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

Fluctuations of attention network activation during the resting state reflect fluctuations in subjective attentional state.

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The Dynamics of Central-Peripheral Stress Responses after Acute Psychosocial Stress: a Multimodal Perspective

An acute stress response is a complex interaction of central and peripheral psychophysiological systems with unique temporal characteristics. Interestingly, the interaction represents a unique temporal characteristic. Investigating the dynamics of both brain and body signals during and after an encounter with a stressor allows us to understand the underlying principle of the acute stress response, which has been shown to be atypical in various psychiatric disorders. However, a detailed understanding of stress response is rarely investigated. Therefore, this thesis investigates two major approaches for understanding the acute stress response dynamics using simultaneous electroencephalography (EEG)-photoplethysmographyfunctional magnetic resonance imaging experiments in 39 subjects before and after the ScanStress task. The EEG-derived vigilance indexes reveal a continuous decline at rest. Given the role of alertness in an efficient stress response, the effects of acute stress induction on EEG-derived vigilance metrics are of interest. Therefore, the first approach uses the dynamic analysis of psychophysiological stress responses after the acute psychosocial stress induction. The first study investigates the carry-over effect of acute psychosocial stress on vigilance and its modulation by the multicomponent over-thecounter drug neurexan, which has been shown to modulate the neuroendocrine stress response. By using dynamic analysis, six vigilance scores were calculated every two minutes before and after the stress induction during the resting state. The study revealed that stress delays the continuous decline of vigilance at rest. In addition, the stress-induced increase in mean vigilance levels at rest was correlated positively with the levels of perceived stress during the last month. In addition, the mean vigilance level exhibited a decrease after neurexan treatment compared to placebo intake. Heart rate variability (HRV) can be viewed as an indicator of how well the adaptive regulation system in the brain reacts the peripheral environment. However, the relationship between the HRV and functional connectivity patterns in the brain networks in stressful situations is rarely investigated. Therefore, the second approach uses the multimodal approach to examine the interaction between different stress response systems. The study investigated the temporal association between HRV and FC between the three core brain networks, namely the central executive network, salience network, and default mode network at baseline and after the psychosocial stress induction. In this study, the functional connectivity between three core brain networks and the HRV was examined by taking 60s window length. Furthermore, the temporal association between HRV and functional connectivity was investigated. A significant association was found between HRV and default mode network-central executive network functional connectivity at rest, which was significantly reduced after acute stress induction compared to baseline. These findings suggest that HRV cofluctuates with the core brain networks selectively depending on the stress conditions. In summary, acute psychological stress affects brain dynamics by exhibiting a delay in the continuously declining vigilance and keeping the brain in a more alert state even after the stressor disappears. Furthermore, the results suggest that EEG-derived vigilance metrics index not only stress-response but also the temporal dynamics of vigilance regulation. It can serve as a potential biomarker for the diagnosis and prognosis for stress-related disorders disrupting temporal characteristics of stress response dynamics and showing atypical stress response. In addition, the study revealed that stress affects the interactions among the core large-scale functional networks and physiological dynamics of the heart. The dynamic adaptation of the resources is crucial in a stressful situation; therefore, the stress alters the interaction between the brain and heart. The perturbation in this interaction may play an important role in developing and maintaining stress-related disorders. The thesis work provides novel insights and an understanding of the central and peripheral stress response dynamics, which show a huge potential for the diagnosis, prognosis, and therapeutic planning of individuals with neuropsychiatric disorders