Neurotransmitter-informed Dimensional Brain Signature of Schizophrenia
Poster No:
473
Submission Type:
Abstract Submission
Authors:
Chao Xie1, Shitong Xiang1, Tianye Jia1
Institutions:
1Fudan University, Shanghai, Shanghai
First Author:
Co-Author(s):
Introduction:
Psychotic disorders like schizophrenia are major contributors to the global disease burden, yet targeted interventions remain limited due to the disorder's phenotypic, neurobiological, and genetic heterogeneity [1]. A promising solution lies in the 'big data' approach, which combines large sample sizes to ensure reliability with deep phenotyping for greater specificity [2]. However, practical trade-offs hinder its clinical utility. For example, PET imaging has identified striatal dopamine dysregulation as a key feature of psychosis, but its high costs and limited availability restrict sample sizes, making it difficult to achieve precise molecular characterization. In contrast, structural MRI studies, which can accommodate larger cohorts, often fail to capture neurochemical specificity, leading to inconsistent findings, particularly in the context of striatal heterogeneity [3]. Addressing these challenges is critical for advancing our understanding of schizophrenia and developing targeted interventions.
To address these challenges, we developed a novel multi-modal information fusion method. By integrating neurobiological brain maps (e.g., PET, fMRI), we derived personalized biology-informed representational feature scores (ReFS). ReFS demonstrated high reproducibility across imaging conditions and outperformed traditional mean-based gray matter volume (m-GMV) in associating striatal features with schizophrenia diagnosis, symptom severity, and genetic risk. This framework provides a scalable solution to address schizophrenia's multi-level heterogeneity, advancing biomarker discovery and precision psychiatry.
To address these challenges, we developed a novel multi-modal information fusion method. By integrating neurobiological brain maps (e.g., PET, fMRI), we derived personalized biology-informed representational feature scores (ReFS). ReFS demonstrated high reproducibility across imaging conditions and outperformed traditional mean-based gray matter volume (m-GMV) in associating striatal features with schizophrenia diagnosis, symptom severity, and genetic risk. This framework provides a scalable solution to address schizophrenia's multi-level heterogeneity, advancing biomarker discovery and precision psychiatry.
Methods:
We analyzed data from 1,229 schizophrenia patients and 1,237 healthy controls across 13 datasets. Structural MRI data underwent voxel-based morphometry preprocessing to generate gray matter volume map for each participant. ReFS were calculated by correlating individual morphometric map with group-level neurotransmitter maps (e.g., dopamine, GABA) and cognitive-behavioral activation maps from Neurosynth [4-5]. ReFS reliability was evaluated using intraclass correlation coefficients with test-retest datasets. Group differences in ReFS and m-GMV were examined using linear mixed models, controlling for site, age, sex, and intracranial volume. Brain dimensionality, derived from neurotransmitter-cognition coupling matrices, was computed via singular value decomposition.
Results:
We found that subcortical ReFS explained 31.7% of group variance, significantly outperforming m-GMV (6.4%). Dopamine-related ReFS alterations were prominent in striatal subregions, especially the posterior putamen (pPUT), and were associated with schizophrenia pathology. GABA-related ReFS changes, specific to chronic schizophrenia, highlighted the importance of striatal neurotransmitter interactions. ReFS showed higher sensitivity and reproducibility than m-GMV, particularly in the posterior putamen. Additionally, we found that ReFS in the pPUT followed a gradient of genetic risk for schizophrenia across healthy controls, unaffected siblings, and patients. In contrast, m-GMV metrics did not show significant associations with this risk gradient.
Using meta-analytic activation maps, we identified schizophrenia-specific Cognitive ReFS changes in the dorsal striatum, particularly in sensory-motor and social cognition maps. These changes, linked to dopamine dysfunction, reflected motor deficits, social impairments, and hallmark symptoms like delusions. Dimensionality analysis revealed reduced brain dimensionality in key striatal regions (putamen, nucleus accumbens), correlating with symptom severity and reflecting disrupted neurotransmitter-cognition coupling.
Using meta-analytic activation maps, we identified schizophrenia-specific Cognitive ReFS changes in the dorsal striatum, particularly in sensory-motor and social cognition maps. These changes, linked to dopamine dysfunction, reflected motor deficits, social impairments, and hallmark symptoms like delusions. Dimensionality analysis revealed reduced brain dimensionality in key striatal regions (putamen, nucleus accumbens), correlating with symptom severity and reflecting disrupted neurotransmitter-cognition coupling.
·ReFS with neurotransmitter map
·ReFS with cognitive-behaivour map
Conclusions:
This study highlights ReFS as a reliable, sensitive biomarker that integrates molecular, structural, and functional imaging to unravel schizophrenia's heterogeneity. By bridging neurochemical and cognitive domains, ReFS provides a scalable framework for advancing precision psychiatry.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Methods Development
Multivariate Approaches
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals
Keywords:
MRI
Psychiatric
Psychiatric Disorders
Schizophrenia
STRUCTURAL MRI
1|2Indicates the priority used for review
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[2] Lombardo, M. V., M. C. Lai, and S. Baron-Cohen. "Big data approaches to decomposing heterogeneity across the autism spectrum." Molecular Psychiatry (2019).
[3] Chuhma, N., S. Mingote, A. Kalmbach, L. Yetnikoff, and S. Rayport. "Heterogeneity in dopamine neuron synaptic actions across the striatum and its relevance for schizophrenia." Biological Psychiatry 81 (2017): 43-51.
[4] Yarkoni, T., R. A. Poldrack, T. E. Nichols, D. C. Van Essen, and T. D. Wager. "Large-scale automated synthesis of human functional neuroimaging data." Nature Methods 8 (2011): 665-670.
[5] Dukart, J., et al. "JuSpace: A tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps." Human Brain Mapping (2020).