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.

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.

The temporal evolution of schizophrenia symptoms is inherently complex and non-linear. Clinical states fluctuate between relatively stable and highly disorganised phases. These dynamics are only partially captured by conventional diagnostic frameworks. This limitation has motivated the development of models that explicitly account for temporal structure and state transitions. Dynamical systems theory provides a rigorous framework for describing non-linear interactions and evolving symptom trajectories. Recent advances in longitudinal data acquisition and computational analysis have enabled the identification of informative dynamical features and the characterisation of patient-specific trajectories. This narrative review synthesises the growing body of work applying dynamical systems theory to schizophrenia spectrum disorders. It distinguishes theoretical models, data-driven approaches, and frameworks empirically evaluated against real patient data. It also examines methodological limitations, translational challenges, and the need for further longitudinal validation. The aim is to provide a coherent conceptual framework for understanding schizophrenia as a dynamic process, to promote cross-disciplinary dialogue, and to guide future research in translational computational psychiatry.

Depression is a highly heterogeneous disorder with a similarly heterogeneous neurobiological background. Understanding separate depressive phenotypes, driven by distinct aetiological pathways like early trauma and neurobiological contributors, can improve treatment. Furthermore, sex differences must be considered, as both the development and treatment of depression may be sex-specific. We aimed to investigate the sex-dependent association between polygenic risk for chronotypes, childhood traumas, and depression.

Attention-deficit/hyperactivity disorder (ADHD) affects 2.5% of adults and is associated with cognitive decline and dementia. The neurobiological mechanisms contributing to adverse outcomes in ADHD are poorly understood. ADHD-related brain alterations may persist into later life and interact with ageing-related processes, potentially increasing susceptibility to neuropathology. This preregistered systematic review synthesised neuroimaging findings in adults aged 35 years and older with clinical, symptomatic, or genetic risk for ADHD, and summarised cognitive and clinical correlates. A search of five databases produced 13 included studies. Risk of bias was assessed using the Newcastle-Ottawa Scale. Most studies (11/13) had low risk of bias. Compared to controls, ADHD groups exhibited alterations in fronto-striatal, fronto-parietal, and limbic systems implicated in executive control and attention. Middle-aged adults with clinical ADHD showed more widespread cortical structural differences, whereas older adults demonstrated abnormalities primarily in frontal regions, possibly reflecting attenuation of differences through ageing. Two functional studies in those with clinical ADHD reported frontal hypoactivation alongside parietal hyperactivation, consistent with compensatory recruitment. Among undiagnosed samples, there were interactions between genetic risk for ADHD and Alzheimer's disease-related pathology affecting brain and cognitive outcomes. Overall, ADHD-associated neurobiological alterations appear to persist into older age. Longitudinal investigations are needed to clarify these relationships.

The present review summarises recommendations for the management of posttraumatic stress disorder (PTSD) based on a consensus among international experts in the field. Cognitive-behavioural therapy (CBT) has the best evidence base as a psychotherapy modality. Virtual reality exposure therapy appears to be an emerging treatment option for PTSD. Eye Movement Desensitisation and Reprocessing (EMDR) therapy and digital CBT-based interventions did not demonstrate superiority compared to controls. Controlled studies supporting the use of psychodynamic therapy are lacking. Early prophylactic psychotherapy for trauma victims is ineffective and may be harmful. First-line pharmacotherapy includes selective serotonin reuptake inhibitors (SSRIs) and the serotonin-noradrenaline reuptake inhibitor (SNRI) venlafaxine. Several second- and third-line medications are available for treatment-refractory patients. Currently, no medications can be recommended for prophylactic use in trauma victims. Although evidence is incomplete regarding combined psychotherapy and pharmacotherapy, available studies generally favour combination treatments over monotherapy. Repetitive transcranial magnetic stimulation (rTMS) showed efficacy in one study. In children and adolescents with PTSD, CBT demonstrated a medium effect size compared to active controls, while studies on SSRI treatment in this population yielded inconsistent results.

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.