Weighted average of shared trajectory: A new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time

Ashkan Faghiri; Armin Iraji; Eswar Damaraju; Aysenil Belger; Judy Ford; Daniel Mathalon; Sarah Mcewen; Bryon Mueller; Godfrey Pearlson; Adrian Preda; Jessica Turner; Jatin G Vaidya; Theo G M Van Erp; Vince D Calhoun

doi:10.1016/j.jneumeth.2020.108600

Weighted average of shared trajectory: A new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time

J Neurosci Methods. 2020 Jan 21:334:108600. doi: 10.1016/j.jneumeth.2020.108600. Online ahead of print.

Authors

Affiliations

¹ The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM, 87106, USA; Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, NM, USA; Department of ECE, University of New Mexico, NM, USA. Electronic address: ashkanfa.shirazu@gmail.com.
² The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM, 87106, USA; Department of ECE, University of New Mexico, NM, USA.
³ Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA; Department of ECE, University of New Mexico, NM, USA.
⁴ Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA; San Francisco VA Medical Center, San Francisco, CA, USA; Department of ECE, University of New Mexico, NM, USA.
⁵ Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA; Department of ECE, University of New Mexico, NM, USA.
⁶ Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA; Department of ECE, University of New Mexico, NM, USA.
⁷ Yale University, School of Medicine, New Haven, CT, USA; Department of ECE, University of New Mexico, NM, USA.
⁸ Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA; Department of ECE, University of New Mexico, NM, USA.
⁹ Department of Psychology, Georgia State University, GA, USA; Department of ECE, University of New Mexico, NM, USA.
¹⁰ Department of Psychiatry, University of Iowa, IA, USA; Department of ECE, University of New Mexico, NM, USA.
¹¹ Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, 5251 California Ave, Irvine, CA, 92617, USA; Center for the Neurobiology of Learning and Memory, University of California Irvine, 309 Qureshey Research Lab, Irvine, CA, 92697, USA.
¹² The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM, 87106, USA; Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, NM, USA; Department of Psychology, Georgia State University, GA, USA; Department of ECE, University of New Mexico, NM, USA.

Abstract

Background: Dynamic functional network connectivity (dFNC) of the brain has attracted considerable attention recently. Many approaches have been suggested to study dFNC with sliding window Pearson correlation (SWPC) being the most well-known. SWPC needs a relatively large sample size to reach a robust estimation but using large window sizes prevents us to detect rapid changes in dFNC.

New method: Here we first calculate the gradients of each time series pair and use the magnitude of these gradients to calculate weighted average of shared trajectory (WAST) as a new estimator for dFNC.

Results: Using WAST to compare healthy control and schizophrenia patients using a large dataset, we show disconnectivity between different regions associated with schizophrenia. In addition, WAST results reveals patients with schizophrenia stay longer in a connectivity state with negative connectivity between motor and sensory regions than do healthy controls.

Comparison with existing methods: We compare WAST with SWPC and multiplication of temporal derivatives (MTD) using different simulation scenarios. We show that WAST enables us to detect very rapid changes in dFNC (undetected by SWPC) while MTD performance is generally lower.

Conclusions: As large window sizes are unable to detect short states, using shorter window size is desirable if the estimator is robust enough. We provide evidence that WAST requires fewer samples (compared to SWPC) to reach a robust estimation. As a result, we were able to identify rapidly varying dFNC patterns undetected by SWPC while still being able to robustly estimate slower dFNC patterns.

Keywords: Brain dynamics; Dynamic functional network connectivity; Functional magnetic resonance imaging; ICA; Phase; Resting state; Shared trajectory; fMRI.

Abstract

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