Inter-subject phase synchronization for exploratory analysis of task-fMRI

Taylor Bolt, Jason Nomi, Shruti G. Vij, Catie Chang, Lucina Q Uddin

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Analysis of task-based fMRI data is conventionally carried out using a hypothesis-driven approach, where blood-oxygen-level dependent (BOLD) time courses are correlated with a hypothesized temporal structure. In some experimental designs, this temporal structure can be difficult to define. In other cases, experimenters may wish to take a more exploratory, data-driven approach to detecting task-driven BOLD activity. In this study, we demonstrate the efficiency and power of an inter-subject synchronization approach for exploratory analysis of task-based fMRI data. Combining the tools of instantaneous phase synchronization and independent component analysis, we characterize whole-brain task-driven responses in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this framework to fMRI data collected during performance of a simple motor task and a social cognitive task. Analyses using an inter-subject phase synchronization approach revealed a large number of brain networks that dynamically synchronized to various features of the task, often not predicted by the hypothesized temporal structure of the task. We suggest that this methodological framework, along with readily available tools in the fMRI community, provides a powerful exploratory, data-driven approach for analysis of task-driven BOLD activity.

Original languageEnglish (US)
Pages (from-to)477-488
Number of pages12
JournalNeuroImage
Volume176
DOIs
StatePublished - Aug 1 2018

Fingerprint

Magnetic Resonance Imaging
Oxygen
Brain
Research Design

Keywords

  • Brain synchronization
  • Exploratory fMRI
  • General linear model
  • Instantaneous phase analysis
  • Inter-subject correlation
  • Task fMRI

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

Inter-subject phase synchronization for exploratory analysis of task-fMRI. / Bolt, Taylor; Nomi, Jason; Vij, Shruti G.; Chang, Catie; Uddin, Lucina Q.

In: NeuroImage, Vol. 176, 01.08.2018, p. 477-488.

Research output: Contribution to journalArticle

Bolt, Taylor ; Nomi, Jason ; Vij, Shruti G. ; Chang, Catie ; Uddin, Lucina Q. / Inter-subject phase synchronization for exploratory analysis of task-fMRI. In: NeuroImage. 2018 ; Vol. 176. pp. 477-488.
@article{aa841d27e7ff45b5a7e99c860677a924,
title = "Inter-subject phase synchronization for exploratory analysis of task-fMRI",
abstract = "Analysis of task-based fMRI data is conventionally carried out using a hypothesis-driven approach, where blood-oxygen-level dependent (BOLD) time courses are correlated with a hypothesized temporal structure. In some experimental designs, this temporal structure can be difficult to define. In other cases, experimenters may wish to take a more exploratory, data-driven approach to detecting task-driven BOLD activity. In this study, we demonstrate the efficiency and power of an inter-subject synchronization approach for exploratory analysis of task-based fMRI data. Combining the tools of instantaneous phase synchronization and independent component analysis, we characterize whole-brain task-driven responses in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this framework to fMRI data collected during performance of a simple motor task and a social cognitive task. Analyses using an inter-subject phase synchronization approach revealed a large number of brain networks that dynamically synchronized to various features of the task, often not predicted by the hypothesized temporal structure of the task. We suggest that this methodological framework, along with readily available tools in the fMRI community, provides a powerful exploratory, data-driven approach for analysis of task-driven BOLD activity.",
keywords = "Brain synchronization, Exploratory fMRI, General linear model, Instantaneous phase analysis, Inter-subject correlation, Task fMRI",
author = "Taylor Bolt and Jason Nomi and Vij, {Shruti G.} and Catie Chang and Uddin, {Lucina Q}",
year = "2018",
month = "8",
day = "1",
doi = "10.1016/j.neuroimage.2018.04.015",
language = "English (US)",
volume = "176",
pages = "477--488",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - Inter-subject phase synchronization for exploratory analysis of task-fMRI

AU - Bolt, Taylor

AU - Nomi, Jason

AU - Vij, Shruti G.

AU - Chang, Catie

AU - Uddin, Lucina Q

PY - 2018/8/1

Y1 - 2018/8/1

N2 - Analysis of task-based fMRI data is conventionally carried out using a hypothesis-driven approach, where blood-oxygen-level dependent (BOLD) time courses are correlated with a hypothesized temporal structure. In some experimental designs, this temporal structure can be difficult to define. In other cases, experimenters may wish to take a more exploratory, data-driven approach to detecting task-driven BOLD activity. In this study, we demonstrate the efficiency and power of an inter-subject synchronization approach for exploratory analysis of task-based fMRI data. Combining the tools of instantaneous phase synchronization and independent component analysis, we characterize whole-brain task-driven responses in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this framework to fMRI data collected during performance of a simple motor task and a social cognitive task. Analyses using an inter-subject phase synchronization approach revealed a large number of brain networks that dynamically synchronized to various features of the task, often not predicted by the hypothesized temporal structure of the task. We suggest that this methodological framework, along with readily available tools in the fMRI community, provides a powerful exploratory, data-driven approach for analysis of task-driven BOLD activity.

AB - Analysis of task-based fMRI data is conventionally carried out using a hypothesis-driven approach, where blood-oxygen-level dependent (BOLD) time courses are correlated with a hypothesized temporal structure. In some experimental designs, this temporal structure can be difficult to define. In other cases, experimenters may wish to take a more exploratory, data-driven approach to detecting task-driven BOLD activity. In this study, we demonstrate the efficiency and power of an inter-subject synchronization approach for exploratory analysis of task-based fMRI data. Combining the tools of instantaneous phase synchronization and independent component analysis, we characterize whole-brain task-driven responses in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this framework to fMRI data collected during performance of a simple motor task and a social cognitive task. Analyses using an inter-subject phase synchronization approach revealed a large number of brain networks that dynamically synchronized to various features of the task, often not predicted by the hypothesized temporal structure of the task. We suggest that this methodological framework, along with readily available tools in the fMRI community, provides a powerful exploratory, data-driven approach for analysis of task-driven BOLD activity.

KW - Brain synchronization

KW - Exploratory fMRI

KW - General linear model

KW - Instantaneous phase analysis

KW - Inter-subject correlation

KW - Task fMRI

UR - http://www.scopus.com/inward/record.url?scp=85047189620&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85047189620&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2018.04.015

DO - 10.1016/j.neuroimage.2018.04.015

M3 - Article

C2 - 29654878

AN - SCOPUS:85047189620

VL - 176

SP - 477

EP - 488

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -