WashU weekly Neuroscience publications

Dorsal striatal response to taste is modified by obesity and insulin resistance
(2023) Obesity, 31 (8), pp. 2065-2075. 

Dunn, J.P.^a b , Lamichhane, B.^c , Smith, G.I.^b , Garner, A.^a , Wallendorf, M.^d , Hershey, T.^e , Klein, S.^b

^a VA St. Louis Health Care System, St. Louis, MO, United States
^b Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^c Department of Neurosurgery, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^d Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^e Departments of Psychiatry and Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States

Abstract
Objective: In preclinical models, insulin resistance in the dorsal striatum (DS) contributes to overeating. Although human studies support the concept of central insulin resistance, they have not investigated its effect on consummatory reward-induced brain activity. Methods: Taste-induced activation was assessed in the caudate and putamen of the DS with blood oxygen level-dependent (BOLD) functional magnetic resonance imaging. Three phenotypically distinct groups were studied: metabolically healthy lean, metabolically healthy obesity, and metabolically unhealthy obesity (MUO; presumed to have central insulin resistance). Participants with MUO also completed a weight loss intervention followed by a second functional magnetic resonance imaging session. Results: The three groups were significantly different at baseline consistent with the design. The metabolically healthy lean group had a primarily positive BOLD response, the MUO group had a primarily negative BOLD response, and the metabolically healthy obesity group had a response in between the two other groups. Food craving was predicted by taste-induced activation. After weight loss in the MUO group, taste-induced activation increased in the DS. Conclusions: These data support the hypothesis that insulin resistance and obesity contribute to aberrant responses to taste in the DS, which is only partially attenuated by weight loss. Aberrant responses to food exposure may be a barrier to weight loss. © 2023 The Obesity Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Funding details
National Institutes of HealthNIHDK20579, DK56341, UL1 TR000445, UL1TR002345
U.S. Department of Veterans AffairsVA1IK2CX000943
Washington University in St. LouisWUSTL
Foundation for Barnes-Jewish HospitalFBJH
Centene CorporationP19‐00559

Document Type: Article
Publication Stage: Final
Source: Scopus

Perceived Utility of Intracranial Pressure Monitoring in Traumatic Brain Injury: A Seattle International Brain Injury Consensus Conference Consensus-Based Analysis and Recommendations
(2023) Neurosurgery, 93 (2), pp. 399-408. 

Chesnut, R.M.^a b c d , Aguilera, S.^e f , Buki, A.^g , Bulger, E.M.^h , Citerio, G.^i j , Cooper, D.J.^k l , Arrastia, R.D.^m , Diringer, M.^n o , Figaji, A.^p , Gao, G.^q , Geocadin, R.G.^r , Ghajar, J.^s , Harris, O.^t , Hawryluk, G.W.J.^u v w , Hoffer, A.^x , Hutchinson, P.^y , Joseph, M.^z , Kitagawa, R.^aa , Manley, G.^ab ac , Mayer, S.^ad , Menon, D.K.^ae , Meyfroidt, G.^af , Michael, D.B.^ag , Oddo, M.^ah , Okonkwo, D.O.^ai , Patel, M.B.^aj , Robertson, C.^ak , Rosenfeld, J.V.^al am , Rubiano, A.M.^an ao , Sahuquillo, J.^ap , Servadei, F.^aq , Shutter, L.^ar , Stein, D.M.^as , Stocchetti, N.^at au , Taccone, F.S.^av , Timmons, S.D.^aw , Tsai, E.C.^ax , Ullman, J.S.^ay , Videtta, W.^az , Wright, D.W.^ba , Zammit, C.^bb

^a Department of Neurological Surgery, University of Washington, Seattle, WA, United States
^b Department of Orthopaedic Surgery, University of Washington, Seattle, WA, United States
^c School of Global Health, University of Washington, Seattle, WA, United States
^d Harborview Medical Center, University of Washington, Seattle, WA, United States
^e Almirante Nef Naval Hospital, Valparaiso University, Viña Del Mar, Chile
^f Valparaiso UniversityValparaiso, Chile
^g Department of Neurosurgery, Faculty of Medicine and Health, Örebro University, Sweden
^h Department of Surgery, Harborview Medical Center, University of Washington, Seattle, WA, United States
ⁱ School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
^j Neuroscience Department, NeuroIntensive Care Unit, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
^k Intensive Care Medicine, Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia
^l Department of Intensive Care and Hyperbaric Medicine, The Alfred Hospital, Melbourne, VIC, Australia
^m Department of Neurology, Penn Presbyterian Medical Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
ⁿ Department of Neurology, Washington University School of Medicine, St Louis, United States
^o Department of Neurology, Barnes-Jewish Hospital, St Louis, MO, United States
^p Division of Neurosurgery and Neuroscience Institute, Groote Schuur Hospital, University of Cape Town, Observatory 7925, South Africa
^q Department of Neurosurgery, Renji Hospital, Shanghai Institute of Head Trauma, Shanghai Jiaotong University School of MedicineShanghai, China
^r Departments of Neurology, Neurological Surgery, Anesthesiology-Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
^s Department of Neurosurgery, Stanford Neuroscience Health Center, Palo Alto, CA, United States
^t Department of Neurosurgery, Stanford University School of Medicine, Center for Academic Medicine, Stanford, CA, United States
^u Cleveland Clinic Akron General Neurosciences Center, Fairlawn, OH, United States
^v Uniformed Services University, Bethesda, MD, United States
^w Brain Trauma Foundation, New York City, NY, United States
^x UH Cleveland Medical Center, Cleveland, OH, United States
^y Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke’s Hospital, University of Cambridge and Cambridge Biomedical Campus, Cambridge, United Kingdom
^z Department of Neurological Sciences, Christian Medical College, Vellore, Tamil Nadu, India
^aa Vivian L Smith Department of Neurosurgery, McGovern Medical School at UTHealth, Houston, TX, United States
^ab Department of Neurological Surgery, University of California, San Francisco, CA, United States
^ac Department of Neurosurgery, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, United States
^ad Westchester Health Network, New York Medical College, Valhalla, NY, United States
^ae Division of Anaesthesia, Addenbrooke’s Hospital, University of Cambridge and Addenbrooke’s Hospital, Cambridge, United Kingdom
^af Department of Intensive Care Medicine, University Hospitals Leuven and Laboratory of Intensive Care Medicine, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
^ag Department of Neurosurgery, Beaumont Health, Michigan Head and Spine Institute, Oakland University William Beaumont School of Medicine, Southfield, MI, United States
^ah CHUV Medical Directorate and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
^ai Department of Neurosurgery, University of Pittsburgh Medical Center Presbyterian, Pittsburgh, PA, United States
^aj Department of Surgery, Division of Acute Care Surgery, Vanderbilt University Medical Center, Nashville, TN, United States
^ak Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX, United States
^al Department of Neurosurgery, Alfred Hospital, Melbourne, Australia
^am Department of Surgery, Monash University, Melbourne, Australia
^an INUB/MEDITECH Research Group, Neurosciences Institute, El Bosque University, Bogotá, Colombia
^ao MEDITECH Foundation, Clinical Research, Cali, Colombia
^ap Department of Neurosurgery, Universitat Autònoma de Barcelona: Neurotraumatology and Neurosurgery Research Unit (UNINN), Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron University Hospital, Barcelona, Spain
^aq Department of Biomedical Sciences, Humanitas University and IRCCS Humanitas Research Hospital, Milano, Italy
^ar Department of Critical Care Medicine, Neurology and Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
^as University of Maryland School of Medicine, Adult Critical Care Services, University of Maryland Medical Center, Baltimore, MD, United States
^at Department of Physiopathology and Transplantation, Milan University, Milan, Italy
^au Neuroscience Intensive Care Unit, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
^av Department of Intensive Care, Hôpital Universitaire de Bruxelles (HUB), Université Libre de Bruxelles (ULB), Brussels, Belgium
^aw Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, United States
^ax Ottawa Hospital, Department of Surgery, Division of Neurosurgery, University of Ottawa, Civic Campus, Suruchi Bhargava Chair in Spinal Cord and Brain Regeneration Research, Ottawa, ON, Canada
^ay Department of Neurosurgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, North Shore University Hospital, Manhasset, NY, United States
^az Intensive Care, Posadas HospitalBuenos Aires, Argentina
^ba Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, United States
^bb Department of Emergency Medicine, University of Rochester Medical Center, Rochester, NY, United States

Abstract
BACKGROUND: Intracranial pressure (ICP) monitoring is widely practiced, but the indications are incompletely developed, and guidelines are poorly followed. OBJECTIVE: To study the monitoring practices of an established expert panel (the clinical working group from the Seattle International Brain Injury Consensus Conference effort) to examine the match between monitoring guidelines and their clinical decision-making and offer guidance for clinicians considering monitor insertion. METHODS: We polled the 42 Seattle International Brain Injury Consensus Conference panel members’ ICP monitoring decisions for virtual patients, using matrices of presenting signs (Glasgow Coma Scale [GCS] total or GCS motor, pupillary examination, and computed tomography diagnosis). Monitor insertion decisions were yes, no, or unsure (traffic light approach). We analyzed their responses for weighting of the presenting signs in decision-making using univariate regression. RESULTS: Heatmaps constructed from the choices of 41 panel members revealed wider ICP monitor use than predicted by guidelines. Clinical examination (GCS) was by far the most important characteristic and differed from guidelines in being nonlinear. The modified Marshall computed tomography classification was second and pupils third. We constructed a heatmap and listed the main clinical determinants representing 80% ICP monitor insertion consensus for our recommendations. CONCLUSION: Candidacy for ICP monitoring exceeds published indicators for monitor insertion, suggesting the clinical perception that the value of ICP data is greater than simply detecting and monitoring severe intracranial hypertension. Monitor insertion heatmaps are offered as potential guidance for ICP monitor insertion and to stimulate research into what actually drives monitor insertion in unconstrained, real-world conditions. Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Congress of Neurological Surgeons.

Document Type: Article
Publication Stage: Final
Source: Scopus

Epigenetic age acceleration mediates the relationship between neighborhood deprivation and pain severity in adults with or at risk for knee osteoarthritis pain
(2023) Social Science and Medicine, 331, art. no. 116088, . 

Jackson, P.^a , Spector, A.L.^b d , Strath, L.J.^c d , Antoine, L.H.^e , Li, P.^f , Goodin, B.R.^g , Hidalgo, B.A.^a , Kempf, M.-C.^f , Gonzalez, C.E.^e , Jones, A.C.^a , Foster, T.C.ⁱ , Peterson, J.A.^d , Quinn, T.^e , Huo, Z.^h , Fillingim, R.^c d , Cruz-Almeida, Y.^c d i , Aroke, E.N.^f

^a School of Public Health, University of Alabama at Birmingham, 1720 2nd Avenue South, Birmingham, AL 35294, United States
^b School of Rehabilitation Sciences and Technology, University of Wisconsin-Milwaukee, PO Box 413, Milwaukee, WI 53201, United States
^c Department of Community Dentistry and Behavioral Science, University of Florida, 1329 16th Street Southwest, Gainesville, FL 32608, United States
^d Pain Research and Intervention Center of Excellence (PRICE), University of Florida, 2004 Mowry Road, Gainesville, FL 32610, United States
^e Department of Psychology, University of Alabama at Birmingham, 1720 2nd Avenue South, Birmingham, AL 35294, United States
^f School of Nursing, University of Alabama at Birmingham, 1720 2nd Avenue South, Birmingham, AL 35294, United States
^g Washington University Pain Center, Department of Anesthesiology, Washington University School of Medicine in St. Louis, United States
^h Department of Biostatistics, University of Florida, 2004 Mowry Road, Gainesville, FL 32603, United States
ⁱ Department of Neuroscience, University of Florida, 1149 Newell Dr, Gainesville, FL 32610, United States

Abstract
An estimated 250 million people worldwide suffer from knee osteoarthritis (KOA), with older adults having greater risk. Like other age-related diseases, residents of high-deprivation neighborhoods experience worse KOA pain outcomes compared to their more affluent neighbors. The purpose of this study was to examine the relationship between neighborhood deprivation and pain severity in KOA and the influence of epigenetic age acceleration (EpAA) on that relationship. The sample of 128 participants was mostly female (60.9%), approximately half non-Hispanic Black (49.2%), and had a mean age of 58 years. Spearman bivariate correlations revealed that pain severity positively correlated with EpAA (ρ = 0.47, p ≤ 0.001) and neighborhood deprivation (ρ = 0.25, p = 0.004). We found a positive significant relationship between neighborhood deprivation and EpAA (ρ = 0.47, p ≤ 0.001). Results indicate a mediating relationship between neighborhood deprivation (predictor), EpAA (mediator), and pain severity (outcome variable). There was a significant indirect effect of neighborhood deprivation on pain severity through EpAA, as the mediator accounted for a moderate portion of the total effect, PM = 0.44. Epigenetic age acceleration may act as a mechanism through which neighborhood deprivation leads to worse KOA pain outcomes and may play a role in the well-documented relationship between the neighborhood of residence and age-related diseases. © 2023

Author Keywords
Age-related conditions;  Epigenetic aging;  Neighborhood deprivation;  Pain disparities

Funding details
National Institutes of HealthNIH
National Institute on AgingNIAR01AG059809, R01AG067757, R36AG077084, R37AG033906
National Institute of Arthritis and Musculoskeletal and Skin DiseasesNIAMSR01AR079178

Document Type: Article
Publication Stage: Final
Source: Scopus

Slow-wave modulation analysis during states of unconsciousness using the novel tau-modulation method
(2023) Journal of Neural Engineering, 20 (4), . 

Xie, T.^a b , Wu, Z.^c , Foutz, T.J.^d , Sheng, X.^e , Zhu, X.^e , Leuthardt, E.C.^a b , Willie, J.T.^a b , Chen, L.^c , Brunner, P.^a b f

^a Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States
^b National Center for Adaptive Neurotechnologies, St. Louis, MO, United States
^c Department of Neurosurgery, Huashan Hospital, Fudan UniversityShanghai, China
^d Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^e State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong UniversityShanghai, China
^f Department of Neurology, Albany Medical College, Albany, NY, United States

Abstract
Objective. Slow-wave modulation occurs during states of unconsciousness and is a large-scale indicator of underlying brain states. Conventional methods typically characterize these large-scale dynamics by assuming that slow-wave activity is sinusoidal with a stationary frequency. However, slow-wave activity typically has an irregular waveform shape with a non-stationary frequency, causing these methods to be highly unpredictable and inaccurate. To address these limitations, we developed a novel method using tau-modulation, which is more robust than conventional methods in estimating the modulation of slow-wave activity and does not require assumptions on the shape or stationarity of the underlying waveform.Approach. We propose a novel method to estimate modulatory effects on slow-wave activity. Tau-modulation curves are constructed from cross-correlation between slow-wave and high-frequency activity. The resultant curves capture several aspects of modulation, including attenuation or enhancement of slow-wave activity, the temporal synchrony between slow-wave and high-frequency activity, and the rate at which the overall brain activity oscillates between states.Main results. The method’s performance was tested on an open electrocorticographic dataset from two monkeys that were recorded during propofol-induced anesthesia, with electrodes implanted over the left hemispheres. We found a robust propagation of slow-wave modulation along the anterior-posterior axis of the lateral aspect of the cortex. This propagation preferentially originated from the anterior superior temporal cortex and anterior cingulate gyrus. We also found the modulation frequency and polarity to track the stages of anesthesia. The algorithm performed well, even with non-sinusoidal activity and in the presence of real-world noise.Significance. The novel method provides new insight into several aspects of slow-wave modulation that have been previously difficult to evaluate across several brain states. This ability to better characterize slow-wave modulation, without spurious correlations induced by non-sinusoidal signals, may lead to robust and physiologically-plausible diagnostic tools for monitoring brain functions during states of unconsciousness. Creative Commons Attribution license.

Author Keywords
broadband gamma;  cross-correlation;  electrocorticography;  modulation;  phase-amplitude coupling;  slow-wave;  unconsciousness

Document Type: Article
Publication Stage: Final
Source: Scopus

Kappa opioid receptor activation increases thermogenic energy expenditure which drives increased feeding
(2023) iScience, 26 (7), art. no. 107241, . 

Cone, A.L.^a , Wu, K.K.^a , Kravitz, A.V.^a b c , Norris, A.J.^a

^a Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, United States
^b Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States
^c Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Opioid receptors, including the kappa opioid receptor (KOR), exert control over thermoregulation and feeding behavior. Notably, activation of KOR stimulates food intake, leading to postulation that KOR signaling plays a central role in managing energy intake. KOR has also been proposed as a target for treating obesity. Herein, we report studies examining how roles for KOR signaling in regulating thermogenesis, feeding, and energy balance may be interrelated using pharmacological interventions, genetic tools, quantitative thermal imaging, and metabolic profiling. Our findings demonstrate that activation of KOR in the central nervous system causes increased energy expenditure via brown adipose tissue activation. Importantly, pharmacologic, or genetic inhibition of brown adipose tissue thermogenesis prevented the elevated food intake triggered by KOR activation. Furthermore, our data reveal that KOR-mediated thermogenesis elevation is reversibly disrupted by chronic high-fat diet, implicating KOR signaling as a potential mediator in high-fat diet-induced weight gain. © 2023 The Author(s)

Author Keywords
Cell biology;  Endocrinology;  Neuroscience

Funding details
National Institutes of HealthNIH5K08MH119538, P30 DK020579

Document Type: Article
Publication Stage: Final
Source: Scopus

Phosphorylation of αB-Crystallin Involves Interleukin-1β-Mediated Intracellular Retention in Retinal Müller Cells: A New Mechanism Underlying Fibrovascular Membrane Formation
(2023) Investigative Ophthalmology & Visual Science, 64 (10), p. 20. 

Yamamoto, T.^a b , Kase, S.^a , Shinkai, A.^a , Murata, M.^a , Kikuchi, K.^a , Wu, D.^c , Kageyama, Y.^d , Shinohara, M.^d , Sasase, T.^e , Ishida, S.^a

^a Laboratory of Ocular Cell Biology and Visual Science, Department of Ophthalmology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
^b Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, MO, United States
^c Eye Center of the Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
^d Tokyo Animal & Diet Department, CLEA Japan, Inc.Tokyo, Japan
^e Biological/Pharmacological Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc.Osaka, Japan

Abstract
Purpose: Chronic inflammation plays a pivotal role in the pathology of proliferative diabetic retinopathy (PDR), in which biological alterations of retinal glial cells are one of the key elements. The phosphorylation of αB-crystallin/CRYAB modulates its molecular dynamics and chaperone activity, and attenuates αB-crystallin secretion via exosomes. In this study, we investigated the effect of phosphorylated αB-crystallin in retinal Müller cells on diabetic mimicking conditions, including interleukin (IL)-1β stimuli. Methods: Human retinal Müller cells (MIO-M1) were used to examine gene and protein expressions with real-time quantitative PCR, enzyme linked immunosorbent assay (ELISA), and immunoblot analyses. Cell apoptosis was assessed by Caspase-3/7 assay and TdT-mediated dUTP nick-end labeling staining. Retinal tissues isolated from the Spontaneously Diabetic Torii (SDT) fatty rat, a type 2 diabetic animal model with obesity, and fibrovascular membranes from patients with PDR were examined by double-staining immunofluorescence. Results: CRYAB mRNA was downregulated in MIO-M1 cells with the addition of 10 ng/mL IL-1β; however, intracellular αB-crystallin protein levels were maintained. The αB-crystallin serine 59 (Ser59) residue was phosphorylated with IL-1β application in MIO-M1 cells. Cell apoptosis in MIO-M1 cells was induced by CRYAB knockdown. Immunoreactivity for Ser59-phosphorylated αB-crystallin and glial fibrillary acidic protein was colocalized in glial cells of SDT fatty rats and fibrovascular membranes. Conclusions: The Ser59 phosphorylation of αB-crystallin was modulated by IL-1β in Müller cells under diabetic mimicking inflammatory conditions, suggesting that αB-crystallin contributes to the pathogenesis of PDR through an anti-apoptotic effect.

Document Type: Article
Publication Stage: Final
Source: Scopus

The natural history of ALS: Baseline characteristics from a multicenter clinical cohort
(2023) Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, . 

Berger, A.^a , Locatelli, M.^a , Arcila-Londono, X.^b , Hayat, G.^c , Olney, N.^d , Wymer, J.^e , Gwathmey, K.^f , Lunetta, C.^g h , Heiman-Patterson, T.ⁱ , Ajroud-Driss, S.^j , Macklin, E.A.^a k , Bind, M.-A.^a k , Goslin, K.^d , Stuchiner, T.^d , Brown, L.^d , Bazan, T.^d , Regan, T.^d , Adamo, A.^d , Ferment, V.^l , Schroeder, C.^l , Somers, M.^m , Manousakis, G.^l , Faulconer, K.^a , Sinani, E.^a , Mirochnick, J.^a , Yu, H.^a , Sherman, A.V.^a n , Walk, D.^l , The Pooled Resource Open-Access ALS Clinical Trials Consortium^o

^a Neurological Clinical Research Institute, Massachusetts General Hospital, Boston, MA, United States
^b Harry J. Hoenselaar ALS Clinic, Henry Ford University, Detroit, MI, United States
^c SLUCare ALS Clinic, Washington University, St Louis, MO, United States
^d Providence Portland Medical Center, Providence Brain and Spine Institute, Portland, OR, United States
^e Norman Fixel Institute for Neurological Diseases,University of Florida, Gainesville, FL, United States
^f Neuromuscular and ALS Clinic, Virginia Commonwealth University, Richmond, VA, United States
^g U.O. Riabilitazione Specialistica Neurologica, Istituti Clinici Scientifici Maugeri IRCCS, Milano, Italy
^h Neuromuscular Omnicentre, Milano, Italy
ⁱ MDA/ALS Center of Hope, Temple University, Philadelphia, PA, United States
^j Les Turner ALS Center, Northwestern University, Chicago, IL, United States
^k Biostatistics, Harvard Medical School, Boston, MA, United States
^l Neurology, University of Minnesota, Minneapolis, MN, United States
^m Neurology, Marquette University, Milwaukee, WI, United States
ⁿ Neurology, Harvard Medical School, Boston, MA, United States

Abstract
Background: Amyotrophic lateral sclerosis (ALS) is a rare disease with urgent need for improved treatment. Despite the acceleration of research in recent years, there is a need to understand the full natural history of the disease. As only 40% of people living with ALS are eligible for typical clinical trials, clinical trial datasets may not generalize to the full ALS population. While biomarker and cohort studies have more generous inclusion criteria, these too may not represent the full range of phenotypes, particularly if the burden for participation is high. To permit a complete understanding of the heterogeneity of ALS, comprehensive data on the full range of people with ALS is needed. Methods: The ALS Natural History Consortium (ALS NHC) consists of nine ALS clinics and was created to build a comprehensive dataset reflective of the ALS population. At each clinic, most patients are asked to participate and about 95% do. After obtaining consent, a minimum dataset is abstracted from each participant’s electronic health record. Participant burden is therefore minimal. Results: Data on 1925 ALS patients were submitted as of 9 December 2022. ALS NHC participants were more heterogeneous relative to anonymized clinical trial data from the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database. The ALS NHC includes ALS patients of older age of onset and a broader distribution of El Escorial categories, than the PRO-ACT database. Conclusions: ALS NHC participants had a higher diversity of diagnostic and demographic data compared to ALS clinical trial participants.Key MessagesWhat is already known on this topic: Current knowledge of the natural history of ALS derives largely from regional and national registries that have broad representation of the population of people living with ALS but do not always collect covariates and clinical outcomes. Clinical studies with rich datasets of participant characteristics and validated clinical outcomes have stricter inclusion and exclusion criteria that may not be generalizable to the full ALS population. What this study adds: To bridge this gap, we collected baseline characteristics for a sample of the population of people living with ALS seen at a consortium of ALS clinics that collect extensive, pre-specified participant-level data, including validated outcome measures. How this study might affect research, practice, or policy: A clinic-based longitudinal dataset can improve our understanding of the natural history of ALS and can be used to inform the design and analysis of clinical trials and health economics studies, to help the prediction of clinical course, to find matched controls for open label extension trials and expanded access protocols, and to document real-world evidence of the impact of novel treatments and changes in care practice. © 2023 World Federation of Neurology on behalf of the Research Group on Motor Neuron Diseases.

Author Keywords
epidemiology;  models;  Natural history;  prognostic

Funding details
RO1-FD007630
National Institutes of HealthNIHDP5OD021412
U.S. Food and Drug AdministrationFDA
Office of the DirectorOD
ALS AssociationALSA
ALS Hope FoundationALSHF
University of Minnesota FoundationUMF

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

Analysis of brain edema in RHAPSODY
(2023) International Journal of Stroke, . 

Schleicher, R.L.^a , Vorasayan, P.^a b , McCabe, M.E.^c , Bevers, M.B.^d , Davis, T.P.^e , Griffin, J.H.^f , Hinduja, A.^g , Jadhav, A.P.^h , Lee, J.-M.ⁱ , Sawyer, R.N., Jr^j , Zlokovic, B.V.^k , Sheth, K.N.^l , Fedler, J.K.^c , Lyden, P.^k m , Kimberly, W.T.^a , on behalf of the NN104 Investigatorsⁿ

^a Division of Neurocritical Care and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
^b Division of Neurology, Department of Medicine, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
^c Department of Biostatistics, University of Iowa, Iowa City, IA, United States
^d Divisions of Stroke, Cerebrovascular and Critical Care Neurology, Brigham and Women’s Hospital, Boston, MA, United States
^e Department of Pharmacology, University of Arizona Health Sciences, Tucson, AZ, United States
^f Department of Molecular Medicine, Scripps Research, La JollaCA, United States
^g Department of Neurology, Ohio State University Wexner Medical Center, Columbus, OH, United States
^h Barrow Neurological Institute, Phoenix, AZ, United States
ⁱ Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^j Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States
^k Department of Physiology and Neuroscience, Keck School of Medicine of USC, Los Angeles, CA, United States
^l Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale School of Medicine, Yale University, New Haven, CT, United States
^m Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, United States

Abstract
Background: Cerebral edema is a secondary complication of acute ischemic stroke, but its time course and imaging markers are not fully understood. Recently, net water uptake (NWU) has been proposed as a novel marker of edema. Aims: Studying the RHAPSODY trial cohort, we sought to characterize the time course of edema and test the hypothesis that NWU provides distinct information when added to traditional markers of cerebral edema after stroke by examining its association with other markers. Methods: A total of 65 patients had measurable supratentorial ischemic lesions. Patients underwent head computed tomography (CT), brain magnetic resonance imaging (MRI) scans, or both at the baseline visit and after 2, 7, 30, and 90 days following enrollment. CT and MRI scans were used to measure four imaging markers of edema: midline shift (MLS), hemisphere volume ratio (HVR), cerebrospinal fluid (CSF) volume, and NWU using semi-quantitative threshold analysis. Trajectories of the markers were summarized, as available. Correlations of the markers of edema were computed and the markers compared by clinical outcome. Regression models were used to examine the effect of 3K3A-activated protein C (APC) treatment. Results: Two measures of mass effect, MLS and HVR, could be measured on all imaging modalities, and had values available across all time points. Accordingly, mass effect reached a maximum level by day 7, normalized by day 30, and then reversed by day 90 for both measures. In the first 2 days after stroke, the change in CSF volume was associated with MLS (ρ = –0.57, p = 0.0001) and HVR (ρ = –0.66, p < 0.0001). In contrast, the change in NWU was not associated with the other imaging markers (all p ⩾ 0.49). While being directionally consistent, we did not observe a difference in the edema markers by clinical outcome. In addition, baseline stroke volume was associated with all markers (MLS (p < 0.001), HVR (p < 0.001), change in CSF volume (p = 0.003)) with the exception of NWU (p = 0.5). Exploratory analysis did not reveal a difference in cerebral edema markers by treatment arm. Conclusions: Existing cerebral edema imaging markers potentially describe two distinct processes, including lesional water concentration (i.e. NWU) and mass effect (MLS, HVR, and CSF volume). These two types of imaging markers may represent distinct aspects of cerebral edema, which could be useful for future trials targeting this process. © 2023 World Stroke Organization.

Author Keywords
edema;  imaging markers;  Ischemic stroke

Funding details
Biogen

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

Functional trajectories during innate spinal cord repair
(2023) Frontiers in Molecular Neuroscience, 16, art. no. 1155754, . 

Jensen, N.O.^a b , Burris, B.^a b , Zhou, L.^a b , Yamada, H.^a b , Reyes, C.^a b , Pincus, Z.^a c , Mokalled, M.H.^a b

^a Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, United States
^b Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, United States
^c Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Adult zebrafish are capable of anatomical and functional recovery following severe spinal cord injury. Axon growth, glial bridging and adult neurogenesis are hallmarks of cellular regeneration during spinal cord repair. However, the correlation between these cellular regenerative processes and functional recovery remains to be elucidated. Whereas the majority of established functional regeneration metrics measure swim capacity, we hypothesize that gait quality is more directly related to neurological health. Here, we performed a longitudinal swim tracking study for 60 individual zebrafish spanning 8 weeks of spinal cord regeneration. Multiple swim parameters as well as axonal and glial bridging were integrated. We established rostral compensation as a new gait quality metric that highly correlates with functional recovery. Tensor component analysis of longitudinal data supports a correspondence between functional recovery trajectories and neurological outcomes. Moreover, our studies predicted and validated that a subset of functional regeneration parameters measured 1 to 2 weeks post-injury is sufficient to predict the regenerative outcomes of individual animals at 8 weeks post-injury. Our findings established new functional regeneration parameters and generated a comprehensive correlative database between various functional and cellular regeneration outputs. Copyright © 2023 Jensen, Burris, Zhou, Yamada, Reyes, Pincus and Mokalled.

Author Keywords
functional recovery;  spinal cord injury;  spinal cord regeneration;  swim assay;  zebrafish

Funding details
National Institutes of HealthNIHR01 NS113915, R01 NS123708
University of WashingtonUW

Document Type: Article
Publication Stage: Final
Source: Scopus

Experimentally induced active and quiet sleep engage non-overlapping transcriptomes in Drosophila
(2023) eLife, 12, . 

Anthoney, N.^a , Tainton-Heap, L.A.L.^a , Luong, H.^b , Notaras, E.^a , Zhao, Q.^a , Perry, T.^b , Batterham, P.^b , Shaw, P.J.^c , van Swinderen, B.^a

^a Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
^b School of BioSciences, The University of Melbourne, Melbourne, VIC 3052, Australia
^c Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Sleep in mammals is broadly classified into two different categories: rapid eye movement (REM) sleep and slow wave sleep (SWS), and accordingly REM and SWS are thought to achieve a different set of functions. The fruit fly Drosophila melanogaster is increasingly being used as a model to understand sleep functions, although it remains unclear if the fly brain also engages in different kinds of sleep as well. Here, we compare two commonly used approaches for studying sleep experimentally in Drosophila: optogenetic activation of sleep-promoting neurons and provision of a sleep-promoting drug, Gaboxadol. We find that these different sleep-induction methods have similar effects on increasing sleep duration, but divergent effects on brain activity. Transcriptomic analysis reveals that drug-induced deep sleep (‘quiet’ sleep) mostly downregulates metabolism genes, whereas optogenetic ‘active’ sleep upregulates a wide range of genes relevant to normal waking functions. This suggests that optogenetics and pharmacological induction of sleep in Drosophila promote different features of sleep, which engage different sets of genes to achieve their respective functions. © 2023, eLife Sciences Publications Ltd. All rights reserved.

Funding details
National Institutes of HealthNIHNS076980
National Health and Medical Research CouncilNHMRCGNT1164499

Document Type: Article
Publication Stage: Final
Source: Scopus

Assessment of cochlear synaptopathy by electrocochleography to low frequencies in a preclinical model and human subjects
(2023) Frontiers in Neurology, 14, art. no. 1104574, . 

Haggerty, R.A.^a , Hutson, K.A.^a , Riggs, W.J.^b , Brown, K.D.^a c , Pillsbury, H.C.^a c , Adunka, O.F.^b , Buchman, C.A.^d , Fitzpatrick, D.C.^a

^a Department of Otolaryngology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^b Department of Otolaryngology, The Ohio State University, Columbus, OH, United States
^c University of North Carolina School of Medicine, Chapel Hill, NC, United States
^d Department of Otolaryngology, Washington University in St. Louis, St. Louis, MO, United States

Abstract
Cochlear synaptopathy is the loss of synapses between the inner hair cells and the auditory nerve despite survival of sensory hair cells. The findings of extensive cochlear synaptopathy in animals after moderate noise exposures challenged the long-held view that hair cells are the cochlear elements most sensitive to insults that lead to hearing loss. However, cochlear synaptopathy has been difficult to identify in humans. We applied novel algorithms to determine hair cell and neural contributions to electrocochleographic (ECochG) recordings from the round window of animal and human subjects. Gerbils with normal hearing provided training and test sets for a deep learning algorithm to detect the presence of neural responses to low frequency sounds, and an analytic model was used to quantify the proportion of neural and hair cell contributions to the ECochG response. The capacity to detect cochlear synaptopathy was validated in normal hearing and noise-exposed animals by using neurotoxins to reduce or eliminate the neural contributions. When the analytical methods were applied to human surgical subjects with access to the round window, the neural contribution resembled the partial cochlear synaptopathy present after neurotoxin application in animals. This result demonstrates the presence of viable hair cells not connected to auditory nerve fibers in human subjects with substantial hearing loss and indicates that efforts to regenerate nerve fibers may find a ready cochlear substrate for innervation and resumption of function. Copyright © 2023 Haggerty, Hutson, Riggs, Brown, Pillsbury, Adunka, Buchman and Fitzpatrick.

Author Keywords
auditory nerve;  cochlear microphonic;  deep learning;  electrocochleography;  hair cells

Funding details
W81XWH-19-1-0609

Document Type: Article
Publication Stage: Final
Source: Scopus

Glioblastoma induces whole-brain spectral change in resting state fMRI: Associations with clinical comorbidities and overall survival
(2023) NeuroImage: Clinical, 39, art. no. 103476, . 

Park, K.Y.^a b c d , Snyder, A.Z.^e f , Olufawo, M.^a , Trevino, G.^a , Luckett, P.H.^a c d , Lamichhane, B.^a c d g , Xie, T.^a , Lee, J.J.^e , Shimony, J.S.^e , Leuthardt, E.C.^a c d

^a Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States
^b Medical Scientist Training Program, Washington University School of Medicine, St. Louis, MO, United States
^c Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO, United States
^d Division of Neurotechnology, Washington University School of Medicine, St. Louis, MO, United States
^e Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
^f Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^g Center for Health Sciences, Oklahoma State University, 1013 E 66th Pl, Tulsa, OK 74136, United States

Abstract
Glioblastoma, a highly aggressive form of brain tumor, is a brain-wide disease. We evaluated the impact of tumor burden on whole brain resting-state functional magnetic resonance imaging (rs-fMRI) activity. Specifically, we analyzed rs-fMRI signals in the temporal frequency domain in terms of the power-law exponent and fractional amplitude of low-frequency fluctuations (fALFF). We contrasted 189 patients with newly-diagnosed glioblastoma versus 189 age-matched healthy reference participants from an external dataset. The patient and reference datasets were matched for age and head motion. The principal finding was markedly flatter spectra and reduced grey matter fALFF in the patients as compared to the reference dataset. We posit that the whole-brain spectral change is attributable to global dysregulation of excitatory and inhibitory balance and metabolic demand in the tumor-bearing brain. Additionally, we observed that clinical comorbidities, in particular, seizures, and MGMT promoter methylation, were associated with flatter spectra. Notably, the degree of change in spectra was predictive of overall survival. Our findings suggest that frequency domain analysis of rs-fMRI activity provides prognostic information in glioblastoma patients and offers a means of noninvasively studying the effects of glioblastoma on the whole brain. © 2023 The Author(s)

Author Keywords
Clinical comorbidities;  Fractional amplitude of low-frequency fluctuations (fALFF);  Glioblastoma;  Intrinsic brain activity;  Power-law exponent;  Prognosis;  Resting-state fMRI

Funding details
National Institutes of HealthNIHP41EB018783, R01CA203861, R01EB026439, U24NS109103
McDonnell Center for Systems Neuroscience
Washington University School of Medicine in St. LouisWUSM
Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine in St. LouisIDDRCP50 HD103525

Document Type: Article
Publication Stage: Final
Source: Scopus

Age-Related Changes in Temporal Binding Involving Auditory and Vestibular Inputs
(2023) Seminars in Hearing, . 

Malone, A.K.^a , Hungerford, M.E.^b c , Smith, S.B.^d , Chang, N.-Y.N.^e , Uchanski, R.M.^f , Oh, Y.-H.^g , Lewis, R.F.^h , Hullar, T.E.^b c

^a ENT and Allergy Associates of Florida, Boca Raton, FL, United States
^b VA RR&D National Center for Rehabilitative Auditory Research, VA Portland Health Care System, Portland, OR, United States
^c Department of OtolaryngologyHead and Neck Surgery, Oregon Health and Science University, Portland, OR, United States
^d Department of Speech, Language, and Hearing Sciences, University of Texas, Austin, TX, United States
^e Department of Oral and Maxillofacial Surgery, Oregon Health and Science University, Portland, OR, United States
^f Department of Otolaryngology – Head and Neck Surgery, Washington University in St. Louis, St. Louis, MO, United States
^g University of Louisville, Louisville, KY, United States
^h Departments of Otolaryngology and Neurology, Harvard Medical School, Boston, MA, United States

Abstract
Maintaining balance involves the combination of sensory signals from the visual, vestibular, proprioceptive, and auditory systems. However, physical and biological constraints ensure that these signals are perceived slightly asynchronously. The brain only recognizes them as simultaneous when they occur within a period of time called the temporal binding window (TBW). Aging can prolong the TBW, leading to temporal uncertainty during multisensory integration. This effect might contribute to imbalance in the elderly but has not been examined with respect to vestibular inputs. Here, we compared the vestibular-related TBW in 13 younger and 12 older subjects undergoing 0.5 Hz sinusoidal rotations about the earth-vertical axis. An alternating dichotic auditory stimulus was presented at the same frequency but with the phase varied to determine the temporal range over which the two stimuli were perceived as simultaneous at least 75% of the time, defined as the TBW. The mean TBW among younger subjects was 286 ms (SEM ± 56 ms) and among older subjects was 560 ms (SEM ± 52 ms). TBW was related to vestibular sensitivity among younger but not older subjects, suggesting that a prolonged TBW could be a mechanism for imbalance in the elderly person independent of changes in peripheral vestibular function. © 2023. The Author(s).

Author Keywords
labyrinth/physiology;  motion perception;  proprioception/physiology;  reaction time;  rotation;  sensory thresholds/physiology;  time factors;  vestibule

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

Precipitating Mechanisms of Falls in Preclinical Alzheimer’s Disease
(2023) Journal of Alzheimer’s Disease Reports, 7 (1), pp. 739-750. 

Keleman, A.A.^a , Nicosia, J.^b , Bollinger, R.M.^a , Wisch, J.K.^b , Hassenstab, J.^b c , Morris, J.C.^b e , Ances, B.M.^b d e , Balota, D.A.^c , Stark, S.L.^a b

^a Program in Occupational Therapy, Washington University in St. Louis, St. Louis, MO, United States
^b Department of Neurology, Washington University in St. Louis, St. Louis, MO, United States
^c Department of Psychology, Washington University in St. Louis, St. Louis, MO, United States
^d Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
^e Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, MO, United States

Abstract
Background: Individuals with Alzheimer’s disease (AD) are more than twice as likely to incur a serious fall as the general population of older adults. Although AD is commonly associated with cognitive changes, impairments in other clinical measures such as strength or functional mobility (i.e., gait and balance) may precede symptomatic cognitive impairment in preclinical AD and lead to increased fall risk. Objective: To examine mechanisms (i.e., functional mobility, cognition, AD biomarkers) associated with increased falls in cognitively normal older adults. Methods: This 1-year study was part of an ongoing longitudinal cohort study. We examined the relationships among falls, clinical measures of functional mobility and cognition, and neuroimaging AD biomarkers in cognitively normal older adults. We also investigated which domain(s) best predicted fall propensity and severity through multiple regression models. Results: A total of 182 older adults were included (mean age 75 years, 53% female). A total of 227 falls were reported over the year; falls per person ranged from 0-16 with a median of 1. Measures of functional mobility were the best predictors of fall propensity and severity. Cognition and AD biomarkers were associated with each other but not with the fall outcome measures. Conclusion: These results suggest that, although subtle changes in cognition may be more closely associated with AD neuropathology, functional mobility indicators better predict falls in cognitively normal older adults. This study adds to our understanding of the mechanisms underlying falls in older adults and could lead to the development of targeted fall prevention strategies. © 2023 – The authors. Published by IOS Press.

Author Keywords
Alzheimer’s disease;  cognition;  falls;  functional mobility

Funding details
National Institutes of HealthNIH5TL1TR002344-05, P01AG026276, P01AG03991, P30AG066444, R01AG057680, UL1 TR002345

Document Type: Article
Publication Stage: Final
Source: Scopus

Benefits of a 12-Week Non-Drug ‘Brain Fitness Program’ for Patients with Attention-Deficit/Hyperactive Disorder, Post-Concussion Syndrome, or Memory Loss
(2023) Journal of Alzheimer’s Disease Reports, 7 (1), pp. 675-697. 

Fotuhi, M.^a b , Khorrami, N.D.^b , Raji, C.A.^c d

^a Department of Psychological Brain Sciences, George Washington University, Washington, DC, United States
^b NeuroGrow Brain Fitness Center, McLean, VA, United States
^c Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, United States
^d Department of Neurology, Washington University, School of Medicine, St. Louis, MO, United States

Abstract
Background: Non-pharmacologic interventions can potentially improve cognitive function, sleep, and/or mood in patients with attention-deficit/hyperactive disorder (ADHD), post-concussion syndrome (PCS), or memory loss. Objective: We evaluated the benefits of a brain rehabilitation program in an outpatient neurology practice that consists of targeted cognitive training, lifestyle coaching, and electroencephalography (EEG)-based neurofeedback, twice weekly (90 minutes each), for 12 weeks. Methods: 223 child and adult patients were included: 71 patients with ADHD, 88 with PCS, and 64 with memory loss (mild cognitive impairment or subjective cognitive decline). Patients underwent a complete neurocognitive evaluation, including tests for Verbal Memory, Complex Attention, Processing Speed, Executive Functioning, and Neurocognition Index. They completed questionnaires about sleep, mood, diet, exercise, anxiety levels, and depression – as well as underwent quantitative EEG – at the beginning and the end of the program. Results: Pre-post test score comparison demonstrated that all patient subgroups experienced statistically significant improvements on most measures, especially the PCS subgroup, which experienced significant score improvement on all measures tested (p≤0.0011; dz≥0.36). After completing the program, 60% to 90% of patients scored higher on cognitive tests and reported having fewer cognitive and emotional symptoms. The largest effect size for pre-post score change was improved executive functioning in all subgroups (ADHD dz= 0.86; PCS dz= 0.83; memory dz= 1.09). Conclusion: This study demonstrates that a multimodal brain rehabilitation program can have benefits for patients with ADHD, PCS, or memory loss and supports further clinical trials in this field. © 2023 – The authors. Published by IOS Press.

Author Keywords
Alzheimer’s disease;  attention-deficit/hyperactivity disorder;  electroencephalography;  memory;  neurofeedback;  post-concussion syndrome;  rehabilitation;  subjective cognitive decline;  subjective cognitive impairment;  traumatic brain injury

Document Type: Article
Publication Stage: Final
Source: Scopus

Identification of PROK2 gene polymorphisms as predictors of methamphetamine use disorder risk and indicators of craving scale in the Chinese Han population
(2023) Frontiers in Pharmacology, 14, art. no. 1217382, . 

Jiang, Z.^a b c , Zhang, T.^d , Han, W.^a b , Xiao, J.^a , Zhang, W.^a , Wang, X.^a , Liu, J.^a , Yang, Y.^d , Yang, C.^d , Guan, F.^a b , Li, T.^a b , Rice, J.P.^e

^a Department of Forensic Medicine, School of Medicine and Forensics, Xi’an Jiaotong University, Shaanxi, Xi’an, China
^b Key Laboratory of National Health Commission for Forensic Sciences, Xi’an Jiaotong University Health Science Center, Xi’an, China
^c Department of Neurology, Honghui Hospital of Xi’an Jiaotong University, Shaanxi, Xi’an, China
^d Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Shaanxi, Xi’an, China
^e Department of Psychiatry, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States

Abstract
Background: Methamphetamine use disorder (MUD) has become a global problem due to the highly addictive nature of methamphetamine. Earlier research have demonstrated that PROK2 functions as a compensatory and protective response against neurotoxic stress by stimulating astrocyte reactivity. The aim of our study was to evaluate the correlation between the PROK2 gene and both MUD risk susceptibility and craving scale in the Chinese Han population. Methods: A total of 5,282 participants (1,796 MUD patients and 3,486 controls) were recruited. Seven tag SNPs of the PROK2 gene were chosen and genotyped in the samples. Genetic association analyses were performed to capture the significant SNPs. To investigate the relationship between PROK2 levels and craving scores with the associated-SNP genotypes, we conducted a linear model. Results: SNP rs75433452 was significantly linked with MUD risk (p-value = 1.54 × 10−8), with the A allele being positively correlated with an increased risk of MUD. Moreover, the average serum level of PROK2 decreased when more copies of the A allele were presented in both MUD patients (p-value = 4.57 × 10−6) and controls (p-value = 1.13 × 10−5). Furthermore, the genotypes of SNP rs75433452 were strongly correlated with the craving scores in MUD patients (p-value = 4.05 × 10−4). Conclusion: Our study identified a significant association signal of the PROK2 gene with MUD risk susceptibility and methamphetamine craving scores in the Chinese Han population, providing potential valuable insights into the underlying mechanisms of METH dependence. Copyright © 2023 Jiang, Zhang, Han, Xiao, Zhang, Wang, Liu, Yang, Yang, Guan, Li and Rice.

Author Keywords
case-control study;  craving degree;  genetic polymorphism;  methamphetamine use disorder;  prokineticin 2 gene

Funding details
National Natural Science Foundation of ChinaNSFC82171873, 82222031
Fundamental Research Funds for the Central Universities

Document Type: Article
Publication Stage: Final
Source: Scopus

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium
(2023) Neuroradiology, . 

Lee, M.D.^a , Patel, S.H.^b , Mohan, S.^c , Akbari, H.^d e , Bakas, S.^d e f , Nasrallah, M.L.P.^f g , Calabrese, E.^h , Rudie, J.ⁱ , Villanueva-Meyer, J.^j , LaMontagne, P.^k , Marcus, D.S.^k , Colen, R.R.^l m , Balana, C.ⁿ , Choi, Y.S.^o , Badve, C.^p , Barnholtz-Sloan, J.S.^q r , Sloan, A.E.^s t , Booth, T.C.^u v , Palmer, J.D.^w , Dicker, A.P.^x , Flanders, A.E.^y , Shi, W.^x , Griffith, B.^z , Poisson, L.M.^aa , Chakravarti, A.^w , Mahajan, A.^ab , Chang, S.^ac , Orringer, D.^ad ae , Davatzikos, C.^d e af , Jain, R.^a ad , Bagley, S.J.^ag , Bilello, M.^ag , Brem, S.^ag , Baid, U.^ag , Desai, A.S.^ag , Lustig, R.A.^ag , Mamourian, E.^ag , Kazerooni, A.F.^ag , Garcia, J.A.^ag , O’Rourke, D.M.^ag , Binder, Z.A.^ag , Milchenko, M.^ag , Nazeri, A.^ag , Sotiras, A.^ag , Ak, M.^ag , Capellades, J.^ag , Puig, J.^ag , Ahn, S.S.^ag , Chang, J.H.^ag , Lee, S.-K.^ag , Park, Y.W.^ag , Vadmal, V.^ag , Waite, K.A.^ag , Gongala, S.^ag , Chelliah, A.^ag , Karami, G.^ag , Alexander, G.S.^ag , Ali, A.S.^ag , Liem, S.^ag , Lombardo, J.^ag , Shukla, G.^ag , Sharif, M.^ag , Rogers, L.R.^ag , Taylor, W.^ag , Cepeda, S.^ag , Kotrotsou, A.^ag , Fathallah-Shaykh, H.^ag , Santonocito, O.S.^ag , Di Stefano, A.L.^ag , Rulseh, A.M.^ag , Matsumoto, Y.^ag , Alexander, K.^ag , Satgunaseelan, L.^ag , Wiestler, B.^ag , Gullapalli, R.P.^ag , Melhem, E.R.^ag , Woodworth, G.F.^ag , Kamel, P.I.^ag , Perez-Garcia, V.M.^ag , Vamvakas, A.^ag , Tsougos, Y.^ag , Valdes, P.^ag , Tiwari, P.^ag , Aboian, M.^ag , the ReSPOND Consortium^ag

^a Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States
^b Department of Radiology, University of Virginia School of Medicine, Charlottesville, VA, United States
^c Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
^d Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, United States
^e Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
^f Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
^g Glioblastoma Multiforme Translational Center of Excellence, Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
^h Department of Radiology, Division of Neuroradiology, Duke University, Durham, NC, United States
ⁱ Department of Radiology, University of California San Diego, San Diego, CA, United States
^j Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
^k Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
^l Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States
^m Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
ⁿ Medical Oncology Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
^o Department of Radiology, Section of Neuroradiology, Yonsei University Health System, Seoul, South Korea
^p Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, OH, United States
^q Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, United States
^r Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States
^s Department of Neurosurgery, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, OH, United States
^t Seidman Cancer Center and Case Comprehensive Cancer Center, Cleveland, OH, United States
^u School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
^v Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, Ruskin WingLondon, United Kingdom
^w Department of Radiation Oncology and Neurosurgery, The James Cancer Hospital at the Ohio State University Wexner Medical Center, Columbus, OH, United States
^x Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, United States
^y Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, United States
^z Department of Radiology, Henry Ford Health, Detroit, MI, United States
^aa Department of Public Health Sciences, Center for Bioinformatics, Henry Ford Health, Detroit, MI, United States
^ab The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, United Kingdom
^ac Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, United States
^ad Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, United States
^ae Department of Pathology, NYU Grossman School of Medicine, New York, NY, United States
^af Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States

Abstract
Purpose: While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas. Methods: Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS). Results: One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS. Conclusion: Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Author Keywords
Astrocytoma;  Glioblastoma;  Isocitrate dehydrogenase;  Magnetic resonance imaging;  T2-FLAIR mismatch

Funding details
National Institutes of HealthNIH
National Cancer InstituteNCIR01CA269948

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

Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP)
(2023) Nature Cell Biology, . 

Jain, S.^a b c , Pei, L.^d , Spraggins, J.M.^e , Angelo, M.^f , Carson, J.P.^g , Gehlenborg, N.^h , Ginty, F.ⁱ , Gonçalves, J.P.^j , Hagood, J.S.^k , Hickey, J.W.^l , Kelleher, N.L.^m , Laurent, L.C.ⁿ , Lin, S.^o , Lin, Y.^p , Liu, H.^q , Naba, A.^r , Nakayasu, E.S.^s , Qian, W.-J.^s , Radtke, A.^t , Robson, P.^u , Stockwell, B.R.^v , Van de Plas, R.^w , Vlachos, I.S.^x y , Zhou, M.^z , Ahn, K.J.^ac , Allen, J.^e , Anderson, D.M.^ad , Anderton, C.R.^z , Curcio, C.^ae , Angelin, A.^d , Arvanitis, C.^q , Atta, L.^af , Awosika-Olumo, D.^ag , Bahmani, A.^ah , Bai, H.^ac , Balderrama, K.^ai , Balzano, L.^aj , Bandyopadhyay, G.^ak , Bandyopadhyay, S.^al , Bar-Joseph, Z.^am , Barnhart, K.^al , Barwinska, D.^an , Becich, M.^ao , Becker, L.^ah , Becker, W.^ah , Bedi, K.^al , Bendall, S.^ah , Benninger, K.^ap , Betancur, D.^ap , Bettinger, K.^ah , Billings, S.^ap , Blood, P.^ap , Bolin, D.^aq , Border, S.^aj , Bosse, M.^ah , Bramer, L.^z , Brewer, M.^ar , Brusko, M.^aj , Bueckle, A.^aq , Burke, K.^ao , Burnum-Johnson, K.^z , Butcher, E.^ah , Butterworth, E.^aj , Cai, L.^as , Calandrelli, R.^at , Caldwell, M.^m , Campbell-Thompson, M.^aj , Cao, D.^ae , Cao-Berg, I.^ap , Caprioli, R.^ad , Caraccio, C.^ah , Caron, A.^au , Carroll, M.^ap , Chadwick, C.ⁱ , Chen, A.^av , Chen, D.^ah , Chen, F.^ai , Chen, H.^am , Chen, J.^aj , Chen, L.^aw , Chen, L.^ax , Chiacchia, K.^ap , Cho, S.ⁱ , Chou, P.^ay , Choy, L.^h , Cisar, C.^az , Clair, G.^s , Clarke, L.^ba , Clouthier, K.A.^ar , Colley, M.E.^e , Conlon, K.^a , Conroy, J.^h , Contrepois, K.^ah , Corbett, A.^ak , Corwin, A.^bb , Cotter, D.^ah , Courtois, E.^u , Cruz, A.^ao , Csonka, C.^ap , Czupil, K.^az , Daiya, V.^aq , Dale, K.^bc , Davanagere, S.A.^aq , Dayao, M.^am , de Caestecker, M.P.^ar , Decker, A.^v , Deems, S.^ap , Degnan, D.^z , Desai, T.^ah , Deshpande, V.^aq , Deutsch, G.^bd , Devlin, M.^ap , Diep, D.^as at be , Dodd, C.^af , Donahue, S.^am , Dong, W.^bf , dos Santos Peixoto, R.^af , Duffy, M.^al , Dufresne, M.^e , Duong, T.E.^at , Dutra, J.^ak , Eadon, M.T.^an , El-Achkar, T.M.^an , Enninful, A.^bg , Eraslan, G.^ai , Eshelman, D.^ap , Espin-Perez, A.^ah , Esplin, E.D.^ah , Esselman, A.^bh , Falo, L.D.^bi , Falo, L.^ao , Fan, J.^af , Fan, R.^bg , Farrow, M.A.^ad , Farzad, N.^bg , Favaro, P.^ah , Fermin, J.^aj , Filiz, F.^ah , Filus, S.^ap , Fisch, K.ⁿ , Fisher, E.^bj , Fisher, S.^al , Flowers, K.^ai , Flynn, W.F.^u , Fogo, A.B.^bk , Fu, D.A.^aj , Fulcher, J.^z , Fung, A.^at , Furst, D.^ao , Gallant, M.^aq , Gao, F.^bg , Gao, Y.^r , Gaulton, K.^at , Gaut, J.P.^c , Gee, J.^al , Ghag, R.R.^a , Ghazanfar, S.^ba , Ghose, S.ⁱ , Gisch, D.^an , Gold, I.^h , Gondalia, A.^aq , Gorman, B.^z , Greenleaf, W.^ah , Greenwald, N.^ah , Gregory, B.^al , Guo, R.^al , Gupta, R.^bf , Hakimian, H.^aj , Haltom, J.^d , Halushka, M.^af , Han, K.S.^af , Hanson, C.^ah , Harbury, P.^ah , Hardi, J.^ah , Harlan, L.^aj , Harris, R.C.^ar , Hartman, A.^ba , Heidari, E.^bl , Helfer, J.^ao , Helminiak, D.^ay , Hemberg, M.^bf , Henning, N.^m , Herr, B.W., II^aq , Ho, J.^bi , Holden-Wiltse, J.^ak , Hong, S.-H.^bm , Hong, Y.-K.^bn , Honick, B.^ap , Hood, G.^ap , Hu, P.^ac , Hu, Q.^bf , Huang, M.ⁿ , Huyck, H.^ak , Imtiaz, T.^al , Isberg, O.G.^e , Itkin, M.^al , Jackson, D.^o , Jacobs, M.ⁿ , Jain, Y.^aq , Jewell, D.^aj , Jiang, L.^ah , Jiang, Z.G.^av , Johnston, S.^al , Joshi, P.^bm , Ju, Y.^aq , Judd, A.^e , Kagel, A.^ah , Kahn, A.^ag , Kalavros, N.^av , Kalhor, K.^at , Karagkouni, D.^av , Karathanos, T.^ah , Karunamurthy, A.^ao , Katari, S.^aj , Kates, H.^aj , Kaushal, M.^a , Keener, N.^az , Keller, M.^h , Kenney, M.^ap , Kern, C.^at , Kharchenko, P.^h , Kim, J.^al , Kingsford, C.^am , Kirwan, J.^aj , Kiselev, V.^am , Kishi, J.^bo , Kitata, R.B.^z , Knoten, A.^a , Kollar, C.^ao , Krishnamoorthy, P.^a , Kruse, A.R.S.^e , Da, K.^al , Kundaje, A.^ah , Kutschera, E.^d , Kwon, Y.^s , Lake, B.B.^at be , Lancaster, S.^ah , Langlieb, J.^ai , Lardenoije, R.^bp , Laronda, M.^bq , Laskin, J.^br , Lau, K.^bs , Lee, H.^ah , Lee, M.^bn , Lee, M.^bm , Strekalova, Y.L.^aj , Li, D.^am , Li, J.^ah , Li, J.^ai , Li, X.^br , Li, Z.^at , Liao, Y.-C.^z , Liaw, T.^h , Lin, P.^at , Lin, Y.^al , Lindsay, S.ⁿ , Liu, C.^d , Liu, Y.^bg , Liu, Y.^at , Lott, M.^d , Lotz, M.^bm , Lowery, L.ⁱ , Lu, P.^d , Lu, X.^am , Lucarelli, N.^aj , Lun, X.^bf , Luo, Z.^at , Ma, J.^am , Macosko, E.^ai , Mahajan, M.^bf , Maier, L.^aq , Makowski, D.^ac , Malek, M.^e , Manthey, D.^aj , Manz, T.^h , Margulies, K.^al , Marioni, J.^au , Martindale, M.^aq , Mason, C.ⁿ , Mathews, C.^aj , Maye, P.^bm , McCallum, C.^h , McDonough, E.ⁱ , McDonough, L.ⁱ , Mcdowell, H.^bt , Meads, M.ⁿ , Medina-Serpas, M.^aj , Ferreira, R.M.^an , Messinger, J.^ae , Metis, K.^ao , Migas, L.G.^w , Miller, B.^af , Mimar, S.^aj , Minor, B.^a , Misra, R.^ak , Missarova, A.^ba , Mistretta, C.^av , Moens, R.^j , Moerth, E.^h , Moffitt, J.^bu , Molla, G.^ba , Monroe, M.^s , Monte, E.^ah , Morgan, M.^ba , Muraro, D.^bv , Murphy, B.R.^am , Murray, E.^ai , Musen, M.A.^ah , Naglah, A.^aj , Nasamran, C.ⁿ , Neelakantan, T.^v , Nevins, S.^ah , Nguyen, H.^al , Nguyen, N.^am , Nguyen, T.^h , Nguyen, T.^at , Nigra, D.^ap , Nofal, M.^bf , Nolan, G.^ah , Nwanne, G.^a , O’Connor, M.^ah , Okuda, K.^bw , Olmer, M.^aw , O’Neill, K.^al , Otaluka, N.^av , Pang, M.^al , Parast, M.ⁿ , Pasa-Tolic, L.^z , Paten, B.^az , Patterson, N.H.^ad , Peng, T.^d , Phillips, G.^ap , Pichavant, M.^ah , Piehowski, P.^z , Pilner, H.^bd , Pingry, E.^e , Pita-Juarez, Y.^av , Plevritis, S.^ah , Ploumakis, A.^av , Pouch, A.^al , Pryhuber, G.^ak , Puerto, J.^ap , Qaurooni, D.^aq , Qin, L.^al , Quardokus, E.M.^aq , Rajbhandari, P.^v , Rakow-Penner, R.ⁿ , Ramasamy, R.^u , Read, D.^o , Record, E.G.^aq , Reeves, D.^e , Ricarte, A.^am , Rodríguez-Soto, A.ⁿ , Ropelewski, A.^ap , Rosario, J.^al , Roselkis, M.-A.^h , Rowe, D.^aw , Roy, T.K.^bx , Ruffalo, M.^am , Ruschman, N.^aq , Sabo, A.^an , Sachdev, N.^ai , Saka, S.^bo , Salamon, D.^a , Sarder, P.^aj , Sasaki, H.^bo , Satija, R.^ba , Saunders, D.^ar , Sawka, R.^ac , Schey, K.^ad , Schlehlein, H.^aq , Scholten, D.^q , Schultz, S.^am , Schwartz, L.^al , Schwenk, M.^ao , Scibek, R.^ap , Segre, A.^bf , Serrata, M.^bo , Shands, W.^az , Shen, X.^ah , Shendure, J.^bd , Shephard, H.^ap , Shi, L.^at , Shi, T.^z , Shin, D.-G.^aw , Shirey, B.^ao , Sibilla, M.^ao , Silber, M.^al , Silverstein, J.^ap , Simmel, D.^ap , Simmons, A.^ao , Singhal, D.^av , Sivajothi, S.^u , Smits, T.^h , Soncin, F.^at , Song, Q.^am , Stanley, V.ⁿ , Stuart, T.^ba , Su, H.^bo , Su, P.^m , Sun, X.^at , Surrette, C.ⁱ , Swahn, H.^aw , Tan, K.^ac , Teichmann, S.^bv , Tejomay, A.^aq , Tellides, G.^bg , Thomas, K.^al , Thomas, T.^al , Thompson, M.^ap , Tian, H.^ao , Tideman, L.^w , Trapnell, C.^bd , Tsai, A.G.^ah , Tsai, C.-F.^z , Tsai, L.^av , Tsui, E.^bq , Tsui, T.^e , Tung, J.^ah , Turner, M.^h , Uranic, J.^ap , Vaishnav, E.D.^ai , Varra, S.R.^ah , Vaskivskyi, V.^am , Velickovic, D.^z , Velickovic, M.^z , Verheyden, J.^at , Waldrip, J.^ao , Wallace, D.^d , Wan, X.^at , Wang, A.^at , Wang, F.^ax , Wang, M.^ah , Wang, S.^av , Wang, X.^as , Wasserfall, C.^aj , Wayne, L.^aj , Webber, J.^ai , Weber, G.M.^bf , Wei, B.^ah , Wei, J.-J.^q , Weimer, A.^ah , Welling, J.^ap , Wen, X.^at , Wen, Z.^am , Williams, M.K.^aj , Winfree, S.^by , Winograd, N.^bz , Woodard, A.^al , Wright, D.^aq , Wu, F.^af , Wu, P.-H.^af , Wu, Q.^at , Wu, X.^bx , Xing, Y.^d , Xu, T.^at , Yang, M.^br , Yang, M.^bg , Yap, J.^av , Ye, D.H.^br , Yin, P.^bo , Yuan, Z.^ao , Yun, C.J.^as , Zahraei, A.^e , Zemaitis, K.^z , Zhang, B.^a , Zhang, C.^aw , Zhang, C.^aw , Zhang, C.^at , Zhang, K.^at be , Zhang, S.^d , Zhang, T.^am , Zhang, Y.^bf , Zhao, B.^ah , Zhao, W.^at , Zheng, J.W.^bf , Zhong, S.^at , Zhu, B.^ah , Zhu, C.^ah , Zhu, D.^at , Zhu, Q.^at , Zhu, Y.^s , Börner, K.^aa , Snyder, M.P.^ab , HuBMAP Consortium^ca

^a Department of Medicine, Washington University School of Medicine, St Louis, MO, United States
^b Department of Pediatrics, Washington University School of Medicine, St Louis, MO, United States
^c Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
^d Center for Mitochondrial and Epigenomic Medicine, Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^e Department of Cell and Developmental Biology and the Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
^f Department of Pathology, Stanford School of Medicine, Stanford, CA, United States
^g Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, United States
^h Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
ⁱ GE Research Center, Niskayuna, NY, United States
^j Department of Intelligent Systems, Delft University of Technology, Delft, Netherlands
^k Department of Pediatrics (Pulmonology) and Program for Rare and Interstitial Lung Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^l Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
^m Departments of Medicine, Chemistry and Molecular Biosciences, Northwestern University, Evanston, IL, United States
ⁿ Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, United States
^o Division of Cardiology, University of Washington School of Medicine, Seattle, WA, United States
^p Department of Surgery, Washington University School of Medicine, St Louis, MO, United States
^q Departments of Pharmacology, Medicine (Hematology and Oncology), Lurie Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
^r Department of Physiology and Biophysics, University of Illinois at Chicago, Chicago, IL, United States
^s Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
^t Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, United States
^u The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States
^v Department of Biological Sciences and Department of Chemistry, Columbia University, New York, NY, United States
^w Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands
^x Broad Institute of MIT and Harvard, Cambridge, MA, United States
^y Spatial Technologies Unit, Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, United States
^z Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, United States
^aa Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States
^ab Department of Genetics, Stanford School of Medicine, Stanford, CA, United States
^ac Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
^ad Department of Biochemistry and the Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
^ae University of Alabama at Birmingham, Birmingham, AL, United States
^af Johns Hopkins University, Baltimore, MD, United States
^ag The University of Texas at Austin, Austin, TX, United States
^ah Stanford University, Stanford, CA, United States
^ai Broad Institute, Boston, MA, United States
^aj University of Florida, Gainesville, FL, United States
^ak University of Rochester Medical Center, Rochester, NY, United States
^al University of Pennsylvania, Philadelphia, PA, United States
^am Carnegie Mellon University, Pittsburgh, PA, United States
^an Indiana University, Indianapolis, IN, United States
^ao University of Pittsburgh, Pittsburgh, PA, United States
^ap PSC (Pittsburgh Supercomputing Center)/CMU, Pittsburgh, PA, United States
^aq Indiana University, Bloomington, IN, United States
^ar Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
^as California Institute of Technology, Pasadena, CA, United States
^at University of California San Diego, San Diego, CA, United States
^au European Bioinformatics Institute, Hinxton, United Kingdom
^av Beth Israel Deaconess Medical Center, Boston, MA, United States
^aw University of Connecticut/Scripps, Mansfield, United States
^ax Stony Brook University, Stony Brook, NY, United States
^ay Marquette University, Milwaukee, WI, United States
^az University of California Santa Cruz, Santa Cruz, CA, United States
^ba New York Genome Center, New York City, NY, United States
^bb GE Research Cente, Niskayuna, NY, United States
^bc Native BioData Consortium, Oklahoma City, OK, United States
^bd University of Washington, Seattle, WA, United States
^be Altos Labs, San Diego Institute of Science, San Diego, CA, United States
^bf Harvard Medical School, Boston, MA, United States
^bg Yale University, New Haven, CT, United States
^bh Department of Chemistry, Vanderbilt University, Nashville, TN, United States
^bi Department of Dermatology, University of Pittsburgh, Pittsburgh, PA, United States
^bj University of Cambridge, Cambridge, United Kingdom
^bk Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
^bl University of Zurich, Zurich, Switzerland
^bm University of Connecticut, Mansfield, CT, United States
^bn University of Southern California, Los Angeles, CA, United States
^bo Harvard University, Boston, MA, United States
^bp Delft University of Technology, Delft, Netherlands
^bq Lurie Children’s Hospital, Chicago, IL, United States
^br Purdue University, Purdue, IN, United States
^bs Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, United States
^bt Northwestern University, Chicago, IL, United States
^bu Boston Children’s Hospital, Boston, MA, United States
^bv Wellcome Sanger Institute, Hinxton, United Kingdom
^bw University of North Carolina, Chapel Hill, NC, United States
^bx University of Iowa, Iowa City, IA, United States
^by University of Nebraska Medical Center, Omaha, NE, United States
^bz Pennsylvania State University, State College, PA, United States

Abstract
The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research. © 2023, Springer Nature Limited.

Funding details
National Institutes of HealthNIHT32CA196585
U.S. Department of DefenseDODW81XWH-22-1-0058
American Cancer SocietyACSPF-20-032-01-CSM
National Cancer InstituteNCI
National Institute of Allergy and Infectious DiseasesNIAID
Additional VenturesAV

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

Locus specific endogenous retroviral expression associated with Alzheimer’s disease
(2023) Frontiers in Aging Neuroscience, 15, art. no. 1186470, . 

Dawson, T.^a b , Rentia, U.^a , Sanford, J.^c , Cruchaga, C.^c , Kauwe, J.S.K.^d , Crandall, K.A.^a b

^a Computational Biology Institute, The George Washington University, Washington, DC, United States
^b Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
^c Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^d Department of Biology, Brigham Young University, Provo, UT, United States

Abstract
Introduction: Human endogenous retroviruses (HERVs) are transcriptionally-active remnants of ancient retroviral infections that may play a role in Alzheimer’s disease. Methods: We combined two, publicly available RNA-Seq datasets with a third, novel dataset for a total cohort of 103 patients with Alzheimer’s disease and 45 healthy controls. We use telescope to perform HERV quantification for these samples and simultaneously perform gene expression analysis. Results: We identify differentially expressed genes and differentially expressed HERVs in Alzheimer’s disease patients. Differentially expressed HERVs are scattered throughout the genome; many of them are members of the HERV-K superfamily. A number of HERVs are correlated with the expression of dysregulated genes in Alzheimer’s and are physically proximal to genes which drive disease pathways. Discussion: Dysregulated expression of ancient retroviral insertions in the human genome are present in Alzheimer’s disease and show localization patterns that may explain how these elements drive pathogenic gene expression. Copyright © 2023 Dawson, Rentia, Sanford, Cruchaga, Kauwe and Crandall.

Author Keywords
Alzheimer’s disease;  endogenous retrovirus;  gene expression;  HERV;  RNA-Seq

Funding details
National Institutes of HealthNIHP01AG003991, P01AG026276, P01AG0399, P30AG06644, R01AG044546, R01AG078964, RF1AG053303, RF1AG058501, RF1AG071706, U01AG058922
U.S. Department of DefenseDODLI- W81XWH2010849
Michael J. Fox Foundation for Parkinson’s ResearchMJFF
Alzheimer’s AssociationAAZEN-22-848604
Brigham Young UniversityBYU
Hope Center for Neurological Disorders
Chan Zuckerberg InitiativeCZI

Document Type: Article
Publication Stage: Final
Source: Scopus

Prediction of Normal Tissue Complication Probability (NTCP) After Radiation Therapy Using Imaging and Molecular Biomarkers and Multivariate Modelling
(2023) Journal of Molecular Neuroscience, . 

Alirezaei, Z.^a , Amouheidari, A.^b , Iraji, S.^c , Hassanpour, M.^d , Hejazi, S.H.^e , Davanian, F.^f , Nami, M.T.^g , Rastaghi, S.^h , Shokrani, P.^a , Tsien, C.I.ⁱ , Nazem-Zadeh, M.-R.^c d

^a Medical Physics Department, Isfahan University of Medical Science, Isfahan, Iran
^b Research & Education, Department of Radiation Oncology, Isfahan Milad Hospital, Isfahan, Iran
^c Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
^d Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
^e Skin Diseases and Leishmaniosis Research Center, Department of Parasitology and Mycology, School of Medicine, Isfahan University of Medical Science, Isfahan, Iran
^f Radiology Department, School of Medicine, Isfahan University of Medical Science, Isfahan, Iran
^g Dana Health Center, Shiraz University of Medical Sciences, Shiraz, Iran
^h Biostatistics Department, Mashhad University of Medical Sciences, Mashhad, Iran
ⁱ Radiation Oncology Department, Washington University, St. Louis, MO, United States

Abstract
The aim of this study was to design a predictive radiobiological model of normal brain tissue in low-grade glioma following radiotherapy based on imaging and molecular biomarkers. Fifteen patients with primary brain tumors prospectively participated in this study and underwent radiation therapy. Magnetic resonance imaging (MRI) was obtained from the patients, including T1- and T2-weighted imaging and diffusion tensor imaging (DTI), and a generalized equivalent dose (gEUD) was calculated. The radiobiological model of the normal tissue complication probability (NTCP) was performed using the variables gEUD; axial diffusivity (AD) and radial diffusivity (RD) of the corpus callosum; and serum protein S100B by univariate and multivariate logistic regression XLIIIrd Sir Peter Freyer Memorial Lecture and Surgical Symposium (2018). Changes in AD, RD, and S100B from baseline up to the 6 months after treatment had an increasing trend and were significant in some time points (P-value < 0.05). The model resulting from RD changes in the 6 months after treatment was significantly more predictable of necrosis than other univariate models. The bivariate model combining RD changes in Gy40 dose-volume and gEUD, as well as the trivariate model obtained using gEUD, RD, and S100B, had a higher predictive value among multivariate models at the sixth month of the treatment. Changes in RD diffusion indices and in serum protein S100B value were used in the early-delayed stage as reliable biomarkers for predicting late-delayed damage (necrosis) caused by radiation in the corpus callosum. Current findings could pave the way for intervention therapies to delay the severity of damage to white matter structures, minimize cognitive impairment, and improve the quality of life of patients with low-grade glioma. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Author Keywords
Imaging biomarker;  Low-grade glioma;  Molecular biomarker;  NTCP (normal tissue complication probability);  Radiobiological modelling;  Radiotherapy

Funding details
Isfahan University of Medical SciencesIUMS397151

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