Disrupted modular organization of primary sensory brain areas in schizophrenia.

Cécile Bordier,Giulia Forcellini,Carlo Nicolini,Angelo Bifone

doi:10.1016/j.nicl.2018.02.035

Abstract

Abnormal brain resting-state functional connectivity has been consistently observed in patients affected by schizophrenia (SCZ) using functional MRI and other neuroimaging techniques. Graph theoretical methods provide a framework to investigate these defective functional interactions and their effects on the organization of brain connectivity networks. A few studies have shown altered distribution of connectivity within and between functional modules in SCZ patients, an indication of imbalanced functional segregation ad integration. However, no major alterations of modular organization have been reported in patients, and unambiguous identification of the neural substrates affected remains elusive. Recently, it has been demonstrated that current modularity analysis methods suffer from a fundamental and severe resolution limit, as they fail to detect features that are smaller than a scale determined by the size of the entire connectivity network. This resolution limit is likely to have hampered the ability to resolve differences between patients and controls in previous studies. Here, we apply Surprise, a novel resolution limit-free approach, to study the modular organization of resting state functional connectivity networks in a large cohort of SCZ patients and in matched healthy controls. Leveraging these important methodological advances we find new evidence of substantial fragmentation and reorganization involving primary sensory, auditory and visual areas in SCZ patients. Conversely, frontal and prefrontal areas, typically associated with higher cognitive functions, appear to be largely unaffected, with changes selectively involving language and speech processing areas. Our findings support the hypothesis that cognitive dysfunction in SCZ may involve deficits occurring already at early stages of sensory processing.

Highlights

Schizophrenia has been associated with aberrant functional connectivity as measured by neuroimaging methods in a number of studies (Friston and Frith, 1995; Liang et al, 2006; Liu et al, 2008; Calhoun et al, 2009; Karbasforoushan and Woodward, 2012; Garrity et al, 2007)
This growing evidence is in keeping with the disconnectivity hypothesis of schizophrenia (Friston and Frith, 1995) that posits that the core dysfunction of this disease may correspond to alterations of the functional interactions between specialized brain areas (Bullmore et al, 1998; Ellison-Wright and Bullmore, 2009; Fornito et al, 2009; Kubicki et al, 2005), resulting in defective integration of activity in distributed networks and in cognitive disintegration (Tononi and Edelman, 2000)
We have recently shown that maximization of Asymptotical Surprise enables detection of heterogeneously distributed communities (Nicolini et al, 2017), making it possible to resolve differences in the modular organization of different networks representing functional connectivity in different subjects or experimental groups (Nicolini and Bifone, 2016)

Summary

Introduction

Schizophrenia has been associated with aberrant functional connectivity as measured by neuroimaging methods in a number of studies (Friston and Frith, 1995; Liang et al, 2006; Liu et al, 2008; Calhoun et al, 2009; Karbasforoushan and Woodward, 2012; Garrity et al, 2007). Understanding the nature of connectivity alterations in SCZ patients and their effects on brain functional integration may provide important insights into the etiology of this devastating disease, as well as potential diagnostic or prognostic markers To this end, graph theoretical approaches have been proposed as a powerful framework to assess topological features of functional connectivity networks (Bassett and Bullmore, 2006; Bullmore and Sporns, 2009; Kaiser, 2011; Stam and Reijneveld, 2007; Reijneveld et al, 2007), in which nodes correspond to anatomically defined brain regions and the edges to interregional correlations. Several alterations in graph-related metrics of resting state connectivity have been identified in schizophrenia patients, including reduction in global network efficiency (Bassett et al, 2008; Liu et al, 2008; Bullmore and Sporns, 2009), small worldness (Anderson Ariana and Cohen, 2013; Yu et al, 2011; Liu et al, 2008) and rich-club organization of high-connectivity nodes (van den Heuvel et al, 2013)

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Conclusion