A machine learning approach to automated structural network analysis: application to neonatal encephalopathy.

ABSTRACT: Neonatal encephalopathy represents a heterogeneous group of conditions associated with life-long developmental disabilities and neurological deficits. Clinical measures and current anatomic brain imaging remain inadequate predictors of outcome in children with neonatal encephalopathy. Some studies have suggested that brain development and, therefore, brain connectivity may be altered in the subgroup of patients who subsequently go on to develop clinically significant neurological abnormalities. Large-scale structural brain connectivity networks constructed using diffusion tractography have been posited to reflect organizational differences in white matter architecture at the mesoscale, and thus offer a unique tool for characterizing brain development in patients with neonatal encephalopath

SUBMITTER: Ziv E 

PROVIDER: S-EPMC3840059 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature