Transdiagnostic Mood, Anxiety and Trauma Symptom Factors in Alcohol Use Disorder: Neural Correlates Across Three Brain Networks.

BACKGROUND: Alcohol use disorder (AUD) is associated with high rates of trauma, mood, and anxiety disorders. Individual symptoms highly overlap across diagnoses, highlighting the need for a transdiagnostic approach. Further, there is limited research on how transdiagnostic psychopathology impacts the neural correlates of AUD. Thus, we aimed to identify symptom factors spanning diagnoses and how they relate to the neurocircuitry of addiction. METHODS: Eighty-six Veterans with AUD completed self-report measures and reward, incentive salience and cognitive control fMRI tasks. Factor analysis was performed on self-reported trauma, depression, anxiety, and stress symptoms to obtain transdiagnostic symptom compositions. Neural correlates with a-priori-defined regions of interest in the three networks were assessed. Independent samples t-tests compared the same nodes by DSM-5 diagnosis. RESULTS: Four symptom factors were identified: trauma distress, negative affect, hyperarousal, and somatic anxiety. Trauma distress score was associated with increased cognitive control activity regions during response inhibition (dACC). Negative affect related to lower activation in reward regions (R.Caudate) but higher activation in cognitive control regions during response inhibition (L.dlPFC). Hyperarousal related to lower reward activity during monetary reward anticipation (L.Caudate, R.Caudate) Somatic anxiety was not significantly associated with brain activation. No difference in neural activity was found by PTSD, MDD or GAD diagnosis CONCLUSION: These preliminary, hypothesis-generating findings offer transdiagnostic symptom factors that are differentially associated with neural function and could guide us towards a brain-based classification of psychiatric dysfunction in AUD. Results warrant further investigation of transdiagnostic approaches to symptoms in addiction.

Mediating Effects of Neural Targets on Depression, Weight, and Anxiety Outcomes of an Integrated Collaborative Care Intervention: The ENGAGE-2 Mechanistic Pilot Randomized Clinical Trial.

Background: Integrated treatments for comorbid depression (often with anxiety) and obesity are lacking; mechanisms are poorly investigated. Methods: In a mechanistic pilot trial, adults with body mass index ≥30 and Patient Health Questionnaire-9 scores ≥10 were randomized to usual care (n = 35) or an integrated behavioral intervention (n = 71). Changes at 6 months in body mass index and Depression Symptom Checklist-20 scores were co-primary outcomes, and Generalized Anxiety Disorder Scale-7 score was a secondary outcome. Changes at 2 months in the activation and functional connectivity of regions of interest in the negative affect circuit were primary neural targets, and secondary targets were in the cognitive control, default mode, and positive affect circuits. Results: Participants were 47.0 years (SD = 11.9 years), 76% women, 55% Black, and 20% Latino. Depression Symptom Checklist-20 (between-group difference, -0.3 [95% CI: -0.6 to -0.1]) and Generalized Anxiety Disorder Scale-7 (-2.9 [-4.7 to -1.1]) scores, but not body mass index, decreased significantly at 6 months in the intervention versus usual care groups. Only Generalized Anxiety Disorder Scale-7 score changes at 6 months significantly correlated with neural target changes at 2 months in the negative affect (anterior insula, subgenual/pregenual anterior cingulate cortex, amygdala) and cognitive control circuits (dorsal lateral prefrontal cortex, dorsal anterior cingulate cortex). Effects were medium to large (0.41-1.18 SDs). Neural target changes at 2 months in the cognitive control circuit only differed by treatment group. Effects were medium (0.58-0.79 SDs). Conclusions: Compared with usual care, the study intervention led to significantly improved depression but not weight loss, and the results on neural targets were null for both outcomes. The significant intervention effect on anxiety might be mediated through changes in the cognitive control circuit, but this warrants replication.

Amygdala neurocircuitry at the interface between emotional regulation and narcolepsy with cataplexy.

Narcolepsy is a sleep disorder characterized by chronic and excessive daytime sleepiness, and sudden intrusion of sleep during wakefulness that can fall into two categories: type 1 and type 2. Type 1 narcolepsy in humans is widely believed to be caused as a result of loss of neurons in the brain that contain the key arousal neuropeptide Orexin (Orx; also known as Hypocretin). Patients with type 1 narcolepsy often also present with cataplexy, the sudden paralysis of voluntary muscles which is triggered by strong emotions (e.g., laughter in humans, social play in dogs, and chocolate in rodents). The amygdala is a crucial emotion-processing center of the brain; however, little is known about the role of the amygdala in sleep/wake and narcolepsy with cataplexy. A collection of reports across human functional neuroimaging analyses and rodent behavioral paradigms points toward the amygdala as a critical node linking emotional regulation to cataplexy. Here, we review the existing evidence suggesting a functional role for the amygdala network in narcolepsy, and build upon a framework that describes relevant contributions from the central nucleus of the amygdala (CeA), basolateral amygdala (BLA), and the extended amygdala, including the bed nucleus of stria terminalis (BNST). We propose that detailed examinations of amygdala neurocircuitry controlling transitions between emotional arousal states may substantially advance progress in understanding the etiology of narcolepsy with cataplexy, leading to enhanced treatment opportunities.

Effects of Acceptance and Commitment Therapy (ACT) and Mindfulness-Based Stress Reduction (MBSR) on symptoms and emotional competencies in individuals with multiple sclerosis.

BACKGROUND: Compared to the general population, individuals with multiple sclerosis (MS) report higher levels of insomnia, depression, fatigue, and paresthesia, and lower levels of emotional competencies (understanding emotions in self and others). Available treatments are limited, and novel approaches to reducing symptoms and enhancing emotional competencies in MS are needed. Two potentially beneficial treatments are Acceptance and Commitment Therapy (ACT) and Mindfulness-Based Stress Reduction (MBSR). The aim of the present study was to investigate the impact of ACT and MBSR on symptoms and emotional competencies in patients with MS. METHODS: A total of 76 individuals with MS (81.6% females; mean age: 38.88 years; EDSS median: 2; range: 0-5) were randomly assigned to an 8-week ACT treatment, an 8-week MBSR treatment, or a wait-list control condition. At baseline and study-end (week 8), participants completed a series of questionnaires covering symptoms and emotional competencies. At mid-term (week 4), participants rated their insomnia and depression. RESULTS: Over time, symptoms of MS decreased (medium effect size for insomnia, fatigue, and paresthesia, and large effect size for depression) and emotional competencies improved (large effect size), but more so in the MBSR and ACT conditions, compared with the control condition. At study-end, the outcome improvement did not differ between the ACT and MBSR conditions. CONCLUSIONS: Both ACT and MBSR led to reduced symptoms and enhanced emotional competencies. Psychotherapeutic interventions such as these should be considered as a means of decreasing symptoms and increasing emotional competencies among individuals with MS.

Viability of an Early Sleep Intervention to Mitigate Poor Sleep and Improve Well-being in the COVID-19 Pandemic: Protocol for a Feasibility Randomized Controlled Trial.

BACKGROUND: The COVID-19 pandemic has led to drastic increases in the prevalence and severity of insomnia symptoms. These increases in insomnia complaints have been paralleled by significant decreases in well-being, including increased symptoms of depression, anxiety, and suicidality and decreased quality of life. However, the efficacy and impact of early treatment of insomnia symptoms on future sleep and well-being remain unknown. OBJECTIVE: Here, we present the framework and protocol for a novel feasibility, pilot study that aims to investigate whether a brief telehealth insomnia intervention targeting new insomnia that developed during the pandemic prevents deterioration of well-being, including symptoms of insomnia, depression, anxiety, suicidality, and quality of life. METHODS: The protocol details a 2-arm randomized controlled feasibility trial to investigate the efficacy of a brief, telehealth-delivered, early treatment of insomnia and evaluate its potential to prevent deterioration of well-being. Participants with clinically significant insomnia symptoms that began during the pandemic were randomized to either a treatment group or a 28-week waitlist control group. Treatment consists of 4 telehealth sessions of cognitive behavioral therapy for insomnia (CBT-I) delivered over 5 weeks. All participants will complete assessments of insomnia symptom severity, well-being, and daily habits checklist at baseline (week 0) and at weeks 1-6, 12, 28, and 56. RESULTS: The trial began enrollment on June 3, 2020 and closed enrollment on June 17, 2021. As of October 2021, 49 participants had been randomized to either immediate treatment or a 28-week waitlist; 23 participants were still active in the protocol. CONCLUSIONS: To our knowledge, this protocol would represent the first study to test an early sleep intervention for improving insomnia that emerged during the COVID-19 pandemic. The findings of this feasibility study could provide information about the utility of CBT-I for symptoms that emerge in the context of other stressors before they develop a chronic course and deepen understanding of the relationship between sleep and well-being. TRIAL REGISTRATION: ClinicalTrials.gov NCT04409743; https://clinicaltrials.gov/ct2/show/NCT04409743. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34409.

Mapping Neural Circuit Biotypes to Symptoms and Behavioral Dimensions of Depression and Anxiety.

BACKGROUND: Despite tremendous advances in characterizing human neural circuits that govern emotional and cognitive functions impaired in depression and anxiety, we lack a circuit-based taxonomy for depression and anxiety that captures transdiagnostic heterogeneity and informs clinical decision making. METHODS: We developed and tested a novel system for quantifying 6 brain circuits reproducibly and at the individual patient level. We implemented standardized circuit definitions relative to a healthy reference sample and algorithms to generate circuit clinical scores for the overall circuit and its constituent regions. RESULTS: In new data from primary and generalizability samples of depression and anxiety (N = 250), we demonstrated that overall disconnections within task-free salience and default mode circuits map onto symptoms of anxious avoidance, loss of pleasure, threat dysregulation, and negative emotional biases-core characteristics that transcend diagnoses-and poorer daily function. Regional dysfunctions within task-evoked cognitive control and affective circuits may implicate symptoms of cognitive and valence-congruent emotional functions. Circuit dysfunction scores also distinguished response to antidepressant and behavioral intervention treatments in an independent sample (n = 205). CONCLUSIONS: Our findings articulate circuit dimensions that relate to transdiagnostic symptoms across mood and anxiety disorders. Our novel system offers a foundation for deploying standardized circuit assessments across research groups, trials, and clinics to advance more precise classifications and treatment targets for psychiatry.

Problem-solving therapy-induced amygdala engagement mediates lifestyle behavior change in obesity with comorbid depression: a randomized proof-of-mechanism trial.

BACKGROUND: Depression hinders obesity treatment; elucidating mechanisms may enable treatment enhancements. OBJECTIVES: The aim was to investigate whether changes in neural targets in the negative affect circuit following psychotherapy mediate subsequent changes in weight and behaviors. METHODS: Adults (n = 108) with obesity and depression were randomly assigned to usual care or an intervention that delivered problem-solving therapy (PST) for depression over 2 mo. fMRI for brain imaging was performed at baseline and 2 mo. BMI, physical activity, and diet were measured at baseline and 12 mo. Mediation analysis assessed between-group differences in neural target changes using t test and correlations between neural target changes and outcome changes (simple and interaction effect) using ordinary least-squares regression. RESULTS: Compared with usual care, PST led to reductions in left amygdala activation (-0.75; 95% CI: -1.49, -0.01) and global scores of the negative affect circuit (-0.43; -0.81, -0.06), engaged by threat stimuli. Increases in amygdala activation and global circuit scores at 2 mo correlated with decreases in physical activity outcomes at 12 mo in the usual-care group; these relations were altered by PST. In relation to change in leisure-time physical activity, standardized ß-coefficients were -0.67 in usual care and -0.01 in the intervention (between-group difference: 0.66; 0.02, 1.30) for change in left amygdala activation and -2.02 in usual care and -0.11 in the intervention (difference: 1.92; 0.64, 3.20) for change in global circuit scores. In relation to change in total energy expenditure, standardized ß-coefficients were -0.65 in usual care and 0.08 in the intervention (difference: 0.73; 0.29, 1.16) for change in left amygdala activation and -1.65 in usual care and 0.08 in the intervention (difference: 1.74; 0.85, 2.63) for change in global circuit scores. Results were null for BMI and diet. CONCLUSIONS: Short-term changes in the negative affect circuit engaged by threat stimuli following PST for depression mediated longer-term changes in physical activity. This trial was registered at www.clinicaltrials.gov as NCT02246413 (https://clinicaltrials.gov/ct2/show/NCT02246413).

Early changes in neural circuit function engaged by negative emotion and modified by behavioural intervention are associated with depression and problem-solving outcomes: A report from the ENGAGE randomized controlled trial.

BACKGROUND: Depression exerts a staggering toll that is worsened with co-occurring chronic conditions such as obesity. It is imperative to develop more effective interventions for depression and to identify objective and biological plausible neural mechanisms to understand intervention outcomes. The current study uses functional neuroimaging to determine whether a behavioural intervention changes the negative affect circuit and whether these changes relate to subsequent improvements in both symptom and problem-solving outcomes in depressed patients with co-occurring obesity. METHODS: This study ('ENGAGE') was a pre-planned element of the randomized controlled trial, 'RAINBOW' (ClinicalTrials.gov NCT02246413). 108 depressed patients with obesity were randomized to receive an integrated collaborative care intervention (I-CARE) or usual care. Participants underwent functional neuroimaging using an established facial emotion task at baseline and two months (coinciding with the first two months of intervention focused on problem-solving therapy ('PST')). Amygdala, insula and anterior cingulate cortex activation was extracted using pre-planned definitions and standardized methods. The primary health and behavioural outcomes were depression symptom severity and problem-solving ability respectively, assessed at baseline, the main 6-month outcome point and at 12-month follow up. Mediation analyses used an intent-to-treat approach. FINDINGS: PST, relative to usual care, reduced amygdala activation engaged by threat stimuli at two months. This reduction mediated subsequent improvements in depression severity in an intervention-dependent manner. PST did not change insula activation at two months but did temper the strength of the relationship between insula activation and improvements in problem-solving ability. INTERPRETATION: The negative affect circuit may be an important neural target and potential mediator of PST in patients with comorbid obesity. FUNDING: US National Institutes of Health/National Heart Lung and Blood Institute R01 HL119453 and UH2/UH3 HL132368.

Kleine-Levin syndrome is associated with birth difficulties and genetic variants in the TRANK1 gene loci.

Kleine-Levin syndrome (KLS) is a rare disorder characterized by severe episodic hypersomnia, with cognitive impairment accompanied by apathy or disinhibition. Pathophysiology is unknown, although imaging studies indicate decreased activity in hypothalamic/thalamic areas during episodes. Familial occurrence is increased, and risk is associated with reports of a difficult birth. We conducted a worldwide case-control genome-wide association study in 673 KLS cases collected over 14 y, and ethnically matched 15,341 control individuals. We found a strong genome-wide significant association (rs71947865, Odds Ratio [OR] = 1.48, P = 8.6 × 10-9) within the 3'region of TRANK1 gene locus, previously associated with bipolar disorder and schizophrenia. Strikingly, KLS cases with rs71947865 variant had significantly increased reports of a difficult birth. As perinatal outcomes have dramatically improved over the last 40 y, we further stratified our sample by birth years and found that recent cases had a significantly reduced rs71947865 association. While the rs71947865 association did not replicate in the entire follow-up sample of 171 KLS cases, rs71947865 was significantly associated with KLS in the subset follow-up sample of 59 KLS cases who reported birth difficulties (OR = 1.54, P = 0.01). Genetic liability of KLS as explained by polygenic risk scores was increased (pseudo R2 = 0.15; P < 2.0 × 10-22 at P = 0.5 threshold) in the follow-up sample. Pathway analysis of genetic associations identified enrichment of circadian regulation pathway genes in KLS cases. Our results suggest links between KLS, circadian regulation, and bipolar disorder, and indicate that the TRANK1 polymorphisms in conjunction with reported birth difficulties may predispose to KLS.

Understanding mechanisms of integrated behavioral therapy for co-occurring obesity and depression in primary care: a mediation analysis in the RAINBOW trial.

The RAINBOW trial demonstrated that an integrated collaborative care intervention was effective for improving weight and depression. This study examined mediation of the treatment effect by a priori specified lifestyle behaviors and cognitive functioning. Participants were randomized to a 12-month integrated intervention (n = 204) or usual care (n = 205). Body mass index (BMI) and 20-item Depression Symptom Check List (SCL-20) were co-primary outcomes (Y). To examine mediation, we assessed (a) the effect of the integrated intervention (X) on lifestyle behaviors (diet and physical activity) and cognitive functioning (problem-solving; M, X→M path a) and (b) the association of these behaviors with BMI and SCL-20 (M→Y path b). Mediation existed if paths a and b were significant or if path a and the product of coefficients test (paths a and b) were significant. Compared with usual care, the intervention led to significant improvements in leisure time physical activity (201.3 MET minutes/week [SD, 1,457.6]) and total calorie intake (337.4 kcal/day [818.3]) at 6 months but not 12 months (path a). These improvements were not significantly associated with improvements in BMI or SCL-20 (path b). However, avoidant problem-solving style score and increased fruit and vegetable intake significantly correlated with improvements in BMI at 6 and 12 months, respectively. Also, increased fruit and vegetable intake, higher dietary quality, and better problem-solving abilities significantly correlated with improvements in SCL-20 at 6 and 12 months. These findings did not support the hypothesized mediation, but suggest lifestyle behaviors and cognitive functioning to target in future intervention optimization.

The ENGAGE-2 study: Engaging self-regulation targets to understand the mechanisms of behavior change and improve mood and weight outcomes in a randomized controlled trial (Phase 2).

Despite evidence for effective integrated behavior therapy for treating comorbid obesity and depression, treatment response is highly variable and the underlying neurobiological mechanisms remain unknown. This hampers efforts to identify mechanistic targets in order to optimize treatment precision and potency. Funded within the NIH Science of Behavior Change (SOBC) Research Network, the 2-phased ENGAGE research project applies an experimental precision medicine approach to address this gap. The Phase 1 study focused on demonstrating technical feasibility, target engagement and potential neural mechanisms of responses to an integrated behavior therapy. This therapy combines a video-based behavioral weight loss program and problem-solving therapy for depression, with as-needed intensification of antidepressant medications, and its clinical effectiveness was demonstrated within a parent randomized clinical trial. Here, we describe the ENGAGE Phase 2 (ENGAGE-2) study protocol which builds on Phase 1 in 2 ways: (1) pilot testing of an motivational interviewing-enhanced, integrated behavior therapy in an independent, primarily minority patient sample, and (2) evaluation of a priori defined neural targets, specifically the negative affect (threat and sadness) circuits which demonstrated engagement and malleability in Phase 1, as mediators of therapeutic outcomes. Additionally, the Phase 2 study includes a conceptual and methodological extension to explore the role of microbiome-gut-brain and systemic immunological pathways in integrated behavioral treatment of obesity and depression. This protocol paper documents the conceptualization, design and the transdisciplinary methodologies in ENGAGE-2, which can inform future clinical and translational research in experimental precision medicine for behavior change and chronic disease management. Trial registration: ClinicalTrials.gov #NCT 03,841,682.

Effect of an Intervention for Obesity and Depression on Patient-Centered Outcomes: An RCT.

INTRODUCTION: An integrated collaborative care intervention was successful for treating comorbid obesity and depression. The effect of the integrated intervention on secondary outcomes of quality of life and psychosocial functioning were examined, as well as whether improvements in these secondary outcomes were correlated with improvements in the primary outcomes of weight and depressive symptoms. STUDY DESIGN: This RCT compared an integrated collaborative care intervention for obesity and depression to usual care. Data were analyzed in 2018. SETTING/PARTICIPANTS: Adult primary care patients (n=409) with a BMI ≥30 (≥27 if Asian) and 9-Item Patient Health Questionnaire score ≥10 were recruited from September 30, 2014 to January 12, 2017 from primary care clinics in Northern California. INTERVENTION: The 12-month intervention integrated a behavioral weight loss program and problem-solving therapy with as-needed antidepressant medications for depression. MAIN OUTCOME MEASURES: A priori secondary outcomes included health-related quality of life (Short Form-8 Health Survey), obesity-specific quality of life (Obesity-Related Problems Scale), sleep disturbance and sleep-related impairment (Patient-Reported Outcomes Measurement Information System), and functional disability (Sheehan Disability Scale) at baseline and 6 and 12 months. RESULTS: Participants randomized to the intervention experienced significantly greater improvements in obesity-specific problems, mental health-related quality of life, sleep disturbance, sleep-related impairment, and functional disability at 6 months but not 12 months. Improvements in obesity-related problems (ß=0.01, 95% CI=0.01, 0.02) and sleep disturbance (ß= -0.02, 95% CI= -0.04, 0) were associated with lower BMI. Improvements in the physical (ß= -0.01, 95% CI= -0.01, 0) and mental health components (ß= -0.02, 95% CI= -0.03, -0.02) of the Short Form-8 Health Survey as well as sleep disturbance (ß=0.01, 95% CI=0.01, 0.02) and sleep-related impairment (ß=0.01, 95% CI=0, 0.01) were associated with fewer depressive symptoms. CONCLUSIONS: An integrated collaborative care intervention for obesity and depression that was shown previously to improve weight and depressive symptoms may also confer benefits for quality of life and psychosocial functioning over 6 months. TRIAL REGISTRATION: This study is registered at clinicaltrials.gov NCT02246413.

Integrating sleep, neuroimaging, and computational approaches for precision psychiatry.

In advancing precision psychiatry, we focus on what imaging technology and computational approaches offer for the future of diagnostic subtyping and personalized tailoring of interventions for sleep impairment in mood and anxiety disorders. Current diagnostic criteria for mood and anxiety tend to lump different forms of sleep disturbance together. Parsing the biological features of sleep impairment and brain circuit dysfunction is one approach to identifying subtypes within these disorders that are mechanistically coherent and offer targets for intervention. We focus on two large-scale neural circuits implicated in sleep impairment and in mood and anxiety disorders: the default mode network and negative affective network. Through a synthesis of existing knowledge about these networks, we pose a testable framework for understanding how hyper- versus hypo-engagement of these networks may underlie distinct features of mood and sleep impairment. Within this framework we consider whether poor sleep quality may have an explanatory role in previously observed associations between network dysfunction and mood symptoms. We expand this framework to future directions including the potential for connecting circuit-defined subtypes to more distal features derived from digital phenotyping and wearable technologies, and how new discovery may be advanced through machine learning approaches.

Sleep-wake disorders in Alzheimer's disease: further genetic analyses in relation to objective sleep measures.

This paper presents updated analyses on the genetic associations of sleep disruption in individuals with Alzheimer's disease (AD). We published previously a study of the association between single nucleotide polymorphisms (SNPs) found in eight genes related to circadian rhythms and objective measures of sleep-wake disturbances in 124 individuals with AD. Here, we present new relevant analyses using polygenic risk scores (PRS) and variable number tandem repeats (VNTRs) enumerations. PRS were calculated using the genetic data from the original participants and relevant genome wide association studies (GWAS). VNTRs for the same circadian rhythm genes studied with SNPs were obtained from a separate cohort of participants using whole genome sequencing (WGS). Objectively (wrist actigraphy) determined wake after sleep onset (WASO) was used as a measure of sleep disruption. None of the PRS were associated with sleep disturbance. Computer analyses using VNTRseek software generated a total of 30 VNTRs for the circadian-related genes but none appear relevant to our objective sleep measure. In addition, of 71 neurotransmitter function-related genes, 29 genes had VNTRs that differed from the reference VNTR, but it was not clear if any of these might affect circadian function in AD patients. Although we have not found in either the current analyses or in our previous published analyses of SNPs any direct linkages between identified genetic factors and WASO, research in this area remains in its infancy.

Connectivity of the Cognitive Control Network During Response Inhibition as a Predictive and Response Biomarker in Major Depression: Evidence From a Randomized Clinical Trial.

BACKGROUND: In treating major depressive disorder, we lack tests anchored in neurobiology that predict antidepressant efficacy. Cognitive impairments are a particularly disabling feature of major depressive disorder. We tested whether functional connectivity during a response-inhibition task can predict response to antidepressants and whether its changes over time are correlated to symptom changes. METHODS: We analyzed data from outpatients with major depressive disorder (n = 124) randomized to receive escitalopram, sertraline, or venlafaxine (8 weeks) and healthy control subjects (n = 59; age 18-65 years). Before and after treatment, participants were interviewed and scanned using functional magnetic resonance imaging, and functional connectivity was measured using generalized psychophysiological interaction during response inhibition (Go-NoGo task). We investigated the interaction between treatment type and response (≥50% reduction on self-reported symptoms), coupling differences between responders and nonresponders at baseline, their correlation with symptom improvement, and their changes in time. RESULTS: During response inhibition, connectivity between the dorsolateral prefrontal cortex/supramarginal gyrus and supramarginal gyrus/middle temporal gyrus was associated with response to sertraline and venlafaxine, but not escitalopram. Sertraline responders had higher functional connectivity between these regions compared with nonresponders, whereas venlafaxine responders had lower functional connectivity. For sertraline, attenuation of connectivity in the precentral and superior temporal gyri correlated with posttreatment symptom improvement. For venlafaxine, enhancement of connectivity between the orbitofrontal cortex and subcortical regions correlated with symptom improvement. CONCLUSIONS: Connectivity of the cognitive control circuit during response inhibition selectively and differentially predicts antidepressant treatment response and correlates with symptom improvement. These quantitative markers tied to the neurobiology of cognitive features of depression could be used translationally to predict and evaluate treatment response.

Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder.

Although major depressive disorder (MDD) is associated with altered functional coupling between disparate neural networks, the degree to which such measures are ameliorated by antidepressant treatment is unclear. It is also unclear whether functional connectivity can be used as a predictive biomarker of treatment response. Here, we used whole-brain functional connectivity analysis to identify neural signatures of remission following antidepressant treatment, and to identify connectomic predictors of treatment response. 163 MDD and 62 healthy individuals underwent functional MRI during pre-treatment baseline and 8-week follow-up sessions. Patients were randomized to escitalopram, sertraline or venlafaxine-XR antidepressants and assessed at follow-up for remission. Baseline measures of intrinsic functional connectivity between each pair of 333 regions were analyzed to identify pre-treatment connectomic features that distinguish remitters from non-remitters. We then interrogated these connectomic differences to determine if they changed post-treatment, distinguished patients from controls, and were modulated by medication type. Irrespective of medication type, remitters were distinguished from non-remitters by greater connectivity within the default mode network (DMN); specifically, between the DMN, fronto-parietal and somatomotor networks, the DMN and visual, limbic, auditory and ventral attention networks, and between the fronto-parietal and somatomotor networks with cingulo-opercular and dorsal attention networks. This baseline hypo-connectivity for non-remitters also distinguished them from controls and increased following treatment. In contrast, connectivity for remitters was higher than controls at baseline and also following remission, suggesting a trait-like connectomic characteristic. Increased functional connectivity within and between large-scale intrinsic brain networks may characterize acute recovery with antidepressants in depression.

Intrinsic functional connectivity predicts remission on antidepressants: a randomized controlled trial to identify clinically applicable imaging biomarkers.

Default mode network (DMN) dysfunction (particularly within the anterior cingulate cortex (ACC) and medial prefrontal cortex (mPFC)) has been implicated in major depressive disorder (MDD); however, its contribution to treatment outcome has not been clearly established. Here we tested the role of DMN functional connectivity as a general and differential biomarker for predicting treatment outcomes in a large, unmedicated adult sample with MDD. Seventy-five MDD outpatients completed fMRI scans before and 8 weeks after randomization to escitalopram, sertraline, or venlafaxine-XR. A whole-brain voxel-wise t-test identified profiles of pretreatment intrinsic functional connectivity that distinguished patients who were subsequently classified as remitters or non-remitters at follow-up. Connectivity was seeded in the PCC, an important node of the DMN. We further characterized differences between remitters, non-remitters, and 31 healthy controls and characterized changes pretreatment to posttreatment. Remitters were distinguished from non-remitters by relatively intact connectivity between the PCC and ACC/mPFC, not distinguishable from healthy controls, while non-remitters showed relative hypo-connectivity. In validation analyses, we demonstrate that PCC-ACC/mPFC connectivity predicts remission status with >80% cross-validated accuracy. In analyses testing whether intrinsic connectivity differentially relates to outcomes for a specific type of antidepressant, interaction models did not survive the corrected threshold. Our findings demonstrate that the overall capacity to remit on commonly used antidepressants may depend on intact organization of intrinsic functional connectivity between PCC and ACC/mPFC prior to treatment. The findings highlight the potential utility of functional scans for advancing a more precise approach to tailoring antidepressant treatment choices.

Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders.

Importance: The symptoms that define mood, anxiety, and trauma disorders are highly overlapping across disorders and heterogeneous within disorders. It is unknown whether coherent subtypes exist that span multiple diagnoses and are expressed functionally (in underlying cognition and brain function) and clinically (in daily function). The identification of cohesive subtypes would help disentangle the symptom overlap in our current diagnoses and serve as a tool for tailoring treatment choices. Objective: To propose and demonstrate 1 approach for identifying subtypes within a transdiagnostic sample. Design, Setting, and Participants: This cross-sectional study analyzed data from the Brain Research and Integrative Neuroscience Network Foundation Database that had been collected at the University of Sydney and University of Adelaide between 2006 and 2010 and replicated at Stanford University between 2013 and 2017. The study included 420 individuals with a primary diagnosis of major depressive disorder (n = 100), panic disorder (n = 53), posttraumatic stress disorder (n = 47), or no disorder (healthy control participants) (n = 220). Data were analyzed between October 2016 and October 2017. Main Outcomes and Measures: We followed a data-driven approach to achieve the primary study outcome of identifying transdiagnostic subtypes. First, machine learning with a hierarchical clustering algorithm was implemented to classify participants based on self-reported negative mood, anxiety, and stress symptoms. Second, the robustness and generalizability of the subtypes were tested in an independent sample. Third, we assessed whether symptom subtypes were expressed at behavioral and physiological levels of functioning. Fourth, we evaluated the clinically meaningful differences in functional capacity of the subtypes. Findings were interpreted relative to a complementary diagnostic frame of reference. Results: Four hundred twenty participants with a mean (SD) age of 39.8 (14.1) years were included in the final analysis; 256 (61.0%) were female. We identified 6 distinct subtypes characterized by tension (n=81; 19%), anxious arousal (n=55; 13%), general anxiety (n=38; 9%), anhedonia (n=29; 7%), melancholia (n=37; 9%), and normative mood (n=180; 43%), and these subtypes were replicated in an independent sample. Subtypes were expressed through differences in cognitive control (F5,383 = 5.13, P < .001, ηp2 = 0.063), working memory (F5,401 = 3.29, P = .006, ηp2 = 0.039), electroencephalography-recorded ß power in a resting paradigm (F5,357 = 3.84, P = .002, ηp2 = 0.051), electroencephalography-recorded ß power in an emotional paradigm (F5,365 = 3.56, P = .004, ηp2 = 0.047), social functional capacity (F5,414 = 21.33, P < .001, ηp2 = 0.205), and emotional resilience (F5,376 = 15.10, P < .001, ηp2 = 0.171). Conclusions and Relevance: These findings offer a data-driven framework for identifying robust subtypes that signify specific, coherent, meaningful associations between symptoms, behavior, brain function, and observable real-world function, and that cut across DSM-IV-defined diagnoses of major depressive disorder, panic disorder, and posttraumatic stress disorder.

The ENGAGE study: Integrating neuroimaging, virtual reality and smartphone sensing to understand self-regulation for managing depression and obesity in a precision medicine model.

Precision medicine models for personalizing achieving sustained behavior change are largely outside of current clinical practice. Yet, changing self-regulatory behaviors is fundamental to the self-management of complex lifestyle-related chronic conditions such as depression and obesity - two top contributors to the global burden of disease and disability. To optimize treatments and address these burdens, behavior change and self-regulation must be better understood in relation to their neurobiological underpinnings. Here, we present the conceptual framework and protocol for a novel study, "Engaging self-regulation targets to understand the mechanisms of behavior change and improve mood and weight outcomes (ENGAGE)". The ENGAGE study integrates neuroscience with behavioral science to better understand the self-regulation related mechanisms of behavior change for improving mood and weight outcomes among adults with comorbid depression and obesity. We collect assays of three self-regulation targets (emotion, cognition, and self-reflection) in multiple settings: neuroimaging and behavioral lab-based measures, virtual reality, and passive smartphone sampling. By connecting human neuroscience and behavioral science in this manner within the ENGAGE study, we develop a prototype for elucidating the underlying self-regulation mechanisms of behavior change outcomes and their application in optimizing intervention strategies for multiple chronic diseases.