Resting-state fMRI correlations: From link-wise unreliability to whole brain stability

Pannunzi, Mario; Hindriks, Rikkert; Bettinardi, Ruggero G.; Wenger, Elisabeth; Lisofsky, Nina; Mårtensson, Johan; Butler, Oisín; Filevich, Elisa; Becker, Maxi; Lochstet, Martyna; Kühn, Simone; Deco, Gustavo

doi:10.1016/j.neuroimage.2017.06.006

Cited by 82 publications

(59 citation statements)

References 43 publications

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“…Similar findings have been reported by others as the basis for connectivity-based ‘fingerprinting’ of individuals (Anderson et al, 2011; Finn et al, 2015; Miranda-Dominguez et al, 2014; Pannunzi et al, 2017; Xu et al, 2016). However, the ability to distinguish subjects may at least in part be explained by differences in the spatial locations of brain networks across individuals (Gordon et al, 2017a; Laumann et al, 2015; Satterthwaite and Davatzikos, 2015).…”

Section: Discussionsupporting

confidence: 90%

Precision Functional Mapping of Individual Human Brains

Gordon

Laumann

Gilmore

et al. 2017

Neuron

1,164

1,346

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Summary Human functional MRI (fMRI) research primarily focuses on analyzing data averaged across groups, which limits the detail, specificity, and clinical utility of fMRI resting-state functional connectivity (RSFC) and task activation maps. To push our understanding of functional brain organization to the level of individual humans, we assembled a novel MRI dataset containing five hours of RSFC data, six hours of task fMRI, multiple structural MRIs, and neuropsychological tests from each of ten adults. Using these data, we generated ten high fidelity, individual-specific functional connectomes. This individual connectome approach revealed several new types of spatial and organizational variability in brain networks, including unique network features and topologies that corresponded with structural and task-derived brain features. We are releasing this highly-sampled, individual-focused dataset as a resource for neuroscientists, and we propose precision individual connectomics as a model for future work examining the organization of healthy and diseased individual human brains.

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Section: Discussionsupporting

confidence: 90%

Precision Functional Mapping of Individual Human Brains

Gordon

Laumann

Gilmore

et al. 2017

Neuron

1,164

1,346

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“…It is important to note that there is an ongoing discussion in the literature regarding whether DFC estimates are largely due to sampling variability versus true changes in FC (e.g. fluctuations in wakefulness) (Pannunzi et al, 2017;Tagliazucchi and Laufs, 2014;Laumann et al, 2017;Haimovici et al, 2017;Liégeois et al, 2017). The present work does not make any claims as to whether DFC estimates reflect dynamics changes in brain functional connectivity as opposed to sampling variability or other artifactual 835 factors.…”

Section: Resultsmentioning

confidence: 71%

Nuisance Effects and the Limitations of Nuisance Regression in Dynamic Functional Connectivity fMRI

Nalci

Rao

Liu

2018

Preprint

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In resting-state fMRI, dynamic functional connectivity (DFC) measures are used to characterize temporal changes in the brain's intrinsic functional connectivity. A widely used approach for DFC estimation is the computation of the sliding window correlation between blood oxygenation level dependent (BOLD) signals from di↵erent brain regions. Although the source of temporal fluctuations in DFC estimates remains largely unknown, there is growing evidence that they may reflect dynamic shifts between functional brain networks. At the same time, recent findings suggest that DFC estimates might be prone to the influence of nuisance factors such as the physiological modulation of the BOLD signal. Therefore, nuisance regression is used in many DFC studies to regress out the e↵ects of nuisance terms prior to the computation of DFC estimates. In this work we examined the relationship between DFC estimates and nuisance factors.We found that DFC estimates were significantly correlated with temporal fluctuations in the magnitude (norm) of various nuisance regressors, with significant correlations observed in the majority (76%) of the cases examined. Significant correlations between the DFC estimates and nuisance regressor norms were found even when the underlying correlations between the nuisance and fMRI time courses were relatively small. We then show that nuisance regression does not eliminate the relationship between DFC estimates and nuisance norms, with significant correlations observed in the majority (71%) of the cases examined after nuisance regression. We present theoretical bounds on the di↵erence between DFC estimates obtained before and after nuisance regression and relate these bounds to limitations in the ⇤ Corresponding Author ⇤⇤ Principal Corresponding Author Email addresses: analci@ucsd.edu (Alican Nalci), ttliu@ucsd.edu (Thomas T. Liu). CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/285239 doi: bioRxiv preprint first posted online Mar. 21, 2018; e cacy of nuisance regression with regards to DFC estimates.

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“…This is not the first study trying to address reproducibility and consistency in resting state sessions. Many studies have shown that the longer the scanning time is, the better the consistency and reliability of the analysis is (Pannunzi et al, ). Interestingly, some have found that the optimal scanning time to have reproducible results is greater than 14 to 20 min (Anderson, Ferguson, Lopez‐Larson, & Yurgelun‐Todd, ; Birn et al, ; Laumann et al, ), which represents around three to four sessions in the present study, corroborating the idea of a minimal scanning time to have reproducible results.…”

Section: Discussionmentioning

confidence: 99%

“…This represents a major issue given the well-known large anatomical and functional variability across individuals (Frost & Goebel, 2012;Pannunzi et al, 2017). Moreover, functional variability is also expected within a single subject depending on many factors such as the state of the subject and environmental noise (Mueller et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Reliable local dynamics in the brain across sessions are revealed by whole‐brain modeling of resting state activity

Donnelly-Kehoe

Saenger

Lisofsky

et al. 2019

Human Brain Mapping

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Resting state fMRI is a tool for studying the functional organization of the human brain. Ongoing brain activity at “rest” is highly dynamic, but procedures such as correlation or independent component analysis treat functional connectivity (FC) as if, theoretically, it is stationary and therefore the fluctuations observed in FC are thought as noise. Consequently, FC is not usually used as a single‐subject level marker and it is limited to group studies. Here we develop an imaging‐based technique capable of reliably portraying information of local dynamics at a single‐subject level by using a whole‐brain model of ongoing dynamics that estimates a local parameter, which reflects if each brain region presents stable, asynchronous or transitory oscillations. Using 50 longitudinal resting‐state sessions of one single subject and single resting‐state sessions from a group of 50 participants we demonstrate that brain dynamics can be quantified consistently with respect to group dynamics using a scanning time of 20 min. We show that brain hubs are closer to a transition point between synchronous and asynchronous oscillatory dynamics and that dynamics in frontal areas have larger heterogeneity in its values compared to other lobules. Nevertheless, frontal regions and hubs showed higher consistency within the same subject while the inter‐session variability found in primary visual and motor areas was only as high as the one found across subjects. The framework presented here can be used to study functional brain dynamics at group and, more importantly, at individual level, opening new avenues for possible clinical applications.

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Resting-state fMRI correlations: From link-wise unreliability to whole brain stability

Cited by 82 publications

References 43 publications

Precision Functional Mapping of Individual Human Brains

Precision Functional Mapping of Individual Human Brains

Nuisance Effects and the Limitations of Nuisance Regression in Dynamic Functional Connectivity fMRI

Reliable local dynamics in the brain across sessions are revealed by whole‐brain modeling of resting state activity

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