Depression, Vascular Burden, and Dementia Prevalence in Late Middle-Aged and Older Black Adults

OBJECTIVES

Late-life depression and white matter hyperintensities (WMH) have been linked to increased dementia risk. However, there is a dearth of literature examining these relationships in Black adults. We investigated whether depression or WMH volume are associated with a higher likelihood of dementia diagnosis in a sample of late middle-aged to older Black adults, and whether dementia prevalence is highest in individuals with both depression and higher WMH volume.

METHODS

Secondary data analysis involved 443 Black participants aged 55+ with brain imaging within 1 year of baseline visit in the National Alzheimer's Coordinating Center Uniform Data Set. Chi-square analyses and logistic regression models controlling for demographic variables examined whether active depression in the past 2 years, WMH volume, or their combination were associated with higher odds of all-cause dementia.

RESULTS

Depression and higher WMH volume were associated with a higher prevalence of dementia. These associations remained after controlling for demographic factors, as well as vascular disease burden. Dementia risk was highest in the depression/high WMH volume group compared to the depression-only group, high WMH volume-only group, and the no depression/low WMH volume group. Post hoc analyses comparing the Black sample to a demographically matched non-Hispanic White sample showed associations of depression and the combination of depression and higher WMH burden with dementia were greater in Black compared to non-Hispanic White individuals.

DISCUSSION

Results suggest late-life depression and WMH have independent and joint relationships with dementia and that Black individuals may be particularly at risk due to these factors.

A deep learning approach for mental health quality prediction using functional network connectivity and assessment data

While one can characterize mental health using questionnaires, such tools do not provide direct insight into the underlying biology. By linking approaches that visualize brain activity to questionnaires in the context of individualized prediction, we can gain new insights into the biology and behavioral aspects of brain health. Resting-state fMRI (rs-fMRI) can be used to identify biomarkers of these conditions and study patterns of abnormal connectivity. In this work, we estimate mental health quality for individual participants using static functional network connectivity (sFNC) data from rs-fMRI. The deep learning model uses the sFNC data as input to predict four categories of mental health quality and visualize the neural patterns indicative of each group. We used guided gradient class activation maps (guided Grad-CAM) to identify the most discriminative sFNC patterns. The effectiveness of this model was validated using the UK Biobank dataset, in which we showed that our approach outperformed four alternative models by 4-18% accuracy. The proposed model's performance evaluation yielded a classification accuracy of 76%, 78%, 88%, and 98% for the excellent, good, fair, and poor mental health categories, with poor mental health accuracy being the highest. The findings show distinct sFNC patterns across each group. The patterns associated with excellent mental health consist of the cerebellar-subcortical regions, whereas the most prominent areas in the poor mental health category are in the sensorimotor and visual domains. Thus the combination of rs-fMRI and deep learning opens a promising path for developing a comprehensive framework to evaluate and measure mental health. Moreover, this approach had the potential to guide the development of personalized interventions and enable the monitoring of treatment response. Overall this highlights the crucial role of advanced imaging modalities and deep learning algorithms in advancing our understanding and management of mental health.

Intersectionality in Alzheimer's Disease: The Role of Female Sex and Black American Race in the Development and Prevalence of Alzheimer's Disease

It is well known that vascular factors and specific social determinants of health contribute to dementia risk and that the prevalence of these risk factors differs according to race and sex. In this review, we discuss the intersection of sex and race, particularly female sex and Black American race. Women, particularly Black women, have been underrepresented in Alzheimer's disease clinical trials and research. However, in recent years, the number of women participating in clinical research has steadily increased. A greater prevalence of vascular risk factors such as hypertension and type 2 diabetes, coupled with unique social and environmental pressures, puts Black American women particularly at risk for the development of Alzheimer's disease and related dementias. Female sex hormones and the use of hormonal birth control may offer some protective benefits, but results are mixed, and studies do not consistently report the demographics of their samples. We argue that as a research community, greater efforts should be made to not only recruit this vulnerable population, but also report the demographic makeup of samples in research to better target those at greatest risk for the disease.

Race modifies default mode connectivity in Alzheimer's disease

Background

Older African Americans are more likely to develop Alzheimer's disease (AD) than older Caucasians, and this difference cannot be readily explained by cerebrovascular and socioeconomic factors alone. We previously showed that mild cognitive impairment and AD dementia were associated with attenuated increases in the cerebrospinal fluid (CSF) levels of total and phosphorylated tau in African Americans compared to Caucasians, even though there was no difference in beta-amyloid 1-42 level between the two races.

Methods

We extended our work by analyzing early functional magnetic resonance imaging (fMRI) biomarkers of the default mode network in older African Americans and Caucasians. We calculated connectivity between nodes of the regions belonging to the various default mode network subsystems and correlated these imaging biomarkers with non-imaging biomarkers implicated in AD (CSF amyloid, total tau, and cognitive performance).

Results

We found that race modifies the relationship between functional connectivity of default mode network subsystems and cognitive performance, tau, and amyloid levels.

Conclusion

These findings provide further support that race modifies the AD phenotypes downstream from cerebral amyloid deposition, and identifies key inter-subsystem connections for deep imaging and neuropathologic characterization.

Genetics Modulate Gray Matter Variation Beyond Disease Burden in Prodromal Huntington's Disease

Huntington’s disease (HD) is a neurodegenerative disorder caused by an expansion mutation of the cytosine–adenine–guanine (CAG) trinucleotide in the HTT gene. Decline in cognitive and motor functioning during the prodromal phase has been reported, and understanding genetic influences on prodromal disease progression beyond CAG will benefit intervention therapies. From a prodromal HD cohort (N = 715), we extracted gray matter (GM) components through independent component analysis and tested them for associations with cognitive and motor functioning that cannot be accounted for by CAG-induced disease burden (cumulative effects of CAG expansion and age). Furthermore, we examined genetic associations (at the genomic, HD pathway, and candidate region levels) with the GM components that were related to functional decline. After accounting for disease burden, GM in a component containing cuneus, lingual, and middle occipital regions was positively associated with attention and working memory performance, and the effect size was about a tenth of that of disease burden. Prodromal participants with at least one dystonia sign also had significantly lower GM volume in a bilateral inferior parietal component than participants without dystonia, after controlling for the disease burden. Two single-nucleotide polymorphisms (SNPs: rs71358386 in NCOR1 and rs71358386 in ADORA2B) in the HD pathway were significantly associated with GM volume in the cuneus component, with minor alleles being linked to reduced GM volume. Additionally, homozygous minor allele carriers of SNPs in a candidate region of ch15q13.3 had significantly higher GM volume in the inferior parietal component, and one minor allele copy was associated with a total motor score decrease of 0.14 U. Our findings depict an early genetical GM reduction in prodromal HD that occurs irrespective of disease burden and affects regions important for cognitive and motor functioning.

Whole-Brain Connectivity in a Large Study of Huntington's Disease Gene Mutation Carriers and Healthy Controls

Huntington's disease (HD) is an inherited brain disorder characterized by progressive motor, cognitive, and behavioral dysfunctions. It is caused by abnormally large trinucleotide cytosine-adenine-guanine (CAG) repeat expansions on exon 1 of the Huntingtin gene. CAG repeat length (CAG-RL) inversely correlates with an earlier age of onset. Region-based studies have shown that HD gene mutation carrier (HDgmc) individuals (CAG-RL ≥36) present functional connectivity alterations in subcortical (SC) and default mode networks. In this analysis, we expand on previous HD studies by investigating associations between CAG-RL and connectivity in the whole brain, as well as between CAG-dependent connectivity and motor and cognitive performances. We used group-independent component analysis on resting-state functional magnetic resonance imaging scans of 261 individuals (183 HDgmc and 78 healthy controls) from the PREDICT-HD study, to obtain whole-brain resting state networks (RSNs). Regression analysis was applied within and between RSNs connectivity (functional network connectivity [FNC]) to identify CAG-RL associations. Connectivity within the putamen RSN is negatively correlated with CAG-RL. The FNC between putamen and insula decreases with increasing CAG-RL, and also shows significant associations with motor and cognitive measures. The FNC between calcarine and middle frontal gyri increased with CAG-RL. In contrast, FNC in other visual (VIS) networks declined with increasing CAG-RL. In addition to observed effects in SC areas known to be related to HD, our study identifies a strong presence of alterations in VIS regions less commonly observed in previous reports and provides a step forward in understanding FNC dysfunction in HDgmc.