Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders

Abstract

To survive in an ever-changing environment, the brain must seamlessly integrate a rich stream of incoming information into coherent internal representations that can then be used to efficiently plan for action. The brain must, however, balance its ability to information from various sources with a complementary capacity to information into modules which perform specialized computations in local circuits. Importantly, evidence suggests that imbalances in the brain's ability to bind together and/or segregate information over both space and time is a common feature of several neuropsychiatric disorders. Most studies have, however, until recently strictly attempted to characterize the principles of integration and segregation in static (i.e. time-invariant) representations of human brain networks, hence disregarding the complex spatio-temporal nature of these processes. In the present Review, we describe how the emerging discipline of may be used to study the causal mechanisms of the integration and segregation of information on behaviourally relevant timescales. We emphasize how novel methods from network science and whole-brain computational modelling can expand beyond traditional neuroimaging paradigms and help to uncover the neurobiological determinants of the abnormal integration and segregation of information in neuropsychiatric disorders.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.

Conscious Perception as Integrated Information Patterns in Human Electrocorticography

“…Looking at synapse-level connectivity of large groups of single neurons (Reimann et al, 2017) have recently shown how neural systems can be understood in terms of their topological features, treating them as sets of overlapping groups of connected neurons. In another study, Lord et al (2017) have described how a different set of methods to distinguish “integration” (the holistic property of a neural system) from “segregation” (the way the parts are differentiated from one another) can be used to understand psychiatric disorder and other aspects of whole-brain function.…”

Mathematical methods in medicine: neuroscience, cardiology and pathology

“…-better data analysis and diagnoses [9,12,16,17]; -better therapies or instrumentation [15,[19][20][21]; -better understanding of biological processes [10,13,14]; and -better understanding of medical disorders [11,18].…”

“…This theme issue on the applications of mathematics to medicine is composed of six papers belonging to the area of neuroscience [9][10][11][12][13][14], three papers belonging to cardiology [15][16][17] and four papers belonging to pathology [18][19][20][21], totalling 13 papers from collaborations among 55 scientists.…”

“…Lord et al [11] provide a thorough review of integration and segregation from the perspective of whole-brain connectnomics. Importantly, they push the boundary from static network structure to time-varying systems.…”

Resting-state network topology and planning ability in healthy adults

“…Different properties of the brain network can be calculated using this graph. For example, efficiency and clustering describe the ability of a network to integrate and segregate information, respectively (Cohen and D'Esposito 2016;Lord et al 2017).…”