Barbara Jacoquelyn Sahakian

Background and aims: Gambling causes harms for the individual, their families and wider society. Psychological interventions have been shown to improve gambling severity, but their relative effectiveness is not well understood. Methods: Systematic review and network meta-analysis (NMA) of psychological interventions for harmful gambling. Outcomes were gambling symptom severity and gambling frequency (expressed as standardized mean differences [SMDs]). Results: We included 52 randomized controlled trials (RCTs), 9013 participants and 14 intervention classes. For gambling symptom severity, group CBT was the most effective intervention (SMD −1.09, 95% credible intervals [95%CrIs] −1.75 to −0.44) followed by individual CBT (SMD −0.56, 95%CrIs −1.07 to −0.07). Waitlist showed increased symptom severity compared with no treatment (SMD 0.96, 95%CrIs 0.47 to 1.46). All other intervention effects were characterized by uncertainty. For gambling frequency, all active interventions showed similar effects to treatment as usual. Discussion: Included studies were of low or very low quality and there was moderate-to-high heterogeneity for the gambling symptom severity NMA. However, threshold analysis suggested that conclusions based on the NMA were robust to potential biases and sampling variation in the included evidence. Bias adjustment analyses did not show evidence of bias associated with small studies or industry funding. Conclusions: Group CBT shows clinically important benefits and should be regarded as the best intervention for harmful gambling. For individuals who decline group CBT, or where this is not possible or suitable, individual CBT shows the next best evidence of benefit.

Growing recognition of the importance of prevention in mental health has led to increased interest in modifiable lifestyle factors that support brain health, cognition, and well-being. This editorial examines evidence for an accessible and low-cost protective factor across the lifespan, namely reading for pleasure. Drawing on subjective surveys and objective studies using longitudinal, neuroimaging, and behavioral methods, we highlight converging findings that link reading engagement with improved brain and mental health, better cognitive performance, and reduced risk of cognitive decline. Survey data in adults suggested that reading was associated with reduced stress, greater empathy, enhanced well-being, and decreased loneliness. Longitudinal cohort studies further indicate that cognitively stimulating activities, particularly reading, are associated with a lower risk of dementia and slower cognitive decline, even among individuals with lower educational attainment. In childhood and adolescence, early reading for pleasure has been linked to better attention, memory, executive function, academic achievement, and fewer mental health problems. Neuroimaging findings suggest that reading is associated with structural and functional differences in brain regions supporting executive control, emotional regulation, and social cognition. Importantly, these benefits appear independent of socioeconomic background. Book club studies may suggest additional social benefits of reading in groups. Together, the evidence supports reading for pleasure and engaging in book clubs as scalable interventions, which may promote cognitive reserve, resilience, and lifelong mental well-being.

Understanding how a stable structural connectome supports flexible cognition, especially in aging, is a fundamental question in neuroscience. While static structure-function coupling (SFC) is well-studied, the temporal variability of structure-function coupling, referred to as dynamic SFC (DSFC), remains poorly characterized, particularly in relation to aging and cognitive outcomes. Leveraging MRI data from 34,067 UK Biobank participants (ages 45-82), we characterized the distinct roles of SFC and DSFC in aging, cognition, and health. We found that SFC and DSFC followed spatially divergent aging trajectories: SFC declined primarily in sensorimotor systems, whereas DSFC decreased most prominently in higher-order networks. Both SFC and DSFC in higher-order networks were positively correlated with cognitive performance (e.g., fluid intelligence). However, the associations with mental and physical health diverged spatially between the two measures: reduced DSFC was predominantly linked to health burden in high-order default/limbic networks, whereas weakened SFC was primarily associated with health burden in low-order sensory-motor networks. Genetic analyses revealed that Alzheimer's risk, specifically APOE ε4 dosage, significantly reduced DSFC in higher-order cognitive networks and SFC in low-order visual cortex. Mediation analyses further suggested that SFC and DSFC partially mediated the associations of aging and APOE ε4 dosage with cognitive performance. These findings suggest that static and dynamic coupling capture complementary aspects of brain organization: static SFC reflects the degree of functional alignment with structural architecture, whereas DSFC captures temporal variability in this alignment. Together, our results highlight these dual mechanisms as crucial for maintaining cognitive flexibility, providing potential biomarkers for age-related neurodegeneration.

Background: Longitudinal studies of seed‐based functional connectivity (SBFC) in young adult Huntington's disease gene‐expanded (HDGE) individuals are rare, and none, to our knowledge, have examined adult cohorts decades from predicted clinical motor diagnosis. Objectives: To examine longitudinal functional connectivity (FC) changes over ~4.8 years in adult HDGE individuals before clinical motor diagnosis compared with matched controls, all selected from the HD Young Adult Study (HD‐YAS) cohort, focusing on bilateral caudate and putamen as seeds. Methods: A subset of 71 right‐handed individuals (43 HDGEs and 28 controls) from the HD‐YAS underwent resting‐state functional magnetic resonance imaging (fMRI) at two visits ~4.8 years apart. SBFC analyses focused on bilateral caudate and putamen seeds. Mixed‐effects analysis of variance (ANOVA) tested main effects of group, time, and group × time interactions, with false discovery rate (FDR) correction. Post hoc tests explored significant findings. Results: HDGEs showed reduced FC between the putamen and cerebellar vermis/lobules and brainstem ( P ‐FDR ≤ 0.01), alongside increased connectivity with precuneus ( P ‐FDR = 0.04), supramarginal, and angular gyri ( P ‐FDR = 0.04). Over ~4.8 years, HDGEs exhibited different FC trajectories compared with controls, displaying FC reductions between the right caudate and paracingulate, frontal ( P ‐FDR = < 0.001), occipital ( P ‐FDR = < 0.01), and striatal regions ( P ‐FDR = ≤ 0.03). Conclusions: These findings provide the first longitudinal evidence of early cortico‐striatal and cerebellar functional network changes in adult HDGEs decades before clinical motor diagnosis, alongside possible compensatory processes in key hubs of the default mode network. These early FC changes likely reflect a dynamic interplay between neurodegenerative processes and adaptive reorganization, a balance that may ultimately fail as pathology progresses. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Early detection and prevention of psychiatric disorders, particularly depression, remain as major global health challenges, yet reliable tools for identifying individuals before symptom onset are lacking. Here, we combine functional neuroimaging with computational modeling to identify a mechanistic biomarker of depression risk. In a population-based adolescent cohort (IMAGEN, N = 1332), we found that weakened neural representations of emotional signals were linked to depressive symptoms. Perturbation experiments in a brain-aligned deep learning model showed that this deficit reflects overregularized emotion perception, producing a negative perceptual bias. A neurocomputational signature of this mechanism predicted depression symptom onset up to 4 years later at the IMAGEN follow-up ( N = 725), was associated with both a genetic-risk variant and polygenic risk for depression, and improved depression classification in a patient cohort (STRATIFY, N = 411). These findings suggest a possible mechanism linking genetic vulnerability to altered emotion perception and future depression, and propose a predictive computational marker with potential for early detection and prevention.

Attention deficit hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental condition characterised by impairments in sustained attention, executive functioning, and emotional regulation, with significant lifelong consequences if untreated. Recent years have seen substantial increases in ADHD diagnoses and stimulant prescriptions across children, adolescents, and adults, alongside a parallel rise in the non-medical use of cognitive-enhancing drugs by neurotypical individuals. This review examines five key issues: factors driving rising ADHD diagnoses and prescriptions; the cognitive profile of ADHD and mechanisms of pharmacological treatments; reasons for increasing cognitive enhancer use in neurotypical populations; evidence for their efficacy in neurotypical individuals; and associated ethical implications. We review evidence implicating digital technology, parenting styles, educational incentives, and diagnostic practices in rising ADHD prevalence. Cognitive studies demonstrate that ADHD is associated with deficits in sustained attention, working memory, inhibitory control, and emotional regulation, underpinned by fronto-striatal dysfunction. Stimulant medications such as methylphenidate and lisdexamfetamine effectively improve 'cold' cognitive functions and change dysfuntional neural circuitry to a more typically developing pattern, though emotional dysregulation may persist. In neurotypical individuals, drugs such as methylphenidate and modafinil produce small-to-moderate improvements in cognition, particularly by increasing cognitive effort and motivation, with greatest benefits in those operating below optimal levels. However, their growing use raises ethical concerns regarding safety, fairness, coercion, and regulation. We argue that careful risk-benefit evaluation, long-term research studies, and informed policy are urgently needed to guide responsible use.

Unraveling the heterogeneity of autism spectrum disorder (ASD) remains a major challenge yet crucial for elucidating the etiological underpinnings. Leveraging large-scale MRI data from 60,234 healthy participants and 3,117 individuals with ASD, we established lifespan brain charts of cortical thickness (CT) and surface area (SA) and derived personalized centile scores indexing deviations from typical neurodevelopment. Using a categorical multilayer community detection framework, we identified three CT-based and three SA-based neuroanatomical subtypes, each characterized by distinct spatial patterns and clinical profiles. Polygenic and plasma proteomic analyses further revealed genetic susceptibility and immune-related pathways underlying the observed heterogeneity. Moreover, the spatial patterns of these subtypes exhibited differential associations with cortical microarchitectural features and aligned closely with multimodal disease epicenters. Collectively, our findings indicate neuroanatomical heterogeneity in ASD as an integrative substrate linking molecular dysregulation to behavioral variability, providing a biologically grounded and clinically relevant framework for parsing the complex mechanisms of autism.

Background: Depression is associated with pathological dysregulations affecting both the brain and the body, with the latter being reflected in plasma proteins. While plasma protein signatures of depression have been increasingly recognized, a holistic examination of interactions with brain features is lacking. Methods: Leveraging data from 3,966 UK Biobank participants, we identified a multimodal neuroimaging-plasma protein component of depression (NeuroPro-Dep) by integrating plasma proteins and five brain modalities via an ICD-10 diagnosis-constrained multimodal fusion approach. Results: Notably, NeuroPro-Dep demonstrates detectable associations with depression symptoms across datasets from diverse populations, underscoring its clinical potential. This capability is anchored in its five brain modalities alterations, including hippocampal atrophy, reduced cortical sensorimotor network functional connectivity, and impaired internetwork structural connectivity of the frontoparietal network. The multimodal neuroimaging-derived plasma protein modality of NeuroPro-Dep is enriched in metabolic pathways, as further supported by association analysis linking this modality to body mass index (BMI), type 2 diabetes, and other metabolic indicators. Crucially, two-step Mendelian randomization analysis revealed that the NeuroPro-Dep plasma protein modality exerts a causal effect on depression through BMI (plasma protein to BMI: or=0.28, p=0.035; BMI to depression: or=1.14, p=4.37×10 ⁻¹¹ ). Conclusions: Overall, this study underscores metabolic dysfunction as a bridge between brain changes, depression, and physical diseases, while providing a novel multimodal biological signature and valuable insights that may inform future treatment strategies.

Background. Artificial intelligence (AI) and related digital technologies are rapidly entering mental health care, psychosomatic medicine, and psychotherapy. Yet the clinical value of these tools cannot be judged by technical performance alone. The field is characterized by diagnostic heterogeneity, reliance on language and self-report, complex biopsychosocial causation, and substantial variability in response to interventions. Clinically meaningful progress therefore requires integration of biological markers with patients’ lived experience, personal history and context, alongside person-centered precision decision-making and intervention.Summary. This review examines how AI may contribute to mental health care across multiple levels, including diagnostic assessment, biomarker development, treatment selection, AI-enabled non-invasive neuromodulation, closed-loop adaptation, and emerging chatbot-based interventions. We argue that AI should support and improve, not replace, clinical assessments and care by integrating complex data into person-centered care, while raising challenges of validation, safety, equity, commodification and governance.Key Messages. AI can strengthen and improve clinically effective, context-sensitive, and person-centered mental health care. AI may enhance diagnosis, treatment selection, and therapeutic options while preserving the human-human therapeutic alliance. The future of AI-enabled mental health depends on combining computational precision with human judgment, ethical oversight, and clinical relevance to improve patient outcomes and quality of life. Keywords: Artificial intelligence; Mental health; Personalized treatment; Psychotherapy; Psychosomatic medicine