Altered Dynamic Functional Network Connectivity in Healthy Adults with Acute Pain: Findings from the Human Connectome Project.

Characterizing the neural signature of pain and its modulation is critical for assessing treatment efficacy and conducting translational clinical research. However, the dynamics of pain processing in the brain have remained largely unknown. In this study, we employed independent component analysis (ICA) as a data-driven clustering method on resting-state functional magnetic resonance imaging (fMRI) to obtain intrinsic connectivity networks (ICNs) in a cohort of healthy adults from the Human Connectome Project (HCP) who were identified as having acute pain.

Relationships Between Brain Intrinsic Connectivity Networks and Measures of Cognition and Emotion: A Study of the Human Connectome Project Data.

Growing evidence suggests that variations in cognitive and emotional behavior are associated with variations in brain function. To achieve a more comprehensive assessment, data-driven techniques, specifically independent component analysis (ICA), can be employed to generate outcome variables that describe unique but complementary aspects of functional connectivity within and between networks. In this study, resting-state fMRI and behavioral data were collected from 50 healthy participants in the Human Connectome Project.

A combined exercise and cognitive training intervention induces fronto-cingulate cortical plasticity in first-episode psychosis patients.

Objective: Schizophrenia (SZ) is characterized by neurobiological and associated cognitive and functional deficits, including pronounced cortical thinning, that lead to acute and long-term functional impairment. Research with older adults supports the role of non-pharmacological interventions, such as exercise (E) and cognitive training (CT), for cognitive impairments. This literature influenced the development of combined CT&E treatments for individuals with SZ.

Examining the Influence of Spatial Smoothing on Spatiotemporal Features of Intrinsic Connectivity Networks at Low ICA Model Order.

Using a relatively high model order of independent component analysis (ICA with 75 ICs) of functional magnetic resonance imaging (fMRI) data, we have reported a clear effect of spatial smoothing Gaussian kernel size on spatiotemporal properties of intrinsic connectivity networks (ICNs). However, many if not the majority of ICA fMRI studies are usually performed at low model order, e.g.

The Influence of Spatial Smoothing Kernel Size on the Whole-brain Dynamic Functional Network Connectivity and Meta-state Parameters.

In functional magnetic resonance imaging (fMRI), spatial smoothing procedure is generally a stable step in the preprocessing stream. Previous research (including ours) suggested dependency of the static functional connectivity on the size of the spatial smoothing kernel size. But its impact on the time-varying patterns of functional connectivity has not been investigated.