Pleased to announce that data from four new meta-analytic studies have been added to the ANIMA database over the past month. This brings our total to 30 studies, and it's encouraging to see a steady and growing interest in our freely available resource.
Genon et al. (Cerebral Cortex, 2016) used connectivity-based parcellation (CBP) to parcellate the right dorsal premotor cortex (PMd) into five distinct subregions. Their findings were complemented by clustering approaches using resting-state fMRI and DWI-based probabilistic tractography.
Bzdok et al. (Neuroimage, 2015) also applied CBP, in order to parcellate the posterior medial cortex (PMC). They evaluated functional connectivity using meta-analytic connectivity modelling (MACM) and resting-state fMRI, and performed a functional characterization of the results, using forward and reverse inference.
Robinson et al. (Human Brain Mapping, 2015) used ultra-high-field structural and functional MRI to arrive at a fine parcellation of the hippocampus, complementing this with a meta-analytic CBP of the same region. They found a strong 3-cluster solution in the left hippocampus, while the right was less certain, with both 2- and 5-cluster solutions.
Finally, Chase et al. (Cogn. Affect. Behav. Neurosci., 2015) used activation likelihood estimation (ALE) to isolate brain regions which are activated during algorithmic reinforcement learning tasks. They report a specific set of brain areas that are associated with the processing of prediction errors in these paradigms.
