Evaluation of the GTRACT diffusion tensor tractography algorithm: a validation and reliability study.

Fiber tracking, based on diffusion tensor imaging (DTI), is the only approach available to non-invasively study the three-dimensional structure of white matter tracts. Two major obstacles to this technique are partial volume artifacts and tracking errors caused by image noise. In this paper, a novel fiber tracking algorithm called Guided Tensor Restore Anatomical Connectivity Tractography (GTRACT) is presented. This algorithm utilizes a multi-pass approach to fiber tracking. In the first pass, a 3D graph search algorithm is utilized. The second pass incorporates anatomical connectivity information generated in the first pass to guide the tracking in this stage. This approach improves the ability to reconstruct complex fiber paths as well as the tracking accuracy. Validation and reliability studies using this algorithm were performed on both synthetic phantom data and clinical human brain data. A method is also proposed for the evaluating reliability of fiber tract generation based both on the position of the fiber tracts, as well the anisotropy values along the path. The results demonstrate that the GTRACT algorithm is less sensitive to image noise and more capable of handling areas of complex fiber crossing, compared to conventional streamline methods.

White matter volume and cognitive dysfunction in early Huntington's disease.

BACKGROUND: Structural abnormalities of the striatum and cognitive impairments have consistently been shown in patients with Huntington's disease (HD). Fewer studies have examined other cerebral structures in early HD and potential associations with cognition. METHOD: Ten patients with early HD and 10 matched control subjects underwent magnetic resonance imaging to provide quantitative measures (volumes) of cortical gray and white matter and the caudate, putamen, and thalamus. Patients completed the Unified Huntington's Disease Rating Scale, including three cognitive tasks. RESULTS: Although striatal volumes were clearly reduced, white matter was also morphologically abnormal. Cortical gray matter volume was not significantly correlated with cognitive performance. However, the cognitive tasks were most highly correlated with cerebral white matter and, to a lesser degree, striatal volume. CONCLUSIONS: Cerebral white matter volume may be an important variable to examine in future studies of HD.

Clinical correlates of cognitive decline in vascular dementia.

OBJECTIVE: To determine whether demographic data, dementia severity, functional status, whole brain volume (WBV), or subcortical hyperintensity volume (SH) predict subsequent cognitive decline in vascular dementia (VaD). BACKGROUND: The identification of variables that accurately predict progressive cognitive decline in dementia has important clinical implications. METHODS: A cohort of 30 patients with VaD completed neurologic and neuropsychologic examinations and magnetic resonance imaging of the brain at baseline and again after 12 months. All participants met clinical and research criteria for VaD according to standard guidelines. Change scores were computed for measures of verbal fluency, verbal learning, and visual learning. Potential correlates of cognitive change included age, education, score on the Hachinski scale, WBV, SH, and functional ability. RESULTS: As a group, lower WBV and lower Hachinski score correlated with decline in verbal fluency and visual learning, whereas lower Hachinski score correlated with decline in verbal learning. However, when subdivided by disease type, this pattern held only for individuals with evidence of a cortical stroke at baseline. No clinical variables correlated with cognitive decline among individuals without a cortical infarction. CONCLUSIONS: Assessment of cognitive decline in VaD should be guided by dementia subtype, with particular attention directed at severity of cerebral atrophy rather than classic symptoms of infarction.