Using pre-treatment electroencephalography data to predict response to transcranial magnetic stimulation therapy for major depression

We investigate the use of machine learning methods based on the pre-treatment electroencephalograph (EEG) to predict response to repetitive transcranial magnetic stimulation (rTMS), which is a non-pharmacological form of therapy for treating major depressive disorder (MDD). The learning procedure involves the extraction of a large number of candidate features from EEG data, from which a very small subset of most statistically relevant features is selected for further processing. A statistical prediction model based on mixture of factor analysis (MFA) model is constructed from a training set that classifies the respective subject into responder and non-responder classes. A leave-2-out (L2O) cross-validation procedure is used to evaluate the prediction performance. This pilot study involves 27 subjects who received either left high-frequency (HF) active rTMS therapy or simultaneous left HF and right low-frequency active rTMS therapy. Our results indicate that it is possible to predict rTMS treatment efficacy of either treatment modality with a specificity of 83% and a sensitivity of 78%, for a combined accuracy of 80%.

[1]  J. Lefaucheur.  Methods of therapeutic cortical stimulation , 2009, Neurophysiologie Clinique/Clinical Neurophysiology.

[2]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[3]  J. Reilly,et al.  A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy , 2010, Clinical Neurophysiology.

[4]  Ahmad Khodayari-Rostamabad,et al.  Diagnosis of psychiatric disorders using EEG data and employing a statistical decision model , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[5]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[6]  Geoffrey E. Hinton,et al.  The EM algorithm for mixtures of factor analyzers , 1996 .

[7]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[8]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  B. Druss,et al.  Health and disability costs of depressive illness in a major U.S. corporation. , 2000, The American journal of psychiatry.

[10]  G. Price,et al.  Appraisal of Sessional EEG Features as a Correlate of Clinical Changes in an rTMS Treatment of Depression , 2008, Clinical EEG and neuroscience.