MEA-NAP compares microscale functional connectivity, topology, and network dynamics in organoid or monolayer neuronal cultures.

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time-series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and, thus, can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches.

Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework.

Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with an agent-based SIR model to assess the relative risks of different intervention strategies. By applying our model on MIT's Stata center, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that reducing the frequency at which individuals leave their workstations combined with lowering the number of individuals admitted below the current recommendations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the agent-based SIR model, we compare relative infection risk of four respiratory illnesses, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.

Collision frequency locality-sensitive hashing for prediction of critical events.

We present a fast, efficient method to predict future critical events for a patient. The prediction method is based on retrieving and leveraging similar waveform trajectories from a large medical database. Locality-sensitive hashing (LSH), our theoretical foundation, is a model-free, sub-linear time, approximate search method enabling a fast retrieval of a nearest neighbor set for a given query. We propose a new variant of LSH, namely Collision Frequency LSH (CFLSH), to further improve the prediction accuracy without sacrificing any speed. The key idea is that the more frequently an element and a query collide across multiple LSH hash tables, the more similar they are. Unlike the standard LSH which only utilizes the linear distance calculation, in CFLSH, the short-listing step from a pool of pre-selected candidates filtered by hash functions to the final nearest neighbor set relies upon the frequency of collision along with distance information. We evaluate CFLSH versus the standard LSH using the L1 and cosine distances, for predicting acute hypotensive episodes on arterial blood pressure time series data extracted from the MIMIC II database. Our results show that CFLSH for the L1 distance has a higher prediction accuracy and further accelerates the sub-linear querying time obtained by the standard LSH.

Stratified locality-sensitive hashing for accelerated physiological time series retrieval.

We introduce stratified locality-sensitive hashing (SLSH) for retrieving similar physiological waveform time series. SLSH further accelerates the sublinear retrieval time obtained by the standard locality-sensitive hashing (LSH) method. The standard family of locality-sensitive hash functions is limited to provide only a single perspective on the data due to its one-to-one relationship to a distinct distance function for measuring similarity. SLSH incorporates multiple locality-sensitive hash families with various distance functions enabling it to examine the data with more diverse and refined perspectives. We provide the procedures of SLSH with locality-sensitive hash families for the l1 and the cosine distances, and compare its performance to the standard LSH on an arterial blood pressure time series data extracted from the physiological waveform repository of the MIMIC2 database. The time to retrieve five most similar waveforms by SLSH is 14 times faster than the linear search and 1.7 times faster than the standard LSH when we allow 5% decrease in accuracy as a trade-off.