SSRI Treatment Response Prediction in Depression Based on Brain Activation by Emotional Stimuli

The Task-Based fMRI Using von Zerssen Scale in Recurrent Depression Disorder: A Replication Study

Currently, translation into clinical practice of scientific knowledge on pathological reorganization of brain mechanisms in psychiatric disorders, as suggested by functional neuroimaging data, remains limited. This situation calls for the exploration of new approaches, which were recently proposed for the combined use of the simultaneous application of psychodiagnostic testing and fMRI scanning. Consequently, a self-rated psychodiagnostic scale was used as an experimental task during fMRI scanning for patients with major depressive disorder. Given the promising neuroimaging results obtained in these studies, in current research, our objective was to replicate these results and conduct an fMRI study using statements from the von Zerssen depression as experimental conditions. Eighteen patients with recurrent depressive disorder and healthy volunteers participated in the study to replicate the group size of previous research. The results obtained showed that patients with recurrent depressive disorder exhibited greater activity in the right precuneus and bilateral supramarginal gyrus than healthy controls while responding to diagnostically specific (DS) statements compared to diagnostically neutral (DN) ones. These findings replicate the main results of the original study and emphasize the potential of this approach in the field of translational psychiatry. In addition, they contribute to understanding the pathophysiological mechanisms of depression through the use of this unique fMRI paradigm.

Thalamocortical Dysconnectivity in Treatment-Resistant Depression

Thalamocortical connectivity is associated with cognitive and affective processing. The role of thalamocortical connectivity in the pathomechanism of treatment-resistant depression (TRD) remains unclear. This study included 48 patients with TRD and 48 healthy individuals. We investigated thalamocortical connectivity by performing resting-state functional MRI with the bilateral thalamus as the seed. In addition, patients with TRD were evaluated using the Montgomery-Åsberg Depression Rating Scale (MADRS). Compared with the healthy individuals, the patients with TRD exhibited increased functional connectivity (FC) of the thalamus with the insula and superior temporal cortex and reduced the FC of the thalamus with the anterior paracingulate cortex and cerebellum crus II. Our study may support the crucial role of thalamocortical dysconnectivity in the TRD pathomechanism. However, the small sample size may limit the statistical power. A future study with a large sample size of patients with TRD would be required to validate our findings.

Motor performance and functional connectivity between the posterior cingulate cortex and supplementary motor cortex in bipolar and unipolar depression

Although implicated in unsuccessful treatment, psychomotor deficits and their neurobiological underpinnings in bipolar (BD) and unipolar (UD) depression remain poorly investigated. Here, we hypothesized that motor performance deficits in depressed patients would relate to basal functional coupling of the hand primary motor cortex (M1) and the posterior cingulate cortex (PCC) with the supplementary motor area (SMA). We performed a longitudinal, naturalistic study in BD, UD and matched healthy controls comprising of two resting-state functional MRI measurements five weeks apart and accompanying assessments of motor performance using a finger tapping task (FTT). A subject-specific seed-based analysis describing functional connectivity between PCC-SMA as well as M1-SMA was conducted. The basal relationships with motor performance were investigated using linear regression models and all measures were compared across groups. Performance in FTT was impaired in BD in comparison to HC in both sessions. Behavioral performance across groups correlated significantly with resting state functional coupling of PCC-SMA, but not of M1-SMA regions. This relationship was partially reflected in a reduced PCC-SMA connectivity in BD vs HC in the second session. Exploratory evaluation of large-scale networks coupling (SMN-DMN) exhibited no correlation to motor performance. Our results shed new light on the association between the degree of disruption in the SMA-PCC anticorrelation and the level of motor impairment in BD.

A randomized controlled trial of desvenlafaxine-induced structural brain changes in the treatment of persistent depressive disorder

The anatomical changes that antidepressant medications induce in the brain and through which they exert their therapeutic effects remain largely unknown. We randomized 61 patients with Persistent Depressive Disorder (PDD) to receive either desvenlafaxine or placebo in a 12-week trial and acquired anatomical MRI scans in 42 of those patients at baseline before randomization and immediately at the end of the trial. We also acquired MRIs once in 39 age- and sex-matched healthy controls. We assessed whether the serotonin-norepinephrine reuptake inhibitor, desvenlafaxine, differentially changed cortical thickness during the trial compared with placebo. Patients relative to controls at baseline had thinner cortices across the brain. Although baseline thickness was not associated with symptom severity, thicker baseline cortices predicted greater reduction in symptom severity in those treated with desvenlafaxine but not placebo. We did not detect significant treatment-by-time effects on cortical thickness. These findings suggest that baseline thickness may serve as predictive biomarkers for treatment response to desvenlafaxine. The absence of treatment-by-time effects may be attributable either to use of insufficient desvenlafaxine dosing, a lack of desvenlafaxine efficacy in treating PDD, or the short trial duration.

Depression, stress and regional cerebral blood flow

Decreased cerebral blood flow (CBF) may be an important mechanism associated with depression. In this study we aimed to determine if the association of CBF and depression is dependent on current level of depression or the tendency to experience depression over time (trait depression), and if CBF is influenced by depression-related factors such as stressful life experiences and antidepressant medication use. CBF was measured in 254 participants from the Amish Connectome Project (age 18-76, 99 men and 154 women) using arterial spin labeling. All participants underwent assessment of symptoms of depression measured with the Beck Depression Inventory and Maryland Trait and State Depression scales. Individuals diagnosed with a unipolar depressive disorder had significantly lower average gray matter CBF compared to individuals with no history of depression or to individuals with a history of depression that was in remission at time of study. Trait depression was significantly associated with lower CBF, with the associations strongest in cingulate gyrus and frontal white matter. Use of antidepressant medication and more stressful life experiences were also associated with significantly lower CBF. Resting CBF in specific brain regions is associated with trait depression, experience of stressful life events, and current antidepressant use, and may provide a valuable biomarker for further studies.

AI-assisted prediction of differential response to antidepressant classes using electronic health records

Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after antidepressant initiation. The final data set comprised 17,556 patients. Predictors were derived from both structured and unstructured EHR data and models accounted for features predictive of treatment selection to minimize confounding by indication. Outcome labels were derived through expert chart review and AI-automated imputation. Regularized generalized linear model (GLM), random forest, gradient boosting machine (GBM), and deep neural network (DNN) models were trained and their performance compared. Predictor importance scores were derived using SHapley Additive exPlanations (SHAP). All models demonstrated similarly good prediction performance (AUROCs ≥ 0.70, AUPRCs ≥ 0.68). The models can estimate differential treatment response probabilities both between patients and between antidepressant classes for the same patient. In addition, patient-specific factors driving response probabilities for each antidepressant class can be generated. We show that antidepressant response can be accurately predicted from real-world EHR data with AI modeling, and our approach could inform further development of clinical decision support systems for more effective treatment selection.

Altered coordination between frontal delta and parietal alpha networks underlies anhedonia and depressive rumination in major depressive disorder

BACKGROUND

A hyperactive default mode network (DMN) has been observed in people with major depressive disorder (MDD), and weak DMN suppression has been linked to depressive symptoms. However, whether dysregulation of the DMN contributes to blunted positive emotional experience in people with MDD is unclear.

METHODS

We recorded 128-channel electroencephalograms (EEGs) from 24 participants with MDD and 31 healthy controls in a resting state (RS) and an emotion-induction state (ES), in which participants engaged with emotionally positive pictures. We combined Granger causality analysis and data-driven decomposition to extract latent brain networks shared among states and groups, and we further evaluated their interactions across individuals.

RESULTS

We extracted 2 subnetworks. Subnetwork 1 represented a delta (δ)-band (1~4 Hz) frontal network that was activated more in the ES than the RS (i.e., task-positive). Subnetwork 2 represented an alpha (α)-band (8~13 Hz) parietal network that was suppressed more in the ES than the RS (i.e., task-negative). These subnetworks were anticorrelated in both the healthy control and MDD groups, but with different sensitivities: for participants with MDD to achieve the same level of task-positive (subnetwork 1) activation as healthy controls, more suppression of task-negative (subnetwork 2) activation was necessary. Furthermore, the anticorrelation strength in participants with MDD correlated with the severity of 2 core MDD symptoms: anhedonia and rumination.

LIMITATIONS

The sample size was small.

CONCLUSION

Our findings revealed altered coordination between 2 functional networks in MDD and suggest that weak suppression of the task-negative α-band parietal network contributes to blunted positive emotional responses in adults with depression. The subnetworks identified here could be used for diagnosis or targeted for treatment in the future.

MRI predictors of pharmacotherapy response in major depressive disorder

Major depressive disorder is among the most prevalent psychiatric disorders, exacting a substantial personal, social, and economic toll. Antidepressant treatment typically involves an individualized trial and error approach with an inconsistent success rate. Despite a pressing need, no reliable biomarkers for predicting treatment outcome have yet been discovered. Brain MRI measures hold promise in this regard, though clinical translation remains elusive. In this review, we summarize structural MRI and functional MRI (fMRI) measures that have been investigated as predictors of treatment outcome. We broadly divide these into five categories including three structural measures: volumetric, white matter burden, and white matter integrity; and two functional measures: resting state fMRI and task fMRI. Currently, larger hippocampal volume is the most widely replicated predictor of successful treatment. Lower white matter hyperintensity burden has shown robustness in late life depression. However, both have modest discriminative power. Higher fractional anisotropy of the cingulum bundle and frontal white matter, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli, and hyperconnectivity within the default mode network (DMN) and between the DMN and executive control network also show promise as predictors of successful treatment. Such network-focused measures may ultimately provide a higher-dimensional measure of treatment response with closer ties to the underlying neurobiology.

Neuroimaging Correlates of Treatment Response with Psychedelics in Major Depressive Disorder: A Systematic Review

Preliminary evidence supports the use of psychedelics for major depressive disorder (MDD). However, less attention has been given to the neural mechanisms behind their effects. We conducted a systematic review examining the neuroimaging correlates of antidepressant response following psychedelic interventions for MDD. Through MEDLINE, Embase, and APA PsycINFO, 187 records were identified and 42 articles were screened. Six published studies and one conference abstract were included. Five ongoing trials were included from subjective outcomesTrials.gov. Our search covered several psychedelics, though included studies were specific to psilocybin, ayahuasca, and lysergic acid diethylamide. Three psilocybin studies noted amygdala activity and functional connectivity (FC) alterations that correlated with treatment response. Two psilocybin studies reported that FC changes in the medial and ventromedial prefrontal cortices correlated with treatment response. Two trials from a single study reported global decreases in brain network modularity which correlated with antidepressant response. One ayahuasca study reported increased activity in the limbic regions following treatment. Preliminary evidence suggests that the default mode and limbic networks may be a target for future research on the neural mechanisms of psychedelics. More data is required to corroborate these initial findings as the evidence summarized in this review is based on four datasets.

From Therapeutic Factors to Mechanisms of Change in the Creative Arts Therapies: A Scoping Review

Empirical studies in the creative arts therapies (CATs; i.e., art therapy, dance/movement therapy, drama therapy, music therapy, psychodrama, and poetry/bibliotherapy) have grown rapidly in the last 10 years, documenting their positive impact on a wide range of psychological and physiological outcomes (e.g., stress, trauma, depression, anxiety, and pain). However, it remains unclear how and why the CATs have positive effects, and which therapeutic factors account for these changes. Research that specifically focuses on the therapeutic factors and/or mechanisms of change in CATs is only beginning to emerge. To gain more insight into how and why the CATs influence outcomes, we conducted a scoping review (N studies = 67) to pinpoint therapeutic factors specific to each CATs discipline, joint factors of CATs, and more generic common factors across all psychotherapy approaches. This review therefore provides an overview of empirical CATs studies dealing with therapeutic factors and/or mechanisms of change, and a detailed analysis of these therapeutic factors which are grouped into domains. A framework of 19 domains of CATs therapeutic factors is proposed, of which the three domains are composed solely of factors unique to the CATs: "embodiment," "concretization," and "symbolism and metaphors." The terminology used in change process research is clarified, and the implications for future research, clinical practice, and CATs education are discussed.