Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia

Abstract

The complex organization of connectivity in the human brain is incompletely understood. Recently, topological measures based on graph theory have provided a new approach to quantify large-scale cortical networks. These methods have been applied to anatomical connectivity data on nonhuman species, and cortical networks have been shown to have small-world topology, associated with high local and global efficiency of information transfer. Anatomical networks derived from cortical thickness measurements have shown the same organizational properties of the healthy human brain, consistent with similar results reported in functional networks derived from resting state functional magnetic resonance imaging (MRI) and magnetoencephalographic data. Here we show, using anatomical networks derived from analysis of inter-regional covariation of gray matter volume in MRI data on 259 healthy volunteers, that classical divisions of cortex (multimodal, unimodal, and transmodal) have some distinct topological attributes. Although all cortical divisions shared nonrandom properties of small-worldness and efficient wiring (short mean Euclidean distance between connected regions), the multimodal network had a hierarchical organization, dominated by frontal hubs with low clustering, whereas the transmodal network was assortative. Moreover, in a sample of 203 people with schizophrenia, multimodal network organization was abnormal, as indicated by reduced hierarchy, the loss of frontal and the emergence of nonfrontal hubs, and increased connection distance. We propose that the topological differences between divisions of normal cortex may represent the outcome of different growth processes for multimodal and transmodal networks and that neurodevelopmental abnormalities in schizophrenia specifically impact multimodal cortical organization.

MR connectomics: Principles and challenges

“…Bassett et al, 2008), and they may become important diagnostic tools in future clinical applications of the connectome.…”

“…Schizophrenia and Alzheimer's disease are the two most prominent examples (Bassett et al, 2008;Buckner et al, 2009). In both cases it has been shown that widespread changes in cortical thickness, white matter structure, brain electrophysiology and metabolism occur and are hypothesized to be related to changes in the underlying connectivity, which likely precisely translates into the related cognitive impairment.…”

Structure and dynamics of functional networks in child-onset schizophrenia

“…The description of the large-scale human brain organization has allowed studying the hypothesis that some neuropsychiatric disorders are related to disarrays of connectivity between cortical areas. Indeed, aberrant structural patterns have been verified in the brain organization of subjects with autism, schizophrenia, depression, anxiety disorders, Alzheimer's disease and frontotemporal dementia (Supekar et al, 2008;He et al, 2008;Bassett et al, 2008;Bassett and Bullmore, 2009;Lynall et al, 2010;Menon, 2011). Revealing these disorder related differences has improved the understanding of how dysfunction of cognitive and emotion regulation processes are related to the altered brain network organization.…”

“…In case of schizophrenia in general, data obtained from fMRI suggest that cortical networks of schizophrenic adults are less small-world type, less hierarchical, less clustered, less efficiently wired (Bassett et al, 2008;Bullmore and Sporns, 2009) and have a less hub-dominated configuration (Lynall et al, 2010). Moreover, Basset et al (Bassett et al, 2008) showed that multimodal network organization of patients with schizophrenia are abnormal, presenting increased connection distance and reduced hierarchy.…”

“…Moreover, Basset et al (Bassett et al, 2008) showed that multimodal network organization of patients with schizophrenia are abnormal, presenting increased connection distance and reduced hierarchy. In adult schizophrenic patients previous studies already illustrated the usefulness of non-invasive diagnosis based on the analysis of restingstate fMRI data.…”