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Vocal Markers of Schizophrenia: Assessing the Generalizability of Machine Learning Models and Their Clinical Applicability
No full textBackground and Hypothesis Machine learning (ML) models have been argued to reliably predict diagnosis and symptoms of schizophrenia based on voice data only. However, it is unclear to what extent such ML markers would generalize to different clinical samples and different languages, a crucial assessment to move toward clinical applicability. In this study, we systematically assessed the generalizability of current ML models of vocal markers of schizophrenia across contexts and languages. Study Design We trained models relying on a large cross-linguistic dataset (Danish, German, Chinese) of 217 patients with schizophrenia and 221 controls, and used a conservative pipeline to minimize overfitting. We tested the models' generalizability on: (Q1) new participants, speaking the same language; (Q2) new participants, speaking a different language; (Q3-Q4) further, we assessed whether training on data with multiple languages would improve generalizability using Mixture of Expert (MoE) and multilingual models. Results Model performance was comparable to state-of-the-art findings (F1-score similar to 0.75) within the same language; however, models did not generalize well-showing a substantial decrease-when tested on new languages. The performance of MoE and multilingual models was generally low (F1-score similar to 0.50). Conclusions Overall, the cross-linguistic generalizability of vocal markers of schizophrenia is limited. We argue that more emphasis should be placed on collecting large open cross-linguistic datasets to systematically test the generalizability of voice-based ML models, and on identifying more precise mechanisms of how the clinical features of schizophrenia are expressed in language and voice, and how different languages vary in that expression.</p
Identifying Opponent's Neuroticism Based on Behavior in Wargame
No full textTraditional neuroticism assessments primarily rely on self-report questionnaires, which can be difficult to implement in highly confrontational scenarios and are susceptible to subjective biases. To overcome these limitations, this study develops a machine learning-based approach using behavioral data to predict an opponent's neuroticism in competitive environments. We analyzed behavioral records from 167 participants on the MiaoSuan Wargame platform. After data cleaning and feature selection, key behavioral features associated with neuroticism were identified, and predictive models were developed. Neuroticism was assessed using the 8-item neuroticism subscale of the Big Five Inventory. Results indicate that this method can effectively infer an individual's neuroticism level. The best-performing model was LinearSVR, which balances interpretability, robustness to noise, and the ability to capture moderate nonlinear relationships-making it suitable for behavior-based psychological inference tasks. The correlation between predicted scores and self-reported questionnaire scores was 0.606, the R-squared value was 0.354, and the test-retest reliability was 0.516. These behavioral features provide valuable insights into neuroticism prediction and have practical applications in psychological assessment, particularly in competitive environments where conventional methods are impractical. This study demonstrates the feasibility of behavior-based neuroticism assessment and suggests future research directions, including refining feature selection techniques and expanding the application scenarios
What Increases the Risk of Sleep Problems for Train Drivers? Evidence From Network Analysis
No full textPrevious studies have established robust associations between sleep quality in shift workers and factors such as cognition, stressors, mental states, and positive traits. However, the hierarchical relationships among these factors, such as proximal versus distal influences, and their mechanistic interactions in shaping sleep outcomes, remain unclear. In this study, we assessed 769 train drivers at baseline (T1), with 694 participants completing a follow-up sleep assessment 6 months later (T2). Using cross-sectional (T1) and longitudinal (T1-T2) network analyses, we mapped the interrelationships among these variables. Our findings indicate that mental states (e.g., anxiety, somatisation) serve as the most proximal predictors of sleep disturbances, while positive traits (e.g., mindfulness) function as intermediate factors. Cognition and external stressors emerged as the most distal influences. Both cross-sectional and longitudinal networks highlighted anxiety, somatisation, and sleep-related symptoms as key bridge nodes with high centrality. Notably, mindfulness exhibited strong bridging properties in the longitudinal analysis. These results suggest that mental states, particularly anxiety and somatisation, play a critical and immediate role in sleep dysfunction among train drivers. Interventions targeting mindfulness may offer a promising therapeutic avenue for improving sleep in this population
Subgenual anterior cingulate cortex functional connectivity abnormalities in depression: insights from brain imaging big data and precision-guided personalized intervention via transcranial magnetic stimulation
No full textThe subgenual anterior cingulate cortex (sgACC) plays a central role in the pathophysiology of major depressive disorder (MDD). Its functional interactive profile with the left dorsal lateral prefrontal cortex (DLPFC) is associated with transcranial magnetic stimulation (TMS) treatment outcomes. Previous research on sgACC functional connectivity (FC) in MDD has yielded inconsistent results, partly due to small sample sizes and limited statistical power. Furthermore, calculating sgACC-FC to target TMS individually is challenging. We used a large multi-site cross-sectional sample (1660 patients with MDD vs. 1341 healthy controls) from Phase II of the Depression Imaging REsearch ConsorTium (DIRECT) to systematically delineate case-control difference maps of sgACC-FC. We explored the potential impact of group-level abnormality profiles on TMS target localization and clinical efficacy. Next, we developed an MDD big data-guided, individualized TMS targeting algorithm to integrate group-level statistical maps with individual-level brain activity to individually localize TMS targets. We found enhanced sgACC-DLPFC FC in patients with MDD compared with healthy controls (HC). These group differences altered the position of the sgACC anti-correlation peak in the left DLPFC. We showed that the magnitude of case-control differences in the sgACC-FC was related to clinical improvement in two independent clinical samples. This targeting algorithm may generate targets demonstrating stronger associations with clinical efficiency than group-level targets. We reliably delineated MDD-related abnormalities of sgACC-FC profiles in a large, independently ascertained sample and demonstrated the potential impact of such case-control differences on FC-guided localization of TMS targets. (c) 2025 The Authors. Published by Elsevier B.V. and Science China Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Altered neural reward activation predicts clinical depression improvement after a novel loving-kindness meditation: a multimodal neuroimaging study
No full textObjective: Major depressive disorder (MDD) has become the second largest risk factor affecting human health, with a progress in its treatment especially non-pharmacological therapies. The loving-kindness meditation (LKM) has been introduced to depression but is not popular due to requirement on awareness and concentration, and its utilization in clinical MDD is absent as well as exploration on neural mechanism. This study aims to develop a more feasible novel therapy-loving-kindness meditation integrating cognition and behavior (LKM-CB), examine its effect on clinical depression, and further explore its neural mechanism by multimodal neuroimaging. Method: In study 1, the knowledge about love and the behavior of love were integrated into the LKM to form a LKM-CB, to better activate patients with cognitive and behavioral approach. It was further utilized to 30 MDD patients (31 controls). Study 2 further explored the neural mechanism behind the LKM-CB with 16 MDD patients, who underwent a structural MRI, resting-state fMRI, and reward card-guessing task fMRI before and after the LKM-CB. Results: Study 1 developed a novel 8-week LKM-CB and found that compared with control group, LKM-CB significantly improved clinical depression in intervention group. Study 2 further showed that after LKM-CB intervention, patients showed lower activation in frontal-striatum especially middle orbito-frontal cortex (OFC) and anterior cingulate (AC) and insula for win and neutral outcome and anticipation following a loss feedback, while they showed higher activation in frontal-striatum including medial/middle-OFC and hippocampus for loss outcome and anticipation following a win feedback. Similar increased ALFF activation and grey matter in frontal cortex was also found. In contrast, patients showed higher activation in non-reward temporaloccipital cortex for loss and neutral outcome and anticipation following a loss feedback, while they showed decreased temporal-occipital ALFF activation and grey matter. Conclusions: This study develops a novel LKM-CB, which is effective in improving clinical depression. After the LKM-CB, there is dissociation in the neural reward activation pattern between reward anticipation (hyperactivation) and reward outcome (hypoactivation), and a hypoactivation in non-reward temporal-occipital cortex. This study provides a new feasible LKM-CB for non-pharmacological therapy of MDD, and to our knowledge, this is the first study to explore the neural mechanism behind the efficacy of LKM in depression therapy.</p
Hyperhomocysteinemia in chronic schizophrenia: prevalence, clinical correlates, and paradoxical associations with symptom severity
No full textBackgroundElevated homocysteine levels, known as hyperhomocysteinemia (HHcy), have been implicated in the pathophysiology of schizophrenia. Most prior studies focused on first-episode or acute-phase schizophrenia patients, leaving the prevalence, determinants, and clinical correlates of HHcy in chronic schizophrenia understudied. This study aims to investigate the prevalence and determinants of HHcy in patients with chronic schizophrenia, as well as its clinical correlates.MethodsA cross-sectional study was conducted involving 509 patients diagnosed with chronic schizophrenia, recruited from multiple psychiatric hospitals in China. Demographic, clinical, and lifestyle data were collected through structured interviews and medical record reviews. Blood samples were analyzed for homocysteine levels and other biochemical parameters. The Positive and Negative Syndrome Scale (PANSS), Hamilton Depression Rating Scale (HAMD), Insomnia Severity Index (ISI), and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) were used to assess clinical symptoms and cognitive function. Binary logistic regression analysis was performed to identify independent predictors of HHcy.ResultsThe prevalence of HHcy in the study population was 56.2%. Patients with HHcy were significantly older (mean age: 52.1 +/- 12.2 years) and had a higher proportion of males (67.1%) compared to those without HHcy. The HHcy group exhibited milder positive and general psychopathology symptoms, as indicated by lower PANSS scores, revealing unexpected inverse associations with symptom severity. Elevated levels of C-reactive protein (CRP), total bilirubin (TBIL), and creatine phosphokinase (CPK) were observed in the HHcy group. Binary logistic regression analysis identified female gender and older age as independent predictors of HHcy.ConclusionsThis study highlights a high prevalence of HHcy in patients with chronic schizophrenia, associated with older age and male gender. Contrary to expectations, HHcy was linked to milder symptom severity, suggesting a potential paradoxical relationship
A Replicable and Generalizable Neuroimaging-Based Indicator of Pain Sensitivity Across Individuals
No full textRevealing the neural underpinnings of pain sensitivity is crucial for understanding how the brain encodes individual differences in pain and advancing personalized pain treatments. Here, six large and diverse functional magnetic resonance imaging (fMRI) datasets (total N = 1046) are leveraged to uncover the neural mechanisms of pain sensitivity. Replicable and generalizable correlations are found between nociceptive-evoked fMRI responses and pain sensitivity for laser heat, contact heat, and mechanical pains. These fMRI responses correlate more strongly with pain sensitivity than with tactile, auditory, and visual sensitivity. Moreover, a machine learning model is developed that accurately predicts not only pain sensitivity (r = 0.20 similar to 0.56, ps < 0.05) but also analgesic effects of different treatments in healthy individuals (r = 0.17 similar to 0.25, ps < 0.05). Notably, these findings are influenced considerably by sample sizes, requiring >200 for univariate whole brain correlation analysis and >150 for multivariate machine learning modeling. Altogether, this study demonstrates that fMRI activations encode pain sensitivity across various types of pain, thus facilitating interpretations of subjective pain reports and promoting more mechanistically informed investigations into pain physiology.</p
Learning from financial rewards and punishments reduces the in-group bias in social approach without changing the in-group bias in impressions
No full textHumans' approach behaviour and impressions are biased towards individuals from their own group (in-group) compared with different groups (out-group). There is evidence that learning from specific interactions with in-group and out-group members can reduce these in-group biases, but it is yet unclear if learning from non-social reinforcers, such as financial rewards and punishments, can have similar effects. Here, we conducted three independent studies by using different versions of a novel approach-avoidance learning task, intergroup impression ratings and computational learning models. In the approach-avoidance learning task, participants moved a manikin representing themselves towards or away from one of two symbols, representing in-group or out-group individuals or which had no social meaning. Approach was financially rewarded with varying probabilities. Our results confirmed initial in-group biases in approach and impression ratings. Rewarding out-group approach significantly reduced the in-group bias in approach, with stronger learning from rewards compared with punishments. In contrast, the in-group bias in impressions remained unchanged. Two further studies showed that learning-related changes in approach are larger in social compared with non-social contexts and require varying reward probabilities. Together, these findings show that learning from financial rewards or punishments can improve out-group approach but not out-group impressions
Gratitude Expressions in Teams: Effectiveness and Loss
No full text感激表达作为促进人际交往的重要机制，其在二元人际互动中的积极效应已得到充分验证。然而，在以团队为基本单元的现代组织背景下，传统基于二元人际互动的研究模式难以全面捕捉团队中成员感激表达的复杂互动特征及其对团队过程和效能的潜在影响。因此，亟需进一步检验团队情境下成员间感激表达对团队效能的影响机制。
尽管感激表达在二元人际交往中具有显著的积极影响，但其实际发生率往往低于预期。鉴于团队情境与二元人际情境在互动结构与动力机制上的显著差异，有必要探讨团队中是否也存在感激表达不足的现象；同时，团队情境下普遍存在的责任分散效应可能会对成员的感激表达产生抑制作用，从而引发感激表达损失的现象。然而，目前对于这一现象及其内在机制仍缺乏系统性的实证检验。
为了克服当前研究的局限性，本研究旨在解决以下三个递进的研究问题：（1）在团队情境下，成员间的感激表达是否以及如何影响团队过程和团队效能？（2）团队情境下是否存在感激表达损失的现象，即相较于二元人际情境，在团队情境下成员表达感激的可能性是否显著降低？（3）如果存在团队情境下感激表达损失的现象，感知到表达感激的“责任”是否是其中的内在机制？此外，鉴于文化背景对感激表达的影响，本研究还补充考察了该现象在不同文化情境下的适用性。
本研究共包含四个研究，总计九个子研究。研究一通过对国内某外资汽车企业 90 个团队的领导及其 458 名成员进行三阶段问卷调查，探索了团队情境下成员间感激表达的效能机制。结果显示，高水平的团队感激表达网络密度能够显著增强团队亲社会动机，从而有效提升团队工作绩效和团队组织公民行为；同时，感激表达网络中心势对这一积极效应具有显著的正向调节作用。
研究二采用情境实验法，通过两项子研究验证了团队情境下感激表达损失的现象。在四个样本中均发现了一致的结果，即与二元人际情境相比，在团队情境下成员表达感激的可能性相对更低。
研究三基于情境实验法，在三项子研究中进一步验证了团队情境下存在感激表达损失的现象，同时检验了感知到表达感激的“责任”在这一现象中的中介机制。
最后，考虑到文化因素对于感激表达的潜在影响，研究四结合西方文化背景下的真实样本以及大语言模型的模拟数据，通过三项子研究进一步探索西方文化环境下是否也存在团队情境感激表达损失的现象。结果显示，在西方文化背景中，与二元人际情境相比，团队情境下成员表达感激的可能性并无显著差异，在西方文化情境下并未观察到团队情境下感激表达损失的现象。
本研究突破了以往仅依赖二元互动视角探讨感激表达的局限，系统地揭示了团队情境下成员间感激表达的效能机制及其损失现象。研究结果表明，团队情境下成员间感激表达有助于提升团队效能，但可能因责任分散效应而受到抑制，这一损失效应在集体主义文化背景下更为显著。这些发现不仅为深入理解团队中情感互动的复杂性提供了新的视角，也为后续的干预措施和实践应用提供了理论依据，为组织在提升团队效能和有效促进成员间感激表达方面提供了新的实践启示和参考。</p
Thalamic-Specific Pain Representation and Analgesic Effects of Deep Brain Stimulation in Rats
No full text疼痛作为“第五大生命体征”，是一种复杂的生理和心理体验，其高发病率及对个体生活质量的深远影响使其成为全球性的健康挑战。深入理解疼痛的神经机制并开发有效的治疗方法具有重要的科学意义和临床价值。丘脑作为中枢神经系统中的关键脑区，在疼痛感知和调控过程中发挥着重要作用。既往研究将丘脑核团及其皮层投射区分为负责疼痛感觉和疼痛情绪处理的独立通路，并在宏观层面（尤其是皮层）刻画了疼痛的脑响应特征。然而，研究者对丘脑的疼痛表征特性及其调控作用的认识仍存在局限性，突出表现为以下四个关键科学问题：（1）丘脑对疼痛刺激的特异性响应模式是什么，其与皮层响应存在哪些异同？（2）丘脑内部是否存在特异性编码疼痛信息的核团，其神经响应特征如何？（3）靶向调控疼痛特异性丘脑核团能否产生即时性镇痛效应，其神经机制是什么？（4）靶向调控疼痛特异性核团的策略是否具有普适性，在慢性疼痛模型中是否也具有镇痛效果？基于上述问题，本文设计了四项研究，系统探究了大鼠丘脑特异性疼痛表征及其深部脑刺激（deep brain stimulation, DBS）调控的镇痛效果。
研究一解析了丘脑特异性疼痛脑响应，明确其表征特性。该研究利用多脑区电生理记录技术，同步采集大鼠内外侧疼痛通路中丘脑与皮层的疼痛和触觉诱发脑响应，并评估动物的行为表现。研究发现，丘脑表现出与皮层相似但不同的脑响应特征，均能编码疼痛强度信息。相似特征包括疼痛刺激诱发的 N1 成分和高频神经振荡（gamma band oscillations, GBOs）等；差异在于丘脑具有独特的疼痛场电位响应——早期负波（early negativity, EN）成分，其潜伏期显著早于丘脑和皮层的 N1 成分。EN 成分仅能在疼痛诱发的丘脑场电位中提取，而在触觉场电位中无法提取，该成分同时存在于腹外侧丘脑核（ventroposterior thalamic nucleus, VPL）和背内侧丘脑核（mediodorsal thalamic nucleus, MD），代表了丘脑对疼痛表征的普遍特征。
研究二鉴别了丘脑疼痛特异性核团，通过进一步分析疼痛和触觉刺激诱发的大鼠群体神经元放电数据，在脑区层面和丘脑-皮层环路层面鉴别了疼痛特异性核团及其神经响应特征。研究发现，MD 在疼痛处理中具有独特作用：与 VPL 对多种躯体感觉的广泛响应不同，MD 表现出疼痛特异性——其对疼痛刺激的神经元放电响应显著强于触觉刺激，并与前扣带回皮层（anterior cingulate cortex, ACC）形成了疼痛特异性的功能连接；此外，MD 的疼痛特异性响应模式与既往人类研究结果类似，因而具有跨物种一致性。这些发现不仅强调了 MD 在丘脑疼痛表征中的核心地位，也为开发新型镇痛靶点和策略提供了重要的实验依据。
研究三明确了DBS 调控 MD 镇痛的刺激参数，揭示了高频 DBS 调控 MD 的即时性镇痛效果。实验一对大鼠 MD 分别施加低频和高频电刺激，观测其热痛缩足潜伏期；结果显示，靶向 MD 的高频 DBS 显著延长了大鼠的热痛缩足潜伏期，有效缓解了急性疼痛。实验二在大鼠激光诱发急性疼痛模型中进一步探究了高频 DBS 对急性疼痛的影响；结果显示，靶向 MD 的高频 DBS 显著降低大鼠的疼痛行为评分，并下调了激光诱发脑响应，包括 MD 和 ACC 的 N1 成分、GBOs 以及 MD-ACC 之间的神经同步和信息交互，该研究验证了 MD 作为潜在镇痛靶点的 有效性。
研究四检验了高频 DBS 调控 MD 对慢性疼痛的镇痛效果。该研究通过两个实验分别对大鼠进行慢性炎症性疼痛造模和神经病理性疼痛造模，在多个时间点检测动物的热痛和机械疼痛阈值，并同步记录 MD 和 ACC 的静息态场电位信号。 结果表明，高频 DBS 调控丘脑 MD 能够显著缓解两种慢性疼痛伴随的热痛和机械疼痛敏化，同时下调 ACC 脑区的神经振荡；该研究表明高频 DBS 调控 MD 具有跨疼痛模型的广泛镇痛效果。
综上所述，本研究明确了 MD 在疼痛感知和调控中的重要性，揭示了其作为疼痛干预靶点的潜在价值。这一发现不仅为疼痛的认知神经机制研究提供了新视角，也为疼痛管理提供了新的干预策略。未来通过微创或无创神经调控技术靶向 MD，并结合丘脑特异性疼痛表征进行闭环调控，有望为慢性疼痛患者提供更有 效且安全的治疗选择，从而改善其生活质量。</p
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