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Abstract: Background: Stroop task is used to evaluate inhibition, a core executive function. Alpha Event‐Related Desynchronization (ERD) from analysis of electroencephalogram (EEG) during Stroop task reflects brain interference processing. We previously reported different relationships between heart rate variability (HRV) and alpha ERD during Stroop task. However, HRV proxied autonomic function can be confounded by increasing erratic rhythm with age, quantified by heart rate fragmentation (HRF). The knowledge of HRF is limited in a Cognitively Healthy (CH) cohort. To fill this gap, we studied these measurements in CH participants who underwent a Stroop task. Method: We studied HRF in an established EEG dataset during Stroop task from CH participants when their CSF amyloid/tau ratio were normal (≥2.71, CH‐NATs) or pathological (

Electroencephalographic (EEG) alpha oscillations have been related to heart rate variability (HRV) and both change in Alzheimer's disease (AD). We explored if task switching reveals altered alpha power and HRV in cognitively healthy individuals with AD pathology in cerebrospinal fluid (CSF) and whether HRV improves the AD pathology classification by alpha power alone. We compared low and high alpha event-related desynchronization (ERD) and HRV parameters during task switch testing between two groups of cognitively healthy participants classified by CSF amyloid/tau ratio: normal (CH-NAT, n = 19) or pathological (CH-PAT, n = 27). For the task switching paradigm, participants were required to name the color or word for each colored word stimulus, with two sequential stimuli per trial. Trials include color (cC) or word (wW) repeats with low load repeating, and word (cW) or color switch (wC) for high load switching. HRV was assessed for RR interval, standard deviation of RR-intervals (SDNN) and root mean squared successive differences (RMSSD) in time domain, and low frequency (LF), high frequency (HF), and LF/HF ratio in frequency domain. Results showed that CH-PATs compared to CH-NATs presented: 1) increased (less negative) low alpha ERD during low load repeat trials and lower word switch cost (low alpha: p = 0.008, Cohen's d = -0.83, 95% confidence interval -1.44 to -0.22, and high alpha: p = 0.019, Cohen's d = -0.73, 95% confidence interval -1.34 to -0.13); 2) decreasing HRV from rest to task, suggesting hyper-activated sympatho-vagal responses. 3) CH-PATs classification by alpha ERD was improved by supplementing HRV signatures, supporting a potentially compromised brain-heart interoceptive regulation in CH-PATs. Further experiments are needed to validate these findings for clinical significance.

Neurodegenerative disorders, including Alzheimer's disease and AD‐related dementias (AD/ADRD), pose significant challenges to health care systems globally, particularly in Africa. With the advances in medical technology and research capabilities, especially in next‐generation sequencing and imaging, vast amounts of data have been generated from AD/ADRD research. Given that the greatest increase in AD/ADRD prevalence is expected to occur in Africa, it is critical to establish comprehensive bioinformatics training programs to help African scientists leverage existing data and collect additional information to untangle AD/ADRD heterogeneity in African populations. The South Texas Alzheimer's Disease Research Center, with efforts from the National Institutes of Health and the Global Brain Health Institute, has partnered with the Brain Research Africa Initiative to develop the African Initiative on Bioinformatics Online Training in Neurodegenerative Disease (AI‐BOND). AI‐BOND is a comprehensive and accessible training program, the aim of which is to advance biostatistics and bioinformatics expertise in Africa in studying neurodegenerative diseases. This expertise is essential to enable African scientists to utilize the extensive AD/ADRD data and enhance the continent's ability to contribute to global research efforts in this field. The training addresses the gap in analyzing neurodegenerative disease data by providing skills and knowledge in genetic epidemiology, biostatistics, and bioinformatics to African students and researchers. This innovative online training program will last 6 months and provide training in skill sets R, SAS, and Python programing, genome‐wide association studies, genomics, transcriptomics, proteomics, metabolomics, microbiome analysis, and advanced statistical methods. Additional training will include study design and manuscript and grant writing. The first cohort of the AI‐BOND program will graduate in June 2024. The AI‐BOND program is expected to build research computational capacities in Africa that will improve the ability of graduates to conduct and utilize large‐scale studies, with the goal of curbing the growing incidence of neurodegenerative diseases in Africa. Highlights: Alzheimer's disease (AD) and AD‐related dementias (ADRD) pose significant health challenges globally, particularly in Africa.The most significant AD/ADRD prevalence increase is predicted to occur in Africa.It is crucial to establish a bioinformatics training capacity in Africa to leverage the vast number of multi‐omics and imaging biomarkers of AD/ADRD data being generated.The African Initiative on Bioinformatics Online Training in Neurodegenerative Disease (AI‐BOND) training addresses the gaps in study design, biostatistics, genetic epidemiology, and bioinformatics related to neurodegenerative diseases in Africa.The success of AI‐BOND is anticipated to help build computational research capacities in Africa. [ABSTRACT FROM AUTHOR]

ImportanceHigher dietary intake of the essential fatty acid docosahexaenoic (DHA) has been associated with better cognitive performance in several epidemiological studies. Animal and in vitro studies also indicate that DHA prevents amyloid deposition in the brain.ObjectiveTo determine the association between serum DHA levels, cerebral amyloidosis, and the volumes of brain areas affected by Alzheimer disease.Design, settings, and participantsCross-sectional analysis of serum DHA levels together with measures of amyloid deposition (Pittsburgh Compound B index), brain volumes, and neuropsychological testing scores from 61 participants in the Aging Brain Study. The study was conducted between June 2008 and May 2013, and the data were analyzed between October 2015 and April 2016. Linear models were adjusted for age, sex, years of education, and apolipoprotein E status.Main outcomes and measuresSerum DHA levels with cerebral amyloidosis measured using PIB PET.ResultsSamples were available from 61 Aging Brain Study participants (41 women and 20 men) who underwent amyloid PET imaging. The mean (SD) age of the participants was 77 (6) years and ranged from 67 to 88 years. Serum DHA levels (percentage of total fatty acids) were 23% lower in participants with cerebral amyloidosis than those without (0.97 vs 1.25, P = .007) and were inversely correlated with brain amyloid load (r = -0.32, P = .01) independent of age, sex, apolipoprotein E genotype, and years of education. Moreover, greater serum DHA levels were positively associated with brain volume in several subregions affected by AD, in particular the left subiculum (r = 0.38, P = .005) and the left entorhinal volumes (r = 0.51, P = .001). Serum DHA levels were also associated with nonverbal memory scores (r = 0.28, P = .03).Conclusions and relevanceIn this study, serum DHA levels were associated with pathogenesis of cerebral amyloidosis and with preservation of entorhinal and hippocampal volumes. These findings suggest an important role for DHA metabolism in brain amyloid deposition during the preclinical or early symptomatic stages of Alzheimer disease.