Knowledge-based classification of neuronal fibers in entire brain.

This work presents a framework driven by parcellation of brain gray matter in standard normalized space to classify the neuronal fibers obtained from diffusion tensor imaging (DTI) in entire human brain. Classification of fiber bundles into groups is an important step for the interpretation of DTI data in terms of functional correlates of white matter structures. Connections between anatomically delineated brain regions that are considered to form functional units, such as a short-term memory network, are identified by first clustering fibers based on their terminations in anatomically defined zones of gray matter according to Talairach Atlas, and then refining these groups based on geometric similarity criteria. Fiber groups identified this way can then be interpreted in terms of their functional properties using knowledge of functional neuroanatomy of individual brain regions specified in standard anatomical space, as provided by functional neuroimaging and brain lesion studies.