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INNOVATIVE METHODOLOGY

Differentially Variable Component Analysis: Identifying Multiple Evoked Components Using Trial-to-Trial Variability

¹Department of Physics, University at Albany, State University of New York, Albany; ²Department of Neuroscience, Albert Einstein College of Medicine, Bronx; ³Cognitive Neuroscience and Schizophrenia Program, Nathan Kline Institute, Orangeburg; ⁴Department of Psychiatry, Columbia College of Physicians and Surgeons, New York, New York; ⁵Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; ⁶Neuroscience Department, Brown University, Providence, Rhode Island; ⁷The J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville; and ⁸Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, Florida

Submitted 24 June 2005; accepted in final form 2 February 2006

Electric potentials and magnetic fields generated by ensembles of synchronously active neurons, either spontaneously or in response to external stimuli, provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult because detectors record signals simultaneously generated by various regions throughout the brain. We introduce a novel approach to this problem, the differentially variable component analysis (dVCA) algorithm, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components. Using simulations we demonstrate the importance of response variability to component identification, the robustness of dVCA to noise, and its ability to characterize single-trial data. We then compare the source-separation capabilities of dVCA with those of principal component analysis and independent component analysis. Finally, we apply dVCA to neural ensemble activity recorded from an awake, behaving macaque—demonstrating that dVCA is an important tool for identifying and characterizing multiple components in the single trial.