WashU weekly Neuroscience publications

Resting state functional connectivity predictors of treatment response to electroconvulsive therapy in depression
(2019) Scientific Reports, 9 (1), art. no. 5071, . 

Moreno-Ortega, M.^{a b} , Prudic, J.^a , Rowny, S.^a , Patel, G.H.^a , Kangarlu, A.^d , Lee, S.^c , Grinband, J.^a , Palomo, T.^{b e} , Perera, T.^a , Glasser, M.F.^f , Javitt, D.C.^a

^a Division of Experimental Therapeutics, Department of Psychiatry, New York State Psychiatric Institute/Columbia University Medical Center, New York, NY, United States
^b Centro de Investigacion Biomedica en Red de Salud Mental (CIBERSAM), Madrid, Spain
^c Department of Psychiatry and Biostatistics, New York State Psychiatric Institute/Columbia University, New York, NY, United States
^d Department of Psychiatry, Radiology and Biomedical Engineering, Columbia University, New York, NY, United States
^e Department of Psychiatry, Complutense University, Madrid, Spain
^f Departments of Radiology and Neuroscience, Washington University Medical School, St. Louis, MO, United States

Abstract
There is increasing focus on use of resting-state functional connectivity (RSFC) analyses to subtype depression and to predict treatment response. To date, identification of RSFC patterns associated with response to electroconvulsive therapy (ECT) remain limited, and focused on interactions between dorsal prefrontal and regions of the limbic or default-mode networks. Deficits in visual processing are reported in depression, however, RSFC with or within the visual network have not been explored in recent models of depression. Here, we support prior studies showing in a sample of 18 patients with depression that connectivity between dorsal prefrontal and regions of the limbic and default-mode networks serves as a significant predictor. In addition, however, we demonstrate that including visual connectivity measures greatly increases predictive power of the RSFC algorithm (>80% accuracy of remission). These exploratory results encourage further investigation into visual dysfunction in depression, and use of RSFC algorithms incorporating the visual network in prediction of response to both ECT and transcranial magnetic stimulation (TMS), offering a new framework for the development of RSFC-guided TMS interventions in depression. © 2019, The Author(s).

Document Type: Article
Publication Stage: Final
Source: Scopus
Access Type: Open Access

HDAC5 promotes optic nerve regeneration by activating the mTOR pathway
(2019) Experimental Neurology, 317, pp. 271-283. 

Pita-Thomas, W.^a , Mahar, M.^a , Joshi, A.^a , Gan, D.^{a d} , Cavalli, V.^{a b c}

^a Department of Neuroscience, 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 Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, United States
^d Department of Neuroscience, Brandeis University, Waltham, MA 02453, United States

Abstract
Neurons in the central nervous system (CNS) regenerate poorly compared to their counterparts in the peripheral nervous system. We previously showed that, in peripheral sensory neurons, nuclear HDAC5 inhibits the expression of regenerative associated genes. After nerve injury, HDAC5 is exported to the cytoplasm to promote axon regeneration. Here we investigated the role of HDAC5 in retinal ganglion cells (RGCs), a CNS neuron which fails to survive and regenerate axons after injury. In contrast to PNS neurons, we found that HDAC5 is mostly cytoplasmic in naïve RGCs and its localization is not affected by optic nerve injury, suggesting that HDAC5 does not directly suppress regenerative associated genes in these cells. Manipulation of the PKCμ pathway, the canonical pathway that regulates HDAC5 localization in PNS neurons by phosphorylating serine 259 and 498, and other pathways that regulate nuclear/cytoplasmic transport, did not affect HDAC5 cytoplasmic localization in RGC. Also, an HDAC5 mutant whose serine 259 and 488 were replaced by alanine (HDAC5 AA ) to prevent phosphorylation and nuclear export showed a predominantly cytoplasmic localization, suggesting that HDAC5 resides mostly in the cytoplasm in RGCs. Interestingly, expression of HDAC5 AA , but not HDAC5 wild type, in RGCs in vivo promoted optic nerve regeneration and RGC survival. Mechanistically, we found that HDAC5 AA stimulated the survival and regeneration of RGCs by activating the mTOR pathway. Consistently, the combination of HDAC5 AA expression and the stimulation of the immune system by zymosan injection had an additive effect in promoting robust axon regeneration. These results reveal the potential of manipulating HDAC5 phosphorylation state to activate the mTOR pathway, offering a new therapeutic target to design drugs that promote axon regeneration in the optic nerve. © 2019 Elsevier Inc.

Author Keywords
HDAC5;  mTOR;  Optic nerve regeneration;  Retinal ganglion cells;  Zymosan

Document Type: Article
Publication Stage: Final
Source: Scopus

Gene regulation and the architecture of complex human traits in the genomics era
(2019) Current Opinion in Psychology, 27, pp. 93-97. 

Boutwell, B.B.^a , White, M.A.^b

^a Criminology and Criminal Justice, Saint Louis University, 3550 Lindell Blvd., St. Louis, MO 63013, United States
^b Department of Genetics and Edison Family Center for Genome Sciences and Systems Biology, Washington University in St. Louis School of Medicine, Couch Biomedical Research Building, St. Louis, MO 63110, United States

Abstract
Virtually all human psychological and behavioral traits are at least partially heritable. For nearly a century, classical genetic studies have sought to understand how genetic variation contributes to human variation in these traits. More recently, genome-wide association studies have identified large numbers of specific genetic variants linked with complex traits. Many of these variants fall outside of protein-coding genes, in putative gene regulatory elements. This suggests that some fraction of causal human genetic variation acts through gene regulation. New developments in the field of regulatory genomics offer resources and methods to understand how genetic variants that alter gene expression contribute to human psychology and risk for psychiatric disease. © 2019 Elsevier Ltd

Document Type: Review
Publication Stage: Final
Source: Scopus

Association of Amyloid Positron Emission Tomography with Subsequent Change in Clinical Management among Medicare Beneficiaries with Mild Cognitive Impairment or Dementia
(2019) JAMA – Journal of the American Medical Association, 321 (13), pp. 1286-1294. Cited 1 time.

Rabinovici, G.D.^{a b} , Gatsonis, C.^{c d} , Apgar, C.^e , Chaudhary, K.^a , Gareen, I.^{c f} , Hanna, L.^c , Hendrix, J.^g , Hillner, B.E.^h , Olson, C.^e , Lesman-Segev, O.H.^a , Romanoff, J.^c , Siegel, B.A.ⁱ , Whitmer, R.A.^{j k} , Carrillo, M.C.^g

^a Memory and Aging Center, Department of Neurology, University of California, 675 Nelson Rising Ln, Ste 190, San Francisco, CA 94158, United States
^b JAMA Neurology, United States
^c Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, United States
^d Department of Biostatistics, Brown University School of Public Health, Providence, RI, United States
^e American College of Radiology, Reston, VA, United States
^f Department of Epidemiology, Brown University School of Public Health, Providence, RI, United States
^g Alzheimer’s Association, Chicago, IL, United States
^h Department of Medicine, Virginia Commonwealth University, Richmond, United States
ⁱ Edward Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
^j Division of Research, Kaiser Permanente, Oakland, CA, United States
^k Department of Public Health Sciences, University of California, Davis, United States

Abstract
Importance: Amyloid positron emission tomography (PET) detects amyloid plaques in the brain, a core neuropathological feature of Alzheimer disease. Objective: To determine if amyloid PET is associated with subsequent changes in the management of patients with mild cognitive impairment (MCI) or dementia of uncertain etiology. Design, Setting, and Participants: The Imaging Dementia – Evidence for Amyloid Scanning (IDEAS) study was a single-group, multisite longitudinal study that assessed the association between amyloid PET and subsequent changes in clinical management for Medicare beneficiaries with MCI or dementia. Participants were required to meet published appropriate use criteria stating that etiology of cognitive impairment was unknown, Alzheimer disease was a diagnostic consideration, and knowledge of PET results was expected to change diagnosis and management. A total of 946 dementia specialists at 595 US sites enrolled 16008 patients between February 2016 and September 2017. Patients were followed up through January 2018. Dementia specialists documented their diagnosis and management plan before PET and again 90 (±30) days after PET. Exposures: Participants underwent amyloid PET at 343 imaging centers. Main Outcomes and Measures: The primary end point was change in management between the pre- and post-PET visits, as assessed by a composite outcome that included Alzheimer disease drug therapy, other drug therapy, and counseling about safety and future planning. The study was powered to detect a 30% or greater change in the MCI and dementia groups. One of 2 secondary end points is reported: the proportion of changes in diagnosis (from Alzheimer disease to non-Alzheimer disease and vice versa) between pre- and post-PET visits. Results: Among 16008 registered participants, 11409 (71.3%) completed study procedures and were included in the analysis (median age, 75 years [interquartile range, 71-80]; 50.9% women; 60.5% with MCI). Amyloid PET results were positive in 3817 patients with MCI (55.3%) and 3154 patients with dementia (70.1%). The composite end point changed in 4159 of 6905 patients with MCI (60.2% [95% CI, 59.1%-61.4%]) and 2859 of 4504 patients with dementia (63.5% [95% CI, 62.1%-64.9%]), significantly exceeding the 30% threshold in each group (P <.001, 1-sided). The etiologic diagnosis changed from Alzheimer disease to non-Alzheimer disease in 2860 of 11409 patients (25.1% [95% CI, 24.3%-25.9%]) and from non-Alzheimer disease to Alzheimer disease in 1201 of 11409 (10.5% [95% CI, 10.0%-11.1%]). Conclusions and Relevance: Among Medicare beneficiaries with MCI or dementia of uncertain etiology evaluated by dementia specialists, the use of amyloid PET was associated with changes in clinical management within 90 days. Further research is needed to determine whether amyloid PET is associated with improved clinical outcomes. Trial Registration: ClinicalTrials.gov Identifier: NCT02420756. © 2019 American Medical Association. All rights reserved.

Document Type: Article
Publication Stage: Final
Source: Scopus

The Behavioral Medicine Research Council: Its origins, mission, and methods
(2019) Health Psychology, 38 (4), pp. 277-289. 

Freedland, K.E.

Behavioral Medicine Center, Department of Psychiatry, Washington University School of Medicine, 4320 Forest Park Avenue #301, St. Louis, MO 63108, United States

Abstract
The Behavioral Medicine Research Council (BMRC) is a new, autonomous joint committee of 4 of the leading behavioral medicine research organizations, including the Academy of Behavioral Medicine Research, the American Psychosomatic Society, the Society for Health Psychology, and the Society of Behavioral Medicine. The BMRC’s work has important implications for the science and practice of behavioral medicine. The distinguished senior scientists who comprise this new committee will identify a series of strategic research goals for behavioral medicine and promote systematic, interdisciplinary efforts to achieve them. This special report discusses the developments that led to the formation of the BMRC, describes the BMRC’s mission, and explains the methods that its members will use. © 2019 American Psychological Association.

Author Keywords
Behavioral medicine;  Health behavior;  Multicenter studies;  Randomized controlled trials as topic;  Societies

Document Type: Article
Publication Stage: Final
Source: Scopus

Intraoperative Methadone in Same-Day Ambulatory Surgery: A Randomized, Double-Blinded, Dose-Finding Pilot Study
(2019) Anesthesia and analgesia, 128 (4), pp. 802-810. 

Komen, H.^a , Brunt, L.M.^b , Deych, E.^c , Blood, J.^a , Kharasch, E.D.^{a d e}

^a From the Department of Anesthesiology, Germany
^b Department of Surgery, Washington University in St Louis, St Louis, MO, United States
^c BioRankings, St Louis, MO, United States
^d Department of Biochemistry and Molecular Biophysics, Washington University in St Louis, St Louis, MO, United States
^e Center for Clinical Pharmacology, St Louis College of Pharmacy, Washington University in St Louis, St Louis, MO, United States

Abstract
BACKGROUND: Approximately 50 million US patients undergo ambulatory surgery annually. Postoperative opioid overprescribing is problematic, yet many patients report inadequate pain relief. In major inpatient surgery, intraoperative single-dose methadone produces better analgesia and reduces opioid use compared with conventional repeated dosing of short-duration opioids. This investigation tested the hypothesis that in same-day ambulatory surgery, intraoperative methadone, compared with short-duration opioids, reduces opioid consumption and pain, and determined an effective intraoperative induction dose of methadone for same-day ambulatory surgery. METHODS: A double-blind, dose-escalation protocol randomized 60 patients (2:1) to intraoperative single-dose intravenous methadone (initially 0.1 then 0.15 mg/kg ideal body weight) or conventional as-needed dosing of short-duration opioids (eg, fentanyl, hydromorphone; controls). Intraoperative and postoperative opioid consumption, pain, and opioid side effects were assessed before discharge. Patient home diaries recorded pain, opioid use, and opioid side effects daily for 30 days postoperatively. Primary outcome was in-hospital (intraoperative and postoperative) opioid use. Secondary outcomes were 30 days opioid consumption, pain intensity, and opioid side effects. RESULTS: Median (interquartile range) methadone doses were 6 (5-6) and 9 (8-9) mg in the 0.1 and 0.15 mg/kg methadone groups, respectively. Total opioid consumption (morphine equivalents) in the postanesthesia care unit was significantly less compared with controls (9.3 mg, 1.3-11.0) in subjects receiving 0.15 mg/kg methadone (0.1 mg, 0.1-3.3; P < .001) but not 0.1 mg/kg methadone (5.0 mg, 3.3-8.1; P = .60). Dose-escalation ended at 0.15 mg/kg methadone. Total in-hospital nonmethadone opioid use after short-duration opioid, 0.1 mg/kg methadone, and 0.15 mg/kg methadone was 35.3 (25.0-44.0), 7.1 (3.7-10.0), and 3.3 (0.1-5.8) mg morphine equivalents, respectively (P < .001 for both versus control). In-hospital pain scores and side effects were not different between groups. In the 30 days after discharge, patients who received methadone 0.15 mg/kg had less pain at rest (P = .02) and used fewer opioid pills than controls (P < .0001), whereas patients who received 0.1 mg/kg had no difference in pain at rest (P = .69) and opioid use compared to controls (P = .08). CONCLUSIONS: In same-day discharge surgery, this pilot study identified a single intraoperative dose of methadone (0.15 mg/kg ideal body weight), which decreased intraoperative and postoperative opioid requirements and postoperative pain, compared with conventional intermittent short-duration opioids, with similar side effects.

Document Type: Article
Publication Stage: Final
Source: Scopus

Restoring mitofusin balance prevents axonal degeneration in a Charcot-Marie-Tooth type 2A model
(2019) The Journal of clinical investigation, 130, . 

Zhou, Y.^{a b} , Carmona, S.^b , Muhammad, A.K.M.G.^{a b} , Bell, S.^{a b} , Landeros, J.^{a b} , Vazquez, M.^{a b} , Ho, R.^b , Franco, A.^c , Lu, B.^b , Dorn, G.W., 2nd^c , Wang, S.^b , Lutz, C.M.^d , Baloh, R.H.^{a b e}

^a Center for Neural Science and Medicine
^b Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
^c Center for Pharmacogenomics, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, United States
^d Jackson Laboratory, Bar Harbor, ME, United States
^e Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, United States

Abstract
Mitofusin-2 (MFN2) is a mitochondrial outer-membrane protein that plays a pivotal role in mitochondrial dynamics in most tissues, yet mutations in MFN2, which cause Charcot-Marie-Tooth disease type 2A (CMT2A), primarily affect the nervous system. We generated a transgenic mouse model of CMT2A that developed severe early onset vision loss and neurological deficits, axonal degeneration without cell body loss, and cytoplasmic and axonal accumulations of fragmented mitochondria. While mitochondrial aggregates were labeled for mitophagy, mutant MFN2 did not inhibit Parkin-mediated degradation, but instead had a dominant negative effect on mitochondrial fusion only when MFN1 was at low levels, as occurs in neurons. Finally, using a transgenic approach, we found that augmenting the level of MFN1 in the nervous system in vivo rescued all phenotypes in mutant MFN2R94Q-expressing mice. These data demonstrate that the MFN1/MFN2 ratio is a key determinant of tissue specificity in CMT2A and indicate that augmentation of MFN1 in the nervous system is a viable therapeutic strategy for the disease.

Author Keywords
Mouse models;  Neurodegeneration;  Neuromuscular disease;  Neuroscience

Document Type: Article
Publication Stage: Final
Source: Scopus
Access Type: Open Access

Cortical degeneration in chronic traumatic encephalopathy and Alzheimer’s disease neuropathologic change
(2019) Neurological Sciences, 40 (3), pp. 529-533. 

Armstrong, R.A.^a , McKee, A.C.^{b c d} , Stein, T.D.^{d e} , Alvarez, V.E.^{c e} , Cairns, N.J.^f

^a Vision Sciences, Aston University, Birmingham, B4 7ET, United Kingdom
^b VA Boston HealthCare System, Boston, MA 02130, United States
^c Department of Neurology, Boston University School of Medicine, Boston, MA 02118, United States
^d Department of Pathology & Laboratory Medicine, Boston University School of Medicine, Boston, MA 02118, United States
^e Department of Veterans Affairs Medical Center, Bedford, MA 01730, United States
^f Departments of Neurology and Pathology & Immunology, Washington University School of Medicine, Saint Louis, MO 63110, United States

Abstract
Objectives: An observational study to compare the laminar distributions in frontal and temporal cortex of the tau-immunoreactive pathologies in chronic traumatic encephalopathy (CTE) and Alzheimer’s disease neuropathologic change (ADNC). Patients: Post-mortem material of (1) four cases of CTE without ADNC, (2) seven cases of CTE with ADNC (CTE/ADNC), and (3) seven cases of ADNC alone. Results: In CTE and CTE/ADNC, neurofibrillary tangles (NFT), neuropil threads (NT), and dot-like grains (DLG) were distributed either in upper cortex or across all layers. Low densities of astrocytic tangles (AT) and abnormally enlarged neurons (EN) were not localized to any specific layer. Surviving neurons exhibited peaks of density in both upper and lower cortex, and vacuole density was greatest in superficial layers. In ADNC, neuritic plaques (NP) were more frequent, AT rare, NFT and NT were more widely distributed, NT affected lower layers more frequently, and surviving neurons were less frequently bimodal than in CTE and CTE/ADNC. Conclusion: Tau pathology in CTE and CTE/ADNC consistently affected the upper cortex but was more widely distributed in ADNC. The presence of CTE may encourage the development of ADNC pathology later in the course of the disease. © 2018, The Author(s).

Author Keywords
Alzheimer’s disease neuropathologic change (ADNC);  Chronic traumatic encephalopathy (CTE);  Laminar distribution;  Tauopathy

Document Type: Article
Publication Stage: Final
Source: Scopus

Evaluation of 11 C-methionine PET and anatomic MRI associations in diffuse intrinsic pontine glioma
(2019) Journal of Nuclear Medicine, 60 (3), pp. 312-319. Cited 1 time.

Tinkle, C.L.^a , Duncan, E.C.^b , Doubrovin, M.^c , Han, Y.^d , Li, Y.^d , Kim, H.^e , Broniscer, A.^f , Snyder, S.E.^c , Merchant, T.E.^a , Shulkin, B.L.^c

^a Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN, United States
^b University of Tennessee Health Science Center, Memphis, TN, United States
^c Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, TN, United States
^d Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN, United States
^e Department of Radiation Oncology, Washington University, St. Louis, MO, United States
^f Department of Oncology, University of Pittsburgh Medical Center, Pittsburgh, PA, United States

Abstract
The role of metabolic imaging in the diagnosis, treatment, and response assessment of diffuse intrinsic pontine glioma (DIPG) is poorly defined. We investigated the uptake of 11 C-methionine in pediatric patients with newly diagnosed DIPG and evaluated the associations of 11 C-methionine PET metrics with conventional MRI indices and survival outcomes. Methods: Twenty-two patients with newly diagnosed DIPG were prospectively enrolled on an institutional review board–approved investigational study of 11 C-methionine PET. All patients underwent baseline 11 C-methionine PET/CT, and initial treatment-response scans after chemotherapy or radiation therapy were obtained for 17 patients. Typical and atypical DIPGs were assessed clinically and radiographically and defined by multidisciplinary consensus. Three-dimensional regions of interest, reviewed by consensus between a nuclear medicine physician and a radiation oncologist, were delineated after coregistration of PET and MR images. Associations of 11 C-methionine uptake intensity and uniformity with survival, along with associations between 11 C-methionine uptake and conventional MRI tumor indices over time, were evaluated. 11 C-methionine PET voxel values within regions of interest were assessed as threshold values across proportions of the study population, and 11 C-methionine uptake at baseline was assessed relative to MRI-defined tumor progression. Results: 11 C-methionine uptake above that of uninvolved brain tissue was observed in 18 of 22 baseline scans (82%) and 15 of 17 initial response scans (88%). 11 C-methionine avidity within MRI-defined tumor was limited in extent, with 11 of 18 positive baseline 11 C-methionine PET scans (61%) showing less than 25% 11 C-methionine–avid tumor. The increase in total tumor volume with 11 C-methionine PET was relatively limited (17.2%; interquartile range, 6.53%–38.90%), as was the extent of 11 C-methionine uptake beyond the MRI-defined tumor (2.2%; interquartile range, 0.55%–10.88%). Although baseline 11 C-methionine PET intensity and uniformity metrics did not correlate with survival outcomes, initial 11 C-methionine avidity overlapped with recurrent tumor in 100% of cases. A clinical diagnosis of atypical DIPG was associated with borderline significantly prolonged progression-free survival (P 5 0.07), yet 11 C-methionine PET indices at diagnosis did not differ significantly between atypical and typical DIPGs. Conclusion: Most newly diagnosed DIPGs are successfully visualized by 11 C-methionine PET. Baseline 11 C-methionine uptake delineates regions at increased risk for recurrence, yet intensity and uniformity metrics did not correlate with treatment outcomes in children with DIPG in this study. Copyright © 2019 by the Society of Nuclear Medicine and Molecular Imaging.

Author Keywords
11 C-methionine PET;  Brainstem glioma;  Diffuse midline glioma;  DIPG;  MRI;  Pediatric

Document Type: Article
Publication Stage: Final
Source: Scopus

16S rRNA gene profiling and genome reconstruction reveal community metabolic interactions and prebiotic potential of medicinal herbs used in neurodegenerative disease and as nootropics
(2019) PLoS ONE, 14 (3), art. no. e0213869, . 

Peterson, C.T.^a , Sharma, V.^b , Iablokov, S.N.^{c d} , Albayrak, L.^e , Khanipov, K.^e , Uchitel, S.^f , Chopra, D.^{a g} , Mills, P.J.^a , Fofanov, Y.^e , Rodionov, D.A.^{b c} , Peterson, S.N.^{b h}

^a UC San Diego, School of Medicine, Center of Excellence for Research and Training in Integrative Health, Department of Family Medicine and Public Health, La Jolla, CA, United States
^b Sanford Burnham Prebys Medical Discovery Institute, Bioinformatics and Structural Biology Program, La Jolla, CA, United States
^c Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russian Federation
^d P.G. Demidov Yaroslavl State University, Yaroslavl, Russian Federation
^e Department of Pharmacology and Toxicology, Sealy Center for Structural Biology, University of Texas Medical Branch, Galveston, TX, United States
^f Washington University, Department of Biology, St. Louis, MO, United States
^g Chopra Foundation, Department of Ayurveda and Yoga Research, Carlsbad, CA, United States
^h Sanford Burnham Prebys Medical Discovery Institute, Tumor Microenvironment and Cancer Immunology Program, La Jolla, CA, United States

Abstract
10.1371/journal.pone.0213869

The prebiotic potential of nervine herbal medicines has been scarcely studied. We therefore used anaerobic human fecal cultivation to investigate whether medicinal herbs commonly used as treatment in neurological health and disease in Ayurveda and other traditional systems of medicine modulate gut microbiota. Profiling of fecal cultures supplemented with either Kapikacchu, Gotu Kola, Bacopa/Brahmi, Shankhapushpi, Boswellia/Frankincense, Jatamansi, Bhringaraj, Guduchi, Ashwagandha or Shatavari by 16S rRNA sequencing revealed profound changes in diverse taxa. Principal coordinate analysis highlights that each herb drives the formation of unique microbial communities predicted to display unique metabolic potential. The relative abundance of approximately one-third of the 243 enumerated species was altered by all herbs. Additional species were impacted in an herb-specific manner. In this study, we combine genome reconstruction of sugar utilization and short chain fatty acid (SCFA) pathways encoded in the genomes of 216 profiled taxa with monosaccharide composition analysis of each medicinal herb by quantitative mass spectrometry to enhance the interpretation of resulting microbial communities and discern potential drivers of microbiota restructuring. Collectively, our results indicate that gut microbiota engage in both protein and glycan catabolism, providing amino acid and sugar substrates that are consumed by fermentative species. We identified taxa that are efficient amino acid fermenters and those capable of both amino acid and sugar fermentation. Herb-induced microbial communities are predicted to alter the relative abundance of taxa encoding SCFA (butyrate and propionate) pathways. Co-occurrence network analyses identified a large number of taxa pairs in medicinal herb cultures. Some of these pairs displayed related culture growth relationships in replicate cultures highlighting potential functional interactions among medicinal herb-induced taxa. © 2019 Peterson et al. 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.

Document Type: Article
Publication Stage: Final
Source: Scopus
Access Type: Open Access

Multiple functional neurosteroid binding sites on GABAA receptors
(2019) PLoS biology, 17 (3), p. e3000157. 

Chen, Z.-W.^{a b} , Bracamontes, J.R.^a , Budelier, M.M.^a , Germann, A.L.^a , Shin, D.J.^a , Kathiresan, K.^c , Qian, M.-X.^c , Manion, B.^a , Cheng, W.W.L.^a , Reichert, D.E.^{b d} , Akk, G.^a , Covey, D.F.^{a b c} , Evers, A.S.^{a b c}

^a Department of Anesthesiology, Washington University in St Louis, St Louis, MO, United States
^b Taylor Family Institute for Innovative Psychiatric Research, St Louis, MO, United States
^c Department of Developmental Biology, Washington University in St Louis, St Louis, MO, United States
^d Department of Radiology, Washington University in St Louis, St Louis, MO, United States

Abstract
Neurosteroids are endogenous modulators of neuronal excitability and nervous system development and are being developed as anesthetic agents and treatments for psychiatric diseases. While gamma amino-butyric acid Type A (GABAA) receptors are the primary molecular targets of neurosteroid action, the structural details of neurosteroid binding to these proteins remain ill defined. We synthesized neurosteroid analogue photolabeling reagents in which the photolabeling groups were placed at three positions around the neurosteroid ring structure, enabling identification of binding sites and mapping of neurosteroid orientation within these sites. Using middle-down mass spectrometry (MS), we identified three clusters of photolabeled residues representing three distinct neurosteroid binding sites in the human α1β3 GABAA receptor. Novel intrasubunit binding sites were identified within the transmembrane helical bundles of both the α1 (labeled residues α1-N408, Y415) and β3 (labeled residue β3-Y442) subunits, adjacent to the extracellular domains (ECDs). An intersubunit site (labeled residues β3-L294 and G308) in the interface between the β3(+) and α1(-) subunits of the GABAA receptor pentamer was also identified. Computational docking studies of neurosteroid to the three sites predicted critical residues contributing to neurosteroid interaction with the GABAA receptors. Electrophysiological studies of receptors with mutations based on these predictions (α1-V227W, N408A/Y411F, and Q242L) indicate that both the α1 intrasubunit and β3-α1 intersubunit sites are critical for neurosteroid action.

Document Type: Article
Publication Stage: Final
Source: Scopus
Access Type: Open Access

Association of Prenatal Cannabis Exposure with Psychosis Proneness among Children in the Adolescent Brain Cognitive Development (ABCD) Study
(2019) JAMA Psychiatry, . 

Fine, J.D.^a , Moreau, A.L.^a , Karcher, N.R.^b , Agrawal, A.^b , Rogers, C.E.^b , Barch, D.M.^{a b b} , Bogdan, R.^a

^a Department of Psychological and Brain Sciences, Washington University in St Louis, One Brookings Drive, St Louis, MO 63110, United States
^b Department of Psychiatry, Washington University in St Louis, St Louis, MO, United States

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

Genetic mimics of cerebral palsy
(2019) Movement Disorders, . 

Pearson, T.S.^a , Pons, R.^b , Ghaoui, R.^c , Sue, C.M.^d

^a Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^b First Department of Pediatrics, National and Kapodistrian University of Athens, Aghia Sofia Hospital, Athens, Greece
^c Department of Neurology, Royal Adelaide Hospital, Adelaide, SA, Australia
^d Department of Neurogenetics, Kolling Institute, Royal North Shore Hospital and University of Sydney, St Leonards, NSW, Australia

Abstract
The term “cerebral palsy mimic” is used to describe a number of neurogenetic disorders that may present with motor symptoms in early childhood, resulting in a misdiagnosis of cerebral palsy. Cerebral palsy describes a heterogeneous group of neurodevelopmental disorders characterized by onset in infancy or early childhood of motor symptoms (including hypotonia, spasticity, dystonia, and chorea), often accompanied by developmental delay. The primary etiology of a cerebral palsy syndrome should always be identified if possible. This is particularly important in the case of genetic or metabolic disorders that have specific disease-modifying treatment. In this article, we discuss clinical features that should alert the clinician to the possibility of a cerebral palsy mimic, provide a practical framework for selecting and interpreting neuroimaging, biochemical, and genetic investigations, and highlight selected conditions that may present with predominant spasticity, dystonia/chorea, and ataxia. Making a precise diagnosis of a genetic disorder has important implications for treatment, and for advising the family regarding prognosis and genetic counseling. © 2019 International Parkinson and Movement Disorder Society. © 2019 International Parkinson and Movement Disorder Society

Author Keywords
ataxia;  cerebral palsy;  dystonia;  inborn errors of metabolism;  spasticity

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

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk
(2019) Nature Genetics, . 

Huckins, L.M.^{a b c d} , Dobbyn, A.^{a b} , Ruderfer, D.M.^e , Hoffman, G.^{a d} , Wang, W.^{a b} , Pardiñas, A.F.^f , Rajagopal, V.M.^{g h i} , Als, T.D.^{g h i} , T. Nguyen, H.^{a b} , Girdhar, K.^{a b} , Boocock, J.^j , Roussos, P.^{a b c d} , Fromer, M.^{a b} , Kramer, R.^k , Domenici, E.^l , Gamazon, E.R.^{e m} , Purcell, S.^{a b d} , Johnson, J.S.^a , Shah, H.R.^{b d} , Klein, L.L.^p , Dang, K.K.^q , Logsdon, B.A.^q , Mahajan, M.C.^{b d} , Mangravite, L.M.^q , Toyoshiba, H.^s , Gur, R.E.^t , Hahn, C.-G.^u , Schadt, E.^{b d} , Lewis, D.A.^p , Haroutunian, V.^{a q v w} , Peters, M.A.^q , Lipska, B.K.^k , Buxbaum, J.D.^{x y} , Hirai, K.^z , Perumal, T.M.^q , Essioux, L.^aa , Ripke, S.^{aj ak} , Neale, B.M.^{aj ak al am} , Corvin, A.^an , Walters, J.T.R.^f , Farh, K.-H.^aj , Holmans, P.A.^{f ao} , Lee, P.^{aj ak am} , Bulik-Sullivan, B.^{aj ak} , Collier, D.A.^{ap aq} , Huang, H.^{aj al} , Pers, T.H.^{al ar as} , Agartz, I.^{at au av} , Agerbo, E.^{h ae af} , Albus, M.^aw , Alexander, M.^ax , Amin, F.^{ay az} , Bacanu, S.A.^ba , Begemann, M.^bb , Belliveau, R.A., Jr^ak , Bene, J.^{bc bd} , Bergen, S.E.^{ak be} , Bevilacqua, E.^ak , Bigdeli, T.B.^ba , Black, D.W.^bf , Bruggeman, R.^bg , Buccola, N.G.^bh , Buckner, R.L.^{bi bj bk} , Byerley, W.^bl , Cahn, W.^bm , Cai, G.^{b c} , Campion, D.^bn , Cantor, R.M.^j , Carr, V.J.^{bo bp} , Carrera, N.^f , Catts, S.V.^{bo bq} , Chambert, K.D.^ak , Chan, R.C.K.^br , Chen, R.Y.L.^bs , Chen, E.Y.H.^{bs bt} , Cheng, W.ⁿ , Cheung, E.F.C.^bu , Chong, S.A.^bv , Cloninger, C.R.^bw , Cohen, D.^bx , Cohen, N.^by , Cormican, P.^an , Craddock, N.^{f ao} , Crowley, J.J.^bz , Curtis, D.^{ca cb} , Davidson, M.^cc , Davis, K.L.^c , Degenhardt, F.^{cd ce} , Del Favero, J.^cf , Demontis, D.^{g h i} , Dikeos, D.^cg , Dinan, T.^ch , Djurovic, S.^{av ci} , Donohoe, G.^{an cj} , Drapeau, E.^c , Duan, J.^{ck cl} , Dudbridge, F.^cm , Durmishi, N.^cn , Eichhammer, P.^co , Eriksson, J.^{cp cq cr} , Escott-Price, V.^f , Essioux, L.^cs , Fanous, A.H.^{ct cu cv cw} , Farrell, M.S.^bz , Frank, J.^cx , Franke, L.^cy , Freedman, R.^cz , Freimer, N.B.^da , Friedl, M.^db , Friedman, J.I.^c , Fromer, M.^{a aj ak am} , Genovese, G.^ak , Georgieva, L.^f , Giegling, I.^{db dc} , Giusti-Rodríguez, P.^bz , Godard, S.^dd , Goldstein, J.I.^{aj al} , Golimbet, V.^de , Gopal, S.^df , Gratten, J.^dg , de Haan, L.^dh , Hammer, C.^bb , Hamshere, M.L.^f , Hansen, M.^di , Hansen, T.^{h ac} , Haroutunian, V.^{c v x} , Hartmann, A.M.^db , Henskens, F.A.^{bo dj dk} , Herms, S.^{cd ce dl} , Hirschhorn, J.N.^{al as dm} , Hoffmann, P.^{cd ce dl} , Hofman, A.^{cd ce} , Hollegaard, M.V.^ai , Hougaard, D.M.^ai , Ikeda, M.^dn , Joa, I.^do , Julia, A.^dp , Kahn, R.S.^bm , Kalaydjieva, L.^{dq dr} , Karachanak-Yankova, S.^ds , Karjalainen, J.^cy , Kavanagh, D.^f , Keller, M.C.^dt , Kennedy, J.L.^{du dv dw} , Khrunin, A.^dx , Kim, Y.^bz , Klovins, J.^dy , Knowles, J.A.^dz , Konte, B.^db , Kucinskas, V.^ea , Kucinskiene, Z.A.^ea , Kuzelova-Ptackova, H.^eb , Kahler, A.K.^be , Laurent, C.^{ax ec} , Keong, J.L.C.^{bv ed} , Lee, S.H.^dg , Legge, S.E.^f , Lerer, B.^ee , Li, M.^{bs bt ef} , Li, T.^eg , Liang, K.-Y.^eh , Lieberman, J.^ei , Limborska, S.^dx , Loughland, C.M.^{bo ej} , Lubinski, J.^ek , Lonnqvist, J.^el , Macek, M., Jr.^eb , Magnusson, P.K.E.^be , Maher, B.S.^em , Maier, W.^en , Mallet, J.^eo , Marsal, S.^dp , Mattheisen, M.^{g h i ep} , Mattingsdal, M.^{av eq} , McCarley, R.W.^{er es} , McDonald, C.^et , McIntosh, A.M.^{eu ev} , Meier, S.^cx , Meijer, C.J.^dh , Melegh, B.^{bc bd} , Melle, I.^{av ew} , Mesholam-Gately, R.I.^{er ex} , Metspalu, A.^ey , Michie, P.T.^{bo ez} , Milani, L.^ey , Milanova, V.^fa , Mokrab, Y.^ap , Morris, D.W.^{an cj} , Mors, O.^{h i fb} , Murphy, K.C.^fc , Murray, R.M.^fd , Myin-Germeys, I.^fe , Muller-Myhsok, B.^{ff fg fh} , Nelis, M.^ey , Nenadic, I.^fi , Nertney, D.A.^fj , Nestadt, G.^fk , Nicodemus, K.K.^fl , Nikitina-Zake, L.^dy , Nisenbaum, L.^fm , Nordin, A.^fn , O’Callaghan, E.^fo , O’Dushlaine, C.^ak , O’Neill, F.A.^fp , Oh, S.-Y.^fq , Olincy, A.^du , Olsen, L.^{h bk} , Van Os, J.^{fe fr} , Pantelis, C.^{bo fs} , Papadimitriou, G.N.^cg , Papiol, S.^bb , Parkhomenko, E.^c , Pato, M.T.^dz , Paunio, T.^{ft fu} , Pejovic-Milovancevic, M.^fv , Perkins, D.O.^fw , Pietiläinen, O.^{fu fx} , Pimm, J.^cb , Pocklington, A.J.^f , Powell, J.^fd , Price, A.^{al fy} , Pulver, A.E.^fk , Purcell, S.M.^a , Quested, D.^fz , Rasmussen, H.B.^{ac ap} , Reichenberg, A.^c , Reimers, M.A.^ga , Richards, A.L.^f , Roffman, J.L.^{bi bk} , Roussos, P.^{a d} , Ruderfer, D.M.^{a e f} , Salomaa, V.^cr , Sanders, A.R.^{ck cl} , Schall, U.^{bo ej} , Schubert, C.R.^gb , Schulze, T.G.^{cx gc} , Schwab, S.G.^gd , Scolnick, E.M.^ak , Scott, R.J.^{bo ge gf} , Seidman, L.J.^{em ex} , Shi, J.^gg , Sigurdsson, E.^gh , Silagadze, T.^gi , Silverman, J.M.^{c gj} , Sim, K.^bv , Slominsky, P.^dx , Smoller, J.W.^{ak am} , So, H.-C.^bs , Spencer, C.C.A.^gk , Stahl, E.A.^{a b c d} , Stefansson, H.^gl , Steinberg, S.^gl , Stogmann, E.^gm , Straub, R.E.^gn , Strengman, E.^{bm go} , Strohmaier, J.^cx , Stroup, T.S.^ei , Subramaniam, M.^bv , Suvisaari, J.^el , Svrakic, D.M.^bw , Szatkiewicz, J.P.^bz , Soderman, E.^at , Thirumalai, S.^gp , Toncheva, D.^ds , Tosato, S.^gq , Veijola, J.^{gr gs} , Waddington, J.^gt , Walsh, D.^gu , Wang, D.^df , Wang, Q.^eg , Webb, B.T.^ba , Weiser, M.^cc , Wildenauer, D.B.^gv , Williams, N.M.^f , Williams, S.^bz , Witt, S.H.^cx , Wolen, A.R.^ga , Wong, E.H.M.^bs , Wormley, B.K.^ba , Xi, H.S.^gw , Zai, C.C.^{du dv} , Zheng, X.^gx , Zimprich, F.^gm , Wray, N.R.^dg , Stefansson, K.^gl , Visscher, P.M.^dg , Adolfsson, R.^fn , Andreassen, O.A.^{av ew} , Blackwood, D.H.R.^ev , Bramon, E.^gy , Buxbaum, J.D.^{b c w x} , Børglum, A.D.^{g h i fb} , Cichon, S.^{cd ce dl gz} , Darvasi, A.^ha , Domenici, E.^hb , Ehrenreich, H.^bb , Esko, T.^{al as dm ey} , Gejman, P.V.^{ck cl} , Gill, M.^an , Gurling, H.^cb , Hultman, C.M.^be , Iwata, N.^dn , Jablensky, A.V.^{bo dr gv hc} , Jonsson, E.G.^{at av} , Kendler, K.S.^hd , Kirov, G.^f , Knight, J.^{dt dv dw} , Lencz, T.^{he hf hg} , Levinson, D.F.^ax , Li, Q.S.^df , Liu, J.^{gx hh} , Malhotra, A.K.^{he hf hg} , McCarroll, S.A.^{ak dm} , McQuillin, A.^cb , Moran, J.L.^ak , Mortensen, P.B.^{h ae af} , Mowry, B.J.^{dg hi} , Nothen, M.M.^{cd ce} , Ophoff, R.A.^{j bm cz} , Owen, M.J.^{f ao} , Palotie, A.^{ak am fx} , Pato, C.N.^dz , Petryshen, T.L.^{ak er hj} , Posthuma, D.^{hk hl hm} , Rietschel, M.^cx , Riley, B.P.^hd , Rujescu, D.^{db dc} , Sham, P.C.^{bs bt ef} , Sklar, P.^{a b c d x} , Clair, D.S.^hn , Weinberger, D.R.^{gn ho} , Wendland, J.R.^gb , Werge, T.^{h ac hp} , Daly, M.J.^{aj ak al} , Sullivan, P.F.^{be bz fw} , O’Donovan, M.C.^{f ao} , Børglum, A.D.^{g h i} , Demontis, D.^{g h i} , Rajagopal, V.M.^{g h i} , Als, T.D.^{g h i} , Mattheisen, M.^{g h i} , Grove, J.^{g h i ab} , Werge, T.^{h ac ad} , Mortensen, P.B.^{g h ae af} , Pedersen, C.B.^{h ae af} , Agerbo, E.^{h ae af} , Pedersen, M.G.^{h ae af} , Mors, O.^{h ag} , Nordentoft, M.^{h ah} , Hougaard, D.M.^{h ai} , Bybjerg-Grauholm, J.^{h ai} , Bækvad-Hansen, M.^{h ai} , Hansen, C.S.^{h ai} , Demontis, D.^{g h i} , Børglum, A.D.^{g h i} , Walters, J.T.R.^f , O’Donovan, M.C.^f , Sullivan, P.^{n o} , Owen, M.J.^f , Devlin, B.^p , Sieberts, S.K.^q , Cox, N.J.^e , Im, H.K.^r , Sklar, P.^{a b c d} , Stahl, E.A.^{a b c d} , CommonMind Consortium^hq , The Schizophrenia Working Group of the PsyUniversity of Copenhagenchiatric Genomics Consortium^hq , iPSYCH-GEMS Schizophrenia Working Group^hr

^a Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^b Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^c Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^d Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^e Vanderbilt University Medical Center, Nashville, TN, United States
^f MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
^g Department of Biomedicine, Aarhus University, Aarhus, Denmark
^h The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
ⁱ Center for Integrative Sequencing, Aarhus University, Aarhus, Denmark
^j Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
^k Human Brain Collection Core, National Institute of Mental Health, Bethesda, MD, United States
^l Laboratory of Neurogenomic Biomarkers, Centre for Integrative Biology (CIBIO), University of Trento, Trento, Italy
^m Clare Hall, University of Cambridge, Cambridge, United Kingdom
ⁿ University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^o Karolinska Institutet, Stockholm, Sweden
^p Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
^q Systems Biology, Sage Bionetworks, Seattle, WA, United States
^r Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, United States
^s Integrated Technology Research Laboratories, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, Fujisawa, Japan
^t Neuropsychiatry Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^u Neuropsychiatric Signaling Program, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^v Psychiatry, JJ Peters Virginia Medical Center, Bronx, NY, United States
^w Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, NY, United States
^x Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^y Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^z CNS Drug Discovery Unit, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, Fujisawa, Japan
^aa F. Hoffman-La Roche Ltd, Basel, Switzerland
^ab Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
^ac Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
^ad Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
^ae National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
^af Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
^ag Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark
^ah Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark
^ai Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
^aj Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, United States
^ak Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
^al Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, United States
^am Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, United States
^an Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
^ao NationalCentre for Mental Health, Cardiff University, Cardiff, United Kingdom
^ap Eli Lilly and Company Limited, Erl Wood Manor, Sunninghill Road, Windlesham, Surrey, United Kingdom
^aq Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, London, United Kingdom
^ar Center for BiologicalSequence Analysis, Department of Systems Biology, Technical University of Denmark, Kongens Lyngby, Denmark
^as Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA, United States
^at Department of Clinical Neuroscience, Psychiatry Section, Karolinska Institutet, Stockholm, Sweden
^au Department of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway
^av NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
^aw State Mental Hospital, Haar, Germany
^ax Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
^ay Department of Psychiatry and Behavioral Sciences, Atlanta Veterans Affairs Medical Center, Atlanta, GA, United States
^az Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, United States
^ba Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
^bb Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Gottingen, Germany
^bc Department of Medical Genetics, University of Pécs, Pécs, Hungary
^bd Szentagothai Research Center, University of Pécs, Pécs, Hungary
^be Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
^bf Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, United States
^bg University Medical Center Groningen, Department of Psychiatry, University of Groningen, Groningen, Netherlands
^bh School of Nursing, Louisiana State University Health Sciences Center, New Orleans, LA, United States
^bi Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
^bj Center for Brain Science, Harvard University, Cambridge, MA, United States
^bk Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
^bl Department of Psychiatry, University of California at San Francisco, San Francisco, CA, United States
^bm University Medical Center Utrecht, Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, Utrecht, Netherlands
^bn Centre Hospitalier du Rouvray and INSERM U1079 Faculty of Medicine, Rouen, France
^bo Schizophrenia Research Institute, Sydney, NSW, Australia
^bp School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
^bq Royal Brisbane and Women’s Hospital, University of Queensland, Brisbane, QLD, Australia
^br Institute of Psychology, Chinese Academy of Science, Beijing, China
^bs Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
^bt State Key Laboratory for Brain and Cognitive Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
^bu Castle Peak Hospital, Hong Kong
^bv Institute of Mental Health, Singapore, Singapore
^bw Department of Psychiatry, Washington University, St. Louis, MO, United States
^bx Department of Child and Adolescent Psychiatry, Assistance Publique Hopitaux de Paris, Pierre and Marie Curie Faculty of Medicine and Institute for Intelligent Systems and Robotics, Paris, France
^by Blue Note Biosciences, Princeton, NJ, United States
^bz Department of Genetics, University of North Carolina, Chapel Hill, NC, United States
^ca Department of Psychological Medicine, Queen Mary University of London, London, United Kingdom
^cb Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, United Kingdom
^cc Sheba Medical Center, Tel Hashomer, Israel
^cd Department of Genomics, Life and Brain Center, Bonn, Germany
^ce Institute of Human Genetics, University of Bonn, Bonn, Germany
^cf AppliedMolecular Genomics Unit, VIB Department of Molecular Genetics, University of Antwerp, Antwerp, Belgium
^cg First Department of Psychiatry, University of Athens Medical School, Athens, Greece
^ch Department of Psychiatry, University College Cork, Co, Cork, Ireland
^ci Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
^cj Cognitive Genetics and Therapy Group, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, Co, Galway, Ireland
^ck Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States
^cl Department of Psychiatry and Behavioral Sciences, North Shore University Health System, Evanston, IL, United States
^cm Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
^cn Department of Child and Adolescent Psychiatry, University Clinic of Psychiatry, Skopje, Macedonia
^co Department of Psychiatry, University of Regensburg, Regensburg, Germany
^cp Department of General Practice, Helsinki University Central Hospital, University of Helsinki, Helsinki, Finland
^cq Folkhälsan Research Center, Helsinki, Finland, Biomedicum Helsinki, Helsinki, Finland
^cr National Institute for Health and Welfare, Helsinki, Finland
^cs Translational Technologies and Bioinformatics, Pharma Research and Early Development, F. Hoffman-La Roche, Basel, Switzerland
^ct Department of Psychiatry, Georgetown University School of Medicine, Washington, DC, United States
^cu Department of Psychiatry, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
^cv Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
^cw Mental Health Service Line, Washington VA Medical Center, Washington, DC, United States
^cx Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Mannheim, Germany
^cy Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands
^cz Department of Psychiatry, University of Colorado Denver, Aurora, CO, United States
^da Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, United States
^db Department of Psychiatry, University of Halle, Halle, Germany
^dc Department of Psychiatry, University of Munich, Munich, Germany
^dd Departments of Psychiatry and Human and Molecular Genetics, INSERM, Institut de Myologie, Hôpital de la Pitiè-Salpêtrière, Paris, France
^de Mental Health Research Centre, Russian Academy of Medical Sciences, Moscow, Russian Federation
^df Neuroscience Therapeutic Area, Janssen Research and Development, Raritan, NJ, United States
^dg Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
^dh Academic Medical Centre University of Amsterdam, Department of Psychiatry, Amsterdam, Netherlands
^di Illumina, La Jolla, CA, United States
^dj Priority Research Centre for Health Behaviour, University of Newcastle, Newcastle, NSW, Australia
^dk School of Electrical Engineering and Computer Science, University of Newcastle, Newcastle, NSW, Australia
^dl Division of Medical Genetics, Department of Biomedicine, University of Basel, Basel, Switzerland
^dm Department of Genetics, Harvard Medical School, Boston, MA, United States
^dn Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan
^do Regional Centre for Clinical Researchin Psychosis, Department of Psychiatry, Stavanger University Hospital, Stavanger, Norway
^dp Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
^dq Centre for Medical Research, The University of Western Australia, Perth, WA, Australia
^dr The Perkins Institute for Medical Research, The University of Western Australia, Perth, WA, Australia
^ds Department of Medical Genetics, Medical University, Sofia, Bulgaria
^dt Department of Psychology, University of Colorado Boulder, Boulder, CO, United States
^du Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
^dv Department of Psychiatry, University of Toronto, Toronto, ON, Canada
^dw Institute of Medical Science, University of Toronto, Toronto, ON, Canada
^dx Institute of Molecular Genetics, Russian Academy of Sciences, Moscow, Russian Federation
^dy Latvian Biomedical Research and Study Centre, Riga, Latvia
^dz Department of Psychiatry and Zilkha Neurogenetics Institute, Keck School of Medicine at University of Southern California, Los Angeles, CA, United States
^ea Faculty of Medicine, Vilnius University, Vilnius, Lithuania
^eb Department of Biology and Medical Genetics, 2nd Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
^ec Department of Child and Adolescent Psychiatry, Pierre and Marie Curie Faculty of Medicine, Paris, France
^ed Duke-NUS Graduate Medical School, Singapore, Singapore
^ee Department of Psychiatry, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
^ef Centre for Genomic Sciences, The University of Hong Kong, Hong Kong
^eg Mental Health Centre and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China
^eh Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States
^ei Department of Psychiatry, Columbia University, New York, New York, NY, United States
^ej Priority Centre for Translational Neuroscience and Mental Health, University of Newcastle, Newcastle, NSW, Australia
^ek Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University in Szczecin, Szczecin, Poland
^el Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Helsinki, Finland
^em Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
^en Department of Psychiatry, University of Bonn, Bonn, Germany
^eo Centre National de la Recherche Scientifique, Laboratoire de Génétique Moléculaire de la Neurotransmission et des Processus Neurodénégératifs, Hôpital de la Pitiè-Salpêtrière, Paris, France
^ep Department of Genomics Mathematics, University of Bonn, Bonn, Germany
^eq Research Unit, Sørlandet Hospital, Kristiansand, Norway
^er Department of Psychiatry, Harvard Medical School, Boston, MA, United States
^es VA Boston Health Care System, Brockton, MA, United States
^et Department of Psychiatry, National University of Ireland Galway, Co, Galway, Ireland
^eu Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
^ev Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
^ew Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
^ex Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess Medical Center, Boston, MA, United States
^ey Estonian Genome Center, University of Tartu, Tartu, Estonia
^ez School of Psychology, University of Newcastle, Newcastle, NSW, Australia
^fa First Psychiatric Clinic, Medical University, Sofia, Bulgaria
^fb Department P, Aarhus University Hospital, Risskov, Denmark
^fc Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
^fd King’s College London, London, United Kingdom
^fe Maastricht University Medical Centre, South Limburg Mental Health Research and TeachingNetwork, EURON, Maastricht, Netherlands
^ff Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
^fg Max Planck Institute of Psychiatry, Munich, Germany
^fh Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
^fi Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
^fj Department of Psychiatry, Queensland Brain Institute and Queensland Centre for Mental Health Research, University of Queensland, Brisbane, QLD, Australia
^fk Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
^fl Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
^fm Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States
^fn Department of Clinical Sciences, Psychiatry, Umeå University, Umeå, Sweden
^fo DETECT Early Intervention Service for Psychosis, Blackrock, Co, Dublin, Ireland
^fp Centre for Public Health, Institute of Clinical Sciences, Queen’s University Belfast, Belfast, United Kingdom
^fq Lawrence Berkeley National Laboratory, University of California at Berkeley, Berkeley, CA, United States
^fr Institute of Psychiatry, King’s College London, London, United Kingdom
^fs Melbourne Neuropsychiatry Centre, University of Melbourne & Melbourne Health, Melbourne, VIC, Australia
^ft Department of Psychiatry, University of Helsinki, Helsinki, Finland
^fu Public Health Genomics Unit, National Institute for Health and Welfare, Helsinki, Finland
^fv Medical Faculty, University of Belgrade, Belgrade, Serbia
^fw Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States
^fx Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
^fy Department of Epidemiology, Harvard School of Public Health, Boston, MA, United States
^fz Department of Psychiatry, University of Oxford, Oxford, United Kingdom
^ga Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
^gb Pharma Therapeutics Clinical Research, Pfizer Worldwide Research and Development, Cambridge, MA, United States
^gc Department of Psychiatry and Psychotherapy, University of Gottingen, Göttingen, Germany
^gd Psychiatry and Psychotherapy Clinic, University of Erlangen, Erlangen, Germany
^ge Hunter New England Health Service, Newcastle, NSW, Australia
^gf School of Biomedical Sciences, University of Newcastle, Newcastle, NSW, Australia
^gg Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States
^gh University of Iceland, Landspitali, National University Hospital, Reykjavik, Iceland
^gi Department of Psychiatry and Drug Addiction, Tbilisi State Medical University (TSMU), Tbilisi, Georgia
^gj Research and Development, Bronx Veterans Affairs Medical Center, New York, NY, United States
^gk WellcomeTrust Centre for Human Genetics, Oxford, United Kingdom
^gl deCODE Genetics, Reykjavik, Iceland
^gm Department of Clinical Neurology, Medical University of Vienna, Wien, Austria
^gn Lieber Institute for Brain Development, Baltimore, MD, United States
^go Department of Medical Genetics, University Medical Centre Utrecht, Utrecht, Netherlands
^gp Berkshire Healthcare NHS Foundation Trust, Bracknell, United Kingdom
^gq Section of Psychiatry, University of Verona, Verona, Italy
^gr Department of Psychiatry, University of Oulu, Oulu, Finland
^gs University Hospital of Oulu, Oulu, Finland
^gt Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland
^gu Health Research Board, Dublin, Ireland
^gv School of Psychiatry and Clinical Neurosciences, The University of Western Australia, Perth, WA, Australia
^gw Computational Sciences CoE, Pfizer Worldwide Research and Development, Cambridge, MA, United States
^gx Human Genetics, Genome Institute of Singapore, A*STAR, Singapore, Singapore
^gy University College London, London, United Kingdom
^gz Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany
^ha Department of Genetics, The Hebrew University of Jerusalem, Jerusalem, Israel
^hb NeuroscienceDiscovery and Translational Area, Pharma Research and Early Development, F. Hoffman-La Roche, Basel, Switzerland
^hc Centre for Clinical Research in Neuropsychiatry, School of Psychiatry and Clinical Neurosciences, The University of Western Australia, Medical Research Foundation Building, Perth, WA, Australia
^hd Virginia Institute for Psychiatric and Behavioral Genetics, Departments of Psychiatry and Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, United States
^he The Feinstein Institute for Medical Research, Manhasset, NY, United States
^hf The Hofstra NS-LIJ School of Medicine, Hempstead, NY, United States
^hg The Zucker Hillside Hospital, Glen Oaks, NY, United States
^hh Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
^hi Queensland Centre for Mental Health Research, University of Queensland, Brisbane, QLD, Australia
^hj Center for HumanGenetic Research and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
^hk Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, Netherlands
^hl Department of Complex Trait Genetics, Neuroscience Campus Amsterdam, VU University Medical Center Amsterdam, Amsterdam, Netherlands
^hm Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University, Amsterdam, Netherlands
^hn University of Aberdeen, Institute of Medical Sciences, Aberdeen, United Kingdom
^ho Departments of Psychiatry, Neurology, Neuroscience and Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States
^hp Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark

Abstract
Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.

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

Editorial: Elevating the Narrative About Adverse Events During Development
(2019) Journal of the American Academy of Child and Adolescent Psychiatry, . 

Glowinski, A.L.

William Greenleaf Eliot Division of Child Psychiatry, Washington University School of Medicine, St. Louis, MO, United States

Abstract
US middle- or high-school–age children are taught about the perils of cyber bullying in health classes. They learn that they are at risk of suicide because of online harassment behaviors and that resources are being expanded to prevent, report, or interrupt such bullying. However, the perspective that suicide victims likely have other salient predisposing or precipitating risk factors (eg, summarized comprehensively by Turecki and Brent 1 ) is usually not emphasized simultaneously. In the context of an abundance of studies documenting clear associations between childhood or adolescence adverse experiences and many aspects of health, as reviewed recently in the Lancet Public Health, 2 it is not an exaggeration to observe that a crucial axiom of first science classes, correlation is not causation, can be overlooked when a small number of specific adverse events emerges narratively as the major etiologic cause of much of psychopathology, including youth suicide. However, traumatic events, such as youth sexual victimization 3 or physical punishment/maltreatment, 4 have long been known to correlate to social contexts and/or heritable genotypes, which also might mediate or co-mediate the risk for adverse outcomes. That some methodologic designs are superior to others to disentangle such confounds and examine causation is well known in behavior genetics. 5 This was highlighted 2 decades ago, in this very journal, by Dr. Naimah Weinberg of the National Institutes of Health, in an article reviewing the cognitive and behavioral deficits associated with parental alcohol use, 6 where she noted the growing field of behavior genetics offers an approach to understanding such complex problems. This is a necessary perspective to move the science of child psychiatry forward. © 2019 American Academy of Child and Adolescent Psychiatry

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

A farnesyltransferase inhibitor activates lysosomes and reduces tau pathology in mice with tauopathy
(2019) Science Translational Medicine, 11 (485), . 

Hernandez, I.^a , Luna, G.^a , Rauch, J.N.^a , Reis, S.A.^b , Giroux, M.^a , Karch, C.M.^c , Boctor, D.^a , Sibih, Y.E.^a , Storm, N.J.^d , Diaz, A.^d , Kaushik, S.^d , Zekanowski, C.^e , Kang, A.A.^a , Hinman, C.R.^a , Cerovac, V.^a , Guzman, E.^a , Zhou, H.^a , Haggarty, S.J.^b , Goate, A.M.^f , Fisher, S.K.^{a g} , Cuervo, A.M.^d , Kosik, K.S.^{a g}

^a Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, United States
^b Department of Neurology, Massachusetts General Hospital, Chemical Neurobiology Lab, Center for Genomic Medicine, Harvard Medical School, Boston, MA 02114, United States
^c Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
^d Department of Developmental and Molecular Biology, Institute for Aging Studies, Albert Einstein College of Medicine, New York, NY 10461, United States
^e Laboratory of Neurogenetics, Mossakowski Medical Research Centre, Polish Academy of Sciences, 5 Pawinskiego St., Warsaw, 02-106, Poland
^f Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
^g Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, United States

Abstract
Tau inclusions are a shared feature of many neurodegenerative diseases, among them frontotemporal dementia caused by tau mutations. Treatment approaches for these conditions include targeting posttranslational modifications of tau proteins, maintaining a steady-state amount of tau, and preventing its tendency to aggregate. We discovered a new regulatory pathway for tau degradation that operates through the farnesylated protein, Rhes, a GTPase in the Ras family. Here, we show that treatment with the farnesyltransferase inhibitor lonafarnib reduced Rhes and decreased brain atrophy, tau inclusions, tau sumoylation, and tau ubiquitination in the rTg4510 mouse model of tauopathy. In addition, lonafarnib treatment attenuated behavioral abnormalities in rTg4510 mice and reduced microgliosis in mouse brain. Direct reduction of Rhes in the rTg4510 mouse by siRNA reproduced the results observed with lonafarnib treatment. The mechanism of lonafarnib action mediated by Rhes to reduce tau pathology was shown to operate through activation of lysosomes. We finally showed in mouse brain and in human induced pluripotent stem cell-derived neurons a normal developmental increase in Rhes that was initially suppressed by tau mutations. The known safety of lonafarnib revealed in human clinical trials for cancer suggests that this drug could be repurposed for treating tauopathies. Copyright © 2019 The Authors, some rights reserved.

Document Type: Article
Publication Stage: Final
Source: Scopus

Adventures in spacetime: circadian rhythms and the dynamics of protein catabolism
(2019) Autophagy, . 

Ryzhikov, M., Ehlers, A., Haspel, J.A.

Division of Pulmonary and Critical Care Medicine, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Circadian rhythms help cells to organize complex processes, but how they shape the kinetics of protein catabolism is unclear. In a recent paper, we employed proteomics to map daily biological rhythms in autophagic flux in mouse liver, and correlated these rhythms with proteasome activity. We also explored the effect of inflammation caused by endotoxin on autophagy dynamics. Our data provide insight into how circadian rhythms serve as a framework for connecting the spatial, temporal, and metabolic aspects of autophagy at a system-wide level. Our observations also have implications for how to optimize autophagy-directed therapies in patients. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Author Keywords
Autophagy;  circadian rhythms;  endotoxin;  inflammation;  macroautophagy;  proteasome;  proteomics

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

Internet-delivered cognitive behavioral therapies for late-life depressive symptoms: a systematic review and meta-analysis
(2019) Aging and Mental Health, . 

Xiang, X.^a , Wu, S.^b , Zuverink, A.^a , Tomasino, K.N.^c , An, R.^{d f} , Himle, J.A.^{a e}

^a School of Social Work, University of Michigan, Ann Arbor, MI, United States
^b School of Social Work, Arizona State University, Phoenix, AZ, United States
^c Gastroenterology and Hepatology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
^d Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois-Urbana Champaign, Champaign, IL, United States
^e Department of Psychiatry, Medical School, University of Michigan, Ann Arbor, MI, United States
^f Brown School, Washington University in St. Louis, St. Louis, MO 63130, United States

Abstract
Background: This study aimed to review and synthesize evidence related to the effectiveness of internet-based cognitive behavioral therapy (iCBT) for reducing depressive symptoms in older adults. Method: The authors conducted a systematic review of intervention studies testing iCBT for symptoms of depression in older adults. An initial search of PubMed, PsychINFO, and Web of Science was undertaken, followed by a manual search of reference lists of the relevant articles. The Cochrane Risk of Bias Tool was used to appraise study quality. The mean effect size for included studies was estimated in a random effects model. Meta-regression was used to examine potential moderators of effect sizes. Results: Nine studies met the inclusion criteria, including 1272 participants averaging 66 years of age. The study design included randomized controlled trials (k = 3), controlled trials without randomization (k = 2), uncontrolled trials (k = 2), and naturalistic evaluation (k = 2). Seven studies tested iCBT with some level of therapist involvement and 2 examined self-guided iCBT. Six studies tested interventions specifically adapted for older adults. The mean within-group effect size was 1.27 (95% CI = 1.09, 1.45) and the mean between-group effect size was 1.18 (95% CI = 0.63, 1.73). Participants’ age was negatively associated with within-group effect sizes (b = −0.06, p =.016). Conclusions: iCBT is a promising approach for reducing depressive symptoms among older adults with mild to moderate depressive symptoms. However, studies involving older adults in iCBT trials were limited, had considerable heterogeneity, and were of low quality, calling for more studies with rigorous designs to produce a best-practice guideline. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Author Keywords
Cognitive behavioral therapy;  computer-assisted therapy;  depression

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

Longitudinal cost of services in a homeless sample with cocaine use disorder
(2019) Journal of Social Distress and the Homeless, . 

Ayvaci, E.R.^a , Pollio, D.E.^b , Hong, B.A.^c , North, C.S.^{a d}

^a Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
^b Department of Social Work, The University of Alabama at Birmingham, Birmingham, AL, United States
^c Department of Psychiatry, The Washington University School of Medicine, St. Louis, MO, United States
^d Metrocare Services, The Altshuler Center for Education & Research, Dallas, TX, United States

Abstract
Homeless people with cocaine use disorder have multiple comorbidities and costly service needs. This study examined service costs associated with cocaine use and substance service use in substance, psychiatric, and medical service sectors. 127 homeless participants with cocaine use disorder were interviewed annually. Self-report and agency-report service use and cost data were combined. Pairwise comparisons were made with cocaine abstinence and substance service use in relation to mean and yearly proportional service costs in 3 service sectors. Among substance service users, the achievement of abstinence was not associated with decreased substance service costs. Cocaine abstinence was associated with proportional reduction of substance service costs over time. Substance service use was associated with proportional reduction of psychiatric service costs over time among the abstinent subgroup. Conversely, substance service use was associated with continuing higher medical service expenditures in the abstinent subgroup and higher psychiatric service expenditures in those not abstinent. Homeless individuals who achieved cocaine abstinence after using substance services had decreased substance service expenditures. Individuals with continued substance service use had greater medical and psychiatric service costs. Policy-based on maximizing benefits while minimizing costs appears insufficiently complex to incorporate the multiple needs and associated with the costs of treating homeless populations. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Author Keywords
Cocaine use;  cost of health services;  homelessness;  service utilization;  substance service use

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

Structural signature of sporadic Creutzfeldt–Jakob disease
(2019) European Journal of Neurology, . 

Navid, J.^a , Day, G.S.^{a b} , Strain, J.^a , Perrin, R.J.^{b c d} , Bucelli, R.C.^a , Dincer, A.^e , Wisch, J.K.^a , Soleimani-Meigooni, D.^f , Morris, J.C.^{a b c d} , Benzinger, T.L.S.^{b e} , Ances, B.M.^{a b d e}

^a Department of Neurology, Washington University in St Louis, St Louis, MO, United States
^b Knight Alzheimer’s Disease Research Center, Washington University in St Louis, St Louis, MO, United States
^c Department of Pathology, Washington University in St Louis, St Louis, MO, United States
^d Hope Center for Neurological Disorders, Washington University in St Louis, St Louis, MO, United States
^e Department of Radiology, Washington University in St Louis, St Louis, MO, United States
^f Department of Neurology, University of California, San Francisco, San Francisco, CA, United States

Abstract
Background and purpose: Sporadic Creutzfeldt–Jakob disease (sCJD) is a rapidly progressive neurodegenerative disease caused by an abnormal isoform of the human prion protein. Structural magnetic resonance imaging in patients with pathologically confirmed sCJD was compared with cognitively normal individuals to identify a cortical thickness signature of sCJD. Methods: This retrospective cross-sectional study compared patients with autopsy-confirmed sCJD with dementia (n = 11) with age- and sex-matched cognitively normal individuals (n = 22). We identified regions of interest (ROIs) in which cortical thickness was most affected by sCJD. Within patients with sCJD, the relationship between ROI cortical thickness and clinical measures (disease duration, cerebrospinal fluid tau and diffusion-weighted imaging abnormalities) was evaluated. Results: Compared with cognitively normal individuals, patients with sCJD had significantly reduced cortical thickness in multiple ROIs, including the fusiform gyrus, precentral gyrus, precuneus and superior temporal gyrus bilaterally; the caudal middle frontal gyrus, superior frontal gyrus, postcentral gyrus, inferior temporal gyrus and transverse temporal gyrus in the left hemisphere; and the superior parietal lobule in the right hemisphere. Only one patient with sCJD had co-pathology consistent with Alzheimer’s disease. Reduced cortical thickness did not correlate with disease duration, presence of diffusion restriction or elevated cerebrospinal fluid tau. Conclusion: Cortical signature changes in sCJD may reflect brain changes not captured by standard clinical measures. This information may be used with clinical measures to inform the progression of sCJD and patterns of prion protein spread throughout the brain. These results may have implications for prediction of symptomatic progression and plausibly for development of therapeutic strategies. © 2019 EAN

Author Keywords
biomarker;  cortical signature;  cortical thickness;  Creutzfeldt–Jakob disease;  magnetic resonance imaging;  neurodegenerative disorders;  prion diseases;  rapidly progressive dementia

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

Impact of aneurysm morphology on safety and effectiveness of flow diverter treatment of vertebrobasilar aneurysms
(2019) Journal of Neuroradiology, . 

Wallace, A.N.^{a b} , CreveCoeur, T.S.^c , Grossberg, J.A.^d , Kamran, M.^e , Osbun, J.W.^{c e} , Delgado Almandoz, J.E.^a , Cross, D.T.^e , Moran, C.J.^e

^a Division of Neurointerventional Radiology, Neuroscience Institute, Abbott Northwestern Hospital, Minneapolis, MN, United States
^b Department of Radiology, University of Iowa, Iowa City, IA, United States
^c Department of Neurosurgery, Washington University, St Louis, MO, United States
^d Department of Neurosurgery, Emory University, Atlanta, GA, United States
^e Mallinckrodt Institute of Radiology, Washington UniversitySt LouisMO, United States

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

Tau positron emission tomography imaging in C9orf72 repeat expansion carriers
(2019) European Journal of Neurology, . 

Ly, C.V.^a , Koenig, L.^b , Christensen, J.^b , Gordon, B.^{b c} , Beaumont, H.^a , Dahiya, S.^d , Chen, J.^d , Su, Y.^e , Nelson, B.^a , Jockel-Balsarotti, J.^a , Drain, C.^a , Jerome, G.^a , Morris, J.C.^{a c} , Fagan, A.M.^{a c f} , Harms, M.B.^g , Benzinger, T.L.S.^{b c h} , Miller, T.M.^{a f} , Ances, B.M.^{a b c f}

^a Department of Neurology, Washington University, Saint Louis, MO, United States
^b Department of Radiology, Washington University, Saint Louis, MO, United States
^c Knight Alzheimer’s Disease Research Center, Washington University, Saint Louis, MO, United States
^d Department of Pathology and Immunology, Washington University, Saint Louis, MO, United States
^e Banner Alzheimer’s Institute, Phoenix, AZ, United States
^f Hope Center for Neurological Disorders, Washington University, Saint Louis, MO, United States
^g Department of Neurology, Columbia University, New York, NY, United States
^h Department of Neurosurgery, Washington University, Saint Louis, MO, United States

Abstract
Background and purpose: AV-1451 ( 18 F-AV-1451, flortaucipir) positron emission tomography was performed in C9orf72 expansion carriers to assess tau accumulation and disease manifestation. Methods: Nine clinically characterized C9orf72 expansion carriers and 18 age- and gender- matched cognitively normal individuals were psychometrically evaluated and underwent tau positron emission tomography imaging. The regional AV-1451 standard uptake value ratios from multiple brain regions were analyzed. Spearman correlation was performed to relate the AV-1451 standard uptake value ratio to clinical, psychometric and cerebrospinal fluid measures. Results: C9orf72 expansion carriers had increased AV-1451 binding in the entorhinal cortex compared to controls. Primary age-related tauopathy was observed postmortem in one patient. AV-1451 uptake did not correlate with clinical severity, disease duration, psychometric performance or cerebrospinal fluid markers. Conclusion: C9orf72 expansion carriers exhibited increased AV-1451 uptake in entorhinal cortex compared to cognitively normal controls, suggesting a propensity for primary age-related tauopathy. However, AV-1451 accumulation was not associated with psychometric performance in our cohort. © 2019 EAN

Author Keywords
amyotrophic lateral sclerosis;  AV-1451;  C9orf72;  positron emission tomography;  tau

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

Neural crest-derived neurons invade the ovary but not the testis during mouse gonad development
(2019) Proceedings of the National Academy of Sciences of the United States of America, 116 (12), pp. 5570-5575. 

McKey, J.^a , Bunce, C.^a , Batchvarov, I.S.^a , Ornitz, D.M.^b , Capel, B.^a

^a Department of Cell Biology, Duke University Medical Center, Durham, NC 27710, United States
^b Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, United States

Abstract
Testes and ovaries undergo sex-specific morphogenetic changes and adopt strikingly different morphologies, despite the fact that both arise from a common precursor, the bipotential gonad. Previous studies showed that recruitment of vasculature is critical for testis patterning. However, vasculature is not recruited into the early ovary. Peripheral innervation is involved in patterning development of many organs but has been given little attention in gonad development. In this study, we show that while innervation in the male reproductive complex is restricted to the epididymis and vas deferens and never invades the interior of the testis, neural crest-derived innervation invades the interior of the ovary around E16.5. Individual neural crest cells colonize the ovary, differentiate into neurons and glia, and form a dense neural network within the ovarian medulla. Using a sex-reversing mutant mouse line, we show that innervation is specific to ovary development, is not dependent on the genetic sex of gonadal or neural crest cells, and may be blocked by repressive guidance signals elevated in the male pathway. This study reveals another aspect of sexually dimorphic gonad development, establishes a precise timeline and structure of ovarian innervation, and raises many questions for future research. © 2019 National Academy of Sciences. All Rights Reserved.

Author Keywords
Innervation;  Neural crest;  Organogenesis;  Ovary;  Testis

Document Type: Article
Publication Stage: Final
Source: Scopus

Cross-national validity of the Beck Hopelessness Scale for children and adolescents: findings from the YouthSave-Impact Study Kenya
(2018) International Journal of Culture and Mental Health, 11 (4), pp. 457-469. 

Kagotho, N.^a , Bowen, N.K.^a , Ssewamala, F.M.^{b c} , Vaughn, M.G.^d , Kirkbride, G.^{b c}

^a College of Social Work, The Ohio State University, Columbus, OH, United States
^b Columbia University School of Social Work, International Center for Child Health and Asset Development, Columbia UniversityNY, United States
^c Brown School, Washington University in St. Louis, International Center for Child Health and Development (ICHAD), St. Louis, MO, United States
^d School of Social Work, Saint Louis University, St. Louis, MO, United States

Abstract
A sense of hopelessness is common but under-identified among poor children in sub-Saharan Africa (SSA). Given the concomitant inadequate screening, assessment, and a lack of culturally and developmentally appropriate measures in much of SSA, identifying reliable and valid measures of hopelessness is needed. One promising candidate, the Beck Hopelessness Scale (BHS), has undergone limited evaluation in the region and none with non-adult populations. The present study assesses the psychometric properties of the BHS using data from a diverse sample of 3965 school-going youth (M = 12.2, SD = 1.1) in Kenya. Given inconclusive results from model comparisons of previously established factor structures, we used parallel analysis, exploratory factor analysis, and confirmatory factor analysis to ascertain the factor structure of the BHS in this sample. We also evaluated measurement invariance of the scale across two key developmental ages (9–12 and 13–18), given distinct cognitive and emotional differences. Models supported a one-factor 20-item structure with partial invariance across child and adolescent samples. Concurrent criterion validity correlations were in the small to medium range. Findings provide evidence of the utility of the BHS as a psychometrically sound measure that possesses cultural relevance to Kenyan youth. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Author Keywords
Beck Hopelessness Scale;  factor analysis;  psychosocial functioning;  sub-Saharan Africa;  youth

Document Type: Article
Publication Stage: Final
Source: Scopus

Associations of alcohol use disorder, alcohol use, housing, and service use in a homeless sample of 255 individuals followed over 2 years
(2018) Substance Abuse, 39 (4), pp. 497-504. Cited 1 time.

Asana, O.O.^a , Ayvaci, E.R.^b , Pollio, D.E.^c , Hong, B.A.^d , North, C.S.^{b e}

^a Department of Psychiatry, New York University Langone Health, New York, NY, United States
^b Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
^c Department of Social Work, The University of Alabama at Birmingham, Birmingham, AL, United States
^d Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^e The Altshuler Center for Education & Research, Metrocare Services, Dallas, TX, United States

Abstract
Background: Homeless individuals with alcohol use disorders have multiple comorbidities and therefore various service needs. Despite need for services, homeless individuals face numerous barriers to treatment. Little is known about the associations of specific services in relation to homelessness in the context of alcohol problems. The current study analyzed 2-year prospective longitudinal data on a homeless sample, examining relationships between alcohol use disorder, alcohol use, housing status, and service use over time. Methods: Two hundred fifty-five of 400 individuals recruited systematically from shelters and street locations completed 3 annual assessments (69% completion). Data on lifetime and current psychiatric disorders, housing status, and past-year service use were obtained and merged with service use data gathered from local agencies. Generalized estimating equation (GEE) models were created to predict dependent outcome variables of stable housing, alcohol use, and service use in both follow-up years. Results: Lifetime alcohol use disorder was positively associated with substance and medical service use. Alcohol problems did not hinder attainment of stable housing, and placement in housing did not necessarily increase risk for alcohol use. Stable housing was negatively associated with psychiatric and substance service use. In the second year, when alcohol use was finally associated with receiving substance services, it appears that these services provided a gateway to psychiatric services. The psychiatric services in turn appeared to provide a gateway to medical services. Conclusions: Alcohol use behaved differently compared with lifetime alcohol use disorder in relation to service use. Lack of association between alcohol use and housing supports Housing First policy. Obtaining housing may have ameliorative effects on mental health, diminishing perceived need for psychiatric services. Services may also be more accessible during homelessness. Obtaining substance treatment may provide a gateway for those who use alcohol after becoming homeless to connect with psychiatric and medical services, informing policy and practice. © 2018, © 2018 Taylor & Francis Group, LLC.

Author Keywords
Alcohol use;  homelessness;  Housing First;  service utilization

Document Type: Article
Publication Stage: Final
Source: Scopus