Mutual connectivity analysis of resting-state functional MRI data with local models

DSouza, Adora M.; Abidin, Anas Z.; Chockanathan, Udaysankar; Schifitto, Giovanni; Wismüller, Axel

doi:10.1016/j.neuroimage.2018.05.038

Cited by 28 publications

(9 citation statements)

References 74 publications

(141 reference statements)

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“…Dynamic time warping has been demonstrated to capture the non-stationarity in simulated functional MRI data 26,27 . The importance of non-linear and directional dependencies among BOLD signals is highlighted by means of mutual connectivity 28 .…”

Section: Discussionmentioning

confidence: 99%

Rethinking Measures of Functional Connectivity via Feature Extraction

Mohanty

Sethares

Nair

et al. 2020

Sci Rep

106

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Functional magnetic resonance imaging (fMRI)-based functional connectivity (FC) commonly characterizes the functional connections in the brain. Conventional quantification of FC by Pearson's correlation captures linear, time-domain dependencies among blood-oxygen-level-dependent (BOLD) signals. We examined measures to quantify FC by investigating: (i) Is Pearson's correlation sufficient to characterize FC? (ii) Can alternative measures better quantify FC? (iii) What are the implications of using alternative FC measures? FMRI analysis in healthy adult population suggested that: (i) Pearson's correlation cannot comprehensively capture BOLD inter-dependencies. (ii) Eight alternative FC measures were similarly consistent between task and resting-state fMRI, improved age-based classification and provided better association with behavioral outcomes. (iii) Formulated hypotheses were: first, in lieu of Pearson's correlation, an augmented, composite and multi-metric definition of FC is more appropriate; second, canonical large-scale brain networks may depend on the chosen FC measure. A thorough notion of FC promises better understanding of variations within a given population.open Scientific RepoRtS | (2020) 10:1298 | https://doi.org/10.1038/s41598-020-57915-wwww.nature.com/scientificreports www.nature.com/scientificreports/ dependencies). These different measures were compared and contrasted with three experiments which assessed the consistency of information provided by the FC measures, utility of the FC measures in population-based discrimination and the biological plausibility of using these FC measures.Experiment 1 (E1). The goal of the first experiment was to understand the relative consistency of the different FC measures when evaluated in young and healthy individuals. Within-individual consistency was first established by comparing the overlap (Sørensen-Dice similarity coefficient) of the FC pattern observed in task (motor and verbal) and resting-state functional MRI and then averaging across individuals for each FC measure. Findings from E1: Consistency of functional connectivity. Consistency of all of the identified FCmeasures was evaluated by comparing task functional MRI and resting-state functional MRI in young healthy adults (demographics in Table 1). Since the connectivity pattern may be different for each FC measure, the task functional MRI data offer a form of ground truth as they activate specific regions in the brain which can bear correspondence with those in resting condition. Thresholded FC maps (threshold = one standard deviation above grand mean) between task and resting-state conditions are illustrated for Pearson's correlation in Fig. 2 (subfigure A) and remaining FC measures in Supplementary Fig. 1.Consistency of resting-state FC, quantified by Sørensen-Dice similarity coefficient, is tabulated in Table 2 for the motor and language networks. While mostly comparable across many FC measures, the overlap coefficients for Pearson's correlation are not necessarily the best in any of these tested net...

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Section: Discussionmentioning

confidence: 99%

Rethinking Measures of Functional Connectivity via Feature Extraction

Mohanty

Sethares

Nair

et al. 2020

Sci Rep

106

View full text Add to dashboard Cite

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“…Though CCM does not concern itself with the dimension of the attractor (d), it relates to the embedding dimension of the reconstructed manifold (E) through the Whitney embedding theorem (E ≤ 2d + 1). DSouza et al (2018) showed the deleterious effect that a high repetition time (TR) has on attractor reconstruction. Following the example of McFarlin et al (2013) who reported improvements in connectivity analysis 1 Convergence is a key property for inferring causality, it is limited by observational error, process noise, and time series length.…”

Section: Data Preparationmentioning

confidence: 99%

Causal Analysis of Activity in Social Brain Areas During Human-Agent Conversation

Martins

Chaminade

Cavazza

2022

Front. Neuroergonomics

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This article investigates the differences in cognitive and neural mechanisms between human-human and human-virtual agent interaction using a dataset recorded in an ecologically realistic environment. We use Convergent Cross Mapping (CCM) to investigate functional connectivity between pairs of regions involved in the framework of social cognitive neuroscience, namely the fusiform gyrus, superior temporal sulcus (STS), temporoparietal junction (TPJ), and the dorsolateral prefrontal cortex (DLPFC)—taken as prefrontal asymmetry. Our approach is a compromise between investigating local activation in specific regions and investigating connectivity networks that may form part of larger networks. In addition to concording with previous studies, our results suggest that the right TPJ is one of the most reliable areas for assessing processes occurring during human-virtual agent interactions, both in a static and dynamic sense.

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“…该方法可以表述为 [47] S = argmintr( ) log + , 式如下: [54] . 因其所需数据量较少, 并且能够有效地反映非线性特征等特点, 非常适合用于基于功能核磁共振数据进行脑区间功能连接的分析 [55,56] . 通过收敛交叉映射方法构建脑功能连接网络的流程如图2所示.…”

Section: 如上所述 Dmn内部激活模式及功能连接的异常往往unclassified