TY - JOUR
T1 - Resting-State fMRI dynamics and null models
T2 - Perspectives, sampling variability, and simulations
AU - Miller, Robyn L.
AU - Abrol, Anees
AU - Adali, Tulay
AU - Levin-Schwarz, Yuri
AU - Calhoun, Vince D.
N1 - Funding Information:
This work was supported by National Institutes of Health (NIH) via a COBRE grant P20GM103472, R01 grants R01EB005846, 1R01EB006841, 1R01DA040487 and REB020407, and National Science Foundation (NSF) grants 1539067 and 1631838.
Publisher Copyright:
© 2018 Miller, Abrol, Adali, Levin-Schwarz and Calhoun.
PY - 2018/9/6
Y1 - 2018/9/6
N2 - Studies of resting state functional MRI (rs-fRMI) are increasingly focused on "dynamics", or on those properties of brain activation that manifest and vary on timescales shorter than the scan's full duration. This shift in focus has led to a flurry of interest in developing hypothesis testing frameworks and null models applicable to the dynamical setting. Thus far however, these efforts have been weakened by a number of crucial shortcomings that are outlined and discussed in this article. We focus here on aspects of recently proposed null models that, we argue, are poorly formulated relative to the hypotheses they are designed to test, i.e., their potential role in separating functionally relevant BOLD signal dynamics from noise or intermittent background and maintenance type processes is limited by factors that are fundamental rather than merely quantitative or parametric. In this short position paper, we emphasize that (1) serious care must be exercised in building null models for rs-fMRI dynamics from distributionally stationary univariate or multivariate timeseries, i.e., timeseries whose values are each independently drawn from one pre-specified probability distribution; and (2) measures such as kurtosis that quantify over-concentration of observed values in the far tails of some reference distribution may not be particularly suitable for capturing signal features most plausibly contributing to functionally relevant brain dynamics. Other metrics targeted, for example, at capturing the type of epochal signal variation that is often viewed as a signature of brain responsiveness to stimuli or experimental tasks, could play a more scientifically clarifying role. As we learn more about the phenomenon of functionally relevant brain dynamics and its imaging correlates, scientifically meaningful null hypotheses and well-tuned null models will naturally emerge. We also revisit the important concept of distributional stationarity, discuss how it manifests within realizations vs. across multiple realizations, and provide guidance on the benefits and limitations of employing this type of stationarity in modeling the absence of functionally relevant temporal dynamics in resting state fMRI. We hope that the discussions herein are useful, and promote thoughtful consideration of these important issues.
AB - Studies of resting state functional MRI (rs-fRMI) are increasingly focused on "dynamics", or on those properties of brain activation that manifest and vary on timescales shorter than the scan's full duration. This shift in focus has led to a flurry of interest in developing hypothesis testing frameworks and null models applicable to the dynamical setting. Thus far however, these efforts have been weakened by a number of crucial shortcomings that are outlined and discussed in this article. We focus here on aspects of recently proposed null models that, we argue, are poorly formulated relative to the hypotheses they are designed to test, i.e., their potential role in separating functionally relevant BOLD signal dynamics from noise or intermittent background and maintenance type processes is limited by factors that are fundamental rather than merely quantitative or parametric. In this short position paper, we emphasize that (1) serious care must be exercised in building null models for rs-fMRI dynamics from distributionally stationary univariate or multivariate timeseries, i.e., timeseries whose values are each independently drawn from one pre-specified probability distribution; and (2) measures such as kurtosis that quantify over-concentration of observed values in the far tails of some reference distribution may not be particularly suitable for capturing signal features most plausibly contributing to functionally relevant brain dynamics. Other metrics targeted, for example, at capturing the type of epochal signal variation that is often viewed as a signature of brain responsiveness to stimuli or experimental tasks, could play a more scientifically clarifying role. As we learn more about the phenomenon of functionally relevant brain dynamics and its imaging correlates, scientifically meaningful null hypotheses and well-tuned null models will naturally emerge. We also revisit the important concept of distributional stationarity, discuss how it manifests within realizations vs. across multiple realizations, and provide guidance on the benefits and limitations of employing this type of stationarity in modeling the absence of functionally relevant temporal dynamics in resting state fMRI. We hope that the discussions herein are useful, and promote thoughtful consideration of these important issues.
KW - Brain dynamics
KW - Dynamic connectivity
KW - FMRI
KW - FMRI methods
KW - Functional network connectivity
UR - http://www.scopus.com/inward/record.url?scp=85053151119&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053151119&partnerID=8YFLogxK
U2 - 10.3389/fnins.2018.00551
DO - 10.3389/fnins.2018.00551
M3 - Article
C2 - 30237758
AN - SCOPUS:85053151119
SN - 1662-4548
VL - 12
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
IS - SEP
M1 - 551
ER -