Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study

Abstract

Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large‐scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model‐based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large‐scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self‐inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole‐brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging‐based cognitive assessment.

A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network

“…The more immediate application of causal centrality would be to elucidate the influence of different regions in the causal models of neurotypical functional networks (such as the eDMN (Alves et al 2019) and attention network (Alves et al 2022)), during resting state and task conditions. A follow-up application would be to compare the neurotypical (causal centrality) results with those obtained in pathological conditions, e.g., in schizophrenia Zarghami et al 2023). In a recent study, (Mana et al 2022) used perturbation analysis to identify a set of "critical" regions whose modulation can push a healthy brain towards the pathological dynamics of schizophrenia.…”

Increasing spectral DCM flexibility and speed by leveraging Julia’s ModelingToolkit and automated differentiation

“…DCM is one of the most widely used and validated methods of extracting underlying neural connectivity from fMRI data (5,(10)(11)(12)(13)(14)17). The aim of the work presented here is two-fold: to improve the computational efficiency and accessibility of the well-established spectral DCM framework provided by SPM12 (2), and increase the application domain by providing a modular framework for model-fitting, thus empowering researchers to tailor their models to specific experimental setups and research questions.…”

“…DCM provides methods and an associated pipeline for modeling interactions between neuronal populations at the cortical level, fitting models from local field potential (LFP) recordings (5–7), electroencephalography (EEG) data (8), and functional MRI (fMRI) sequences (9–14). Neuroscience applications of DCM have provided insights into both healthy (3, 4, 15, 16) and impaired (17) neuronal functioning, in response to task-based (3, 4, 16) and resting-state (2, 17) stimuli. Additionally, the underlying mathematical approach of DCM has been extended to applications outside of neuroscience that benefit from causal inference (18).…”

“…Moran, Mallet, et al, 2011), electroencephalography (EEG) data (David et al, 2006), and functional MRI (fMRI) sequences (Bernal-Casas et al, 2017;Cha et al, 2016;Rupprechter et al, 2020;Seghier & Friston, 2013;Sladky et al, 2015;Tik et al, 2018). Neuroscience applications of DCM have provided insights into both healthy (Bukhari et al, 2022;Hahn et al, 2020;Lee et al, 2022;Sladky et al, 2022) and impaired (Zarghami et al, 2023) neuronal functioning, in response to task-based (Hahn et al, 2020;Lee et al, 2022;Sladky et al, 2022) and resting-state (K. J. Friston et al, 2014;Zarghami et al, 2023) stimuli.…”

Resting-state perfusion in motor and fronto-limbic areas is linked to diminished expression of emotion and speech in schizophrenia

“…However, altered gesture production in schizophrenia has in addition been associated with altered activity of the praxis network 58 – 60 . Apart from the above mentioned key role in the motor system evidence suggests the pre-SMA/SMA to be highly relevant for cognitive functions such as attention, temporal processing 61 – 64 , problem solving and working memory capabilities in schizophrenia 65 , 66 , as well as in early psychosis 67 , 68 . Finally SMA dysfunction has been associated with altered sense of agency 52 , 69 – 71 .…”