The amygdala is a complex structure composed of a heterogenous group of nuclei and subnuclei, which are primarily defined by distinct cytoarchitectonics and differing connectivity patterns (Freese and Amaral, 2005, 2006, 2009; Alheid, 2003; Price et al., 1987; Aggleton, 2000; Gloor, 1972, 1978, 1997; McDonald, 1998). Although the names and boundaries of these nuclei remain disputed, they are commonly grouped into four main divisions: lateral (LA), basal and accessory basal (BA), medial and cortical (ME), and central (CE) (e.g. (LeDoux, 1998). These structures are also functionally distinct. For example, LA is involved in learning new stimulus-affect associations (Johansen et al., 2010), whereas ME is involved in olfactory associations and sexual behavior (Lehman et al., 1980; Bian et al., 2008). These functions are likely determined by the afferent and efferent connectivity patterns to each region (LeDoux, 1996; Swanson and Petrovich, 1998; Pitkanen et al., 1997). For example, LA and BA are engaged in updating current stimulus value associations, primarily through connections with orbitofrontal regions (Baxter and Murray, 2002), whereas CE is believed to mediate behavioral responses to potentially harmful stimuli through its connectivity with hypothalamus, basal forebrain, and the brainstem (Kalin et al., 2004).
The distinct functions of the amygdala nucleus groups are not well-understood in the human brain, however, because the nuclei cannot be differentiated in standard magnetic resonance imaging. This is regrettable, because multiple studies suggest amygdalar involvement in psychopathology, such as mood (Phillips et al., 2003), anxiety (Rauch et al., 2003), and developmental disorders (Baron-Cohen et al., 2000). Some attempts have been made to segment the amygdala, either manually through visual approximation based on a single-subject histological atlas (Etkin et al., 2004), or automatically by normalizing the subject’s brain to a template brain and applying a thresholded probabilistic atlas (Amunts et al., 2005). The former approach is labor intensive and susceptible to human error, whereas the latter approach is prey to normalization errors. Further, the use of any atlas necessarily disregards individual differences in nucleic anatomy. Without an easily accessible and robust technique with which to compartmentalize the amygdala, it is difficult to elucidate the separate roles of the human amygdaloid nuclei, as well as the impact of individual differences in nucleus structure and function. Moreover, progress towards mechanistic theories of dysfunction and abnormal development will remain hindered until these structures can be explored in vivo.
Given the unique set of extrinsic connections for each nucleus, it may be possible to differentiate the distinct nuclei by their anatomic connectivity patterns. A metric of structural connectivity can be acquired non-invasively through diffusion weighted imaging (DWI), an MRI method that utilizes the propensity of water to travel along myelinated axons. Fibers can then be reconstructed using a variety of methods collectively termed tractography.
We adapted and extended methods that used probabilistic tractography (Behrens et al., 2003a) to divide each subject’s set of amygdaloid voxels into logical subsets, using Boolean expressions. Boolean logic has several properties that make it potentially advantageous for segmenting regions with highly overlapping connectivity patterns such as the amygdaloid nuclei. First, Boolean expressions can define precise combinations of connectivity patterns through specifically defined sets of unions, intersections, and negations. This should be an effective approach in disambiguating the similar connectivity profiles among amygdaloid nuclei. Second, we expected that this would be particularly useful when combining several smaller nuclei or subnuclei with distinct connectivity patterns. For example, LA is composed of dorsal, dorsal intermediate, ventral intermediate, and ventral subnuclei (Pitkanen and Amaral, 1998; Price et al., 1987), but these subdivisions are too small for typical scan resolutions and so are combined here for practical purposes. Boolean logic can easily combine connectivity patterns of these small subnuclei into a single unit. Finally, Boolean logic is especially appropriate when connectivity patterns are known a priori and are well-explored; a single expression can then be directly constructed from actual anatomical data.
Here we present a novel method, TractSeg (Tractography-based Segmentation), that localizes the four main nucleus groups in the living human amygdala (BA, LA, CE, and ME) using probabilistic tractography on DWI scans that take less than ten minutes to acquire. We hypothesized that it was possible to delineate subregions in the human amygdala based on connectivity patterns derived mainly from animal studies. To validate this method, we compared these subregions with the known topography of their corresponding nuclei, and tested how well they mapped on to the nucleic boundaries observable with a high-resolution scan. In addition, we assessed the across-subject consistency of TractSeg by measuring the spatial overlap between subjects’ nuclei, in a reference frame produced by rigid-body rotation based on each subject’s own amygdalae.