Format

Send to

Choose Destination
See comment in PubMed Commons below
Front Hum Neurosci. 2013 Jun 14;7:275. doi: 10.3389/fnhum.2013.00275. eCollection 2013.

Parcellation of the cingulate cortex at rest and during tasks: a meta-analytic clustering and experimental study.

Author information

  • 1Department of Psychology, UniversitĂ  di Torino Torino, Italy ; CCS fMRI-Brain Connectivity and Complex Systems Unit, Koelliker Hospital Torino, Italy.

Abstract

Anatomical, morphological, and histological data have consistently shown that the cingulate cortex can be divided into four main regions. However, less is known about parcellations of the cingulate cortex when involved in active tasks. Here, we aimed at comparing how the pattern of clusterization of the cingulate cortex changes across different levels of task complexity. We parcellated the cingulate cortex using the results of a meta-analytic study and of three experimental studies. The experimental studies, which included two active tasks and a resting state protocol, were used to control the results obtained with the meta-analytic parcellation. We explored the meta-analytic parcellation by applying a meta-analytic clustering (MaC) to papers retrieved from the BrainMap database. The MaC is a meta-analytic connectivity driven parcellation technique recently developed by our group which allowed us to parcellate the cingulate cortex on the basis of its pattern of co-activations during active tasks. The MaC results indicated that the cingulate cortex can be parcellated into three clusters. These clusters covered different percentages of the cingulate parenchyma and had a different density of foci, with the first cluster being more densely connected. The control experiments showed different clusterization results, suggesting that the co-activations of the cingulate cortex are highly dependent on the task that is tested. Our results highlight the importance of the cingulate cortex as a hub, which modifies its pattern of co-activations depending on the task requests and on the level of task complexity. The neurobiological meaning of these results is discussed.

KEYWORDS:

activation likelihood estimation; data driven parcellation; hierarchical clustering; k-means clustering; meta-analytic clustering; meta-analytic connectivity modeling; voronoi parcellation; voxel-based meta-analysis

PMID:
23785324
PMCID:
PMC3682391
DOI:
10.3389/fnhum.2013.00275
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Frontiers Media SA Icon for PubMed Central
    Loading ...
    Support Center