Measuring, Tracking and Modulating Human Decision-Making Neural Circuits In the Wild

PROJECT ABSTRACTIn understanding how our brains generate perception, cognition and emotion, importantquestions are ~what are the dynamics of these processes in ecologically realistic settings?~ and~how might we manipulate the dynamics of these processes so as to potentially augment oroptimize behavior?~ In this project we address these questions by making measurements of neuralactivity, physiology and human behavior ~in-the-wild~, and testing interventions that manipulatethese dynamics for real-world tasks. Specifically, we propose a framework that engages macroscalebrain circuitry central to rapid decision-making, specifically a circuit that includes theanterior cingulate (ACC), dorsal lateral prefrontal cortex (dlPFC) and locus coeruleus (LC). Ouroverarching scientific hypothesis is that unique functional dynamics of the ACC-dlPFC-LC,particularly those reflecting modulation by stress, are observable by linking neuro-physiobehavioralmeasurements from controlled laboratory settings with those conducted in naturalisticsettings.This project proposes a new approach that uses neuroimaging and neurostimulationtechnologies, already approved for human use, and state-of-the-art machine learning to fuse andintegrate the resulting information revealed across different measurement modalities and taskenvironments. Specifically we will develop a testable model of human brain dynamics thatgoverns rapid decision-making in natural environments, and use machine learning, particularlydeep learning, to make linkages between EEG and fMRI neuroimaging measurements and tasksthat are carried out in both highly controlled laboratory environments and those that are acquiredin less well-controlled, but more ecologically realistic virtual (VR) and augmented reality (AR).Our project will develop a new computational framework that enables fusion and ~transcoding~ ofneural and physiological measurements between different modalities and noise conditions,enabling development of a macro-scale neural circuit model that is based on high resolutionneuroimaging data as well as real-world tasks and behavior. Also novel and innovative, is that wewill use closed-loop, endogenously triggered, neuromodulation to test causal interactions in thiscircuit and how these relate to behavior. The three primary objectives of the project are,Objective 1: Develop an experimental paradigm and instrumentation for measuring andmodulating activity of ACC-dlPFC-LC circuit in both the laboratory and naturalisticenvironments:Objective 2: Develop a hypothesis driven theoretical model of ACC-dlPFC-LC functionbased on switching multivariate dynamical systems.Objectives 3: Develop and evaluate a computational framework for relating lab and in-thewildACC-dlPFC-LC circuit activity, with a key innovation being development of a deep learningframework enabling ~neural transcoding~ - i.e. generating one neuroimaging modality fromanother.In addition to shedding new light on how the human brain rapidly makes decisions in realworldsituations, we believe this project will have broad implications to cognitive neuroscience aswell having revolutionary impact on DoD capabilities in the areas of human-to-human and humanmachineinteraction. The translation of models of brain dynamics from the laboratory to real-worldsetting opens the door for multi-human neural integration. These models create a natural ~language~by which neural systems can communicate. This communication could be both human-to-humanand human-to-machine. For example, these models would facilitate human-agent teaming, byproviding machine-based agents/actors with neurally-informed models of group and/orindividual~s ~state-of-mind~ when making decisions.