Controllability of Functional Brain Networks and Its Clinical Significance in First-Episode Schizophrenia

Abstract

Background and Hypothesis Disrupted control of brain state transitions may contribute to the diverse dysfunctions of cognition, emotion, and behavior that are fundamental to schizophrenia. Control theory provides the rationale for evaluating brain state transitions from a controllability perspective, which may help reveal the brain mechanism for clinical features such as cognitive control deficits associated with schizophrenia. We hypothesized that brain controllability would be altered in patients with schizophrenia, and that controllability of brain networks would be related to clinical symptomatology. Study Design Controllability measurements of functional brain networks, including average controllability and modal controllability, were calculated and compared between 125 first-episode never-treated patients with schizophrenia and 133 healthy controls (HCs). Associations between controllability metrics and clinical symptoms were evaluated using sparse canonical correlation analysis. Study Results Compared to HCs, patients showed significantly increased average controllability (PFDR = .023) and decreased modal controllability (PFDR = .023) in dorsal anterior cingulate cortex (dACC). General psychopathology symptoms and positive symptoms were positively correlated with average controllability in regions of default mode network and negatively associated with average controllability in regions of sensorimotor, dorsal attention, and frontoparietal networks. Conclusions Our findings suggest that altered controllability of functional activity in dACC may play a critical role in the pathophysiology of schizophrenia, consistent with the importance of this region in cognitive and brain state control operations. The demonstration of associations of functional controllability with psychosis symptoms suggests that the identified alterations in average controllability of brain function may contribute to the severity of acute psychotic illness in schizophrenia.

Morphology of Anterior Cingulate Cortex and Its Relation to Schizophrenia

“…Brugger and Howes [9] in their meta-analysis from 2017 based on MRI morphometry from 3901 patients with first-episode schizophrenia and 4040 controls discovered greater homogeneity of the ACC volume and significantly lower mean volume, which may signalize schizophrenia. Modified controllability of functional activity in dorsal ACC may also play an important role in the pathophysiology of schizophrenia, consistent with the importance of this region in cognitive and brain state control operations [10]. At the same time, available findings point to the fact that the ACC gray matter reductions precede psychosis onset and that neurodevelopment of the ACC region might thus be in play [8].…”

Network controllability measures of subnetworks: implications for neurosciences

“…It is common practice in the applications of network control theory in neuroimaging applications to obtain the adjacency matrix A from structural connectome data extracted from diffusion imaging [39]. Alternative to this approach is extracting the adjacency matrix based on the temporal correlations observed in the functional data [42], an approach that is also heavily used across psychological sciences [41] although more complex models based on generative models are being employed as well [43,44]. Critically, since these models are often arbitrarily weighted, they are commonly first normalized before estimating the controllability properties [23].…”

Altered dynamic amplitude of low‐frequency fluctuation in individuals at high risk for Alzheimer's disease

“…CCA, a multivariate analysis technique, was performed to determine the relationships between the aberrant dALFF values and clinical measurements. CCA investigates the correlations between the linear combinations of variables in two multivariate datasets (Li et al, 2023), resulting in pairs of typical variables. Specifically, CCA finds a linear combination of brain activity that is maximally correlated with a linear combination of cognitive behaviour variables, as defined in Y * A = U ~ V = X * B, where Y is the set of brain measures, X is the set of cognitive behaviours, A and B are the linear weights for X and Y, respectively, and U and V are canonical variate pairs.…”

“…Numerous studies have identified abnormalities in ALFFs as well as functional connectivity changes in multiple brain regions among SCD and MCI patients compared to the connectivity in healthy controls (HCs; López‐Sanz et al, 2017; McDonough et al, 2020; Sun et al, 2016). However, the human brain is a highly dynamic system (Fu et al, 2018; Luo et al, 2023) with non‐stationary neural activity, which is vital for adaptive cortical interaction and brain adaptability (De Pasquale et al, 2018). Previous research has confirmed that the non‐stationary nature of spontaneous brain activity persists even during periods of rest (Shine et al, 2019).…”

“…The dALFF method based on rs‐fMRI enables the capture of spontaneously occurring patterns of activity, which play an essential role in understanding temporal variabilities in the intrinsic organization of the brain with high reliability and repeatability (Zhong et al, 2022). Therefore, this study aimed to explore the dynamic changes in neural activity in individuals at high risk for AD and examine the bi‐multivariate correlations of neuropsychological scales with resting‐state dALFF based on canonical correlation analysis (CCA; Li et al, 2023), to provide an effective imaging tool for comprehending the pathophysiology of AD.…”