Optimizing precision medicine for second-step depression treatment: a machine learning approach.

BACKGROUND: Less than a third of patients with depression achieve successful remission with standard first-step antidepressant monotherapy. The process for determining appropriate second-step care is often based on clinical intuition and involves a protracted course of trial and error, resulting in substantial patient burden and unnecessary delay in the provision of optimal treatment. To address this problem, we adopt an ensemble machine learning approach to improve prediction accuracy of remission in response to second-step treatments. METHOD: Data were derived from the Level 2 stage of the STAR*D dataset, which included 1439 patients who were randomized into one of seven different second-step treatment strategies after failing to achieve remission during first-step antidepressant treatment. Ensemble machine learning models, comprising several individual algorithms, were evaluated using nested cross-validation on 155 predictor variables including clinical and demographic measures. RESULTS: The ensemble machine learning algorithms exhibited differential classification performance in predicting remission status across the seven second-step treatments. For the full set of predictors, AUC values ranged from 0.51 to 0.82 depending on the second-step treatment type. Predicting remission was most successful for cognitive therapy (AUC = 0.82) and least successful for other medication and combined treatment options (AUCs = 0.51-0.66). CONCLUSION: Ensemble machine learning has potential to predict second-step treatment. In this study, predictive performance varied by type of treatment, with greater accuracy in predicting remission in response to behavioral treatments than to pharmacotherapy interventions. Future directions include considering more informative predictor modalities to enhance prediction of second-step treatment response.

A Randomized Controlled Trial of Community-Delivered Heated Hatha Yoga for Moderate-to-Severe Depression.

Objective: To evaluate feasibility, acceptability, and preliminary efficacy of heated yoga to treat moderate-to-severe depression.Design: An 8-week randomized controlled trial (RCT) of heated yoga versus waitlist control was conducted from March 2017 to August 2019.Methods: Participants in the yoga condition were asked to attend heated yoga classes at 2 community heated yoga studios at least twice weekly. We assessed acceptability and feasibility using exit interview and attendance data, respectively. The primary intervention efficacy outcome variable was change in the Inventory of Depressive Symptomatology-Clinician Rated (IDS-CR) score from baseline to post-intervention (week 8).Results: We randomized 80 participants and included 65 (mean [± SD] age 32.7 [± 11.7] years; 81.5% female) in the analyses (yoga n = 33, waitlist n = 32). The mean IDS-CR score at baseline was 35.6 (± 7.9) for the full sample, 36.9 (± 8.8) for yoga participants, and 34.4 (± 6.7) for waitlist participants. Participants attended an average of 10.3 (± 7.1) total classes over the 8-week intervention period. Yoga participants had a significantly greater pre- to post-intervention reduction in IDS-CR scores than waitlist participants (Cohen d = 1.04, P < .001). More yoga participants (59.3%; n = 16) than waitlist participants (6.3%; n = 2) evidenced larger treatment responses (IDS-CR ≥ 50% decrease in symptoms). Participants rated the heated yoga and its aftereffects positively in exit interviews.Conclusions: Approximately 1 heated yoga session per week (mean of 10.3 classes over 8 weeks) was associated with significantly greater reduction in depression symptoms than a waitlist control. Participants rated heated yoga positively. Taken together, results suggest feasibility, acceptability, and preliminary efficacy for patients with depression and warrant further research using active control conditions.Trial Registration: ClinicalTrials.gov identifier: NCT02607514.

Rising early warning signals in affect associated with future changes in depression: a dynamical systems approach.

BACKGROUND: Predicting future states of psychopathology such as depressive episodes has been a hallmark initiative in mental health research. Dynamical systems theory has proposed that rises in certain 'early warning signals' (EWSs) in time-series data (e.g. auto-correlation, temporal variance, network connectivity) may precede impending changes in disorder severity. The current study investigates whether rises in these EWSs over time are associated with future changes in disorder severity among a group of patients with major depressive disorder (MDD). METHODS: Thirty-one patients with MDD completed the study, which consisted of daily smartphone-delivered surveys over 8 weeks. Daily positive and negative affect were collected for the time-series analyses. A rolling window approach was used to determine whether rises in auto-correlation of total affect, temporal standard deviation of total affect, and overall network connectivity in individual affect items were predictive of increases in depression symptoms. RESULTS: Results suggested that rises in auto-correlation were significantly associated with worsening in depression symptoms (r = 0.41, p = 0.02). Results indicated that neither rises in temporal standard deviation (r = -0.23, p = 0.23) nor in network connectivity (r = -0.12, p = 0.59) were associated with changes in depression symptoms. CONCLUSIONS: This study more rigorously examines whether rises in EWSs were associated with future depression symptoms in a larger group of patients with MDD. Results indicated that rises in auto-correlation were the only EWS that was associated with worsening future changes in depression.

How family dementia caregivers perceive benefits of a 4-week Mentalizing Imagery Therapy program: a pilot study.

Background: Family caregivers of dementia patients experience high levels of interpersonal stress that often results in elevated anxiety, and depression, and negative impacts on interpersonal relationships. Changes in behaviors and the structure of relationships with the care recipient (CR) and others in the social milieu challenge the caregivers' ability to mentalize, or understand the links between mental states and behaviors. This study investigates the experiences and perceived benefits of family dementia caregivers who underwent Mentalizing Imagery Therapy (MIT), a treatment aiming to improve balanced self-other mentalizing and reduce psychological symptoms. Methods: Purposeful sampling was used to select 11 family dementia caregivers who underwent a 4-week pilot trial of MIT. Semi-structured interviews were completed post-intervention to identify subjective benefits, putative psychological mediators and perceived active components. Results: Caregivers reported improvements in well-being, mood, anxiety, and sleep, and a majority stated MIT helped with forming and maintaining healthier relationships. Some participants noted benefits extending to how they reacted to their social environment and perceived themselves more objectively from others' perspectives. Specific elements of MIT, including self-compassion, self-care, and the ability to reflect on emotionally arousing challenges, might have mediated these improvements. Conclusion: Family dementia caregivers perceived salutary benefits of MIT on multiple domains of well-being. The self reports suggest MIT holds promise for improving well-being, reducing non-mentalizing patterns of thought, and facilitating improvements in balanced mentalization within the caregivers' relationships.

Quantifying and addressing the impact of measurement error in network models.

Network psychometric models are often estimated using a single indicator for each node in the network, thus failing to consider potential measurement error. In this study, we investigate the impact of measurement error on cross-sectional network models. First, we conduct a simulation study to evaluate the performance of models based on single indicators as well as models that utilize information from multiple indicators per node, including average scores, factor scores, and latent variables. Our results demonstrate that measurement error impairs the reliability and performance of network models, especially when using single indicators. The reliability and performance of network models improves substantially with increasing sample size and when using methods that combine information from multiple indicators per node. Second, we use empirical data from the STAR*D trial (n = 3,731) to further evaluate the impact of measurement error. In the STAR*D trial, depression symptoms were assessed via three questionnaires, providing multiple indicators per symptom. Consistent with our simulation results, we find that when using sub-samples of this dataset, the discrepancy between the three single-indicator networks (one network per questionnaire) diminishes with increasing sample size. Together, our simulated and empirical findings provide evidence that measurement error can hinder network estimation when working with smaller samples and offers guidance on methods to mitigate measurement error.

Mentalizing imagery therapy to augment skills training for dementia caregivers: Protocol for a randomized, controlled trial of a mobile application and digital phenotyping.

More than 50 million people worldwide live with a dementia, and most are cared for by family members. Family caregivers often experience chronic stress and insomnia, resulting in decreased mental and physical health. Accessibility of in-person stress reduction therapy is limited due to caregiver time constraints and distance from therapy sites. Mentalizing imagery therapy (MIT) provides mindfulness and guided imagery tools to reduce stress, promote self and other understanding, and increase feelings of interconnectedness. Combining MIT with caregiver skills training might enable caregivers to both reduce stress and better utilize newly learned caregiving skills, but this has never been studied. Delivering MIT through a smartphone application (App) has the potential to overcome difficulties with scalability and dissemination and offers caregivers an easy-to-use format. Harnessing passive smartphone data provides an important opportunity to study behavioral changes continuously and with higher granularity than routine clinical assessments. This protocol describes a randomized, controlled, superiority trial in which 120 family dementia caregivers, aged 60 years or older, will be assigned to smartphone App delivery of caregiver skills with MIT (experimental condition) or without MIT (control condition). The primary objectives of the trial are to assess whether the experimental condition is superior to control on reducing family caregiver stress, insomnia and related outcomes and to demonstrate the feasibility of developing behavioral markers from passive smartphone data that predict health outcomes in older adults. Trial outcomes may inform the suitability of our intervention for caregivers and provide new methods for assessment of older adults.

Use of Staging Models for Treatment-Resistant Depression Is Not Helpful in Predicting Nonresponse to Acute Intravenous Ketamine Treatment.

BACKGROUND: Some staging models for treatment-resistant depression (TRD) have been developed in the attempt to predict treatment outcome, in particular with electroconvulsive therapy. However, these models have not been tested in predicting clinical outcome of ketamine treatment. We assessed the relationship between patients' classification with different TRD staging models and subsequent nonresponse to acute intravenous ketamine treatment. METHODS: A sample of 120 patients with TRD who received acute ketamine treatment from October 2018 to November 2020 were included. Intravenous ketamine was administered twice weekly for 3 weeks as acute treatment. Generalized linear models were fitted to examine if staging classification at baseline could predict percent change in the 16-item Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR16) scale. Potential confounders such as age, sex, and primary diagnosis were included in the models. Other generalized linear models were also fitted with the Bonferroni correction to investigate if other clinical variables of potential relevance could predict percent change in the QIDS-SR16. RESULTS: No TRD staging model proved accurate in predicting depressive improvement after acute ketamine treatment. Clinical variables such as age (F = 6.68, P = 0.01) and history of neuromodulation therapy (F = 5.12, P = 0.03) were negatively associated with subsequent percent improvement in the QIDS-SR16 with acute ketamine treatment. CONCLUSIONS: The efficacy of acute intravenous ketamine treatment was similar in subjects with higher and lower level of treatment resistance, using definitions based on different TRD staging models. Further exploration of ketamine treatment predictors such as age and neuromodulation therapy is warranted.

Transitioning From In-Person to Remote Clinical Research on Depression and Traumatic Brain Injury During the COVID-19 Pandemic: Study Modifications and Preliminary Feasibility From a Randomized Controlled Pilot Study.

BACKGROUND: Telehealth has provided many researchers, especially those conducting psychosocial research, with the tools necessary to transition from in-person to remote clinical trials during the COVID-19 pandemic. A growing body of research supports the effectiveness of telemental health for a variety of psychiatric conditions, but few studies have examined telemental health for individuals with comorbid medical diagnoses. Furthermore, little is known about the remote implementation of clinical trials examining telemental health interventions. OBJECTIVE: This paper outlines the procedural modifications used to facilitate conversion of an in-person randomized controlled trial of cognitive behavioral therapy (CBT) for depression in individuals with traumatic brain injury (TBI; CBT-TBI) to a telemental health study administered remotely. METHODS: Given the nature of remote implementation and specific challenges experienced by individuals with TBI, considerations related to treatment delivery, remote consent, data management, neuropsychological assessment, safety monitoring, and delivery of supportive material have been discussed. Feasibility, acceptability, and safety were evaluated by examining attendance and participant responses on self-report measures of treatment satisfaction and suicidal behavior. RESULTS: High rates of treatment attendance, assessment completion, study retention, and satisfaction with the intervention and modality were reported by participants who completed at least one telemental health CBT-TBI session. CONCLUSIONS: Study modifications are necessary when conducting a study remotely, and special attention should be paid to comorbidities and population-specific challenges (eg, cognitive impairment). Preliminary data support the feasibility, acceptability, and safety of remotely conducting a randomized controlled trial of CBT-TBI. TRIAL REGISTRATION: ClinicalTrials.gov NCT03307070; https://clinicaltrials.gov/ct2/show/NCT03307070.

Relationship between cannabis use and psychotic experiences in college students.

BACKGROUND: Emerging data suggest cannabis use is a component cause of psychotic disorders; however, the sequence of processes accounting for this association is poorly understood. Some clues have come from studies in laboratory settings showing that acute cannabis intoxication is associated with subclinical hallucinations and delusional thinking, i.e., "psychotic experiences". Although psychotic experiences are relatively common, those that are severe and distressing are linked to an increased risk of developing a psychotic disorder. This study aimed to investigate the association between the frequency of cannabis use and psychotic experiences in young adults. METHODS: 1034 U.S. college students completed questionnaires to assess: cannabis use in the past week, delusional ideation (Peters Delusions Inventory), hallucinations (Launay-Slade Hallucinations Scale-Extended), and depression (Beck Depression Inventory). RESULTS: Participants reporting higher rates of weekly cannabis use were more likely to report hallucinatory experiences and delusional ideation. The relationship between cannabis use and hallucinatory experiences, but not the relationship between cannabis use and delusional ideation, remained significant after controlling for levels of depression. Moreover, those who reported greater amounts of cannabis use had more distressing delusional ideas, that were held with more conviction. CONCLUSIONS: Cannabis use is linked to the presence of subclinical hallucinations and delusional ideation in U.S. college students.

Validation of brief screening measures for depression and anxiety in young people with substance use disorders.

BACKGROUND: It is critical to promptly identify and monitor mood and anxiety symptoms in young people with SUD. The primary aim of this study was to conduct a psychometric validation of the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder scale (GAD-7) for depression and anxiety screening in young people seeking outpatient treatment for SUD. Our secondary aim was to compare the performance of the PHQ-9 and GAD-7 to their briefer two-item versions (PHQ-2 and GAD-2) in terms of detecting probable mood and anxiety disorders. METHOD: Data were extracted from the electronic health records of patients (ages 14 to 26) who received a diagnostic evaluation following clinical implementation of the PHQ-9 and GAD-7 at a hospital-based outpatient SUD treatment program (N=121, average age 19.1 ± 3.1 years). RESULTS: The PHQ-9 and GAD-7 showed excellent internal consistency. A PHQ-9 cut score of 7 or 8 (PHQ-2 cut score: 2) and GAD-7 cut score of 6 (GAD-2 cut score: 2) had the best balance of sensitivity, specificity, and positive and negative predictive power in these data. These measures also showed good convergent and acceptable discriminant validity. LIMITATIONS: The sample was predominantly White and non-Hispanic, and a validated (semi-)structured diagnostic interview was not used to establish mood and anxiety disorder diagnoses. CONCLUSIONS: Results suggest the PHQ-9 and GAD-7 are reliable and potentially clinically useful screening tools for depression and anxiety in young people with SUD, and that the two-item versions may have similar clinical utility as the full measures.

Change processes in cognitive behavioral therapy and motivational interviewing for depression and heavy alcohol use: A network approach.

Objective: Both cognitive behavioral therapy for depression (CBT-D) combined with brief motivational interviewing (CBT-D + BMI) and alone are associated with symptom improvement among college students with co-occurring depression and heavy episodic drinking (HED). However, little is known about change processes underlying these different treatments.The current study uses a network approach to examine change process that may differentially underlie CBT-D + BMI relative to CBT alone. Methods: Participants included 94 college students with depression and HED who were randomized to either eight weeks of CBT-D + BMI or CBT alone. A network approach was adopted to examine how treatment condition influenced changes in the network structure of depression symptoms, heavy drinking, drinking motives, and consequences of alcohol. Network analyses were conducted using change scores representing the eight-week difference from pre-treatment to post-treatment assessments. Results: Relative to CBT-D alone, the combined CBT-D + BMI treatment influenced the symptom network structure by preferentially targeting reductions in drinking to cope motives and in the depression symptom 'loss of interest'. Conclusion: The current study revealed that combined CBT-D + BMI may confer therapeutic benefit through different network structure pathways than CBT-D alone. Specifically, augmenting CBT-D with BMI may influence change processes related to drinking motives, such as drinking to cope.

Sexual assault, sense of belonging, depression and suicidality among LGBQ and heterosexual college students.

OBJECTIVE: To compare the effect of sexual orientation, history of sexual assault and sense of belonging on depression and suicidality among lesbian, gay, bisexual, queer and questioning (LGBQ) and heterosexual students. Participants: A total of 60,194 students from 60 US campuses who participated in the 2017-2018 Healthy Minds Study. Methods: Three-way interaction effects were analyzed using PROCESS macro models in SPSS. Results: Significant three-way interaction effects were found for sexual orientation, sexual assault and sense of belonging predicting depressive symptoms (b = -0.06, p= 0.042, CI: -0.12 to -0.00), and suicidality (b = -0.10, p= 0.004, CI: -0.01 to -0.003). The effect of sexual assault on mental health differed depending on sexual orientation and sense of belonging. Particularly among LGBQ students, high sense of belonging was protective in the presence of sexual assault, and its absence had a deleterious effect on mental health. Conclusion: Enhancing sense of belonging may represent a strategy to mitigate depression and promote suicide prevention, particularly among LGBQ students.

Long-term outcome in outpatients with depression treated with acute and maintenance intravenous ketamine: A retrospective chart review.

BACKGROUND: Little is known about the long-term outcomes of repeated ketamine infusions for depression. We conducted a retrospective chart review to investigate outcomes of maintenance intravenous ketamine treatment at Massachusetts General Hospital. METHODS: Eighty-five patients with treatment-resistant depression (TRD) who started intravenous ketamine from October 2018 to November 2019 were examined. Symptom severity was evaluated with the 16-item Quick Inventory of Depressive Symptomatology-Self Report scale (QIDS-SR16) at every visit prior to administration. The initial ketamine dose was usually 0.5 mg/kg infused over 40 min. Intravenous ketamine was administered twice-weekly for three weeks in an induction phase, followed by maintenance with a variable administration schedule and dose. Response was defined as a ≥50% reduction in total QIDS-SR16 score from baseline. RESULTS: Forty (47.1%) of the 85 patients who started treatment discontinued during or right after the induction phase; 3 (3.5%) were still on induction at the time of this report, and 42 (49.4%) transitioned to maintenance after completing induction. Among these patients, 14 (16.5%) discontinued during maintenance and 28 (32.9%) continued on maintenance. The mean ketamine dosage during maintenance was 0.91±0.28 mg/kg. Fifteen out of 82 patients (18.3%) responded to induction treatment and 6 (7.3%) remained in responder status at the time of data analysis during maintenance. Three patients discontinued ketamine due to side-effects. CONCLUSIONS: Despite the apparently low response rate in QIDS-SR16 scores and considerable out-of-pocket costs, almost half of real-world outpatients with TRD decided to continue with maintenance ketamine treatment due to perceived significant improvement.

Low-Dose Testosterone Augmentation for Antidepressant-Resistant Major Depressive Disorder in Women: An 8-Week Randomized Placebo-Controlled Study.

OBJECTIVE: Low-dose testosterone has been shown to improve depression symptom severity, fatigue, and sexual function in small studies in women not formally diagnosed with major depressive disorder. The authors sought to determine whether adjunctive low-dose transdermal testosterone improves depression symptom severity, fatigue, and sexual function in women with antidepressant-resistant major depression. A functional MRI (fMRI) substudy examined effects on activity in the anterior cingulate cortex (ACC), a brain region important in mood regulation. METHODS: The authors conducted an 8-week randomized double-blind placebo-controlled trial of adjunctive testosterone cream in 101 women, ages 21-70, with antidepressant-resistant major depression. The primary outcome measure was depression symptom severity as assessed by the Montgomery-Åsberg Depression Rating Scale (MADRS). Secondary endpoints included fatigue, sexual function, and safety measures. The primary outcome of the fMRI substudy (N=20) was change in ACC activity. RESULTS: The participants' mean age was 47 years (SD=14) and their mean baseline MADRS score was 26.6 (SD=5.9). Eighty-seven (86%) participants completed 8 weeks of treatment. MADRS scores decreased in both study arms from baseline to week 8 (testosterone arm: from 26.8 [SD=6.3] to 15.3 [SD=9.6]; placebo arm: from 26.3 [SD=5.4] to 14.4 [SD=9.3]), with no significant difference between groups. Improvement in fatigue and sexual function did not differ between groups, nor did side effects. fMRI results showed a relationship between ACC activation and androgen levels before treatment but no difference in ACC activation with testosterone compared with placebo. CONCLUSIONS: Adjunctive transdermal testosterone, although well tolerated, was not more effective than placebo in improving symptoms of depression, fatigue, or sexual dysfunction. Imaging in a subset of participants demonstrated that testosterone did not result in greater activation of the ACC.

Effects of Open-Label, Adjunctive Ganaxolone on Persistent Depression Despite Adequate Antidepressant Treatment in Postmenopausal Women: A Pilot Study.

OBJECTIVE: The neuroactive steroid metabolite of progesterone, allopregnanolone, is a positive allosteric modulator of γ-aminobutyric acid-A (GABAA) receptors and a putative treatment for mood disorders. This pilot study was performed to determine whether an oral allopregnanolone analog (ganaxolone) may be effective adjunctive therapy for persistent depression despite adequate antidepressant treatment in postmenopausal women. METHOD: Ten postmenopausal women (mean ± SD age: 62.8 ± 6.3 years; range, 53-69 years) with persistent depression despite adequate antidepressant treatment (current DSM-IV-TR major depressive episode per the Structured Clinical Interview for DSM-IV-TR, Montgomery-Asberg Depression Rating Scale [MADRS] score ≥ 16, and treated with an adequately dosed antidepressant for ≥ 6 weeks) were studied from December 2016 to April 2018. Open-label ganaxolone (225 mg twice daily, increased to 450 mg twice daily if tolerated) was administered for 8 weeks, followed by a 2-week taper. RESULTS: Mean ± SEM total MADRS score (primary endpoint) decreased by 8 weeks (24.4 ± 1.6 to 12.8 ± 2.9, P = .015), and the decrease persisted over the 2-week taper (P = .019); of the 9 subjects who completed the full 8-week treatment period, 44% (4/9) experienced response (MADRS score decrease ≥ 50%) and remission (final MADRS score < 10), which persisted in 100% and 50% of subjects at 10 weeks, respectively. Secondary endpoints showed significant improvement, including Inventory of Depressive Symptomatology-Self-Report score (P = .003), MADRS reduced sleep subscale score (P < .001), total Symptoms of Depression Questionnaire (SDQ) score (P = .012), and scores on SDQ subscales for disruptions in sleep quality (P = .003) and changes in appetite and weight (P = .009) over 8 weeks. No significant effects were observed on quality of life or sexual function. All subjects experienced sleepiness and fatigue; 60% experienced dizziness. CONCLUSIONS: In this open-label, uncontrolled pilot study, adjunctive ganaxolone appears to exert antidepressant effects but produces sedation with twice-daily dosing. Ganaxolone may also improve sleep, which may be useful in patients with depression and insomnia. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02900092.

Major depressive disorder and insomnia: Exploring a hypothesis of a common neurological basis using waking and sleep-derived heart rate variability.

It remains unclear whether neurobiological dysfunction observed in major depressive disorder (MDD) and insomnia is an expression of common or independent bases. The present investigation sought to explore differences in heart rate variability (HRV)-a widely utilized biomarker of neurobiological functioning-among individuals with MDD, insomnia, and healthy controls, while awake and during distinct sleep stages (REM, N2), with the goal of improving our understanding of shared neurobiological factors in depression and insomnia. Participants were 73 adults who underwent home polysomnography. All N2 and REM sleep epochs with a duration greater than or equal to 5 min were identified for HRV analysis. Additionally, a single waking epoch was defined for each participant. From waking to N2 sleep, and waking to REM sleep, changes in HRV indices indicated participants experienced reductions in sympathetic arousal and increases in parasympathetic arousal. Contrary to hypotheses, however, no between group differences were observed in HRV. Though the present findings do not support the hypotheses of a shard neurobiological pathway between MDD and insomnia, more work is warranted to advance our understanding of the neurobiological bases of these common, debilitating, and frequently co-occurring psychiatric conditions, to improve early detection and identify novel intervention targets for these disorders.

Elevated Amygdala Activity in Young Adults With Familial Risk for Depression: A Potential Marker of Low Resilience.

BACKGROUND: Amygdala overactivity has been frequently observed in patients with depression, as well as in nondepressed relatives of patients with depression. A remaining unanswered question is whether elevated amygdala activity in those with familial risk for depression is related to the presence of subthreshold symptoms or to a trait-level vulnerability for illness. METHODS: To examine this question, functional magnetic resonance imaging data were collected in nondepressed young adults with (family history [FH+]) (n = 27) or without (FH-) (n = 45) a first-degree relative with a history of depression while they viewed images of "looming" or withdrawing stimuli (faces and cars) that varied in salience by virtue of their apparent proximity to the subject. Activation of the amygdala and 2 other regions known to exhibit responses to looming stimuli, the dorsal intraparietal sulcus (DIPS) and ventral premotor cortex (PMv), were measured, as well as levels of resilience, anxiety, and psychotic and depressive symptoms. RESULTS: Compared with the FH- group, the FH+ group exhibited significantly greater responses of the amygdala, but not the dorsal intraparietal sulcus or ventral premotor cortex, to looming face stimuli. Moreover, amygdala responses in the FH+ group were negatively correlated with levels of resilience and unrelated to levels of subthreshold symptoms of psychopathology. CONCLUSIONS: These findings indicate that elevated amygdala activity in nondepressed young adults with a familial history of depression is more closely linked to poor resilience than to current symptom state.

Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors.

Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performance of assessing depressive symptom severity by using behavioral and physiological features obtained from wristband and smartphone sensors. Method: Participants were thirty-one individuals with Major Depressive Disorder (MDD). The protocol included 8 weeks of behavioral and physiological monitoring through smartphone and wristband sensors and six in-person clinical interviews during which depression was assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Results: Participants wore the right and left wrist sensors 92 and 94% of the time respectively. Three machine-learning models estimating depressive symptom severity were developed-one combining features from smartphone and wearable sensors, one including only features from the smartphones, and one including features from wrist sensors-and evaluated in two different scenarios. Correlations between the models' estimate of HDRS scores and clinician-rated HDRS ranged from moderate to high (0.46 [CI: 0.42, 0.74] to 0.7 [CI: 0.66, 0.74]) and had moderate accuracy with Mean Absolute Error ranging between 3.88 ± 0.18 and 4.74 ± 1.24. The time-split scenario of the model including only features from the smartphones performed the best. The ten most predictive features in the model combining physiological and mobile features were related to mobile phone engagement, activity level, skin conductance, and heart rate variability. Conclusion: Monitoring of MDD patients through smartphones and wrist sensors following a clinician-rated HDRS assessment is feasible and may provide an estimate of changes in depressive symptom severity. Future studies should further examine the best features to estimate depressive symptoms and strategies to further enhance accuracy.