Cross-Frequency rs-fMRI Network Connectivity Patterns Manifest Differently for Schizophrenia Patients and Healthy Controls

Abstract

Patterns of resting state fMRI functional network connectivity in schizophrenia patients have been shown to differ markedly from that of healthy controls. While some studies have explored connectivity within fixed frequency bands, the question of network phase synchrony across disparate frequency bands, or cross-frequency connectivity, has remained surprisingly underexplored. Computational modeling at the neuronal scale however has long acknowledged the existence of coupled fast and slow subsystems. Here we present preliminary evidence that cross-frequency coupling exists at the network level, that it patterns in meaningful ways over functional domains, and that this patterning differs between the healthy population and individuals with diagnosed schizophrenia.

Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional Brain Motifs

“…The evolving motifs fluidly move through transient states of connectivity that resemble familiar formations obtained from the basic time-blind clustering into single transiently realized connectivity patterns ( Figure 2 ). Consistent with published results [35, 49, 53, 54, 57] on occupancy rates of time-blind SNAPdFNC states, we find here that: strongly modularized and hyperconnected patterns feature more prominently in EVOdFNCs with greater representational importance in controls (1, 5, 7 and 8) and in EVOdFNCs whose representational importance in controls is not statistically distinguishable from that in patients (4, 6, 9); weak connectivity and modularized negative DMN-to-other (DMNneg) patterns (2, 3) feature more prominently in EVOdFNCs with significantly higher representational importance in patients. A novel modularized pattern of functional organization, not seen in time-blind SNAPdFNC states, appears in EVOdFNC 10, which features a persistent stretch of strong modularized negative SM-VIS/CC/DMN connectivity.…”

“…After dropping the first 3 and final TRs, this procedure yields a 47 47 1 2 ⁄ 1081-dimensional dFNC measure on each of 136 windows of length 22TRs for each subject. Clustering this collection of time-resolved connectivity observations using Matlab's implementation of k-means clustering (Euclidean distance, 2000 iterations, 250 repetitions, 5 clusters chosen according the elbow criterion) produces five non-varying cluster centroids, often referred to as "dynamic states" or dFNC states (Figure 2) reported in previous studies [35,[49][50][51][52][53][54][55][56][57][58]. We mention them because they are referenced later in the results section of this paper.…”

Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions

“…The statistical rigor and use of both frequency and time information taken by this approach are appealing, but difficult to transfer to whole-brain studies due to the explosion in the dimensionality of the data, perhaps explaining why this approach has not been widely utilized. More recently, approaches to obtain information about the multiple frequencies that mediate dynamic functional connectivity at the level of the whole brain have been introduced (Miller et al, 2016a;Yaesoubi et al, 2015.…”

A joint time-frequency analysis of resting-state functional connectivity reveals novel patterns of connectivity shared between or unique to schizophrenia patients and healthy controls

“…This includes observing differences in the frequency of activation and co-activation between different regions or functional networks of the brain( Calhoun et al, 2011 ; Yu et al, 2014 ; Meda et al, 2015 ; Hoptman et al, 2010 ) and also, more recently, it includes observation in the temporal dynamic changes of the frequency of co-activation between the same pair of regions or networks( Chang and Glover, 2010 ; Yaesoubi et al, 2015 ). The former observations have shown that evaluations of frequency specific activation and co-activation enable us to capture significant differences between groups of patients and healthy controls ( Miller et al, 2016 ) as it is shown to carry useful information related to the underlying neurophysiological processes. For example, the default-mode network has been shown to exhibit significantly more high frequency fluctuations in patients and significantly less low frequency fluctuations in controls, perhaps related to decreased cognitive efficiency ( Garrity, 2007 ).…”