Exploring the brain's structural connectome: A quantitative stroke lesion-dysfunction mapping study.
| Publication Type | Academic Article |
| Authors | Kuceyeski A, Navi B, Kamel H, Relkin N, Villanueva M, Raj A, Toglia J, O'Dell M, Iadecola C |
| Journal | Hum Brain Mapp |
| Volume | 36 |
| Issue | 6 |
| Pagination | 2147-60 |
| Date Published | 02/05/2015 |
| ISSN | 1097-0193 |
| Keywords | Brain, Brain Ischemia, Connectome, Stroke |
| 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. |
| DOI | 10.1002/hbm.22761 |
| PubMed ID | 25655204 |
| PubMed Central ID | PMC4414746 |