WashU weekly Neuroscience publications

Sensory Over-responsivity: A Feature of Childhood Psychiatric Illness Associated With Altered Functional Connectivity of Sensory Networks
(2023) Biological Psychiatry, 93 (1), pp. 92-101. 

Schwarzlose, R.F.^a , Tillman, R.^a , Hoyniak, C.P.^a , Luby, J.L.^a , Barch, D.M.^a b

^a Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
^b Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri

Abstract
Background: Sensory over-responsivity (SOR) is recognized as a common feature of autism spectrum disorder. However, SOR is also common among typically developing children, in whom it is associated with elevated levels of psychiatric symptoms. The clinical significance and neurocognitive bases of SOR in these children remain poorly understood and actively debated. Methods: This study used linear mixed-effects models to identify psychiatric symptoms and network-level functional connectivity (FC) differences associated with parent-reported SOR in the Adolescent Brain Cognitive Development (ABCD) Study, a large community sample (9 to 12 years of age) (N = 11,210). Results: Children with SOR constituted 18% of the overall sample but comprised more than half of the children with internalizing or externalizing scores in the clinical range. Controlling for autistic traits, both mild and severe SOR were associated with greater concurrent symptoms of depression, anxiety, obsessive-compulsive disorder, and attention-deficit/hyperactivity disorder. Controlling for psychiatric symptoms and autistic traits, SOR predicted increased anxiety, attention-deficit/hyperactivity disorder, and prodromal psychosis symptoms 1 year later and was associated with FC differences in brain networks supporting sensory and salience processing in datasets collected 2 years apart. Differences included reduced FC within and between sensorimotor networks, enhanced sensorimotor-salience FC, and altered FC between sensory networks and bilateral hippocampi. Conclusions: SOR is a common, clinically relevant feature of childhood psychiatric illness that provides unique predictive information about risk. It is associated with differences in brain networks that subserve tactile processing, implicating a neural basis for sensory differences in affected children. © 2022 Society of Biological Psychiatry

Author Keywords
Anxiety;  Attention-deficit/hyperactivity disorder;  Autism spectrum disorder;  Depression;  Obsessive-compulsive disorder;  Sensory over-responsivityFunding details
National Institutes of HealthNIHU01DA041022, U01DA041025, U01DA041028, U01DA041048, U01DA041089, U01DA041093, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U01DA050987, U01DA050988, U01DA050989, U01DA051016, U01DA051018, U01DA051037, U01DA051038, U01DA051039, U24DA041123, U24DA041147
National Institute of Mental HealthNIMHK23 MH127305-01, MH014677-40, MH100019-05
National Institute of Child Health and Human DevelopmentNICHDK99 HD109454-01

Document Type: Article
Publication Stage: Final
Source: Scopus

Prognostic significance and extra-hypothalamus dysfunction of hyponatremia in anti-leucine-rich glioma-inactivated protein 1 encephalitis
(2022) Journal of Neuroimmunology, 373, art. no. 578000, . 

Liu, X.^a c , Li, G.^a c , Yu, T.^a c , Lv, R.^a c , Cui, T.^a c , Hogan, R.E.^b , Wang, Q.^a c d

^a Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
^b Department of Neurology, Washington University School of Medicine in St LouisMO, United States
^c China National Clinical Research Center for Neurological Diseases, Beijing, China
^d Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China

Abstract
This study aimed to investigate prognostic significance and brain metabolic mechanism of hyponatremia in anti-leucine-rich glioma-inactivated protein 1 (LGI1) encephalitis. After adjusting for confounders, patients with moderate and severe hyponatremia had significantly increased risk of poor functional outcome and sequelae of seizures. In addition, serum sodium was negatively correlated with normalized ratio of the standardized uptake value of medial temporal lobe (MTL), basal ganglia (BG), and hypothalamus on positron emission tomography (PET) and which was further validated using voxel-wise analysis, suggesting an extra-hypothalamus (BG and MTL) localization for hyponatremia. © 2022

Author Keywords
Autoimmune encephalitis;  Hyponatremia;  Leucine-rich glioma-inactivated protein 1;  Positron emission tomography;  Prognosis

Funding details
H2018206435
Natural Science Foundation of Beijing MunicipalityZ200024
Capital Health Research and Development of Special Fund
National Key Research and Development Program of ChinaNKRDPC2017YFC1307500

Document Type: Article
Publication Stage: Final
Source: Scopus

Brain transplantation of genetically corrected Sanfilippo type B neural stem cells induces partial cross-correction of the disease
(2022) Molecular Therapy – Methods and Clinical Development, 27, pp. 452-463. 

Pearse, Y.^a , Clarke, D.^a , Kan, S.-H.^a b , Le, S.Q.^a , Sanghez, V.^a , Luzzi, A.^a , Pham, I.^c , Nih, L.R.^a c d , Cooper, J.D.^e , Dickson, P.I.^e , Iacovino, M.^a f

^a Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, United States
^b CHOC Research Institute, Orange, CA 92868, United States
^c Department of Neurology, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, United States
^d Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States
^e Department of Pediatrics, Washington University, Saint Louis, MO 63110, United States
^f Department of Pediatrics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States

Abstract
Sanfilippo syndrome type B (mucopolysaccharidosis type IIIB) is a recessive genetic disorder that severely affects the brain due to a deficiency in the enzyme α-N-acetylglucosaminidase (NAGLU), leading to intra-lysosomal accumulation of partially degraded heparan sulfate. There are no effective treatments for this disorder. In this project, we carried out an ex vivo correction of neural stem cells derived from Naglu−/− mice (iNSCs) induced pluripotent stem cells (iPSC) using a modified enzyme in which human NAGLU is fused to an insulin-like growth factor II receptor binding peptide in order to improve enzyme uptake. After brain transplantation of corrected iNSCs into Naglu−/− mice and long-term evaluation of their impact, we successfully detected NAGLU-IGFII activity in all transplanted animals. We found decreased lysosomal accumulation and reduced astrocytosis and microglial activation throughout transplanted brains. We also identified a novel neuropathological phenotype in untreated Naglu−/− brains with decreased levels of the neuronal marker Map2 and accumulation of synaptophysin-positive aggregates. Upon transplantation, we restored levels of Map2 expression and significantly reduced formation of synaptophysin-positive aggregates. Our findings suggest that genetically engineered iNSCs can be used to effectively deliver the missing enzyme to the brain and treat Sanfilippo type B-associated neuropathology. © 2022

Author Keywords
cell therapy;  LSD;  MPS;  neural progenitor cells;  Sanfilippo type B

Funding details
National Institute of Neurological Disorders and StrokeNINDS1R01 NS088766, 1R41NS092221-0181, 4R33NS096044-03, 5T32 GM8243-28

Document Type: Article
Publication Stage: Final
Source: Scopus

Structural basis of synthetic agonist activation of the nuclear receptor REV-ERB
(2022) Nature Communications, 13 (1), art. no. 7131, . 

Murray, M.H.^a b , Valfort, A.C.^c , Koelblen, T.^c , Ronin, C.^d , Ciesielski, F.^d , Chatterjee, A.^a , Veerakanellore, G.B.^b e , Elgendy, B.^b e , Walker, J.K.^a , Hegazy, L.^b e , Burris, T.P.^c

^a Department of Pharmacology and Physiology, Saint Louis University School of Medicine, St. Louis, MO 63104, United States
^b Center for Clinical Pharmacology, Washington University School of Medicine, University of Health Sciences & Pharmacy, St. Louis, MO 63110, United States
^c University of Florida Genetics Institute, Gainesville, FL 32610, United States
^d NovAliX SAS, Strasbourg, France
^e Department of Pharmaceutical and Administrative Sciences, University of Health Sciences & Pharmacy, St. Louis, MO 63110, United States

Abstract
The nuclear receptor REV-ERB plays an important role in a range of physiological processes. REV-ERB behaves as a ligand-dependent transcriptional repressor and heme has been identified as a physiological agonist. Our current understanding of how ligands bind to and regulate transcriptional repression by REV-ERB is based on the structure of heme bound to REV-ERB. However, porphyrin (heme) analogues have been avoided as a source of synthetic agonists due to the wide range of heme binding proteins and potential pleotropic effects. How non-porphyrin synthetic agonists bind to and regulate REV-ERB has not yet been defined. Here, we characterize a high affinity synthetic REV-ERB agonist, STL1267, and describe its mechanism of binding to REV-ERB as well as the method by which it recruits transcriptional corepressor both of which are unique and distinct from that of heme-bound REV-ERB. © 2022, The Author(s).

Funding details
National Institutes of HealthNIHAG060769
Congressionally Directed Medical Research ProgramsCDMRPW81XWH-19-1-0632, W81XWH-19-1-0633

Document Type: Article
Publication Stage: Final
Source: Scopus

EEG-based grading of immune effector cell-associated neurotoxicity syndrome
(2022) Scientific Reports, 12 (1), art. no. 20011, . 

Jones, D.K.^a b c d , Eckhardt, C.A.^a b c e , Sun, H.^a b c , Tesh, R.A.^a b c , Malik, P.^a b c , Quadri, S.^a b c , Firme, M.S.^a b c , van Sleuwen, M.^a b c , Jain, A.^a b c , Fan, Z.^a b c , Jing, J.^a b c , Ge, W.^a b c , Nascimento, F.A.^h , Sheikh, I.S.^a b , Jacobson, C.^e f , Frigault, M.^a b f , Kimchi, E.Y.^a b , Cash, S.S.^a b , Lee, J.W.^b e , Dietrich, J.^a b f , Westover, M.B.^a b c g

^a Department of Neurology, Massachusetts General Hospital (MGH), 50 Staniford St. Suite 401, Boston, MA, United States
^b Harvard Medical School, Boston, MA, United States
^c Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
^d Brigham Young University, Provo, UT, United States
^e Department of Neurology, Brigham and Women’s Hospital (MGH), Boston, MA, United States
^f Dana Farber Cancer Institute (DFCI), Boston, MA, United States
^g MGH Cancer Center for Brain Health, Boston, MA, United States
^h Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States

Abstract
CAR-T cell therapy is an effective cancer therapy for multiple refractory/relapsed hematologic malignancies but is associated with substantial toxicity, including Immune Effector Cell Associated Neurotoxicity Syndrome (ICANS). Improved detection and assessment of ICANS could improve management and allow greater utilization of CAR-T cell therapy, however, an objective, specific biomarker has not been identified. We hypothesized that the severity of ICANS can be quantified based on patterns of abnormal brain activity seen in electroencephalography (EEG) signals. We conducted a retrospective observational study of 120 CAR-T cell therapy patients who had received EEG monitoring. We determined a daily ICANS grade for each patient through chart review. We used visually assessed EEG features and machine learning techniques to develop the Visual EEG-Immune Effector Cell Associated Neurotoxicity Syndrome (VE-ICANS) score and assessed the association between VE-ICANS and ICANS. We also used it to determine the significance and relative importance of the EEG features. We developed the Visual EEG-ICANS (VE-ICANS) grading scale, a grading scale with a physiological basis that has a strong correlation to ICANS severity (R = 0.58 [0.47–0.66]) and excellent discrimination measured via area under the receiver operator curve (AUC = 0.91 for ICANS ≥ 2). This scale shows promise as a biomarker for ICANS which could help to improve clinical care through greater accuracy in assessing ICANS severity. © 2022, The Author(s).

Funding details
National Science FoundationNSFK08-MH116135, SCH-2014431
National Institutes of HealthNIH
National Institute of Diabetes and Digestive and Kidney DiseasesNIDDK
National Institute of Neurological Disorders and StrokeNINDS
American Federation for Aging ResearchAFAR
Glenn Foundation for Medical ResearchGFMR
National Sleep FoundationNSF
Center for Hierarchical Manufacturing, National Science FoundationCHM, NSF
Brigham Young UniversityBYU
American Academy of Sleep MedicineAASM
American Academy of Sleep Medicine FoundationAASMF
Nick Simons FoundationNSF
Office of Research Infrastructure Programs, National Institutes of HealthORIP, NIH, NIH-ORIP, ORIPR01AG062989, R01AG073410, R01NS102190, R01NS102574, R01NS107291, RF1AG064312

Document Type: Article
Publication Stage: Final
Source: Scopus

Does clinically measured walking capacity contribute to real-world walking performance in Parkinson’s disease?
(2022) Parkinsonism and Related Disorders, 105, pp. 123-127. 

Zajac, J.A.^a , Cavanaugh, J.T.^b , Baker, T.^a , Duncan, R.P.^c d , Fulford, D.^e , Girnis, J.^a , LaValley, M.^f , Nordahl, T.^a , Porciuncula, F.^a , Rawson, K.S.^c , Saint-Hilaire, M.^g , Thomas, C.A.^g , Earhart, G.M.^c d h , Ellis, T.D.^a

^a Department of Physical Therapy and Athletic Training, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, United States
^b Department of Physical Therapy, University of New England, Portland, ME, United States
^c Program in Physical Therapy, Washington University in St Louis School of Medicine, St Louis, MO, United States
^d Department of Neurology, Washington University in St Louis School of Medicine, St Louis, MO, United States
^e Department of Occupational Therapy, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, United States
^f School of Public Health, Boston University, Boston, MA, United States
^g Department of Neurology, Parkinson’s Disease and Movement Disorders Center, Boston University, Boston, MA, United States
^h Department of Neuroscience, Washington University in St Louis School of Medicine, St Louis, MO, United States

Abstract
Objective: The study examined how clinically measured walking capacity contributes to real-world walking performance in persons with Parkinson’s disease (PD). Methods: Cross-sectional baseline data (n = 82) from a PD clinical trial were analyzed. The 6-Minute Walk Test (6MWT) and 10-Meter Walk Test (10MWT) were used to generate capacity metrics of walking endurance and fast gait speed, respectively. An activity monitor worn for seven days was used to generate performance metrics of mean daily steps and weekly moderate intensity walking minutes. Univariate linear regression analyses were used to examine associations between each capacity and performance measure in the full sample and less and more active subgroups. Results: Walking capacity significantly contributed to daily steps in the full sample (endurance: R2=.13, p <.001; fast gait speed: R2=.07, p =.017) and in the less active subgroup (endurance: R2 =.09, p =.045). Similarly, walking capacity significantly contributed to weekly moderate intensity minutes in the full sample (endurance: R2=.13, p <.001; fast gait speed: R2=.09, p =.007) and less active subgroup (endurance: R2 =.25, p <.001; fast gait speed: R2 =.21, p =.007). Walking capacity did not significantly contribute to daily steps or moderate intensity minutes in the more active subgroup. Conclusions: Walking capacity contributed to, but explained a relatively small portion of the variance in, real-world walking performance. The contribution was somewhat greater in less active individuals. The study adds support to the idea that clinically measured walking capacity may have limited benefit for understanding real-world walking performance in PD. Factors beyond walking capacity may better account for actual walking behavior. © 2022 Elsevier Ltd

Author Keywords
Ambulatory activity;  Intensity;  Physical activity;  Steps

Funding details
National Institutes of HealthNIH
National Institute of Child Health and Human DevelopmentNICHD03517371, 1RO1HD092444-01A1, K23HD100569, NCT03517371
Foundation for Physical TherapyFPT

Document Type: Article
Publication Stage: Final
Source: Scopus

Septin-5 and -7-IgGs: Neurologic, Serologic, and Pathophysiologic Characteristics
(2022) Annals of Neurology, 92 (6), pp. 1090-1101. 

Hinson, S.R.^a , Honorat, J.A.^a , Grund, E.M.^b , Clarkson, B.D.^b , Miske, R.^c , Scharf, M.^c , Zivelonghi, C.^a , Al-Lozi, M.T.^d , Bucelli, R.C.^d , Budhram, A.^b , Cho, T.^e , Choi, E.^f , Grell, J.^a , Lopez-Chiriboga, A.S.^g , Levin, M.^h , Merati, M.ⁱ , Montalvo, M.^b , Pittock, S.J.^a b , Wilson, M.R.^j , Howe, C.L.^b , McKeon, A.^a b

^a Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
^b Department of Neurology, Mayo Clinic, Rochester, MN, United States
^c Institute for Experimental Immunology, affiliated to EUROIMMUN Medizinische Labordiagnostika, Lubeck, Germany
^d Department of Neurology, Washington University, St. Louis, MO, United States
^e Department of Neurology, University of Iowa, Iowa City, IA, United States
^f Overlake Neurosciences Institute, Overlake Hospital, Bellevue, WA, United States
^g Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
^h Department of Ophthalmology, Palo Alto Medical Foundation, Palo Alto, CA, United States
ⁱ Department of Neurology, Michigan State University, Lansing, MI, United States
^j Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, United States

Abstract
Background and Objectives: We sought to determine clinical significance of neuronal septin autoimmunity and evaluate for potential IgG effects. Methods: Septin-IgGs were detected by indirect immunofluorescence assays (IFAs; mouse tissue and cell based) or Western blot. IgG binding to (and internalization of) extracellular septin epitopes were evaluated for by live rat hippocampal neuron assay. The impact of purified patient IgGs on murine cortical neuron function was determined by recording extracellular field potentials in a multielectrode array platform. Results: Septin-IgGs were identified in 23 patients. All 8 patients with septin-5-IgG detected had cerebellar ataxia, and 7 had prominent eye movement disorders. One of 2 patients with co-existing septin-7-IgG had additional psychiatric phenotype (apathy, emotional blunting, and poor insight). Fifteen patients had septin-7 autoimmunity, without septin-5-IgG detected. Disorders included encephalopathy (11; 2 patients with accompanying myelopathy, and 2 were relapsing), myelopathy (3), and episodic ataxia (1). Psychiatric symptoms (≥1 of agitation, apathy, catatonia, disorganized thinking, and paranoia) were prominent in 6 of 11 patients with encephalopathic symptoms. Eight of 10 patients with data available (from 23 total) improved after immunotherapy, and a further 2 patients improved spontaneously. Staining of plasma membranes of live hippocampal neurons produced by patient IgGs (subclasses 1 and 2) colocalized with pre- and post-synaptic markers. Decreased spiking and bursting behavior in mixed cultures of murine glutamatergic and GABAergic cortical neurons produced by patient IgGs were attributable to neither antigenic crosslinking and internalization nor complement activation. Interpretation: Septin-IgGs are predictive of distinct treatment-responsive autoimmune central nervous system (CNS) disorders. Live neuron binding and induced electrophysiologic effects by patient IgGs may support septin-specific pathophysiology. ANN NEUROL 2022;92:1090–1101. © 2022 American Neurological Association.

Funding details
Mayo Clinic

Document Type: Article
Publication Stage: Final
Source: Scopus

Detection and discrimination of electrical stimuli from an upper limb cuff electrode in M. Mulatta
(2022) Journal of Neural Engineering, 19 (6), art. no. 066009, . 

Schlichenmeyer, T.C., Zellmer, E.R., Burton, H., Ray, W.Z., Moran, D.W.

Washington University in St Louis, 1 Brookings Dr, St Louis, MO 63118, United States

Abstract
Objective. Peripheral nerve interfaces seek to restore nervous system function through electrical stimulation of peripheral nerves. In clinical use, these devices should function reliably for years or decades. In this study, we assessed evoked sensations from multi-channel cuff electrode stimulation in macaque monkeys up to 711 d post-implantation. Approach. Three trained macaque monkeys received multi-channel cuff electrode implants at the median or ulnar nerves in the upper arm. Electrical stimuli from the cuff interfaces evoked sensations, which we measured via standard psychophysical tasks. We adjusted pulse amplitude or pulse width for each block with various electrode channel configurations to examine the effects of stimulus parameterization on sensation. We measured detection thresholds and just-noticeable differences (JNDs) at irregular, near-daily intervals for several months using Bayesian inferencing from trial data. We examined data trends using classical models such as Weber’s Law and the strength-duration relationship using linear regression. Main results. Detection thresholds were similar between blocks with pulse width modulation and blocks with pulse amplitude modulation when represented as charge per pulse, the product of the amplitude and the pulse width. Conversely, Weber fractions—calculated as the slope of the regression between JND charge values and reference stimulus charge—were significantly different between pulse width and pulse amplitude modulation blocks for the discrimination task. Significance. Weber fractions were lower in blocks with amplitude modulation than in blocks with pulse width modulation, suggesting that pulse amplitude modulation allows finer resolution of sensory encoding above threshold. Consequently, amplitude modulation may enable a greater dynamic range for sensory perception with neuroprosthetic devices. © 2022 IOP Publishing Ltd.

Author Keywords
neural prosthetics;  peripheral nerve;  psychophysics;  somatosensation

Funding details
HR0011-15-2-0007
Defense Advanced Research Projects AgencyDARPA

Document Type: Article
Publication Stage: Final
Source: Scopus

Structural basis for mechanotransduction in a potassium-dependent mechanosensitive ion channel
(2022) Nature Communications, 13 (1), art. no. 6904, . 

Mount, J.^a b , Maksaev, G.^a b , Summers, B.T.^c , Fitzpatrick, J.A.J.^a c d e , Yuan, P.^a b

^a Department of Cell Biology and Physiology, Washington University School of Medicine, Saint Louis, MO, United States
^b Center for the Investigation of Membrane Excitability Diseases, Washington University School of Medicine, Saint Louis, MO, United States
^c Washington University Center for Cellular Imaging, Washington University School of Medicine, Saint Louis, MO, United States
^d Department of Neuroscience, Washington University School of Medicine, Saint Louis, MO, United States
^e Department of Biomedical Engineering, Washington University in Saint Louis, Saint Louis, MO, United States

Abstract
Mechanosensitive channels of small conductance, found in many living organisms, open under elevated membrane tension and thus play crucial roles in biological response to mechanical stress. Amongst these channels, MscK is unique in that its activation also requires external potassium ions. To better understand this dual gating mechanism by force and ligand, we elucidate distinct structures of MscK along the gating cycle using cryo-electron microscopy. The heptameric channel comprises three layers: a cytoplasmic domain, a periplasmic gating ring, and a markedly curved transmembrane domain that flattens and expands upon channel opening, which is accompanied by dilation of the periplasmic ring. Furthermore, our results support a potentially unifying mechanotransduction mechanism in ion channels depicted as flattening and expansion of the transmembrane domain. © 2022, The Author(s).

Funding details
National Institutes of HealthNIHR01GM143440, R01NS099341

Document Type: Article
Publication Stage: Final
Source: Scopus

Collective genomic segments with differential pleiotropic patterns between cognitive dimensions and psychopathology
(2022) Nature Communications, 13 (1), art. no. 6868, . 

Lam, M.^{a b c d e} , Chen, C.-Y.^f , Hill, W.D.^g , Xia, C.^g , Tian, R.^h , Levey, D.F.^i j , Gelernter, J.^i j k l , Stein, M.B.^m n o , Hatoum, A.S.^p , Huang, H.^c d , Malhotra, A.K.^a b q r , Runz, H.^f , Ge, T.^c s t , Lencz, T.^a b q r

^a Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell, Glen Oaks, NY, United States
^b Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, United States
^c Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
^d Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, United States
^e Institute of Mental Health, Singapore, Singapore
^f Translational Biology, Research and Development, Biogen Inc, Cambridge, MA, United States
^g Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
^h Computational Biology and Human Genetics, Dewpoint Therapeutics, Boston, MA, United States
ⁱ Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
^j VA Connecticut Healthcare System, West Haven, CT, United States
^k Department of Genetics, Yale University School of Medicine, New Haven, CT, United States
^l Department of Neuroscience, Yale University School of Medicine, New Haven, CT, United States
^m VA San Diego Healthcare System, San Diego, CA, United States
ⁿ Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
^o Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, United States
^p Department of Psychiatry, Washington University in St. Louis Medical School, St. Louis, MO, United States
^q Department of Psychiatry, Zucker School of Medicine at Hofstra/Norwell, Hempstead, NY, United States
^r Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Norwell, Hempstead, NY, United States
^s Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
^t Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States

Abstract
Cognitive deficits are known to be related to most forms of psychopathology. Here, we perform local genetic correlation analysis as a means of identifying independent segments of the genome that show biologically interpretable pleiotropic associations between cognitive dimensions and psychopathology. We identify collective segments of the genome, which we call “meta-loci”, showing differential pleiotropic patterns for psychopathology relative to either cognitive task performance (CTP) or performance on a non-cognitive factor (NCF) derived from educational attainment. We observe that neurodevelopmental gene sets expressed during the prenatal-early childhood period predominate in CTP-relevant meta-loci, while post-natal gene sets are more involved in NCF-relevant meta-loci. Further, we demonstrate that neurodevelopmental gene sets are dissociable across CTP meta-loci with respect to their spatial distribution across the brain. Additionally, we find that GABA-ergic, cholinergic, and glutamatergic genes drive pleiotropic relationships within dissociable meta-loci. © 2022, The Author(s).

Funding details
National Institutes of HealthNIHR01 MH117646
National Institute of Mental HealthNIMH
Medical Research CouncilMRCMR/T03052/1

Document Type: Article
Publication Stage: Final
Source: Scopus

The SOD1-mediated ALS phenotype shows a decoupling between age of symptom onset and disease duration
(2022) Nature Communications, 13 (1), art. no. 6901, . 

Opie-Martin, S.^a , Iacoangeli, A.^a b c , Topp, S.D.^a , Abel, O.^d , Mayl, K.^a , Mehta, P.R.^a , Shatunov, A.^a e f , Fogh, I.^a , Bowles, H.^a , Limbachiya, N.^a , Spargo, T.P.^a , Al-Khleifat, A.^a , Williams, K.L.^g , Jockel-Balsarotti, J.^h , Bali, T.^h , Self, W.^h , Henden, L.^g , Nicholson, G.A.^g i , Ticozzi, N.^j k , McKenna-Yasek, D.^l , Tang, L.^m , Shaw, P.J.ⁿ , Chio, A.^o p , Ludolph, A.^q r , Weishaupt, J.H.^s t , Landers, J.E.^l , Glass, J.D.^u , Mora, J.S.^v , Robberecht, W.^w , Damme, P.V.^w x , McLaughlin, R.^y , Hardiman, O.^z , van den Berg, L.^aa , Veldink, J.H.^aa , Corcia, P.^ab ac , Stevic, Z.^ad , Siddique, N.^ae , Silani, V.^j k , Blair, I.P.^g , Fan, D.-S.^m , Esselin, F.^af , de la Cruz, E.^af , Camu, W.^af , Basak, N.A.^ag , Siddique, T.^ae , Miller, T.^h , Brown, R.H.^l , Al-Chalabi, A.^a , Shaw, C.E.^ah ai

^a Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 9NU, United Kingdom
^b Department of Biostatistics and Health Informatics, Institute of Psychiatry Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
^c NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
^d Homerton University Hospital, Homerton Row, London, E9 6SR, United Kingdom
^e Department of Molecular and Clinical Pharmacology, University of Liverpool, Blue Block 1.09, Sherrington Building, Crown St, Liverpool, L693BX, United Kingdom
^f Institute of Medicine, North-Eastern Federal University, 58 Belinsky str, Yakutsk, 677000, Russian Federation
^g Macquarie University Centre for MND Research, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
^h Department of Neurology, Washington University School of Medicine, St Louis, MO 63110, United States
ⁱ Concord Clinical School, ANZAC Research Institute, Concord Repatriation Hospital, Sydney, NSW 2139, Australia
^j Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, MiIan, Cusano Milanino, 20095, Italy
^k Dino Ferrari Center, Department of Pathophysiology and Transplantation, Center for Neurotechnology and Brain Therapeutics, Università degli Studi di Milano, Milan, Italy
^l Department of Neurology, University of Massachusetts Medical School, Worcester, MA 02125, United States
^m Department of Neurology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China
ⁿ Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, S10 2HQ, United Kingdom
^o Rita Levi Montalcini’ Department of Neuroscience, University of Turin, Turin, Italy
^p Neurology 1, AOU Città della Salute e della Scienza of Torino, Torino, Turin, 10124, Italy
^q Department of Neurology, Ulm University, Oberer Eselsberg 45, Ulm, 89081, Germany
^r German Center for Neurodegenerative Diseases, DZNE, Ulm, Germany
^s Department of Neurology, University of Ulm, Oberer Eselsberg 45, Ulm, 89081, Germany
^t Division of Neurodegenerative Disorders, Department of Neurology, Mannheim Center for Translational Neuroscience, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
^u Department Neurology, Emory University School of Medicine, Atlanta, GA 30322, United States
^v ALS Unit, Department of Neurology, Hospital San Rafael, Madrid, 28016, Spain
^w Neurology Department, Univeristy Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium
^x Neuroscience Department, KU Leuven and Center for Brain & Disease Research VIB Leuven, Leuven, Belgium
^y Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, D02 PN40, Ireland
^z Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, D02 PN40, Ireland
^aa Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, Netherlands
^ab Centre de Référence pour la SLA et les Autres Maladies du Motoneurone (FILSLAN), 2 Avenue Martin Luther King, Limoges Cedex, 87042, France
^ac Centre de Compétences Neuropathies Amyloïdes Familiales et Autres Neuropathies Périphériques Rares (NNERF), Poitiers, France
^ad Neurology Clinic, Clinical Center of Serbia, School of Medicine, University of Belgrade, Studentski trg 1, Belgrade, Serbia
^ae Neuromuscular Disorders Program, Northwestern University, Feinberg School of Medicine, Chicago, IL 60208, United States
^af Reference Center for ALS and Other Rare Motoneuron Disorders, University Hospital Gui de Chauliac, Montpellier, 34295, France
^ag Koç University, School of Medicine Translational Medicine Research Center KUTTAM-NDAL, Sarıyer, Istanbul, 34450, Turkey
^ah UK Dementia Research Institute Centre at King’s College London, School of Neuroscience, King’s College London, Strand, London, WC2R 2LS, United Kingdom
^ai Centre for Brain Research, University of Auckland, 85 Park Road, Grafton, Auckland, 1023, New Zealand

Abstract
Superoxide dismutase (SOD1) gene variants may cause amyotrophic lateral sclerosis, some of which are associated with a distinct phenotype. Most studies assess limited variants or sample sizes. In this international, retrospective observational study, we compare phenotypic and demographic characteristics between people with SOD1-ALS and people with ALS and no recorded SOD1 variant. We investigate which variants are associated with age at symptom onset and time from onset to death or censoring using Cox proportional-hazards regression. The SOD1-ALS dataset reports age of onset for 1122 and disease duration for 883 people; the comparator population includes 10,214 and 9010 people respectively. Eight variants are associated with younger age of onset and distinct survival trajectories; a further eight associated with younger onset only and one with distinct survival only. Here we show that onset and survival are decoupled in SOD1-ALS. Future research should characterise rarer variants and molecular mechanisms causing the observed variability. © 2022, The Author(s).

Funding details
ALS AssociationALSA
King’s College LondonKCL
Horizon 2020 Framework ProgrammeH2020633413, 772376
Seventh Framework ProgrammeFP7259867
EU Joint Programme – Neurodegenerative Disease ResearchJPND
Psychiatry Research TrustPRTAAC/CES/AI/SOM
Medical Research CouncilMRCMR/L501529/1
Economic and Social Research CouncilESRCES/ L008238/1
National Institute for Health and Care ResearchNIHR
Motor Neurone Disease AssociationMNDA
European Research CouncilERC
KU Leuven
Vlaamse regering
Stichting ALS NederlandALS

Document Type: Article
Publication Stage: Final
Source: Scopus

Transfer of learned cognitive control settings within and between tasks
(2022) Neurobiology of Learning and Memory, 196, art. no. 107689, . 

Ileri-Tayar, M., Moss, C., Bugg, J.M.

Department of Psychological and Brain Sciences, Washington University in St. Louis, United States

Abstract
Cognitive control is modulated based on learned associations between conflict probability and stimulus features such as color. We investigated whether such learning-guided control transfers to novel stimuli and/or a novel task. In Experiments 1 and 2, participants experienced an item-specific proportion congruence (ISPC) manipulation in a Stroop (Experiment 1) or Flanker (Experiment 2) task with mostly congruent (MC) and mostly incongruent (MI) colors in training blocks. During a transfer block, participants performed the same task and encountered novel transfer stimuli paired with MC or MI colors. Evidencing within-task transfer, in both experiments, responses were faster to incongruent transfer stimuli comprising an MI color compared with an MC color. In Experiment 3, we investigated between-task transfer from Stroop to Flanker. After training with an ISPC manipulation in the Stroop task, a Flanker task was completed with the same colors but without an ISPC manipulation (i.e., 50% congruent). Responses were faster to incongruent transfer stimuli paired with the previously-MI colors compared with the previously-MC colors. Additionally, transfer was evident in the first half of the Flanker task but not the second half. The evidence for within-task transfer, in combination with the novel evidence for between-task transfer, suggests learned control settings are flexibly retrieved and executed when predictive cues signaling these control settings are encountered in novel stimuli or a novel task. Theoretical implications are discussed alongside potential neural mechanisms mediating transfer of learning-guided control. © 2022 Elsevier Inc.

Author Keywords
Cognitive control;  Episodic retrieval;  Item-specific proportion congruence;  Learning;  Transfer

Document Type: Article
Publication Stage: Final
Source: Scopus

Polony gels enable amplifiable DNA stamping and spatial transcriptomics of chronic pain
(2022) Cell, 185 (24), pp. 4621-4633.e17. 

Fu, X.^a b , Sun, L.^a b c , Dong, R.^a b d , Chen, J.Y.^a e , Silakit, R.^a f , Condon, L.F.^a e g , Lin, Y.^h , Lin, S.^f , Palmiter, R.D.^a e , Gu, L.^a b

^a Department of Biochemistry, University of Washington, Seattle, WA 98195, United States
^b Institute for Protein Design, University of Washington, Seattle, WA 98195, United States
^c TopoGene Inc., Seattle, WA 98195, United States
^d Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, United States
^e Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, United States
^f Division of Cardiology, Department of Medicine, University of Washington, Seattle, WA 98195, United States
^g Graduate Programs in Medical Scientist Training and Neuroscience, University of Washington, Seattle, WA, United States
^h Department of Surgery, Washington University School of Medicine, St. Louis, MO 63110, United States

Abstract
Methods for acquiring spatially resolved omics data from complex tissues use barcoded DNA arrays of low- to sub-micrometer features to achieve single-cell resolution. However, fabricating such arrays (randomly assembled beads, DNA nanoballs, or clusters) requires sequencing barcodes in each array, limiting cost-effectiveness and throughput. Here, we describe a vastly scalable stamping method to fabricate polony gels, arrays of ∼1-micrometer clonal DNA clusters bearing unique barcodes. By enabling repeatable enzymatic replication of barcode-patterned gels, this method, compared with the sequencing-dependent array fabrication, reduced cost by at least 35-fold and time to approximately 7 h. The gel stamping was implemented with a simple robotic arm and off-the-shelf reagents. We leveraged the resolution and RNA capture efficiency of polony gels to develop Pixel-seq, a single-cell spatial transcriptomic assay, and applied it to map the mouse parabrachial nucleus and analyze changes in neuropathic pain-regulated transcriptomes and cell-cell communication after nerve ligation. © 2022 Elsevier Inc.

Author Keywords
chronic pain;  DNA array;  DNA stamping;  microcontact printing;  olfactory bulb;  parabrachial nucleus;  Pixel-seq;  polony gel;  polony sequencing;  spatial transcriptomics

Funding details
National Institutes of HealthNIHR01DA024908, R21DA051194, R21DA051555, R35GM128918, R41MH130299, UG3CA268096
University of WashingtonUW

Document Type: Article
Publication Stage: Final
Source: Scopus

Myostatin is a negative regulator of adult neurogenesis after spinal cord injury in zebrafish
(2022) Cell Reports, 41 (8), art. no. 111705, . 

Saraswathy, V.M.^a b , Zhou, L.^a b , McAdow, A.R.^a b , Burris, B.^a b , Dogra, D.^c d , Reischauer, S.^c e f , Mokalled, M.H.^a b

^a Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, United States
^b Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63110, United States
^c Department of Developmental Genetics, Max Planck Institute for Heart and Lung Research, Bad Nauheim, 61231, Germany
^d Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
^e Medical Clinic I, (Cardiology/Angiology) and Campus Kerckhoff, Justus Liebig University, Giessen, Giessen, 35392, Germany
^f The Cardio-Pulmonary Institute, Frankfurt, Germany

Abstract
Intrinsic and extrinsic inhibition of neuronal regeneration obstruct spinal cord (SC) repair in mammals. In contrast, adult zebrafish achieve functional recovery after complete SC transection. While studies of innate SC regeneration have focused on axon regrowth as a primary repair mechanism, how local adult neurogenesis affects functional recovery is unknown. Here, we uncover dynamic expression of zebrafish myostatin b (mstnb) in a niche of dorsal SC progenitors after injury. mstnb mutants show impaired functional recovery, normal glial and axonal bridging across the lesion, and an increase in the profiles of newborn neurons. Molecularly, neuron differentiation genes are upregulated, while the neural stem cell maintenance gene fgf1b is downregulated in mstnb mutants. Finally, we show that human fibroblast growth factor 1 (FGF1) treatment rescues the molecular and cellular phenotypes of mstnb mutants. These studies uncover unanticipated neurogenic functions for mstnb and establish the importance of local adult neurogenesis for innate SC repair. © 2022 The Author(s)

Author Keywords
adult neurogenesis;  CP: Developmental biology;  CP: Neuroscience;  eural stem cells;  myostatin;  neuronal differentiation;  regeneration;  spinal cord injury;  zebrafish

Funding details
National Institutes of HealthNIHR01 NS113915
University of WashingtonUW
National Institutes of Natural SciencesNINS

Document Type: Article
Publication Stage: Final
Source: Scopus

Parenchymal border macrophages regulate the flow dynamics of the cerebrospinal fluid
(2022) Nature, 611 (7936), pp. 585-593. 

Drieu, A.^a b , Du, S.^a b c , Storck, S.E.^a b , Rustenhoven, J.^a b , Papadopoulos, Z.^a b c , Dykstra, T.^a b , Zhong, F.^d , Kim, K.^a b , Blackburn, S.^a b , Mamuladze, T.^a b c , Harari, O.^e , Karch, C.M.^e f , Bateman, R.J.^f , Perrin, R.^b f , Farlow, M.^g , Chhatwal, J.^h , Brosch, J.^g , Buck, J.^g , Farlow, M.^g , Ghetti, B.^g , Adams, S.ⁱ , Barthelemy ⁱ, N. , Benzinger, T.ⁱ , Brandon, S.ⁱ , Buckles, V.ⁱ , Cash, L.ⁱ , Chen, C.ⁱ , Chua, J.ⁱ , Cruchaga, C.ⁱ , Denner, D.ⁱ , Dincer, A.ⁱ , Donahue, T.ⁱ , Fagan, A.ⁱ , Feldman, B.ⁱ , Flores, S.ⁱ , Franklin, E.ⁱ , Joseph-Mathurin, N.ⁱ , Gonzalez, A.ⁱ , Gordon, B.ⁱ , Gray, J.ⁱ , Gremminger, E.ⁱ , Groves, A.ⁱ , Hassenstab, J.ⁱ , Hellm, C.ⁱ , Herries, E.ⁱ , Hoechst-Swisher, L.ⁱ , Holtzman, D.ⁱ , Hornbeck, R.ⁱ , Jerome, G.ⁱ , Keefe, S.ⁱ , Koudelis, D.ⁱ , Li, Y.ⁱ , Marsh, J.ⁱ , Martinez, R.ⁱ , Mawuenyega, K.ⁱ , McCullough, A.ⁱ , McDade, E.ⁱ , Morris, J.ⁱ , Norton, J.ⁱ , Shady, K.ⁱ , Sigurdson, W.ⁱ , Smith, J.ⁱ , Wang, P.ⁱ , Wang, Q.ⁱ , Xiong, C.ⁱ , Xu, J.ⁱ , Xu, X.ⁱ , Allegri, R.^j , Mendez, P.C.^j , Egido, N.^j , Araki, A.^k , Ikeuchi, T.^k , Ishii, K.^k l , Kasuga, K.^k , Bechara, J.^m , Brooks, W.^m , Schofield, P.^m , Berman, S.ⁿ , Goldberg, S.ⁿ , Ikonomovic, S.ⁿ , Klunk, W.ⁿ , Lopez, O.ⁿ , Mountz, J.ⁿ , Nadkarni, N.ⁿ , Patira, R.ⁿ , Smith, L.ⁿ , Snitz, B.ⁿ , Thompson, S.ⁿ , Weamer, E.ⁿ , Bodge, C.^o , Salloway, S.^o , Carter, K.^p , Duong, D.^p , Johnson, E.^p , Levey, A.^p , Ping, L.^p , Seyfried, N.T.^p , Fitzpatrick, C.^q , Chui, H.^r , Ringman, J.^r , Day, G.S.^s , Graff-Radford, N.^s , Graham, M.^s , Stephens, S.^s , Cruz, C.D.L.^t , Goldman, J.^t , Mejia, A.^t , Neimeyer, K.^t , Noble, J.^t , Diffenbacher, A.^u , Yakushev, I.^u , Levin, J.^u , Vöglein, J.^u , Douglas, J.^v , Fox, N.^v , Grilo, M.^v , Mummery, C.^v , O’Connor, A.^v , Esposito, B.^w , Goate, A.^w , Renton, A.^w , Fujii, H.^x , Senda, M.^x , Shimada, H.^x , Gardener, S.^y , Martins, R.^y , Sohrabi, H.^y , Taddei, K.^y , Gräber-Sultan, S.^z , Häsler, L.^z , Hofmann, A.^z , Jucker, M.^z , Käser, S.^z , Kuder-Buletta, E.^z , Laske, C.^z , Preische, O.^z , Haass, C.^aa , Morenas-Rodriguez, E.^aa , Nuscher, B.^aa , Ihara, R.^l , Nagamatsu, A.^l , Niimi, Y.^l , Jack, C.^ab , Koeppe, R.^ac , Mason, N.S.^ad , Masters, C.^ae , Obermüller, U.^af , Hu, S.^d , Randolph, G.J.^b , Smirnov, I.^a b , Kipnis, J.^a b c , Dominantly Inherited Alzheimer Network^ag

^a Center for Brain Immunology and Glia (BIG), Washington University in St Louis, St Louis, MO, United States
^b Department of Pathology and Immunology, School of Medicine, Washington University in St Louis, St Louis, MO, United States
^c Immunology Graduate Program, School of Medicine, Washington University in St Louis, St Louis, MO, United States
^d Department of Biomedical Engineering, Washington University in St Louis, Danforth Campus, St Louis, MO, United States
^e Department of Psychiatry, Washington University in St Louis, St Louis, MO, United States
^f Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer’s Disease Research Center, School of Medicine, Washington University in St Louis, St Louis, MO, United States
^g Indiana School of Medicine, Indianapolis, IN, United States
^h Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
ⁱ School of Medicine, Washington University in St Louis, St Louis, MO, United States
^j Institute of Neurological Research Fleni, Buenos Aires, Argentina
^k Niigata University, Niigata, Japan
^l Tokyo University, Tokyo, Japan
^m Neuroscience Research Australia, Sydney, NSW, Australia
ⁿ University of Pittsburgh, Pittsburgh, PA, United States
^o Brown University–Butler Hospital, Providence, RI, United States
^p Emory University School of Medicine, Atlanta, GA, United States
^q Brigham and Women’s Hospital–Massachusetts General Hospital, Boston, MA, United States
^r University of Southern California, Los Angeles, CA, United States
^s Mayo Clinic Jacksonville, Jacksonville, FL, United States
^t Columbia University, New York, NY, United States
^u German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
^v University College London, London, United Kingdom
^w Icahn School of Medicine at Mount Sinai, New York, NY, United States
^x Osaka City University, Osaka, Japan
^y Edith Cowan University, Perth, WA, Australia
^z German Center for Neurodegenerative Diseases (DZNE), Tubingen, Germany
^aa Ludwig–Maximilian’s University, Munich, Germany
^ab Mayo Clinic, Rochester, NY, United States
^ac University of Michigan, Ann Arbor, MI, United States
^ad University of Pittsburgh Medical Center, Pittsburgh, PA, United States
^ae University of Melbourne, Parkville, VIC, Australia
^af Hertie Institute for Clinical Brain Research, Tubingen, Germany

Abstract
Macrophages are important players in the maintenance of tissue homeostasis1. Perivascular and leptomeningeal macrophages reside near the central nervous system (CNS) parenchyma2, and their role in CNS physiology has not been sufficiently well studied. Given their continuous interaction with the cerebrospinal fluid (CSF) and strategic positioning, we refer to these cells collectively as parenchymal border macrophages (PBMs). Here we demonstrate that PBMs regulate CSF flow dynamics. We identify a subpopulation of PBMs that express high levels of CD163 and LYVE1 (scavenger receptor proteins), closely associated with the brain arterial tree, and show that LYVE1+ PBMs regulate arterial motion that drives CSF flow. Pharmacological or genetic depletion of PBMs led to accumulation of extracellular matrix proteins, obstructing CSF access to perivascular spaces and impairing CNS perfusion and clearance. Ageing-associated alterations in PBMs and impairment of CSF dynamics were restored after intracisternal injection of macrophage colony-stimulating factor. Single-nucleus RNA sequencing data obtained from patients with Alzheimer’s disease (AD) and from non-AD individuals point to changes in phagocytosis, endocytosis and interferon-γ signalling on PBMs, pathways that are corroborated in a mouse model of AD. Collectively, our results identify PBMs as new cellular regulators of CSF flow dynamics, which could be targeted pharmacologically to alleviate brain clearance deficits associated with ageing and AD. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.

Funding details
AG057777, AG062734, AG067764
National Institutes of HealthNIH
National Institute on AgingNIAAG034113, AG057496, AG078106
National Institute of General Medical SciencesNIGMSP41 GM103422, R24GM136766
National Center for Advancing Translational SciencesNCATSUL1 TR000448
Alzheimer’s Disease Research Center, Emory UniversityADRC
Cure Alzheimer’s FundCAF
University of WashingtonUW
Institute of Clinical and Translational SciencesICTS
University of VirginiaUV
Japan Agency for Medical Research and DevelopmentAMED
Cosmetic Surgery FoundationCSF
Center for Cellular Imaging, Washington UniversityWUCCI
Alvin J. Siteman Cancer CenterNCI P30 CA091842, P01AG026276, P01AG03991, P30 AG066444, UF1AG032438
Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in St. LouisKGAD
Korea Health Industry Development InstituteKHIDI
Deutsches Zentrum für Neurodegenerative ErkrankungenDZNE
Fleni

Document Type: Article
Publication Stage: Final
Source: Scopus

Dopamine in the rodent tail of striatum regulates behavioral variability in response to threatening novel objects
(2022) Neuron, 110 (22), pp. 3653-3655. 

Pai, J.^a , Monosov, I.E.^b

^a Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States
^b Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA; Pain Center, Washington University, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University, St. Louis, MO, USA; Department of Neurosurgery, Washington University, St. Louis, MO, USA; Department of Electrical Engineering, Washington University, St. Louis, MO, USA

Abstract
Mice display variability in fear-like responses to many external salient events, such as encountering unexpected novel objects, but the neural basis of this variability has been unclear. Akiti et al. (2022) demonstrate that dopamine in the tail of the rodent striatum predicts and regulates salience-related variability in individuals’ behavioral responses to unexpected novel objects. Copyright © 2022 Elsevier Inc. All rights reserved.

Document Type: Article
Publication Stage: Final
Source: Scopus

The maturation of exploratory behavior in adolescent Mus spicilegus on two photoperiods
(2022) Frontiers in Behavioral Neuroscience, 16, art. no. 988033, . 

Cryns, N.G.^a d e , Lin, W.C.^b , Motahari, N.^a , Krentzman, O.J.^b d e , Chen, W.^a , Prounis, G.^b , Wilbrecht, L.^b c

^a Department of Molecular and Cellular Biology, University of California, Berkeley, Berkeley, CA, United States
^b Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
^c Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
^d Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States
^e Washington University School of Medicine, St. Louis, MO, United States

Abstract
Dispersal from the natal site or familial group is a core milestone of adolescent development in many species. A wild species of mouse, Mus spicilegus, presents an exciting model in which to study adolescent development and dispersal because it shows different life history trajectory depending on season of birth. M. spicilegus born in spring and summer on long days (LD) disperse in the first 3 months of life, while M. spicilegus born on shorter autumnal days (SD) delay dispersal through the wintertime. We were interested in using these mice in a laboratory context to compare age-matched mice with differential motivation to disperse. To first test if we could find a proxy for dispersal related behavior in the laboratory environment, we measured open field and novel object investigation across development in M. spicilegus raised on a LD 12 h:12 h light:dark cycle. We found that between the first and second month of life, distance traveled and time in center of the open field increased significantly with age in M. spicilegus. Robust novel object investigation was observed in all age groups and decreased between the 2nd and 3rd month of life in LD males. Compared to male C57BL/6 mice, male M. spicilegus traveled significantly longer distances in the open field but spent less time in the center of the field. However, when a novel object was placed in the center of the open field, Male M. spicilegus, were significantly more willing to contact and mount it. To test if autumnal photoperiod affects exploratory behavior in M. spicilegus in a laboratory environment, we reared a cohort of M. spicilegus on a SD 10 h:14 h photoperiod and tested their exploratory behavior at P60-70. At this timepoint, we found SD rearing had no effect on open field metrics, but led to reduced novel object investigation. We also observed that in P60-70 males, SD reared M. spicilegus weighed less than LD reared M. spicilegus. These observations establish that SD photoperiod can delay weight gain and blunt some, but not all forms of exploratory behavior in adolescent M. spicilegus. Copyright © 2022 Cryns, Lin, Motahari, Krentzman, Chen, Prounis and Wilbrecht.

Author Keywords
adolescence;  dispersal;  novel object;  open field;  seasonality;  wild mouse

Funding details
University of California BerkeleyUCB

Document Type: Article
Publication Stage: Final
Source: Scopus

Correlating electroconvulsive therapy response to electroencephalographic markers: Study protocol
(2022) Frontiers in Psychiatry, 13, art. no. 996733, . 

Subramanian, S.^a b c , Labonte, A.K.^d e , Nguyen, T.^d , Luong, A.H.^d f , Hyche, O.^d , Smith, S.K.^d g , Hogan, R.E.^h , Farber, N.B.^a , Palanca, B.J.A.^{a d g i j k} , Kafashan, M.^d g , CET-REM Study Group^l

^a Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^b Department of Neurology, Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, United States
^c Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
^d Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^e Neuroscience Graduate Program, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^f Department of Health Policy and Management, Columbia University, New York, NY, United States
^g Center on Biological Rhythms and Sleep, Washington University School of Medicine in St. LouisMO, United States
^h Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
ⁱ Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^j Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
^k Neuroimaging Labs Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, United States

Abstract
Introduction: Electroconvulsive therapy (ECT) is an effective intervention for patients with major depressive disorder (MDD). Despite longstanding use, the underlying mechanisms of ECT are unknown, and there are no objective prognostic biomarkers that are routinely used for ECT response. Two electroencephalographic (EEG) markers, sleep slow waves and sleep spindles, could address these needs. Both sleep microstructure EEG markers are associated with synaptic plasticity, implicated in memory consolidation, and have reduced expression in depressed individuals. We hypothesize that ECT alleviates depression through enhanced expression of sleep slow waves and sleep spindles, thereby facilitating synaptic reconfiguration in pathologic neural circuits. Methods: Correlating ECT Response to EEG Markers (CET-REM) is a single-center, prospective, observational investigation. Wireless wearable headbands with dry EEG electrodes will be utilized for at-home unattended sleep studies to allow calculation of quantitative measures of sleep slow waves (EEG SWA, 0.5–4 Hz power) and sleep spindles (density in number/minute). High-density EEG data will be acquired during ECT to quantify seizure markers. Discussion: This innovative study focuses on the longitudinal relationships of sleep microstructure and ECT seizure markers over the treatment course. We anticipate that the results from this study will improve our understanding of ECT. Copyright © 2022 Subramanian, Labonte, Nguyen, Luong, Hyche, Smith, Hogan, Farber, Palanca, Kafashan and CET-REM Study Group.

Author Keywords
depression;  electroconvulsive therapy (ECT);  electroencephalography (EEG);  seizure;  sleep;  sleep spindle;  slow wave (NREM) sleep

Funding details
P50 MH122351, R25 MH11247
National Institutes of HealthNIHTL1TR002344
National Institute of Mental HealthNIMHK01 MH128663
National Institute of General Medical SciencesNIGMS1U01MH128483, T32GM108539
McDonnell Center for Systems Neuroscience

Document Type: Article
Publication Stage: Final
Source: Scopus

Diabetic retinopathy predicts cardiovascular disease independently of subclinical atherosclerosis in individuals with type 2 diabetes: A prospective cohort study
(2022) Frontiers in Cardiovascular Medicine, 9, art. no. 945421, . 

Castelblanco, E.^a b , Granado-Casas, M.^c d , Hernández, M.^e f , Pinyol, M.^g , Correig, E.^h , Julve, J.^c d , Rojo-López, M.I.^d , Alonso, N.^c i , Avogaro, A.^j , Ortega, E.^k l m , Mauricio, D.^c d n

^a Division of Endocrinology, Metabolism and Lipid Research, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^b DAP-Cat Group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain
^c Center for Biomedical Research on Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III, Barcelona, Spain
^d Department of Endocrinology and Nutrition, Hospital de la Santa Creu i Sant Pau and Sant Pau Biomedical Research Institute (IIB Sant Pau), Barcelona, Spain
^e Lleida Institute for Biomedical Research Dr. Pifarré Foundation IRBLleida, University of Lleida, Lleida, Spain
^f Department of Endocrinology and Nutrition, University Hospital Arnau de Vilanova, Lleida, Spain
^g Consorcio de Atención Primaria del Eixample (CAPSE), Grup Transversal de Recerca en Atenció Primària, Institut d’Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
^h Department of Biostatistics, Universitat Rovira i Virgili, Reus, Spain
ⁱ Department of Endocrinology and Nutrition, Germans Trias i Pujol Hospital and Research Institute, Universitat Autònoma de Barcelona, Badalona, Spain
^j Department of Medicine, Università di Padova, Padua, Italy
^k Diabetes Unit, Department of Endocrinology and Nutrition, Hospital Clínic de Barcelona, Barcelona, Spain
^l Center for Biomedical Research on Pathophysiology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Barcelona, Spain
^m Institut d’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
ⁿ Faculty of Medicine, University of Vic (UVic-UCC), Barcelona, Spain

Abstract
Background: Diabetic retinopathy (DR) and preclinical atherosclerosis are associated with higher cardiovascular risk. However, no studies have investigated the predictive role of DR and preclinical atherosclerosis jointly on cardiovascular events in subjects with type 2 diabetes (T2D). We aimed to assess the contribution of DR and subclinical atherosclerosis on the risk of adverse cardiovascular events in subjects with T2D without previous cardiovascular disease (CVD). Methods: We included two prospective cohorts of subjects with T2D from the same geographical area. Assessment of subclinical atherosclerosis was performed by carotid ultrasound. An ophthalmologist classified DR according to standard criteria. Cardiovascular outcomes considered for analysis were the following: ischemic heart disease, stroke, heart failure, peripheral artery disease, revascularization procedures, and cardiovascular mortality. Bivariable and multivariable predictive models were performed. Results: From a total of 374 subjects with T2D 44 developed cardiovascular events during the 7.1 years of follow-up. Diabetes duration, total cholesterol, and glycated hemoglobin (HbA1c) at baseline were higher in subjects who developed cardiovascular outcomes (p < 0.001, p = 0.026, and p = 0.040, respectively). Compared with subjects without events, those developing cardiovascular events had higher prevalence of retinopathy (65.9% vs. 38.8%, p = 0.001; respectively) and more than mild retinopathy (43.2% vs. 31.8%, p = 0.002; respectively). Furthermore, all-cause mortality was higher in subjects with MACE than those without events (13.6% vs. 3.3%, p = 0.009; respectively). The multivariable analyses showed that HbA1c and the presence of DR at baseline were predictive of cardiovascular outcomes (p = 0.045 and p = 0.023, respectively). However, the burden of subclinical atherosclerosis was not (p = 0.783 and p = 0.071, respectively). Conclusion: DR is a strong predictor of cardiovascular events in T2D individuals at primary CVD prevention, even after accounting for the presence of preclinical carotid atherosclerosis. These results may help to individualize CVD prevention strategies in T2D. Copyright © 2022 Castelblanco, Granado-Casas, Hernández, Pinyol, Correig, Julve, Rojo-López, Alonso, Avogaro, Ortega and Mauricio.

Author Keywords
cardiovascular disease;  diabetic retinopathy;  major adverse cardiovascular events;  subclinical atherosclerosis;  type 2 diabetes

Funding details
SLT017/20/000107
RED2018-102799-T
Federación Española de Enfermedades RarasFEDER
Ministerio de Economía y CompetitividadMINECO
Instituto de Salud Carlos IIIISCIII

Document Type: Article
Publication Stage: Final
Source: Scopus

Optimizing Neurodevelopmental Outcomes in Neonates With Congenital Heart Disease
(2022) Pediatrics, 150, . 

Ortinau, C.M.^a b , Smyser, C.D.^a b c d , Arthur, L.^e , Gordon, E.E.^f , Heydarian, H.C.^g , Wolovits, J.^f , Nedrelow, J.^h , Marino, B.S.ⁱ , Levy, V.Y.^j

^a Departments of Pediatrics
^b Contributed equally as co-first authors
^c Neurology
^d Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States
^e Department of Pediatrics, University of Arkansas for Medical Sciences, Little RockAR, United States
^f Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, United States
^g Department of Pediatrics, University of Cincinnati College of Medicine, Division of Cardiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
^h Department of Neonatology, Cook Children’s Medical Center, Fort Worth, TX, United States
ⁱ Department of Pediatrics, Northwestern University Feinberg School of Medicine, Divisions of Cardiology and Critical Care Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, United States
^j Department of Pediatrics, Stanford University School of Medicine, Lucile Packard Children’s Hospital, Palo Alto, CA, United States

Abstract
Neurodevelopmental impairment is a common and important long-term morbidity among infants with congenital heart disease (CHD). More than half of those with complex CHD will demonstrate some form of neurodevelopmental, neurocognitive, and/or psychosocial dysfunction requiring specialized care and impacting long-term quality of life. Preventing brain injury and treating long-term neurologic sequelae in this high-risk clinical population is imperative for improving neurodevelopmental and psychosocial outcomes. Thus, cardiac neurodevelopmental care is now at the forefront of clinical and research efforts. Initial research primarily focused on neurocritical care and operative strategies to mitigate brain injury. As the field has evolved, investigations have shifted to understanding the prenatal, genetic, and environmental contributions to impaired neurodevelopment. This article summarizes the recent literature detailing the brain abnormalities affecting neurodevelopment in children with CHD, the impact of genetics on neurodevelopmental outcomes, and the best practices for neonatal neurocritical care, focusing on developmental care and parental support as new areas of importance. A framework is also provided for the infrastructure and resources needed to support CHD families across the continuum of care settings. Copyright © 2022 by the American Academy of Pediatrics.

Document Type: Article
Publication Stage: Final
Source: Scopus

The incidence of candidate binding sites for β-arrestin in Drosophila neuropeptide GPCRs
(2022) PLoS ONE, 17 (11 November), art. no. e0275410, . 

Taghert, P.H.

Department of Neuroscience, Washington University, School of Medicine, St. Louis, MO, United States

Abstract
To support studies of neuropeptide neuromodulation, I have studied beta-arrestin binding sites (BBS’s) by evaluating the incidence of BBS sequences among the C terminal tails (CTs) of each of the 49 Drosophila melanogaster neuropeptide GPCRs. BBS were identified by matches with a prediction derived from structural analysis of rhodopsin:arrestin and vasopressin receptor: arrestin complexes [1]. To increase the rigor of the identification, I determined the conservation of BBS sequences between two long-diverged species D. melanogaster and D. virilis. There is great diversity in the profile of BBS’s in this group of GPCRs. I present evidence for conserved BBS’s in a majority of the Drosophila neuropeptide GPCRs; notably some have no conserved BBS sequences. In addition, certain GPCRs display numerous conserved compound BBS’s, and many GPCRs display BBS-like sequences in their intracellular loop (ICL) domains as well. Finally, 20 of the neuropeptide GPCRs are expressed as protein isoforms that vary in their CT domains. BBS profiles are typically different across related isoforms suggesting a need to diversify and regulate the extent and nature of GPCR:arrestin interactions. This work provides the initial basis to initiate future in vivo, genetic analyses in Drosophila to evaluate the roles of arrestins in neuropeptide GPCR desensitization, trafficking and signaling. © 2022 Paul H. Taghert. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding details
National Institutes of HealthNIH
National Institute of Neurological Disorders and StrokeNINDSR01 NS108393-20

Document Type: Article
Publication Stage: Final
Source: Scopus

Stroke genetics informs drug discovery and risk prediction across ancestries
(2022) Nature, 611 (7934), pp. 115-123. Cited 2 times.

Mishra, A.^a , Malik, R.^b , Hachiya, T.^c , Jürgenson, T.^d e , Namba, S.^f , Posner, D.C.^g , Kamanu, F.K.^h i , Koido, M.^j k , Le Grand, Q.^a , Shi, M.^k , He, Y.^k , Georgakis, M.K.^b l m , Caro, I.^a , Krebs, K.^d , Liaw, Y.-C.^n o , Vaura, F.C.^p q , Lin, K.^r , Winsvold, B.S.^s t u , Srinivasasainagendra, V.^v , Parodi, L.^l m , Bae, H.-J.^w , Chauhan, G.^x , Chong, M.R.^y z , Tomppo, L.^aa , Akinyemi, R.^ab ac , Roshchupkin, G.V.^ad ae , Habib, N.^af , Jee, Y.H.^ag , Thomassen, J.Q.^ah , Abedi, V.^ai aj , Cárcel-Márquez, J.^ak al , Nygaard, M.^am an , Leonard, H.L.^ao ap aq , Yang, C.^ar as , Yonova-Doing, E.^at au , Knol, M.J.^ad , Lewis, A.J.^av , Judy, R.L.^aw , Ago, T.^ax , Amouyel, P.^ay az ba , Armstrong, N.D.^bb , Bakker, M.K.^bc , Bartz, T.M.^bd be , Bennett, D.A.^bf , Bis, J.C.^bd , Bordes, C.^a , Børte, S.^t bg bh , Cain, A.^af , Ridker, P.M.^bi bj , Cho, K.^g , Chen, Z.^r bk , Cruchaga, C.^bl bm , Cole, J.W.^bn bo , de Jager, P.L.^m bp , de Cid, R.^bq , Endres, M.^{br bs bt bu} , Ferreira, L.E.^bv , Geerlings, M.I.^bw , Gasca, N.C.^be , Gudnason, V.^bx by , Hata, J.^bz , He, J.^av , Heath, A.K.^ca , Ho, Y.-L.^g , Havulinna, A.S.^cb cc , Hopewell, J.C.^cd , Hyacinth, H.I.^ce , Inouye, M.^{at cf cg ch ci} , Jacob, M.A.^cj , Jeon, C.E.^ck , Jern, C.^cl cm , Kamouchi, M.^cn , Keene, K.L.^co , Kitazono, T.^ax , Kittner, S.J.^bo cp , Konuma, T.^f , Kumar, A.^x , Lacaze, P.^cq , Launer, L.J.^cr , Lee, K.-J.^cs , Lepik, K.^{d ct cu cv} , Li, J.^ai , Li, L.^cw , Manichaikul, A.^ar , Markus, H.S.^cx , Marston, N.A.^h i , Meitinger, T.^cy cz , Mitchell, B.D.^da db , Montellano, F.A.^dc dd , Morisaki, T.^j , Mosley, T.H.^de , Nalls, M.A.^ao ap aq , Nordestgaard, B.G.^df dg , O’Donnell, M.J.^dh , Okada, Y.^{f di dj dk dl dm} , Onland-Moret, N.C.^bw , Ovbiagele, B.^dn , Peters, A.^do dp dq , Psaty, B.M.^bd dr ds , Rich, S.S.^ar , Rosand, J.^l m dt , Sabatine, M.S.^h i , Sacco, R.L.^du dv , Saleheen, D.^dw , Sandset, E.C.^dx dy , Salomaa, V.^cb , Sargurupremraj, M.^dz , Sasaki, M.^c , Satizabal, C.L.^dz ea , Schmidt, C.O.^eb , Shimizu, A.^c , Smith, N.L.^dr ec ed , Sloane, K.L.^ee , Sutoh, Y.^c , Sun, Y.V.^ef eg , Tanno, K.^c , Tiedt, S.^b , Tatlisumak, T.^eh , Torres-Aguila, N.P.^ak , Tiwari, H.K.^v , Trégouët, D.-A.^a , Trompet, S.^ei ej , Tuladhar, A.M.^cj , Tybjærg-Hansen, A.^ah dg , van Vugt, M.^ek , Vibo, R.^el , Verma, S.S.^em , Wiggins, K.L.^bd , Wennberg, P.^en , Woo, D.^ce , Wilson, P.W.F.^ef eo , Xu, H.^da , Yang, Q.^ea ep , Yoon, K.^eq , Millwood, I.Y.^r bk , Gieger, C.^er , Ninomiya, T.^bz , Grabe, H.J.^es et , Jukema, J.W.^ej eu ev , Rissanen, I.L.^bw , Strbian, D.^aa , Kim, Y.J.^eq , Chen, P.-H.^o , Mayerhofer, E.^l m , Howson, J.M.M.^at au , Irvin, M.R.^bb , Adams, H.^ew ex , Wassertheil-Smoller, S.^ey , Christensen, K.^am an ez , Ikram, M.A.^ad , Rundek, T.^du dv , Worrall, B.B.^fa fb , Lathrop, G.M.^fc , Riaz, M.^cq , Simonsick, E.M.^fd , Kõrv, J.^el , França, P.H.C.^bv , Zand, R.^fe ff , Prasad, K.^x , Frikke-Schmidt, R.^ah dg , de Leeuw, F.-E.^cj , Liman, T.^bs bw fg , Haeusler, K.G.^dd , Ruigrok, Y.M.^bc , Heuschmann, P.U.^dc fh fi , Longstreth, W.T.^dr fj , Jung, K.J.^r fk , Bastarache, L.^av , Paré, G.^y z fl fm , Damrauer, S.M.^fn fo , Chasman, D.I.^bi bj , Rotter, J.I.^fp , Anderson, C.D.^l m dt fq , Zwart, J.-A.^s t bg , Niiranen, T.J.^p q fr , Fornage, M.^fs ft , Liaw, Y.-P.^o fu , Seshadri, S.^dz ea fv , Fernández-Cadenas, I.^ak , Walters, R.G.^r bk , Ruff, C.T.^h i , Owolabi, M.O.^ab fw , Huffman, J.E.^g , Milani, L.^d , Kamatani, Y.^k , Dichgans, M.^b fx fy , Debette, S.^a fz , COMPASS Consortium^ga , INVENT Consortium^gb , Dutch Parelsnoer Initiative (PSI) Cerebrovascular Disease Study Group^gc , Estonian Biobank^gd , PRECISEQ Consortium^ge , FinnGen Consortium^gf , NINDS Stroke Genetics Network (SiGN)^gg , MEGASTROKE Consortium^gh , SIREN Consortium^gi , China Kadoorie Biobank Collaborative Group^gj , VA Million Veteran Program^gk , International Stroke Genetics Consortium (ISGC)^gl , Biobank Japan^gm , CHARGE Consortium^gn , GIGASTROKE Consortium^go

^a Bordeaux Population Health Research Center, University of Bordeaux, Inserm, Bordeaux, France
^b Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
^c Iwate Tohoku Medical Megabank Organization, Iwate Medical UniversityIwate, Japan
^d Estonian Genome Centre, Institute of Genomics, University of TartuTartu, Estonia
^e Institute of Mathematics and Statistics, University of TartuTartu, Estonia
^f Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
^g Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, United States
^h TIMI Study Group, Boston, MA, United States
ⁱ Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
^j Division of Molecular Pathology, Institute of Medical Sciences, University of TokyoTokyo, Japan
^k Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, University of TokyoTokyo, Japan
^l Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
^m Program in Medical and Population Genetics, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, United States
ⁿ Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of TokyoTokyo, Japan
^o Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung, Taiwan
^p Department of Internal Medicine, University of Turku, Turku, Finland
^q Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Turku, Finland
^r Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
^s Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University HospitalOslo, Norway
^t K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
^u Department of Neurology, Oslo University HospitalOslo, Norway
^v Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, United States
^w Department of Neurology and Cerebrovascular Disease Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea
^x Rajendra Institute of Medical Sciences, Ranchi, India
^y Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
^z Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
^aa Department of Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
^ab Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria
^ac Neuroscience and Ageing Research Unit Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan, Ibadan, Nigeria
^ad Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
^ae Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
^af Edmond and Lily Safra Center for Brain Sciences, Hebrew University of JerusalemJerusalem, Israel
^ag Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
^ah Department of Clinical Biochemistry, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
^ai Department of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, VA, United States
^aj Department of Public Health Sciences, College of Medicine, Pennsylvania State University, State CollegePA, United States
^ak Stroke Pharmacogenomics and Genetics Laboratory, Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain
^al Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
^am Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
^an Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
^ao Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, United States
^ap Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
^aq Data Tecnica International, Glen Echo, MD, United States
^ar Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
^as Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, United States
^at British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
^au Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, United Kingdom
^av Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
^aw Department of Surgery, University of Pennsylvania, Philadelphia, PA, United States
^ax Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu UniversityFukuoka, Japan
^ay University of Lille, INSERM U1167, RID-AGE, LabEx DISTALZ, Risk Factors and Molecular Determinants of Aging-Related Diseases, Lille, France
^az Public Health Department, CHU Lille, Lille, France
^ba Institut Pasteur de Lille, Lille, France
^bb Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
^bc UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, University UtrechtUtrecht, Netherlands
^bd Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
^be Department of Biostatistics, University of Washington, Seattle, WA, United States
^bf Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
^bg Institute of Clinical Medicine, Faculty of Medicine, University of OsloOslo, Norway
^bh Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University HospitalOslo, Norway
^bi Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, United States
^bj Harvard Medical School, Boston, MA, United States
^bk MRC Population Health Research Unit, University of Oxford, Oxford, United Kingdom
^bl Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
^bm NeuroGenomics and Informatics Center, Washington University School of Medicine, Saint Louis, MO, USA
^bn VA Maryland Health Care System, Baltimore, MD, United States
^bo Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, United States
^bp Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
^bq GenomesForLife-GCAT Lab Group, Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain
^br Klinik und Hochschulambulanz für Neurologie, Charité-Universitätsmedizin BerlinBerlin, Germany
^bs Center for Stroke Research BerlinBerlin, Germany
^bt German Center for Neurodegenerative Diseases (DZNE), partner site BerlinBerlin, Germany
^bu German Centre for Cardiovascular Research (DZHK), partner site BerlinBerlin, Germany
^bv Post-Graduation Program on Health and Environment, Department of Medicine and Joinville Stroke Biobank, University of the Region of JoinvilleSanta Catarina, Brazil
^bw Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht UniversityUtrecht, Netherlands
^bx Icelandic Heart Association, Kopavogur, Iceland
^by Faculty of Medicine, University of Iceland, Reykjavik, Iceland
^bz Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu UniversityFukuoka, Japan
^ca Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
^cb Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
^cc Institute for Molecular Medicine Finland, Helsinki, Finland
^cd Clinical Trial Service and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
^ce Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States
^cf Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
^cg Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
^ch Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
^ci British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
^cj Department of Neurology, Donders Center for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
^ck Los Angeles County Department of Public Health, Los Angeles, CA, United States
^cl Institute of Biomedicine, Department of Laboratory Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
^cm Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
^cn Department of Health Care Administration and Management, Graduate School of Medical Sciences, Kyushu UniversityFukuoka, Japan
^co Department of Biology, Brody School of Medicine Center for Health Disparities, East Carolina University, Greenville, NC, United States
^cp Department of Neurology and Geriatric Research and Education Clinical Center, VA Maryland Health Care System, Baltimore, MD, United States
^cq Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
^cr Intramural Research Program, National Institute on Aging, NIH, Baltimore, MD, United States
^cs Department of Neurology, Korea University Guro HospitalSeoul, South Korea
^ct Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
^cu Swiss Institute of Bioinformatics, Lausanne, Switzerland
^cv University Center for Primary Care and Public Health, Lausanne, Switzerland
^cw Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science CenterBeijing, China
^cx Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
^cy Institute of Human Genetics, Technical University of Munich, Munich, Germany
^cz Institute of Human Genetics, German Research Center for Environmental Health, Helmholtz Zentrum MünchenNeuherberg, Germany
^da Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
^db Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, United States
^dc Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
^dd Department of Neurology, University Hospital Würzburg, Würzburg, Germany
^de MIND Center, University of Mississippi Medical Center, Jackson, MS, United States
^df Department of Clinical Biochemistry, Copenhagen University Hospital-Herlev and Gentofte, Copenhagen, Denmark
^dg Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
^dh College of Medicine Nursing and Health Science, NUI Galway, Galway, Ireland
^di Department of Genome Informatics, Graduate School of Medicine, University of TokyoTokyo, Japan
^dj Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
^dk Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
^dl Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
^dm Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
^dn Weill Institute for Neurosciences, University of California, San Francisco, CA, United States
^do Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum MünchenNeuherberg, Germany
^dp Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University Munich, Munich, Germany
^dq German Centre for Cardiovascular Research (DZHK), partner site Munich, Munich, Germany
^dr Department of Epidemiology, University of Washington, Seattle, WA, United States
^ds Department of Health Systems and Population Health, University of Washington, Seattle, WA, United States
^dt McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
^du Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, United States
^dv Evelyn F. McKnight Brain Institute, Gainesville, FL, United States
^dw Division of Cardiology, Department of Medicine, Columbia University, New York, NY, USA
^dx Stroke Unit, Department of Neurology, Oslo University HospitalOslo, Norway
^dy Research and Development, Norwegian Air Ambulance FoundationOslo, Norway
^dz Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, United States
^ea Framingham Heart Study, Framingham, MA, United States
^eb Institute for Community Medicine, Greifswald, University Medicine Greifswald, Germany
^ec Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
^ed Department of Veterans Affairs Office of Research and Development, Seattle Epidemiologic Research and Information Center, Seattle, WA, United States
^ee Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
^ef Atlanta VA Health Care System, Decatur, GA, United States
^eg Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, United States
^eh Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Unviersity Hospital, Gothenburg, Sweden
^ei Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
^ej Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands
^ek Division Heart & Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht UniversityUtrecht, Netherlands
^el Department of Neurology and Neurosurgery, University of TartuTartu, Estonia
^em Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
^en Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
^eo Department of Medicine, Division of Cardiovascular Disease, Emory University School of Medicine, Atlanta, GA, United States
^ep Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
^eq Division of Genome Science, Department of Precision Medicine, Cheongju, National Institute of Health, South Korea
^er Research Unit Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum MünchenNeuherberg, Germany
^es Department of Psychiatry and Psychotherapy, Greifswald, University Medicine Greifswald, Germany
^et German Center for Neurodegenerative Diseases (DZNE), site Rostock/Greifswald, Rostock, Germany
^eu Netherlands Heart InstituteUtrecht, Netherlands
^ev Einthoven Laboratory for Experimental Vascular Medicine, LUMC, Leiden, Netherlands
^ew Department of Clinical Genetics, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands
^ex Latin American Brain Health (BrainLat), Universidad Adolfo IbáñezSantiago, Chile
^ey Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA
^ez Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
^fa Department of Neurology, University of Virginia, Charlottesville, VA, United States
^fb Department of Public Health Science, University of Virginia, Charlottesville, VA, United States
^fc McGill Genome Centre, Montreal, QC, Canada
^fd Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, United States
^fe Geisinger Neuroscience Institute, Geisinger Health System, Danville, PA, United States
^ff Department of Neurology, College of Medicine, Pennsylvania State University, State CollegePA, United States
^fg Klinik für Neurologie, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany
^fh Comprehensive Heart Failure Center, University Hospital Würzburg, Würzburg, Germany
^fi Clinical Trial Center, University Hospital Würzburg, Würzburg, Germany
^fj Department of Neurology, University of Washington, Seattle, WA, United States
^fk Institute for Health Promotion, Graduate School of Public Health, Yonsei UniversitySeoul, South Korea
^fl Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
^fm Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
^fn Department of Surgery and Department of Genetics, University of Pennsylvania, Philadelphia, PA, United States
^fo Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, United States
^fp Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
^fq Department of Neurology, Brigham and Women’s Hospital, Boston, MA, United States
^fr Division of Medicine, Turku University Hospital, Turku, Finland
^fs Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
^ft Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States
^fu Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan
^fv Department of Neurology, Boston University School of Medicine, Boston, MA, United States
^fw Department of Medicine, University of Ibadan, Ibadan, Nigeria
^fx Munich Cluster for Systems Neurology, Munich, Germany
^fy German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
^fz Department of Neurology, Institute for Neurodegenerative Diseases, CHU de Bordeaux, Bordeaux, France

Abstract
Previous genome-wide association studies (GWASs) of stroke – the second leading cause of death worldwide – were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries. © 2022. The Author(s).

Document Type: Article
Publication Stage: Final
Source: Scopus

Genetic Addiction Risk and Psychological Profiling Analyses for “Preaddiction” Severity Index
(2022) Journal of Personalized Medicine, 12 (11), art. no. 1772, . 

Blum, K.^{a b c d e f g h} , Han, D.ⁱ , Bowirrat, A.^h , Downs, B.W.^f , Bagchi, D.^f j , Thanos, P.K.^k l , Baron, D.^a b , Braverman, E.R.^b , Dennen, C.A.^m , Gupta, A.ⁿ , Elman, I.^o , Badgaiyan, R.D.^p q , Llanos-Gomez, L.^b , Khalsa, J.^r s , Barh, D.^g t , McLaughlin, T.^b , Gold, M.S.^u

^a Division of Addiction Research & Education, Center for Sports, Exercise, and Mental Health, Western University of Health Sciences, Pomona, CA 91766, United States
^b Division of Nutrigenomics, The Kenneth Blum Behavioral Neurogenetic Institute, LLC, Austin, TX 78701, United States
^c Institute of Psychology, ELTE Eötvös Loránd University, Budapest, 1075, Hungary
^d Department of Psychiatry, University of Vermont, Burlington, VT 05405, United States
^e Department of Psychiatry, Wright University Boonshoft School of Medicine, Dayton, OH 45324, United States
^f Division of Nutrigenomics, Victory Nutrition International, Inc., Harleysville, PA 19329, United States
^g Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology, Nonakuri, Purba Medinipur, West Bengal721172, India
^h Department of Molecular Biology, Adelson School of Medicine, Ariel University, Ariel, 40700, Israel
ⁱ Department of Management Science and Statistics, University of Texas at San Antonio, San Antonio, TX 78249, United States
^j Department of Pharmaceutical Sciences, College of Pharmacy, Southern University, Houston, TX 77004, United States
^k Behavioral Neuropharmacology and Neuroimaging Laboratory on Addictions, Clinical Research Institute on Addictions, Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biosciences, State University of New York at Buffalo, Buffalo, NY 14260, United States
^l Department of Psychology, State University of New York at Buffalo, Buffalo, NY 14260, United States
^m Department of Family Medicine, Jefferson Health Northeast, Philadelphia, PA 19107, United States
ⁿ Future Biologics, Lawrenceville, GA 30043, United States
^o Department of Psychiatry, Harvard School of Medicine, Cambridge, MA 02115, United States
^p Department of Psychiatry, South Texas Veteran Health Care System, Audie L. Murphy Memorial VA Hospital, Long School of Medicine, University of Texas Health Science Center, San Antonio, TX 78229, United States
^q Department of Psychiatry, MT. Sinai School of Medicine, New York, NY 10003, United States
^r Department of Microbiology, Immunology and Tropical Medicine, School of Medicine, George Washington University, Washington, DC 20052, United States
^s Medical Consequences of Drug Abuse and Infections Branch, National Institute on Drug Abuse, NIH, BethesdaMD 20892, United States
^t Department of Genetics, Ecology and Evolution, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil
^u Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States

Abstract
Since 1990, when our laboratory published the association of the DRD2 Taq A1 allele and severe alcoholism in JAMA, there has been an explosion of genetic candidate association studies, including genome-wide association studies (GWAS). To develop an accurate test to help identify those at risk for at least alcohol use disorder (AUD), a subset of reward deficiency syndrome (RDS), Blum’s group developed the genetic addiction risk severity (GARS) test, consisting of ten genes and eleven associated risk alleles. In order to statistically validate the selection of these risk alleles measured by GARS, we applied strict analysis to studies that investigated the association of each polymorphism with AUD or AUD-related conditions, including pain and even bariatric surgery, as a predictor of severe vulnerability to unwanted addictive behaviors, published since 1990 until now. This analysis calculated the Hardy–Weinberg Equilibrium of each polymorphism in cases and controls. Pearson’s χ2 test or Fisher’s exact test was applied to compare the gender, genotype, and allele distribution if available. The statistical analyses found the OR, 95% CI for OR, and the post risk for 8% estimation of the population’s alcoholism prevalence revealed a significant detection. Prior to these results, the United States and European patents on a ten gene panel and eleven risk alleles have been issued. In the face of the new construct of the “preaddiction” model, similar to “prediabetes”, the genetic addiction risk analysis might provide one solution missing in the treatment and prevention of the neurological disorder known as RDS. © 2022 by the authors.

Author Keywords
behavioral octopus;  dopamine homeostasis;  epigenetics;  genetic addiction risk analysis;  neurobiology;  preaddiction;  reward deficiency syndrome (RDS)

Document Type: Article
Publication Stage: Final
Source: Scopus

Acute and postacute sequelae associated with SARS-CoV-2 reinfection
(2022) Nature Medicine, 28 (11), pp. 2398-2405. 

Bowe, B.^a b , Xie, Y.^a b , Al-Aly, Z.^{a b c d e}

^a Clinical Epidemiology Center, Research and Development Service, Veteran Affairs Saint Louis Health Care System, St. Louis, MO, United States
^b Veterans Research and Education Foundation of St. Louis, St. Louis, MO, United States
^c Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
^d Nephrology Section, Medicine Service, Veteran Affairs St. Louis Health Care System, St. Louis, MO, United States
^e Institute for Public Health, Washington University in St. Louis, St. Louis, MO, United States

Abstract
First infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is associated with increased risk of acute and postacute death and sequelae in various organ systems. Whether reinfection adds to risks incurred after first infection is unclear. Here we used the US Department of Veterans Affairs’ national healthcare database to build a cohort of individuals with one SARS-CoV-2 infection (n = 443,588), reinfection (two or more infections, n = 40,947) and a noninfected control (n = 5,334,729). We used inverse probability-weighted survival models to estimate risks and 6-month burdens of death, hospitalization and incident sequelae. Compared to no reinfection, reinfection contributed additional risks of death (hazard ratio (HR) = 2.17, 95% confidence intervals (CI) 1.93–2.45), hospitalization (HR = 3.32, 95% CI 3.13–3.51) and sequelae including pulmonary, cardiovascular, hematological, diabetes, gastrointestinal, kidney, mental health, musculoskeletal and neurological disorders. The risks were evident regardless of vaccination status. The risks were most pronounced in the acute phase but persisted in the postacute phase at 6 months. Compared to noninfected controls, cumulative risks and burdens of repeat infection increased according to the number of infections. Limitations included a cohort of mostly white males. The evidence shows that reinfection further increases risks of death, hospitalization and sequelae in multiple organ systems in the acute and postacute phase. Reducing overall burden of death and disease due to SARS-CoV-2 will require strategies for reinfection prevention. © 2022, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.

Funding details
U.S. Department of Veterans AffairsVA
American Society of NephrologyASN

Document Type: Article
Publication Stage: Final
Source: Scopus

Increased white matter glycolysis in humans with cerebral small vessel disease
(2022) Nature Aging, 2 (11), pp. 991-999. 

Brier, M.R.^a , Blazey, T.^b , Raichle, M.E.^b , Morris, J.C.^a , Benzinger, T.L.S.^b , Vlassenko, A.G.^b , Snyder, A.Z.^a b b , Goyal, M.S.^a b b

^a Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^b Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States

Abstract
White matter lesions in cerebral small vessel disease are related to ischemic injury and increase the risk of stroke and cognitive decline. Pathological changes due to cerebral small vessel disease are increasingly recognized outside of discrete lesions, but the metabolic alterations in nonlesional tissue has not been described. Aerobic glycolysis is critical to white matter myelin homeostasis and repair. In this study, we examined cerebral metabolism of glucose and oxygen as well as blood flow in individuals with and without cerebral small vessel disease using multitracer positron emission tomography. We show that glycolysis is relatively elevated in nonlesional white matter in individuals with small vessel disease relative to healthy, age-matched controls. On the other hand, in young healthy individuals, glycolysis is relatively low in areas of white matter susceptible to lesion formation. These results suggest that increased white matter glycolysis is a marker of pathology associated with small vessel disease. © 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.

Funding details
National Institutes of HealthNIH
National Institute on AgingNIAKL2TR002346, P01AG003991, P01AG026276, P50AG005681, R01AG053503, R01AG057536, R25NS090978, RF1AG073210
Massachusetts General HospitalMGH
Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in St. LouisKGAD
Siemens Healthineers

Document Type: Article
Publication Stage: Final
Source: Scopus

Correlating Psychotropic Use to Major Depressive Disorder and ADHD Research Diagnoses: Trends in a Prospective Pediatric Cohort From Ages 3 to 21
(2022) The Journal of Clinical Psychiatry, 83 (6), . 

Liang, M.U.^a b , Charatcharungkiat, N.^c , Tillman, R.^c , Patel, H.M.^c , Vogel, A.C.^c , Luby, J.L.^c

^a Washington University School of Medicine, St. Louis, MO, United States
^b Corresponding author: Miranda U. Liang, BS, Washington University School of Medicine, 4444 Forest Park Ave, St. Louis, United States
^c Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Objective: To examine the associations of psychotropic usage to clinical characteristics in a pediatric research cohort with research diagnoses and severity scores. Methods: The cohort (N = 348) was enriched for children with mood and externalizing symptoms. Prospective longitudinal data were collected from ages 3 to 21 (September 2003-December 2019). At up to 10 time points, data on psychotropic medication use were collected by caregiver- and self-report from the MacArthur Health and Behavior Questionnaire, Parent Version and as part of the diagnostic interview, and research diagnoses (DSM-IV and DSM-5) and disease severity scores were acquired using an age-appropriate standardized research interview (Preschool Age Psychiatric Assessment, Child and Adolescent Psychiatric Assessment, Kiddie-Schedule for Affective Disorders and Schizophrenia). Results: The percentage of children with attention-deficit/hyperactivity disorder (ADHD) taking ADHD medications was preschool, 20.7%; school-age, 65.4%; and adolescence/early adulthood, 84.0%. The percentage with major depressive disorder (MDD) who were taking antidepressants was preschool, 0%; school-age, 21.6%; and adolescence/early adulthood, 42.6%. Antipsychotic use in children with research diagnoses of ADHD or MDD peaked in school-age: ADHD, 30.8%, and MDD, 21.6%. Children who were taking an antipsychotic concurrently with an ADHD medication or antidepressant had more comorbid conditions and higher disease severity than those taking ADHD medications or antidepressants without concurrent antipsychotics. Black children with MDD used antidepressants significantly less than White children with MDD (Black = 12.1%, White = 31.9%, FDR P = .0495). Conclusions: Concordance between research diagnosis and psychotropic use increased with age. Antipsychotic use was quite high, though more frequent in children with higher disease severity. Both findings suggest that psychotropic use is less tied to discrete diagnoses at earlier ages and that antipsychotic medication use may be motivated by severity/impairment rather than diagnosis. © Copyright 2022 Physicians Postgraduate Press, Inc.

Document Type: Article
Publication Stage: Final
Source: Scopus

The effects of wakeful rest on memory consolidation in an online memory study
(2022) Frontiers in Psychology, 13, art. no. 932592, . 

King, O.^a , Nicosia, J.^b

^a Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States
^b Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Memory consolidation is the process in which memory traces are strengthened over time for later retrieval. Although some theories hold that consolidation can only occur during sleep, accumulating evidence suggests that brief periods of wakeful rest may also facilitate consolidation. Interestingly, however, Varma and colleagues reported that a demanding 2-back task following encoding produced a similar performance to a wakeful reset condition. We tested whether participants’ recall would be best following a wakeful rest condition as compared to other distractor conditions, consistent with the extant wakeful rest literature, or whether we would replicate the finding by Varma and colleagues such that participants’ memory benefitted from both a rest and a 2-back task following encoding. Across two experiments, we used similar (Experiment 1) and the same (Experiment 2) encoding material as used the one by Varma and colleagues, employed a wakeful rest condition adapted for online testing, and compared participants’ recall across post-encoding conditions. In the first experiment, we used a between-subjects design and compared participants’ cued recall performance following a period of wakeful rest, a 2-back task, or a rest + sounds condition. The second experiment more closely replicated the experimental design used by Varma and colleagues using a within-subjects manipulation. Ultimately, our findings more consistently aligned with the canonical wakeful rest finding, such that recall was better following the rest condition than all other post-encoding conditions. These results support the notion that wakeful rest may allow for consolidation by protecting recently encoded information from interference, thereby improving memory performance. Copyright © 2022 King and Nicosia.

Author Keywords
consolidation;  memory;  online studies;  prolific;  wakeful rest

Document Type: Article
Publication Stage: Final
Source: Scopus

CROI 2022: neurologic complications of HIV-1, SARS-CoV-2, and other pathogens
(2022) Topics in Antiviral Medicine, 30 (4), pp. 475-489. 

Anderson, A.M.^a , Letendre, S.L.^b , Ances, B.M.^c

^a Emory University, Atlanta, GA, United States
^b University of Califoria San Diego, San Diego, CA, United States
^c Washington University at St Louis, St Louis, MO, United States

Abstract
The 2022 Conference on Retroviruses and Opportunistic Infections featured new and important findings about the neurologic complications of HIV-1, COVID-19, and other infections. Long-term analyses identified that cognitive decline over time, phenotypic aging, and stroke are associated with various comorbidities in people with HIV. Neuroimaging studies showed greater neuroinflammation, white matter damage, demyelination, and overall brain aging in people with chronic HIV infection. Childhood trauma and exposure to environmental pollutants contribute to these neuroimaging findings. Studies of blood and cerebrospinal fluid biomarkers showed that systemic inflammation, neurodegeneration, endothelial activation, oxidative stress, and iron dysregulation are associated with worse cognition in people with HIV. Some animal studies focused on myeloid cells of the central nervous system, but other animal and human studies showed that lymphoid cells also contribute to HIV neuropathogenesis. The deleterious central nervous system effects of polypharmacy and anticholinergic drugs in people with HIV were demonstrated. In contrast, a large randomized controlled trial showed that integrase strand transfer inhibitor therapy was not associated with neurotoxicity. Studies of cryptococcal meningitis demonstrated he cost-effectiveness of single high-dose liposomal amphotericin and the prognostic value of the cryptococcal antigen lateral flow assay. People hospitalized with COVID-19 had more anxiety over time after discharge. The SARS-CoV-2 nucleocapsid antigen is present in cerebrospinal fluid in the absence of viral RNA. Systemic inflammation, astrocyte activation, and tryptophan metabolism pathways are associated with post-COVID-19 neurologic syndromes. Whether these processes are independent or intertwined during HIV-1 and COVID-19 infections requires further study.

Document Type: Article
Publication Stage: Final
Source: Scopus

A Seizure-Like Episode in a Healthy 12-Month Old
(2022) Clinical Chemistry, 68 (8), pp. 1108-1110. 

Tawiah, K.^a , Roper, S.M.^a b

^a Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, United States
^b Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States

Document Type: Article
Publication Stage: Final
Source: Scopus

Optical imaging and spectroscopy for the study of the human brain: status report
(2022) Neurophotonics, 9, art. no. S24001, . 

Ayaz, H.^a b , Baker, W.B.^c d , Blaney, G.^e , Boas, D.A.^f g , Bortfeld, H.^h , Brady, K.^i j , Brake, J.^a k , Brigadoi, S.^a l , Buckley, E.M.^a m , Carp, S.A.^a n , Cooper, R.J.^a o , Cowdrick, K.R.^a p , Culver, J.P.^a q , Dan, I.^a r , Dehghani, H.^a s , Devor, A.^g t , Durduran, T.^a u , Eggebrecht, A.T.^b v , Emberson, L.L.^b w , Fang, Q.^b x , Fantini, S.^e y , Franceschini, M.A.^a z , Fischer, J.B.^a aa , Gervain, J.^a ab , Hirsch, J.^b ac , Hong, K.-S.^b ad , Horstmeyer, R.^b ae , Kainerstorfer, J.M.^c af , Ko, T.S.^c ag , Licht, D.J.^c ah , Liebert, A.^c ai , Luke, R.^c aj , Lynch, J.M.^c ak , Mesquida, J.^c al , Mesquita, R.C.^c am , Naseer, N.^d an , Novi, S.L.^c ao , Orihuela-Espina, F.^a ap , O’Sullivan, T.D.^d aq , Peterka, D.S.^d ar , Pifferi, A.^d as , Pollonini, L.^d at , Sassaroli, A.^e au , Sato, J.R.^d av , Scholkmann, F.^d aw , Spinelli, L.^e ax , Srinivasan, V.J.^e ay , St. Lawrence, K.^e az , Tachtsidis, I.^b ba , Tong, Y.^e bb , Torricelli, A.^d bc , Urner, T.^a bd , Wabnitz, H.^e be , Wolf, M.^d bf , Wolf, U.^d bg , Xu, S.^b bh , Yang, C.^e , Yodh, A.G.^e , Yücel, M.A.^f , Zhou, W.^e

^a Drexel University, School of Biomedical Engineering, Science, and Health Systems, Philadelphia, PA, United States
^b Drexel University, College of Arts and Sciences, Department of Psychological and Brain Sciences, Philadelphia, PA, United States
^c Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, PA, United States
^d Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^e Tufts University, Department of Biomedical Engineering, Medford, MA, United States
^f Boston University Neurophotonics Center, Boston, MA, United States
^g Boston University, College of Engineering, Department of Biomedical Engineering, Boston, MA, United States
^h University of California, Merced, Departments of Psychological Sciences and Cognitive and Information Sciences, Merced, CA, United States
ⁱ Lurie Children’s Hospital, Northwestern University Feinberg School of Medicine, Department of Anesthesiology, Chicago, IL, United States
^j Harvey Mudd College, Department of Engineering, Claremont, CA, United States
^k University of Padua, Department of Developmental and Social Psychology, Padua, Italy
^l Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, GA, United States
^m Emory University School of Medicine, Department of Pediatrics, Atlanta, GA, United States
ⁿ Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
^o University College London, Department of Medical Physics and Bioengineering, DOT-HUB, London, United Kingdom
^p Washington University School of Medicine, Department of Radiology, St. Louis, MO, United States
^q Chuo University, Faculty of Science and Engineering, Tokyo, Japan
^r University of Birmingham, School of Computer Science, Birmingham, United Kingdom
^s Icfo – the Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
^t Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
^u Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, MO, United States
^v University of British Columbia, Department of Psychology, Vancouver, BC, Canada
^w Northeastern University, Department of Bioengineering, Boston, MA, United States
^x Université Paris Cité, Cnrs, Integrative Neuroscience and Cognition Center, Paris, France
^y Yale School of Medicine, Department of Psychiatry, Neuroscience, and Comparative Medicine, New Haven, CT, United States
^z University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
^aa Pusan National University, School of Mechanical Engineering, Busan, South Korea
^ab Qingdao University, School of Automation, Institute for Future, Qingdao, China
^ac Duke University, Department of Biomedical Engineering, Durham, NC, United States
^ad Duke University, Department of Electrical and Computer Engineering, Durham, NC, United States
^ae Duke University, Department of Physics, Durham, NC, United States
^af Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, PA, United States
^ag Carnegie Mellon University, Neuroscience Institute, Pittsburgh, PA, United States
^ah Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, PA, United States
^ai Polish Academy of Sciences, Nalecz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Poland
^aj Macquarie University, Department of Linguistics, Sydney, NSW, Australia
^ak Macquarie University Hearing, Australia Hearing Hub, Sydney, NSW, Australia
^al Parc Taulí Hospital Universitari, Critical Care Department, Sabadell, Spain
^am University of Campinas, Institute of Physics, São Paulo, Brazil
^an Brazilian Institute of Neuroscience and Neurotechnology, São Paulo, Brazil
^ao Air University, Department of Mechatronics and Biomedical Engineering, Islamabad, Pakistan
^ap Western University, Department of Physiology and Pharmacology, London, ON, Canada
^aq University of Notre Dame, Department of Electrical Engineering, Notre Dame, IN, United States
^ar Columbia University, Zuckerman Mind Brain Behaviour Institute, New York, United States
^as Politecnico di Milano, Dipartimento di Fisica, Milan, Italy
^at University of Houston, Department of Engineering Technology, Houston, TX, United States
^au Federal University of Abc, Center of Mathematics, Computing and Cognition, São Bernardo do Campo, São Paulo, Brazil
^av University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
^aw University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
^ax National Research Council (CNR), Ifn – Institute for Photonics and Nanotechnologies, Milan, Italy
^ay University of California Davis, Department of Biomedical Engineering, Davis, CA, United States
^az Nyu Langone Health, Department of Ophthalmology, New York, NY, United States
^ba Nyu Langone Health, Department of Radiology, New York, NY, United States
^bb Lawson Health Research Institute, Imaging Program, London, ON, Canada
^bc Western University, Department of Medical Biophysics, London, ON, Canada
^bd Purdue University, Weldon School of Biomedical Engineering, West Lafayette, IN, United States
^be Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany
^bf California Institute of Technology, Department of Electrical Engineering, Pasadena, CA, United States
^bg University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, PA, United States
^bh China Jiliang University, College of Optical and Electronic Technology, Hangzhou, Zhejiang, China

Abstract
This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Author Keywords
DCS;  diffuse optics;  functional neuroscience;  NIRS;  optical imaging;  optical spectroscopy

Funding details
R01NS100750, R21NS116571
SAP4100083087
National Science FoundationNSFOAC1919691
National Institutes of HealthNIH
National Institute of Mental HealthNIMHR01MH122751
National Institute on Drug AbuseNIDA1UG3DA050325-01
National Institute on Deafness and Other Communication DisordersNIDCD1R01DC018701, GR-2019-12368539, R01-NS113945
National Institute of Nursing ResearchNINR1R01NR018425-01A1
Air Force Office of Scientific ResearchAFOSRFA9550-18-1-0455
Bill and Melinda Gates FoundationBMGF2020/INV-005792
James S. McDonnell FoundationJSMF2017/AWD1005451
American Heart AssociationAHA17SDG33700047, R21-EB024675
U.S. Army
Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNICHD1R21HD100997-01
Engineering Research CentersERC773202
Center for Machine Learning and Health, School of Computer Science, Carnegie Mellon UniversityCMLH, SCS, CMU
National Centre of Robotics and AutomationNCRA2016/22990-0, 2018/04654-9, 2018/21934-5, 2019/21962-1, 2021/05332-8, CNS1650536, EB029747, EB032840, EY031469, NCRA-RF-027, NS094681, P41EB015893, R01EB029595, TIP1919269, U01NS113273, U19NS104649
Canadian Institutes of Health ResearchIRSC14171, CGP-130391
Natural Sciences and Engineering Research Council of CanadaNSERCCHRP 478470
Canada Foundation for InnovationCFI2021/41724, R01-EB026998, R01-EB029414, R01-GM114365, R01-NS095334, U24-NS124027
Engineering and Physical Sciences Research CouncilEPSRCEP/N025946/1
Fundação de Amparo à Pesquisa do Estado de São PauloFAPESP2013/07559-3
National Natural Science Foundation of ChinaNSFC62105315
Ministero della SaluteR01HL152322, R01NS115994
Conselho Nacional de Desenvolvimento Científico e TecnológicoCNPq311768/2019-9
Ministry of Science, ICT and Future PlanningMSIP2020R1A2B5B03096000, R01MH107513, R01MH111629, R01MH119430
National Research Foundation of KoreaNRF
Higher Education Commision, PakistanHEC

Document Type: Article
Publication Stage: Final
Source: Scopus

ALSUntangled #68: ozone therapy
(2022) Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, . 

Sun, Y.^a , Barkhaus, P.^b , Barnes, B.^c , Beauchamp, M.^d , Benatar, M.^e , Bertorini, T.^f , Bromberg, M.^g , Carter, G.T.^h , Crayle, J.ⁱ , Cudkowicz, M.^j , Dimachkie, M.^k , Feldman, E.L.^l , Fullam, T.^m , Heiman-Patterson, T.ⁿ , Jhooty, S.^o , Lund, I.^p , Mcdermott, C.^q , Pattee, G.^r , Pierce, K.^s , Ratner, D.^t , Wicks, P.^u , Bedlack, R.^v

^a Neurology Department, University of Kentucky, Lexington, KY, United States
^b Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, United States
^c Department of Neurology, Medical College of Georgia, Augusta, GA, United States
^d Neurosciences Clinical Trials Unit, UNC Chapel Hill NC, Chapel Hill, NC, United States
^e Department of Neurology, University of Miami, Miami, FL, United States
^f Neurology Department, University of Tennessee Health Science Center, Memphis, TN, United States
^g Department of Neurology, University of Utah, Salt Lake City, UT, United States
^h Department of Rehabilitation, Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, United States
ⁱ Neurology Department, Washington University, St. Louis, MO, United States
^j Department of Neurology, Harvard Medical School, Boston, MA, United States
^k Department of Neurology, University of Kansas, Kansas City, KS, United States
^l Department of Neurology, University of Michigan, Ann Arbor, MI, United States
^m Department of Neurology, UTSA, San Antonio, TX, United States
ⁿ Department of Neurology, Temple Health, Philadelphia, PA, United States
^o Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^p Green Hope High School, Cary, NC, United States
^q Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
^r Department of Neurology, University of Nebraska Medical Center, Omaha, NE, United States
^s Department of Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^t Longmeadow High School, Longmeadow, MA, United States
^u Independent Consultant, Lichfield, United Kingdom
^v Department of Neurology, Duke University, Durham, NC, United States

Abstract
ALSUntangled reviews alternative and off-label treatments for people living with amyotrophic lateral sclerosis (PALS). Here we review ozone therapy. Ozone therapy has possible mechanisms for slowing ALS progression based on its antioxidant, anti-inflammatory, and mitochondrial effects. A non-peer-reviewed report suggests that ozone treatment may slow progression in a mTDP-43 mouse model of ALS. One verified “ALS reversal” occurred on a cocktail of alternative treatments including ozone. There are no ALS trials using ozone to treat PALS. There can be potentially serious side effects associated with ozone therapy, depending on the dose. Based on the above information, we support an investigation of ozone therapy in ALS cell or animal models but cannot yet recommend it as a treatment in PALS. © 2022 World Federation of Neurology on behalf of the Research Group on Motor Neuron Diseases.

Author Keywords
ALS;  alternative therapy;  neurodegeneration;  oxidative stress;  ozone therapy

Funding details
CytokineticsCYTK

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Identification of a Stress-Sensitive Anorexigenic Neurocircuit From Medial Prefrontal Cortex to Lateral Hypothalamus
(2022) Biological Psychiatry, . 

Clarke, R.E.^a , Voigt, K.^b , Reichenbach, A.^a , Stark, R.^a , Bharania, U.^a , Dempsey, H.^a , Lockie, S.H.^a , Mequinion, M.^a , Lemus, M.^a , Wei, B.^a , Reed, F.^a , Rawlinson, S.^a , Nunez-Iglesias, J.^c , Foldi, C.J.^a , Kravitz, A.V.^d , Verdejo-Garcia, A.^b , Andrews, Z.B.^a

^a Monash Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, Australia
^b Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
^c Monash Biomedicine Discovery Institute and Department of Anatomy and Developmental Biology, Monash University, Clayton, VIC, Australia
^d Departments of Psychiatry, Anesthesiology, and Neuroscience, Washington University in St. Louis, St. Louis, MO, United States

Abstract
Background: A greater understanding of how the brain controls appetite is fundamental to developing new approaches for treating diseases characterized by dysfunctional feeding behavior, such as obesity and anorexia nervosa. Methods: By modeling neural network dynamics related to homeostatic state and body mass index, we identified a novel pathway projecting from the medial prefrontal cortex (mPFC) to the lateral hypothalamus (LH) in humans (n = 53). We then assessed the physiological role and dissected the function of this mPFC-LH circuit in mice. Results: In vivo recordings of population calcium activity revealed that this glutamatergic mPFC-LH pathway is activated in response to acute stressors and inhibited during food consumption, suggesting a role in stress-related control over food intake. Consistent with this role, inhibition of this circuit increased feeding and sucrose seeking during mild stressors, but not under nonstressful conditions. Finally, chemogenetic or optogenetic activation of the mPFC-LH pathway is sufficient to suppress food intake and sucrose seeking in mice. Conclusions: These studies identify a glutamatergic mPFC-LH circuit as a novel stress-sensitive anorexigenic neural pathway involved in the cortical control of food intake. © 2022 Society of Biological Psychiatry

Author Keywords
Calcium imaging;  Chemogenetics;  FED3;  Feeding behavior;  Network modeling;  Optogenetics

Funding details
National Health and Medical Research CouncilNHMRCAPP1126724, APP1154974, APP2013243

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Targeted Memory Reactivation and Consolidation-Like Processes During Mind-Wandering in Younger and Older Adults
(2022) Journal of Experimental Psychology: Learning Memory and Cognition, . 

Nicosia, J., Balota, D.A.

Department of Psychological and Brain Sciences, Washington University in St. Louis School of Medicine, United States

Abstract
Mind-wandering (MW) is a universal cognitive process that is estimated to comprise ~30% of our everyday thoughts. Despite its prevalence, the functional utility of MW remains a scientific blind spot. The present study sought to investigate whether MW serves a functional role in cognition. Specifically, we investigated whether MW contributes to memory consolidation-like processes, and if age differences in the ability to reactivate episodic memories during MW may contribute to age-related declines in episodic memory. Younger and older adults encoded paired associates, received targeted reactivation cues during an interval filled with a task that promotes MW, and were tested on their memory for the cued and uncued stimuli from the initial encoding task. Thought probes were presented during the retention (MW) interval to assess participants’ thought contents. Across four experiments, we compared the effect of different cue modalities (i.e., auditory, visual) on cued recall performance, and examined both correct retrieval RTs as well as accuracy. Across experiments, there was evidence that stimuli that were cued during the MW task were correctly retrieved more quickly than uncued stimuli and that this effect was more robust for younger adults than older adults. Additionally, the more MW a participant reported during the retention interval, the stronger the cuing effect they produced during retrieval. The results from these experiments are interpreted within a retrieval facilitation framework wherein cues serve to reactivate the earlier traces during MW, and this reactivation benefits retrieval speed for cued items compared with uncued items. © 2022 American Psychological Association

Author Keywords
Aging;  Consolidation;  Episodic memory;  Mind-wandering

Funding details
National Institute on AgingNIAT32-AG000030-44
Washington University in St. LouisWUSTL
College and Graduate School of Arts and Sciences

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Working Memory Capacity Preferentially Enhances Implementation of Proactive Control
(2022) Journal of Experimental Psychology: Learning Memory and Cognition, . 

Lin, Y., Brough, R.E., Tay, A., Jackson, J.J., Braver, T.S.

Department of Psychological and Brain Sciences, Washington University, St. Louis, United States

Abstract
Previous research has linked working memory capacity (WMC) with enhanced proactive control. However, it remains unclear the extent to which this relationship reflects the influence of WMC on the tendency to engage proactive control, or rather, the ability to implement it. The current study sought to clarify this ambiguity by leveraging the Dual Mechanisms of Cognitive Control (DMCC) version of the AX-CPT task, in which the mode of cognitive control is experimentally manipulated across distinct testing sessions. To adjudicate between competing hypotheses, Bayesian mixed modeling was used to conduct sequential analyses involving two separate data sets. Posterior parameter estimates obtained from the initial analysis were entered as informed priors during the replication analysis to evaluate the influence of new data on previous estimates. Results yielded strong evidence demonstrating that the influence of WMC on proactive control is most robust under experimentally controlled conditions, during which use of proactive control is standardized across participants via explicit training and instruction. Critically, the observed pattern of findings suggests that the relationship between WMC and proactive control may be better characterized as individual differences in the ability to implement proactive control, rather than a more generalized tendency to engage it. © 2022 American Psychological Association

Author Keywords
Cognition;  Cognitive control;  Dual mechanisms of control;  Individual differences

Funding details
National Institutes of HealthNIHF32 AG069499, R37 MH066078

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Cortical adaptation of the night monkey to a nocturnal niche environment: a comparative non-invasive T1w/T2w myelin study
(2022) Brain Structure and Function, . 

Ikeda, T.^a , Autio, J.A.^a , Kawasaki, A.^a , Takeda, C.^a , Ose, T.^a , Takada, M.^b , Van Essen, D.C.^c , Glasser, M.F.^c d , Hayashi, T.^a e

^a Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
^b Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Japan
^c Department of Neuroscience, Washington University Medical School, St Louis, MO, United States
^d Department of Radiology, Washington University Medical School, St Louis, MO, United States
^e Department of Brain Connectomics, Kyoto University Graduate School of Medicine, Kyoto, Japan

Abstract
Night monkeys (Aotus) are the only genus of monkeys within the Simian lineage that successfully occupy a nocturnal environmental niche. Their behavior is supported by their sensory organs’ distinctive morphological features; however, little is known about their evolutionary adaptations in sensory regions of the cerebral cortex. Here, we investigate this question by exploring the cortical organization of night monkeys using high-resolution in-vivo brain MRI and comparative cortical-surface T1w/T2w myeloarchitectonic mapping. Our results show that the night monkey cerebral cortex has a qualitatively similar but quantitatively different pattern of cortical myelin compared to the diurnal macaque and marmoset monkeys. T1w/T2w myelin and its gradient allowed us to parcellate high myelin areas, including the middle temporal complex (MT +) and auditory cortex, and a low-myelin area, Brodmann area 7 (BA7) in the three species, despite species differences in cortical convolutions. Relative to the total cortical-surface area, those of MT + and the auditory cortex are significantly larger in night monkeys than diurnal monkeys, whereas area BA7 occupies a similar fraction of the cortical sheet in all three species. We propose that the selective expansion of sensory areas dedicated to visual motion and auditory processing in night monkeys may reflect cortical adaptations to a nocturnal environment. © 2022, The Author(s).

Author Keywords
Area MT;  Auditory cortex;  Comparative neuroanatomy;  Myelin;  Night monkey;  Primate

Funding details
National Institutes of HealthNIHMH060974
Japan Agency for Medical Research and DevelopmentAMEDJP18dm0307006, JP21dm0525006, JP22dm037006
Japan Society for the Promotion of ScienceKAKENJP15K08707, JP20K15945

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Functional and clinical studies reveal pathophysiological complexity of CLCN4-related neurodevelopmental condition
(2022) Molecular Psychiatry, . 

Palmer, E.E.^a b , Pusch, M.^c , Picollo, A.^c , Forwood, C.^a , Nguyen, M.H.^b d , Suckow, V.^e , Gibbons, J.^e , Hoff, A.^c f , Sigfrid, L.^c f , Megarbane, A.^g h , Nizon, M.^i j , Cogné, B.^i j , Beneteau, C.ⁱ , Alkuraya, F.S.^k , Chedrawi, A.^l , Hashem, M.O.^k , Stamberger, H.^m n , Weckhuysen, S.^m n o , Vanlander, A.^p , Ceulemans, B.^q , Rajagopalan, S.^d , Nunn, K.^r , Arpin, S.^s , Raynaud, M.^s , Motter, C.S.^t , Ward-Melver, C.^t , Janssens, K.^u , Meuwissen, M.^u , Beysen, D.^v , Dikow, N.^w , Grimmel, M.^x , Haack, T.B.^x , Clement, E.^y , McTague, A.^z aa , Hunt, D.^ab , Townshend, S.^ac , Ward, M.^ac , Richards, L.J.^ad ae , Simons, C.^af ag , Costain, G.^ah , Dupuis, L.^ah , Mendoza-Londono, R.^ah , Dudding-Byth, T.^ai aj , Boyle, J.^ai , Saunders, C.^ak al , Fleming, E.^am , El Chehadeh, S.^an ao , Spitz, M.-A.^ap , Piton, A.^aq , Gerard, B.^aq , Abi Warde, M.-T.^ap ar , Rea, G.^as , McKenna, C.^as , Douzgou, S.^at au , Banka, S.^au av , Akman, C.^aw , Bain, J.M.^aw , Sands, T.T.^aw , Wilson, G.N.^ax , Silvertooth, E.J.^ay , Miller, L.^az , Lederer, D.^ba , Sachdev, R.^a b , Macintosh, R.^a b , Monestier, O.^ba , Karadurmus, D.^ba , Collins, F.^bb , Carter, M.^bc , Rohena, L.^bd be , Willemsen, M.H.^bf , Ockeloen, C.W.^bf , Pfundt, R.^bf , Kroft, S.D.^bg , Field, M.^ai , Laranjeira, F.E.R.^bh , Fortuna, A.M.^bi , Soares, A.R.^bi , Michaud, V.^bj bk , Naudion, S.^bj , Golla, S.^bl , Weaver, D.D.^bm , Bird, L.M.^bn , Friedman, J.^bn , Clowes, V.^bo bp , Joss, S.^bq , Pölsler, L.^br , Campeau, P.M.^bs , Blazo, M.^bt , Bijlsma, E.K.^bu , Rosenfeld, J.A.^bv bw , Beetz, C.^bx , Powis, Z.^by , McWalter, K.^bz , Brandt, T.^bz , Torti, E.^bz , Mathot, M.^ca , Mohammad, S.S.^r cb , Armstrong, R.^cc , Kalscheuer, V.M.^e

^a Centre for Clinical Genetics, Sydney Children’s Hospital Network, Randwick, NSW, Australia
^b Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, University of New South Wales, Randwick, NSW, Australia
^c Istituto di Biofisica, CNR, Genova, Italy
^d Department of Clinical Genetics, Liverpool Hospital, Liverpool, NSW, Australia
^e Max Planck Institute for Molecular Genetics, Group Development and Disease, Berlin, Germany
^f Department of Biomedical and Clinical Sciences, Linköping University, Linköping, 581 83, Sweden
^g Department of Human Genetics, Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Byblos, Lebanon
^h Institut Jerome Lejeune, Paris, France
ⁱ Service de Génétique Médicale, CHU de Nantes, Nantes Université, Nantes, France
^j Nantes Université, CNRS, INSERM, l’Institut du Thorax, Nantes, France
^k Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
^l Department of Neurosciences, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
^m Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
ⁿ Neurology Department, Antwerp University Hospital, Antwerp, Belgium
^o Translational Neurosciences, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
^p Department of Child Neurology & Metabolism, Ghent University Hospital, Ghent, Belgium
^q Department of Pediatric Neurology, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium
^r Children’s Hospital at Westmead, Sydney Children’s Hospitals Network, Sydney, Australia
^s Service de Génétique Clinique, Centre Hospitalier Régional Universitaire de Tours, Tours, France
^t Genetic Center, Akron Children’s Hospital, Akron, OH, United States
^u Center of Medical Genetics, University Hospital Antwerp/University of Antwerp, Edegem, Belgium
^v Department of Pediatric Neurology, University Hospital Antwerp/University of Antwerp, Edegem, Belgium
^w Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
^x Institute of Medical Genetics and Applied Genomics, University of Tuebingen, Tuebingen, Germany
^y Department of Clinical Genetics, Great Ormond Street Hospital for Children, London, United Kingdom
^z Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
^aa Department of Neurology, Great Ormond Street Hospital, London, United Kingdom
^ab Wessex Clinical Genetics Service, Princess Anne Hospital, Southampton, United Kingdom
^ac Genetic Services of WA, King Edward Memorial Hospital, Subiaco, WA, Australia
^ad Department of Neuroscience, Washington University in St Louis School of Medicine, St Louis, MI, United States
^ae The University of Queensland, Queensland Brain Institute, St Lucia, QLD, Australia
^af Centre for Population Genomics, Murdoch Children’s Research Institute, Melbourne, Australia
^ag Garvan Institute of Medical Research, UNSW, Sydney, NSW, Australia
^ah Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, ON, Canada
^ai Genetics of Learning Disability Service, Newcastle, NSW, Australia
^aj University of Newcastle Grow Up Well Priority Research Centre, Newcastle, NSW, Australia
^ak Department of Pathology and Laboratory Medicine, Children’s Mercy Hospital and Clinics, Kansas City, MI, United States
^al Kansas City School of Medicine, University of Missouri, Kansas City, MI, United States
^am Division of Clinical Genetics, Children’s Mercy Hospital and Clinics, Kansas City, MI, United States
^an Service de Génétique Médicale, Institut de Génétique Médicale d’Alsace (IGMA), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
^ao Laboratoire de Génétique Médicale, UMRS_1112, Institut de Génétique Médicale d’Alsace (IGMA), Université de Strasbourg et INSERM, Strasbourg, France
^ap Service de Pédiatrie, Hôpital de Hautepierre, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
^aq Laboratoires de Diagnostic Génétique, Institut de Génétique Médicale d’Alsace (IGMA), Hôpitaux Universitaires de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France
^ar Pediatric Neurology Department, CHU de Strasbourg, Strasbourg, France
^as Northern Ireland Regional Genetics Service, Belfast, United Kingdom
^at Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
^au Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
^av Manchester Centre for Genomic Medicine, Saint Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom
^aw Department of Neurology, Division of Child Neurology, Columbia University Irving Medical Center, New York, United States
^ax Texas Tech Health Sciences Center Lubbock and KinderGenome Medical Genetics, Dallas, TX, United States
^ay Texas Sports Psychiatry and Integrative Health, Austin, TX, United States
^az Hillcrest Internal Medicine, Waco, TX, United States
^ba Centre de Génétique Humaine, Institut de Pathologie et de Génétique ASBL, Gosselies, Belgium
^bb Department of Medical Genomics/Clinical Genetics, Royal Prince Alfred Hospital, Camperdown, Sydney, NSW, Australia
^bc Department of Genetics, Children’s Hospital of Eastern Ontario, Ottawa, ON, Canada
^bd Division of Medical Genetics, Department of Pediatrics, San Antonio Military Medical Center, San Antonio, TX, United States
^be Department of Pediatrics, Long School of Medicine-UT Health San Antonio, San Antonio, TX, United States
^bf Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
^bg Pluryn, Residential Care Setting, Groesbeek, Netherlands
^bh Centro de Genética Médica Jacinto Magalhães, Centro Hospitalar Universitário do Porto, Porto, Portugal
^bi Unit for Multidisciplinary Research in Biomedicine, School of Medicine and Biomedical Sciences, Porto University, Porto, Portugal
^bj Service de Génétique Médicale, CHU Bordeaux, Bordeaux, France
^bk INSERM U1211, Laboratoire Maladies Rares: Génétique et Métabolisme, Bordeaux, Univ., Bordeaux, France
^bl Child Neurology and Neurodevelopmental Medicine Thompson Autism Center, CHOC Hospital, Orange County, CA, United States
^bm Indiana University School of Medicine, Indianapolis, United States
^bn University of California, San Diego, Rady Children’s Hospital San Diego, San Diego, CA, United States
^bo North West Thames Regional Genetics Service, London North West University Healthcare NHS Trust, Harrow, London, United Kingdom
^bp Imperial College London, London, United Kingdom
^bq West of Scotland Centre for Genomic Medicine, Queen Elizabeth University Hospital, Glasgow, United Kingdom
^br Centrum Medische Genetica, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussels, Belgium
^bs CHU Sainte-Justine Research Center, University of Montreal, Montreal, QC, Canada
^bt Division Clinical Genetics Texas A&M University Health Science Center, College Station, TX, United States
^bu Department of Clinical Genetics, Leiden University Medical Centre, Leiden, Netherlands
^bv Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
^bw Baylor Genetics Laboratories, Houston, TX, United States
^bx Centogene GmbH, Rostock, Germany
^by Clinical Genomics, Ambry Genetics, Aliso Viejo, CA, United States
^bz GeneDx LLC, Gaithersburg, MA, United States
^ca Neuropediatric Unit, CHU UCL-Namur, Namur, Belgium
^cb Children’s Hospital at Westmead, Sydney Children’s Hospitals Network, Sydney, NSW, Australia
^cc East Anglian Medical Genetics Service, Clinical Genetics, Addenbrooke’s Treatment Centre, Addenbrooke’s Hospital, Cambridge, United Kingdom

Abstract
Missense and truncating variants in the X-chromosome-linked CLCN4 gene, resulting in reduced or complete loss-of-function (LOF) of the encoded chloride/proton exchanger ClC-4, were recently demonstrated to cause a neurocognitive phenotype in both males and females. Through international clinical matchmaking and interrogation of public variant databases we assembled a database of 90 rare CLCN4 missense variants in 90 families: 41 unique and 18 recurrent variants in 49 families. For 43 families, including 22 males and 33 females, we collated detailed clinical and segregation data. To confirm causality of variants and to obtain insight into disease mechanisms, we investigated the effect on electrophysiological properties of 59 of the variants in Xenopus oocytes using extended voltage and pH ranges. Detailed analyses revealed new pathophysiological mechanisms: 25% (15/59) of variants demonstrated LOF, characterized by a “shift” of the voltage-dependent activation to more positive voltages, and nine variants resulted in a toxic gain-of-function, associated with a disrupted gate allowing inward transport at negative voltages. Functional results were not always in line with in silico pathogenicity scores, highlighting the complexity of pathogenicity assessment for accurate genetic counselling. The complex neurocognitive and psychiatric manifestations of this condition, and hitherto under-recognized impacts on growth, gastrointestinal function, and motor control are discussed. Including published cases, we summarize features in 122 individuals from 67 families with CLCN4-related neurodevelopmental condition and suggest future research directions with the aim of improving the integrated care for individuals with this diagnosis. © 2022, The Author(s).

Funding details
IG 21558
Wellcome TrustWT
Heart of England NHS Foundation TrustHEFT
Medical Research CouncilMRCMR/T007087/1, VS0122
National Institute for Health and Care ResearchNIHR
Cancer Research UKCRUK
Rosetrees TrustCF2\100018, GNT20081
National Health and Medical Research CouncilNHMRCGNT1120615
Deutsche ForschungsgemeinschaftDFG418081722, 433158657
Fonds Wetenschappelijk OnderzoekFWO1861419N
Ministero dell’Istruzione, dell’Università e della RicercaMIURPRIN 20174TB8KW
King Salman Center for Disability ResearchKSCDRRG-2022-010, RG-2022-011
Cerebral Palsy AllianceCPAPG01217
Fondation Médicale Reine ElisabethFMRE

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Spoken Word Recognition in Listeners with Mild Dementia Symptoms
(2022) Journal of Alzheimer’s Disease: JAD, 90 (2), pp. 749-759. 

McClannahan, K.S.^a , Mainardi, A.^a , Luor, A.^a , Chiu, Y.-F.^b , Sommers, M.S.^c , Peelle, J.E.^a d

^a Department of Otolaryngology, Washington University in St. Louis, St. Louis, MO, United States
^b Department of Speech, Language and Hearing Sciences, Saint Louis University, St. Louis, MO, United States
^c Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States
^d Center for Cognitive and Brain Health, Department of Communication Sciences and Disorders, Department of Psychology, Northeastern University, Boston, MA, United States

Abstract
BACKGROUND: Difficulty understanding speech is a common complaint of older adults. In quiet, speech perception is often assumed to be relatively automatic. However, higher-level cognitive processes play a key role in successful communication in noise. Limited cognitive resources in adults with dementia may therefore hamper word recognition. OBJECTIVE: The goal of this study was to determine the impact of mild dementia on spoken word recognition in quiet and noise. METHODS: Participants were 53-86 years with (n = 16) or without (n = 32) dementia symptoms as classified by the Clinical Dementia Rating scale. Participants performed a word identification task with two levels of word difficulty (few and many similar sounding words) in quiet and in noise at two signal-to-noise ratios, +6 and +3 dB. Our hypothesis was that listeners with mild dementia symptoms would have more difficulty with speech perception in noise under conditions that tax cognitive resources. RESULTS: Listeners with mild dementia symptoms had poorer task accuracy in both quiet and noise, which held after accounting for differences in age and hearing level. Notably, even in quiet, adults with dementia symptoms correctly identified words only about 80% of the time. However, word difficulty was not a factor in task performance for either group. CONCLUSION: These results affirm the difficulty that listeners with mild dementia may have with spoken word recognition, both in quiet and in background noise, consistent with a role of cognitive resources in spoken word identification.

Author Keywords
Cognition;  dementia;  hearing;  speech intelligibility;  word processing

Document Type: Article
Publication Stage: Final
Source: Scopus

Neonatal motor functional connectivity and motor outcomes at age two years in very preterm children with and without high-grade brain injury
(2022) NeuroImage: Clinical, 36, art. no. 103260, . 

Cyr, P.E.P.^a , Lean, R.E.^b , Kenley, J.K.^a , Kaplan, S.^a , Meyer, D.E.^a , Neil, J.J.^a , Alexopoulos, D.^a , Brady, R.G.^a , Shimony, J.S.^c , Rodebaugh, T.L.^d , Rogers, C.E.^b e , Smyser, C.D.^a c e

^a Washington University School of Medicine, Department of Neurology, United States
^b Washington University School of Medicine, Department of Psychiatry, United States
^c Washington University School of Medicine, Mallinckrodt Institute of Radiology, United States
^d Washington University in St. Louis, Department of Psychology, United States
^e Washington University School of Medicine, Department of Pediatrics, United States

Abstract
Preterm-born children have high rates of motor impairments, but mechanisms for early identification remain limited. We hypothesized that neonatal motor system functional connectivity (FC) would relate to motor outcomes at age two years; currently, this relationship is not yet well-described in very preterm (VPT; born <32 weeks’ gestation) infants with and without brain injury. We recruited 107 VPT infants – including 55 with brain injury (grade III–IV intraventricular hemorrhage, cystic periventricular leukomalacia, post-hemorrhagic hydrocephalus) – and collected FC data at/near term-equivalent age (35–45 weeks postmenstrual age). Correlation coefficients were used to calculate the FC between bilateral motor and visual cortices and thalami. At two years corrected-age, motor outcomes were assessed with the Bayley Scales of Infant and Toddler Development, 3rd edition. Multiple imputation was used to estimate missing data, and regression models related FC measures to motor outcomes. Within the brain-injured group only, interhemispheric motor cortex FC was positively related to gross motor outcomes. Thalamocortical and visual FC were not related to motor scores. This suggests neonatal alterations in motor system FC may provide prognostic information about impairments in children with brain injury. © 2022 The Authors

Author Keywords
Brain injury;  Cerebral palsy;  Functional connectivity;  Motor cortex;  Preterm birth

Funding details
National Institutes of HealthNIHF30 HD105336, GM07200, K01 MH122735, K02 NS089852, K23 MH105179, P30 NS098577, P50 HD103525, R01 HD057098, R01 HD061619, R01 MH113570
March of Dimes FoundationMDF
Cerebral Palsy International Research FoundationCPIRF
Child Neurology FoundationCNF
University of WashingtonUW
Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNICHD

Document Type: Article
Publication Stage: Final
Source: Scopus

Genetic and Environmental Variation in Continuous Phenotypes in the ABCD Study®
(2022) Behavior Genetics, . 

Maes, H.H.M.^a b c , Lapato, D.M.^a , Schmitt, J.E.^d , Luciana, M.^e , Banich, M.T.^f g , Bjork, J.M.^b , Hewitt, J.K.^g h , Madden, P.A.ⁱ , Heath, A.C.ⁱ , Barch, D.M.ⁱ , Thompson, W.K.^j , Iacono, W.G.^e , Neale, M.C.^a b

^a Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033, United States
^b Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
^c Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, United States
^d Departments of Radiology and Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
^e Department of Psychology, University of Minnesota, Minneapolis, United States
^f Department of Psychology and Neuroscience, University of Colorado, Boulder, United States
^g Institute of Cognitive Science, University of Colorado, Boulder, United States
^h Institute for Behavioral Genetics, University of Colorado, Boulder, United States
ⁱ Department of Psychiatry, Washington University in St Louis, St Louis, MO, United States
^j Division of Biostatistics and Department of Radiology, Population Neuroscience and Genetics Lab, University of California at San Diego, La Jolla, CA, United States

Abstract
Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9–10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background. © 2022, The Author(s).

Author Keywords
ABCD;  Adolescence;  Children;  Cognition;  Cognitive abilities;  Environment;  FAIR data;  Genetics;  Heritability;  Neuroscience;  Open science;  Personality;  Psychiatric disorders;  Substance use;  Twin

Funding details
National Institutes of HealthNIHU01DA041022, U01DA041025, U01DA041028, U01DA041048, U01DA041089, U01DA041093, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U01DA050987, U01DA050988, U01DA050989, U01DA051018, U01DA051037, U01DA051038, U01DA051039, U24DA041123, U24DA041147

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias
(2022) Human Brain Mapping, . 

Jirsaraie, R.J.^a , Kaufmann, T.^b c , Bashyam, V.^d , Erus, G.^d , Luby, J.L.^e , Westlye, L.T.^c f g , Davatzikos, C.^d , Barch, D.M.^h , Sotiras, A.ⁱ

^a Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, United States
^b Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
^c Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
^d Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
^e Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
^f Department of Psychology, University of Oslo, Oslo, Norway
^g KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
^h Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States
ⁱ Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States

Abstract
Machine learning has been increasingly applied to neuroimaging data to predict age, deriving a personalized biomarker with potential clinical applications. The scientific and clinical value of these models depends on their applicability to independently acquired scans from diverse sources. Accordingly, we evaluated the generalizability of two brain age models that were trained across the lifespan by applying them to three distinct early-life samples with participants aged 8–22 years. These models were chosen based on the size and diversity of their training data, but they also differed greatly in their processing methods and predictive algorithms. Specifically, one brain age model was built by applying gradient tree boosting (GTB) to extracted features of cortical thickness, surface area, and brain volume. The other model applied a 2D convolutional neural network (DBN) to minimally preprocessed slices of T1-weighted scans. Additional model variants were created to understand how generalizability changed when each model was trained with data that became more similar to the test samples in terms of age and acquisition protocols. Our results illustrated numerous trade-offs. The GTB predictions were relatively more accurate overall and yielded more reliable predictions when applied to lower quality scans. In contrast, the DBN displayed the most utility in detecting associations between brain age gaps and cognitive functioning. Broadly speaking, the largest limitations affecting generalizability were acquisition protocol differences and biased brain age estimates. If such confounds could eventually be removed without post-hoc corrections, brain age predictions may have greater utility as personalized biomarkers of healthy aging. © 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

Author Keywords
brain age;  brain development;  computational neuroscience;  generalizability;  machine learning

Funding details
National Science FoundationNSFDGE‐1745038, DGE‐2139839
National Institute of Mental HealthNIMHMH064769, MH090876, U01MH109589

Document Type: Article
Publication Stage: Article in Press
Source: Scopus