Stephen LaConte, PhD
Assistant Professor, Virginia Tech Carilion Research Institute
Assistant Professor, Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences
Assistant Professor of Emergency Medicine, Virginia Tech Carilion School of Medicine
Research Program Summary
Research in the LaConte lab is devoted to advanced neuroimaging acquisition and data analysis approaches, aimed at basic scientific discovery as well as understanding and rehabilitating neurological and psychiatric diseases. A major focus of the lab is an innovation in functional magnetic resonance imaging (fMRI) that we developed and call “temporally adaptive brain state” (TABS) fMRI. The inception of TABS arose from two major recent advances in neuroimaging, namely 1) the recognition that multi-voxel patterns of fMRI data can be used to decode brain states (determine what the volunteer was “doing,” such as receiving sensory input, effecting motor output, or otherwise internally focusing on a prescribed task or thought) and 2) continued advances in MR imaging systems and experimental sophistication with fMRI that have led to the emergence of real-time fMRI as a viable tool for biofeedback.
For a full listing of Dr. LaConte's publications, visit PubMed.
Education and Training
- University of Denver: B.S., Biomedical Science and Electrical Engineering
- University of Minnesota: Ph.D., Biomedical Engineering
- Baylor College of Medicine Assistant Professor, Neuroscience
Awards and Honors
- Dean’s Award for Outstanding New Assistant Professor, Virginia Tech College of Engineering, 2014
- Eklund A, Dufort P, Forsberg D, LaConte SM. (2013). Medical image processing on the GPU – Past, present and future. Medical Image Analysis 17(8), 1073-94.
- Craddock RC, Milham MP, LaConte SM. (2013). Predicting intrinsic brain activity. Neuroimage 82, 127-36.
- Sulzer J, Haller S, Scharnowski F, Weiskopf N, Birbaumer N, Blefari ML, Bruehl AB, Cohen LG, DeCharms RC, Gassert R, Goebel R, Herwig U, LaConte S, Linden D, Luft A, Seifritz E, Sitaram R. (2013). Real-time fMRI neurofeedback: Progress and challenges. Neuroimage 76, 386-99.
- Eklund A, Villani M, LaConte SM. (2013). Harnessing graphics processing units for improved neuroimaging statistics. Cogn Affect Behav Neurosci 13, 587-97.
- Yang Z, Zuo XN, Wang P, Li Z, LaConte SM, Bandettini PA, Hu XP. (2012). Generalized RAICAR: Discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks. Neuroimage 63(1), 403-14.
- M. A. McHenry and S. M. LaConte. (2011). Computer Speech Recognition as an Objective Measure of Intelligibility. J Med Speech Lang Pathol 18, 99-103.
- S. M. LaConte. (2011). Decoding fMRI brain states in real-time. NeuroImage 56, 440-54.
- Z. Yang, S. LaConte, X. Weng, and X. Hu. (2008). Ranking and averaging independent component analysis by reproducibility (RAICAR). Hum Brain Mapp 29, 711–725.
- S. M. LaConte, S. J. Peltier, and X. P. Hu. (2007). Real-time fMRI using brain-state classification. Hum Brain Mapp 208, 1033–1044.
- S. LaConte, S. Strother, V. Cherkassky, J. Anderson, and X. Hu. (2005). Support vector machines for temporal classification of block design fMRI data. Neuroimage 26, 317–329.