Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia

Abstract

Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical–subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences.

Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

“…may be a more sensitive marker for mental conditions than metrics about stable characteristics of the brain (16). Preliminary research has already revealed differences in dwell time between controls and both Alzheimer's disease patients (42) and schizophrenics (43). In two recent reviews on resting state dynamics (4,16), it was acknowledged that a better understanding of the relationship between BOLD dynamics and behavior was still needed.…”

Ongoing dynamics in large-scale functional connectivity predict perception

“…These investigations have established the nonstationarity of large-scale functional connectivity that previously had been largely neglected. Some studies have shown characteristic changes of dynamic connectivity in different patient populations (7). However, none of these resting-state studies have directly investigated the functional consequences of ongoing dynamics during performance (in other words, how moment to moment changes in baseline functional connectivity relate to cognition and behavior).…”

Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks

“…The first of these models emphasizes the spatial dimension, and parcellates brain activity into spatially localized static functional networks 2,3,5 . A second model emphasizes the temporal dimension, and parcellates brain activity into dynamically recurring states 32,35,36,51 . While both network and state models are supported by an extensive literature, it remains unclear how dynamic brain states and static brain networks relate.…”

“…Because we used a high-dimensional ICA decomposition of the data, however, the independent components that we used as network nodes were, for the most part, localised to specific brain regions. Following conventions from previous work that used a similar approach 25,32,35,36 , nodes were named according to the spatial localisation of their peak coordinates in combination with a qualitative visual inspection of component maps. The activation timeseries of each node was subject to additional preprocessing steps in the GIFT toolbox, including demeaning and detrending, interpolation over artifact-related outliers ('despiking'), removal of frequencies less than 0.01 Hz or greater than 0.15 Hz using a bandpass filter, and variance normalization of signal intensities.…”

“…That is, the brain may occupy any of a number of connectivity states over time, each with a distinct network architecture 33 . Such a multistable model has been applied to discover novel biomarkers for pathology [34][35][36] and to track changing cognitive demands 37 . Figure 1.…”