Individuals with schizophrenia shows a wide range of cognitive deficits, and lack in the ability to control the variety of inputs (whether internal or external) to create a unitary “self”. This problem has been often associated with a supramodal attentional deficit, but its causes are yet unknown. In the past twenty years, the quest to identify brain regions or networks showing abnormal functioning in schizophrenia has been unproductive; but one question is left unanswered: is it possible that it is the white matter tracts supporting attention (i.e., the superior longitudinal fasciculi I, II, and III) to be compromised in this psychiatric disorder? The present project will attempt to dissect the fronto-parietal branches with DTI by analyzing over 400 images from the Open Access SchizConnect.Org repository (data already available to the PI). Funds will be used to support the work of a junior or senior-year undergraduate student (possibly from URMs) to conduct the data analysis during the summer of AY2020. Results will be made available with the scientific community and published in open access peer-review journals (e.g., Schizophrenia Research). Co-mentoring of the undergraduate student will be done by Dr. Henrietta Howells (https://scholar.google.it/citations?user=nudmODkAAAAJ&hl=en&oi=ao), opening the student to an international collaboration.

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A central issue facing systems neuroscience is defining the rich naturalistic behavioral repertoire that mice engage in under psychiatrically relevant situations. Recent advances in deep learning (e.g., DeepLabCut) have made frame by frame detailed pose estimation possible. However, this dense behavioral data requires new techniques for defining the ethogram (full description of behavior). To date, researchers have used frequency based time series approaches to tackle this problem, with significant limitations. An alternative approach would be to take advantage of new topology methods (persistent homology and directed algebraic topology) to characterize the shapes formed by mouse limb trajectories. Such an approach would have broad application in systems neuroscience. For this project, the student will use machine learning to label animal body parts, then topology to characterize the ethogram and compare the results to existing approaches.

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The visual cortex has a distinctive deep hierarchical organization as a result of ontogenetic and phylogenetic optimization. It is unclear what the factors are that shape this particular hierarchical organization. One factor is the compositional and hierarchical nature of our world’s appearance, which may be optimally processed by a hierarchical visual system. Another factor is the need for space and energy efficiency, which constrains the number of neurons and connections. The project will employ computational modeling to understand the contribution of these constraints to shaping the combination of breadth, depth, and skipping connections employed by primate visual cortex.

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The goal of this project is to develop and validate a deep neural network that predicts a child’s emotion and cognition. DSI scholars will implement 3D convolutional neural networks on brain imaging data from thousands of children to predict cognitive, emotional, and socio-developmental variables. Statistical evaluation of the model performance will be conducted. The scalable deep neural network analysis will help find brain underpinnings of cognition and emotion.

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Analysis of the relation between the activity of all the neurons in the cnidarian hydra and its behavioral repertoire.

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Given calcium imaging data of active neurons, can we detect groups of co-firing neurons, called neuronal ensembles? We have a number of datasets consisting of hundreds of neurons imaged for thousands of time steps, and seek to extend an existing CRF model to consider temporal relationships. The goal is to be able to detect neuronal ensembles that span multiple time steps, and that are not conditioned on external stimuli.

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Microelectrode array recordings from patients undergoing surgical evaluation have captured typical clinical seizures. Because of the extreme pathological conditions at these times, identifying single units from extracellular data is a particular challenge. Our group has developed techniques for tracking neurons through the ictal transition. We are applying them to newly acquired data and addressing fundamental questions about the activity of different cell classes at seizure initiation.

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