Disrupted Emergent Properties of the Brain in Schizophrenia: Insight From Integrated Information Decomposition of Resting State fMRI

BACKGROUND: Schizophrenia is a severe psychiatric disorder marked by specific cognitive and clinical disturbances, for which neuroimaging biomarkers remain elusive. Novel theoretical and computational frameworks, such as integrated information decomposition, offer promising approaches to provide interpretable biomarkers for neuroimaging alterations in schizophrenia, potentially capturing disruptions relevant to consciousness and self-experience. METHODS: In this preliminary methodological exploration study, resting-state functional MRI (rsFMRI) data from 72 patients with schizophrenia and 74 healthy controls were retrieved and analyzed. Integrated information decomposition was leveraged to assess pairwise brain connectivity according to redundant, transferred, and synergistic components of information processing, as well as an overall metric of emergent consciousness/information integration: Φ. Clinical correlates with the Positive and Negative Syndrome Scale and the Wechsler Adult Intelligence Scale were assessed by partial Spearman correlations. Diagnostic accuracy was assessed through L1-regularized logistic regressions, after 5-fold cross-validation. RESULTS: Redundancy was positively correlated with intelligence quotient (IQ) across both groups (rho = 0.187, p-value = 0.033). Within patients, information metrics were positively correlated with stereotyped thinking (min rho = 0.343, max p-value = 0.006) and preoccupation (min rho = 0.250, max p-value = 0.046). Positive symptoms were positively correlated with redundancy (min rho = 0.250, max p-value = 0.047). Promising diagnostic accuracy was reached with Φ (balanced accuracy = 64.38%, area under the curve = 70.89%), redundancy (balanced accuracy = 84.93%, area under the curve = 92.30%), and synergy (balanced accuracy = 65.75%, area under the curve = 70.93%). CONCLUSIONS: These preliminary findings suggest that information metrics may offer clinically relevant, interpretable biomarkers for schizophrenia.

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Publication Details

Journal
Brain and Behavior
Published
2026-03-31
DOI
https://doi.org/10.1002/brb3.71352
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Disrupted Emergent Properties of the Brain in Schizophrenia: Insight From Integrated Information Decomposition of Resting State fMRI

L. D'Incerti, Valdo RICCA, G. Castellini, Tiziana Pisano et al.
Brain and Behavior
Functional Brain Connectivity Studies
article

Disrupted Emergent Properties of the Brain in Schizophrenia: Insight From Integrated Information Decomposition of Resting State fMRI

L. D'Incerti, Valdo RICCA, G. Castellini, Tiziana Pisano, A. Di Giacomo, Lorenzo Lasagni, Andrea Bianchi, Luca Zompa, L. Ubaldi, Jessica Bottacin, Enrico Lodovici, Livio Tarchi
article en

Abstract

BACKGROUND: Schizophrenia is a severe psychiatric disorder marked by specific cognitive and clinical disturbances, for which neuroimaging biomarkers remain elusive. Novel theoretical and computational frameworks, such as integrated information decomposition, offer promising approaches to provide interpretable biomarkers for neuroimaging alterations in schizophrenia, potentially capturing disruptions relevant to consciousness and self-experience. METHODS: In this preliminary methodological exploration study, resting-state functional MRI (rsFMRI) data from 72 patients with schizophrenia and 74 healthy controls were retrieved and analyzed. Integrated information decomposition was leveraged to assess pairwise brain connectivity according to redundant, transferred, and synergistic components of information processing, as well as an overall metric of emergent consciousness/information integration: Φ. Clinical correlates with the Positive and Negative Syndrome Scale and the Wechsler Adult Intelligence Scale were assessed by partial Spearman correlations. Diagnostic accuracy was assessed through L1-regularized logistic regressions, after 5-fold cross-validation. RESULTS: Redundancy was positively correlated with intelligence quotient (IQ) across both groups (rho = 0.187, p-value = 0.033). Within patients, information metrics were positively correlated with stereotyped thinking (min rho = 0.343, max p-value = 0.006) and preoccupation (min rho = 0.250, max p-value = 0.046). Positive symptoms were positively correlated with redundancy (min rho = 0.250, max p-value = 0.047). Promising diagnostic accuracy was reached with Φ (balanced accuracy = 64.38%, area under the curve = 70.89%), redundancy (balanced accuracy = 84.93%, area under the curve = 92.30%), and synergy (balanced accuracy = 65.75%, area under the curve = 70.93%). CONCLUSIONS: These preliminary findings suggest that information metrics may offer clinically relevant, interpretable biomarkers for schizophrenia.

Brain and BehaviorVol. 16(4)
Meyer Children's Hospital (IT), University of Florence (IT)
Ministero dell'Università e della Ricerca
Openalex Percentile: Top 38%
Functional Brain Connectivity Studies
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