Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation

Abstract

The organisation of the cerebral cortex into distinct modules may be described along several dimensions, most importantly, structure, connectivity and function. Identification of cortical modules by differences in whole-brain connectivity profiles derived from diffusion tensor imaging or resting state correlations have already been shown. These approaches, however, carry no task-related information. Hence, inference on the functional relevance of the ensuing parcellation remains tentative. Here, we demonstrate, that Meta-Analytic Connectivity Modelling (MACM) allows the delineation of cortical modules based on their whole-brain co-activation pattern across databased neuroimaging results. Using a model free approach, two regions of the medial pre-motor cortex, SMA and pre-SMA were differentiated solely based on their functional connectivity. Assessing the behavioural domain and paradigm class meta-data of the experiments associated the clusters derived from the co-activation based parcellation moreover allows the identification of their functional characteristics. The ensuing hypotheses about functional differentiation and distinct functional connectivity between pre-SMA and SMA were then explicitly tested and confirmed in independent datasets using functional and resting state fMRI. Co-activation based parcellation thus provides a new perspective for identifying modules of functional connectivity and linking them to functional properties, hereby generating new and subsequently testable hypotheses about the organization of cortical modules.

Cognitive relevance of the community structure of the human brain functional coactivation network

“…This provides an alternative measure of functional connectivity between regions, without describing a possible direction of the influence (unlike causal or effective connectivity) (14). Such meta-analytic measures of task-related coactivation have been explored using "seed-based" analysis (identifying which other regions are coactivated with an arbitrary region or "seed") (15,16) and by independent component analysis (ICA) (identifying a set of components or systems of regions that are consistently coactivated with each other) (17). Thus, it has been shown that brain coactivation components can be metaanalytically linked to specific cognitive domains (18) and that they are anatomically similar to the systems identified by ICA of resting-state fMRI data (17).…”

Characterizing acupuncture stimuli using brain imaging with fMRI – a systematic review and meta-analysis of the literature

“…As there exists no correction for multiple comparison with this approach, the threshold was set at p,0.05 (uncorrected) with a min. cluster size = 200 mm 3 [13].…”

The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI

“…Most of the recent open‐source fMRI datasets such as Human Connectome Project, ADHD‐200 or ABIDE, already support the functional parcellation of the data (Craddock, James, Holtzheimer, Hu, & Mayberg, 2012; Glasser et al., 2016; Rosenberg et al., 2016). Multiple functional parcellations are available in the field of fMRI (Bellec et al., 2006; Bellec, Rosa‐Neto, Lyttelton, Benali, & Evans, 2010; Blumensath et al., 2013; Chen et al., 2013; Craddock et al., 2012; Eickhoff et al., 2011; Flandin et al., 2002; Glasser et al., 2016; Golland, Golland, & Malach, 2007; Janssen, Jylänki, Kessels, & van Gerven, 2015; Janssen, Jylänki, & van Gerven, 2016; Kahnt, Chang, Park, Heinzle, & Haynes, 2012; Lashkari et al., 2012; Lashkari, Vul, Kanwisher, & Golland, 2010; Michel et al., 2012; Orban et al., 2014; Thirion et al., 2006; Tucholka et al., 2008; van den Heuvel, Mandl, & Pol, 2008; Yeo et al., 2011), and the issue of optimal functional parcellation is broadly discussed in the field (Stanley et al., 2013). In particular, in cognitive paradigms, the ROIs can be built in a data‐driven way and on the basis of the patterns of activation only (task localizers, Fedorenko, Hsieh, Nieto‐Castañón, Whitfield‐Gabrieli, & Kanwisher, 2010; Heinzle, Wenzel, & Haynes, 2012).…”