Functional connectivity correlates of reaction time variability in treatment-resistant major depression

Normative Modeling of sgACC-DLPFC Functional Connectivity Identifies a Personalized rTMS Target Associated with Clinical Response in Major Depressive Disorder.

BACKGROUND Response to repetitive transcranial magnetic stimulation (rTMS) in major depressive disorder (MDD) varies substantially. Normative modeling of functional connectivity can disentangle disease-related pathophysiology from demographic variability, potentially refining personalized targeting. METHODS We constructed a normative model of subgenual anterior cingulate cortex (sgACC)-dorsolateral prefrontal cortex (DLPFC) functional connectivity using exclusively healthy controls (HCs; DIRECT dataset, n = 1,313). Individual Z-score maps were generated for 1,583 MDD patients; the DLPFC voxel with the most negative Z-score defined the functional connectivity normative deviation (FCND)-guided target. Clinical utility was tested in two independent rTMS cohorts (active: n=39, sham: n=23; accelerated iTBS: n=15) by correlating Euclidean distance from the stimulation site to the FCND target with improvement. We explored whether Z-score normalization statistically mediated this relationship. RESULTS In the DIRECT dataset, patients with MDD showed significantly more negative values for the most negative Z-score (Min Z-score) than HCs, and this Min Z-score correlated negatively with depressive symptom severity, as measured by the 17-item Hamilton Depression Rating Scale. In the YT active group, shorter distance to the FCND-guided target was associated with greater clinical improvement, while no association was observed in the sham group. This association was also significant in the aiTBS dataset. Exploratory mediation analysis revealed an indirect effect statistically consistent with the hypothesis that distance influences improvement through Z-score normalization in the active group, with a directionally consistent trend in the iTBS dataset. CONCLUSION The FCND-guided target framework provides a personalized, mechanism-informed strategy for precision neuromodulation in MDD.

Electrophysiological Markers of Within-Network Connectivity in Major Depression

Depression and treatment-resistant depression (TRD) are significant public health issues, but the associated network-level neurobiological mechanisms remain poorly understood. This study used magnetoencephalography (MEG) to identify altered resting-state connectivity within the default mode (DMN), executive control (ECN), salience (SN), dorsal attention (DAN), motor (MN), and visual (VN) networks as potential biomarkers of depression and treatment resistance. The study recruited 168 participants (80 healthy volunteers (HVs) and 88 currently experiencing a major depressive episode (74 with TRD and 14 without TRD (noTRD))). Data Integration Analysis for Biomarker Discovery using Latent Variable Approaches for Omics Studies (DIABLO) was used to differentiate the depression, TRD, and HV subgroups and identify neural markers of depression and treatment resistance. For differentiating the depression and HV groups, the triple network model (area under the receiver operating curve (AUROC): 0.759-0.787) - which includes the DMN, ECN, and SN - outperformed the six-network model (AUROC: 0.747-0.762) across different bandwidths. For differentiating the TRD and HV groups, the triple network model demonstrated reasonable prediction across different bandwidths (AUROC: 0.737-0.807); potential within-network connectivity differences distinguished those with TRD from HVs, especially DMN within-network connectivity between the inferior parietal lobule and precuneus in the beta band (FDR-corrected p<.05). Hyperconnectivity within the SN (superior parietal lobule and frontal operculum in the alpha band) and DMN (inferior parietal lobule and lateral prefrontal cortex in the beta band) was associated with number of treatment failures (ps<.05). These findings highlight key brain regions and connectivity patterns, advancing our understanding of neural mechanisms underlying depression and treatment resistance.

Longitudinal Brain Correlates of Cognitive Performance in Early Psychosis

Pharmacologic and expectancy effects in depression: Associations with inter-network resting-state connectivity.

Depression involves dysregulation across large-scale neural networks, yet substantial heterogeneity in resting-state functional connectivity (rsFC) across patients limits our understanding of treatment mechanisms. A key unresolved question is whether baseline network architecture differentially predicts response to pharmacological versus expectancy-driven treatment effects. In this mechanistic, hypothesis-generating study, 60 depressed participants completed resting-state fMRI at baseline and after 8 weeks of double-blind randomization to an SSRI or placebo. We tested whether connectivity between three networks - the dorsal attention (DAN), salience (SN), and default mode (DMN) networks - predicted treatment response as a function of drug assignment and treatment beliefs. We identified dissociable neural pathways for pharmacological and expectancy effects. Baseline connectivity between attention and salience networks, circuits involved in contextual processing, predicted response specifically among participants who developed placebo beliefs. Baseline connectivity between salience and default mode networks, circuits involved in mood regulation and internal state prediction, showed a double dissociation by drug assignment: higher connectivity predicted better outcomes with the SSRI, while lower connectivity predicted better outcomes with placebo. Network reorganization over treatment followed rather than predicted mood improvement and occurred only when drug assignment and belief aligned. These findings suggest that baseline connectivity patterns serve as trait-like neural markers differentiating pharmacological from expectancy-driven response pathways, and that network reorganization reflects a consequence of aligned pharmacological and psychological treatment effects. Replication in larger samples is warranted, but these results offer a novel framework for understanding and ultimately resolving heterogeneity in antidepressant treatment response.

Functional connectivity changes in obsessive-compulsive disorder following exposure and response prevention.

Obsessive-compulsive disorder (OCD) has been increasingly linked to alterations in functional connectivity (FC) across large-scale brain networks, particularly the default mode (DMN), salience (SN), and central executive network (CEN), known as the triple-networks. However, little is known about how connectivity patterns evolve following treatment. We examined both static and dynamic resting-state FC in 25 adults with OCD and 21 matched healthy controls (HCs) at baseline, one week, and three months following intensive exposure and response prevention (ERP) using the Bergen 4-Day Treatment (B4DT). Independent component analysis (ICA) and sliding-window approaches were used to evaluate network connectivity within and between the triple networks. At baseline, adults with OCD showed stronger static connectivity within the DMN and SN and spent more time in a dynamic connectivity state characterized by strong connectivity within the DMN and decoupling between the DMN and SN compared to controls. Following the 4-day intensive treatment, these abnormal patterns normalized and remained stable at the three-month follow-up. A higher number of transitions between states was associated with reductions in the severity of OCD, anxiety, and depressive symptoms. Additional correlation analyses indicated that higher baseline SN-CEN connectivity was associated with greater symptom severity, while post-treatment reductions in CEN and CEN-SN static connectivity were linked to improvements in depressive symptoms. These findings suggest that effective ERP promotes a normalization of both static and dynamic connectivity within and between the triple networks. Thus, dynamic metrics, alongside static measures, may serve as clinically meaningful biomarkers of therapeutic response.

Neural markers of working memory and emotional regulation are predictors of future depression severity in at risk young adults: a replication study.

Objective neural markers that reflect the underlying pathophysiological mechanisms of affective disorders are needed to facilitate early identification of individuals most at risk of future affective disorders and ultimately provide neural targets to guide therapeutic interventions. Using an emotional n-back paradigm designed to examine working memory (WM) and emotional regulation (ER) capacity, we previously showed that WM-related elevated left dlPFC activity (a key node of the central executive network (CEN)) and elevated right precuneus activity (a key node of the default mode network (DMN)), as well as ER-related elevated left dlPFC activity were positively associated with future depression severity in young adults at risk for affective disorders. We now aimed to replicate and extend these previous longitudinal findings by examining relationships among right precuneus activity and left dlPFC activity during WM and ER tasks and future depression severity in a new independent young adult sample (n = 77: 50 female, age = 24.68), and a larger combined sample (n = 121: 83 female, age = 23.81) comprising the original and new samples. The Hamilton Rating Scale for Depression (HAM-D) and Young Mania Rating Scale (YMRS) were measured at 12 months post scan to assess future depression and mania/hypomania severity respectively. In the new sample, we showed patterns of left dlPFC activity and right precuneus activity during WM, and left dlPFC activity during ER that were consistent with the original sample. In both the new and combined samples, future depression severity was robustly predicted by WM-related left dlPFC activity and right precuneus activity, and ER-related left dlPFC activity (all ps < 0.05 qFDR). These findings were specific to future depression severity. The effect sizes (pseudo R-squared values) for the full models including all IVs in the new and combined samples ranged from approximately 25-43%, with left dlPFC and right precuneus activity during WM explaining 15.59% of variance in future depression severity in the new sample; and left dlPFC activity during ER explaining 14.63% of variance in future depression severity in the combined sample. These replicated, longitudinal findings provide candidate neural markers to guide risk identification and targeting of new interventions for individuals with and those at risk for future affective disorders.