For a while now, one of the big ideas in clinical psych has been that we may be able to use functional neuroimaging (techniques like EEG, fMRI, PET, etc) to improve our treatment strategies for various mental illnesses. Diseases like depression seem to be characterized by a wide range of related symptoms, and it's well-established that people respond to different medications in a pattern that's hard to characterized. The logic goes, if we can see what's happening in the brain, we may be able to build up a database that links patients' neural activation patterns to what treatments they respond to, and then we can use that information to help future patients.
A soon-to-be-published paper from a group at McMaster University is doing just this, with schizophrenic patients, EEG, and clozapine. Clozapine is an anti-psychotic, but like most psych meds, it doesn't work for everyone. The stakes are higher because the side effects are pretty nasty. Thus, motivation to assess in advance whether a patient is likely to respond to the medication.
The group took resting EEG from 23 schizophrenic patients whose clozapine responses were known, and extracted a bunch of potentially predictive features:
In our study, these features are statistical quantities including coherence
between all electrode pairs at various frequencies, correlation and
cross-correlation coefficients, mutual information between all sensor
pairs, absolute and relative power levels at various frequencies, the left-to-right hemisphere power ratio,
the anterior/posterior power gradient across many frequencies and
between electrodes (calculated using logarithm difference of power
spectral density values). These quantities can all be readily calculated
from the measured EEG signal.
They then used a machine learning algorithm (which I'm not going into – I know a little bit about this, enough to understand their explanation of their algorithm, but not enough to place it in a larger context) to identify 8 features that were significantly predictive of whether a patient would respond to clozapine. That is, they created an 8-dimensional space, with each patient's data represented as a point in that space defined by its score on each of these features/metrics, in which the clozapine responders and the clozapine non-responders formed two distinct clusters.
This is a 2-dimensional collapse of the 8-D space, and you can see the clustering of the responders (blue) and non-responders (white). Note that even on this training data set, there's some prediction error, which shows up as overlap between the clusters. The accuracy of the model at predicting response of patients in the training set was about 87%.
Which is nifty and all, but what's really nice is that they then used the model to predict clozapine responsiveness in a new group of 14 patients. (This is standard operating procedure in developing this sort of algorithm – find a set of parameters that works well on your training data, and then test it on unfamiliar data and see if it still performs well. The concern is that you might overfit the training data, and develop an algorithm that sorts those 23 subjects perfectly, but is at close to chance if you give it unfamiliar data.) The algorithm's accuracy on this test group was about 86%, very similar to the accuracy on the training set, and definitely high enough to be useful as a clinical tool.
Of course this is clearly still pilot work – 37 participants is not enough to change the standard of treatment – but it's a very cool study, both for the EEG analysis/feature selection process, and the accuracy of response prediction.
Khodayari-Rostamabad, A., Hasey, G. M., MacCrimmon, D. J., Reilly, J. P., and de Bruin, H. (in press). A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy. Clinical Neurophysiology. doi:10.1016/j.clinph.2010.05.009