Integrating large-scale neural mass modeling and deep learning

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Background Progress in the neuroimaging field in recent years has made it possible to create diffusion MRI-based structural connectivity maps as well as their functional counterparts, with functional MRI (fMRI). The identification of important features and mechanisms of network structures and their dynamics that are critical to understand brain function have necessitated the formulation of methods to unravel the links between brain structure, function, and characteristic biophysical mechanisms underlying neuroimaging signals. Multiscale modeling is one such strategy. However, multiscale modelling can fail to efficiently combine large datasets from different sources and different levels of resolution (e.g, EEG/MEG and fMRI datasets) and is constrained to current but ever-changing theoretical understandings in neuroscience. On the other hand, deep artificial neural networks (dANNs) are used to classify high-dimensional neuroimaging data for establishing links between specific data features and clinical variables. However, dANNs methods operating on noisy and incomplete data cannot provide mechanistic insights into neurophysiology. This suggests that multiscale biophysical modeling and deep learning can effectively complement each other when analyzing large neuroimaging dataset: where deep learning reveals correlation, biophysical modeling can unpack cause into mechanisms at lower scales. Overarching goal To propose and validate a large-scale neural mass model of brain activity that has integrated a realistic deep learning algorithm. Objective 1. Develop a biologically realistic learning method with asymmetric connections. Objective 2. Integrate the learning method developed in Objective 1 into a large-scale neural mass model of brain activity. Objective 3. Calibrate and test the model with simulated data and well-understood benchmark datasets. Objective 4. Estimate parameters and predict system dynamics from fMRI and EEG/MEG data recorded from healthy human subjects. Integrating deep learning and large-scale neural mass models will allow us to link findings of brain structure, function, and neurophysiological mechanisms. Our work can potentially shift conventional thinking in the field, which has focused mainly on integrating biology and machine learning at the cellular level, with dANNs units modeled after neurons. Here we propose a mean field approach that focuses on the computing capabilities of `neural masses' rather than modeling each neuron in the network individually.