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| Home > Publications database > The effect of outliers and their exclusion on restingstateconnectivity-based parcellation |
| Poster (Other) | FZJ-2018-04164 |
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2018
Please use a persistent id in citations: http://hdl.handle.net/2128/21481
Abstract: TitleThe effect of outliers and their exclusion on resting-state connectivity-based parcellationAuthorsNiels Reuter, Sarah Genon, Shahrzad Kharabian, Tobias Kalenscher, Felix Hoffstaedter, RainerGoebel, Simon Eickhoff, Kaustubh PatilContent [3957 characters inc. spaces]IntroductionRegional connectivity-based parcellation (CBP) aims to find biologically meaningful parcels orsubregions. This is achieved by clustering the voxels in a region of interest (ROI) based on theirconnectivity profiles. Using a large resting-state fMRI (rs-fMRI) sample, we show that deviantconnectivity profiles substantially influence group-based clustering results. Such outliers can arisedue to various reasons and we investigated one possible reason for high dimensional data:difference in intrinsic dimensionality.MethodsThe Right (R) insula ROI (Fig. 2C), subject to repeated CBP analyses [1], was defined using theHarvard Oxford Atlas [2]. rs-fMRI data from 408 healthy unrelated subjects (2mm isotropic,TR=0.72s, age 22-37, 205 males) from the Human Connectome Project [3] were included. FIXdenoiseddata was preprocessed with SPM8 [4] using unified segmentation [5], 5mm FWHMsmoothed, WM-CSF signal regressed, and frequency-filtered (0.01-0.08 Hz). Correlations betweentime-series of each ROI voxel and all brain gray-matter voxels were computed and Fisher Ztransformed,yielding an ROI-to-whole-brain connectivity matrix per subject. k-means (with k from2 to 5) was performed on each connectivity matrix. To identify outliers, for each subject a nearestneighborsubject was identified using Euclidean distance between connectivity matrices. Theresulting vector d was Z-scored (Fig. 1A). k-means (k=2) clustering of d revealed a separationaround 0, providing a conservative threshold (Fig.1B). Two further thresholds were chosen: 1.69(.95 left tail area on a standard normal distribution) and a liberal 2.5. Group parcellations for each kusing hierarchical clustering with average linkage and Hamming distance were calculated afterexcluding outliers based on these thresholds. The adjusted rand index (ARI) between k-meanscluster results of all subjects was computed, retaining the highest values per subject as a similarityvector a (Fig. 1C). Lastly, principal component analysis was performed on the connectivitymatrices, noting the number of components retaining 95% of variance. Correlating the componentnumbers to d uncovers whether there is a relationship between intrinsic dimensionality of theconnectivity matrices and their distances to one another.ResultsApplying the thresholds of 0, 1.69, and 2.5 removed 134, 32, and 14 subjects, respectively. Whencorrelating distances d (Fig. 1A) to the similarity vector a (Fig. 1C), we found correlations of -.38,-.41, -.49, and -.53, for k=2, 3, 4, and 5, respectively. This result suggests outliers clusterdifferently, thus including them into a group-level consensus might be detrimental. Accordingly,differences were found between group-level parcellations (Fig. 2A). For instance, when comparingthe liberal 2.5 threshold-removed group parcellation (Fig. 2D, column two) with a groupparcellation without outlier removal (Fig. 2D, column one), there was only an 81% overlap,ARI=.55 for k=3 (ARI=.67 and .71 for k=4, 5, resp.). Further comparisons are illustrated in Figure2D. The distances d were related to the number of principal components retaining 95% of variancewith correlation of -.79 (Fig. 2B). That is, if intrinsic dimensionality was low for a subject, theassociated connectivity matrix would be more distant to the rest of the sample (Fig. 2D).ConclusionThe differences in clusterings highlights the influence of outliers. While assessment of the grouplevelparcellations reveals that clustering results were relatively stable across thresholds for k=2(Fig. 2D), ample evidence suggests more than 2 clusters in the R-insula [6,7,8]. As linkagealgorithms in hierarchical clustering as well as k-means clustering are sensitive to outliers [9], it isimportant to remove them by using a proper identification threshold. In the future we will focus onautomatic identification of parameters that lead to biologically meaningful parcellations.
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