Psychiatry and Clinical Neurosciences最新文献

Shishida and colleagues report that hypnotic-associated fall risk in hospitalized patients varies by drug class and time of day. We propose that the observed pattern may reflect a temporal misalignment between reactive as-needed hypnotic prescribing and the circadian architecture of in-hospital delirium, rather than purely pharmacokinetic effects. This reframing has implications for consultation-liaison workflow, suggesting that nighttime hypnotic requests should trigger structured delirium assessment rather than automatic dispensing.

Aims: Electroconvulsive therapy (ECT) is an effective intervention for treatment-resistant depression (TRD). Although prior studies have identified cerebellar functional alterations following ECT, cerebellar-cerebral functional connectivity (FC) changes remain insufficiently explored.

Methods: Cerebellar seeds were defined based on prior connectivity studies, targeting cerebellar regions functionally coupled with executive, default-mode, and affective-limbic networks. Seed-based cerebellar-cerebral FC analyses were conducted using neuroimaging data from 72 patients with TRD (52 of whom completed post-ECT assessment) and 63 healthy controls (HCs). Correlation analyses assessed clinical associations, and support vector regression was applied to predict treatment response using baseline FC. Neuroimaging-transcriptomic association analyses were conducted to identify gene expression patterns correlated with post-ECT FC alterations.

Results: Compared to HCs, patients with TRD exhibited decreased cerebellar connectivity with the sensorimotor network (SMN) and the default-mode network (DMN). Following ECT, TRD patients showed significant alleviation of depressive symptoms, which was accompanied by a marked increase in cerebellar-DMN connectivity. Enhanced FCs between the left Crus II_Exec2 and the bilateral medial superior frontal gyrus were positively correlated with Hamilton Depression Rating Scale (HAMD) score reduction (r = 0.343, P = 0.026). Additionally, baseline FC of the left Crus II_Exec2 significantly predicted HAMD reduction (r = 0.412, P < 0.001). Neuroimaging-transcription association analysis revealed these FC alterations were associated with normative gene patterns enriched in synaptic signaling, ion channel, and chromatin remodeling.

Conclusions: TRD is associated with reduced cerebellar-SMN and cerebellar-DMN connectivity. ECT may enhance cerebellar-DMN connectivity, potentially contributing to antidepressant effects.

Bipolar disorder (BD) is a clinically heterogenous mental disorder with a diversity in clinical trajectories and treatment response. Twin studies have shown that BD is highly heritable, with estimates ranging from 60% to 90%. However, our understanding of this genetic component has yet to be translated into clinical interventions or prediction tools. In this review, we summarize findings from large-scale international collaborations, with the latest and largest study finding 298 genome-wide significant loci and 36 credible mapped genes associated with BD, alongside the first genes with an increased burden of rare genetic variants. We next highlight key biological insights from these findings, including widespread genetic overlap but distinct patterns of genetic correlation with other mental disorders and related traits; enrichment of gene expression within both brain-specific tissues and cell types and non-brain tissues, including pancreatic and large intestinal tissues; and the identification of novel drug targets and repurposing candidates, including calcium channel blockers. While these insights may take several years before they impact clinical practice, the potential for genetic advances to impact patient care through more accurate nosology and the development of clinically relevant prediction and stratification tools is more imminently achievable. We go on to summarize the most prominent methods for genetic prediction, including artificial intelligence-based methods, before discussing promising use-cases and key challenges to implementation. Looking to the future, cross-disciplinary collaboration, access to real-world data for robust validation, and pragmatic solutions that facilitate implementation across diverse healthcare systems will be crucial to achieving material advances in clinical care and improving outcomes for people with BD.

Major depressive disorder (MDD) remains a highly heterogeneous condition, presenting significant challenges for effective diagnosis and treatment. Traditional diagnostic systems often fail to capture the diverse clinical and biological phenotypes of MDD, limiting the efficacy and predictability of therapeutic interventions. The advent of wearable technology has enabled the continuous collection of real-time, objective data. By leveraging advanced artificial intelligence (AI) methodologies, these data streams can be transformed into dynamic digital phenotypes that may correlate with the complex psychopathological manifestations of depression. This integration offers a novel, data-driven approach to augment traditional subjective assessments, paving the way for more precise classification and personalized treatment strategies. This review explores the potential of AI-enhanced digital phenotyping to revolutionize depression diagnosis and management, advocating for a paradigm shift toward a more personalized, precision-based approach in psychiatric practice.

Aim: Despite being one of the most disabling symptoms of post-traumatic stress disorder (PTSD)-disrupting sleep continuity, reinforcing hyperarousal, and worsening psychiatric comorbidity-the physiological signature of Trauma-related nightmares (TRNs) under naturalistic sleep conditions remains poorly characterized.

Methods: We used home-based, multi-sensor devices from the SOMMEPT cohort to assess whether TRNs display distinct autonomic dynamics before and after awakening. TRN awakenings were self-marked with the wristband button and validated through electroencephalography (EEG) inspection by a psychiatrist and a sleep physician. Each TRN event (N = 412) was matched 1:1 to three control conditions: spontaneous awakenings in 60 healthy military participants, awakenings in 34 military PTSD patients without TRNs, and non-nightmare awakenings from 74 military PTSD patients with TRNs. Pre-awakening autonomic activity was analyzed over 10 min, and post-awakening reactivity over a 2-min window using nonparametric statistics with cluster-based permutation correction.

Results: TRNs were associated with heightened sudomotor activity and reduced vagal heart rate variability compared with spontaneous awakenings in healthy controls; differences were weaker versus PTSD patients without TRNs or non-TRN awakenings. Phasic Electrodermal activity (EDA) showed earlier peaks and prolonged recovery, while movement was lower before TRN-related awakenings. Post-awakening, TRNs elicited an abrupt surge: heart rate accelerated within ~40 s and normalized by ~2 min; tonic EDA remained elevated, phasic EDA bursts were longer, slower to recover, and motor activity rose during the first ~80 s.

Conclusions: TRNs display a distinctive autonomic pattern with pre-awakening sudomotor buildup and post-awakening cardiovascular-electrodermal surges, supporting biomarker-based detection and targeted intervention.

Trial registration: ClinicalTrials.gov Identifier: NCT04581850.

Aim: Previous studies assessed dynamic functional connectivity in obsessive-compulsive disorder (OCD) by the sliding-window approach, limiting sensitivity to rapid neural fluctuations. Moreover, the mechanisms underlying dynamic transitions between brain states also remain unknown. Therefore, the study aimed to explore the dynamic neural mechanisms of OCD by characterizing state dynamic patterns and the underlying energy basis.

Methods: The study recruited 198 OCD patients and 109 healthy controls, characterizing altered state dynamic patterns and underlying control energy in OCD by integrating co-activation pattern (CAP) analysis with network control theory (NCT).

Results: OCD patients showed increased occurrences of the states characterized by ventral attention and somatomotor network co-activation with default mode network suppression (VAN-SMN⁺/DMN^-) and default mode network activation with ventral attention network suppression (DMN⁺/VAN^-), reduced persistence of the actively frontoparietal network with suppressively visual network (FPN⁺/VIS^-) state, and elevated transitions among DMN⁺/VAN^-, VAN-SMN⁺/DMN^-, and SMN-VAN⁺/VIS^- states. Moreover, computational enhancement of FPN⁺/VIS^- state persistence via virtual perturbation partially improved abnormal brain-state dynamics in OCD. NCT further revealed that state transitions from DMN⁺/VAN^- to VAN-SMN⁺/DMN^- or SMN-VAN⁺/VIS^- required reduced control energy under modulation by GABAergic signaling and mitochondrial respiratory capacity, forming a "low-cost pathological shortcut" associated with greater symptom severity; state transitions from SMN-VAN⁺/VIS^- to VAN-SMN⁺/DMN^- were intrinsically energy-demanding, modulated by dopaminergic and serotonergic systems, constituting an "inefficient pathological transition" consistent with repetitive behavior observed clinically.

Conclusions: OCD is characterized by a maladaptive brain-state cycle marked by excessive DMN dominance, frequent shifts to VAN/SMN activation states, and attenuated FPN engagement. Computationally enhancing the persistence of the FPN⁺/VIS^- state via virtual perturbation partially improved the dysregulated cycle in OCD. Within this cycle, two distinct pathological transition modes emerged: a "low-cost shortcut" from DMN to VAN/SMN modulated by GABAergic and an "inefficient transition" from SMN to VAN linked to dopaminergic and serotonergic. These reveal neurochemically grounded alterations in the energy control of abnormal brain-state transitions, offering mechanistic insights into the disrupted neural dynamics of OCD.

Aim: The altered ratio of neural excitation (E) and inhibition (I) has been proposed to the etiology of autism spectrum disorder (ASD). Previous studies suggested a lower E/I ratio in autistic individuals compared to non-autistic comparisons (NACs) in specific brain regions. Whether the E/I imbalance was present in ASD in adolescence and adulthood and how the ratio correlated with clinical manifestations are unclear. This study aimed to investigate the E/I ratio in autistic adolescents and adults by using resting-state electroencephalography (EEG) signals.

Methods: The study recruited 63 autistic adolescents and adults, and 53 NAC. EEG was recorded while participants were asked to stare at the cross mark on the monitor and close their eyes for 3 min each. We used fitting oscillations and one-over-F function to separate periodic and aperiodic components, then fitting the curve to a linear line.

Results: Autistic participants exhibited a flatter spectrum power slope and a smaller offset compared to NAC. Group differences existed in the relationship between slope and Autism Spectrum Quotient (AQ) scores, with higher AQ total scores correlated with lower aperiodic exponent in ASD. In ASD, a lower aperiodic exponent was significantly correlated with greater difficulty in mindreading, low registration, and working memory difficulty.

Conclusions: The slope of the aperiodic component in ASD was flatter across frontal and posterior regions, supporting that the ASD group may have an E/I imbalance. Besides, the slope might be associated with overall autistic severity, empathy, sensory characteristics, and real-world working memory difficulties that warrants further investigation.

Aim: This network meta-analysis of randomized controlled trials (RCTs) aimed to investigate which hypnotics are associated with the most favorable sleep architecture and respiratory outcomes in adults with obstructive sleep apnea.

Methods: Primary outcomes included total sleep time (TST) and apnea-hypopnea index (AHI) during TST. Other outcomes were rapid eye movement (REM) sleep time, latency to persistent sleep (LPS), wake after sleep onset (WASO), sleep efficiency (SE), AHI during non-REM or REM sleep, mean peripheral oxygen saturation (SpO₂) during TST, mean SpO₂ nadir during TST, arousal index (AI), all-cause discontinuation, adverse event-related discontinuation, and incidence of individual adverse events. Effect sizes with 95% confidence intervals were calculated.

Results: This systematic review included 32 RCTs (n = 1871, average age = 51.60 years, 62.52% male, mean AHI = 23.60). Our network meta-analysis evaluated brotizolam, daridorexant, eszopiclone, flurazepam, lemborexant, nitrazepam, ramelteon, temazepam, triazolam, zaleplon, zolpidem, zopiclone, and placebo. Compared with placebo, lemborexant increased TST, REM sleep time, and SE and decreased LPS and WASO, whereas both daridorexant and zolpidem increased TST and SE and decreased WASO. These three medications demonstrated respiratory safety and discontinuation profiles similar to those of placebo. Eszopiclone increased TST and SE and decreased LPS, WASO, AHI during TST, and AI, but its effects on LPS, WASO, AHI during TST, and AI disappeared in the sensitivity analysis, excluding continuous positive airway pressure titration studies.

Conclusion: Our network meta-analysis identified different effects of various hypnotics on sleep architecture and respiratory parameters; however, the lack of data prevented a formal synthesis of subjective outcomes. Therefore, these results should be interpreted with caution in clinical practice.