NeuroImage
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Resting-state data sets contain coherent fluctuations unrelated to neural processes originating from residual motion artefacts, respiration and cardiac action. Such confounding effects may introduce correlations and cause an overestimation of functional connectivity strengths. In this study we applied several multidimensional linear regression approaches to remove artificial coherencies and examined the impact of preprocessing on sensitivity and specificity of functional connectivity results in simulated data and resting-state data sets from 40 subjects. ⋯ Results in simulated data sets compared with result of human data strongly suggest that anticorrelations are indeed introduced by global signal regression and should therefore be interpreted very carefully. In addition, global signal regression may also reduce the sensitivity for detecting true correlations, i.e. increase the number of false negatives. Concluding from our results we suggest that is highly recommended to apply correction against realignment parameters, white matter and ventricular time courses, as well as the global signal to maximize the specificity of positive resting-state correlations.
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Comparative Study
Direct quantitative comparison between cross-relaxation imaging and diffusion tensor imaging of the human brain at 3.0 T.
Cross-relaxation imaging (CRI) describes the magnetization transfer within tissues between mobile water protons and macromolecular protons. Whole-brain parametric maps of the principle kinetic components of magnetization transfer, the fraction of macromolecular protons (f) and the rate constant (k), revealed detailed anatomy of white matter (WM) fiber tracts at 1.5 T. In this study, CRI was first adapted to 3.0 T, and constraints for transverse relaxation times of water and macromolecular protons were identified to enable unbiased f and k estimation. ⋯ The lack of association between CRI and FA in WM is consistent with differences in the underlying physical principles between techniques - fiber density vs. directionality, respectively. The association in GM may be attributable to variable axonal density unique to each structure. Our findings suggest that whole-brain CRI provides distinct quantitative information compared to DTI, and CRI parameters may prove constructive as biomarkers in neurological diseases.