Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders

Abstract

SEE MATTAR ET AL DOI101093/AWW151 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demonstrate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architecture is modulated by local blood oxygen level-dependent activity and α-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dyna...

Functional connectivity-based subtypes of individuals with and without autism spectrum disorder

“…http://dx.doi.org/10.1101/198093 doi: bioRxiv preprint first posted online Oct. 4, 2017; total scores, with most connections involving the right medial superior frontal gyrus and left middle temporal pole (Chen et al, 2017), and positive correlations between temporal variability in several DMN regions and RRB scores (Zhang et al, 2016). Crucially, we did not find reliable relationships between FC and behaviour when all ASD participants were considered as a single group, which reveals the importance of defining subtypes of ASD in order to elucidate relationships between FC and behavioural measures.…”

“…Network-level analyses for the second LV showed that positive BSRs (ASD > TD) were reliable within the FPN and OCC, and negative BSRs (TD > ASD) were reliable within the DMN and SMN. Decreased temporal stability, or increased variability, has been reported in recent studies of dynamic FC in individuals with ASD (Chen et al, 2017;Falahpour et al, 2016;Zhang et al, 2016). Chen et al (2017) found a cluster of connections that were more variable in ASD, and were particularly prominent in prefrontal and temporal regions, especially the right medial superior frontal gyrus.…”

“…Overall, these "hypervariant" connections tended to occur for long-range connections. Zhang et al (2016) reported increased variability of connections involving primarily medial frontal regions. Falahpour et al (2016) reported increased connection variability amongst DMN regions, specifically between the PCC and medial PFC, and between the medial PFC and lateral parietal cortex.…”

“…It is particularly valuable to study dynamic FC in ASD, as it has been suggested that the disorder is characterized by a decreased signal to noise ratio in the brain; thus, noisier, more variable signaling can be expected (Rubenstein & Merzenich, 2003) and may mediate the atypical patterns of static FC (Falahpour et al, 2016). While little research has been conducted on the dynamic nature of FC in individuals with ASD, recent studies have reported increased variability of FC, particularly for connections involving the default mode network (Chen et al, 2017;Falahpour et al, 2016;Zhang et al, . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder.…”

A Hypothesis of Autism Approached with the Nonlinear Model

“…It is also well-recognized that visual inputs or behavior activates specific areas of the patient's brain, and Jie Zhang and other researchers have experimentally found that autistic patients exhibit higher variability in specific brain areas [9].The patients obtain some details of the information through some action, which will activate the brain mirror reflection area, thus the abnormal brain structures would lead abnormal behaviors, Then a positive feedback system is formed.…”