Multimodal Psychiatry

Depression remains a significant global health challenge, with Intermittent Theta Burst Stimulation (iTBS) targeting the left dorsolateral prefrontal cortex (DLPFC) as a promising intervention for treatment-resistant depression. However, the neurophysiological mechanisms driving its clinical efficacy are not fully understood. 

A recent symptom-based model of psychopathology suggests the existence of hierarchical organization across disorders[1]. Studies in youth utilizing functional connectivity (FC) have uncovered both shared and distinct neural patterns associated with disorder dimensions[2, 3]. However, limited research has explored the relationship between psychopathology dimensions and imaging biomarkers in adults, particularly in middle-aged populations. In this study, we attempt to address this gap by investigating the brain functional phenotypes relating to mental health in the middle-aged and older adults using the UK Biobank cohort, while assessing its predictive utility within and across datasets. 

Subthreshold depression (StD) poses a high risk for major depressive disorder (MDD) and is characterized by significant clinical heterogeneity among individuals (Cuijpers and Smit, 2004; Eaton, et al., 1995). However, the neurobiological substrates of this heterogeneity remain largely unknown, posing substantial challenges for early detection and effective intervention. Previous studies using resting-state functional MRI (r-fMRI) have documented disruptions in the functional connectome in StD participants (Gao, et al., 2016; Hwang, et al., 2016; Yin, et al., 2024; Yokoyama, et al., 2018; Zhang, et al., 2021), advancing our understanding of its neurobiological basis. However, these studies primarily focused on group-averaged alterations, largely overlooking individual differences among StD participants. Here, we aimed to characterize the age-related trajectory of the functional connectome in a large healthy dataset using normative models, identifying clinically significant neurobiological subtypes based on each participant's deviations from this model. This exploration would deepen our understanding of the distinct neurobiological mechanism underlying clinical heterogeneity in StD and inspire imaging-derived candidate phenotypes for the guidance of precise diagnosis and treatment. 

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. 

Schizophrenia (SZ) is a complex psychiatric disorder characterized by delusions, hallucinations, disorganized speech, and atypical behaviors. It is associated with altered brain dynamics (Georgiadis, et al., 2024; Menon, 2011; Roll, 2021). However, the exact nature of these disruptions remains unknown. We recently identified five fundamental large-scale signal propagation modes that effectively predict future BOLD activity and encompass various dynamic and operational aspects within the brain (Song et al., 2024). Here, we hypothesized that altered signal propagation patterns exist in the SZ brain. We applied this framework to investigate SZ-related brain dynamics, aiming to identify potential novel biomarkers for psychiatric disorders. 

Major Depressive Disorder (MDD) is a prevalent and debilitating psychiatric condition, yet its neurobiological mechanisms remain insufficiently understood. Increasing evidence points to the involvement of alterations in the principal neurotransmitters, including gamma-aminobutyric acid (GABA) and glutamate, in the pathophysiology of MDD. However, prior studies have often been restricted to specific brain regions (Fries et al., 2023), leading to inconsistent findings. This study aimed to address these gaps by employing whole-brain in vivo mapping of GABA and glutamate concentrations in patients with MDD and healthy controls, thus providing a comprehensive analysis of region-specific alterations in these key neurotransmitters.