To detrend, or not to detrend, that is the question? The effects of detrending on cross-lagged effects in panel models.

Intervention studies in psychology often focus on identifying mechanisms that explain change over time. Cross-lagged panel models (CLPMs) are well suited to study mechanisms, but there is a controversy regarding the importance of detrending-defined here as separating longer-term time trends from cross-lagged effects-when modeling these change processes. The aim of this study was to present and test the arguments for and against detrending CLPMs in the presence of an intervention effect. We conducted Monte Carlo simulations to examine the impact of trends on estimates of cross-lagged effects from several longitudinal structural equation models. Our simulations suggested that ignoring time trends led to biased estimates of auto- and cross-lagged effects in some conditions, while detrending did not introduce bias in any of the models. We used real data from an intervention study to illustrate how detrending may affect results. This example showed that models that separated trends from cross-lagged effects fit better to the data and showed nonsignificant effect of the mechanism on outcome, while models that ignored trends showed significant effects. We conclude that ignoring trends increases the risk of bias in estimates of auto- and cross-lagged parameters and may lead to spurious findings. Researchers can test for the presence of trends by comparing model fit of models that take into account individual differences in trends (e.g., autoregressive latent trajectory model, the latent curve model with structured residuals, or the general cross-lagged model). (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Improved outcomes for depressed elder abuse victims with video-delivered psychotherapy during COVID-19.

OBJECTIVE: There is a lack of evidence-based scalable therapies for elder abuse victims, with no current remotely delivered tailored psychotherapy. The purpose of this manuscript is to (a) examine the effectiveness of a brief therapy for depression for elder abuse victims, and (b) to compare remote intervention delivery via phone or video to the traditional in-person delivery. METHOD: PROTECT, Providing Options to Elderly Clients Together, is a brief therapy developed in collaboration with partners at the Department for the Aging (DFTA) of New York City. During the COVID-19 outbreak, PROTECT delivery shifted from in-person to phone or video delivery. Depression severity was tracked using the Patient Health Questionaire-9 (PHQ-9). Reduction in depression severity was evaluated using a linear mixed effects model with non-inferiority test to compare the effectiveness of video vs in-person delivery of PROTECT. RESULTS: PROTECT reduced depression (average 5.15 PHQ-9 points). Video and phone delivery were non-inferior to in-person delivery. The video group completed therapy more quickly than the in-person group and had a more rapid improvement in depression symptoms. CONCLUSIONS: PROTECT therapy delivered remotely reduces depression among diverse elder abuse victims. Video delivery of PROTECT could increase reach and scalability to serve more vulnerable older depressed victims.

Improving social reward responsivity and social connectedness in psychotherapies for late-life depression: Engage & Connect as an example.

Psychotherapies are effective in reducing late-life depression. Yet, about half of patients remain depressed at treatment end. Advances in neuroscience can inform simplified interventions that target key brain networks impacted by depression. Behavioral activation therapies that increase social connectedness may improve social reward responsivity and alter abnormalities of the Positive Valence System (PVS). Engage & Connect is an example for a scalable and simple neuroscience-informed psychotherapy, aimed to improve PVS functions and social reward responsivity by increasing engagement in rewarding social activities. Interventions that improve social reward responsivity can be promising first-line treatments for late-life depression in the community.

Social reward processing in depressed and healthy individuals across the lifespan: A systematic review and a preliminary coordinate-based meta-analysis of fMRI studies.

BACKGROUND: Social rewards (e.g., social feedback, praise, and social interactions) are fundamental to social learning and relationships across the life span. Exposure to social rewards is linked to activation in key brain regions, that are impaired in major depression. This is the first summary of neuroimaging literature on social reward processing in depressed and healthy individuals. METHOD: We screened 409 studies and identified 25 investigating task-based fMRI activation during exposure to social stimuli in depressed and healthy populations across the lifespan. We conducted a systematic review followed by an Activation Likelihood Estimation (ALE) analysis of three main contrasts: a) positive social feedback vs. neutral stimuli; b) negative social feedback vs. neutral stimuli; c) positive vs. negative social feedback. We also compared activation patterns in depressed versus healthy controls. RESULTS: Systematic review revealed that social rewards elicit increased activation in subcortical reward regions (NAcc, amygdala, ventral striatum, thalamus) in healthy and depressed individuals; and decreased activation in prefrontal reward regions (medial prefrontal cortex, orbitofrontal cortex) among depressed persons. Our meta-analysis showed, in both depressed and healthy individuals, increased cluster activation of the putamen and caudate in response to negative social stimuli vs. positive stimuli. We also found increased cluster activation in the inferior frontal gyrus (IFG) and the medial frontal gyrus (MFG) in healthy controls vs. depressed individuals, in response to negative social stimuli. CONCLUSIONS: Processing of social stimuli elicits activation of key brain regions involved in affective and social information processing. Interventions for depression can increase social reward responsivity to improve outcomes.

Expansion of a frontostriatal salience network in individuals with depression.

Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, 2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

Therapists' oxytocin response mediates the association between patients' negative emotions and psychotherapy outcomes.

BACKGROUND: Existing literature suggests that patients' experiences of emotions, especially negative emotions, predict outcomes in psychotherapies for major depressive disorder. However, the specific mechanisms underlying this effect remain unclear. Based on studies pointing to the role of oxytocin (OT) in attachment relationships, we proposed and tested a mediation model where the therapists' hormonal responses, as represented by increases in their OT levels, mediates the association between negative emotions and symptomatic change. METHOD: OT saliva samples (pre- and post-session, N = 435) were collected on a fixed schedule over 16 sessions from the therapists of 62 patients receiving psychotherapy for major depression. The Hamilton Rating Scale for Depression was administered to the patients before the sessions, and the patients reported their in-session emotions after the sessions. RESULTS: The findings support the proposed within-person mediation model: (a) higher levels of negative emotions in patients predicted greater increases in therapist OT levels pre- to post-session throughout treatment; (b) greater OT levels in therapists, in turn, predicted reduction in patients' depressive symptoms on the subsequent assessment; and (c) the therapists' OT levels significantly mediated the association between patients' negative emotions and reduction in their depressive symptoms. LIMITATIONS: This design precluded establishing a time sequence between patients' negative emotions and therapists' OT; thus, causality could not be inferred. CONCLUSION: These findings point to a possible biological mechanism underlying the effects of patients' experiences of negative emotions on treatment outcomes. The findings suggest that therapists' OT responses could potentially serve as a biomarker of an effective therapeutic processes.

Course of Subtypes of Late-Life Depression Identified by Bipartite Network Analysis During Psychosocial Interventions.

Importance: Approximately half of older adults with depression remain symptomatic at treatment end. Identifying discrete clinical profiles associated with treatment outcomes may guide development of personalized psychosocial interventions. Objective: To identify clinical subtypes of late-life depression and examine their depression trajectory during psychosocial interventions in older adults with depression. Design, Setting, and Participants: This prognostic study included older adults aged 60 years or older who had major depression and participated in 1 of 4 randomized clinical trials of psychosocial interventions for late-life depression. Participants were recruited from the community and outpatient services of Weill Cornell Medicine and the University of California, San Francisco, between March 2002 and April 2013. Data were analyzed from February 2019 to February 2023. Interventions: Participants received 8 to 14 sessions of (1) personalized intervention for patients with major depression and chronic obstructive pulmonary disease, (2) problem-solving therapy, (3) supportive therapy, or (4) active comparison conditions (treatment as usual or case management). Main Outcomes and Measures: The main outcome was the trajectory of depression severity, assessed using the Hamilton Depression Rating Scale (HAM-D). A data-driven, unsupervised, hierarchical clustering of HAM-D items at baseline was conducted to detect clusters of depressive symptoms. A bipartite network analysis was used to identify clinical subtypes at baseline, accounting for both between- and within-patient variability across domains of psychopathology, social support, cognitive impairment, and disability. The trajectories of depression severity in the identified subtypes were compared using mixed-effects models, and time to remission (HAM-D score ≤10) was compared using survival analysis. Results: The bipartite network analysis, which included 535 older adults with major depression (mean [SD] age, 72.7 [8.7] years; 70.7% female), identified 3 clinical subtypes: (1) individuals with severe depression and a large social network; (2) older, educated individuals experiencing strong social support and social interactions; and (3) individuals with disability. There was a significant difference in depression trajectories (F2,2976.9 = 9.4; P < .001) and remission rate (log-rank χ22 = 18.2; P < .001) across clinical subtypes. Subtype 2 had the steepest depression trajectory and highest likelihood of remission regardless of the intervention, while subtype 1 had the poorest depression trajectory. Conclusions and Relevance: In this prognostic study, bipartite network clustering identified 3 subtypes of late-life depression. Knowledge of patients' clinical characteristics may inform treatment selection. Identification of discrete subtypes of late-life depression may stimulate the development of novel, streamlined interventions targeting the clinical vulnerabilities of each subtype.

A 50-state survey study of thoughts of suicide and social isolation among older adults in the United States.

BACKGROUND: We aimed to characterize the prevalence of social disconnection and thoughts of suicide among older adults in the United States, and examine the association between them in a large naturalistic study. METHODS: We analyzed data from 6 waves of a fifty-state non-probability survey among US adults conducted between February and December 2021. The internet-based survey collected the PHQ-9, as well as multiple measures of social connectedness. We applied multiple logistic regression to analyze the association between presence of thoughts of suicide and social disconnection. Exploratory analysis, using generalized random forests, examined heterogeneity of effects across sociodemographic groups. RESULTS: Of 16,164 survey respondents age 65 and older, mean age was 70.9 (SD 5.0); the cohort was 61.4 % female and 29.6 % male; 2.0 % Asian, 6.7 % Black, 2.2 % Hispanic, and 86.8 % White. A total of 1144 (7.1 %) reported thoughts of suicide at least several days in the prior 2 week period. In models adjusted for sociodemographic features, households with 3 or more additional members (adjusted OR 1.73, 95 % CI 1.28-2.33) and lack of social supports, particularly emotional supports (adjusted OR 2.60, 95 % CI 2.09-3.23), were independently associated with greater likelihood of reporting such thoughts, as was greater reported loneliness (adjusted OR 1.75, 95 % CI 1.64-1.87). The effects of emotional support varied significantly across sociodemographic groups. CONCLUSIONS: Thoughts of suicide are common among older adults in the US, and associated with lack of social support, but not with living alone. TRIAL REGISTRATION: NA.

Functional Connectivity Mapping for rTMS Target Selection in Depression.

OBJECTIVE: Repetitive transcranial magnetic stimulation (rTMS) protocols increasingly use subgenual anterior cingulate cortex (sgACC) functional connectivity to individualize treatment targets. However, the efficacy of this approach is unclear, with conflicting findings and varying effect sizes across studies. Here, the authors investigated the effect of the stimulation site's functional connectivity with the sgACC (sgACC-StimFC) on treatment outcome to rTMS in 295 patients with major depression. METHODS: The reliability and accuracy of estimating sgACC functional connectivity were validated with data from individuals who underwent extensive functional MRI testing. Electric field modeling was used to analyze associations between sgACC-StimFC and clinical improvement using standardized assessments and to evaluate sources of heterogeneity. RESULTS: An imputation-based method provided reliable and accurate sgACC functional connectivity estimates. Treatment responses weakly but robustly correlated with sgACC-StimFC (r=-0.16), but only when the stimulated cortex was identified using electric field modeling. Surprisingly, this association was driven by patients with strong global signal fluctuations stemming from a specific periodic respiratory pattern (r=-0.49). CONCLUSIONS: Functional connectivity between the sgACC and the stimulated cortex was correlated with individual differences in treatment outcomes, but the association was weaker than those observed in previous studies and was accentuated in a subgroup of patients with distinct, respiration-related signal patterns in their scans. These findings indicate that in a large representative sample of patients with major depressive disorder, individual differences in sgACC-StimFC explained only ∼3% of the variance in outcomes, which may limit the utility of existing sgACC-based targeting protocols. However, these data also provide strong evidence for a true-albeit small-effect and highlight opportunities for incorporating additional functional connectivity measures to generate models of rTMS response with enhanced predictive power.

Strength-based methods - a narrative review and comparative multilevel meta-analysis of positive interventions in clinical settings.

OBJECTIVE: In psychotherapy, strength-based methods (SBM) represent efforts to build on patients' strengths while addressing the deficits and challenges that led them to come to therapy. SBM are incorporated to some extent in all major psychotherapy approaches, but data on their unique contribution to psychotherapy efficacy is scarce. METHODS: First, we conducted a systematic review and narrative synthesis of eight process-outcome psychotherapy studies that investigated in-session SBM and their relation to immediate outcomes. Second, we conducted a systematic review and multilevel comparative meta-analysis contrasting strength-based bona fide psychotherapy vs. other bona fide psychotherapy at post-treatment (57 effect sizes nested in 9 trials). RESULTS: Despite their methodological variability, the pattern of results in the process-outcome studies was generally positive, such that SBM were linked with more favorable immediate, session-level patient outcomes. The comparative meta-analysis found an overall weighted average effect size of g = 0.17 (95% CIs [0.03, 0.31], p < .01) indicating a small but significant effect in favor of strength-based bona fide psychotherapies. There was non-significant heterogeneity among the effect sizes (Q(56) = 69.1, p = .11; I2 = 19%, CI [16%, 22%]). CONCLUSION: Our findings suggest that SBMs may not be a trivial by-product of treatment progress and may provide a unique contribution to psychotherapy outcomes. Thus, we recommend integration of SBM to clinical training and practice across treatment models.

Efficacy and moderators of short-term psychodynamic psychotherapy for depression: A systematic review and meta-analysis of individual participant data.

BACKGROUND: Short-term psychodynamic psychotherapy (STPP) is frequently used to treat depression, but it is unclear which patients might benefit specifically. Individual participant data (IPD) meta-analyses can provide more precise effect estimates than conventional meta-analyses and identify patient-level moderators. This IPD meta-analysis examined the efficacy and moderators of STPP for depression compared to control conditions. METHODS: PubMed, PsycInfo, Embase, and Cochrane Library were searched September 1st, 2022, to identify randomized trials comparing STPP to control conditions for adults with depression. IPD were requested and analyzed using mixed-effects models. RESULTS: IPD were obtained from 11 of the 13 (84.6%) studies identified (n = 771/837, 92.1%; mean age = 40.8, SD = 13.3; 79.3% female). STPP resulted in significantly lower depressive symptom levels than control conditions at post-treatment (d = -0.62, 95%CI [-0.76, -0.47], p < .001). At post-treatment, STPP was more efficacious for participants with longer rather than shorter current depressive episode durations. CONCLUSIONS: These results support the evidence base of STPP for depression and indicate episode duration as an effect modifier. This moderator finding, however, is observational and requires prospective validation in future large-scale trials.

Perception of Social Support and Cognitive Performance in Older Adults With Depression.

This cross-sectional study assesses the association between perceived social support and cognitive performance in older adults with depression.

Research-Practice Partnership to Develop and Implement Routine Mental Health Symptom Tracking Tool Among Older Adults During COVID-19.

OBJECTIVE: Older adults are disproportionally impacted by the COVID-19 pandemic, causing a mental health crisis in late life, due to physical restrictions (e.g., quarantine), limited access to services, and lower literacy and access to technology. Despite established benefits, systematic screening of mental health needs of older adults in community and routine care settings is limited and presents multiple challenges. Cross-disciplinary collaborations are essential for identification and evaluation of mental health needs and service delivery. METHODS: Using a research-practice partnership model, we developed and implemented a routine mental health needs identification and tracking tool at a community-based social services organization. Repeated screenings were conducted remotely over 5 months and included depression, anxiety, perceived loneliness, social support, and related domains such as sleep quality, resilience, and trauma symptoms linked to COVID-19. We examined symptomatic distress levels and associations between different domains of functioning. RESULTS: Our project describes the process of establishing a research-practice partnership during the COVID-19 pandemic. We collected 292 screenings from 124 individuals; clients were mildly to moderately depressed and anxious, reporting large amounts of time alone and moderate levels of loneliness. Those reporting higher depressive symptoms reported higher anxiety symptoms, poorer sleep quality, lower quality of life, lower capacity to adapt to challenging situations, and greater trauma symptoms due to COVID-19. CONCLUSION: Our routine screening tool can serve as a blueprint for case management agencies and senior centers nationwide, beyond the pressing mental health crisis due to COVID-19, to continue identifying needs as they emerge in the community.

Proof of Concept of the Contribution of the Interaction between Trait-like and State-like Effects in Identifying Individual-Specific Mechanisms of Action in Biological Psychiatry.

Background: Identifying individual-specific mechanisms of action may facilitate progress toward precision medicine. Most studies seeking to identify mechanisms of action collapse together two distinct components: pre-treatment trait-like characteristics differentiating between individuals and state-like characteristics changing within each individual over the course of treatment. We suggest a conceptual framework highlighting the importance of studying interactions between trait-like and state-like components in the development of moderated mediation models that can guide personalized targeted interventions. Methods: To facilitate implementation of this framework, two empirical demonstrations are presented from a recent clinical trial and neuroimaging study. The first examines limbic reactivity during an emotional face task; the second concerns striatal activation in a monetary reward task. Results: In both tasks, considering the interaction between trait-like and state-like components predicted treatment outcome more robustly than did the trait-like or state-like components examined individually. Conclusions: These findings suggest that the extent to which state-like modulation of neural activations can serve as a potential treatment target depends on the pre-treatment, trait-like levels of activation in these regions. Thus, the interaction between trait-like and state-like components can serve as a promising path to the development of personalized interventions within a precision medicine framework in which mechanisms of action are individual-specific.

How to model and interpret cross-lagged effects in psychotherapy mechanisms of change research: A comparison of multilevel and structural equation models.

OBJECTIVE: Modeling cross-lagged effects in psychotherapy mechanisms of change studies is complex and requires careful attention to model selection and interpretation. However, there is a lack of field-specific guidelines. We aimed to (a) describe the estimation and interpretation of cross lagged effects using multilevel models (MLM) and random-intercept cross lagged panel model (RI-CLPM); (b) compare these models' performance and risk of bias using simulations and an applied research example to formulate recommendations for practice. METHOD: Part 1 is a tutorial focused on introducing/describing dynamic effects in the form of autoregression and bidirectionality. In Part 2, we compare the estimation of cross-lagged effects in RI-CLPM, which takes dynamic effects into account, with three commonly used MLMs that cannot accommodate dynamics. In Part 3, we describe a Monte Carlo simulation study testing model performance of RI-CLPM and MLM under realistic conditions for psychotherapy mechanisms of change studies. RESULTS: Our findings suggested that all three MLMs resulted in severely biased estimates of cross-lagged effects when dynamic effects were present in the data, with some experimental conditions generating statistically significant estimates in the wrong direction. MLMs performed comparably well only in conditions which are conceptually unrealistic for psychotherapy mechanisms of change research (i.e., no inertia in variables and no bidirectional effects). DISCUSSION: Based on conceptual fit and our simulation results, we strongly recommend using fully dynamic structural equation modeling models, such as the RI-CLPM, rather than static, unidirectional regression models (e.g., MLM) to study cross-lagged effects in mechanisms of change research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

How does hostile resistance interfere with the benefits of cognitive-behavioral therapy for panic disorder? The role of therapist adherence and working alliance.

Objective Although clients' hostile behavior directed at therapists (hostile resistance) predicts worse outcomes in cognitive-behavioral therapy (CBT) for panic disorder, the process by which this happens remains unknown. This study examines two putative mechanisms: working alliance and therapist adherence. Method: Seventy-one adults with primary panic disorder received CBT in a larger trial. Hostile resistance and adherence in Sessions 2 and 10 were reliably coded using observer-rated measures; client- and therapist-rated questionnaires assessed working alliance. Outcome measures were attrition and symptomatic improvement, assessed at multiple timepoints with the Panic Disorder Severity Scale. Results: Hostile resistance was significantly related to both preexisting (r = -.36, p = .04) and subsequent declines (r = -.58, p < .0001) in the working alliance. Nevertheless, hierarchical linear modeling revealed that neither a declining alliance nor therapist adherence (whether treated as linear or curvilinear) was independently predictive of symptom change, nor did these factors mediate hostile resistance's association with worse symptomatic improvement. Exploratory logistic regressions similarly indicated that neither adherence nor alliance moderated whether hostilely resistant clients dropped out. Conclusion: This is the first study to establish a bidirectional association between hostile resistance and a declining working alliance. Findings also add to a mixed literature on the adherence-outcome relationship.