Motion sickness as metaphor: engaging with diversity in STEM.

Science, technology, engineering, and math (STEM) continue to work to increase the diversity of the fields, yet there are still significant historical and societal hurdles to be overcome before we reach full representation throughout STEM. The concept of science identity has become a point of interest in this process; it has been suggested that development of one's identity as a scientist is critical to persistence in the field. Metaphors that are rooted in bodily experience can provide a starting point to understand abstract concepts, including science identity and how we as STEM educators respond to increasing diversity within our fields. Given the history of STEM being predominantly populated by people who are white and male, disorientation or discomfort with increasing diversity is not unexpected, and many women and people of color report discrimination and marginalization as a part of their experience in STEM. Here I present a neuroscience-based metaphor that can serve as a starting point for understanding some of the potential disorientation or discomfort that we may experience as we engage with the increasing diversity of STEM and acknowledge this experience as a normal but temporary part of the process of growth and development as a field. I encourage the development and use of further discipline-based metaphors to enhance our discussion and understanding of this important work.

A predictive epilepsy index based on probabilistic classification of interictal spike waveforms.

Quantification of interictal spikes in EEG may provide insight on epilepsy disease burden, but manual quantification of spikes is time-consuming and subject to bias. We present a probability-based, automated method for the classification and quantification of interictal events, using EEG data from kainate- and saline-injected mice (C57BL/6J background) several weeks post-treatment. We first detected high-amplitude events, then projected event waveforms into Principal Components space and identified clusters of spike morphologies using a Gaussian Mixture Model. We calculated the odds-ratio of events from kainate- versus saline-treated mice within each cluster, converted these values to probability scores, P(kainate), and calculated an Hourly Epilepsy Index for each animal by summing the probabilities for events where the cluster P(kainate) > 0.5 and dividing the resultant sum by the record duration. This Index is predictive of whether an animal received an epileptogenic treatment (i.e., kainate), even if a seizure was never observed. We applied this method to an out-of-sample dataset to assess epileptiform spike morphologies in five kainate mice monitored for ~1 month. The magnitude of the Index increased over time in a subset of animals and revealed changes in the prevalence of epileptiform (P(kainate) > 0.5) spike morphologies. Importantly, in both data sets, animals that had electrographic seizures also had a high Index. This analysis is fast, unbiased, and provides information regarding the salience of spike morphologies for disease progression. Future refinement will allow a better understanding of the definition of interictal spikes in quantitative and unambiguous terms.

Automated identification of multiple seizure-related and interictal epileptiform event types in the EEG of mice.

Visual scoring of murine EEG signals is time-consuming and subject to low inter-observer reproducibility. The Racine scale for behavioral seizure severity does not provide information about interictal or sub-clinical epileptiform activity. An automated algorithm for murine EEG analysis was developed using total signal variation and wavelet decomposition to identify spike, seizure, and other abnormal signal types in single-channel EEG collected from kainic acid-treated mice. The algorithm was validated on multi-channel EEG collected from γ-butyrolacetone-treated mice experiencing absence seizures. The algorithm identified epileptiform activity with high fidelity compared to visual scoring, correctly classifying spikes and seizures with 99% accuracy and 91% precision. The algorithm correctly identifed a spike-wave discharge focus in an absence-type seizure recorded by 36 cortical electrodes. The algorithm provides a reliable and automated method for quantification of multiple classes of epileptiform activity within the murine EEG and is tunable to a variety of event types and seizure categories.