Two distinguishable clusters emerged, with one showing widespread cortical thinning, anergy-related symptoms, childhood trauma, and diabetes comorbidity, and one showing widespread cortical thinning, anergy-related symptoms, childhood trauma, and diabetes comorbidity.
Abstract
Identifying generalizable brain-based biotypes across independent cohorts is critical for parsing heterogeneity in Major Depressive Disorder (MDD), yet robust subtypes spanning micro- and macroscales remain poorly defined. We applied stability-based clustering to cortical thickness data from 1,531 MDD individuals in UK Biobank (UKB), with external validation in 144 inpatients from IRCCS Ospedale San Raffaele (HSR). Two distinguishable clusters emerged (accuracy=87.5%), with one showing widespread cortical thinning, anergy-related symptoms, childhood trauma, and diabetes comorbidity. This profile generalized with 96.5% accuracy in a hold-out UKB sample and 80.6% in HSR. Mapping clusters cortical profiles onto Neurosynth meta-analytic activation patterns revealed a ventral-dorsal gradient linked with emotion regulation, interoceptive, and motivational processes. Spatial correlations with 19 neurotransmitter receptors and transporters obtained from positron emission tomography identified dopamine transporter as the dominant contributor in UKB, and histamine receptor H3 in HSR. These findings provide a reproducible framework linking MDD subtypes to multiscale biological complexity.
Abstract Background Major depressive disorder (MDD) is a clinically and biologically heterogeneous condition associated with a high rate of somatic morbidity and mortality, largely driven by cardiometabolic comorbidities [1]. Elucidating the mechanisms underlying this elevated risk remains challenging, as MDD spans wide inter-individual variability across neuroanatomical, microstructural, functional, and genetic domains [2]. Data-driven stratification approaches may therefore identify biologically meaningful subtypes with differential vulnerability to medical complications, and inform personalized prevention and treatment strategies [3]. Aims & Objectives We integrated multimodal neuroimaging and polygenic scores (PGSs) with unsupervised and supervised machine-learning techniques to (i) derive neurobiologically informed subgroups of individuals with lifetime MDD and (ii) identify subtype-specific biological features associated with cardiovascular diseases and diabetes risk. Method The study included 3,887 UK Biobank participants with lifetime MDD who underwent standardized multimodal neuroimaging and genotyping. Neuroimaging features encompassed cortical thickness, surface area, grey-matter volumes, diffusion tensor metrics (fractional anisotropy, axial and radial diffusivity), resting-state functional connectivity strengths, and task-related activation during emotional processing. PGSs for psychiatric, metabolic, and inflammatory traits were computed using LDpred2-auto [4]. Consensus clustering [5] was applied to identify homogeneous patient subgroups. K-means clustering was iterated 1,000 times for clustering solutions ranging from k=2 to k=5, with each iteration sampling 80% of participants. The optimal cluster number was determined by internal validity indices, while statistical significance was assessed via 10,000 Monte Carlo simulations. Between-cluster differences in biological and clinical variables were tested using the Wilcoxon rank-sum and χ2 tests, correcting for False Discovery Rate. Within each cluster, nested cross-validated elastic-net models predicted cardiovascular disease and diabetes, accounting for class imbalance, computing model significance via 1,000 permutations, and estimating feature stability through 5,000 non-parametric bootstrap samplings. Results Two statistically significant clusters emerged (Cluster0: N=2,030; Cluster1: N=1,857; p<0.001). One subgroup (Cluster1) exhibited widespread cortical thinning (q<0.001), reduced cortical and subcortical volumes (q<0.001), diffuse white-matter microstructural abnormalities (q<0.001), diminished resting-state connectivity (q<0.001) and emotional-task activation (q<0.05), and elevated PGSs for MIF (q=0.047) and RANTES (q=0.046), with respect to the other. Clinically, the same cluster showed a higher prevalence of cardiovascular diseases (q=0.013) and diabetes (q=0.021), alongside poorer motor (q=0.015) and processing (q=0.030) speed performance. Elastic-net models revealed distinct cardiometabolic risk signatures across clusters (q=0.009-0.047). While genetic liability for type 2 diabetes contributed to diabetes risk in both subgroups, white-matter diffusivity measures were most influential in the less neurobiologically compromised cluster (Cluster0), whereas grey-matter structural metrics predominated in the more affected subgroup (Cluster1). Cardiovascular disease prediction involved multimodal features in both clusters, but genetic and neuroimaging contributions were broader and stronger in the more impaired subtype (Cluster1). Discussion & Conclusions This large-scale, multimodal, data-driven analysis identified two neurobiologically distinct MDD subtypes with distinct cardiometabolic vulnerability profiles. The subtype characterized by more marked structural, microstructural, functional, and inflammatory-genetic alterations showed increased cardiometabolic burden and cognitive slowing, aligning with immuno-metabolic models of depression. Importantly, the biological pathways linking MDD to cardiometabolic disease differed across subtypes, underscoring the value of stratified approaches for advancing precision psychiatry and targeting preventive and therapeutic interventions.
B. Vai, L. F. Uyà, F. Colombo et al.· International Journal of Neu...· 0 citations
BACKGROUND
Major depressive disorder (MDD) is often accompanied by cognitive impairment; however, the cognitive heterogeneity of MDD and its neurobiological context remain poorly understood.
METHODS
A total of 198 participants with MDD and 275 HCs underwent multi-domain cognitive assessments and multi-modal MRI acquisition. A semi-supervised approach was applied to identify cognitive dimensions of MDD, and individual-level structural-enriched functional networks (SFNs) were constructed. Network-based statistics were applied to characterize network-level associations between structural-functional coupling deviation and cognitive dimensions. Furthermore, the correlations between the spatial pattern of SFN deviation and meta-analytic neurocognitive terms, cortical transcriptome, and neurotransmitter density distribution maps were detected.
RESULTS
In the MDD group, 103 individuals were assigned to Cluster 1, presenting widespread cognitive impairments, whereas 95 were assigned to Cluster 2, presenting cognitive preservations. An abnormally enhanced SFN subnetwork (PPerm = 0.042) was identified, which showed significant spatial correlation with meta-analytic neurocognitive maps (r = 0.181, P < 0.001). The SFN deviation pattern was spatially associated with 2458 genes enriched primarily in neuronal and synaptic function, and these genes also showed enrichment for pathways annotated to neurodegenerative diseases (PFDR < 0.05). In addition, SFN deviation was spatially associated with three neurotransmitter maps, including N-methyl-D-aspartate receptor, cannabinoid type-1 receptor, and norepinephrine transporter (PFDR = 0.028).
CONCLUSIONS
The study provides a data-driven characterization of cognitive heterogeneity in MDD, identifying two cognitive dimensions spanning from relative preservation to widespread impairment. By integrating structural-functional coupling deviations with transcriptomic and neurotransmitter maps, these findings provide preliminary biological context for interpreting cognitive heterogeneity in MDD.
Yu-Shun Yan, Jia'ao Yu, Liang Zuo et al.· Psychiatry and Clinical Neur...· 0 citations
Results indicate that multi-omics integration, in addition to explaining the molecular architecture of MDD, also characterizes patient subgroups with pathophysiological mechanisms, dimensions of symptoms, and disease treatment, which demonstrates that there is a shift in psychiatry toward a more mechanistic approach.
Elham Amjad, B. Sokouti· OBM Neurobiology· 0 citations
Abstract Background Major depressive disorder (MDD) shows substantial clinical and neurobiological heterogeneity, yet current diagnostic frameworks treat this highly disabling condition as a unitary syndrome. This mismatch has limited efforts to identify reproducible neural markers and to develop biologically informed treatment strategies. Data-driven approaches that integrate symptom measures with functional neuroimaging offer a promising path toward parsing this heterogeneity, but many reported subtypes fail to generalize beyond the original sample. Robust and replicable methods are therefore needed to derive clinically meaningful and biologically grounded dimensions of depression that can inform personalized treatment strategies like non-invasive neuromodulation. Aims & Objectives We aimed to identify stable, generalizable dimensional and categorical representations of depression heterogeneity using multivariate modeling of clinical symptoms and resting-state functional connectivity (RSFC). Specifically, we sought to (1) derive latent brain–behavior dimensions associated with core depressive symptom domains, (2) assess the stability and generalizability of these dimensions using cross-validation, and (3) determine whether dimensional solutions yield clinically meaningful categorical subtypes with distinct neurobiological profiles and differential treatment response. Method We analyzed clinical and RSFC data from 328 individuals with MDD. We applied regularized canonical correlation analysis (rCCA) to identify latent dimensions capturing shared variance between symptom measures and RSFC while minimizing overfitting. We evaluated model performance using cross-validated held-out test sets. We then clustered individuals with MDD based on their dimensional scores using hierarchical clustering to identify categorical subtypes. Finally, we compared subtypes on symptom profiles, functional connectivity patterns, and response to repetitive transcranial magnetic stimulation (rTMS). Results The optimal rCCA model identified three reproducible brain–behavior dimensions that generalized to held-out data. These dimensions primarily reflected variation in (1) depressed mood and somatic symptoms, (2) anhedonia, and (3) insomnia. Each dimension mapped onto distinct patterns of RSFC involving default mode, limbic, and frontoparietal networks. Clustering of dimensional scores revealed four depression subtypes characterized by dissociable clinical profiles and connectivity signatures. Subtypes differed in the relative prominence of affective versus somatic symptoms and showed distinct alterations in default mode network connectivity and limbic–cortical interactions. Importantly, subtype membership predicted differential response to rTMS, indicating that the identified neurobiological patterns carried treatment-relevant information. Subtype assignment was stable post-rTMS, irrespective of response or remission status. Discussion & Conclusions By combining multivariate brain–behavior modeling with clustering, we identified robust dimensional and categorical representations of depression heterogeneity that generalized beyond the training data. The derived dimensions captured clinically salient symptom domains and corresponded to distinct functional connectivity patterns, supporting their neurobiological relevance. The resulting subtypes showed differential treatment response, underscoring their potential utility for stratifying patients and guiding personalized intervention strategies. These findings demonstrate that integrating dimensional and categorical approaches can bridge symptom heterogeneity with circuit-level variation and provide a scalable framework for biologically informed subtyping in MDD. This approach advances efforts toward precision neuropsychopharmacology by identifying reproducible brain–behavior relationships that may inform treatment selection and mechanistic targeting.
K. Dunlop· International Journal of Neu...· 0 citations
Abstract Background Major Depressive Disorder (MDD) is characterized by substantial heterogeneity in both symptomatic presentation and underlying neurobiology, posing significant challenges for accurate diagnosis and effective intervention. While prior research has attempted to delineate MDD subtypes using only neuroimaging features, the clinical interpretability of these subtypes often remains limited. Aims & Objectives Aiming to identify clinically meaningful MDD subtypes through an integrative analysis of clinical symptoms and multimodal neuroimaging data, this study leveraged samples from the REST-meta-MDD consortium. The final dataset included 474 MDD patients with Hamilton Depression Rating Scale (HAMD) scores and 418 healthy controls (HCs) across six sites. Method Structural and functional MRI data underwent surface-based preprocessing. Partial correlation analyses were performed between imaging metrics and HAMD scores to identify brain regions exhibiting clinically meaningful associations. Subsequently, Regularized Canonical Correlation Analysis (rCCA) was applied to these selected regions and clinical scores for feature dimensionality reduction. K-means clustering was then implemented on the derived components to delineate MDD subtypes. Finally, the identified subtypes were compared with HC to characterize their distinct profiles in brain structure, function, and clinical symptomatology. Results Two distinct MDD subtypes were identified (Subtype 1: n=299; Subtype 2: n=175). Relative to HC, Subtype 1 exhibited reduced cortical thickness and volume in multiple brain regions, including the frontal, temporal, limbic, and parietal lobes, whereas no significant functional alterations were detected. Conversely, Subtype 2 showed decreased amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) values across several resting-state networks—such as the default mode, salience/ventral attention, dorsal attention, visual, and control networks—without notable structural deficits. Clinically, Subtype 1 demonstrated higher scores than Subtype 2 in items related to depressive mood and psychic anxiety. Discussion & Conclusions This study delineated two MDD subtypes with distinct neuropathological profiles: one characterized by structural abnormalities across multiple brain regions and the other by widespread functional disruptions. Notably, the subtype defined by structural alterations presented greater severity in depressive symptoms and psychic anxiety. These findings elucidate the clinical and biological heterogeneity of MDD, offering a theoretical foundation for refined diagnostic classification and the development of targeted therapeutic strategies.
Z. Chen, Q. Bo, C. Wang· International Journal of Neu...· 0 citations
Major Depressive Disorder (MDD) is a prevalent and disabling psychiatric disorder. Its clinical heterogeneity undermines diagnostic precision and treatment effectiveness. While DSM-5 defines melancholic and anxious subtypes based on symptoms, their neurophysiological bases remain unclear. Resting-state electroencephalography (EEG) provides a non-invasive approach for investigating neurophysiological heterogeneity in depression. Beta-band oscillations are associated with reward processing, motivation, and cortical arousal, which may differ across subtypes.
We analyzed 325 patients with MDD, classified into melancholic (
n
= 108), anxious (
n
= 102), and non-melancholic and non-anxious (
n
= 115) groups. Relative spectral power and phase-locking value (PLV) in beta1 (12–20 Hz) and beta2 (20–30 Hz) bands were compared. Logistic regression and subtype-specific correlation analyses assessed classification and clinical associations.
Melancholic depression was associated with widespread reductions in beta2 power and higher suicide risk, whereas anxious depression showed altered beta-band functional connectivity, particularly involving left temporal-region connections. Both subtypes shared reduced prefrontal–temporal connectivity. Exploratory classification analyses showed modest discriminative performance after leakage-controlled cross-validation. Associations between beta1 activity and symptom severity were observed only in the anxious subtype.
Melancholic and anxious depression showed partially distinct beta-band EEG patterns. These findings provide preliminary evidence that beta-band EEG features may capture subtype-related neurophysiological heterogeneity in MDD, with subtype-specific associations between neural activity and symptom severity. Further validation is required before these features can be considered clinically applicable.
The trial was registered at the Chinese Clinical Trial Registry on 04/23/2022 ( www.chictr.org.cn ChiCTRID ChiCTR2200059053).
Meng-Yuan Cheng, Ziyao Su, Haoran Zhang et al.· BMC Psychiatry· 0 citations
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