Open AccessStructural connectome disruption at baseline predicts 6‐months post‐stroke outcome
Author(s) -
Kuceyeski Amy,
Navi Babak B.,
Kamel Hooman,
Raj Ashish,
Relkin Norman,
Toglia Joan,
Iadecola Costantino,
O'Dell Michael
Publication year - 2016
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.23198
Subject(s) - connectome , diffusion mri , disconnection , generalizability theory , stroke (engine) , pairwise comparison , bootstrapping (finance) , psychology , artificial intelligence , neuroscience , computer science , magnetic resonance imaging , medicine , functional connectivity , radiology , mathematics , developmental psychology , econometrics , mechanical engineering , political science , law , engineering
In this study, models based on quantitative imaging biomarkers of post‐stroke structural connectome disruption were used to predict six‐month outcomes in various domains. Demographic information and clinical MRIs were collected from 40 ischemic stroke subjects (age: 68.1 ± 13.2 years, 17 female, NIHSS: 6.8 ± 5.6). Diffusion‐weighted images were used to create lesion masks, which were uploaded to the Network Modification (NeMo) Tool. The NeMo Tool, using only clinical MRIs, allows estimation of connectome disruption at three levels: whole brain, individual gray matter regions and between pairs of gray matter regions. Partial Least Squares Regression models were constructed for each level of connectome disruption and for each of the three six‐month outcomes: applied cognitive, basic mobility and daily activity. Models based on lesion volume were created for comparison. Cross‐validation, bootstrapping and multiple comparisons corrections were implemented to minimize over‐fitting and Type I errors. The regional disconnection model best predicted applied cognitive ( R 2 = 0.56) and basic mobility outcomes ( R 2 = 0.70), while the pairwise disconnection model best predicted the daily activity measure ( R 2 = 0.72). These results demonstrate that models based on connectome disruption metrics were more accurate than ones based on lesion volume and that increasing anatomical specificity of disconnection metrics does not always increase model accuracy, likely due to statistical adjustments for concomitant increases in data dimensionality. This work establishes that the NeMo Tool's measures of baseline connectome disruption, acquired using only routinely collected MRI scans, can predict 6‐month post‐stroke outcomes in various functional domains including cognition, motor function and daily activities. Hum Brain Mapp, 2016 . © 2016 Wiley Periodicals, Inc .