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

By Simon B. Eickhoff, Danilo Bzdok, Angela R. Laird, Christian Roski, Svenja Caspers, Karl Zilles, Peter T. Fox. NeuroImage, 2011

477 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ef9af360f36b6e1c1964fdb33b046973485641f0

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In Cognitive Neuroscience

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Citations in the corpus

The BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data (2011, methodology): “GingerALE 2.1 also includes a more streamlined interface and a revision of the subtraction analysis [40] originally developed by Laird et al. [37] that has been substantially improved using the new statistical framework developed by Eickhoff et al. [38].”
Informatic parcellation of the network involved in the computation of subjective value. (2014, methodology): “This procedure should effectively balance inference on reliable areas of activation without generating a bias toward false positives (Laird et al., 2005; Eickhoff et al., 2011).”
Neural network of cognitive emotion regulation — An ALE meta-analysis and MACM analysis (2013, methodology): “MACM is based on assessing the brain-wise co-activation patterns of a seed region across a large number of data based neuroimaging results (Eickhoff et al., 2011; Laird et al., 2009b).”
Cognitive relevance of the community structure of the human brain functional coactivation network (2013, methodology): “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 tha”
Which fMRI clustering gives good brain parcellations? (2014, background): “…as gyro-66 sulcal anatomy ( Desikan et al. , 2006; Klein and Tourville , 2012), post-mortem in vitro receptor autoradiography for cyto-architecture ( Eickhoff et al. , 2008; Fischl et al. , 2008), anatomical connectivity ( Roca et al. , 2010) with diffusion imaging, or functional features with…”
Neural substrates of smoking cue reactivity: A meta-analysis of fMRI studies (2011, methodology): “The technique of ALE subtraction analysis is new (Eickhoff et al., 2011), and the effect of unbalanced groups on subtraction analysis results has not yet been determined.”
Meta-analytical definition and functional connectivity of the human vestibular cortex (2012, methodology): “05, cluster-level corrected for multiple comparisons (Eickhoff et al., 2011).”
Modelling neural correlates of working memory: A coordinate-based meta-analysis (2011, unknown): “Differences between conditions were tested by first performing separate ALE analyses for each condition and computing the voxel-wise difference between the ensuing ALE maps (cf. Eickhoff et al., 2011b).”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, background): “, 2015), meta-analytic connectivity techniques (Eickhoff et al., 2011; Power et al., 2011), dictionary learning (Varoquaux et al.”
The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture (2016, unknown): “…brain region or provide more comprehensive maps of the cerebral cortex (Tzourio-Mazoyer et al. 2002; Desikan et al. 2006; Cohen, Fair, et al. 2008; Cohen, Lombardo, et al. 2008; Eickhoff et al. 2011; Wang et al. 2012; Fan et al. 2014; Wig et al. 2014; Laumann Timothy et al. 2015; Figure 4.”