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

doi:10.1016/J.NEUROIMAGE.2011.05.021

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

Journal Article•DOI•

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

Simon B. Eickhoff¹, Danilo Bzdok¹, Danilo Bzdok², Angela R. Laird³, Christian Roski², Svenja Caspers², Karl Zilles⁴, Karl Zilles³, Peter T. Fox - Show less +5 more•Institutions (4)

RWTH Aachen University¹, Forschungszentrum Jülich², University of Texas Health Science Center at San Antonio³, University of Düsseldorf⁴

01 Aug 2011-NeuroImage (Academic Press Inc.)-Vol. 57, Iss: 3, pp 938-949

TL;DR: It is demonstrated that Meta-Analytic Connectivity Modeling (MACM) allows the delineation of cortical modules based on their whole-brain co-activation pattern across databased neuroimaging results, and 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 cortex modules.

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About: This article is published in NeuroImage.The article was published on 2011-08-01 and is currently open access. It has received 418 citations till now. The article focuses on the topics: Resting state fMRI.

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Journal Article•DOI•

The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture

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Lingzhong Fan, Hai Li, Junjie Zhuo¹, Yu Zhang, Jiaojian Wang¹, Liangfu Chen, Zhengyi Yang, Congying Chu, Sangma Xie, Angela R. Laird², Peter T. Fox³, Simon B. Eickhoff⁴, Chunshui Yu⁵, Tianzi Jiang¹ - Show less +10 more•Institutions (5)

University of Electronic Science and Technology of China¹, Florida International University², University of Texas Health Science Center at San Antonio³, University of Düsseldorf⁴, Tianjin Medical University General Hospital⁵

01 Aug 2016-Cerebral Cortex

TL;DR: A connectivity-based parcellation framework is designed that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture and provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections.

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Abstract: The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.

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1,717 citations

Cites background or methods from "Co-activation patterns distinguish ..."

...…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....
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...…resonance imaging (MRI), including structural, functional, and diffusion MRI, has offered alternative ways to tackle the challenge of cortical cartography (Behrens et al. 2003; JohansenBerg et al. 2004; Cohen, Fair, et al. 2008; Cohen, Lombardo, et al. 2008; Kim et al. 2010; Eickhoff et al. 2011)....
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...…connectivity-based parcellation-yielded subregions was based on behavioral domain and paradigm class meta data labels of the BrainMap database (cf. http://www.brainmap.org/taxonomy) using forward and reverse inferences (Eickhoff et al. 2011; Cieslik et al. 2013; Clos et al. 2013; Fox et al. 2014)....
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...…al. 2004), resting-state functional connectivity (Cohen, Fair, et al. 2008; Cohen, Lombardo, et al. 2008; Nelson et al. 2010), structural covariance (Cohen, Fair, et al. 2008; Cohen, Lombardo, et al. 2008; Kelly et al. 2012), and meta-analysis-based functional coactivation (Eickhoff et al. 2011)....
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Journal Article•DOI•

Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI

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Alexander Schaefer¹, Ru Kong¹, Evan M. Gordon, Timothy O. Laumann², Xi-Nian Zuo³, Avram J. Holmes⁴, Simon B. Eickhoff⁵, B.T. Thomas Yeo¹, B.T. Thomas Yeo⁶ - Show less +5 more•Institutions (6)

National University of Singapore¹, Washington University in St. Louis², Chinese Academy of Sciences³, Yale University⁴, University of Düsseldorf⁵, Harvard University⁶

01 Sep 2018-Cerebral Cortex

TL;DR: The results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data.

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Abstract: A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).

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1,567 citations

Cites background from "Co-activation patterns distinguish ..."

...2013), others have focused on finer subdivisions (Cohen et al. 2008; Eickhoff et al. 2011; Craddock et al. 2012; Blumensath et al. 2013; Ryali et al. 2013; Shen et al. 2013; Wig et al. 2014a; Honnorat et al. 2015; Glasser et al. 2016)....
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...Contributions from other modalities might be necessary (Toga et al. 2006; Eickhoff et al. 2011; Bzdok et al. 2013; Wang et al. 2015a; Glasser et al. 2016) if there were certain biological boundaries where rs-fMRI is truly intrinsically insensitive....
[...]
...…et al. 2010; Power et al. 2011; Lee et al. 2012; Zuo et al. 2012; Hacker et al. 2013), others have focused on finer subdivisions (Cohen et al. 2008; Eickhoff et al. 2011; Craddock et al. 2012; Blumensath et al. 2013; Ryali et al. 2013; Shen et al. 2013; Wig et al. 2014a; Honnorat et al. 2015;…...
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...Contributions from other modalities might be necessary (Toga et al., 2006; Eickhoff et al., 2011; Bzdok et al., 2013; Wang et al., 2015a; Glasser et al., 2016) if there were certain biological boundaries where rs-fMRI is truly intrinsically insensitive....
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Journal Article•DOI•

Identification of a common neurobiological substrate for mental illness.

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Madeleine S. Goodkind¹, Simon B. Eickhoff², Desmond J. Oathes¹, Desmond J. Oathes³, Ying Jiang¹, Ying Jiang³, Andrew Chang¹, Andrew Chang³, Laura B. Jones-Hagata¹, Laura B. Jones-Hagata³, Brissa N. Ortega¹, Brissa N. Ortega³, Yevgeniya V. Zaiko³, Yevgeniya V. Zaiko¹, Erika L. Roach¹, Erika L. Roach³, Mayuresh S. Korgaonkar⁴, Stuart M. Grieve⁴, Isaac R. Galatzer-Levy⁵, Peter T. Fox⁶, Amit Etkin¹, Amit Etkin³ - Show less +18 more•Institutions (6)

Stanford University¹, University of Düsseldorf², Veterans Health Administration³, University of Sydney⁴, New York University⁵, University of Texas Health Science Center at San Antonio⁶

01 Apr 2015-JAMA Psychiatry

TL;DR: A concordance is identified in terms of integrity of an anterior insula/dorsal anterior cingulate-based network, which may relate to executive function deficits observed across diagnoses, which provides an organizing model that emphasizes the importance of shared neural substrates across psychopathology.

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Abstract: Importance Psychiatric diagnoses are currently distinguished based on sets of specific symptoms. However, genetic and clinical analyses find similarities across a wide variety of diagnoses, suggesting that a common neurobiological substrate may exist across mental illness. Objective To conduct a meta-analysis of structural neuroimaging studies across multiple psychiatric diagnoses, followed by parallel analyses of 3 large-scale healthy participant data sets to help interpret structural findings in the meta-analysis. Data Sources PubMed was searched to identify voxel-based morphometry studies through July 2012 comparing psychiatric patients to healthy control individuals for the meta-analysis. The 3 parallel healthy participant data sets included resting-state functional magnetic resonance imaging, a database of activation foci across thousands of neuroimaging experiments, and a data set with structural imaging and cognitive task performance data. Data Extraction and Synthesis Studies were included in the meta-analysis if they reported voxel-based morphometry differences between patients with an Axis I diagnosis and control individuals in stereotactic coordinates across the whole brain, did not present predominantly in childhood, and had at least 10 studies contributing to that diagnosis (or across closely related diagnoses). The meta-analysis was conducted on peak voxel coordinates using an activation likelihood estimation approach. Main Outcomes and Measures We tested for areas of common gray matter volume increase or decrease across Axis I diagnoses, as well as areas differing between diagnoses. Follow-up analyses on other healthy participant data sets tested connectivity related to regions arising from the meta-analysis and the relationship of gray matter volume to cognition. Results Based on the voxel-based morphometry meta-analysis of 193 studies comprising 15 892 individuals across 6 diverse diagnostic groups (schizophrenia, bipolar disorder, depression, addiction, obsessive-compulsive disorder, and anxiety), we found that gray matter loss converged across diagnoses in 3 regions: the dorsal anterior cingulate, right insula, and left insula. By contrast, there were few diagnosis-specific effects, distinguishing only schizophrenia and depression from other diagnoses. In the parallel follow-up analyses of the 3 independent healthy participant data sets, we found that the common gray matter loss regions formed a tightly interconnected network during tasks and at resting and that lower gray matter in this network was associated with poor executive functioning. Conclusions and Revelance We identified a concordance across psychiatric diagnoses in terms of integrity of an anterior insula/dorsal anterior cingulate–based network, which may relate to executive function deficits observed across diagnoses. This concordance provides an organizing model that emphasizes the importance of shared neural substrates across psychopathology, despite likely diverse etiologies, which is currently not an explicit component of psychiatric nosology.

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1,033 citations

Journal Article•DOI•

Modelling neural correlates of working memory: a coordinate-based meta-analysis.

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Claudia Rottschy¹, Robert Langner², Robert Langner³, Imis Dogan¹, Kathrin Reetz³, Kathrin Reetz¹, Angela R. Laird⁴, Jörg B. Schulz¹, Peter T. Fox, Simon B. Eickhoff², Simon B. Eickhoff³ - Show less +7 more•Institutions (4)

RWTH Aachen University¹, University of Düsseldorf², Forschungszentrum Jülich³, University of Texas Health Science Center at San Antonio⁴

01 Mar 2012-NeuroImage

TL;DR: Meta-analyses are a powerful tool to integrate the data of functional imaging studies on a (broader) psychological construct, probing the consistency across various paradigms as well as the differential effects of different experimental implementations.

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782 citations

Posted Content•DOI•

Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI

[...]

Alexander Schaefer¹, Ru Kong¹, Evan M. Gordon, Timothy O. Laumann², Xi-Nian Zuo³, Avram J. Holmes⁴, Simon B. Eickhoff⁵, B.T. Thomas Yeo¹ - Show less +4 more•Institutions (5)

National University of Singapore¹, Washington University in St. Louis², Chinese Academy of Sciences³, Yale University⁴, University of Düsseldorf⁵

06 Jun 2017-bioRxiv

...read moreread less

Abstract: A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological “atoms”. Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in-vivo human cortical parcellation. Almost all previous parcellations relied on one of two approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than four previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured sub-areal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multi-resolution parcellations generated from 1489 participants are available at FREESURFER_WIKI LINK_TO_BE_ADDED.

...read moreread less

698 citations

Cites background from "Co-activation patterns distinguish ..."

...Contributions from other modalities might be necessary (Toga et al., 2006; Eickhoff et al., 2011; Bzdok et al., 2013; Wang et al., 2015a; Glasser et al., 2016) if there were certain biological boundaries where rs-fMRI is truly intrinsically insensitive....
[...]
...…et al. 2010; Power et al. 2011; Lee et al. 2012; Zuo et al. 2012; Hacker et al. 2013), others have focused on finer subdivisions (Cohen et al. 2008; Eickhoff et al. 2011; Craddock et al. 2012; Blumensath et al. 2013; Ryali et al. 2013; Shen et al. 2013; Wig et al. 2014a; Honnorat et al. 2015;…...
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References

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Open Access

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Journal Article•DOI•

Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.

[...]

Bharat B. Biswal¹, F Z Yetkin¹, Victor M. Haughton¹, James S. Hyde¹•Institutions (1)

Medical College of Wisconsin¹

01 Oct 1995-Magnetic Resonance in Medicine

TL;DR: It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.

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Abstract: An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.

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8,766 citations

"Co-activation patterns distinguish ..." refers background in this paper

...…likely conveying meaningful functional relationships between brain regions are illustrated by the fact that they were reported to widely correspond with both task-state networks (Biswal et al., 1995; Smith et al., 2009) and structural connectivity (Greicius et al., 2009; Hagmann et al., 2008)....
[...]
...Those signal fluctuations likely conveying meaningful functional relationships between brain regions are illustrated by the fact that they were reported to widely correspond with both task-state networks (Biswal et al., 1995; Smith et al., 2009) and structural connectivity (Greicius et al....
[...]

Journal Article•DOI•

Correspondence of the brain's functional architecture during activation and rest.

[...]

Stephen M. Smith¹, Peter T. Fox², Karla L. Miller¹, David C. Glahn², P. Mickle Fox², Clare E. Mackay¹, Nicola Filippini¹, Kate E. Watkins¹, Roberto Toro³, Angela R. Laird², Christian F. Beckmann⁴, Christian F. Beckmann¹ - Show less +8 more•Institutions (4)

University of Oxford¹, University of Texas Health Science Center at San Antonio², Pasteur Institute³, Imperial College London⁴

04 Aug 2009-Proceedings of the National Academy of Sciences of the United States of America

TL;DR: It is concluded that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.”

...read moreread less

Abstract: Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is “at rest.” In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.”

...read moreread less

4,768 citations

"Co-activation patterns distinguish ..." refers background or result in this paper

...…physiology of correlations in the absence of a structured task remains somewhat elusive, it seems plausible that “resting” state, as a mixture of various cognitive processes, may likewise sample the repertoire of operations brain networks can perform (Buckner and Vincent, 2007; Smith et al., 2009)....
[...]
...This congruency strongly supports the correspondence of the brain's functional architecture during rest and activation revealed by independent component analysis (Smith et al., 2009) and demonstrated that it also extends to seed-based analyses and connectivity based parcellations....
[...]
...Those signal fluctuations likely conveying meaningful functional relationships between brain regions are illustrated by the fact that they were reported to widely correspond with both task-state networks (Biswal et al., 1995; Smith et al., 2009) and structural connectivity (Greicius et al....
[...]
...…likely conveying meaningful functional relationships between brain regions are illustrated by the fact that they were reported to widely correspond with both task-state networks (Biswal et al., 1995; Smith et al., 2009) and structural connectivity (Greicius et al., 2009; Hagmann et al., 2008)....
[...]
...While the underlying physiology of correlations in the absence of a structured task remains somewhat elusive, it seems plausible that “resting” state, as a mixture of various cognitive processes, may likewise sample the repertoire of operations brain networks can perform (Buckner and Vincent, 2007; Smith et al., 2009)....
[...]

Journal Article•DOI•

Dynamic causal modelling.

[...]

Karl J. Friston, Lee M. Harrison, William D. Penny

01 Aug 2003-NeuroImage

TL;DR: As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling, but unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.

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4,182 citations

"Co-activation patterns distinguish ..." refers background in this paper

...Among those, effective connectivity analyses, such as dynamic causal modeling (Friston et al., 2003) or structural equation modeling (Buchel and Friston, 1997), allow the investigation of taskdependent influences among cortical areas (Grefkes et al., 2008)....
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Journal Article•DOI•

Mapping the Structural Core of Human Cerebral Cortex

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Patric Hagmann¹, Leila Cammoun², Xavier Gigandet², Reto Meuli¹, Christopher J. Honey³, Van J. Wedeen⁴, Olaf Sporns³ - Show less +3 more•Institutions (4)

University of Lausanne¹, École Polytechnique Fédérale de Lausanne², Indiana University³, Harvard University⁴

01 Jul 2008-PLOS Biology

TL;DR: The spatial and topological centrality of the core within cortex suggests an important role in functional integration and a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants.

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Abstract: Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these pathways within and across cortical hemispheres in individual human participants. An analysis of the resulting large-scale structural brain networks reveals a structural core within posterior medial and parietal cerebral cortex, as well as several distinct temporal and frontal modules. Brain regions within the structural core share high degree, strength, and betweenness centrality, and they constitute connector hubs that link all major structural modules. The structural core contains brain regions that form the posterior components of the human default network. Looking both within and outside of core regions, we observed a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants. The spatial and topological centrality of the core within cortex suggests an important role in functional integration.

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4,035 citations

"Co-activation patterns distinguish ..." refers background in this paper

...…likely conveying meaningful functional relationships between brain regions are illustrated by the fact that they were reported to widely correspond with both task-state networks (Biswal et al., 1995; Smith et al., 2009) and structural connectivity (Greicius et al., 2009; Hagmann et al., 2008)....
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..., 2009) and structural connectivity (Greicius et al., 2009; Hagmann et al., 2008)....
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Journal Article•DOI•

A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data

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Simon B. Eickhoff¹, Klaas E. Stephan², Hartmut Mohlberg¹, Christian Grefkes³, Gereon R. Fink³, Katrin Amunts¹, Karl Zilles¹ - Show less +3 more•Institutions (3)

Forschungszentrum Jülich¹, University College London², Max Planck Society³

01 May 2005-NeuroImage

TL;DR: A new, MATLAB based toolbox for the SPM2 software package is introduced which enables the integration of probabilistic cytoarchitectonic maps and results of functional imaging studies and an easy-to-use tool for the integrated analysis of functional and anatomical data in a common reference space.

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3,911 citations

"Co-activation patterns distinguish ..." refers background in this paper

...In addition, a necessary step toward understanding cortical organization is to relate connectivity-defined modules and functional differentiations to microstructural maps of the human cerebral cortex (Amunts et al., 2007; Eickhoff et al., 2005; Eickhoff et al., 2008b; Zilles et al., 2002)....
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