European psychiatry : the journal of the Association of European Psychiatrists · 2026
Structural brain complexity is associated with linguistic complexity in psychosis.
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Korda Alexandra, Hinzen Wolfram, He Rui, van Dyken Peter, Mackinley Michael, Toyota Eric, Avram Mihai, Andreou Christina, Borgwardt Stefan, Palaniyappan Lena
Аннотация
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.
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.