Mediterranean-Type Diet and Brain Structural Change from 73 to 79 Years in the Lothian Birth Cohort 1936.

OBJECTIVES: To test whether Mediterranean-type Diet (MeDi) at age 70 years is associated with longitudinal trajectories of total brain MRI volume over a six-year period from age 73 to 79. DESIGN: Cohort study which uses a correlational design. SETTING: Participants residing in the Lothian region of Scotland and living independently in the community. PARTICIPANTS: A relatively healthy Scottish sample drawn from the Lothian Birth Cohort 1936. MEASUREMENTS: Total brain volume measurements were available at ages 73, 76 and 79 (N ranged 332 to 563). Adherence to the MeDi was based on food frequency questionnaire data collected three years before the baseline imaging scans, and was used in growth curve models to predict the trajectory of total brain volume change. RESULTS: No association was found (p>.05) between adherence to the MeDi at age 70 and total brain volume change from 73 to 79 years in minimally-adjusted (sex) or fully adjusted models controlling for additional health confounders. CONCLUSIONS: Variation in adherence to the MeDi was not predictive of total brain atrophy over a six-year period. This suggests that previous findings of dietary associations with brain volume are not long lasting or become less important as ageing-related conditions account for greater variation in brain volume change. More frequent collection of dietary intake data is needed to clarify these findings.

Three major dimensions of human brain cortical ageing in relation to cognitive decline across the eighth decade of life.

Different brain regions can be grouped together, based on cross-sectional correlations among their cortical characteristics; this patterning has been used to make inferences about ageing processes. However, cross-sectional brain data conflate information on ageing with patterns that are present throughout life. We characterised brain cortical ageing across the eighth decade of life in a longitudinal ageing cohort, at ages ~73, ~76, and ~79 years, with a total of 1376 MRI scans. Volumetric changes among cortical regions of interest (ROIs) were more strongly correlated (average r = 0.805, SD = 0.252) than were cross-sectional volumes of the same ROIs (average r = 0.350, SD = 0.178). We identified a broad, cortex-wide, dimension of atrophy that explained 66% of the variance in longitudinal changes across the cortex. Our modelling also discovered more specific fronto-temporal and occipito-parietal dimensions that were orthogonal to the general factor and together explained an additional 20% of the variance. The general factor was associated with declines in general cognitive ability (r = 0.431, p < 0.001) and in the domains of visuospatial ability (r = 0.415, p = 0.002), processing speed (r = 0.383, p < 0.001) and memory (r = 0.372, p < 0.001). Individual differences in brain cortical atrophy with ageing are manifest across three broad dimensions of the cerebral cortex, the most general of which is linked with cognitive declines across domains. Longitudinal approaches are invaluable for distinguishing lifelong patterns of brain-behaviour associations from patterns that are specific to aging.

White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks.

White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the effects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist). The problem of WMH delineation is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense regions. Recently, several automated methods aiming to tackle the challenges of WMH segmentation have been proposed. Most of these methods have been specifically developed to segment WMH in MRI but cannot differentiate between WMHs and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. Therefore, a task specific, reliable, fully automated method that can segment and differentiate between these two pathological manifestations on MRI has not yet been fully identified. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. The proposed fully convolutional CNN architecture, called uResNet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to outperform other well established and state-of-the-art algorithms in terms of overlap with manual expert annotations. Clinically, the extracted WMH volumes were found to correlate better with the Fazekas visual rating score than competing methods or the expert-annotated volumes. Additionally, a comparison of the associations found between clinical risk-factors and the WMH volumes generated by the proposed method, was found to be in line with the associations found with the expert-annotated volumes.

Brain age predicts mortality.

Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, 'brain-predicted age', derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death.

Morphologic, distributional, volumetric, and intensity characterization of periventricular hyperintensities.

BACKGROUND AND PURPOSE: White matter hyperintensities are characteristic of old age and identifiable on FLAIR and T2-weighted MR imaging. They are typically separated into periventricular or deep categories. It is unclear whether the innermost segment of periventricular white matter hyperintensities is truly abnormal or is imaging artifacts. MATERIALS AND METHODS: We used FLAIR MR imaging from 665 community-dwelling subjects 72-73 years of age without dementia. Periventricular white matter hyperintensities were visually allocated into 4 categories: 1) thin white line; 2) thick rim; 3) penetrating toward or confluent with deep white matter hyperintensities; and 4) diffuse ill-defined, labeled as "subtle extended periventricular white matter hyperintensities." We measured the maximum intensity and width of the periventricular white matter hyperintensities, mapped all white matter hyperintensities in 3D, and investigated associations between each category and hypertension, stroke, diabetes, hypercholesterolemia, cardiovascular disease, and total white matter hyperintensity volume. RESULTS: The intensity patterns and morphologic features were different for each periventricular white matter hyperintensity category. Both the widths (r = 0.61, P < .001) and intensities (r = 0.51, P < .001) correlated with total white matter hyperintensity volume and with each other (r = 0.55, P < .001) for all categories with the exception of subtle extended periventricular white matter hyperintensities, largely characterized by evidence of erratic, ill-defined, and fragmented pale white matter hyperintensities (width: r = 0.02, P = .11; intensity: r = 0.02, P = .84). The prevalence of hypertension, hypercholesterolemia, and neuroradiologic evidence of stroke increased from periventricular white matter hyperintensity categories 1 to 3. The mean periventricular white matter hyperintensity width was significantly larger in subjects with hypertension (mean difference = 0.5 mm, P = .029) or evidence of stroke (mean difference = 1 mm, P < .001). 3D mapping revealed that periventricular white matter hyperintensities were discontinuous with deep white matter hyperintensities in all categories, except only in particular regions in brains with category 3. CONCLUSIONS: Periventricular white matter hyperintensity intensity levels, distribution, and association with risk factors and disease suggest that in old age, these are true tissue abnormalities and therefore should not be dismissed as artifacts. Dichotomizing periventricular and deep white matter hyperintensities by continuity from the ventricle edge toward the deep white matter is possible.

Brain white matter tract integrity as a neural foundation for general intelligence.

General intelligence is a robust predictor of important life outcomes, including educational and occupational attainment, successfully managing everyday life situations, good health and longevity. Some neuronal correlates of intelligence have been discovered, mainly indicating that larger cortices in widespread parieto-frontal brain networks and efficient neuronal information processing support higher intelligence. However, there is a lack of established associations between general intelligence and any basic structural brain parameters that have a clear functional meaning. Here, we provide evidence that lower brain-wide white matter tract integrity exerts a substantial negative effect on general intelligence through reduced information-processing speed. Structural brain magnetic resonance imaging scans were acquired from 420 older adults in their early 70s. Using quantitative tractography, we measured fractional anisotropy and two white matter integrity biomarkers that are novel to the study of intelligence: longitudinal relaxation time (T1) and magnetisation transfer ratio. Substantial correlations among 12 major white matter tracts studied allowed the extraction of three general factors of biomarker-specific brain-wide white matter tract integrity. Each was independently associated with general intelligence, together explaining 10% of the variance, and their effect was completely mediated by information-processing speed. Unlike most previously established neurostructural correlates of intelligence, these findings suggest a functionally plausible model of intelligence, where structurally intact axonal fibres across the brain provide the neuroanatomical infrastructure for fast information processing within widespread brain networks, supporting general intelligence.