NeuroImage
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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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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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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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Brain white matter connections have become a focus of major interest with important maturational processes occurring in newborns. To study the complex microstructural developmental changes in-vivo, it is imperative that non-invasive neuroimaging approaches are developed for this age-group. Multi-b-value diffusion weighted imaging data were acquired in 13 newborns, and the biophysical compartment diffusion models CHARMED-light and NODDI, providing new microstructural parameters such as intra-neurite volume fraction (νin) and neurite orientation dispersion index (ODI), were developed for newborn data. ⋯ Late maturing regions (external capsule and periventricular crossroads of pathways) had lower νin values, but displayed significant differences in ODI. The compartmented models CHARMED-light and NODDI bring new indices corroborating the cellular architectures, with the lowest νin, reflecting the late maturation of areas with thin non-myelinated fibers, and with highest ODI indicating the presence of fiber crossings and fanning. The application of biophysical compartment diffusion models adds new insights to the brain white matter development in vivo.
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Stimulus repetition can produce neural response attenuation in stimulus-category selective networks within the occipito-temporal lobe. It is hypothesized that this neural suppression reflects the functional sharpening of local neuronal assemblies which boosts information processing efficiency. This neural suppression phenomenon has been mainly reported during conditions of conscious stimulus perception. ⋯ By measuring the temporal dynamics of high-frequency broadband gamma activity in VOTC and testing for main and interaction effects, we report that early processing of words in word-form selective networks exhibits a temporal cascade of modulations by stimulus repetition and masking: neuronal attenuation initially is observed in response to repeated words (irrespective of consciousness), that is followed by a second modulation contingent upon word reportability (irrespective of stimulus repetition). Later on (>300ms post-stimulus), a significant effect of conscious perception on the extent of repetition suppression was observed. The temporal dynamics of consciousness, the recognition memory processes and their interaction revealed in this study advance our understanding of their contributions to the neural mechanisms of word processing in VOTC.