Arts & Sciences Brown School McKelvey School Medicine Weekly Publications

WashU weekly Neuroscience publications

“Predicting Alzheimer’s disease progression using multi-modal deep learning approach” (2019) Scientific Reports

Predicting Alzheimer’s disease progression using multi-modal deep learning approach
(2019) Scientific Reports, 9 (1), art. no. 1952, . 

Lee, G.a b , Nho, K.c d , Kang, B.a , Sohn, K.-A.a , Kim, D.b e , Weiner, M.W.f , Aisen, P.g , Petersen, R.h , Jack, C.R., Jr.h , Jagust, W.i , Trojanowki, J.Q.j , Toga, A.W.k , Beckett, L.l , Green, R.C.m , Saykin, A.J.n , Morris, J.o , Shaw, L.M.j , Khachaturian, Z.p , Sorensen, G.q , Carrillo, M.r , Kuller, L.s , Raichle, M.o , Paul, S.t , Davies, P.u , Fillit, H.v , Hefti, F.w , Holtzman, D.o , Mesulam, M.M.x , Potter, W.y , Snyder, P.z , Montine, T.aa , Thomas, R.G.g , Donohue, M.g , Walter, S.g , Sather, T.g , Jiminez, G.g , Balasubramanian, A.B.g , Mason, J.g , Sim, I.g , Harvey, D.l , Bernstein, M.h , Fox, N.ab , Thompson, P.ac , Schuff, N.f , DeCArli, C.l , Borowski, B.h , Gunter, J.h , Senjem, M.h , Vemuri, P.h , Jones, D.h , Kantarci, K.h , Ward, C.h , Koeppe, R.A.ad , Foster, N.ae , Reiman, E.M.af , Chen, K.af , Mathis, C.s , Landau, S.i , Cairns, N.J.o , Householder, E.o , Taylor-Reinwald, L.o , Lee, V.ac , Korecka, M.ac , Figurski, M.ac , Crawford, K.k , Neu, S.k , Foroud, T.M.n , Potkin, S.ag , Shen, L.n , Faber, K.n , Kim, S.n , Tha, L.q , Frank, R.ai , Hsiao, J.aj , Kaye, J.ak , Quinn, J.ak , Silbert, L.ak , Lind, B.ak , Carter, R.ak , Dolen, S.ak , Ances, B.o , Carroll, M.o , Creech, M.L.o , Franklin, E.o , Mintun, M.A.o , Schneider, S.o , Oliver, A.o , Schneider, L.S.k , Pawluczyk, S.k , Beccera, M.k , Teodoro, L.k , Spann, B.M.k , Brewer, J.g , Vanderswag, H.g , Fleisher, A.g , Marson, D.al , Griffith, R.al , Clark, D.al , Geldmacher, D.al , Brockington, J.al , Roberson, E.al , Love, M.N.ah , Heidebrink, J.L.i , Lord, J.L.i , Mason, S.S.h , Albers, C.S.h , Knopman, D.h , Johnson, K.h , Grossman, H.ai , Mitsis, E.am , Shah, R.C.an , deToledo-Morrell, L.an , Doody, R.S.ao , Villanueva-Meyer, J.ao , Chowdhury, M.ao , Rountree, S.ao , Dang, M.ao , Duara, R.ap , Varon, D.ap , Greig, M.T.ap , Roberts, P.ap , Stern, Y.aq , Honig, L.S.aq , Bell, K.L.aq , Albert, M.ah , Onyike, C.ah , D’Agostino, D., IIah , Kielb, S.ah , Galvin, J.E.ar , Cerbone, B.ar , Michel, C.A.ar , Pogorelec, D.M.ar , Rusinek, H.ar , de Leon, M.J.ar , Glodzik, L.ar , De Santi, S.ar , Womack, K.as , Mathews, D.as , Quiceno, M.as , Doraiswamy, P.M.at , Petrella, J.R.at , Borges-Neto, S.at , Wong, T.Z.at , Coleman, E.at , Levey, A.I.au , Lah, J.J.au , Cella, J.S.au , Burns, J.M.av , Swerdlow, R.H.av , Brooks, W.M.av , Arnold, S.E.j , Karlawish, J.H.j , Wolk, D.j , Clark, C.M.j , Apostolova, L.ac , Tingus, K.ac , Woo, E.ac , Silverman, D.H.S.ac , Lu, P.H.ac , Bartzokis, G.ac , Smith, C.D.aw , Jicha, G.aw , Hardy, P.aw , Sinha, P.aw , Oates, E.aw , Conrad, G.aw , Graff-Radford, N.R.ax , Parfitt, F.ax , Kendall, T.ax , Johnson, H.ax , Lopez, O.L.s , Oakley, M.A.s , Simpson, D.M.s , Farlow, M.R.n , Hake, A.M.n , Matthews, B.R.n , Brosch, J.R.n , Herring, S.n , Hunt, C.n , Porsteinsson, A.P.ay , Goldstein, B.S.ay , Martin, K.ay , Makino, K.M.ay , Ismail, M.S.ay , Brand, C.ay , Mulnard, R.A.ay , Thai, G.ay , Mc-Adams-Ortiz, C.ay , van Dyck, C.H.az , Carson, R.E.az , MacAvoy, M.G.az , Varma, P.az , Chertkow, H.ba , Bergman, H.ba , Hosein, C.ba , Black, S.bb , Stefanovic, B.bb , Caldwell, C.bb , Hsiung, G.-Y.R.bc , Feldman, H.bc , Mudge, B.bc , Assaly, M.bc , Finger, E.bd , Pasternack, S.bd , Rachisky, I.bd , Trost, D.bd , Kertesz, A.bd bm , Bernick, C.be , Munic, D.be , Lipowski, K.x , Weintraub, M.x , Bonakdarpour, B.x , Kerwin, D.x , Wu, C.-K.x , Johnson, N.x , Sadowsky, C.bf , Villena, T.bf , Turner, R.S.bg , Johnson, K.bg , Reynolds, B.bg , Sperling, R.A.m , Johnson, K.A.m , Marshall, G.m , Yesavage, J.bh , Taylor, J.L.bh , Lane, B.bh , Rosen, A.bh , Tinklenberg, J.bh , Sabbagh, M.N.af , Belden, C.M.af , Jacobson, S.A.af , Sirrel, S.A.af , Kowall, N.bi , Killiany, R.bi , Budson, A.E.bi , Norbash, A.bi , Johnson, P.L.bi , Obisesan, T.O.bj , Wolday, S.bj , Allard, J.bj , Lerner, A.bk , Ogrocki, P.bk , Tatsuoka, C.bk , Fatica, P.bk , Fletcher, E.l , Maillard, P.l , Olichney, J.l , Carmichael, O.l , Kittur, S.bl , Borrie, M.bm , Lee, T.-Y.bm , Bartha, R.bm , Johnson, S.bm , Asthana, S.bm , Carlsson, C.M.bm , Preda, A.ag , Nguyen, D.ag , Tariot, P.af , Burke, A.af , Trncic, N.af , Fleisher, A.af , Reeder, S.af , Bates, V.bn , Capote, H.bn , Rainka, M.bn , Scharre, D.W.bo , Kataki, M.bo , Adeli, A.bo , Zimmerman, E.A.bp , Celmins, D.bp , Brown, A.D.bp , Pearlson, G.D.bq , Blank, K.bq , Anderson, K.bq , Flashman, L.A.br , Seltzer, M.br , Hynes, M.L.br , Santulli, R.B.br , Sink, K.M.bs , Gordineer, L.bs , Williamson, J.D.bs , Garg, P.bs , Watkins, F.bs , Ott, B.R.z , Querfurth, H.z , Tremont, G.z , Salloway, S.z , Malloy, P.z , Correia, S.z , Rosen, H.J.f , Miller, B.L.f , Perry, D.f , Mintzer, J.bt , Spicer, K.bt , Bachman, D.bt , Finger, E.bm , Pasternak, S.bm , Rachinsky, I.bm , Rogers, J.bm , Drost, D.bm , Pomara, N.bu , Hernando, R.bu , Sarrael, A.bu , Schultz, S.K.bv , Ponto, L.L.B.bv , Shim, H.bv , Smith, K.E.bv , Relkin, N.t , Chaing, G.t , Lin, M.t , Ravdin, L.t , Smith, A.bw , Raj, B.A.bw , Fargher, K.bw , for Alzheimer’s Disease Neuroimaging Initiativebx

a Department of Software and Computer Engineering, Ajou University, Suwon, South Korea
b Biomedical & Translational Informatics Institute, Geisinger, Danville, United States
c Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
d Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
e The Huck Institute of the Life Sciences, Pennsylvania State University, University Park, United States
f UC San Francisco, San Francisco, CA 94107, United States
g UC San Diego, La Jolla, CA 92093, United States
h Mayo Clinic, Rochester, MN, United States
i UC Berkeley, Berkeley, San Francisco, United States
j University of Pennsylvania, Philadelphia, PA 19104, United States
k USC, Los Angeles, CA 90032, United States
l UC Davis, Sacramento, CA, United States
m Brigham and Women’s Hospital/Harvard Medical School, Boston, MA 02215, United States
n Indiana University, Bloomington, IN 47405, United States
o Washington University St. Louis, St. Loui, MO 63110, United States
p Prevent Alzheimer’s Disease 2020, Rockville, MD 20850, United States
q Siemens, Erlangen, Germany
r Alzheimer’s Association, Chicago, IL 60631, United States
s University of Pittsburg, Pittsburgh, PA 15213, United States
t Cornell University, Ithaca, NY 14853, United States
u Albert Einstein College of Medicine of Yeshiva University, Bronx, NY 10461, United States
v AD Drug Discovery Foundation, New York, NY 10019, United States
w Acumen Pharmaceuticals, Livermore, CA 94551, United States
x Northwestern University, Chicago, IL 60611, United States
y National Institute of Mental Health, Bethesda, MD 20892, United States
z Brown University, Providence, RI 02912, United States
aa University of Washington, Seattle, WA 98195, United States
ab University of London, London, United Kingdom
ac UCLA, Torrance, CA 90509, United States
ad University of Michigan, Ann Arbor, MI 48109-2800, United States
ae University of Utah, Salt Lake City, UT 84112, United States
af Banner Alzheimer’s Institute, Phoenix, AZ 85006, United States
ag UUC Irvine, Orange, CA 92868, United States
ah Johns Hopkins University, Baltimore, MD 21205, United States
ai Richard Frank Consulting, Consulting, United States
aj National Institute on Aging, Baltimore, MD, United States
ak Oregon Health and Science University, Portland, OR 97239, United States
al University of Alabama, Birmingham, AL, United States
am Mount Sinai School of Medicine, New York, NY, United States
an Rush University Medical Center, Chicago, IL 60612, United States
ao Baylor College of Medicine, Houston, TX, United States
ap Wien Center, Miami Beach, FL 33140, United States
aq Columbia University Medical Center, New York, NY, United States
ar New York University, New York, NY, United States
as University of Texas Southwestern Medical School, Galveston, TX 77555, United States
at Duke University Medical Center, Durham, NC, United States
au Emory University, Atlanta, GA 30307, United States
av University of Kansas Medical Center, Kansas City, KS, United States
aw University of Kentucky, Lexington, KY, United States
ax Mayo Clinic, Jacksonville, FL, United States
ay University of Rochester Medical Center, Rochester, NY 14642, United States
az Yale University School of Medicine, New Haven, CT, United States
ba McGill Univ. Montreal-Jewish General Hospital, Montreal, PQ H3A 2A7, Canada
bb Sunnybrook Health Sciences, Toronto, ON, Canada
bc U.B.C. Clinic for AD & Related Disorders, Vancouver, BC, Canada
bd Cognitive Neurology – St. Joseph’s, London, ON, Canada
be Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, United States
bf Premiere Research Inst (Palm Beach Neurology), W Palm Beach, FL, United States
bg Georgetown University Medical Center, Washington, DC 20007, United States
bh Stanford University, Stanford, CA 94305, United States
bi Boston University, BostonMA, United States
bj Howard University, Washington, DC 20059, United States
bk Case Western Reserve University, Cleveland, OH 44106, United States
bl Neurological Care of CNY, Liverpool, NY 13088, United States
bm St. Joseph’s Health Care, London, ON N6A 4H1, Canada
bn Dent Neurologic Institute, Amherst, NY 14226, United States
bo Ohio State University, Columbus, OH 43210, United States
bp Albany Medical College, Albany, NY 12208, United States
bq Hartford Hospital Olin Neuropsychiatry Research Center, Hartford, CT 06114, United States
br Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States
bs Wake Forest University Health Sciences, Winston-Salem, NC, United States
bt Medical University South Carolina, Charleston, SC 29425, United States
bu Nathan Kline Institute, Orangeburg, NY, United States
bv University of Iowa College of Medicine, Iowa City, IA 52242, United States
bw University of South Florida: USF Health Byrd Alzheimer’s Institute, Tampa, FL 33613, United States

Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials. © 2019, The Author(s).

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

“Salivary melatonin onset in youth at familial risk for bipolar disorder” (2019) Psychiatry Research

Salivary melatonin onset in youth at familial risk for bipolar disorder
(2019) Psychiatry Research, 274, pp. 49-57. 

Ghaziuddin, N.a , Shamseddeen, W.a b , Bertram, H.a , McInnis, M.a , Wilcox, H.C.c , Mitchell, P.B.d e , Fullerton, J.M.i j , Roberts, G.M.P.d e , Glowinski, A.L.f , Kamali, M.g h , Stapp, E.h , Hulvershorn, L.A.k , Nurnberger, J.k , Armitage, R.a , Bipolar High Risk Research Groupl

a Department of Psychiatry, University of Michigan, Ann arbor, MI, United States
b Department of Psychiatry, American University of Beirut, Lebanon
c Johns Hopkins Schools of Public Health and Medicine, Baltimore, MD, United States
d School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
e Black Dog Institute, Prince of Wales Hospital, Sydney, NSW, Australia
f Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
g Department of Psychiatry, Massachusetts General HospitalMA, United States
h National Institute of Mental Health, Intramural Research Program, Bethesda, MD, United States
i Neuroscience Research Australia, Randwick, New South Wales, Australia
j School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
k Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States

Abstract
Melatonin secretion and polysomnography (PSG) were compared among a group of healthy adolescents who were at high familial risk for bipolar disorder (HR) and a second group at low familial risk (LR). Adolescent participants (n = 12) were a mean age 14 ± 2.3 years and included 8 females and 4 males. Saliva samples were collected under standardized condition light (red light) and following a 200 lux light exposure over two consecutive nights in a sleep laboratory. Red Light Melatonin onset (RLMO) was defined as saliva melatonin level exceeding the mean of the first 3 readings plus 2 standard deviations. Polysomnography was also completed during each night. HR youth, relative to LR, experienced a significantly earlier melatonin onset following 200 lux light exposure. Polysomnography revealed that LR youth, relative to HR, spent significantly more time in combined stages 3 and 4 (deep sleep) following red light exposure. Additionally, regardless of the group status (HR or LR), there was no significant difference in Red Light Melatonin Onset recorded at home or in the laboratory, implying its feasibility and reliability. © 2019

Author Keywords
Adolescent;  Bipolar;  Melatonin;  Polysomnography;  Risk

Document Type: Article
Publication Stage: Final
Source: Scopus

“Exploring the relationship between electrical impedance myography and quantitative ultrasound parameters in Duchenne muscular dystrophy” (2019) Clinical Neurophysiology

Exploring the relationship between electrical impedance myography and quantitative ultrasound parameters in Duchenne muscular dystrophy
(2019) Clinical Neurophysiology, 130 (4), pp. 515-520. 

Roy, B.a , Darras, B.T.b , Zaidman, C.M.c , Wu, J.S.a , Kapur, K.b , Rutkove, S.B.a

a Beth Israel Deaconess Medical Center, Boston, MA, United States
b Boston Children’s Hospital, Boston, MA, United States
c Washington University in St. LouisMO, United States

Abstract
Objectives: Quantitative ultrasound (QUS), including grayscale level analysis (GLA) and quantitative backscatter analysis (QBA), and electrical impedance myography (EIM) have been proposed as biomarkers in Duchenne muscular dystrophy (DMD). However, the relationship between these methods has not been assessed. Methods: QUS values (including GLA and QBA) and several EIM measures were recorded from six muscles in 36 DMD and 29 healthy boys between ages 5 and 13 years at baseline, 6-months, and 12-months. Results: In the DMD boys, a moderate correlation was noted between QUS and EIM parameters, with the strongest correlations being identified for averaged muscle values. Of the individual muscles, biceps brachii and deltoid showed the strongest correlations. For example, in biceps, the QBA/EIM correlation coefficient (Spearman rho) was ≥0.70 (p < 0.01). Importantly, changes in QUS values over 12 months also correlated moderately with changes in EIM parameters and EIM/QBA rho values mostly varied between −0.53 and −0.70 (p ≤ 0.02). No significant correlations were identified in the healthy boys. Conclusions: A moderate correlation of QUS with EIM in DMD boys suggests that the two technologies provide related data but are sensitive to different pathological features of muscle. Significance: The use of both technologies jointly in assessing DMD progression and response to therapy should be considered. © 2019

Author Keywords
Duchenne muscular dystrophy;  Electrical impedance myography;  Outcome measures;  Quantitative ultrasound

Document Type: Article
Publication Stage: Final
Source: Scopus

“Association between circadian rhythms and neurodegenerative diseases” (2019) The Lancet Neurology

Association between circadian rhythms and neurodegenerative diseases
(2019) The Lancet Neurology, 18 (3), pp. 307-318. 

Leng, Y.a b , Musiek, E.S.c , Hu, K.d e , Cappuccio, F.P.f , Yaffe, K.a b

a Department of Psychiatry, Neurology, and Epidemiology and Biostatistics, University of California, San Francisco, CA, United States
b San Francisco VA Medical Center, San Francisco, CA, United States
c Hope Center for Neurological Disorders and Knight Alzheimer Disease Research Center, Department of Neurology, Washington University School of Medicine, St Louis, MO, United States
d Medical Biodynamics Program, Division of Sleep and Circadian Disorders, Department of Medicine and Department of Neurology, Brigham and Women’s Hospital, Boston, MA, United States
e Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA, United States
f Division of Health Sciences (Mental Health and Wellbeing), Warwick Medical School, University of Warwick, Coventry, United Kingdom

Abstract
Dysfunction in 24-h circadian rhythms is a common occurrence in ageing adults; however, circadian rhythm disruptions are more severe in people with age-related neurodegenerative diseases, including Alzheimer’s disease and related dementias, and Parkinson’s disease. Manifestations of circadian rhythm disruptions differ according to the type and severity of neurodegenerative disease and, for some patients, occur before the onset of typical clinical symptoms of neurodegeneration. Evidence from preliminary studies suggest that circadian rhythm disruptions, in addition to being a symptom of neurodegeneration, might also be a potential risk factor for developing Alzheimer’s disease and related dementias, and Parkinson’s disease, although large, longitudinal studies are needed to confirm this relationship. The mechanistic link between circadian rhythms and neurodegeneration is still not fully understood, although proposed underlying pathways include alterations of protein homoeostasis and immune and inflammatory function. While preliminary clinical studies are promising, more studies of circadian rhythm disruptions and its mechanisms are required. Furthermore, clinical trials are needed to determine whether circadian interventions could prevent or delay the onset of neurodegenerative diseases. © 2019 Elsevier Ltd

Document Type: Review
Publication Stage: Final
Source: Scopus

“Optic Nerve Head Drusen: The Relationship Between Intraocular Pressure and Optic Nerve Structure and Function: Response” (2019) Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society

Optic Nerve Head Drusen: The Relationship Between Intraocular Pressure and Optic Nerve Structure and Function: Response
(2019) Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society, 39 (1), pp. 143-144. 

Shyne, M., Van Stavern, G.P., Nolan, K.W., Lee, M.S., McClelland, C.M.

Biostatistical Design and Analysis Center, University of Minnesota, Minneapolis, Minnesota Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, Saint Louis, Missouri Department of Ophthalmology and Visual Neurosciences, University of Minnesota, Minneapolis, Minnesota

Document Type: Article
Publication Stage: Final
Source: Scopus

“Cerebellar Functional Connectivity in Term- and Very Preterm-Born Infants” (2019) Cerebral cortex (New York, N.Y. : 1991)

Cerebellar Functional Connectivity in Term- and Very Preterm-Born Infants
(2019) Cerebral cortex (New York, N.Y. : 1991), 29 (3), pp. 1174-1184. 

Herzmann, C.S.a , Snyder, A.Z.a b , Kenley, J.K.a , Rogers, C.E.c d , Shimony, J.S.b , Smyser, C.D.a b d

a Department of Neurology, Washington University School of Medicine, Saint Louis, MO, United States
b Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, United States
c Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, United States
d Department of Pediatrics, Washington University School of Medicine, Saint Louis, MO, United States

Abstract
Cortical resting state networks have been consistently identified in infants using resting state-functional connectivity magnetic resonance imaging (rs-fMRI). Comparable studies in adults have demonstrated cerebellar components of well-established cerebral networks. However, there has been limited investigation of early cerebellar functional connectivity. We acquired non-sedated rs-fMRI data in the first week of life in 57 healthy, term-born infants and at term-equivalent postmenstrual age in 20 very preterm infants (mean birth gestational age 27 ± 2 weeks) without significant cerebral or cerebellar injury. Seed correlation analyses were performed using regions of interests spanning the cortical and subcortical gray matter and cerebellum. Parallel analyses were performed using rs-fMRI data acquired in 100 healthy adults. Our results demonstrate that cortico-cerebellar functional connectivity is well-established by term. Intra- and cortico-cerebellar functional connectivity were largely similar in infants and adults. However, infants showed more functional connectivity structure within the cerebellum, including stronger homotopic correlations and more robust anterior-posterior anticorrelations. Prematurity was associated with reduced correlation magnitudes, but no alterations in intra- and cortico-cerebellar functional connectivity topography. These results add to the growing evidence that the cerebellum plays an important role in shaping early brain development during infancy.

Document Type: Article
Publication Stage: Final
Source: Scopus

“Comorbid Conditions Explain the Association Between Posttraumatic Stress Disorder and Incident Cardiovascular Disease” (2019) Journal of the American Heart Association

Comorbid Conditions Explain the Association Between Posttraumatic Stress Disorder and Incident Cardiovascular Disease
(2019) Journal of the American Heart Association, 8 (4), p. e011133. 

Scherrer, J.F.a b , Salas, J.a b , Cohen, B.E.c , Schnurr, P.P.d , Schneider, F.D.e , Chard, K.M.f , Tuerk, P.g , Friedman, M.J.d , Norman, S.B.h , van den Berk-Clark, C.a , Lustman, P.J.i

a Department of Family and Community Medicine Saint Louis University School of Medicine St. Louis MO
b Harry S. Truman Veterans Administration Medical Center Research Service Columbia MO
c Department of Medicine University of California San Francisco School of Medicine and San Francisco VAMC San Francisco CA
d National Center for PTSD and Department of Psychiatry Geisel School of Medicine at Dartmouth Darmouth HanoverNH, United States
e Department of Family and Community Medicine University of Texas Southwestern Dallas TX
f 6 Trauma Recovery Center Cincinnati VAMC and Department of Psychiatry and Behavioral Neuroscience University of Cincinnati Cincinnati OH, France
g 7 Sheila C. Johnson Center for Clinical Services Department of Human Services University of Virginia Charlottesville VA, France
h National Center for PTSD and Department of Psychiatry University of California San Diego CA
i Department of Psychiatry Washington University School of Medicine St. Louis MO

Abstract
Background Posttraumatic stress disorder ( PTSD ) is associated with risk of cardiovascular disease ( CVD ). Biopsychosocial factors associated with PTSD likely account for some or all of this association. We determined whether 1, or a combination of comorbid conditions explained the association between PTSD and incident CVD . Methods and Results Eligible patients used 1 of 5 Veterans Health Affairs medical centers distributed across the United States. Data were obtained from electronic health records. At index date, 2519 Veterans Health Affairs ( VA ) patients, 30 to 70 years of age, had PTSD diagnoses and 1659 did not. Patients had no CVD diagnoses for 12 months before index date. Patients could enter the cohort between 2008 and 2012 with follow-up until 2015. Age-adjusted Cox proportional hazard models were computed before and after adjusting for comorbidities. Patients were middle aged (mean=50.1 years, SD ±11.0), mostly male (87.0%), and 60% were white. The age-adjusted association between PTSD and incident CVD was significant (hazard ratio=1.41; 95% CI : 1.21-1.63). After adjustment for metabolic conditions, the association between PTSD and incident CVD was attenuated but remained significant (hazard ratio=1.23; 95% CI : 1.06-1.44). After additional adjustment for smoking, sleep disorder, substance use disorder, anxiety disorders, and depression, PTSD was not associated with incident CVD (hazard ratio=0.96; 95% CI : 0.81-1.15). Conclusions PTSD is not an independent risk factor for CVD . Physical and psychiatric conditions and smoking that co-occur with PTSD explain why this patient population has an increased risk of CVD . Careful monitoring may limit exposure to CVD risk factors and subsequent incident CVD .

Author Keywords
cardiovascular disease;  epidemiology;  posttraumatic stress disorder;  veterans

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

“Agent-based representations of objects and actions in the monkey pre-supplementary motor area” (2019) Proceedings of the National Academy of Sciences of the United States of America

Agent-based representations of objects and actions in the monkey pre-supplementary motor area
(2019) Proceedings of the National Academy of Sciences of the United States of America, 116 (7), pp. 2691-2700. 

Livi, A.a c , Lanzilotto, M.a , Maranesi, M.a , Fogassi, L.a , Rizzolatti, G.a b , Bonini, L.a

a Department of Medicine and Surgery, Neuroscience Unit, University of Parma, Parma, 43125, Italy
b Parma Unit, Institute of Neuroscience of Centro Nazionale delle Ricerche (CNR), Parma, 43125, Italy
c Department of Neuroscience, Washington University, St. Louis, MO 63130, United States

Abstract
Information about objects around us is essential for planning actions and for predicting those of others. Here, we studied pre-supplementary motor area F6 neurons with a task in which monkeys viewed and grasped (or refrained from grasping) objects, and then observed a human doing the same task. We found “action-related neurons” encoding selectively monkey’s own action [self-type (ST)], another agent’s action [other-type (OT)], or both [self- and other-type (SOT)]. Interestingly, we found “object-related neurons” exhibiting the same type of selectivity before action onset: Indeed, distinct sets of neurons discharged when visually presented objects were targeted by the monkey’s own action (ST), another agent’s action (OT), or both (SOT). Notably, object-related neurons appear to signal self and other’s intention to grasp and the most likely grip type that will be performed, whereas action-related neurons encode a general goal attainment signal devoid of any specificity for the observed grip type. Time-resolved cross-modal population decoding revealed that F6 neurons first integrate information about object and context to generate an agent-shared signal specifying whether and how the object will be grasped, which progressively turns into a broader agent-based goal attainment signal during action unfolding. Importantly, shared representation of objects critically depends upon their location in the observer’s peripersonal space, suggesting an “object-mirroring” mechanism through which observers could accurately predict others’ impending action by recruiting the same motor representation they would activate if they were to act upon the same object in the same context. © 2019 National Academy of Sciences. All Rights Reserved.

Author Keywords
Action prediction;  Macaque;  Mirror neuron;  Object processing;  Pre-SMA

Document Type: Article
Publication Stage: Final
Source: Scopus

“Regulation of BK Channels by Beta and Gamma Subunits” (2019) Annual Review of Physiology

Regulation of BK Channels by Beta and Gamma Subunits
(2019) Annual Review of Physiology, 81, pp. 113-137. 

Gonzalez-Perez, V., Lingle, C.J.

Department of Anesthesiology, Washington University, School of Medicine, St. Louis, MO 63110, United States

Abstract
Ca 2+ – and voltage-gated K + channels of large conductance (BK channels) are expressed in a diverse variety of both excitable and inexcitable cells, with functional properties presumably uniquely calibrated for the cells in which they are found. Although some diversity in BK channel function, localization, and regulation apparently arises from cell-specific alternative splice variants of the single pore-forming α subunit (KCa1.1, Kcnma1, Slo1) gene, two families of regulatory subunits, β and γ, define BK channels that span a diverse range of functional properties. We are just beginning to unravel the cell-specific, physiological roles served by BK channels of different subunit composition. Copyright © 2019 by Annual Reviews. All rights reserved.

Author Keywords
auxiliary subunits;  beta subunits;  BK channels;  Ca2+-and voltage-dependent;  gamma subunits;  K+channels;  KCa1.1;  KCNMB;  LRRC26

Document Type: Review
Publication Stage: Final
Source: Scopus

“Multi-Modal Home Sleep Monitoring in Older Adults” (2019) Journal of visualized experiments : JoVE

Multi-Modal Home Sleep Monitoring in Older Adults
(2019) Journal of visualized experiments : JoVE, (143), . 

Toedebusch, C.D.a , McLeland, J.S.a , Schaibley, C.M.a , Banks, I.R.a , Boyd, J.a , Morris, J.C.b , Holtzman, D.M.b , Lucey, B.P.c

a Department of Neurology, Washington University School of Medicine, United States
b Department of Neurology, Washington University School of Medicine; Hope Center for Neurological Disorders, Washington University School of Medicine; Knight Alzheimer’s Disease Research Center, Washington University School of Medicine
c Department of Neurology, Washington University School of Medicine; Hope Center for Neurological Disorders, Washington University School of Medicine;

Abstract
The gold standard for sleep monitoring is attended in-lab polysomnography; however, this method may be cost-prohibitive and inconvenient for patients and research participants. Home sleep testing has gained momentum in the field of sleep medicine due to its convenience and lower cost, as well as being more naturalistic. The accuracy and quality of home sleep testing, however, may be variable because studies are not monitored by sleep technologists. There has been some success in improving the accuracy of home sleep studies by having trained sleep technicians assist participants inside their homes with putting on the devices, but this can be intrusive and time-consuming for those involved. In this protocol, participants undergo at-home sleep monitoring with multiple devices: 1) a single-channel EEG device; 2) a home sleep test for sleep-disordered breathing and periodic limb movements; 3) actigraphy; and 4) sleep logs. A major challenge of this study is obtaining high-quality sleep monitoring data on the first attempt in order to minimize participant burden. This protocol describes the implementation of educational manuals with step-by-step instructions and photos. The goal is to improve the quality of home sleep testing.

Document Type: Article
Publication Stage: Final
Source: Scopus

“Early administration of Fab antivenom resulted in faster limb recovery in copperhead snake envenomation patients” (2019) Clinical Toxicology

Early administration of Fab antivenom resulted in faster limb recovery in copperhead snake envenomation patients
(2019) Clinical Toxicology, 57 (1), pp. 25-30. Cited 1 time.

Anderson, V.E.a , Gerardo, C.J.b , Rapp-Olsson, M.a , Bush, S.P.c , Mullins, M.E.d , Greene, S.e , Toschlog, E.A.f , Quackenbush, E.g , Rose, S.R.h , Schwartz, R.B.i , Charlton, N.P.j , Lewis, B.k , Kleinschmidt, K.C.l , Sharma, K.l , Lavonas, E.J.m

a Rocky Mountain Poison and Drug Center, Denver Health and Hospital Authority, Denver, CO, United States
b Division of Emergency Medicine, Duke University School of Medicine, Durham, NC, United States
c Department of Emergency Medicine, Brody School of Medicine, Greenville, NC, United States
d Division of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, United States
e Department of Emergency Medicine, Baylor College of Medicine, Houston, TX, United States
f Department of Surgery, Brody School of Medicine, Greenville, NC, United States
g Department of Emergency Medicine, University of North Carolina, Chapel Hill, NC, United States
h Department of Emergency Medicine, Virginia Commonwealth University, Richmond, VA, United States
i Department of Emergency Medicine and Hospital Services, Medical College of Georgia, Augusta, GA, United States
j Department of Emergency Medicine, University of Virginia, Charlottesville, VA, United States
k Texas A&M Health Science Center, College Station, TX, United States
l Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States
m Department of Emergency Medicine and Rocky Mountain Poison and Drug Center, Denver Health and Hospital Authority, Denver, CO, United States

Abstract
Background: No previous research has studied whether early snake antivenom administration leads to better clinical outcomes than late antivenom administration in North American pit viper envenomation. Methods: A secondary analysis of data from a clinical trial of Fab antivenom (FabAV) versus placebo for copperhead snake envenomation was conducted. Patients treated before the median time to FabAV administration were classified as receiving early treatment and those treated after the median time were defined as the late treatment group. A Cox proportional hazards model was used to compare time to full recovery on the Patient-Specific Functional Scale (PSFS) instrument between groups. Secondary analyses compared estimated mean PSFS scores using a generalized linear model and the estimated proportion of patients with full recovery at each time point using logistic regression. To evaluate for confounding, the main analysis was repeated using data from placebo-treated subjects. Results: Forty-five subjects were treated with FabAV at a median of 5.47 h after envenomation. Patients in the early treatment group had a significantly shorter time to full recovery than those treated late (median time: 17 versus 28 days, p =.025). Model-estimated PSFS scores were numerically higher at each time point in the early group. No difference was found between patients treated early versus late with placebo. Conclusions: In this secondary analysis of trial data, recovery of limb function was faster when Fab antivenom was administered soon after envenomation, as opposed to late administration. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Author Keywords
Agkistrodon;  antivenins;  recovery of function;  snake bites

Document Type: Article
Publication Stage: Final
Source: Scopus

“Variants in TCF20 in neurodevelopmental disability: description of 27 new patients and review of literature” (2019) Genetics in Medicine

Variants in TCF20 in neurodevelopmental disability: description of 27 new patients and review of literature
(2019) Genetics in Medicine, . 

Torti, E.a , Keren, B.b , Palmer, E.E.c d , Zhu, Z.a , Afenjar, A.e f g , Anderson, I.J.h , Andrews, M.V.i , Atkinson, C.j , Au, M.k , Berry, S.A.l , Bowling, K.M.m , Boyle, J.c , Buratti, J.b , Cathey, S.S.n , Charles, P.b o p , Cogne, B.q r , Courtin, T.b , Escobar, L.F.s , Finley, S.L.t , Graham, J.M., Jr.k , Grange, D.K.i , Heron, D.b e o p , Hewson, S.j , Hiatt, S.M.m , Hibbs, K.A.u , Jayakar, P.v , Kalsner, L.w x , Larcher, L.b , Lesca, G.y z , Mark, P.R.aa , Miller, K.ab , Nava, C.b ac , Nizon, M.q r , Pai, G.S.ad , Pappas, J.ae , Parsons, G.aa , Payne, K.af , Putoux, A.y z , Rabin, R.ae , Sabatier, I.ag , Shinawi, M.i , Shur, N.ab , Skinner, S.A.n , Valence, S.ah , Warren, H.n , Whalen, S.ai , Crunk, A.a , Douglas, G.a , Monaghan, K.G.a , Person, R.E.a , Willaert, R.a , Solomon, B.D.a , Juusola, J.a

a GeneDx, Gaithersburg, MD, United States
b Département de génétique, Hôpital Pitié-Salpêtrière, Assistance Publique–Hôpitaux de Paris, Paris, France
c Genetics of Learning Disability Service, Hunter New England Health, Waratah, NSW, Australia
d Australia School of Women’s’ and Children’ Health, University of New South Wales, Sydney, NSW, Australia
e Département de génétique et embryologie médicale, Hôpital Trousseau, Assistance publique–Hôpitaux de Paris, Paris, France
f Centre de Référence malformations et maladies congénitales du cervelet, Paris, France
g Sorbonne Universités, GRC ConCer-LD, Hôpital Armand Trousseau, Paris, France
h Department of Medicine, Division of Genetics, the University of Tennessee Graduate School of Medicine, University Genetics, Knoxville, TN, United States
i Department of Pediatrics, Division of Genetics and Genomic Medicine, Washington University School of Medicine, St. Louis, MO, United States
j Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
k Cedars-Sinai Medical Center, Los Angeles, CA, United States
l Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
m HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States
n Greenwood Genetic Center, Greenwood, SC, United States
o Centre de Référence Déficiences Intellectuelles de Causes Rares, Paris, France
p Sorbonne Université, GRC “Déficience Intellectuelle et Autisme”, Paris, France
q CHU Nantes, Service de Génétique Médicale, Nantes, France
r l’Institut du Thorax, INSERM, CNRS, UNIV Nantes, Nantes, France
s St. Vincent Hospital and Health Services, Indianapolis, IN, United States
t University Genetics, University of Tennessee Medical Center, Knoxville, TN, United States
u University of Minnesota Masonic Children’s Hospital, Minneapolis, MN, United States
v Division of Genetics and Metabolism, Nicklaus Children’s Hospital, Miami, FL, United States
w Connecticut Children’s Medical Center, Farmington, CT, United States
x School of Medicine, University of Connecticut, Farmington, CT, United States
y Department of Medical Genetics, Lyon University Hospitals, Lyon, France
z Lyon Neuroscience Research Centre, CNRS UMR5292, INSERM U1028, Claude Bernard Lyon I University, Lyon, France
aa Spectrum Health Medical Genetics, Grand Rapids, MI, United States
ab Albany Medical Center, Albany, NY, United States
ac Sorbonne Universités, Institut du Cerveau et de la Moelle épinière, ICM, Inserm U1127, CNRS UMR 7225, Paris, France
ad Department of Pediatrics, Medical University of South Carolina, Charleston, SC, United States
ae Department of Pediatrics, New York University School of Medicine, New York, NY, United States
af Riley Hospital for Children, Indianapolis, IN, United States
ag Department of Pediatric Neurology, Women Mother and Children Hospital, Lyon University Hospitals, Lyon, France
ah Service de neuropédiatrie, Hôpital Trousseau, Assistance publique–Hôpitaux de Paris, Paris, France
ai Unité Fonctionnelle de génétique clinique, Hôpital Armand Trousseau, Assistance publique–Hôpitaux de Paris, Centre de Référence des anomalies du développement et syndromes malformatifs, Paris, France

Abstract
Purpose: To define the clinical characteristics of patients with variants in TCF20, we describe 27 patients, 26 of whom were identified via exome sequencing. We compare detailed clinical data with 17 previously reported patients. Methods: Patients were ascertained through molecular testing laboratories performing exome sequencing (and other testing) with orthogonal confirmation; collaborating referring clinicians provided detailed clinical information. Results: The cohort of 27 patients all had novel variants, and ranged in age from 2 to 68 years. All had developmental delay/intellectual disability. Autism spectrum disorders/autistic features were reported in 69%, attention disorders or hyperactivity in 67%, craniofacial features (no recognizable facial gestalt) in 67%, structural brain anomalies in 24%, and seizures in 12%. Additional features affecting various organ systems were described in 93%. In a majority of patients, we did not observe previously reported findings of postnatal overgrowth or craniosynostosis, in comparison with earlier reports. Conclusion: We provide valuable data regarding the prognosis and clinical manifestations of patients with variants in TCF20. © 2019, American College of Medical Genetics and Genomics.

Author Keywords
autism;  developmental delay;  exome;  intellectual disability;  TCF20

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

“Psychosis risk is associated with decreased resting-state functional connectivity between the striatum and the default mode network” (2019) Cognitive, Affective and Behavioral Neuroscience

Psychosis risk is associated with decreased resting-state functional connectivity between the striatum and the default mode network
(2019) Cognitive, Affective and Behavioral Neuroscience, . 

Hua, J.P.Y.a , Karcher, N.R.a b , Merrill, A.M.a , O’Brien, K.J.a b , Straub, K.T.a , Trull, T.J.a , Kerns, J.G.a

a Department of Psychological Sciences, University of Missouri, Columbia, MO, United States
b Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Psychosis is linked to aberrant salience or to viewing neutral stimuli as self-relevant, suggesting a possible impairment in self-relevance processing. Psychosis is also associated with increased dopamine in the dorsal striatum, especially the anterior caudate (Kegeles et al., 2010). Critically, the anterior caudate is especially connected to (a) the cortical default mode network (DMN), centrally involved in self-relevance processing, and (b) to a lesser extent, the cortical frontoparietal network (FPN; Choi, Yeo, & Buckner, 2012). However, no previous study has directly examined striatal–cortical DMN connectivity in psychosis risk. In Study 1, we examined resting-state functional connectivity in psychosis risk (n = 18) and control (n = 19) groups between (a) striatal DMN and FPN subregions and (b) cortical DMN and FPN. The psychosis risk group exhibited decreased connectivity between the striatal subregions and the cortical DMN. In contrast, the psychosis risk group exhibited intact connectivity between the striatal subregions and the cortical FPN. Additionally, recent distress was also associated with decreased striatal–cortical DMN connectivity. In Study 2, to determine whether the decreased striatal–cortical DMN connectivity was specific to psychosis risk or was related to recent distress more generally, we examined the relationship between connectivity and distress in individuals diagnosed with nonpsychotic emotional distress disorders (N = 25). In contrast to Study 1, here we found that distress was associated with evidence of increased striatal–cortical DMN connectivity. Overall, the present results suggest that decreased striatal–cortical DMN connectivity is associated with psychosis risk and could contribute to aberrant salience. © 2019, The Psychonomic Society, Inc.

Author Keywords
Attenuated psychotic symptoms;  Corticostriatal loops;  Dorsal caudate;  Positive schizotypy;  Temporal lobe

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

“Microglia in Alzheimer’s disease: A target for immunotherapy” (2019) Journal of Leukocyte Biology

Microglia in Alzheimer’s disease: A target for immunotherapy
(2019) Journal of Leukocyte Biology, . 

Wang, S., Colonna, M.

Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States

Abstract
Microglia are resident Mϕs of the CNS that play pleiotropic functions in brain development and homeostasis. Impaired microglial functions are thought to be involved in the onset and progression of various neurodevelopmental and neurodegenerative diseases. Thus, understanding microglia in these settings may indicate new approaches for therapeutic intervention. Here, we review recent evidence implicating microglia in Alzheimer’s disease and discuss potential therapeutic strategies targeting microglia and their receptors in this disease. ©2019 Society for Leukocyte Biology

Author Keywords
Alzheimer’s disease;  immunotherapy;  microglia;  neuroinflammation;  phagocytosis;  TREM2

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

“The sleep-wake cycle regulates brain interstitial fluid tau in mice and csf tau in humans” (2019) Science

The sleep-wake cycle regulates brain interstitial fluid tau in mice and csf tau in humans
(2019) Science, . 

Holth, J.K.a , Fritschi, S.K.a , Wang, C.a , Pedersen, N.P.b , Cirrito, J.R.a , Mahan, T.E.a , Finn, M.B.a , Manis, M.a , Geerling, J.C.c , Fuller, P.M.d , Lucey, B.P.a , Holtzman, D.M.a

a Department of Neurology, Hope Center for Neurological Disorders, and Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, United States
b Department of Neurology, Emory Epilepsy Center and Program in Neuroscience, Emory University, Atlanta, GA 30322, United States
c Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, United States
d Department of Neurology, Beth Israel Deaconess Medical Center, Division of Sleep Medicine, Harvard Medical School, Boston, MA 02215, United States

Abstract
The sleep-wake cycle regulates interstitial fluid (ISF) and cerebrospinal (CSF) levels of amyloid-β (Aβ) that accumulates in Alzheimer disease (AD). Furthermore, chronic sleep deprivation (SD) increases Aβ plaques. However, tau, not Aβ, accumulation appears to drive AD neurodegeneration. Here, we tested whether ISF/CSF tau and tau seeding/spreading was influenced by the sleep-wake cycle and SD. Mouse ISF tau was increased ~90% during normal wakefulness vs. sleep and ~100% during SD. Human CSF tau also increased over 50% during SD. In a tau seeding and spreading model, chronic SD increased tau pathology spreading. Chemogenetically-driven wakefulness in mice also significantly increased both ISF Aβ and tau. Thus, the sleep-wake cycle regulates ISF tau and sleep deprivation increases ISF and CSF tau as well as tau pathology spreading. © 2019, American Association for the Advancement of Science. All Rights Reserved.

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

“Results of disseminating an online screen for eating disorders across the U.S.: Reach, respondent characteristics, and unmet treatment need” (2019) International Journal of Eating Disorders

Results of disseminating an online screen for eating disorders across the U.S.: Reach, respondent characteristics, and unmet treatment need
(2019) International Journal of Eating Disorders, . 

Fitzsimmons-Craft, E.E.a , Balantekin, K.N.b , Graham, A.K.c , Smolar, L.d , Park, D.d , Mysko, C.d , Funk, B.e , Taylor, C.B.f g , Wilfley, D.E.a

a Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
b Department of Exercise and Nutrition Sciences, University at Buffalo, Buffalo, NY, United States
c Department of Medical Social Sciences, Northwestern University, Chicago, IL, United States
d National Eating Disorders Association, New York, NY, United States
e Institute of Information Systems, Leuphana University, Lüneburg, Germany
f Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
g Center for m2Health, Palo Alto University, Palo Alto, CA, United States

Abstract
Objective: The treatment gap between those who need and those who receive care for eating disorders is wide. Scaling a validated, online screener that makes individuals aware of the significance of their symptoms/behaviors is a crucial first step for increasing access to care. The objective of the current study was to determine the reach of disseminating an online eating disorder screener in partnership with the National Eating Disorders Association (NEDA), as well to examine the probable eating disorder diagnostic and risk breakdown of adult respondents. We also assessed receipt of any treatment. Method: Participants completed a validated eating disorder screen on the NEDA website over 6 months in 2017. Results: Of 71,362 respondents, 91.0% were female, 57.7% 18–24 years, 89.6% non-Hispanic, and 84.7% White. Most (86.3%) screened positive for an eating disorder. In addition, 10.2% screened as high risk for the development of an eating disorder, and only 3.4% as not at risk. Of those screening positive for an eating disorder, 85.9% had never received treatment and only 3.0% were currently in treatment. Discussion: The NEDA online screen may represent an important eating disorder detection tool, as it was completed by >71,000 adult respondents over just 6 months, the majority of whom screened positive for a clinical/subclinical eating disorder. The extremely high percentage of individuals screening positive for an eating disorder who reported not being in treatment suggests a wide treatment gap and the need to offer accessible, affordable, evidence-based intervention options, directly linked with screening. © 2019 Wiley Periodicals, Inc.

Author Keywords
eating disorders;  referral;  screening

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

“Pride and Prejudice in the Treatment of Depression and Anxiety in Acutely Ill Older Adults” (2019) American Journal of Geriatric Psychiatry

Pride and Prejudice in the Treatment of Depression and Anxiety in Acutely Ill Older Adults
(2019) American Journal of Geriatric Psychiatry, . 

Lenze, E.J., Avidan, M.S.

Department of Psychiatry, Washington University, St. Louis, United States

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

“Neonatal Skin-to-Skin Contact: Implications for Learning and Autonomic Nervous System Function in Infants With Congenital Heart Disease” (2019) Biological Research for Nursing

Neonatal Skin-to-Skin Contact: Implications for Learning and Autonomic Nervous System Function in Infants With Congenital Heart Disease
(2019) Biological Research for Nursing, . 

Harrison, T.M.a , Chen, C.-Y.b , Stein, P.c , Brown, R.d , Heathcock, J.C.e

a The Ohio State University College of Nursing, Columbus, OH, United States
b Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
c Washington University in St. Louis, St. Louis, MO, United States
d Medical Research Consulting, Middleton, WI, United States
e The Ohio State University School of Health and Rehabilitation Sciences, Columbus, OH, United States

Abstract
Background: Infants with complex congenital heart disease (CCHD) often develop neurodevelopmental disabilities. Cognitive abilities are associated with vagally mediated autonomic function. Skin-to-skin contact (SSC) interventions enhance infant neurodevelopment and autonomic function in other high-risk populations. Aim: To examine the effects of a neonatal SSC intervention on learning and autonomic function in 3-month-old infants: infants with CCHD who received neonatal SSC (n = 10), typically developing (TD) infants (n = 16), and infants with CCHD without SSC (n = 10). Methods: This secondary data analysis measured cognitive function using the mobile paradigm (MP), a classic measure of learning based on operant conditioning. Autonomic function was assessed with heart rate (HR) and HR variability (HRV). Data were analyzed with repeated-measures general linear mixed modeling with α =.10 for this exploratory study. Results: Learning rates were TD = 75%, cardiac-SSC = 70%, and cardiac-control = 40%. Learners demonstrated significant reductions in HRV during the MP; nonlearners exhibited no change. TD and cardiac-SSC groups exhibited increases in HR and reductions in HRV during the MP. No significant changes occurred in the cardiac-control group. Nonlinear HRV during the MP differed only in the TD group. Conclusions: Findings suggest improvements in cognitive and autonomic development in 3-month-old infants with CCHD who received neonatal SSC. Learning and autonomic function results in infants with CCHD who had not received SSC suggest reduced capacity to muster the physiologic resources to carry out this cognitive task. Findings provide preliminary evidence in support of implementation of SSC with infants with CCHD and support additional research. © The Author(s) 2019.

Author Keywords
autonomic nervous system;  congenital heart disease;  heart rate variability;  neurodevelopment;  skin-to-skin contact

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

“Challenges to curing primary brain tumours” (2019) Nature Reviews Clinical Oncology

Challenges to curing primary brain tumours
(2019) Nature Reviews Clinical Oncology, . 

Aldape, K.a , Brindle, K.M.b , Chesler, L.c , Chopra, R.c , Gajjar, A.d , Gilbert, M.R.e , Gottardo, N.f , Gutmann, D.H.g , Hargrave, D.h , Holland, E.C.i , Jones, D.T.W.j , Joyce, J.A.k , Kearns, P.l , Kieran, M.W.m , Mellinghoff, I.K.n , Merchant, M.o , Pfister, S.M.p , Pollard, S.M.q , Ramaswamy, V.r , Rich, J.N.s , Robinson, G.W.d , Rowitch, D.H.t , Sampson, J.H.u , Taylor, M.D.v , Workman, P.c , Gilbertson, R.J.b w

a Department of Pathology, University Health Network, Toronto, ON, Canada
b CRUK Cambridge Institute, Li Ka Shing Centre, Cambridge, United Kingdom
c The Institute of Cancer Research, London, United Kingdom
d Department of Oncology, St Jude Children’s Research Hospital, Memphis, TN, United States
e Center for Cancer Research, National Cancer Institute, Bethesda, MD, United States
f Telethon Kids Institute, Subiaco, WA, Australia
g Department of Neurology, Washington University School of Medicine, St Louis, MO, United States
h Great Ormond Street Hospital for Children, London, United Kingdom
i Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
j Pediatric Glioma Research Group, Hopp Children’s Cancer Center at the NCT Heidelberg, Heidelberg, Germany
k Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
l Cancer Research UK Clinical Trials Unit, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
m Dana–Farber/Boston Children’s Cancer and Blood Disorders Center and Harvard Medical School, Boston, MA, United States
n Human Oncology and Pathogenesis Program and Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
o Oncology, AstraZeneca IMED Biotech Unit, Boston, MA, United States
p Division of Pediatric Oncology, Hopp Children’s Cancer Center at the NCT Heidelberg, Heidelberg, Germany
q Cancer Research UK Edinburgh Centre and Medical Research Council Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom
r Department of Paediatrics, The Hospital for Sick Children, Toronto, ON, Canada
s Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, CA, United States
t Department of Paediatrics, University of Cambridge and Wellcome Trust-MRC Stem Cell Institute, Cambridge, United Kingdom
u The Preston Robert Tisch Brain Tumor Center, Duke Cancer Center, Durham, NC, United States
v The Arthur and Sonia Labatt Brain Tumour Research Centre and Division of Neurosurgery, The Hospital for Sick Children, Toronto, ON, Canada
w CRUK Cambridge Institute and Department of Oncology, University of Cambridge, Hutchison/MRC Research Centre, Cambridge Biomedical Campus, Cambridge, United Kingdom

Abstract
Despite decades of research, brain tumours remain among the deadliest of all forms of cancer. The ability of these tumours to resist almost all conventional and novel treatments relates, in part, to the unique cell-intrinsic and microenvironmental properties of neural tissues. In an attempt to encourage progress in our understanding and ability to successfully treat patients with brain tumours, Cancer Research UK convened an international panel of clinicians and laboratory-based scientists to identify challenges that must be overcome if we are to cure all patients with a brain tumour. The seven key challenges summarized in this Position Paper are intended to serve as foci for future research and investment. © 2019, Springer Nature Limited.

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

“Genome-wide analyses as part of the international FTLD-TDP whole-genome sequencing consortium reveals novel disease risk factors and increases support for immune dysfunction in FTLD” (2019) Acta Neuropathologica

Genome-wide analyses as part of the international FTLD-TDP whole-genome sequencing consortium reveals novel disease risk factors and increases support for immune dysfunction in FTLD
(2019) Acta Neuropathologica, . 

Pottier, C.a , Ren, Y.b , Perkerson, R.B., IIIa , Baker, M.a , Jenkins, G.D.c , van Blitterswijk, M.a , DeJesus-Hernandez, M.a , van Rooij, J.G.J.d , Murray, M.E.a , Christopher, E.a , McDonnell, S.K.c , Fogarty, Z.c , Batzler, A.c , Tian, S.c , Vicente, C.T.a , Matchett, B.a , Karydas, A.M.e , Hsiung, G.-Y.R.f , Seelaar, H.d , Mol, M.O.d , Finger, E.C.g , Graff, C.h i , Öijerstedt, L.h i , Neumann, M.j k , Heutink, P.j l , Synofzik, M.j l , Wilke, C.j l , Prudlo, J.j m , Rizzu, P.j , Simon-Sanchez, J.j l , Edbauer, D.n o , Roeber, S.p , Diehl-Schmid, J.q , Evers, B.M.r , King, A.s t , Mesulam, M.M.u , Weintraub, S.u v , Geula, C.u , Bieniek, K.F.a w , Petrucelli, L.a , Ahern, G.L.x , Reiman, E.M.y , Woodruff, B.K.z , Caselli, R.J.z , Huey, E.D.aa , Farlow, M.R.ab , Grafman, J.ac , Mead, S.ad , Grinberg, L.T.e ae , Spina, S.e , Grossman, M.af , Irwin, D.J.af , Lee, E.B.ag , Suh, E.R.ag , Snowden, J.ah , Mann, D.ai , Ertekin-Taner, N.a aj , Uitti, R.J.aj , Wszolek, Z.K.aj , Josephs, K.A.ak , Parisi, J.E.ak , Knopman, D.S.ak , Petersen, R.C.ak , Hodges, J.R.al , Piguet, O.am , Geier, E.G.e , Yokoyama, J.S.e , Rissman, R.A.an ao , Rogaeva, E.ap , Keith, J.aq ar , Zinman, L.aq , Tartaglia, M.C.ap as , Cairns, N.J.at , Cruchaga, C.au , Ghetti, B.av , Kofler, J.aw , Lopez, O.L.x ax , Beach, T.G.ay , Arzberger, T.n p az , Herms, J.n p , Honig, L.S.ba , Vonsattel, J.P.bb , Halliday, G.M.al bc , Kwok, J.B.al bc , White, C.L., IIIr , Gearing, M.bd , Glass, J.bd , Rollinson, S.be , Pickering-Brown, S.be , Rohrer, J.D.bf , Trojanowski, J.Q.ag , Van Deerlin, V.ag , Bigio, E.H.u , Troakes, C.s , Al-Sarraj, S.s t , Asmann, Y.b , Miller, B.L.e , Graff-Radford, N.R.aj , Boeve, B.F.ak , Seeley, W.W.e ae , Mackenzie, I.R.A.bg , van Swieten, J.C.d , Dickson, D.W.a , Biernacka, J.M.c , Rademakers, R.a

a Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, United States
b Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, United States
c Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
d Department of Neurology, Erasmus Medical Center, Wytemaweg 80, Rotterdam, 3015 CN, Netherlands
e Department of Neurology, Memory and Aging Center, University of California, San Francisco, CA, United States
f Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC V6T 2B5, Canada
g Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON N6A 2E2, Canada
h Division of Neurogeriatrics, Department NVS, Karolinska Institutet, Visionsgatan 4, J10:20, Solna, 171 64, Sweden
i Theme Aging, Unit for Hereditary Dementias, Karolinska University Hospital, Solna, Sweden
j German Center for Neurodegenerative Diseases (DZNE), Rostock, 18147, Germany
k Department of Neuropathology, University of Tübingen, Tübingen, 72076, Germany
l Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, 72076, Germany
m Department of Neurology, Rostock University Medical Center, Rostock, 18147, Germany
n German Center for Neurodegenerative Diseases (DZNE), Feodor-Lynen-Str 17, Munich, 81377, Germany
o Munich Cluster of Systems Neurology (SyNergy), Feodor-Lynen-Str 17, Munich, 81377, Germany
p Center for Neuropathology and Prion Research, Ludwig-Maximilians-University of Munich, Feodor-Lynen-Straße 23, Munich, 81377, Germany
q Department of Psychiatry and Psychotherapy, Technische Universität München, Munich, Germany
r Division of Neuropathology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9073, United States
s London Neurodegenerative Diseases Brain Bank, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
t Department of Clinical Neuropathology, King’s College Hospital NHS Foundation Trust, London, SE5 9RS, United Kingdom
u Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Northwestern University, Chicago, IL 60611, United States
v Department of Psychiatry and Behavioral Sciences and Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
w Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio, TX 78229, United States
x Department of Neurology, University of Arizona Health Sciences Center, 1501 North Campbell Avenue, Tucson, AZ 85724-5023, United States
y Banner Alzheimer’s Institute, Phoenix, AZ 85006, United States
z Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ 85259, United States
aa Departments of Psychiatry and Neurology, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, 630 West 168th St P&S Box 16, New York, NY 10032, United States
ab Indiana University School of Medicine, 355 West 16th Street, GH 4700 Neurology, Indianapolis, IN 46202, United States
ac Department of Physical Medicine and Rehabilitation, Neurology, Cognitive Neurology and Alzheimer’s Center, Department of Psychiatry, Feinberg School of Medicine, Northwestern University, 355 E Erie Street, Chicago, IL 60611-5146, United States
ad MRC Prion Unit at University College London, Institute of Prion Diseases, London, United Kingdom
ae Department of Pathology, Memory and Aging Center, University of California, San Francisco, CA, United States
af Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, United States
ag Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, United States
ah Cerebral Function Unit, Greater Manchester Neurosciences Centre, Salford Royal Hospital, Salford, United Kingdom
ai Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Salford Royal Hospital, Salford, United Kingdom
aj Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
ak Department of Neurology, Mayo Clinic, Rochester, MN, United States
al Central Clinical School and Brain and Mind Centre, The University of Sydney, Sydney, 2050, Australia
am School of Psychology and Brain and Mind Centre, The University of Sydney, Sydney, 2050, Australia
an Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, United States
ao Veterans Affairs San Diego Healthcare System, San Diego, CA 92161, United States
ap Krembil Discovery Tower, Tanz Centre for Research in Neurodegenerative Disease, University of Toronto, 60 Leonard Av, 4th Floor – 4KD481, Toronto, ON M5T 0S8, Canada
aq Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada
ar Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A1, Canada
as Krembil Neuroscience Center, Movement Disorder’s Clinic, Toronto Western Hospital, 399 Bathurst Street, Toronto, ON M5T 2S8, Canada
at Department of Neurology, Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63108, United States
au Department of Psychiatry, Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63108, United States
av Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, 635 Barnhill Drive, MS A138, Indianapolis, IN 46202, United States
aw Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15213, United States
ax Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, United States
ay Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ 85351, United States
az Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University of Munich, Nussbaumstraße 7, Munich, 80336, Germany
ba Department of Neurology, Taub Institute, and GH Sergievsky Center, Columbia University Irving Medical Center, 630 West 168th St (P&S Unit 16), New York, NY 10032, United States
bb Department of Pathology and Taub Institute, Columbia University Irving Medical Center, 630 West 168th St, New York, NY 10032, United States
bc UNSW Medicine and NeuRA, Randwick, 2031, Australia
bd Department of Pathology and Laboratory Medicine and Department of Neurology, Emory University, Atlanta, GA 30322, United States
be Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
bf Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, United Kingdom
bg Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada

Abstract
Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) represents the most common pathological subtype of FTLD. We established the international FTLD-TDP whole-genome sequencing consortium to thoroughly characterize the known genetic causes of FTLD-TDP and identify novel genetic risk factors. Through the study of 1131 unrelated Caucasian patients, we estimated that C9orf72 repeat expansions and GRN loss-of-function mutations account for 25.5% and 13.9% of FTLD-TDP patients, respectively. Mutations in TBK1 (1.5%) and other known FTLD genes (1.4%) were rare, and the disease in 57.7% of FTLD-TDP patients was unexplained by the known FTLD genes. To unravel the contribution of common genetic factors to the FTLD-TDP etiology in these patients, we conducted a two-stage association study comprising the analysis of whole-genome sequencing data from 517 FTLD-TDP patients and 838 controls, followed by targeted genotyping of the most associated genomic loci in 119 additional FTLD-TDP patients and 1653 controls. We identified three genome-wide significant FTLD-TDP risk loci: one new locus at chromosome 7q36 within the DPP6 gene led by rs118113626 (p value = 4.82e − 08, OR = 2.12), and two known loci: UNC13A, led by rs1297319 (p value = 1.27e − 08, OR = 1.50) and HLA-DQA2 led by rs17219281 (p value = 3.22e − 08, OR = 1.98). While HLA represents a locus previously implicated in clinical FTLD and related neurodegenerative disorders, the association signal in our study is independent from previously reported associations. Through inspection of our whole-genome sequence data for genes with an excess of rare loss-of-function variants in FTLD-TDP patients (n ≥ 3) as compared to controls (n = 0), we further discovered a possible role for genes functioning within the TBK1-related immune pathway (e.g., DHX58, TRIM21, IRF7) in the genetic etiology of FTLD-TDP. Together, our study based on the largest cohort of unrelated FTLD-TDP patients assembled to date provides a comprehensive view of the genetic landscape of FTLD-TDP, nominates novel FTLD-TDP risk loci, and strongly implicates the immune pathway in FTLD-TDP pathogenesis. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Author Keywords
DPP6;  HLA;  Immunity;  TBK1;  UNC13A;  Whole-genome sequencing FTLD-TDP

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

“Dried blood spot compared to plasma measurements of blood-based biomarkers of brain injury in neonatal encephalopathy” (2019) Pediatric Research

Dried blood spot compared to plasma measurements of blood-based biomarkers of brain injury in neonatal encephalopathy
(2019) Pediatric Research, . 

Massaro, A.N.a , Wu, Y.W.b , Bammler, T.K.c , MacDonald, J.W.c , Mathur, A.d , Chang, T.e , Mayock, D.f , Mulkey, S.B.e , van Meurs, K.g , Afsharinejad, Z.c , Juul, S.E.f

a Pediatrics – Division of Neonatology, Children’s National Health Systems and The George Washington University School of Medicine, Washington, DC, United States
b Neurology and Pediatrics, UCSF, San Francisco, CA, United States
c Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, United States
d Pediatrics, Washington University School of Medicine, St. Louis, MO, United States
e Neurology and Pediatrics, Children’s National Health Systems and The George Washington University School of Medicine, Washington, DC, United States
f Pediatrics—Division of Neonatology, University of Washington, Seattle, WA, United States
g Pediatrics, Stanford, Palo Alto, CA, United States

Abstract
Background: Data correlating dried blood spots (DBS) and plasma concentrations for neonatal biomarkers of brain injury are lacking. We hypothesized that candidate biomarker levels determined from DBS can serve as a reliable surrogate for plasma levels. Methods: In the context of a phase II multi-center trial evaluating erythropoietin for neuroprotection in neonatal encephalopathy (NE), DBS were collected at enrollment (< 24 h), day 2, 4, and 5. Plasma was collected with the first and last DBS. The relationship between paired DBS-plasma determinations of brain-specific proteins and cytokines was assessed by correlation and Bland–Altman analyses. For analytes with consistent DBS-plasma associations, DBS-derived biomarker levels were related to brain injury by MRI and 1-year outcomes. Results: We enrolled 50 newborns with NE. While S100B protein, tumor necrosis factor α, interleukin (IL)1 β, IL-6, IL-8 demonstrated significant DBS-plasma correlations, Bland–Altman plots demonstrated that the methods are not interchangeable, with a 2 to 4-fold error between measurements. No significant relationships were found between DBS levels of TNFα, IL-6, and IL-8 and outcomes. Conclusion: Further work is needed to optimize elution and assay methods before using DBS specimens as a reliable surrogate for plasma levels of candidate brain injury biomarkers in NE. © 2019, International Pediatric Research Foundation, Inc.

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

“Sleep disturbances are common in patients with autoimmune encephalitis” (2019) Journal of Neurology

Sleep disturbances are common in patients with autoimmune encephalitis
(2019) Journal of Neurology, . 

Blattner, M.S.a , de Bruin, G.S.a , Bucelli, R.C.a , Day, G.S.a b

a Department of Neurology, Washington University School of Medicine, Saint Louis, MO, United States
b The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, 4488 Forest Park Avenue, Suite 160, Saint Louis, MO 63108, United States

Abstract
Objectives: Autoimmune encephalitis (AE) is increasingly recognized as an important cause of subacute cognitive decline, seizures, and encephalopathy, with an ever-broadening clinical phenotype. Sleep disturbances are reported in AE patients, including rapid eye movement sleep behavior disorder, hypersomnia, fragmented sleep, and sleep-disordered breathing; however, the prevalence of sleep disturbances and contributions to outcomes in AE patients remain unknown. There is a need to determine the prevalence of sleep disturbances in AE patients, and to clarify the relationship between specific autoantibodies and disruptions in sleep. Methods: Clinical history, results of serum and cerebrospinal fluid testing, electroencephalography, and neuroimaging were reviewed from 26 AE patients diagnosed and managed at our tertiary care hospital. Polysomnography was performed in patients with clinical indications, yielding data from 12 patients. Results: The median age of AE patients was 53 years (range 18–83). Autoantibodies against intracellular antigens (including Ma and Hu autoantibodies) were identified in 6/26 (23%) patients, while autoantibodies against cell-surface neuronal antigens (including NMDAR and LGI1) were identified in 20/26 (77%) patients. New sleep complaints were reported by 19/26 (73%) AE patients, including gasping or snoring (9/19, 47%), dream enactment behavior (6/19, 32%), insomnia (5/19, 29%), hypersomnia (4/19, 21%), other parasomnias (4/19, 21%), and dream-wake confusional states (2/19, 11%). Dream enactment behaviors were particularly common in AE associated with LGI1 autoantibodies, reported in 4/7 (57%) patients. Polysomnography showed reduced total sleep time, stage 3 and rapid eye movement sleep, and prominent sleep fragmentation. Conclusion: Sleep disturbances are common in AE, warranting active surveillance in affected patients. Improved identification and treatment of sleep disorders may reduce morbidity associated with AE and improve long-term outcomes. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Author Keywords
Autoimmune encephalitis;  LGI1 autoantibodies;  NMDAR encephalitis;  Polysomnography;  REM behavior disorder;  Restless legs;  Sleep apnea

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