Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior Journal of Neural Engineering, (2020). We identify and characterize separate groups of interacting brain areas that predict learning in a brain computer interface (BCI) task and suggest a key role for networks that support learning in sustaining attention. We employ non-negative matrix factorization to obtain a single, data-driven decomposition of brain and behavioral data taken during BCI learning and use models from network control theory to test theories about their role. BCIs have marked potential as a therapy to restore neural function, but their application is limited by the difficulty, and heterogeneity of learning to reliably control a BCI. Previous work has demonstrated that neural activity in diverse cognitive systems predicts learning across individuals, but is unclear how these different systems interact through time to cohesively support the volitional control of neural activity. In 20 individuals learning to control a BCI, we investigate time varying interactions across the whole brain that predict learning, and find a separation of different neural process that work to selectively facilitate sustained attention. Our results inform avenues of research seeking to identify candidates for BCI based therapies based on their time varying brain activity.
White matter network architecture guides direct electrical stimulation through optimal state transitions Cell Reports, (2019). A mathematical model of stimulation spread across white matter tracts in the brain makes predictions about which regions, and patterns of brain activity will be eneficial for stimulation targetted to improve memory. We use electrocorticographic recordings of brain activity from a stimulation experiment conducted in epilepsy patients to show how important an individual’s patterns of white matter connections is for determining how their brain responds to stimulation and how they influence predictions of when and where to stimulate. Brain stimulation is an effective therapy for depression, epilepsy, Parkinson’s, and other neurological disorders. Personalizing and optimizing these therapies to treat other disorders is an active area of research, but progress is hampered by how difficult it is to predict how the entire brain will respond to stimulation at a given region. We used features of an individual’s brain activity, and pattern of connections between brain regions to model what happens to the whole brain when we stimulate a specific region. This work is an important, early step towards developing a fast, generalizable model of an individual’s response to a specific stimulation therapy.
Spatial embedding imposes constraints on neuronal network architectures Trends in Cognitive Science, (2018). Recent progress towards understanding circuit function has capitalized on tools from network science to parsimoniously describe the spatiotemporal architecture of neural systems. Such tools often address systems topology divorced from its physical instantiation. Nevertheless, for embedded systems such as the brain, physical laws directly constrain the processes of network growth, development, and function. We review here the rules imposed by the space and volume of the brain on the development of neuronal networks, and show that these rules give rise to a specific set of complex topologies. These rules also affect the repertoire of neural dynamics that can emerge from the system, and thereby inform our understanding of network dysfunction in disease. We close by discussing new tools and models to delineate the effects of spatial embedding.
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