Altered Resting State Complexity in Schizophrenia

By D. Bassett, Brent G. Nelson, Bryon A. Mueller, Jazmin Camchong, Kelvin O. Lim. NeuroImage, 2011

424 citations recorded by the source corpus.

The complexity of the human brain’s activity and connectivity varies over temporal scales and is altered in disease states such as schizophrenia. Using a multi-level analysis of spontaneous low-frequency fMRI data stretching from the activity of individual brain regions to the coordinated connectivity pattern of the whole brain, we investigate the role of brain signal complexity in schizophrenia. Specifically, we quantitatively characterize the univariate wavelet entropy of regional activity, the bivariate pairwise functional connectivity between regions, and the multivariate network organization of connectivity patterns. Our results indicate that univariate measures of complexity are less sensitive to disease state than higher level bivariate and multivariate measures. While wavelet entropy is unaffected by disease state, the magnitude of pairwise functional connectivity is significantly decreased in schizophrenia and the variance is increased. Furthermore, by considering the network structure as a function of correlation strength, we find that network organization specifically of weak connections is strongly correlated with attention, memory, and negative symptom scores and displays potential as a clinical biomarker, providing up to 75% classification accuracy and 85% sensitivity. We also develop a general statistical framework for the testing of group differences in network properties, which is broadly applicable to studies where changes in network organization are crucial to the understanding of brain function.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/e4dc2999995aef67f0f9cd492d63dda855119d2b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

Structural connectomics in brain diseases (2013, neutral): “…are testing for between-groups cost alterations prior further analyses, or adopting cost-integration, i.e. AC C EP TE D M AN U SC R IP T ACCEPTED MANUSCRIPT integrate network measures over a range of thresholds on the network cost (Ginestet et al., 2011; He et al., 2009; Bassett et al., 2012).”
Clinical applications of the functional connectome (2013, neutral): “Others have focused on regional measures of variance (Cheng et al., 2012; Sato et al., 2012), and regional or global graph statistics (Bassett et al., 2012; Cheng et al., 2012; Dai et al., 2012; Dey et al., 2012; Lord et al., 2012; Sato et al., 2012; Zhang et al., 2012; Zhu et al., 2008).”
Small-World Brain Networks Revisited (2016, methodology): “Binary graph analysis has usefully measured high connection density, due to the existence of many new anatomical connections, but binarization of these data is not the best way to understand their complex topology and its economical embedding in anatomical space [31, 38, 39, 81, 84].”
Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls (2016, background): “t3:44 rsfMRI Schizophrenia Size of connected components in graphs build from correlation among time-courses for 90 AAL regions N/A SVM HC = 29, SZ = 29 Total = 58 75% Bassett et al. (2012)”
Two’s company, three (or more) is a simplex (2016, methodology): “However it is diﬃcult to make a principled choice of threshold (Ginestet et al, 2011; Bassett et al, 2012; Garrison et al, 2015; Drakesmith et al, 2015; Sala et al, 2014; Langer et al, 2013), and the resulting network discards a great deal of information.”
GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, background): “, 2013), Functional Data Analysis (Bassett et al., 2012) or re-sampling approach (Gong et al.”
Cliques and cavities in the human connectome (2016, background): “Weaker connections have been shown to distinguish between health individuals and those with schizophrenia (Bassett et al. 2012), and have also been shown to predict individual differences in intelligence (Cole et al.”
GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks (2012, methodology): “For structural network analysis, GAT accepts gray matter volume/surface area/cortical thickness data of groups, extracts structural correlation networks, applies different thresholding schemes for comparing networks between groups, calculates network measures for different thresholding schemes, esti”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, neutral): “The disadvantage of the AUC is that significant experimental effects present at only a small range of densities can be lost after integrating AC C EP TE D M AN U SC R IP T over a larger range of non-significant densities.”
Resting state fMRI: A review of methods and clinical applications (2012, neutral): “fMRI has been applied to distinguish patients with psychiatric diseases from controls, including major depressive disorder(75) and schizophrenia.(76,77) RS-fMRI has also been used to identify patients with autism(78) and attention deficit/hyperactivity disorder.”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “This type of approach is seeing increasing use [150, 151, 111], with some state-of-the-art results obtained with this technique [147], and clinical prediction starting to be quite successful [152].”
Structural foundations of resting-state and task-based functional connectivity in the human brain (2013, background): “Note that these resting-state scans are similar in length to those used in previous studies to identify meaningful resting state networks [2, 3, 4, 5], and they are significantly longer than those used to examine the temporal evolution of brain network dynamics [2, 6].”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, methodology): “Generally, functional connectivity with group difference are first identified using different statistical tests such as t-test, Welch’s t-test and ranksum-test and then these functional connections are used as input features of classification approaches (Calhoun et al., 2008; Anderson et al., 2011; ”
Activity Dependent Degeneration Explains Hub Vulnerability in Alzheimer's Disease (2012, background): “Interestingly, the relation between regional activity and large-scale functional connectivity has recently also addressed with respect to schizophrenia [78,79].”