Department of Biomedical Engineering, Vanderbilt University, Nashville, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, United States
Advanced MRI Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
Pinar S Özbay
Advanced MRI Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
Dante Picchioni
Advanced MRI Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
Jeff Duyn
Advanced MRI Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
Dario J Englot
Department of Biomedical Engineering, Vanderbilt University, Nashville, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, United States; Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, United States; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, United States
Victoria L Morgan
Department of Biomedical Engineering, Vanderbilt University, Nashville, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, United States; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, United States
Department of Biomedical Engineering, Vanderbilt University, Nashville, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, United States; Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, United States
Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.
