Psychophysical and physiological experiments have demonstrated that attending to an anticipated stimulus before its actual onset can enhance its sensory processing. Disruption of the neural mechanism that controls anticipatory attention is symptomatic of a number of psychiatric and neurological disorders including schizophrenia and epilepsy. Neuroimaging studies have identified the brain areas that contribute to anticipatory attention and suggested a top-down control mechanism. What remains not well understood is the neurophysiological signal that implements such control. The present project has three objectives. First, we wish to establish that alpha range (10 Hz) activities in local field potentials and in surface EEGs are the signals that mediate different aspects of top-down attentional control in the visual cortex. Second, we wish to establish that the inferotemporal cortex may serve as a way station that directs the top-down control signal from the prefrontal cortex to lower visual areas. Third, by comparing the attentional effect before and after stimulus onset, we wish to examine the interplay between top-down control and bottom-up processing. These objectives will be addressed by utilizing a unique resource - 120 GB of high quality multielectrode local field potential, surface EEG and multiunit activity data recorded from monkeys performing an intermodal selective attention task. A key enabling component for accomplishing these objectives is a set of signal processing methods for analyzing multivariate neural recordings developed and validated over the past decade by the PI and his colleagues. Initial applications of the analysis methods to the data have already yielded a number of insights in support of our research aims which lay the foundation for the successful execution of the proposed project.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZRG1-IFCN-E (02))
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Rossi, Andrew
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University of Florida
Biomedical Engineering
Schools of Engineering
United States
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Wen, Xiaotong; Rangarajan, Govindan; Ding, Mingzhou (2013) Is Granger causality a viable technique for analyzing fMRI data? PLoS One 8:e67428
Wen, Xiaotong; Rangarajan, Govindan; Ding, Mingzhou (2013) Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix. Philos Trans A Math Phys Eng Sci 371:20110610
Wen, Xiaotong; Yao, Li; Liu, Yijun et al. (2012) Causal interactions in attention networks predict behavioral performance. J Neurosci 32:1284-92
Wen, Xiaotong; Mo, Jue; Ding, Mingzhou (2012) Exploring resting-state functional connectivity with total interdependence. Neuroimage 60:1587-95
Nedungadi, Aatira G; Ding, Mingzhou; Rangarajan, Govindan (2011) Block coherence: a method for measuring the interdependence between two blocks of neurobiological time series. Biol Cybern 104:197-207
Mo, Jue; Schroeder, Charles E; Ding, Mingzhou (2011) Attentional modulation of alpha oscillations in macaque inferotemporal cortex. J Neurosci 31:878-82
Ding, Mingzhou; Mo, Jue; Schroeder, Charles E et al. (2011) Analyzing coherent brain networks with Granger causality. Conf Proc IEEE Eng Med Biol Soc 2011:5916-8
Bollimunta, Anil; Mo, Jue; Schroeder, Charles E et al. (2011) Neuronal mechanisms and attentional modulation of corticothalamic ? oscillations. J Neurosci 31:4935-43
Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj et al. (2009) Analyzing multiple spike trains with nonparametric Granger causality. J Comput Neurosci 27:55-64
Nalatore, Hariharan; Ding, Mingzhou; Rangarajan, Govindan (2009) Denoising neural data with state-space smoothing: method and application. J Neurosci Methods 179:131-41

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