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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 Ć  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

Corrected p-values associated with one-sample Student-t () and WSR () tests performed on resting-state ([A,C,E]) and task-related multifractal parameters ([B,D,F]) for assessing  (blue curves) and  (red curves) on the the averaged map types ([A-B]), networks š’© ([C-D]) and artifact types š’Æ ([E-F]), respectively. Significance level (Ī±ā€‰=ā€‰0.05) is shown in .
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Figure 6: Corrected p-values associated with one-sample Student-t () and WSR () tests performed on resting-state ([A,C,E]) and task-related multifractal parameters ([B,D,F]) for assessing (blue curves) and (red curves) on the the averaged map types ([A-B]), networks š’© ([C-D]) and artifact types š’Æ ([E-F]), respectively. Significance level (Ī±ā€‰=ā€‰0.05) is shown in .

Mentions: Then, we focused on the statistical analysis at different macroscopic scales, first by averaging all F/A and U-maps respectively so as to derive a mean behavior for F/A/U-maps. Finally, we looked at functional networks and artifact types in more details. Blue curves in Figures 6A,B report such results for the rest and task-related datasets, respectively. We still observed a significant level of self-similarity in all averaged groups (blue curves) irrespective of the brain state: is systematically rejected for jā€‰=ā€‰(R, T). However, we still noticed a reduction of statistical significance induced by task irrespective of the map type. More interestingly, we found at this macroscopic level that all averaged maps were multifractal at rest whereas only the functional one remained multifractal during task: see red curves in Figures 6A,B. Further, statistical analysis of functional networks defined in Table 1 was conducted to understand which network drives this effect. When comparing p-values in Figures 6C,D on functional networks, we observed that all remained significantly self-similar in both states, while the DMN is close to the significance level Ī±ā€‰=ā€‰0.05 during task (blue curves). Regarding multifractality, only the non-cortical regions appeared monofractal at rest and all networks kept a significant amount of multifractality during task. In contrast, this observation did not hold for artifacts: when looking at Figures 6E,F in detail, the signal related to ventricles became monofractal during task.


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)

Corrected p-values associated with one-sample Student-t () and WSR () tests performed on resting-state ([A,C,E]) and task-related multifractal parameters ([B,D,F]) for assessing  (blue curves) and  (red curves) on the the averaged map types ([A-B]), networks š’© ([C-D]) and artifact types š’Æ ([E-F]), respectively. Significance level (Ī±ā€‰=ā€‰0.05) is shown in .
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Corrected p-values associated with one-sample Student-t () and WSR () tests performed on resting-state ([A,C,E]) and task-related multifractal parameters ([B,D,F]) for assessing (blue curves) and (red curves) on the the averaged map types ([A-B]), networks š’© ([C-D]) and artifact types š’Æ ([E-F]), respectively. Significance level (Ī±ā€‰=ā€‰0.05) is shown in .
Mentions: Then, we focused on the statistical analysis at different macroscopic scales, first by averaging all F/A and U-maps respectively so as to derive a mean behavior for F/A/U-maps. Finally, we looked at functional networks and artifact types in more details. Blue curves in Figures 6A,B report such results for the rest and task-related datasets, respectively. We still observed a significant level of self-similarity in all averaged groups (blue curves) irrespective of the brain state: is systematically rejected for jā€‰=ā€‰(R, T). However, we still noticed a reduction of statistical significance induced by task irrespective of the map type. More interestingly, we found at this macroscopic level that all averaged maps were multifractal at rest whereas only the functional one remained multifractal during task: see red curves in Figures 6A,B. Further, statistical analysis of functional networks defined in Table 1 was conducted to understand which network drives this effect. When comparing p-values in Figures 6C,D on functional networks, we observed that all remained significantly self-similar in both states, while the DMN is close to the significance level Ī±ā€‰=ā€‰0.05 during task (blue curves). Regarding multifractality, only the non-cortical regions appeared monofractal at rest and all networks kept a significant amount of multifractality during task. In contrast, this observation did not hold for artifacts: when looking at Figures 6E,F in detail, the signal related to ventricles became monofractal during task.

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 Ć  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