Public health and safety professionals, including firefighters, police officers, and emergency medical personnel, serve as critical pillars of public safety and societal well-being. These professions require navigating environments characterized by ‘brittleness’, ‘anxiety’, ‘nonlinearity’, and ‘incomprehensibility’—conditions that lead to chronic physical and psychological stress. This stress significantly elevates the risk of mental health disorders such as post-traumatic stress disorder, anxiety disorders, mood disorders, and sleep disturbances, while also increasing the likelihood of human errors driven by cognitive lapses. Such challenges extend beyond individual health, undermining organizational efficiency and ultimately jeopardizing public safety and societal welfare. Existing mental health management systems predominantly depend on reactive interventions, which are insufficient to meet the dynamic and unpredictable demands of high-risk occupational environments. As a proactive alternative, this paper introduces the concept of ‘Mind Vital Signs,’ an innovative framework that expands the traditional concept of vital signs into the mental health domain. Mind Vital Signs integrates physiological indicators—including heart rate variability, physical activity, respiratory rates, and sleep patterns—with psychological and behavioral data such as ecological momentary assessments and life logs. By employing real-time monitoring and advanced analytics, this multidimensional system facilitates early detection of mental health risks and supports targeted and timely preventive interventions. The implementation of Mind Vital Signs not only bolsters individual resilience and organizational stability but also enhances operational efficiency and strengthens public safety and societal well-being. Future research should prioritize clinical validation and policy development to ensure the effective integration and scalability of Mind Vital Signs in high-risk occupational settings.

Public health and safety professionals, including firefighters, police officers, and emergency medical personnel, serve as critical pillars of public safety and societal well-being. These professions require navigating environments characterized by ‘brittleness’, ‘anxiety’, ‘nonlinearity’, and ‘incomprehensibility’—conditions that lead to chronic physical and psychological stress. This stress significantly elevates the risk of mental health disorders such as post-traumatic stress disorder, anxiety disorders, mood disorders, and sleep disturbances, while also increasing the likelihood of human errors driven by cognitive lapses. Such challenges extend beyond individual health, undermining organizational efficiency and ultimately jeopardizing public safety and societal welfare. Existing mental health management systems predominantly depend on reactive interventions, which are insufficient to meet the dynamic and unpredictable demands of high-risk occupational environments. As a proactive alternative, this paper introduces the concept of ‘Mind Vital Signs,’ an innovative framework that expands the traditional concept of vital signs into the mental health domain. Mind Vital Signs integrates physiological indicators—including heart rate variability, physical activity, respiratory rates, and sleep patterns—with psychological and behavioral data such as ecological momentary assessments and life logs. By employing real-time monitoring and advanced analytics, this multidimensional system facilitates early detection of mental health risks and supports targeted and timely preventive interventions. The implementation of Mind Vital Signs not only bolsters individual resilience and organizational stability but also enhances operational efficiency and strengthens public safety and societal well-being. Future research should prioritize clinical validation and policy development to ensure the effective integration and scalability of Mind Vital Signs in high-risk occupational settings.

Objectives: Recent advances in brain age prediction models reveal accelerated brain aging in major depressive disorder (MDD) patients. This review investigates the complex relationship between brain aging and biological age gap (BAG) in MDD, emphasizing the influences of clinical characteristics, treatment responses, and various neuroimaging techniques on this dynamic interplay. Methods: A systematic review of the existing literature was conducted, focusing on 18 studies that analyze brain aging patterns in MDD patients. Key factors such as age, clinical features, and lifestyle choices were examined to assess their impact on BAG and the overall neurobiological health of individuals with MDD. Results: The findings indicate that MDD patients frequently experience accelerated brain aging, particularly in elderly populations, with BAG serving as a valuable biomarker for assessing biological aging rates. The review highlights the urgent need for more granular approaches, considering variables such as age, gender, and socioeconomic status. Specific local brain aging patterns were observed in regions related to emotional regulation, suggesting that localized BAG changes may provide critical insights into the pathophysiology of MDD and its neurobiological underpinnings. Conclusions BAG is a significant biomarker for evaluating accelerated brain aging in MDD, informing personalized treatment strategies. Future research should incorporate diverse clinical characteristics and advanced neuroimaging techniques in representative samples to enhance the clinical applicability of BAG and deepen the understanding of its role in depression and biological aging.

Anosmia, characterized by the loss of smell, is associated not only with dysfunction in the peripheral olfactory system but also with changes in several brain regions involved in olfactory processing. Specifically, the orbitofrontal cortex is recognized for its pivotal role in integrating olfactory information, engaging in bidirectional communication with the primary olfactory regions, including the olfactory cortex, amygdala, and entorhinal cortex. However, little is known about alterations in structural connections among these brain regions in patients with anosmia. In this study, highresolution T1-weighted images were obtained from participants. Utilizing the volumes of key brain regions implicated in olfactory function, we employed a structural covariance approach to investigate brain reorganization patterns in patients with anosmia (n=22) compared to healthy individuals (n=30). Our structural covariance analysis demonstrated diminished connectivity between the amygdala and entorhinal cortex, components of the primary olfactory network, in patients with anosmia compared to healthy individuals (z=-2.22, FDR-corrected p=0.039). Conversely, connectivity between the orbitofrontal cortex—a major region in the extended olfactory network—and amygdala was found to be enhanced in the anosmia group compared to healthy individuals (z=2.32, FDR-corrected p=0.039). However, the structural connections between the orbitofrontal cortex and entorhinal cortex did not differ significantly between the groups (z=0.04, FDR-corrected p=0.968). These findings suggest a potential structural reorganization, particularly of higher-order cortical regions, possibly as a compensatory effort to interpret the limited olfactory information available in individuals with olfactory loss.

Cognitive dysfunction, a significant complication of type 2 diabetes mellitus (T2DM), can potentially manifest even from the early stages of the disease. Despite evidence of global brain atrophy and related cognitive dysfunction in early-stage T2DM patients, specific regions vulnerable to these changes have not yet been identified. The study enrolled patients with T2DM of less than five years’ duration and without chronic complications (T2DM group, n=100) and demographically similar healthy controls (control group, n=50). High-resolution T1-weighted magnetic resonance imaging data were subjected to independent component analysis to identify structurally significant components indicative of morphometric networks. Within these networks, the groups’ gray matter volumes were compared, and distinctions in memory performance were assessed. In the T2DM group, the relationship between changes in gray matter volume within these networks and declines in memory performance was examined. Among the identified morphometric networks, the T2DM group exhibited reduced gray matter volumes in both the precuneus (Bonferronicorrected p=0.003) and insular-opercular (Bonferroni-corrected p=0.024) networks relative to the control group. Patients with T2DM demonstrated significantly lower memory performance than the control group (p=0.001). In the T2DM group, reductions in gray matter volume in both the precuneus (r=0.316, p=0.001) and insular-opercular (r=0.199, p=0.047) networks were correlated with diminished memory performance. Our findings indicate that structural alterations in the precuneus and insular-opercular networks, along with memory dysfunction, can manifest within the first 5 years following a diagnosis of T2DM.

Objectives: Recent advances in brain age prediction models reveal accelerated brain aging in major depressive disorder (MDD) patients. This review investigates the complex relationship between brain aging and biological age gap (BAG) in MDD, emphasizing the influences of clinical characteristics, treatment responses, and various neuroimaging techniques on this dynamic interplay. Methods: A systematic review of the existing literature was conducted, focusing on 18 studies that analyze brain aging patterns in MDD patients. Key factors such as age, clinical features, and lifestyle choices were examined to assess their impact on BAG and the overall neurobiological health of individuals with MDD. Results: The findings indicate that MDD patients frequently experience accelerated brain aging, particularly in elderly populations, with BAG serving as a valuable biomarker for assessing biological aging rates. The review highlights the urgent need for more granular approaches, considering variables such as age, gender, and socioeconomic status. Specific local brain aging patterns were observed in regions related to emotional regulation, suggesting that localized BAG changes may provide critical insights into the pathophysiology of MDD and its neurobiological underpinnings. Conclusions BAG is a significant biomarker for evaluating accelerated brain aging in MDD, informing personalized treatment strategies. Future research should incorporate diverse clinical characteristics and advanced neuroimaging techniques in representative samples to enhance the clinical applicability of BAG and deepen the understanding of its role in depression and biological aging.

Objectives: Recent advances in brain age prediction models reveal accelerated brain aging in major depressive disorder (MDD) patients. This review investigates the complex relationship between brain aging and biological age gap (BAG) in MDD, emphasizing the influences of clinical characteristics, treatment responses, and various neuroimaging techniques on this dynamic interplay. Methods: A systematic review of the existing literature was conducted, focusing on 18 studies that analyze brain aging patterns in MDD patients. Key factors such as age, clinical features, and lifestyle choices were examined to assess their impact on BAG and the overall neurobiological health of individuals with MDD. Results: The findings indicate that MDD patients frequently experience accelerated brain aging, particularly in elderly populations, with BAG serving as a valuable biomarker for assessing biological aging rates. The review highlights the urgent need for more granular approaches, considering variables such as age, gender, and socioeconomic status. Specific local brain aging patterns were observed in regions related to emotional regulation, suggesting that localized BAG changes may provide critical insights into the pathophysiology of MDD and its neurobiological underpinnings. Conclusions BAG is a significant biomarker for evaluating accelerated brain aging in MDD, informing personalized treatment strategies. Future research should incorporate diverse clinical characteristics and advanced neuroimaging techniques in representative samples to enhance the clinical applicability of BAG and deepen the understanding of its role in depression and biological aging.

Public health and safety professionals, including firefighters, police officers, and emergency medical personnel, serve as critical pillars of public safety and societal well-being. These professions require navigating environments characterized by ‘brittleness’, ‘anxiety’, ‘nonlinearity’, and ‘incomprehensibility’—conditions that lead to chronic physical and psychological stress. This stress significantly elevates the risk of mental health disorders such as post-traumatic stress disorder, anxiety disorders, mood disorders, and sleep disturbances, while also increasing the likelihood of human errors driven by cognitive lapses. Such challenges extend beyond individual health, undermining organizational efficiency and ultimately jeopardizing public safety and societal welfare. Existing mental health management systems predominantly depend on reactive interventions, which are insufficient to meet the dynamic and unpredictable demands of high-risk occupational environments. As a proactive alternative, this paper introduces the concept of ‘Mind Vital Signs,’ an innovative framework that expands the traditional concept of vital signs into the mental health domain. Mind Vital Signs integrates physiological indicators—including heart rate variability, physical activity, respiratory rates, and sleep patterns—with psychological and behavioral data such as ecological momentary assessments and life logs. By employing real-time monitoring and advanced analytics, this multidimensional system facilitates early detection of mental health risks and supports targeted and timely preventive interventions. The implementation of Mind Vital Signs not only bolsters individual resilience and organizational stability but also enhances operational efficiency and strengthens public safety and societal well-being. Future research should prioritize clinical validation and policy development to ensure the effective integration and scalability of Mind Vital Signs in high-risk occupational settings.

Objectives: Recent advances in brain age prediction models reveal accelerated brain aging in major depressive disorder (MDD) patients. This review investigates the complex relationship between brain aging and biological age gap (BAG) in MDD, emphasizing the influences of clinical characteristics, treatment responses, and various neuroimaging techniques on this dynamic interplay. Methods: A systematic review of the existing literature was conducted, focusing on 18 studies that analyze brain aging patterns in MDD patients. Key factors such as age, clinical features, and lifestyle choices were examined to assess their impact on BAG and the overall neurobiological health of individuals with MDD. Results: The findings indicate that MDD patients frequently experience accelerated brain aging, particularly in elderly populations, with BAG serving as a valuable biomarker for assessing biological aging rates. The review highlights the urgent need for more granular approaches, considering variables such as age, gender, and socioeconomic status. Specific local brain aging patterns were observed in regions related to emotional regulation, suggesting that localized BAG changes may provide critical insights into the pathophysiology of MDD and its neurobiological underpinnings. Conclusions BAG is a significant biomarker for evaluating accelerated brain aging in MDD, informing personalized treatment strategies. Future research should incorporate diverse clinical characteristics and advanced neuroimaging techniques in representative samples to enhance the clinical applicability of BAG and deepen the understanding of its role in depression and biological aging.

Public health and safety professionals, including firefighters, police officers, and emergency medical personnel, serve as critical pillars of public safety and societal well-being. These professions require navigating environments characterized by ‘brittleness’, ‘anxiety’, ‘nonlinearity’, and ‘incomprehensibility’—conditions that lead to chronic physical and psychological stress. This stress significantly elevates the risk of mental health disorders such as post-traumatic stress disorder, anxiety disorders, mood disorders, and sleep disturbances, while also increasing the likelihood of human errors driven by cognitive lapses. Such challenges extend beyond individual health, undermining organizational efficiency and ultimately jeopardizing public safety and societal welfare. Existing mental health management systems predominantly depend on reactive interventions, which are insufficient to meet the dynamic and unpredictable demands of high-risk occupational environments. As a proactive alternative, this paper introduces the concept of ‘Mind Vital Signs,’ an innovative framework that expands the traditional concept of vital signs into the mental health domain. Mind Vital Signs integrates physiological indicators—including heart rate variability, physical activity, respiratory rates, and sleep patterns—with psychological and behavioral data such as ecological momentary assessments and life logs. By employing real-time monitoring and advanced analytics, this multidimensional system facilitates early detection of mental health risks and supports targeted and timely preventive interventions. The implementation of Mind Vital Signs not only bolsters individual resilience and organizational stability but also enhances operational efficiency and strengthens public safety and societal well-being. Future research should prioritize clinical validation and policy development to ensure the effective integration and scalability of Mind Vital Signs in high-risk occupational settings.