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. 2013 Dec 10;7:237.
doi: 10.3389/fnins.2013.00237. eCollection 2013.

ICA Model Order Selection of Task Co-Activation Networks

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Free PMC article

ICA Model Order Selection of Task Co-Activation Networks

Kimberly L Ray et al. Front Neurosci. .
Free PMC article

Abstract

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

Keywords: BrainMap; co-activations; functional brain networks; functional connectivity; independent component analysis; intrinsic connectivity networks; meta-analysis; resting state networks.

Figures

Figure 1
Figure 1
The BrainMap ICA processing stream included four steps. Step 1: Peak activation coordinates from 8,637 experiments in the BrainMap database were smoothed at a FWHM of 12 mm to create a 4D modeled activation map (space × experiment ID). Step 2: ICA was applied to the 4D data using FSL's MELODIC at a model order of d to create a set of d spatial components. Step 3: Metadata matrices were created at each d weighting how strongly each component related to the behavioral domain and paradigm metadata classes in BrainMap (125 metadata classes × d components). Step 4: HCA was performed on metadata matrices, and the CCc of the resultant dendrogram was computed to determine the fit of the clustering for that model order. These 4 steps were repeated for a range of d, from 20 to 200 in intervals of 10.
Figure 2
Figure 2
The mean number of significant voxels (z > 4) of ICA components decreased as model order increased (solid, bold), while the mean z-score of significant voxels in ICA components increased almost linearly (R2 = 0.975) as model order increased (dashed line).
Figure 3
Figure 3
Cophenetic correlation coefficients (CCc) were computed for the BrainMap metadata matrices across ICA model orders. CCc values indicate how well the HCA results fit the corresponding BrainMap metadata. The ICA model orders yielding the two highest CCc values were generated with metadata from the d = 20 (CCc = 0.5119) and d = 70 (CCc = 0.5198) decompositions.
Figure 4
Figure 4
Hierarchical clustering dendrograms are shown for ICA model orders of 20, 50, 70, and 190. Model orders of 20 and 70 resulted in the two highest CCc values, while d = 50 and d = 190 resulted in the lowest CCc values. The dissimilarity scale (y-axis) of each dendrogram indicates how strongly the behaviors and paradigms were found to cluster together. High branching points along the dissimilarity axis in the d = 50 and d = 190 dendrograms indicate less agreement across variables, whereas lower branching points in the d = 20 and d = 70 networks indicate a more tightly clustered solution.
Figure 5
Figure 5
The spatial topography of ICA model orders yielding high quality decompositions identified by producing the highest CCc values at d = 20 and d = 70. The d = 20 components are presented in the same order as provided in Laird et al., . The d = 70 components mirror the hierarchical network organization of their respective model order, beginning with the most similar components followed by the least similar. ICA maps were converted to z statistic images via a normalized mixture model fit, thresholded at z > 4, and viewed in standard (Talairach) brain space. Orthogonal slices of the representative point in space are shown for each component.
Figure 6
Figure 6
HCA of BrainMap-based ICNs at d = 20 and d = 70 provide insight into how low-model order networks fractionate into high-model order sub-networks. (A) Network clustering of the d = 20 decomposition revealed three clear groupings of highly similar networks: emotional and interoceptive networks (blue), motor and visuospatial networks (green), and visual networks (cyan). The remaining components were associated with higher cognitive processes (warm colors). The cognitive networks were behaviorally dissimilar across components and other network groups, as indicated by high branching points in its dendrogram. (B) Network clustering of the d = 70 decomposition exhibited generally similar network organizational properties with the d = 20 dendrogram, but included subtle fractionation properties indicative of non-homogenous behavioral functions. The left–to–right ordering of networks in the above dendrograms are the same as presented in Figure 5. Vis, visual; M/V, motor and visuospatial; Cog, cognitive; Emot/Int, emotional and interoceptive.
Figure 7
Figure 7
Topological fractionation of select BrainMap-based ICNs are shown from low (d = 20) to high (d = 70) model orders. The spatial cross correlation between the d = 20 and d = 70 networks are indicated below each respective d = 70 network. (A) The Motor/Visuospatial networks, which were grouped into one cluster at d = 20 (green ICNs in Figure 6), split into three separate clusters at d = 70. Metadata associated with these ICNs indicated that two clusters were more closely related to visuospatial tasks while the other cluster was highly linked motor tasks. (B) The Emotion/Interoception networks (blue ICNs in Figure 6) showed a more complex fractionation at d = 70 when splitting into two main clusters. Both of these clusters contained sub-networks from nearly all of their d = 20 ICNs; however, the metadata associated with these d = 70 ICNs showed a clear division in functional characterization into an initial cluster linked to internal and emotional processes and another cluster linked to external and physical processes.
Figure 8
Figure 8
Additional analyses were performed on 10 random subsets of 90% of the experiments in the BrainMap database at the time of the initial analysis following the same procedure as outlined in Figure 1 (up to d = 100). The CCc values resulting from the 90% subsets follow a similar trend as shown in Figure 3 when averaging across all subsets.

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References

    1. Abou Elseoud A., Littow H., Remes J., Starck T., Nikkinen J., Nissilä J., et al. (2011). Group-ICA model order highlights patterns of functional brain connectivity. Front. Syst. Neurosci. 5:37 10.3389/fnsys.2011.00037 - DOI - PMC - PubMed
    1. Abou Elseoud A., Starck T., Remes J., Nikkinen J., Tervonen O., Kiviniemi V. J. J. (2010). The effect of model order selection in group PICA. Hum. Brain Mapp. 31, 1207–1216 10.1002/hbm.20929 - DOI - PMC - PubMed
    1. Allen E. A., Damaraju E., Plis S. M., Erhardt E. B., Eichele T., Calhoun V. D. (2012). Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 34, 2154–2177 10.1093/cercor/bhs352 [Epub ahead of print] - DOI - PubMed
    1. Allen E. A., Erhardt E. B., Damaraju E., Gruner W., Segall J. M., Silva R. F., et al. (2011). A Baseline for the multivariate comparison of resting-state networks. Front. Hum. Neurosci. 5:2 10.3389/fnsys.2011.00002 - DOI - PMC - PubMed
    1. Beckmann C. F. (2012). Modelling with independent components. Neuroimage 62, 891–901 10.1016/j.neuroimage.2012.02.020 - DOI - PubMed
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