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Humans are exposed to extreme environmental stressors during spaceflight and return with alterations in brain structure and shifts in intracranial fluids. To date, no studies have evaluated the effects of spaceflight on perivascular spaces (PVSs) within the brain, which are believed to facilitate fluid drainage and brain homeostasis. Here, we examined how the number and morphology of magnetic resonance imaging (MRI)-visible PVSs are affected by spaceflight, including prior spaceflight experience. Fifteen astronauts underwent sixT₁-weighted 3 T MRI scans, twice prior to launch and four times following their return to Earth after ~ 6-month missions to the International Space Station. White matter MRI-visible PVS number and morphology were calculated using an established, automated segmentation algorithm. We validated our automated segmentation algorithm by comparing algorithm PVS counts with those identified by two trained raters in 50 randomly selected slices from this cohort; the automated algorithm performed similarly to visual ratings (r(48) = 0.77,p < 0.001). In addition, we found high reliability for four of five PVS metrics across the two pre-flight time points and across the four control time points (ICC(3,k) > 0.50). Among the astronaut cohort, we found that novice astronauts showed an increase in total PVS volume from pre- to post-flight, whereas experienced crewmembers did not (p = 0.020), suggesting that experienced astronauts may exhibit holdover effects from prior spaceflight(s). Greater pre-flight PVS load was associated with more prior flight experience (r = 0.60–0.71), though these relationships did not reach statistical significance (p > 0.05). Pre- to post-flight changes in ventricular volume were not significantly associated with changes in PVS characteristics, and the presence of spaceflight associated neuro-ocular syndrome (SANS) was not associated with PVS number or morphology. Together, these findings demonstrate that PVSs can be consistently identified onT₁-weighted MRI scans, and that spaceflight is associated with PVS changes. Specifically, prior spaceflight experience may be an important factor in determining PVS characteristics.

 

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.