Inter-subject phase synchronization for exploratory analysis of task-fMRI

Bolt, Taylor; Nomi, Jason S.; Vij, Shruti Gopal; Chang, Catie; Uddin, Lucina Q.

doi:10.1016/j.neuroimage.2018.04.015

Cited by 19 publications

(16 citation statements)

References 47 publications

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“…Firm conclusions will also depend on other factors not examined here. For example, GLM beta maps used here as representative of “task‐evoked” activity hardly exhaust the characterization of the brain's BOLD task response (Bolt, Nomi, Vij, Chang, & Uddin, ; Cole, Bassett, Power, Braver, & Petersen, ; Gonzalez‐Castillo et al, ). In other words, the extent of between‐person variability in task‐evoked activity will depend on how one defines “task‐evoked” activity.…”

Section: Resultsmentioning

confidence: 99%

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The situation or the person? Individual and task‐evoked differences in BOLD activity

Bolt¹,

Nomi

Bainter

et al. 2019

Human Brain Mapping

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Investigations of between‐person variability are enjoying a recent resurgence in functional magnetic resonance imaging (fMRI) research. Several recent studies have found persistent between‐person differences in blood‐oxygenated‐level dependent (BOLD) activation patterns and resting‐state functional connectivity. Conflicting findings have been reported regarding the extent to which (a) between‐person or (b) within‐person cognitive state differences explain differences in BOLD activation patterns. These discrepancies may arise due to statistical analysis choices, parcellation resolution, and limited sampling of task‐fMRI datasets. We attempt to address these issues in a large‐scale analysis of several task‐fMRI paradigms. Using a novel application of multivariate distance matrix regression, we examine between‐person and task‐condition variability estimates across varying levels of “resolution”, from a coarse region‐of‐interest level to the vertex‐level, and across different distance metrics. These analyses revealed that under most circumstances, differences in task conditions explained a greater amount of variance in activation map differences than between‐person differences. However, this finding was reversed when comparing activation maps at a “high‐resolution” vertex level. More generally, we observed that when moving from “low” to “high” resolutions, the variance explained by between‐person differences increased while variance explained by task conditions decreased. We further analyzed the relationships among subject‐level activation maps across all task‐conditions using an unsupervised clustering approach and identified a superordinate task structure. This structure went beyond conventional task labels and highlighted those experimental manipulations across task conditions that produce contrasting versus similar whole‐brain activation patterns. Overall, these analyses suggest that the question of the subject‐ versus task‐effects on BOLD activation patterns is nontrivial, and depends on the comparison “resolution,” choice of distance metric, and the coding of task‐conditions.

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Section: Resultsmentioning

confidence: 99%

“…Firm conclusions will also depend on other factors not examined here. For example, GLM beta maps used here as representative of "task-evoked" activity hardly exhaust the characterization of the brain's BOLD task response (Bolt, Nomi, Vij, Chang, & Uddin, 2018;Cole, Bassett, Power, Braver, & Petersen, 2014;Gonzalez-Castillo et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

The situation or the person? Individual and task‐evoked differences in BOLD activity

Bolt¹,

Nomi

Bainter

et al. 2019

Human Brain Mapping

Self Cite

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“…Following previous work (Bolt et al, 2018), preprocessing for the synchronization analysis additionally included detrending and filtering (0.01-0.1 Hz) using DPARSF (http://www.restfmri.net/forum/DPARSF, Yan & Zang, 2010). For each subject, time-series of each voxel were extracted and z-transformed.…”

Section: Inter-subject Phase Synchronization (Isps) Analysismentioning

confidence: 99%

“…These studies are based on earlier work demonstrating that brain regions produce similar temporal dynamics across participants experiencing the same task event concurrently (Hanson, Gagliardi, & Hanson, 2009). Recently, an inter-subject phase synchronization (ISPS) analysis which combines the instantaneous phase synchronization measure (Glerean et al, 2012;Kauppi, Pajula, & Tohka, 2014) and independent component analysis (ICA) (Beckmann & Smith, 2004Calhoun, Kiehl, & Pearlson, 2008) has been introduced as a means for conducting exploratory analysis of task-based fMRI data (Bolt, Nomi, Vij, Chang, & Uddin, 2018). This approach estimates the task relevant brain networks that dynamically synchronize during the task across participants in a data-driven manner, without dependence on a priori reference functions.…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of such an approach over the traditional GLM approach is that one can potentially gain information about brain responses that is not predicted by the hypothesized temporal structure of the task. The efficiency and power of the ISPS approach have recently been demonstrated in a simple motor task and a social cognitive task provided by the Human Connectome Project (Barch et al, 2013;Bolt et al, 2018). In the current study, we applied this data-driven approach to a large sample of fMRI data collected during performance of a pain empathy task employing affective and physical pain empathy stimuli as well as corresponding non-painful control stimuli in order to explore neural network synchronization during vicarious pain empathy.…”

Section: Introductionmentioning

confidence: 99%

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Inter-subject phase synchronization differentiates neural networks underlying physical pain empathy

Lei

Bolt

Nomi

et al. 2019

Preprint

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Recent approaches for understanding the neural basis of pain empathy emphasize the dynamic construction of neural networks underlying this multifaceted social cognitive process. Inter-subject phase synchronization (ISPS)is an approach for exploratory analysis of task-based fMRI data that reveals brain networks dynamically synchronized to task-features across subjects. We applied ISPS to task-fMRI data assessing vicarious pain empathy in healthy subjects (n=238). The task employed physical (limb) and affective (faces) painful and corresponding non-painful visual stimuli. ISPS revealed two distinct networks synchronized during physical pain observation, one encompassing anterior insula and midcingulate regions strongly engaged in (vicarious) pain, and another encompassing parietal and inferior frontal regions associated with social cognitive processes which may further modulate and support the pain empathic response. No robust network synchronization was observed while processing facial expressions of pain, possibly reflecting high inter-individual variation in response to socially transmitted pain experience. ISPS also revealed networks related to task onset or general processing of physical (limb) or affective (face) stimuli which encompassed networks engaged in object manipulation or face processing, respectively. Together, the ISPS approach permits segregation of networks engaged in different psychological processes, providing additional insight into shared neural mechanisms of empathy for physical pain across individuals.

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Measuring functional connectivity in frequency‐domain helps to better characterize brain function

Peng,

Su,

et al. 2024

Human Brain Mapping

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Resting‐state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence‐based and correlation‐based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.

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Inter-subject phase synchronization for exploratory analysis of task-fMRI

Cited by 19 publications

References 47 publications

The situation or the person? Individual and task‐evoked differences in BOLD activity

The situation or the person? Individual and task‐evoked differences in BOLD activity

Inter-subject phase synchronization differentiates neural networks underlying physical pain empathy

Measuring functional connectivity in frequency‐domain helps to better characterize brain function

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