Brain ventricle and choroid plexus morphology as predictor of treatment response in major depression: Findings from the EMBARC study.

Recent observations suggest a role of the volume of the cerebral ventricle volume, corpus callosum (CC) segment volume, in particular that of the central-anterior part, and choroid plexus (CP) volume for treatment resistance of major depressive disorder (MDD). An increased CP volume has been associated with increased inflammatory activity and changes in the structure of the ventricles and corpus callosum. We attempt to replicate and confirm that these imaging markers are associated with clinical outcome in subjects from the EMBARC study, as implied by a recent pilot study. The EMBARC study is a placebo controlled randomized study comparing sertraline vs. placebo in patients with MDD to identify biological markers of therapy resistance. Association of baseline volumes of the lateral ventricles (LVV), choroid plexus volume (CPV) and volume of segments of the CC with treatment response after 4 weeks treatment was evaluated. 171 subjects (61 male, 110 female) completed the 4 week assessments; gender and age were taken into account for this analyses. As previously reported, no treatment effect of sertraline vs. placebo was observed, therefore the study characterized prognostic markers of response in the pooled population. Change in depression severity was identified by the ratio of the Hamilton-Depression rating scale 17 (HAMD-17) at week 4 divided by the HAMD-17 at baseline (HAMD-17 ratio). Volumes of the lateral ventricles and choroid plexi were positively correlated with the HAMD-17 ratio, indication worse outcome with larger ventricles and choroid plexus volumes, whereas the volume of the central-anterior corpus callosum was negatively correlated with the HAMD-17 ratio. Responders (n = 54) had significantly smaller volumes of the lateral ventricles and CP compared to non-responders (n = 117), whereas the volume of mid-anterior CC was significantly larger compared to non-responders (n = 117), confirming our previous findings. In an exploratory way associations between enlarged LVV and CPV and signs of lipid dysregulation were observed. In conclusion, we confirmed that volumes of lateral ventricles, choroid plexi and the mid-anterior corpus callosum are associated with clinical improvement of depression and may be indicators of metabolic/inflammatory activity.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Neural substrates of emotional conflict with anxiety in major depressive disorder: Findings from the Establishing Moderators and biosignatures of Antidepressant Response in Clinical Care (EMBARC) randomized controlled trial.

BACKGROUND: The brain circuitry of depression and anxiety/fear is well-established, involving regions such as the limbic system and prefrontal cortex. We expand prior literature by examining the extent to which four discrete factors of anxiety (immediate state anxiety, physiological/panic, neuroticism/worry, and agitation/restlessness) among depressed outpatients are associated with differential responses during reactivity to and regulation of emotional conflict. METHODS: A total of 172 subjects diagnosed with major depressive disorder underwent functional magnetic resonance imaging while performing an Emotional Stroop Task. Two main contrasts were examined using whole brain voxel wise analyses: emotional reactivity and emotion regulation. We also evaluated the association of these contrasts with the four aforementioned anxiety factors. RESULTS: During emotional reactivity, participants with higher immediate state anxiety showed potentiated activation in the rolandic operculum and insula, while individuals with higher levels of physiological/panic demonstrated decreased activation in the posterior cingulate. No significant results emerged for any of the four factors on emotion regulation. When re-analyzing these statistically-significant brain regions through analyses of a subsample with (n = 92) and without (n = 80) a current anxiety disorder, no significant associations occurred among those without an anxiety disorder. Among those with an anxiety disorder, results were similar to the full sample, except the posterior cingulate was associated with the neuroticism/worry factor. CONCLUSIONS: Divergent patterns of task-related brain activation across four discrete anxiety factors could be used to inform treatment decisions and target specific aspects of anxiety that involve intrinsic processing to attenuate overactive responses to emotional stimuli.

Anatomically-Informed Data Augmentation for Functional MRI with Applications to Deep Learning.

The application of deep learning to build accurate predictive models from functional neuroimaging data is often hindered by limited dataset sizes. Though data augmentation can help mitigate such training obstacles, most data augmentation methods have been developed for natural images as in computer vision tasks such as CIFAR, not for medical images. This work helps to fills in this gap by proposing a method for generating new functional Magnetic Resonance Images (fMRI) with realistic brain morphology. This method is tested on a challenging task of predicting antidepressant treatment response from pre-treatment task-based fMRI and demonstrates a 26% improvement in performance in predicting response using augmented images. This improvement compares favorably to state-of-the-art augmentation methods for natural images. Through an ablative test, augmentation is also shown to substantively improve performance when applied before hyperparameter optimization. These results suggest the optimal order of operations and support the role of data augmentation method for improving predictive performance in tasks using fMRI.

Psychometric Properties of the Concise Associated Symptom Tracking Scale and Validation of Clinical Utility in the EMBARC Study.

Objective: The authors aimed to evaluate psychometric properties of the Concise Associated Symptom Tracking (CAST) Scale and validate the clinical utility of measuring irritability by updating and replicating a previously published outcome calculator from the Combining Medications to Enhance Depression Outcomes (CO-MED) trial. Methods: Participants were 292 adults from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study who had completed the CAST scale at baseline. The scale's five-domain (irritability, anxiety, mania, insomnia, and panic) structure was evaluated with confirmatory factor analysis. Correlations with other clinical measures were used to confirm convergent and divergent validity. Logistic regression analyses from CO-MED were used to estimate individual outcomes in EMBARC. Results: Cronbach's alpha for the CAST scale was 0.78. Model fit for the five-domain structure was adequate (goodness of fit index=0.93, comparative fit index=0.92, root mean square error of approximation=0.06). Scores on irritability, anxiety, panic, insomnia, and mania were correlated with scores on the Anger Attack Questionnaire irritability item (rs=0.50), Hamilton Rating Scale for Depression anxiety subscale (rs=0.24), Mood and Anxiety Symptoms Questionnaire anxious arousal scale (rs=0.44), Quick Inventory of Depressive Symptomatology Self-Report insomnia items (rs=0.38), and Altman Self-Rating Mania Scale (rs=0.39), respectively. Individual outcomes of remission (area under the curve [AUC]=0.805) and no meaningful benefit (AUC=0.779) were predicted with high accuracy among EMBARC participants using their baseline and week 4 scores for depression and irritability and model estimates from CO-MED. Conclusions: Measuring irritability may help predict clinical course. The CAST scale is a valid measure of depression-associated symptoms, including irritability.

Dysfunctional adaptive immune response in adolescents and young adults with suicide behavior.

BACKGROUND: Immune system dysfunction has been implicated in the pathophysiology of suicide behavior. Here, we conducted an exploratory analysis of immune profile differences of three groups of adolescents and young adults (ages 10-25 years): healthy controls (n = 39), at risk of major depressive disorder (MDD; at-risk, n = 33), and MDD with recent suicide behavior/ ideation (suicide behavior, n = 37). METHODS: Plasma samples were assayed for chemokines and cytokines using Bio-Plex Pro Human Chemokine 40-plex assay. Log-transformed cytokine and chemokine levels were compared after controlling for age, gender, body mass index, race, ethnicity, and C-reactive protein (CRP) levels. In post-hoc analyses to understand the effect of dysregulated immune markers identified in this exploratory analysis, their association with autoantibodies was tested in an unrelated sample (n = 166). RESULTS: Only levels of interleukin 4 (IL-4) differed significantly among the three groups [false discovery rate (FDR) adjusted p = 0.0007]. Participants with suicide behavior had lower IL-4 [median = 16.8 pg/ml, interquartile range (IQR) = 7.9] levels than healthy controls (median = 29.1 pg/ml, IQR = 16.1, effect size [ES] = 1.30) and those at-risk (median = 24.4 pg/ml, IQR = 16.3, ES = 1.03). IL-4 levels were negatively correlated with depression severity (r= -0.38, p = 0.024). In an unrelated sample of outpatients with MDD, levels of IL-4 were negatively correlated (all FDR p < 0.05) with several autoantibodies [54/117 in total and 12/18 against innate immune markers]. CONCLUSIONS: Adolescent and young adult patients with recent suicide behavior exhibit lower IL-4 levels. One biological consequence of reduced IL-4 levels may be increased risk of autoimmunity.

Sensitivity of derived clinical biomarkers to rs-fMRI preprocessing software versions.

When common software packages (CONN and SPM) are used to process fMRI, results such as functional connectivity measures can substantially differ depending on the versions of the packages used and the tools used to convert image formats such as DICOM to NIFTI. The significance of these differences are illustrated within the context of a realistic research application: finding moderators of antidepressant response from a large psychiatric study of 288 major depressive disorder (MDD) patients. Significant differences in functional connectivity measurements and discrepancies in derived moderators were found between nearly all software configurations. These results should encourage researchers to be vigilant of software versions during fMRI preprocessing, to maintain consistency throughout each project, and to carefully report versions to facilitate reproducibility.

Predicting Response to the Antidepressant Bupropion using Pretreatment fMRI.

Major depressive disorder is a primary cause of disability in adults with a lifetime prevalence of 6-21% worldwide. While medical treatment may provide symptomatic relief, response to any given antidepressant is unpredictable and patient-specific. The standard of care requires a patient to sequentially test different antidepressants for 3 months each until an optimal treatment has been identified. For 30-40% of patients, no effective treatment is found after more than one year of this trial-and-error process, during which a patient may suffer loss of employment or marriage, undertreated symptoms, and suicidal ideation. This work develops a predictive model that may be used to expedite the treatment selection process by identifying for individual patients whether the patient will respond favorably to bupropion, a widely prescribed antidepressant, using only pretreatment imaging data. This is the first model to do so for individuals for bupropion. Specifically, a deep learning predictor is trained to estimate the 8-week change in Hamilton Rating Scale for Depression (HAMD) score from pretreatment task-based functional magnetic resonance imaging (fMRI) obtained in a randomized controlled antidepressant trial. An unbiased neural architecture search is conducted over 800 distinct model architecture and brain parcellation combinations, and patterns of model hyperparameters yielding the highest prediction accuracy are revealed. The winning model identifies bupropion-treated subjects who will experience remission with the number of subjects needed-to-treat (NNT) to lower morbidity of only 3.2 subjects. It attains a substantially high neuroimaging study effect size explaining 26% of the variance (R2 = 0.26) and the model predicts post-treatment change in the 52-point HAMD score with an RMSE of 4.71. These results support the continued development of fMRI and deep learning-based predictors of response for additional depression treatments.