Dialogues in Clinical Neuroscience最新文献

Background: Although treatment approaches have been explored in internet gaming disorder (IGD), however, little is known about the specific neural activity that is associated with treatment outcomes. This study aims to establish a treatment-sensitive network that reliably predicts treatment outcomes across different interventions for IGD.

Methods: Resting-state fMRI data were collected from 90 individuals with IGD who received one of three different intervention protocols (mindfulness meditation, approach bias modification, or retrieval-aversive conditioning training). Connectome-based predictive modelling (CPM) was used to build a treatment-sensitive network to predict individual symptom reduction, then validated them on the external dataset.

Results: The negative (r = 0.401, p <.001) and combined network (r = 0.480, p <.001) can significantly predict a reduction of addiction severity (baseline minus post-test). Specifically, the negative network was primarily centred on the motor, subcortical, and salience networks. The positive network primarily involved cerebellar-related connections. Incorporating these into a combined model significantly improved predictive accuracy, suggesting that cerebellar-related connectivity may contribute to the prediction of intervention-related clinical improvement. Finally, the treatment-sensitive network was validated in a separate data.

Conclusions: This study established a treatment-sensitive network based on the whole-brain connectome. These findings provide valuable empirical evidence for identifying biomarkers for IGD treatment and advance personalised therapeutic strategies.

Clinical trials registration: The trial protocol was pre-registered with the Chinese Clinical Trial Registry (www.chictr.org.cn; ChiCTR2300077831(MM), ChiCTR2300075869(ApBm), ChiCTR2500095444(R-AC), ChiCTR2400090834(SAE)).

Introduction: Resting‑state fMRI is widely used to map brain connectivity, yet the field lacks a clear, data‑driven standard for acquisition duration. We compared traditional Single‑Band (SB) imaging with an accelerated Multiband factor 4 (MB4) protocol to identify a time‑efficient approach for capturing stable network topology.

Methods: Twenty‑five healthy young adults completed two sessions one month apart, each including counterbalanced 13‑minute SB and MB4 scans. Test-retest reliability was evaluated across multiple durations using Portrait Divergence, a multiscale graph‑theoretic metric. Supplementary analyses compared Portrait Divergence with the field-standard Intraclass Correlation Coefficient, assessed robustness across preprocessing pipelines and parcellation schemes, and examined MB4's ability to capture both cortical and subcortical connectivity.

Results: MB4 consistently outperformed SB across all tested durations, with peak topological stability at 9 minutes. Stability declined beyond this point, likely due to cumulative motion artefacts. Supplementary analyses confirmed MB4's robustness across preprocessing strategies and atlas choices and demonstrated effective whole‑brain coverage.

Conclusions: In healthy young adults, MB4 captures network topology more effectively than SB and reaches peak stability at 9 minutes before declining, challenging the assumption that longer scans are always better. These findings require validation in clinical, paediatric, and geriatric cohorts.

The integration of Artificial Intelligence (AI) into psychiatry is progressing rapidly, with promising yet underexplored applications in suicide prevention. While most existing studies have focused on suicide risk prediction, AI could be applied more broadly across the suicide prevention continuum. This review explores AI's potential in improving the detection of suicidal ideation (SI), enhancing clinician training, and expanding patient access to psychotherapeutic support. Studies suggest that AI models may identify SI more sensitively than clinicians by detecting subtle linguistic or semantic cues. Furthermore, AI-based virtual patients have been shown to improve clinicians' confidence and accuracy in suicide risk assessments, particularly when used alongside traditional training methods. On the patient side, CBT-inspired AI tools offer scalable, on-demand support, but most remain unregulated and clinically unvalidated. Despite their perceived empathy and availability, such tools pose ethical and clinical risks. Many patients use AI tools without professional guidance, a trend clinicians often underestimate. This disconnect calls for urgent regulatory oversight and professional engagement. Without clear clinical validation, AI-based tools may evolve outside medical frameworks, exposing vulnerable individuals to preventable harm. Psychiatry must not only adapt but also lead, ensuring that innovation is harnessed responsibly to serve patients with empathy, safety, and scientific rigor.

Based on a synthesis of recent advances, this review elucidates the distinct yet synergistic roles of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep in memory consolidation. During NREM sleep, cortical slow oscillations (SOs) establish a temporal framework by alternating between UP and DOWN states, facilitating cross-regional communication. SOs phase-couple with thalamic spindles to coordinate hippocampal sharp-wave ripples (SWRs), thereby driving the reactivation and transfer of labile hippocampal memories to neocortical networks for long-term storage. Concurrent synaptic homeostasis optimises network efficiency through global downscaling while selectively strengthening memory-relevant connections. In REM sleep, theta oscillations synchronise limbic-prefrontal-hippocampal circuits, and ponto-geniculo-occipital (PGO) waves further integrate emotional and procedural memories. The cholinergically dominant, monoamine-depleted neurochemical milieu of REM sleep promotes dendritic spine remodelling and sparse neuronal reactivation, enabling affective decoupling and synaptic refinement. Despite progress, key challenges remain in understanding cross-frequency oscillatory coupling among slow oscillations, spindles, ripples and REM-specific rhythms, glial contributions and clinical translation. Future integration of high-density electrophysiology with computational approaches may yield mechanistic models and clinically relevant biomarkers for sleep-based cognitive monitoring and intervention.

Objective: The aim of our study was to investigate neurobiological markers and gender difference for diagnosing anxious major depressive disorder (aMDD) through a meta-analysis.

Methods: We systematically searched multiple databases for whole-brain neuroimaging studies comparing anxious major depression disorder (aMDD), pure MDD, and healthy controls, with publication dates through December 2024. We extracted brain coordinates and their corresponding peaks, for further analysis using the Seed-based d Mapping software package.

Results: A total of 11 studies were included in this study, encompassing 829 aMDD patients, 681 MDD patients, and 865 healthy controls. The meta-analysis revealed that aMDD patients exhibited increased functional alteration in the left middle temporal gyrus compared to individuals diagnosed with MDD. In the comparison between individuals with aMDD and healthy controls, the meta-analysis revealed increased functional alteration in the anterior commissure and decreased functional alteration the right middle frontal gyrus. Furthermore, the meta-regression analysis revealed heightened neural activity of the left middle cingulate gyrus and right anterior thalamic regions, as well as weakened neural activity of the left rolandic operculum in females with aMDD compared to the control group.

Conclusions: We identified specific functional alterations in brain regions that may serve as potential neurobiological markers for aMDD and associated differences.

Objectives: This study explored the plasma metabolites' mediation effect between gut microbiomes and insomnia through Mendelian randomisation (MR).

Methods: Using publicly accessible GWAS data from 5959 individuals for gut microbiota and 8299 individuals for plasma metabolites, we employed MR analysis to explore their causal effects on insomnia. Insomnia outcome data were obtained from Pan-UKB, GERA, and FinnGen, covering 9007 cases and 871,802 controls. Mediation effects of identified bacterial taxa on insomnia through plasma metabolites were computed using the product of coefficients approach.

Results: Our MR analysis included participants with a mean age of 45.7 years (SD = 11.5) for gut microbiota and 63 years (range 45-85) for plasma metabolites. The analysis identified 10 gut microbiomes and 35 plasma metabolites potentially associated with insomnia respectively. Specifically, increased abundances of certain gut microbiomes, such as species CAG-145 sp000435615, were linked to a higher risk of insomnia. Mediation analysis revealed that the plasma metabolite 3-ethylcatechol sulphate levels significantly mediated the effects of these microbiomes on insomnia, explaining up to 31.49% of the total effect.

Conclusion: This study highlights the role of gut microbiota in influencing insomnia risk, mediated through specific plasma metabolites. These findings provide valuable insights into the gut-brain axis and may inform the development of therapeutic targets for managing sleep disorders.

Introduction: Disruptive behaviours of children with intellectual disabilities predispose mothers to mental and physical morbidities, leading to caregiving burnout, lower childcare quality, and poor child progress.

Methods: This cross-sectional study investigated the psychometrics of the Arabic version of the Generalised Anxiety Disorder 2-item scale (GAD-2) among 85 Saudi mothers of children with intellectual disabilities through latent variable model and receiver-operating characteristic curve analyses.

Results: The unidimensional GAD-2 demonstrated good construct validity, invariance at the configural, metric, and scalar levels across age groups, and adequate convergent/divergent validity-It was negatively predicted by high mood and happiness and positively predicted by stress, and it mediated the effect of stress and happiness on depression. Its known-group validity was determined by elevated anxiety levels among mothers using psychotropic drugs. Two cut-offs (≥2.5 and ≥3.5) flagged the best trade-off between sensitivity and specificity for predicting low mood, poor sleep quality, nightmares, high stress, low general physical health, and willingness to join a psychological support program. The positive predictive value for the cut-off ≥3.5 was higher for all outcomes than that of the cut-off ≥2.5.

Discussion: The GAD-2 is a valid and reliable tool, which at thresholds ≥3.5 can identify anxious mothers, aiding early diagnosis and intervention.

Second-generation antipsychotics are frequently linked to weight gain and metabolic dysfunction, yet the mechanisms driving these effects remain elusive. The gut microbiome has been proposed as a potential mediator of these adverse outcomes. This study aimed to investigate the role of the gut microbiota in antipsychotic-induced weight gain. A systematic search of PubMed and Embase was conducted. In total, 24 publications were included in this review, including clinical and preclinical observational and intervention studies. Collectively, there is strong evidence that atypical antipsychotic-induced weight gain and metabolic dysfunction is accompanied by microbiota alterations. However, there is a lack of consensus with regards to the exact mechanisms and involvement of the microbiome in antipsychotic-induced weight gain. Nevertheless, a few patterns and common observations were found across studies, such as reduced diversity, increased Firmicutes/Bacteroidetes ratio and a reduction in Akkermansia species. While microbiota-targeted interventions had generally weak effects on weight gain and metabolic dysfunction in clinical cohorts, the use of specific probiotic strains and microbiota metabolites showed promise in preclinical studies. Thus, while the relationship between antipsychotic-induced weight gain, metabolic dysfunction, and changes in the gut microbiome are evident, further research is warranted to establish definitive causal relationships and to aid in the development of precision microbiota-targeted interventions to counteract these adverse effects.

Background: Cognitive impairment is widely reported in migraineurs. A Mendelian randomisation (MR) approach, similar to a randomised-controlled trial, employs single-nucleotide polymorphisms (SNPs) to investigate causal relationships.

Methods: This study comprised one- and two-sample MR analyses of the Taiwan Biobank. Three strategies were used to obtain causal estimates: (1) a polygenic risk score (PRS) method-several SNPs associated with migraines were constructed as a single instrument variable; (2) a meta-analysis of genome-wide association study (GWAS) statistics for traits of migraines and cognitive impairment in the framework of a one-sample MR; and (3) a two-sample MR analysis with a meta-analysis of GWAS statistics in two distinct datasets (IEU GWAS database and the Taiwan Biobank).

Results: In strategy 1, the PRS constructed by 18 selected SNPs exhibited a causal association with cognitive impairment (β = -2.31, 95% confidence interval [CI]: -4.56 to -0.06). In strategy 2, a one-sample MR showed migraines were causally associated with cognitive impairment (inverse-variance weighted [IVW] estimator β = 2.90; 95% CI: 0.90-4.89). In strategy 3, a two-sample MR validated migraines to be causally associated with cognitive impairment (IVW estimator β = 2.43; 95% CI: 1.08-3.78).

Conclusions: Migraine, a polygenic disorder, is causally associated with cognitive impairment.

Introduction: Reproductive health impacts mental health. Consequently, reproductive factors should be assessed in psychotherapy to ensure correct differential diagnosis, precise case conceptualisation and efficient treatment. It has not been analysed to which extent psychotherapists assess reproductive factors in routine care.

Methods: Analyses were based on data from Germany-wide surveys with n = 390 psychotherapists and n = 291 patients. From their respective perspective, we examined how frequently psychotherapists proactively assessed whether patients experienced a menstrual cycle, menopause, used hormonal contraception, experienced a pregnancy, fertility treatment, or birth, whether these factors were associated with patients' symptoms, and how relevant they were perceived to be. Further, we examined how psychotherapists acquired knowledge about the reproductive factors and which psychotherapist characteristics increased the likelihood of considering them.

Results: We identified a gap between low proactive assessment and high perceived relevance of reproductive factors. Psychotherapists acquired their knowledge through personal experience rather than clinical training or further education. Greater knowledge and perceived relevance increased the likelihood of proactively assessing reproductive factors.

Conclusions: There is a clear need to incorporate a standardised, proactive assessment of reproductive factors into routine care. The responsibility to disclose them should not be left to the patients. Developing appropriate teaching content and tailored anamnestic instruments can help to overcome the neglect.