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Scale-Free and Multifractal Time Dynamics of fMRI Signals during Rest and Task.

Ciuciu P, Varoquaux G, Abry P, Sadaghiani S, Kleinschmidt A - Front Physiol (2012)

Bottom Line: Also, it benefits from improved estimation performance compared to tools previously used in the literature.Results stemming from those analysis confirm the already reported task-related decrease of long memory in functional networks, but also show that it occurs in artifacts, thus making this feature not specific to functional networks.Task-related modulation of multifractality appears only significant in functional networks and thus can be considered as the key property disentangling functional networks from artifacts.

View Article: PubMed Central - PubMed

Affiliation: Life Science Division, Biomedical Imaging Department, NeuroSpin Center, Commissariat à l'Energie Atomique et aux Energies Alternatives Gif-sur-Yvette, France.

ABSTRACT
Scaling temporal dynamics in functional MRI (fMRI) signals have been evidenced for a decade as intrinsic characteristics of ongoing brain activity (Zarahn et al., 1997). Recently, scaling properties were shown to fluctuate across brain networks and to be modulated between rest and task (He, 2011): notably, Hurst exponent, quantifying long memory, decreases under task in activating and deactivating brain regions. In most cases, such results were obtained: First, from univariate (voxelwise or regionwise) analysis, hence focusing on specific cognitive systems such as Resting-State Networks (RSNs) and raising the issue of the specificity of this scale-free dynamics modulation in RSNs. Second, using analysis tools designed to measure a single scaling exponent related to the second order statistics of the data, thus relying on models that either implicitly or explicitly assume Gaussianity and (asymptotic) self-similarity, while fMRI signals may significantly depart from those either of those two assumptions (Ciuciu et al., 2008; Wink et al., 2008). To address these issues, the present contribution elaborates on the analysis of the scaling properties of fMRI temporal dynamics by proposing two significant variations. First, scaling properties are technically investigated using the recently introduced Wavelet Leader-based Multifractal formalism (WLMF; Wendt et al., 2007). This measures a collection of scaling exponents, thus enables a richer and more versatile description of scale invariance (beyond correlation and Gaussianity), referred to as multifractality. Also, it benefits from improved estimation performance compared to tools previously used in the literature. Second, scaling properties are investigated in both RSN and non-RSN structures (e.g., artifacts), at a broader spatial scale than the voxel one, using a multivariate approach, namely the Multi-Subject Dictionary Learning (MSDL) algorithm (Varoquaux et al., 2011) that produces a set of spatial components that appear more sparse than their Independent Component Analysis (ICA) counterpart. These tools are combined and applied to a fMRI dataset comprising 12 subjects with resting-state and activation runs (Sadaghiani et al., 2009). Results stemming from those analysis confirm the already reported task-related decrease of long memory in functional networks, but also show that it occurs in artifacts, thus making this feature not specific to functional networks. Further, results indicate that most fMRI signals appear multifractal at rest except in non-cortical regions. Task-related modulation of multifractality appears only significant in functional networks and thus can be considered as the key property disentangling functional networks from artifacts. These finding are discussed in the light of the recent literature reporting scaling dynamics of EEG microstate sequences at rest and addressing non-stationarity issues in temporally independent fMRI modes.

No MeSH data available.


Related in: MedlinePlus

(A) Welch (blue curves) vs. Wavelet (black curves) spectra associated with a F-map (f18). Solid and dashed lines correspond to rest and task, respectively. (B) Corresponding multifractal spectra 𝒟(h).
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Figure 2: (A) Welch (blue curves) vs. Wavelet (black curves) spectra associated with a F-map (f18). Solid and dashed lines correspond to rest and task, respectively. (B) Corresponding multifractal spectra 𝒟(h).

Mentions: where Κ0 denotes the Fourier transform of ψ0. This indicates that can be read as a wavelet based estimate of the PSD and is hence referred to as the wavelet spectrum. It measures the amount of energy of Y around the frequency fj = f0/2j where f0 is a constant that depends on the explicit choice of ψ0 (for the Daubechies wavelet used here, f0 ≃ 3fs/4 with fs the sampling frequency). This correspondence between the Fourier and wavelet spectra is illustrated on fMRI signals in Figure 2. For scale-free processes satisfying equation (2), it implies:


Scale-Free and Multifractal Time Dynamics of fMRI Signals during Rest and Task.

Ciuciu P, Varoquaux G, Abry P, Sadaghiani S, Kleinschmidt A - Front Physiol (2012)

(A) Welch (blue curves) vs. Wavelet (black curves) spectra associated with a F-map (f18). Solid and dashed lines correspond to rest and task, respectively. (B) Corresponding multifractal spectra 𝒟(h).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3375626&req=5

Figure 2: (A) Welch (blue curves) vs. Wavelet (black curves) spectra associated with a F-map (f18). Solid and dashed lines correspond to rest and task, respectively. (B) Corresponding multifractal spectra 𝒟(h).
Mentions: where Κ0 denotes the Fourier transform of ψ0. This indicates that can be read as a wavelet based estimate of the PSD and is hence referred to as the wavelet spectrum. It measures the amount of energy of Y around the frequency fj = f0/2j where f0 is a constant that depends on the explicit choice of ψ0 (for the Daubechies wavelet used here, f0 ≃ 3fs/4 with fs the sampling frequency). This correspondence between the Fourier and wavelet spectra is illustrated on fMRI signals in Figure 2. For scale-free processes satisfying equation (2), it implies:

Bottom Line: Also, it benefits from improved estimation performance compared to tools previously used in the literature.Results stemming from those analysis confirm the already reported task-related decrease of long memory in functional networks, but also show that it occurs in artifacts, thus making this feature not specific to functional networks.Task-related modulation of multifractality appears only significant in functional networks and thus can be considered as the key property disentangling functional networks from artifacts.

View Article: PubMed Central - PubMed

Affiliation: Life Science Division, Biomedical Imaging Department, NeuroSpin Center, Commissariat à l'Energie Atomique et aux Energies Alternatives Gif-sur-Yvette, France.

ABSTRACT
Scaling temporal dynamics in functional MRI (fMRI) signals have been evidenced for a decade as intrinsic characteristics of ongoing brain activity (Zarahn et al., 1997). Recently, scaling properties were shown to fluctuate across brain networks and to be modulated between rest and task (He, 2011): notably, Hurst exponent, quantifying long memory, decreases under task in activating and deactivating brain regions. In most cases, such results were obtained: First, from univariate (voxelwise or regionwise) analysis, hence focusing on specific cognitive systems such as Resting-State Networks (RSNs) and raising the issue of the specificity of this scale-free dynamics modulation in RSNs. Second, using analysis tools designed to measure a single scaling exponent related to the second order statistics of the data, thus relying on models that either implicitly or explicitly assume Gaussianity and (asymptotic) self-similarity, while fMRI signals may significantly depart from those either of those two assumptions (Ciuciu et al., 2008; Wink et al., 2008). To address these issues, the present contribution elaborates on the analysis of the scaling properties of fMRI temporal dynamics by proposing two significant variations. First, scaling properties are technically investigated using the recently introduced Wavelet Leader-based Multifractal formalism (WLMF; Wendt et al., 2007). This measures a collection of scaling exponents, thus enables a richer and more versatile description of scale invariance (beyond correlation and Gaussianity), referred to as multifractality. Also, it benefits from improved estimation performance compared to tools previously used in the literature. Second, scaling properties are investigated in both RSN and non-RSN structures (e.g., artifacts), at a broader spatial scale than the voxel one, using a multivariate approach, namely the Multi-Subject Dictionary Learning (MSDL) algorithm (Varoquaux et al., 2011) that produces a set of spatial components that appear more sparse than their Independent Component Analysis (ICA) counterpart. These tools are combined and applied to a fMRI dataset comprising 12 subjects with resting-state and activation runs (Sadaghiani et al., 2009). Results stemming from those analysis confirm the already reported task-related decrease of long memory in functional networks, but also show that it occurs in artifacts, thus making this feature not specific to functional networks. Further, results indicate that most fMRI signals appear multifractal at rest except in non-cortical regions. Task-related modulation of multifractality appears only significant in functional networks and thus can be considered as the key property disentangling functional networks from artifacts. These finding are discussed in the light of the recent literature reporting scaling dynamics of EEG microstate sequences at rest and addressing non-stationarity issues in temporally independent fMRI modes.

No MeSH data available.


Related in: MedlinePlus