Structural connectome disruption at baseline predicts 6-months post-stroke outcome

• Published in: Human brain mapping Vol. 37; no. 7; pp. 2587 - 2601
• Main Authors: Kuceyeski, Amy, Navi, Babak B., Kamel, Hooman, Raj, Ashish, Relkin, Norman, Toglia, Joan, Iadecola, Costantino, O'Dell, Michael
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.07.2016; John Wiley & Sons, Inc; John Wiley and Sons Inc
• Subjects: Aged; Brain - diagnostic imaging; Brain Ischemia - diagnostic imaging; Connectome; Diffusion Magnetic Resonance Imaging; Female; Follow-Up Studies; Humans; Image Interpretation, Computer-Assisted; imaging biomarkers; Least-Squares Analysis; magnetic resonance imaging; Male; outcome assessment; Prognosis; statistical modeling; stroke; Stroke - diagnostic imaging; Stroke Rehabilitation; Treatment Outcome; imaging biomarkers; connectome; statistical modeling; magnetic resonance imaging; outcome assessment; stroke
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.23198

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 (R2 = 0.56) and basic mobility outcomes (R2 = 0.70), while the pairwise disconnection model best predicted the daily activity measure (R2 = 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.