Understanding and Predicting Cognitive Fatigue across Multiple Timescales, Distinct Aspects of Cognition, and Different Individuals with Multiscale Whole Cortex Models
Abstract
Cognitive fatigue (CF) is a crucial challenge for the Armed Forces, where 24/7 operations require readiness, alertness, and high-level performance across diverse cognitive tasks at all hours of the day. The primary challenge to understanding and mitigating CF is its multidimensional, multiscale nature. CF has been linked with global arousal state and neurotransmitter activity, neuronal synapses and neuronal/glial interactions, local release of sleep regulatory substances, and intracellular circadian timekeeping, with changes occurring over time scales from milliseconds to days. CF affects brain regions differently, and the manifestations of CF are task-dependent and vary significantly between individuals, mediated in part by genetics. Neurons fire on a millisecond timescale, yet human CF builds over minutes (time-on-task effect), hours (homeostatic regulation), and days (allostatic response). Harnessing the multiscale nature of CF to ensure and enhance military readiness and performance is hampered by critical knowledge gaps, which this proposal seeks to address through multiscale modeling. Our new mathematical developments make a new generation of multiscale models possible, including 1) new ways to rapidly simulate and visualize the behavior of millions of neurons on desktop computers; 2) asymmetric particle population density methods that resolve the stochastic nature of individual ion channels, while predicting the behavior of a network of millions of neurons; 3) New techniques to study learning in neuronal networks, and 4) gene-set enrichment with mathematical biology techniques that allow us to determine the role individual genes play in overall neuronal systems. Based on extensive published data from animal experimentation, we have previously modeled the biophysics of synaptic learning. We can simulate this in each of more than a billion synapses in the mouse brain with experimentally measured positions and connectivity, enabling detailed predictions of neuronal activity and the biomolecular substrates of cognitive processing. Here, we expand our modeling substantially to address the characteristics mentioned above of human CF, informed by previous large-scale data sets and new data. The proposed multiscale model development for human CF will be further supported and validated by targeted experimentation explicitly focused on relationships between the overt cognitive performance and underlying molecular substrate manifestations of CF over time, across brain regions, and between individuals. We will measure distinct aspects of CF through psychomotor vigilance and cognitive flexibility tasks in the laboratory and errors in smartphone keyboard strokes in the field and link these through the model to specific biophysical mechanisms that occur during sleep deprivation and circadian alignment. The temporal and spatial dynamics of the brain-based molecular underpinnings will be measured with unprecedented accuracy through repeated measurements of cerebral spinal fluid (CSF) under constant routine conditions and neuroimaging to assess functional connectivity, diffusion tensor imaging (DTI), and fiber tractography. Individuals in real-world 24/7 settings involving sleep deprivation and circadian misalignment will be tested with a mobile app. We will then apply our model to predict the individual differences that predominate CF more accurately and test these models in the real world with genetic and breath metabolomic biomarkers. Our specific aims are to 1) develop and optimize our multiscale model; 2) further develop our mathematical tools to bridge potential differences between the mouse and human brain, as well as simplify our detailed model to the point that it can be deployed on smartphones; 3) understand and predict CF outcomes for distinct aspects of cognition and across different individuals; and 4) test and deploy our individualized CF predictions through a mobile app.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 08, 2022
- Source ID
- W911NF2210223
Entities
People
- Daniel Forger
Organizations
- Army Contracting Command
- United States Army
- University of Michigan