Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency bands and temporal scales

Godfrey, Megan; Singh, Krish D.

doi:10.1016/j.neuroimage.2020.117551

Cited by 12 publications

(11 citation statements)

References 65 publications

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“…Ninety healthy participants (58 females, age: 19–34 years, mean age: 23.7 years, SD : 3.4 years) were scanned at the Cardiff University Brain Research Imaging Centre (CUBRIC). This is a subset of the participants from the “100 Brains” and UK MEG Partnership scanning projects ( Godfrey & Singh, 2020 ) and includes the participants who successfully went through the scans described below. All procedures were given ethical approval by the Cardiff University School of Psychology Ethics Committee, and all participants gave written informed consent before taking part.…”

Section: Methodsmentioning

confidence: 99%

“…Amplitude correlations were calculated by correlating the 90 downsampled Hilbert envelopes to each other, and were converted to variance-normalized z-scores by applying a Fisher transform. This choice was motivated by the fact that such correlations have been shown to be one of the most robust and repeatable electrophysiological connectivity measures ( Colclough et al, 2016 ; Godfrey & Singh, 2020 ). We adjusted each participant’s FC matrix to correct for global session effects ( Siems, Pape, Hippa, & Siegel, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

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Predicting MEG resting-state functional connectivity from microstructural information

Messaritaki

Foley

Schiavi

et al. 2021

Network Neuroscience

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Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from ninety healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity and a myelin measure (derived from multi-component relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer timeseries in the delta, theta, alpha and beta frequency bands. Non-negative matrix factorization was performed to identify the components of the functional connectivity. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest-path-length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting MEG resting-state functional connectivity from microstructural information

Messaritaki

Foley

Schiavi

et al. 2021

Network Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…Amplitude correlations were calculated by correlating the 90 down-sampled Hilbert envelopes to each other, and were converted to variance-normalized z-scores by applying a Fisher transform. This choice was motivated by the fact that such correlations have been shown to be one of the most robust and repeatable electrophysiological connectivity measures (Colclough et al, 2016; Godfrey and Singh, 2020). We adjusted each participant’s FC matrix to correct for global session effects (Siems et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

“…Ninety healthy participants (58 females, age: 19 − 34 years, mean age: 23.7 years, SD: 3.4 years) were scanned at the Cardiff University Brain Research Imaging Centre (CUBRIC). This is a subset of the participants from the "100 Brains" and UK MEG Partnership scanning projects (Godfrey and Singh, 2020), and includes the participants who successfully went through the scans described below. All procedures were given ethical approval by the Cardiff University School of Psychology Ethics Committee, and all participants gave written informed consent before taking part.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

Predicting MEG brain functional connectivity using microstructural information

Messaritaki

Foley

Schiavi

et al. 2020

Preprint

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Understanding the role that human brain microstructure plays in the formation of functional connectivity is an important endeavor that could inform therapies for patients with neurological disorders. In this work, magnetic resonance imaging data from ninety healthy participants were used to perform tractography and calculate structural connectivity matrices, using four microstructural metrics to assign connection strength: number of streamlines, fractional anisotropy, radial diffusivity and a myelin measure (derived from the myelin water fraction computed from multi-component relaxometry). Unweighted binarized structural connectivity matrices, marking with 1 existing structural connections and with 0 non-existing ones, and assigning no specific strength to the connections, were also constructed. Magnetoencephalography resting-state data from the same participants were used to calculate their functional connectivity matrices, via correlations of the Hilbert envelopes of the beamformer time series, at four frequency bands: delta (1 – 4 Hz), theta (3 – 8 Hz), alpha (8 – 13 Hz) and beta (13 – 30 Hz). Non-negative matrix factorization (NMF) was then performed to identify the components of the observed resting-state functional connectivity in those bands. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for both the total resting-state functional connectivity and its NMF components.The observed functional connectivity was stronger for brain areas that were closer to each other. The microstructurally-informed algorithms predicted the NMF-derived components of the observed functional connectivity more accurately than they predicted the total (non-factorized) observed functional connectivity. The shortest-path-length algorithm outperformed the searchinformation algorithm in terms of prediction accuracy. When comparing the weights of the structural connectivity matrices, the number of streamlines and the myelin measure gave the most accurate predictions of functional connectivity, while the fractional anisotropy consistently performed poorly. Overall, different structural metrics paint very different pictures of the human structural connectome, of its relationship to functional connectivity, and of which white matter tracts are implicated in the communication between brain areas.

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“…MEG based functional connectome can reliably extract intrinsic fluctuations between spatially distant brain regions [93,9,68,94,95]. Though general agreement between interindividual variability across brain in alpha and beta band oscillations, there are differences in variability especially in visual, motor and temporal cortices.…”

Section: Structural and Functional Interindividual Variability Is Heterogenous Across Cortical Surfacementioning

confidence: 99%

Individual variability in the human connectome maintains selective cross-modal consistency and shares microstructural signatures

Karahan¹,

Tait²,

Si³

et al. 2021

Preprint

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Individuals are different in their behavioural responses and cognitive abilities. Neural underpinnings of individual differences are largely unknown. Here, by using multimodal imaging data including diffusion MRI, functional MRI and MEG, we show the consistency of interindividual variation of connectivity across modalities. We demonstrated that regional differences in individual variability of structural and functional connectomes is characterized by higher variability in association cortices and lower variability in sensory and visual cortices. This pattern is consistent across all modalities at varying degrees as shown by significant alignment between functional and structural connectome variabilities at several clusters of brain regions. Variability in connectivity is associated with cortical myelin content and microstructural properties of connections. Our findings contribute to understanding of individual differences in functional and structural organization of brain and facilitate fingerprinting applications.

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Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency bands and temporal scales

Cited by 12 publications

References 65 publications

Predicting MEG resting-state functional connectivity from microstructural information

Predicting MEG resting-state functional connectivity from microstructural information

Predicting MEG brain functional connectivity using microstructural information

Individual variability in the human connectome maintains selective cross-modal consistency and shares microstructural signatures

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