title: Predicting future brain tissue loss from white matter connectivity disruption in ischemic stroke.
abstract: OBJECTIVE
  The Network Modification (NeMo) Tool uses a library of brain connectivity maps from normal subjects to quantify the amount of structural connectivity loss caused by focal brain lesions. We hypothesized that the Network Modification Tool could predict remote brain tissue loss caused by poststroke loss of connectivity.
  
  
  METHODS
  Baseline and follow-up MRIs (10.7±7.5 months apart) from 26 patients with acute ischemic stroke (age, 74.6±14.1 years, initial National Institutes of Health Stroke Scale, 3.1±3.1) were collected. Lesion masks derived from diffusion-weighted images were superimposed on the Network Modification Tool's connectivity maps, and regional structural connectivity losses were estimated via the Change in Connectivity (ChaCo) score (ie, the percentage of tracks connecting to a given region that pass through the lesion mask). ChaCo scores were correlated with subsequent atrophy.
  
  
  RESULTS
  Stroke lesions' size and location varied, but they were more frequent in the left hemisphere. ChaCo scores, generally higher in regions near stroke lesions, reflected this lateralization and heterogeneity. ChaCo scores were highest in the postcentral and precentral gyri, insula, middle cingulate, thalami, putamen, caudate nuclei, and pallidum. Moderate, significant partial correlations were found between baseline ChaCo scores and measures of subsequent tissue loss (r=0.43, P=4.6×10(-9); r=0.61, P=1.4×10(-18)), correcting for the time between scans.
  
  
  CONCLUSIONS
  ChaCo scores varied, but the most affected regions included those with sensorimotor, perception, learning, and memory functions. Correlations between baseline ChaCo and subsequent tissue loss suggest that the Network Modification Tool could be used to identify regions most susceptible to remote degeneration from acute infarcts.
authors: From the Department of Radiology (A.K., A.R.), Brain and Mind Research Institute (A.K., H.K., B.B.N., A.R., C.I.), and Department of Neurology (H.K., B.B.N., C.I.), Weill Cornell Medical College, New York, NY.
authors: Amy
authors: A
authors: Kuceyeski
authors: Hooman
authors: H
authors: Kamel
authors: Babak B
authors: BB
authors: Navi
authors: Ashish
authors: A
authors: Raj
authors: Costantino
authors: C
authors: Iadecola
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001921:Brain / Q000473:pathology*; D002545:Brain Ischemia / Q000473:pathology*; D002544:Cerebral Infarction / Q000473:pathology; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D009415:Nerve Net / Q000473:pathology*; D009434:Neural Pathways / Q000473:pathology; D015588:Observer Variation; D011237:Predictive Value of Tests; D020521:Stroke / Q000473:pathology*; D057188:Workflow
keywords: brain imaging; brain infarction; diffusion magnetic resonance imaging; neural pathways; stroke