One Nanostep for Technology, One Quantum Leap for Psychiatry

Update: Serious methodological concerns have been raised about the Debattista paper, check out Neuroskeptic’s coverage here.

In 1996 we witnessed a computer beat the world chess champion, something many never thought would be possible. Is it possible that in 2011 a computer could actually beat psychiatrists in something as intrinsically human as diagnosing mental disorder and even deciding the most effective medication? According to preliminary results from research currently being conducted by a team at Stanford University this is already rapidly becoming a reality.The key question for psychiatrists today is not the naming of the disorder but deciding (when a prescription is necessary) which prescription is most likely to be effective. The wrong prescription can do much more harm than good. It is accepted that to a large extent psychiatrists still rely on trial and error. Unlike other areas of medicine, psychiatric problems tend to lack biomarkers that inform pharmacotherapy such as bacterial assays that guide antibiotic treatment or histological and genetic tests that guide chemotherapy. The massive STAR*D clinical trial of antidepressants demonstrated just how much of a lottery the choice can be.

A study published in the January volume of the Journal of Psychiatric Research by a team at Stanford demonstrates a computer system that appears to be able to tackle this problem in the precribing of anti-depressant medicaton . The method used is called “referenced EEG” (rEEG). This uses mathematical algorithms to compare the brain patterns of a patient to a database of the brain patterns of previous patients with a similar condition and their treatment outcomes. Essentially the patient is given the treatment that is demonstrated to work best for patients with similar brain patterns.

This technique has been suggested before but only now is it seriously beginning to present a major challenge to the traditional method. In November a research group in Canada demonstrated an rEEG method that categorised depressive, bipolar and schizophrenia patients with 85% accuracy. A month later the same researchers published another paper demonstrating that the program successfully classified whether Schizophrenia patients would respond positively to clozapine, again in 85% of cases. Now the Stanford team led by Charles DeBattista has published preliminary findings that appear to demonstrate rEEG can select notoriously hard to predict depression medications with 65% accuracy. This is significantly higher than the 38% score achieved using the STAR*D approach which is widely considered best practice amongst Psychatrists.

The critically minded amongst you may well baulk at the methodological conundrums involved in comparing an rEEG diagnosis to a human one. If these results are valid, they are truly astounding. In 1949 Ash demonstrated that only 20% of Psychiatrists agreed on diagnosis, as recently as 1962 that figure was only 42%. More recently the “DSM” has assured agreement is now closer to 90%. Whether the DSM diagnosis is valid is another debate however. The suggestion that rEEG may be able choose an appropriate prescription after a human psychiatrist has performed a diagnosis certainly seems more tangible a possibility at this point in time.

It is important to recognise the findings are only preliminary. There are always methodological issues inherent in a pilot study that prevent results being as earth shattering as they may sound. The medications prescribed by the rEEG program were far more varied and qualitatively different from the limited selection of STAR*D. The issue may be that psychiatrists are exercising greater restraint in prescribing higher risk medication at the expense of better results. (This is in no way a criticism of psychiatrists, caution is obviously of paramount importance when dealing with such powerful medications.) Regardless, research groups around the world are joining the race to test and expand the method. Psychiatrists (and EEG technicians) will doubtless be awaiting these results with bated breath.

If this news came as a shock to you I’d recommend taking a look at the work of Ray Kurzweil, a remarkable professor who has made some amazing discoveries himself and continues to make astounding technological predictions.

Khodayari-Rostamabad A, Reilly JP, Hasey G, Debruin H, & Maccrimmon D (2010). Diagnosis of psychiatric disorders using EEG data and employing a statistical decision model. Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2010, 4006-9 PMID: 21097280

Khodayari-Rostamabad, A., Hasey, G., MacCrimmon, D., Reilly, J., & Bruin, H. (2010). A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy. Clinical Neurophysiology, 121 (12), 1998-2006 DOI: 10.1016/j.clinph.2010.05.009

Charles DeBattista, Gustavo Kinrys, Daniel Hoffman, Corey Goldstein, John Zajecka, James Kocsis, Martin Teicher, Steven Potkin, Adrian Preda, Gurmeet Multani, Len Brandt, Mark Schiller, Dan Iosifescu, Maurizio Fava (2011). The use of referenced-EEG (rEEG) in assisting medication selection for the treatment of depression.  Psychiatric Research, 15 (12), 64-75 DOI: The use of referenced-EEG (rEEG) in assisting medication selection for the treatment of depression

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