Deep and superficial amygdala nuclei projections revealed in vivo by probabilistic tractography

Abstract

Despite a homogenous macroscopic appearance on magnetic resonance images, subregions of the amygdala express distinct functional profiles as well as corresponding differences in connectivity. In particular, histological analysis shows stronger connections for superficial (i.e., centromedial and cortical), compared with deep (i.e., basolateral and other), amygdala nuclei to lateral orbitofrontal cortex and stronger connections of deep compared with superficial, nuclei to polymodal areas in the temporal pole. Here, we use diffusion weighted imaging with probabilistic tractography to investigate these connections in humans. We use a data-driven approach to segment the amygdala into two subregions using k-means clustering. The identified subregions are spatially contiguous and their location corresponds to deep and superficial nuclear groups. Quantification of the connection strength between these amygdala clusters and individual target regions corresponds to qualitative histological findings in non-human primates, indicating such findings can be extrapolated to humans. We propose that connectivity profiles provide a potentially powerful approach for in vivo amygdala parcellation and can serve as a guide in studies that exploit functional and anatomical neuroimaging.

Figure 1.

Illustration of the clustering method on data from an individual participant. A, Reordered cross-correlation matrix after k-means clustering with k = 2 clusters. B, Sagittal view of resulting clusters shows spatial contiguity and localization in accordance with a deep/superficial segmentation.

Figure 2.

Group probability maps for the two clusters and two datasets in dataset-specific DARTEL space.

Figure 3.

Three-dimensional rendering of group maximum probability maps in dataset-specific DARTEL space, thresholded at p = 0.3 and seen from a frontal, slightly elevated angle. The deep cluster 1 is shown in blue, and the superficial cluster 2 in red.

Figure 4.

Group maximum probability maps of binary images that indicate whether any target voxel had relatively stronger connections to one or the other seed cluster, as determined by the clustering algorithm. Target voxels that have stronger connections to the deep cluster 1 are shown in blue; target voxels that connect more strongly to the superficial cluster 2 are red.

Figure 5.

We determined the average connection likelihood from each amygdala cluster to each of the two target subregions. Here, we show the average percentage of traces arriving at each target region for dataset 2, where connections from the deep cluster are shown in blue, and connections from the superficial cluster in red. The interaction shown here is significant at p < 0.05.