List of publications for week of April 11, 2022
Accuracy and reliability of diffusion imaging models
(2022) NeuroImage, 254, art. no. 119138, .
Seider, N.A.a , Adeyemo, B.b , Miller, R.a b , Newbold, D.J.b p , Hampton, J.M.a , Scheidter, K.M.a b , Rutlin, J.c , Laumann, T.O.a , Roland, J.L.d , Montez, D.F.b , Van, A.N.b k , Zheng, A.b , Marek, S.a , Kay, B.P.b , Bretthorst, G.L.c e , Schlaggar, B.L.f g h , Greene, D.J.i , Wang, Y.c j k , Petersen, S.E.a c k l m , Barch, D.M.a c m , Gordon, E.M.c , Snyder, A.Z.b c , Shimony, J.S.c l , Dosenbach, N.U.F.b c k n o
a Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States
b Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
c Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States
d Department of Neurological Surgery, Washington University School of Medicine, St Louis, MO 63110, United States
e Department of Chemistry, Washington University in St Louis, MO, St. Louis, 63110, United States
f Kennedy Krieger Institute, Baltimore, MD 21205, United States
g Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
h Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
i Department of Cognitive Science, University of California, San Diego, La Jolla, CA, United States
j Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, United States
k Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States
l Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States
m Department of Psychological and Brain Sciences, Washington University in St. LouisMO 63110, United States
n Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, United States
o Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
p Department of Neurology, New York University Langone Medical Center, New York, NY 10016, United States
Abstract
Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain’s white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927–1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL’s BedpostX [BPX], DSI Studio’s Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3’s Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI. © 2022
Funding details
14–011
1IK2CX001680
National Institutes of HealthNIHAG053548, HD087011, HD094381, MH1000872, MH104592, MH112473, MH121276, MH121518, MH122066, MH124567, MH96773, NS088590, NS090978, NS098577, NS110332, NS115672, T32MH100019
March of Dimes FoundationMDF
James S. McDonnell FoundationJSMF
BrightFocus FoundationBFFA2017330S
Child Neurology FoundationCNF
McDonnell Center for Systems Neuroscience
Hope Center for Neurological Disorders
Jacobs Foundation2016121703
Document Type: Article
Publication Stage: Final
Source: Scopus
Hold that pose: capturing cervical dystonia’s head deviation severity from video
(2022) Annals of Clinical and Translational Neurology, .
Zhang, Z.a , Cisneros, E.a , Lee, H.Y.a , Vu, J.P.a , Chen, Q.a , Benadof, C.N.a , Whitehill, J.b , Rouzbehani, R.a , Sy, D.T.a , Huang, J.S.c , Sejnowski, T.J.d , Jankovic, J.e , Factor, S.f , Goetz, C.G.g , Barbano, R.L.h , Perlmutter, J.S.i j , Jinnah, H.A.f k , Berman, B.D.l , Richardson, S.P.m n , Stebbins, G.T.g , Comella, C.L.g , Peterson, D.A.a d
a Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
b Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, United States
c Department of Pediatrics, University of California, San Diego, La Jolla, CA, United States
d Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, United States
e Parkinson’s Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, TX, United States
f Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States
g Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
h Department of Neurology, University of Rochester, Rochester, NY, United States
i Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
j Departments of Radiology, Neuroscience, Physical Therapy, and Occupational Therapy, Washington University School of Medicine, St. Louis, MO, United States
k Departments of Human Genetics, Emory University School of Medicine, Atlanta, GA, United States
l Department of Neurology, Virginia Commonwealth University, Richmond, VA, United States
m Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, NM, United States
n Neurology Service, New Mexico Veterans Affairs Health Care System, Albuquerque, NM, United States
Abstract
Objective: Deviated head posture is a defining characteristic of cervical dystonia (CD). Head posture severity is typically quantified with clinical rating scales such as the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS). Because clinical rating scales are inherently subjective, they are susceptible to variability that reduces their sensitivity as outcome measures. The variability could be circumvented with methods to measure CD head posture objectively. However, previously used objective methods require specialized equipment and have been limited to studies with a small number of cases. The objective of this study was to evaluate a novel software system—the Computational Motor Objective Rater (CMOR)—to quantify multi-axis directionality and severity of head posture in CD using only conventional video camera recordings. Methods: CMOR is based on computer vision and machine learning technology that captures 3D head angle from video. We used CMOR to quantify the axial patterns and severity of predominant head posture in a retrospective, cross-sectional study of 185 patients with isolated CD recruited from 10 sites in the Dystonia Coalition. Results: The predominant head posture involved more than one axis in 80.5% of patients and all three axes in 44.4%. CMOR’s metrics for head posture severity correlated with severity ratings from movement disorders neurologists using both the TWSTRS-2 and an adapted version of the Global Dystonia Rating Scale (rho = 0.59–0.68, all p <0.001). Conclusions: CMOR’s convergent validity with clinical rating scales and reliance upon only conventional video recordings supports its future potential for large scale multisite clinical trials. © 2022 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.
Funding details
W81XWH‐17‐1‐0393, W81XWH‐19‐1‐0146
National Institutes of HealthNIH
National Institute of Neurological Disorders and StrokeNINDSU54 NS065701, U54 NS116025
National Center for Advancing Translational SciencesNCATSU54 TR001456
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Return of the lesion: a meta-analysis of 1134 angiographically cured pediatric arteriovenous malformations
(2021) Journal of Neurosurgery: Pediatrics, 28 (6), pp. 677-684.
Lauzier, D.C.a d , Vellimana, A.K.a , Chatterjee, A.R.e , Osbun, J.W.e , Moran, C.J.a b , Zipfel, G.J.b c , Kansagra, A.P.a c
a Mallinckrodt Institute of Radiology, United States
b Department of Neurological Surgery, United States
c Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
d Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
Abstract
OBJECTIVE Brain arteriovenous malformations (AVMs) carry a risk of rupture and subsequent morbidity or mortality unless fully treated. AVMs in pediatric patients are known to occasionally recur after obliteration. The objective of this study was to characterize the risk of AVM recurrence following angiographically confirmed obliteration in children. METHODS Consecutive pediatric AVMs treated at a single center were identified from a prospective database. Patients with angiographically confirmed AVM obliteration following treatment were included in this study. Associations between AVM recurrence and patient or procedural factors were characterized using the two-tailed Fisher exact test or Mann-Whitney U-test. A literature search was conducted using PubMed, Scopus, Embase, and the Clarivate Web of Science with defined search criteria, and eligible studies were included alongside this study cohort in a meta-analysis. Rates of AVM recurrence following obliteration were pooled across studies with a random-effects model and reported with 95% confidence intervals (CIs). RESULTS Recurrence after angiographic confirmation of AVM obliteration was observed in 10.4% (7/67) of pediatric AVMs treated at the authors’ center. Patients with recurrent AVMs were significantly younger than those without recurrence (p = 0.002). In the meta-analysis, which included 1134 patients across 24 studies, the rate of recurrence was 4.8% (95% CI 3.0%–6.7%). The rate of AVM recurrence following radiosurgery was 0.7% (95% CI 0%–1.6%), which was significantly lower than the 8.5% rate (95% CI 5.0%–12.0%) following microsurgery. CONCLUSIONS Recurrence of obliterated brain AVMs is common in children. Recurrence is more common in young children and following microsurgery. ©AANS 2021, except where prohibited by US copyright law
Author Keywords
angiography; arteriovenous malformation; obliteration; recurrence; vascular disorders
Document Type: Article
Publication Stage: Final
Source: Scopus