Depressive Symptom Dimensions in Treatment-Resistant Major Depression and Their Modulation With Electroconvulsive Therapy

Electroconvulsive therapy improves somatic symptoms before mood in patients with depression: A directed network analysis

BACKGROUND

The recent network perspective of depression conceptualizes depression as a dynamic network of causally related symptoms, that contrasts with the traditional view of depression as a discrete latent entity that causes all symptoms. Electroconvulsive therapy (ECT) is an effective treatment for severe depression, but little is known about the temporal trajectories of symptom improvement during a course of ECT.

OBJECTIVE

To gain insight into the dynamics of depressive symptoms in individuals treated with ECT.

METHODS

The Quick Inventory of Depressive Symptomatology (QIDS) was used to assess symptoms twice a week in 68 participants with a unipolar or bipolar depression treated with ECT, with an average of 12 assessments per participant. Dynamic time warping (DTW) was used to analyze individual time series data, which were subsequently aggregated to calculate a directed symptom network and the in- and out-strength for each symptom.

RESULTS

Participants had a mean age of 49.6 (SD = 12.8) and 60% were female. Somatic symptoms (e.g., decreased weight) and suicidal ideation showed the highest out-strength values, indicating that their improvement tended to precede improvements in mood symptoms, which showed high in-strength. Sad mood had the highest in-strength, and thus appeared to be the last symptom to improve during ECT treatment (p < 0.001).

CONCLUSION

This study addresses a gap in the existing literature on ECT, by first analysing the temporal trajectories of symptoms within individual patients and subsequently aggregating them to the group level. The results show that somatic symptoms tend to improve before mood symptoms during ECT.

Functional connectivity changes between amygdala and prefrontal cortex after ECT are associated with improvement in distinct depressive symptoms

Electroconvulsive therapy (ECT) is one of the most effective treatments for treatment-resistant depression. However, the underlying mechanisms of action are not yet fully understood. The investigation of depression-specific networks using resting-state fMRI and the relation to differential symptom improvement might be an innovative approach providing new insights into the underlying processes. In this naturalistic study, we investigated the relationship between changes in resting-state functional connectivity (rsFC) and symptom improvement after ECT in 21 patients with treatment-resistant depression. We investigated rsFC before and after ECT and focused our analyses on FC changes directly related to symptom reduction and on FC at baseline to identify neural targets that might predict individual clinical responses to ECT. Additional analyses were performed to identify the direct relationship between rsFC change and symptom dimensions such as sadness, negative thoughts, detachment, and neurovegetative symptoms. An increase in rsFC between the left amygdala and left dorsolateral prefrontal cortex (DLPFC) after ECT was related to overall symptom reduction (Bonferroni-corrected p = 0.033) as well as to a reduction in specific symptoms such as sadness (r = 0.524, uncorrected p = 0.014), negative thoughts (r = 0.700, Bonferroni-corrected p = 0.002) and detachment (r = 0.663, p = 0.004), but not in neurovegetative symptoms. Furthermore, high baseline rsFC between the left amygdala and the right frontal pole (FP) predicted treatment outcome (uncorrected p = 0.039). We conclude that changes in FC in regions of the limbic-prefrontal network are associated with symptom improvement, particularly in affective and cognitive dimensions. Frontal-limbic connectivity has the potential to predict symptom improvement after ECT. Further research combining functional imaging biomarkers and a symptom-based approach might be promising.

Predicting Dimensional Antidepressant Response to Repetitive Transcranial Magnetic Stimulation using Pretreatment Resting-state Functional Connectivity

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression and has been shown to modulate resting-state functional connectivity (RSFC) of depression-relevant neural circuits. To date, however, few studies have investigated whether individual treatment-related symptom changes are predictable from pretreatment RSFC. We use machine learning to predict dimensional changes in depressive symptoms using pretreatment patterns of RSFC. We hypothesized that changes in dimensional depressive symptoms would be predicted more accurately than scale total scores. Patients with depression (n=26) underwent pretreatment RSFC MRI. Depressive symptoms were assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Random forest regression (RFR) models were trained and tested to predict treatment-related symptom changes captured by the HDRS-17, HDRS-6 and three previously identified HDRS subscales: core mood/anhedonia (CMA), somatic disturbances, and insomnia. Changes along the CMA, HDRS-17, and HDRS-6 were predicted significantly above chance, with 9%, 2%, and 2% of out-of-sample outcome variance explained, respectively (all p<0.01). CMA changes were predicted more accurately than the HDRS-17 (p<0.05). Higher baseline global connectivity (GC) of default mode network (DMN) subregions and the somatomotor network (SMN) predicted poorer symptom reduction, while higher GC of the right dorsal attention (DAN) frontoparietal control (FPCN), and visual networks (VN) predicted reduced CMA symptoms. HDRS-17 and HDRS-6 changes were predicted with similar GC patterns. These results suggest that RSFC spanning the DMN, SMN, DAN, FPCN, and VN subregions predict dimensional changes with greater accuracy than syndromal changes following rTMS. These findings highlight the need to assess more granular clinical dimensions in therapeutic studies, particularly device neuromodulation studies, and echo earlier studies supporting that dimensional outcomes improve model accuracy.

Dimensions of the Hamilton Depression Rating Scale Correlate with Impulsivity and Personality Traits among Youth Patients with Depression

The heterogeneity of symptoms in young patients with major depression disorder makes it difficult to properly identify and diagnose. Therefore, the appropriate evaluation of mood symptoms is important in early intervention. The aim of this study was to (a) establish dimensions of the Hamilton Depression Rating Scale (HDRS-17) in adolescents and young adults and (b) perform correlations between the identified dimensions and psychological variables (impulsivity, personality traits). This study enrolled 52 young patients with major depression disorder (MDD). The severity of the depressive symptoms was established using the HDRS-17. The factor structure of the scale was studied using the principal component analysis (PCA) with varimax rotation. The patients completed the self-reported Barratt Impulsiveness Scale (BIS-11) and Temperament and Character Inventory (TCI). The three dimensions of the HDRS-17 identified as core in adolescent and young patients with MDD were (1) psychic depression/motor retardation, (2) disturbed thinking, and (3) sleep disturbances/anxiety. In our study, dimension 1 correlated with reward dependence and cooperativeness; dimension 2 correlated with non-planning impulsivity, harm avoidance, and self-directedness; and dimension 3 correlated with reward dependence. Conclusions: Our study supports the previous findings, which indicate that a certain set of clinical features (including the HDRS-17 dimensions, not only total score) may represent a vulnerability pattern that characterizes patients with depression.

Fronto-medial electrode placement for electroconvulsive treatment of depression

Electroconvulsive therapy (ECT) is the most effective treatment for severe treatment-resistant depression but concern about cognitive side-effects, particularly memory loss, limits its use. Recent observational studies on large groups of patients who have received ECT report that cognitive side-effects were associated with electric field (EF) induced increases in hippocampal volume, whereas therapeutic efficacy was associated with EF induced increases in sagittal brain structures. The aim in the present study was to determine whether a novel fronto-medial (FM) ECT electrode placement would minimize electric fields in bilateral hippocampi (HIP) whilst maximizing electric fields in dorsal sagittal cortical regions. An anatomically detailed computational head model was used with finite element analysis, to calculate ECT-induced electric fields in specific brain regions identified by translational neuroimaging studies of treatment-resistant depressive illness, for a range of electrode placements. As hypothesized, compared to traditional bitemporal (BT) electrode placement, a specific FM electrode placement reduced bilateral hippocampal electric fields two-to-three-fold, whilst the electric fields in the dorsal anterior cingulate (dAC) were increased by approximately the same amount. We highlight the clinical relevance of this specific FM electrode placement for ECT, which may significantly reduce cognitive and non-cognitive side-effects and suggest a clinical trial is indicated.

Anterior default mode network and posterior insular connectivity is predictive of depressive symptom reduction following serial ketamine infusion

BACKGROUND

Ketamine is a rapidly-acting antidepressant treatment with robust response rates. Previous studies have reported that serial ketamine therapy modulates resting state functional connectivity in several large-scale networks, though it remains unknown whether variations in brain structure, function, and connectivity impact subsequent treatment success. We used a data-driven approach to determine whether pretreatment multimodal neuroimaging measures predict changes along symptom dimensions of depression following serial ketamine infusion.

METHODS

Patients with depression (n = 60) received structural, resting state functional, and diffusion MRI scans before treatment. Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale (HDRS-17), the Inventory of Depressive Symptomatology (IDS-C), and the Rumination Response Scale (RRS) before and 24 h after patients received four (0.5 mg/kg) infusions of racemic ketamine over 2 weeks. Nineteen unaffected controls were assessed at similar timepoints. Random forest regression models predicted symptom changes using pretreatment multimodal neuroimaging and demographic measures.

RESULTS

Two HDRS-17 subscales, the HDRS-6 and core mood and anhedonia (CMA) symptoms, and the RRS: reflection (RRSR) scale were predicted significantly with 19, 27, and 1% variance explained, respectively. Increased right medial prefrontal cortex/anterior cingulate and posterior insula (PoI) and lower kurtosis of the superior longitudinal fasciculus predicted reduced HDRS-6 and CMA symptoms following treatment. RRSR change was predicted by global connectivity of the left posterior cingulate, left insula, and right superior parietal lobule.

CONCLUSIONS

Our findings support that connectivity of the anterior default mode network and PoI may serve as potential biomarkers of antidepressant outcomes for core depressive symptoms.

Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls

PURPOSE

Functional seizures (FS), also known as psychogenic nonepileptic seizures (PNES), are physical manifestations of acute or chronic psychological distress. Functional and structural neuroimaging have identified objective signs of this disorder. We evaluated whether magnetic resonance imaging (MRI) morphometry differed between patients with FS and clinically relevant comparison populations.

METHODS

Quality-screened clinical-grade MRIs were acquired from 666 patients from 2006 to 2020. Morphometric features were quantified with FreeSurfer v6. Mixed-effects linear regression compared the volume, thickness, and surface area within 201 regions-of-interest for 90 patients with FS, compared to seizure-naïve patients with depression (n = 243), anxiety (n = 68), and obsessive-compulsive disorder (OCD, n = 41), respectively, and to other seizure-naïve controls with similar quality MRIs, accounting for the influence of multiple confounds including depression and anxiety based on chart review. These comparison populations were obtained through review of clinical records plus research studies obtained on similar scanners.

RESULTS

After Bonferroni-Holm correction, patients with FS compared with seizure-naïve controls exhibited thinner bilateral superior temporal cortex (left 0.053 mm, p = 0.014; right 0.071 mm, p = 0.00006), thicker left lateral occipital cortex (0.052 mm, p = 0.0035), and greater left cerebellar white-matter volume (1085 mm³, p = 0.0065). These findings were not accounted for by lower MRI quality in patients with FS.

CONCLUSIONS

These results reinforce prior indications of structural neuroimaging correlates of FS and, in particular, distinguish brain morphology in FS from that in depression, anxiety, and OCD. Future work may entail comparisons with other psychiatric disorders including bipolar and schizophrenia, as well as exploration of brain structural heterogeneity within FS.

Parsing the Network Mechanisms of Electroconvulsive Therapy

Electroconvulsive therapy (ECT) is one of the oldest and most effective forms of neurostimulation, wherein electrical current is used to elicit brief, generalized seizures under general anesthesia. When electrodes are positioned to target frontotemporal cortex, ECT is arguably the most effective treatment for severe major depression, with response rates and times superior to other available antidepressant therapies. Neuroimaging research has been pivotal in improving the field's mechanistic understanding of ECT, with a growing number of magnetic resonance imaging studies demonstrating hippocampal plasticity after ECT, in line with evidence of upregulated neurotrophic processes in the hippocampus in animal models. However, the precise roles of the hippocampus and other brain regions in antidepressant response to ECT remain unclear. Seizure physiology may also play a role in antidepressant response to ECT, as indicated by early positron emission tomography, single-photon emission computed tomography, and electroencephalography research and corroborated by recent magnetic resonance imaging studies. In this review, we discuss the evidence supporting neuroplasticity in the hippocampus and other brain regions during and after ECT, and their associations with antidepressant response. We also offer a mechanistic, circuit-level model that proposes that core mechanisms of antidepressant response to ECT involve thalamocortical and cerebellar networks that are active during seizure generalization and termination over repeated ECT sessions, and their interactions with corticolimbic circuits that are dysfunctional prior to treatment and targeted with the electrical stimulus.

Circuit-Targeted Neuromodulation for Anhedonia

Despite the prevalence of anhedonia across multiple psychiatric disorders, its relevance to treatment selection and prognostication can be unclear (Davey et al., Psychol Med 42(10):2071-81, 2012). Given the challenges in pharmacological and psychosocial treatment, there has been increasing attention devoted to neuroanatomically-targeted treatments. This chapter will present a brief introduction to circuit-targeted therapeutics in psychiatry (Sect. 1), an overview of brain mapping as it relates to anhedonia (Sect. 2), a review of existing studies on brain stimulation for anhedonia (Sect. 3), and a description of emerging approaches to circuit-based neuromodulation for anhedonia (Sect. 4).

Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy

Depression symptom heterogeneity limits the identifiability of treatment‐response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing three latent symptom dimensions within the 17‐item Hamilton Depression Rating Scale (HDRS) and use data‐driven methods to relate multivariate patterns of patient clinical, demographic, and brain structural changes over electroconvulsive therapy (ECT) to dimensional changes in depressive symptoms. We included 110 ECT patients from Global ECT‐MRI Research Collaboration (GEMRIC) sites who underwent structural MRI and HDRS assessments before and after treatment. Cross validated random forest regression models predicted change along symptom dimensions. HDRS symptoms clustered into dimensions of somatic disturbances (SoD), core mood and anhedonia (CMA), and insomnia. The coefficient of determination between predicted and actual changes were 22%, 39%, and 39% (all p < .01) for SoD, CMA, and insomnia, respectively. CMA and insomnia change were predicted more accurately than HDRS‐6 and HDRS‐17 changes (p < .05). Pretreatment symptoms, body‐mass index, and age were important predictors. Important imaging predictors included the right transverse temporal gyrus and left frontal pole for the SoD dimension; right transverse temporal gyrus and right rostral middle frontal gyrus for the CMA dimension; and right superior parietal lobule and left accumbens for the insomnia dimension. Our findings support that recovery along depressive symptom dimensions is predicted more accurately than HDRS total scores and are related to unique and overlapping patterns of clinical and demographic data and volumetric changes in brain regions related to depression and near ECT electrodes.