Psychiatry and Clinical Neurosciences最新文献

Aims: People with anorexia nervosa (AN) often engage in compulsive exercise to control weight and manage emotions, altering the reward associated with physical activity. Delay-discounting evaluates preference for immediate over delayed rewards, potentially indicating struggles with prioritizing long-term gains. However, the impact of AN on these factors remains unexplored. This study aims to assess the attitudes of individuals with AN using a modified exercise delay discount task, comparing them with the general population.

Methods: A sample of 76 individuals (70 women, 92.1%) with a diagnosis of AN was compared with a sample of 124 people (115 women, 92.7%) without a lifetime diagnosis of any eating disorder. Two different delay discount tasks were used to evaluate different constructs: a standardized monetary delay discount task and a modified version focused on physical activity.

Results: The standardized monetary delay discount task confirmed the existence of varied monetary rewards in different subtypes of AN. The exercise-related task demonstrated a specific emphasis on exercise within AN, showing a tendency to delay physical activity by looking for a greater amount of exercise. On the contrary, the general population opted for immediate exercise proposals.

Conclusions: Our findings suggest that the tendency to delay physical activity could be related to the compulsive nature of exercise in patients with AN, with a possible change in cognitive evaluation based on the magnitude of exercise. Finally, in addition to psychological problems related to eating disorders, additional cognitive factors likely contribute to exercise compulsiveness, necessitating further research exploration.

Aims: Borderline personality disorder (BPD) patients show negative emotional reactions to both excluding and including social scenarios, with levels normalizing only during extreme inclusion. Prior research among healthy individuals highlights the right ventrolateral prefrontal cortex (rVLPFC) role in regulating emotional responses to social exclusion, since transcranial direct current stimulation (tDCS) of rVLPFC decreases rejection-related emotions following social exclusion. This pilot study investigated whether, in BPD patients, tDCS over the rVLPFC reduces rejection-related emotions not only after social exclusion but also after fair social inclusion.

Methods: Forty BPD patients randomly received either real or sham tDCS on rVLPFC before participating in the Cyberball paradigm, which involved phases of inclusion, exclusion, and over-inclusion. Participants self-reported their level of rejection-related emotions following each phase.

Results: Transcranial direct current stimulation reduced rejection-related emotions during both social exclusion and fair inclusion, but not during over-inclusion. Specifically, those in the Real tDCS group exhibited comparable emotional responses to fair and over-including scenarios, unlike those in the Sham group who experienced heightened rejection-related emotions during fair inclusion compared to over-inclusion.

Conclusions: Transcranial direct current stimulation over the rVLPFC reduces BPD patients' tendency to feel rejected both in fairly including and excluding scenarios. These findings confirm the rVLPFC involvement in emotional regulation and highlight a therapeutic potential for tDCS in moderating BPD's typical heightened rejection-related emotional responses to fairly including scenarios. This study supports the application of tDCS in BPD treatment, providing new insights into neuromodulatory interventions that can aid BPD patients to better regulate their emotions during varying social scenarios.

Aims: The way that individuals subjectively experience the world greatly influences their own mental well-being. However, it remains a considerable challenge to precisely characterize the breadth and depth of such experiences. One persistent problem is the lack of objective tools for directly quantifying and comparing narrative reports of subjective experiences. Here, we develop a new approach to map and compare reports of experience using the latent space of artificial neural networks.

Methods: Using a series of 31 prompts, including 30 images and one open-ended question, we quantified how the verbal reports provided by participants (n = 210, 50% female) deviate from one another and how these variations are linked to subjective experience and mental health.

Results: We found that latent space embeddings of experience can accurately predict subjective judgments of valence and arousal in a series of emotional pictures. Furthermore, we show that narrative reports to ambiguous images can accurately predict transdiagnostic factors of mental health. While distortions in the latent space of artificial neural networks are notoriously difficult to interpret, we propose a new approach to synthesize visual stimuli with generative artificial intelligence that can be used to explore semantic distortions in reported experiences.

Conclusions: In sum, latent space cartography could offer a promising avenue for objectively quantifying distortions of subjective experience in mental health and could ultimately help identify new therapeutic targets for clinical interventions.

Aim: Epigenetic clocks, quantifying biological age through DNA methylation (DNAmAge), have emerged as potential indicators of brain aging. As the variety of DNAmAge algorithms grows, consensus on their efficacy in predicting age-related changes is lacking. This study aimed to explore the intricate relationship between diverse DNAmAge algorithms and structural and cognitive markers of brain aging.

Methods: Within a cohort of 796 elderly patients (mean age, 65.8 ± 7.9 years), we scrutinized 11 DNAmAge algorithms, including Horvath, Hannum, Zhang's clocks, PhenoAge, GrimAge, DunedinPACE, and principal component (PC)-based PCHorvath, PCHannum, PCPhenoAge, and PCGrimAge. We evaluated their association with baseline cognition and cognitive decline, assessed through follow-up evaluations at three (T1) and six (T2) years postbaseline. Additionally, we examined their relationship with structural magnetic resonance imaging markers of brain aging, including white matter.

Results: Zhang's clock was the best predictor of decline in memory (β = -0.04) and global cognition (β = -0.03), whereas PCGrimAge was the best predictor of speed decline (β = -0.17). The DNAmAge algorithms were the second-best predictors in explaining cognitive variability after education in memory and global cognition (R² _partial = 1.66% to 2.82%) and the best predictors for speed decline (R² _partial = 2.13%). PC-trained DNAmAge algorithms outperformed their respective original version.

Conclusion: DNAmAge algorithms are strong and independent predictors of cognitive decline in the normal elderly population and explain additional variability in cognitive decline beyond that accounted for by conventional risk factors.

Aim: Up to 10% of SARS-CoV-2 infected individuals suffer from post-COVID-19 condition, marked by fatigue and cognitive dysfunction as major symptoms. Longitudinal studies on neuropsychological and clinical trajectories and related brain changes are scarce. Here, we aimed to examine their evolution up to 2 years post-infection.

Methods: In a multi-center, longitudinal study of 79 post-COVID patients (mean age 46, 48 female) with persistent symptoms and 21 age- and sex-matched never-infected, healthy controls (mean age 42, eight female), we analyzed neuropsychological performance, self-reported outcomes and associated neuroimaging alterations of resting-state functional and structural magnetic resonance imaging data 23 months post-infection.

Results: In post-COVID patients 23 months after SARS-CoV-2 infection we observed (1) that fatigue severity had reduced but still remained present in most patients, (2) widespread brain changes involving the brainstem, the pre- and postcentral gyrus and the limbic olfactory network, (3) a weakening of self-reported fatigue and its cerebral associations. Notably, findings of brain aberrations were more pronounced in hospitalized patients.

Conclusion: Our findings indicate that complex brain adaptations take place up to 2 years following SARS-CoV-2 infection. Some regions manifest enduring abnormalities while others undergo restitution. The attenuation of radio-clinical associations suggests a compensatory function for these regions, pointing to non-brain intrinsic factors to sustain persistent fatigue.

Aim: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that may persist into adulthood, with no established objective diagnostic tool yet. This study aims to propose a multimodal objective assessment tool involving clinical assessments, functional neuroimaging, and oculomotricity measurement for ADHD in young adults.

Methods: Seventy-one medication-naïve patients and 71 healthy controls (HCs) aged 18 to 28 underwent clinical interviews, Conners' Adult ADHD Rating Scale (CAARS) questionnaire, functional near-infrared spectroscopy (fNIRS), oculomotricity task, and Conners' Continuous Performance Task (CPT) 3rd edition. Student's t-tests with Bonferroni's correction were performed to compare the performance between groups, and logistic regression was used for classification.

Results: ADHD patients had significantly lower frontal hemodynamic response during verbal fluency task (VFT) (P = 0.0003), more anticipatory eye movements during overlap task (P = 0.0006), higher latency (P < 0.0001), anticipatory (P < 0.0001), and errors (P < 0.0001) during anti-saccade task, as well as higher commission errors (P < 0.0001) and standard deviation in hit reaction time (HRT) (P = 0.0018). The multivariate logistic regression model featuring these seven parameters from the three objective tests (fNIRS-VFT, oculomotricity, and CPT) yielded an area under the receiver operating characteristic curve (AUC) value of 0.892 (95% confidence interval (CI): 0.840-0.944), with sensitivity and specificity of 80.28% and 84.51%, respectively.

Conclusion: This multimodal assessment offered an accurate diagnostic tool for ADHD in young adults and laid the foundation for future machine-learning approaches.

Aims: Prompt diagnosis and intervention are crucial for first-episode psychosis (FEP) outcomes, but predicting the response to antipsychotics remains challenging. We studied whether adding electroencephalography (EEG) characteristics improves clinical prediction models for treatment response and whether EEG-based predictors are influenced by initial treatment.

Methods: We included 115 antipsychotic-naïve patients with FEP. Positive and Negative Syndrome Scale (PANSS) and sociodemographic items were included as clinical features. Additionally, we analyzed resting-state EEG data (n = 45) for (relative) power, functional connectivity, and network organization. Treatment response, measured as change in PANSS positive subscale scores (∆PANSS+), was predicted using a random forest regression model. We analyzed whether the most predictive EEG characteristics were influenced after treatment.

Results: The clinical model explained 12% variance in symptom reduction in the training set and 32% in the validation set. Including EEG variables in the model led to a nonsignificant increase of 2% (total 34%) explained variance in symptom reduction. High hallucination symptom scores and a more hierarchical organization of alpha band networks (tree hierarchy) were associated with ∆PANSS+ reduction. The tree hierarchy in the alpha band decreased after medication. EEG source analysis revealed that this change was driven by alterations in the degree and centrality of frontal and parietal nodes in the functional brain network.

Conclusions: Both clinical and EEG characteristics can inform treatment response prediction in patients with FEP, but the combined model may not be beneficial over a clinical model. Nevertheless, adding a more objective marker such as EEG could be valuable in selected cases.

Aim: Methamphetamine use disorders (MUDs) cause widespread disruptions in metabolomic and immunologic processes, highlighting the need for new therapeutic approaches. The purpose of this study was to find molecular and neuroimaging biomarkers for methamphetamine addiction.

Methods: In this study, we recruited 231 patients with MUD at varying stages of withdrawal and 40 healthy controls to quantify the blood levels of 52 molecules using enzyme-linked immunosorbent assay.

Results: The overall molecular disruption caused by methamphetamine was inversely related to withdrawal time (P = 0.0008), with partial recovery observed after 1 year of follow-up (P = 2.20 × 10^-5). Molecules related to stress, immune activation, oxidative products, and cardiac injury were significantly elevated in all MUD groups, while antioxidation enzymes were downregulated. Additionally, the blood level of brain-derived neurotrophic factor was significantly correlated with gray matter volumes in nine brain regions (fusiform gyrus, orbitofrontal cortex, temporal pole, caudate, cerebellum crus, and vermis, adjusted P < 0.05) among patients with MUD.

Conclusion: These findings suggest that patients with MUD exhibit elevated levels of immune response, stress, and oxidative stress, which are associated with brain structural abnormalities.