NeuroImage
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Network analysis has become a tool of choice for the study of functional and structural Magnetic Resonance Imaging (MRI) data. Little research, however, has investigated connectivity dynamics in relation to varying cognitive load. In fMRI, correlations among slow (<0.1 Hz) fluctuations of blood oxygen level dependent (BOLD) signal can be used to construct functional connectivity networks. ⋯ The results were found to be highly sensitive to the frequency band used for the computation of the between-region correlations, with the relationship between weighted cost and behavioral performance being most salient at very low frequencies (0.01-0.03 Hz). These findings are discussed in relation to the integration/specialization functional dichotomy. The pruning of functional networks under increasing cognitive load may permit greater modular specialization, thereby enhancing performance.
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Resting-state MRI (rs-fMRI) is a powerful procedure for studying whole-brain neural connectivity. In this study we provide the first empirical evidence of the longitudinal reliability of rs-fMRI in children. We compared rest-retest measurements across spatial, temporal and frequency domains for each of six cognitive and sensorimotor intrinsic connectivity networks (ICNs) both within and between scan sessions. ⋯ For the visual network, within-session T1 correlated with the T2 low-frequency power, across participants. These measures from resting-state data in children were consistent across multiple domains (spatial, temporal, and frequency). Resting-state connectivity is therefore a reliable method for assessing large-scale brain networks in children.
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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.