Sheikhattar, A. | Search bioRxiv

bioRxiv2026

Astrocyte-induced internal state transitions reshape brainwide sensory, integrative, and motor computations

Animals rapidly adapt to changing circumstances by shifting how they perceive, integrate, and act. Such flexibility is often attributed to transitions between internal states that exert widespread influence across the brain. Yet the mechanisms that drive state transitions and how they reconfigure brainwide computation remain unclear. Larval zebrafish, when actions are rendered futile by decoupling visual flow feedback from swimming in virtual reality, enter a temporary passive, energy-preserving state. In this state, astrocyte calcium levels are elevated, and swim reinitiation requires greater accumulated visual motion. Using whole-brain, cellular-resolution activity imaging, we observed widespread circuit alterations underlying this disengaged state: neuronal visual responses weakened, visual motion integration over time became dramatically leakier, motor inhibition increased, and motor preparation slowed, together suppressing conversion of sensory evidence into action. Astrocyte calcium rose during futile swimming, tracked the emergence and resolution of these brainwide changes, and was both necessary and sufficient to drive them. Thus, astrocytes orchestrate internal states that profoundly reshape neural computations, most powerfully at intermediate integrative processing stages, to meet changing demands. HighlightsO_LIInternal state change alters brainwide neuronal processing at every stage of the sensorimotor transformation C_LIO_LIEffects are most powerful at integrative stages through stimulus memory collapse C_LIO_LIAs state resolves, amplification of sensory representations synergizes with reduced motor inhibition for action reinitiation C_LIO_LIAstrocyte activity drives these brainwide adaptive shifts in neuronal dynamics C_LI

neuroscience↗

bioRxiv2017

Real-Time Tracking of Selective Auditory Attention from M/EEG: A Bayesian Filtering Approach

Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). These procedures operate in an offline fashion, i.e., require the entire duration of the experiment and multiple trials to provide robust results. Therefore, they cannot be used in emerging applications such as smart hearing aid devices, where a single trial must be used in real-time to decode the attentional state. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: 1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, 2) Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and 3) Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and reliable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, {ell}1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurate as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.

neuroscience↗