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

Back

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

Journal article

Open access

Peer reviewed

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, …

Journal of neuroscience methods, Vol.334, pp.108600-108600

03/15/2020

DOI: 10.1016/j.jneumeth.2020.108600

PMID: 31978489

Files and links (1)

url

https://doi.org/10.1016/j.jneumeth.2020.108600View

Published (Version of record) Open Access

Abstract

•New estimator for dynamic connectivity can detect both rapid and slow changes.•We found a new dynamic connectivity pattern prevalent in schizophrenia patients.•The new pattern showed negative connectivity between motor and sensory regions.•This pattern cannot be detected using sliding window Pearson correlation. 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. 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. 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. 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. 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.

Shared trajectory

fMRI

Resting state

Phase

ICA

Functional magnetic resonance imaging

Brain dynamics

Dynamic functional network connectivity

Details

Title: Subtitle: Weighted average of shared trajectory: A new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time
Creators: Ashkan Faghiri - The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM, 87106, USA
Armin Iraji - The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM, 87106, USA
Eswar Damaraju - The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM, 87106, USA
Aysenil Belger - Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
Judy Ford - Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
Daniel Mathalon - Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
Sarah Mcewen - Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
Bryon Mueller - Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
Godfrey Pearlson - Yale University, School of Medicine, New Haven, CT, USA
Adrian Preda - Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
Jessica Turner - Department of Psychology, Georgia State University, GA, USA
Jatin G Vaidya - Department of Psychiatry, University of Iowa, IA, USA
Theo G.M Van Erp - Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, 5251 California Ave, Irvine, CA, 92617, USA
Vince D Calhoun - The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM, 87106, USA
Resource Type: Journal article
Publication Details: Journal of neuroscience methods, Vol.334, pp.108600-108600
Publisher: Elsevier B.V
DOI: 10.1016/j.jneumeth.2020.108600
PMID: 31978489
ISSN: 0165-0270
eISSN: 1872-678X
Grant note: DOI: 10.13039/501100011605, name: National Science Foundation; DOI: 10.13039/100000097, name: National Center for Research Resources
Language: English
Date published: 03/15/2020
Academic Unit: Psychiatry; Iowa Neuroscience Institute; University College Courses
Record Identifier: 9984070608402771

Metrics

35 Record Views

21 Times Cited - Web of Science

See more details