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Stephen Laconte
Baylor College of MedicineDepartment: NeuroscienceAddress: One Baylor Plaza Baylor College of Medicine Houston TX, 77030 Phone: 713-798-8499 Fax: 713-798-3946 Email: laconte@bcm.edu Web: www.cpu.bcm.edu/laconte/ |
Education
Ph.D., University of Minnesota, 2002
Honors
Research Topic
Research Description
Research in the LaConte lab is devoted to advanced neuroimaging acquisition and data analysis approaches, aimed at understanding and rehabilitating neurological and psychiatric diseases. A major focus of the lab is an innovation in functional magnetic resonance imaging (fMRI) which 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” – e.g. 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.
TABS uses brain states for feedback, which is fundamentally different from existing real-time fMRI implementations. Specifically, other approaches use time series fluctuations in localized brain regions to derive biofeedback signals. While TABS does have the capability to focus on specific regions of interest, we have demonstrated it as a whole brain, multivariate technique that can be applied across a wide range of behaviors and cognitive domains. Thus TABS can complement region of interest–based studies and potentially avoid biases in structure-function relationships (and even enable applications in which the neural substrates are not well characterized) by utilizing distributed patterns of activation. The change in emphasis from anatomy to stimulus/response brain states is profound. Compared to real-time fMRI approaches that track localized signal fluctuations, TABS is computationally more efficient and, moreover, leads to more direct experiments in which the patient is asked to perform a task rather than coached to find strategies to modulate a specific anatomic substrate.
Selected Publications
- LaConte S, Anderson J, Muley S, Ashe J, Frutiger S, Rehm K, Hansen LK, Yacoub E, Hu X, Rottenberg D, Strother S. 2003. The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics, Neuroimage, 18:10-27.
- LaConte S, Strother S, Cherkassky V, Hu X. 2005. Support vector machines for temporal classification of block design fMRI data. Neuroimage, 26,317-329.
- Deshpande G, LaConte, SM, Peltier, SJ, Hu, X. 2006. Tissue specificity of nonlinear dynamics in baseline fMRI. Magn Reson Med, 55:626-632.
- Stilla R, Deshpande G, LaConte S, Hu X, Sathian K. 2007. Posteromedial parietal cortical activity and inputs predict tactile spatial acuity. J Neurosci, 20:11091-102.
- LaConte SM, Peltier SJ, Hu XP. 2007. Real-time fMRI using brain state classification. Hum Brain Mapp. 28:1033-44.
- Zhuang J, Peltier S, He S, LaConte S, Hu X. 2008. Mapping the connectivity with structural equation modeling in an fMRI study of shape-from-motion task. Neuroimage, 42:799-806.
- Yang, Z, LaConte, S, Weng, X, Hu, X. 2008, Ranking and averaging independent component analysis of reproducibility (RAICAR). Hum Brain Mapp, 29:711-25.
Lab Members
Lab Photos
Last edited on: October 21, 2009