Substance use disorders (SUDs) are a major public health problem in the United States and cause substantial morbidity and mortality. There are meaningful gaps in the available SUD treatment options, and the development of new therapies is urgently needed. While medications with U.S. Food and Drug Administration approval are available for alcohol, nicotine, and opioid use disorders, there are no approved pharmacotherapies for cannabis, cocaine, or methamphetamine use disorders. Behavioral treatments for SUDs have significant limitations in effectiveness and accessibility, and there is a need for the development of both new behavioral treatment options and new models of treatment delivery. The next generation of treatments for SUDs will likely come from a diverse set of interventions, including new drug classes, new technologies, and new methods of delivery.
OBJECTIVE:Different aspects of social-emotional processing are linked to unique whole-brain patterns of neural activity termed "activation states." Autistic traits in youths include impairments in socioemotional processing that vary depending on age and comorbidity. Characterizing differences in activation states associated with these impairments may explain why youths with high autistic traits process socioemotional information differently than peers. METHODS:This study examined the frequency of three previously identified activation states in 545 youths (ages 5-15 years, 39% female, 54% White, 68% non-Hispanic) from the Healthy Brain Network study as they watched a socially engaging movie. Regressions examined associations between autistic traits (measured by the Social Responsiveness Scale, 2nd ed.) and overall time spent in activation states, and whether these associations varied with age or co-occurring attention deficit hyperactivity disorder (ADHD) and anxiety symptoms (measured by the Strengths and Weaknesses Assessment of ADHD and Normal Behavior and the Screen for Child Anxiety Related Disorders). RESULTS:At younger ages (<9 years), children with more autistic traits spent more time in an activation state associated with increased activation in somatomotor and visual networks. At older ages (>14 years), children with more autistic traits spent more time in an activation state characterized by greater default mode and ventral attention network activation. Across all ages, children with more ADHD symptoms spent less time in an activation state associated with greater cingulo-opercular network activation. CONCLUSIONS:Autistic traits are associated with differences in brain activation during socioemotional processing that vary with age and co-occurring mental health symptoms. Youths with more autistic traits may experience an altered developmental trajectory of social-emotional processing compared to peers with fewer autistic traits. Results inform personalized interventions and progress monitoring for youths with social impairments.
OBJECTIVE:Scalp-based repetitive transcranial magnetic stimulation targeting the left dorsolateral prefrontal cortex (DLPFC) does not account for interindividual variability. This trial was designed to determine whether individualized connectivity-guided intermittent theta-burst stimulation (iTBS) improves antidepressant outcomes compared with 5-cm targeting. METHODS:In this single-center, randomized, double-blind, three-arm trial (February 2023-March 2025), adults ages 18-65 years with major depressive disorder or bipolar II depression (≥1 antidepressant failure) were randomly assigned to receive 20 sessions of iTBS over 2 weeks using robotic neuronavigation with one of three targeting strategies: 5-cm rule, functional connectivity (FC)-guided targeting showing negative resting-state connectivity with the subgenual anterior cingulate cortex (sgACC), or structural connectivity (SC)-guided sites defined by probabilistic tractography to the sgACC. The primary outcome was percentage reduction in 17-item Hamilton Depression Rating Scale (HAM-D) score at week 2. Secondary outcomes included HAM-D score reduction at weeks 6 and 12, response or remission, self-reported symptoms, performance on a cognitive battery, and adverse events. RESULTS:Of 123 randomized participants, 119 were included in the modified intention-to-treat analyses (5-cm, N=40; SC-guided, N=39; FC-guided, N=40). SC-guided iTBS produced significantly greater HAM-D score reduction than the 5-cm rule at week 2 (least squares mean difference [LSMD]=8.79 percentage points, 95% CI=2.19, 15.40; Cohen's d=0.70). At week 6, both SC-guided and FC-guided groups showed significantly greater improvement than the 5-cm group (SC-guided LSMD=12.87 percentage points, 95% CI=6.21, 19.54; Cohen's d=1.03; and FC-guided LSMD=9.41 percentage points, 95% CI=2.84, 15.98; Cohen's d=0.75). By week 12, between-group differences were no longer significant. Adverse events were comparable across groups, with no seizures or mania. CONCLUSIONS:SC-guided iTBS produced promising preliminary evidence of improved antidepressant response compared with 5-cm targeting, supporting sgACC-based connectivity-guided precision neuromodulation in depression.
The discovery of potentially many hundreds of risk genes for schizophrenia does not resolve the mystery of the illness at the level of an individual. The diversity of implicated gene functions has encouraged speculation that there are convergent biological pathways that mediate risk at the systems level, perhaps represented in gene coexpression patterns. The authors emphasize that gene coexpression varies across development, with some molecular elements related to schizophrenia risk losing importance or gaining momentum over time. The systems biology of risk and environmental exposures associated with risk are both time-dependent. The authors propose that the dynamic gene-environment interplay subtended by shifting coexpression patterns may explain variable expressivity of genetic risk during development. In particular, gene-environment correlations provide a mechanism for the individual-hence for their genes-to affect the environment and thus individual experience, promoting chains of life events. The authors envision the paired study of molecular and behavioral patterns over time as a way to identify novel treatments and preventive strategies to change the course of schizophrenia.
Social determinants of health (SDoHs) are increasingly recognized as important contributors to the development, course, and outcomes of psychiatric disorders. However, their integration into clinical psychiatry and mechanistic models remains limited. This overview synthesizes emerging evidence on the biopsychosocial mechanisms through which SDoHs influence mental health. There is a need to distinguish between individual-level, clinically actionable health-related social needs and family-, community-, and society-level structural SDoHs, and to consider both adverse and protective social factors. Converging research demonstrates that social experiences are biologically embedded through interacting pathways, including exposomics, epigenetics, allostatic load, accelerated inflammaging, immune dysregulation, and gut-brain-microbiome signaling. These mechanisms influence neural circuitry underlying stress regulation, reward processing, and social cognition. Psychological processes-including individual differences in resilience, wisdom, compassion, and purpose in life-shape responses to SDoHs and are supported by identifiable neurobiological substrates. Social connection has emerged as a central, potentially modifiable SDoH that is strongly associated with whole health and longevity. Loneliness and social isolation have become major global public health challenges. The authors propose a biopsychosocial framework that integrates social exposures, biological mechanisms, neural systems, and psychological processes to better understand the risk, course, and prevention of mental illnesses. Clinical and public health implications include the need for routine assessment of SDoHs, incorporation of protective factors at individual and societal levels, and development of pragmatic, multidomain interventions. Finally, rapidly evolving digital technologies, including artificial intelligence, offer new opportunities but also require careful governance. Advancing toward human-centered "artificial wisdom" may enhance the capacity of technology to promote whole health in individuals with mental illnesses globally.
