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Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation
Open accessAbstract
The organization 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 has 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 Modeling (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 behavioral domain and paradigm class meta-data of the experiments associated with 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.
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“advancements in the study of structural and functional connectomics have provided substantial evidence of distinct patterns of brain activity organization, with segregated, yet integrated modules that dynamically interact during rest and task-based activity (van den Heuvel et al. 2009; Eickhoff et al. 2011; Laird et al. 2013; Raichle 2015; Margulies et al. 2016; Basile et al. 2022a)”
From Neuroanatomical correlates of peripersonal space: bridging the gap between perception, action, emotion and social cognition (2024) · cites this paper as Eickhoff et al. 2011
Experimental — accuracy not yet measuredIn the citing paper
“The localization of our (pre)SMA VOI corresponds to the localization of both the anterior and posterior cluster of the SMA identified by functional characterization (Eickhoff et al., 2011), which were associated to cognitive and executive aspects of motor behavior, respectively.”
From Neural network of cognitive emotion regulation—an ALE meta-analysis and MACM analysis (2014) · cites this paper as Eickhoff et al., 2011
In the citing paper
“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).”
From Informatic parcellation of the network involved in the computation of subjective value (2014) · cites this paper as Eickhoff et al., 2011
In the citing paper
“Using a binomial test, Bonferroni-corrected for multiple comparisons, the functional roles of the convergent network as a whole were identified”
From Parsing the neural correlates of moral cognition: ALE meta-analysis on morality, theory of mind, and empathy (2012) · cites this paper as Eickhoff et al. 2011
In the citing paper
“These resulting non-parametric p values were thresholded at p < 0.001.”
From Parsing the neural correlates of moral cognition: ALE meta-analysis on morality, theory of mind, and empathy (2012) · cites this paper as Eickhoff et al. 2011
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Cites
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BackgroundA new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data
Eickhoff, S. B., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., & Zilles, K. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.034 · Cited by 4,033 · cited 1×
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MethodCharacterization and propagation of uncertainty in diffusion-weighted MR imaging
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Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network
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“…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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“…described co-activation parcellation of the seed region, ALE scores for the MACM analysis of this cluster were compared to a null-distribution that reflects a random spatial association between experiments, but regards the within-experiment distribution of foci as fixed (Eickhoff et al., 2009).”
MethodResultSelf-citation
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Yan, H., Wu, H., Cai, Z., Du, S., Li, L., Xu, B., Chang, C., & Wang, N. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1181558 · Cited by 17
Improved connectivity and cognition due to cognitive stimulation in Alzheimer’s disease
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“…of each subarea in the Brainnetome Atlas are characterized, based on the behavioral domains and paradigm class metadata labels of the BrainMap database, 1 using forward and reverse inferences (Eickhoff et al., 2011; Cieslik et al., 2013; Clos et al., 2013; Fox et al., 2014; Fan et al., 2016).”
Cummine, J., Hanif, W., Dymouriak-Tymashov, I., Anchuri, K., Chiu, S. H., & Boliek, C. A. (2017) · Brain Topography · doi:10.1007/s10548-017-0553-3 · Cited by 16
“Based on the neuroimaging literature, it has been shown that there are differences in the structural composition and neural connections between the anterior portion of the SMA, namely the pre-SMA, and the posterior portion, namely the SMA-proper (Crespo-Farcorro et al. 1999; Crippa et al. 2010; Eickhoff et al. 2011; N…”
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Anatomically Informed Metrics for Connectivity-Based Cortical Parcellation From Diffusion MRI
Tungaraza, R. F., Mehta, S. H., Haynor, D. R., & Grabowski, T. J. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2444917 · Cited by 15
“, by resting state fMRI [27], [45]–[47], fMRI metaanalysis [48], or myelin content [49]) coincide with those defined ex vivo by histologic structure [19], [20], [25] is an ongoing focus of work.”
Shi, H., Wang, X., & Yao, S. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00459 · Cited by 15
“The comparison between the ALE maps generated by different tasks was obtained by subtraction of ALE values in each voxel using GingerALE too (Eickhoff et al., 2011).”
MethodNetwork neuroscience and the connectomics revolution
Betzel, R. F. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821861-7.00002-6 · Cited by 18
“[118] Simon B Eickhoff, Danilo Bzdok, Angela R Laird, Christian Roski, Svenja Caspers, Karl Zilles, and Peter T Fox, “Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation,” Neuroimage 57, 938–949 (2011).”
BackgroundMüller, V. I., Cieslik, E. C., Ficco, L., Tyralla, S., Sepehry, A. A., Aziz-Safaie, T., Feng, C., Eickhoff, S. B., & Langner, R. (2024) · Neuropsychology Review · doi:10.1007/s11065-024-09647-1 · Cited by 17
“First, the real difference between the two individual analyses was determined by computing the voxel-wise difference between the unthresholded ALE maps of each analysis (Eickhoff et al., 2011).”
Self-citationKulik, V., Reyes, L. D., & Sherwood, C. C. (2022) · Progress in brain research · doi:10.1016/bs.pbr.2022.10.003 · Cited by 14
Fascher, M., Nowaczynski, S., Spindler, C., Strobach, T., & Muehlhan, M. (2023) · Psychopharmacology · doi:10.1007/s00213-023-06498-1 · Cited by 16
“MACM is a valuable method for investigating how ALE-derived clusters are functionally organized within larger brain networks (Eickhoff et al. 2011; Fox et al. 2014; Langner et al. 2014).”
Cheng, H., Wu, H. Q., & Yong Fan, A. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.09.004 · Cited by 14
Langner, R., & Camilleri, J. A. (2021) · Springer eBooks · doi:10.1007/978-3-030-59797-9_5 · Cited by 13
Classical Statistics and Statistical Learning in Imaging Neuroscience
Bzdok, D. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00543 · Cited by 158
“…considerations also reveal the intimate relationship of ClSt models to the notion of forward inference, while StLe methods are probably more often used for formal reverse inference in functional neuroimaging (Eickhoff et al., 2011; Poldrack, 2006; Varoquaux and Thirion, 2014; Yarkoni et al., 2011).”
MethodSelf-citationMcKenna, R., Rushe, T., & Woodcock, K. A. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00154 · Cited by 13
Clustering techniques for neuroimaging applications
Derntl, A., & Plant, C. (2015) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1174 · Cited by 13
“37,38 Parcellations can be calculated from so called dictionary learning techniques such as various notions of independent component analysis (ICA) 39 – 41 and principal component analysis (PCA).”
MethodWiafe, S.-L., Faghiri, A., Fu, Z., Miller, R. L., Preda, A., & Calhoun, V. D. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00187 · Cited by 13
“…et al., 2023; Leonardi & Van De Ville, 2015; Sako ğ lu et al., 2010), phase synchrony (PS), ( Glerean et al., 2012; Honari et al., 2021; Pedersen et al., 2018), wavelet coherence ( Chang & Glover, 2010; Yaesoubi et al., 2015), and coactivation patterns ( Eickhoff et al., 2011; Liu et al., 2018).”
BackgroundCosta, C., Pezzetta, R., Masina, F., Lago, S., Gastaldon, S., Frangi, C., Genon, S., Arcara, G., & Scarpazza, C. (2024) · Human Brain Mapping · doi:10.1002/hbm.26817 · Cited by 18
“To assess the differences between cross-domain prediction incongruency and prediction congruency, the voxel-wise difference between the ensuing ALE maps was computed (Eickhoff et al., 2011).”
Gan, R., Qiu, Y.-D., Liao, J., Zhang, Y.-T., Wu, J., Peng, X., Lee, T. M., & Huang, R. (2024) · Neuroscience and Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105918 · Cited by 14
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“Both can result in the loss of relevant information.”
Gao, J., Zeng, M., Dai, X., Yang, X., Yu, H., Chen, K., Hu, Q., Xu, J., Cheng, B., & Wang, J. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00427 · Cited by 15
To learn or to gain: neural signatures of exploration in human decision-making
Zhen, S., Yaple, Z. A., Eickhoff, S. B., & Yu, R. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02389-3 · Cited by 13
“The ALE method can also perform the conjunction and contrast analysis to assess the overlaps and differences between activation patterns of two ALE maps (Eickhoff et al. 2011).”
MethodSelf-citationForebrain Network Associated With Cardiovascular Control in Exercising Humans
Shoemaker, J. (2022) · Exercise and sport sciences reviews · doi:10.1249/jes.0000000000000299 · Cited by 7
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“…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;…”
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Functional Characterization of Atrophy Patterns Related to Cognitive Impairment
Schnellbächer, G. J., Hoffstaedter, F., Eickhoff, S. B., Caspers, S., Nickl‐Jockschat, T., Fox, P. T., Laird, A. R., Schulz, J. B., Reetz, K., & Dogan, I. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00018 · Cited by 13
“Additionally, we performed contrast analyses of network connectivity between seed regions to assess divergent connectivity profiles between the three atrophy seeds (28).”
MethodSelf-citationMetaireau, M., Osiurak, F., Seye, A., & Lesourd, M. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105720 · Cited by 12
White matter tract strength correlates with therapy outcome in persistent developmental stuttering
Neef, N. E., Korzeczek, A., Primaßin, A., von Gudenberg, A. W., Dechent, P., Riedel, C. H., Paulus, W., & Sommer, M. (2022) · Human Brain Mapping · doi:10.1002/hbm.25853 · Cited by 12
“with posterior sites related to motor and action and anterior sites related to cognition and emotion (Eickhoff et al., 2011; Hartwigsen, Neef, Camilleri, Margulies, & Eickhoff, 2019).”
BackgroundMulti-Modal Imaging of Neural Correlates of Motor Speed Performance in the Trail Making Test
Camilleri, J. A., Reid, A., Müller, V. I., Grefkes, C., Amunts, K., & Eickhoff, S. B. (2015) · Frontiers in Neurology · doi:10.3389/fneur.2015.00219 · Cited by 14
Self-citationA Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation
Albughdadi, M., Chaâri, L., Tourneret, J., Forbes, F., & Ciuciu, P. (2017) · Signal Processing · doi:10.1016/j.sigpro.2017.01.005 · Cited by 12
Emotional prediction: An ALE meta-analysis and MACM analysis
Ran, G., Cao, X., & Chen, X. (2017) · Consciousness and Cognition · doi:10.1016/j.concog.2017.10.019 · Cited by 13
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“Furthermore, subtraction analyses such as “CI (lexical–baseline)–NH (lexical–baseline)” or “CI (nonlexical–baseline)–NH (nonlexical–baseline)” did not show significant foci of relative activation because the number of included studies was not enough for yielding significant results [Eickhoff et al., 2011].”
ResultLaboratory test utilization in the diagnosis of hypercoagulability.
McKenzie, S. B., Clare, C. N., Smith, L., & Sang, J. (2000) · PubMed · Cited by 10
Linguistic network in early deaf individuals: A neuroimaging meta-analysis
Yang, T., Fan, X., Hou, B., Wang, J., & Chen, X. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120720 · Cited by 13
“The null distribution was manipulated by pooling the original experiments, randomly dividing them 20,000 times into new datasets and computing the corresponding voxel-wise differences (Eickhoff et al., 2011).”
MethodZhao, M., Li, R., Xiang, S., & Liu, N. (2024) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsae068 · Cited by 13
Mansueto, S. P., Romeo, Z., Angrilli, A., & Spironelli, C. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120986 · Cited by 11
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“…et al., 2015; Parisot et al., 2016b), Gaussian mixture models (Lashkari et al., 2010; Yeo et al., 2011), meta-analytic connectivity techniques (Eickhoff et al., 2011; Power et al., 2011), dictionary learning (Varoquaux et al., 2011), edge detection (Cohen et al., 2008; Laumann et al., 2015;…”
MethodNeural networks underlying visual illusions: An activation likelihood estimation meta-analysis
von Gal, A., Boccia, M., Nori, R., Verde, P., Giannini, A. M., & Piccardi, L. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120335 · Cited by 10
Social perception in deaf individuals: A meta‐analysis of neuroimaging studies
Arioli, M., Segatta, C., Papagno, C., Tettamanti, M., & Cattaneo, Z. (2023) · Human Brain Mapping · doi:10.1002/hbm.26444 · Cited by 10
“…uncorrected threshold for comparison analysis (i.e., conjunction and contrasts): this is the standard accepted procedure when using GingerALE (see Eickhoff et al., 2011) that does not provide an established method for multiple comparison corrections in contrast analysis (for other meta-analyses…”
Zhang, Y., Lu, H., Ren, X., Zhang, J., Wang, Y., Zhang, C., & Zhao, X. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1392002 · Cited by 10
Chu, C., Fan, L., Eickhoff, C. R., Liu, Y., Yang, Y., Eickhoff, S. B., & Jiang, T. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.069 · Cited by 10
“In addition, MACM has been used to model ROIbased co-activation patterns from the data in the BrainMap database (Eickhoff et al., 2011; Robinson et al., 2010).”
Self-citationRofes, A., D'Agata, F., Guerrini, F., & Spena, G. (2024) · Journal of Neurosurgery · doi:10.3171/2023.10.jns231493 · Cited by 7
Wang, M., Deng, Y., Liu, Y., Suo, T., Guo, B., Eickhoff, S. B., Xu, J., & Rao, H. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105607 · Cited by 10
Self-citationFascher, M., Nowaczynski, S., & Muehlhan, M. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105712 · Cited by 8
“Then the merge function implemented in GingerALE was used to merge all foci of the individual MACMs for C1 and C2 into one text file.”
InfluentialThe effects of childhood adversity: Two specific neural patterns
Yan, L., Kraaijenvanger, E. J., Wennekers, R., Müller, V. I., Eickhoff, S. B., Fernández, G., Holz, N. E., & Kohn, N. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106176 · Cited by 9
“We further aimed to functionally describe the significant clusters of ALE results based on the ‘Behavioral Domain (BD) ’ and ‘Paradigm Class (PC) ’ meta-data categories included in the BrainMap database (Eickhoff et al., 2011).”
Self-citationPotential Targets for Noninvasive Brain Stimulation on Depersonalization-Derealization Disorder
Zheng, S., Song, N., Wang, S., Ning, Y., Zhu, H. Y., Song, M., Jia, Y., & Jia, H. (2022) · Brain Sciences · doi:10.3390/brainsci12081112 · Cited by 10
Neural correlates of paired associate recollection: A neuroimaging meta-analysis.
Kim, H. (2022) · Brain Research · doi:10.1016/j.brainres.2022.148200 · Cited by 9
Appetitive and aversive cue reactivities differentiate neural subtypes of alcohol drinkers
Chen, Y., & Li, C. R. (2023) · Addiction Neuroscience · doi:10.1016/j.addicn.2023.100089 · Cited by 9
Does losing money truly hurt? The shared neural bases of monetary loss and pain
Tan, H., Duan, Q., Liu, Y., Qiao, X., & Luo, S. (2022) · Human Brain Mapping · doi:10.1002/hbm.25840 · Cited by 8
Neuroimaging Research: From Null-Hypothesis Falsification to Out-of-Sample Generalization
Bzdok, D., Varoquaux, G. P., & Thirion, B. (2016) · Educational and Psychological Measurement · doi:10.1177/0013164416667982 · Cited by 9
Self-citationKim, H. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00321 · Cited by 7
“This meta-analysis used the Activation Likelihood Estimation (ALE) algorithm, as detailed by Eickhoff et al. (2009, 2011), utilizing the latest version of GingerALE software (version 3.02), available at http://www . brainmap . org / ale.”
MethodInfluentialLiu, A., Friedman, D., Barron, D. S., Wang, X., Thesen, T., & Dugan, P. (2020) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2019.106644 · Cited by 8
“org) software platform, using methods previously described [29].”
MethodNeural correlates of the inverse base rate effect
Inkster, A. B., Milton, F., Edmunds, C. E. R., Benattayallah, A., & Wills, A. J. (2021) · Human Brain Mapping · doi:10.1002/hbm.25729 · Cited by 6
“For all analyses, normalized MNI space coordinates were transformed to Talairach space using GingerALE (Eickhoff et al., 2011) and assigned anatomical labels using the Talairach Client (http://talairach.org/client.html) as per the atlas of Talairach and Tournoux (1988).”
Skipper, J. I., & Hasson, U. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/139550 · Cited by 8
“Significance was assessed by permutation analysis of above-chance clustering between experiments (S. B. Eickhoff, Heim, Zilles, & Amunts, 2009; Simon B. Eickhoff et al., 2011; Simon B. Eickhoff, Bzdok, Laird, Kurth, & Fox, 2012; Turkeltaub et al., 2012).”
MethodA meta-analysis of task-based differences in bilingual L1 and L2 language networksPreprint
Comstock, L. B., & Oliver, B. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.28.474335 · Cited by 8
Rogojin, A., Gorbet, D. J., Hawkins, K. M., & Sergio, L. E. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1054523 · Cited by 7
Kim, H. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00098 · Cited by 7
“All meta-analyses were conducted using the ALE algo-rithm ( Eickhoff et al., 2009, 2011) through GingerALE software version 3.0.2 (http://www . brainmap . org / ale).”
MethodInfluentialPallanti, S., Ponzio, M. D., Levola, J. M., Lioumis, P., Paunio, T., Kičić, D., Ilmoniemi, R. J., & Makris, N. (2024) · International Journal of Mental Health and Addiction · doi:10.1007/s11469-024-01261-9 · Cited by 7
“This array of functions is reflected by pre-SMA’s distinct structural connectivity (Eickhoff et al., 2011; Picard & Strick, 1996; Rizzolatti & Luppino, 2001; Nachev et al., 2005, 2007, 2008; Shima et al., 1996).”
Toffoli, L., Zdorovtsova, N., Epihova, G., Duma, G. M., Cristaldi, F. D. P., Pastore, M., Astle, D. E., & Mento, G. (2024) · Human Brain Mapping · doi:10.1002/hbm.70011 · Cited by 7
“In fact, we aimed at characterizing the spatial configuration of functional organization patterns of cortical dynamics, allowing inference regarding co-activations across brain regions (Ding et al., 2022; Eickhoff et al., 2011).”
MethodThe role of the intraparietal sulcus in numeracy: A review of parietal lesion cases
Duricy, E., Durisko, C., & Fiez, J. A. (2025) · Behavioural Brain Research · doi:10.1016/j.bbr.2025.115453 · Cited by 8
“As a follow-up analysis to examine possible anatomical associations with distinct domains of numeracy, we performed activation likelihood estimation (ALE) contrast analyses for each task category, using the software BrainMap GingerALE 3.0.2 [46,47,121].”
InfluentialPresti, S. L., Origlia, S., Gianelli, C., & Canessa, N. (2023) · Human Brain Mapping · doi:10.1002/hbm.26312 · Cited by 7
Reduced Gray Matter Volume and Cortical Thickness in Patients With Small-Fiber Neuropathy
Scheliga, S., Dohrn, M. F., Habel, U., Lampert, A., Rolke, R., Lischka, A., van den Braak, N. W. M., Spehr, M., Jo, H., & Kellermann, T. (2024) · Journal of Pain · doi:10.1016/j.jpain.2024.01.001 · Cited by 7
BrainMap Database as a Resource for Computational Modeling
Barron, D. S., & Fox, P. T. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00350-x · Cited by 7
Self-citationHeterogeneous fractionation profiles of meta-analytic coactivation networks
Laird, A. R., Riedel, M. C., Okoe, M., Jianu, R., Ray, K. L., Eickhoff, S. B., Smith, S. M., Fox, P. T., & Sutherland, M. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.037 · Cited by 7
“…and paradigms, are complementary to seed-based resting state correlations, and have been validated in a series of papers comparing findings to other network mapping techniques (Robinson et al., 2010; 2012; Eickhoff et al., 2010; 2011; Narayana et al., 2012; Jakobs et al., 2012; Reetz et al., 2012).”
MethodResultSelf-citationSassenberg, T. A., Safron, A., & DeYoung, C. G. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae391 · Cited by 8
Yeung, A. W. K. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103496 · Cited by 6
““Studies based on ROIs or SVC must be excluded because a prerequisite for fMRI meta-analyses is that convergence across experiments is tested against a null-hypothesis of random spatial associations across the entire brain, under the assumption that each voxel has the same a priori chance of being activated (Eickhoff…”
BackgroundMethodBrain Network Dysconnection in Neuropsychiatric Disorders: The Practice of “Normal Science”
Diwadkar, V. A., & Eickhoff, S. B. (2021) · Springer eBooks · doi:10.1007/978-3-030-59797-9_1 · Cited by 7
Self-citationHan, H. (2022) · Mathematics · doi:10.3390/math10030356 · Cited by 5
“Once all meta-analysis procedures were completed, one unthresholded statistical image reporting activation likelihood in analyzed voxels and one thresholded image were generated for each completed meta-analysis [27,38].”
MethodProchnow, D., Brunheim, S., Kossack, H., Eickhoff, S. B., Markowitsch, H. J., & Seitz, R. J. (2014) · F1000Research · doi:10.12688/f1000research.4734.1 · Cited by 6
Self-citationBrain dynamics and connectivity networks under natural auditory stimulation
Kuo, P., Tseng, Y., Zilles, K., Suen, S., Eickhoff, S. B., Lee, J., Cheng, P. E., & Liou, M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116042 · Cited by 6
Self-citationPresti, S. L., Bonavita, M., Piga, V., Lozito, S., Doricchi, F., & Lasaponara, S. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106153 · Cited by 5
“…ROI were excluded, as a prerequisite for fMRI meta-analyses is that convergence across experiments be tested against a null hypothesis of random spatial associations in the whole brain, assuming each voxel has an equal a priori probability of activation (Eickhoff et al., 2011; Müller et al., 2018).”
Gan, X., Zhang, R., Zheng, Z., Wang, L., Yang, X., Klugah‐Brown, B., Xu, T., Qiu, N., Kendrick, K. M., Mathiak, K., et al. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106356 · Cited by 7
Ni, H., Xue, J., Qin, J., & Zhang, Y. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108281 · Cited by 5
Yang, Z., & Li, P. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106205 · Cited by 6
Allen, A., Zhang, Z., & Nobel, A. B. (2024) · Network Neuroscience · doi:10.1162/netn_a_00409 · Cited by 5
Ramage, A. E., Cote, K., Thorson, J. C., Lerner, K., Reidel, M. C., & Laird, A. R. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00355 · Cited by 8
Self-citationHill-Bowen, L. D., Riedel, M. C., Poudel, R., Salo, T., Flannery, J. S., Camilleri, J. A., Eickhoff, S. B., Laird, A. R., & Sutherland, M. T. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.26.966549 · Cited by 6
“Based on prior work [69, 70, 128], we selected the standardized Euclidean ‘distance’ method and Ward’s minimum variance ‘linkage’, how the outcomes reported herein would differ as a function of the distance and linkage methods employed was beyond the scope of this work.”
Self-citationLevy, D. F., Greicius, Q., Wang, C., Ko, E., Xu, D., Andrews, J. P., & Chang, E. F. (2025) · Neurobiology of Language · doi:10.1162/nol_a_00166 · Cited by 4
“…of the body relevant for movement, the pre-SMA projects instead to prefrontal cortex and is modulated by more abstract motor processes, such as action selection, motor learning, and/or preparation for complex movement (Eickhoff et al., 2011; Fontaine et al., 2002; Potgieser et al., 2014).”
Common neural activations of creativity and exploration: A meta-analysis of task-based fMRI studies
Liu, Y., Wang, M., & Rao, H. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106158 · Cited by 4
Fascher, M., Thomsen, H., & Muehlhan, M. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106537 · Cited by 4
Different Neural Correlates of Sexually Preferred and Sexually Nonpreferred Stimuli
Long, X., Tian, F., Zhou, Y., Cheng, B., & Jia, Z. (2020) · The Journal of Sexual Medicine · doi:10.1016/j.jsxm.2020.03.007 · Cited by 4
Neurocognition and placebo analgesia: Linking in functional magnetic resonance imaging
Palermo, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820589-1.00028-2 · Cited by 4
Ibrahim, K., Iturmendi-Sabater, I., Vasishth, M., Barron, D. S., Guardavaccaro, M., Funaro, M. C., Holmes, A. J., McCarthy, G., Eickhoff, S. B., & Sukhodolsky, D. G. (2022) · Schizophrenia Research · doi:10.1016/j.schres.2023.12.003 · Cited by 5
Self-citationKim, H. (2024) · Brain Research · doi:10.1016/j.brainres.2024.148794 · Cited by 4
Wang, Y., & Yao, X. (2024) · Physiology & Behavior · doi:10.1016/j.physbeh.2024.114481 · Cited by 4
Ren, H., Li, Y., Bi, H., & Yang, Y. (2024) · International Journal of Clinical and Health Psychology · doi:10.1016/j.ijchp.2024.100519 · Cited by 5
Prochnow, D., Brunheim, S., Kossack, H., Eickhoff, S. B., Markowitsch, H. J., & Seitz, R. J. (2015) · F1000Research · doi:10.12688/f1000research.4734.3 · Cited by 4
“Subsequently, two groups of experiments were formed by pooling and randomly assigning them to same-size groups (Eickhoff et al., 2011b).”
Self-citationMonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain
He, B., Yang, Z., Fan, L., Gao, B., Li, H., Ye, C., You, B., & Jiang, T. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2020.00014 · Cited by 4
Neural Correlates of Anti-appetite Medications: An fMRI Meta-analysis
Yeung, A. W. K. (2021) · Current Neuropharmacology · doi:10.2174/1570159x19666210914142227 · Cited by 4
“The ALE method is the commonest approach of coordinate-based meta-analysis that pools neuroimaging data from multiple studies to identify brain regions with consistent activation based on probability distribution modeling [7].”
MethodNgo, G. H., Eickhoff, S. B., Fox, P. T., & Yeo, B. T. T. (2016) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2016.7552332 · Cited by 4
“An alternative approach is to analyze large collections of brain activation coordinates without dividing task contrasts into psychological constructs a priori [8, 9, 10, 11].”
BackgroundSelf-citationBrain reorganization in anticipation of predictable wordsPreprint
Skipper, J. I., & Zevin, J. D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/101113 · Cited by 4
Research on the Brain-inspired Cross-media Neural Cognitive Computing Framework.Preprint
Liu, Y. (2018) · arXiv (Cornell University) · Cited by 4
A Review on MR Based Human Brain Parcellation MethodsPreprint
Moghimi, P., Dang, A. T., Netoff, T. I., Lim, K. O., & Atluri, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.03475 · Cited by 7
“Of note are two approaches known as meta-analytic connectivity modeling (MACM) (Eickhoff et al. 2011) and meta-analytic activation modeling-based parcellation (MAMP) (Yang et al. 2016).”
BackgroundMethodInfluentialKelkar, A. S., & Medaglia, J. D. (2018) · Springer eBooks · doi:10.1007/978-3-319-16999-6_2422-1 · Cited by 4
Comstock, L. B. (2024) · Brain and Language · doi:10.1016/j.bandl.2023.105377 · Cited by 7
“As a result, all but one of the bilingual neuroimaging meta-analyses considered here implement the activation likelihood estimation (ALE) technique (Eickhoff et al., 2009; Eickhoff, Bzdok, Laird, Roski, Caspers, Zilles, & Fox, 2011; Turkeltaub et al., 2012).”
BackgroundBao, S., Boyd, B. D., Kanakaraj, P., Ramadass, K., Meyer, F. A. C., Liu, Y., Duett, W. E., Huo, Y., Lyu, I., Zald, D. H., et al. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00679-8 · Cited by 5
“The fMRI data is categorized into three types of fMRI tasks: (1) go/no go task [45] with three runs, (2) monetary incentive delay (MID) task [46] with two runs, and (3) co-activation pattern [47] with two runs.”
MethodResearch on Brain and Mind Inspired Intelligence.
Liu, Y., & Wei, J. (2023) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2023.07.004 · Cited by 3
Metadata
- Authors
- Eickhoff, S. B., Bzdok, D., Laird, A. R., Roski, C., Caspers, S., Zilles, K., & Fox, P. T.
- Affiliations
- Forschungszentrum Jülich (DE)
- The University of Texas at San Antonio Health Science Center (US)
- Corresponding author
- Eickhoff, Simon B.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-08
- Volume
- 57
- Issue
- 3
- Pages
- 938-949
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3129435
- OpenAlex
- W2001483470
- Cited by
- 484
- References
- 60
- Citations by year
- 2026: 20 · 2025: 20 · 2024: 38 · 2023: 22 · 2022: 40 · 2021: 40 · 2020: 27 · 2019: 25 · 2018: 27 · 2017: 41 · 2016: 43 · 2015: 48 · 2014: 34 · 2013: 27 · 2012: 23
- FWCI
- 14.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- JuSER (Forschungszentrum Jülich) (submittedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Funders
- Human Brain Project (R01-MH074457-01A1)
- Helmholtz Alliance on Systems Biology
- DFG (IRTG 1328)
- Helmholtz Alliance for Mental Health in an Aging Society
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- cortical modules, co-activation patterns, meta-analytic connectivity modeling, whole-brain functional connectivity, pre-supplementary motor area, supplementary motor area, functional parcellation
- MeSH terms
- Algorithms, Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neural Pathways, Cluster Analysis
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