WashU weekly Neuroscience publications | Office of Neuroscience Research

“Tracking white matter degeneration in asymptomatic and symptomatic MAPT mutation carriers” (2019) Neurobiology of Aging

Tracking white matter degeneration in asymptomatic and symptomatic MAPT mutation carriers
(2019) Neurobiology of Aging, 83, pp. 54-62.

Chen, Q.^a ^b , Boeve, B.F.^c , Schwarz, C.G.^b , Reid, R.^b , Tosakulwong, N.^d , Lesnick, T.G.^d , Bove, J.^e , Brannelly, P.^f , Brushaber, D.^d , Coppola, G.^g , Dheel, C.^c , Dickerson, B.C.^h , Dickinson, S.ⁱ , Faber, K.^j , Fields, J.^k , Fong, J.^l , Foroud, T.^j , Forsberg, L.^c , Gavrilova, R.H.^c , Gearhart, D.^c , Ghoshal, N.^m , Goldman, J.ⁿ , Graff-Radford, J.^c , Graff-Radford, N.R.^o , Grossman, M.^e , Haley, D.^o , Heuer, H.W.^l , Hsiung, G.-Y.R.^p , Huey, E.ⁿ , Irwin, D.J.^e , Jack, C.R.^b , Jones, D.T.^c , Jones, L.^q , Karydas, A.M.^l , Knopman, D.S.^c , Kornak, J.^r , Kramer, J.^l , Kremers, W.^d , Kukull, W.A.^s , Lapid, M.^k , Lucente, D.^h , Lungu, C.^t , Mackenzie, I.R.A.^u , Manoochehri, M.ⁿ , McGinnis, S.^h , Miller, B.L.^l , Pearlman, R.^v , Petrucelli, L.^w , Potter, M.^j , Rademakers, R.^w , Ramos, E.M.^g , Rankin, K.P.^l , Rascovsky, K.^e , Sengdy, P.^p , Shaw, L.^x , Syrjanen, J.^d , Tatton, N.ⁱ , Taylor, J.^l , Toga, A.W.^y , Trojanowski, J.^x , Weintraub, S.^z , Wong, B.^h , Boxer, A.L.^l , Rosen, H.^l , Wszolek, Z.^o , Kantarci, K.^b , LEFFTDS Consortium^aa

^a Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
^b Department of Radiology, Mayo Clinic, Rochester, MN, United States
^c Department of Neurology, Mayo Clinic, Rochester, MN, United States
^d Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
^e Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^f Tau Consortium, Rainwater Charitable Foundation, Fort Worth, TX, United States
^g Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
^h Department of Neurology, Frontotemporal Disorders Unit, Massachusetts General Hospital, Harvard University, Boston, MA, United States
ⁱ Association for Frontotemporal Degeneration, Radnor, PA, United States
^j National Cell Repository for Alzheimer’s Disease (NCRAD), Indiana University, Indianapolis, IN, United States
^k Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
^l Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States
^m Departments of Neurology and Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
ⁿ Department of Neurology, Columbia University, New York, NY, United States
^o Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
^p Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
^q Department of Radiology, Washington University School of Medicine, Washington University, St. Louis, MO, United States
^r Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States
^s National Alzheimer Coordinating Center (NACC), University of Washington, Seattle, WA, United States
^t National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, United States
^u Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
^v Bluefield Project, San Francisco, CA, United States
^w Department of Neurosciences, Mayo Clinic, Jacksonville, FL, United States
^x Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^y Departments of Ophthalmology, Neurology, Psychiatry and the Behavioral Sciences, Laboratory of Neuroimaging (LONI), USC, Los Angeles, CA, United States
^z Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States

Abstract
Our aim was to investigate the patterns and trajectories of white matter (WM) diffusion abnormalities in microtubule-associated protein tau (MAPT) mutations carriers. We studied 22 MAPT mutation carriers (12 asymptomatic, 10 symptomatic) and 20 noncarriers from 8 families, who underwent diffusion tensor imaging (DTI) and a subset (10 asymptomatic, 6 symptomatic MAPT mutation carriers, and 10 noncarriers) were followed annually (median = 4 years). Cross-sectional and longitudinal changes in mean diffusivity (MD) and fractional anisotropy were analyzed. Asymptomatic MAPT mutation carriers had higher MD in entorhinal WM, which propagated to the limbic tracts and frontotemporal projections in the symptomatic stage compared with noncarriers. Reduced fractional anisotropy and increased MD in the entorhinal WM were associated with the proximity to estimated and actual age of symptom onset. The annualized change of entorhinal MD on serial DTI was accelerated in MAPT mutation carriers compared with noncarriers. Entorhinal WM diffusion abnormalities precede the symptom onset and track with disease progression in MAPT mutation carriers. Our cross-sectional and longitudinal data showed a potential clinical utility for DTI to track neurodegenerative disease progression for MAPT mutation carriers in clinical trials. © 2019 Elsevier Inc.

Author Keywords
Asymptomatic; Diffusion tensor image; Frontotemporal dementia; Longitudinal; MAPT

Document Type: Article
Publication Stage: Final
Source: Scopus

“Stroke detection with 3 different PET tracers” (2019) Radiology Case Reports

Stroke detection with 3 different PET tracers
(2019) Radiology Case Reports, 14 (11), pp. 1447-1451.

Dundar, A.^a , Bold, M.S.^a , Agac, B.^b , Kendi, A.T.^a , Friedman, S.N.^a ^c

^a Department of Radiology, Mayo Clinic, , 150 3rd Street SW, Rochester, MN 55905, USA, United States
^b Department of Neurology, Mayo Clinic, Rochester, MN, United States
^c Division of Nuclear Medicine, Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, United States

Abstract
Stroke is a common cause of patient morbidity and mortality, being the fifth leading cause of death in the United States. Positron emission tomography (PET) is a proven tool for oncology patients, and may have utility in patients with stroke. We demonstrate findings of stroke incidentally detected on oncologic PET/CTs using 18F-FDG, 11C-Choline, and 68Ga-DOTATATE radiotracers. Specifically, focal 11C-Choline or 68Ga-DOTATATE uptakes localized in areas of MRI confirmed ischemia, and paradoxically increased 18F-FDG activity was visualized surrounding a region of hemorrhage, in different patients. These cases demonstrate that PET may have utility in evaluating patients with stroke based on flow dynamics, metabolic activity, and receptor expression. © 2019 The Authors

Author Keywords
11C-Choline; 18F-FDG; 68Ga-DOTATATE; PET; Stroke

Document Type: Article
Publication Stage: Final
Source: Scopus

“An overview of the current state of evidence for the role of specific diets in multiple sclerosis” (2019) Multiple Sclerosis and Related Disorders

An overview of the current state of evidence for the role of specific diets in multiple sclerosis
(2019) Multiple Sclerosis and Related Disorders, 36, art. no. 101393, .

Evans, E.^a , Levasseur, V.^b , Cross, A.H.^c , Piccio, L.^d ^e

^a Former Multiple Sclerosis Fellow, Washington University in St. Louis, Current Neurologist, Mercy MS Care, St. Louis, MO, United States
^b Neurology Resident, Washington University in St. Louis, United States
^c The Manny and Rosalyn Rosenthal – Dr. John Trotter MS Chair in Neuroimmunology, Professor of Neurology, Washington University in St. Louis, United States
^d Associate Professor of Neurology, Washington University in St. Louis, United States
^e Brain and Mind Centre, University of Sydney, Australia, Australia

Abstract
Background: Surveys of people with multiple sclerosis (MS) report that most are interested in using dietary modifications to potentially reduce the severity and symptoms of their disease. This review provides an updated overview of the current state of evidence for the role of specific diets in MS and its animal models, with an emphasis on recent studies including efficacy and safety issues related to dietary manipulations in people with MS. Methods: Studies were identified using a PubMed search for each diet in both MS and experimental autoimmune encephalomyelitis, by review of the reference list of papers identified in the search process, and by searching clinicaltrials.gov for ongoing studies. Each study was evaluated and the data was summarized. Each diet was assigned a level of evidence for its use in MS based on the Quality Rating Scheme for Studies and Other Evidence. Results: Several diets have been explored in people with MS and animal models of MS. Most human trials have been small and non-blinded, limiting their generalizability. Many have also been of short-duration, potentially limiting their ability to find clinically meaningful changes. Presently, insufficient evidence exists to recommend the routine use of any specific diet by people with MS. Clinical trials are ongoing or planned for many diets including the Swank Diet, Wahl’s diet, McDougall diet, Mediterranean diet, and intermittent fasting. Results of these studies may help guide clinical recommendations. Conclusion: There is insufficient evidence to recommend the routine use of any specific diet by people with MS. Some diets touted for MS may have potential negative health consequences. It is important that clinicians inquire regarding dietary manipulations, so they can educate patients on any known efficacy data and potential adverse effects of individual diets. Consultation with a registered dietician is recommended for patients undertaking restrictive diets. © 2019 Elsevier B.V.

Author Keywords
Diet; Multiple sclerosis; Neuroimmunology; Nutrition

Document Type: Review
Publication Stage: Final
Source: Scopus

“Pathological Progression Induced by the Frontotemporal Dementia-Associated R406W Tau Mutation in Patient-Derived iPSCs” (2019) Stem Cell Reports

Pathological Progression Induced by the Frontotemporal Dementia-Associated R406W Tau Mutation in Patient-Derived iPSCs
(2019) Stem Cell Reports, 13 (4), pp. 684-699.

Nakamura, M.^a ^b , Shiozawa, S.^a , Tsuboi, D.^c , Amano, M.^c , Watanabe, H.^a , Maeda, S.^a , Kimura, T.^d , Yoshimatsu, S.^a , Kisa, F.^a , Karch, C.M.^e , Miyasaka, T.^f , Takashima, A.^g , Sahara, N.^d , Hisanaga, S.-I.^h , Ikeuchi, T.ⁱ , Kaibuchi, K.^c , Okano, H.^a

^a Department of Physiology, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
^b Department of Biomedical Chemistry, School of International Health, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan
^c Department of Cell Pharmacology, Graduate School of Medicine, Nagoya University, 65 Tsurumai, Showa, Nagoya, Aichi 466-8550, Japan
^d Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, 4-9-1 Anagawa, Inage, Chiba 266-8555, Japan
^e Department of Psychiatry and Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, MO 63110, United States
^f Department of Neuropathology, Faculty of Life and Medical Sciences, Doshisha University, Kyotanabe-shi, Kyoto, 610-0394, Japan
^g Faculty of Science, Gakushuin University, Toshima-ku, Tokyo, 171-8588, Japan
^h Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji-shi, Tokyo, 192-0397, Japan
ⁱ Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachidori, Chuo-ku, Niigata, 951-8585, Japan

Abstract
Mutations in the microtubule-associated protein tau (MAPT) gene are known to cause familial frontotemporal dementia (FTD). The R406W tau mutation is a unique missense mutation whose patients have been reported to exhibit Alzheimer’s disease (AD)-like phenotypes rather than the more typical FTD phenotypes. In this study, we established patient-derived induced pluripotent stem cell (iPSC) models to investigate the disease pathology induced by the R406W mutation. We generated iPSCs from patients and established isogenic lines using CRISPR/Cas9. The iPSCs were induced into cerebral organoids, which were dissociated into cortical neurons with high purity. In this neuronal culture, the mutant tau protein exhibited reduced phosphorylation levels and was increasingly fragmented by calpain. Furthermore, the mutant tau protein was mislocalized and the axons of the patient-derived neurons displayed morphological and functional abnormalities, which were rescued by microtubule stabilization. The findings of our study provide mechanistic insight into tau pathology and a potential for therapeutic intervention. © 2019 The Author(s)

In this article, Nakamura and colleagues establish an iPSC-derived neuronal model from frontotemporal dementia patients with the tau R406W mutation and gain insight into the disease pathology. Here, the R406W mutant tau exhibited reduced phosphorylation levels and was prone to fragmentation by calpain. Furthermore, the patients’ neurons displayed multiple axonal defects caused by microtubule destabilization. © 2019 The Author(s)

Author Keywords
disease modeling; FTD; iPSC; neurodegenerative disease; tau

Document Type: Article
Publication Stage: Final
Source: Scopus

“Health care resource utilization and cost before initial schizophrenia diagnosis” (2019) Journal of Managed Care and Specialty Pharmacy

Health care resource utilization and cost before initial schizophrenia diagnosis
(2019) Journal of Managed Care and Specialty Pharmacy, 25 (10), pp. 1102-1110.

Wallace, A.^a , Barron, J.^a , York, W.^a , Isenberg, K.^b ^c , Franchino-Elder, J.^d , Sidovar, M.^d , Sand, M.^d

^a HealthCore, Wilmington, DE, United States
^b Anthem, Indianapolis, IN, United States
^c Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^d Boehringer Ingelheim Pharmaceuticals, Ridgefield, CT, United States

Abstract
BACKGROUND: The management of schizophrenia, a chronic, multifaceted mental health condition, is associated with considerable health care resource utilization (HCRU) and costs. Current evidence indicates that a high-risk and costly prodromal period, during which patients are likely symptomatic, precedes diagnosis. Better characterization and disease management during this stage could help to improve patient outcomes. OBJECTIVE: To describe and compare HCRU and costs for up to 5 years before diagnosis in a cohort with schizophrenia versus a demographically matched cohort without schizophrenia in a commercially insured U.S. population. METHODS: This retrospective study identified newly diagnosed schizophrenia patients using enrollee claims in the HealthCore Integrated Research Database between January 1, 2007, and April 30, 2016. The index date was defined as the date of the first medical claim with a schizophrenia diagnosis code. Schizophrenia patients were directly matched (1:4) by age, sex, and region to comparators without schizophrenia who were assigned the same index dates as their matched schizophrenia counterparts. Observation periods were 0-12, 13-24, 25-36, 37-48, and 49-60 months before the index date. Outcomes included HCRU and costs for inpatient admissions, emergency room visits, outpatient care (office visits and other outpatient services), and medications. Means, standard deviations, medians, and 95% confidence intervals were calculated for continuous variables; relative frequencies and percentages were calculated for categorical variables. Cohorts were compared with t-tests for continuous variables and chi-square tests for categorical variables. Differences across cohorts were estimated with individual generalized linear models for each observation period, controlling for gender, age, geographic region of residence, health plan type and subscriber status, behavioral pre-index comorbidities and chronic comorbidities during the period before diagnosis. RESULTS: 6,732 schizophrenia patients were matched to 26,928 patients without schizophrenia. All-cause inpatient admissions were more prevalent among schizophrenia patients than their comparators for all time periods (49-60 months prediagnosis: 9% vs. 4%; 0-12 months prediagnosis: 33% vs. 4%). The schizophrenia cohort had higher adjusted all-cause per-patient per-month health care costs relative to comparators from the earliest period of 49-60 months prediagnosis ($557 [95% CI=474-639] vs. $321 [95% CI=288-355]) through 0-12 months prediagnosis ($1,058 [95% CI=998-1,115] vs. $338 [95% CI=320-355]). Behavioral health-related costs were different in each time period as were cost ratios (schizophrenia costs: comparator costs), which increased from 5.4 in the earliest period to 14.8 in the year before diagnosis. CONCLUSIONS: Schizophrenia patients had higher all-cause and behavioral health-related HCRU and costs before diagnosis than matched controls. Costs increased from 5 years to 1 year prediagnosis for schizophrenia patients driven primarily by inpatient hospital stays and prescription drug costs, but remained stable for comparators. Additional research is needed for the development of predictive models to aid in the identification of high-risk patients. Copyright © 2019, Academy of Managed Care Pharmacy. All rights reserved.

Document Type: Article
Publication Stage: Final
Source: Scopus

“Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility” (2019) Science

Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility
(2019) Science, 365 (6460), art. no. eaav7188, . Cited 1 time.

Patsopoulos, N.A.^a ^b ^c ^d , Baranzini, S.E.^e , Santaniello, A.^e , Shoostari, P.^d ^f ^g ^cr , Cotsapas, C.^d ^f ^g , Wong, G.^a ^c , Beecham, A.H.^h , James, T.ⁱ , Replogle, J.^b ^c ^d ^j , Vlachos, I.S.^a ^c ^d , McCabe, C.^d , Pers, T.H.^k , Brandes, A.^d , White, C.^d ^j , Keenan, B.^l , Cimpean, M.^j , Winn, P.^j , Panteliadis, I.-P.^a ^d , Robbins, A.^j , Andlauer, T.F.M.^m ⁿ ^o , Zarzycki, O.^a ^d , Dubois, B.^p , Goris, A.^p , Søndergaard, H.B.^q , Sellebjerg, F.^q , Sorensen, P.S.^q , Ullum, H.^r , Thørner, L.W.^r , Saarela, J.^s , Cournu-Rebeix, I.^t , Damotte, V.^t ^u , Fontaine, B.^t ^v , Guillot-Noel, L.^t , Lathrop, M.^w ^x ^y , Vukusic, S.^z ^aa ^ab ^ac , Berthele, A.ⁿ ^o , Pongratz, V.ⁿ ^o , Buck, D.ⁿ ^o , Gasperi, C.ⁿ ^o , Graetz, C.^o ^ad , Grummel, V.ⁿ ^o , Hemmer, B.ⁿ ^o ^ae , Hoshi, M.ⁿ ^o , Knier, B.ⁿ ^o , Korn, T.ⁿ ^o ^ae , Lill, C.M.^o ^af ^ag , Luessi, F.^o ^af , Mühlau, M.ⁿ ^o , Zipp, F.^o ^af , Dardiotis, E.^ah , Agliardi, C.^ai , Amoroso, A.^aj , Barizzone, N.^ak , Benedetti, M.D.^al ^am , Bernardinelli, L.^an , Cavalla, P.^ao , Clarelli, F.^ap , Comi, G.^ap ^aq , Cusi, D.^ar , Esposito, F.^ap ^as , Ferrè, L.^as , Galimberti, D.^at ^au , Guaschino, C.^ap ^as , Leone, M.A.^av , Martinelli, V.^as , Moiola, L.^as , Salvetti, M.^aw ^ax , Sorosina, M.^ap , Vecchio, D.^ay , Zauli, A.^ap , Santoro, S.^ap , Mancini, N.^az , Zuccalà, M.^ba , Mescheriakova, J.^bb , Van Duijn, C.^bb ^bc , Bos, S.D.^bd , Celius, E.G.^bd ^be , Spurkland, A.^bf , Comabella, M.^bg , Montalban, X.^bg , Alfredsson, L.^bh , Bomfim, I.L.^bi , Gomez-Cabrero, D.^bi ^bj ^bk , Hillert, J.^bi , Jagodic, M.^bi , Lindén, M.^bi , Piehl, F.^bi , Jelčić, I.^bl ^bm , Martin, R.^bl ^bm , Sospedra, M.^bl ^bm , Baker, A.^bn , Ban, M.^bo , Hawkins, C.^bo , Hysi, P.^bp , Kalra, S.^bq , Karpe, F.^bq , Khadake, J.^br , Lachance, G.^bp , Molyneux, P.^bp , Neville, M.^bq , Thorpe, J.^bs , Bradshaw, E.^j , Caillier, S.J.^e , Calabresi, P.^bt , Cree, B.A.C.^e , Cross, A.^bu , Davis, M.^bv , De Bakker, P.W.I.^b ^c ^d ^cs , Delgado, S.^bw , Dembele, M.^bt , Edwards, K.^bx , Fitzgerald, K.^bt , Frohlich, I.Y.^j , Gourraud, P.-A.^e ^by , Haines, J.L.^bz , Hakonarson, H.^ca ^cb , Kimbrough, D.^c ^cc , Isobe, N.^e ^cd , Konidari, I.^h , Lathi, E.^ce , Lee, M.H.^j , Li, T.^cf , An, D.^cf , Zimmer, A.^cf , Madireddy, L.^e , Manrique, C.P.^h , Mitrovic, M.^d ^f ^g , Olah, M.^j , Patrick, E.^j ^cg ^ch , Pericak-Vance, M.A.^h , Piccio, L.^bt , Schaefer, C.^ci , Weiner, H.^cj , Lage, K.^ce , Compston, A.^bm , Hafler, D.^d ^ck , Harbo, H.F.^bc ^bd , Hauser, S.L.^e , Stewart, G.^cl , D’Alfonso, S.^cm , Hadjigeorgiou, G.^ah , Taylor, B.^cn , Barcellos, L.F.^co , Booth, D.^cp , Hintzen, R.^cq , Kockum, I.ⁱ , Martinelli-Boneschi, F.^ap ^aq , McCauley, J.L.^h , Oksenberg, J.R.^e , Oturai, A.^p , Sawcer, S.^bk , Ivinson, A.J.^cp , Olsson, T.ⁱ , De Jager, P.L.^d ^j , International Multiple Sclerosis Genetics Consortium^ct , ANZgene, IIBDGC, WTCCC2^ct

^a Systems Biology and Computer Science Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women’s Hospital, Boston, MA 02115, United States
^b Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
^c Harvard Medical School, Boston, MA 02115, United States
^d Broad Institute of Harvard, Massachusetts Institute of Technology, Cambridge, MA, United States
^e Department of Neurology, University of California at San Francisco, Sandler Neurosciences Center, 675 Nelson Rising Lane, San Francisco, CA 94158, United States
^f Department of Neurology, Yale University School of Medicine, New Haven, CT 06520, United States
^g Department of Genetics, Yale School of Medicine, New Haven, CT 06520, United States
^h John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL 33136, United States
ⁱ Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
^j Center for Translational and Computational Neuroimmunology, Multiple Sclerosis Center, Department of Neurology, Columbia University Medical Center, New York, NY, United States
^k Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2100, Denmark
^l Center for Sleep and Circadian Neurobiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
^m Max Planck Institute of Psychiatry, Munich, 80804, Germany
ⁿ Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, 81675, Germany
^o German competence network for multiple sclerosis, Germany
^p KU Leuven Department of Neurosciences, Laboratory for Neuroimmunology, Herestraat 49 bus 1022, Leuven, 3000, Belgium
^q Danish Multiple Sclerosis Center, Department of Neurology, Rigshospitalet, University of Copenhagen, Section 6311, Copenhagen, 2100, Denmark
^r Department of Clinical Immunology, Rigshospitalet, University of Copenhagen, Section 2082, Copenhagen, 2100, Denmark
^s Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
^t ICM-UMR 1127, INSERM, Sorbonne University, Hôpital Universitaire Pitié-Salpêtrière, 47 Boulevard de l’Hôpital, Paris, F-75013, France
^u UMR1167 Université de Lille, Inserm, CHU Lille, Institut Pasteur de Lille, France
^v CRM-UMR974, Department of Neurology, Hôpital Universitaire Pitié-Salpêtrière, 47 Boulevard de l’Hôpital, Paris, F-75013, France
^w Commissariat à l′Energie Atomique, Institut Genomique, Centre National de Génotypage, Evry, France
^x Fondation Jean Dausset – Centre d’Etude du Polymorphisme Humain, Paris, France
^y McGill University, Genome Quebec Innovation Center, Montreal, Canada
^z Hospices Civils de Lyon, Service de Neurologie, sclérose en Plaques, Pathologies de la Myéline et Neuro-inflammation, Bron, F-69677, France
^aa Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, INSERM 1028 et CNRS UMR 5292, Lyon, F-69003, France
^ab Université de Lyon, Université Claude Bernard Lyon 1, Lyon, F-69000, France
^ac Eugène Devic EDMUS Foundation against multiple sclerosis, Bron, F-69677, France
^ad Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), Johannes Gutenberg University-Medical Center, Mainz, Germany
^ae Munich Cluster for Systems Neurology (SyNergy), Munich, 81377, Germany
^af Department of Neurology, Focus Program Translational Neuroscience (FTN), and Immunology (FZI), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
^ag Genetic and Molecular Epidemiology Group, Institute of Neurogenetics, University of Luebeck, Luebeck, Germany
^ah Neurology Department, Neurogenetics Lab, University Hospital of Larissa, Greece
^ai Laboratory of Molecular Medicine and Biotechnology, Don C. Gnocchi Foundation ONLUS, IRCCS S. Maria Nascente, Milan, Italy
^aj Department of Medical Sciences, Torino University, Turin, Italy
^ak Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Novara, Italy
^al Centro Regionale Sclerosi Multipla, Neurologia B, AOUI Verona, Italy
^am Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Italy
^an Medical Research Council Biostatistics Unit, Robinson Way, Cambridge, CB2 0SR, United Kingdom
^ao MS Center, Department of Neuroscience, A.O. Città della Salute e della Scienza di Torino, University of Turin, Torino, Italy
^ap Laboratory of Human Genetics of Neurological complex disorder, Institute of Experimental Neurology (INSPE), Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, Milan, 20132, Italy
^aq Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
^ar University of Milan, Department of Health Sciences, San Paolo Hospital and Filarete Foundation, viale Ortles 22/4, Milan, 20139, Italy
^as Department of Neurology, Institute of Experimental Neurology (INSPE), Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, Milan, 20132, Italy
^at Neurology Unit, Department of Pathophysiology and Transplantation, University of Milan, Dino Ferrari Center, Milan, Italy
^au Fondazione IRCCS Ca’ Granda, Ospedale Policlinico, Milan, Italy
^av Fondazione IRCCS Casa Sollievo della Sofferenza, Unit of Neurology, San Giovanni Rotondo (FG), Italy
^aw Center for Experimental Neurological Therapies, Sant’Andrea Hospital, Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University, Rome, Italy
^ax Istituto Neurologico Mediterraneo (INM) Neuromed, Pozzilli, Isernia, Italy
^ay Department of Neurology, Ospedale Maggiore, Novara, Italy
^az Laboratory of Microbiology and Virology, University Vita-Salute San Raffaele, Hospital San Raffaele, Milan, Italy
^ba Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Novara, Italy
^bb Department of Neurology, Erasmus MC, Rotterdam, Netherlands
^bc Nuffield Department of Population Health, Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Oxford, OX3 7LF, United Kingdom
^bd Department of Neurology, Institute of Clinical Medicine, University of Oslo, Norway
^be Department of Neurology, Oslo University Hospital, Oslo, Norway
^bf Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
^bg Servei de Neurologia-Neuroimmunologia, Centre d’Esclerosi Múltiple de Catalunya (Cemcat), Institut de Recerca Vall d’Hebron (VHIR), Hospital Universitari Vall d’Hebron, Spain
^bh Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
^bi Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
^bj Translational Bioinformatics Unit, NavarraBiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Navarra, Spain
^bk Mucosal and Salivary Biology Division, King’s College, London Dental Institute, London, United Kingdom
^bl Neuroimmunology and MS Research (nims), Neurology Clinic, University Hospital Zurich, Frauenklinikstrasse 26, Zurich, 8091, Switzerland
^bm Department of Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zürich, Frauenklinikstrasse 26, Zürich, 8091, Switzerland
^bn University of Cambridge, Department of Clinical Neurosciences, Addenbrooke’s Hospital, BOX 165, Hills Road, Cambridge, CB2 0QQ, United Kingdom
^bo Keele University Medical School, University Hospital of North Staffordshire, Stoke-on-Trent, ST4 7NY, United Kingdom
^bp Department of Twin Research and Genetic Epidemiology, King’s College London, London, SE1 7EH, United Kingdom
^bq NIHR Oxford Biomedical Research Centre, Diabetes and Metabolism Theme, OCDEM, Churchill Hospital, Oxford, United Kingdom
^br NIHR BioResource, University of Cambridge, Cambridge University Hospitals, NHS Foundation Trust, Hills Road, Cambridge, CB2 0QQ, United Kingdom
^bs Department of Neurology, Peterborough City Hospital, Edith Cavell Campus, Bretton Gate, Peterborough, PE3 9GZ, United Kingdom
^bt Department of Neurology, Johns Hopkins University School of medicine, Baltimore, MD, United States
^bu Multiple sclerosis center, Department of neurology, School of medicine, Washington University St Louis, St Louis, MO, United States
^bv Center for Human Genetics Research, Vanderbilt University Medical Center, 525 Light Hall, 2215 Garland Avenue, Nashville, TN 37232, United States
^bw Multiple Sclerosis Division, Department of Neurology, University of Miami, Miller School of Medicine, Miami, FL 33136, United States
^bx MS Center of Northeastern NY, 1205 Troy Schenectady Rd, Latham, NY 12110, United States
^by Université de Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ATIP-Avenir, Equipe 5, Nantes, France
^bz Population and Quantitative Health Sciences, Department of Epidemiology and Biostatistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106-4945, United States
^ca Center for Applied Genomics, Children’s Hospital of Philadelphia, 3615 Civic Center Blvd., Philadelphia, PA 19104, United States
^cb Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^cc Department of Neurology, Brigham and Women’s Hospital, Boston, MA 02115, United States
^cd Departments of Neurology and Neurological Therapeutics, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan
^ce Elliot Lewis Center, 110 Cedar St, Wellesley, MA 02481, United States
^cf Broad Institute of Harvard University and, MIT, Cambridge, MA 02142, United States
^cg School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
^ch Westmead Institute for Medical Research, University of Sydney, Westmead, NSW 2145, Australia
^ci Kaiser Permanente Division of Research, Oakland, CA, United States
^cj Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women’s Hospital, Boston, MA 02115, United States
^ck Departments of Neurology and Immunobiology, Yale University School of Medicine, New Haven, CT 06520, United States
^cl Westmead Millennium Institute, University of SydneyNSW, Australia
^cm Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Novara, Italy
^cn Menzies Research Institute Tasmania, University of Tasmania, Australia
^co UC Berkeley School of Public Health, Center for Computational Biology, United States
^cp Westmead Millennium Institute, University of SydneyNSW, Australia
^cq Department of Neurology, Department of Immunology, Erasmus MC, Rotterdam, Netherlands
^cr Center for Computational Medicine, Peter Gilgan Centre for Research and Learning, Hospital for Sick Children (SickKids), Toronto, ON M5G 0A4, Canada
^cs Vertex Pharmaceuticals, 50 Northern Avenue, Boston, MA 02210, United States

Abstract
We analyzed genetic data of 47,429 multiple sclerosis (MS) and 68,374 control subjects and established a reference map of the genetic architecture of MS that includes 200 autosomal susceptibility variants outside the major histocompatibility complex (MHC), one chromosome X variant, and 32 variants within the extended MHC. We used an ensemble of methods to prioritize 551 putative susceptibility genes that implicate multiple innate and adaptive pathways distributed across the cellular components of the immune system. Using expression profiles from purified human microglia, we observed enrichment for MS genes in these brain-resident immune cells, suggesting that these may have a role in targeting an autoimmune process to the central nervous system, although MS is most likely initially triggered by perturbation of peripheral immune responses. © 2019 American Association for the Advancement of Science. All rights reserved.

Document Type: Article
Publication Stage: Final
Source: Scopus

“Cell type-specific modulation of sensory and affective components of itch in the periaqueductal gray” (2019) Nature Communications

Cell type-specific modulation of sensory and affective components of itch in the periaqueductal gray
(2019) Nature Communications, 10 (1), p. 4356.

Samineni, V.K.^a ^b , Grajales-Reyes, J.G.^a ^b ^c ^d , Sundaram, S.S.^a ^b , Yoo, J.J.^a ^b , Gereau, R.W., 4th^a ^b ^e

^a Department of Anesthesiology, Washington University School of Medicine, 660 S. Euclid Ave, Box 8054, St. Louis, MO, 63110, USA
^b Washington University Pain Center, Washington University School of Medicine, 660 S. Euclid Ave, Box 8054, St. Louis, MO, 63110, USA
^c Medical Scientist Training Program, Washington University School of Medicine, 660 S. Euclid Ave, Box 8054, St. Louis, MO, 63110, USA
^d Neuroscience Program, Washington University School of Medicine, 660 S. Euclid Ave, Box 8054, St. Louis, MO, 63110, USA
^e Department of Neuroscience, Department of Biomedical Engineering, Washington University School of Medicine, 660 S. Euclid Ave, Box 8054, St. Louis, MO, 63110, USA

Abstract
Itch is a distinct aversive sensation that elicits a strong urge to scratch. Despite recent advances in our understanding of the peripheral basis of itch, we know very little regarding how central neural circuits modulate acute and chronic itch processing. Here we establish the causal contributions of defined periaqueductal gray (PAG) neuronal populations in itch modulation in mice. Chemogenetic manipulations demonstrate bidirectional modulation of scratching by neurons in the PAG. Fiber photometry studies show that activity of GABAergic and glutamatergic neurons in the PAG is modulated in an opposing manner during chloroquine-evoked scratching. Furthermore, activation of PAG GABAergic neurons or inhibition of glutamatergic neurons resulted in attenuation of scratching in both acute and chronic pruritis. Surprisingly, PAG GABAergic neurons, but not glutamatergic neurons, may encode the aversive component of itch. Thus, the PAG represents a neuromodulatory hub that regulates both the sensory and affective aspects of acute and chronic itch.

Document Type: Article
Publication Stage: Final
Source: Scopus

“A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images” (2019) Frontiers in Neuroinformatics

A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images
(2019) Frontiers in Neuroinformatics, 13, art. no. 53, .

Chauhan, S.^a , Vig, L.^b , De Filippo De Grazia, M.^c , Corbetta, M.^d ^e , Ahmad, S.^a , Zorzi, M.^c ^f

^a School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
^b TCS Research, New Delhi, India
^c Department of General Psychology, Padova Neuroscience Center, University of Padova, Padua, Italy
^d Department of Neurosciences, Padova Neuroscience Center, University of Padova, Padua, Italy
^e Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^f IRCCS San Camillo Hospital, Venice, Italy

Abstract
Stroke causes behavioral deficits in multiple cognitive domains and there is a growing interest in predicting patient performance from neuroimaging data using machine learning techniques. Here, we investigated a deep learning approach based on convolutional neural networks (CNNs) for predicting the severity of language disorder from 3D lesion images from magnetic resonance imaging (MRI) in a heterogeneous sample of stroke patients. CNN performance was compared to that of conventional (shallow) machine learning methods, including ridge regression (RR) on the images’ principal components and support vector regression. We also devised a hybrid method based on re-using CNN’s high-level features as additional input to the RR model. Predictive accuracy of the four different methods was further investigated in relation to the size of the training set and the level of redundancy across lesion images in the dataset, which was evaluated in terms of location and topological properties of the lesions. The Hybrid model achieved the best performance in most cases, thereby suggesting that the high-level features extracted by CNNs are complementary to principal component analysis features and improve the model’s predictive accuracy. Moreover, our analyses indicate that both the size of training data and image redundancy are critical factors in determining the accuracy of a computational model in predicting behavioral outcome from the structural brain imaging data of stroke patients. © Copyright © 2019 Chauhan, Vig, De Filippo De Grazia, Corbetta, Ahmad and Zorzi.

Author Keywords
brain lesion; cognitive deficit; deep learning; machine learning; magnetic resonance imaging; stroke

Document Type: Article
Publication Stage: Final
Source: Scopus