Translational Psychiatry最新文献

Bipolar disorder (BD) is a complex mood disorder characterized by recurrent depressive and manic/hypomanic episodes, accompanied by significant cognitive dysfunction and emotional dysregulation. Accurate and timely diagnosis, especially the differentiation between subtypes, remains a challenge due to overlapping symptoms, variable onset times for more specific symptoms (e.g., psychotic features), and the reliance on subjective assessments. This study examines the use of a multimodal approach combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to identify patterns of BD emotional dysregulation, aiming to enhance its diagnosis and subtype differentiation. The protocol employed an emotional visual task to evaluate the interference of emotional content on cognitive function. EEG data were collected using a whole-head cap, while fNIRS focused on hemodynamic changes in the frontal cortex. Furthermore, the feasibility of using a potential simplified, portable EEG-fNIRS system was explored by focusing the analysis on frontal regions. The cohort included BD patients [BP] of two main subtypes, and healthy controls [HC]. Behavioral analysis revealed significant performance differences between BP and HC groups. While EEG alone enabled groups' classification, integrating EEG and fNIRS improved accuracy by reducing misclassification rates. Although classification using only frontal EEG regions was slightly less accurate than the full-head cap, fNIRS integration ensured robust results, supporting the feasibility for a potential simplified system. These findings underscore the complementary strengths of EEG and fNIRS in capturing neural and vascular markers of emotional dysregulation in BD and support the development of multimodal diagnostic tools for BD.

A core clinical feature of posttraumatic stress disorder (PTSD) is recurrent reexperiencing of the traumatic event in the form of intrusive memories. Doxycycline is a matrix metalloproteinase 9 (MMP-9) inhibitor. MMP-9 is required for late-phase, NMDA receptor-dependent long-term potentiation in the hippocampus and the basal and central amygdala nuclei, which are important to various forms of learning and memory. Here we examined the effect of doxycycline on the development of intrusive memories in a pre-registered randomized, double-blind, placebo-controlled trial (https://osf.io/72ys9). Healthy females (N = 80) received 200 mg doxycycline or placebo 4.5 h before exposure to film footage depicting strong interpersonal violence. Participants then completed an intrusion diary for one week. Most participants, 92%, experienced intrusive memories following the trauma film. There was no evidence that doxycycline and placebo groups differed in frequency, distress, and vividness of daily intrusive memories models. The doxycycline group showed enhanced arousal, indexed by skin conductance when exposed to reminder cues, and better performance in a memory task about film content compared to placebo one-week post-film. Based on our findings, the MMP9-inhibitor doxycycline did not impair the development of intrusive memories and was associated with increased arousal and improved retrieval of experimental trauma memory one week later.

Artificial intelligence (AI) is increasingly used in mental health, yet its rehabilitation-oriented applications in schizophrenia have not been systematically mapped. We conducted a systematic scoping review of PubMed, Web of Science, IEEE Xplore and the ACM Digital Library (January 1, 2012-October 31, 2025; two search rounds), applying operationalized rehabilitation boundaries and excluding diagnostics-only case-control studies. We extracted data on data sources, feature engineering, model families, validation, calibration, interpretability, application domains, outcomes and implementation readiness. Eighty-three studies met inclusion criteria (median sample size 160; 55% longitudinal). Applications focused on symptom monitoring (48/83), medication management (19/83) and risk management (16/83), whereas functional training (1/83) and psychosocial support (3/83) were rarely targeted. Supervised learning predominated (53/83, 63.9%) over representation learning (20/83, 24%), most commonly using speech/text, electronic health records and smartphone sensing. Across classification tasks, the median AUC was 0.79 (IQR 0.71-0.86); relapse early-warning models showed a median sensitivity of 31.5% at 88.0% specificity. Only four studies reported external validation and three described closed-loop deployment, including one randomized trial that improved adherence. Proxy endpoints were more common than clinical endpoints, and reporting of calibration/uncertainty and fairness auditing was sparse. Overall, AI shows promise for monitoring, adherence support and relapse risk stratification, but routine-care deployment will require externally validated and calibrated human-in-the-loop decision support, privacy-preserving multimodal pipelines and pragmatic trials targeting functional outcomes and participation.

Artificial intelligence (AI) and machine learning (ML) have seen remarkable growth in mental health applications over the past few decades, demonstrating significant potential to transform psychiatric care. Despite these advancements, the translation of AI systems into clinical practice remains fraught with challenges. This Perspective examines critical hurdles in psychiatric AI research, emphasizing limitations in research rigor, model reliability, interpretability, clinical utility, and ethical considerations. We argue that a human-assisted AI framework-incorporating incremental feedback, self-adaptation, and dynamic collaboration-can address biases, enhance transparency, and build trust in AI systems. Moreover, initiatives in clinical education, cultural adaptation, and data/software sharing are essential to fostering public engagement, data transparency, and research reproducibility. By focusing on these areas, we aim to bridge the gap between AI potential and its successful, ethical implementation in mental health care, guiding the development of trustworthy, effective, and culturally adaptive AI-powered psychiatric tools.

Symptoms of common mental health problems often pertain to complex inference and decision problems (for example around future social interactions). Such patterns may reflect the breakdown of the fundamental computational processes that ordinarily underpin these behaviours, with the use of flexible goal-directed decision-making being a prime candidate. Here, we used a validated, naturalistic threat inference task to assess the use of goal-directed decision-making in complex interactive decision problems. Participants (n = 1025) completed this task alongside a battery of self-report measures of mental health symptoms and neurodevelopmental characteristics. Participants higher in inattentive/neurodevelopmental symptoms were better able to predict the predator's behaviour, while those higher in externalising symptoms made more incorrect inferences. Variability in behaviour was better explained by these specific symptom dimensions than by more general factors. Using computational modelling, we show that these associations are mediated by the degree to which individuals use goal-directed decision-making to make inferences about the predator's behaviour. Our results suggest that symptoms and traits that manifest in real-world environments may result from alterations in the use of complex computational mechanisms.

Internalizing disorders such as depressive and anxiety disorders are prevalent, disabling, and characterized by difficulty managing emotions particularly in the context of negative information. Despite the availability of empirically supported treatments, response to these treatments remains heterogeneous. Accruing data indicate neural predictors of treatment response have the potential to contribute to precision medicine to improve treatment outcomes. This review evaluates functional magnetic resonance imaging studies that examined explicit (e.g., effortful) and implicit (e.g., automatic) emotion regulation involving negative stimuli and treatment response. Results showed treatments mostly consisted of cognitive behavioral therapy, exposure therapy, and/or pharmacotherapy. Explicit regulation findings were predominantly based on cognitive reappraisal, which showed pre-treatment activity in medial and lateral prefrontal cortices frequently served as predictors of treatment response. Regarding directionality, greater symptom improvement was generally associated with lower baseline activity in these regions. Results suggest patients with less baseline explicit regulation capacity may benefit more from treatment. For implicit regulation, most studies utilized emotional interference tasks. Predictors frequently involved prefrontal cortical regions and anterior cingulate cortex; here, the direction was largely that of more baseline activity predicting greater symptom improvement. Findings suggest treatment may leverage greater pre-existing implicit regulatory capacity. While baseline activity in other regions during explicit and implicit regulation were reported, including regions central to emotion processing (e.g., amygdala), results were less consistent. Despite these insights, substantial gaps in the literature were observed. For explicit regulation, studies predominantly focused on cognitive reappraisal, an adaptive regulation approach. Furthermore, the majority of studies consisted of major depressive disorder, anxiety disorders, and posttraumatic stress disorder with insufficient representation of other internalizing disorders. Findings underscore the relevance of neural predictors of treatment outcome through emotion regulation in internalizing disorders. However, further study is needed to determine their contribution in precision medicine.

Chronic pain is increasingly recognized as a potential risk factor for cognitive decline, yet findings from observational studies are inconsistent. We conducted a meta-analysis to evaluate the long-term association between chronic pain and cognitive impairment. PubMed, Embase, and the Cochrane Library were searched from inception to January 2025 for longitudinal cohort studies assessing this relationship. Twenty-eight eligible cohorts comprising 7,914,407 participants were included. Adjusted odds ratios (ORs) were pooled using random-effects models; subgroup, sensitivity, and meta-regression analyses were performed to explore heterogeneity. Chronic pain was associated with a higher risk of cognitive impairment (pooled adjusted OR = 1.30; 95% CI: 1.14-1.47), an effect driven by dementia (pooled OR = 1.43; 95% CI: 1.23-1.65) rather than by global cognitive performance scores (pooled OR = 0.99; 95% CI: 0.88-1.11). Associations were stronger in studies with follow-up ≥5 years (OR = 1.37), in older populations (OR = 1.30), and in cohorts focusing on headache-related pain (OR = 1.42). Meta-regression indicated that depression was a key moderator of the association. These findings suggest that chronic pain is linked specifically to an increased risk of dementia, particularly among older individuals and those with headache-related pain. Integrative clinical strategies addressing pain and co-occurring depression, along with mechanistic and interventional studies using standardized cognitive endpoints, are warranted.

Treatment-resistant depression (TRD) accounts for approximately 30% of major depressive disorder cases and has been characterized by altered functional connectivity within and between the Default Mode (DMN) and Frontoparietal networks (FPN). Ketamine can be an effective treatment for TRD, and its antidepressant response has been associated with alterations in resting state functional connectivity (rsFC). Here, we evaluated the effect of a single subanesthetic dose of racemic ketamine (0.5 mg/kg) on electroencephalogram (EEG) derived source-based measures of rsFC from 24 participants with TRD (16 women; aged 44.35 ± 15.86 years). Ninety-six channel resting state EEG data were collected 24 h before and after ketamine infusion. Exact low-resolution electromagnetic tomography (eLORETA) was used to estimate theta and beta-band rsFC within and between the DMN and FPN. Ruminative symptoms were assessed using the Ruminative Response Scale. Analogous data were collected from 34 healthy control participants (25 women, aged 32.49 ± 14.07 years) who did not receive any intervention. Twenty-four hours post-infusion, depressive, anhedonic, and ruminative symptoms for the TRD sample were significantly reduced. Interestingly, symptom reduction was not correlated with any changes in rsFC but was associated with initial pre-ketamine rsFC. Moreover, individuals with TRD displayed broad increases in rsFC within the DMN and FPN as well as between these two networks. Based on preclinical findings, we posit that ketamine's synaptogenic effects may be driving this general increase in connectivity. However, these synaptogenic effects can be short lived, and future work probing the full time-course of rsFC via EEG pre- and post-ketamine administration is warranted.

Postoperative depression adversely influences breast cancer patients' clinical outcomes. Our prior study demonstrated that intraoperative esketamine ameliorated postoperative depression in breast cancer patients, yet the underlying neural mechanism remains incompletely understood. We performed a double-blind randomized controlled trial in 35 breast cancer patients with preoperative depressive symptoms, who were randomly given intraoperative esketamine 0.25 mg·kg⁻¹ (n = 18) or saline placebo (n = 17) over the initial 40 min of anesthesia. Resting-state functional magnetic resonance imaging data were collected at preoperative baseline and postoperative day 1 follow-up to calculate brain functional network measures. In contrast to no significant change in the placebo group, the esketamine group showed increased degree centrality of the left inferior frontal gyrus, opercular part from baseline to follow-up, which was related to improvement in depressive symptoms. Additionally, we found significant associations of baseline network measures at the global, nodal, and edge levels with short-term and long-term improvements in depressive symptoms following esketamine administration. These findings may not only provide novel insights into the neural mechanism by which esketamine exerts its antidepressant efficacy during the perioperative period, but also highlight the prospect of functional network measures as useful predictors of antidepressant response to esketamine in patients with breast cancer.

Resting-state functional connectivity (rsFC) studies have revealed altered regional homogeneity (ReHo) and degree centrality (DC) in individuals with anorexia nervosa (AN) compared to healthy controls (HC), but the underlying mechanisms remain unclear. Here we explored the spatial alignment with neurotransmitter receptor and transporter densities (i.e., "chemoarchitecture", based on "reference" PET studies) as a potential explanatory factor. We investigated rsFC alterations in acutely underweight patients with AN (n = 87) and age-matched HC (n = 87) cross-sectionally at admission and then again after successful weight-restoration treatment. Global ReHo and DC maps were associated with the spatial distribution of neurotransmitter receptors, transporters and/or metabolic glucose uptake. First, the correlation between rsFC alterations in AN and chemoarchitecture was evaluated at the group/timepoint-level. Second, individual-level correlations of neuroreceptor maps with rsFC alterations were calculated to test for possible associations with early weight restoration. The acute state of AN was characterized by higher DC (but not ReHo) in brain regions with a higher cortical density of vesicular acetylcholine transporter (VAChT), dopamine transporter (DAT) and serotonin transporter (SERT). Conversely, weight restoration was associated with normalization of DC, especially in areas with a higher DAT density. Importantly, individual-level spatial correlations between VAChT, DAT and SERT densities and DC alterations at admission significantly predicted early weight gain over first 90 days of treatment. These results suggest that neurochemical context may underlie functional brain alterations, providing a preliminary step toward identifying biological risk signatures. Replication with individualized PET data will be crucial to validate their potential utility for treatment stratification and personalization.