WashU weekly Neuroscience publications | Office of Neuroscience Research

“Long range synchronization within the enteric nervous system underlies propulsion along the large intestine in mice” (2021) Communications Biology

Long range synchronization within the enteric nervous system underlies propulsion along the large intestine in mice
(2021) Communications Biology, 4 (1), art. no. 955, .

Spencer, N.J.^a , Travis, L.^a , Wiklendt, L.^b , Costa, M.^a , Hibberd, T.J.^a , Brookes, S.J.^a , Dinning, P.^b , Hu, H.^c , Wattchow, D.A.^d , Sorensen, J.^a

^a Visceral Neurophysiology Laboratory, College of Medicine and Public Health, Centre for Neuroscience, Flinders University, Bedford Park, SA, Australia
^b Discipline of Gastroenterology, College of Medicine and Public Health, Flinders Medical Centre, Bedford Park, SA, Australia
^c Department of Anesthesiology, The Center for the Study of Itch, Washington University, St Louis, MO, United States
^d Discipline of Surgery, College of Medicine and Public Health, Flinders Medical Centre, Bedford Park, SA, Australia

Abstract
How the Enteric Nervous System (ENS) coordinates propulsion of content along the gastrointestinal (GI)-tract has been a major unresolved issue. We reveal a mechanism that explains how ENS activity underlies propulsion of content along the colon. We used a recently developed high-resolution video imaging approach with concurrent electrophysiological recordings from smooth muscle, during fluid propulsion. Recordings showed pulsatile firing of excitatory and inhibitory neuromuscular inputs not only in proximal colon, but also distal colon, long before the propagating contraction invades the distal region. During propulsion, wavelet analysis revealed increased coherence at ~2 Hz over large distances between the proximal and distal regions. Therefore, during propulsion, synchronous firing of descending inhibitory nerve pathways over long ranges aborally acts to suppress smooth muscle from contracting, counteracting the excitatory nerve pathways over this same region of colon. This delays muscle contraction downstream, ahead of the advancing contraction. The mechanism identified is more complex than expected and vastly different from fluid propulsion along other hollow smooth muscle organs; like lymphatic vessels, portal vein, or ureters, that evolved without intrinsic neurons. © 2021, The Author(s).

Funding details
Australian Research CouncilARC190103628
National Health and Medical Research CouncilNHMRC1156416

Document Type: Article
Publication Stage: Final
Source: Scopus

“Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization” (2021) Scientific Reports

Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization
(2021) Scientific Reports, 11 (1), art. no. 15265, .

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

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

Abstract
Alzheimer’s disease (AD) is a complex and heterogeneous disease that can be affected by various genetic factors. Although the cause of AD is not yet known and there is no treatment to cure this disease, its progression can be delayed. AD has recently been recognized as a brain-specific type of diabetes called type 3 diabetes. Several studies have shown that people with type 2 diabetes (T2D) have a higher risk of developing AD. Therefore, it is important to identify subgroups of patients with AD that may be more likely to be associated with T2D. We here describe a new approach to identify the correlation between AD and T2D at the genetic level. Subgroups of AD and T2D were each generated using a non-negative matrix factorization (NMF) approach, which generated clusters containing subsets of genes and samples. In the gene cluster that was generated by conventional gene clustering method from NMF, we selected genes with significant differences in the corresponding sample cluster by Kruskal–Wallis and Dunn-test. Subsequently, we extracted differentially expressed gene (DEG) subgroups, and candidate genes with the same regulation direction can be extracted at the intersection of two disease DEG subgroups. Finally, we identified 241 candidate genes that represent common features related to both AD and T2D, and based on pathway analysis we propose that these genes play a role in the common pathological features of AD and T2D. Moreover, in the prediction of AD using logistic regression analysis with an independent AD dataset, the candidate genes obtained better prediction performance than DEGs. In conclusion, our study revealed a subgroup of patients with AD that are associated with T2D and candidate genes associated between AD and T2D, which can help in providing personalized and suitable treatments. © 2021, The Author(s).

Funding details
U24 AG21886
National Institutes of HealthNIHU01 AG024904
U.S. Department of DefenseDODW81XWH-12-2-0012
National Institute on AgingNIA
National Institute of Biomedical Imaging and BioengineeringNIBIB
Alzheimer’s AssociationAA
Alzheimer’s Drug Discovery FoundationADDF
Genentech
Johnson and JohnsonJ&J
Merck
Janssen Research and DevelopmentJRD
GE Healthcare
F. Hoffmann-La Roche
Medpace
Alzheimer’s Disease Neuroimaging InitiativeADNI
Takeda Pharmaceutical CompanyTPC
Ministry of Health and WelfareMOHWHI18C0460
Korea Health Industry Development InstituteKHIDI
Ministry of Science and ICT, South KoreaMSITNRF-2018M3C7A1054935
IXICO

Document Type: Article
Publication Stage: Final
Source: Scopus

“Face mask type affects audiovisual speech intelligibility and subjective listening effort in young and older adults” (2021) Cognitive Research: Principles and Implications

Face mask type affects audiovisual speech intelligibility and subjective listening effort in young and older adults
(2021) Cognitive Research: Principles and Implications, 6 (1), art. no. 49, .

Brown, V.A.^a , Van Engen, K.J.^a , Peelle, J.E.^b

^a Department of Psychological & Brain Sciences, Washington University in Saint Louis, St. Louis, United States
^b Department of Otolaryngology, Washington University in Saint Louis, St. Louis, United States

Abstract
Identifying speech requires that listeners make rapid use of fine-grained acoustic cues—a process that is facilitated by being able to see the talker’s face. Face masks present a challenge to this process because they can both alter acoustic information and conceal the talker’s mouth. Here, we investigated the degree to which different types of face masks and noise levels affect speech intelligibility and subjective listening effort for young (N = 180) and older (N = 180) adult listeners. We found that in quiet, mask type had little influence on speech intelligibility relative to speech produced without a mask for both young and older adults. However, with the addition of moderate (− 5 dB SNR) and high (− 9 dB SNR) levels of background noise, intelligibility dropped substantially for all types of face masks in both age groups. Across noise levels, transparent face masks and cloth face masks with filters impaired performance the most, and surgical face masks had the smallest influence on intelligibility. Participants also rated speech produced with a face mask as more effortful than unmasked speech, particularly in background noise. Although young and older adults were similarly affected by face masks and noise in terms of intelligibility and subjective listening effort, older adults showed poorer intelligibility overall and rated the speech as more effortful to process relative to young adults. This research will help individuals make more informed decisions about which types of masks to wear in various communicative settings. © 2021, The Author(s).

Author Keywords
Aging; Face masks; Speech intelligibility; Subjective listening effort

Funding details
National Science FoundationNSFDGE-1745038
National Institutes of HealthNIHR01 DC014281

Document Type: Article
Publication Stage: Final
Source: Scopus

“Genetic association study of childhood aggression across raters, instruments, and age” (2021) Translational Psychiatry

Genetic association study of childhood aggression across raters, instruments, and age
(2021) Translational Psychiatry, 11 (1), art. no. 413, .

Ip, H.F.^a , van der Laan, C.M.^a ^b , Krapohl, E.M.L.^c , Brikell, I.^d , Sánchez-Mora, C.^e ^f ^g , Nolte, I.M.^h , St Pourcain, B.ⁱ ^j ^k , Bolhuis, K.^l , Palviainen, T.^m , Zafarmand, H.ⁿ ^o , Colodro-Conde, L.^p , Gordon, S.^p , Zayats, T.^q ^r ^s , Aliev, F.^t ^u , Jiang, C.^v ^w , Wang, C.A.^x , Saunders, G.^y , Karhunen, V.^z , Hammerschlag, A.R.^a ^aa ^ab , Adkins, D.E.^ac ^ad , Border, R.^ae ^af ^ag , Peterson, R.E.^ah , Prinz, J.A.^ai , Thiering, E.^aj ^ak , Seppälä, I.^al , Vilor-Tejedor, N.^am ^an ^ao ^ap ^aq , Ahluwalia, T.S.^ar ^as , Day, F.R.^at , Hottenga, J.-J.^a , Allegrini, A.G.^c , Rimfeld, K.^c , Chen, Q.^d , Lu, Y.^d , Martin, J.^d ^au , Soler Artigas, M.^e ^f ^g , Rovira, P.^e ^f ^g , Bosch, R.^e ^f ^av , Español, G.^e , Ramos Quiroga, J.A.^e ^f ^g ^av , Neumann, A.^l ^aw , Ensink, J.^ax ^ay , Grasby, K.^p , Morosoli, J.J.^p , Tong, X.^v ^w , Marrington, S.^az , Middeldorp, C.^a ^aa ^ba , Scott, J.G.^az ^bb ^bc , Vinkhuyzen, A.^bd , Shabalin, A.A.^ad , Corley, R.^ae ^be , Evans, L.M.^ae ^be , Sugden, K.^ai ^bf , Alemany, S.^am ^an ^ao , Sass, L.^ar , Vinding, R.^ar , Ruth, K.^bg , Tyrrell, J.^bg , Davies, G.E.^bh , Ehli, E.A.^bh , Hagenbeek, F.A.^a , De Zeeuw, E.^a , Van Beijsterveldt, T.C.E.M.^a , Larsson, H.^d ^bi , Snieder, H.^h , Verhulst, F.C.^l ^bj , Amin, N.^bk , Whipp, A.M.^m , Korhonen, T.^m , Vuoksimaa, E.^m , Rose, R.J.^bl , Uitterlinden, A.G.^bk ^bm ^bn , Heath, A.C.^bo , Madden, P.^bo , Haavik, J.^q ^bp , Harris, J.R.^bq , Helgeland, Ø.^br , Johansson, S.^q ^bs , Knudsen, G.P.S.^bq , Njolstad, P.R.^bt , Lu, Q.^v ^w , Rodriguez, A.^z ^bu , Henders, A.K.^bd , Mamun, A.^bv , Najman, J.M.^az , Brown, S.^bw , Hopfer, C.^bx , Krauter, K.^by , Reynolds, C.^bz , Smolen, A.^ae , Stallings, M.^ae ^af , Wadsworth, S.^ae , Wall, T.L.^bw , Silberg, J.L.^ah ^ca , Miller, A.^cb , Keltikangas-Järvinen, L.^cc , Hakulinen, C.^cc , Pulkki-Råback, L.^cc , Havdahl, A.^cd ^ce , Magnus, P.^cf , Raitakari, O.T.^cg ^ch ^ci , Perry, J.R.B.^at , Llop, S.^cj ^ck , Lopez-Espinosa, M.-J.^cj ^ck ^cl , Bønnelykke, K.^ar , Bisgaard, H.^ar , Sunyer, J.^am ^an ^ao ^cm , Lehtimäki, T.^al , Arseneault, L.^cn , Standl, M.^aj , Heinrich, J.^aj ^co ^cp , Boden, J.^cq , Pearson, J.^cr , Horwood, L.J.^cq , Kennedy, M.^cs , Poulton, R.^ct , Eaves, L.J.^ah ^ca , Maes, H.H.^ah ^ca ^cu , Hewitt, J.^ae ^af , Copeland, W.E.^cv , Costello, E.J.^cw , Williams, G.M.^az , Wray, N.^bd ^cx , Järvelin, M.-R.^z ^cy , McGue, M.^y , Iacono, W.^y , Caspi, A.^ai ^bf ^cn ^cz , Moffitt, T.E.^ai ^bf ^cn ^cz , Whitehouse, A.^da , Pennell, C.E.^x , Klump, K.L.^db , Burt, S.A.^db , Dick, D.M.^t ^dc ^dd , Reichborn-Kjennerud, T.^ce ^de , Martin, N.G.^p , Medland, S.E.^p , Vrijkotte, T.ⁿ , Kaprio, J.^m ^df , Tiemeier, H.^l ^dg , Davey Smith, G.ⁱ ^dh , Hartman, C.A.^di , Oldehinkel, A.J.^di , Casas, M.^e ^f ^g ^av , Ribasés, M.^e ^f ^g , Lichtenstein, P.^d , Lundström, S.^dj ^dk , Plomin, R.^c , Bartels, M.^a ^ab , Nivard, M.G.^a , Boomsma, D.I.^a ^ab

^a Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^b Netherlands Institute for the Study of Crime and Law Enforcement, Amsterdam, Netherlands
^c Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
^d Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
^e Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Spain
^f Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Barcelona, Spain
^g Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
^h Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
ⁱ MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
^j Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
^k Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
^l Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Rotterdam, Netherlands
^m Institute for Molecular Medicine FIMM, HiLife, University of Helsinki, Helsinki, Finland
ⁿ Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
^o Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam Public Health Research Institute, Amsterdam UMC, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
^p QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
^q Department of Biomedicine, University of Bergen, Bergen, Norway
^r Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
^s Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
^t Department of Psychology, Virginia Commonwealth University, Richmond, VA, United States
^u Faculty of Business, Karabuk University, Karabuk, Turkey
^v Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, United States
^w Department of Biostatistics, University of Florida, Gainesville, FL, United States
^x Faculty of Medicine and Health, School of Medicine and Public Health, The University of Newcastle, Callaghan, NSW, Australia
^y Department of Psychology, University of Minnesota, Minneapolis, MN, United States
^z Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
^aa Child Health Research Centre, the University of Queensland, Brisbane, QLD, Australia
^ab Amsterdam Public Health Research Institute, Amsterdam, Netherlands
^ac Department of Sociology, College of Social and Behavioral Science, University of Utah, Salt Lake City, UT, United States
^ad Department of Psychiatry, School of Medicine, University of Utah, Salt Lake City, UT, United States
^ae Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States
^af Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States
^ag Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, United States
^ah Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
^ai Center for Genomic and Computational Biology, Duke University, Durham, NC, United States
^aj Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
^ak Division of Metabolic Diseases and Nutritional Medicine, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians-University of Munich, Munich, Germany
^al Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center – Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
^am ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
^an Universitat Pompeu Fabra (UPF), Barcelona, Spain
^ao CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
^ap Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
^aq Barcelona Beta Brain Research Center, Pasqual Maragall Foundation (FPM), Barcelona, Spain
^ar COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
^as Steno Diabetes Center Copenhagen, Gentofte, Denmark
^at MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge Biomedical Campus, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
^au MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
^av Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
^aw Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
^ax Department of Child and Adolescent Psychiatry, Academic Medical Center, Amsterdam, Netherlands
^ay De Bascule, Academic Centre for Child and Adolescent Psychiatry, Amsterdam, Netherlands
^az School of Public Health, Faculty of Medicine, The University of Queensland, Herston, QLD, Australia
^ba Children’s Health Queensland Hospital and Health Service, Child and Youth Mental Health Service, Brisbane, QLD, Australia
^bb Metro North Mental Health, University of Queensland, St Lucia, QLD, Australia
^bc Queensland Centre for Mental Health Research, St Lucia, QLD, Australia
^bd Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia
^be Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, United States
^bf Department of Psychology and Neuroscience and Center for Genomic and Computational Biology, Duke University, Durham, NC, United States
^bg Genetics of Complex Traits, Royal Devon & Exeter Hospital, University of Exeter Medical School, Exeter, United Kingdom
^bh Avera Institute for Human Genetics, Sioux Falls, SD, United States
^bi School of Medical Sciences, Orebro University, Orebro, Sweden
^bj Child and Adolescent Mental Health Centre, Mental Health Services Capital Region, Research Unit, Copenhagen University Hospital, Copenhagen, Denmark
^bk Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
^bl Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
^bm Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
^bn Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, Netherlands
^bo Washington University, St Louis, MO, United States
^bp Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
^bq Division of Health Data and Digitalisation, The Norwegian Institute of Public Health, Oslo, Norway
^br Department of Genetics and Bioinformatics, Division of Health Data and Digitalization, The Norwegian Institute of Public Health, Bergen, Norway
^bs K.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Clinical Science, University of Bergen, Bergen, Norway
^bt Department of Clinical Science, University of Bergen, Bergen, Norway
^bu School of Psychology, University of Lincoln, Lincolnshire, United Kingdom
^bv Institute for Social Science Research, University of Queensland, Long Pocket, QLD, Australia
^bw Department of Psychiatry, University of California, San Diego, CA, United States
^bx University of Colorado School of Medicine, Aurora, CO, United States
^by Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, United States
^bz Department of Psychology, University of California Riverside, Riverside, CA, United States
^ca Department of Human & Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
^cb Department of Pathology and Biomedical Science, and Carney Centre for Pharmacogenomics, University of Otago Christchurch, Christchurch Central City, New Zealand
^cc Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
^cd Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
^ce Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
^cf Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
^cg Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
^ch Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
^ci Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
^cj Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, Valencia, Spain
^ck Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
^cl Faculty of Nursing and Chiropody, Universitat de València, Valencia, Spain
^cm IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
^cn Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
^co Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University of Munich Medical Center, Ludwig-Maximilians-Universität München, Munich, Germany
^cp Allergy and Lung Health Unit, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
^cq Christchurch Health and Development Study, Department of Psychological Medicine, University of Otago Christchurch, Christchurch Central City, New Zealand
^cr Biostatistics and Computational Biology Unit, Department of Pathology and Biomedical Science, University of Otago Christchurch, Christchurch Central City, New Zealand
^cs Department of Pathology and Biomedical Science, and Carney Centre for Pharmacogenomics, University of Otago Christchurch, Christchurch Central City, New Zealand
^ct Dunedin Multidisciplinary Health and Development Research Unit, University of Otago, Dunedin, New Zealand
^cu Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, United States
^cv Department of Psychiatry, College of Medicine, University of Vermont, Burlington, VT, United States
^cw Department of Psychiatry, School of Medicine, Duke University, Durham, United Kingdom
^cx Queensland Brain Institute, Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia
^cy Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
^cz Social, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
^da Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia
^db Department of Psychology, Michigan State University, East Lansing, MI, United States
^dc Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, United States
^dd College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, United States
^de Institute of Clinical Medicine, University of Oslo, Oslo, Norway
^df Department of Public Health, Medical Faculty, University of Helsinki, Helsinki, Finland
^dg Department of Social and Behavioral Science, Harvard TH Chan School of Public Health, Boston, MA, United States
^dh Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
^di Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
^dj Gillberg Neuropsychiatry Centre, University of Gothenburg, Gothenburg, Sweden
^dk Centre for Ethics, Law and Mental Health, University of Gothenburg, Gothenburg, Sweden

Abstract
Childhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association meta-analysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis (AGGoverall) was 3.31% (SE = 0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P = 1.6E–06), PCDH7 (P = 2.0E–06), and IPO13 (P = 2.5E–06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations (rg) among rater-specific assessment of AGG ranged from rg = 0.46 between self- and teacher-assessment to rg = 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range ∣ rg∣ : 0.19–1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg = ~−0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range ∣ rg∣ : 0.46–0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG. © 2021, The Author(s).

Funding details
Seventh Framework ProgrammeFP7602768
Cilag
Shire
Seventh Framework ProgrammeFP7
H. Lundbeck A/S

Document Type: Article
Publication Stage: Final
Source: Scopus

“Different roles of T-type calcium channel isoforms in hypnosis induced by an endogenous neurosteroid epipregnanolone” (2021) Neuropharmacology

Different roles of T-type calcium channel isoforms in hypnosis induced by an endogenous neurosteroid epipregnanolone
(2021) Neuropharmacology, 197, art. no. 108739, .

Coulter, I.^a , Timic Stamenic, T.^a , Eggan, P.^a , Fine, B.R.^a , Corrigan, T.^g , Covey, D.F.^e ^f , Yang, L.^d , Pan, J.Q.^d , Todorovic, S.M.^a ^b ^c

^a Department of Anesthesiology, University of Colorado, Anschutz Medical Campus, Aurora, 80045, United States
^b Neuroscience and University of Colorado, Anschutz Medical Campus, Aurora, 80045, United States
^c Pharmacology Graduate Programs, University of Colorado, Anschutz Medical Campus, Aurora, 80045, United States
^d Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, United States
^e Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, United States
^f Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO 63110, United States
^g Department of Pediatrics, Division of Neurology, Translational Epilepsy Research Program, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, United States

Abstract
Background: Many neuroactive steroids induce sedation/hypnosis by potentiating γ-aminobutyric acid (GABAA) currents. However, we previously demonstrated that an endogenous neuroactive steroid epipregnanolone [(3β,5β)-3-hydroxypregnan-20-one] (EpiP) exerts potent peripheral analgesia and blocks T-type calcium currents while sparing GABAA currents in rat sensory neurons. This study seeks to investigate the behavioral effects elicited by systemic administration of EpiP and to characterize its use as an adjuvant agent to commonly used general anesthetics (GAs). Methods: Here, we utilized electroencephalographic (EEG) recordings to characterize thalamocortical oscillations, as well as behavioral assessment and mouse genetics with wild-type (WT) and different knockout (KO) models of T-channel isoforms to investigate potential sedative/hypnotic and immobilizing properties of EpiP. Results: Consistent with increased oscillations in slower EEG frequencies, EpiP induced an hypnotic state in WT mice when injected alone intra-peritoneally (i.p.) and effectively facilitated anesthetic effects of isoflurane (ISO) and sevoflurane (SEVO). The CaV3.1 (Cacna1g) KO mice demonstrated decreased sensitivity to EpiP-induced hypnosis when compared to WT mice, whereas no significant difference was noted between CaV3.2 (Cacna1h), CaV3.3 (Cacna1i) and WT mice. Finally, when compared to WT mice, onset of EpiP-induced hypnosis was delayed in CaV3.2 KO mice but not in CaV3.1 and CaV3.3 KO mice. Conclusion: We posit that EpiP may have an important role as novel hypnotic and/or adjuvant to volatile anesthetic agents. We speculate that distinct hypnotic effects of EpiP across all three T-channel isoforms is due to their differential expression in thalamocortical circuitry. © 2021

Author Keywords
Calcium; Isoflurane; Low-voltage-activated; Righting reflex; Thalamus; Withdrawal reflex

Funding details
National Institutes of HealthNIHP30 NS048154, R01GM102525

Document Type: Article
Publication Stage: Final
Source: Scopus

“An examination between treatment type and treatment retention in persons with opioid and co-occurring alcohol use disorders” (2021) Drug and Alcohol Dependence

An examination between treatment type and treatment retention in persons with opioid and co-occurring alcohol use disorders
(2021) Drug and Alcohol Dependence, 226, art. no. 108886, .

Mintz, C.M.^a , Presnall, N.J.^b , Xu, K.Y.^a , Hartz, S.M.^a , Sahrmann, J.M.^c , Bierut, L.J.^a , Grucza, R.A.^d

^a Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Ave, Campus, Box 8134, St. Louis, MO 63110, United States
^b Department of Social Work, Washington University, One Brookings Drive, Campus Box 1196, St. Louis, MO 63130, United States
^c Department of Medicine, Division of Infectious Diseases, Washington University School of Medicine, 4990 Children’s Place, St. Louis, MO 63110, United States
^d Departments of Family and Community Medicine and Health and Outcomes Research, St. Louis University, 1008 South Spring, SLUCare Academic Pavilion, 3rd Floor, St. Louis, MO 63110, United States

Abstract
Background and Aims: Persons with opioid use disorder (OUD) and co-occurring alcohol use disorder (AUD) are understudied. We identified whether co-occurring AUD was associated with OUD treatment type, compared associations between treatment type and six-month treatment retention and determined whether co-occurring AUD moderated these relationships. Methods: We used an observational cohort study design to analyze insurance claims data from 2011 to 2016 from persons aged 12–64 with an opioid abuse or opioid dependence diagnosis and OUD treatment claim. Our unit of analysis was the treatment episode; we used logistic regression for analyses. Results: Of 211,047 treatment episodes analyzed, 14 % had co-occurring alcohol abuse or dependence diagnoses. Among persons with opioid dependence, persons with co-occurring alcohol dependence were 25 % less likely to receive medication treatment relative to those without AUD. Further, alcohol dependence was associated with decreased likelihood of treatment with buprenorphine (AOR 0.47, 95 % CI 0.44−0.49) or methadone (AOR 0.31, 95 % CI 0.28−0.35) and increased likelihood of treatment with extended-release (AOR 1.36, 95 % CI 1.21–1.54) or oral (AOR 1.73, 95 % CI 1.57–1.90) naltrexone relative to psychosocial treatment. Buprenorphine and methadone were associated with highest retention prevalence regardless of OUD or AUD severity. Co-occurring alcohol abuse or dependence did not meaningfully change retention prevalence associated with buprenorphine or methadone. Co-occurring AUD was not associated with improved retention among persons receiving either formulation of naltrexone. Conclusions: Buprenorphine and methadone are associated with relatively high likelihood of treatment retention among persons opioid and alcohol dependence, but are disproportionately under-prescribed. © 2021

Author Keywords
Alcohol use disorder; Buprenorphine; Methadone; Naltrexone; Opioid use disorder; Treatment retention

Funding details
National Institutes of HealthNIHR24 HS19455
National Institute on Alcohol Abuse and AlcoholismNIAAAR01 AA029308, R21 AA024888
Substance Abuse and Mental Health Services AdministrationSAMHSAH79TI082566
Agency for Healthcare Research and QualityAHRQ
National Center for Advancing Translational SciencesNCATS
Institute of Clinical and Translational SciencesICTSUL1 TR002345
Saint Louis UniversitySLU

Document Type: Article
Publication Stage: Final
Source: Scopus

“A multicenter validation of the condylar-C2 sagittal vertical alignment in Chiari malformation type I: A study using the Park-Reeves Syringomyelia Research Consortium” (2021) Journal of Neurosurgery: Pediatrics

A multicenter validation of the condylar-C2 sagittal vertical alignment in Chiari malformation type I: A study using the Park-Reeves Syringomyelia Research Consortium
(2021) Journal of Neurosurgery: Pediatrics, 28 (2), pp. 176-182.

Ravindra, V.M.^a ^b ^c , Iyer, R.R.^a , Yahanda, A.T.^d , Bollo, R.J.^a , Zhu, H.^e , Joyce, E.^a , Bethel-Anderson, T.^d , Meehan, T.^d , Smyth, M.D.^d , Strahle, J.M.^d , Park, T.S.^d , Limbrick, D.D., Jr.^d , Brockmeyer, D.L.^a , the Park-Reeves Syringomyelia Research Consortium^f

^a Division of Pediatric Neurosurgery, Primary Children’s Hospital, University of Utah, Salt Lake City, UT, United States
^b Division of Neurosurgery, University of California, San Diego, CA, United States
^c Department of Neurosurgery, Naval Medical Center, San Diego, CA, United States
^d Department of Neurological Surgery, Washington University, School of Medicine, St. Louis, MO, United States
^e Division of Pediatric Neurosurgery, Texas Children’s Hospital, Houston, TX, United States

Abstract
OBJECTIVE The condylar-C2 sagittal vertical alignment (C-C2SVA) describes the relationship between the occipitoatlantal joint and C2 in patients with Chiari malformation type I (CM-I). It has been suggested that a C-C2SVA ≥ 5 mm is predictive of the need for occipitocervical fusion (OCF) or ventral brainstem decompression (VBD). The authors’ objective was to validate the predictive utility of the C-C2SVA by using a large, multicenter cohort of patients. METHODS This validation study used a cohort of patients derived from the Park-Reeves Syringomyelia Research Consortium; patients < 21 years old with CM-I and syringomyelia treated from June 2011 to May 2016 were identified. The primary outcome was the need for OCF and/or VBD. After patients who required OCF and/or VBD were identified, 10 age- and sex-matched controls served as comparisons for each OCF/VBD patient. The C-C2SVA (defined as the position of a plumb line from the midpoint of the O-C1 joint relative to the posterior aspect of the C2-3 disc space), pBC2 (a line perpendicular to a line from the basion to the posteroinferior aspect of the C2 body), and clival-axial angle (CXA) were measured on sagittal MRI. The secondary outcome was the need for ≥ 2 CM-related operations. RESULTS Of the 206 patients identified, 20 underwent OCF/VBD and 14 underwent repeat posterior fossa decompression. A C-C2SVA ≥ 5 mm was 100% sensitive and 86% specific for requiring OCF/VBD, with a 12.6% misclassification rate, whereas CXA < 125° was 55% sensitive and 99% specific, and pBC2 ≥ 9 was 20% sensitive and 88% specific. Kaplan-Meier analysis demonstrated that there was a significantly shorter time to second decompression in children with C-C2SVA ≥ 5 mm (p = 0.0039). The mean C-C2SVA was greater (6.13 ± 1.28 vs 3.13 ± 1.95 mm, p < 0.0001), CXA was lower (126° ± 15.4° vs 145° ± 10.7°, p < 0.05), and pBC2 was similar (7.65 ± 1.79 vs 7.02 ± 1.26 mm, p = 0.31) among those who underwent OCF/VBD versus decompression only. The intraclass correlation coefficient for the continuous measurement of C-C2SVA was 0.52; the kappa value was 0.47 for the binary categorization of C-C2SVA ≥ 5 mm. CONCLUSIONS These results validated the C-C2SVA using a large, multicenter, external cohort with 100% sensitivity, 86% specificity, and a 12.6% misclassification rate. A C-C2SVA ≥ 5 mm is highly predictive of the need for OCF/VBD in patients with CM-I. The authors recommend that this measurement be considered among the tools to identify the “high-risk” CM-I phenotype. © AANS 2021, except where prohibited by US copyright law

Author Keywords
Cervical spine; Chiari malformation; Clival-axial angle; Occipitoatlantal joint; Park-Reeves Syringomyelia Research Consortium; PBC2; Sagittal vertical alignment

Document Type: Article
Publication Stage: Final
Source: Scopus

“Associations between Physical Activity, Blood-Based Biomarkers of Neurodegeneration, and Cognition in Healthy Older Adults: The MAPT Study” (2021) Journals of Gerontology – Series A Biological Sciences and Medical Sciences

Associations between Physical Activity, Blood-Based Biomarkers of Neurodegeneration, and Cognition in Healthy Older Adults: The MAPT Study
(2021) Journals of Gerontology – Series A Biological Sciences and Medical Sciences, 76 (8), pp. 1382-1390.

Raffin, J.^a , Rolland, Y.^a ^b , Aggarwal, G.^c ^d , Nguyen, A.D.^c ^d , Morley, J.E.^c , Li, Y.^e ^f , Bateman, R.J.^e , Vellas, B.^a ^b , Barreto, P.D.S.^a ^b

^a Gérontopôle de Toulouse, Institut du Vieillissement, Centre Hospitalo-Universitaire de Toulouse, France
^b Umr Inserm, 1027 University of Toulouse Iii, Faculté de Médecine, France
^c Division of Geriatric Medicine, Saint Louis University School of MedicineMO, United States
^d Henry and Amelia Nasrallah Center for Neuroscience, Saint Louis UniversityMO, United States
^e Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^f Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Physical activity (PA) demonstrated benefits on brain health, but its relationship with blood biomarkers of neurodegeneration remains poorly investigated. We explored the cross-sectional associations of PA with blood concentrations of neurofilament light chain (NFL) and beta amyloid (Aβ)42/40. We further examined whether the interaction between PA and these biomarkers was longitudinally related to cognition. Four-hundred and sixty-five nondemented older adults engaged in an interventional study and who had a concomitant assessment of PA levels and blood measurements of NFL (pg/mL) and Aβ42/40 were analyzed. A composite Z-score combining 4 cognitive tests was used for cognitive assessment up to a 4-year follow-up. Multiple linear regressions demonstrated that people achieving 500-999 and 2000+ MET-min/week of PA had lower (ln)NFL concentrations than their inactive peers. Logistic regressions revealed that achieving at least 90 MET-min/week of PA was associated with a lower probability of having high NFL concentrations (ie, ≥91.961 pg/mL [third quartile]). PA was not associated with (Aβ)42/40. Mixed-model linear regressions demonstrated that the reverse relationship between PA and cognitive decline tended to be more pronounced as Aβ42/40 increased, while it was dampened with increasing levels of (ln)NFL concentrations. This study demonstrates that PA is associated with blood NFL but not with Aβ42/40. Furthermore, it suggests that PA may attenuate the negative association between amyloid load and cognition, while having high NFL levels mitigates the favorable relationship between PA and cognition. More investigations on non demented older adults are required for further validation of the present findings. © 2021 The Author(s). Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved.

Author Keywords
Aging; Neurofilament light chain; Physical exercise; Plasma amyloid

Document Type: Article
Publication Stage: Final
Source: Scopus

“Untargeted metabolomics and infrared ion spectroscopy identify biomarkers for pyridoxine-dependent epilepsy” (2021) Journal of Clinical Investigation

Untargeted metabolomics and infrared ion spectroscopy identify biomarkers for pyridoxine-dependent epilepsy
(2021) Journal of Clinical Investigation, 131 (15), art. no. e148272, .

Engelke, U.F.H.^a , Van Outersterp, R.E.^b , Merx, J.^c , Van Geenen, F.A.M.G.^b , Van Rooij, A.^a , Berden, G.^b , Huigen, M.C.D.G.^a , Kluijtmans, L.A.J.^a , Peters, T.M.A.^a ^d , Al-Shekaili, H.H.^e , Leavitt, B.R.^e , De Vrieze, E.^f , Broekman, S.^f , Van Wijk, E.^f , Tseng, L.A.^g , Kulkarni, P.^a , Rutjes, F.P.J.T.^c , Mecinović, J.^c ^h , Struys, E.A.ⁱ , Jansen, L.A.^j , Gospe, S.M., Jr.^k ^l , Mercimek-Andrews, S.^m ⁿ , Hyland, K.^o , Willemsen, M.A.A.P.^p , Bok, L.A.^q , Van Karnebeek, C.D.M.^g ^r ^s , Wevers, R.A.^a , Boltje, T.J.^c , Oomens, J.^b ^t , Martens, J.^b , Coene, K.L.M.^a

^a Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, Netherlands
^b Institute for Molecules and Materials, FELIX Laboratory, Nijmegen, Netherlands
^c Institute for Molecules and Materials, Synthetic Organic Chemistry, Radboud University, Nijmegen, Netherlands
^d Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
^e Centre for Molecular Medicine and Therapeutics, British Columbia Children’s Hospital Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
^f Department of Otorhinolaryngology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
^g Department of Pediatrics, Emma Children’s Hospital, Amsterdam University Medical Centers, Amsterdam, Netherlands
^h Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense, Denmark
ⁱ Department of Clinical Chemistry, Amsterdam University Medical Centers, Location VU Medical Centre, Amsterdam, Netherlands
^j Division of Pediatric Neurology, Washington University School of Medicine, St. Louis, MO, United States
^k Departments of Neurology and Pediatrics, University of Washington, Seattle, WA, United States
^l Department of Pediatrics, Duke University, Durham, NC, United States
^m Division of Clinical and Metabolic Genetics, Department of Pediatrics, University of Toronto, Toronto, ON, Canada
ⁿ Department of Medical Genetics, University of Alberta, Edmonton, AB, Canada
^o Medical Neurogenetics Laboratories, Atlanta, GA, United States
^p Department of Pediatric Neurology, Radboud University Medical Centre, Nijmegen, Netherlands
^q Department of Pediatrics, Máxima Medical Centre, Veldhoven, Netherlands
^r Department of Pediatrics-Metabolic Diseases, Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen, Netherlands
^s United for Metabolic Diseases (UMD), Netherlands
^t Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, Netherlands

Abstract
Background. Pyridoxine-dependent epilepsy (PDE-ALDH7A1) is an inborn error of lysine catabolism that presents with refractory epilepsy in newborns. Biallelic ALDH7A1 variants lead to deficiency of α-aminoadipic semialdehyde dehydrogenase/ antiquitin, resulting in accumulation of piperideine-6-carboxylate (P6C), and secondary deficiency of the important cofactor pyridoxal-5′-phosphate (PLP, active vitamin B6) through its complexation with P6C. Vitamin B6 supplementation resolves epilepsy in patients, but intellectual disability may still develop. Early diagnosis and treatment, preferably based on newborn screening, could optimize long-term clinical outcome. However, no suitable PDE-ALDH7A1 newborn screening biomarkers are currently available. Methods. We combined the innovative analytical methods untargeted metabolomics and infrared ion spectroscopy to discover and identify biomarkers in plasma that would allow for PDE-ALDH7A1 diagnosis in newborn screening. Results. We identified 2S,6S-/2S,6R-oxopropylpiperidine-2-carboxylic acid (2-OPP) as a PDE-ALDH7A1 biomarker, and confirmed 6-oxopiperidine-2-carboxylic acid (6-oxoPIP) as a biomarker. The suitability of 2-OPP as a potential PDE-ALDH7A1 newborn screening biomarker in dried bloodspots was shown. Additionally, we found that 2-OPP accumulates in brain tissue of patients and Aldh7a1-knockout mice, and induced epilepsy-like behavior in a zebrafish model system. Conclusion. This study has opened the way to newborn screening for PDE-ALDH7A1. We speculate that 2-OPP may contribute to ongoing neurotoxicity, also in treated PDE-ALDH7A1 patients. As 2-OPP formation appears to increase upon ketosis, we emphasize the importance of avoiding catabolism in PDE-ALDH7A1 patients. © 2021, American Society for Clinical Investigation.

Funding details
758913
2019.062, TKI-LIFT 731.017.419, TTW 15769, VICI 724.011.002
184.034.019
UMD-CG-2020-004
Canadian Institutes of Health ResearchCIHR
Radboud UniversiteitRU

Document Type: Article
Publication Stage: Final
Source: Scopus

“Distinct progression patterns across Parkinson disease clinical subtypes” (2021) Annals of Clinical and Translational Neurology

Distinct progression patterns across Parkinson disease clinical subtypes
(2021) Annals of Clinical and Translational Neurology, 8 (8), pp. 1695-1708.

Myers, P.S.^a , Jackson, J.J.^b , Clover, A.K.^a , Lessov-Schlaggar, C.N.^c , Foster, E.R.^a ^c ^d , Maiti, B.^a , Perlmutter, J.S.^a ^d ^e ^f ^g , Campbell, M.C.^a ^e

^a Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^b Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States
^c Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^d Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO, United States
^e Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
^f Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States
^g Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Objective: To examine specific symptom progression patterns and possible disease staging in Parkinson disease clinical subtypes. Methods: We recently identified Parkinson disease clinical subtypes based on comprehensive behavioral evaluations, “Motor Only,” “Psychiatric & Motor,” and “Cognitive & Motor,” which differed in dementia and mortality rates. Parkinson disease participants (“Motor Only”: n = 61, “Psychiatric & Motor”: n = 17, “Cognitive & Motor”: n = 70) and controls (n = 55) completed longitudinal, comprehensive motor, cognitive, and psychiatric evaluations (average follow-up = 4.6 years). Hierarchical linear modeling examined group differences in symptom progression. A three-way interaction among time, group, and symptom duration (or baseline age, separately) was incorporated to examine disease stages. Results: All three subtypes increased in motor dysfunction compared to controls. The “Motor Only” subtype did not show significant cognitive or psychiatric changes compared to the other two subtypes. The “Cognitive & Motor” subtype’s cognitive dysfunction at baseline further declined compared to the other two subtypes, while also increasing in psychiatric symptoms. The “Psychiatric & Motor” subtype’s elevated psychiatric symptoms at baseline remained steady or improved over time, with mild, steady decline in cognition. The pattern of behavioral changes and analyses for disease staging yielded no evidence for sequential disease stages. Interpretation: Parkinson disease clinical subtypes progress in clear, temporally distinct patterns from one another, particularly in cognitive and psychiatric features. This highlights the importance of comprehensive clinical examinations as the order of symptom presentation impacts clinical prognosis. © 2021 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association

Funding details
UL1RR024992
National Institutes of HealthNIHAG050263, AG‐64937, AT010753‐02S1, ES029524, KL2 TR002346, NS065701, NS075527, NS092865, NS097799, NS103957, NS107281, NS109487, U10NS077384, U19 NS110456, U54NS116025
U.S. Department of DefenseDODDOD W81XWH‐217‐1‐0393
National Institute on AgingNIAAG063974, R01AG065214, RO1NS118146
National Institute of Diabetes and Digestive and Kidney DiseasesNIDDKR01DK064832
National Institute of Neurological Disorders and StrokeNINDSNS058714, NS075321, NS097437, NS41509, NS48924, P30 NS048056, TR 001456
Michael J. Fox Foundation for Parkinson’s ResearchMJFF
Huntington’s Disease Society of AmericaHDSA
American Brain FoundationABF
American Academy of NeurologyAAN
CHDI FoundationCHDI
National Center for Advancing Translational SciencesNCATS
American Parkinson Disease AssociationAPDA
Foundation for Barnes-Jewish Hospital
Parkinsonfonden
St. Louis American Parkinson Disease Association
McDonnell Center for Systems Neuroscience

Document Type: Article
Publication Stage: Final
Source: Scopus

“Longitudinal evaluation of neuromuscular dysfunction in long-term survivors of childhood cancer: A report from the childhood cancer survivor study” (2021) Cancer Epidemiology Biomarkers and Prevention

Longitudinal evaluation of neuromuscular dysfunction in long-term survivors of childhood cancer: A report from the childhood cancer survivor study
(2021) Cancer Epidemiology Biomarkers and Prevention, 30 (8), pp. 1536-1545.

Rodwin, R.L.^a , Chen, Y.^b , Yasui, Y.^c , Leisenring, W.M.^d , Gibson, T.M.^e , Nathan, P.C.^f , Howell, R.M.^g , Krull, K.R.^c ^h , Mohrmann, C.ⁱ , Hayashi, R.J.ⁱ , Chow, E.J.^d , Oeffinger, K.C.^j , Armstrong, G.T.^c , Ness, K.K.^c , Kadan-Lottick, N.S.^a ^k

^a Department of Pediatrics, Yale School of Medicine, New Haven, CT, United States
^b School of Public Health, University of Alberta, Edmonton, AB, Canada
^c Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, United States
^d Clinical Research and Public Health Sciences Divisions, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
^e Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States
^f Division of Hematology-Oncology, The Hospital for Sick Children, Toronto, ON, Canada
^g Division of Radiation Oncology, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
^h Department of Psychology, St. Jude Children’s Research Hospital, Memphis, TN, United States
ⁱ Division of Pediatric Hematology/Oncology, Washington University School of Medicine, St. Louis Children’s Hospital, St. Louis, MO, United States
^j Department of Medicine, Duke University, Durham, NC, United States
^k Yale Cancer Center, New Haven, CT, United States

Abstract
Background: Children treated for cancer are at risk for neuromuscular dysfunction, but data are limited regarding prevalence, longitudinal patterns, and long-term impact. Methods: Longitudinal surveys from 25,583 childhood cancer survivors ≥5 years from diagnosis and 5,044 siblings from the Childhood Cancer Survivor Study were used to estimate the prevalence and cumulative incidence of neuromuscular dysfunction. Multivariable models adjusted for age, sex, race, and ethnicity estimated prevalence ratios (PR) of neuromuscular dysfunction in survivors compared with siblings, and associations with treatments and late health/socioeconomic outcomes. Results: Prevalence of neuromuscular dysfunction was 14.7% in survivors 5 years postdiagnosis versus 1.5% in siblings [PR, 9.9; 95% confidence interval (CI), 7.9–12.4], and highest in survivors of central nervous system (CNS) tumors (PR, 27.6; 95% CI, 22.1–34.6) and sarcomas (PR, 11.5; 95% CI, 9.1–14.5). Cumulative incidence rose to 24.3% in survivors 20 years postdiagnosis (95% CI, 23.8–24.8). Spinal radiotherapy and increasing cranial radiotherapy dose were associated with increased prevalence of neuromuscular dysfunction. Platinum exposure (vs. none) was associated with neuromuscular dysfunction (PR, 1.8; 95% CI, 1.5–2.1), even after excluding survivors with CNS tumors, cranial/spinal radiotherapy, or amputation. Neuromuscular dysfunction was associated with concurrent or later obesity (PR, 1.1; 95% CI, 1.1–1.2), anxiety (PR, 2.5; 95% CI, 2.2–2.9), depression (PR, 2.1; 95% CI, 1.9–2.3), and lower likelihood of graduating college (PR, 0.92; 95% CI, 0.90–0.94) and employment (PR, 0.8; 95% CI, 0.8–0.9). Conclusions: Neuromuscular dysfunction is prevalent in childhood cancer survivors, continues to increase posttherapy, and is associated with adverse health and socioeconomic outcomes. Impact: Interventions are needed to prevent and treat neuromuscular dysfunction, especially in survivors with platinum and radiation exposure. © 2021 American Association for Cancer Research.

Funding details
CA21765
National Institutes of HealthNIHCA55727
National Cancer InstituteNCI
American Lebanese Syrian Associated CharitiesALSACT32 CA250803

Document Type: Article
Publication Stage: Final
Source: Scopus

“Top-down modeling of distributed neural dynamics for motion control” (2021) Proceedings of the American Control Conference

Top-down modeling of distributed neural dynamics for motion control
(2021) Proceedings of the American Control Conference, 2021-May, art. no. 9482782, pp. 2757-2762.

Mallik, S.^a , Ching, S.^a ^b

^a Washington University, Department of Electrical and Systems Engineering, St. Louis, MO 63130, United States
^b Washington University, Department of Biomedical Engineering, St. Louis, MO 63130, United States

Abstract
In neuroscience a topic of interest pertains to understanding the neural circuit and network mechanisms that enable a range of motor functions, including motion and navigation. While engineers have strong mathematical conceptualizations regarding how these functions can be achieved using control-theoretic frameworks, it is far from clear whether similar strategies are embodied within neural circuits. In this work, we adopt a ‘top-down’ strategy to postulate how certain nonlinear control strategies might be achieved through the actions of a network of biophysical neurons acting on multiple time-scales. Specifically, we study how neural circuits might interact to learn and execute an optimal strategy for spatial control. Our approach is comprised of an optimal nonlinear control problem where a high-level objective function encapsulates the fundamental requirements of the task at hand. We solve this optimization using an iterative method based on Pontryagin’s Maximum Principle. It turns out that the proposed solution methodology can be translated into the dynamics of neural populations that act to produce the optimal solutions in a distributed fashion. Importantly, we are able to provide conditions under which these networks are guaranteed to arrive at an optimal solution. In total, this work provides an iterative optimization framework that confers a novel interpretation regarding how nonlinear control can be achieved in neural circuits. © 2021 American Automatic Control Council.

Funding details
National Science FoundationNSF
Burroughs Wellcome FundBWF1653589, 1724218

Document Type: Conference Paper
Publication Stage: Final
Source: Scopus

“Polysubstance use trends and variability among individuals with opioid use disorder in rural versus urban settings” (2021) Preventive Medicine

Polysubstance use trends and variability among individuals with opioid use disorder in rural versus urban settings
(2021) Preventive Medicine, art. no. 106729, .

Ellis, M.S., Kasper, Z.A., Cicero, T.J.

Washington University in St. Louis School of Medicine, Department of Psychiatry, Campus Box 8134, 660 S. Euclid Avenue, St. Louis, MO 63110, United States

Abstract
Rural areas of the United States have been disproportionately impacted by the opioid epidemic, exacerbated by COVID-19-related economic upheavals. While polysubstance use is an important determinant of overdose risk, variability in polysubstance use as a result of numerous factors (e.g., access, preference) has yet to be described, particularly among rural persons with opioid use disorder (PWOUD). Survey data on past-month use of prescription and illicit opioids and 12 non-opioid psychoactive drug classes were analyzed from a national sample of rural (n = 3872) and urban (n = 8153) residents entering treatment for OUD from 2012 to 2019. Trend analyses for opioid and stimulant use were compared between rural and urban PWOUD. Latent class analyses assessed substance use trends through identified typologies of rural/urban PWOUD, which then underwent comparative analyses. By 2019, prescription opioid use remained greater in rural versus urban PWOUD, and methamphetamine use showed greater growth in rural, compared to urban areas. Latent class analyses identified variability in polysubstance use, with five identical subgroups in rural/urban PWOD: high polysubstance, polyprescription, prescription opioid-focused, prescription opioid-focused with polysubstance use, and illicit opioid-focused. Polyprescription was highest in rural areas, with illicit opioid-focused use highest in urban areas. Demographic characteristics, co-morbid conditions and healthcare coverage were all associated with between-group differences. There is significant variability in polysubstance use that may identify specific prevention and treatment needs for subpopulations of OUD patients: interventions focused on reducing opioid prescriptions, early engagement with mental health resources, wider distribution of naloxone, and screening/treatment plans that take into account the use of multiple substances. © 2021 Elsevier Inc.

Author Keywords
Opioid use disorder; Polysubstance use; Rural

Funding details
Washington University in St. LouisWUSTL

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

“Assessment of intellectual impairment, health-related quality of life, and behavioral phenotype in patients with neurotransmitter related disorders: Data from the iNTD registry” (2021) Journal of Inherited Metabolic Disease

Assessment of intellectual impairment, health-related quality of life, and behavioral phenotype in patients with neurotransmitter related disorders: Data from the iNTD registry
(2021) Journal of Inherited Metabolic Disease, .

Keller, M.^a , Brennenstuhl, H.^a , Kuseyri Hübschmann, O.^a , Manti, F.^b , Julia Palacios, N.A.^c , Friedman, J.^d , Yıldız, Y.^e , Koht, J.A.^f , Wong, S.-N.^g , Zafeiriou, D.I.^h , López-Laso, E.ⁱ , Pons, R.^j , Kulhánek, J.^k , Jeltsch, K.^a , Serrano-Lomelin, J.^l , Garbade, S.F.^a ^m , Opladen, T.^a , Goez, H.ⁿ , Burlina, A.^o , Cortès-Saladelafont, E.^c ^p , Fernández Ramos, J.A.ⁱ , García-Cazorla, A.^c , Hoffmann, G.F.^a , Kiat Hong, S.T.^q , Honzík, T.^k , Kavecan, I.^r , Kurian, M.A.^s , Leuzzi, V.^b , Lücke, T.^t , Manzoni, F.^o , Mastrangelo, M.^b , Mercimek-Andrews, S.^u ^v , Mir, P.^w , Oppebøen, M.^x , Pearson, T.S.^y , Sivri, H.S.^e , Steel, D.^s , Stevanović, G.^z , Fung, C.-W.^g , International Working Group on Neurotransmitter related Disorders (iNTD)^aa

^a Division of Child Neurology and Metabolic Medicine, University Children’s Hospital Heidelberg, Heidelberg, Germany
^b Department of Human Neuroscience, Unit of Child Neurology and Psychiatry, Università degli Studi di Roma La Sapienza, Rome, Italy
^c Inborn errors of metabolism Unit, Department of Neurology, Institut de Recerca Sant Joan de Déu and CIBERER-ISCIII, Barcelona, Spain
^d UCSD Departments of Neuroscience and Pediatrics; Rady Children’s Hospital Division of Neurology, Rady Children’s Institute for Genomic Medicine, San Diego, CA, United States
^e Hacettepe University, Faculty of Medicine, Department of Pediatrics, Section of Pediatric Metabolism, Ankara, Turkey
^f Department of Neurology, Oslo University Hospital, Oslo, Norway
^g Department of Pediatrics and Adolescent Medicine, The Hong Kong Children’s Hospital, Hong Kong, Hong Kong
^h First Department of Pediatrics Aristotle University of Thessaloniki, Thessaloniki, Greece
ⁱ Pediatric Neurology Unit, Department of Pediatrics, University Hospital Reina Sofía, IMIBIC and CIBERER, Córdoba, Spain
^j First Department of Pediatrics of the University of Athens, Aghia Sofia Hospital, Athens, Greece
^k Department of Pediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
^l Women and Children’s Health Research Institute, University of Alberta, Edmonton, AB, Canada
^m Dietmar-Hopp Metabolic Center, University Children’s Hospital Heidelberg, Heidelberg, Germany
ⁿ Department of Pediatrics, University of Alberta, Glenrose Rehabilitation Hospital, Edmonton, AB, Canada
^o U.O.C. Malattie Metaboliche Ereditarie, Dipartimento della Salute della Donna e del Bambino, Azienda Ospedaliera Universitaria di Padova – Campus Biomedico Pietro d’Abano, Padova, Italy
^p Inborn Errors of Metabolism and Child Neurology Unit, Department of Pediatrics, Hospital Germans Trias i Pujol, Badalona and Faculty of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
^q KTP-National University Children’s Medical Institute, National University Health System, Singapore, Singapore
^r Faculty of Medicine, University of Novi Sad, Institute for Children and Youth Health Care of Vojvodina, Novi Sad, Serbia
^s Developmental Neurosciences, UCL Great Ormond Street-Institute of Child Health and Department of Neurology, Great Ormond Street Hospital, London, United Kingdom
^t University Children’s Hospital, St. Josef-Hospital, Ruhr-University Bochum, Bochum, Germany
^u Division of Clinical and Metabolic Genetics, Department of Pediatrics, University of Toronto, The Hospital for Sick Children, Toronto, ON, Canada
^v Department of Medical Genetics, University of Alberta, Women and Children’s Health Research Institute, Stollery Children’s Hospital, Edmonton, AB, Canada
^w Unidad de Trastornos del Movimiento Servicio de Neurología y Neurofisiología Clínica Unidad de Gestión Clínica de Neurociencias Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío, Sevilla, Spain
^x Children’s Department Division of Child Neurology Oslo University Hospital Rikshospitalet, Oslo, Norway
^y Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^z Clinic of Neurology and Psychiatry for Children and Youth, School of Medicine, University of Belgrade, Belgrade, Serbia

Abstract
Inherited disorders of neurotransmitter metabolism are a group of rare diseases, which are caused by impaired synthesis, transport, or degradation of neurotransmitters or cofactors and result in various degrees of delayed or impaired psychomotor development. To assess the effect of neurotransmitter deficiencies on intelligence, quality of life, and behavior, the data of 148 patients in the registry of the International Working Group on Neurotransmitter Related Disorders (iNTD) was evaluated using results from standardized age-adjusted tests and questionnaires. Patients with a primary disorder of monoamine metabolism had lower IQ scores (mean IQ 58, range 40-100) within the range of cognitive impairment (<70) compared to patients with a BH4 deficiency (mean IQ 84, range 40-129). Short attention span and distractibility were most frequently mentioned by parents, while patients reported most frequently anxiety and distractibility when asked for behavioral traits. In individuals with succinic semialdehyde dehydrogenase deficiency, self-stimulatory behaviors were commonly reported by parents, whereas in patients with dopamine transporter deficiency, DNAJC12 deficiency, and monoamine oxidase A deficiency, self-injurious or mutilating behaviors have commonly been observed. Phobic fears were increased in patients with 6-pyruvoyltetrahydropterin synthase deficiency, while individuals with sepiapterin reductase deficiency frequently experienced communication and sleep difficulties. Patients with BH4 deficiencies achieved significantly higher quality of life as compared to other groups. This analysis of the iNTD registry data highlights: (a) difference in IQ and subdomains of quality of life between BH4 deficiencies and primary neurotransmitter-related disorders and (b) previously underreported behavioral traits. © 2021 The Authors. Journal of Inherited Metabolic Disease published by John Wiley & Sons Ltd on behalf of SSIEM.

Author Keywords
behavioral phenotype; cognitive impairment; iNTD; intelligence; neurotransmitter deficiencies; quality of life

Funding details
Universität Heidelberg

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

“Development and external validation of the KIIDS-TBI tool for managing children with mild traumatic brain injury and intracranial injuries” (2021) Academic Emergency Medicine

Development and external validation of the KIIDS-TBI tool for managing children with mild traumatic brain injury and intracranial injuries
(2021) Academic Emergency Medicine, .

Greenberg, J.K.^a , Ahluwalia, R.^b , Hill, M.^c , Johnson, G.^a , Hale, A.T.^b , Belal, A.^d , Baygani, S.^d , Olsen, M.A.^e , Foraker, R.E.^e , Carpenter, C.R.^f , Yan, Y.^g , Ackerman, L.^d , Noje, C.^h , Jackson, E.ⁱ , Burns, E.^j , Sayama, C.M.^k , Selden, N.R.^k , Vachhrajani, S.^c , Shannon, C.N.^b ^l , Kuppermann, N.^m , Limbrick, D.D., Jr.^a

^a Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^b Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, United States
^c Department of Neurological Surgery, Dayton Children’s Hospital, Dayton, OH, United States
^d Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, United States
^e Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^f Department of Emergency Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^g Department of Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^h Department of Anesthesiology, Johns Hopkins School of Medicine, Baltimore, MD, United States
ⁱ Department of Neurological Surgery, Johns Hopkins School of Medicine, Baltimore, MD, United States
^j Department of Pediatrics, Oregon Health and Science University, Portland, OR, United States
^k Department of Neurological Surgery, Oregon Health and Science University, Portland, OR, United States
^l American Society for Reproductive Medicine, University of California Davis, Davis, CA, United States
^m Department of Emergency Medicine, University of California–Davis, Davis, CA, United States

Abstract
Background: Clinical decision support (CDS) may improve the postneuroimaging management of children with mild traumatic brain injuries (mTBI) and intracranial injuries. While the CHIIDA score has been proposed for this purpose, a more sensitive risk model may have broader use. Consequently, this study’s objectives were to: (1) develop a new risk model with improved sensitivity compared to the CHIIDA model and (2) externally validate the new model and CHIIDA model in a multicenter data set. Methods: We analyzed children ≤18 years old with mTBI and intracranial injuries included in the PECARN head injury data set (2004–2006). We used binary recursive partitioning to predict the composite outcome of neurosurgical intervention, intubation for > 24 h due to TBI, or death due to TBI. The new model was externally validated in a separate data set that included children treated at any one of six centers from 2006 to 2019. Results: Based on 839 patients from the PECARN data set, a new risk model, the KIIDS-TBI model, was developed that incorporated imaging (e.g., midline shift) and clinical (e.g., Glasgow Coma Scale score) findings. Based on the model-predicted probability of the composite outcome, three cutoffs were evaluated to classify patients as “high risk” for level of care decisions. In the external validation data set consisting of 1,630 patients, the most conservative cutoff (i.e., any predictor present) identified 119 of 119 children with the composite outcome (sensitivity = 100%), but had the lowest specificity (26.3%). The other two decision-making cutoffs had worse sensitivity (94.1%–96.6%) but improved specificity (67.4%–81.3%). The CHIIDA model lacked the most conservative cutoff and otherwise showed the same or slightly worse performance compared to the other two cutoffs. Conclusions: The KIIDS-TBI model has high sensitivity and moderate specificity for risk stratifying children with mTBI and intracranial injuries. Use of this CDS tool may help improve the safe, resource-efficient management of this important patient population. © 2021 by the Society for Academic Emergency Medicine

Funding details
Agency for Healthcare Research and QualityAHRQ1F32HS027075‐01A1
Pfizer
Merck
Thrasher Research FundTRF15024
Sanofi Pasteur

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

“Circadian clock synchrony and chronotherapy opportunities in cancer treatment” (2021) Seminars in Cell and Developmental Biology

Circadian clock synchrony and chronotherapy opportunities in cancer treatment
(2021) Seminars in Cell and Developmental Biology, .

Damato, A.R., Herzog, E.D.

Department of Biology, Washington University, Box 1137, St. Louis, MO 63130, United States

Abstract
Cell-autonomous, tissue-specific circadian rhythms in gene expression and cellular processes have been observed throughout the human body. Disruption of daily rhythms by mistimed exposure to light, food intake, or genetic mutation has been linked to cancer development. Some medications are also more effective at certain times of day. However, a limited number of clinical studies have examined daily rhythms in the patient or drug timing as treatment strategies. This review highlights advances and challenges in cancer biology as a function of time of day. Recent evidence for daily rhythms and their entrainment in tumors indicate that personalized medicine should include understanding and accounting for daily rhythms in cancer patients. © 2021 Elsevier Ltd

Author Keywords
Astrocyte; Chronomedicine; Chronotherapy; Glioblastoma; Glucocorticoid; Insulin

Funding details
National Institutes of HealthNIHF31CA250161, R21NS120003
Foundation for Barnes-Jewish Hospital
University of WashingtonUW

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

“Insights on Neural Response to Racist Threats” (2021) JAMA Psychiatry

Insights on Neural Response to Racist Threats
(2021) JAMA Psychiatry, .

Motley, R.O., Jr.^a , Rogers, C.^b

^a Boston College School of Social Work, Chestnut Hill, MA, United States
^b Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO, United States

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

“MAPT R406W increases tau T217 phosphorylation in absence of amyloid pathology” (2021) Annals of Clinical and Translational Neurology

MAPT R406W increases tau T217 phosphorylation in absence of amyloid pathology
(2021) Annals of Clinical and Translational Neurology, .

Sato, C.^a , Mallipeddi, N.^a , Ghoshal, N.^a ^b , Wright, B.A.^c , Day, G.S.^d , Davis, A.A.^a ^e , Kim, A.H.^e ^f , Zipfel, G.J.^e ^f , Bateman, R.J.^a ^e ^g , Gabelle, A.^h , Barthélemy, N.R.^a

^a Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^b Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^c Department of Neurosciences, University of California San Diego School of Medicine, La Jolla, CA, United States
^d Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, United States
^e Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, United States
^f Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States
^g Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, United States
^h Department of Neurology, Memory Research and Resources Center, University Hospital of Montpellier, Neurosciences Institute of Montpellier, University of Montpellier, Montpellier, France

Abstract
Objective: Tau hyperphosphorylation at threonine 217 (pT217) in cerebrospinal fluid (CSF) has recently been linked to early amyloidosis and could serve as a highly sensitive biomarker for Alzheimer’s disease (AD). However, it remains unclear whether other tauopathies induce pT217 modifications. To determine if pT217 modification is specific to AD, CSF pT217 was measured in AD and other tauopathies. Methods: Using immunoprecipitation and mass spectrometry methods, we compared CSF T217 phosphorylation occupancy (pT217/T217) and amyloid-beta (Aβ) 42/40 ratio in cognitively normal individuals and those with symptomatic AD, progressive supranuclear palsy, corticobasal syndrome, and sporadic and familial frontotemporal dementia. Results: Individuals with AD had high CSF pT217/T217 and low Aβ42/40. In contrast, cognitively normal individuals and the majority of those with 4R tauopathies had low CSF pT217/T217 and normal Aβ 42/40. We identified a subgroup of individuals with increased CSF pT217/T217 and normal Aβ 42/40 ratio, most of whom were MAPT R406W mutation carriers. Diagnostic accuracies of CSF Aβ 42/40 and CSF pT217/T217, alone and in combination were compared. We show that CSF pT217/T217 × CSF Aβ 42/40 is a sensitive composite biomarker that can separate MAPT R406W carriers from cognitively normal individuals and those with other tauopathies. Interpretation: MAPT R406W is a tau mutation that leads to 3R+4R tauopathy similar to AD, but without amyloid neuropathology. These findings suggest that change in CSF pT217/T217 ratio is not specific to AD and might reflect common downstream tau pathophysiology common to 3R+4R tauopathies. © 2021 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association

Author Keywords
Alzheimer’s Disease; biomarker; cerebrospinal fluid; mass spectrometry; tau phosphorylation; Tauopathies

Funding details
P41 GM103422
National Institutes of HealthNIH
National Institute on AgingNIAK01AG062796, K23AG064029
Foundation for Barnes-Jewish Hospital3945
Washington University School of Medicine in St. Louis
Hope Center for Neurological Disorders
Tau Consortium

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

“Modeling Sporadic Alzheimer’s Disease in Human Brain Organoids under Serum Exposure” (2021) Advanced Science

Modeling Sporadic Alzheimer’s Disease in Human Brain Organoids under Serum Exposure
(2021) Advanced Science, .

Chen, X.^a , Sun, G.^a , Tian, E.^a , Zhang, M.^a , Davtyan, H.^b , Beach, T.G.^c , Reiman, E.M.^d , Blurton-Jones, M.^e , Holtzman, D.M.^f , Shi, Y.^a

^a Division of Stem Cell Biology Research, Department of Developmental and Stem Cell Biology, Beckman Research Institute of City of Hope, 1500 E. Duarte Rd, Duarte, CA 91010, United States
^b Institute for Memory Impairments & Neurological Disorders and Sue & Bill Gross Stem Cell Research Center, University of California Irvine, Irvine, CA 92697, United States
^c Banner Sun Health Research Institute, 105015 West Santa Fe Drive, Sun City, AZ 85351, United States
^d Banner Alzheimer Institute, 901 East Willetta Street, Phoenix, AZ 95006, United States
^e Department of Neurobiology & Behavior, Institute for Memory Impairments & Neurological Disorders and Sue & Bill Gross Stem Cell Research Center, University of California Irvine, Irvine, CA 92697, United States
^f Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, St. Louis, MO 63110, United States

Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with no cure. Huge efforts have been made to develop anti-AD drugs in the past decades. However, all drug development programs for disease-modifying therapies have failed. Possible reasons for the high failure rate include incomplete understanding of complex pathophysiology of AD, especially sporadic AD (sAD), and species difference between humans and animal models used in preclinical studies. In this study, sAD is modeled using human induced pluripotent stem cell (hiPSC)-derived 3D brain organoids. Because the blood–brain barrier (BBB) leakage is a well-known risk factor for AD, brain organoids are exposed to human serum to mimic the serum exposure consequence of BBB breakdown in AD patient brains. The serum-exposed brain organoids are able to recapitulate AD-like pathologies, including increased amyloid beta (Aβ) aggregates and phosphorylated microtubule-associated tau protein (p-Tau) level, synaptic loss, and impaired neural network. Serum exposure increases Aβ and p-Tau levels through inducing beta-secretase 1 (BACE) and glycogen synthase kinase-3 alpha / beta (GSK3α/β) levels, respectively. In addition, single-cell transcriptomic analysis of brain organoids reveals that serum exposure reduced synaptic function in both neurons and astrocytes and induced immune response in astrocytes. The human brain organoid-based sAD model established in this study can provide a powerful platform for both mechanistic study and therapeutic development in the future. © 2021 The Authors. Advanced Science published by Wiley-VCH GmbH

Author Keywords
brain organoids; disease modeling; induced pluripotent stem cells; serum exposure; sporadic Alzheimer’s disease

Funding details
National Institutes of HealthNIHP30 AG066519, R01 AG056303, R01 AG056305, R56 AG061171, RF1 AG061794, RF1 DA048813
National Institute on AgingNIAP30 AG19610
National Cancer InstituteNCIP30CA33572
National Institute of Neurological Disorders and StrokeNINDSU24 NS072026
Michael J. Fox Foundation for Parkinson’s ResearchMJFF
Arizona Department of Health ServicesADHS211002
Arizona Biomedical Research CommissionABRC0011, 05‐901, 1001, 4001

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

“Alterations in resting-state functional connectivity in pediatric patients with tuberous sclerosis complex” (2021) Epilepsia Open

Alterations in resting-state functional connectivity in pediatric patients with tuberous sclerosis complex
(2021) Epilepsia Open, .

Lobanov, O.V.^a , Shimony, J.S.^b , Kenley, J.^a , Kaplan, S.^a , Alexopoulos, D.^a , Roland, J.L.^c , Smyth, M.D.^d ^e , Smyser, C.D.^a ^b ^e

^a Department of Neurology, Washington University, St. Louis, MO, United States
^b Department of Radiology, Washington University, St. Louis, MO, United States
^c Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, United States
^d Department of Neurological Surgery, Washington University, St. Louis, MO, United States
^e Department of Pediatrics, Washington University, St. Louis, MO, United States

Abstract
Objective: To investigate resting-state functional connectivity (FC) in pediatric patients with tuberous sclerosis complex and intractable epilepsy requiring surgery. Methods: Resting-state functional MRI was utilized to investigate functional connectivity in 13 pediatric patients with tuberous sclerosis complex (TSC) and intractable epilepsy requiring surgery. Results: The majority of patients demonstrated a resting-state network architecture similar to those reported in healthy individuals. However, preoperative differences were evident between patients with high versus low tuber burden, as well as those with good versus poor neurodevelopmental outcomes, most notably in the cingulo-opercular and visual resting-state networks. One patient with high tuber burden and poor preoperative development and seizure control had nearly normal development and seizure resolution after surgery. This was accompanied by significant improvement in resting-state network architecture just one day postoperatively. Significance: Although many patients with tuberous sclerosis complex and medically refractory epilepsy demonstrate functional connectivity patterns similar to healthy children, relationships within and between RSNs demonstrate clear differences in patients with higher tuber burden and worse outcomes. Improvements in resting-state network organization postoperatively may be related to epilepsy surgery outcomes, providing candidate biomarkers for clinical management in this high-risk population. © 2021 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.

Author Keywords
epilepsy; epilepsy surgery; functional connectivity; functional MRI; tuberous sclerosis complex

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

“Predictive modeling of spread in adult-onset isolated dystonia: Key properties and effect of tremor inclusion” (2021) European Journal of Neurology

Predictive modeling of spread in adult-onset isolated dystonia: Key properties and effect of tremor inclusion
(2021) European Journal of Neurology, .

Wang, M.^a , Sajobi, T.^a , Morgante, F.^b ^c , Adler, C.^d , Agarwal, P.^e , Bäumer, T.^f , Berardelli, A.^g ^h , Berman, B.D.ⁱ , Blumin, J.^j , Borsche, M.^k , Brashear, A.^l , Deik, A.^m , Duque, K.ⁿ , Espay, A.J.ⁿ , Ferrazzano, G.^g , Feuerstein, J.^o , Fox, S.^p , Frank, S.^q , Hallett, M.^r , Jankovic, J.^s , LeDoux, M.S.^t , Leegwater-Kim, J.^u , Mahajan, A.^v , Malaty, I.A.^w , Ondo, W.^x ^y , Pantelyat, A.^z , Pirio-Richardson, S.^aa , Roze, E.^ab , Saunders-Pullman, R.^ac , Suchowersky, O.^ad , Truong, D.^ae ^af , Vidailhet, M.^ab , Shukla, A.W.^w , Perlmutter, J.S.^ag , Jinnah, H.A.^ah , Martino, D.^ai

^a Department of Community Health Sciences, Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
^b Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St. George’s, University of London, London, United Kingdom
^c Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
^d Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ, United States
^e Booth Gardner Parkinson’s Center, Evergreen Health, Kirkland, WA, United States
^f Institute of Systems Motor Science, Center for Rare Diseases, University Medical Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany
^g Department of Human Neurosciences, University of Rome “La Sapienza”, Rome, Italy
^h IRCCS Neuromed, Pozzilli, Italy
ⁱ Department of Neurology, Virginia Commonwealth University, Richmond, VA, United States
^j Department of Otolaryngology & Communication Sciences, Medical College of Wisconsin, Milwaukee, WI, United States
^k Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
^l Department of Neurology, University of California, Davis, Sacramento, CA, United States
^m Disease and Movement Disorders Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
ⁿ Department of Neurology and Rehabilitation Medicine, Gardner Family Center for Parkinson’s Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, United States
^o Department of Neurology, University of Colorado, Aurora, CO, United States
^p Movement Disorder Clinic, Edmond J Safra Program in Parkinson Disease, Toronto Western Hospital, and Division of Neurology, University of Toronto, Toronto, ON, Canada
^q Beth Israel Deaconess Medical Center, Boston, MA, United States
^r Human Motor Control Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, United States
^s Parkinson’s Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, TX, United States
^t Department of Psychology and School of Health Sciences, University of Memphis, and Veracity Neuroscience, Memphis, TN, United States
^u Lahey Hospital and Medical Center, Tufts University School of Medicine, Burlington, MA, United States
^v Rush Parkinson’s disease and movement disorders program, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
^w Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
^x Houston Methodist Hospital, Houston, TX, United States
^y Weill Cornell Medical School, New York, NY, United States
^z Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
^aa Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, NM, United States
^ab Sorbonne Université, Institut du Cerveau – Paris Brain Institute – ICM, Inserm, CNRS, AP-HP, Hôpital Salpetriere, Paris, France
^ac Department of Neurology, Icahn School of Medicine at Mount Sinai and Mount Sinai Beth Israel, New York, NY, United States
^ad Department of Medicine, University of Alberta, Edmonton, AB, Canada
^ae Department of Neurosciences, UC Riverside, Riverside, CA, United States
^af The Parkinson and Movement Disorder Institute, Fountain Valley, CA, United States
^ag Departments of Neurology, Psychiatry, Radiology, Neurobiology, Physical Therapy and Occupational Therapy, Washington University School of Medicine, St. Louis, MO, United States
^ah Departments of Neurology, Human Genetics, and Pediatrics, Emory University School of Medicine, Atlanta, GA, United States
^ai Department of Clinical Neurosciences & Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada

Abstract
Background and purpose: Several clinical and demographic factors relate to anatomic spread of adult-onset isolated dystonia, but a predictive model is still lacking. The aims of this study were: (i) to develop and validate a predictive model of anatomic spread of adult-onset isolated dystonia; and (ii) to evaluate whether presence of tremor associated with dystonia influences model predictions of spread. Methods: Adult-onset isolated dystonia participants with focal onset from the Dystonia Coalition Natural History Project database were included. We developed two prediction models, one with dystonia as sole disease manifestation (“dystonia-only”) and one accepting dystonia OR tremor in any body part as disease manifestations (“dystonia OR tremor”). Demographic and clinical predictors were selected based on previous evidence, clinical plausibility of association with spread, or both. We used logistic regressions and evaluated model discrimination and calibration. Internal validation was carried out based on bootstrapping. Results: Both predictive models showed an area under the curve of 0.65 (95% confidence intervals 0.62–0.70 and 0.62–0.69, respectively) and good calibration after internal validation. In both models, onset of dystonia in body regions other than the neck, older age, depression and history of neck trauma were predictors of spread. Conclusions: This predictive modeling of spread in adult-onset isolated dystonia based on accessible predictors (demographic and clinical) can be easily implemented to inform individuals’ risk of spread. Because tremor did not influence prediction of spread, our results support the argument that tremor is a part of the dystonia syndrome, and not an independent or coincidental disorder. © 2021 European Academy of Neurology

Author Keywords
isolated dystonia; neurological diseases; predictive models; spread; tremor

Funding details
U54 NS065701, U54 NS116025
National Institute of Neurological Disorders and StrokeNINDS
Dystonia Coalition
Rare Diseases Clinical Research NetworkRDCRNU54 TR001456

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

“Commentary: Cognitive Dysfunction After Coronary Revascularization in Older Adults- An Unsolved Mystery” (2021) Seminars in Thoracic and Cardiovascular Surgery

Commentary: Cognitive Dysfunction After Coronary Revascularization in Older Adults- An Unsolved Mystery
(2021) Seminars in Thoracic and Cardiovascular Surgery, .

Inkollu, S.K.^a , Khetarpaul, V.^b

^a Department of Cardiovascular Surgery, Division of Vascular Surgery, Marshfield Clinic Health Systems, Marshfield, WI, United States
^b Department of Surgery, Division of Vascular Surgery, Washington University in St Louis, St. Louis, MO, United States

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

“How Will Aducanumab Approval Impact AD Research?” (2021) Journal of Prevention of Alzheimer’s Disease

How Will Aducanumab Approval Impact AD Research?
(2021) Journal of Prevention of Alzheimer’s Disease, .

Weiner, M.W.^a ^b ^c ^r , Aisen, P.S.^d , Beckett, L.A.^e , Green, R.C.^f , Jagust, W.^g , Morris, J.C.^h , Okonkwo, O.ⁱ , Perrin, R.J.^h , Petersen, R.C.^j , Rivera Mindt, M.^k ^q , Saykin, A.J.^l ^m , Shaw, L.M.ⁿ , Toga, A.W.^o , Trojanowski, J.Q.^p , Alzheimer’s Disease Neuroimaging Initiative (ADNI)^s

^a Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, United States
^b Departments of Radiology, Medicine, Psychiatry, and Neurology, University of California, San Francisco, CA, United States
^c Principal Investigator of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Brain Health Registry, New York, United States
^d Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, United States
^e Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, United States
^f Department of Neurology and Center for Neuroscience, University of California, Davis, Davis, CA, United States
^g Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, United States
^h Departments of Pathology and Immunology and of Neurology, Washington University School of Medicine, Saint Louis, MO, United States
ⁱ Wisconsin Alzheimer’s Disease Research Center and Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
^j Department of Neurology, Mayo Clinic, Rochester, MN, United States
^k Department of Psychology, Latin American Latino Studies Institute, and African and African American Studies, Fordham University, New York, NY, United States
^l Department of Radiology and Imaging Sciences and Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, United States
^m Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
ⁿ Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^o Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
^p Department of Geriatric Medicine and Gerontology, University of Pennsylvania Health System, Philadelphia, PA, United States
^q Departments of Neurology and Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^r NCIRE, 4150 Clement St, San Francisco, CA 94121, United States

Funding details
G-89294, P30 AG010133, P30 AG072976, R01 AG019771, R01 AG057739, R01 AG068193, R01 LM013463, T32 AG071444, U01 AG068057
National Institutes of HealthNIHAG024904, AG047866, B639943, HG008685, HG009922, HL143295, OD026553, R01AG062517, TR003201
National Institute on AgingNIA
Alzheimer’s AssociationAA
GenentechP01AG003991, P01AG026276, P30 AG0620677, P30 AG066444, U01 AG006786, U01 AG024904, U19 AG032438, U24 AG057437
Merck
Roche
Biogen
AbbVie
EisaiAARGD-16-446038, R01AG065110, R01AG066471-01A1, R13AG071313, SC3GM141996, U19 AG024904

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

“Developmental variation in testosterone: cortisol ratio alters cortical- And amygdala-based cognitive processes” (2021) Journal of Developmental Origins of Health and Disease

Developmental variation in testosterone:cortisol ratio alters cortical- And amygdala-based cognitive processes
(2021) Journal of Developmental Origins of Health and Disease, .

Lew, J.^a ^b , Jones, S.L.^b ^c , Caccese, C.^b ^c , Orfi, I.^d , Little, C.^d , Botteron, K.N.^e ^f , McCracken, J.T.^f ^g , Nguyen, T.-V.^b ^c ^h

^a Integrated Program in Neuroscience, McGill University, Montreal, QC H3A2B4, Canada
^b Research Institute of the McGill University Health Center, Montreal, QC H4A3J1, Canada
^c Department of Psychiatry, McGill University, Montreal, QC H3A1A1, Canada
^d Department of Psychology, McGill University, Montreal, QC H4A3J1, Canada
^e Department of Psychiatry, Washington University, School of Medicine, St. Louis, MO 63110, United States
^f Brain Development Cooperative Group, United States
^g Department of Child and Adolescent Psychiatry, University of California in Los Angeles, Los Angeles, CA 90024, United States
^h Department of Obstetrics-Gynecology, McGill University Health Center, Montreal, QC H4A3J1, Canada

Abstract
Testosterone (T) and cortisol (C) are the end products of neuroendocrine axes that interact with the process of shaping brain structure and function. Relative levels of T:C (TC ratio) may alter prefrontal-amygdala functional connectivity in adulthood. What remains unclear is whether TC-related effects are rooted to childhood and adolescence. We used a healthy cohort of 4-22-year-olds to test for associations between TC ratios, brain structure (amygdala volume, cortical thickness (CTh), and their coordinated growth), as well as cognitive and behavioral development. We found greater TC ratios to be associated with the growth of specific brain structures: 1) parietal CTh; 2) covariance of the amygdala with CTh in visual and somatosensory areas. These brain parameters were in turn associated with lower verbal/executive function and higher spatial working memory. In sum, individual TC profiles may confer a particular brain phenotype and set of cognitive strengths and vulnerabilities, prior to adulthood. © The Author(s), 2021. Published by Cambridge University Press in association with International Society for Developmental Origins of Health and Disease.

Author Keywords
adolescence; Androgens; brain development; cognition; TC ratio

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