Associations of neuroinflammatory IL-6 and IL-8 with brain atrophy, memory decline, and core AD biomarkers - in cognitively unimpaired older adults.

Concentrations of pro-inflammatory cytokines -interleukin-6 (IL-6) and interleukin-8 (IL-8) - are increased with age and in Alzheimer's disease (AD). It is not clear whether concentrations of IL-6 and IL-8 in the central nervous system predict later brain and cognitive changes over time nor whether this relationship is mediated by core AD biomarkers. Here, 219 cognitively healthy older adults (62-91 years), with baseline cerebrospinal fluid (CSF) measures of IL-6 and IL-8 were followed over time - up to 9 years - with assessments that included cognitive function, structural magnetic resonance imaging, and CSF measurements of phosphorylated tau (p-tau) and amyloid-ß (Aß-42) concentrations (for a subsample). Higher baseline CSF IL-8 was associated with better memory performance over time in the context of lower levels of CSF p-tau and p-tau/Aß-42 ratio. Higher CSF IL-6 was related to less CSF p-tau changes over time. The results are in line with the hypothesis suggesting that an up-regulation of IL-6 and IL-8 in the brain may play a neuroprotective role in cognitively healthy older adults with lower load of AD pathology.

Deep neural networks learn general and clinically relevant representations of the ageing brain.

The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in data acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one of the largest and most diverse datasets assembled (n=53542), and trained convolutional neural networks (CNNs) to predict age. We achieved state-of-the-art performance on unseen data from unknown scanners (n=2553), and showed that higher brain age delta is associated with diabetes, alcohol intake and smoking. Using transfer learning, the intermediate representations learned by our model complemented and partly outperformed brain age delta in predicting common brain disorders. Our work shows we can achieve generalizable and biologically plausible brain age predictions using CNNs trained on heterogeneous datasets, and transfer them to clinical use cases.

Differential age-related gray and white matter impact mediates educational influence on elders' cognition.

High education, as a proxy of cognitive reserve (CR), has been associated with cognitive advantage amongst old adults and may operate through neuroprotective and/or compensation mechanisms. In neuromaging studies, indirect evidences of neuroprotection can be inferred from positive relationships between CR and brain integrity measures. In contrast, compensation allows high CR elders to sustain greater brain damage. We included 100 cognitively normal old-adults and investigated the associations and interactions between education, speed of processing (SP), memory and two brain integrity measures: cortical thickness (CTh) of gray matter (GM) and fractional anisotropy (FA) in the white matter (WM). High education was associated with better cognitive performance, enlarged CTh in frontal lobe areas and reduced measures of FA in several areas. Better SP performance in higher educated subjects was related to more preserved GM and WM, while memory status amongst high educated elders was better explained by a putative compensatory mechanism and independently from cerebrovascular risk indicators. Moreover, we analyzed the direct effect of age on measures of brain integrity and found a stronger negative effect on WM than in CTh, which was accentuated amongst the high CR sample. Our study suggests that the cognitive advantage associated to high education among healthy aging is related to the coexistence of both neuroprotective and compensatory mechanisms. In particular, high educated elders seem to have greater capacity to counteract a more abrupt age impact on WM integrity.