Physiological underpinnings of healthy brain ageing

Changes in cerebral perfusion or metabolism can occur as a result of healthy ageing, and in conditions of impaired ageing such as mild cognitive impairment (MCI) or Alzheimer’s disease (AD). Overarchingly, this thesis aimed to explore physiological magnetic resonance imaging (MRI) measures to study both cerebral perfusion and metabolism in the healthy ageing brain. Specifically, arterial spin labelling (ASL) and functional magnetic resonance spectroscopy (1H-fMRS) were employed in the elucidation of healthy ageing. Investigation of cerebral functionality is clinically important, enabling understanding of healthy ageing and disease pathology beyond that provided by structural measures. Given the necessity for tightly-regulated tissue perfusion in the delivery of oxygen to the brain, assessment of brain perfusion can enable elucidation of related brain health. Firstly, this thesis focused on changes in brain perfusion within a cross-sectional retrospective cohort of healthy subjects. This study aimed to assess the utility of univariate and multivariate pattern analysis (MVPA) techniques, and determine whether spatial coefficient of variation (sCoV) measures – which provide a method for inferring spatial heterogeneity of blood flow from single post-label delay (PLD) ASL data – are more significantly associated with age than standard perfusion metrics (ml/100g/min values). The impact of data processing steps on quantification of perfusion was initially assessed. Particularly, the influence of partial volume effect (PVE) correction and how this affected quantification of cerebral perfusion was of interest. The relationship between measures of cerebral perfusion – in regions of interest, vascular territories, and grey matter – and age were assessed, before grey matter (GM) spatial covariance patterns were identified, with MVPA hypothesised to elucidate more subtle age-related change than univariate, voxel-wise methodology. The executive control network (ECN) was the only network exhibiting a significant decline in perfusion with age, after controlling for relevant covariates. Interestingly, whilst the PCA approach resulted in a pattern of both positive and negative associations with age across cerebral GM, the surviving clusters in voxel-wise approaches were deemed spurious. Five-fold cross validation of PCA findings was used to assess whether the resultant spatial covariance patterns were able to predict subject age. This prediction was successful, with related r2 values of between 0.5316 and 0.7297 (p < 0.001 for all), however validation of these findings in an unseen dataset is required. The utility of the sCoV metric was also compared with standard tissue perfusion values, finding that sCoV may be more closely associated with ageing than ml/100g/min in certain regions. Particularly, a significant increase in whole GM sCoV with age was notable, given the absence of significant changes in perfusion with age in the same region. Additionally, a MVPA approach was used to establish the complex unknown relationship between cerebral perfusion and the Montreal Cognitive Assessment (MoCA), before graph visualisation was used to further understand the regional relatedness of the spatial covariance pattern. PCA resulted in a model which provided a moderate explanation of the aforementioned relationship, but this may be improved by inclusion of additional covariates in subsequent work, such as those pertaining to genetic status, such as apolipoprotein E (APOE). This study also replicates an FDG PET cognitive resilience signature in an ASL cohort for the first time, with a trend towards declining perfusion with age found (p = .08). Lastly, as ageing is associated with metabolic failure in the brain, which is often investigated using methodology which employs ionising radiation, the final study was motivated to investigate possible metabolic markers of brain ageing which can be measured using MRI. Metabolic-functional coupling can be studied using functional stimulation, and functional magnetic resonance spectroscopy (fMRS) is perfectly poised to elucidate certain metabolic behaviour. Given the close relationship between glucose (Glc) – the key fuel for cerebral functionality – and lactate (Lac) metabolism, an optimised long echo time (TE) semi-localized by adiabatic selective refocusing (semi-LASER) sequence (TE=144ms) with optimised J-modulation selection at 7T was employed to assess the effects of age on the dynamic behaviour of Lac, and determine its absolute concentrations throughout the time course, whilst a visual stimulation paradigm was viewed. Successful quantification of metabolite concentrations – including Lac, tCr and tNAA – was achieved in both the young and old cohorts, and their Lac peaks clearly visually identifiable throughout the time course. A significant increase in Lac concentration was observed between rest and stimulation, but not stimulation and recovery, in the young cohort. No significant Lac time course changes were identified in the full old cohort. This thesis concluded by summarising and contextualising the key findings herein, and discussion of possible directions for further associated research. The findings of this thesis broaden the field of knowledge around healthy ageing, and therefore may contribute to subsequent translation efforts for both clinical diagnostics and treatment approaches.

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