Guido I Guberman1, Sonja Stojanovski2,3, Eman Nishat2,3, Alain Ptito1, Danilo Bzdok4,5,6, Anne L Wheeler2,3, Maxime Descoteaux7,8. 1. Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Canada. 2. Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Canada. 3. Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, Canada. 4. McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada. 5. Department of Biomedical Engineering, Faculty of Medicine, School of Computer Science, McGill University, Montreal, Canada. 6. Mila - Quebec Artificial Intelligence Institute, Montreal, Canada. 7. Department of Computer Science, Université de Sherbrooke, Sherbrooke, Canada. 8. Imeka Solutions Inc, Sherbrooke, Canada.
Abstract
Background: The heterogeneity of white matter damage and symptoms in concussion has been identified as a major obstacle to therapeutic innovation. In contrast, most diffusion MRI (dMRI) studies on concussion have traditionally relied on group-comparison approaches that average out heterogeneity. To leverage, rather than average out, concussion heterogeneity, we combined dMRI and multivariate statistics to characterize multi-tract multi-symptom relationships. Methods: Using cross-sectional data from 306 previously concussed children aged 9-10 from the Adolescent Brain Cognitive Development Study, we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first representing microstructural complexity, the second representing axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 symptom measures. Results: Early multi-tract multi-symptom pairs explained the most covariance and represented broad symptom categories, such as a general problems pair, or a pair representing all cognitive symptoms, and implicated more distributed networks of white matter tracts. Further pairs represented more specific symptom combinations, such as a pair representing attention problems exclusively, and were associated with more localized white matter abnormalities. Symptom representation was not systematically related to tract representation across pairs. Sleep problems were implicated across most pairs, but were related to different connections across these pairs. Expression of multi-tract features was not driven by sociodemographic and injury-related variables, as well as by clinical subgroups defined by the presence of ADHD. Analyses performed on a replication dataset showed consistent results. Conclusions: Using a double-multivariate approach, we identified clinically-informative, cross-demographic multi-tract multi-symptom relationships. These results suggest that rather than clear one-to-one symptom-connectivity disturbances, concussions may be characterized by subtypes of symptom/connectivity relationships. The symptom/connectivity relationships identified in multi-tract multi-symptom pairs were not apparent in single-tract/single-symptom analyses. Future studies aiming to better understand connectivity/symptom relationships should take into account multi-tract multi-symptom heterogeneity. Funding: Financial support for this work came from a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (G.I.G.), an Ontario Graduate Scholarship (S.S.), a Restracomp Research Fellowship provided by the Hospital for Sick Children (S.S.), an Institutional Research Chair in Neuroinformatics (M.D.), as well as a Natural Sciences and Engineering Research Council CREATE grant (M.D.).
Background: The heterogeneity of white matter damage and symptoms in concussion has been identified as a major obstacle to therapeutic innovation. In contrast, most diffusion MRI (dMRI) studies on concussion have traditionally relied on group-comparison approaches that average out heterogeneity. To leverage, rather than average out, concussion heterogeneity, we combined dMRI and multivariate statistics to characterize multi-tract multi-symptom relationships. Methods: Using cross-sectional data from 306 previously concussed children aged 9-10 from the Adolescent Brain Cognitive Development Study, we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first representing microstructural complexity, the second representing axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 symptom measures. Results: Early multi-tract multi-symptom pairs explained the most covariance and represented broad symptom categories, such as a general problems pair, or a pair representing all cognitive symptoms, and implicated more distributed networks of white matter tracts. Further pairs represented more specific symptom combinations, such as a pair representing attention problems exclusively, and were associated with more localized white matter abnormalities. Symptom representation was not systematically related to tract representation across pairs. Sleep problems were implicated across most pairs, but were related to different connections across these pairs. Expression of multi-tract features was not driven by sociodemographic and injury-related variables, as well as by clinical subgroups defined by the presence of ADHD. Analyses performed on a replication dataset showed consistent results. Conclusions: Using a double-multivariate approach, we identified clinically-informative, cross-demographic multi-tract multi-symptom relationships. These results suggest that rather than clear one-to-one symptom-connectivity disturbances, concussions may be characterized by subtypes of symptom/connectivity relationships. The symptom/connectivity relationships identified in multi-tract multi-symptom pairs were not apparent in single-tract/single-symptom analyses. Future studies aiming to better understand connectivity/symptom relationships should take into account multi-tract multi-symptom heterogeneity. Funding: Financial support for this work came from a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (G.I.G.), an Ontario Graduate Scholarship (S.S.), a Restracomp Research Fellowship provided by the Hospital for Sick Children (S.S.), an Institutional Research Chair in Neuroinformatics (M.D.), as well as a Natural Sciences and Engineering Research Council CREATE grant (M.D.).
Authors: Sonja Stojanovski; Daniel Felsky; Joseph D Viviano; Saba Shahab; Rutwik Bangali; Christie L Burton; Gabriel A Devenyi; Lauren J O'Donnell; Peter Szatmari; M Mallar Chakravarty; Stephanie Ameis; Russell Schachar; Aristotle N Voineskos; Anne L Wheeler Journal: Biol Psychiatry Date: 2018-07-12 Impact factor: 13.382
Authors: Gabriel Girard; Roberto Caminiti; Alexandra Battaglia-Mayer; Etienne St-Onge; Karen S Ambrosen; Simon F Eskildsen; Kristine Krug; Tim B Dyrby; Maxime Descoteaux; Jean-Philippe Thiran; Giorgio M Innocenti Journal: Neuroimage Date: 2020-07-30 Impact factor: 6.556
Authors: J David Cassidy; Linda J Carroll; Paul M Peloso; Jörgen Borg; Hans von Holst; Lena Holm; Jess Kraus; Victor G Coronado Journal: J Rehabil Med Date: 2004-02 Impact factor: 2.912
Authors: B J Casey; Tariq Cannonier; May I Conley; Alexandra O Cohen; Deanna M Barch; Mary M Heitzeg; Mary E Soules; Theresa Teslovich; Danielle V Dellarco; Hugh Garavan; Catherine A Orr; Tor D Wager; Marie T Banich; Nicole K Speer; Matthew T Sutherland; Michael C Riedel; Anthony S Dick; James M Bjork; Kathleen M Thomas; Bader Chaarani; Margie H Mejia; Donald J Hagler; M Daniela Cornejo; Chelsea S Sicat; Michael P Harms; Nico U F Dosenbach; Monica Rosenberg; Eric Earl; Hauke Bartsch; Richard Watts; Jonathan R Polimeni; Joshua M Kuperman; Damien A Fair; Anders M Dale Journal: Dev Cogn Neurosci Date: 2018-03-14 Impact factor: 6.464
Authors: Bing Si; Gina Dumkrieger; Teresa Wu; Ross Zafonte; Alex B Valadka; David O Okonkwo; Geoffrey T Manley; Lujia Wang; David W Dodick; Todd J Schwedt; Jing Li Journal: PLoS One Date: 2018-07-11 Impact factor: 3.240
Authors: Guido I Guberman; Sonja Stojanovski; Eman Nishat; Alain Ptito; Danilo Bzdok; Anne L Wheeler; Maxime Descoteaux Journal: Elife Date: 2022-05-17 Impact factor: 8.713