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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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BackgroundA new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data
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MethodResultSelf-citation
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Self-citationThe Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture
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InfluentialSelf-citationIdentification of a Common Neurobiological Substrate for Mental Illness
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Self-citationNeural network of cognitive emotion regulation—an ALE meta-analysis and MACM analysis
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“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).”
BackgroundMethodSelf-citationInformatic parcellation of the network involved in the computation of subjective value
Clithero, J. A., & Rangel, A. (2014) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nst106 · Cited by 706
“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).”
MethodLangner, R., & Eickhoff, S. B. (2013) · Psychological Bulletin · doi:10.1037/a0030694 · Cited by 746
Self-citationBzdok, D., Schilbach, L., Vogeley, K., Schneider, K., Laird, A. R., Langner, R., & Eickhoff, S. B. (2012) · Brain Structure and Function · doi:10.1007/s00429-012-0380-y · Cited by 653
“The third and last step distinguished between random and ‘true’ convergence by comparing the ensuing ALE scores against an empirical null distribution reflecting a random spatial association between the experiments’ MA maps (Eickhoff et al. 2012).”
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“In recent years, several openaccess frameworks have been developed to pool data from thousands of neuroimaging studies (Eickhoff et al., 2011; Poldrack and Yarkoni, 2016).”
BackgroundUnderstanding the minds of others: A neuroimaging meta-analysis
Molenberghs, P., Johnson, H., Henry, J. D., & Mattingley, J. B. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.03.020 · Cited by 570
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Eickhoff, S. B., Yeo, B. T. T., & Genon, S. (2018) · Nature reviews. Neuroscience · doi:10.1038/s41583-018-0071-7 · Cited by 484
Self-citationLong-term neural and physiological phenotyping of a single human
Poldrack, R. A., Laumann, T. O., Koyejo, S., Gregory, B., Hover, A. M., Chen, M.-Y., Gorgolewski, K. J., Luci, J. J., Joo, S. J., Boyd, R. L., et al. (2015) · Nature Communications · doi:10.1038/ncomms9885 · Cited by 486
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“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…”
MethodMorawetz, C., Bode, S., Derntl, B., & Heekeren, H. R. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.11.014 · Cited by 458
“groups, keeping the sample size of the two original data sets (Eickhoff et al., 2011; Rottschy et al., 2012).”
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Meta-analytical definition and functional connectivity of the human vestibular cortex
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“05, cluster-level corrected for multiple comparisons (Eickhoff et al., 2011).”
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“In light of this, connectivity-based parcellation informed by MACM might serve as a useful tool to generate hypotheses for targeted experiments assessing structural, connectional, or functional properties and their disturbances [Eickhoff et al., 2011].”
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“For these contrasts, a subtraction analysis was run to identify distinct areas of activation (Eickhoff et al., 2011), by repeating the following process 5,000 times: (i) all the results that contributed to either of the two contrasts being examined, were pooled and randomly split into two groups that were the same siz…”
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“, 2015), meta-analytic connectivity techniques (Eickhoff et al., 2011; Power et al., 2011), dictionary learning (Varoquaux et al.”
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“Keywords: action, connectivity, database, fMRI, prefrontal”
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Engelmann, J. M., Versace, F., Robinson, J. D., Minnix, J. A., Lam, C. Y., Cui, Y., Brown, V. L., & Cinciripini, P. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.024 · Cited by 394
“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.”
MethodThree key regions for supervisory attentional control: Evidence from neuroimaging meta-analyses
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Which fMRI clustering gives good brain parcellations?
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“…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…”
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Self-citationConnectivity‐based parcellation: Critique and implications
Eickhoff, S. B., Thirion, B., Varoquaux, G. P., & Bzdok, D. (2015) · Human Brain Mapping · doi:10.1002/hbm.22933 · Cited by 278
“That is, RSFC and MACM (but not dMRI) may be driven by indirect connections.”
BackgroundMethodResultInfluentialSelf-citationImplementation errors in the GingerALE Software: Description and recommendations
Eickhoff, S. B., Laird, A. R., Fox, P. M., Lancaster, J. L., & Fox, P. T. (2016) · Human Brain Mapping · doi:10.1002/hbm.23342 · Cited by 270
“In addition, algorithms have been provided for meta-analytic contrast analyses using fixed-effects [Laird et al., 2005] and random-effects [Eickhoff et al., 2011] models.”
MethodSelf-citationRobinson, J. L., Laird, A. R., Glahn, D. C., Blangero, J., Sanghera, M. K., Pessoa, L., Fox, P. M., Uecker, A. M., Friehs, G. M., Young, K. A., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.010 · Cited by 269
Self-citationBzdok, D., Langner, R., Schilbach, L., Jakobs, O., Roski, C., Caspers, S., Laird, A. R., Fox, P. T., Zilles, K., & Eickhoff, S. B. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.046 · Cited by 262
“Hence, pooling across the close spatial neighborhood has become the dominant approach in MACM analysis (Cauda et al., 2011; Eickhoff et al., 2011).”
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“It has to be further emphasised that our approach with definition of seed and target areas is different from the connectivity-based parcellationwhich aims at parcellation of a cortical region based on its connectivity pattern to every other voxel in the brain (Anwander et al., 2007; Behrens et al., 2003a; Eickhoff et…”
BackgroundSelf-citationDong, H.-M., Margulies, D. S., Zuo, X., & Holmes, A. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2024448118 · Cited by 253
“organization from neural circuits through large-scale cortical networks relate to behavior (18-20), the age-dependent changes that underpin hierarchical shifts across”
Segregation of the human medial prefrontal cortex in social cognition
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“All experiments contributing to either analysis were then pooled and randomly divided into two groups of the same size as the two original sets of experiments defined by activation in the first or second cluster (Eickhoff et al., 2011).”
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“The observed discrepancies might have resulted from different causes, such as systematic or unsystematic measurement errors and noise or conceptual differences between anatomical and FC [Eickhoff et al., 2011; Eickhoff et al., 2010].”
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“Contrasts and conjunctions (i.e., overlaps) between ALE meta-analyses were all done using ten thousand permutations to derive p-values (Eickhoff et al., 2011).”
Definition and characterization of an extended social-affective default network
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“Second, hierarchical clustering is an approach in which clusters are formed by first linking the seeds that were more similar to each other and then performing a complete linkage of the clusters (Eickhoff et al. 2011).”
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“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].”
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“j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / y n i m g (Johansen-Berg et al., 2004), resting state functional connectivity (Zhang and Li, 2012), and meta-analytic connectivity modeling (Cauda et al., 2012; Eickhoff et al., 2011).”
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ICA model order selection of task co-activation networks
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“…co-activation networks, but also functional brain networks derived from meta-analytic connectivity modeling (Laird et al., 2009; Eickhoff et al., 2010; Robinson et al., 2010; Zald et al., 2012) and connectivity-based parcellation (Eickhoff et al., 2011; Bzdok et al., 2013; Cieslik et al., 2013).”
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“Conjunction and contrast analyses were computed to examine overlapping and distinct brain regions for the two ALE maps that included both active and passive tasks for symbolic and nonsymbolic number processing (Eickhoff et al., 2011).”
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Gray matter asymmetries in aging and neurodegeneration: a review and meta-analysis.
Minkova, L., Habich, A., Peter, J., Kaller, C. P., Eickhoff, S. B., & Klöppel, S. (2017) · Human Brain Mapping · doi:10.1002/hbm.23772 · Cited by 194
“A subtraction analysis [Eickhoff et al., 2011] between the original and the flipped foci was then performed to compute the voxel-wise difference between the two ALE maps, respectively.”
MethodSelf-citationClustering of Resting State Networks
Lee, M. H., Hacker, C. D., Snyder, A. Z., Corbetta, M., Zhang, D., Leuthardt, E. C., & Shimony, J. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040370 · Cited by 203
“The SMA and pre-SMA division has also been seen by others [52–54].”
Stawarczyk, D., & D’Argembeau, A. (2015) · Human Brain Mapping · doi:10.1002/hbm.22818 · Cited by 203
“These analyses were computed using the voxel-wise minimum value of the input ALE images such that only significant clusters in the individual analyses were included [Eickhoff et al., 2011].”
Left inferior parietal lobe engagement in social cognition and language
Bzdok, D., Hartwigsen, G., Reid, A., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.02.024 · Cited by 190
Self-citationChuan-Peng, H., Di, X., Eickhoff, S. B., Zhang, M., Peng, K., Guo, H., & Sui, J. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.12.003 · Cited by 188
Self-citationYaple, Z. A., Stevens, W. D., & Arsalidou, M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.074 · Cited by 190
“Permutations at the contrast level are used to correct for variability among studies (Eickhoff et al., 2011).”
BackgroundNetworks of task co-activations
Laird, A. R., Eickhoff, S. B., Rottschy, C., Bzdok, D., Ray, K. L., & Fox, P. T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.073 · Cited by 168
“In light of the as-yet limited number of studies combining MACM and resting state functional connectivity analyses (Cieslik et al., 2013; Eickhoff et al., 2011; Jakobs et al., 2012; Reetz et al., 2012; Rottschy et al., in press), the above results obtained for the V5 region that was shown to be…”
InfluentialSelf-citationSubspecialization in the human posterior medial cortex
Bzdok, D., Heeger, A., Langner, R., Laird, A. R., Fox, P. T., Palomero‐Gallagher, N., Vogt, B. A., Zilles, K., & Eickhoff, S. B. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.009 · Cited by 180
“05; Eickhoff et al., 2011; Nickl-Jockschat et al., 2012).”
MethodInfluentialSelf-citationZald, D. H., McHugo, M., Ray, K. L., Glahn, D. C., Eickhoff, S. B., & Laird, A. R. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs308 · Cited by 184
“Contrast Analysis To determine whether regions showed significantly different levels of coactivation with the mOFC versus lOFC, we performed a contrast analysis that computed the voxel-wise difference between ALE scores for the 2 sets of coactivation foci (Eickhoff et al. 2011).”
MethodInfluentialSelf-citationNeuroanatomical and neurofunctional markers of social cognition in autism spectrum disorder
Patriquin, M. A., DeRamus, T. P., Libero, L. E., Laird, A. R., & Kana, R. K. (2016) · Human Brain Mapping · doi:10.1002/hbm.23288 · Cited by 179
“Analyses of activation peaks were performed using activation likelihood estimation via GingerALE software developed by the Human Brain Mapping Project (ALE) [Eickhoff et al., 2009, 2011, 2012; Turkeltaub et al., 2012].”
MethodSelf-citationMeta-Analysis in Human Neuroimaging: Computational Modeling of Large-Scale Databases
Fox, P. T., Lancaster, J. L., Laird, A. R., & Eickhoff, S. B. (2014) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062012-170320 · Cited by 176
“Eickhoff and colleagues (2011) applied connectivity-based parcellation to BrainMap data for the same brain regions (SMA and preSMA) and obtained virtually identical borders (Eickhoff et al. 2011).”
BackgroundSelf-citationHoffstaedter, F., Grefkes, C., Caspers, S., Roski, C., Palomero‐Gallagher, N., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2014) · Human Brain Mapping · doi:10.1002/hbm.22363 · Cited by 164
“…of different methods should be less susceptible to drawbacks inherent in each individual method (MACM, resting-state analysis), the conjunction analyses should moreover represent a conservative approach to FC [Eickhoff et al., 2011; Jakobs et al., 2012; Reetz et al., 2012; Smith et al., 2009].”
MethodSelf-citationThe role of the putamen in language: a meta-analytic connectivity modeling study
Viñas‐Guasch, N., & Wu, Y. J. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1450-y · Cited by 169
Functional segregation of the human dorsomedial prefrontal cortex
Eickhoff, S. B., Laird, A. R., Fox, P. T., Bzdok, D., & Hensel, L. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhu250 · Cited by 159
“All experiments contributing to either analysis were then pooled and randomly divided into 2 groups of the same size as the 2 original sets of experiments defined by activation in the first or second cluster (Eickhoff et al. 2011).”
BackgroundMethodSelf-citationALE meta‐analysis reveals dissociable networks for affective and discriminative aspects of touch
Morrison, I. (2016) · Human Brain Mapping · doi:10.1002/hbm.23103 · Cited by 164
“To discover clusters with a higher likelihood of activation by affective touch compared with discriminative touch, a contrast between the affective and discriminative ALE maps was performed [Eickhoff et al., 2011].”
ResultTimmers, I., Park, A. L., Fischer, M. D., Kronman, C. A., Heathcote, L. C., Hernandez, J. M., & Simons, L. E. (2018) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2018.00289 · Cited by 154
“In addition to examining convergence across all empathy-related studies, we performed several contrasts to test for statistical differences across convergence (Eickhoff et al., 2011).”
Definition and characterization of an extended multiple-demand network
Camilleri, J. A., Müller, V. I., Fox, P. M., Laird, A. R., Hoffstaedter, F., Kalenscher, T., & Eickhoff, S. B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.020 · Cited by 162
“This was achieved through an agglomerative approach in which clusters initially formed by individual regions that are subsequently merged according to their similarity using standardized Euclidean distances and Ward's incremental sum of squares method (Eickhoff et al., 2011; Timm, 2002).”
Self-citationBzdok, D. (2017) · HAL · Cited by 160
Self-citationPain anticipation: An activation likelihood estimation meta‐analysis of brain imaging studies
Palermo, S., Benedetti, F., Costa, T., & Amanzio, M. (2014) · Human Brain Mapping · doi:10.1002/hbm.22727 · Cited by 159
“To explore the BD associated with the FC network, we added to the ALE analysis a MACM. r 1656 r Importantly, the MACM allows meta-analyses, which instead of pooling studies that share a common experimental design, look for global coactivation patterns across a diverse range of tasks, thus responding to the question “f…”
MethodInfluentialAutomated regional behavioral analysis for human brain images
Lancaster, J. L., Laird, A. R., Eickhoff, S. B., Martinez, M. J., Fox, P. M., & Fox, P. T. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2012.00023 · Cited by 150
“…connected regions using connectivity determined by stimulation based TMS/PET (Fox et al., 1997, 2006; Paus et al., 1997; Laird et al., 2008) and by meta-analytic connectivity modeling (MACM) (Fox et al., 1998; Robinson et al., 2010; Cauda et al., 2011; Eickhoff et al., 2011; Torta and Cauda, 2011).”
MethodSelf-citationHartwigsen, G., Neef, N. E., Camilleri, J. A., Margulies, D. S., & Eickhoff, S. B. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy049 · Cited by 167
Self-citationLangner, R., Leiberg, S., Hoffstaedter, F., & Eickhoff, S. B. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.04.022 · Cited by 156
Self-citationMeta-analysis of functional brain imaging in specific phobia
Ipser, J. C., Singh, L., & Stein, D. J. (2013) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.12055 · Cited by 142
“The SPM generated from these groups are subsequently subtracted from one another to yield ALE difference maps, with the difference statistics averaged across 10000 iterations to obtain the null distribution ALE SPM used to determine the likelihood of the foci observed in the review.(24) Although Britton et al.”
MethodFunctional connectivity mapping of regions associated with self‐ and other‐processing
Murray, R. J., Debbané, M., Fox, P. T., Bzdok, D., & Eickhoff, S. B. (2014) · Human Brain Mapping · doi:10.1002/hbm.22703 · Cited by 152
“Significance was established using a binomial test [P< 0.001; Eickhoff et al., 2011; Laird et al., 2009].”
MethodInfluentialSelf-citationL1 and L2 processing in the bilingual brain: A meta-analysis of neuroimaging studies
Liu, H., & Cao, F. (2016) · Brain and Language · doi:10.1016/j.bandl.2016.05.013 · Cited by 178
“, Set A: 13 early bilingual studies, 27 late bilingual studies) can be corrected by this approach (Eickhoff et al., 2011): Simulated data is created by pooling the foci data sets and randomly dividing them into two groups of the same size as the original data sets (e.”
Tahmasian, M., Eickhoff, S. B., Giehl, K., Schwartz, F., Herz, D. M., Drzezga, A. E., van Eimeren, T., Laird, A. R., Fox, P. T., Khazaie, H., et al. (2017) · Cortex · doi:10.1016/j.cortex.2017.03.016 · Cited by 138
Self-citationArioli, M., Cattaneo, Z., Ricciardi, E., & Canessa, N. (2021) · Human Brain Mapping · doi:10.1002/hbm.25570 · Cited by 151
“For each comparison, a conjunction image was created, using the voxel-wise minimum value of the included ALE images, to display the similarity between datasets (Eickhoff et al., 2011).”
MethodPawliczek, C. M., Derntl, B., Kellermann, T., Kohn, N., Gur, R. C., & Habel, U. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.104 · Cited by 138
“A recent meta-analysis on pre-SMA and SMA, including coordinates very similar to the regions found in our study, suggested that the pre-SMA is more involved in complex or ‘cognitive’ aspects of motor inhibition, for example (successful) motor inhibition (Eickhoff et al., 2011).”
Neural correlates of formal thought disorder: An activation likelihood estimation meta‐analysis
Wensing, T., Cieslik, E. C., Müller, V. I., Hoffstaedter, F., Eickhoff, S. B., & Nickl‐Jockschat, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23706 · Cited by 135
“Clusters of overlap between MACM and RS-connectivity analyses for the left vp-MTG (A), left STG (B), and left dp-MTG (C) seed region.”
MethodInfluentialSelf-citationA supramodal role of the basal ganglia in memory and motor inhibition: meta-analytic evidence
Guo, Y., Schmitz, T. W., Mur, M., Ferreira, C. S., & Anderson, M. C. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.11.033 · Cited by 133
“The limited number of studies should not exert a strong influence on the results (Eickhoff et al., 2009, 2011).”
BackgroundMethodTahmasian, M., Rosenzweig, I., Eickhoff, S. B., Sepehry, A. A., Laird, A. R., Fox, P. T., Morrell, M. J., Khazaie, H., & Eickhoff, C. R. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.03.026 · Cited by 134
Self-citationKim, H. (2014) · Hippocampus · doi:10.1002/hipo.22387 · Cited by 133
“A comparison (subtraction) analysis of two ALE results was conducted using the algorithm in Eickhoff et al. (2011).”
MethodKalmady, S. V., Greiner, R., Agrawal, R., Shivakumar, V., Narayanaswamy, J. C., Brown, M. R. G., Greenshaw, A. J., Dursun, S. M., & Venkatasubramanian, G. (2019) · Schizophrenia · doi:10.1038/s41537-018-0070-8 · Cited by 131
Gu, R., Huang, W., Camilleri, J. A., Xu, P., Wei, P., Eickhoff, S. B., & Feng, C. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.02.017 · Cited by 127
“This is achieved through an agglomerative approach in which clusters initially formed by individual regions are subsequently merged according to their similarity using standardized Euclidean distances and Ward's incremental sum of squares method (Eickhoff et al., 2011; Timm and Kruse, 2002).”
Self-citationWeiss, P. H., Ubben, S. D., Kaesberg, S., Kalbe, E., Kessler, J., Liebig, T., & Fink, G. R. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0925-3 · Cited by 123
Grace, S. A., Rossell, S. L., Heinrichs, M., Kordsachia, C., & Labuschagne, I. (2018) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2018.05.031 · Cited by 120
“Notably, there are still no methods to correct for ALE difference maps for multiple comparisons (Eickhoff et al., 2011), and given the small number of papers in the sub-sample analyses (8–24 experiments), an uncorrected threshold of p < 0.”
InfluentialNeurofunctional topography of the human hippocampus
Robinson, J. L., Barron, D. S., Kirby, L. A. J., Bottenhorn, K. L., Hill, A. C., Murphy, J. E., Katz, J. S., Salibi, N., Eickhoff, S. B., & Fox, P. T. (2015) · Human Brain Mapping · doi:10.1002/hbm.22987 · Cited by 125
“Functional magnetic resonance imaging (fMRI) We performed high resolution resting state fMRI to further characterize the neurofunctional segments identified by CBP. Twenty-three healthy individuals (21 righthanded, 7 males, 16 females, M 6 SD 5 21.17 6 1.44)1 were 1For both resting state and DTI, we carried out analys…”
MethodInfluentialSelf-citationA meta-analysis of changes in brain activity in clinical depression
Palmer, S. M., Crewther, S. G., Carey, L. M., & The START Project Team (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.01045 · Cited by 116
Balsters, J. H., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2014) · Human Brain Mapping · doi:10.1002/hbm.22392 · Cited by 113
“MACM assesses brain-wise co-activation patterns of an anatomical region across a large number of databased neuroimaging results [Eickhoff et al., 2011; Laird et al., 2009a].”
MethodInfluentialSelf-citationApšvalka, D., Ferreira, C. S., Schmitz, T. W., Rowe, J. B., & Anderson, M. C. (2022) · Nature Communications · doi:10.1038/s41467-021-27926-w · Cited by 121
“We then submitted the obtained thresholded ALE maps from the three individual meta-analyses to a meta-analytic contrast analysis (Eickhoff et al., 2011), which produced the conjunction of the Stop > Go & No-Think > Think contrasts.”
MethodSocial exclusion reliably engages the default network: A meta-analysis of Cyberball
Mwilambwe-Tshilobo, L., & Spreng, R. N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117666 · Cited by 116
Affordance processing in segregated parieto-frontal dorsal stream sub-pathways
Sakreida, K., Alkhasli, I., Thill, S., Menz, M. M., Jirak, D., Eickhoff, C. R., Tom Ziemke, B., Eickhoff, S. B., Borghi, A. M., & Binkofski, F. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.07.032 · Cited by 110
Self-citationIndividual parcellation of resting fMRI with a group functional connectivity prior
Chong, M., Bhushan, C., Joshi, A. A., Choi, S., Haldar, J. P., Shattuck, D. W., Spreng, R. N., & Leahy, R. M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.054 · Cited by 109
“Identification of contiguous areas of cortex that exhibit similar network connectivity can be used to define a functional parcellation (Raichle et al., 2001; Sporns et al., 2005, Smith et al., 2013, Kim et al., 2010; Eickhoff et al., 2011; Eickhoff et al., 2015).”
Wang, J., Xie, S., Guo, X., Becker, B., Fox, P. T., Eickhoff, S. B., & Jiang, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23488 · Cited by 106
“05 corrected for multiple comparisons using FDR method) [Eickhoff et al., 2011].”
MethodInfluentialSelf-citationHuman pulvinar functional organization and connectivity
Barron, D. S., Eickhoff, S. B., Clos, M., & Fox, P. T. (2015) · Human Brain Mapping · doi:10.1002/hbm.22781 · Cited by 107
“Connectivity-based parcellation approaches have been applied to diffusion tractography [Jbabdi et al., 2009], resting state functional connectivity [Kim et al., 2010], and MACM [Eickhoff et al., 2011] to define a region’s functional anatomy.”
BackgroundMethodInfluentialSelf-citationRuan, J., Bludau, S., Palomero‐Gallagher, N., Caspers, S., Mohlberg, H., Eickhoff, S. B., Seitz, R. J., & Amunts, K. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1738-6 · Cited by 103
Self-citationAndrzejewski, J. A., Greenberg, T., & Carlson, J. M. (2019) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-019-00747-7 · Cited by 100
Witteman, J., van Heuven, V. J., & Schiller, N. O. (2012) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2012.07.026 · Cited by 105
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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