NeuroImage
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Voxel-based morphometry (VBM) is a widely applied method in computational neurosciences but it is currently recommended to compare only data collected at a single MRI scanner. Multi-site VBM would be a desirable approach to increase group size and, thus, statistical power. We aimed to assess if multi-site VBM is feasible on similar hardware and compare the magnitude of inter- and intra-scanner differences. 18 healthy subjects were scanned in two identical 3T MRI scanners using different head coil designs, twice in scanner A and once in scanner B. 3D T1-weighted images were processed with SPM8 and FSL4.1 and compared as paired t-test (scan versus re-scan) on a voxel basis by means of a general linear model (GLM). ⋯ Intra-scanner scan/re-scan differences were generally weaker and did not exceed a p<0.05 (FWE corrected) threshold in the GLM analysis. At 3T profound inter-scanner differences are to be expected that could severely confound an unbalanced VBM analysis. These are like related to the receive bias of the radio-frequency hardware.
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Huntington's disease (HD) displays progressive striatal atrophy that occurs long before the onset of clinical motor symptoms. As there is no treatment for the disease once overt symptoms appear, it has been suggested that neuroprotective therapy given during this presymptomatic period might slow progression of the disease. This requires biomarkers that can reliably detect early changes and are sensitive to treatment response. ⋯ We report here that N171-82Q HD mice exhibit adult-onset and progressive brain atrophy in the striatum and neocortex as well as in whole brain; the progressive atrophy in striatum and neocortex is positively correlated with motor deficits. Most notably, MRI also detected neuroprotective effects of sertraline treatment, a neuroprotective agent confirmed in our previous studies. Our present studies provide the first evidence that longitudinal structural MRI measures can detect the therapeutic effect in HD mice, suggesting that such measures in brain could be valuable biomarkers in HD clinical trials.
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Characterization and quantification of magnetic resonance perfusion images is important for clinical interpretation, though this calls for a reproducible and accurate method of analysis and a robust healthy reference. The few studies which have examined the perfusion of the healthy brain using dynamic susceptibility contrast (DSC) imaging were largely limited to manual definition of the regions of interest (ROI) and results were dependent on the location of the ROI. The current study aimed to develop a methodology for DSC data analysis and to obtain reference values of healthy subjects. ⋯ Additionally, regional perfusion differences were studied and revealed a prolonged mean transient time and a trend for higher vascularity in the posterior compared with the anterior and middle cerebral vascular territories. While additional studies are required to confirm our findings, this result may have important clinical implications. The proposed unsupervised multiparametric method enabled accurate tissue differentiation, is easy replicable and has a wide range of applications in both pathological and healthy brains.
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The cognitive activity of the human brain benefits from the functional connectivity of multiple brain regions that form specific, functional brain networks. Recent studies have indicated that the relationship between brain regions can be investigated by examining the temporal interaction (known as functional connectivity) of spontaneous blood oxygen level-dependent (BOLD) signals derived from resting-state functional MRI. Most of these studies plausibly assumed that inter-regional interactions were temporally stationary. ⋯ This dynamic pattern was also observed for the interactions between different functional networks. In addition, the spatial pattern of dynamic connectivity maps obtained from neighboring time points had a high similarity. Overall, this study provides insights into the dynamic properties of resting-state functional networks.
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In investigations of the brain's resting state using functional magnetic resonance imaging (fMRI), a seed-based approach is commonly used to identify brain regions that are functionally connected. The seed is typically identified based on anatomical landmarks, coordinates, or the location of brain activity during a separate task. However, anatomical boundaries may be difficult to discern, and designing a task to interrogate desired brain regions of interest may be difficult, especially when subject compliance is in question, as in many patient studies. ⋯ Connectivity maps generated by rest-based seeds and task-based seeds were statistically equivalent; however, only 3 min of data were required to reach significance for rest-based seeds compared to an estimated 6 min for task-based seeds. Rest-based seeds also exhibited good inter-experimenter reproducibility. These findings suggest that seed regions based on inter-voxel cross-correlation of resting-state signals can be used as an alternative approach for connectivity analysis when task-related activity is not available or difficult to acquire, as in some patient studies.