Abstract
Resting state fMRI (rsfMRI) is frequently used to study brain function, including in clinical populations. Similarity of blood-oxygen-level-dependent (BOLD) fluctuations during rsfMRI between brain regions is thought to reflect intrinsic functional connectivity (FC), potentially due to history of coactivation. To quantify similarity, studies have almost exclusively relied on Pearson correlation, which assumes linearity and can therefore underestimate FC if the hemodynamic response function differs regionally or there is BOLD signal lag between timeseries. Here we show in three cohorts of children, adolescents and adults, with and without autism spectrum disorders (ASDs), that measuring the similarity of BOLD signal fluctuations using non-linear dynamic time warping (DTW) is more robust to global signal regression (GSR), has higher test-retest reliability and is more sensitive to task-related changes in FC. Additionally, when comparing FC between individuals with ASDs and typical controls, more group differences are detected using DTW. DTW estimates are also more related to ASD symptom severity and executive function, while Pearson correlation estimates of FC are more strongly associated with respiration during rsfMRI. Together these findings suggest that non-linear methods such as DTW improve estimation of resting state FC, particularly when studying clinical populations whose hemodynamics or neurovascular coupling may be altered compared to typical controls.
Original language | English (US) |
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Article number | 117383 |
Journal | Neuroimage |
Volume | 223 |
DOIs | |
State | Published - Dec 2020 |
Funding
We are grateful to all participants and families who participated in this study. This work was supported by the National Institutes of Health ( R01 MH081023 , R01 MH101173 , and R01 MH103494 to RAM, and K01 MH097972 to IF).
Keywords
- Autism spectrum disorder
- Dynamic time warping
- Functional MRI
- Functional connectivity
- Resting state
- Timeseries analysis
ASJC Scopus subject areas
- Neurology
- Cognitive Neuroscience