title: Exploring the brain's structural connectome: A quantitative stroke lesion-dysfunction mapping study.
abstract: The aim of this work was to quantitatively model cross-sectional relationships between structural connectome disruptions caused by cerebral infarction and measures of clinical performance. Imaging biomarkers of 41 ischemic stroke patients (72.0 ± 12.0 years, 20 female) were related to their baseline performance in 18 cognitive, physical and daily life activity assessments. Individual estimates of structural connectivity disruption in gray matter regions were computed using the Change in Connectivity (ChaCo) score. ChaCo scores were utilized because they can be calculated using routinely collected clinical magnetic resonance imagings. Partial Least Squares Regression (PLSR) was used to predict various acute impairment and activity measures from ChaCo scores and patient demographics. Statistical methods of cross-validation, bootstrapping and multiple comparisons correction were implemented to minimize over-fitting and Type I errors. Multiple linear regression models based on lesion volume and lateralization information were constructed for comparison. All models based on connectivity disruption had lower Akaike Information Criterion and almost all had better goodness-of-fit values (R(2) : 0.26-0.92) than models based on lesion characteristics (R(2) : 0.06-0.50). Confidence intervals of PLSR coefficients identified brain regions important in predicting each clinical assessment. Appropriate mapping of eloquent functions, that is, language and motor, and replication of results across pathologies provided validation of this method. Models of complex functions provided new insights into brain-behavior relationships. In addition to the potential applications in prognostication and rehabilitation development, this quantitative approach provides insight into the structural networks underlying complex functions like activities of daily living and cognition. Quantitative analysis of big data will be invaluable in understanding complex brain-behavior relationships.
authors: Department of Radiology, Weill Cornell Medical College, New York; The Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York.
authors: Amy
authors: A
authors: Kuceyeski
authors: Babak B
authors: BB
authors: Navi
authors: Hooman
authors: H
authors: Kamel
authors: Norman
authors: N
authors: Relkin
authors: Mark
authors: M
authors: Villanueva
authors: Ashish
authors: A
authors: Raj
authors: Joan
authors: J
authors: Toglia
authors: Michael
authors: M
authors: O'Dell
authors: Costantino
authors: C
authors: Iadecola
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D001921:Brain / Q000473:pathology*; D002545:Brain Ischemia / Q000473:pathology*; D063132:Connectome / Q000379:methods*; D005260:Female; D066128:Gray Matter / Q000473:pathology; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D009434:Neural Pathways / Q000473:pathology; D009483:Neuropsychological Tests; D020521:Stroke / Q000473:pathology*
keywords: biological markers; cognition; connectome; infarction; linear models; magnetic resonance imaging; outcome assessment (health care); stroke
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