Receptor architecture of human cingulate cortex: Evaluation of the four-region neurobiological model

Institute of Neurosciences and Biophysics-Medicine, Research Centre Jülich, Jülich, Germany.
Human Brain Mapping (Impact Factor: 5.97). 08/2009; 30(8):2336-55. DOI: 10.1002/hbm.20667
Source: PubMed


The structural and functional organization of the human cingulate cortex is an ongoing focus; however, human imaging studies continue to use the century-old Brodmann concept of a two region cingulate cortex. Recently, a four-region neurobiological model was proposed based on structural, circuitry, and functional imaging observations. It encompasses the anterior cingulate, midcingulate, posterior cingulate, and retrosplenial cortices (ACC, MCC, PCC, and RSC, respectively). For the first time, this study performs multireceptor autoradiography of 15 neurotransmitter receptor ligands and multivariate statistics on human whole brain postmortem samples covering the entire cingulate cortex. We evaluated the validity of Brodmann's duality concept and of the four-region model using a hierarchical clustering analysis of receptor binding according to the degree of similarity of each area's receptor architecture. We could not find support for Brodmann's dual cingulate concept, because the anterior part of his area 24 has significantly higher AMPA, kainate, GABA(B), benzodiazepine, and M(3) but lower NMDA and GABA(A) binding site densities than the posterior part. The hierarchical clustering analysis distinguished ACC, MCC, PCC, and RSC as independent regions. The ACC has highest AMPA, kainate, alpha(2), 5-HT(1A), and D(1) but lowest GABA(A) densities. The MCC has lowest AMPA, kainate, alpha(2), and D(1) densities. Area 25 in ACC is similar in receptor-architecture to MCC, particularly the NMDA, GABA(A), GABA(B), and M(2) receptors. The PCC and RSC differ in the higher M(1) and alpha(1) but lower M(3) densities of PCC. Thus, multireceptor autoradiography supports the four-region neurobiological model of the cingulate cortex.

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    • "= nucleus; ITG = inferior temporal gyrus. a References for cytoarchitectonically defined areas: Amunts et al., 2005; Bludau et al., 2014; Caspers et al., 2006; Palomero-Gallagher et al., 2009. b Forming a separate cluster with modified MACM (k M = 221). "
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    ABSTRACT: Co-activation of distinct brain regions is a measure of functional interaction, or connectivity, between those regions. The co-activation pattern of a given region can be investigated using seed-based activation likelihood estimation meta-analysis of functional neuroimaging data stored in databases such as BrainMap. This method reveals inter-regional functional connectivity by determining brain regions that are consistently co-activated with a given region of interest (the "seed") across a broad range of experiments. In current implementations of this meta-analytic connectivity modelling (MACM), significant spatial convergence (i.e. consistent co-activation) is distinguished from noise by comparing it against an unbiased null-distribution of random spatial associations between experiments according to which all grey-matter voxels have the same chance of convergence. As the a priori probability of finding activation in different voxels markedly differs across the brain, computing such a quasi-rectangular null-distribution renders the detection of significant convergence more likely in those voxels that are frequently activated. Here, we propose and test a modified MACM approach that takes this activation frequency bias into account. In this new specific co-activation likelihood estimation (SCALE) algorithm, a null-distribution is generated that reflects the base rate of reporting activation in any given voxel and thus equalizes the a priori chance of finding across-study convergence in each voxel of the brain. Using four exemplary seed regions (right visual area V4, left anterior insula, right intraparietal sulcus, and subgenual cingulum), our tests corroborated the enhanced specificity of the modified algorithm, indicating that SCALE may be especially useful for delineating distinct core networks of co-activation.
    NeuroImage 06/2014; 99:559-570. DOI:10.1016/j.neuroimage.2014.06.007 · 6.36 Impact Factor
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    • "It is not clear, however, which functions of the Cg1 might be affected by AIS-induced changes in the RGS4-GABABR complex, as the Cg1 takes part in a variety of neural processes including attention (24), emotion (25), and cognition (26). Although normal and disordered functions of the Cg1 are modulated by stressful environments (27), many GPCRs are involved in these processes, including M2-3muscarinic, μ-opioid, α2-adrenergic, 5-hydroxytryptamine1A, and GABABR (28). Interestingly, with the exception of GABABR, these receptors have already been recognized as RGS4-related GPCRs (8-11), and GABABR was found to be an RGS4-bound GPCR in the present study (Fig. 2). "
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    ABSTRACT: Regulators of G-protein signaling (RGS) proteins regulate certain G-protein-coupled receptor (GPCR)-mediated signaling pathways. The GABAB receptor (GABABR) is a GPCR that plays a role in the stress response. Previous studies indicate that acute immobilization stress (AIS) decreases RGS4 in the prefrontal cortex (PFC) and hypothalamus (HY) and suggest the possibility of a signal complex composed of RGS4 and GABABR. Therefore, in the present study, we tested whether RGS4 associates with GABABR in these brain regions. We found the co-localization of RGS4 and GABABR subtypes in the PFC and HY using double immunohistochemistry and confirmed a direct association between GABAB2R and RGS4 proteins using co-immunoprecipitation. Furthermore, we found that AIS decreased the amount of RGS4 bound to GABAB2R and the number of double-positive cells. These results indicate that GABABR forms a signal complex with RGS4 and suggests that RGS4 is a regulator of GABABR.
    BMB reports 12/2013; 47(6). DOI:10.5483/BMBRep.2014.47.6.162 · 2.60 Impact Factor
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    • "The seed region for the following analyses was taken from a recent fMRI study which examined neural effects of self-initiated movements by letting subjects choose between left or right finger movements to be initiated at an freely chosen point in time [Hoffstaedter et al., 2013]. A part of the aMCC (areas a24', 32' [Palomero-Gallagher et al., 2009]; Fig. 2) was found to be the only brain region showing increased activity not only for movement selection but additionally for the free timing of movement initiation . This functionally defined volume of interest (VOI) associated with intentional movement initiation is used here as seed for the FC analyses (center of mass x 5 23, y 5 18, z 5 42; volume 5 1,873 mm 3 ). "
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    ABSTRACT: The rostral cingulate cortex has been associated with a multitude of cognitive control functions. Recent neuroimaging data suggest that the anterior midcingulate cortex (aMCC) has a key role for cognitive aspects of movement generation, i.e., intentional motor control. We here tested the functional connectivity of this area using two complementary approaches: (1) resting-state connectivity of the aMCC based on fMRI scans obtained in 100 subjects, and (2) functional connectivity in the context of explicit task conditions using meta-analytic connectivity modeling (MACM) over 656 imaging experiment. Both approaches revealed a convergent functional network architecture of the aMCC with prefrontal, premotor and parietal cortices as well as anterior insula, area 44/45, cerebellum and dorsal striatum. To specifically test the role of the aMCC's task-based functional connectivity in cognitive motor control, separate MACM analyses were conducted over "cognitive" and "action" related experimental paradigms. Both analyses confirmed the same task-based connectivity pattern of the aMCC. While the "cognition" domain showed higher convergence of activity in supramodal association areas in prefrontal cortex and anterior insula, "action" related experiments yielded higher convergence in somatosensory and premotor areas. Secondly, to probe the functional specificity of the aMCC's convergent functional connectivity, it was compared with a neural network of intentional movement initiation. This exemplary comparison confirmed the involvement of the state independent FC network of the aMCC in the intentional generation of movements. In summary, the different experiments of the present study suggest that the aMCC constitute a key region in the network realizing intentional motor control. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.
    Human Brain Mapping 09/2013; 35(6). DOI:10.1002/hbm.22363 · 5.97 Impact Factor
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