Humans exhibit significant interindividual variability in behavioral reaction time (RT) performance yet the underlying neural mechanisms for this variability remain largely unknown. It has been proposed that interindividual variability in RT performance may be due to differences in white matter (WM) physiological properties, although such a relationship has never been demonstrated in cortical projection or association pathways in healthy young adults. Using diffusion tensor MRI (DTI), we sought to test whether diffusion tensor fractional anisotropy (FA), a measure of the orientational coherence of water self-diffusion, is regionally correlated with RT on a visual self-paced choice RT (CRT) task. CRT was found to be significantly correlated with FA in projection and association pathways supporting visuospatial attention including the right optic radiation, right posterior thalamus, and right medial precuneus WM. Significant correlations were also observed in left superior temporal sulcus WM and the left parietal operculum. The lateralization of the CRT-FA correlation to right visual and parietal WM pathways is consistent with the specialization of right visual and parietal cortices for visuospatial attention. The localization of the CRT-FA correlations to predominately visual and parietal WM pathways, but not to motor pathways or the corpus callosum indicates that individual differences in visual CRT performance are associated with variations in the WM underlying the visuospatial attention network as opposed to pathways supporting motor movement or interhemispheric transmission. diffusion tensor MRI B ehavioral reaction time (RT) performance is widely used in cognitive neuroscience research as a measure of information processing speed. RT performance is known to vary significantly across individuals (1-4), yet little is known about the neural basis for this variability. Identifying the neural substrates for interindividual differences in RT performance would provide invaluable insight into the mechanisms of behavioral performance and information processing speed in health, aging, and psychiatric disorders.It has been proposed that interindividual differences in RT performance may be due to differences in white matter (WM) physiology, particularly, myelination (1, 5-8). Increased myelination would result in faster (or less variable) nerve conduction velocity (NCV), which would result in faster RTs. The relationship between NCV and WM physiological properties such as myelination and axon diameter is well established (9). However, it is not clear whether interindividual differences in WM physiology are responsible for interindividual differences in RT.Until recently, it has been difficult to evaluate such a relationship because of the lack of a method for measuring WM microstructural properties in vivo. Diffusion tensor imaging (DTI) (10-12) is an MRI technique developed over the last decade that has been shown to provide high sensitivity to WM pathology (13). DTI measures the water self-diffusion tensor within each...

Cerebral amyloid angiopathy (CAA) is a common cause of symptomatic intracerebral hemorrhage (ICH), as well as small asymptomatic hemorrhage in the elderly. We used gradient-echo MRI to analyze spatial distribution of 321 hemorrhages in 59 patients with probable CAA-related ICH. Hemorrhagic lesions were found preferentially in the temporal (ratio of actual to expected hemorrhages = 1.37) and occipital lobes (ratio = 1.45, p < 0.0001). Within individuals, hemorrhages tended to cluster, regardless of lobe (p < 0.0001). Among subjects followed prospectively for recurrence, clustering of new symptomatic and asymptomatic hemorrhages was observed. These data suggest that regional differences within the brain play a role in the development of CAA-related hemorrhage.

Estimation of noise-induced variability in diffusion tensor imaging (DTI) is needed to objectively follow disease progression in therapeutic monitoring and to provide consistent readouts of pathophysiology. The noise variability of nonlinear quantities of the diffusion tensor (e.g., fractional anisotropy, fiber orientation, etc.) have been quantified using the bootstrap, in which the data are resampled from the experimental averages, yet this approach is only applicable to DTI scans that contain multiple averages from the same sampling direction. It has been shown that DTI acquisitions with a modest to large number of directions, in which each direction is only sampled once, outperform the multiple averages approach. These acquisitions resist the traditional (regular) bootstrap analysis though. In contrast to the regular bootstrap, the wild bootstrap method can be applied to such protocols in which there is only one observation per direction. Here, we compare and contrast the wild bootstrap with the regular bootstrap using Monte Carlo numerical simulations for a number of diffusion scenarios. The regular and wild bootstrap methods are applied to human DTI data and empirical distributions are obtained for fractional anisotropy and the diffusion tensor eigensystem. Spatial maps of the estimated variability in the diffusion tensor principal eigenvector are provided. The wild bootstrap method can provide empirical distributions for tensor-derived quantities, such as fractional anisotropy and principal eigenvector direction, even when the exact distributions are not easily derived.