Abstract
Despite the well-described deleterious effects of aging on cognition, some individuals are able to show stability. Here, we aimed to describe the functional and structural brain characteristics of older individuals, particularly focusing on those with stable working memory (WM) performance, as measured with a verbal N-back task across a 2-year follow-up interval. Forty-seven subjects were categorized as stables or decliners based on their WM change. Stables were further subdivided into high performers (SHP) and low performers (SLP), based on their baseline scores. At both time points, magnetic resonance imaging (MRI) data were acquired, including task-based functional MRI (fMRI) and structural T1-MRI. Although there was no significant interaction between overall stables and decliners as regards fMRI patterns, decliners exhibited over-activation in the right superior parietal lobule at follow-up as compared to baseline, while SHP showed reduced the activity in this region. Further, at follow-up, decliners exhibited more activity than SHP but in left temporo-parietal cortex and posterior cingulate (i.e., non-task-related areas). Also, at the cross-sectional level, SLP showed lower activity than SHP at both time points and less activity than decliners at follow-up. Concerning brain structure, a generalized significant cortical thinning over time was identified for the whole sample. Notwithstanding, the decliners evidenced a greater rate of atrophy comprising the posterior middle and inferior temporal gyrus as compared to the stable group. Overall, fMRI data suggest unsuccessful compensation in the case of decliners, shown as increases in functional recruitment during the task in the context of a loss in WM performance and brain atrophy. On the other hand, among older individuals with WM cognitive stability, differences in baseline performance might determine dissimilar fMRI trajectories. In this vein, the findings in the SHP subgroup support the brain maintenance hypothesis, suggesting that stable and high WM performance in aging is sustained by functional efficiency and maintained brain structure rather than compensatory changes.
Highlights
Radiomics is revolutionizing medical image assessment and interpretation, moving from a subjective evaluation to a quantifiable -omics image assessment method [1, 2]
We explore the role of image resampling and batch effect as post-imageacquisition correction methods for reducing radiomics variability, thereby improving the classification accuracy of radiomics in phantom and clinical applications
We evaluated the influence of image resampling and the effect of radiomics data normalization by means of batch effect correction to reduce the variability and improve the tissueclassification capacity of radiomics in a phantom and clinical application
Summary
Radiomics is revolutionizing medical image assessment and interpretation, moving from a subjective evaluation to a quantifiable -omics image assessment method [1, 2]. Multiple studies have shown that radiomics provides meaningful information about cancer and correlates with histological and molecular tumor phenotypes, creating opportunities to develop novel predictive and prognostic biomarkers for cancer [3, 4]. The maximum benefit for cancer patients has been shown when tailoring treatments to specific cancer characteristics [5]. Radiomics can play a key role in improving personalized medicine. Radiomics features are influenced by the image-acquisition technique and the reconstruction parameters [6,7,8,9]. Studies performed at a single institution usually do not account for this source of variability, and the results entail low scalability of the signatures for multicentric applications
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