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Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation
Open accessAbstract
Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis.
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“However, new studies have since been published, and existing ALE meta-analyses do not meet the current standards for type I error control (Eickhoff et al., 2016).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
In the citing paper
“Following current best-practice guidelines, we assessed the significance of convergence of activation foci using a voxel-level threshold of and a cluster-level familywise error-corrected threshold of , and performed ALE meta-analyses only when at least 17 experiments reported activation foci for a contrast (Eickhoff et al., 2016, Tahmasian et al., 2019).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
In the citing paper
“Notably, this null-finding occurred in a well-powered meta-analysis: the sample of 35 experiments is considered adequate by present guidelines for neuroimaging meta-analyses (Müller et al., 2018, Tahmasian et al., 2019) and gave us more than 80 % power to detect a convergence of activation differences in areas that were identified in just 7 (20 %) of the experiments (Eickhoff et al., 2016)”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
In the citing paper
“which has been shown to be inappropriate for neuroimaging data (Chumbley and Friston, 2009) and to entail both low sensitivity and high susceptibility to spurious, false positive findings (Eickhoff et al., 2016).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
In the citing paper
“Brem et al. (2012); Eng et al. (2015) and Menzies et al. (2008) performed meta-analyses on activation differences obtained from less than the recommended minimum of 17 fMRI experiments (Eickhoff et al., 2016).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
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Citations
Cites
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“Moreover, it may be discussed whether indeed something like “the” true location of an effect exists, given recent accounts of distributed coding and representation (Bzdok et al., 2015; Haxby, 2012; Kriegeskorte, 2009; Rissman and Wagner, 2012).”
Thresholding of statistical maps in functional neuroimaging using the false discovery rate
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“In turn, a higher number of significant voxels at the target location allow for more false positive voxels when the false positive rate is controlled (Genovese et al., 2002).”
Self-citationLarge-scale automated synthesis of human functional neuroimaging data.
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011) · Nature Methods · doi:10.1038/nmeth.1635 · Cited by 3,686 · cited 1×
“…topics from cognitive (Chase et al., 2015; Molenberghs et al., 2012), affective (Kohn et al., 2014; Lamm et al., 2011) and motor (King et al., 2014; Yuan and Brown, 2015) neuroscience to the neurobiology of neurological (Herz et al., 2014; Rehme et al., 2012) and psychiatric (Bludau et al., 2015;…”
Self-citationAn Introduction to the Bootstrap.
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“Particularly ALE has been applied to a wide range of different topics from cognitive (Chase et al., 2015; Molenberghs et al., 2012), affective (Kohn et al., 2014; Lamm et al., 2011) and motor (King et al., 2014; Yuan and Brown, 2015) neuroscience to the neurobiology of neurological (Herz et al.,…”
Self-citationLamm, C., Decety, J., & Singer, T. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.10.014 · Cited by 2,016
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“More specifically, this is quite likely to happen if one experiment is based on a substantially larger N than the remaining ones and hence features a tighter Gaussian leading to higher voxel-wise probabilities around the reported foci (Eickhoff et al., 2009b).”
InfluentialSelf-citationMeta-analysis of the functional neuroanatomy of single-word reading: method and validation
Turkeltaub, P. E., Eden, G. F., Jones, K. M., & Zeffiro, T. A. (2002) · NeuroImage · doi:10.1006/nimg.2002.1131 · Cited by 1,604 · cited 4×
“Empirically, it has repeatedly been noted that significant effects may be largely driven by even a single experiment if the total number of experiments is relatively low (Bzdok et al., 2011; Raemaekers et al., 2007; Turkeltaub et al., 2012; Wager et al., 2007).”
InfluentialLogothetis, N. K., & Wandell, B. A. (2004) · Annual Review of Physiology · doi:10.1146/annurev.physiol.66.082602.092845 · Cited by 1,568 · cited 1×
“Such structured integration is enabled by community-wide standards of spatial normalization (Amunts et al., 2014; Evans et al., 2012; Mazziotta et al., 2001) and the convention of reporting peak locations in stereotaxic reference spaces.”
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Chiang, F. L., Wang, Q., Yu, F. F., Romero, R. S., Huang, S. Y., Fox, P. M., Tantiwongkosi, B., & Fox, P. T. (2019) · Clinical Radiology · doi:10.1016/j.crad.2019.07.005 · Cited by 23
Self-citationHuman brain responses to gustatory and food stimuli: A meta-evaluation of neuroimaging meta-analyses
Yeung, A. W. K., Wong, N. S. M., Lau, H. C., & Eickhoff, S. B. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116111 · Cited by 23
Self-citationSiew, S. K. H., & Yu, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-45765-1 · Cited by 25
RetractedEmotion 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
“…(CBMA) using the activation likelihood estimation (ALE) approach (Eickhoff et al., 2012; Laird, 2009; Turkeltaub et al., 2012, 2002) have proven successful over the past decade (Eickhoff et al., 2016), enabling researchers to pinpoint brain regions with consistent activation across studies.”
InfluentialSelf-citationLaCroix, A. N., James, E., & Rogalsky, C. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.680933 · Cited by 22
“4ALEs that include fewer than eight tasks should be interpreted with caution due to the small sample size (Eickhoff et al., 2016).”
BackgroundInfluentialLi, Y., Kong, F., Ji, M., Luo, Y.-M., Lan, J.-J., & You, X.-Q. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.01021 · Cited by 19
Social cognition in the blind brain: A coordinate‐based meta‐analysis
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“…on a low number of contrasts, preventing more detailed analyses such as a direct comparison of nodes of the social brain possibly differently engaged by auditory and haptic inputs, our sample is in line with current recommendations for ALE meta-analyses (Eickhoff et al., 2016; Muller et al., 2018).”
Resultkhodadadifar, T., Soltaninejad, Z., Ebneabbasi, A., Eickhoff, C. R., Sorg, C., van Eimeren, T., Vogeley, K. T., Zarei, M., Eickhoff, S. B., & Tahmasian, M. (2021) · Human Brain Mapping · doi:10.1002/hbm.25722 · Cited by 21
Self-citationAction 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
Frahm, L., Cieslik, E. C., Hoffstaedter, F., Satterthwaite, T. D., Fox, P., Langner, R., & Eickhoff, S. (2022) · Human Brain Mapping · doi:10.1002/hbm.25898 · Cited by 17
“Eickhoff et al. (2016) performed ALE analyses on all datasets, identified peaks in the resulting Z-maps and checked the distance of contributing foci to the corresponding peaks.”
BackgroundMethodResultInfluentialSelf-citationSacheli, L. M., Tomasetig, G., Musco, M. A., Pizzi, S., Bottini, G., Pizzamiglio, L., & Paulesu, E. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104768 · Cited by 26
Pankey, B., Riedel, M. C., Cowan, I., Bartley, J. E., Lobo, R. P., Hill-Bowen, L. D., Salo, T., Musser, E. D., Sutherland, M. T., & Laird, A. R. (2022) · Behavioral and Brain Functions · doi:10.1186/s12993-022-00196-2 · Cited by 24
“However, we observed null results for these additional contrasts as well, likely in part due to the underpowered samples (Eickhoff et al., 2016).”
BackgroundMethodResultInfluentialSelf-citationDugre, J. R., Orban, P., & Potvin, S. (2022) · Addiction Biology · doi:10.1111/adb.13257 · Cited by 24
Sader, M., Waiter, G. D., & Williams, J. H. G. (2023) · Brain and Behavior · doi:10.1002/brb3.3286 · Cited by 21
Giehl, K., Tahmasian, M., Eickhoff, S. B., & van Eimeren, T. (2019) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2019.02.015 · Cited by 20
Self-citationVariability in the analysis of a single neuroimaging dataset by many teamsPreprint
Botvinik‐Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford, J. A., Adcock, R. A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/843193 · Cited by 21
Self-citationLi, J., Kuang, S., Liu, Y., Wu, Y., & Li, H. (2024) · Human Brain Mapping · doi:10.1002/hbm.26702 · Cited by 21
“First, ALE analyses with few experiments are unstable and can be largely influenced by a single experiment (Eickhoff et al., 2016).”
BackgroundMencarelli, L., Biagi, M. C., Salvador, R., Romanella, S. M., Ruffini, G., Rossi, S., & Santarnecchi, E. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9030828 · Cited by 22
“ALE maps based on a small number of studies are more affected by study heterogeneity [97], while at the same time focusing on a specific topic/task (and therefore a smaller number of studies) is also important to provide specific contribution to the field of interest.”
BackgroundNeural correlates of morphological processing: An activation likelihood estimation meta-analysis.
Bulut, T. (2022) · Cortex; a journal devoted to the study of the nervous system and behavior · doi:10.1016/j.cortex.2022.02.010 · Cited by 18
Cattarinussi, G., Grimaldi, D. A., & Sambataro, F. (2023) · Schizophrenia Bulletin · doi:10.1093/schbul/sbad044 · Cited by 21
Brain gray matter abnormalities in progressive supranuclear palsy revisited
Pan, P.-L., Liu, Y., Zhang, Y., Zhao, H., Ye, X., & Xu, Y. (2017) · OncoTarget · doi:10.18632/oncotarget.20895 · Cited by 16
“To achieve sufficient power for moderate effects, Eickhoff and co-workers recently recommended that 17 or more experiments were needed for a coordinate-based meta-analysis to detect moderately sized effects [39].”
BackgroundResultA Meta-analysis of Structural and Functional Brain Abnormalities in Early-Onset Schizophrenia
Ioakeimidis, V., Haenschel, C., Yarrow, K., Kyriakopoulos, M., & Dima, D. (2020) · Schizophrenia Bulletin Open · doi:10.1093/schizbullopen/sgaa016 · Cited by 21
“48 Secondly, the resultant voxel-level FWE thresholded functional connectivity (ie, MACM) maps for each seed were used in conjunction analyses to find where these maps overlap using the minimum statistic method.”
Common and distinct neural trends of allocentric and egocentric spatial coding: An ALE meta‐analysis
Derbie, A. Y., Chau, B. K. H., Wong, C. H. Y., Chen, L., Ting, K. H., Lam, B. Y., Lee, T. M. C., Chan, C. C. H., & Smith, Y. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15240 · Cited by 24
The pathoconnectivity network analysis of the insular cortex: A morphometric fingerprinting
Nani, A., Manuello, J., Mancuso, L., Liloia, D., Costa, T., Vercelli, A., Duca, S., & Cauda, F. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117481 · Cited by 19
Groenendijk, I., Mehnert, U., Groen, J., Clarkson, B., Scheepe, J., & Blok, B. (2020) · PLoS ONE · doi:10.1371/journal.pone.0246042 · Cited by 16
“Nevertheless, to advance evidence in this field and since validity of ALE analysis depends on the data amount and quality [13,30] more effort is needed to provide such data.”
BackgroundInfluentialMeta-analytic connectivity modelling of deception-related brain regions
Meier, S. K., Ray, K. L., Mastan, J. C., Salvage, S. R., & Robin, D. A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0248909 · Cited by 18
Boccadoro, S., Wagels, L., Puiu, A. A., Votinov, M., Weidler, C., Veselinović, T., Demko, Z., Raine, A., & Neuner, I. (2021) · Human Brain Mapping · doi:10.1002/hbm.25635 · Cited by 23
“Last, the convergence of foci is tested by comparing the ALE scores against the nullhypothesis of random spatial brain activations (Eickhoff et al., 2016; Turkeltaub et al., 2012).”
Giacobbe, C., Raimo, S., Cropano, M., & Santangelo, G. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00680-3 · Cited by 22
“, implicit or explicit) in action language processing regardless of an effector-specific selection criterion to conduct a robust and sufficiently powered ALE-based metaanalysis (Eickhoff et al., 2016).”
BackgroundUnger, 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
Self-citationCona, G., Wiener, M., Allegrini, F., & Scarpazza, C. (2023) · Neuropsychology Review · doi:10.1007/s11065-023-09609-z · Cited by 20
“Statistical ALE maps were thresholded using cluster level FWE correction at p 0.05 (cluster-forming threshold at voxel-level p 0.001) (Eickhoff et al., 2016) in line with the recent guidelines for coordinate-based meta-analysis (Muller et al., 2018).”
MethodInfluentialMattavelli, G. C., Gorrino, I., Tornaghi, D., & Canessa, N. (2023) · Cortex · doi:10.1016/j.cortex.2023.10.008 · Cited by 21
Bortolini, T., Laport, M. C., Latgé-Tovar, S., Fischer, R. D., Zahn, R., de Oliveira‐Souza, R., & Moll, J. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105584 · Cited by 22
“Additionally, these meta-analyses were limited by insufficient statistical power (Eickhoff et al., 2016) and lacked assessments of the robustness of their results (Müller et al., 2018).”
BackgroundMethodInfluentialSmith, D. V., & Delgado, M. R. (2016) · Human Brain Mapping · doi:10.1002/hbm.23354 · Cited by 19
“Key words: psychophysiological interaction; PPI; CBMA; meta-analysis; fMRI; open science r r”
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Hubs of long‐distance co‐alteration characterize brain pathology
Cauda, F., Mancuso, L., Nani, A., Ficco, L., Premi, E., Manuello, J., Liloia, D., Gelmini, G., Duca, S., & Costa, T. (2020) · Human Brain Mapping · doi:10.1002/hbm.25093 · Cited by 17
“The maps were thresholded at p = .05, with an FWE correction for multiple comparison as indicated by Eickhoff et al. (2016), and a cluster volume threshold of 100 mm.”
Roberts, C. A., Giesbrecht, T., Stančák, A., Fallon, N. B., Thomas, A., & Kirkham, T. C. (2019) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2019.04.007 · Cited by 19
A dual larynx motor networks hypothesis
Belyk, M., Eichert, N., & McGettigan, C. (2021) · Philosophical Transactions of the Royal Society B · doi:10.1098/rstb.2020.0392 · Cited by 17
Funch Uhre, V., Melissa Larsen, K., Marc Herz, D., Baaré, W., Katrine Pagsberg, A., & Roman Siebner, H. (2022) · NeuroImage: Clinical · doi:10.1016/j.nicl.2022.103268 · Cited by 18
“However, Brem et al. (2012); Eng et al. (2015) and Menzies et al. (2008) performed meta-analyses on activation differences obtained from less than the recommended minimum of 17 fMRI experiments (Eickhoff et al., 2016).”
InfluentialNeural mechanisms of behavioral addiction: An ALE meta-analysis and MACM analysis
Li, Z., He, Q., Elhai, J. D., Montag, C., & Yang, H. (2025) · Journal of Behavioral Addictions · doi:10.1556/2006.2024.00082 · Cited by 18
Boer, O. D., Marroun, H. E., & Franken, I. H. A. (2022) · Brain Research · doi:10.1016/j.brainres.2022.148020 · Cited by 20
Feng, C., Huang, W., Xu, K., Stewart, J. L., Camilleri, J. A., Yang, X., Wei, P., Gu, R., Luo, W., & Eickhoff, S. B. (2022) · Clinical Psychology Review · doi:10.1016/j.cpr.2022.102189 · Cited by 19
Self-citationCognitive 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
“In line with recent guidelines (Eickhoff et al ., 2016), the ‘true’ ALE scores were then tested against the ALE scores of the null distribution by adopting a cluster-level family wise error (cFWE)–corrected threshold of p 0.05, while the cluster-forming voxel-wise threshold was set at P 0.001…”
BackgroundMethodResultInfluentialBossier, H., Seurinck, R., Kühn, S., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Martinot, J., Lemaître, H., Paus, T., Millenet, S., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00745 · Cited by 18
Caenegem, E. E. V., Verdú, M. M., Waltzing, B. M., Hamoline, G., McAteer, S. M., Frahm, L., & Hardwick, R. M. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105902 · Cited by 21
Wertheim, J., & Ragni, M. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01531 · Cited by 17
“Eickhoff et al. (2016) recommend the use of the cluster-level family-wise error method in which a randomized data set tantamount to the testing data set is generated.”
MethodIs there a reliable brain morphological signature for migraine?
Wang, H. Z., Wang, W. H., Shi, H. C., & Yuan, C. H. (2020) · The Journal of Headache and Pain · doi:10.1186/s10194-020-01158-7 · Cited by 17
“It has been shown that at least 17 experiments in the ALE analysis would achieve reasonable power for statistical analysis [12].”
BackgroundWu, Y. H., Veerareddy, A., Lee, M. R., Bellucci, G., Camilleri, J. A., Eickhoff, S. B., & Krüeger, F. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.09.053 · Cited by 17
Self-citationBleau, 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
“For the single meta-analyses, an ALE value was calculated for each voxel, using cluster-level family-wise error (FWE) correction (Eickhoff et al., 2016) to correct for multiple comparisons.”
MethodZhang, Y., Mirman, D., & Hoffman, P. (2023) · Brain and Language · doi:10.1016/j.bandl.2023.105287 · Cited by 17
“The number of studies included was slightly below the minimum of 17 studies recommended for a well-powered ALE meta-analysis (Eickhoff et al., 2016).”
BackgroundNewton‐Fenner, A., Hewitt, D., Henderson, J. R., Roberts, H., Mari, T., Gu, Y., Gorelkina, O., Giesbrecht, T., Fallon, N. B., Roberts, C. A., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0286969 · Cited by 19
“Further, to ensure that no single study is driving the ALE scores of each cluster, a maximum FSN was set at 146, requiring at least a 10% contribution from the cohort studies [95].”
BackgroundMethodInfluentialLinguiti, S., Vogel, J. W., Sydnor, V. J., Pines, A. R., Wellman, N., Basbaum, A. I., Eickhoff, C. R., Eickhoff, S. B., Edwards, R. R., Larsen, B., et al. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105421 · Cited by 18
Self-citationAltered Functional Connectivity in Working Memory Network After Acute Sleep Deprivation
Feng, S., Yao, H., Zheng, S., Feng, Z., Liu, X., Liu, R., Dong, L., Cai, Y., Jia, H., & Ning, Y. (2023) · Neuroscience · doi:10.1016/j.neuroscience.2023.11.003 · Cited by 18
Neural bases of reading fluency: A systematic review and meta-analysis
Lee, M. M., & Stoodley, C. J. (2024) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2024.108947 · Cited by 23
Xia, H., Hou, Y., Li, Q., & Chen, A. (2024) · Human Brain Mapping · doi:10.1002/hbm.70031 · Cited by 25
“A recommendation was proposed that meta-analysis based on ALE should include at least 17 experiments to reduce the influence of a single experiment on the general results (Eickhoff et al., 2016; Müller et al., 2018).”
MethodBara, I., Darda, K. M., Kurz, A. S., & Ramsey, R. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15479 · Cited by 17
Universality and language specificity of brain Reading networks: A developmental perspective
Zhang, J., Chen, J., & Ding, G. (2024) · Developmental Science · doi:10.1111/desc.13379 · Cited by 16
Nardoni, L., Tullo, M. G., Stefano, G. D., Fiorelli, M., & Caramia, F. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103759 · Cited by 16
“This overcomes the variability of neuroimaging findings due to interindividual variability, intrinsic signal-to-noise and spatial resolution typical of non-invasive neuro-imaging modalities (Eickhoff et al., 2016).”
Schumann, A., Giuliano, M. D., Schulz, S., de la Cruz, F., Kreuder, T., Seifert, G., & Bär, K. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf315 · Cited by 20
Fronto-parietal numerical networks in relation with early numeracy in young children
Zhang, H., Chong-yaw Wee, A., Poh, J. S., Wang, Q., Shek, L. P., Chong, Y. S., Fortier, M. V., Meaney, M. J., Broekman, B. F. P., & Qiu, A. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1774-2 · Cited by 16
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Feng, W., Wang, W., Liu, J., Wang, Z., Tian, L., & Fan, L. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.666179 · Cited by 23
The Cerebellum Links to Positive Symptoms of Psychosis: A Systematic Review and Meta-analysis
Pinheiro, A. P., Johnson, J. F., Amorim, M., Roberto, M. S., Schwartze, M., Kotz, S. A., & Shenton, M. E. (2021) · Schizophrenia Bulletin Open · doi:10.1093/schizbullopen/sgab039 · Cited by 17
Zapparoli, L., Devoto, F., Giannini, G., Zonca, S., Gallo, F., & Paulesu, E. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103179 · Cited by 16
“…it can be easily explained by the fact that the fMRI paradigms that we scrutinized did not contain studies that were stressing behavioural control, as these studies are still too few to be meta-analysed (see the recommendations on study numerosity needed for meta-analyses in Eickhoff et al. 2016).”
BackgroundMethodInfluentialLiloia, D., Crocetta, A., Cauda, F., Duca, S., Costa, T., & Manuello, J. (2022) · Brain Sciences · doi:10.3390/brainsci12101367 · Cited by 16
“Finally, the meta-analytic map was thresholded applying a family-wise error (FWE) correction for multiple comparisons, with 1000 permutations, and the threshold-free cluster enhancement (TFCE) statistic ( p ≤ 0.05; minimum cluster size = 10 voxels) [38].”
InfluentialWatanuki, S. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.984647 · Cited by 17
Yeung, A. W. K., & Leung, W. K. (2023) · Nutrients · doi:10.3390/nu15071577 · Cited by 16
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
“Based on a recent simulation study (Eickhoff et al., 2016), a recommendation was made to include at least 17–20 experiments in ALE meta-analyses in order to have sufficient power to detect smaller effects and to also make sure that results are not driven by single experiments.”
MethodVuong, V., Hewan, P., Perron, M., Thaut, M. H., & Alain, C. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105423 · Cited by 18
Association between Behavioral Ambidexterity and Brain Health
Kokubun, K., Yamakawa, Y., & Hiraki, K. (2020) · Brain Sciences · doi:10.3390/brainsci10030137 · Cited by 16
“In the field of neuroscience, it has been suggested that grit leads to higher academic performance through the neural link of the right dorsomedial prefrontal cortex, a highly associative center in the frontal cortex [23,52].”
BackgroundMa, W., Tian, M.-J., Yao, Q.-L., Li, Q., Tang, F., Xiao, C., Shi, J., & Chen, J. (2021) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.13775 · Cited by 13
The neural basis of creative production: A cross-modal ALE meta-analysis
Brown, S., & Kim, E. (2021) · Open Psychology · doi:10.1515/psych-2020-0114 · Cited by 19
Hill-Bowen, L. D., Riedel, M. C., Salo, T., Flannery, J. S., Poudel, R., Laird, A. R., & Sutherland, M. T. (2022) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2022.109625 · Cited by 18
Self-citationCoordinate based meta-analysis does not show grey matter atrophy in narcolepsy
Tănăsescu, R., Tench, C. R., Cottam, W. J., Constantinescu, C. S., & Auer, D. P. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.07.016 · Cited by 16
Tahmasian, M., Zarei, M., Noori, K., Khazaie, H., Samea, F., Spiegelhalder, K., Eickhoff, S. B., Someren, E. J. W. V., & Eickhoff, C. R. (2018) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2018.08.004 · Cited by 16
“driven by a single study is an important and clearly established lower bound (13).”
Self-citationSartin, S., Ranzini, M., Scarpazza, C., & Monaco, S. (2022) · Current Research in Neurobiology · doi:10.1016/j.crneur.2022.100070 · Cited by 15
“The ALE scores are then tested against a null hypothesis according to which the concordance in spatial activation between experiments can occur by chance and is therefore random (noise; Eickhoff et al., 2016), by applying a random-effects spatial inference (i.”
MethodJameei, H., Rakesh, D., Zalesky, A., Cairns, M. J., Reay, W. R., Wray, N. R., & Biase, M. A. D. (2023) · Schizophrenia Bulletin · doi:10.1093/schbul/sbad087 · Cited by 15
“This review is qualitative as inter-study design differences, as well as lack of sufficient studies— the activation likelihood estimation meta-analysis model requires at least 17–20 experiments to yield robust analyses 41 —precluded a meta-analysis.”
BackgroundSeparate Neural Systems Value Prosocial Behaviors and Reward: An ALE Meta-Analysis
Wang, H., Zhang, J., & Jia, H. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00276 · Cited by 15
“Separate meta-analyses were only performed if a sufficient number of experiments were available (>17 experiments) (Eickhoff et al., 2016).”
MethodMeta-analytical transdiagnostic neural correlates in common pediatric psychiatric disorders
Dugre, J. R., Eickhoff, S. B., & Potvin, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-08909-3 · Cited by 15
Self-citationBrain responses to stimuli mimicking dental treatment among non‐phobic individuals: A meta‐analysis
Yeung, A. W. K., Goto, T., & Leung, W. K. (2017) · Oral Diseases · doi:10.1111/odi.12819 · Cited by 15
Boyland, E., Maden, M., Coates, A. E., Masterson, T. D., Alblas, M. C., Bruce, A. S., & Roberts, C. A. (2024) · Obesity Reviews · doi:10.1111/obr.13643 · Cited by 14
“We adhered to the recommendations on methodology reported by Eickhoff et al. 52 As such, a cluster-level family-wise error (FWE) correction at p < 0.05 was employed to control for multiple comparisons.”
BackgroundMethodInfluentialVartanian, O., Replete, V., Saint, S. A., Lam, Q., Forbes, S., Beaudoin, M. E., Brunyé, T. T., Bryant, D. J., Feltman, K. A., Heaton, K. J., et al. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.868001 · Cited by 15
Müller, 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
“Cluster-level family-wise error (FWE) correction was performed as suggested by Eickhoff et al. (2016) and Frahm et al. (2022) and described in detail in previous meta-analyses (Bzdok et al., 2012; Rottschy et al., 2012).”
Self-citationNeural Correlates of Repetition Priming: A Coordinate-Based Meta-Analysis of fMRI Studies
Lee, S.-M., Henson, R. N., & Lin, C. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.565114 · Cited by 14
Domín, M., Grimm, N. K., Klepzig, K., Schmidt, C. O., Kordaß, B., & Lotze, M. (2021) · Journal of Pain · doi:10.1016/j.jpain.2021.01.003 · Cited by 14
Fenerci, C., Gurguryan, L., Spreng, R. N., & Sheldon, S. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.06.009 · Cited by 14
Kulik, V., Reyes, L. D., & Sherwood, C. C. (2022) · Progress in brain research · doi:10.1016/bs.pbr.2022.10.003 · Cited by 14
MacLean, M. W., Hadid, V., Spreng, R. N., & Leporé, F. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120088 · Cited by 15
Fascher, M., Nowaczynski, S., Spindler, C., Strobach, T., & Muehlhan, M. (2023) · Psychopharmacology · doi:10.1007/s00213-023-06498-1 · Cited by 16
“…for this by setting cluster-level family-wise-error correction (cFWE) with p < 0.05 for the cluster-forming threshold and p < 0.001 for the voxel-wise threshold (Eickhoff et al. 2016; Flandin and Friston 2019) as this is the gold-standard among correction methods within ALE (Frahm et al. 2022).”
InfluentialGrey matter structural alterations in anxiety disorders: a voxel-based meta-analysis
Liu, H., Hao, Z., Qiu, S., Wang, Q., Zhan, L., Huang, L., Shao, Y., Wang, Q., Su, C., Cao, Y., et al. (2023) · Brain Imaging and Behavior · doi:10.1007/s11682-023-00842-x · Cited by 16
“The thalamus has also been implicated 17–20 studies, which could better detect smaller effects and avoid results driven by few studies (Eickhoff et al., 2016).”
Li, Q., Lai, X., Li, T., Madsen, K. H., Xiao, J. Z., Hu, K., Feng, C., Fu, D., & Liu, X. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120707 · Cited by 15
“It is also possible that the lower number of experiments available for the subdivided processes influences these findings, although our current number of studies is within the recommended quantity for the ALE method (Eickhoff et al., 2016).”
Revisiting the brain activity associated with innocuous and noxious cold exposure
King, M., & Carnahan, H. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.06.021 · Cited by 14
Neuroimaging biomarkers of psychogenic erectile dysfunction: protocol for a systematic review
Yin, T., Li, Z., Xiong, J., Lan, L., Sun, R., Ren, F., & Zhang, P. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-030061 · Cited by 13
Phan, L., Tariq, A., Lam, G., Pang, E. W., & Alain, C. (2020) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-020-04794-9 · Cited by 13
Nilsson, M., & Kalckert, A. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15534 · Cited by 14
“Clusters achieving a family-wise error (FWE) corrected significance of p < .05 were considered significant, reflecting current recommendations for best practices (Eickhoff et al., 2016; Müller et al., 2018).”
BackgroundMethodSex differences in the taste-evoked functional connectivity network
Ponticorvo, S., Prinster, A., Cantone, E., Salle, F. D., Esposito, F., & Canna, A. (2022) · Chemical Senses · doi:10.1093/chemse/bjac015 · Cited by 13
Salera, C., Boccia, M., & Pecchinenda, A. (2023) · Neuropsychology Review · doi:10.1007/s11065-023-09593-4 · Cited by 13
“This together with using cluster-level thresholding allows controlling for the excessive contribution of one experiment (Eickhoff et al., 2016).”
MethodInfluentialMethods for decoding cortical gradients of functional connectivity
Peraza, J. A., Salo, T., Riedel, M. C., Bottenhorn, K. L., Poline, J., Dockès, J., Kent, J. D., Bartley, J. E., Flannery, J. S., Hill-Bowen, L. D., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00081 · Cited by 15
Self-citationCosta, T., Premi, E., Borroni, B., Manuello, J., Cauda, F., Duca, S., & Liloia, D. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120798 · Cited by 11
“…the field, allowing us to integrate the largest available dataset on the topic; therefore, we optimized the balance between sensitivity and susceptibility to false positive effects, increasing the statistical power of our analysis (Eickhoff et al., 2016; Manuello et al., 2022; Müller et al., 2018).”
BackgroundMethodInfluentialCosta, 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
“Datasets for analyses were created by clustering studies and experiments according to the cognitive domain categorization, with a minimum threshold of 17 experiments set to maintain adequate power for each meta-analysis (Eickhoff et al., 2016).”
InfluentialGan, 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
Metadata
- Authors
- Eickhoff, S. B., Nichols, T. E., Laird, A. R., Hoffstaedter, F., Amunts, K., Fox, P. T., Bzdok, D., & Eickhoff, C. R.
- Affiliations
- Forschungszentrum Jülich (DE)
- University of Warwick (GB)
- Florida International University (US)
- The University of Texas at San Antonio Health Science Center (US)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Corresponding author
- Eickhoff, Simon B.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-08
- Volume
- 137
- Pages
- 70-85
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4981641
- OpenAlex
- W2349406327
- Cited by
- 683
- References
- 76
- Citations by year
- 2026: 61 · 2025: 68 · 2024: 58 · 2023: 83 · 2022: 92 · 2021: 93 · 2020: 72 · 2019: 61 · 2018: 46 · 2017: 35 · 2016: 13
- FWCI
- 32.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (EI 816/4-1, LA 3071/3-1, EI 816/6-1)
- National Institute of Mental Health (R01-MH074457)
- Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain” and the European Union Seventh Framework Program (604102)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- activation likelihood estimation, coordinate-based meta-analysis, statistical thresholding, false discovery rate, sample size, statistical power, brain-behavior relationship, BrainMap
- MeSH terms
- Brain, Data Interpretation, Statistical, Humans, Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Meta-Analysis as Topic, Reproducibility of Results, Likelihood Functions, Neuroimaging
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