EEG source derived salience network coupling supports real-world attention switching.

While the brain mechanisms underlying selective attention have been studied in great detail in controlled laboratory settings, it is less clear how these processes function in the context of a real-world self-paced task. Here, we investigated engagement on a real-world computerized task equivalent to a standard academic test that consisted of solving high-school level problems in a self-paced manner. In this task, we used EEG-source derived estimates of effective coupling between brain sources to characterize the neural mechanisms underlying switches of sustained attention from the attentive on-task state to the distracted off-task state. Specifically, since the salience network has been implicated in sustained attention and attention switching, we conducted a hypothesis-driven analysis of effective coupling between the core nodes of the salience network, the anterior insula (AI) and the anterior cingulate cortex (ACC). As per our hypothesis, we found an increase in AI - > ACC effective coupling that occurs during the transitions of attention from on-task focused to off-task distracted state. This research may inform the development of future neural function-targeted brain-computer interfaces to enhance sustained attention.

Markov Chain Abstractions of Electrochemical Reaction-Diffusion in Synaptic Transmission for Neuromorphic Computing.

Progress in computational neuroscience toward understanding brain function is challenged both by the complexity of molecular-scale electrochemical interactions at the level of individual neurons and synapses and the dimensionality of network dynamics across the brain covering a vast range of spatial and temporal scales. Our work abstracts an existing highly detailed, biophysically realistic 3D reaction-diffusion model of a chemical synapse to a compact internal state space representation that maps onto parallel neuromorphic hardware for efficient emulation at a very large scale and offers near-equivalence in input-output dynamics while preserving biologically interpretable tunable parameters.

A Physiologically-Based Pharmacokinetic Model for Targeting Calcitriol-Conjugated Quantum Dots to Inflammatory Breast Cancer Cells.

Quantum dots (QDs) conjugated with 1,25 dihydroxyvitamin D3 (calcitriol) and Mucin-1 (MUC-1) antibodies (SM3) have been found to target inflammatory breast cancer (IBC) tumors and reduce proliferation, migration, and differentiation of these tumors in mice. A physiologically-based pharmacokinetic model has been constructed and optimized to match experimental data for multiple QDs: control QDs, QDs conjugated with calcitriol, and QDs conjugated with both calcitriol and SM3 MUC1 antibodies. The model predicts continuous QD concentration for key tissues in mice distinguished by IBC stage (healthy, early-stage, and late-stage). Experimental and clinical efforts in QD treatment of IBC can be augmented by in silico simulations that predict the short-term and long-term behavior of QD treatment regimens.