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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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“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).”
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“…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).”
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“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).”
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“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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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
“Moreover, to meet criteria of robust unbiased results, clusters were only considered significant if the most dominant experiment (MDE) contributed to the significant cluster on average less than 50% and the two MDEs (2MDEs) contributed on average less than 80% (Bellucci et al., 2017b; Bellucci et al., 2018; Eickhoff e…”
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“The recommended number of studies to yield sufficient statistical power for ALE meta-analyses is 17-20 (Eickhoff et al., 2016; Müller et al., 2018).”
BackgroundLocalizing 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
Self-citationSun, J.-T., Hu, B., Chen, T.-Q., Chen, Z., Shang, Y., Li, Y., Wang, R., & Wang, W. (2023) · Brain Imaging and Behavior · doi:10.1007/s11682-023-00762-w · Cited by 38
“Based on recommendations(Eickhoff et al., 2016), ALE maps were set with a threshold at p 0.05 using a cluster level familywise error (cFWE) with a cluster forming threshold at voxel level p 0.001.”
MethodInfluentialRaimo, S., Santangelo, G., D’Iorio, A., Trojano, L., & Grossi, D. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9959-0 · Cited by 37
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Neural basis of dysphagia in stroke: A systematic review and meta-analysis
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“…results, clusters were defined as significant only if they met the criterion: (i) at least two studies contribute same clusters; (ii) the contribution of the most dominant experiment (MDE) on a significant cluster is <50%, and the 2MDEs are <80% (Eickhoff et al., 2016; Bellucci et al., 2018).”
BackgroundSelf-citationCastelhano, J., Lima, G., Teixeira, M., Soares, C., Pais, M. L., & Castelo-Branco, M. S. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.739053 · Cited by 41
“To identify functional brain imaging studies, our inclusion criteria were: 1) the studies imaged the whole brain; 2) the results presented coordinate-based data in a standard space and were not review papers; 3) the imaging method was fMRI or PET; 4) subjects were healthy controls; 5) sample size N ≥ 8 (Eickhoff et al…”
BackgroundMethodChen, C.-F., Chen, W.-N., & Zhang, B. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00503-x · Cited by 35
“A recent study declared that at least 17–20 investigations into an activation likelihood estimation (ALE) meta-analysis are necessary to achieve sufficient power for moderate effects (Eickhoff et al., 2016).”
BackgroundGlobal Alterations of Whole Brain Structural Connectome in Parkinson’s Disease: A Meta-analysis
Zuo, C., Suo, X., Lan, H., Pan, N., Wang, S., Kemp, G. J., & Gong, Q. (2022) · Neuropsychology Review · doi:10.1007/s11065-022-09559-y · Cited by 35
“…needed for the meta-analysis (Greco et al., 2013; Müller et al., 2018; Pigott, 2012; Valentine et al., 2010): some view 17–20 studies are desirable (Eickhoff et al., 2016), while others argue that meta-analysis can be conducted with as few as 2 studies which meet quality standards and statistical…”
BackgroundThe hippocampal network model: A transdiagnostic metaconnectomic approach
Kotkowski, E., Price, L. R., Fox, P. M., Vanasse, T. J., & Fox, P. T. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.01.002 · Cited by 33
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BackgroundStructural and functional neuroimaging of late-life depression: a coordinate-based meta-analysis
Saberi, A., Mohammadi, E., Zarei, M., Eickhoff, S. B., & Tahmasian, M. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00494-9 · Cited by 33
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“This choice reflects effects that are present in one out of five of all treatment-related intervention studies in PWA,which is an effect size reported to bemuchmore common thanhigher effect sizes (Eickhoff et al., 2016).”
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Brain Responses to Noxious Stimuli in Patients With Chronic Pain
Xu, A., Larsen, B., Henn, A. T., Baller, E. B., Scott, J. C., Sharma, V., Adebimpe, A., Basbaum, A. I., Corder, G. F., Dworkin, R. H., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.32236 · Cited by 32
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An fMRI meta-analysis of the role of the striatum in everyday-life vs laboratory-developed habits
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“As such, it is difficult to form a clear conclusion as to the consensus in the field, or to conduct a useful meta-analysis of WMT brain imaging studies—which will need to be reserved for such a time that a sufficient number of comparable studies can be analyzed with Activation Likelihood Estimation (ALE) for example (…”
Teghil, A., Bonavita, A., Guariglia, C., & Boccia, M. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.06.012 · Cited by 35
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Kamalian, A., khodadadifar, T., Saberi, A., Masoudi, M., Camilleri, J. A., Eickhoff, C. R., Zarei, M., Pasquini, L., Laird, A. R., Fox, P. T., et al. (2022) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12318 · Cited by 31
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“2016), voxel-level FWE allows to more readily interpret all the cluster extent as well as its peak location from the main localizer samples anatomically (Eickhoff et al. 2016).”
InfluentialSoares, C., Mier, G. G., Castelhano, J., & Castelo-Branco, M. S. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105325 · Cited by 30
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Functional neuroanatomy of blindsight revealed by activation likelihood estimation meta-analysis
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The neural basis of gambling disorder: An activation likelihood estimation meta-analysis
Raimo, S., Cropano, M., Trojano, L., & Santangelo, G. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.11.027 · Cited by 33
Raine, P. J., & Rao, H. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00659-0 · Cited by 30
“While significance thresholds for ALE scores in some prior ALE meta-analyses have used uncorrected p values or FDR corrected thresholds, these have been viewed as suboptimal (Eickhoff et al., 2016).”
BackgroundManuello, J., Costa, T., Cauda, F., & Liloia, D. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104659 · Cited by 30
Ding, R., Ren, J., Li, S., Zhu, X., Zhang, K., & Luo, W. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107286 · Cited by 30
Does Meditation Alter Brain Responses to Negative Stimuli? A Systematic Review
Magalhães, A. A., Oliveira, L., Pereira, M. G., & Menezes, C. B. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00448 · Cited by 30
Properties of cross-modal occipital responses in early blindness: An ALE meta-analysis
Zhang, C., Lee, T. M. C., Fu, Y., Ren, C., Chan, C. C. H., & Tao, Q. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102041 · Cited by 32
Imaging Happiness: Meta Analysis and Review
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Saarinen, A. I. L., Huhtaniska, S., Pudas, J., Björnholm, L., Jukuri, T., Tohka, J., Granö, N., Barnett, J. H., Kiviniemi, V. J., Veijola, J., et al. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2019.12.023 · Cited by 33
“4 Methodological considerations The number of fMRI studies investigating social cognition was comparatively low (7 studies), whereas the optimal number of studies for meta-analysis is likely higher (Eickhoff et al., 2016).”
BackgroundMethodMapping common grey matter volume deviation across child and adolescent psychiatric disorders
Li, T., Wang, L., Camilleri, J. A., Chen, X., Li, S., Stewart, J. L., Jiang, Y., Eickhoff, S. B., & Feng, C. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.05.015 · Cited by 30
Self-citationInvolvement of the cerebellum in the serial reaction time task (SRT) (Response to Janacsek et al.)
Baetens, K., Firouzi, M., Overwalle, F. V., & Deroost, N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117114 · Cited by 28
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
“After identification of significant convergence, we computed the average non-linear contribution of the different experiments to each significant (Eickhoff et al., 2016) to ensure that the contribution of the most dominant experiment (MDE) did not exceed 50%, and the contribution of the two MDE did not exceed 80% (Eic…”
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“Second, to achieve 80% power to detect effects that are present in approximately 1/3 of the population, a sample size of 17 experiments is required when using cluster-level FWE (Eickhoff et al., 2016).”
BackgroundResultInfluentialAlthubeati, S., Avery, A., Tench, C. R., Lobo, D. N., Salter, A. M., & Eldeghaidy, S. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104603 · Cited by 28
Reappraisal 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
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“Furthermore, LocalALE does not require a minimum of at least 17 studies to avoid individual studies dominating the significant results (Eickhoff et al., 2016).”
Examination of common and unique brain regions for atypical reading and math: a meta-analysis
Martinez‐Lincoln, A., Fotidzis, T. S., Cutting, L. E., Price, G. R., & Barquero, L. A. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad013 · Cited by 27
No reliable gray matter changes in essential tremor
Luo, R., Pan, P., Xu, Y., & Chen, L. (2019) · Neurological Sciences · doi:10.1007/s10072-019-03933-0 · Cited by 28
Faroqi‐Shah, Y., Sebastian, R., & Woude, A. V. (2018) · Human Brain Mapping · doi:10.1002/hbm.24334 · Cited by 29
Zheng, D., Chen, C., Song, W., Yi, Z., Zhao, P., Zhong, J., Dai, Z., Shi, H., & Pan, P. (2019) · Behavioural Brain Research · doi:10.1016/j.bbr.2019.111973 · Cited by 26
Brain‐based ranking of cognitive domains to predict schizophrenia
Karrer, T. M., Bassett, D. S., Derntl, B., Gruber, O., Aleman, A., Jardri, R., Laird, A. R., Fox, P. T., Eickhoff, S. B., Grisel, O., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24716 · Cited by 26
“By considering only cognitive domains that can be based on a sufficient number of neuroimaging experiments (Bossier et al., 2018; Eickhoff et al., 2016), we could construct robust and meaningful brain–behavior maps, as we will describe in detail in the next section.”
MethodSelf-citationIdiopathic and acquired pedophilia as two distinct disorders: an insight from neuroimaging
Scarpazza, C., Finos, L., Genon, S., Masiero, L., Bortolato, E., Cavaliere, C., Pezzaioli, J., Monaro, M., Navarin, N., Battaglia, U., et al. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00442-z · Cited by 27
Sai, L., Bellucci, G., Wang, C., Fu, G., Camilleri, J. A., Eickhoff, S. B., & Krüeger, F. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109208118 · Cited by 27
“Finally, the relatively low number of studies for SD compared to ID might have prevented us from detecting effects of small effect size (15).”
BackgroundSelf-citationFeng, 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-citationLuo, Y., Dong, D., Huang, H., Zhou, J., Zuo, X., Hu, J. H., He, H., Jiang, S., Duan, M., Yao, D., et al. (2023) · Schizophrenia Bulletin · doi:10.1093/schbul/sbad047 · Cited by 26
Balgova, E., Diveica, V., Jackson, R. L., & Binney, R. J. (2024) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2024.108904 · Cited by 28
Topological learning for brain networks
Songdechakraiwut, T., & Chung, M. K. (2023) · The Annals of Applied Statistics · doi:10.1214/22-aoas1633 · Cited by 32
Liloia, D., Mancuso, L., Uddin, L. Q., Costa, T., Nani, A., Keller, R., Manuello, J., Duca, S., & Cauda, F. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102583 · Cited by 24
Structural and functional neural correlates of schizotypy: A systematic review.
Tonini, E., Quidé, Y., Kaur, M., Whitford, T. J., & Green, M. J. (2021) · Psychological Bulletin · doi:10.1037/bul0000260 · Cited by 25
Jiao, L., Meng, N., Wang, Z., Schwieter, J. W., & Liu, C. (2022) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2022.108273 · Cited by 29
Kokkonen, A., Honkanen, E. A., & Joutsa, J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119473 · Cited by 26
“As per the most recent recommendations (Eickhoff, Bzdok, Laird, Kurth, & Fox, 2012; Eickhoff et al., 2016), each analysis was”
MethodNeural systems underlying RDoC social constructs: An activation likelihood estimation meta-analysis
Lobo, R. P., Bottenhorn, K. L., Riedel, M. C., Toma, A. I., Hare, M. M., Smith, D. D., Moor, A. C., Cowan, I., Valdes, J. A., Bartley, J. E., et al. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104971 · Cited by 26
Self-citationIstomina, A., & Arsalidou, M. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101419 · Cited by 26
“ALE is a coordinate-based meta-analytical method that combines data from multiple experiments investigating a common cognitive phenomenon (Turkeltaub et al., 2002; 2012; Eickhoff et al., 2016; 2017).”
Coordinate based random effect size meta-analysis of neuroimaging studies
Tench, C. R., Tănăsescu, R., Constantinescu, C. S., Auer, D. P., & Cottam, W. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.002 · Cited by 25
“Each study included an average, after censoring, of 10 randomly (uniformly from a GM mask) distributed coordinates, and up to 5 (depending on the censoring) coordinates spatially distributed about known clustering points; this spatial distribution was Gaussian, using a standard deviation of the distances from the cent…”
BackgroundMethodInfluentialA meta-analysis of fMRI studies of semantic cognition in children
Enge, A., Rahman, R. A., & Skeide, M. A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118436 · Cited by 24
Chen, Y., & Canli, T. (2022) · Personality Neuroscience · doi:10.1017/pen.2021.5 · Cited by 26
“Although no study, to our knowledge, has empirically evaluated the minimal number of studies required for meta-analysis using SDM, Eickhoff et al. (2016) suggested that between 17 to 20 studies are required to achieve adequate power using activation likelihood estimator (ALE) meta-analysis,…”
Yeung, A. W. K., Robertson, M., Uecker, A. M., Fox, P. T., & Eickhoff, S. B. (2022) · Human Brain Mapping · doi:10.1002/hbm.26177 · Cited by 25
“Unfortunately, we found that only 30.3% of the surveyed ALE papers had an average of >17 Experiments per analysis as recommended by (Eickhoff et al., 2016; Müller et al., 2018).”
BackgroundMethodInfluentialSelf-citationLin, H.-M., Chang, Y.-T., Chen, M.-H., Liu, S.-T., Chen, B.-S., Li, L., Lee, C.-Y., Sue, Y.-R., Sung, T.-M., Sun, C.-K., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192316277 · Cited by 26
“Therefore, CBA is recommended for ALE studies [28].”
BackgroundTablante, J., Krossa, L., Azimi, T., & Chen, L. (2023) · Human Brain Mapping · doi:10.1002/hbm.26240 · Cited by 24
“We converted all coordinates to MNI space using Talairach to MNI (SPM) transform for analytic consistency provided by GingerALE (version 3.0.2; Eickhoff et al., 2009, 2012, 2016), and all the reported analyses were conducted in the same version of the GingerALE.”
MethodInfluentialElectroconvulsive therapy modulates grey matter increase in a hub of an affect processing network
Camilleri, J. A., Hoffstaedter, F., Zavorotnyy, M., Zöllner, R., Wolf, R. C., Thomann, P. A., Redlich, R., Opel, N., Dannlowski, U., Grözinger, M., et al. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102114 · Cited by 26
“05 after correction for multiple comparisons (Eickhoff et al., 2016).”
Self-citationThe 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
“However, for performing robust and valid ALE meta-analyses, a minimum number of 17 to 20 experiments is needed (Eickhoff et al., 2016).”
BackgroundSelf-citationSaberi, A., Ebneabbasi, A., Rahimi, S., Sarebannejad, S., Şen, Z. D., Graf, H., Walter, M., Sorg, C., Camilleri, J. A., Laird, A. R., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02780-6 · Cited by 26
“The analyses were performed only if 12 or more experiments were included in each category, as ALE analyses with too few experiments are likely to be largely driven by a single experiment and, therefore, lack suf fi cient statistical power to provide valid results [41].”
Self-citationEllmore, T. M., Suescun, J., Castriotta, R. J., & Schiess, M. C. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00815 · Cited by 24
“These tables can be used to confirm replication of results in other exploratory analyses (65) and facilitate meta-analyses (66).”
Age differences in predicting working memory performance from network-based functional connectivity
Pläschke, R. N., Patil, K. R., Cieslik, E. C., Nostro, A. D., Varikuti, D. P., Plachti, A., Lösche, P., Hoffstaedter, F., Kalenscher, T., Langner, R., et al. (2020) · Cortex · doi:10.1016/j.cortex.2020.08.012 · Cited by 24
“Here we addressed this question by taking a novel approach leveraging the power of coordinate-based meta-analyses (Eickhoff et al., 2016; Müller et al., 2018) to robustly define regions of the brain that are consistently recruited across dozens to hundreds of neuroimaging studies examining a particular mental function.”
MethodSelf-citationCosta, C., Cristea, I. A., Bò, E. D., Melloni, C., & Gentili, C. (2021) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13412 · Cited by 26
Janiri, D., Moser, D. A., Doucet, G. E., Luber, M. J., Rasgon, A., Lee, W. H., Murrough, J. W., Sani, G., Eickhoff, S. B., & Frangou, S. (2021) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.19206 · Cited by 28
Self-citationLiloia, D., Cauda, F., Uddin, L. Q., Manuello, J., Mancuso, L., Keller, R., Nani, A., & Costa, T. (2022) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2022.01.007 · Cited by 23
Parcellation‐based tractographic modeling of the salience network through meta‐analysis
Briggs, R. G., Young, I. M., Dadario, N. B., Fonseka, R. D., Hormovas, J., Allan, P. G., Larsen, M. L., Lin, Y., Tanglay, O., Maxwell, B. D., et al. (2022) · Brain and Behavior · doi:10.1002/brb3.2646 · Cited by 24
Arioli, M., Cattaneo, Z., Parimbelli, S., & Canessa, N. (2023) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsad003 · Cited by 27
“The number of experiments included in most of these meta-analyses is in line with the recent prescriptions for ALE (Eickhoff et al., 2016; Müller et al., 2018), suggesting a minimum of 17 experiments to ensure that results would not be driven by single experiments (Xiong et al.”
ResultWojtasik, 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-citationA lack of consistent brain grey matter alterations in migraine
Sheng, L., Zhao, P., Ma, H., Yuan, C., Zhong, J., Dai, Z., & Pan, P. (2020) · Brain · doi:10.1093/brain/awaa123 · Cited by 24
Bulut, T. (2023) · Brain and Behavior · doi:10.1002/brb3.3046 · Cited by 26
Tannou, T., Magnin, É., Comte, A., Aubry, R., & Joubert, S. (2021) · Brain Sciences · doi:10.3390/brainsci11081043 · Cited by 23
“0) to combine the activation coordinates from several studies [34].”
BackgroundCargnelutti, E., Tomasino, B., & Fabbro, F. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.744489 · Cited by 23
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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