User-Friendly Chatbot to Mitigate the Psychological Stress of Older Adults During the COVID-19 Pandemic: Development and Usability Study.

BACKGROUND: To safeguard the most vulnerable individuals during the COVID-19 pandemic, numerous governments enforced measures such as stay-at-home orders, social distancing, and self-isolation. These social restrictions had a particularly negative effect on older adults, as they are more vulnerable and experience increased loneliness, which has various adverse effects, including increasing the risk of mental health problems and mortality. Chatbots can potentially reduce loneliness and provide companionship during a pandemic. However, existing chatbots do not cater to the specific needs of older adult populations. OBJECTIVE: We aimed to develop a user-friendly chatbot tailored to the specific needs of older adults with anxiety or depressive disorders during the COVID-19 pandemic and to examine their perspectives on mental health chatbot use. The primary research objective was to investigate whether chatbots can mitigate the psychological stress of older adults during COVID-19. METHODS: Participants were older adults belonging to two age groups (≥65 years and <65 years) from a psychiatric outpatient department who had been diagnosed with depressive or anxiety disorders by certified psychiatrists according to the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria. The participants were required to use mobile phones, have internet access, and possess literacy skills. The chatbot's content includes monitoring and tracking health data and providing health information. Participants had access to the chatbot for at least 4 weeks. Self-report questionnaires for loneliness, depression, and anxiety were administered before and after chatbot use. The participants also rated their attitudes toward the chatbot. RESULTS: A total of 35 participants (mean age 65.21, SD 7.51 years) were enrolled in the trial, comprising 74% (n=26) female and 26% (n=9) male participants. The participants demonstrated a high utilization rate during the intervention, with over 82% engaging with the chatbot daily. Loneliness significantly improved in the older group ≥65 years. This group also responded positively to the chatbot, as evidenced by changes in University of California Los Angeles Loneliness Scale scores, suggesting that this demographic can derive benefits from chatbot interaction. Conversely, the younger group, <65 years, exhibited no significant changes in loneliness after the intervention. Both the older and younger age groups provided good scores in relation to chatbot design with respect to usability (mean scores of 6.33 and 6.05, respectively) and satisfaction (mean scores of 5.33 and 5.15, respectively), rated on a 7-point Likert scale. CONCLUSIONS: The chatbot interface was found to be user-friendly and demonstrated promising results among participants 65 years and older who were receiving care at psychiatric outpatient clinics and experiencing relatively stable symptoms of depression and anxiety. The chatbot not only provided caring companionship but also showed the potential to alleviate loneliness during the challenging circumstances of a pandemic.

Life After Traumatic Brain Injury: Effects on the Lifestyle and Quality of Life of Community-Dwelling Patients.

Persons who have experienced traumatic brain injury (TBI) may encounter a range of changes in their physical, mental, and cognitive functions as well as high fatigue levels. To gain a comprehensive understanding of the challenges faced by persons after TBI, we conducted multi-domain assessments among community-dwelling persons with a history of TBI and compared them with age- and sex-matched controls from the Northeastern Taiwan Community Medicine Research Cohort between 2019 and 2021. A total of 168 persons with TBI and 672 non-TBI controls were not different in terms of demographics, comorbidities, and physiological features. However, compared with the non-TBI group, the TBI group had a distinct lifestyle that involved increased reliance on analgesics (6.9% vs. 15.0%, respectively; p = 0.001) and sleep aids (p = 0.008), which negatively affected their quality of life. Moreover, they consumed more coffee (p < 0.001), tea (p < 0.001), cigarettes (p = 0.002), and betel nuts (p = 0.032) than did the non-TBI group. Notably, the use of coffee had a positive effect on the quality of life of the TBI group (F = 4.034; p = 0.045). Further, compared with the non-TBI group, the TBI group had increased risks of sarcopenia (p = 0.003), malnutrition (p = 0.003), and anxiety (p = 0.029) and reduced blood levels of vitamin D (29.83 ± 10.39 vs. 24.20 ± 6.59 ng/mL, respectively; p < 0.001). Overall, the TBI group had a reduced health-related quality of life, with significant challenges related to physical health, mental well-being, social interactions, pain management, and fatigue levels. Moreover, the TBI group experienced poorer sleep quality and efficiency than did the non-TBI group. In conclusion, persons who have sustained brain injuries that require comprehensive and holistic care that includes lifestyle modification, mental and physical healthcare plans, and increased long-term support from their communities. ClinicalTrials.gov (identifier: NCT04839796).

Associations of depression and perceived physical fatigability with white matter integrity in older adults.

BACKGROUNDS: Fatigability is prevalent in older adults. However, it is often associated with depressed mood. We aim to investigate these two psychobehavioral constructs by examining their underpinning of white matter structures in the brain and their associations with different medical conditions. METHODS: Twenty-seven older adults with late-life depression (LLD) and 34 cognitively normal controls (CN) underwent multi-shell diffusion MRI. Fatigability was measured with the Pittsburgh Fatigability Scale. We examined white matter integrity by measuring the quantitative anisotropy (QA), a fiber tracking parameter with better accuracy than the traditional imaging technique. RESULTS: We found those with LLD had lower QA in the 2nd branch of the left superior longitudinal fasciculus (SLF-II), and those with more physical fatigability had lower QA in more widespread brain regions. In tracts associated with more physical fatigability, the lower QA in left acoustic radiation and left superior thalamic radiation correlated with higher blood glucose (r = - 0.46 and - 0.49). In tracts associated with depression, lower QA in left SLF-II correlated with higher bilirubin level (r = - 0.58). DISCUSSION: Depression and fatigability were associated with various white matter integrity changes, which correlated with biochemistry biomarkers all related to inflammation.

Predicting suicidality in late-life depression by 3D convolutional neural network and cross-sample entropy analysis of resting-state fMRI.

BACKGROUND: Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD). METHODS: We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation. RESULTS: We found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide. CONCLUSION: Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality.

Cognitive protection and brain entropy changes from omega-3 polyunsaturated fatty acids supplement in late-life depression: A 52-week randomized controlled trial.

BACKGROUND: Late-life depression (LLD) is associated with risk of dementia, yet intervention of LLD provides an opportunity to attenuate subsequent cognitive decline. Omega-3 polyunsaturated fatty acids (PUFAs) supplement is a potential intervention due to their beneficial effect in depressive symptoms and cognitive function. To explore the underlying neural mechanism, we used resting-state functional MRI (rs-fMRI) before and after omega-3 PUFAs supplement in older adults with LLD. METHODS: A 52-week double-blind randomized controlled trial was conducted. We used multi-scale sample entropy to analyze rs-fMRI data. Comprehensive cognitive tests and inflammatory markers were collected to correlate with brain entropy changes. RESULTS: A total of 20 patients completed the trial with 11 under omega-3 PUFAs and nine under placebo. While no significant global cognitive improvement was observed, a marginal enhancement in processing speed was noted in the omega-3 PUFAs group. Importantly, participants receiving omega-3 PUFAs exhibited decreased brain entropy in left posterior cingulate gyrus (PCG), multiple visual areas, the orbital part of the right middle frontal gyrus, and the left Rolandic operculum. The brain entropy changes of the PCG in the omega-3 PUFAs group correlated with improvement of language function and attenuation of interleukin-6 levels. LIMITATIONS: Sample size is small with only marginal clinical effect. CONCLUSION: These findings suggest that omega-3 PUFAs supplement may mitigate cognitive decline in LLD through anti-inflammatory mechanisms and modulation of brain entropy. Larger clinical trials are warranted to validate the potential therapeutic implications of omega-3 PUFAs for deterring cognitive decline in patients with late-life depression.

Potential Mobile Health Applications for Improving the Mental Health of the Elderly: A Systematic Review.

The rapid aging of the global population presents challenges in providing mental health care resources for older adults aged 65 and above. The COVID-19 pandemic has further exacerbated the global population's psychological distress due to social isolation and distancing. Thus, there is an urgent need to update scholarly knowledge on the effectiveness of mHealth applications to improve older people's mental health. This systematic review summarizes recent literature on chatbots aimed at enhancing mental health and well-being. Sixteen papers describing six apps or prototypes were reviewed, indicating the practicality, feasibility, and acceptance of chatbots for promoting mental health in older adults. Engaging with chatbots led to improvements in well-being and stress reduction, as well as a decrement in depressive symptoms. Mobile health applications addressing these studies are categorized for reference.

Vascular risk factors and astrocytic marker for the glymphatic system activity.

OBJECTIVES: Glymphatic system maintains brain fluid circulation via active transportation of astrocytic aquaporin-4 in perivascular space. The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) is an established method measuring perivascular glymphatic activity, but comprehensive investigations into its influential factors are lacking. METHODS: Community-dwelling older adults underwent brain MRI scans, neuropsychiatric, and multi-domain assessments. Blood biomarker tests included glial fibrillary acidic protein (GFAP) for astrocyte injury. RESULTS: In 71 enrolled participants, the DTI-ALPS index was associated with modifiable factors, including lipid profile (high-density lipoprotein, r = 0.396; very-low-density lipoprotein, r = - 0.342), glucose intolerance (diabetes mellitus, standardized mean difference (SMD) = 0.7662; glycated hemoglobin, r = - 0.324), obesity (body mass index, r = - 0.295; waist, r = - 0.455), metabolic syndrome (SMD = - 0.6068), cigarette-smoking (SMD = - 0.6292), and renal clearance (creatinine, r = - 0.387; blood urea nitrogen, r = - 0.303). Unmodifiable associative factors of DTI-ALPS were age (r = - 0.434) and sex (SMD = 1.0769) (all p < 0.05). A correlation of DTI-ALPS and blood GFAP was noticed (r = - 0.201, one-tailed t-test for the assumption that astrocytic injury impaired glymphatic activity, p = 0.046). Their cognitive correlations diverged, domain-specific for DTI-ALPS (Facial Memory Test, r = 0.272, p = 0.022) but global cognition-related for blood GFAP (MoCA, r = - 0.264, p = 0.026; ADAS-cog, r = 0.304, p = 0.010). CONCLUSION: This correlation analysis revealed multiple modifiable and unmodifiable association factors to the glymphatic image marker. The DTI-ALPS index correlated with various metabolic factors that are known to increase the risk of vascular diseases such as atherosclerosis. Furthermore, the DTI-ALPS index was associated with renal indices, and this connection might be a link of water regulation between the two systems. In addition, the astrocytic biomarker, plasma GFAP, might be a potential marker of the glymphatic system; however, more research is needed to confirm its effectiveness.

Review of EEG-based neurofeedback as a therapeutic intervention to treat depression.

Depression, or major depressive disorder, is a common mental disorder that affects individuals' behavior, mood, and physical health, and its prevalence has increased during the lockdowns implemented to curb the COVID-19 pandemic. There is an urgent need to update the treatment recommendations for mental disorders during such crises. Conventional interventions to treat depression include long-term pharmacotherapy and cognitive behavioral therapy. Electroencephalogram-neurofeedback (EEG-NF) training has been suggested as a non-invasive option to treat depression with minimal side effects. In this systematic review, we summarize the recent literature on EEG-NF training for treating depression. The 12 studies included in our final sample reported that despite several issues related to EEG-NF practices, patients with depression showed significant cognitive, clinical, and neural improvements following EEG-NF training. Given its low cost and the low risk of side effects due to its non-invasive nature, we suggest that EEG-NF is worth exploring as an augmented tool for patients who already receive standard medications but remain symptomatic, and that EEG-NF training may be an effective intervention tool that can be utilized as a supplementary treatment for depression. We conclude by providing some suggestions related to experimental designs and standards to improve current EEG-NF training practices for treating depression.

Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI.

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.

Functional Connectivity Dynamics Altered of the Resting Brain in Subjective Cognitive Decline.

Background: Subjective cognitive decline (SCD) appears in the preclinical stage of the Alzheimer's disease continuum. In this stage, dynamic features are more sensitive than static features to reflect early subtle changes in functional brain connectivity. Therefore, we studied local and extended dynamic connectivity of the resting brain of people with SCD to determine their intrinsic brain changes. Methods: We enrolled cognitively normal older adults from the communities and divided them into SCD and normal control (NC) groups. We used mean dynamic amplitude of low-frequency fluctuation (mdALFF) to evaluate region of interest (ROI)-wise local dynamic connectivity of resting-state functional MRI. The dynamic functional connectivity (dFC) between ROIs was tested by whole-brain-based statistics. Results: When comparing SCD (N = 40) with NC (N = 45), mdALFFmean decreased at right inferior parietal lobule (IPL) of the frontoparietal network (FPN). Still, it increased at the right middle temporal gyrus (MTG) of the ventral attention network (VAN) and right calcarine of the visual network (VIS). Also, the mdALFFvar (variance) increased at the left superior temporal gyrus of AUD, right MTG of VAN, right globus pallidum of the cingulo-opercular network (CON), and right lingual gyrus of VIS. Furthermore, mdALFFmean at right IPL of FPN are correlated negatively with subjective complaints and positively with objective cognitive performance. In the dFC seeded from the ROIs with local mdALFF group differences, SCD showed a generally lower dFCmean and higher dFCvar (variance) to other regions of the brain. These weakened and unstable functional connectivity appeared among FPN, CON, the default mode network, and the salience network, the large-scale networks of the triple network model for organizing neural resource allocations. Conclusion: The local dynamic connectivity of SCD decreased in brain regions of cognitive executive control. Meanwhile, compensatory visual efforts and bottom-up attention rose. Mixed decrease and compensatory increase of dynamics of intrinsic brain activity suggest the transitional nature of SCD. The FPN local dynamics balance subjective and objective cognition and maintain cognitive preservation in preclinical dementia. Aberrant triple network model features the dFC alternations of SCD. Finally, the right lateralization phenomenon emerged early in the dementia continuum and affected local dynamic connectivity.

Normative Data of Mini-Mental State Examination, Montreal Cognitive Assessment, and Alzheimer's Disease Assessment Scale-Cognitive Subscale of Community-Dwelling Older Adults in Taiwan.

INTRODUCTION: Appropriate tools and references are essential for evaluating individuals' cognitive levels. This study validated the Taiwan version of the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-cog) and provided normative data for the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and ADAS-cog in community-dwelling older adults. METHODS: MMSE, MoCA, and ADAS-cog were administered to 150 nondemented healthy adults aged 55-85 years during 2018-2020 as part of the Northeastern Taiwan Community Medicine Research Cohort. ADAS-cog was translated from the original English version to traditional Chinese with cultural and language considerations in Taiwan. Cronbach's alpha (α) tested the reliability of ADAS-cog, and Pearson correlations examined its external validity using MMSE and MoCA as comparisons. Normative data were generated and stratified by age and education, and the one-way analysis of variance compared scores between age and education groups. Another 20 hospital-acquired participants with cognitive impairment joined the 150 healthy participants. Comparisons in the Clinical Dementia Rating (CDR) tiers tested the discriminability of the tests for different cognitive levels. The area under the receiver operating characteristic curve (AUROC) analyzed the power of ADAS-cog in predicting CDR 0.5 from CDR 0. RESULTS: The Taiwan version of ADAS-cog had fair reliability between items (α = 0.727) and good correlations to MMSE (r = -0.673, p < 0.001) and MoCA (r = -0.746, p < 0.001). The normative data of MMSE, MoCA, and ADAS-cog showed ladder changes with age (p = 0.006, 0.001, and 0.437) and education (p < 0.001, <0.001, and <0.001) in the 150 nondemented older adults. Next, in the 170 mixed participants from the communities and the hospital, MMSE, MoCA, and ADAS-cog scores were well differentiable between CDR 0, 0.5, and 1. In addition, ADAS-cog discriminated CDR 0.5 from 0 by an AUROC of 0.827 (p < 0.001). DISCUSSION/CONCLUSION: The three structured cognitive tests consistently reflect cognitive levels of healthy older adults. The Taiwan version of ADAS-cog is compatible with MMSE and MoCA to distinguish people with mildly impaired from normal cognition. In addition, this study derived MMSE, MoCA, and ADAS-cog norms tailored to demographic factors. The findings highlight the need for stratification of age and education rather than applying a fixed cutoff for defining normal and abnormal cognition.

Sex Differences in the Relationship between Excessive Alcohol Consumption and Metabolic Abnormalities: A Community-Based Study in Taiwan.

Excessive alcohol consumption, as part of an unhealthy lifestyle, can contribute to metabolic abnormalities. This study investigated the sex differences in the relationship between excessive drinking and the risk of metabolic abnormalities. This community-based study included 3387 participants (age range: 30-103 years, mean age ± SD: 57 ± 13.5 years, 38.2% males) from the northeastern region of Taiwan. All participants completed a demographic survey and were subjected to blood tests. The risks of excessive drinking were evaluated using the Alcohol Use Disorder Identification Test (AUDIT). The results showed that males were at higher risks of obesity, hypertension, and hypertriglyceridemia, but at a lower risk of abdominal obesity than females. Males with hazardous drinking were at greater risks of hypertension, hyperglycemia, low serum levels of high-density lipoprotein cholesterol, and hypertriglyceridemia compared to those with no drinking. Females with hazardous drinking were at a greater risk of hypertension than those with no drinking. There was no interaction effect of sex and excessive drinking on the risks of metabolic abnormalities after controlling for demographics and lifestyle-related habits. Future studies are warranted to explore the sex-specific risk factors for metabolic abnormalities and to elucidate the mechanism underlying this association between alcohol consumption and metabolic abnormalities.

Meta-analytic evidence for the cognitive control model of loneliness in emotion processing.

Loneliness is strongly related to affective dysregulation. However, the neuropsychological mechanisms underpinning the loneliness-affective processing relationships remain unclear. Here, we first utilised the coordinate-based activation likelihood estimation method to confirm functional clusters related to loneliness, including the striatum, superior and medial frontal gyrus, insula, and cuneus. Meta-analytic connectivity modelling was then performed to characterise the functional connectivity of these clusters across studies using emotion tasks. Our results revealed that these clusters co-activated with the cognitive control networks. From the literature, we understand that loneliness and its neural correlates are highly related to regulating the attention biases to social rewards and social cues. Therefore, our findings provide a proof-of-concept that loneliness up-regulates the cognitive control networks to process socio-affective information. Prolonged up-regulation thus exhausts cognitive resources and hence, affective dysregulation. This study offers insight into the intricate role of cognitive and affective regulation in loneliness and social perception and provides meta-analytic evidence of the cognitive control model of loneliness and loneliness-related affective dysregulation, bringing significant clinical implications.

Validation of the Traditional Chinese Version of the Pittsburgh Fatigability Scale for Older Adults.

OBJECTIVES: The Pittsburgh Fatigability Scale (PFS) is a self-administered 10-item tool to measure physical and mental fatigability in older adults. The aim of the current study was to validate the psychometric properties of the traditional Chinese version of PFS (TC-PFS). METHODS: We recruited 114 community-dwellingolder adults, where 35 were diagnosed with late-life depression (LLD), 26 with mild cognitive impairment (MCI), and 53 were cognitively normal (CN) from a larger community study of older adults. Statistical analyses were done separately for TC-PFS Physical and Mental subscales. Factor analysis was used for reliability, Cronbach's alpha for internal consistency, Pearson's correlation for construct validity, and group comparison for discriminative validity. RESULTS: Factor analysis revealed a two-factor structure for both the TC-PFS Physical and Mental subscales with high reliability (α = 0.89 and 0.89, respectively). Patients with LLD had the highest PFS scores, with 80.0% and 82.9% classified as having greater physical and mental fatigability. For concurrent validity, we found moderate associations with the vitality and physical functioning subscales of the 36-Item Short Form Health Survey. For convergent validity, TC-PFS showed moderate association with emotional-related psychometrics, particularly for the Physical subscale in those with LLD. In contrast, TC-PFS Mental subscale showed correlations with cognitive function, particularly in the MCI group. CONCLUSIONS: Our results indicate that the TC-PFS is a valid instrument to measure perceived physical and mental fatigability in older Taiwanese adults.Clinical implications: Perceived fatigability reflects the underlying physical, mental or cognitive function in older adults with or without depression.

Multimodal Neural Evidence on the Corticostriatal Underpinning of Suicidality in Late-Life Depression.

BACKGROUND: Suicidality involves thoughts (ideations and plans) and actions related to self-inflicted death. To improve management and prevention of suicidality, it is essential to understand the key neural mechanisms underlying suicidal thoughts and actions. Following empirically informed neural framework, we hypothesized that suicidal thoughts would be primarily characterized by alterations in the default mode network indicating disrupted self-related processing, whereas suicidal actions would be characterized by changes in the lateral prefrontal corticostriatal circuitries implicating compromised action control. METHODS: We analyzed the gray matter volume and resting-state functional connectivity of 113 individuals with late-life depression, including 45 nonsuicidal patients, 33 with suicidal thoughts but no action, and 35 with past suicidal action. Between-group analyses revealed key neural features associated with suicidality. The functional directionality of the identified resting-state functional connectivity was examined using dynamic causal modeling to further elucidate its mechanistic nature. Post hoc classification analysis examined the contribution of the neural measures to suicide classification. RESULTS: As expected, reduced gray matter volumes in the default mode network and lateral prefrontal regions characterized patients with suicidal thoughts and those with past suicidal actions compared with nonsuicidal patients. Furthermore, region-of-interest analyses revealed that the directionality and strength of the ventrolateral prefrontal cortex-caudate resting-state functional connectivity were related to suicidal thoughts and actions. The neural features significantly improved classification of suicidal thoughts and actions over that based on clinical and suicide questionnaire variables. CONCLUSIONS: Gray matter reductions in the default mode network and lateral prefrontal regions and the ventrolateral prefrontal cortex-caudate connectivity alterations characterized suicidal thoughts and actions in patients with late-life depression.

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset.

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

Characteristics of Optic Disc and Visual Field Changes in Patients with Thyroid-Associated Orbitopathy and Open-Angle Glaucoma.

This study aimed to characterize the changes in the visual field (VF) patterns and disc morphology of patients with thyroid-associated orbitopathy (TAO) and open-angle glaucoma (OAG). A retrospective review of the medical records at the Tri-Service General Hospital in Taiwan identified 396 eyes of 198 patients with thyroid-associated glaucoma. A final follow-up of VF examination in 140 eyes revealed 114 eyes with VF defects, indicating disease progression. The characteristics of and changes in disc morphology, optical coherence tomography findings, and VF defects were statistically analyzed. The most common VF defects at the initial diagnosis and the end of the follow-up period were inferior partial arcuate (17%) and paracentral (15%) defects, respectively. The most common VF defect in patients with unspecific disc signs was an unspecific scotoma (13%). The most common optic disc feature was disc cupping (51%), followed by parapapillary atrophy (48%). The most frequent location of nerve fiber layer thinning was the inferotemporal region (48%). VF defects showed a significantly more pronounced progression in the non-nerve fiber bundle group than in the nerve fiber bundle group (p < 0.001). This study details the characteristics and progression of disc morphology and VF defects in patients with TAO and OAG.

White Matter Integrity Underlies the Physical-Cognitive Correlations in Subjective Cognitive Decline.

Objective: Although previous studies postulated that physical and cognitive decline codeveloped in preclinical dementia, the interconnected relationship among subjective cognitive complaints (SCCs), objective cognitive performance, and physical activity remained hazy. We investigated the mediating roles of physical activity between subjective and objective cognition. Diffusion tensor imaging (DTI) was utilized to test our hypothesis that brain white matter microstructural changes underlie the physical-cognitive decline in subjective cognitive decline (SCD). Methods: We enrolled cognitively normal older adults aged > 50 years in the Community Medicine Research Center of Keelung Chang Gung Memorial Hospital during 2017-2020. Regression models analyzed mediation effects of physical activity between subjective and objective cognition. The self-reported AD8 questionnaire assessed SCCs. The SCD group, defined by AD8 score ≥ 2, further underwent diffusion MRI scans. Those who agreed to record actigraphy also wore the SOMNOwatch™ for 72 h. Spearman's correlation coefficients evaluated the associations of diffusion indices with physical activity and cognitive performance. Results: In 95 cognitively normal older adults, the AD8 score and the Montreal Cognitive Assessment (MoCA) score were mediated partially by the metabolic equivalent of the International Physical Activity Questionnaire-Short Form (IPAQ-SF MET) and fully by the sarcopenia score SARC-F. That is, the relation between SCCs and poorer cognitive performance was mediated by physical inactivity. The DTI analysis of 31 SCD participants found that the MoCA score correlated with mean diffusivity at bilateral inferior cerebellar peduncles and the pyramids segment of right corticospinal tract [p < 0.05, false discovery rate (FDR) corrected]. The IPAQ-SF MET was associated with fractional anisotropy (FA) at the right posterior corona radiata (PCR) (p < 0.05, FDR corrected). In 15 SCD participants who completed actigraphy recording, the patterns of physical activity in terms of intradaily variability and interdaily stability highly correlated with FA of bilateral PCR and left superior corona radiata (p < 0.05, FDR corrected). Conclusions: This study addressed the role of physical activity in preclinical dementia. Physical inactivity mediated the relation between higher SCCs and poorer cognitive performance. The degeneration of specific white matter tracts underlay the co-development process of physical-cognitive decline in SCD.