Dynamic Time Warping Identifies Functionally Distinct fMRI Resting State Cortical Networks Specific to VTA and SNc: A Proof of Concept

Abstract

Functional connectivity (FC) is determined by similarity between functional magnetic resonance imaging (fMRI) signals from distinct brain regions. However, traditional FC analyses ignore temporal phase differences. Here, we addressed this limitation, using dynamic time warping (DTW) within a machine-learning framework, to study cortical FC patterns of 2 spatially adjacent but functionally distinct subcortical regions, namely Substantia Nigra Pars Compacta (SNc) and ventral tegmental area (VTA). We evaluate: 1) the influence of pair of brain regions considered, 2) the influence of warping window sizes, 3) the classification efficacy of DTW, and 4) the uniqueness of features identified. Whole brain 7 Tesla resting state fMRI scans from 81 healthy participants were used. FC between 2 subcortical regions of interests (ROIs) and 360 cortical parcels were computed using: 1) Pearson correlations (PCs), 2) dynamic time-warped PCs (DTW-PC). The separability of SNc-cortical and VTA-cortical network was validated on 40 participants and tested on the remaining 41, using a support vector machine (SVM). The SVM separated the SNc-cortical versus VTA-cortical network with 74.39 and 97.56% test accuracy using PC and DTW-PC, respectively. SVM-recursive feature elimination yielded 20 DTW-PC features that most strongly contributed to the separation of the networks and revealed novel VTA versus SNc preferential connections (P < 0.05, Bonferroni-Holm corrected).

Trial registration: ClinicalTrials.gov NCT00047853.

Keywords: 7 Tesla fMRI; Recursive feature elimination; cortical parcellation; machine learning; phase difference.

© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Figures

Figure 1

Influence of warping window sizes on the correlation between left versus right ROI pairs selected to include 1) primary regions 2) associative regions and 3) higher cognitive regions. Primary regions such as the primary visual cortex, primary motor cortex, and primary sensory motor cortex have similarly high left versus right correlation scores, irrespective of the warping window used. Increasing the warping caused a small increase in correlation scores for regions with moderate correlation, prior to warping (e.g., left vs. right parietal cortex, PCC, ACC, insula, hippocampus). The bilateral ROI pairs with the largest effect of warping were vmPFC, dlPFC, and Broca’s area. The y-axis has rows that correspond to individual participants repeated for 4 blocks with different maxsamp.

Figure 2

LOOCV accuracy (A) and test accuracy (B) for different warping window sizes (maxsamp). Larger window sizes resulted in higher accuracy levels. Similarly, increasing the number of top features (determined using the RFE), resulted in an initial increase in accuracy which saturated at around 7 features. The maxsamp = 0 (while not matlab acceptable) is merely used to denote that the is no warping done prior to the PC. Similarly, a maxsamp of 6 corresponded to a warping window of 6 TRs, that is, 15 s. (C) From left to right: VTA ROI, SNc ROI, Glasser atlas parcels, and reduced opacity Glasser atlas parcels superimposed on the averaged standard space anatomical image.

Figure 3

Correlations of the top 20 features of the “training data” using (A) DTW (maxsamp = 12), (C) No warping (maxsamp = 0). (B) The top 20 features identified using DTW (maxsamp = 12), unwarped (maxsamp = 0) correlations. (D) Correlations in the top 20 features of the “testing data” using DTW (maxsamp = 12). The top half of rows correspond to correlations between the SNc (subscript indicates participant number) and the cortical parcels, whereas the bottom half corresponds to those between VTA (subscript indicates participant number) and the corticial parcels. The correlations were normalized across participants [(x-mean)/SD].