Structural brain complexity is associated with linguistic complexity in psychosis
6 affiliations
- 1Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Germany
- 2Department of Translation & Language Sciences, Universitat Pompeu Fabra, Carrer Roc Boronat, Barcelona, Spain
- 3Institut Català de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- 4Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- 5Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada
- 6Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
Abstract
Background: Brain-structural and language abnormalities in people with psychotic symptoms (PSx) have long been reported. A key question is whether language alterations are a potential behavioral readout of brain cortical gray matter changes. Adding to previous evidence for this in the cases of cortical thinning and gyrification, we here used structural MRI (sMRI) to relate a complexity-theoretical metric, the Largest Lyapunov Exponent (lambda [λ]), to computational linguistic complexity metrics extracted from spontaneous speech. This study aimed to identify psychosis-related regional changes in structural complexity using λ and an explainable AI (XAI) approach. We hypothesized that altered structural complexity will relate to computational language features that have previously been shown to track brain dysconnectivity in schizophrenia in fMRI.
Methods: MRI data were acquired from 92 patients with PSx and 38 healthy controls (HCs) from the TOPSY study. Nonlinear analysis of gray matter distribution was performed by extracting the λ for gray matter voxels. XAI was employed to identify the voxels contributing significantly to classifying PSx against HCs. Finally, the brain voxels’ contribution was tested for associations with word perplexity and syntactic and semantic complexity metrics, from spontaneous speech as obtained from picture descriptions, and with clinical symptom severity (SOFAS and PANSS scores).
Results: The classification framework resulted in a balanced accuracy of 75.9%. The voxels contributing most to the classification decision were located in the temporal lobe, cingulum, angular, lingual, calcarine, occipital, fusiform, and parietal cortices, and parts of the cerebellum and vermis. Structural complexity in these regions was significantly associated with clinical variables (SOFAS and PANSS), word perplexity, and lexical diversity.
Conclusions: We present a new analytical framework using spatial series extracted from sMRI, demonstrating that alterations in structural complexity are an identifiable feature of psychosis, which co-vary with probabilistic and structural changes in speech as a behavioral readout.
Related
- Reconstructing digital phenotyping of psychological distress: integrating visual complexity and linguistic sentiment
- Temporal imprecision of the self in drug-free psychosis: Abnormal infraslow phase dynamics and brain complexity
- Large language model linguistic perplexity in childhood onset psychosis: unique features and developmental trends
- ALS-associated mutations reveal a conserved C-terminal self-assembly module in the TDP-43 low-complexity domain
- Dual-eligible Medicare–Medicaid status is associated with lower surgeon reimbursement despite greater medical and psychosocial complexity in total shoulder arthroplasty
BibTeX
@article{korda2026structural,
title = {{Structural brain complexity is associated with linguistic complexity in psychosis}},
author = {Alexandra Korda and Wolfram Hinzen and Rui He and Peter van Dyken and Michael Mackinley and Eric Toyota and Mihai Avram and Christina Andreou and Stefan Borgwardt and Lena Palaniyappan},
journal = {European Psychiatry},
year = {2026},
month = jul,
doi = {10.1192/j.eurpsy.2026.12242},
url = {https://doi.org/10.1192/j.eurpsy.2026.12242},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Structural brain complexity is associated with linguistic complexity in psychosis
AU - Alexandra Korda
AU - Wolfram Hinzen
AU - Rui He
AU - Peter van Dyken
AU - Michael Mackinley
AU - Eric Toyota
AU - Mihai Avram
AU - Christina Andreou
AU - Stefan Borgwardt
AU - Lena Palaniyappan
PY - 2026
DA - 2026/07/21
JO - European Psychiatry
DO - 10.1192/j.eurpsy.2026.12242
UR - https://doi.org/10.1192/j.eurpsy.2026.12242
AB - Background: Brain-structural and language abnormalities in people with psychotic symptoms (PSx) have long been reported. A key question is whether language alterations are a potential behavioral readout of brain cortical gray matter changes. Adding to previous evidence for this in the cases of cortical thinning and gyrification, we here used structural MRI (sMRI) to relate a complexity-theoretical metric, the Largest Lyapunov Exponent (lambda [λ]), to computational linguistic complexity metrics extracted from spontaneous speech. This study aimed to identify psychosis-related regional changes in structural complexity using λ and an explainable AI (XAI) approach. We hypothesized that altered structural complexity will relate to computational language features that have previously been shown to track brain dysconnectivity in schizophrenia in fMRI. Methods: MRI data were acquired from 92 patients with PSx and 38 healthy controls (HCs) from the TOPSY study. Nonlinear analysis of gray matter distribution was performed by extracting the λ for gray matter voxels. XAI was employed to identify the voxels contributing significantly to classifying PSx against HCs. Finally, the brain voxels’ contribution was tested for associations with word perplexity and syntactic and semantic complexity metrics, from spontaneous speech as obtained from picture descriptions, and with clinical symptom severity (SOFAS and PANSS scores). Results: The classification framework resulted in a balanced accuracy of 75.9%. The voxels contributing most to the classification decision were located in the temporal lobe, cingulum, angular, lingual, calcarine, occipital, fusiform, and parietal cortices, and parts of the cerebellum and vermis. Structural complexity in these regions was significantly associated with clinical variables (SOFAS and PANSS), word perplexity, and lexical diversity. Conclusions: We present a new analytical framework using spatial series extracted from sMRI, demonstrating that alterations in structural complexity are an identifiable feature of psychosis, which co-vary with probabilistic and structural changes in speech as a behavioral readout.
SN - 0924-9338
SN - 1778-3585
ER -