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Sleep duration has been associated with dementia and stroke. Few studies have evaluated sleep pattern–related outcomes of brain disease in diverse Hispanics/Latinos.

The SOL‐INCA (Study of Latinos‐Investigation of Neurocognitive Aging) magnetic resonance imaging (MRI) study recruited diverse Hispanics/Latinos (35–85 years) who underwent neuroimaging. The main exposure was self‐reported sleep duration. Our main outcomes were total and regional brain volumes.

The final analytic sample includedn = 2334 participants. Increased sleep was associated with smaller brain volume (β_{total_brain} = −0.05,p < 0.01) and consistently so in the 50+ subpopulation even after adjusting for mild cognitive impairment status. Sleeping >9 hours was associated with smaller gray (β_{combined_gray} = −0.17,p < 0.05) and occipital matter volumes (β_{occipital_gray} = −0.18,p < 0.05).

We found that longer sleep duration was associated with lower total brain and gray matter volume among diverse Hispanics/Latinos across sex and background. These results reinforce the importance of sleep on brain aging in this understudied population.

Longer sleep was linked to smaller total brain and gray matter volumes.

Longer sleep duration was linked to larger white matter hyperintensities (WMHs) and smaller hippocampal volume in an obstructive sleep apnea (OSA) risk group.

These associations were consistent across sex and Hispanic/Latino heritage groups.

 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk. 

In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression ofCR1(complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.