Effects of sleep on the glymphatic functioning and multimodal human brain network affecting memory in older adults.

Understanding how sleep affects the glymphatic system and human brain networks is crucial for elucidating the neurophysiological mechanism underpinning aging-related memory declines. We analyzed a multimodal dataset collected through magnetic resonance imaging (MRI) and polysomnographic recording from 72 older adults. A proxy of the glymphatic functioning was obtained from the Diffusion Tensor Image Analysis along the Perivascular Space (DTI-ALPS) index. Structural and functional brain networks were constructed based on MRI data, and coupling between the two networks (SC-FC coupling) was also calculated. Correlation analyses revealed that DTI-ALPS was negatively correlated with sleep quality measures [e.g., Pittsburgh Sleep Quality Index (PSQI) and apnea-hypopnea index]. Regarding human brain networks, DTI-ALPS was associated with the strength of both functional connectivity (FC) and structural connectivity (SC) involving regions such as the middle temporal gyrus and parahippocampal gyrus, as well as with the SC-FC coupling of rich-club connections. Furthermore, we found that DTI-ALPS positively mediated the association between sleep quality and rich-club SC-FC coupling. The rich-club SC-FC coupling further mediated the association between DTI-ALPS and memory function in good sleepers but not in poor sleepers. The results suggest a disrupted glymphatic-brain relationship in poor sleepers, which underlies memory decline. Our findings add important evidence that sleep quality affects cognitive health through the underlying neural relationships and the interplay between the glymphatic system and multimodal brain networks.

The N-3 polyunsaturated fatty acids supplementation to prevent depression recurrence in patients with late-life depression: A 52-week double-blind, placebo-controlled trial.

BACKGROUND: Reports on the efficacy of omega-3 fatty acids (n-3 PUFAs) for the treatment of late-life depression (LLD) are mixed, and most studies focus on the modification of depressive symptoms rather than depression prevention. The aim of the present study was to investigate the efficacy of n-3 PUFAs in preventing depressive recurrence in patients with late-life depression. In addition, we investigated the effects of n-3 PUFAs on changes in depressive and anxiety symptoms and inflammatory markers in LLD. METHODS: A 52-week, double-blind, randomized, controlled trial was conducted. We enrolled a total of 39 euthymic patients with LLD. They were randomized to receive either n-3 PUFAs (1.2 g per day of eicosapentaenoic acid and 1 g of docosahexaenoic acid) or placebo for 52 weeks. Recurrence of depression and severity of depression symptoms were assessed at baseline and weeks 4, 8, 16, 24, 32, 40, and 52. RESULTS: A total of 39 patients completed the trial with 19 in the n-3 PUFAs group and 20 in the placebo group. Cox proportional hazard regression indicated that n-3 PUFAs had significant protective effect on depression recurrence (Hazard Ratio: 0.295, 95 % Confidence Interval: 0.093-0.931, p value =0.037). But n-3 PUFAs intervention had no significant effect in reducing depressive or anxiety symptoms, inflammatory markers over the placebo group. LIMITATION: The results should be interpreted with consideration of the modest sample size. CONCLUSION: These findings suggest that n-3 PUFAs may have a prophylactic effect in currently euthymic patients with LLD.

Development and validation of a geriatric depression knowledge scale for older adults with depression.

Poor adherence to antidepressants increases the risk of suicide, while greater mental health awareness promotes seeking appropriate treatment, highlighting the urgent need to assess depression knowledge. This study aimed to develop and assess the psychometrics of a Geriatric Depression Knowledge Scale (GDKS) for older adults with depression. In phase 1, 18 items were generated through an intensive literature review and clinical experiences. Phase 2 involved assessing content and face validities of the GDKS. In phase 3, a cross-sectional study (206 older adults, 100 psychiatric professionals) determined construct validity, internal consistency, and test-retest reliability. GDKS demonstrated excellent content and face validity. Older participants scored significantly lower than psychiatric professionals, confirming excellent construct validity. Reliability was evident with a Kuder-Richardson formula 20 score of 0.72 and a 4-week test-retest reliability of 0.86 (p < 0.01). The GDKS provides a reliable tool for evaluating geriatric depression knowledge in psychiatric outpatient settings.

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.

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.

Traces of EEG-fMRI coupling reveals neurovascular dynamics on sleep inertia.

Upon emergence from sleep, individuals experience temporary hypo-vigilance and grogginess known as sleep inertia. During the transient period of vigilance recovery from prior nocturnal sleep, the neurovascular coupling (NVC) may not be static and constant as assumed by previous neuroimaging studies. Stemming from this viewpoint of sleep inertia, this study aims to probe the NVC changes as awakening time prolongs using simultaneous EEG-fMRI. The time-lagged coupling between EEG features of vigilance and BOLD-fMRI signals, in selected regions of interest, was calculated with one pre-sleep and three consecutive post-awakening resting-state measures. We found marginal changes in EEG theta/beta ratio and spectral slope across post-awakening sessions, demonstrating alterations of vigilance during sleep inertia. Time-varying EEG-fMRI coupling as awakening prolonged was evidenced by the changing time lags of the peak correlation between EEG alpha-vigilance and fMRI-thalamus, as well as EEG spectral slope and fMRI-anterior cingulate cortex. This study provides the first evidence of potential dynamicity of NVC occurred in sleep inertia and opens new avenues for non-invasive neuroimaging investigations into the neurophysiological mechanisms underlying brain state transitions.

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.

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.

[Effects of Multicomponent Case Management on the Problem Behaviors of Patients With Dementia and Family Caregivers' Distress, Self-Efficacy, Depression, Burden and Health Promotion Behaviors].

BACKGROUND: Few of the interventions currently available for family caregivers (FCGs) of persons with dementia (PWDs) with long-term follow-ups have a grounding in theory and incorporate multicomponent case management formats. PURPOSE: Based on Pearlin's Caregiving and Stress Process model, this study was developed to examine the effectiveness of a family-centered case management program for PWDs with early to moderate dementia in terms of reducing PWDs behavioral problems and improve FCG outcomes, including distress, self-efficacy, depression, caregiver burden, and health-promoting behaviors. METHODS: This randomized, single-blind, parallel-controlled trial included 76 dyads of PWDs and their FCGs. The dyads were recruited from outpatient clinics at dementia centers in three district hospitals in northern Taiwan. The dyads were randomly assigned to the intervention group (IG, n = 39) and control group (CG, n = 37). The dyads in the IG received a four-month intervention with two home or clinic visits and two telephone interviews. The multi-component interventions provided assessment, education, consultations, support, and referrals to long-term care resources. The CG received routine care and two social phone calls. Data were collected upon enrollment (T0 = baseline) and at 4-,6-, and 12-months post-intervention (T1, T2, and T3, respectively). Generalized estimating equations were conducted to analyze the effects of the intervention. RESULTS: By controlling for the interaction between group and time, we made a comparison between IG and the CG. The results showed significant improvements from baseline measures in behavioral problems in the PWDs for mood, psychosis, and social engagement, and improvements in the FCGs for distress and self-efficacy for obtaining respite as well as for better control of distressing thoughts, feelings of depression, caregiver burden, and overall health promoting behaviors at T1 and T2 (p < 0.5). Significant improvements were also found in the IG for psychomotor regulation among PWDs and the self-efficacy of FCGs in managing the PWDs' disturbing behaviors and health promotion behaviors for nutrition at T1 (p < 0.5). There were no significant improvements in the outcome variables at T3. CONCLUSIONS / IMPLICATIONS FOR PRACTICE: Significant interactions between group and time were found at the 6-month assessment (T2) for improvements in problem behaviors of PWDs and depression, caregiver burden, and distress in the FCGs. Positive effects on self-efficacy and health promotion behaviors among the FCGs were also achieved. The results suggest that a multicomponent case management intervention should be referenced in dementia care policymaking for FCGs and PWDs.

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.

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.

Association of Sleep, Neuropsychological Performance, and Gray Matter Volume With Glymphatic Function in Community-Dwelling Older Adults.

BACKGROUND AND OBJECTIVES: The glymphatic system, which is robustly enabled during some stages of sleep, is a fluid-transport pathway that clears cerebral waste products. Most contemporary knowledge regarding the glymphatic system is inferred from rodent experiments and human research is limited. Our objective is to explore the associations between human glymphatic function, sleep, neuropsychological performance, and cerebral gray matter volumes. METHODS: This cross-sectional study included individuals 60 years or older who had participated in the Integrating Systemic Data of Geriatric Medicine to Explore the Solution for Health Aging study between September 2019 and October 2020. Community-dwelling older adults were enrolled at 2 different sites. Participants with dementia, major depressive disorders, and other major organ system abnormalities were excluded. Sleep profile was accessed using questionnaires and polysomnography. Administered neuropsychological test batteries included Everyday Cognition (ECog) and the Consortium to Establish a Registry for Alzheimer's Disease Neuropsychological Battery (CERAD-NB). Gray matter volumes were estimated based on MRI. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index was used as the MRI marker of glymphatic function. RESULTS: A total of 84 participants (mean [SD] age 73.3 [7.1] years, 47 [56.0%] women) were analyzed. Multivariate linear regression model determined that age (unstandardized ß, -0.0025 [SE 0.0001]; p = 0.02), N2 sleep duration (unstandardized ß, 0.0002 [SE 0.0001]; p = 0.04), and the apnea-hypopnea index (unstandardized ß, -0.0011 [SE 0.0005]; p = 0.03) were independently associated with DTI-ALPS. Higher DTI-ALPS was associated with better ECog language scores (unstandardized ß, -0.59 [SE 0.28]; p = 0.04) and better CERAD-NB word list learning delayed recall subtest scores (unstandardized ß, 6.17 [SE 2.31]; p = 0.009) after covarying for age and education. Higher DTI-ALPS was also associated with higher gray matter volume (unstandardized ß, 107.00 [SE 43.65]; p = 0.02) after controlling for age, sex, and total intracranial volume. DISCUSSION: Significant associations were identified between glymphatic function and sleep, stressing the importance of sleep for brain health. This study also revealed associations between DTI-ALPS, neuropsychological performance, and cerebral gray matter volumes, suggesting the potential of DTI-ALPS as a biomarker for cognitive disorders.

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.

A Case of Lung Cancer with Brain Metastasis following Late-Onset Bipolar Disorder.

OBJECTIVE: To describe a case of lung cancer with brain metastasis in a patient who developed new late-onset bipolar disorder 2 years previously. BACKGROUND: The typical onset age of bipolar disorder is approximately 20, and the first episode is usually a depressive episode. It is still not clear which age-specific factors contribute to the underlying risk. MATERIALS AND METHODS: A 65-year-old male patient presented with a new-onset manic episode characterized by labile mood, impulsivity, decreased need for sleep, and grandiosity. He was diagnosed with late-onset bipolar disorder after excluding other possible physiological conditions. He was hospitalized in the acute psychiatric ward, and a combination of mood stabilizers and antipsychotics was prescribed. His mental condition improved, and he remained stable for 2 years. However, he experienced abrupt cognitive decline for 2 months and was referred to the emergency room for physiological examination. RESULTS: The patient was diagnosed with lung cancer with brain metastasis by brain magnetic resonance imaging and whole-body positron emission tomography. CONCLUSION: In geriatric patients, who are at high risk of multiple medical conditions, excluding secondary causes of bipolar disorder is important.

Greater white matter hyperintensities and the association with executive function in suicide attempters with late-life depression.

Late-life depression (LLD) is associated with greater risk of suicide and white matter hyperintensities (WMH), which are also found in suicide attempters regardless of age. Greater periventricular WMH are related to worse cognitive function. We investigated the spatial distribution of WMH in suicide attempters with LLD and its association with cognitive function. We recruited 114 participants with LLD (34 with history of suicide attempt and 80 without) and 47 older adult controls (individuals without LLD or history of suicide attempt). WMH were quantified by an automated segmentation algorithm and were classified into different regions. Suicide attempters with LLD had significantly higher global WMH (F3, 150 = 2.856, p = 0.039) and periventricular WMH (F3, 150 = 3.635, p = 0.014) compared to other groups. Suicide attempters with high WMH had significantly lower executive function, which could be an underlying mechanism for cognitive decline in older adults with suicidality.