Metastability as a 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 neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. Group-level discrimination, individual-level classification, and pathophysiological relevance were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n=82, non-affective psychosis n=53) and the Cobre study (controls n=71, cases n=59). In this work we introduce a new framework that uses Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implements a novel approach to estimate metastability. We used non-parametric testing to evaluate group-level differences and a naive 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. 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 neuromechanistic biomarker of schizophrenia pathology.

EiDA: A lossless approach for the dynamic analysis of connectivity patterns in signals; application to resting state fMRI of a model of ageing

“…Given the considerations above, the first eigenvalue can be considered a global information metric about the instantaneous phase locking of the signals, that is conceptually similar to the Kuramoto Order Parameter ((29; 2; 27; 24; 48; 21; 49; 50)); note that the latter, however, is based on a specific model of the structure of the phase interactions, that is mean phase. Therefore, we define a new measure of metastability, the standard deviation over time of λ 1 , which is also equal to the standard deviation of λ 2 .…”

“…A simple measure of metastability can be obtained from one of the most popular measures of synchronisation: the Kuramoto Order Parameter that can be computed for each time θ 1 ( t ), using the phases of the analytical signals((29; 2; 24; 27; 48; 21; 49; 50)). The modulus of r ∈ [0, 1] is a measure of synchronization: if all signals are in phase, this number approaches 1, while, if they are distributed uniformly, it will approach 0.…”

“…It can be conceived as a centroid of instantaneous phases. Starting from this number, metastability is defined as its standard deviation over time ((29; 2; 24; 27; 48; 21; 49; 50)), and we refer to it as Kuramoto metastability to clearly differentiate it from the spectral metastability introduced above. …”

“…The methodology can be used to extract discrete states whenever the time-series of the two eigenvectors would warrant the use of clustering; and in addition, continuous EiDA can provide a complementary perspective to discrete states by capturing the evolution of the dynamics in 2-dimensional plots. Metrics from both discrete and continuous EiDA may provide more accurate estimations of differences between case and control groups, and so may improve discrimination and classification accuracy of putative neuromechanistic biomarkers (24).…”

“…connectivity patterns that are consistently explored by the brain during the data recording, as also proposed by (19). LEiDA has been applied with significant impact to study sleep-wake transition ((20)), psychedelics ((21; 22)), neurodevelopment ((23)), schizophrenia ((24)), and depression ((25; 26)).…”