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https://orcid.org/0000-0003-2113-5943All Publications
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Generalizable structure-function covariation predictive of antidepressant response revealed by target-oriented multimodal fusion.
Nature. Mental health
2026; 4 (1): 85-101
Abstract
Major depressive disorder (MDD) is a prevalent condition that profoundly impairs quality of life across diverse populations. Despite widespread use, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates. Progress in developing effective therapies is hampered by the insufficiently understood heterogeneity of MDD and its elusive underlying mechanisms. Here, to address these challenges, we develop a novel machine learning framework that identifies structure-function covariation through target-oriented fusion of structural and functional connectivity, which robustly predicts individual-level antidepressant response (sertraline, R 2 = 0.31; placebo, R 2 = 0.22). Validation in an independent escitalopram-medicated MDD cohort confirms the biomarker's generalizability (P = 0.01) and suggests an overlap of psychopharmacological signatures across selective serotonin reuptake inhibitors. Our models highlight the right precuneus as a common key region for both sertraline and placebo responses, with the right middle frontal gyrus and left fusiform gyrus specific to sertraline and the left inferior and middle frontal gyri to placebo. We also find that structural connectivity is more predictive of sertraline response, while functional connectivity better predicts placebo response. The framework further decomposes the overall predictive patterns into three constitutive network constellations (default-mode regulatory, affective and sensory processing), which exhibit distinct generalizable structure-function covariation and treatment-specific association with personality traits and behavioral/cognitive profiles. These findings provide unique insights to the structure-function covariation in patients with MDD, its association to the heterogeneity in antidepressant response and the dissection of the intricate MDD neuropsychopharmacology, paving the way for precision medicine and development of more targeted antidepressant therapeutics. Clinicaltrials.gov registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT01407094.
View details for DOI 10.1038/s44220-025-00541-0
View details for PubMedID 42460432
View details for PubMedCentralID PMC13372467
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Symptom dimensions of resting-state electroencephalographic functional connectivity in autism.
Nature. Mental health
2024; 2 (3): 287-298
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social and communication deficits (SCDs), restricted and repetitive behaviors (RRBs) and fixated interests. Despite its prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To comprehensively explore these heterogeneities, we developed a new analytical framework combining contrastive learning and sparse canonical correlation analysis that identifies symptom-linked resting-state electroencephalographic connectivity dimensions within 392 ASD samples. We present two dimensions with multivariate connectivity basis exhibiting significant correlations with SCD and RRB, confirm their robustness through cross-validation and demonstrate their conceptual generalizability using an independent dataset (n = 222). Specifically, the right inferior parietal lobe is the core region for RRB, while connectivity between the left angular gyrus and the right middle temporal gyrus show key contribution to SCD. These findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for ASD treatment development and precision medicine.
View details for DOI 10.1038/s44220-023-00195-w
View details for PubMedID 39219688
View details for PubMedCentralID PMC11361313
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Generalizable structure-function covariation predictive of antidepressant response revealed by target-oriented multimodal fusion
NATURE MENTAL HEALTH
2025
View details for DOI 10.1038/s44220-025-00541-0
View details for Web of Science ID 001637006900001
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Deep Learning of Brain-Behavior Dimensions Identifies Transdiagnostic Biotypes in Youth with ADHD and Anxiety Disorders.
bioRxiv : the preprint server for biology
2025
Abstract
Attention-deficit/hyperactivity disorder and anxiety disorders are highly prevalent in youth and are characterized by substantial heterogeneity and frequent co-occurrence. This transdiagnostic complexity challenges conventional diagnostic frameworks that rely on symptom-based categories, which often obscure underlying dimensional and neurobiological mechanisms and offer limited neurobiological specificity. To address these issues, we developed a deep learning-based brain-behavior modeling framework that integrates clinically salient functional connectivity with cognitive and behavioral measures to identify interpretable dimensions and biologically grounded subtypes (biotypes). We applied our model to the Adolescent Brain Cognitive Development (ABCD) dataset comprising 3,508 children aged 9-11 years and revealed two reproducible brain-behavior dimensions that captured variation in cognitive control and emotion-attention regulation. These dimensions further yielded three distinct biotypes, each exhibiting unique symptom profiles and distinct brain development. We tested the robustness and generalizability of the dimensions and corresponding biotypes in an independent cohort of 224 age-matched participants from the Healthy Brain Network (HBN) and documented their early expression before symptom onset during adolescence. These findings highlight the utility of brain-behavior dimensions for elucidating heterogeneous psychiatric presentations and advance a biologically grounded framework for early classification and potential clinical translation in youth mental health.
View details for DOI 10.1101/2025.10.13.682243
View details for PubMedID 41279186
View details for PubMedCentralID PMC12632924
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Early Brain Functional Connectivity Changes Induced by Antidepressants and Placebo.
bioRxiv : the preprint server for biology
2025
Abstract
Major depressive disorder (MDD) is a common and debilitating condition with limited treatment precision. While brain imaging has linked neural features to MDD diagnosis and treatment response, the underlying circuits and their early modulation by treatment remain unclear. To examine changes in functional connectivity within the first two weeks of treatment, we analyzed two independent cohorts of MDD patients receiving antidepressants or placebo. Across patients, a visual-precuneus-thalamus network showed increased functional connectivity regardless of treatment arm or clinical outcome. Placebo response involved attention and striatal systems, while drug-specific effects were localized to the amygdala, mid-cingulate, orbitofrontal cortex, and cerebellum, emerging only in a subset of medicated patients. Notably, the responses of those without drug-specific changes can be predicted with a placebo response prediction model. These early functional connectivity changes reveal common and distinct mechanisms of treatment effects, offering insights that could inform more personalized interventions for MDD.
View details for DOI 10.1101/2025.08.22.671857
View details for PubMedID 40909518
View details for PubMedCentralID PMC12407754
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Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD.
Journal of affective disorders
2025: 119483
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major depression disorder (MDD), particularly for treatment-resistant cases. However, identifying translatable biomarkers predictive of treatment outcomes remains underexplored.Participants with treatment resistant depression from the TDBRAIN dataset underwent either high frequency rTMS (10 Hz) at the left dorsolateral prefrontal cortex (DLPFC) (Protocol 1, n = 44) or low frequency rTMS (1 Hz) at the right DLPFC (Protocol 2, n = 73). Pre-treatment electroencephalograms (EEG) was collected, and changes in Beck Depression Inventory were measured post-treatment. EEG oscillations were decomposed into multiband intrinsic mode functions (IMF) and integrated under a latent space predictive modeling framework to identify signatures for predicting treatment outcomes.Multiband signatures significantly predicted rTMS outcomes (Protocol 1: r = 0.40, p < 0.01; Protocol 2: r = 0.26, p < 0.05). Key spatial patterns linked to treatment outcomes were identified, revealing three main oscillations: IMF-Alpha, IMF-Beta, and the residual signal. In Protocol 1, critical regions included the left frontal and parietal regions for IMF-Alpha, left frontal-central and right parietal regions for IMF-Beta, and bi-hemispheric central and left parietal-occipital regions for residual signals. In Protocol 2, critical regions involved the left frontal and parietal regions for IMF-Alpha, left frontal-central region IMF-Beta, and right frontal, left frontal-central, midline central, and left parietal-occipital regions for residual signals. These oscillatory features also showed correlations with specific personality measures, suggesting their potential clinical relevance.Our findings demonstrate the promise of machine learning-driven multiband EEG signatures for personalized MDD treatment prediction, offering a translatable pathway for improved patient outcomes.
View details for DOI 10.1016/j.jad.2025.119483
View details for PubMedID 40441660
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Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression.
Cell reports. Medicine
2025: 102151
Abstract
Major depressive disorder (MDD) is highly heterogeneous, posing challenges for effective treatment due to complex interactions between clinical symptoms and neurobiological features. To address this, we apply contrastive principal-component analysis to fMRI-based resting-state functional connectivity, isolating disorder-specific variations by contrasting data from 233 MDD patients and 285 healthy controls. Subsequently, we use sparse canonical correlation analysis to identify two significant dimensions linking distinct brain circuits with clinical profiles. One dimension relates to an internalizing-externalizing symptom spectrum involving visual and limbic networks and is associated with cognitive task reaction times. The other dimension, linked to personality traits protective against depression (e.g., extraversion), is driven by dorsal attention network connections and correlates with cognitive control and psychomotor performance. This approach illuminates stable symptom dimensions and their neurophysiological underpinnings, aiding in precision phenotyping for MDD and supporting the development of targeted, individualized therapeutic strategies for mental health care.
View details for DOI 10.1016/j.xcrm.2025.102151
View details for PubMedID 40441140
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Contrastive Functional Connectivity Defines Neurophysiology-informed Symptom Dimensions in Major Depression.
bioRxiv : the preprint server for biology
2024
Abstract
Major depressive disorder (MDD) is a prevalent psychiatric disorder characterized by substantial clinical and neurobiological heterogeneity. Conventional studies that solely focus on clinical symptoms or neuroimaging metrics often fail to capture the intricate relationship between these modalities, limiting their ability to disentangle the complexity in MDD. Moreover, patient neuroimaging data typically contains normal sources of variance shared with healthy controls, which can obscure disorder-specific variance and complicate the delineation of disease heterogeneity.We employed contrastive principal component analysis to extract disorder-specific variations in fMRI-based resting-state functional connectivity (RSFC) by contrasting MDD patients (N=233) with age-matched healthy controls (N=285). We then applied sparse canonical correlation analysis to identify latent dimensions in the disorder variations by linking the extracted contrastive connectivity features to clinical symptoms in MDD patients.Two significant and generalizable dimensions linking distinct brain circuits and clinical profiles were discovered. The first dimension, associated with an apparent "internalizing-externalizing" symptom dimension, was characterized by self-connections within the visual network and also associated with choice reaction times of cognitive tasks. The second dimension, associated with personality facets such as extraversion and conscientiousness typically inversely associated with depression symptoms, is primarily driven by self-connections within the dorsal attention network. This "depression-protective personality" dimension is also associated with multiple cognitive task performances related to psychomotor slowing and cognitive control.Our contrastive RSFC-based dimensional approach offers a new avenue to dissect clinical heterogeneity underlying MDD. By identifying two stable, neurophysiology-informed symptom dimensions in MDD patients, our findings may enhance disease mechanism insights and facilitate precision phenotyping, thus advancing the development of targeted therapeutics for precision mental health.
View details for DOI 10.1101/2024.10.04.616707
View details for PubMedID 39416217
View details for PubMedCentralID PMC11482755
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Multiband EEG signature decoded using machine learning for predicting rTMS treatment response in major depression.
medRxiv : the preprint server for health sciences
2024
Abstract
Major depressive disorder (MDD) is a global health challenge with high prevalence. Further, many diagnosed with MDD are treatment resistant to traditional antidepressants. Repetitive transcranial magnetic stimulation (rTMS) offers promise as an alternative solution, but identifying objective biomarkers for predicting treatment response remains underexplored. Electroencephalographic (EEG) recordings are a cost-effective neuroimaging approach, but traditional EEG analysis methods often do not consider patient-specific variations and fail to capture complex neuronal dynamics. To address this, we propose a data-driven approach combining iterated masking empirical mode decomposition (itEMD) and sparse Bayesian learning (SBL). Our results demonstrated significant prediction of rTMS outcomes using this approach (Protocol 1: r=0.40, p<0.01; Protocol 2: r=0.26, p<0.05). From the decomposition, we obtained three key oscillations: IMF-Alpha, IMF-Beta, and the remaining residue. We also identified key spatial patterns associated with treatment outcomes for two rTMS protocols: for Protocol 1 (10Hz left DLPFC), important areas include the left frontal and parietal regions, while for Protocol 2 (1Hz right DLPFC), the left and frontal, left parietal regions are crucial. Additionally, our exploratory analysis found few significant correlations between oscillation specific predictive features and personality measures. This study highlights the potential of machine learning-driven EEG analysis for personalized MDD treatment prediction, offering a pathway for improved patient outcomes.
View details for DOI 10.1101/2024.09.22.24314146
View details for PubMedID 39399007
View details for PubMedCentralID PMC11469383
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Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience.
Nature neuroscience
2024
Abstract
The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.
View details for DOI 10.1038/s41593-024-01649-9
View details for PubMedID 38778146
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Identifying Brain-Clinical Dimensions in Major Depression Using Contrastive Connectivity Analysis
ELSEVIER SCIENCE INC. 2024: S166
View details for Web of Science ID 001282811900389
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Optimizing Antidepressant Efficacy: Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response.
medRxiv : the preprint server for health sciences
2024
Abstract
Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R2 = 0.31; placebo: R2 = 0.22). Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel insights into the intricate neuropsychopharmacology of antidepressant treatment and paves the way for advances in precision medicine and development of more targeted antidepressant therapeutics.
View details for DOI 10.1101/2024.04.11.24305583
View details for PubMedID 38645124
View details for PubMedCentralID PMC11030479
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Individual deviations from normative electroencephalographic connectivity predict antidepressant response.
Journal of affective disorders
2024
Abstract
Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo, partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment. Here we develop a novel normative modeling framework to quantify individual deviations in psychopathological dimensions that offers a promising avenue for the personalized treatment for psychiatric disorders.We built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients (102 sertraline-medicated and 119 placebo-medicated). Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after the eight-week antidepressant treatment.We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between sertraline and placebo responses.Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective personalized MDD treatment.Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094.
View details for DOI 10.1016/j.jad.2024.01.177
View details for PubMedID 38281595
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Individual Deviations from Normative Electroencephalographic Connectivity Predict Antidepressant Response.
medRxiv : the preprint server for health sciences
2023
Abstract
Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo. This modest efficacy is partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment - the approved antidepressants only benefit a portion of patients, calling for personalized psychiatry based on individual-level prediction of treatment responses. Normative modeling, a framework that quantifies individual deviations in psychopathological dimensions, offers a promising avenue for the personalized treatment for psychiatric disorders. In this study, we built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients. We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between treatment responses. Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective MDD treatment.
View details for DOI 10.1101/2023.05.24.23290434
View details for PubMedID 37292874
View details for PubMedCentralID PMC10246152