NeuroImage
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The average pathlength map: a diffusion MRI tractography-derived index for studying brain pathology.
Magnetic resonance diffusion tractography provides a powerful tool for the assessment of white matter architecture in vivo. Quantitative tractography metrics, such as streamline length, have successfully been used in the study of brain pathology. To date, these studies have relied on a priori knowledge of which tracts are affected by injury or pathology and manual delineation of regions of interest (ROIs) for use as waypoints in tractography. ⋯ Our analysis shows that voxel-wise average pathlength values are comparable to fractional anisotropy (FA) in terms of reproducibility and variability. For the TBI patient, we observed a significant reduction in streamline pathlength in the genu of the corpus callosum and its projections into the frontal lobe. This study demonstrates that the average pathlength map can be used for voxel-based analysis of a quantitative tractography metric within the whole brain, removing both the dependence on a priori knowledge of affected pathways and time-consuming manual delineation of ROIs.
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Exploratory analysis of functional MRI data allows activation to be detected even if the time course differs from that which is expected. Independent Component Analysis (ICA) has emerged as a powerful approach, but current extensions to the analysis of group studies suffer from a number of drawbacks: they can be computationally demanding, results are dominated by technical and motion artefacts, and some methods require that time courses be the same for all subjects or that templates be defined to identify common components. We have developed a group ICA (gICA) method which is based on single-subject ICA decompositions and the assumption that the spatial distribution of signal changes in components which reflect activation is similar between subjects. ⋯ In the chemosensory task, additional regions are identified by FENICA and temporal concatenation ICA that we show is related to the stimulus, but exhibit a delayed response. FENICA is a fully exploratory method that allows activation to be identified without assumptions about temporal evolution, and isolates activation from other sources of signal fluctuation in fMRI. It has the advantage over other gICA methods that it is computationally undemanding, spotlights components relating to activation rather than artefacts, allows the use of familiar statistical thresholding through deployment of a higher level GLM analysis and can be applied to studies where the paradigm is different for all subjects.
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Patients with Parkinson's disease (PD) have difficulty in performing self-initiated movements. The neural mechanism of this deficiency remains unclear. In the current study, we used functional MRI (fMRI) and psychophysiological interaction (PPI) methods to investigate the changes in effective connectivity of the brain networks during performance of self-initiated movement in PD patients. ⋯ The striatum-cortical and striatum-cerebellar connections are weakened. In contrast, the connections between cortico-cerebellar motor regions are strengthened and may compensate for basal ganglia dysfunction. These altered interregional connections are more deviant when the disorder is more severe, and, therefore, our results give further insight into the explanation for the difficulty in performing self-initiated movements in PD.
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The human insula is hidden in the depth of the cerebral hemisphere by the overlying frontal and temporal opercula, and consists of three cytoarchitectonically distinct regions: the anterior agranular area, posterior granular area, and the transitional dysgranular zone; each has distinct histochemical staining patterns and specific connectivity. Even though there are several studies reporting the functional connectivity of the insula with the cingulated cortex, its relationships with other brain areas remain elusive in humans. Therefore, we decided to use resting state functional connectivity to elucidate in details its connectivity, in terms of cortical and subcortical areas, and also of lateralization. ⋯ The clear bipartition of the insula was confirmed by negative correlation analysis. Correlation maps are partially lateralized: the salience network, related to the ventral anterior insula, displays stronger connections with the anterior cingulate cortex on the right side, and with the frontal cortex on the left side; the posterior network has stronger connections with the superior temporal cortex and the occipital cortex on the right side. These results are in agreement with connectivity studies in primates, and support the use of resting state functional analysis to investigate connectivity in the living human brain.