Search for: All records

Transvascular water exchange plays a key role in the functional integrity of the blood–brain barrier (BBB). In white matter (WM), a variety of imaging modalities have demonstrated age‐related changes in structure and metabolism, but the extent to which water exchange is altered remains unclear. Here, we investigated the cumulative effects of healthy aging on WM capillary water exchange.

A total of 38 healthy adults (aged 36‐80 years) were studied using 7T dynamic contrast enhanced MRI. Blood volume fraction (v_b) and capillary water efflux rate constant (k_po) were determined by fitting changes in the¹H₂O longitudinal relaxation rate constant (R₁) during contrast agent bolus passage to a two‐compartment exchange model. WM volume was determined by morphometric analysis of structural images.

R₁values and WM volume showed similar trajectories of age‐related decline. Among all subjects,v_bandk_poaveraged 1.7 (±0.5) mL/100 g of tissue and 2.1 (±1.1) s⁻¹, respectively. Whilev_bshowed minimal changes over the 40‐year‐age span of participants,k_podeclined 0.06 s⁻¹(ca. 3%) per year (r= −.66;P < .0005), from near 4 s⁻¹at age 30 to ca. 2 s⁻¹at age 70. The association remained significant after controlling for WM volume.

Previous studies have shown thatk_potracks Na⁺, K⁺‐ATPase activity‐dependent water exchange at the BBB and likely reflects neurogliovascular unit (NGVU) coupled metabolic activity. The age‐related decline ink_poobserved here is consistent with compromised NGVU metabolism in older individuals and the dysregulated cellular bioenergetics that accompany normal brain aging.

 

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.