Our research lies at the intersection of multimodal integration, network modeling and clinical neuroscience. By combining different viewpoints, such as imaging features, behavioral characteristics and genetic markers, with key neuroscientific insights, our goal is to characterize complex processes within the brain. In particular, we focus on modeling how the brain changes given a neurological impairment, and how we might use this information to improve clinical care. Our current projects include:
Characterizing the Heterogeneity of Autism via Bayesian Matrix Factorization
Autism Spectrum Disorder (ASD) affects an estimated 1 in 68 children in the United States. It is characterized by impaired social-communicative skill and awareness across multiple sensory domains, coupled with restricted/repetitive behaviors. These diffculties can result in ostracization by peers, educational problems and limited employment opportunities. Despite ongoing efforts, the breadth and inconsistency of clinical symptoms have greatly impeded our ability to understand and treat the disorder. This project will leverage both the group-level imaging information and patient-specific behavioral variability to provide a complete picture of ASD.
We leverage a matrix factorization objective to decompose the connectivity differences between a neurotypical and an autistic cohort into a set of canonical networks, each of which isolates the contribution of a particular clinical measure (ex. behavioral score or demographic variable) to the global functional organization. We consider two complementary network topologies for the altered pathways. A community architecture suggests that a particular deficit arises from a subset of abnormally communicating brain regions. In contrast, a spreading model assumes that the deficit is linked to
a sparse subset of regions, which abnormally interact with the rest of the brain.
Modeling Neural Plasticity Induced by an Acute Lesion
Neural plasticity refers to the anato-functional reorganization of the brain across the lifespan, and it plays a key role in our natural ability to compensate for injuries to the brain. For example, low grade gliomas are slow-growing tumors that originate in the central nervous system. Preoperative and intraoperative imaging suggest a progressive redistribution of the affected functional areas, from non-primary perilesional areas to homologous regions in the contralateral hemisphere. However, our understanding of functional reorganization is still largely heuristic and limited in scope.
This proposal tackles a fundamental yet unexplored challenge in the study of recuperative neural plasticity: how do we develop a statistical model of whole-brain functional reorganization induced by a glioma? Our method will account for varying tumor characteristics (volume, location, grade), while gaining statistical power from group-level information. Unlike conventional studies, we hypothesize that a lesion will produce distributed functional migration patterns across multiple brain systems, from the directly impacted functionality to auxiliary compensation mechanisms.
Noninvasive Seizure Localization for Epilepsy
The CDC estimates that between 2-3 million people in the United States suffer from epilepsy; over 20% of these cases are medically refractory and do not respond to drug therapy. The alternative for many of these patients is to surgically remove the areas of the brain responsible for triggering an epileptic seizure. However, the gold-standard procedure to localize the epileptic foci requires a craniotomy and implantation of electrode grids directly onto the cortical surface. This evaluation procedure is understandably traumatic and increases the patient risk for infection and injury.
Hence, our goal is to automatically and noninvasively localize the seizure onset zones, which will both streamline the pre-surgical planning process and reduce the amount of time the patients spend in the hospital. Our preliminary results suggest that we can extract meaningful information about the ictal areas via functional MRI. Moving forward, we will combine multiple snapshots of the brain, such as structural MRI, functional MRI and electroencephalography (EEG) in order to refine the localization.
Treating Autism by Manipulating Speech Prosody
Spoken language is a fundamental part of our society, both to convey information and to sustain interpersonal relationships. Beyond grammatical syntax and semantics, prosody refers to suprasegmental vocal inflections in speech, which are crucially tied to meaning, attitudes and emotions. One of the earliest observations of ASD was a struggle with both the production and interpretation of prosodic information, particularly within the context of emotions. Recent findings suggest that the neural circuitry required to parse emotional prosody is present, but possibly under-utilized, in autistic individuals, relative to their neurotypical peers. Hence, artificially amplifying the emotional content of human speech may allow individuals with ASD to perceive emotional cues in the same way that a non-autistic individual would discern them in the original signals.
Our initial effort is designed to bridge the knowledge gap between prosody and perception in a systematic and data-driven manner. In particular, we address the following question: given a naturally produced speech signal, how do we alter its perceived emotional content? Our goal is to extract and systematically manipulate the features, which best predict the emotional state of the speaker.