Connectome-Based Attractor Dynamics Underlie Brain Activity in Rest, Task, and Disease
Abstract
Understanding large-scale brain dynamics is a grand challenge in neuroscience. We propose functional connectome-based Hopfield Neural Networks (fcHNNs) as a model of macro-scale brain dynamics, arising from recurrent activity flow among brain regions. An fcHNN is neither optimized to mimic certain brain characteristics, nor trained to solve specific tasks; its weights are simply initialized with empirical functional connectivity values. In the fcHNN framework, brain dynamics are understood in relation to so-called attractor states, i.e. neurobiologically meaningful low-energy activity configurations. Analyses of 7 distinct datasets demonstrate that fcHNNs can accurately reconstruct and predict brain dynamics under a wide range of conditions, including resting and task states and brain disorders. By establishing a mechanistic link between connectivity and activity, fcHNNs offer a simple and interpretable computational alternative to conventional descriptive analyses of brain function. Being a generative framework, fcHNNs can yield mechanistic insights and hold potential to uncover novel treatment targets.
Key Points
We present a simple yet powerful phenomenological model for large-scale brain dynamics
The model uses a functional connectome-based Hopfield artificial neural network (fcHNN) architecture to compute recurrent “activity flow” through the network of brain regions
fcHNN attractor dynamics accurately reconstruct several characteristics of resting state brain dynamics
fcHNNs conceptualize both task-induced and pathological changes in brain activity as a non-linear alteration of these dynamics
Our approach is validated using large-scale neuroimaging data from seven studies
fcHNNs offers a simple and interpretable computational alternative to conventional descriptive analyses of brain function
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