A co-alteration parceling of the cingulate cortex

Abstract

The cingulate cortex is known to be a complex structure, involved in several cognitive and emotional functions, as well as being altered by a variety of brain disorders. This heterogeneity is reflected in the multiple parceling models proposed in the literature. At the present, sub-regions of the cingulate cortex had been identified taking into account functional and structural connectivity, as well as cytological and electrochemical properties. In the present work, we propose an innovative node-wise parceling approach based on meta-analytic Bayesian co-alteration. To this aim, 193 case–control voxel-based morphometry experiments were analyzed, and the Patel’s κ index was used to assess probability of morphometric co-alteration between nodes placed in the cingulate cortex and in the rest of the brain. Hierarchical clustering was then applied to identify nodes in the cingulate cortex exhibiting a similar pattern of whole-brain co-alteration. The obtained dendrogram highlighted a robust fronto-parietal cluster compatible with the default mode network, and being supported by the interplay between the retrosplenial cortex and the anterior and posterior cingulate cortex, rarely described in the literature. This ensemble was further confirmed by the analysis of functional patterns. Leveraging on co-alteration to investigate cortical organization could, therefore, allow to combine multimodal information, resolving conflicting results sometimes coming from the separate use of singular modalities. Crucially, this provides a valuable way to understand the pathological brain using data driven, whole-brain informed and context-specific evidence in a way not yet explored in the field.

CBMAT: a data assistant for coordinate-based meta-analyses

“…A more correct approach to be used in these cases is Meta-Analytic Connectivity Modeling (MACM) (Robinson et al, 2010). Briefly, it allows to compute meta-analysis considering only experiments that reported at least one focus of effect in a given region of interest (ROI), this way assuring that every other peak of effect found in the brain has a direct connection with the selected ROI (for an implementation of MACM in the context of brain disease see Nani et al (2021) and Manuello et al (2022b)). Manually preparing data to compute MACM is particularly hard, as it implies checking the foci location one by one.…”

Editorial for “Effect of Physical Exercise on MRI‐Assessed Brain Perfusion in Chemotherapy‐Treated Breast Cancer Patients: A Randomized Controlled Trial”

“…[2][3][4] One brain region can be divided into several subregions according to function and structure, such as the cingulate cortex. 10 So how to select the regions of interesting is needed to further investigate thoroughly for this issue of JMRI. More smaller sub-regions could be selected for the regions of interesting considering that different parts of brain regions may have different roles for cognitive function.…”

“…The selected brain regions related with memory function seems not enough for this issue of JMRI, also it is needed to notice that only part of the region (not the whole region) was involved for some previous studies 2–4 . One brain region can be divided into several sub‐regions according to function and structure, such as the cingulate cortex 10 . So how to select the regions of interesting is needed to further investigate thoroughly for this issue of JMRI.…”