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.
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.
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:Stress contributes to substance use (SU) involvement (SUI) and differences in brain white matter (WM). However, longitudinal relationships involving SUI, stress hormones, and WM are not well understood. This study examined the moderating role of SUI on the prospective association between cortisol response (CR) and WM microstructure. METHODS:Data were drawn from a larger longitudinal study on SU disorder (SUD) aetiology. Participants were enrolled at ages 10-12 years when salivary cortisol was assayed. The Drug Use Chart assessed cumulative involvement with 10 substance categories by age 18 years. Diffusion-weighted data were acquired from sixty-nine participants after reaching age 18 years and were used to compute crossing-fibre measures. Analyses were conducted in a priori regions of interest linked to SU and stress. RESULTS:CR levels were related to secondary- and tertiary-fibre anisotropy in the cingulum. SUI moderated associations between CR and right cingulum secondary-fibre anisotropy wherein CR levels associated with greater anisotropy at heightened SUI levels. CONCLUSIONS:This study identified stress-linked differences in distinct cingulum WM fibres among individuals with greater SUI. Involvement with SU overall, may modulate stress effects and enhance WM anisotropy in region- and fibre-specific manners. Potential biomarkers associated with SUI in WM among vulnerable stress-exposed youth require further investigation with respect to SUD aetiology.
