Cognitive: Executive FunctionThe functional connectivity of the human caudate: An application of meta-analytic connectivity modeling with behavioral filtering
Highlights
► Meta-analytic connectivity modeling methods were used to examine the human caudate. ► MACM revealed behavioral domain specific circuits and topographical organization. ► Results were compared to DTI data to parse direct and indirect influences. ► MACM is a robust and sensitive method for modeling functional connectivity.
Introduction
The human dorsal striatum contains the primary input to the basal ganglia (Grahn et al., 2008, Haber, 2003). Composed of the caudate and putamen, it receives axons from all regions of the cortex with the exception of the primary visual, auditory, and olfactory cortices (Grahn et al., 2008). Anatomical, functional, and/or connectivity abnormalities of the caudate nuclei have been noted in a wide range of disorders including autism (Turner et al., 2006), Huntington's disease (Bohanna et al., 2011), Parkinson's disease (PD) (Rowe et al., 2008), human immunodeficiency virus (HIV) (Melrose et al., 2008), drug addiction (Ma et al., 2011), depression (Bluhm et al., 2009), and attention deficit hyperactivity disorder (ADHD) (Casey et al., 2007). Despite its involvement in a range of psychiatric and neurological disorders, few studies have examined the functional connectivity of the human caudate (Di Martino et al., 2008, Postuma and Dagher, 2006), and no study, to our knowledge, has examined functional connectivity in this structure using advanced meta-analytic techniques. In the present study, we used meta-analytic connectivity modeling (MACM) (Robinson et al., 2010) to provide an initial model of functional connectivity utilizing decades worth of neuroimaging data collected across various behavioral domains. Describing connectivity models in this manner has the potential to facilitate discovery of specific pathways that are aberrant in populations with known dysfunction of the caudate, which may ultimately lead to the identification of novel interventions.
Early models of the basal ganglia assigned to the caudate a primary role of integrating information from the cortical association and sensorimotor areas of the brain before sending it to distinct ventrolateral thalamic sub-regions, which would then relay the information almost exclusively to the primary motor cortex. These early models have largely been replaced by more complex ones based on evidence of reciprocating but interconnected circuits that link the cortex, basal ganglia, and thalamus (Alexander and Crutcher, 1990, Alexander et al., 1986, DeLong et al., 1983). Five primary circuits have been proposed in the nonhuman primate literature: motor, oculomotor, dorsolateral prefrontal, lateral orbitofrontal, and anterior cingulate (Alexander et al., 1986). In each of these proposed circuits, the basal ganglia receive input from multiple cortical regions, pass this information to the thalamus where integration operations occur before information is passed to specific cortical regions of one of the segregated functional circuits (Alexander et al., 1986). Thus, each cycle within a thalamocortical-basal ganglia circuit concludes with the thalamocortical pathway terminating in specific regions of the cortex, unique to that particular loop. To date, these looping circuits have not been adequately described in human functional neuroimaging studies.
Topographic mapping within the caudate has been demonstrated in animal models. Specifically, segmentation of the caudate nucleus into head and body components has revealed consistent, distinct compartments such that the head of the caudate has been associated with more cognitive and emotional processing whereas the body/tail of the caudate has been associated with action and perceptual processes. However, similar to the proposed looping circuits, no study to our knowledge has tested this organization in humans.
Here, we test whether the human caudate connectivity patterns support the major circuits identified in the nonhuman primate system, and investigate whether it demonstrates anterior–posterior somatotopic and behavioral topography. Because it is not feasible to investigate this in a single study, we capitalize on the power of meta-analyses and the organization of the BrainMap database (Fox and Lancaster, 2002, Fox et al., 2005, Laird et al., 2005b) database to 1) identify behavioral domain-specific networks that we predict will correspond to the circuits described in the primate literature, and 2) determine if the anterior and posterior portions of the caudate demonstrate behavioral domain segmentation as previously described with action and perception networks mapping primarily to the posterior body/tail of the caudate, and cognitive and emotional systems relying on more anterior aspects of the structure. To do so, we use a robust, unbiased meta-analytic approach, coupled with a tractography analysis using diffusion tensor imaging (DTI).
Section snippets
Methods
Meta-analytic connectivity modeling (MACM) was employed to assess human caudate functional connectivity. Below, we describe methods for region of interest (ROI) selection as well as the implementation of MACM.
Modeling of functional connectivity
We observed significant functional connectivity of both the left and right caudate to regions of the left anterior (BA32) and posterior cingulate (BA23), left and right insula (BA13), thalamus (medial dorsal nucleus), and inferior frontal gyrus (BA9), and left middle frontal (BA6) and precentral gyri (Table 1). In addition, we found multiple regions of functional connectivity that were spatially distinct with different Talairach Daemon labels between the right and left caudate maps.
Discussion
Our results provide strong evidence for a behavior-based topographic organization previously suggested by histological and functional imaging studies, while also demonstrating the utility of using MACM to develop models of functional connectivity that account for both direct and indirect influences. Below, we discuss these differences in the context of their categorical influences.
Conflict of interest
All authors report having no financial, personal, or organizational conflict of interest with the work outlined in this manuscript.
Acknowledgments
This work was supported by the following grants: NIMH R01-MH074457 (PIs: PTF and ARL), NIMH R01-MH0708143 (PI: DCG), NIMH R01-MH078111 (PI: JB), and NIMH R01-MH083824 (PI: DCG).
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