Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study

Mind and body connection in expert meditators: a computational study based on central and peripheral nervous system

A meditative 'technique' is conceived as a continuum of different affective states involving mind and body jointly. Meditative practices can involve cognitive effort (e.g., focused attention and open-minded techniques), as well as automatic and implicit practices (e.g., transcendental techniques). The NGALSO tantric self-healing meditation technique is a brief, comprehensive meditation technique relying on mind and body connection. In this study, we aimed to investigate the state and the trait neurophysiological correlates of NGALSO meditation practice. First, 19 EEG channels and a 3-lead ECG signal were recorded from 10 expert meditators (more than 7 years of daily meditation) and 10 healthy inexpert participants (controls) who underwent the same meditative procedure. The neuropsychological profiles of experts and controls were compared. Results showed that expert meditators had significantly higher power spectra on alpha, theta and beta, and a higher sympathetic tone with lower parasympathetic tone after meditation. Conversely, the control group had significantly less power spectra on alpha, theta and beta, and a higher parasympathetic tone with lower sympathetic tone after meditation. A machine learning approach also allowed us to classify experts vs. controls correctly by using only EEG Theta bands before or after meditation. ECG results allowed us to show a significantly higher effort by expert meditators vs. controls, thus suggesting that a higher effort is required for this meditation, in line with the principle 'no pain, no gain' in body and mind.

Machine learning in Alzheimer's disease drug discovery and target identification

Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.

Mental flexibility assessment: A research protocol for patients with Parkinson's Disease and Anorexia Nervosa

Mental Flexibility oscillates between adaptive variability in behavior and the capacity to restore homeostasis, linked to mental health. It has recently been one of the most investigated abilities in mental and neurological diseases such as Anorexia nervosa and Parkinson's disease, studied for rigidity or cognitive inflexibility. Patients with anorexia nervosa have rigid cognitive processes about food and weight, which leads to restrictive eating and excessive exercise. People who struggle to adapt their cognitive processes and actions to change their diet and exercise habits may have a harder time recovering from the disorder. On the other hand, research suggests that Parkinson's disease patients may have cognitive flexibility impairments that impair their ability to perform daily tasks and adapt to new environments. Although of clinical interest, mental flexibility lacks theoretical liberalization and unified assessment. This study introduces "IntellEGO" a protocol for a new, multidimensional psychometric assessment of flexibility. This assessment evaluates a person's authentic ability to handle daily challenges using cognitive, emotional, and behavioral factors. Since traditional assessments often focus on one domain, we aim to examine flexibility from multiple angles, acknowledging the importance of viewing people as whole beings with mental and physical aspects. The study protocol includes two assessment phases separated by a rehabilitation period. T0, the acute phase upon admission, and T1, the post-rehabilitation phase lasting 15 days for Parkinson's patients and 4 weeks for eating disorder patients, will be assessed. Neuropsychological performance, self-report questionnaires, psychophysiological measures, and neuroendocrine measures will be collected from Anorexia Nervosa and Parkinson's Disease patients during each study phase. The objective of this procedure is to provide clinicians with a comprehensive framework for conducting meticulous assessments of mental flexibility. This framework considers emotional, cognitive, and behavioral factors, and is applicable to various patient populations.

Outlining a novel psychometric model of mental flexibility and affect dynamics

Theoretically, affective states have always been conceived as complex phenomena enabling individuals to respond flexibly and dynamically to environmental demands. Methodologically, the novel field of Affect Dynamics has started to analyze affective states as inherently dynamic and interdependent phenomena by focusing on how and why they fluctuate over time. Fluctuations of affective states can also be conceived as a function of individuals' ability to flexibly modulate their responses according to environmental demands. However, this ability has been sparsely investigated in different disciplines and domains, thus, engendering a plethora of terms and models. In this conceptual analysis, we first aimed to disentangle the puzzle of flexibility by outlining the distinctive cross-domain features of this concept, thus providing a novel comprehensive operationalization. We termed this novel unitary concept "mental flexibility," the general ability to variably adapt to environmental demands. Then, we outlined the interplay between individuals' mental flexibility and affect dynamics by proposing a novel psychometric model of affect dynamics, using Markovian chain.