Metastability as a candidate neuromechanistic biomarker of schizophrenia pathology

Abstract

The disconnection hypothesis of schizophrenia proposes that symptoms of the disorder arise as a result of aberrant functional integration between segregated areas of the brain. The concept of metastability characterizes the coexistence of competing tendencies for functional integration and functional segregation in the brain, and is therefore well suited for the study of schizophrenia. In this study, we investigate metastability as a candidate neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. Group-level discrimination, individual-level classification, pathophysiological relevance, and explanatory power were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n = 53, non-affective psychosis n = 82) and the Cobre study (controls n = 71, cases n = 59). In this work we extend Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implement a novel approach to estimate metastability. We used non-parametric testing to evaluate group-level differences and a naïve Bayes classifier to discriminate cases from controls. Our results show that our new approach is capable of discriminating cases from controls with elevated effect sizes relative to published literature, reflected in an up to 76% area under the curve (AUC) in out-of-sample classification analyses. Additionally, our new metric showed explanatory power of between 81–92% for measures of integration and segregation. Furthermore, our analyses demonstrated that patients with early psychosis exhibit intermittent disconnectivity of subcortical regions with frontal cortex and cerebellar regions, introducing new insights about the mechanistic bases of these conditions. Overall, these findings demonstrate reliability and face validity of metastability as a candidate neuromechanistic biomarker of schizophrenia pathology.

Exploring global and local processes underlying alterations in resting-state functional connectivity and dynamics in schizophrenia

“…( 55 ) on the same dataset but in contrast to very recent work from Hancock et al. ( 56 ), proposing metastability as a candidate biomarker for schizophrenia. However, we have to note that Hancock et al.…”

“…However, we have to note that Hancock et al. ( 56 ) introduced a new measure of metastability with increased sensitivity to detect the differences between healthy controls and ScZ patients. This new measure of metastability did not rely on predefined brain parcellations but rather flexibly defined recurring spatio-temporal modes, so-called ‘communities’ where single brain regions may be grouped into more than one community.…”

Exploring psychedelic-assisted therapy in the treatment of functional seizures: A review of underlying mechanisms and associated brain networks

“…As such, by applying a complex system approach, a deeper understanding of the therapeutic mechanisms can be achieved. For example, neuroimaging investigations of FS could apply analyses derived from complexity science which assess system-level properties such as modularity, entropy, metastability, and criticality ( Hancock et al, 2022a , 2022b ; Wilting and Priesemann, 2019 )—as have been fruitfully applied to characterize psychedelic effects ( Carhart-Harris et al, 2014 , 2018 ; Daws et al, 2022 ; Girn et al, 2023 ; Toker et al, 2022 ), as well as other mental states and conditions ( Hancock et al, 2023 ; Turkheimer et al, 2021 ).…”

DySCo: a general framework for dynamic Functional Connectivity

“…Starting from a seminal work on schizophrenia ( [6]), the field of "Chronnectomics" has shown great potential in elucidating and characterizing differences in typical versus atypical brain dynamics ( [7]). Some exemplar applications are in psychiatric disorders ( [8,9,10,11]), neurodevelopment ( [12]), ageing and neurodegeneration ( [13,14,15,16]), cognitive performance and flexibility ( [17]), the effect of psychedelics ( [18]), and neurological conditions, like epilepsy ( [19]) or traumatic brain injury ( [20]).…”

“…This switches the problem from one of identification of an appropriate time-window size to one of finding a bandwidth of interest, where signals can be legitimately approximated by simple oscillations ( [34]). This approach has been especially successful in fMRI signals, which are usually narrow-band, and its application to the study of wake-sleep ( [35]), psychedelics ( [36,18]), neurodevelopment ( [12]), psychiatric disorders ( [11]), ageing ( [15]).…”