Impulsivity, trauma history, and interoceptive awareness contribute to completion of a criminal diversion substance use treatment program for women.

Introduction: In the US, women are one of the fastest-growing segments of the prison population and more than a quarter of women in state prison are incarcerated for drug offenses. Substance use criminal diversion programs can be effective. It may be beneficial to identify individuals who are most likely to complete the program versus terminate early as this can provide information regarding who may need additional or unique programming to improve the likelihood of successful program completion. Prior research investigating prediction of success in these programs has primarily focused on demographic factors in male samples. Methods: The current study used machine learning (ML) to examine other non-demographic factors related to the likelihood of completing a substance use criminal diversion program for women. A total of 179 women who were enrolled in a criminal diversion program consented and completed neuropsychological, self-report symptom measures, criminal history and demographic surveys at baseline. Model one entered 145 variables into a machine learning (ML) ensemble model, using repeated, nested cross-validation, predicting subsequent graduation versus termination from the program. An identical ML analysis was conducted for model two, in which 34 variables were entered, including the Women's Risk/Needs Assessment (WRNA). Results: ML models were unable to predict graduation at an individual level better than chance (AUC = 0.59 [SE = 0.08] and 0.54 [SE = 0.13]). Post-hoc analyses indicated measures of impulsivity, trauma history, interoceptive awareness, employment/financial risk, housing safety, antisocial friends, anger/hostility, and WRNA total score and risk scores exhibited medium to large effect sizes in predicting treatment completion (p < 0.05; ds = 0.29 to 0.81). Discussion: Results point towards the complexity involved in attempting to predict treatment completion at the individual level but also provide potential targets to inform future research aiming to reduce recidivism.

Whole-brain mechanism of neurofeedback therapy: predictive modeling of neurofeedback outcomes on repetitive negative thinking in depression.

Real-time fMRI neurofeedback (rtfMRI-NF) has emerged as a promising intervention for psychiatric disorders, yet its clinical efficacy remains underexplored due to an incomplete mechanistic understanding. This study aimed to delineate the whole-brain mechanisms underpinning the effects of rtfMRI-NF on repetitive negative thinking in depression. In a double-blind randomized controlled trial, forty-three depressed individuals underwent NF training targeting the functional connectivity (FC) between the posterior cingulate cortex and the right temporoparietal junction, linked to rumination severity. Participants were randomly assigned to active or sham groups, with the sham group receiving synthesized feedback mimicking real NF signal patterns. The active group demonstrated a significant reduction in brooding rumination scores (d = -1.52, p < 0.001), whereas the sham group did not (d = -0.23, p = 0.503). While the target FC did not show discernible training effects or group differences, connectome-based predictive modeling (CPM) analysis revealed that the interaction between brain activity during regulation and brain response to the feedback signal was the critical factor in explaining treatment outcomes. The model incorporating this interaction successfully predicted rumination changes across both groups. The FCs significantly contributing to the prediction were distributed across brain regions, notably the frontal control, salience network, and subcortical reward processing areas. These results underscore the importance of considering the interplay between brain regulation activities and brain response to the feedback signal in understanding the therapeutic mechanisms of rtfMRI-NF. The study affirms rtfMRI-NF's potential as a therapeutic intervention for repetitive negative thinking and highlights the need for a nuanced understanding of the whole-brain mechanisms contributing to its efficacy.

Neuroimaging Biomarkers in Addiction.

As a neurobiological process, addiction involves pathological patterns of engagement with substances and a range of behaviors with a chronic and relapsing course. Neuroimaging technologies assess brain activity, structure, physiology, and metabolism at scales ranging from neurotransmitter receptors to large-scale brain networks, providing unique windows into the core neural processes implicated in substance use disorders. Identified aberrations in the neural substrates of reward and salience processing, response inhibition, interoception, and executive functions with neuroimaging can inform the development of pharmacological, neuromodulatory, and psychotherapeutic interventions to modulate the disordered neurobiology. Based on our systematic search, 409 protocols registered on ClinicalTrials.gov include the use of one or more neuroimaging paradigms as an outcome measure in addiction, with the majority (N=268) employing functional magnetic resonance imaging (fMRI), followed by positron emission tomography (PET) (N=71), electroencephalography (EEG) (N=50), structural magnetic resonance imaging (MRI) (N=35) and magnetic resonance spectroscopy (MRS) (N=35). Furthermore, in a PubMed systematic review, we identified 61 meta-analyses including 30 fMRI, 22 structural MRI, 8 EEG, 7 PET, and 3 MRS meta-analyses suggesting potential biomarkers in addictions. These studies can facilitate the development of a range of biomarkers that may prove useful in the arsenal of addiction treatments in the coming years. There is evidence that these markers of large-scale brain structure and activity may indicate vulnerability or separate disease subtypes, predict response to treatment, or provide objective measures of treatment response or recovery. Neuroimaging biomarkers can also suggest novel targets for interventions. Closed or open loop interventions can integrate these biomarkers with neuromodulation in real-time or offline to personalize stimulation parameters and deliver the precise intervention. This review provides an overview of neuroimaging modalities in addiction, potential neuroimaging biomarkers, and their physiologic and clinical relevance. Future directions and challenges in bringing these putative biomarkers from the bench to the bedside are also discussed.

Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility.

Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.

Increased expression of ER stress, inflammasome activation, and mitochondrial biogenesis-related genes in peripheral blood mononuclear cells in major depressive disorder.

A subset of major depressive disorder (MDD) is characterized by immune system dysfunction, but the intracellular origin of these immune changes remains unclear. Here we tested the hypothesis that abnormalities in endoplasmic reticulum (ER) stress, inflammasome activity and mitochondrial biogenesis contribute to the development of systemic inflammation in MDD. RT-qPCR was used to measure mRNA expression of key organellar genes from peripheral blood mononuclear cells (PBMCs) isolated from 186 MDD and 67 healthy control (HC) subjects. The comparative CT (2-ΔΔCT) method was applied to quantify mRNA expression using GAPDH as the reference gene. After controlling for age, sex, BMI, and medication status using linear regression models, expression of the inflammasome (NLRC4 and NLRP3) and the ER stress (XBP1u, XBP1s, and ATF4) genes was found to be significantly increased in the MDD versus the HC group. Sensitivity analyses excluding covariates yielded similar results. After excluding outliers, expression of the inflammasome genes was no longer statistically significant but expression of the ER stress genes (XBP1u, XBP1s, and ATF4) remained significant and the mitochondrial biogenesis gene, MFN2, was significantly increased in the MDD group. NLRC4 and MFN2 were positively correlated with serum C-reactive protein concentrations, while ASC trended significant. The altered expression of inflammasome activation, ER stress, and mitochondrial biogenesis pathway components suggest that dysfunction of these organelles may play a role in the pathogenesis of MDD.

The unique face of comorbid anxiety and depression: Increased frontal, insula and cingulate cortex response during Pavlovian fear-conditioning.

BACKGROUND: Dysregulation of fear processing through altered sensitivity to threat is thought to contribute to the development of anxiety disorders and major depressive disorder (MDD). However, fewer studies have examined fear processing in MDD than in anxiety disorders. The current study used propensity matching to examine the hypothesis that comorbid MDD and anxiety (AnxMDD) shows greater neural correlates of fear processing than MDD, suggesting that the co-occurrence of AnxMDD is exemplified by exaggerated defense related processes. METHODS: 195 individuals with MDD (N = 65) or AnxMDD (N = 130) were recruited from the community and completed multi-level assessments, including a Pavlovian fear learning task during functional imaging. Visual images paired with threat (conditioned stimuli: CS+) were compared to stimuli not paired with threat (CS-). RESULTS: MDD and AnxMDD showed significantly different patterns of activation for CS+ vs CS- in the dorsal anterior insula/inferior frontal gyrus (partial eta squared; ηp2 = 0.02), dorsolateral prefrontal cortex (ηp2 = 0.01) and dorsal anterior/mid cingulate cortex (ηp2 = 0.01). These differences were driven by greater activation to the CS+ in AnxMDD versus MDD. LIMITATIONS: Limitations include the cross-sectional design, a scream US rather than shock and half the number of MDD as AnxMDD participants. CONCLUSIONS: AnxMDD showed a pattern of increased activation in regions identified with fear processing. Effects were consistently driven by threat, further suggesting fear signaling as the emergent target process. Differences emerged in regions associated with salience processing, attentional orienting/conflict, self-relevant processing and executive functioning in comorbid anxiety and depression, thereby highlighting potential treatment targets for this prevalent and treatment resistant group.

Directed exploration is elevated in affective disorders but reduced by an aversive interoceptive state induction.

Elevated anxiety and uncertainty avoidance are known to exacerbate maladaptive choice in individuals with affective disorders. However, the differential roles of state vs. trait anxiety remain unclear, and underlying computational mechanisms have not been thoroughly characterized. In the present study, we investigated how a somatic (interoceptive) state anxiety induction influences learning and decision-making under uncertainty in individuals with clinically significant levels of trait anxiety. A sample of 58 healthy comparisons (HCs) and 61 individuals with affective disorders (iADs; i.e., depression and/or anxiety) completed a previously validated explore-exploit decision task, with and without an added breathing resistance manipulation designed to induce state anxiety. Computational modeling revealed a pattern in which iADs showed greater information-seeking (i.e., directed exploration; Cohen's d=.39, p=.039) in resting conditions, but that this was reduced by the anxiety induction. The affective disorders group also showed slower learning rates across conditions (Cohen's d=.52, p=.003), suggesting more persistent uncertainty. These findings highlight a complex interplay between trait anxiety and state anxiety. Specifically, while elevated trait anxiety is associated with persistent uncertainty, acute somatic anxiety can paradoxically curtail exploratory behaviors, potentially reinforcing maladaptive decision-making patterns in affective disorders.

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (ß = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

Computational Mechanisms Underlying Multi-Step Planning Deficits in Methamphetamine Use Disorder.

Current theories suggest individuals with methamphetamine use disorder (iMUDs) have difficulty considering long-term outcomes in decision-making, which could contribute to risk of relapse. Aversive interoceptive states (e.g., stress, withdrawal) are also known to increase this risk. The present study analyzed computational mechanisms of planning in iMUDs, and examined the potential impact of an aversive interoceptive state induction. A group of 40 iMUDs and 49 healthy participants completed two runs of a multi-step planning task, with and without an anxiogenic breathing resistance manipulation. Computational modeling revealed that iMUDs had selective difficulty identifying the best overall plan when this required enduring negative short-term outcomes - a mechanism referred to as aversive pruning. Increases in reported craving before and after the induction also predicted greater aversive pruning in iMUDs. These results highlight a novel mechanism that could promote poor choice in recovering iMUDs and create vulnerability to relapse.

Sensorimotor Feedback Control Dysfunction as a Marker of Posttraumatic Stress Disorder.

BACKGROUND: Posttraumatic stress disorder (PTSD) is characterized not only by its direct association with traumatic events but also by a potential deficit in inhibitory control across emotional, cognitive, and sensorimotor domains. Recent research has shown that a continuous sensorimotor feedback control task, the rapid assessment of motor processing (RAMP) paradigm, can yield reliable measures of individual sensorimotor control performance. This study used this paradigm to investigate control deficits in PTSD relative to both healthy volunteer and a non-PTSD psychiatric comparison group. METHODS: We examined control processing using the RAMP paradigm in a sample of 40 individuals with PTSD, along with matched groups of 40 individuals with mood and anxiety (MA) complaints and 40 healthy controls (HC). We estimated Kp (drive) and Kd (damping) parameters using a proportion-derivative (PD) control modeling approach. RESULTS: The Kp parameter was lower in the PTSD group compared to the HC (Cohen's d = .86) and MA groups (Cohen's d = 0.63). After controlling for color-word inhibition, Kp remained lower in the PTSD group versus HC (Cohen's d = 0.79) and versus MA (Cohen's d = 0.62). Mediation analysis showed that Kd significantly mediated the relationship between PTSD and control deficits in the Kp parameter, with 96% of the effect mediated by Kd. CONCLUSIONS: These findings underscore the potential of using dynamic control paradigms to elucidate the control dysfunctions in PTSD and suggests that different psychiatric conditions may distinctly influence subcomponents of sensorimotor control.

Genetic Correlates of Treatment-Resistant Depression: Insights from Polygenic Scores Across Cognitive, Temperamental, and Sleep Traits in the All of US cohort.

Background: Treatment-resistant depression (TRD) is a major challenge in mental health, affecting a significant number of patients and leading to considerable economic and social burdens. The etiological factors contributing to TRD are complex and not fully understood. Objective: To investigate the genetic factors associated with TRD using polygenic scores (PGS) across various traits, and to explore their potential role in the etiology of TRD using large-scale genomic data from the All of Us Research Program (AoU). Methods: Data from 292,663 participants in the AoU were analyzed using a case-cohort design. Treatment resistant depression (TRD), treatment responsive Major Depressive Disorder (trMDD), and all others who have no formal diagnosis of Major Depressive Disorder (non-MDD) were identified through diagnostic codes and prescription patterns. Polygenic scores (PGS) for 61 unique traits from seven domains were used and logistic regressions were conducted to assess associations between PGS and TRD. Finally, Cox proportional hazard models were used to explore the predictive value of PGS for progression rate from the diagnostic event of Major Depressive Disorder (MDD) to TRD. Results: In the discovery set (104128 non-MDD, 16640 trMDD, and 4177 TRD), 44 of 61 selected PGS were found to be significantly associated with MDD, regardless of treatment responsiveness. Eleven of them were found to have stronger associations with TRD than with trMDD, encompassing PGS from domains in education, cognition, personality, sleep, and temperament. Genetic predisposition for insomnia and specific neuroticism traits were associated with increased TRD risk (OR range from 1.05 to 1.15), while higher education and intelligence scores were protective (ORs 0.88 and 0.91, respectively). These associations are consistent across two other independent sets within AoU (n = 104,388 and 63,330). Among 28,964 individuals tracked over time, 3,854 developed TRD within an average of 944 days (95% CI: 883 ~ 992 days) after MDD diagnosis. All eleven previously identified and replicated PGS were found to be modulating the conversion rate from MDD to TRD. Thus, those having higher education PGS would experiencing slower conversion rates than those who have lower education PGS with hazard ratios in 0.79 (80th versus 20th percentile, 95% CI: 0.74 ~ 0.85). Those who had higher insomnia PGS experience faster conversion rates than those who had lower insomnia PGS, with hazard ratios in 1.21 (80th versus 20th percentile, 95% CI: 1.13 ~ 1.30). Conclusions: Our results indicate that genetic predisposition related to neuroticism, cognitive function, and sleep patterns play a significant role in the development of TRD. These findings underscore the importance of considering genetic and psychosocial factors in managing and treating TRD. Future research should focus on integrating genetic data with clinical outcomes to enhance our understanding of pathways leading to treatment resistance.

Blunted stimulus-preceding negativity during reward anticipation in major depressive disorder.

BACKGROUND: Reward processing dysfunction is a core characteristic of major depressive disorder (MDD), yet event-related potential (ERP) research in MDD has predominantly focused on reward receipt as opposed to anticipation. The stimulus-preceding negativity (SPN) ERP reflects anticipatory brain processing. This study examines whether individuals with MDD exhibit deficits during reward anticipation as evidenced by altered SPN amplitude. METHODS: We assessed prefeedback-SPN amplitudes during a monetary incentive delay (MID) task in individuals with MDD (n = 142, 99 with comorbid anxiety disorders [MDD + ANX]) compared to Controls (n = 37). A mixed analysis of variance was performed on prefeedback-SPN amplitude and behavioral measures, with group (MDD, MDD + ANX, Control) as the between-subjects factor, and feedback (gain, loss) and electrode (F3, F4, Fz, C3, C4, Cz, P3, P4, Pz) as within-subjects factors. RESULTS: A group main effect revealed faster reaction times for the Control group than MDD and MDD + ANX groups. A group x feedback interaction indicated that the MDD subgroup had smaller prefeedback-SPN amplitudes than MDD + ANX and Control groups when anticipating gain feedback. Additionally, individuals with current MDD, irrespective of past MDD and comorbid anxiety, exhibited smaller SPN amplitudes than Controls prior to gain feedback. LIMITATIONS: The MID paradigm, designed for functional magnetic resonance imaging (fMRI) data acquisition, lacks optimization for ERP analysis. Moreover, the clinical groups included more females than the Control group. CONCLUSIONS: Reduced resource allocation to reward anticipation may differentiate MDD from MDD + ANX and Control groups. Further investigation of the neural mechanisms of distinct MDD phenotypes is warranted.

A randomized controlled safety and feasibility trial of floatation-REST in anxious and depressed individuals.

BACKGROUND: Reduced Environmental Stimulation Therapy via floatation (floatation-REST) is a behavioral intervention designed to attenuate exteroceptive sensory input to the nervous system. Prior studies in anxious and depressed individuals demonstrated that single sessions of floatation-REST are safe, well-tolerated, and associated with an acute anxiolytic and antidepressant effect that persists for over 48 hours. However, the feasibility of using floatation-REST as a repeated intervention in anxious and depressed populations has not been well-investigated. METHODS: In this single-blind safety and feasibility trial, 75 individuals with anxiety and depression were randomized to complete six sessions of floatation-REST in different formats: pool-REST (weekly 1-hour float sessions), pool-REST preferred (float sessions with flexibility of duration and frequency), or an active comparator (chair-REST; weekly 1-hour sessions in a Zero Gravity chair). Feasibility (primary outcome) was assessed via an 80% rate of adherence to the assigned intervention; tolerability via study dropout and duration/frequency of REST utilization; and safety via incidence of adverse events and ratings about the effects of REST. RESULTS: Of 1,715 individuals initially screened, 75 participants were ultimately randomized. Six-session adherence was 85% for pool-REST (mean, M = 5.1 sessions; standard deviation, SD = 1.8), 89% for pool-REST preferred (M = 5.3 sessions; SD = 1.6), and 74% for chair-REST (M = 4.4 sessions; SD = 2.5). Dropout rates at the end of the intervention did not differ significantly between the treatment conditions. Mean session durations were 53.0 minutes (SD = 12.3) for pool-REST, 75.4 minutes (SD = 29.4) for pool-REST preferred, and 58.4 minutes (SD = 4.3) for chair-REST. There were no serious adverse events associated with any intervention. Positive experiences were endorsed more commonly than negative ones and were also rated at higher levels of intensity. CONCLUSIONS: Six sessions of floatation-REST appear feasible, well-tolerated, and safe in anxious and depressed individuals. Floatation-REST induces positively-valenced experiences with few negative effects. Larger randomized controlled trials evaluating markers of clinical efficacy are warranted. CLINICAL TRIAL REGISTRATION IDENTIFIER: NCT03899090.

Transdiagnostic failure to adapt interoceptive precision estimates across affective, substance use, and eating disorders: A replication and extension of previous results.

Recent Bayesian theories of interoception suggest that perception of bodily states rests upon a precision-weighted integration of afferent signals and prior beliefs. In a previous study, we fit a computational model of perception to behavior on a heartbeat tapping task to test whether aberrant precision-weighting could explain misestimation of cardiac states in psychopathology. We found that, during an interoceptive perturbation designed to amplify afferent signal precision (inspiratory breath-holding), healthy individuals increased the precision-weighting assigned to ascending cardiac signals (relative to resting conditions), while individuals with anxiety, depression, substance use disorders, and/or eating disorders did not. In this pre-registered study, we aimed to replicate and extend our prior findings in a new transdiagnostic patient sample (N = 285) similar to the one in the original study. As expected, patients in this new sample were also unable to adjust beliefs about the precision of cardiac signals - preventing the ability to accurately perceive changes in their cardiac state. Follow-up analyses combining samples from the previous and current study (N = 719) also afforded power to identify group differences between narrower diagnostic categories, and to examine predictive accuracy when logistic regression models were trained on one sample and tested on the other. With this confirmatory evidence in place, future studies should examine the utility of interoceptive precision measures in predicting treatment outcomes and test whether these computational mechanisms might represent novel therapeutic targets.

Individuals with Methamphetamine Use Disorder Show Reduced Directed Exploration and Learning Rates Independent of an Aversive Interoceptive State Induction.

Methamphetamine Use Disorder (MUD) is associated with substantially reduced quality of life. Yet, decisions to use persist, due in part to avoidance of anticipated withdrawal states. However, the specific cognitive mechanisms underlying this decision process, and possible modulatory effects of aversive states, remain unclear. Here, 56 individuals with MUD and 58 healthy comparisons (HCs) performed a decision task, both with and without an aversive interoceptive state induction. Computational modeling measured the tendency to test beliefs about uncertain outcomes (directed exploration) and the ability to update beliefs in response to outcomes (learning rates). Compared to HCs, those with MUD exhibited less directed exploration and slower learning rates, but these differences were not affected by aversive state induction. These results suggest novel, state-independent computational mechanisms whereby individuals with MUD may have difficulties in testing beliefs about the tolerability of abstinence and in adjusting behavior in response to consequences of continued use.

Rumination and Overrecruitment of Cognitive Control Circuits in Depression.

BACKGROUND: Rumination is associated with greater cognitive dysfunction and treatment resistance in major depressive disorder (MDD), but its underlying neural mechanisms are not well understood. Because rumination is characterized by difficulty in controlling negative thoughts, the current study investigated whether rumination was associated with aberrant cognitive control in the absence of negative emotional information. METHODS: Individuals with MDD (n = 176) and healthy control individuals (n = 52) completed the stop signal task with varied stop signal difficulty during functional magnetic resonance imaging. In the task, a longer stop signal asynchrony made stopping difficult (hard stop), whereas a shorter stop signal asynchrony allowed more time for stopping (easy stop). RESULTS: In participants with MDD, higher rumination intensity was associated with greater neural activity in response to difficult inhibitory control in the frontoparietal regions. Greater activation for difficult inhibitory control associated with rumination was also positively related to state fear. The imaging results provide compelling evidence for the neural basis of inhibitory control difficulties in individuals with MDD with high rumination. CONCLUSIONS: The association between higher rumination intensity and greater neural activity in regions involved in difficult inhibitory control tasks may provide treatment targets for interventions aimed at improving inhibitory control and reducing rumination in this population.

C-reactive protein concentrations diverge as a function of substance use disorder: A pre-registered replication in a clinical sample.

BACKGROUND: Inflammatory biomarkers may differentiate clinical disorders, which could lead to more targeted interventions. Analyses within a clinical sample (May et al., 2021) revealed that females with substance use disorders (SUD) exhibited lower C-reactive protein (CRP) and higher interleukin (IL)-8 and -10 concentrations than females without SUD who met criteria for mood/anxiety disorders. We aimed to replicate these findings in a new sample. METHODS: Hypotheses and analyses were preregistered. Treatment-seeking individuals with mood/anxiety disorders and/or SUD (N = 184) completed a blood draw, clinical interview, and questionnaires. Participants were categorized as SUD+ (45F, 43M) and SUD- (78F, 18M). Principal component analysis (PCA) of questionnaire data resulted in two factors reflecting appetitive and aversive emotional states. SUD group and nuisance covariates (PCA factors, age, body mass index [BMI], medication, nicotine [and hormones in females]) predicted biomarker concentrations (CRP, IL-8, and IL-10) in regressions. RESULTS: In females, the omnibus CRP model [F(8, 114) = 8.02, p <.001, R²-adjusted =.32] indicated that SUD+ exhibited lower CRP concentrations than SUD- (ß = -.33, t = -3.09, p =.002, 95% CI [-.54, -.12]) and greater BMI was associated with higher CRP levels (ß =.58, t = 7.17, p <.001, 95% CI [.42,.74]). SUD+ exhibited higher IL-8 levels than SUD- in simple but not omnibus regression models. CONCLUSION: Findings across two samples bolster confidence that females with SUD show attenuated CRP-indexed inflammation. As SUD+ comorbidity was high, replication is warranted with respect to specific SUD classes (i.e., stimulants versus cannabis).

Leptin's Inverse Association With Brain Morphology and Depressive Symptoms: A Discovery and Confirmatory Study Across 2 Independent Samples.

BACKGROUND: Major depressive disorder has a complex, bidirectional relationship with metabolic dysfunction, but the neural correlates of this association are not well understood. METHODS: In this cross-sectional investigation, we used a 2-step discovery and confirmatory strategy utilizing 2 independent samples (sample 1: 288 participants, sample 2: 196 participants) to examine the association between circulating indicators of metabolic health (leptin and adiponectin) and brain structures in individuals with major depressive disorder. RESULTS: We found a replicable inverse correlation between leptin levels and cortical surface area within essential brain areas responsible for emotion regulation, such as the left posterior cingulate cortex, right pars orbitalis, right superior temporal gyrus, and right insula (standardized beta coefficient range: -0.27 to -0.49, puncorrected < .05). Notably, this relationship was independent of C-reactive protein levels. We also identified a significant interaction effect of leptin levels and diagnosis on the cortical surface area of the right superior temporal gyrus (standardized beta coefficient = 0.26 in sample 1, standardized beta coefficient = 0.30 in sample 2, puncorrected < .05). We also observed a positive correlation between leptin levels and atypical depressive symptoms in both major depressive disorder groups (r = 0.14 in sample 1, r = 0.29 in sample 2, puncorrected < .05). CONCLUSIONS: The inverse association between leptin and cortical surface area in brain regions that are important for emotion processing and leptin's association with atypical depressive symptoms support the hypothesis that metabolic processes may be related to emotion regulation. However, the molecular mechanisms through which leptin may exert these effects should be explored further.

Screen time, sleep, brain structural neurobiology, and sequential associations with child and adolescent psychopathology: Insights from the ABCD study.

Background and Aims: The precise roles of screen media activity (SMA) and sleep problems in relation to child/adolescent psychopathology remain ambiguous. We investigated temporal relationships among sleep problems, SMA, and psychopathology and potential involvement of thalamus-prefrontal-cortex (PFC)-brainstem structural covariation. Methods: This study utilized data from the Adolescent Brain Cognitive Development study (n = 4,641 ages 9-12) at baseline, Year1, and Year2 follow-up. Cross-Lagged Panel Models (CLPMs) investigated reciprocal predictive relationships between sleep duration/problems, SMA, and psychopathology symptoms. A potential mediating role of baseline Thalamus-PFC-brainstem covariation on SMA-externalizing relationships was examined. Results: Participants were divided into discovery (n = 2,359, 1,054 girls) and replication (n = 2,282, 997 girls) sets. CLPMs showed 1) bidirectional associations between sleep duration and SMA in late childhood, with higher frequency SMA predicting shorter sleep duration (ß = -0.10 [95%CI: -0.16, -0.03], p = 0.004) and vice versa (ß = -0.11 [95%CI: -0.18, -0.05], p < 0.001); 2) externalizing symptoms at age 10-11 predicting sleep problems (ß = 0.11 [95%CI: 0.04, 0.19], p = 0.002), SMA (ß = 0.07 [95%CI: 0.01, 0.13], p = 0.014), and internalizing symptoms (ß = 0.09 [95%CI: 0.05, 0.13], p < 0.001) at age 11-12; and 3) externalizing behavior at age 10-11 partially mediating the relationship between baseline thalamus-PFC-brainstem covariation and SMA at age 11-12 (indirect effect = 0.032 [95%CI: 0.003, 0.067], p-value = 0.030). Findings were replicable. Conclusion: We found bi-directional SMA-sleep-duration associations in late childhood. Externalizing symptoms preceded future SMA and sleep disturbances and partially mediated relationships between structural brain covariation and SMA. The findings emphasize the need for understanding individual differences and developing and implementing integrated strategies addressing both sleep concerns and screen time to mitigate potential impacts on psychopathology.

Medication Use is Associated with Distinct Microbial Features in Anxiety and Depression.

This study investigated the relationship between gut microbiota and neuropsychiatric disorders (NPDs), specifically anxiety disorder (ANXD) and/or major depressive disorder (MDD), as defined by DSM-IV or V criteria. The study also examined the influence of medication use, particularly antidepressants and/or anxiolytics, classified through the Anatomical Therapeutic Chemical (ATC) Classification System, on the gut microbiota. Both 16S rRNA gene amplicon sequencing and shallow shotgun sequencing were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and NeuroMAP CoBRE cohorts. The results highlight the significant influence of medication use; antidepressant use is associated with significant differences in gut microbiota beta diversity and has a larger effect size than NPD diagnosis. Next, specific microbes were associated with ANXD and MDD, highlighting their potential for non-pharmacological intervention. Finally, the study demonstrated the capability of Random Forest classifiers to predict diagnoses of NPD and medication use from microbial profiles, suggesting a promising direction for the use of gut microbiota as biomarkers for NPD. The findings suggest that future research on the gut microbiota's role in NPD and its interactions with pharmacological treatments are needed.