Stephen LaConte, Ph.D.

Associate Professor

Lab

LaConte Lab

Research Areas

  • Biomedical imaging
  • Neuroengineering

Research Interests

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.

Education

  • University of Minnesota: Ph.D., Biomedical Engineering
  • University of Denver: B.S., Biomedical Science and Electrical Engineering

Awards, Honors, and Service

  • Dean’s Award for Outstanding New Assistant Professor, Virginia Tech College of Engineering, 2014

Recent Publications

Snider SE, Deshpande HU, Lisinski JM, Koffarnus MN, LaConte SM, Bickel WK. (2018). Working memory training improves alcohol users’ episodic future thinking: a rate dependent analysis. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 3(2): 160-7.

Lee-Park JJ, Deshpande H, Lisinki J, LaConte S, Ramey SL, DeLuca S. (2018). Neuroimaging strategies addressing challenges in using fMRI for the children with cerebral palsy. Journal of Behavioral and Brain Science 8: 306-318.

Koffarnus MN, Deshpande HU, Lisinski JM, Eklund A, Bickel WK, LaConte SM. (2017). An adaptive, individualized fMRI delay discounting procedure to increase flexibility and optimize scanner time. Neuroimage 161: 56-66.

Li T, Murphy S, Kiselev B, Bakshi KS, Zhang J, Eltahir A, Zhang Y, Chen Y, Zhu J, Davis RM, Madsen LA, Morris JR, Karolyi DR, LaConte SM, Sheng Z, Dorn HC. (2015). A New Interleukin-13 Amino-Coated Gadolinium Metallofullerene Nanoparticle for Targeted MRI Detection of Glioblastoma Tumor Cells. Journal of the American Chemical Society 137(24): 7881-8.

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. Neuroimage63(1): 403-14.

Chiew M, LaConte SM, Graham SJ. (2012). Investigation of fMRI neurofeedback of differential primary motor cortex activity using kinesthetic motor imagery. Neuroimage 61(1): 21-31.

 

Stephen M. LaConte, Ph.D., Associate Professor, Virginia Tech Carilion Research Institute (David Hungate for VTCRI)

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  • (540) 526-2008
  • slaconte@vt.edu
  • Virginia Tech Carilion Research Institute
    2 Riverside Circle
    Office #1112
    Roanoke VA, 24016