This invention relates to a method for significantly increasing te accuracy of predicting and selecting an antidepressant agent, or other pharmacological agent for treatment of a disease state, that will be effective based on pre-treatment or baseline, placebo treatment and/or active treatment, or other post-treatment time period data, early changes quantitative EEG or other brain imaging functional state and/or anatomical data (such as magnetoencephalography (MEG), quantitative MEG (QMEG), fMRI, CAT scan, PET, functional PET, X-ray, etc.), time change/time series, weighted factor, principal component, regional ensemble and/or artificial intelligence analysis. Utilization of such methods may also be applied to enhance individual statement verification and/or lie detection. In addition, such methods can be used to identify physiological state, pathophysiological state, including disease diagnosis, disease progression and/or remission, and other health and/or disease states and changes of interest. Furthermore, the invention may be used to discover novel applications for therapeutic entities, deduce the mode of action of one or more therapeutic entities, improve testing of candidate therapeutic entities, and be used by the pharmaceutical industry or research community to eliminate or select agents or therapeutic modalities for further development as therapeutic agents or treatment modalities.
MEDICATION TREATMENT SELECTION STATEGY AND CLINICAL MODEL METHODS