Submitted on June 24, 2005
Accepted on February 2, 2006
Differentially Variable Component Analysis (dVCA): Identifying Multiple Evoked Components using Trial-to-Trial Variability
Kevin H. Knuth1*, Ankoor S. Shah2, Wilson Truccolo3, Mingzhou Ding4, Steven L. Bressler5, and Charles E. Schroeder6
1 Department of Physics, University at Albany (SUNY), Albany, NY, USA
2 Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA; Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA; Cognitive Neuroscience and Schizophrenia Program, Nathan Kline Institute, Orangeburg, NY, USA
3 Neuroscience Department, Brown University, Providence, RI, USA
4 The J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
5 Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, USA
6 Cognitive Neuroscience and Schizophrenia Program, Nathan Kline Institute, Orangeburg, NY, USA; Department of Psychiatry, Columbia College of Physicians and Surgeons, New York, NY, USA
* To whom correspondence should be addressed. E-mail: kknuth{at}albany.edu.
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 since 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 PCA and ICA. 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.
