OBJECTIVE:Repetitive transcranial magnetic stimulation (rTMS) and intranasal esketamine are FDA-approved for treatment-resistant depression (TRD), yet comparative real-world data on response trajectories and predictors of outcomes remain limited. METHODS:A retrospective analysis was performed using electronic medical records from UC San Diego Health. Adults with TRD treated with rTMS (n = 279) or intranasal esketamine (n = 93) between 2017 and 2025 were included. The primary outcome was clinical response (≥50% PHQ-9 reduction). Time-to-response was assessed using inverse probability treatment weighted (IPTW) Cox models; Kaplan-Meier curves and log-rank tests provided descriptive comparisons. Covariates included age, trauma history, anxiety comorbidity, benzodiazepine use, tobacco use history, BMI, and baseline symptom severity. Secondary outcomes included remission (PHQ-9 < 5) and suicidal ideation (SI). RESULTS:Esketamine demonstrated earlier response over 90 days (RMST difference = -11.94 days) and faster time-to-response in the IPTW Cox model (HR = 1.62, p = 0.005); KM estimates showed median response at 36 vs. 49 days (p = 0.0096) with convergence by ∼90 days. Cumulative response and remission rates were numerically higher for esketamine (68.8% / 45.2%) than rTMS (59.4% / 40.1%), supporting a speed-of-response difference rather than superior overall efficacy. SI improved more rapidly with esketamine (median 9 vs. 26 days; p = 0.001). In the rTMS cohort, comorbid anxiety (HR = 0.69, p = 0.039) and benzodiazepine use (HR = 0.73, p = 0.046) predicted slower response, while former tobacco use predicted faster response (HR = 1.31, p = 0.006). No significant predictors emerged for esketamine. CONCLUSIONS:Esketamine was associated with earlier observed antidepressant and anti-suicidal improvement than rTMS. Baseline factors, including benzodiazepine use, may help inform expectations regarding rTMS response trajectory.

Major depressive disorder (MDD) is the most common mood disorder in adolescents, affecting up to 20% of adolescents in the US. Identifying neurophysiological functional connectivity biomarkers that track treatment response and provide mechanistic insights could inform early interventions and improve depression outcomes in adolescents. Resting-state quantitative electroencephalography (qEEG) provides a low-cost, non-invasive method to examine functional connectivity and link connectivity dynamics to symptom changes during treatment. We utilized qEEG to identify associations between functional connectivity measures and depressive symptom severity over time by analyzing longitudinal data from 21 adolescents with moderate-to-severe MDD (85.7% female) recruited at University Hospitals Cleveland Medical Center, Ohio. Participants completed EEG recordings and depressive symptom assessments using the Children’s Depression Rating Scale-Revised (CDRS-R) at baseline (pre-treatment), week 4, and week 16. Participants received fluoxetine or escitalopram alongside evidence-based therapy (EBT). We computed baseline functional connectivity measures for selected channel pairs across five canonical EEG frequency bands. Principal component analysis (PCA) reduced the dimensionality of baseline functional connectivity metrics to seven components, which were projected onto subsequent time points. Longitudinal associations between PCs and CDRS-R Total and subscale scores were inspected with a linear mixed effects model (LMEM). We observed robust clinical improvement over time. PC2 and PC3 showed significant time-dependent interactions with CDRS total, while PC3 revealed a significant interaction with CDRS Morbidity over time. These findings suggest that fronto-parietal and fronto-temporal connectivity features, especially involving Weighted Pairwise Phase Consistency (WPPC) and Coherence, longitudinally track treatment response in adolescents undergoing antidepressant treatment.

Background: Improving early recognition and accurate diagnosis of major depressive disorder (MDD) in childhood is a pressing concern. Quantitative electroencephalogram (qEEG) may be an effective, noninvasive diagnostic biomarker for MDD. Prior work by our team demonstrated decreased resting connectivity, as measured by qEEG coherence, in a heterogeneous group of adolescents with MDD compared with age and gender-matched healthy controls (HCs). This study explored qEEG coherence as a predictor of MDD diagnosis in a prospective, longitudinal sample of medication-free, adolescents with MDD versus HCs. Methods: Twenty-eight adolescents with MDD (Children's Depression Rating Scale score ≥40) and 27 age and gender-matched HCs (age 14-17, 78% female) received a baseline resting 32-channel EEG. Brain-wide coherence between channel pairs was calculated for the frequency bands (alpha, beta, theta, and delta) and compared between MDD youth and HC. Random forest classifiers were used to predict individual MDD status using baseline qEEG coherence. Models were trained and tested using 10-repeated, 10-fold cross-validation, and performance was evaluated with the area under the receiver operating characteristic curve (AUC-ROC). The contribution of individual predictors was assessed using permutation importance. Model significance was assessed using permutation testing (B = 1000 resamples). Results: Random forest models predicted depression status with a trend-level of significance (mean AUC-ROC = 0.65, p = 0.08). Among the most predictive channel pairs, adolescent MDD was characterized by lower coherence in T7-P7 (p < 0.05), Fz-Cz, and Fp2-F8 as well as higher coherence in P4-O2 and Cz-Pz. Conclusions: This study provides preliminary evidence that multivariate patterns of qEEG may inform the diagnosis of adolescent MDD. Specific aberrant patterns of coherence within the default mode network and cognitive control network were characteristic of adolescent MDD. Ongoing work will seek to replicate these findings in a larger cohort.

Introduction:Electroconvulsive therapy (ECT) and ketamine are two effective treatments for depression with similar efficacy; however, individual patient outcomes may be improved by models that predict optimal treatment assignment. Here, we adapt the Personalized Advantage Index (PAI) algorithm using machine learning to predict optimal treatment assignment between ECT and ketamine using medical record data from a large, naturalistic patient cohort. We hypothesized that patients who received a treatment predicted to be optimal would have significantly better outcomes following treatment compared to those who received a non-optimal treatment. Methods:Data on 2526 ECT and 235 mixed IV ketamine and esketamine patients from McLean Hospital was aggregated. Depressive symptoms were measured using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Patients were matched between treatments on pretreatment QIDS, age, inpatient status, and psychotic symptoms using a 1:1 ratio yielding a sample of 470 patients (n=235 per treatment). Random forest models were trained and predicted differential patientwise minimum QIDS scores achieved during acute treatment (min-QIDS) scores for ECT and ketamine using pretreatment patient measures. Analysis of Shapley Additive exPlanations (SHAP) values identified predictors of differential outcomes between treatments. Results:Twenty-seven percent of patients with the largest PAI scores who received a treatment predicted optimal had significantly lower min-QIDS scores compared to those who received a non-optimal treatment (mean difference=1.6, t=2.38, q<0.05, Cohen's D=0.36). Analysis of SHAP values identified prescriptive pretreatment measures. Conclusions:Patients assigned to a treatment predicted to be optimal had significantly better treatment outcomes. Our model identified pretreatment patient factors captured in medical records that can provide interpretable and actionable guidelines treatment selection.

An extensive library of symptom inventories has been developed over time to measure clinical symptoms of traumatic brain injury (TBI), but this variety has led to several long-standing issues. Most notably, results drawn from different settings and studies are not comparable. This creates a fundamental problem in TBI diagnostics and outcome prediction, namely that it is not possible to equate results drawn from distinct tools and symptom inventories. Here, we present an approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories by ranking item text similarities according to their conceptual likeness. We tested the ability of four pretrained deep learning models to screen thousands of symptom description pairs for related content-a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. Correlation and factor analysis found the properties of the scales were broadly preserved under conversion. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding broad gains for the harmonization of TBI assessment.

The Global ECT MRI Research Collaboration (GEMRIC) has collected clinical and neuroimaging data of patients treated with electroconvulsive therapy (ECT) from around the world. Results to date have focused on neuroimaging correlates of antidepressant response. GEMRIC sites have also collected longitudinal cognitive data. Here, we summarize the existing GEMRIC cognitive data and provide recommendations for prospective data collection for future ECT-imaging investigations. We describe the criteria for selection of cognitive measures for mega-analyses: Trail Making Test Parts A (TMT-A) and B (TMT-B), verbal fluency category (VFC), verbal fluency letter (VFL), and percent retention from verbal learning and memory tests. We performed longitudinal data analysis focused on the pre-/post-ECT assessments with healthy comparison (HC) subjects at similar timepoints and assessed associations between demographic and ECT parameters with cognitive changes. The study found an interaction between electrode placement and treatment number for VFC (F(1,107) = 4.14, p = 0.04). Higher treatment was associated with decreased VFC performance with right unilateral electrode placement. Percent retention showed a main effect for group, with post-hoc analysis indicating decreased cognitive performance among the HC group. However, there were no significant effects of group or group interactions observed for TMT-A, TMT-B, or VFL. We assessed the current GEMRIC cognitive data and acknowledge the limitations associated with this data set including the limited number of neuropsychological domains assessed. Aside from the VFC and treatment number relationship, we did not observe ECT-mediated neurocognitive effects in this investigation. We provide prospective cognitive recommendations for future ECT-imaging investigations focused on strong psychometrics and minimal burden to subjects.

Dysfunctional reward processing in major depressive disorder (MDD) involves functional circuitry of the habenula (Hb) and nucleus accumbens (NAc). Ketamine elicits rapid antidepressant and alleviates anhedonia in MDD. To clarify how ketamine perturbs reward circuitry in MDD, we examined how serial ketamine infusions (SKI) modulate static and dynamic functional connectivity (FC) in Hb and NAc networks. MDD participants (n=58, mean age=40.7 years, female=28) received four ketamine infusions (0.5mg/kg) 2-3 times weekly. Resting-state fMRI scans and clinical assessments were collected at baseline and 24 hours post-SKI completion. Static FC (sFC) and dynamic FC variability (dFCv) were calculated from left and right Hb and NAc seeds to all other brain regions. Paired t-tests examined changes in FC pre-to-post SKI, and correlations were used to determine relationships between FC changes with mood and anhedonia. Following SKI, significant increases in left Hb-bilateral visual cortex FC, decreases in left Hb-left inferior parietal cortex FC, and decreases in left NAc-right cerebellum FC occurred. Decreased dFCv between left Hb and right precuneus and visual cortex, and decreased dFCv between right NAc and right visual cortex both significantly correlated with improvements in Hamilton Depression Rating Scale. Decreased FC between left Hb and bilateral visual/parietal cortices as well as increased FC between left NAc and right visual/parietal cortices both significantly correlated with improvements in anhedonia. Subanesthetic ketamine modulates functional pathways linking the Hb and NAc with visual, parietal, and cerebellar regions. Overlapping effects between Hb and NAc functional systems were associated with ketamine's therapeutic response.

INTRODUCTION:MRI represents one of the clinical tools at the forefront of research efforts aimed at identifying diagnostic and prognostic biomarkers following traumatic brain injury (TBI). Both volumetric and diffusion MRI findings in mild TBI (mTBI) are mixed, making the findings difficult to interpret. As such, additional research is needed to continue to elucidate the relationship between the clinical features of mTBI and quantitative MRI measurements. MATERIAL AND METHODS:Volumetric and diffusion imaging data in a sample of 976 veterans and service members from the Chronic Effects of Neurotrauma Consortium and now the Long-Term Impact of Military-Relevant Brain Injury Consortium observational study of the late effects of mTBI in combat with and without a history of mTBI were examined. A series of regression models with link functions appropriate for the model outcome were used to evaluate the relationships among imaging measures and clinical features of mTBI. Each model included acquisition site, participant sex, and age as covariates. Separate regression models were fit for each region of interest where said region was a predictor. RESULTS:After controlling for multiple comparisons, no significant main effect was noted for comparisons between veterans and service members with and without a history of mTBI. However, blast-related mTBI were associated with volumetric reductions of several subregions of the corpus callosum compared to non-blast-related mTBI. Several volumetric (i.e., hippocampal subfields, etc.) and diffusion (i.e., corona radiata, superior longitudinal fasciculus, etc.) MRI findings were noted to be associated with an increased number of repetitive mTBIs versus. CONCLUSIONS:In deployment-related mTBI, significant findings in this cohort were only observed when considering mTBI sub-groups (blast mechanism and total number/dose). Simply comparing healthy controls and those with a positive mTBI history is likely an oversimplification that may lead to non-significant findings, even in consortium analyses.

Mild traumatic brain injury (mTBI) is the most common form of brain injury. While most individuals recover from mTBI, roughly 20% experience persistent symptoms, potentially including reduced fine motor control. We investigate relationships between regional white matter organization and subcortical volumes associated with performance on the Grooved Pegboard (GPB) test in a large cohort of military Service Members and Veterans (SM&Vs) with and without a history of mTBI(s). Participants were enrolled in the Long-term Impact of Military-relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium. SM&Vs with a history of mTBI(s) (n = 847) and without mTBI (n = 190) underwent magnetic resonance imaging and the GPB test. We first examined between-group differences in GPB completion time. We then investigated associations between GPB performance and regional structural imaging measures (tractwise diffusivity, subcortical volumes, and cortical thickness) in SM&Vs with a history of mTBI(s). Lastly, we explored whether mTBI history moderated associations between imaging measures and GPB performance. SM&Vs with mTBI(s) performed worse than those without mTBI(s) on the non-dominant hand GPB test at a trend level (p < 0.1). Higher fractional anisotropy (FA) of tracts including the posterior corona radiata, superior longitudinal fasciculus, and uncinate fasciculus were associated with better GPB performance in the dominant hand in SM&Vs with mTBI(s). These findings support that the organization of several white matter bundles are associated with fine motor performance in SM&Vs. We did not observe that mTBI history moderated associations between regional FA and GPB test completion time, suggesting that chronic mTBI may not significantly influence fine motor control.