Deep Brain Stimulation for Obsessive-Compulsive Disorder and Depression.

The field of stereotactic neurosurgery developed more than 70 years ago to address a therapy gap for patients with severe psychiatric disorders. In the decades since, it has matured tremendously, benefiting from advances in clinical and basic sciences. Deep brain stimulation (DBS) for severe, treatment-resistant psychiatric disorders is currently poised to transition from a stage of empiricism to one increasingly rooted in scientific discovery. Current drivers of this transition are advances in neuroimaging, but rapidly emerging ones are neurophysiological-as we understand more about the neural basis of these disorders, we will more successfully be able to use interventions such as invasive stimulation to restore dysfunctional circuits to health. Paralleling this transition is a steady increase in the consistency and quality of outcome data. Here, we focus on obsessive-compulsive disorder and depression, two topics that have received the most attention in terms of trial volume and scientific effort.

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.

Subcallosal cingulate deep brain stimulation evokes two distinct cortical responses via differential white matter activation.

Subcallosal cingulate (SCC) deep brain stimulation (DBS) is an emerging therapy for refractory depression. Good clinical outcomes are associated with the activation of white matter adjacent to the SCC. This activation produces a signature cortical evoked potential (EP), but it is unclear which of the many pathways in the vicinity of SCC is responsible for driving this response. Individualized biophysical models were built to achieve selective engagement of two target bundles: either the forceps minor (FM) or cingulum bundle (CB). Unilateral 2 Hz stimulation was performed in seven patients with treatment-resistant depression who responded to SCC DBS, and EPs were recorded using 256-sensor scalp electroencephalography. Two distinct EPs were observed: a 120 ms symmetric response spanning both hemispheres and a 60 ms asymmetrical EP. Activation of FM correlated with the symmetrical EPs, while activation of CB was correlated with the asymmetrical EPs. These results support prior model predictions that these two pathways are predominantly activated by clinical SCC DBS and provide first evidence of a link between cortical EPs and selective fiber bundle activation.

Deep brain stimulation for refractory major depressive disorder: a comprehensive review.

Deep brain stimulation (DBS) has emerged as a promising treatment for select patients with refractory major depressive disorder (MDD). The clinical effectiveness of DBS for MDD has been demonstrated in meta-analyses, open-label studies, and a few controlled studies. However, randomized controlled trials have yielded mixed outcomes, highlighting challenges that must be addressed prior to widespread adoption of DBS for MDD. These challenges include tracking MDD symptoms objectively to evaluate the clinical effectiveness of DBS with sensitivity and specificity, identifying the patient population that is most likely to benefit from DBS, selecting the optimal patient-specific surgical target and stimulation parameters, and understanding the mechanisms underpinning the therapeutic benefits of DBS in the context of MDD pathophysiology. In this review, we provide an overview of the latest clinical evidence of MDD DBS effectiveness and the recent technological advancements that could transform our understanding of MDD pathophysiology, improve the clinical outcomes for MDD DBS, and establish a path forward to develop more effective neuromodulation therapies to alleviate depressive symptoms.

Whole brain network effects of subcallosal cingulate deep brain stimulation for treatment-resistant depression.

Ongoing experimental studies of subcallosal cingulate deep brain stimulation (SCC DBS) for treatment-resistant depression (TRD) show a differential timeline of behavioral effects with rapid changes after initial stimulation, and both early and delayed changes over the course of ongoing chronic stimulation. This study examined the longitudinal resting-state regional cerebral blood flow (rCBF) changes in intrinsic connectivity networks (ICNs) with SCC DBS for TRD over 6 months and repeated the same analysis by glucose metabolite changes in a new cohort. A total of twenty-two patients with TRD, 17 [15 O]-water and 5 [18 F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) patients, received SCC DBS and were followed weekly for 7 months. PET scans were collected at 4-time points: baseline, 1-month after surgery, and 1 and 6 months of chronic stimulation. A linear mixed model was conducted to examine the differential trajectory of rCBF changes over time. Post-hoc tests were also examined to assess postoperative, early, and late ICN changes and response-specific effects. SCC DBS had significant time-specific effects in the salience network (SN) and the default mode network (DMN). The rCBF in SN and DMN was decreased after surgery, but responder and non-responders diverged thereafter, with a net increase in DMN activity in responders with chronic stimulation. Additionally, the rCBF in the DMN uniquely correlated with depression severity. The glucose metabolic changes in a second cohort show the same DMN changes. The trajectory of PET changes with SCC DBS is not linear, consistent with the chronology of therapeutic effects. These data provide novel evidence of both an acute reset and ongoing plastic effects in the DMN that may provide future biomarkers to track clinical improvement with ongoing treatment.

Elevated phase amplitude coupling as a depression biomarker in epilepsy.

Depression is prevalent in epilepsy patients and their intracranial brain activity recordings can be used to determine the types of brain activity that are associated with comorbid depression. We performed case-control comparison of spectral power and phase amplitude coupling (PAC) in 34 invasively monitored drug resistant epilepsy patients' brain recordings. The values of spectral power and PAC for one-minute segments out of every hour in a patient's study were correlated with pre-operative assessment of depressive symptoms by Beck Depression Inventory-II (BDI). We identified an elevated PAC signal (theta-alpha-beta phase (5-25 Hz)/gamma frequency (80-100 Hz) band) that is present in high BDI scores but not low BDI scores adult epilepsy patients in brain regions implicated in primary depression, including anterior cingulate cortex, amygdala and orbitofrontal cortex. Our results showed the application of PAC as a network-specific, electrophysiologic biomarker candidate for comorbid depression and its potential as treatment target for neuromodulation.

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.

Metabolomic and inflammatory signatures of symptom dimensions in major depression.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions. METHODS: Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism. RESULTS: The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids. CONCLUSION: The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

ERICH3: vesicular association and antidepressant treatment response.

Selective serotonin reuptake inhibitors (SSRIs) are standard of care for major depressive disorder (MDD) pharmacotherapy, but only approximately half of these patients remit on SSRI therapy. Our previous genome-wide association study identified a single-nucleotide polymorphism (SNP) signal across the glutamate-rich 3 (ERICH3) gene that was nearly genome-wide significantly associated with plasma serotonin (5-HT) concentrations, which were themselves associated with SSRI response for MDD patients enrolled in the Mayo Clinic PGRN-AMPS SSRI trial. In this study, we performed a meta-analysis which demonstrated that those SNPs were significantly associated with SSRI treatment outcomes in four independent MDD trials. However, the function of ERICH3 and molecular mechanism(s) by which it might be associated with plasma 5-HT concentrations and SSRI clinical response remained unclear. Therefore, we characterized the human ERICH3 gene functionally and identified ERICH3 mRNA transcripts and protein isoforms that are highly expressed in central nervous system cells. Coimmunoprecipitation identified a series of ERICH3 interacting proteins including clathrin heavy chain which are known to play a role in vesicular function. Immunofluorescence showed ERICH3 colocalization with 5-HT in vesicle-like structures, and ERICH3 knock-out dramatically decreased 5-HT staining in SK-N-SH cells as well as 5-HT concentrations in the culture media and cell lysates without changing the expression of 5-HT synthesizing or metabolizing enzymes. Finally, immunofluorescence also showed ERICH3 colocalization with dopamine in human iPSC-derived neurons. These results suggest that ERICH3 may play a significant role in vesicular function in serotonergic and other neuronal cell types, which might help explain its association with antidepressant treatment response.

From bed to bench side: Reverse translation to optimize neuromodulation for mood disorders.

The advent of neuroimaging has provided foundational insights into the neural basis of psychiatric conditions, such as major depression. Across countless studies, dysfunction has been localized to distinct parts of the limbic system. Specific knowledge about affected locations has led to the development of circuit modulation therapies to correct dysfunction, notably deep brain stimulation (DBS). This and other emerging neuromodulation approaches have shown great promise, but their refinement has been slow and fundamental questions about their mechanisms of action remain. Here, we argue that their continued development requires reverse translation to animal models with close homology to humans, namely, nonhuman primates. With a particular focus on DBS approaches for depression, we highlight the parts of the brain that have been targeted by neuromodulation in humans, their efficacy, and why nonhuman primates are the most suitable model in which to conduct their refinement. We finish by highlighting key gaps in our knowledge that need to be filled to allow more rapid progress toward effective therapies in patients for whom all other treatment attempts have failed.

Psychotherapy or medication for depression? Using individual symptom meta-analyses to derive a Symptom-Oriented Therapy (SOrT) metric for a personalised psychiatry.

BACKGROUND: Antidepressant medication (ADM) and psychotherapy are effective treatments for major depressive disorder (MDD). It is unclear, however, if treatments differ in their effectiveness at the symptom level and whether symptom information can be utilised to inform treatment allocation. The present study synthesises comparative effectiveness information from randomised controlled trials (RCTs) of ADM versus psychotherapy for MDD at the symptom level and develops and tests the Symptom-Oriented Therapy (SOrT) metric for precision treatment allocation. METHODS: First, we conducted systematic review and meta-analyses of RCTs comparing ADM and psychotherapy at the individual symptom level. We searched PubMed Medline, PsycINFO, and the Cochrane Central Register of Controlled Trials databases, a database specific for psychotherapy RCTs, and looked for unpublished RCTs. Random-effects meta-analyses were applied on sum-scores and for individual symptoms for the Hamilton Rating Scale for Depression (HAM-D) and Beck Depression Inventory (BDI) measures. Second, we computed the SOrT metric, which combines meta-analytic effect sizes with patients' symptom profiles. The SOrT metric was evaluated using data from the Munich Antidepressant Response Signature (MARS) study (n = 407) and the Emory Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study (n = 234). RESULTS: The systematic review identified 38 RCTs for qualitative inclusion, 27 and 19 for quantitative inclusion at the sum-score level, and 9 and 4 for quantitative inclusion on individual symptom level for the HAM-D and BDI, respectively. Neither meta-analytic strategy revealed significant differences in the effectiveness of ADM and psychotherapy across the two depression measures. The SOrT metric did not show meaningful associations with other clinical variables in the MARS sample, and there was no indication of utility of the metric for better treatment allocation from PReDICT data. CONCLUSIONS: This registered report showed no differences of ADM and psychotherapy for the treatment of MDD at sum-score and symptom levels. Symptom-based metrics such as the proposed SOrT metric do not inform allocation to these treatments, but predictive value of symptom information requires further testing for other treatment comparisons.

Common genes associated with antidepressant response in mouse and man identify key role of glucocorticoid receptor sensitivity.

Response to antidepressant treatment in major depressive disorder (MDD) cannot be predicted currently, leading to uncertainty in medication selection, increasing costs, and prolonged suffering for many patients. Despite tremendous efforts in identifying response-associated genes in large genome-wide association studies, the results have been fairly modest, underlining the need to establish conceptually novel strategies. For the identification of transcriptome signatures that can distinguish between treatment responders and nonresponders, we herein submit a novel animal experimental approach focusing on extreme phenotypes. We utilized the large variance in response to antidepressant treatment occurring in DBA/2J mice, enabling sample stratification into subpopulations of good and poor treatment responders to delineate response-associated signature transcript profiles in peripheral blood samples. As a proof of concept, we translated our murine data to the transcriptome data of a clinically relevant human cohort. A cluster of 259 differentially regulated genes was identified when peripheral transcriptome profiles of good and poor treatment responders were compared in the murine model. Differences in expression profiles from baseline to week 12 of the human orthologues selected on the basis of the murine transcript signature allowed prediction of response status with an accuracy of 76% in the patient population. Finally, we show that glucocorticoid receptor (GR)-regulated genes are significantly enriched in this cluster of antidepressant-response genes. Our findings point to the involvement of GR sensitivity as a potential key mechanism shaping response to antidepressant treatment and support the hypothesis that antidepressants could stimulate resilience-promoting molecular mechanisms. Our data highlight the suitability of an appropriate animal experimental approach for the discovery of treatment response-associated pathways across species.

Neuroanatomical predictors of response to subcallosal cingulate deep brain stimulation for treatment-resistant depression

Background: Deep brain stimulation targeting the subcallosal cingulate gyrus (SCG DBS) improves the symptoms of treatment-resistant depression in some patients, but not in others. We hypothesized that there are pre-existing structural brain differences between responders and nonresponders to SCG DBS, detectable using structural MRI. Methods: We studied preoperative, T1-weighted MRI scans of 27 patients treated with SCG DBS from 2003 to 2011. Responders (n = 15) were patients with a >50% improvement in Hamilton Rating Scale for Depression score following 12 months of SCG DBS. Preoperative subcallosal cingulate gyrus grey matter volume was obtained using manual segmentation by a trained observer blinded to patient identity. Volumes of hippocampus, thalamus, amygdala, whole-brain cortical grey matter and white matter volume were obtained using automated techniques. Results: Preoperative subcallosal cingulate gyrus, thalamic and amygdalar volumes were significantly larger in patients who went on to respond to SCG-DBS. Hippocampal volume did not differ between groups. Cortical grey matter volume was significantly smaller in responders, and cortical grey matter:white matter ratio distinguished between responders and nonresponders with high sensitivity and specificity. Limitations: Normalization by intracranial volume nullified some between-group differences in volumetric measures. Conclusion: There are structural brain differences between patients with treatment-resistant depression who respond to SCG DBS and those who do not. Specifically, the structural integrity of the subcallosal cingulate gyrus target region and its connected subcortical areas, and variations in cortical volume across the entire brain, appear to be important determinants of response. Structural MRI shows promise as a biomarker in deep brain stimulation for depression, and may play a role in refining patient selection for future trials.

Somatic symptoms in treatment-naïve Hispanic and non-Hispanic patients with major depression.

BACKGROUND: Somatic complaints are a major driver of health care costs among patients with major depressive disorder (MDD). Some epidemiologic and clinical data suggest that Hispanic and non-Hispanic Black patients with MDD endorse higher levels of somatic symptoms than non-Hispanic White patients. METHODS: Somatic symptoms in 102 Hispanic, 61 non-Hispanic Black, and 156 non-Hispanic White patients with treatment-naïve MDD were evaluated using the somatic symptom subscale of the Hamilton anxiety rating scale (HAM-A). The other seven items of the HAM-A comprise the psychic anxiety subscale, which was also evaluated across ethnicities. RESULTS: Hispanic patients reported significantly greater levels of somatic symptoms than non-Hispanic patients, but levels of psychic anxiety symptoms did not differ by ethnicity. Levels of somatic symptoms did not significantly differ between Black and White non-Hispanic patients. Within the Hispanic sample, somatic symptom levels were higher only among those who were evaluated in Spanish; Hispanics who spoke English showed no significant differences versus non-Hispanics. CONCLUSIONS: In this medically healthy sample of patients with MDD, monolingual Spanish-speaking Hispanic patients endorsed high levels of somatic symptoms. Clinicians should be mindful that the depressive experience may manifest somatically and be judicious in determining when additional medical work-up is warranted for somatic complaints.

Default mode network connectivity change corresponds to ketamine's delayed glutamatergic effects.

Ketamine exerts rapid antidepressant effects peaking 24 h after a single infusion, which have been suggested to be reflected by both reduced functional connectivity (FC) within default mode network (DMN) and altered glutamatergic levels in the perigenual anterior cingulate cortex (pgACC) at 24 h. Understanding the interrelation and time point specificity of ketamine-induced changes of brain circuitry and metabolism is thus key to future therapeutic developments. We investigated the correlation of late glutamatergic changes with FC changes seeded from the posterior cingulate cortex (PCC) and tested the prediction of the latter by acute fractional amplitude of low-frequency fluctuations (fALFF). In a double-blind, randomized, placebo-controlled study of 61 healthy subjects, we compared effects of subanesthetic ketamine infusion (0.5 mg/kg over 40 min) on resting-state fMRI and MR-Spectroscopy at 7 T 1 h and 24 h post-infusion. FC decrease between PCC and dorsomedial prefrontal cortex (dmPFC) was found at 24 h post-infusion (but not 1 h) and this FC decrease correlated with glutamatergic changes at 24 h in pgACC. Acute increase in fALFF was found in ventral PCC at 1 h which was not observed at 24 h and inversely correlated with the reduced dPCC FC towards the dmPFC at 24 h. The correlation of metabolic and functional markers of delayed ketamine effects and their temporal specificity suggest a potential mechanistic relationship between glutamatergic modulation and reconfiguration of brain regions belonging to the DMN.

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.

A Shared Vision for Machine Learning in Neuroscience.

With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data we can collect and toward what we actually do with the data. There has been a growing interest in leveraging this vast volume of data across levels of analysis, measurement techniques, and experimental paradigms to gain more insight into brain function. Such efforts are visible at an international scale, with the emergence of big data neuroscience initiatives, such as the BRAIN initiative (Bargmann et al., 2014), the Human Brain Project, the Human Connectome Project, and the National Institute of Mental Health's Research Domain Criteria initiative. With these large-scale projects, much thought has been given to data-sharing across groups (Poldrack and Gorgolewski, 2014; Sejnowski et al., 2014); however, even with such data-sharing initiatives, funding mechanisms, and infrastructure, there still exists the challenge of how to cohesively integrate all the data. At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.

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.

Deep brain stimulation for psychiatric disorders.

Medications, psychotherapy, and other treatments are effective for many patients with psychiatric disorders. However, with currently available interventions, a substantial number of patients experience incomplete resolution of symptoms, and relapse rates are high. In the search for better treatments, increasing interest has focused on focal neuromodulation. This focus has been driven by improved neuroanatomical models of mood, thought, and behavior regulation, as well as by more advanced strategies for directly and focally altering neural activity. Deep brain stimulation (DBS) is one of the most invasive focal neuromodulation techniques available; data have supported its safety and efficacy in a number of movement disorders. Investigators have produced preliminary data on the safety and efficacy of DBS for several psychiatric disorders, as well. In this review, we describe the development and justification for testing DBS for various psychiatric disorders, carefully consider the available clinical data, and briefly discuss potential mechanisms of action.

A difference degree test for comparing brain networks.

Recently, there has been a proliferation of methods investigating functional connectivity as a biomarker for mental disorders. Typical approaches include massive univariate testing at each edge or comparisons of network metrics to identify differing topological features. Limitations of these methods include low statistical power due to the large number of comparisons and difficulty attributing overall differences in networks to local variation. We propose a method to capture the difference degree, which is the number of edges incident to each region in the difference network. Our difference degree test (DDT) is a two-step procedure for identifying brain regions incident to a significant number of differentially weighted edges (DWEs). First, we select a data-adaptive threshold which identifies the DWEs followed by a statistical test for the number of DWEs incident to each brain region. We achieve this by generating an appropriate set of null networks which are matched on the first and second moments of the observed difference network using the Hirschberger-Qi-Steuer algorithm. This formulation permits separation of the network's true topology from the nuisance topology induced by the correlation measure that alters interregional connectivity in ways unrelated to brain function. In simulations, the proposed approach outperforms competing methods in detecting differentially connected regions of interest. Application of DDT to a major depressive disorder dataset leads to the identification of brain regions in the default mode network commonly implicated in this ruminative disorder.