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Title: A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity
Title: Multi-speaker Emotion Conversion via Latent Variable Regularization and A Chained Encoder-Decoder-Predictor Network
Title: Non-parallel Emotion Conversion using a Deep-Generative Hybrid Network and an Adversarial Pair Discriminator
Title: Integrating Convolutional Neural Networks and Probabilistic Graphical Models for Epileptic Seizure Detection and Localization
Abstract: Epilepsy affects 1% of the population worldwide, and roughly 30% of these patients do not respond to medication. If we can trace the seizures to a singe brain region, then the best course of action is to surgically resect this seizure onset zone (SOZ). Electroencephalography (EEG) is the first and foremost modality used in epilepsy management. However, due to the wide variation in epileptic pathologies, seizure detection and localization rely almost exclusively on expert visual inspection of the EEG signals. This process is time consuming and prone to human error. Moreover, the electrographic signatures can be difficult, if not impossible, to isolate in many patients due to motion artifacts, apparent multifocal onsets, and rapid spreading patterns.
This scenario provides an ideal opportunity for automated methods to simultaneously mine and integrate discriminative cues in the data that can augment expert review. In this talk, I will describe a new framework for seizure detection and localization from multichannel EEG data. The crux of our approach is that the propagation of seizure activity provides valuable information about its onset. Our unique modeling strategy combines the interpretability of probabilistic graphical models with the representational power of deep learning. Specifically, the latent variables in our PGM will capture the spread of seizure activity; they are complemented by a nonparametric likelihood based on convolutional neural networks. We demonstrate that our approach achieves better detection accuracy than competing baseline models, and that it can identify the SOZ across a heterogeneous patient cohort without any a priori information.
Title: A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism
As the world’s largest honor society for scientists and engineers, Sigma Xi recognizes researchers for the values we hold in high esteem: excellence, integrity, leadership, diversity, cooperation, and scholarship. Membership in Sigma Xi is by nomination only and requires nomination by two members of the Society.
Title: A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data
Abstract: We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term decomposes the correlation matrices into a sparse set of representative subnetworks that define a network manifold. These subnetworks are modeled as rank-one outerproducts which correspond to the elemental patterns of co-activation across the brain; the subnetworks are combined via patient-specific non-negative coefficients. The second term is a linear regression model that uses the patient-specific coefficients to predict a measure of clinical severity. We validate our framework on two separate datasets in a ten fold cross validation setting. The first is a cohort of fifty-eight patients diagnosed with Autism Spectrum Disorder (ASD). The second dataset consists of sixty three patients from a publicly available ASD database. Our method outperforms standard semi-supervised frameworks, which employ conventional graph theoretic and statistical representation learning techniques to relate the rs-fMRI correlations to behavior. In contrast, our joint network optimization framework exploits the structure of the rs-fMRI correlation matrices to simultaneously capture group level effects and patient heterogeneity. Finally, we demonstrate that our proposed framework robustly identifies clinically relevant networks characteristic of ASD.
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