On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI

Abstract

Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition.

Discovering individual fingerprints in resting‐state functional connectivity using deep neural networks

“…Alternatively, the effect might be due to the varying signal quality depending on the phase‐encoding directions. Previous research suggests that the difference between R‐L or L‐R direction of phase‐encoding induces asymmetric dropout in the fMRI signal (Omidvarnia et al, 2022 ; Smith et al, 2013 ). Specifically, the R‐L phase‐encoding direction is known to have a more severe dropout in the left hemisphere than the L‐R phase‐encoding direction, which might lead to increased distortion of the FC edges in the left hemisphere under that encoding (Mori et al, 2018 ).…”

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

“…Consequently, a small number of resting-state fMRI studies have used sample entropy (SampEn) (Richman & Moorman, 2000) to quantify the self-similarity of edge fluctuations (edge-SampEn [ESE]) derived from sliding-window analysis (Hirsch & Wohlschlaeger, 2022; Jia & Gu, 2019b; Jia et al, 2017). SampEn is one way of assessing INT, with higher values corresponding to shorter INT (Omidvarnia, Mesbah, Pedersen, & Jackson, 2018; Sokunbi et al, 2014), and it is also significantly associated with mental abilities and cognitive load in healthy subjects (S. S. Menon & Krishnamurthy, 2019; Nezafati, Temmar, & Keilholz, 2020; Omidvarnia et al, 2022; Omidvarnia et al, 2021).…”

The heritability and structural correlates of brain entropy

“…In comparison to other types of entropy measure (such as Shannon entropy [13] and approximate entropy [14]), sample entropy is less sensitive to noise and allows for more accurate entropy estimation for signals with a limited number of sampling points, rendering it suitable for the complexity analysis of fMRI [11, 15, 7]. Previous studies have demonstrated that BEN can accurately discriminate between empirical fMRI and simulated signals [7] and show good reproducibility [16, 17] and task-induced sensitivity [9, 18, 19]. Evidence from theoretical models, animal experiments, and imaging data analyses suggests that BEN may inform the capacity of brain information processing at local and distributed levels [20, 21, 22].…”