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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
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“On these, tractography algorithms may be employed to derive information about the most likely fiber tract direction from particular seed or the course and strength of pathways connecting two different regions of the cortex (Behrens et al., 2003; Ramnani et al., 2004).”
MethodCan cognitive processes be inferred from neuroimaging data?
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Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network
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BackgroundEickhoff, S. B., Laird, A. R., Grefkes, C., Wang, L. E., Zilles, K., & Fox, P. T. (2009) · Human Brain Mapping · doi:10.1002/hbm.20718 · Cited by 1,924 · cited 2×
“…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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Self-citationThe role of the corpus callosum in language network connectivity in children
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Microstructural grey matter parcellation and its relevance for connectome analyses
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“resting-state functional connectivity (Fox and Raichle, 2007), co-activation patterns (Cieslik et al., in press; Eickhoff et al., 2011) or diffusion-based structural connectivity using tractography algorithms (Klein et al.”
Self-citationGerchen, M. F., & Kirsch, P. (2017) · Human Brain Mapping · doi:10.1002/hbm.23762 · Cited by 47
“While coactivation patterns over several experiments can be used to characterize cortical modules [e.g., Eickhoff et al., 2011], conclusions from a single experiment could be prone to errors.”
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Meta‐analytic evidence for altered mesolimbic responses to reward in schizophrenia
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“Meta-analytic connectivity modeling (MACM: Eickhoff et al., 2011) is a complementary means of estimating functional connectivity from contrast maps rather than correlations between regional time series.”
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Connectivity and functional profiling of abnormal brain structures in pedophilia
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MethodInfluentialSelf-citationANIMA: A data-sharing initiative for neuroimaging meta-analyses
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Self-citationMapping repetition suppression of the P50 evoked response to the human cerebral cortex
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“For the functional characterization we proceeded as follows: using the BrainMap database (http://www.brainmap.org), all experiments that reported at least one focus of activation within each VOI volume were identified (Eickhoff et al., 2009, 2011).”
Self-citationMachine learning for bioinformatics and neuroimaging
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“…2012; Tucholka et al., 2008), K-means clustering (Flandin et al., 2002; Kahnt, Chang, Park, Heinzle, & Haynes, 2012; Thomas Yeo et al., 2011), hierarchical clustering (Eickhoff et al., 2011; Michel et al., 2012; Orban et al., 2014), and spectral clustering (Chen et al., 2013; Thirion et al., 2006).”
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The functional neural architecture of dysfunctional reward processing in autism
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“In order to further characterize the functional properties of the identified brain regions, we used the clusters resulting from our ALE meta-analyses as seed regions for two different kinds of functional connectivity analysis, task-independent (resting-state) and task-based functional connectivity (based on meta-analy…”
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“Differences in coactivation between STN and GPi MACMs were examined by first computing the voxel-wise difference between ALE maps [see Eickhoff et al., 2011].”
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Trujillo, J. P., Gerrits, N. J. H. M., Vriend, C., Berendse, H. W., van den Heuvel, O. A., & van der Werf, Y. D. (2015) · Human Brain Mapping · doi:10.1002/hbm.22873 · Cited by 40
A neurocognitive model of perceptual decision‐making on emotional signals
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“However, GingerALE accounts for an unbalanced number of studies that are subjected to certain contrasts by means of data simulation and permutation (Eickhoff et al., 2011).”
BackgroundMethodInfluentialThe emerging neuroscience of social punishment: Meta-analytic evidence
Bellucci, G., Camilleri, J. A., Iyengar, V., Eickhoff, S. B., & Krüeger, F. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.04.011 · Cited by 40
“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).”
MethodSelf-citationLocalizing the human brain response to olfactory stimulation: A meta-analytic approach
Torske, A., Koch, K., Eickhoff, S. B., & Freiherr, J. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.12.035 · Cited by 48
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Co-activation based parcellation of the human frontal pole
Ray, K. L., Zald, D. H., Bludau, S., Riedel, M. C., Bzdok, D., Yanes, J. A., Falcone, K. E., Amunts, K., Fox, P. T., Eickhoff, S. B., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.072 · Cited by 35
“To further investigate the task-based functional connectivity of the FP, we conducted co-activation based parcellation (Eickhoff et al., 2011; Johansen-Berg et al., 2004) in conjunctionwithMACM.”
Self-citationMeta-analytic connectivity modeling of the human superior temporal sulcus
Erickson, L. C., Rauschecker, J. P., & Turkeltaub, P. E. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1215-z · Cited by 38
“We performed a series of ALE subtraction analyses (Eickhoff et al. 2011) to identify the brain areas that were more con-”
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“For the conjunction analysis, all the ALE maps were thresholded, and the conjunction map was obtained by simply identifying the intersection between the adult and adolescent ALE results (Eickhoff et al., 2011; Wu et al., 2021).”
MethodSelf-citationVarikuti, D. P., Hoffstaedter, F., Genon, S., Schwender, H. R., Reid, A., & Eickhoff, S. B. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1286-x · Cited by 36
“Both networks were derived from previous meta-analytic studies, which used anatomical likelihood estimation (ALE; (Eickhoff et al. 2009; Eickhoff et al. 2012)) to identify regions that are robustly activated across studies, for specific task paradigms.”
MethodSelf-citationJackson, R. L., Bajada, C. J., Ralph, M. A. L., & Cloutman, L. L. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz079 · Cited by 34
“Information in different modalities may allow parcellations to capture distinct aspects of functional organization, including organization at differing levels of granularity (Eickhoff et al. 2011; Cloutman and Lambon Ralph 2012; Eickhoff et al. 2018).”
BackgroundEffect of corpus callosum agenesis on the language network in children and adolescents
Bartha, L., Schwartz, E., Kollndorfer, K., Fischmeister, F. P. S., Novak, A., Langs, G., Werneck, H., Prayer, D., Seidl, R., & Kasprian, G. J. (2021) · Brain Structure and Function · doi:10.1007/s00429-020-02203-6 · Cited by 36
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“To perform each discriminability analysis, all coordinates of the experiments contributing to the meta-analysis were pooled together and randomly divided into two groups of the same size as the original contrasted sets of data (Eickhoff et al., 2011).”
Speech perception in autism spectrum disorder: An activation likelihood estimation meta-analysis
Tryfon, A., Foster, N. E. V., Sharda, M., & Hyde, K. L. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.10.025 · Cited by 34
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“Secondly, contrast analyses (Eickhoff et al., 2011) were conducted between the think/no-think and emotion regulation tasks.”
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“A contrast meta-analysis can detect clusters with stronger BOLD signals in one condition than another, and a conjunction meta-analysis can detect the overlapping co-activated brain regions between two conditions (Eickhoff et al., 2011).”
BackgroundMethodInfluentialMeta-analytic connectivity modeling of the left and right inferior frontal gyri
Bulut, T. (2022) · Cortex · doi:10.1016/j.cortex.2022.07.003 · Cited by 31
Probabilistic electrical stimulation mapping of human medial frontal cortex
Trevisi, G., Eickhoff, S. B., Chowdhury, F., Jha, A., Rodionov, R., Nowell, M., Miserocchi, A., McEvoy, A. W., Nachev, P., & Diehl, B. (2018) · Cortex · doi:10.1016/j.cortex.2018.06.015 · Cited by 30
“2011(Eickhoff et al., 2011) was then used to derive voxel-wise z score maps that reflect the voxelwise association between stimulation and each specific (rather than a random) behavioural effect.”
Self-citationFunctional connectivity modeling of consistent cortico-striatal degeneration in Huntington's disease
Dogan, I., Eickhoff, C. R., Fox, P. T., Laird, A. R., Schulz, J. B., Eickhoff, S. B., & Reetz, K. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.02.018 · Cited by 31
“The experiments contributing to either analysis were then pooled and randomly divided into two groups of the same size as the sets of contrasted experiments (Eickhoff et al., 2011).”
BackgroundMethodInfluentialSelf-citationA Meta-Analytic Study of the Neural Systems for Auditory Processing of Lexical Tones
Kwok, V. P. Y., Guo, D., Yakpo, K., Matthews, S., Fox, P. T., Li, P., & Tan, L. H. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00375 · Cited by 31
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“Furthermore, while the term “co-activation pattern/map” is commonly used to describe spatial patterns identified by event-detection approaches, the term co-activation pattern (CAP) also refers to a specific category of event detection approaches (Liu, Zhang, Chang, and Duyn, 2018), and the term co-activation has also…”
MethodLi, Y.-Z., & Bi, H.-Y. (2022) · Neuroscience and Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104650 · Cited by 30
Aging and response conflict solution: behavioural and functional connectivity changes
Langner, R., Cieslik, E. C., Behrwind, S. D., Roski, C., Caspers, S., Amunts, K., & Eickhoff, S. B. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0758-0 · Cited by 31
“…reverse inference, we compared the probabilities of a particular behavioural domain being present given activation in one or the other seed Brain Struct Funct (2015) 220:1739–1757 1745 (Bzdok et al. 2013b; Cieslik et al. 2013b; Clos et al. 2013; Eickhoff et al. 2011a; Rottschy et al. 2013).”
Self-citationMultimodal connectivity mapping of the human left anterior and posterior lateral prefrontal cortex
Reid, A., Bzdok, D., Langner, R., Fox, P. T., Laird, A. R., Amunts, K., Eickhoff, S. B., & Eickhoff, C. R. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-1060-5 · Cited by 32
“The time-series data of each voxel were processed as follows (Eickhoff et al. 2011; Fox et al. 2009; Jakobs et al. 2012; Weissenbacher et al. 2009; Zu Eulenburg et al. 2012): in order to reduce spurious correlations, variance that could be explained by the following nuisance variables was removed—(a) the six motion pa…”
Self-citationWertheim, J., & Ragni, M. (2018) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01311 · Cited by 30
“In conjunction and contrast analyses, the ALE maps of two conditions are examined in activation likelihood for their overlap and distinctness, respectively (Eickhoff et al., 2011).”
BackgroundDing, R., Ren, J., Li, S., Zhu, X., Zhang, K., & Luo, W. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107286 · Cited by 30
Imaging Happiness: Meta Analysis and Review
Tanzer, J. R., & Weyandt, L. L. (2019) · Journal of Happiness Studies · doi:10.1007/s10902-019-00195-7 · Cited by 29
Distinct neural networks subserve placebo analgesia and nocebo hyperalgesia
Fu, J., Wu, S., Liu, C., Camilleri, J. A., Eickhoff, S. B., & Yu, R. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117833 · Cited by 30
Self-citationReappraisal and empathic perspective-taking – More alike than meets the eyes
Morawetz, C., Berboth, S., Kohn, N., Jackson, P. L., & Jauniaux, J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119194 · Cited by 30
Robust brain parcellation using sparse representation on resting-state fMRI
Zhang, Y., Caspers, S., Fan, L., Yong Fan, A., Song, M., Liu, C., Mo, Y., Roski, C., Eickhoff, S. B., Amunts, K., et al. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0874-x · Cited by 27
“The MFC, including the supplementary (SMA) and pre-supplementary motor area (pre-SMA), was manually drawn in the MNI152 brain image, extending from y = - 22 to y = 30, with a short distance above the cingulate sulcus (Johansen-Berg et al. 2004; Eickhoff et al. 2011).”
MethodResultSelf-citationSi, R., Rowe, J. B., & Zhang, J. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118468 · Cited by 28
Feng, C., Gu, R., Li, T., Wang, L., Zhang, Z., Luo, W., & Eickhoff, S. B. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.09.041 · Cited by 34
Self-citationTan, Y., Yan, R., Gao, Y., Zhang, M., & Northoff, G. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119500 · Cited by 34
“It contains the shared regions that exist in both ALE images (Eickhoff et al., 2011).”
BackgroundBalgova, E., Diveica, V., Jackson, R. L., & Binney, R. J. (2024) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2024.108904 · Cited by 28
Brain structure changes associated with sexual orientation
Votinov, M., Goerlich, K. S., Puiu, A. A., Smith, E. S., Nickl‐Jockschat, T., Derntl, B., & Habel, U. (2021) · Scientific Reports · doi:10.1038/s41598-021-84496-z · Cited by 32
Li, S., Krüeger, F., Camilleri, J. A., Eickhoff, S. B., & Qu, C. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118731 · Cited by 30
Self-citationJoint Multi-modal Parcellation of the Human Striatum: Functions and Clinical Relevance
Liu, X., Eickhoff, S. B., Hoffstaedter, F., Genon, S., Caspers, S., Reetz, K., Dogan, I., Eickhoff, C. R., Ji, C., Caspers, J., et al. (2020) · Neuroscience Bulletin · doi:10.1007/s12264-020-00543-1 · Cited by 25
Self-citationKellermann, T. S., Caspers, S., Fox, P. T., Zilles, K., Roski, C., Laird, A. R., Turetsky, B. I., & Eickhoff, S. B. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.01.046 · Cited by 25
“The time-series data of each voxel in the brain was then processed as follows (cf. Eickhoff et al., 2011; Fox et al., 2009; Weissenbacher et al., 2009; zu Eulenburg et al., 2012): In order to reduce spurious correlations , variance that could be explained by the following nuisance variables was…”
BackgroundMethodSelf-citationCortical parcellation based on structural connectivity: A case for generative models
Tittgemeyer, M., Rigoux, L., & Knösche, T. R. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.077 · Cited by 24
“1 Note that this functional relationship is often referred to as effective connectivity, that is the c term functional connectivity usually refers to the temporal correlation between functional brain s connectivity where correlation is defined across subject (Eickhoff et al., 2011).”
BackgroundThe effect of task complexity on the neural network for response inhibition: An ALE meta-analysis
Aziz-Safaie, T., Müller, V. I., Langner, R., Eickhoff, S. B., & Cieslik, E. C. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105544 · Cited by 25
“…the other hand, has been associated with selecting the appropriate motor response from different alternatives (for a review, see Nachev et al., 2008) and is strongly related to tasks requiring cognitive control of motor responses, such as task switching or word generation (Eickhoff et al., 2011).”
BackgroundSelf-citationConnectivity Profile and Function of Uniquely Human Cortical Areas
Bryant, K., Camilleri, J. A., Warrington, S., Freches, G. B., Sotiropoulos, S. N., Jbabdi, S., Eickhoff, S. B., & Mars, R. B. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.2017-24.2025 · Cited by 25
Self-citationHuang, K., Kang, Y., Wu, Z., Wang, Y., Cai, S., & Huang, L. (2021) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2021.110322 · Cited by 23
Arioli, M., Cattaneo, Z., Parimbelli, S., & Canessa, N. (2023) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsad003 · Cited by 27
“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).”
MethodYang, Y., Fan, L., Chu, C., Zhuo, J., Wang, J., Fox, P. T., Eickhoff, S. B., & Jiang, T. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.027 · Cited by 24
“Significance was established using a binomial test (p < 0.05; Eickhoff et al., 2011; Nickl-Jockschat et al., 2012).”
InfluentialSelf-citationWojtasik, M., Bludau, S., Eickhoff, S. B., Mohlberg, H., Gerboga, F., Caspers, S., & Amunts, K. (2020) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2020.00002 · Cited by 24
Self-citationBasile, G. A., Tatti, E., Bertino, S., Milardi, D., Genovese, G., Bruno, A., Muscatello, M. R. A., Ciurleo, R., Cerasa, A., Quartarone, A., et al. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02781-9 · Cited by 24
“…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 ).”
BackgroundBulut, T. (2023) · Brain and Behavior · doi:10.1002/brb3.3046 · Cited by 26
“It should be noted that the ALE subtraction analysis applies permutation significance testing, which controls for differences in the number of papers on each side of the comparison (Eickhoff et al., 2011; Erickson et al., 2017).”
BackgroundMethodBrain responses to social punishment: a meta-analysis
Zinchenko, O. O. (2019) · Scientific Reports · doi:10.1038/s41598-019-49239-1 · Cited by 25
Functional differentiation in the human ventromedial frontal lobe: A data‐driven parcellation
Chase, H. W., Grace, A. A., Fox, P. T., Phillips, M. L., & Eickhoff, S. B. (2020) · Human Brain Mapping · doi:10.1002/hbm.25014 · Cited by 21
“All experiments contributing to the two contrasted clusters were 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).”
BackgroundInfluentialSelf-citationPham, T.-P.-T., Rosenthal, M. P., & Diamond, J. J. (1999) · PubMed · Cited by 23
Emotion regulation: From neural circuits to a transdiagnostic perspective
Morawetz, C., Hemetsberger, F. J., Laird, A. R., & Kohn, N. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105960 · Cited by 27
“…analysis, the experiments were divided into two groups: the first group (or pseudo-cluster) had a number of experiments equal to that of the exisiting RDoC domain, and the second group had a number of experiments equal to the sum of emotion regulation (Eickhoff et al., 2011; Rottschy et al., 2012).”
Self-citationLangner, R., Eickhoff, S. B., & Bilalić, M. (2018) · Brain and Cognition · doi:10.1016/j.bandc.2018.09.007 · Cited by 24
Self-citationKenzie, J. M., Ben-Shabat, E., Lamp, G., Dukelow, S. P., & Carey, L. M. (2017) · Brain Imaging and Behavior · doi:10.1007/s11682-017-9756-1 · Cited by 22
Functional Parcellation of the Speech Production Cortex
Tourville, J. A., Nieto-Castañón, A., Heyne, M., & Guenther, F. H. (2019) · Journal of Speech Language and Hearing Research · doi:10.1044/2019_jslhr-s-csmc7-18-0442 · Cited by 22
“Approaches based on task-based functional magnetic resonance imaging (fMRI) co-activation (e.g., Eickhoff et al., 2011) are more promising but, thus far, have relied on meta-analytic methods that suffer from relying on reported activity peak locations rather than full-brain activation patterns and…”
MethodSocial cognition in the blind brain: A coordinate‐based meta‐analysis
Arioli, M., Ricciardi, E., & Cattaneo, Z. (2020) · Human Brain Mapping · doi:10.1002/hbm.25289 · Cited by 25
ATPP: A Pipeline for Automatic Tractography-Based Brain Parcellation
Li, H., Fan, L., Zhuo, J., Wang, J., Zhang, Y., Yang, Z., & Jiang, T. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00035 · Cited by 22
“…et al., 2003; Cohen M. X. et al., 2008; Moreno-Dominguez et al., 2014), functional (Cohen M. X. et al., 2008; Kim et al., 2010; Craddock et al., 2012), or meta-analytic connectivity (Eickhoff et al., 2011; Yang et al., 2016), as well as genetic correlation (Chen et al., 2012; Cui et al., 2016).”
BackgroundKim, H. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.04.007 · Cited by 23
Action and emotion perception in Parkinson’s disease: A neuroimaging meta-analysis
Arioli, M., Cattaneo, Z., Rusconi, M. L., Blandini, F., & Tettamanti, M. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103031 · Cited by 22
White matter substrates of functional connectivity dynamics in the human brain
Basile, G. A., Bertino, S., Nozais, V., Bramanti, A., Ciurleo, R., Anastasi, G., Milardi, D., & Cacciola, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119391 · Cited by 21
Disentangling emotional signals in the brain: an ALE meta-analysis of vocal affect perception
Mauchand, M., & Zhang, S. (2022) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-022-01030-y · Cited by 17
“Additionally, contrast analyses (stimuli type and task type) were performed by subtracting previously thresholded maps from one another (Eickhoff et al., 2011).”
MethodNeural signatures of lexical tone reading
Kwok, V. P. Y., Wang, T., Chen, S., Yakpo, K., Zhu, L., Fox, P. T., & Tan, L. H. (2014) · Human Brain Mapping · doi:10.1002/hbm.22629 · Cited by 21
“All MNI coordinates were converted into Talairach space using the icbm2tal transform [Lancaster et al., 2007] implemented in GingerALE software package [Eickhoff et al., 2009, 2011].”
MethodSelf-citationThe role of dorsal premotor cortex in mental rotation: A transcranial magnetic stimulation study
Cona, G., Panozzo, G., & Semenza, C. (2017) · Brain and Cognition · doi:10.1016/j.bandc.2017.06.002 · Cited by 21
Amanzio, M., & Palermo, S. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00969 · Cited by 23
“Outstandingly, the operculum exhibited changes in coupling for the period of the nociceptive input, as proved by pinpoint differences and decreased connectivity with the basal ganglia, depending on whether a nocebo context has been provided or not (Ellerbrock et al., 2015).”
BackgroundResultInfluentialUnger, N., Haeck, M., Eickhoff, S. B., Camilleri, J. A., Dickscheid, T., Mohlberg, H., Bludau, S., Caspers, S., & Amunts, K. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1087026 · Cited by 20
“Based on the BrainMap database, area-specific functions of Op5, Op6, and Op7 were analyzed.”
BackgroundMethodInfluentialSelf-citationMattavelli, G. C., Gorrino, I., Tornaghi, D., & Canessa, N. (2023) · Cortex · doi:10.1016/j.cortex.2023.10.008 · Cited by 21
Tench, C. R., Tănăsescu, R., Auer, D. P., Cottam, W. J., & Constantinescu, C. S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106735 · Cited by 19
“The correction for many correlated statistical tests is a problem for contrast meta-analysis that has not yet been addressed [12]; indeed contrast meta-analysis has previously been performed without any correction [13], which will inevitably lead to false positive results.”
BackgroundMethodTuresky, T. K., Turkeltaub, P. E., & Eden, G. F. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00238 · Cited by 22
“To identify brain areas likely to be activated in both groups, and brain areas that differed between the two groups, we generated a conjunction map as well as two contrast ALE maps (older adult > young adult and young adult > older adult; Eickhoff et al., 2011).”
BackgroundMethodInfluentialCognitive reappraisal of food craving and emotions: a coordinate-based meta-analysis of fMRI studies
Gerosa, M., Canessa, N., Morawetz, C., & Mattavelli, G. C. (2023) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsad077 · Cited by 19
“…down-regulation’ meta-analyses, meant as a ‘meta-contrast’ between the two resulting meta-analytic maps) were obtained by directly subtracting one input image from the other, with significant ALE subtraction scores converted into Z scores to aid their interpretation (Eickhoff et al ., 2011).”
MethodMultimodal Brain Parcellation Based on Functional and Anatomical Connectivity
Wang, C., Ng, B., & Garbi, R. (2018) · Brain Connectivity · doi:10.1089/brain.2017.0576 · Cited by 17
“…k-means clustering (Flandin et al., 2002; Kahnt et al., 2012), mixture models (Golland et al., 2007; Yeo et al., 2011), and hierarchical clustering (Eickhoff et al., 2011; Michel et al., 2012; Orban et al., 2015) [e.g., the Ward method (Johnson, 1967; Ward, 2012)] have been extensively explored.”
Wertheim, J., & Ragni, M. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01531 · Cited by 17
“GingerALE offers two options for analyzing data sets: (1) analyzing a single data set and (2) comparing and contrasting two different sets (Eickhoff et al., 2011).”
MethodBleau, M., Paré, S., Chebat, D., Kupers, R. C., Nemargut, J. P., & Ptito, M. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1010354 · Cited by 18
“Conjunction meta-analyses (Eickhoff et al., 2011) were conducted to detect overlapping activated brain regions between groups.”
MethodBoeken, O. J., Cieslik, E. C., Langner, R., & Markett, S. A. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02603-w · Cited by 18
“…application of a variety of experimental paradigms to further disentangle the functions of brain regions is, however, constrained by practical factors such as the high costs and the tremendous effort involved in organizing (large-scale) functional neuroimaging studies (Eickhoff et al. 2011).”
BackgroundMethodLang, A. N., Zhong, Y., Lei, W., Xiao, Y., Hang, Y., Xie, Y., Lv, Z., Zhang, Y., Liu, X., Liang, M., et al. (2024) · Comprehensive Psychiatry · doi:10.1016/j.comppsych.2024.152487 · Cited by 17
“Wanting” versus “needing” related value: An fMRI meta‐analysis
Bosulu, J., Allaire, M., Tremblay‐Grénier, L., Luo, Y., Eickhoff, S. B., & Hétu, S. (2022) · Brain and Behavior · doi:10.1002/brb3.2713 · Cited by 17
Self-citationOn Madness and Civilisation: A History of Insanity in the Age of Reason (1961), by Michel Foucault
Bracken, P. J. (2015) · The British Journal of Psychiatry · doi:10.1192/bjp.bp.115.165274 · Cited by 17
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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