NeuroImage
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Comparative Study
Graph theoretical analysis of resting-state MEG data: Identifying interhemispheric connectivity and the default mode.
Interhemispheric connectivity with resting state MEG has been elusive, and demonstration of the default mode network (DMN) yet more challenging. Recent seed-based MEG analyses have shown interhemispheric connectivity using power envelope correlations. The purpose of this study is to compare graph theoretic maps of brain connectivity generated using MEG with and without signal leakage correction to evaluate for the presence of interhemispheric connectivity. ⋯ Graph theoretic analysis of MEG resting state data without signal leakage correction can demonstrate symmetric networks with some resemblance to fMRI networks. These networks however, are an artifact of high local correlation from signal leakage and lack interhemispheric connectivity. Following signal leakage correction, MEG hubs emerge in the DMN, with strong interhemispheric connectivity.
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Why do interactions become more hostile when social relations shift from "me versus you" to "us versus them"? One possibility is that acting with a group can reduce spontaneous self-referential processing in the moral domain and, in turn, facilitate competitor harm. We tested this hypothesis in an fMRI experiment in which (i) participants performed a competitive task once alone and once with a group; (ii) spontaneous self-referential processing during competition was indexed unobtrusively by activation in an independently localized region of the medial prefrontal cortex (mPFC) associated with self-reference; and (iii) we assessed participants' willingness to harm competitors versus teammates. As predicted, participants who showed reduced mPFC activation in response to descriptions of their own moral behaviors while competing in a group were more willing to harm competitors. These results suggest that intergroup competition (above and beyond inter-personal competition) can reduce self-referential processing of moral information, enabling harmful behaviors towards members of a competitive group.
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Comparative Study
Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.
The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. ⋯ Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI.
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We used diffusion tensor imaging (DTI) to investigate relationships between white matter anatomy and different reading subskills in typical-reading adults. A series of analytic approaches revealed that phonological decoding ability is associated with anatomical markers that do not relate to other reading-related cognitive abilities. Thus, individual differences in phonological decoding might relate to connectivity between a network of cortical regions, while skills like sight word reading might rely less strongly on integration across regions. ⋯ In contrast, tract volume underlying the left angular gyrus was related to nonverbal IQ. Finally, connectivity underlying functional ROIs that are differentially active during phonological and semantic processing predicted nonword reading and reading comprehension, respectively. Together, these results provide important insights into how white matter anatomy may relate to both typical reading subskills, and perhaps a roadmap for understanding neural connectivity in individuals with reading impairments.
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Studies employing functional connectivity-type analyses have established that spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals are organized within large-scale brain networks. Meanwhile, fMRI signals have been shown to exhibit 1/f-type power spectra - a hallmark of scale-free dynamics. We studied the interplay between functional connectivity and scale-free dynamics in fMRI signals, utilizing the fractal connectivity framework - a multivariate extension of the univariate fractional Gaussian noise model, which relies on a wavelet formulation for robust parameter estimation. ⋯ Third, in addition to a decrease of the Hurst exponent and inter-regional correlations, task performance modified cross-temporal dynamics, inducing a larger contribution of the highest frequencies within the scale-free range to global correlation. Lastly, we found that across individuals, a weaker task modulation of the frequency contribution to inter-regional connectivity was associated with better task performance manifesting as shorter and less variable reaction times. These findings bring together two related fields that have hitherto been studied separately - resting-state networks and scale-free dynamics, and show that scale-free dynamics of human brain activity manifest in cross-regional interactions as well.