Cingulate dynamics track depression recovery with deep brain stimulation.

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD)1. However, achieving stable recovery is unpredictable2, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting3. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment.

Differences in clinical presentations of patients seeking care for treatment-resistant depression across sexual orientations and gender identities.

BACKGROUND: Sexual and/or gender minority (SGM) individuals experience higher rates and greater severity of depressive disorders than non-SGM persons. SGM individuals are more likely than non-SGM individuals to seek mental health treatment and to present to treatment with unique characteristics that should be accounted for when considering treatment recommendations. Patients seeking care for treatment-resistant depression (TRD) are offered a variety of evidence-based interventions ranging in modality and invasiveness (eg, psychotherapy and neuromodulation). METHODS: The current study used data from a TRD clinical research program to examine whether SGM (N = 52) and non-SGM (N = 202) patients differed in their clinical presentations and the treatment recommendations offered to them. RESULTS: We found that SGM patients were younger, had a more severe history of childhood trauma, and reported greater current suicidality than non-SGM patients. There were no significant differences in treatment recommendations between groups. CONCLUSIONS: This study adds to nascent literature investigating clinical characteristics of SGM populations seeking mental health care and provides foundational evidence for the unique treatment considerations necessary for SGM individuals seeking treatment for TRD. Research into whether treatment outcomes differ for SGM and non-SGM individuals with TRD is encouraged, given clinical differences in trauma history and suicidality.

The impact of adverse childhood experiences on adult depression severity and treatment outcomes.

BACKGROUND: Past research has established that adverse childhood experiences (ACE) are correlated with depression severity. The purpose of the present study was to examine how the number and nature of ACE exposure is associated with symptomatology and treatment outcomes in adult patients with treatment resistant depression (TRD). METHODS: Participants include 454 patients with a diagnosis of major depression or persistent depressive disorder. A one-way analysis of variance (ANOVA) was used to assess whether number of ACEs was associated with certain outcomes. Linear regression analyses were performed to model the associations between the five ACE subtypes (e.g., sexual abuse, physical violence, injury/illness, childhood grief, and parental upheaval) and symptom severity. Logistic regression analyses were then used to model the association between ACE subtypes and history of lifetime suicide attempt(s) and inpatient admission(s). RESULTS: Greater ACE exposure was associated with more severe symptomatology and treatment outcomes, but these differences were only seen between patients reporting no ACEs versus 3+ ACEs. Only the subtypes of violence and illness/injury were significant predictors of more severe symptomatology. The ACE subtypes of sexual trauma and violence uniquely predicted a lifetime suicide attempt(s), and only the subtype of sexual trauma predicted lifetime inpatient admission(s). LIMITATIONS: Limitations of the present study include retrospective adult assessments of childhood trauma, lack of data on ACE severity and timing, and the cross-sectional reporting of multiple study measures. CONCLUSIONS: Exposure to multiple ACE subtypes, particularly sexual and physical trauma, is associated with depression symptom severity, and history of suicidality, and inpatient admission(s).

Mitigating Mismatch Compression in Differential Local Field Potentials.

Deep brain stimulation (DBS) devices capable of measuring differential local field potentials ( ∂ LFP) enable neural recordings alongside clinical therapy. Efforts to identify oscillatory correlates of various brain disorders, or disease readouts, are growing but must proceed carefully to ensure readouts are not distorted by brain environment. In this report we identified, characterized, and mitigated a major source of distortion in ∂ LFP that we introduce as mismatch compression (MC). Using in vivo, in silico, and in vitro models of MC, we showed that impedance mismatches in the two recording electrodes can yield incomplete rejection of stimulation artifact and subsequent gain compression that distorts oscillatory power. We then developed and validated an opensource mitigation pipeline that mitigates the distortions arising from MC. This work enables more reliable oscillatory readouts for adaptive DBS applications.

Neural Interoceptive Processing is Modulated by Deep Brain Stimulation for Treatment Resistant Depression.

Background: A critical advance in depression research is to clarify the hypothesized role of interoceptive processing in neural mechanisms of treatment efficacy. This study tests whether cortical interoceptive processing, as indexed by the heartbeat-evoked potential (HEP), is modulated by deep brain stimulation (DBS) to the subcallosal cingulate (SCC) for treatment resistant depression (TRD). Methods: Eight patients with TRD were enrolled in a study of SCC DBS safety and efficacy. Electroencephalography (EEG) and symptom severity measures were sampled in a laboratory setting over the course of a six-month treatment protocol. The primary outcome measure was an EEG-derived HEP, which reflects cortical processing of heartbeat sensation. Cluster-based permutation analyses were used to test the effect of stimulation and time in treatment on the HEP. The change in signal magnitude after treatment was correlated with change in depression severity as measured by the 17-item Hamilton Depression Rating Scale. Results: HEP amplitude was greater after 24 weeks of treatment ( t (7)=-4.40, p =.003, g= -1.38, 95% Cl [-2.3, -0.42]), and this change was inversely correlated with latency of treatment response (rho = -0.75, 95% Cl [-0.95, -0.11], p= .03). An acute effect of DBS was also observed, but as a decrease in HEP amplitude ( t (6) =6.66, p <.001, g= 2.19, 95% Cl [0.81, 3.54]). HEP differences were most pronounced over left posterior sensors from 405-425 ms post-stimulus. Conclusion: Brain-based evidence substantiates a theorized link between interoception and depression, and suggests an interoceptive contribution to the mechanism of treatment efficacy with deep brain stimulation for severe depression.

Time-frequency signatures evoked by single-pulse deep brain stimulation to the subcallosal cingulate.

Precision targeting of specific white matter bundles that traverse the subcallosal cingulate (SCC) has been linked to efficacy of deep brain stimulation (DBS) for treatment resistant depression (TRD). Methods to confirm optimal target engagement in this heterogenous region are now critical to establish an objective treatment protocol. As yet unexamined are the time-frequency features of the SCC evoked potential (SCC-EP), including spectral power and phase-clustering. We examined these spectral features-evoked power and phase clustering-in a sample of TRD patients (n = 8) with implanted SCC stimulators. Electroencephalogram (EEG) was recorded during wakeful rest. Location of electrical stimulation in the SCC target region was the experimental manipulation. EEG was analyzed at the surface level with an average reference for a cluster of frontal sensors and at a time window identified by prior study (50-150 ms). Morlet wavelets generated indices of evoked power and inter-trial phase clustering. Enhanced phase clustering at theta frequency (4-7 Hz) was observed in every subject and was significantly correlated with SCC-EP magnitude, but only during left SCC stimulation. Stimulation to dorsal SCC evinced stronger phase clustering than ventral SCC. There was a weak correlation between phase clustering and white matter density. An increase in evoked delta power (2-4 Hz) was also coincident with SCC-EP, but was less consistent across participants. DBS evoked time-frequency features index mm-scale changes to the location of stimulation in the SCC target region and correlate with structural characteristics implicated in treatment optimization. Results also imply a shared generative mechanism (inter-trial phase clustering) between evoked potentials evinced by electrical stimulation and evoked potentials evinced by auditory/visual stimuli and behavioral tasks. Understanding how current injection impacts downstream cortical activity is essential to building new technologies that adapt treatment parameters to individual differences in neurophysiology.

Healing in Health Care: Mental Health Promotion, Advocacy, and Voluntarism in Response to COVID-19.

The COVID-19 pandemic increased many known risk factors for mental health problems. In the context of overwhelmed health systems and resource and staffing shortages, the mental health needs of frontline health care workers (HCWs) gained attention as a major public health concern and a threat to high-quality care delivery. In response, mental health promotion initiatives were quickly developed to meet the demands of the public health crisis. Two years later, the context for psychotherapy has changed, especially as it pertains to the health care workforce. Particularly salient experiences-grief, burnout, moral injury, compassion fatigue, and racial trauma-have become routinely discussed as part of everyday clinical practice. Service programs have become more responsive to the needs, schedules, and identities of HCWs. In addition, mental health and other HCWs have contributed to advocacy and volunteer initiatives promoting health equity, culturally responsive care, and access to care across a range of settings. In this article, the authors review the benefits of these activities to individuals, organizations, and communities and summarize example programs. Many of these initiatives began in response to the acute public health crisis; however, engaging in these ways and spaces holds promise for increasing connection and prioritizing equity and structural change over the long term.

Intraoperative neural signals predict rapid antidepressant effects of deep brain stimulation.

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) is a promising intervention for treatment-resistant depression (TRD). Despite the failure of a clinical trial, multiple case series have described encouraging results, especially with the introduction of improved surgical protocols. Recent evidence further suggests that tractography targeting and intraoperative exposure to stimulation enhances early antidepressant effects that further evolve with ongoing chronic DBS. Accelerating treatment gains is critical to the care of this at-risk population, and identification of intraoperative electrophysiological biomarkers of early antidepressant effects will help guide future treatment protocols. Eight patients underwent intraoperative electrophysiological recording when bilateral DBS leads were implanted in the SCC using a connectomic approach at the site previously shown to optimize 6-month treatment outcomes. A machine learning classification method was used to discriminate between intracranial local field potentials (LFPs) recorded at baseline (stimulation-naïve) and after the first exposure to SCC DBS during surgical procedures. Spectral inputs (theta, 4-8 Hz; alpha, 9-12 Hz; beta, 13-30 Hz) to the model were then evaluated for importance to classifier success and tested as predictors of the antidepressant response. A decline in depression scores by 45.6% was observed after 1 week and this early antidepressant response correlated with a decrease in SCC LFP beta power, which most contributed to classifier success. Intraoperative exposure to therapeutic stimulation may result in an acute decrease in symptoms of depression following SCC DBS surgery. The correlation of symptom improvement with an intraoperative reduction in SCC beta power suggests this electrophysiological finding as a biomarker for treatment optimization.

Classifying Major Depressive Disorder and Response to Deep Brain Stimulation Over Time by Analyzing Facial Expressions.

OBJECTIVE: Major depressive disorder (MDD) is a common psychiatric disorder that leads to persistent changes in mood and interest among other signs and symptoms. We hypothesized that convolutional neural network (CNN) based automated facial expression recognition, pre-trained on an enormous auxiliary public dataset, could provide improve generalizable approach to MDD automatic assessment from videos, and classify remission or response to treatment. METHODS: We evaluated a novel deep neural network framework on 365 video interviews (88 hours) from a cohort of 12 depressed patients before and after deep brain stimulation (DBS) treatment. Seven basic emotions were extracted with a Regional CNN detector and an Imagenet pre-trained CNN, both of which were trained on large-scale public datasets (comprising over a million images). Facial action units were also extracted with the Openface toolbox. Statistics of the temporal evolution of these image features over each recording were extracted and used to classify MDD remission and response to DBS treatment. RESULTS: An Area Under the Curve of 0.72 was achieved using leave-one-subject-out cross-validation for remission classification and 0.75 for response to treatment. CONCLUSION: This work demonstrates the potential for the classification of MDD remission and response to DBS treatment from passively acquired video captured during unstructured, unscripted psychiatric interviews. SIGNIFICANCE: This novel MDD evaluation could be used to augment current psychiatric evaluations and allow automatic, low-cost, frequent use when an expert isn't readily available or the patient is unwilling or unable to engage. Potentially, the framework may also be applied to other psychiatric disorders.

Analyzing Non-verbal Behavior Throughout Recovery in a Sample of Depressed Patients Receiving Deep Brain Stimulation.

BACKGROUND: Traditional rating scales for depression rely heavily on patient self-report, and lack detailed measurement of non-verbal behavior. However, there is evidence that depression is associated with distinct non-verbal behaviors, assessment of which may provide useful information about recovery. This study examines non-verbal behavior in a sample of patients receiving Deep Brain Stimulation (DBS) treatment of depression, with the purpose to investigate the relationship between non-verbal behaviors and reported symptom severity. METHODS: Videotaped clinical interviews of twelve patients participating in a study of DBS for treatment-resistant depression were analyzed at three time points (before treatment and after 3 months and 6 months of treatment), using an ethogram to assess the frequencies of 42 non-verbal behaviors. The Beck Depression Inventory (BDI) and Hamilton Depression Rating Scale (HDRS-17) were also collected at all time points. RESULTS: Factor analysis grouped non-verbal behaviors into three factors: react, engage/fidget, and disengage. Two-way repeated measures ANOVA showed that scores on the three factors change differently from each other over time. Mixed effects modelling assessed the relationship between BDI score and frequency of non-verbal behaviors, and provided evidence that the frequency of behaviors related to reactivity and engagement increase as BDI score decreases. LIMITATIONS: This study assesses a narrow sample of patients with a distinct clinical profile at limited time points. CONCLUSIONS: Non-verbal behavior provides information about clinical states and may be reliably quantified using ethograms. Non-verbal behavior may provide distinct information compared to self-report.

Flattening the emotional distress curve: A behavioral health pandemic response strategy for COVID-19.

This article proposes a framework for managing the behavioral health impacts of the COVID-19 global pandemic. This framework aligns and should be integrated with an existing public health pandemic intervals model. It includes six phases of a behavioral health pandemic response strategy: preplanning, response readiness, response mobilization, intervention, continuation, and amelioration. The ways behavioral health specialists can capitalize on their competence in the leadership, prevention, education, service, research, and advocacy domains within each behavioral health pandemic response phase are articulated. Behavioral health expertise can help ensure a more comprehensive, effective pandemic response that facilitates the flattening of the curve of disease spread, along with the corresponding emotional distress curve. A case illustration, the Caring Communities (CC) initiative, is offered as an exemplar of action steps in the leadership, prevention, education, service, research, and advocacy domains that behavioral health professionals can take within each of the behavioral health pandemic response phases. Key CC action steps include providing support groups, offering virtual wellness breaks, participating in educational outreach, creating and disseminating wellness guides, launching and leading a virtual behavioral health clinic for health care staff, participating in behavioral health research and program evaluation, and engaging in advocacy initiatives aimed at improving behavioral health care and addressing and reducing health disparities. Finally, recommendations for optimizing behavioral health contributions to future pandemic responses are proffered. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Concordance between clinician-rated and patient reported outcome measures of depressive symptoms in treatment resistant depression.

BACKGROUND: Calls to implement measurement-based care (MBC) in psychiatry are increasing. A recent Cochrane meta-analysis concluded that there is insufficient evidence that routine application of patient reported outcomes (PROs) improves treatment outcomes for common psychiatric disorders. There is a particular paucity of this information in patients with treatment resistant depression (TRD). METHODS: A TRD sample (n = 302) and a treatment-naïve sample with major depression (n = 344) were assessed for the level of agreement in depression severity between two PROs (the Beck Depression Inventory, BDI, and the Quick Inventory of Depressive Symptomatology Self-report, QIDS-SR) and two Clinician Rated (CRs) measures (Hamilton Depression Rating Scale, HDRS, and the Montgomery-Asberg Depression Rating Scale, MADRS). RESULTS: Correlations between CR and PRO total scores in the TRD sample ranged from 0.57 (HDRS-QIDS-SR) to 0.68 (MADRS-BDI), reflecting a moderate-to-strong relationship between assessment tools. Correlations in the treatment naïve sample were non-significantly lower for most comparisons, ranging from 0.51 (HDRS-QIDS-SR) to 0.64 (MADRS-BDI). Few predictors of discordance between CRs and PROs were identified, though chronicity of the current episode in treatment-naïve patients was associated with greater agreement. LIMITATIONS: Inter-rater reliability of the clinician interviews was conducted separately within the two studies so we could not determine the reliability between the two groups of raters used in the studies. CONCLUSION: Findings generally supported acceptably high levels of agreement between patient and clinician ratings of baseline depression severity. More work is needed to determine the extent to which PROs can improve outcomes in MBC for depression and, more specifically, TRD.

Classifying Depression Severity in Recovery From Major Depressive Disorder via Dynamic Facial Features.

Major depressive disorder is a common psychiatric illness. At present, there are no objective, non-verbal, automated markers that can reliably track treatment response. Here, we explore the use of video analysis of facial expressivity in a cohort of severely depressed patients before and after deep brain stimulation (DBS), an experimental treatment for depression. We introduced a set of variability measurements to obtain unsupervised features from muted video recordings, which were then leveraged to build predictive models to classify three levels of severity in the patients' recovery from depression. Multiscale entropy was utilized to estimate the variability in pixel intensity level at various time scales. A dynamic latent variable model was utilized to learn a low-dimensional representation of factors that describe the dynamic relationship between high-dimensional pixels in each video frame and over time. Finally, a novel elastic net ordinal regression model was trained to predict the severity of depression, as independently rated by standard rating scales. Our results suggest that unsupervised features extracted from these video recordings, when incorporated in an ordinal regression predictor, can discriminate different levels of depression severity during ongoing DBS treatment. Objective markers of patient response to treatment have the potential to standardize treatment protocols and enhance the design of future clinical trials.

Addressing the Credit Assignment Problem in Treatment Outcome Prediction using Temporal Difference Learning.

Mental health patients often undergo a variety of treatments before finding an effective one. Improved prediction of treatment response can shorten the duration of trials. A key challenge of applying predictive modeling to this problem is that often the effectiveness of a treatment regimen remains unknown for several weeks, and therefore immediate feedback signals may not be available for supervised learning. Here we propose a Machine Learning approach to extracting audio-visual features from weekly video interview recordings for predicting the likely outcome of Deep Brain Stimulation (DBS) treatment several weeks in advance. In the absence of immediate treatment-response feedback, we utilize a joint state-estimation and temporal difference learning approach to model both the trajectory of a patient's response and the delayed nature of feedbacks. Our results based on longitudinal recordings from 12 patients with depression show that the learned state values are predictive of the long-term success of DBS treatments. We achieve an area under the receiver operating characteristic curve of 0.88, beating all baseline methods.

Long-Term Outcomes of Subcallosal Cingulate Deep Brain Stimulation for Treatment-Resistant Depression.

OBJECTIVE: Deep brain stimulation of the subcallosal cingulate (SCC DBS) has been studied as a potential treatment for severe and refractory major depressive disorder since 2005. The authors used an open-label, long-term follow-up design to examine participants enrolled in a clinical trial of SCC DBS for treatment-resistant depression. METHODS: Long-term outcome data were collected for 28 patients (20 with major depressive disorder and seven with bipolar II disorder; one patient in the major depression subgroup was later reclassified as having bipolar II disorder) receiving SCC DBS for 4-8 years. RESULTS: Response and remission rates were maintained at ≥50% and ≥30%, respectively, through years 2-8 of the follow-up period. Three-quarters of all participants met the treatment-response criterion for more than half of their duration of participation in the study, with 21% of all patients demonstrating continuous response to treatment from the first year onward. Of 28 participants, 14 completed ≥8 years of follow-up, 11 completed ≥4 years, and three dropped out before 8 years. The procedure itself was generally safe and well tolerated, and there were no side effects of acute or chronic stimulation. The rate of medical or surgical complications was consistent with the rate observed in studies of DBS for other indications. There were no suicides. CONCLUSIONS: In >8 years of observation, most participants experienced a robust and sustained antidepressant response to SCC DBS.

Field potential 1/f activity in the subcallosal cingulate region as a candidate signal for monitoring deep brain stimulation for treatment-resistant depression.

Subcallosal cingulate cortex deep brain stimulation (SCC-DBS) is an experimental therapy for treatment-resistant depression (TRD). Refinement and optimization of SCC-DBS will benefit from increased study of SCC electrophysiology in context of ongoing high-frequency SCC-DBS therapy. The study objective was a 7-mo observation of frequency-domain 1/f slope in off-stimulation local field potentials (SCC-LFPs) alongside standardized measurements of depression severity in 4 patients undergoing SCC-DBS. SCC was implanted bilaterally with a combined neurostimulation-LFP recording system. Following a 1-mo off-stimulation postoperative phase with multiple daily recordings, patients received bilateral SCC-DBS therapy (130 Hz, 90 µs) and weekly resting-state SCC-LFP recordings over a 6-mo treatment phase. 1/f slopes for each time point were estimated via linear regression of log-transformed Welch periodograms. General linear mixed-effects models were constructed to estimate pretreatment sources of 1/f slope variance, and 95% bootstrap confidence intervals were constructed to estimate treatment phase 1/f slope association with treatment response (50% decrease in preimplantation symptom severity). Results show the time of recording was a prominent source of pretreatment 1/f slope variance bilaterally, with increased 1/f slope magnitude observed during night hours (2300-0659). Increase in right 1/f slope was observed in the setting of treatment response, with bootstrap analysis supporting this observation in 3 of 4 subjects. We conclude that 1/f slope can be measured longitudinally in a combined SCC-DBS/LFP recording system and likely conforms to known 1/f circadian variability. The preliminary evidence of 1/f slope increase during treatment response suggests a potential utility as a candidate biomarker for ongoing development of adaptive TRD-neuromodulation strategies.NEW & NOTEWORTHY In four patients with treatment-resistant depression undergoing therapeutic deep brain stimulation (DBS), we present the first longitudinal observations of local field potentials (LFP) from the subcallosal cingulate region outside the postoperative period. Specifically, our results demonstrate that frequency-domain 1/f activity is measurable in a combined DBS-LFP recording system and that right hemisphere recordings appear sensitive to mood state, thus suggesting a potential readout suitable for consideration in ongoing efforts to develop adaptive DBS delivery systems.

Autonomic arousal elicited by subcallosal cingulate stimulation is explained by white matter connectivity.

BACKGROUND: Subcallosal cingulate deep brain stimulation (SCC DBS) is an experimental treatment for severe depression. Surgery is performed with awake patients and intraoperative stimulation produces acute behavioral responses in select contacts. While there have been reports on the relationship between acute intraoperative behaviors and their relation to the location of the contacts, there are no descriptions of the physiological changes that accompany them. OBJECTIVE: The present study sought to examine these physiological readouts, and their association with the anatomical substrates that generated them. METHODS: Nine patients with severe, treatment-resistant depression were tested intraoperatively. The stimulation protocol consisted of 12 three-minute, sham-controlled, double-blind trials. Changes in heart rate and skin conductance were recorded during each stimulation cycle. Probabilistic tractography between the stimulated contacts and predefined regions of the mood regulation network was performed. RESULTS: Acute intraoperative SCC stimulation produced increases in autonomic sympathetic response that correlated with the salience of the behavioral responses. The autonomic changes were observed within seconds of initiating acute stimulation and prior to verbalization of subjective experiences. The probabilistic tractography analysis suggested that structural connectivity between the stimulated area and the midcingulate cortex is the primary pathway that mediates autonomic responsivity to SCC DBS. CONCLUSIONS: These findings demonstrate that acute SCC stimulation produces autonomic and behavioral changes in the operating room that are explained by the modulation of networks associated with long term antidepressant response. Intraoperative autonomic recordings paired with careful behavioral observations and precise anatomical mapping aid in the identification and classification of the intraoperative phenomena.