Brain connectivity
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The default-mode network (DMN) was shown to have aberrant blood oxygenation-level-dependent (BOLD) activity in major depressive disorder (MDD). While BOLD is a relative measure of neural activity, cerebral blood flow (CBF) is an absolute measure. Resting-state CBF alterations have been reported in MDD. ⋯ Hypoperfusion within the DMN in MDD is not specific to the DMN. Still, depression severity was linked to DMN node perfusion, supporting a role of the DMN in depression pathobiology. The finding has implications for the interpretation of BOLD functional magnetic resonance imaging data in MDD.
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Randomized Controlled Trial
Effects of L-dopa and oxazepam on resting-state functional magnetic resonance imaging connectivity: a randomized, cross-sectional placebo study.
Pharmacological functional brain imaging has traditionally focused on neuropharmacological modulations of event-related responses. The current study is a randomized, cross-sectional resting-state functional magnetic resonance imaging study where a single dose of commonly prescribed amounts of either benzodiazepine (oxazepam), L-dopa, or placebo was given to 81 healthy subjects. It was hypothesized that the connectivity in resting-state networks would be altered, and that the strength of connectivity in areas rich in target receptors would be particularly affected. ⋯ L-dopa mainly decreased connectivity between the Am and bilateral inferior frontal gyri and between midline regions of the DMN. The fALFF analysis revealed that L-dopa decreased low-frequency fluctuations in the cerebellum. It was concluded that the overall effects of single administrations of oxazepam and L-dopa on resting-state connectivity were small both in strength and in spatial extent, and were on par with placebo effects as revealed by comparing the two placebo groups.
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In the past decade, the fast and transient coupling and uncoupling of functionally related brain regions into networks has received much attention in cognitive neuroscience. Empirical tools to study network coupling include functional magnetic resonance imaging (fMRI)-based functional and/or effective connectivity, and electroencephalography (EEG)/magnetoencephalography-based measures of neuronal synchronization. Here we use simultaneously recorded EEG and fMRI to assess whether fMRI-based connectivity and frequency-specific EEG power are related. ⋯ The decreased connectivity within the visual system may indicate an enhanced functional inhibition during a higher alpha activity. This higher inhibition level also attenuates long-range intrinsic functional antagonism between the visual cortex and the other thalamic and cortical regions. Together, these results illustrate that power fluctuations in posterior alpha oscillations result in local and long-range neural connectivity changes.
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The human brain is inherently organized as separate networks, as has been widely revealed by resting-state functional magnetic resonance imaging (fMRI). Although the large-scale functional connectivity can be partially explained by the underlying white-matter structural connectivity, the question of whether the underlying functional connectivity is related to brain metabolic factors is still largely unanswered. The present study investigated the presence of metabolic covariant networks across subjects using a set of fluorodeoxyglucose ((18)F, FDG) positron-emission tomography (PET) images. ⋯ In contrast, homotopic intersubject metabolic covariances observed using PET were comparable to the corresponding fMRI resting-state time-series correlations. The current study provides preliminary illustration, suggesting that the human brain metabolism pertains to organized covariance patterns that might partially reflect functional connectivity as revealed by resting-state blood oxygen level dependent (BOLD). The discrepancy between the PET covariance and BOLD functional connectivity might reflect the differences of energy consumption coupling and ongoing neural synchronization within these brain networks.
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Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. ⋯ The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.