Alteration of Basal Ganglia and Right Frontoparietal Network in Early Drug-Naïve Parkinson’s Disease during Heat Pain Stimuli and Resting State

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in healthy individuals as well as in patients with brain disorders. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject's ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients.

Information processing during human cognitive and emotional operations is thought to involve the dynamic interplay of several large-scale neural networks, including the fronto-parietal central executive network (CEN), cingulo-opercular salience network (SN), and the medial prefrontal-medial parietal default mode networks (DMN). It has been theorized that there is a causal neural mechanism by which the CEN/SN negatively regulate the DMN. Support for this idea has come from correlational neuroimaging studies; however, direct evidence for this neural mechanism is lacking. Here we undertook a direct test of this mechanism by combining transcranial magnetic stimulation (TMS) with functional MRI to causally excite or inhibit TMS-accessible prefrontal nodes within the CEN or SN and determine consequent effects on the DMN. Single-pulse excitatory stimulations delivered to only the CEN node induced negative DMN connectivity with the CEN and SN, consistent with the CEN/SN's hypothesized negative regulation of the DMN. Conversely, low-frequency inhibitory repetitive TMS to the CEN node resulted in a shift of DMN signal from its normally low-frequency range to a higher frequency, suggesting disinhibition of DMN activity. Moreover, the CEN node exhibited this causal regulatory relationship primarily with the medial prefrontal portion of the DMN. These findings significantly advance our understanding of the causal mechanisms by which major brain networks normally coordinate information processing. Given that poorly regulated information processing is a hallmark of most neuropsychiatric disorders, these findings provide a foundation for ways to study network dysregulation and develop brain stimulation treatments for these disorders.

The purpose of this work was to evaluate changes in the connectivity patterns of a set of cognitively relevant, dynamically interrelated brain networks in association with cognitive deficits in Parkinson's disease (PD) using resting-state functional MRI. Sixty-five nondemented PD patients and 36 matched healthy controls were included. Thirty-four percent of PD patients were classified as having mild cognitive impairment (MCI) based on performance in attention/executive, visuospatial/visuoperceptual (VS/VP) and memory functions. A data-driven approach using independent component analysis (ICA) was used to identify the default-mode network (DMN), the dorsal attention network (DAN) and the bilateral frontoparietal networks (FPN), which were compared between groups using a dual-regression approach controlling for gray matter atrophy. Additional seed-based analyses using a priori defined regions of interest were used to characterize local changes in intranetwork and internetwork connectivity. Structural group comparisons through voxel-based morphometry and cortical thickness were additionally performed to assess associated gray matter atrophy. ICA results revealed reduced connectivity between the DAN and right frontoinsular regions in MCI patients, associated with worse performance in attention/executive functions. The DMN displayed increased connectivity with medial and lateral occipito-parietal regions in MCI patients, associated with worse VS/VP performance, and with occipital reductions in cortical thickness. In line with data-driven results, seed-based analyses mainly revealed reduced within-DAN, within-DMN and DAN-FPN connectivity, as well as loss of normal DAN-DMN anticorrelation in MCI patients. Our findings demonstrate differential connectivity changes affecting the networks evaluated, which we hypothesize to be related to the pathophysiological bases of different types of cognitive impairment in PD. © 2014 Wiley Periodicals, Inc.