Brain atrophy is correlated with risk of cognitive impairment, functional decline, and dementia. Despite a high infectious disease burden, Tsimane forager-horticulturists of Bolivia have the lowest prevalence of coronary atherosclerosis of any studied population and present few cardiovascular disease (CVD) risk factors despite a high burden of infections and therefore inflammation. This study (A) examines the statistical association between brain volume and age for Tsimane, and (B) compares this association to that of three industrialized populations in the U.S. and Europe. This cohort-based panel study enrolled 746 participants aged 40 to 94 (396 males), from whom computed tomography (CT) head scans were acquired. Brain volume (BV) and intracranial volume (ICV) were calculated from automatic head CT segmentations.
The linear regression coefficient estimate β⌢T of the Tsimane (T), describing the relationship between age (predictor) and BV (response, as a percentage of ICV), was calculated for the pooled sample (including both sexes) and for each sex. β⌢T was compared to the corresponding regression coefficient estimate β⌢R of samples from the industrialized reference (R) countries. For all comparisons, the null hypothesis βT = βR was rejected both for the combined samples of males and females, as well as separately for each sex. Our results indicate that the Tsimane exhibit a significantly slower decrease in brain volume with age than populations in the U.S. and Europe. Such reduced rates of brain volume decrease, together with a subsistence lifestyle and low cardiovascular disease risk, may protect brain health despite considerable chronic inflammation related to infectious burden.
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.
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