Dhivya Srinivasan

Background: Major depressive disorder (MDD) is clinically heterogeneous, hindering identification of reproducible biomarkers. Using a semi-supervised machine learning approach, HYDRA, we previously identified two neuroanatomical dimensions from structural MRI in medication-free MDD from COORDINATE-MDD consortium. These dimensions (D1, D2) showed differential responses to selective serotonin reuptake inhibitor (SSRI) antidepressants and placebo. External replication in UK Biobank linked D2, characterized by widespread subtle neuroanatomical reductions, to an immuno-metabolic profile. Here, we examined whether these dimensions are detectable early in the course of illness. Methods: We applied the pre-trained model to structural MRI data from the multisite PRONIA cohort, comprising individuals with recent-onset depression (ROD; n = 377; mean age 25.8 years, SD 6.0; 51.3% female) and healthy controls (n = 267; mean age 25.5 years, SD 6.4; 61.0% female). Participants were assigned to clusters (C1, C2) corresponding to the previously identified dimensions (D1, D2). Clusters were compared on clinical symptom profiles, peripheral inflammatory markers, and in a subset (n = 107), proteomic ageing indices. Results: Two neuroanatomical clusters were identified in PRONIA. C1 (n = 265) showed higher negative symptom severity and elevated interleukin-2 levels. C2 (n = 140) was associated with higher residual proteomic age. Overall depressive symptom severity did not differ significantly between clusters. Conclusions: Neuroanatomical dimensions of MDD are reproducible and detectable at illness onset. Associations with negative symptom severity, inflammatory signalling, and proteomic ageing suggest these dimensions capture biologically meaningful heterogeneity early in depression. These findings support a biologically informed framework for stratified treatment approaches in MDD.

Background: Accumulation of amyloid (Aß) plaques is an early pathologic change of Alzheimer’s Disease (AD). However, the mechanisms and pathways by which amyloid spreads across the cerebrum are not fully understood. Deriving distinct dimensions of amyloid deposition and their associations with biomedical factors could be useful to understand how amyloid propagates. In this analysis, we identified two distinct subtypes of progression based on spatiotemporal variations using a recently developed data‐driven, deep learning clustering method called Surreal‐GAN¹.Method: We used data from 482 A+ and 801 A‐ subjects with ¹⁸F‐florbetapir PET from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; N = 832) and ¹¹C‐PiB from the Preclinical AD consortium (PAC; N = 451). Standardized uptake value ratio (SUVR) maps (cerebellar GM reference) were transformed to reference space. We derived amyloid status using Gaussian mixture modelling of mean total cortical SUVR for each of 4 sites. We then applied non‐negative matrix factorization² on the SUVR maps to identify data‐driven common patterns of deposition across the sample. Next, we applied Surreal‐GAN on a total of 1283 subjects to determine the amyloid spatiotemporal subtypes. Correlations with clinical and cognitive variables were evaluated.Results: Participants were 30%/22% female and had mean age 74.9 (±7.4)/ 67.9(±9.1) years for ADNI and PAC, respectively. We identified 24 components from NMF (Fig 1A) after excluding 6 as non‐target features. Surreal‐GAN showed optimal agreement indices for two patterns. While both patterns shared involvement of posterior cingulate/precuneus and inferior frontal cortex, pattern r1 shows relatively more occipital deposition and pattern r2 shows more frontal deposition (Fig 1B), as seen when comparing strongly r1 vs strongly r2 participants using voxel‐based morphometry (Fig 2). Both were associated with worse global cognitive function and worsening neurodegeneration (Table 1). r1 correlates with white matter hyperintensity volume, a measure of cerebrovascular disease, while pattern r2 is associated with the presence of APOE e4 alleles.Conclusion: Employing a data‐driven deep learning‐based approach, we derived more than one dissociable pattern of amyloid deposition, potentially differently influenced by vascular disease (r1/occipital) and genetics (r1/frontal). These findings could inform identification of subgroups of individuals who may or may not be responsive to particular clinical intervention.

BACKGROUND AND PURPOSE: The Systolic Blood Pressure Intervention (SPRINT) randomized trial demonstrated that intensive blood pressure management resulted in slower progression of cerebral white matter hyperintensities, compared with standard therapy. We assessed longitudinal changes in brain functional connectivity to determine whether intensive treatment results in less decline in functional connectivity and how changes in brain functional connectivity relate to changes in brain structure. MATERIALS AND METHODS: Five hundred forty-eight participants completed longitudinal brain MR imaging, including resting-state fMRI, during a median follow-up of 3.84 years. Functional brain networks were identified using independent component analysis, and a mean connectivity score was calculated for each network. Longitudinal changes in mean connectivity score were compared between treatment groups using a 2-sample t test, followed by a voxelwise t test. In the full cohort, adjusted linear regression analysis was performed between changes in the mean connectivity score and changes in structural MR imaging metrics. RESULTS: Four hundred six participants had longitudinal imaging that passed quality control. The auditory-salience-language network demonstrated a significantly larger decline in the mean connectivity score in the standard treatment group relative to the intensive treatment group (P = .014), with regions of significant difference between treatment groups in the cingulate and right temporal/insular regions. There was no treatment group difference in other networks. Longitudinal changes in mean connectivity score of the default mode network but not the auditory-salience-language network demonstrated a significant correlation with longitudinal changes in white matter hyperintensities (P = .013). CONCLUSIONS: Intensive treatment was associated with preservation of functional connectivity of the auditory-salience-language network, while mean network connectivity in other networks was not significantly different between intensive and standard therapy. A longitudinal increase in the white matter hyperintensity burden is associated with a decline in mean connectivity of the default mode network.