While I am interested in many computational problems in medicine, I am most fascinated by Computational Neuroscience, a discipline where algorithms and software tools meet neuroscience. I am particularly interested in brain imaging (e.g., MRI) and brain electromagnetic signals (e.g., EEG).
Conventional epilepsy diagnosis based on EEG requires the occurrenc
of seizures or interictal epileptiform discharges (IDEs), such as slow waves and spikes. As a result, long-term
EEG monitoring, often with joint video recording, is needed. However, this
solution is both tedious and costly.
Since a patient is in
the interictal state (i.e., the time between seizures) for most of the time, it would be very useful if epilepsy diagnosis can be done by using short-term (e.g., 20 minutes) scalp interictal EEG.
Because epilepsy is caused by the dysfunction of the brain, normal EEG and epileptic EEG (even during interictal periods) may have different "fingerprints". These differences are probably too subtle for the human eye to see, but it could be detectable using proper computational tools.
My approach extracts features from EEG signals (i.e., quantifying
each EEG segment into an array of numeric values) and then runs
classifiers (e.g., SVMs) on these features to discriminate between
interictal epileptic EEG and normal EEG, and thus distinguish
epilepsy patients from healthy people.
A pilot study was done from 2008 to 2010, delivering promising results from two datasets (one of scalp recordings with artifacts and the other of intrancranial recordings without artifacts). One of the papers of the pilot study was reported on MIT Technology Review Website. The other one was reported by 2009's special research issue (PDF) of epilepsyUSA, the official magazine of Epilepsy Foundation of America.
Please note that this work is different from seizure detection and spike detection, both of which still require long-term EEG recording. Nor is this work the same as seizure prediction.
In addition to this open question, I am also interested in any other EEG/MEG processing topics, including but not limited to, focus localization, portable/wearable devices for epilepsy care giving, seizure prediction, spike detection.
I have been very fortunate to work with a great team on a project called MindBoggle (at GitHub). The project was initialized by Dr. Arno Klein via a 3-year NIH R01 grant at Columbia Medical School, and involves people from MIT/Harvard, Stony Brook University (SUNY Stony Brook), U. Washington, and Université Catholique de Louvain in Belgium. (e.g., our long authors and affiliations list)
MindBoggle aims to extract anatomical landmarks, e.g., fundi at the bottom of sulci, from human brain cortex and to compare morphological features of these landmarks across brains. Previous studies have shown that morphological changes on these landmarks can be related to Alzheimer's Disease, depression (our SfN 2011 presentation), etc. My main contribution to MindBoggle is landmark extraction and characterization.
I work on MEG from time to time. In the summer of 2011, I was involved in a project that registered SAM results from MEG with MRI images. Recently I am working on analyzing MEG data from surgery-verified epilepsy patients and healthy people. The similarity between MEG and EEG allows me to port algorithms for EEG onto processing MEG data.