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Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI

View Article: PubMed Central - PubMed

ABSTRACT

Independent component analysis (ICA) has been widely used to study functional magnetic resonance imaging (fMRI) connectivity. However, the application of ICA in multi-group designs is not straightforward. We have recently developed a new method named “shared and specific independent component analysis” (SSICA) to perform between-group comparisons in the ICA framework. SSICA is sensitive to extract those components which represent a significant difference in functional connectivity between groups or conditions, i.e., components that could be considered “specific” for a group or condition. Here, we investigated the performance of SSICA on realistic simulations, and task fMRI data and compared the results with one of the state-of-the-art group ICA approaches to infer between-group differences. We examined SSICA robustness with respect to the number of allowable extracted specific components and between-group orthogonality assumptions. Furthermore, we proposed a modified formulation of the back-reconstruction method to generate group-level t-statistics maps based on SSICA results. We also evaluated the consistency and specificity of the extracted specific components by SSICA. The results on realistic simulated and real fMRI data showed that SSICA outperforms the regular group ICA approach in terms of reconstruction and classification performance. We demonstrated that SSICA is a powerful data-driven approach to detect patterns of differences in functional connectivity across groups/conditions, particularly in model-free designs such as resting-state fMRI. Our findings in task fMRI show that SSICA confirms results of the general linear model (GLM) analysis and when combined with clustering analysis, it complements GLM findings by providing additional information regarding the reliability and specificity of networks.

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Results of power spectrum analysis on the temporal dynamics of each detected specific network reported in Figure 9. The reliable visual network shows increase in the strength of functional connectivity in the finger tapping with visual cue condition compared to the finger tapping with auditory cue condition (A), whereas the reliable auditory network shows the opposite (B). We chose to illustrate the visual networks in red and the auditory network in blue. X-axis shows the frequency in Hertz and Y-axis indicates the power in decibel. Shaded area shows standard error of the mean (over all subjects).
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Figure 10: Results of power spectrum analysis on the temporal dynamics of each detected specific network reported in Figure 9. The reliable visual network shows increase in the strength of functional connectivity in the finger tapping with visual cue condition compared to the finger tapping with auditory cue condition (A), whereas the reliable auditory network shows the opposite (B). We chose to illustrate the visual networks in red and the auditory network in blue. X-axis shows the frequency in Hertz and Y-axis indicates the power in decibel. Shaded area shows standard error of the mean (over all subjects).

Mentions: Results of power spectrum analysis on the temporal dynamics of the detected specific networks (visual and auditory) show that the extracted visual network shows increased functional connectivity, estimated by the power of its corresponding time-course in the [0–0.25 Hz] frequency band, in the finger-tapping with visual-cue condition compared to the finger-tapping with auditory-cue one (Figure 10A), whereas the auditory network shows increased functional connectivity in finger-tapping with auditory-cue condition compared to the finger-tapping with visual-cue one (Figure 10B).


Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI
Results of power spectrum analysis on the temporal dynamics of each detected specific network reported in Figure 9. The reliable visual network shows increase in the strength of functional connectivity in the finger tapping with visual cue condition compared to the finger tapping with auditory cue condition (A), whereas the reliable auditory network shows the opposite (B). We chose to illustrate the visual networks in red and the auditory network in blue. X-axis shows the frequency in Hertz and Y-axis indicates the power in decibel. Shaded area shows standard error of the mean (over all subjects).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037228&req=5

Figure 10: Results of power spectrum analysis on the temporal dynamics of each detected specific network reported in Figure 9. The reliable visual network shows increase in the strength of functional connectivity in the finger tapping with visual cue condition compared to the finger tapping with auditory cue condition (A), whereas the reliable auditory network shows the opposite (B). We chose to illustrate the visual networks in red and the auditory network in blue. X-axis shows the frequency in Hertz and Y-axis indicates the power in decibel. Shaded area shows standard error of the mean (over all subjects).
Mentions: Results of power spectrum analysis on the temporal dynamics of the detected specific networks (visual and auditory) show that the extracted visual network shows increased functional connectivity, estimated by the power of its corresponding time-course in the [0–0.25 Hz] frequency band, in the finger-tapping with visual-cue condition compared to the finger-tapping with auditory-cue one (Figure 10A), whereas the auditory network shows increased functional connectivity in finger-tapping with auditory-cue condition compared to the finger-tapping with visual-cue one (Figure 10B).

View Article: PubMed Central - PubMed

ABSTRACT

Independent component analysis (ICA) has been widely used to study functional magnetic resonance imaging (fMRI) connectivity. However, the application of ICA in multi-group designs is not straightforward. We have recently developed a new method named “shared and specific independent component analysis” (SSICA) to perform between-group comparisons in the ICA framework. SSICA is sensitive to extract those components which represent a significant difference in functional connectivity between groups or conditions, i.e., components that could be considered “specific” for a group or condition. Here, we investigated the performance of SSICA on realistic simulations, and task fMRI data and compared the results with one of the state-of-the-art group ICA approaches to infer between-group differences. We examined SSICA robustness with respect to the number of allowable extracted specific components and between-group orthogonality assumptions. Furthermore, we proposed a modified formulation of the back-reconstruction method to generate group-level t-statistics maps based on SSICA results. We also evaluated the consistency and specificity of the extracted specific components by SSICA. The results on realistic simulated and real fMRI data showed that SSICA outperforms the regular group ICA approach in terms of reconstruction and classification performance. We demonstrated that SSICA is a powerful data-driven approach to detect patterns of differences in functional connectivity across groups/conditions, particularly in model-free designs such as resting-state fMRI. Our findings in task fMRI show that SSICA confirms results of the general linear model (GLM) analysis and when combined with clustering analysis, it complements GLM findings by providing additional information regarding the reliability and specificity of networks.

No MeSH data available.


Related in: MedlinePlus