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. 2017 Oct 31;7(1):14392.
doi: 10.1038/s41598-017-14613-4.

Graph theory reveals amygdala modules consistent with its anatomical subdivisions

Elisabeth C Caparelli  1 , Thomas J Ross  2 , Hong Gu  2 , Xia Liang  2   3 , Elliot A Stein  2 , Yihong Yang  2
Affiliations

Graph theory reveals amygdala modules consistent with its anatomical subdivisions

Elisabeth C Caparelli et al. Sci Rep. .

Abstract

Similarities on the cellular and neurochemical composition of the amygdaloid subnuclei suggests their clustering into subunits that exhibit unique functional organization. The topological principle of community structure has been used to identify functional subnetworks in neuroimaging data that reflect the brain effective organization. Here we used modularity to investigate the organization of the amygdala using resting state functional magnetic resonance imaging (rsfMRI) data. Our goal was to determine whether such topological organization would reliably reflect the known neurobiology of individual amygdaloid nuclei, allowing for human imaging studies to accurately reflect the underlying neurobiology. Modularity analysis identified amygdaloid elements consistent with the main anatomical subdivisions of the amygdala that embody distinct functional and structural properties. Additionally, functional connectivity pathways of these subunits and their correlation with task-induced amygdala activation revealed distinct functional profiles consistent with the neurobiology of the amygdala nuclei. These modularity findings corroborate the structure-function relationship between amygdala anatomical substructures, supporting the use of network analysis techniques to generate biologically meaningful partitions of brain structures.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Modularity results for the amygdala subdivisions. (A) Correlation matrix for the amygdala template with modules overlaid in semi-transparent colors, greyscale values indicate r-values, (B) modularity results: LBL (696 mm3), LBR (640 mm3), SFL (472 mm3), SFR (464 mm3), CML (440 mm3), CMR (408 mm3), are displayed on coronal slices located at y-axis values: -2, -4, -6, -8 (L/R suffix = left/right). The maximum modularity factors obtained for this parcellation were QL = 0.26, QR = 0.25; (C) Juelich atlas overlaid on a coronal anatomical image (D) Juelich atlas superimposed on the amygdala template (shown in white, under the Juelich atlas); radiological convention.
Figure 2
Figure 2
FC maps for the amygdala subdivisions, CM, LB and SF, obtained from modularity results. MNI standard space; radiological convention; significance: p < 0.05 FWE corrected. Left/Right indicate seed locations.
Figure 3
Figure 3
FC-differential maps contrasting the connectivity map of each amygdala subdivision, CM, LB and SF, against the average connectivity pattern of the other two subdivisions, LB + SF, CM + SF, CM + LB. MNI standard space; radiological convention; significance: p < 0.05 FWE corrected.
Figure 4
Figure 4
(A) Amygdala activation for the threshold of T ≥ 6 (A 1), location of the most significant activated voxels (T > 13, peak value T = 17) (A 2) overlaid over the amygdala subdivisions, CM, LB and SF (color pattern from Fig. 1), radiological convention; (B) Average BOLD signal for each amygdala subdivision (*p < 0.0001); (C) ROI analysis correlating the mean BOLD signal at the CMR with the mean positive and significant (p < 0.05) Fisher transformed correlation values of the CMR connectivity pathway.
See this image and copyright information in PMC

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