Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses
Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate‐based meta‐analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. He...
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| Published in | Human brain mapping Vol. 33; no. 1; pp. 1 - 13 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.01.2012
Wiley-Liss John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate‐based meta‐analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum contribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within‐experiment effects. However, we show that these effects only account for 2–3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE values more than others. This modification decreases cumulative ALE values by 7–9%, changes the relative magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within‐experiment or within‐group effects. We suggest that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within‐experiment and within‐group effects, optimizing the degree to which ALE values represent concordance of findings across independent reports. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc. |
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| AbstractList | Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate‐based meta‐analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum contribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within‐experiment effects. However, we show that these effects only account for 2–3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE values more than others. This modification decreases cumulative ALE values by 7–9%, changes the relative magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within‐experiment or within‐group effects. We suggest that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within‐experiment and within‐group effects, optimizing the degree to which ALE values represent concordance of findings across independent reports. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc. Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate-based meta-analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum contribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within-experiment effects. However, we show that these effects only account for 2-3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE values more than others. This modification decreases cumulative ALE values by 7-9%, changes the relative magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within-experiment or within-group effects. We suggest that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within-experiment and within-group effects, optimizing the degree to which ALE values represent concordance of findings across independent reports.Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate-based meta-analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum contribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within-experiment effects. However, we show that these effects only account for 2-3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE values more than others. This modification decreases cumulative ALE values by 7-9%, changes the relative magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within-experiment or within-group effects. We suggest that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within-experiment and within-group effects, optimizing the degree to which ALE values represent concordance of findings across independent reports. Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate-based meta-analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum contribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within-experiment effects. However, we show that these effects only account for 2-3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE values more than others. This modification decreases cumulative ALE values by 7-9%, changes the relative magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within-experiment or within-group effects. We suggest that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within-experiment and within-group effects, optimizing the degree to which ALE values represent concordance of findings across independent reports. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc. [PUBLICATION ABSTRACT] Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate-based meta-analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum contribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within-experiment effects. However, we show that these effects only account for 2-3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE values more than others. This modification decreases cumulative ALE values by 7-9%, changes the relative magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within-experiment or within-group effects. We suggest that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within-experiment and within-group effects, optimizing the degree to which ALE values represent concordance of findings across independent reports. |
| Author | Turkeltaub, Peter E. Fox, Peter Eickhoff, Simon B. Laird, Angela R. Fox, Mick Wiener, Martin |
| AuthorAffiliation | 1 Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 2 Department of Psychiatry and Psychotherapy, Rheinisch‐Westfälische Technische Hochschule Aachen University, Aachen D‐52074, Germany 4 Jülich Aachen Research Alliance, Translational Brain Medicine, Jülich D‐52425, Germany 5 Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas 3 Institute of Neuroscience and Medicine (INM‐2), Research Centre Jülich, Jülich D‐52425, Germany |
| AuthorAffiliation_xml | – name: 3 Institute of Neuroscience and Medicine (INM‐2), Research Centre Jülich, Jülich D‐52425, Germany – name: 2 Department of Psychiatry and Psychotherapy, Rheinisch‐Westfälische Technische Hochschule Aachen University, Aachen D‐52074, Germany – name: 4 Jülich Aachen Research Alliance, Translational Brain Medicine, Jülich D‐52425, Germany – name: 1 Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania – name: 5 Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas |
| Author_xml | – sequence: 1 givenname: Peter E. surname: Turkeltaub fullname: Turkeltaub, Peter E. email: peter.turkeltaub@uphs.upenn.edu organization: Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania – sequence: 2 givenname: Simon B. surname: Eickhoff fullname: Eickhoff, Simon B. organization: Department of Psychiatry and Psychotherapy, Rheinisch-Westfälische Technische Hochschule Aachen University, Aachen D-52074, Germany – sequence: 3 givenname: Angela R. surname: Laird fullname: Laird, Angela R. organization: Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas – sequence: 4 givenname: Mick surname: Fox fullname: Fox, Mick organization: Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas – sequence: 5 givenname: Martin surname: Wiener fullname: Wiener, Martin organization: Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania – sequence: 6 givenname: Peter surname: Fox fullname: Fox, Peter organization: Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25292443$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/21305667$$D View this record in MEDLINE/PubMed |
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| References_xml | – reference: Karlsgodt KH, Kochunov P, Winkler AM, Laird AR, Almasy L, Duggirala R, Olvera RL, Fox PT, Blangero J, Glahn DC ( 2010): A multimodal assessment of the genetic control over working memory. J Neurosci 30: 8197-8202. – reference: Wiener M, Turkeltaub P, Coslett HB ( 2010): The image of time: A voxel-wise meta-analysis. NeuroImage 49:1728-1740. – reference: Robinson JL, Laird AR, Glahn DC, Lovallo WR, Fox PT ( 2010): Metaanalytic connectivity modeling: Delineating the functional connectivity of the human amygdala. Hum Brain Mapp 31: 173-184. – reference: Binder JR, Desai RH, Graves WW, Conant LL ( 2009): Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb Cortex 19: 2767-2796. – reference: Chouinard PA, Goodale MA ( 2010): Category-specific neural processing for naming pictures of animals and naming pictures of tools: An ALE meta-analysis. Neuropsychologia 48: 409-418. – reference: Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, Filippini N, Watkins KE, Toro R, Laird AR, Beckmann CF ( 2009): Correspondence of the brain's functional architecture during activation and rest. Proc Natl Acad Sci USA 106: 13040-13045. – reference: Vytal K, Hamann S ( 2010): Neuroimaging support for discrete neural correlates of basic emotions: A voxel-based meta-analysis. J Cogn Neurosci 22:2864-2885. – reference: Wager TD, Jonides J, Reading S ( 2004): Neuroimaging studies of shifting attention: A meta-analysis. NeuroImage 22: 1679-1693. – reference: Minzenberg MJ, Laird AR, Thelen S, Carter CS, Glahn DC ( 2009): Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch Gen Psychiatry 66: 811-822. – reference: Spreng RN, Wojtowicz M, Grady CL ( 2010): Reliable differences in brain activity between young and old adults: A quantitative meta-analysis across multiple cognitive domains. Neurosci Biobehav Rev 34: 1178-1194. – reference: Wager TD, Lindquist M, Kaplan L ( 2007): Meta-analysis of functional neuroimaging data: Current and future directions. Social Cogn Affect Neurosci 2: 150-158. – reference: Turkeltaub P, Eden G, Jones K, Zeffiro T ( 2002): Meta-analysis of the functional neuroanatomy of single-word reading: Method and validation. Neuroimage 16( 3 Part 1): 765-780. – reference: Worsley KJ, Marrett S, Neelin P, Vandal AC, Friston KJ, Evans AC ( 1996): A unified statistical approach for determining significant signals in images of cerebral activation. Hum Brain Mapp 4: 58-73. – reference: Eickhoff SB, Jbabdi S, Caspers S, Laird AR, Fox PT, Zilles K, Behrens TE ( 2010): Anatomical and functional connectivity of cytoarchitectonic areas within the human parietal operculum. J Neurosci 30: 6409-6421. – reference: Maisog JM, Einbinder ER, Flowers DL, Turkeltaub PE, Eden GF ( 2008): A meta-analysis of functional neuroimaging studies of dyslexia. Ann N Y Acad Sci 1145: 237-259. – reference: Ferreira LK, Diniz BS, Forlenza OV, Busatto GF, Zanetti MV ( 2009): Neurostructural predictors of Alzheimer's disease: A meta-analysis of VBM studies. Neurobiol Aging. DOI: 10.1016/j.neurobiolaging.2009.11.008. – reference: Laird AR, Eickhoff SB, Li K, Robin DA, Glahn DC, Fox PT ( 2009a): Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. J Neurosci 29: 14496-14505. – reference: Laird AR, Fox PM, Price CJ, Glahn DC, Uecker AM, Lancaster JL, Turkeltaub PE, Kochunov P, Fox PT ( 2005): ALE meta-analysis: controlling the false discovery rate and performing statistical contrasts. Hum Brain Mapp 25: 155-164. – reference: Glahn DC, Laird AR, Ellison-Wright I, Thelen SM, Robinson JL, Lancaster JL, Bullmore E, Fox PT ( 2008): Meta-analysis of gray matter anomalies in schizophrenia: Application of anatomic likelihood estimation and network analysis. 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Progress in neuro-psychopharmacology and biological psychiatry 33: 1390-1394. – reference: Caspers S, Zilles K, Laird AR, Eickhoff SB ( 2010): ALE meta-analysis of action observation and imitation in the human brain. NeuroImage 50: 1148-1167. – reference: Laird AR, Robbins JM, Li K, Price LR, Cykowski MD, Narayana S, Laird RW, Franklin C, Fox PT ( 2008): Modeling motor connectivity using TMS/PET and structural equation modeling. NeuroImage 41: 424-436. – reference: Wager TD, Lindquist MA, Nichols TE, Kober H, Van Snellenberg JX ( 2009): Evaluating the consistency and specificity of neuroimaging data using meta-analysis. NeuroImage 45( 1 Suppl): S210-S221. – reference: Laird AR, Lancaster JL, Fox PT ( 2009b): Lost in localization? The focus is meta-analysis. NeuroImage 48: 18-20. – volume: 34 start-page: 1178 year: 2010 end-page: 1194 article-title: Reliable differences in brain activity between young and old adults: A quantitative meta‐analysis across multiple cognitive domains publication-title: Neurosci Biobehav Rev – volume: 33 start-page: 1390 year: 2009 end-page: 1394 article-title: White matter reduction in patients with schizophrenia as revealed by voxel‐based morphometry: An activation likelihood estimation meta‐analysis publication-title: Progress in neuro‐psychopharmacology and biological psychiatry – volume: 114 start-page: 1 year: 2010 end-page: 15 article-title: Localization of sublexical speech perception components publication-title: Brain Lang – volume: 2 start-page: 150 year: 2007 end-page: 158 article-title: Meta‐analysis of functional neuroimaging data: Current and future directions publication-title: Social Cogn Affect Neurosci – volume: 19 start-page: 2767 year: 2009 end-page: 2796 article-title: Where is the semantic system? 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| Title | Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses |
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