Multimodal Brain Imaging and Plasma Proteins Reveal Links Between Depression and Physical Diseases
Presented During:
Friday, June 27, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
467
Submission Type:
Abstract Submission
Authors:
Zhengxu Lian1, Xinran Wu1, Zhaowen Liu2, Gechang Yu3, Jing Sui4, Vince Calhoun5, Wei Cheng1, JianFeng Feng1, Jie Zhang1
Institutions:
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2Northwestern Polytechnical University, Xian, Shannxi, China, 3The Chinese University of Hong Kong, Hong Kong, China, 4Beijing Normal University, Beijing, China, 5GSU/GATech/Emory, Atlanta, GA
First Author:
Zhengxu Lian
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Co-Author(s):
Xinran Wu
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Wei Cheng
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
JianFeng Feng
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Jie Zhang
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Introduction:
Depression impacts both the brain and body, with peripheral pathological changes increasingly recognized as integral to its pathophysiology. Plasma proteins serve as key indicators of peripheral changes; however, their relationship with depression mediated by brain structure and function remains underexplored. Leveraging data from 3,966 UK Biobank participants, we identified a multimodal neuroimaging-plasma protein component of depression (MNI-PPC-Dep) through a constrained multimodal fusion approach (MCCAR+jICA). The brain modality reveals hippocampal atrophy, reduced sensorimotor network connectivity, and structural deficits in the default mode network. Additionally, abnormality in the subcallosal cingulate is identified, a region linked to metabolic dysfunction in depression and targeted for deep brain stimulation in resistant cases. The plasma protein modality, identified through fusion with brain imaging features, is primarily enriched in metabolic pathways and associated with genetic risks for type 2 diabetes, in contrast to those identified by traditional approaches. Notably, MNI-PPC-Dep demonstrates generalizability by reliably predicting depression symptom severity across datasets, underscoring its clinical potential. Environmental and lifestyle factors, such as air pollution and alcohol use, are linked to MNI-PPC-Dep, which, in turn, predicts the onset of future physical diseases, including cardiovascular and kidney-related conditions. Together, this study highlights metabolic dysfunction as a potential bridge between brain changes, depression, and physical diseases, providing a novel biomarker and valuable insights to inform depression treatment strategies.
·Schematic Overview of the Study Design
Methods:
The data for multimodal fusion were obtained from the UK Biobank, encompassing neuroimaging features, plasma proteins, and ICD-10-based depression diagnosis labels. Neuroimaging modalities included subcortical volumes, cortical surface area, cortical thickness, resting-state functional connectivity, and structural connectivity.
Multimodal fusion was first applied to the neuroimaging and plasma protein data from the discovery cohort using the MCCAR+jICA method. Specifically, the ICD-10 depression diagnosis labels served as the reference signal to jointly decompose the neuroimaging modalities (X1–X5) and the plasma protein modality (X6), resulting in the identification of MNI-PPC-Dep. The supervised fusion method ensured that the multimodal components achieved maximal correlation under this constraint, as described in Equation 1.
max { ∑6k,j=1 { |corr(Ak, Aj)|2 + 2λ * |corr(Ak, dep_label)|2 } }
Multimodal fusion was first applied to the neuroimaging and plasma protein data from the discovery cohort using the MCCAR+jICA method. Specifically, the ICD-10 depression diagnosis labels served as the reference signal to jointly decompose the neuroimaging modalities (X1–X5) and the plasma protein modality (X6), resulting in the identification of MNI-PPC-Dep. The supervised fusion method ensured that the multimodal components achieved maximal correlation under this constraint, as described in Equation 1.
max { ∑6k,j=1 { |corr(Ak, Aj)|2 + 2λ * |corr(Ak, dep_label)|2 } }
Results:
This component highlights key features within the brain modality, including hippocampal atrophy, reduced functional connectivity in the sensorimotor network, and structural connectivity deficits in the default mode network. Notably, the subcallosal, an area associated with metabolic dysfunction in depression and a target for deep brain stimulation in treatment-resistant cases is also identified. We demonstrate that MNI-PPC-Dep generalizes across multiple independent datasets, effectively predicting depressive symptom severity in the UKB, HCP, and SWU Depression cohorts. Meanwhile, the plasma protein modality, informed by its integration with brain imaging features, revealed that disruptions in metabolic pathways-particularly those involving insulin and IGF-1-serve as critical links between depression, brain structure-function abnormalities, and peripheral biomarkers. This contrasts with previous studies, where pathways identified without such integration were predominantly immune-related(as illustrated by figure 2c, using WGCNA, where pro+/- are our proteins,cox protein is from a previous UKB study).
·Biological Pathways of Plasma Proteins in MNI-PPC-Dep
Conclusions:
Together, this research offers a novel multimodal framework that bridges psychiatric and physical health, providing translational insights into comorbidities of depression and physical diseases.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Genetics:
Genetic Association Studies
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Receptors
White Matter Anatomy, Fiber Pathways and Connectivity
Physiology, Metabolism and Neurotransmission:
Physiology, Metabolism and Neurotransmission Other 2
Keywords:
ADULTS
Blood
Cortex
Data analysis
FUNCTIONAL MRI
Morphometrics
Neurotransmitter
Phenotype-Genotype
Psychiatric Disorders
STRUCTURAL MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
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