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Small world network measures predict white matter degeneration in patients with early-stage mild cognitive impairment.
Content Provider | CiteSeerX |
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Author | Nir, Talia Jahanshad, Neda Jack, Clifford R. Weiner, Michael W. Toga, Arthur W. Thompson, Paul M. |
Abstract | Alzheimer’s Disease (AD) has long been considered a cortical degenerative disease, but impaired brain connectivity, due to white matter injury, may exacerbate cognitive problems. Predicting brain changes is critically important for early treatment. In a longitudinal diffusion tensor imaging study, we investigated white matter fiber integrity in 19 patients (mean age: 74.7 +/- 8.4 yrs at baseline) displaying early signs of mild cognitive impairment (eMCI). We first examined whether baseline average fractional anisotropy (FA) measures in the corpus callosum (CC) predicted changes in white matter integrity over the following 6 months. We then examined whether “small world ” architecture measures- calculated from baseline connectivity maps- predicted white matter changes over the next 6 months. While average CC FA measures at baseline were not associated with future changes in FA, network measures were a sensitive biomarker for predicting white matter changes during this critical time before AD strikes. Index Terms — diffusion imaging, graph theory, connectivity, predictive models, Alzheimer’s disease 1. |
File Format | |
Access Restriction | Open |
Subject Keyword | White Matter Change Early Sign Sensitive Biomarker Brain Change Baseline Average Fractional Anisotropy Cortical Degenerative Disease White Matter Fiber Integrity Alzheimer Disease Baseline Connectivity Map Predictive Model Mild Cognitive Impairment Brain Connectivity White Matter Injury Network Measure Future Change Ad Strike Graph Theory Longitudinal Diffusion Tensor Index Term Diffusion Imaging Cognitive Problem Small World Architecture Measure Mean Age Average Cc Fa Critical Time Early Treatment Corpus Callosum White Matter Integrity |
Content Type | Text |