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Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation
Open accessAbstract
Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis.
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“However, new studies have since been published, and existing ALE meta-analyses do not meet the current standards for type I error control (Eickhoff et al., 2016).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
In the citing paper
“Following current best-practice guidelines, we assessed the significance of convergence of activation foci using a voxel-level threshold of and a cluster-level familywise error-corrected threshold of , and performed ALE meta-analyses only when at least 17 experiments reported activation foci for a contrast (Eickhoff et al., 2016, Tahmasian et al., 2019).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
In the citing paper
“Notably, this null-finding occurred in a well-powered meta-analysis: the sample of 35 experiments is considered adequate by present guidelines for neuroimaging meta-analyses (Müller et al., 2018, Tahmasian et al., 2019) and gave us more than 80 % power to detect a convergence of activation differences in areas that were identified in just 7 (20 %) of the experiments (Eickhoff et al., 2016)”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
In the citing paper
“which has been shown to be inappropriate for neuroimaging data (Chumbley and Friston, 2009) and to entail both low sensitivity and high susceptibility to spurious, false positive findings (Eickhoff et al., 2016).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
In the citing paper
“Brem et al. (2012); Eng et al. (2015) and Menzies et al. (2008) performed meta-analyses on activation differences obtained from less than the recommended minimum of 17 fMRI experiments (Eickhoff et al., 2016).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
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Cites
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InfluentialSelf-citationMeta-analysis of the functional neuroanatomy of single-word reading: method and validation
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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×
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InfluentialSelf-citationIdentification of a Common Neurobiological Substrate for Mental Illness
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“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
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“…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
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Self-citationNeural network of cognitive emotion regulation—an ALE meta-analysis and MACM analysis
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“Moreover, it may be discussed whether indeed something like “the” true location of an effect exists, given recent accounts of distributed coding and representation (Bzdok et al., 2015; Haxby, 2012; Kriegeskorte, 2009; Rissman and Wagner, 2012).”
Self-citationModelling neural correlates of working memory: A coordinate-based meta-analysis
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“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
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Self-citationInterference resolution: Insights from a meta-analysis of neuroimaging tasks
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“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
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“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
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“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).”
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“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
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InfluentialOn the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments
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“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-citationActivation likelihood estimation meta-analysis of motor-related neural activity after stroke
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“Moreover, it may be discussed whether indeed something like “the” true location of an effect exists, given recent accounts of distributed coding and representation (Bzdok et al., 2015; Haxby, 2012; Kriegeskorte, 2009; Rissman and Wagner, 2012).”
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Self-citationMultivariate pattern analysis of fMRI: The early beginnings
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“…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-citationChan, R. C. K., Di, X., McAlonan, G. M., & Gong, Q. (2009) · Schizophrenia Bulletin · doi:10.1093/schbul/sbp073 · Cited by 330 · cited 1×
“…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.”
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“…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
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“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-citationDistributed Representations in Memory: Insights from Functional Brain Imaging
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“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).”
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“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
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“…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…”
InfluentialChase, H. W., Kumar, P., Eickhoff, S. B., & Dombrovski, A. Y. (2015) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-015-0338-7 · Cited by 242 · cited 2×
“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-citationEvaluating the consistency and specificity of neuroimaging data using meta-analysis
Wager, T. D., Lindquist, M. A., Nichols, T. E., Kober, H., & Van Snellenberg, J. X. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.061 · Cited by 242 · cited 3×
“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 234 · cited 2×
“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-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 207
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“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).”
InfluentialAmanzio, M., Benedetti, F., Porro, C. A., Palermo, S., & Cauda, F. (2013) · Human Brain Mapping · doi:10.1002/hbm.21471 · Cited by 202 · cited 1×
“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).”
ALE 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).”
Self-citationMeta-Analysis in Human Neuroimaging: Computational Modeling of Large-Scale Databases
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“…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-citationFunctional neuroimaging of motor control in parkinson's disease: A meta-analysis
Herz, D. M., Eickhoff, S. B., Løkkegaard, A., & Siebner, H. R. (2013) · Human Brain Mapping · doi:10.1002/hbm.22397 · Cited by 174 · cited 2×
“, 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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“…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×
“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.”
Networks 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×
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Self-citationStructural correlates of auditory hallucinations in schizophrenia: A meta-analysis
Palaniyappan, L. K., Balain, V., Raduà, J., & Liddle, P. F. (2012) · Schizophrenia Research · doi:10.1016/j.schres.2012.01.038 · Cited by 164 · cited 1×
“…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).”
Molenberghs, P., Sale, M. V., & Mattingley, J. B. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00078 · Cited by 156 · cited 2×
“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.”
Reproducibility of neuroimaging analyses across operating systems
Tristan Glatard, B., 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 134 · cited 1×
“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 131 · cited 1×
“…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 108 · cited 2×
“…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 98
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×
“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 90 · cited 2×
“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-citationInteroperable 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 87
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×
“Derfuss and Mar estimated that until 2007 almost 8,000 neuroimaging studies had been published (Derrfuss and Mar, 2009).”
Meta‐analysis: Weighing the evidence
Jones, D. R. H. (1995) · Statistics in Medicine · doi:10.1002/sim.4780140206 · Cited by 79 · cited 1×
“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×
“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
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×
“Additionally, the measured signals are indirect hemodynamic proxies rather than direct measures of neuronal activity (Logothetis and Wandell, 2004).”
Self-citationClos, M., Rottschy, C., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.030 · Cited by 63 · cited 2×
“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-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×
“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?”
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 50 · cited 1×
“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-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×
“…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;…”
King, M. B., Rauch, L., Stein, D. J., & Brooks, S. J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.064 · Cited by 41 · cited 2×
“, 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×
“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
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
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
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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
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 948
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
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
“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
“Moreover, the limited number of typical rumination/distraction studies provides equivocal statistical power for meta-analysis (Eickhoff et al., 2016).”
Background
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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