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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Existing ALE meta-analyses do not meet the current standards for type I error control.
“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
Following current best-practice guidelines, we assessed significance using a voxel-level threshold and a cluster-level familywise error-corrected threshold, and performed ALE meta-analyses only when at least 17 experiments reported activation foci for a contrast.
“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
The meta-analysis had more than 80% power to detect convergence of activation differences in areas identified in just 7 (20%) of the experiments.
“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
The false-discovery rate correction for multiple comparisons has been shown to be inappropriate for neuroimaging data and to entail both low sensitivity and high susceptibility to spurious, false positive findings.
“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
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.
“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
Efron, B., & Tibshirani, R. (1995) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2983304 · Cited by 22,453 · see citing works
Power failure: why small sample size undermines the reliability of neuroscience
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3475 · Cited by 7,641 · cited 1× · see citing works
“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
Genovese, C. R., Lazar, N. A., & Nichols, T. (2002) · NeuroImage · doi:10.1006/nimg.2001.1037 · Cited by 4,923 · cited 2× · see citing works
“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× · see citing works
“…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.
Buckland, S. T., Efron, B., & Tibshirani, R. J. (1994) · Biometrics · doi:10.2307/2532810 · Cited by 3,236 · see citing works
Mazziotta, J. C., Toga, A. W., Evans, A. C., Fox, P. T., Lancaster, J. L., Zilles, K., Woods, R. P., Paus, T., Simpson, G. V., Pike, G. B., et al. (2001) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2001.0915 · Cited by 2,334 · cited 1× · see citing works
“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 · see citing works
Eickhoff, S. B., Laird, A. R., Grefkes, C., Wang, L. E., Zilles, K., & Fox, P. T. (2009) · Human Brain Mapping · doi:10.1002/hbm.20718 · Cited by 1,924 · cited 5× · see citing works
“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× · see citing works
“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× · see citing works
“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.”
Activation Likelihood Estimation meta-analysis revisited
Eickhoff, S., Bzdok, D., Laird, A., Kurth, F., & Fox, P. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.017 · Cited by 1,448 · cited 9× · see citing works
“a) Family-wise error correction on the voxel level controls the chance of observing a given Z-value if foci were randomly distributed (Eickhoff et al., 2012).”
InfluentialSelf-citationIdentification of a Common Neurobiological Substrate for Mental Illness
Goodkind, M., Eickhoff, S. B., Oathes, D. J., Jiang, Y., Chang, A., Jones-Hagata, L. B., Ortega, B. N., Zaiko, Y. V., Roach, E. L., Korgaonkar, M. S., et al. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.2206 · Cited by 1,404 · cited 2× · see citing works
“, 2012) and psychiatric (Bludau et al., 2015; Chan et al., 2011; Goodkind et al., 2015) disorders.”
Self-citationKurth, F., Zilles, K., Fox, P. T., Laird, A. R., & Eickhoff, S. B. (2010) · Brain Structure and Function · doi:10.1007/s00429-010-0255-z · Cited by 1,293 · cited 3× · see citing works
“The more worrisome pattern, however, is found for FDR correction (Laird et al., 2005).”
InfluentialSelf-citationCluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations
Woo, C.-W., Krishnan, A., & Wager, T. D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.12.058 · Cited by 1,253 · cited 2× · see citing works
“Searches for studies investigating major brain disorders such as schizophrenia, depression, dementia, stroke or Parkinson’s disease likewise revealed many hundred clinical neuroimaging papers.”
Detecting activations in PET and fMRI: levels of inference and power
Friston, K. J., Holmes, A., Poline, J.-B., Price, C. J., & Frith, C. D. (1996) · NeuroImage · doi:10.1006/nimg.1996.0074 · Cited by 1,148 · cited 2× · see citing works
“This is consistent behavior in traditional brain imaging where cluster-level inference typically exhibits superior power to voxel-level inference (Friston et al., 1996), though this difference must be considered in light of cluster-inference's reduced spatial specificity relative to voxel-level inference.”
Turkeltaub, P., Eickhoff, S., Laird, A. R., Fox, M., Wiener, M., & Fox, P. (2011) · Human Brain Mapping · doi:10.1002/hbm.21186 · Cited by 1,114 · cited 2× · see citing works
“…contribution of individual studies 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).”
Self-citationBehavioral interpretations of intrinsic connectivity networks
Laird, A. R., Fox, P. M., Eickhoff, S. B., Turner, J. A., Ray, K. L., McKay, D. R., Glahn, D. C., Beckmann, C. F., Smith, S. M., & Fox, P. T. (2011) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00077 · Cited by 1,023 · see citing works
Self-citationNeural network of cognitive emotion regulation—an ALE meta-analysis and MACM analysis
Kohn, N., Eickhoff, S. B., Scheller, M., Laird, A. R., Fox, P. T., & Habel, U. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.001 · Cited by 956 · cited 1× · see citing works
“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).”
Self-citationModelling neural correlates of working memory: A coordinate-based meta-analysis
Rottschy, C., Langner, R., Dogan, I., Reetz, K., Laird, A. R., Schulz, J. B., Fox, P. T., & Eickhoff, S. B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.050 · Cited by 951 · cited 2× · see citing works
“The first pool contained 15 hand-coded datasets (Bzdok et al., 2012; Cieslik et al., 2015; Hardwick et al., 2013; Kohn et al., 2014; Rottschy et al., 2012).”
Self-citationALE meta-analysis: controlling the false discovery rate and performing statistical contrasts
Laird, A. R., Fox, P. M., Price, C. J., Glahn, D. C., Uecker, A. M., Lancaster, J. L., Turkeltaub, P. E., Kochunov, P., & Fox, P. T. (2005) · Human Brain Mapping · doi:10.1002/hbm.20136 · Cited by 868 · see citing works
Self-citationInterference resolution: Insights from a meta-analysis of neuroimaging tasks
NEE, D. E., WAGER, T. D., & JONIDES, J. (2007) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/cabn.7.1.1 · Cited by 798 · cited 3× · see citing works
“Rather, multi-level kernel density analysis (MKDA (Kober and Wager, 2010; Nee et al., 2007; Wager et al., 2009)) and signed difference map analysis (SDM (Palaniyappan et al., 2012; Radua and Mataix-Cols, 2012; Radua et al., 2010)) have likewise been frequently used to investigate convergence across…”
Evans, A. C., Janke, A. L., Collins, D. L., & Baillet, S. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.024 · Cited by 697 · cited 3× · see citing works
“…meta-analyses, i.e., studies integrating previous neuroimaging findings on a particular type of mental process or task, there is a growing interest in using meta-analytic methods for the assessment of task-based interaction patterns (Eickhoff et al., 2010; Fox et al., 2014; Laird et al., 2013).”
Bzdok, D., Schilbach, L., Vogeley, K., Schneider, K., Laird, A. R., Langner, R., & Eickhoff, S. B. (2012) · Brain Structure and Function · doi:10.1007/s00429-012-0380-y · Cited by 653 · cited 1× · see citing works
“The first pool contained 15 hand-coded datasets (Bzdok et al., 2012; Cieslik et al., 2015; Hardwick et al., 2013; Kohn et al., 2014; Rottschy et al., 2012).”
Self-citationA quantitative meta-analysis and review of motor learning in the human brain
Hardwick, R. M., Rottschy, C., Miall, R. C., & Eickhoff, S. B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.020 · Cited by 619 · cited 1× · see citing works
“The first pool contained 15 hand-coded datasets (Bzdok et al., 2012; Cieslik et al., 2015; Hardwick et al., 2013; Kohn et al., 2014; Rottschy et al., 2012).”
Self-citationThe secret lives of experiments: Methods reporting in the fMRI literature
Carp, J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.07.004 · Cited by 473 · cited 3× · see citing works
“Furthermore, the substantial analytic flexibility of neuroimaging pipelines also leads to low reliability and reproducibility of neuroimaging findings (Carp, 2012b; Glatard et al., 2015; Wager et al., 2009).”
False discovery rate revisited: FDR and topological inference using Gaussian random fields
Chumbley, J. R., & Friston, K. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.021 · Cited by 458 · cited 5× · see citing works
“As previously pointed out, voxel-wise FDR correction, the type considered here and almost exclusively used in ALE analysis is not appropriate for inference on topological features such as regions of significant convergence of a smooth dataset (Chumbley and Friston, 2009).”
InfluentialMeta-analysis of functional neuroimaging data: current and future directions
Wager, T. D., Lindquist, M., & Kaplan, L. (2007) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsm015 · Cited by 434 · cited 4× · see citing works
“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).”
InfluentialRadua, J., van den Heuvel, O. A., Surguladze, S., & Mataix-Cols, D. (2010) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2010.70 · Cited by 400 · cited 1× · see citing works
“…contribution of individual studies 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).”
Salimi-Khorshidi, G., Smith, S. M., Keltner, J. R., Wager, T. D., & Nichols, T. E. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.039 · Cited by 388 · cited 1× · see citing works
“They provide robust insights into the location of psychological and pathological effects (Kober and Wager, 2010; Nickl-Jockschat et al., 2015; Radua and Mataix-Cols, 2012; Schilbach et al., 2012; Yarkoni et al., 2011).”
Self-citationOn the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments
Carp, J. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00149 · Cited by 381 · cited 3× · see citing works
“Furthermore, the substantial analytic flexibility of neuroimaging pipelines also leads to low reliability and reproducibility of neuroimaging findings (Carp, 2012b; Glatard et al., 2015; Wager et al., 2009).”
Activation likelihood estimation meta-analysis of motor-related neural activity after stroke
Rehme, A. K., Eickhoff, S. B., Rottschy, C., Fink, G. R., & Grefkes, C. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.023 · Cited by 377 · cited 1× · see citing works
“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).”
Self-citationLaird, A. R. (2009) · Frontiers in Neuroinformatics · doi:10.3389/neuro.11.023.2009 · Cited by 372 · see citing works
Self-citationMultivariate pattern analysis of fMRI: The early beginnings
Haxby, J. V. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.016 · Cited by 369 · cited 2× · see citing works
“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).”
Eickhoff, S. B., Jbabdi, S., Caspers, S., Laird, A. R., Fox, P. T., Zilles, K., & Behrens, T. E. J. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5664-09.2010 · Cited by 352 · cited 2× · see citing works
“…meta-analyses, i.e., studies integrating previous neuroimaging findings on a particular type of mental process or task, there is a growing interest in using meta-analytic methods for the assessment of task-based interaction patterns (Eickhoff et al., 2010; Fox et al., 2014; Laird et al., 2013).”
Self-citationMetaanalytic connectivity modeling: Delineating the functional connectivity of the human amygdala
Robinson, J. L., Laird, A. R., Glahn, D. C., Lovallo, W. R., & Fox, P. T. (2010) · Human Brain Mapping · doi:10.1002/hbm.20854 · Cited by 327 · cited 1× · see citing works
“…functional imaging using Positron-Emission Tomography (PET) and in particular functional Magnetic Resonance Imaging (fMRI) have provided ample information about the location of cognitive, sensory and motor processes in the human brain (Bandettini, 2012; Poldrack, 2012; Rosen and Savoy, 2012).”
Self-citationThree key regions for supervisory attentional control: Evidence from neuroimaging meta-analyses
Cieslik, E. C., Mueller, V. I., Eickhoff, C. R., Langner, R., & Eickhoff, S. B. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.11.003 · Cited by 322 · cited 2× · see citing works
“It may hence come as no surprise that brain regions reported to be associated with a given psychological process may be found almost anywhere in the brain (Cieslik et al., 2015; Nee et al., 2007).”
Self-citationChan, R. C. K., Di, X., McAlonan, G. M., & Gong, Q. (2009) · Schizophrenia Bulletin · doi:10.1093/schbul/sbp073 · Cited by 322 · cited 1× · see citing works
“…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; Chan et al., 2011; Goodkind et al., 2015) disorders.”
Distributed Representations in Memory: Insights from Functional Brain Imaging
Rissman, J., & Wagner, A. D. (2012) · Annual Review of Psychology · doi:10.1146/annurev-psych-120710-100344 · Cited by 299 · cited 1× · see citing works
“The fundamental idea behind these types of location-based meta-analyses is to assess which regions are co-active above chance given activation in a predefined region of interest as a marker of functional connectivity between them (Robinson et al., 2010; Rottschy et al., 2013).”
Twenty years of functional MRI: The science and the stories
Bandettini, P. A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.026 · Cited by 268 · cited 2× · see citing works
“For more than two decades, functional imaging using PositronEmission Tomography (PET) and in particular functional Magnetic Resonance Imaging (fMRI) have provided ample information about the location of cognitive, sensory and motor processes in the human brain (Bandettini, 2012; Poldrack, 2012; Rosen and Savoy, 2012).”
Meta-analytic methods for neuroimaging data explained
Radua, J., & Mataix-Cols, D. (2012) · Biology of Mood & Anxiety Disorders · doi:10.1186/2045-5380-2-6 · Cited by 246 · cited 7× · see citing works
“…analysis (MKDA (Kober and Wager, 2010; Nee et al., 2007; Wager et al., 2009)) and signed difference map analysis (SDM (Palaniyappan et al., 2012; Radua and Mataix-Cols, 2012; Radua et al., 2010)) have likewise been frequently used to investigate convergence across functional and structural…”
InfluentialEvaluating the consistency and specificity of neuroimaging data using meta-analysis
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“Rather, multi-level kernel density analysis (MKDA (Kober and Wager, 2010; Nee et al., 2007; Wager et al., 2009)) and signed difference map analysis (SDM (Palaniyappan et al., 2012; Radua and Mataix-Cols, 2012; Radua et al., 2010)) have likewise been frequently used to investigate convergence across…”
Self-citationSchilbach, L., Bzdok, D., Timmermans, B., Fox, P. T., Laird, A. R., Vogeley, K. T., & Eickhoff, S. B. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030920 · Cited by 227 · cited 2× · see citing works
“They provide robust insights into the location of psychological and pathological effects (Kober and Wager, 2010; Nickl-Jockschat et al., 2015; Radua and Mataix-Cols, 2012; Schilbach et al., 2012; Yarkoni et al., 2011).”
Self-citationChase, H. W., Kumar, P., Eickhoff, S. B., & Dombrovski, A. Y. (2015) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-015-0338-7 · Cited by 225 · cited 2× · see citing works
“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.”
Self-citationThe BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data
Laird, A. R., Eickhoff, S. B., Fox, P. M., Uecker, A. M., Ray, K. L., Saenz, J. J., McKay, D., Bzdok, D., Laird, R. W., Robinson, J. L., et al. (2011) · BMC Research Notes · doi:10.1186/1756-0500-4-349 · Cited by 213 · see citing works
Self-citationAmanzio, M., Benedetti, F., Porro, C. A., Palermo, S., & Cauda, F. (2013) · Human Brain Mapping · doi:10.1002/hbm.21471 · Cited by 202 · cited 1× · see citing works
“There is no question that results obtained throughout all realizations of the jackknife procedure must be considered robustly present in the dataset and not dependent on any one experiment (Amanzio et al., 2013; Palaniyappan et al., 2012).”
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Poldrack, R. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.007 · Cited by 189 · cited 4× · see citing works
“The notion of effect-size, however, is somewhat tricky in ALE meta-analyses, as in contrast to classical behavioral meta-analyses neuroimaging meta-analyses assess spatial convergence (Eickhoff et al., 2009b; Kober and Wager, 2010; Radua and Mataix-Cols, 2012).”
InfluentialALE meta-analysis on facial judgments of trustworthiness and attractiveness
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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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“, 2014; Yuan and Brown, 2015) neuroscience to the neurobiology of neurological (Herz et al., 2014; Rehme et al., 2012) and psychiatric (Bludau et al.”
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Weinberger, D. R., & Radulescu, E. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.15060753 · Cited by 173 · cited 1× · see citing works
“…a cluster-extent threshold that controls the chance of observing a cluster of that size if foci were randomly distributed (Eickhoff et al., 2012; Woo et al., 2014). c) Correction for multiple comparisons using the false-discovery rate (Laird et al., 2005; Wager et al., 2007) has also been…”
Conducting a meta-analysis: Basics and good practices
Cheung, M. W. ‐., Ho, R. C. M., Lim, Y., & Mak, A. (2012) · International Journal of Rheumatic Diseases · doi:10.1111/j.1756-185x.2012.01712.x · Cited by 172 · cited 1× · see citing works
“In fact, ALE is in stark contrast to behavioral “effect-size” meta-analyses by not considering the size of the reported effects (Cheung et al., 2012; Jones, 1995) but rather their location and associated spatial uncertainty.”
Meta-Analysis in Human Neuroimaging: Computational Modeling of Large-Scale Databases
Fox, P. T., Lancaster, J. L., Laird, A. R., & Eickhoff, S. B. (2014) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062012-170320 · Cited by 172 · cited 4× · see citing works
“…meta-analyses, i.e., studies integrating previous neuroimaging findings on a particular type of mental process or task, there is a growing interest in using meta-analytic methods for the assessment of task-based interaction patterns (Eickhoff et al., 2010; Fox et al., 2014; Laird et al., 2013).”
InfluentialSelf-citationNetworks of task co-activations
Laird, A. R., Eickhoff, S. B., Rottschy, C., Bzdok, D., Ray, K. L., & Fox, P. T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.073 · Cited by 168 · cited 1× · see citing works
“…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., 2014; Rehme et…”
Self-citationMolenberghs, P., Sale, M. V., & Mattingley, J. B. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00078 · Cited by 156 · cited 2× · see citing works
“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.”
Structural correlates of auditory hallucinations in schizophrenia: a meta-analysis.
Palaniyappan, L., Balain, V., Radua, J., & Liddle, P. (2012) · Schizophrenia Research · doi:10.1016/j.schres.2012.01.038 · Cited by 156 · cited 1× · see citing works
“…functional imaging using Positron-Emission Tomography (PET) and in particular functional Magnetic Resonance Imaging (fMRI) have provided ample information about the location of cognitive, sensory and motor processes in the human brain (Bandettini, 2012; Poldrack, 2012; Rosen and Savoy, 2012).”
Reproducibility of neuroimaging analyses across operating systems
Glatard, T., Lewis, L. B., da Silva, R. F., Adalat, R., Beck, N., Lepage, C., Rioux, P., Rousseau, M.-É., Sherif, T., Deelman, E., et al. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2015.00012 · Cited by 137 · cited 1× · see citing works
“Furthermore, the substantial analytic flexibility of neuroimaging pipelines also leads to low reliability and reproducibility of neuroimaging findings (Carp, 2012b; Glatard et al., 2015; Wager et al., 2009).”
Test–retest reliability of fMRI activation during prosaccades and antisaccades
Raemaekers, M. A. H., Vink, M., Zandbelt, B. B., Wezel, R. J. A. V., Kahn, R. S., & Ramsey, N. F. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.03.061 · Cited by 126 · cited 1× · see citing works
“…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; Chan et al., 2011; Goodkind et al., 2015) disorders.”
fMRI at 20: Has it changed the world?
Rosen, B. R., & Savoy, R. L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.004 · Cited by 103 · cited 2× · see citing works
“…states requires operationalization of the experiment, involving choices on the nature of psychological task, the displayed stimuli, the timing and arrangement of individual trials, their modeling in the statistical analysis and the assessed contrast (Carp, 2012a; Rottschy et al., 2012).”
Differentiated parietal connectivity of frontal regions for “what” and “where” memory
Rottschy, C., Caspers, S., Roski, C., Reetz, K., Dogan, I., Schulz, J. B., Zilles, K., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2012) · Brain Structure and Function · doi:10.1007/s00429-012-0476-4 · Cited by 95 · see citing works
Self-citationRelating population-code representations between man, monkey, and computational models
Kriegeskorte, N. (2009) · Frontiers in Neuroscience · doi:10.3389/neuro.01.035.2009 · Cited by 92 · cited 1× · see citing works
“Some brain regions like the anterior insula (Kurth et al., 2010; Yarkoni et al., 2011) and the posteriormedial frontal cortex (Muller et al., 2015) are more often activated than, for instance, the lateral temporal cortex.”
Müller, V. I., Langner, R., Cieslik, E. C., Rottschy, C., & Eickhoff, S. B. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0797-6 · Cited by 84 · cited 2× · see citing works
“Rather, multi-level kernel density analysis (MKDA (Kober and Wager, 2010; Nee et al., 2007; Wager et al., 2009)) and signed difference map analysis (SDM (Palaniyappan et al., 2012; Radua and Mataix-Cols, 2012; Radua et al., 2010)) have likewise been frequently used to investigate convergence across…”
Self-citationLost in localization: The need for a universal coordinate database
Derrfuss, J., & Mar, R. A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.053 · Cited by 83 · cited 2× · see citing works
“Derfuss and Mar estimated that until 2007 almost 8,000 neuroimaging studies had been published (Derrfuss and Mar, 2009).”
Interoperable atlases of the human brain
Amunts, K., Hawrylycz, M., Essen, D. C. V., Horn, J. D. V., Harel, N., Poline, J.-B., Martino, F. D., Bjaalie, J. G., Dehaene‐Lambertz, G., Dehaene, S., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.010 · Cited by 83 · see citing works
Self-citationMeta‐analysis: Weighing the evidence
Jones, D. R. H. (1995) · Statistics in Medicine · doi:10.1002/sim.4780140206 · Cited by 75 · cited 1× · see citing works
“In fact, ALE is in stark contrast to behavioral “effect-size” meta-analyses by not considering the size of the reported effects (Cheung et al., 2012; Jones, 1995) but rather their location and associated spatial uncertainty.”
Meta-analysis of neuroimaging data
Kober, H., & Wager, T. D. (2010) · WIREs Cognitive Science · doi:10.1002/wcs.41 · Cited by 74 · cited 6× · see citing works
“They provide robust insights into the location of psychological and pathological effects (Kober and Wager, 2010; Nickl-Jockschat et al., 2015; Radua and Mataix-Cols, 2012; Schilbach et al., 2012; Yarkoni et al., 2011).”
InfluentialBludau, S., Bzdok, D., Gruber, O., Kohn, N., Riedl, V., Sorg, C., Palomero-Gallagher, N., Müller, V. I., Hoffstaedter, F., Amunts, K., et al. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.15030349 · Cited by 67 · see citing works
Self-citationMeta-analytic connectivity modeling revisited: Controlling for activation base rates
Langner, R., Rottschy, C., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.007 · Cited by 63 · cited 1× · see citing works
“Additionally, the measured signals are indirect hemodynamic proxies rather than direct measures of neuronal activity (Logothetis and Wandell, 2004).”
Self-citationThe future of functional MRI in clinical medicine
Bullmore, E. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.026 · Cited by 60 · cited 2× · see citing works
“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?”
Clos, M., Rottschy, C., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.030 · Cited by 59 · cited 2× · see citing works
“Meta-Analytic Co-Activation Modeling (MACM) thus provides a complementary aspect of long-range integration relative to measures such as resting-state functional connectivity or structural covariance (Clos et al., 2014; Hardwick et al., 2015).”
Self-citationDrawing and writing: An ALE meta-analysis of sensorimotor activations
Yuan, Y., & Brown, S. (2015) · Brain and Cognition · doi:10.1016/j.bandc.2015.05.004 · Cited by 49 · cited 2× · see citing works
“…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;…”
Multimodal connectivity of motor learning-related dorsal premotor cortex
Hardwick, R. M., Lesage, E., Eickhoff, C. R., Clos, M., Fox, P. M., & Eickhoff, S. B. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.024 · Cited by 46 · cited 1× · see citing works
“The first pool contained 15 hand-coded datasets (Bzdok et al., 2012; Cieslik et al., 2015; Hardwick et al., 2013; Kohn et al., 2014; Rottschy et al., 2012).”
Self-citationKing, M. B., Rauch, L., Stein, D. J., & Brooks, S. J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.064 · Cited by 39 · cited 2× · see citing works
“, 2011) and motor (King et al., 2014; Yuan and Brown, 2015) neuroscience to the neurobiology of neurological (Herz et al.”
Nickl‐Jockschat, T., Janouschek, H., Eickhoff, S. B., & Eickhoff, C. R. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.02.030 · Cited by 33 · cited 1× · see citing works
“They provide robust insights into the location of psychological and pathological effects (Kober and Wager, 2010; Nickl-Jockschat et al., 2015; Radua and Mataix-Cols, 2012; Schilbach et al., 2012; Yarkoni et al., 2011).”
Self-citationMeta-Analyses in Basic and Clinical Neuroscience: State of the Art and Perspective
Eickhoff, S. B., & Bzdok, D. (2013) · Springer eBooks · doi:10.1007/978-3-642-34342-1_7 · Cited by 30 · see citing works
Self-citationSemi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data
Bzdok, D., Eickenberg, M., Grisel, O., Thirion, B., & Varoquaux, G. P. (2015) · HAL · Cited by 30 · see citing works
Self-citationBludau, S., Bzdok, D., Gruber, O., Kohn, N., Riedl, V., Sorg, C., Palomero‐Gallagher, N., Müller, V. I., Hoffstaedter, F., Amunts, K., et al. (2016) · Clinical Neurophysiology · doi:10.1016/j.clinph.2016.05.130 · Cited by 2 · see citing works
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- Eickhoff SB Laird AR Grefkes C Wang LE Zilles K Fox PT Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty Hum Brain Mapp 2009b 30 2907 2926 19172646 PMC2872071
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Variability in the analysis of a single neuroimaging dataset by many teams
(2020) · doi:10.1038/s41586-020-2314-9 · Cited by 1,097 · see citing works
Self-citationNeural correlates of action: Comparing meta-analyses of imagery, observation, and execution
Hardwick, R. M., Caspers, S., Eickhoff, S. B., & Swinnen, S. P. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.08.003 · Cited by 908 · see citing works
Self-citationTen simple rules for neuroimaging meta-analysis
Müller, V. I., Cieslik, E. C., Laird, A. R., Fox, P. T., Radua, J., Mataix-Cols, D., Tench, C. R., Yarkoni, T., Nichols, T. E., Turkeltaub, P. E., et al. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.11.012 · Cited by 805 · see citing works
Self-citationMcTeague, L. M., Huemer, J., Carreon, D. M., Jiang, Y., Eickhoff, S. B., & Etkin, A. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.16040400 · Cited by 635 · see citing works
“Domain-specific ALE (as well as domain-by-disorder analyses) was performed for contrasts with at least 20 experiments (31), and these are reported in the online data supplement (see Figures S12 and S13 and Tables S19 and S20).”
BackgroundMethodInfluentialSelf-citationZhou, H.-X., Chen, X., Shen, Y.-Q., Li, L., Chen, N.-X., Zhu, Z.-C., Castellanos, F. X., & Yan, C.-G. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116287 · Cited by 656 · see citing works
“Moreover, the limited number of typical rumination/distraction studies provides equivocal statistical power for meta-analysis (Eickhoff et al., 2016).”
BackgroundSchurz, M., Raduà, J., Tholen, M. G., Maliske, L., Margulies, D. S., Mars, R. B., Sallet, J., & Kanske, P. (2020) · Psychological Bulletin · doi:10.1037/bul0000303 · Cited by 642 · see citing works
“The SDM method is based on the positive features from existing peak probability methods for meta-analysis, such as activation likelihood estimation (ALE, Eickhoff et al., 2016) or multilevel kernel density analysis (MKDA, Wager, Lindquist, & Kaplan, 2007).”
MethodReward Processing in Depression: A Conceptual and Meta-Analytic Review Across fMRI and EEG Studies
Keren, H., O’Callaghan, G., Vidal-Ribas, P., Buzzell, G. A., Brotman, M. A., Leibenluft, E., Pan, P. M., Meffert, L., Kaiser, A., Wolke, S., et al. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.17101124 · Cited by 521 · see citing works
“Because of the small number of studies, we combined loss anticipation and feedback (88), and this may have diluted effects.”
BackgroundGray, J. P., Müller, V. I., Eickhoff, S. B., & Fox, P. T. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.19050560 · Cited by 449 · see citing works
“(20), the minimum number of experiments required for robust ALE analysis is 17.”
BackgroundMethodInfluentialSelf-citationResting‐state network dysfunction in Alzheimer's disease: A systematic review and meta‐analysis
Badhwar, A., Tam, A., Dansereau, C. L., Orban, P., Hoffstaedter, F., & Bellec, P. (2017) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2017.03.007 · Cited by 441 · see citing works
“, scanner make and model, scanning parameters), and processing choices [13,20,61].”
Self-citationOldham, S., Murawski, C., Fornito, A., Youssef, G. J., Yücel, M., & Lorenzetti, V. (2018) · Human Brain Mapping · doi:10.1002/hbm.24184 · Cited by 425 · see citing works
“A false-discovery rate (FDR) threshold, which has often been used in previous meta-analyses, was not used as this method is highly susceptible to false-positives (Chumbley & Friston, 2009; Eickhoff et al., 2012, 2016).”
BackgroundMethodResultInfluentialMorawetz, C., Bode, S., Derntl, B., & Heekeren, H. R. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.11.014 · Cited by 458 · see citing works
Common Dysfunction of Large-Scale Neurocognitive Networks Across Psychiatric Disorders
Sha, Z., Wager, T. D., Mechelli, A., & He, Y. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.11.011 · Cited by 367 · see citing works
Large-scale functional neural network correlates of response inhibition: an fMRI meta-analysis
Zhang, R., Geng, X., & Lee, T. M. C. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1443-x · Cited by 398 · see citing works
McTeague, L. M., Rosenberg, B. M., Lopez, J. W., Carreon, D. M., Huemer, J., Jiang, Y., Chick, C. F., Eickhoff, S. B., & Etkin, A. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.18111271 · Cited by 388 · see citing works
“Subparcellation of the dorsomedial prefrontal cortex has further specified that the observed cluster of convergence comprises two smaller contiguous regions, with the left strongly connected to the fronto-parietal network and the right strongly connected to the salience network (23, 43) (see Figure S21 in the online s…”
BackgroundMethodSelf-citationAltered Brain Activity in Unipolar Depression Revisited: Meta-analyses of Neuroimaging Studies
Müller, V. I., Cieslik, E. C., Serbanescu, I., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2016.2783 · Cited by 304 · see citing works
Self-citationImplementation errors in the GingerALE Software: Description and recommendations
Eickhoff, S. B., Laird, A. R., Fox, P. M., Lancaster, J. L., & Fox, P. T. (2016) · Human Brain Mapping · doi:10.1002/hbm.23342 · Cited by 270 · see citing works
Self-citationHein, T. C., & Monk, C. S. (2017) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12651 · Cited by 257 · see citing works
“The cluster-wise family-wise error (FWE) correction was employed to correct for multiple comparisons at a significance threshold of p .05 (Eickhoff et al., 2016).”
Berboth, S., & Morawetz, C. (2021) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2021.107767 · Cited by 258 · see citing works
Samea, F., Soluki, S., Nejati, V., Zarei, M., Cortese, S., Eickhoff, S. B., Tahmasian, M., & Eickhoff, C. R. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.02.011 · Cited by 245 · see citing works
“Furthermore, we probed for convergence among task-fMRI studies when focusing only on specific cognitive domains and found a sufficient number of experiments (> 17) (Eickhoff et al., 2016) for inhibitory control and attention, but also performed a joint analyses on the remaining cognitive tasks (memory, timing, reasoni…”
InfluentialSelf-citationNg, T. H., Alloy, L. B., & Smith, D. V. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0644-x · Cited by 244 · see citing works
Arithmetic in the developing brain: A review of brain imaging studies
Peters, L., & Smedt, B. D. (2017) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.05.002 · Cited by 234 · see citing works
Multiple large-scale neural networks underlying emotion regulation
Morawetz, C., Riedel, M. C., Salo, T., Berboth, S., Eickhoff, S. B., Laird, A. R., & Kohn, N. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.07.001 · Cited by 219 · see citing works
Self-citationNeuroimaging the menstrual cycle: A multimodal systematic review
Dubol, M., Epperson, C. N., Sacher, J., Pletzer, B. A., Derntl, B., Lanzenberger, R. R., Poromaa, I. S., & Comasco, E. (2020) · Frontiers in Neuroendocrinology · doi:10.1016/j.yfrne.2020.100878 · Cited by 193 · see citing works
“…of different study designs and limited sample size, no quantitative meta-analysis was performed on the reviewed studies, which did not meet the requirements ensuring robust results from an Activation Likelihood Estimation coordinate-based meta-analysis (Eickhoff et al., 2016; Muller et al., 2018).”
BackgroundNovel findings from 2,838 Adult Brains on Sex Differences in Gray Matter Brain Volume
Lotze, M., Domin, M., Gerlach, F. H., Gaser, C., Lueders, E., Schmidt, C. O., & Neumann, N. (2019) · Scientific Reports · doi:10.1038/s41598-018-38239-2 · Cited by 252 · see citing works
Gray matter asymmetries in aging and neurodegeneration: a review and meta-analysis.
Minkova, L., Habich, A., Peter, J., Kaller, C. P., Eickhoff, S. B., & Klöppel, S. (2017) · Human Brain Mapping · doi:10.1002/hbm.23772 · Cited by 194 · see citing works
“Finally, as recommended by a recent simulation study [Eickhoff et al., 2016], the resulting P-values were thresholded at P 0.05, cluster-level family-wise error (cFWE) r 5893 r corrected (cluster-forming threshold at voxel-level P 0.001) and transformed into Z-scores for display.”
BackgroundMethodInfluentialSelf-citationCortese, S., Aoki, Y. Y., Itahashi, T., Castellanos, F. X., & Eickhoff, S. B. (2020) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2020.08.014 · Cited by 182 · see citing works
Self-citationDisrupted Dorsal Mid-Insula Activation During Interoception Across Psychiatric Disorders
Nord, C. L., Lawson, R. P., & Dalgleish, T. (2021) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2020.20091340 · Cited by 171 · see citing works
“Approximately 17-20 experimental datasets are needed for ALE to be adequately powered to detect small effects, and ensure results are not driven by single experiments (47).”
BackgroundMethodComputing the Social Brain Connectome Across Systems and States
Alcalá-López, D., Smallwood, J., Jefferies, E., Overwalle, F. V., Vogeley, K. T., Mars, R. B., Turetsky, B. I., Laird, A. R., Fox, P. T., Eickhoff, S. B., et al. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx121 · Cited by 177 · see citing works
“this significance testing procedure was found beneficial for quantitative meta-analysis experiments based on the ALE algorithm in a recent systematic evaluation (Eickhoff et al. 2016).”
MethodInfluentialSelf-citationYaple, Z. A., Stevens, W. D., & Arsalidou, M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.074 · Cited by 177 · see citing works
“…statistical maps were thresholded at p 0.05 using a cluster-level correction for multiple comparisons and a cluster forming threshold at p 0.001 (Eickhoff et al., 2017), rather than false discovery rate that is not appropriate for inference on topological features (Eickhoff et al., 2016).”
MethodInfluentialJaniri, 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. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.3351 · Cited by 173 · see citing works
Self-citationDefinition and characterization of an extended multiple-demand network
Camilleri, J. A., Müller, V. I., Fox, P. M., Laird, A. R., Hoffstaedter, F., Tobias Kalenscher, A., & Eickhoff, S. B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.020 · Cited by 159 · see citing works
Self-citationNeural signatures of trust in reciprocity: A coordinate‐based meta‐analysis
Bellucci, G., Chernyak, S. V., Goodyear, K., Eickhoff, S. B., & Krüeger, F. (2016) · Human Brain Mapping · doi:10.1002/hbm.23451 · Cited by 156 · see citing works
“These analyses confirmed that our results meet criteria for robust, unbiased results reflecting “true” convergence [Eickhoff et al., 2016].”
MethodResultInfluentialSelf-citationCan't get it off my brain: Meta-analysis of neuroimaging studies on perseverative cognition.
Makovac, E., Fagioli, S., Rae, C., Critchley, H., & Ottaviani, C. (2019) · Psychiatry research. Neuroimaging · doi:10.1016/j.pscychresns.2019.111020 · Cited by 84 · see citing works
Acar, F., Seurinck, R., Eickhoff, S. B., & Moerkerke, B. (2018) · PLOS ONE · doi:10.1371/journal.pone.0208177 · Cited by 154 · see citing works
“The authors of the ALE algorithm advise to employ a voxel- or cluster-level family-wise error correction (FWE) [28].”
BackgroundMethodResultInfluentialSelf-citationHartwigsen, G., Neef, N. E., Camilleri, J. A., Margulies, D. S., & Eickhoff, S. B. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy049 · Cited by 152 · see citing works
“The resulting individual coactivation profiles were then compared between voxels to identify clusters of voxels with similar coactivation patterns (Eickhoff et al. 2016b).”
MethodSelf-citationThe neural correlates of visual imagery: A co-ordinate-based meta-analysis
Winlove, C. I. P., Milton, F., Ranson, J., Fulford, J., MacKisack, M., Macpherson, F., & Zeman, A. (2018) · Cortex · doi:10.1016/j.cortex.2017.12.014 · Cited by 150 · see citing works
“05), an approach which provides the best compromise between sensitivity and specificity (Eickhoff et al., 2016).”
BackgroundInfluentialLangner, R., Leiberg, S., Hoffstaedter, F., & Eickhoff, S. B. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.04.022 · Cited by 144 · see citing works
Self-citationWalenski, M., Europa, E., Caplan, D. N., & Thompson, C. K. (2019) · Human Brain Mapping · doi:10.1002/hbm.24523 · Cited by 149 · see citing works
“Adank (2012b) reports a cluster in this region for language production in a meta-analysis based on 21 studies (we had 15); recent recommendations are for 20 studies to enable adequate power in cluster-based meta-analysis (Eickhoff et al., 2016).”
Jauniaux, J., Khatibi, A., Rainville, P., & Jackson, P. L. (2019) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsz055 · Cited by 143 · see citing works
Janacsek, K., Shattuck, K. F., Tagarelli, K. M., Lum, J. A. G., Turkeltaub, P. E., & Ullman, M. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116387 · Cited by 142 · see citing works
“…if about one-third of the included experiments show the effect of interest (corresponding to an effect size of 0.33), 17 experiments are sufficient to achieve a desired power of 80% using the cFWE correction, though, in general, at least about 20 experiments are recommended (Eickhoff et al., 2016).”
InfluentialArioli, M., Cattaneo, Z., Ricciardi, E., & Canessa, N. (2021) · Human Brain Mapping · doi:10.1002/hbm.25570 · Cited by 136 · see citing works
“The number of studies included in most of these meta-analyses is in line with the recent prescriptions for ALE (Eickhoff et al., 2016; Muller et al., 2018), suggesting a minimum of 17 studies to ensure that results would not be driven by single experiments (see also Wu et al., 2019).”
ResultPractical recommendations to conduct a neuroimaging meta-analysis for neuropsychiatric disorders
Tahmasian, M., Sepehry, A. A., Samea, F., Khodadadifar, T., Soltaninejad, Z., Javaheripour, N., Khazaie, H., Zarei, M., Eickhoff, S. B., & Eickhoff, C. R. (2019) · Human Brain Mapping · doi:10.1002/hbm.24746 · Cited by 126 · see citing works
“It has been shown that voxel-wise FDR correction is not an optimal method and increases the chance of reporting the spurious clusters (Chumbley & Friston, 2009; Eickhoff et al., 2016).”
BackgroundMethodInfluentialSelf-citationNeural correlates of formal thought disorder: An activation likelihood estimation meta‐analysis
Wensing, T., Cieslik, E. C., Müller, V. I., Hoffstaedter, F., Eickhoff, S. B., & Nickl‐Jockschat, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23706 · Cited by 135 · see citing works
“…twofold: first, given the large variations in experimental designs and phenotype in view of an already rather small number of included studies, additional ALE meta-analyses tackling these questions would be unfeasible for the sake of statistical reliability and robustness [Eickhoff et al., 2016].”
BackgroundMethodInfluentialSelf-citationThe Neural Correlates of Time: A Meta-analysis of Neuroimaging Studies
Nani, A., Manuello, J., Liloia, D., Duca, S., Costa, T., & Cauda, F. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01459 · Cited by 109 · see citing works
“These two parameters are pivotal for the implementation of the ALE method and the identification of the effect (Eickhoff et al., 2016).”
BackgroundMethodLi, Y., Kong, F., Luo, Y., Zeng, S., Lan, J., & You, X. (2019) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2019.00128 · Cited by 130 · see citing works
Fallon, N. B., Roberts, C. A., & Stančák, A. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa090 · Cited by 125 · see citing works
“05) and a minimum cluster size of 200 mm3 as previously recommended (Eickhoff et al., 2016).”
BackgroundMethodInfluentialListening under difficult conditions: An activation likelihood estimation meta‐analysis
Alain, C., Du, Y., Bernstein, L. J., Barten, T. R. M., & Banai, K. (2018) · Human Brain Mapping · doi:10.1002/hbm.24031 · Cited by 121 · see citing works
“…and linguistic complexity—because those are the most commonly used paradigms and thus have a sufficiently large number of neuroimaging studies to assess patterns of brain activation (Eickhoff, Bzdok, Laird, Kurth, & Fox, 2012; Eickhoff, Laird, Fox, Lancaster, & Fox, 2017; Eickhoff et al., 2016).”
BackgroundMethodWhere is the “where” in the brain? A meta‐analysis of neuroimaging studies on spatial cognition
Cona, G., & Scarpazza, C. (2019) · Human Brain Mapping · doi:10.1002/hbm.24496 · Cited by 121 · see citing works
“001) (Eickhoff et al., 2016) according with the recent guidelines for coordinate-based meta-analysis (Muller et al.”
MethodInfluentialHuman Pregenual Anterior Cingulate Cortex: Structural, Functional, and Connectional Heterogeneity
Palomero‐Gallagher, N., Hoffstaedter, F., Mohlberg, H., Eickhoff, S. B., Amunts, K., & Zilles, K. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy124 · Cited by 120 · see citing works
Self-citationTahmasian, M., Noori, K., Samea, F., Zarei, M., Spiegelhalder, K., Eickhoff, S. B., Someren, E. J. W. V., Khazaie, H., & Eickhoff, C. R. (2018) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2018.07.004 · Cited by 119 · see citing works
Self-citationDo I feel or do I know? Neuroimaging meta-analyses on the multiple facets of empathy
Kogler, L., Müller, V. I., Werminghausen, E., Eickhoff, S. B., & Derntl, B. (2020) · Cortex · doi:10.1016/j.cortex.2020.04.031 · Cited by 120 · see citing works
“Analyses with a higher number of included experiments can have a higher power (Eickhoff et al., 2016).”
BackgroundMethodSelf-citationSutherland, M. T., Riedel, M. C., Flannery, J. S., Yanes, J. A., Fox, P. T., Stein, E. A., & Laird, A. R. (2016) · Behavioral and Brain Functions · doi:10.1186/s12993-016-0100-5 · Cited by 114 · see citing works
Self-citationXu, A., Larsen, B., Baller, E. B., Scott, J. C., Sharma, V., Adebimpe, A., Basbaum, A. I., Dworkin, R. H., Edwards, R. R., Woolf, C. J., et al. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.01.004 · Cited by 111 · see citing works
“, 2016) as well as an influx of articles standardizing rigorous procedures for meta-analyses (Eickhoff et al., 2016; Müller et al., 2018) suggest that an update is warranted.”
BackgroundSelf-citationSocial exclusion reliably engages the default network: A meta-analysis of Cyberball
Mwilambwe-Tshilobo, L., & Spreng, R. N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117666 · Cited by 110 · see citing works
Affordance processing in segregated parieto-frontal dorsal stream sub-pathways
Sakreida, K., Alkhasli, I., Thill, S., Menz, M. M., Jirak, D., Eickhoff, C. R., Tom Ziemke, B., Eickhoff, S. B., Borghi, A. M., & Binkofski, F. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.07.032 · Cited by 110 · see citing works
Self-citationFunctional neuroimaging of human postural control: a systematic review with meta-analysis.
Dijkstra, B. W., Bekkers, E., Gilat, M., de Rond, V., Hardwick, R., & Nieuwboer, A. (2020) · Neuroscience and Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.04.028 · Cited by 107 · see citing works
Doucet, G. E., Janiri, D., Howard, R., O’Brien, M., Andrews‐Hanna, J. R., & Frangou, S. (2020) · European Psychiatry · doi:10.1192/j.eurpsy.2020.57 · Cited by 112 · see citing works
“Diagnosis-specific ALE analyses were only performed for schizophrenia (number of studies: 31) and MDD (number of studies: 22); the data for all other disorders were insufficient as the minimum number of studies for robust results is currently considered to be 17 [40].”
BackgroundNeurobiological and Cognitive Profile of Young Binge Drinkers: a Systematic Review and Meta-Analysis
Lees, B., Mewton, L., Stapinski, L. A., Squeglia, L. M., Rae, C. D., & Teesson, M. (2019) · Neuropsychology Review · doi:10.1007/s11065-019-09411-w · Cited by 109 · see citing works
“…(Muller et al., 2018) that requires all included experiments use the same search coverage (i.e., the same brain coverage, EEG and ERP components, neurocognitive domains) and that there were a sufficient number of studies included in the analysis (i.e., >17–20 experiments; Eickhoff et al., 2016).”
A view behind the mask of sanity: meta-analysis of aberrant brain activity in psychopaths
Poeppl, T. B., Donges, M. R., Mokros, A., Rupprecht, R., Fox, P. T., Laird, A. R., Bzdok, D., Langguth, B., & Eickhoff, S. B. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0122-5 · Cited by 108 · see citing works
Self-citationSchulze, L., Schulze, A., Renneberg, B., Schmahl, C., & Niedtfeld, I. (2019) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.11.004 · Cited by 102 · see citing works
Minimum statistical standards for submissions to Neuroimage: Clinical
Roiser, J. P., Linden, D. E. J., Gorno‐Tempini, M., Moran, R., Dickerson, B. C., & Grafton, S. T. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.08.002 · Cited by 106 · see citing works
The search for the number form area: A functional neuroimaging meta-analysis
Yeo, D. J. S., Wilkey, E. D., & Price, G. R. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.04.027 · Cited by 100 · see citing works
Dricu, M., & Frühholz, S. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.10.020 · Cited by 102 · see citing works
Papitto, G., Friederici, A. D., & Zaccarella, E. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116321 · Cited by 98 · see citing works
Convergence of semantics and emotional expression within the IFG pars orbitalis
Belyk, M., Brown, S., Lim, J., & Kotz, S. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.020 · Cited by 103 · see citing works
Resting-state abnormalities in Autism Spectrum Disorders: A meta-analysis
Lau, W. K., Leung, M.-K., & Lau, B. W.-M. (2019) · Scientific Reports · doi:10.1038/s41598-019-40427-7 · Cited by 102 · see citing works
Neural signatures of prosocial behaviors
Bellucci, G., Camilleri, J. A., Eickhoff, S. B., & Krüeger, F. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.07.006 · Cited by 106 · see citing works
“This average non-linear contribution to the ALE value was computed from the ratio of the ALE values at the location of the cluster with and without each contributing experiment (Eickhoff et al., 2016).”
MethodSelf-citationBerretz, G., Packheiser, J., Kumsta, R., Wolf, O. T., & Ocklenburg, S. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.01.001 · Cited by 104 · see citing works
N -back Working Memory Task: Meta-analysis of Normative fMRI Studies With Children
Yaple, Z. A., & Arsalidou, M. (2018) · Child Development · doi:10.1111/cdev.13080 · Cited by 99 · see citing works
“A minimum of 17–20 experiments is currently recommended to ensure the robustness of each cluster and sufficient statistical power (Eickhoff et al., 2016, 2017).”
BackgroundKlugah‐Brown, B., Di, X., Zweerings, J., Mathiak, K., Becker, B., & Biswal, B. (2020) · Human Brain Mapping · doi:10.1002/hbm.25085 · Cited by 86 · see citing works
“In line with recent recommendations for the application of cluster-based correction methods (Eickhoff et al., 2016) an initial cluster forming threshold of p < .”
MethodBellucci, G., Feng, C., Camilleri, J. A., Eickhoff, S. B., & Krüeger, F. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.06.024 · Cited by 94 · see citing works
“Finally, it has to be noted that we did not include contrasts for reciprocity decisions in the one-shot TG, distrust decisions in the iterative TG and decisions to propose offers in the one-shot UG, because we could not find enough published studies reporting contrasts for these decisions to reach a reasonable number…”
InfluentialSelf-citationMultiple dimensions underlying the functional organization of the language network
Hodgson, V. J., Ralph, M. A. L., & Jackson, R. L. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118444 · Cited by 99 · see citing works
Li, Q., Yang, G., Li, Z., Qi, Y., Cole, M. W., & Liu, X. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.09.032 · Cited by 96 · see citing works
Cognitive benefits of exercise interventions: an fMRI activation likelihood estimation meta-analysis
Yu, Q., Herold, F., Becker, B., Klugah‐Brown, B., Zhang, Y., Perrey, S., Veronese, N., Müller, N. G., Kramer, A. F., & Zou, L. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02247-2 · Cited by 98 · see citing works
Feng, C., Eickhoff, S. B., Li, T., Wang, L., Becker, B., Camilleri, J. A., Hétu, S., & Luo, Y. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.03.025 · Cited by 96 · see citing works
Self-citationRodríguez-Nieto, G., Seer, C., Sidlauskaite, J., Vleugels, L., Van Roy, A., Hardwick, R., & Swinnen, S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119665 · Cited by 89 · see citing works
Ganesan, S., Beyer, E., Moffat, B. A., Dam, N. T. V., Lorenzetti, V., & Zalesky, A. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104846 · Cited by 89 · see citing works
Xu, P., Peng, S., Luo, Y., & Gong, G. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.05.023 · Cited by 94 · see citing works
Stimuli, presentation modality, and load‐specific brain activity patterns during n‐back task
Mencarelli, L., Neri, F., Momi, D., Menardi, A., Rossi, S., Rossi, A., & Santarnecchi, E. (2019) · Human Brain Mapping · doi:10.1002/hbm.24633 · Cited by 85 · see citing works
“For example, based on a recent simulation study (Eickhoff et al., 2016), it is known that the results of a meta-analysis with a low number of papers could be driven by few experiments (as evidenced by our analysis, see Supporting Information Figure S5).”
BackgroundBrainMap VBM: An environment for structural meta‐analysis
Vanasse, T. J., Fox, P. M., Barron, D. S., Robertson, M., Eickhoff, S. B., Lancaster, J. L., & Fox, P. T. (2018) · Human Brain Mapping · doi:10.1002/hbm.24078 · Cited by 88 · see citing works
“…likelihood estimation (ALE; performed with GingerALE software), multi-level kernel density analysis (MKDA) and signed differential mapping (SDM) (Eickhoff et al., 2016; Radua & Mataix-Cols, 2012; Turkel-taub, Eden, Jones, & Zeffiro, 2002; Wager, Lindquist, Nichols, Kober, & Van Snellenberg,…”
MethodInfluentialSelf-citationA meta-analysis of fMRI studies of language comprehension in children
Enge, A., Friederici, A., & Skeide, M. A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116858 · Cited by 67 · see citing works
“The cluster-wise FWE-corrected threshold was preferred in accordance with a recent simulation study by Eickhoff et al. (2016).”
BackgroundMethodInfluentialZhao, Y., Yang, L., Gong, G., Cao, Q., & Liu, J. (2021) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2021.110477 · Cited by 85 · see citing works
Tagarelli, K. M., Shattuck, K. F., Turkeltaub, P. E., & Ullman, M. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.061 · Cited by 82 · see citing works
Differential vulnerability of the cerebellum in healthy ageing and Alzheimer’s disease
Gellersen, H. M., Guell, X., & Sami, S. A. K. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102605 · Cited by 82 · see citing works
“In the case of this analysis, we chose a p .05 for cluster-level family-wise error correction (i.e. the cluster-level inference option on GingerALE), which provides the best compromise between sensitivity and specificity (Eickhoff et al., 2016).”
Wang, J., Zhang, J., Zang, Y., & Wu, T. (2018) · GigaScience · doi:10.1093/gigascience/giy071 · Cited by 80 · see citing works
“First, it has been proposed that at least 20 experiments should be included in a meta-analysis [72].”
Brain structural alterations are distributed following functional, anatomic and genetic connectivity
Cauda, F., Nani, A., Manuello, J., Premi, E., Palermo, S., Tatu, K., Duca, S., Fox, P. T., & Costa, T. (2018) · Brain · doi:10.1093/brain/awy252 · Cited by 76 · see citing works
“However, a minimum amount of 20 experiments is usually thought to be sufficient to resolve this issue (Eickhoff et al., 2016), so that analyses based on large databases, as the one used here, should not be so much biased as to produce invalid results.”
BackgroundSelf-citationJavaheripour, N., Shahdipour, N., Noori, K., Zarei, M., Camilleri, J. A., Laird, A. R., Fox, P. T., Eickhoff, S. B., Eickhoff, C. R., Rosenzweig, I., et al. (2019) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2019.03.008 · Cited by 83 · see citing works
“Of note, there was not enough experiment per task to perform a statistically sound CBMA [27].”
BackgroundMethodInfluentialSelf-citationNeural correlates of sense of agency in motor control: a neuroimaging meta-analysis
Zito, G. A., Wiest, R., & Aybek, S. (2020) · PLOS ONE · doi:10.1371/journal.pone.0234321 · Cited by 79 · see citing works
“05 corrected with Family-Wise Error (FWE), and 5000 permutation tests to correct for multiple comparisons [49].”
MethodSindermann, L., Redlich, R., Opel, N., Böhnlein, J., Dannlowski, U., & Leehr, E. J. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.07.022 · Cited by 77 · see citing works
Arioli, M., & Canessa, N. (2019) · Human Brain Mapping · doi:10.1002/hbm.24627 · Cited by 75 · see citing works
Zheng, C. J., Drunen, S. V., & Egorova, N. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-01949-3 · Cited by 77 · see citing works
“In fact, both our ALE analyses of pain in depression and depression in pain likely are low in statistical power [23] and should be interpreted with caution.”
BackgroundEres, R., Louis, W. R., & Molenberghs, P. (2017) · Social Neuroscience · doi:10.1080/17470919.2017.1357657 · Cited by 77 · see citing works
Luo, Y., Eickhoff, S. B., Hétu, S., & Feng, C. (2017) · Human Brain Mapping · doi:10.1002/hbm.23854 · Cited by 75 · see citing works
“In addition, the average non-linear contribution of each experiment for each cluster was calculated from the fraction of the ALE values at the cluster with and without the experiment in question [Eickhoff et al., 2016].”
BackgroundMethodSelf-citationLiloia, D., Brasso, C., Cauda, F., Mancuso, L., Nani, A., Manuello, J., Costa, T., Duca, S., & Rocca, P. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.01.010 · Cited by 69 · see citing works
“g-HR = 6 experiments; c-HR = 4; RDSZ = 7; ChSZ = 7; Table S3) was insufficient to achieve robust ALE estimates (Eickhoff et al., 2016).”
BackgroundMethodResultInfluentialFunctional neural correlates of psychopathy: a meta-analysis of MRI data
Deming, P., & Koenigs, M. R. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0816-8 · Cited by 74 · see citing works
Naghibi, N., Jahangiri, N., Khosrowabadi, R., Eickhoff, C. R., Eickhoff, S. B., Coull, J. T., & Tahmasian, M. (2024) · Neuropsychology Review · doi:10.1007/s11065-023-09588-1 · Cited by 69 · see citing works
“However, we could not investigate those methodological impacts because the number of studies in some subcategories (e.g. PET studies) fell below the minimum number required to control the excessive contribution of individual experiments (Eickhoff et al., 2016).”
BackgroundMethodInfluentialSelf-citationImpact of early life adversities on human brain functioning: A coordinate-based meta-analysis
Kraaijenvanger, E. J., Pollok, T. M., Monninger, M., Kaiser, A., Brandeis, D., Banaschewski, T., & Holz, N. E. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.03.008 · Cited by 72 · see citing works
Brain responses to social norms: Meta‐analyses of fMRI studies
Zinchenko, O., & Arsalidou, M. (2018) · Human Brain Mapping · doi:10.1002/hbm.23895 · Cited by 65 · see citing works
“Although there are no available methods for performing robustness analyses with GingerALE, simulations of ALE analyses have been performed to test sensitivity, ensuing cluster sizes, number of incidental clusters, and statistical power (Eickhoff et al., 2016).”
BackgroundMethodMultimodal evaluation of the amygdala's functional connectivity
Kerestes, R., Chase, H. W., Phillips, M. L., Ladouceur, C. D., & Eickhoff, S. B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.023 · Cited by 70 · see citing works
“experiments (Eickhoff et al., 2016), we normalized all MACM maps to a sum of one.”
MethodSelf-citationDisner, S. G., Marquardt, C. A., Mueller, B. A., Burton, P. C., & Sponheim, S. R. (2017) · Human Brain Mapping · doi:10.1002/hbm.23886 · Cited by 71 · see citing works
“…that the more conservative cluster-level FWE correction employed in the present analysis was recommended following massive empirical simulation (Eickhoff et al., 2016), we are confident that the presently reported ROIs reflect the most reliable meta-analytic outcomes available based on the…”
Method
- 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 · 2015: 1
- 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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