Network variants are similar between task and rest states

Kraus, Brian; Perez, Diana C.; Ladwig, Zach; Seitzman, Benjamin A.; Dworetsky, Ally; Petersen, Steven E.; Gratton, Caterina

doi:10.1101/2020.07.30.229492

Cited by 3 publications

(3 citation statements)

References 56 publications

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“…the two large communities discussed in the previous section. These observations align with other studies showing that the individualization of FC is generally a subtle modulation of features that are evident in group-level data, from brain systems [46][47][48] to regional FC fingerprints [8].…”

Section: High-amplitude Cofluctuation Patterns Are Individualized Andsupporting

confidence: 91%

Individualized event structure drives individual differences in whole-brain functional connectivity

Betzel

Cutts

Greenwell

et al. 2021

Preprint

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Resting-state functional connectivity is typically calculated as a correlation between regional activity. It is studied widely, both to gain insight into the brain's intrinsic organization but also to develop markers sensitive to changes in an individual's cognitive, clinical, and developmental state. Despite this, the origins and drivers of functional connectivity, especially at the level of densely sampled individuals, remain elusive. Here, we leverage novel methodology to decompose functional connectivity into its precise framewise contributions. Using two dense sampling datasets, we investigate the origins of individualized functional connectivity, focusing specifically on the role of brain network ``events'' -- short-lived patterns of high-amplitude co-fluctuations. Here, we develop a statistical test to identify events in empirical recordings. We show that the patterns of co-fluctuation expressed during events are repeated across multiple scans of the same individual and represent idiosyncratic variants of template patterns that are expressed at the group level. Lastly, we propose a simple model of functional connectivity based on event co-fluctuations, demonstrating that group-averaged co-fluctuations are suboptimal for explaining subject-specific connectivity. Our work complements recent studies implicating brief instants of high-amplitude co-fluctuations as the primary drivers of static, whole-brain functional connectivity. Our work also extends those studies, demonstrating that co-fluctuations during events are individualized, positing a dynamic basis for functional connectivity.

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Section: High-amplitude Cofluctuation Patterns Are Individualized Andsupporting

confidence: 91%

Individualized event structure drives individual differences in whole-brain functional connectivity

Betzel

Cutts

Greenwell

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Lastly, the spatial resolution of the current data is lower than used in the state-of-the-art studies (Glasser, Smith, et al, 2016; K. L. Miller et al, 2016). However, it is still representative of the numerous sets of legacy data (Kraus et al, 2020; Teipel et al, 2017), with an important advantage of critically sampling cardiac and respiratory noise peaks. This latter allows us a unique advantage to decipher the effects of low-frequency artifacts, although this results in the use of a lower than usual flip-angle (40 degrees).…”

Section: Discussionmentioning

confidence: 99%

Performance of temporal and spatial ICA in identifying and removing low-frequency physiological and motion effects in resting-state fMRI

Golestani

Chen

2021

Preprint

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Effective separation of signal from noise (including physiological processes and head motion) is one of the chief challenges for improving the sensitivity and specificity of resting-state fMRI (rs-fMRI) measurements and has a profound impact when these noise sources vary between populations. Independent component analysis (ICA) is an approach for addressing these challenges. Conventionally, due to the lower amount of temporal than spatial information in rs-fMRI data, spatial ICA (sICA) is the method of choice. However, with recent developments in accelerated fMRI acquisitions, the temporal information is becoming enriched to the point that the temporal ICA (tICA) has become more feasible. This is particularly relevant as physiological processes and motion exhibit very different spatial and temporal characteristics when it comes to rs-fMRI applications, leading us to conduct a comparison of the performance of sICA and tICA in addressing these types of noise. In this study, we embrace the novel practice of using theory (simulations) to guide our interpretation of empirical data. We find empirically that sICA can identify more noise-related signal components than tICA. However, on the merit of functional-connectivity results, we find that while sICA is more adept at reducing whole-brain motion effects, tICA performs better in dealing with physiological effects. These interpretations are corroborated by our simulation results. The overall message of this study is that if ICA denoising is to be used for rs-fMRI, there is merit in considering a hybrid approach in which physiological and motion-related noise are each corrected for using their respective best-suited ICA approach.

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“…Patients underwent presurgical fMRI for language localization. Previous studies have shown that the intraindividual effect of any task or state on network topology is small in comparison to interindividual differences in network topology (Gratton et al 2018;Kraus et al 2021), indicating that we may use these pseudo resting-state data to investigate individual differences in network integration. Resting-state analysis has previously been used on such task data for network analysis in both healthy controls and patients (Harris et al 2014;Krienen et al 2014;Derks et al 2017).…”

Section: Functional Magnetic Resonance Imagingmentioning

confidence: 99%

Cellular substrates of functional network integration and memory in temporal lobe epilepsy

Douw

et al. 2021

Preprint

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Temporal lobe epilepsy patients are heterogeneous regarding cognitive functioning, with predominant risk of memory deficits. Despite major advances within cellular neuroscience, neuroimaging, and neuropsychology, it remains challenging to integrate memory performance with cellular characteristics and brain network topology. In a unique dataset, we investigate these cross-scale individual differences. Preoperatively, drug-resistant temporal lobe epilepsy patients (n = 31, 15 females) underwent functional magnetic resonance imaging, magnetoencephalography and/or memory testing. Macro-scale network centrality was determined, since this number of integrative functional connections a region has is crucial for memory functioning. Subsequently, non-pathological cortical tissue resected from the lateral middle temporal gyrus (default mode network) was used for single cell morphological (total dendritic length) and electrophysiological patch-clamp analysis (action potential rise speed). We expected greater macro-scale centrality to relate to longer micro-scale dendritic length and faster action potentials, and greater centrality to relate to better memory performance. Greater macro-scale centrality correlated with longer dendritic length and faster action potentials (canonical correlation coefficient = 0.329, p < 0.001). Moreover, greater macro-scale centrality was related to better memory performance (canonical correlation coefficient = 0.234, p = 0.013). We conclude that more complex neuronal morphology and faster action potential kinetics are mirrored by more integrative functional network topology of the middle temporal gyrus, which in turn is associated with better memory functioning. Thus, our cross-scale analyses reveal a significant relationship between cellular and imaging measures of network topology in the brain, which support cognitive performance in these patients.

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Network variants are similar between task and rest states

Cited by 3 publications

References 56 publications

Individualized event structure drives individual differences in whole-brain functional connectivity

Individualized event structure drives individual differences in whole-brain functional connectivity

Performance of temporal and spatial ICA in identifying and removing low-frequency physiological and motion effects in resting-state fMRI

Cellular substrates of functional network integration and memory in temporal lobe epilepsy

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