The major research focus of the Brain Imaging and Modeling Section concerns ascertaining how interacting brain regions (i.e., neural networks) implement specific cognitive tasks, especially those associated with audition and language. We also study how these networks are altered in brain disorders. These issues are addressed by combining computational neuroscience techniques with neuroscientific data, especially those acquired using functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). The network analysis methods allow us to evaluate how brain operations differ between tasks, and between normal and patient populations, thus permitting us to determine which networks are dysfunctional and the role neural plasticity plays in enabling compensatory behavior to occur. Central to this research is the use of large-scale biologically realistic network models that relate neuroanatomical and neurophysiological data to the signals measured by functional brain imaging. Not only does computational modeling help interpret the meaning of functional brain imaging data, it also provides a framework to generate and quantitatively test hypotheses concerning the mechanisms by which specific cognitive tasks are implemented in the brain. Research in our section is divided into three main interconnected areas: (1) designing and executing neuroscientific experiments - primarily functional brain imaging studies; (2) network analysis of functional and effective connectivity between important brain regions based on these data; and (3) development and implementation of large-scale neural models aimed at determining how the functional brain imaging signals from some of these experiments are related to the underlying cellular neural activity. In particular, these approaches are applied to high-level auditory and language function. Because many of the analytic and computational methods used were originated by us, ongoing methodological development of these approaches also continues as a major activity.
Dr. Horwitz received his B.A. degree from Washington University in St. Louis and his Ph.D. in physics from the University of Pennsylvania. After several years of teaching physics, he joined the NIA as a Senior Staff Fellow in the Laboratory of Neurosciences, headed by Stanley I. Rapoport. Dr. Horwitz's work focused on developing methods for using positron emission tomography to determine how different brain regions interact in human subjects. In 1989, he obtained tenure and in 1999 he joined the Language Section, NIDCD, as a Senior Investigator. In 2002, he became Chief of the Brain Imaging and Modeling Section of NIDCD.
- Smith JF, Pillai A, Chen K, Horwitz B. Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems. Front Syst Neurosci. 2011;5:104.
- Pillai AS, Gilbert JR, Horwitz B. Early sensory cortex is activated in the absence of explicit input during crossmodal item retrieval: evidence from MEG. Behav Brain Res. 2013;238:265-72.
- Simonyan K, Herscovitch P, Horwitz B. Speech-induced striatal dopamine release is left lateralized and coupled to functional striatal circuits in healthy humans: a combined PET, fMRI and DTI study. Neuroimage. 2013;70:21-32.
- Banerjee A, Kikuchi Y, Mishkin M, Rauschecker JP, Horwitz B. Chronometry on Spike-LFP Responses Reveals the Functional Neural Circuitry of Early Auditory Cortex Underlying Sound Processing and Discrimination. eNeuro. 2018;5(3).
- Ulloa A, Horwitz B. Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex. Front Neuroinform. 2016;10:32.
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This page was last updated on Thursday, August 29, 2019