Perceptual decision making is one of the most basic forms of cognition, for it is how sensory input is mapped to specific behavior. Substantial effort has focused on uncovering the constituent cortical networks involved in this early form of cognition using both single and multi-unit recordings in primates and, more recently, functional neuroimaging in humans. These neuroimaging studies, typically utilizing functional magnetic resonance imaging (fMRI), have identified areas in frontal, parietal, and thalamic cortex in which metabolic activity correlates with decision related variables. However, decision making is a dynamic process, and the localized activations found with fMRI must be a part of cortical networks defined by the relative timing of these activations and their causality. The overall goal of this project is to couple high temporal resolution, single-trial analysis of electroencephalography (EEG) with simultaneously acquired fMRI to infer the constituent cortical networks of perceptual decision making in the human brain.
Specific aims are 1) to replicate and systematically expand upon our prior results showing neural components correlate with task-relevant decision making variables, but for the case of EEG acquired simultaneously with fMRI, 2) to link trial-to-trial variability of EEG components, identified for perceptual decision making, with spatial area simultaneously imaged with fMRI, and 3) to extend our perceptual decision making paradigm from brief stimulus presentation to prolonged and dynamic stimuli and use single-trial analysis of simultaneous EEG/fMRI to differentiate cortical networks involved in evidence accumulation. This project will significantly advance our understanding of decision making in the human brain by providing a more precise cortical network "diagram" which could be used to better compare differences observed between primate and human data. Finally, this research could lead to a better understanding of cortical processing underlying basic cognitive deficits, linking spatial and temporal changes in activations to specific neurological diseases and disease states.

Public Health Relevance

In this project we will use state-of-the art neuroimaging to map the neural networks underlying decision making in the human brain. This project will both shed light on basic neuroscience questions related to decision making in humans as well as lead to a better understanding of cognitive deficits and neurological disease in which decision making is affected.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Cognitive Neuroscience Study Section (COG)
Program Officer
Rossi, Andrew
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Columbia University (N.Y.)
Biomedical Engineering
Schools of Engineering
New York
United States
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Lou, Bin; Li, Yun; Philiastides, Marios G et al. (2014) Prestimulus alpha power predicts fidelity of sensory encoding in perceptual decision making. Neuroimage 87:242-51
Walz, Jennifer M; Goldman, Robin I; Carapezza, Michael et al. (2014) Simultaneous EEG-fMRI reveals a temporal cascade of task-related and default-mode activations during a simple target detection task. Neuroimage 102 Pt 1:229-39
Hong, Linbi; Walz, Jennifer M; Sajda, Paul (2014) Your eyes give you away: prestimulus changes in pupil diameter correlate with poststimulus task-related EEG dynamics. PLoS One 9:e91321
Sherwin, Jason; Sajda, Paul (2013) Musical experts recruit action-related neural structures in harmonic anomaly detection: evidence for embodied cognition in expertise. Brain Cogn 83:190-202
Ratcliff, Roger; Starns, Jeffrey J (2013) Modeling confidence judgments, response times, and multiple choices in decision making: recognition memory and motion discrimination. Psychol Rev 120:697-719
Li, Yun; Lou, Bin; Gao, Xiaorong et al. (2013) Post-stimulus endogenous and exogenous oscillations are differentially modulated by task difficulty. Front Hum Neurosci 7:9
Muraskin, Jordan; Ooi, Melvyn B; Goldman, Robin I et al. (2013) Prospective active marker motion correction improves statistical power in BOLD fMRI. Neuroimage 68:154-61
Pernet, Cyril R; Sajda, Paul; Rousselet, Guillaume A (2011) Single-trial analyses: why bother? Front Psychol 2:322
Debettencourt, Megan; Goldman, Robin; Brown, Truman et al. (2011) Adaptive Thresholding for Improving Sensitivity in Single-Trial Simultaneous EEG/fMRI. Front Psychol 2:91