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Lab of Cognitive Imaging Research

Current Projects

Multimodal Imaging of Cognitive Networks in Epilepsy

This study addresses how structural, functional, and diffusion imaging can be used to predict postoperative outcomes in three important cognitive domains: language, memory, and executive functioning. First, neural activations are examined in frontal, temporal, and parietal regions using task-related and resting-state functional MRI (fMRI) to probe the brain networks that underlie language, memory and executive functioning in patients with TLE. Second, the integrity of critical white matter fiber tracts is quantified using an advanced diffusion technique,restriction spectrum imaging (RSI). Third, the hippocampal volume is quantified from structural MRI (sMRI). Fourth, information from fMRI, RSI, and sMRI is combined to predict individual risk for surgically-induced cognitive changes on measures of language, memory, and executive functioning. This study strives to improve health outcomes in patients with epilepsy (18-65 years) by using advanced, non-invasive technology to identify individual predictors of cognitive decline that can help to guide surgical decisions and possibly reduce morbidity associated with the removal of eloquent cortex.

Prediction of seizure lateralization and postoperative outcome through the use of deep learning applied to multi-site MRI/DTI data: An ENIGMA-Epilepsy study

This study leverages data collected through ENIGMA-Epilepsy to test whether deep learning approaches improve upon a prediction of seizure lateralization or postoperative outcomes compared to simpler, user-driven models. Our primary aim will be to test the ability of dense neural networks to lateralize the seizure focus compared to support vector machines (SVM). In an exploratory aim, we will test the ability of our model to predict postoperative seizure outcomes. ENIGMA's harmonized approach allows us to test our approach in over 24 datasets, diverse in age, ethnicity, age of onset, epilepsy duration, and surgical outcomes.


R01 NS065838-10                                                                                           
Multimodal Imaging of Cognitive Networks in Epilepsy:  Implications for Surgery
The research investigates the utility of non-invasive multimodal imaging in the preoperative evaluation of patients with temporal lobe epilepsy and examines which method or combination of imaging methods (fMRI, DTI, structural morphometry) and clinical variables best predict the postoperative cognitive outcome.

Research Scholar Grant                     
American Cancer Society       
Restriction Spectrum Imaging for Detecting and Monitoring Brain Tumors
This study evaluates the ability of an advanced diffusion imaging technique, restriction spectrum imaging (RSI) to improve the detection of brain tumors and provide a stronger biomarker of response to anti-angiogenic therapy relative to standard diffusion methods.  

R21 NS107739           
Prediction of seizure lateralization and postoperative outcome through the use of deep learning applied to multi-site MRI/DTI data:  An ENIGMA-Epilepsy study
This grant applies three different machine learning algorithms to advanced structural and diffusion-weighted imaging from 24 epilepsy centers world-wide to predict seizure lateralization and postoperative outcome in patients with temporal lobe epilepsy.

General Electric                                                                      
Restriction Spectrum Imaging for the Evaluation of Response to Immunotherapy in Patients with Primary and Metastatic Brain Tumors
This grant application evaluates the use of advanced diffusion imaging and multi-band technology for improving the evaluation of response to therapy on patients with brain tumors who are treated with immunotherapeutic agents.


See our list of publications.