Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries

High-Intensity Interval Training and Biological Age

The emergence of valid predictors of biological age has enabled researchers to test the effects of various interventions on biological aging processes. The established virtues of exercise and its effects on health and longevity make it a suitable candidate for investigation. This dissertation reviews the current state of biological age prediction models and presents a trial in which a specific exercise protocol’s ability to modulate biological age is tested. The specific protocol used is a 10X1 high-intensity interval training (HIIT) protocol, 10X1 referring to the quantity and duration of high intensity exercise intervals in each exercise session. The specific biological age prediction model chosen as the trial’s primary outcome measure relies on transcriptomic inputs to make biological age predictions. A significant difference in biological age was observed between groups. Reduction in biological age was observed in the exercise group, while increased biological age was observed in the control group. Exploratory, hypothesis generation analyses of gene expression revealed potential modification of autophagy, neurotrophin, and cancer biological pathways. This dissertation concludes that HIIT induces transcriptional changes which may in part account for the established beneficial effects of exercise on health and longevity