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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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Cites
An Introduction to the Bootstrap
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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;…”
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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.,…”
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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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Dugré, J., Hopfer, C., & Winters, D. E. (2025) · Aggression and Violent Behavior · doi:10.1016/j.avb.2025.102035 · Cited by 7
“In the ALE meta-analyses, since there is no effect size for any given voxel, variations in brain activity were identified using the usual recommended statistical threshold (p 0.001 at a voxel-level, cFWE 0.05, Eickhoff et al., 2016).”
InfluentialGuo, Z., Xiao, S., Sun, S., Su, T., Tang, X., Chen, G., Chen, P., Chen, R., Chen, C., Gong, J., et al. (2025) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.70218 · Cited by 5
“This discrepancy may be due to the less conservative thresholds employed in earlier studies, increasing the likelihood of false-positive results [23].”
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Large-scale activation likelihood estimation meta-analysis of parkinsonian disorders
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“Given that unreliable estimates may result from a low n in ALE meta-analysis, 25,27 we set a cut-off of 10 studies in order to conduct ALE meta-analysis.”
CBMAT: a MATLAB toolbox for data preparation and post hoc analyses in neuroimaging meta-analyses
Manuello, J., Liloia, D., Crocetta, A., Cauda, F., & Costa, T. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02185-3 · Cited by 5
“One of the main elements of concern in CBMAs is the possibility of the presence of one experiment (or a few experiments) driving the results (Eickhoff et al., 2016).”
BackgroundMethodThe neural networks of touch observation
Schaefer, M., Kuehn, E., Schweitzer, F. M., & Muehlhan, M. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00065 · Cited by 6
“The analysis was performed at a voxel level of p ≤ 0.001, Family Wise Error cluster level corrected (cFWE) at p ≤ 0.05, as recommended for ALE meta-analyses ( Eickhoff et al., 2016; Müller et al., 2018).”
MethodInfluentialBeber, S., Bontempi, G., Miceli, G., & Tettamanti, M. (2024) · Neuropsychology Review · doi:10.1007/s11065-024-09648-0 · Cited by 7
Ramage, A. E., Cote, K., Thorson, J. C., Lerner, K., Reidel, M. C., & Laird, A. R. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00355 · Cited by 8
“The lack of significance between these two ALE groups is likely due to the small number of experiments in each ( Eickhoff et al., 2016).”
BackgroundSelf-citationUsing coordinate-based meta-analyses to explore structural imaging genetics
Janouschek, H., Eickhoff, C. R., Mühleisen, T. W., Eickhoff, S. B., & Nickl‐Jockschat, T. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1670-9 · Cited by 5
“While certainly a higher number of contributing studies would be desirable [36], it should be emphasized that ALE-based meta-analyses yielded significant and robust results enrolling even smaller numbers of studies [37].”
Self-citationCao, H., Lin, F., Ke, B., Song, J., Xue, Y., Fang, X., & Zeng, E. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.1040553 · Cited by 5
Csonka, M., Mardmomen, N., Webster, P. J., Brefczynski‐Lewis, J. A., Frum, C., & Lewis, J. W. (2021) · Cerebral Cortex Communications · doi:10.1093/texcom/tgab002 · Cited by 5
Corrigendum: Brain networks of perceptual decision-making: an fMRI ALE meta-analysis
Keuken, M. C., Müller-Axt, C., Langner, R., Eickhoff, S. B., Forstmann, B. U., & Neumann, J. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00139 · Cited by 5
“The changes in results should also be seen in light of recent recommendations of sample size in coordinatebased meta-analyses (Eickhoff et al., 2016).”
Self-citationDistinct prefrontal lateralization in placebo and reappraisal mechanisms: An ALE meta-analysis
Monachesi, B., Pisanu, E., Chiffi, D., Rumiati, R., & Grecucci, A. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121459 · Cited by 3
Fascher, M., Thomsen, H., & Muehlhan, M. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106537 · Cited by 4
Different Neural Correlates of Sexually Preferred and Sexually Nonpreferred Stimuli
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Plausible Reasoning in Neuroscience
Costa, T., Liloia, D., Ferraro, M., & Manuello, J. (2023) · Springer eBooks · doi:10.1007/978-3-031-10135-9_74 · Cited by 5
Kim, H. (2024) · Brain Research · doi:10.1016/j.brainres.2024.148794 · Cited by 4
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“The statistical significance of the activation within the ALE map was calculated by cluster-level inference, as suggested by Eickhoff et al. [57,60,61].”
MethodResultCommon neural dysfunction of economic decision-making across psychiatric conditions
Feng, C.-L., Liu, Q.-X., Huang, C.-B., Li, T., Wang, L., Liu, F.-L., Eickhoff, S., & Qu, C. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120641 · Cited by 4
“Additionally, neither the mean contribution of the most dominant experiment (MDE) nor the mean contribution of the two most dominant experiments (2MDEs) ought to surpass 50% or 80% (Eickhoff et al., 2016).”
Self-citationRen, H., Li, Y., Bi, H., & Yang, Y. (2024) · International Journal of Clinical and Health Psychology · doi:10.1016/j.ijchp.2024.100519 · Cited by 5
Dedry, M., Maryn, Y., Szmalec, A., van Lith-Bijl, J. T., Dricot, L., & Desuter, G. (2022) · Journal of Voice · doi:10.1016/j.jvoice.2022.02.008 · Cited by 4
Functional decoding and meta-analytic connectivity modeling in thyroid-associated ophthalmopathy
Duan, Q., Wang, Z., Cheung, W., Liu, J., Zhang, H., Qiao, W., & Zhang, Q. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e23749 · Cited by 4
“Eickhoff [65] recommends including at least 17 – 20 experiments in the ALE meta-analysis to obtain significant power.”
Neural Correlates of Anti-appetite Medications: An fMRI Meta-analysis
Yeung, A. W. K. (2021) · Current Neuropharmacology · doi:10.2174/1570159x19666210914142227 · Cited by 4
“This threshold followed the latest recommendations to balance between false positive and false negative [9, 25, 42].”
BackgroundWolters, A. F., van de Weijer, S. C. F., Leentjens, A. F. G., Duits, A., Jacobs, H. I. L., & Kuijf, M. L. (2019) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2019.07.014 · Cited by 4
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Heckner, M. K., Cieslik, E. C., Eickhoff, S. B., Camilleri, J. A., Hoffstaedter, F., & Langner, R. (2020) · doi:10.1101/2020.07.15.204941
Self-citationComstock, L. B. (2024) · Brain and Language · doi:10.1016/j.bandl.2023.105377 · Cited by 7
Neural correlates of religious behavior related to Christianity: an ALE meta-analysis
Yeung, A. W. K., Wong, N. S. M., Tsui, I. S. Y., & Lee, T. C. P. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1557796 · Cited by 3
Alfaraj, K., Hanel, P. H., & Valentini, E. (2026) · Journal of Pain · doi:10.1016/j.jpain.2026.106218 · Cited by 3
Regional topography of auditory and visual attention: An fMRI-based meta-analysis
Salmi, J., Aquilino, B., Pocan, B., Riihimäki, H., Kangas, S., Kärhä, S., Alho, K., & Wikman, P. A. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106698 · Cited by 3
“We included only categories with minimum 20 contrasts (see Eickhoff et al., 2016), which left categories Auditory Bottom-up and Visual Language out from the final analyses.”
Aberrant brain activity in pedophilia links to receptor distribution, gene expression, and behavior
Schnellbächer, G. J., Dukart, J., Hansen, J. Y., Markello, R. D., Mokros, A., Pietsch, V., Ristow, I., Sakreida, K., Walter, M., Eickhoff, S. B., et al. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00105-0 · Cited by 3
“The denoted sample sizes (number of experimental contrasts) have been shown to be sufficient to achieve robust meta-analytic estimates.(55)”
BackgroundSelf-citationJiang, T., Yin, X., Zhu, L., Wang, G. G., Zhang, F., & Guo, J. (2024) · Brain Imaging and Behavior · doi:10.1007/s11682-024-00949-9 · Cited by 3
“…Gaussian probability distributions for focus to compute modeled activation (MA) maps; then, ALE images are obtained by the union of MA maps; finally, the convergence of foci is verified by testing the null-hypothesis of random spatial association between experiments (Eickhoff et al., 2016).”
Küppers, V., Cieslik, E. C., Frahm, L., Hoffstaedter, F., Eickhoff, S. B., Langner, R., & Müller, V. I. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00358 · Cited by 3
Self-citationTeghil, A., Wiener, M., & Boccia, M. (2025) · Neuropsychology Review · doi:10.1007/s11065-025-09662-w · Cited by 3
“All the thresholded ALE maps were computed using p values from the previous step, a cluster-level inference at the 0.05 significance level with 1000 threshold permutations and a cluster-forming threshold of p 0.001 uncorrected (Eickhoff et al., 2016a, 2016b).”
InfluentialWang, S., Zhang, L., Yang, Z., Liu, S., Wang, X., Li, Y., Xu, Y. J., & Zhao, W. (2025) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2025.111454 · Cited by 3
“Although Eickhoff et al. (2016) recommends that studies utilizing activation likelihood estimation (ALE) for imaging meta-analysis should include at least 20 articles, this advice is also a valuable reference for studies employing SDM. Fortunately, SDM (Radua et al., 2012b) has made numerous…”
Common brain areas in spatial navigation and visuo-spatial planning: A meta-analysis
Pepe, S., von Gal, A., Fabiani, G., & Piccardi, L. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106295 · Cited by 6
“A cluster-forming threshold of p 0.001 and a cluster-level threshold of p 0.05, corrected for Family-Wise Error (FWE), were applied to generate the final thresholded ALE map (Eickhoff et al., 2016).”
InfluentialWang, J., Gan, X., Han, M., Dong, W., He, J., Fu, K., Bore, M. C., Xu, T., Klugah‐Brown, B., Ferraro, S., et al. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106478 · Cited by 3
Tench, C. R., Tănăsescu, R., Jethwa, K. D., & Constantinescu, C. S. (2019) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2018.11.452 · Cited by 3
Yaple, Z. A., Tolomeo, S., & Yu, R. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103029 · Cited by 3
“, 2017) rather than false discovery rate that is not appropriate for inference on topological features(Eickhoff et al., 2016).”
BackgroundMethodInfluentialQiu, Z., Zhong, X., Yang, Q., Shi, X., He, L., Zhou, H., & Xu, X. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1349512 · Cited by 3
Yeung, A. W. K. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38084 · Cited by 3
“[57] was the number of experiments to be included in an ALE meta-analysis, rather than the contributing ratio of one or two MDEs, a subsequent guideline for conducting and reporting neuroimaging meta-analyses [58] recommended diagnostics that “ identify and count all experiments that report foci…”
BackgroundMethodInfluentialA bias-accounting meta-analytic approach refines and expands the cerebellar behavioral topography
Magielse, N., Manoli, A., Eickhoff, S. B., Fox, P. T., Saberi, A., & Valk, S. L. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106418 · Cited by 5
“ALE values are furthermore dependent on experimental sample size and experimental consistency (Eickhoff et al., 2016) (Supplementary Figure 1b), as also shown by our subsampling analyses (Fig.”
InfluentialSelf-citationSindermann, L., Redlich, R., Böhnlein, J., Dannlowski, U., & Leehr, E. J. (2020) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2020.09.182 · Cited by 3
Non-selective response inhibition in equiprobable Go/NoGo task: Bayesian analysis of fMRI dataPreprint
Masharipov, R. S., Korotkov, A., Medvedev, S., & Kireev, M. V. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/823625 · Cited by 4
Chuan-Peng, H., Huang, Y., Eickhoff, S. B., Peng, K., & Sui, J. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/081539 · Cited by 3
“Furthermore, it provides more decisive results and greater statistical power than individual studies (Eickhoff et al., 2016).”
Self-citationTench, C. R., Tănăsescu, R., Constantinescu, C. S., Auer, D. P., & Cottam, W. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.03.409953 · Cited by 3
Saberi, A., Ebneabbasi, A., Rahimi, S., Sarebannejad, S., Şen, Z. D., Graf, H., Walter, M., Sorg, C., Camilleri, J. A., Laird, A. R., et al. (2023) · medRxiv · doi:10.1101/2023.11.24.23298991 · Cited by 3
“…included in the main or subgroup analyses, which can limit the power and increase the risk of a single experiment dominating the findings [34], ii) including explicit or hidden ROI-based experiments which are biased to inflate sig-533 nificance in the selected region, iii) using less…”
BackgroundMethodSelf-citationLiloia, D., Rocca, P., Brasso, C., Tanaka, M., Manuello, J., Crocetta, A., Duca, S., Costa, T., & Cauda, F. (2026) · Psychological Medicine · doi:10.1017/s0033291726103511 · Cited by 4
“…design employed the anatomical ALE, the most widely used meta-analytic algorithm worldwide with demonstrated unbiased spatial reconstruction (Eickhoff et al., 2016; Manuello et al., 2022; Müller et al., 2018; Radua & Mataix-Cols, 2012; Tahmasian et al., 2019), this constraint remains…”
InfluentialNeural correlates of musical timbre: an ALE meta-analysis of neuroimaging data
Bellmann, O. T., & Asano, R. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1373232 · Cited by 2
Ambrasė, A., Müller, V. I., Camilleri, J. A., Wong, H. Y., & Derntl, B. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00277 · Cited by 4
“As suggested for ALE, cluster-level family-wise error (FWE) corrected threshold of p < 0.05 and cluster-forming threshold at voxel-level p < 0.001 with 10,000 permutations were used to correct for multiple comparisons ( Eickhoff et al., 2016; Frahm et al., 2022).”
MethodInfluentialKim, H. (2025) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02343 · Cited by 2
Neural networks underlying magnitude perception: a specific meta-analysis of fMRI studies
Şimşek, H., Sırmatel-Bakrıyanık, B., Erdoğan, B. D., Balcı, F., & Çíçek, M. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf163 · Cited by 2
“Each dataset included at least 21 experiments (in each of which at least 10 participants volunteered), which ensured that the results wo uld have sufficient statistical power and were not driven by a single experiment (Eickhoff et al. 2016; Müller et al. 2018).”
InfluentialKim, H. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.124 · Cited by 2
“…was previously studied with uppercase/lowercase judgment, previously studied with abstract/concrete judgment, or is new Regarding statistical power, Eickhoff et al. (2016) recommend including at least 20 independent studies in Activation Likelihood Estimation (ALE) meta-analyses, which was…”
Fu, Y.-J., Wang, D., Liu, J., Wang, H., Wen, F., Chen, W., & Liu, Z. (2025) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaf091 · Cited by 2
Shu, Y., Zhang, Q., Liu, J., He, Q., & Hou, Y.-Z. (2025) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-025-02906-3 · Cited by 2
Shu, Y., Zhang, Q., Zheng, Z.-L., & Hou, Y. (2025) · Journal of Neural Transplantation and Plasticity · doi:10.1155/np/7885279 · Cited by 3
“The resulting thresholded ALE map was then computed using a false discovery rate (FDR) correction with a p value = 0.001 and a minimum cluster size of 10 mm 3 [41].”
InfluentialAging-related changes in cognitive flexibility: fMRI meta‐analysis
Chuikova, Z. V., Faber, A., Filatov, A. A., Myachykov, A., Юрьевич, Ш. Ю., & Arsalidou, M. (2025) · Behavioral and Brain Functions · doi:10.1186/s12993-025-00301-1 · Cited by 3
“For individual analyses, findings were thres-holded at p 0.05 cluster-level family-wise error (cFWE) corrected for multiple comparisons and cluster-forming threshold at p 0.001 [37].”
InfluentialTowne, J. M., Eslami, V., Barron, D. S., Cavazos, J. E., & Fox, P. T. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103908 · Cited by 5
“Detailed explanations of CBMA statistics and the ALE algorithm have been provided in prior work (Eickhoff et al., 2016, 2012, 2009; Turkeltaub et al., 2012, Turkeltaub et al., 2002).”
InfluentialSelf-citationLi, Q., Du, M., Xiao, J., Li, T., Hu, K., Tu, S., Liu, X., Wang, L., & Dai, W. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121735 · Cited by 3
Bittencourt, A. M. L., FAVERO, B. P., Haas, C., Portolan, E. T., Gallardo, E., Rothmann, L. M., Grassi‐Oliveira, R., & Viola, T. W. (2026) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2026.113064 · Cited by 2
Wang, Y., Peng, Y., Steele, J. D., & Tolomeo, S. (2026) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2026.112196 · Cited by 2
Mapping the neural patterns of verbal repetition: an activation likelihood estimation meta-analysis
Awana, A., Berthier, M. L., Torres‐Prioris, M. J., & López-Barroso, D. (2026) · Brain Structure and Function · doi:10.1007/s00429-026-03119-3 · Cited by 2
“Given the limited number of experiments contributing to the pseudoword condition ( n = 10), results from ALE 3 should be interpreted as preliminary (Eickhoff et al. 2016).”
InfluentialWatershed Brain Regions for Characterizing Brand Equity-Related Mental Processes
Watanuki, S. (2021) · Brain Science · doi:10.3390/brainsci11121619 · Cited by 1
“[43] recommended a cluster-forming threshold at p = 0.”
BackgroundPlausible Reasoning in Neuroscience
Costa, T., Liloia, D., Ferraro, M., & Manuello, J. (2022) · Springer eBooks · doi:10.1007/978-3-030-68436-5_74-1 · Cited by 2
Parcellation-Based Connectivity Model of the Judgement Core
Hormovas, J., Dadario, N. B., Tang, S. J., Nicholas, P. J., Dhanaraj, V., Young, I. M., Doyen, S., & Sughrue, M. E. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13091384 · Cited by 2
“Despite this lack of power, all included analyses met the threshold of eight studies, which has been demonstrated to ensure that no more than 50% of the final ALE is attributable to the most dominant experiment [64].”
BackgroundMethodNeural correlates of pronoun processing: An activation likelihood estimation meta-analysis
Ouardi, L. E., Yeou, M., & Yasmeen Faroqi-shah, A. (2023) · Brain and Language · doi:10.1016/j.bandl.2023.105347 · Cited by 2
Acar, F., Maumet, C., Heuten, T., Vervoort, M., Bossier, H., Seurinck, R., & Moerkerke, B. (2023) · Neuroinformatics · doi:10.1007/s12021-023-09643-5 · Cited by 2
“While (Nichols et al., 2016) provides a comprehensive overview of guidelines for the analysis of fMRI studies and touches lightly on the subject of reporting study results, in this paper we focus on the impact of reporting practices on meta-analysis method options.”
Elliott, M., Hsu, M., Uddin, L. Q., Modavi, K., & Johnson, S. L. (2025) · Clinical Psychological Science · doi:10.1177/21677026241305377 · Cited by 2
“ALE meta-analyses require 20 or more experiments to be sufficiently powered (Eickhoff et al., 2016), so we were unable to use this meta-analytic technique to test for more granular convergence of effects by task category.”
Gray Matter Alterations in Medication Overuse Headache: A Voxel-Based Morphometry Meta-Analysis
Luo, W., Teng, Y., Chen, X., Chen, N., Zhang, X., Zhou, J., Tao, S., Lai, P., Song, Q., Hao, X., et al. (2025) · Pain Physician · doi:10.36076/ppj.2025.28.e115 · Cited by 2
The Neural Mechanisms of Relational Reasoning: Dissociating Representational Types
Wertheim, J., & Ragni, M. (2017) · Cognitive Science · Cited by 2
Zhang, Z. (2022) · Developmental Review · doi:10.1016/j.dr.2022.101052 · Cited by 4
Liu, B., Liu, J., Ju, Y., Wang, M., Liu, T., Zhang, Y., Li, L., Potenza, M. N., & Barron, D. S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/550301 · Cited by 2
“food: overall (19), ↑ (8), ↓ (11) 13-15.”
BackgroundMethodInfluentialHubs of long-distance co-alteration in brain pathologyPreprint
Cauda, F., Mancuso, L., Nani, A., Manuello, J., Liloia, D., Gelmini, G., Ficco, L. A., Premi, E., Duca, S., & Costa, T. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/846642 · Cited by 2
Reviews: Topological Distances and Losses for Brain NetworksPreprint
Chung, M. K., Smith, A. D. S., & Shiu, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.08623 · Cited by 2
Default Mode Network spatial configuration varies across task domainsPreprint
Mancuso, L., Cavuoti-Cabanillas, S., Liloia, D., Manuello, J., Buzi, G., Duca, S., Cauda, F., & Costa, T. (2021) · bioRxiv · doi:10.1101/2021.03.17.435799 · Cited by 3
Sensory perception in autism: an fMRI ALE meta-analysisPreprint
Jassim, N., Baron‐Cohen, S., & Suckling, J. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/p9n2z · Cited by 3
Boer, O. D., Marroun, H. E., & Franken, I. H. A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4120189 · Cited by 2
“Considering that performing robust meta-analyses requires a sufficient 153 number of studies (>17-20) to be comparable on these aspects (Eickhoff et al., 2016), we were 154 not able to conduct a meta-analysis.”
Lotter, L., Kohl, S. H., Gerloff, C., Bell, L., Niephaus, A., Kruppa, J. A., Dukart, J., Schulte-Rüther, M., Reindl, V., & Konrad, K. (2022) · bioRxiv · doi:10.1101/2022.07.26.501562 · Cited by 51
Dugre, J. R., Hopfer, C. J., & Winters, D. E. (2023) · doi:10.31234/osf.io/4zafd · Cited by 2
Dugre, J. R., & Potvin, S. (2024) · medRxiv · doi:10.1101/2024.06.02.24308339 · Cited by 3
“33 .”
Meta all the way down: An overview of neuroimaging meta-analysesPreprint
Oudyk, K., Dockès, J., Peraza, J. A., Kent, J. D., Torabi, M., Wang, M., McPherson, B., Mirhakimi, N., de la Vega, A. I., Laird, A. R., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.21.634094 · Cited by 2
Self-citationCauda, F., Manuello, J., Crocetta, A., Duca, S., Costa, T., & Liloia, D. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02867-4 · Cited by 1
“In line with the latest statistical recommendation (Eickhoff et al. 2016), results were family-wise error-cor-rected (FWE-c) for multiple comparisons, with a cluster-level inference of p < 0.05, a cluster-forming threshold of p < 0.001 on the voxel-level (1,000 permutation runs).”
InfluentialWatanuki, S. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1443578 · Cited by 2
No reliable gray matter alterations in idiopathic dystonia
Wang, Z., Chen, F., Sun, H.-H., Li, H., Hu, J.-B., Dai, Z., & Wang, S. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1510115 · Cited by 1
Zheng, Y., Huai, Y.-Y., Qiao, Y., Zang, Y., Luo, H., & Zhao, N. (2025) · Psychiatry Research · doi:10.1016/j.psychres.2025.116495 · Cited by 1
von Gal, A., Papa, D., D’Auria, M., & Piccardi, L. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251337770 · Cited by 1
The role of the human cerebellum in representing social behavior sequences: An SDM-PSI meta-analysis
Zhang, Y., Qiu, Y., Lin, S. T., Zheng, X., Tan, L., Liu, X., & Huang, R. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121277 · Cited by 1
He, Q., Zhaolan, L. I., & Yang, H. (2025) · Acta Psychologica Sinica · doi:10.3724/sp.j.1041.2025.1333 · Cited by 1
A neuroimaging-based meta-analysis of prosocial behavior in childhood and adolescence
Bonandrini, R., Beveridge, F., & Steinbeis, N. (2025) · Social Neuroscience · doi:10.1080/17470919.2025.2541641 · Cited by 1
“The ALE analysis (Eickhoff et al., 2009, 2012, 2016; Turkeltaub et al., 2002, 2012) on the MNI coordinates reported in the selected studies was conducted by means of GingerALE version 3.0.2 ( https://www.brain map.org/ale/).”
InfluentialHe, Y., Hou, Y., Zhou, Y., Gu, R., Gao, F., & Yuan, Z. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03534-w · Cited by 2
“To avoid results being dominated by a few experiments, we only conducted analyses involving 17 or more experiments, as suggested by a previous study [34].”
InfluentialLee, Z. L., Li, J. J., Siew, S. K. H., Billaud, C. H. A., & Yu, J. (2025) · GeroScience · doi:10.1007/s11357-025-01850-z · Cited by 1
Daviddi, S., & Santangelo, V. (2025) · European Journal of Neuroscience · doi:10.1111/ejn.70244 · Cited by 3
“Due to the limited number of experiments resulting from the above procedure (less than 17 experiments), a voxel-level threshold was set up at a p- FWE-corrected < 0.05, with 5000 permutations, and a minimum cluster volume of 200 mm 3 (cf. Eickhoff et al. 2016).”
InfluentialLuo, J., Celine Mylx, L. I., Richter, C. G., Turnbull, A. G., Richlan, F., Park, C. L., & Hoeft, F. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1669164 · Cited by 1
Costa, T., Manuello, J., Cauda, F., Crocetta, A., & Liloia, D. (2026) · Brain Sciences · doi:10.3390/brainsci16010087 · Cited by 1
“The ALE analysis was conducted using the GingerALE software package (v.3.0.2) [16], with state-of-the-art parameters [18]: family-wise error (FWE) correction at the cluster level ( p 0.05), a cluster-forming threshold of p 0.001, and 1000 permutations to estimate the empirical null distribution.”
InfluentialKeller, L., Lotter, L. D., Eickhoff, C. R., Eickhoff, S. B., Otten, K., Herpertz‐Dahlmann, B., & Seitz, J. (2026) · NeuroImage Clinical · doi:10.1016/j.nicl.2026.103950 · Cited by 2
“ALE can overcome common problems of single imaging studies such as being underpow-ered due to small sample sizes involving the danger of false positive and negative findings, low reproducibility, and overgeneralization of results (Eickhoff et al., 2016).”
InfluentialSelf-citationNeural commonalities and dissociations of human social and experiential learning
Li, T., Wang, L., Zhao, Y., Su, C., Eickhoff, S. B., Olsson, A., Feng, C., & Pan, Y. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106611 · Cited by 1
Self-citationNeurofunctional Correlates of Emotional Dysregulation: Systematic Review and ALE Meta‐Analysis
Wong, E., Loconte, R., Terigi, F., Giani, E., Bucci, M., Rossi, M. M. D., Coraci, D., Milone, A., Masi, G., Cecchetti, L., et al. (2026) · Brain and Behavior · doi:10.1002/brb3.71376 · Cited by 1
“Following the recommendations by Eickhoff et al. ( 2016 ) and Müller et al. ( 2018 ), the ALE results were corrected for multiple comparisons using family-wise error correction (FWE-c) with a cluster-level inference threshold of p < 0.05.”
InfluentialYu, X.-C., Ran, W., Chen, G., Chen, T., Cui, J., Wang, Y., & Chan, R. C. K. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121948 · Cited by 1
Evaluating the methodological quality of coordinate-based meta-analyses: the qual-CBMA checklist
Raduà, J., Fortea, L., Albajes‐Eizagirre, A., Eickhoff, C. R., Fullana, M. A., Fusar‐Poli, P., Mataix‐Cols, D., Shin, J. I., Solanes, A., Tahmasian, M., et al. (2026) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2026.1753543 · Cited by 1
Self-citation
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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