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Growing evidence indicates a reciprocal relationship between low-grade systemic inflammation and stress exposure towards increased vulnerability to neuropsychiatric disorders, including posttraumatic stress disorder (PTSD). However, the neural correlates of this reciprocity and their influence on the subsequent development of PTSD are largely unknown. Here we investigated alterations in functional connectivity among brain networks related to low-grade inflammation and stress exposure using two large independent data sets. Functional couplings among the higher-order cognitive network system including the salience, default mode, and central executive networks were reduced in association with low-grade inflammation and stress exposure. This reduced functional coupling may also be related to subsequent posttraumatic stress symptom severity. The current findings propose functional couplings among the higher-order cognitive network system as neural correlates of low-grade inflammation and stress exposure, and suggest that low-grade inflammation, alongside with stress, may render individuals more vulnerable to PTSD. Low-grade systemic inflammation and stress increase vulnerability to neuropsychiatric disorders. Here, the authors show that inflammation and stress-induced changes in higher order cognitive networks increase vulnerability to posttraumatic stress disorder.

Post-traumatic stress disorder (PTSD) is a chronic condition characterized by symptoms of physiological and psychosocial burden. While growing research demonstrated signs of inflammation in PTSD, specific biomarkers that may be representative of PTSD such as the detailed neural correlates underlying the inflammatory responses in relation to trauma exposure are seldom discussed. Here, we review recent studies that explored alterations in key inflammatory markers in PTSD, as well as neuroimaging-based studies that further investigated signs of inflammation within the brain in PTSD, as to provide a comprehensive summary of recent literature with a neurological perspective. A search was conducted on studies published from 2009 through 2019 in PubMed and Web of Science. Fifty original articles were selected. Major findings included elevated levels of serum proinflammatory cytokines in individuals with PTSD across various trauma types, as compared with those without PTSD. Furthermore, neuroimaging-based studies demonstrated that altered inflammatory markers are associated with structural and functional alterations in brain regions that are responsible for the regulation of stress and emotion, including the amygdala, hippocampus, and frontal cortex. Future studies that utilize both central and peripheral inflammatory markers are warranted to elucidate the underlying neurological pathway of the pathophysiology of PTSD.

The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a combined CNN-MLP algorithm (using both structural brain imaging data and sex information) for age prediction. By integrating sex information with T1-weighted imaging data, our proposed CNN-MLP algorithm outperformed not only the CNN-only algorithm but also established algorithms, such as brainageR, in prediction accuracy. Notably, this hybrid CNN-MLP algorithm effectively distinguished between mild cognitive impairment and Alzheimer’s disease groups by identifying variances in brain age gaps between them, highlighting the algorithm’s potential for clinical application. Overall, these results underscore the enhanced precision of the CNN-MLP algorithm in brain age prediction, achieved through the integration of sex information.

Fatigue is a multifactorial phenomenon affecting both physical and psychological performance, particularly in high-stress occupations. Although wearable sensors enable continuous monitoring, conventional machine-learning (ML) models can produce unstable, weakly calibrated, and opaque predictions in real-world settings. To improve reliability and interpretability, we developed a selective Retrieval-Augmented Generation (RAG)–enhanced hybrid ML–LLM framework that integrates the efficiency of ML with the reasoning capability of large language models (LLMs). Using wearable and ecological momentary assessment data from 297 emergency responders (9543 seven-day windows), logistic regression, XGBoost, and LSTM models were trained to classify fatigue levels dichotomized by the median of daily tiredness scores. The LLM was selectively activated only for borderline ML outputs (0.45 ≤ p ≤ 0.55), using symbolic rules and retrieved analog examples. In the uncertainty region, performance improved from 0.556/0.684/0.635/0.659 to 0.617/0.703/0.748/0.725 (accuracy/precision/recall/F1). On the full test set, performance similarly improved from 0.707/0.739/0.918/0.819 to 0.718/0.741/0.937/0.827, with gains confirmed by McNemar’s paired comparison test (p < 0.05). SHAP-based ML interpretation and LLM reasoning analyses independently identified short-term sleep duration and heart-rate variability as dominant predictors, providing transparent explanations for model behavior. This framework enhances classification robustness, interpretability, and efficiency, offering a scalable solution for real-world fatigue monitoring.

Background Growing literature continues to identify brain regions that are functionally altered in bipolar disorder. However, precise functional network correlates of bipolar disorder have yet to be determined due to inconsistent results. The overview of neurological alterations from a large‐scale network perspective may provide more comprehensive results and elucidate the neuropathology of bipolar disorder. Here, we critically review recent neuroimaging research on bipolar disorder using a network‐based approach. Methods A systematic search was conducted on studies published from 2009 through 2019 in PubMed and Google Scholar. Articles that utilized functional magnetic resonance imaging technique to examine altered functional activity of major regions belonging to a large‐scale brain network in bipolar disorder were selected. Results A total of 49 studies were reviewed. Within‐network hypoconnectivity was reported in bipolar disorder at rest among the default mode, salience, and central executive networks. In contrast, when performing a cognitive task, hyperconnectivity among the central executive network was found. Internetwork functional connectivity in the brain of bipolar disorder was greater between the salience and default mode networks, while reduced between the salience and central executive networks at rest, compared to control. Conclusion This systematic review suggests disruption in the functional activity of large‐scale brain networks at rest as well as during a task stimuli in bipolar disorder. Disrupted intra‐ and internetwork functional connectivity that are also associated with clinical symptoms suggest altered functional connectivity of and between large‐scale networks plays an important role in the pathophysiology of bipolar disorder. The investigation of the brain alterations with a large‐scale network perspective may provide a more comprehensive way in elucidating the neuropathology of a disorder. This systematic review evaluates recent literature and suggests alteration in the functional activity of large‐scale brain networks including the default mode, salience, and central executive networks in bipolar disorder. Furthermore, disrupted intra‐ and internetwork functional connectivity are also associated with clinical symptoms of bipolar disorder, which highlight the importance of large‐scale networks in the pathophysiology of bipolar disorder.

Preclinical studies suggest that lithium may exert neurotrophic effects that counteract pathological processes in the brain of patients with bipolar disorder (BD). To describe and compare the course and magnitude of gray matter volume changes in patients with BD who are treated with lithium or valproic acid (VPA) compared to healthy comparison subjects, and to assess clinical relationships to gray matter volume changes induced by lithium in patients with BD, we conducted longitudinal brain imaging and clinical evaluations of treatment response in 22 mood-stabilizing and antipsychotic medications-naive patients with BD who were randomly assigned to either lithium or VPA treatment after baseline assessment. Fourteen healthy comparison subjects did not take any psychotropic medications during follow-up. Longitudinal data analyses of 93 serial magnetic resonance images revealed lithium-induced increases in gray matter volume, which peaked at week 10–12 and were maintained through 16 weeks of treatment. This increase was associated with positive clinical response. In contrast, VPA-treated patients with BD or healthy comparison subjects did not show gray matter volume changes over time. Results suggest that lithium induces sustained increases in cerebral gray matter volume in patients with BD and that these changes are related to the therapeutic efficacy of lithium.

Repeated exposure to traumatic experiences may put professional firefighters at increased risk of developing posttraumatic stress disorder (PTSD). To date, however, the rate of PTSD symptoms, unmet need for mental health treatment, and barriers to treatment have only been investigated in subsamples rather than the total population of firefighters. We conducted a nationwide, total population-based survey of all currently employed South Korean firefighters (n = 39,562). The overall response rate was 93.8% (n = 37,093), with 68.0% (n = 26,887) complete responses for all variables. The rate of current probable PTSD was estimated as 5.4%. Among those with current probable PTSD (n = 1,995), only a small proportion (9.7%) had received mental health treatment during the past month. For those who had not received treatment, perceived barriers of accessibility to treatment (29.3%) and concerns about potential stigma (33.8%) were reasons for not receiving treatment. Although those with higher PTSD symptom severity and functional impairment were more likely to seek treatment, greater symptom severity and functional impairment were most strongly associated with increased concerns about potential stigma. This nationwide study points to the need for new approaches to promote access to mental health treatment in professional firefighters.

BackgroundRecent evidence suggests that hyperbaric oxygenation (HBO), which has been used as an effective treatment for certain types of tissue injury, may change neural activities in the human brain and subsequently improve symptoms of psychiatric disorders. To scrutinize the neural mechanism of HBO in the human brain, we investigated whether 20 sessions of HBO changed regional cerebral blood flow (rCBF) of the limbic system in firefighters with mild traumatic brain injury (mTBI) and subjective emotional distress.MethodsTwenty firefighters with mTBI and mild emotional distress were treated with HBO at a relatively low pressure of 1.3 atmospheres absolute for 45 min a day for 20 consecutive days (the mild emotional distress group). The rCBF of the limbic system was measured using an arterial spin labeling perfusion magnetic resonance imaging before and after the HBO. Analyses were performed on the data from fourteen individuals who completed the study and 14 age- and sex-matched healthy firefighters (the comparison group).ResultsFirefighters in the mild emotional distress group showed increase rCBF following HBO in a cluster encompassing the right hippocampal and parahippocampal regions (peak t = 4.31; cluster size = 248 mm3)(post-hoc analysis, z = 5.92, p < 0.001) that had lower rCBF relative to the comparison group at baseline (post-hoc analysis, t = −2.20, p = 0.04).ConclusionThe current study demonstrated that low-pressure HBO might increase rCBF of the hippocampal and parahippocampal regions, suggesting a potential underpinning mechanism of HBO in the human brain.

The presence of psychiatric disorders is widely recognized as one of the primary risk factors for suicide. A significant proportion of individuals receiving outpatient psychiatric treatment exhibit varying degrees of suicidal behaviors, which may range from mild suicidal ideations to overt suicide attempts. This study aims to elucidate the transdiagnostic symptom dimensions and associated suicidal features among psychiatric outpatients. The study enrolled patients who attended the psychiatry outpatient clinic at a tertiary hospital in South Korea (n = 1, 849, age range = 18–81; 61% women). A data-driven classification methodology was employed, incorporating a broad spectrum of clinical symptoms, to delineate distinctive subgroups among psychiatric outpatients exhibiting suicidality (n = 1189). A reference group of patients without suicidality (n = 660) was included for comparative purposes to ascertain cluster-specific sociodemographic, suicide-related, and psychiatric characteristics. Psychiatric outpatients with suicidality (n = 1189) were subdivided into three distinctive clusters: the low-suicide risk cluster (Cluster 1), the high-suicide risk externalizing cluster (Cluster 2), and the high-suicide risk internalizing cluster (Cluster 3). Relative to the reference group (n = 660), each cluster exhibited distinct attributes pertaining to suicide-related characteristics and clinical symptoms, covering domains such as anxiety, externalizing and internalizing behaviors, and feelings of hopelessness. Cluster 1, identified as the low-suicide risk group, exhibited less frequent suicidal ideation, planning, and multiple attempts. In the high-suicide risk groups, Cluster 2 displayed pronounced externalizing symptoms, whereas Cluster 3 was primarily defined by internalizing and hopelessness symptoms. Bipolar disorders were most common in Cluster 2, while depressive disorders were predominant in Cluster 3. Our findings suggest the possibility of differentiating psychiatric outpatients into distinct, clinically relevant subgroups predicated on their suicide risk. This research potentially paves the way for personalizing interventions and preventive strategies that address cluster-specific characteristics, thereby mitigating suicide-related mortality among psychiatric outpatients. •Hierarchical clustering sorted suicidality patients into 3 unique subgroups.•Each subgroup differed in suicide risk and clinical characteristics.•One group had low suicide risk; two displayed high suicide risks.•High-risk groups showed either externalizing or internalizing traits.

Convolutional neural networks (CNNs) have been used widely to predict biological brain age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on spatially local features and their aggregates and barely on the connective information between distant regions. To overcome this issue, we propose a novel multi-hop graph attention (MGA) module that exploits both the local and global connections of image features when combined with CNNs. After insertion between convolutional layers, MGA first converts the convolution-derived feature map into graph-structured data by using patch embedding and embedding-distance-based scoring. Multi-hop connections between the graph nodes are modeled by using the Markov chain process. After performing multi-hop graph attention, MGA re-converts the graph into an updated feature map and transfers it to the next convolutional layer. We combined the MGA module with sSE (spatial squeeze and excitation)-ResNet18 for our final prediction model (MGA-sSE-ResNet18) and performed various hyperparameter evaluations to identify the optimal parameter combinations. With 2788 three-dimensional T1-weighted MR images of healthy subjects, we verified the effectiveness of MGA-sSE-ResNet18 with comparisons to four established, general-purpose CNNs and two representative brain age prediction models. The proposed model yielded an optimal performance with a mean absolute error of 2.822 years and Pearson’s correlation coefficient (PCC) of 0.968, demonstrating the potential of the MGA module to improve the accuracy of brain age prediction.