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Annual Review of Neuroscience

Volume 37, 2014

Meta-Analysis in Human Neuroimaging: Computational Modeling of Large-Scale Databases

Abstract

Spatial normalization—applying standardized coordinates as anatomical addresses within a reference space—was introduced to human neuroimaging research nearly 30 years ago. Over these three decades, an impressive series of methodological advances have adopted, extended, and popularized this standard. Collectively, this work has generated a methodologically coherent literature of unprecedented rigor, size, and scope. Large-scale online databases have compiled these observations and their associated meta-data, stimulating the development of meta-analytic methods to exploit this expanding corpus. Coordinate-based meta-analytic methods have emerged and evolved in rigor and utility. Early methods computed cross-study consensus, in a manner roughly comparable to traditional (nonimaging) meta-analysis. Recent advances now compute coactivation-based connectivity, connectivity-based functional parcellation, and complex network models powered from data sets representing tens of thousands of subjects. Meta-analyses of human neuroimaging data in large-scale databases now stand at the forefront of computational neurobiology.

Keyword(s): activation likelihood estimation, ALE, fMRI, human brain mapping, magnetic resonance imaging, MRI
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2014-07-08
2024-04-16
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Literature Cited

  1. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. 2006. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci. 26:63–72 [Google Scholar]
  2. Alexander-Bloch A, Giedd JN, Bullmore E. 2013. Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci. 14:322–36 [Google Scholar]
  3. Amunts K, Kedo O, Kindler M, Pieperhoff P, Mohlberg H. et al. 2005. Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat. Embryol. 210:343–52 [Google Scholar]
  4. Ashburner J, Friston KJ. 2000. Voxel-based morphometry—the methods. NeuroImage 11:6805–21 [Google Scholar]
  5. Barron DS, Fox PM, Laird AR, Robinson JL, Fox PT. 2012. Thalamic medial dorsal nucleus atrophy in medial temporal lobe epilepsy: a VBM meta-analysis. NeuroImage Clin. 2:25–32 [Google Scholar]
  6. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34:4537–41 [Google Scholar]
  7. Brown S, Ingham RJ, Ingham JC, Laird AR, Fox PT. 2005. Stuttered and fluent speech production: an ALE meta-analysis of functional neuroimaging studies. Hum. Brain Mapp. 25:105–17 [Google Scholar]
  8. Bullmore ET, Bassett DA. 2011. Brain graphs: graphical models of the human brain connectome. Annu. Rev. Clin. Psychol. 7:113–40 [Google Scholar]
  9. Bullmore ET, Sporns O. 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10:3186–98 [Google Scholar]
  10. Butcher J. 2007. Alzheimer's researchers open the doors to data sharing. Lancet Neurol. 6:480–81 [Google Scholar]
  11. Bzdok D, Laird AR, Zilles K, Fox PT, Eickhoff SB. 2013. An investigation of the structural, connectional, and functional subspecialization in the human amygdala. Hum. Brain Mapp. 34:3247–66 [Google Scholar]
  12. Bzdok D, Langner R, Schilback L, Engemann DA, Laird AR. et al. 2013. Segregation of the human medial prefrontal cortex in social cognition. Front. Hum. Neurosci. 7:232 [Google Scholar]
  13. Cauda F, Cavanna AE, D'agata F, Sacco K, Duca S, Geminiani GC. 2011. Functional connectivity and coactivation of the nucleus accumbens: a combined functional connectivity and structure-based meta-analysis. J. Cogn. Neurosci. 23:2864–77 [Google Scholar]
  14. Cieslik EC, Zilles K, Caspers S, Roski C, Kellermann TS. et al. 2013. Is there “one” DLPFC in cognitive action control? Evidence for heterogeneity from co-activation-based parcellation. Cereb. Cortex 23:2677–89 [Google Scholar]
  15. Chein JM, Fissell K, Jacobs S, Fiez JA. 2002. Functional heterogeneity within Broca's area during verbal working memory. Physiol. Behav. 77:635–39 [Google Scholar]
  16. Clos M, Amunts K, Laird AR, Fox PT, Eickhoff SB. 2013. Tackling the multifunctional nature of Broca's region meta-analytically: co-activation-based parcellation of area 44. NeuroImage 83:174–88 [Google Scholar]
  17. Costafreda SG, Brammer MJ, David AS, Fu CH. 2008. Predictors of amygdala activation during the processing of emotional stimuli: a meta-analysis of 385 PET and fMRI studies. Brain Res. Rev. 58:57–70 [Google Scholar]
  18. Crossley NA, Mechelli A, Vértes PE, Winton-Brown TT, Patel AX. et al. 2013. Cognitive relevance of the community structure of the human brain functional coactivation network. Proc. Natl. Acad. Sci. USA 110:11583–88Presented the first large-scale graph-analytic meta-analysis; derived similar networks for BrainMap and resting-state fMRI data sets. [Google Scholar]
  19. Decety J, Lamm C. 2007. The role of the right temporoparietal junction in social interaction: how low-level computational processes contribute to meta-cognition. Neuroscientist 13:580–93 [Google Scholar]
  20. Derrfuss J, Mar RA. 2009. Lost in localization: the need for a universal coordinate database. NeuroImage 48:1–7 [Google Scholar]
  21. Dogan I, Eickhoff SB, Schulz JB, Shah JN, Laird AR. et al. 2013. Consistent neurodegeneration and its association with clinical progression in Huntington's disease: a coordinate-based meta-analysis. Neurodegener. Dis. 12:123–35 [Google Scholar]
  22. Duerden EG, Mak-Fan KM, Taylor MJ, Roberts SW. 2012. Regional differences in grey and white matter in children and adults with autism spectrum disorders: an activation likelihood estimate (ALE) meta-analysis. Autism Res. 5:49–66 [Google Scholar]
  23. Eickhoff SB, Bzdok D, Laird AR, Kurth F, Fox PT. 2012. Activation likelihood estimation meta-analysis revisited. NeuroImage 59:2349–61 [Google Scholar]
  24. Eickhoff SB, Bzdok D, Laird AR, Roski C, Caspers S. et al. 2011. Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation. NeuroImage 57:3938–49Introduced coactivation-based parcellation as a meta-analytic method. [Google Scholar]
  25. Eickhoff SB, Jbabdi S, Caspers S, Laird AR, Fox PT. et al. 2010. Anatomical and functional connectivity of cytoarchitectonic areas within the human parietal operculum. J. Neurosci. 30:6409–21 [Google Scholar]
  26. Eickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT. 2009. Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty. Hum. Brain Mapp. 30:2907–26 [Google Scholar]
  27. Ellison-Wright I, Bullmore E. 2009. Meta-analysis of diffusion tensor imaging studies in schizophrenia. Schizophr. Res. 108:3–10 [Google Scholar]
  28. Ellison-Wright I, Glahn DC, Laird AR, Thelen SM, Bullmore E. 2008. The anatomy of first-episode and chronic schizophrenia: an anatomical likelihood estimation meta-analysis. Am. J. Psychiatry 165:1015–23 [Google Scholar]
  29. Evans AC, Janke AL, Collins DL, Bailet S. 2012. Brain templates and atlases. NeuroImage 62:2911–22 [Google Scholar]
  30. Fitzgerald PB, Laird AR, Maller J, Daskalakis ZJ. 2008. A meta-analytic study of changes in brain activation in depression. Hum. Brain Mapp. 29:683–95 [Google Scholar]
  31. Fox MD, Raichle ME. 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8:9700–11 [Google Scholar]
  32. Fox PT. 1995a. Broca's area: motor encoding in somatic space. Behav. Brain Sci. 18:344–45 [Google Scholar]
  33. Fox PT. 1995b. Spatial normalization origins: objectives, applications, and alternatives. Hum. Brain Mapp. 3:161–64 [Google Scholar]
  34. Fox PT, Friston KJ. 2012. Distributed processing; distributed functions?. NeuroImage 61:407–26 [Google Scholar]
  35. Fox PT, Huang AY, Parsons LM, Xiong J-H, Rainey L, Lancaster JL. 1999. Functional volumes modeling: scaling for group size in averaged images. Hum. Brain Mapp. 8:143–50 [Google Scholar]
  36. Fox PT, Huang A, Parsons LM, Xiong J-H, Zamarippa F. et al. 2001. Location-probability profiles for the mouth region of human primary motor–sensory cortex: model and validation. NeuroImage 13:196–209 [Google Scholar]
  37. Fox PT, Laird AR, Fox SP, Fox PM, Uecker AM. et al. 2005a. BrainMap taxonomy of experimental design: description and evaluation. Hum. Brain Mapp. 25:185–98Described and validated the meta-data taxonomy of the BrainMap database. [Google Scholar]
  38. Fox PT, Laird AR, Lancaster JL. 2005b. Coordinate-based voxel-wise meta-analysis: dividends of spatial normalization. Report of a virtual workshop. Hum. Brain Mapp. 5:1–5 [Google Scholar]
  39. Fox PT, Lancaster JL. 1994. Neuroscience on the net. Science 266:994–96 [Google Scholar]
  40. Fox PT, Lancaster JL. 2002. Mapping context and content: the BrainMap model. Nat. Rev. Neurosci. 3:319–21Announced the concept, purpose, structure, and online status of the BrainMap database. [Google Scholar]
  41. Fox PT, Lancaster JL, Parsons LM, Xiong JH, Zamarripa F. 1997. Functional volumes modeling: theory and preliminary assessment. Hum. Brain Mapp. 5:306–11 [Google Scholar]
  42. Fox PT, Miezin FM, Allman JM, Van Essen DC, Raichle ME. 1987. Retinotopic organization of human visual cortex mapped with positron-emission tomography. J. Neurosci. 7:3913–22 [Google Scholar]
  43. Fox PT, Mikiten S, Davis G, Lancaster JL. 1994. BrainMap: a database of human functional brain mapping. Functional Neuroimaging: Technical Foundations RW Thatcher, M Hallet, T Zeffiro, ER John, M Huerta San Diego: Academic [Google Scholar]
  44. Fox PT, Mintun MA. 1989. Noninvasive functional brain mapping by change-distribution analysis of average PET images of H215O tissue activity. J. Nucl. Med. 30:141–49 [Google Scholar]
  45. Fox PT, Mintun MA, Raichle ME, Miezin FM, Allman JM, Van Essen DC. 1986. Mapping human visual cortex with position emission tomography. Nature 323:806–9 [Google Scholar]
  46. Fox PT, Mintun MA, Reiman EM, Raichle ME. 1988. Enhanced detection of focal brain responses using intersubject averaging and change-distribution analysis of subtracted PET images. J. Cereb. Blood Flow Metab. 8:642–53 [Google Scholar]
  47. Fox PT, Parsons LM, Lancaster JL. 1998. Beyond the single study: function/location meta-analysis in cognitive neuroimaging. Curr. Opin. Neurobiol. 8:178–87 [Google Scholar]
  48. Fox PT, Perlmutter JS, Raichle ME. 1985. A stereotactic method of anatomical localization for positron emission tomography. J. Comput. Assist. Tomogr. 9:141–53Introduced the use of spatial normalization (standardized coordinates) to human brain mapping. [Google Scholar]
  49. Friston KJ, Ashburner J, Frith CD, Poline J-B, Heather JD, Frackowiak RSJ. 1995. Spatial registration and normalization of images. Hum. Brain Mapp. 3:165–89 [Google Scholar]
  50. Friston KJ, Frith CD, Liddle PF, Frackowiak RSJ. 1991. Comparing functional (PET) images: the assessment of significant change. J. Cereb. Blood Flow Metab. 11:690–99 [Google Scholar]
  51. Frith CD, Friston KJ, Liddle PF, Frackowiak RS. 1991. Willed action and the prefrontal cortex in man: a study with PET. Proc. R. Soc. B 244:241–46 [Google Scholar]
  52. Gibbons A. 1992. Databasing the brain. Science 258:1872–73 [Google Scholar]
  53. Glahn DC, Laird AR, Ellison-Wright I, Thelen SM, Robinson JL. et al. 2008. Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biol. Psychiatry 64:774–81 [Google Scholar]
  54. Glahn DC, Ragland JD, Abramoff A, Barrett J, Laird AR. et al. 2005. Beyond hypofrontality: a quantitative meta-analysis of functional neuroimaging studies of working memory in schizophrenia. Hum. Brain Mapp. 25:60–69 [Google Scholar]
  55. Hagmann P, Kurant M, Gigandet X, Thiran P, Wedeen VJ. et al. 2007. Mapping human whole-brain structural networks with diffusion MRI. PLoS ONE 2:e597 [Google Scholar]
  56. Johansen-Berg H, Behrens TEJ, Robson MD, Drobnjak I, Rushworth MFS. et al. 2004. Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. Proc. Natl. Acad. Sci. USA 101:3613335–40 [Google Scholar]
  57. Karlsgodt KH, Kochunov P, Winkler AM, Laird AR, Almasy L. et al. 2010. A multimodal assessment of the genetic control over working memory. J. Neurosci. 30:8197–202 [Google Scholar]
  58. Koski L, Paus T. 2000. Functional connectivity of the anterior cingulate cortex within the human frontal lobe: a brain-mapping meta-analysis. Exp. Brain Res. 133:55–65Introduced the concept that meta-analysis of coactivations was an index of functional connectivity. [Google Scholar]
  59. Laird AR, Eickhoff SB, Fox PM, Uecker AM, Ray KL. et al. 2011a. The BrainMap strategy for standardization, sharing and meta-analysis of neuroimaging data. BMC Res. Notes 4:349 [Google Scholar]
  60. Laird AR, Eickhoff SB, Li K, Robin DA, Glahn DC, Fox PT. 2009. Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. J. Neurosci. 29:14496–505 [Google Scholar]
  61. Laird AR, Fox PM, Eickhoff SB, Turner JA, Ray KL. et al. 2011b. Behavioral interpretations of intrinsic connectivity networks. J. Cogn. Neurosci. 23:4022–37 [Google Scholar]
  62. Laird AR, Robinson JL, McMillan KM, Tordesillas-Gutiérrez D, Moran ST. et al. 2010. Comparison of the disparity between Talairach and MNI coordinates in functional neuroimaging data: validation of the Lancaster transform. NeuroImage 51:677–83 [Google Scholar]
  63. Laird AR, Robbins JM, Li K, Price LR, Cykowski MD. et al. 2008. Modeling motor connectivity using TMS/PET and structural equation modeling. NeuroImage 41:424–36 [Google Scholar]
  64. Laird AR, Fox PM, Price CJ, Glahn DC, Uecker AM. et al. 2005a. ALE meta-analysis: controlling the false discovery rate and performing statistical contrasts. Hum. Brain Mapp. 25:155–64 [Google Scholar]
  65. Laird AR, Lancaster JL, Fox PT. 2005b. BrainMap: the social evolution of a human brain mapping database. Neuroinformatics 3:65–78 [Google Scholar]
  66. Lambrecq V, Langbour N, Guehl D, Biolac B, Burbaud P, Rotge JY. 2013. Evolution of gray matter loss in Huntington's disease: a meta-analysis. Eur. J. Neurol. 20:315–21 [Google Scholar]
  67. Lancaster JL, Laird AR, Eickhoff SB, Martinez MJ, Fox MP, Fox PT. 2012. Automated regional behavioral analysis for human brain images. Front. Neuroinform. 6:23 [Google Scholar]
  68. Lancaster JL, Laird AR, Fox M, Glahn DE, Fox PT. 2005. Automated analysis of meta-analysis networks. Hum. Brain Mapp. 25:174–84 [Google Scholar]
  69. Lancaster JL, Tordesillas-Gutiérrez D, Martinez M, Salinas F, Evans A. et al. 2007. Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Hum. Brain Mapp. 28:1194–205 [Google Scholar]
  70. Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS. et al. 2000. Automated Talairach atlas labels for functional brain mapping. Hum. Brain Mapp. 10:3120–31 [Google Scholar]
  71. Lohmann G, Bohn S. 2002. Using replicator dynamics for analyzing fMRI data of the human brain. IEEE Trans. Med. Imaging 21:5485–92 [Google Scholar]
  72. Mazziotta JC, Toga TW, Evans A, Fox P, Lancaster J. 1995. A probabilistic atlas of the human brain: theory and rationale for its development. The International Consortium for Brain Mapping (ICBM). NeuroImage 2:2A89–101 [Google Scholar]
  73. Menzies L, Chamberlain SR, Laird AR, Thelen SM, Sahakian BJ, Bullmore ET. 2008. Integrating evidence from neuroimaging and neuropsychological studies of obsessive-compulsive disorder: the orbitofronto-striatal model revisited. Neurosci. Biobehav. Rev. 32:525–49 [Google Scholar]
  74. Meunier D, Achard S, Morcom A, Bullmore E. 2009. Age-related changes in modular organization of human brain functional networks. NeuroImage 44:715–23 [Google Scholar]
  75. Mintun MA, Fox PT, Raichle ME. 1989. A highly accurate method of localizing regions of neuronal activation in the human brain with positron emission tomography. J. Cereb. Blood Flow Metab. 9:196–103 [Google Scholar]
  76. Minzenberg MJ, Laird AR, Thelen SM, Carter CS, Glahn DC. 2009. Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch. Gen. Psychiatry 66:811–22 [Google Scholar]
  77. Narayana S, Laird AR, Tandon N, Franklin C, Lancaster JL, Fox PT. 2012. Electrophysiological and functional connectivity of the human supplementary motor area. NeuroImage 62:250–65 [Google Scholar]
  78. Neumann J, Fox PT, Turner R, Lohmann G. 2010. Learning partially directed functional networks from meta-analysis imaging data. NeuroImage 49:1372–84 [Google Scholar]
  79. Neumann J, Lohmann G, Derrfuss J, Yves von Cramon D. 2005. The meta-analysis of functional imaging data using replicator dynamics. Hum. Brain Mapp. 25:165–73 [Google Scholar]
  80. Neumann J, Yves von Cramon D, Lohmann G. 2008. Model-based clustering of meta-analytic functional imaging data. Hum. Brain Mapp. 29:177–92 [Google Scholar]
  81. Nickl-Jockschat T, Habel U, Michel TM, Manning J, Laird AR. et al. 2012. Brain structure anomalies in autism spectrum disorder (ASD)—a meta-analysis of VBM studies using anatomic likelihood estimation (ALE). Hum. Brain Mapp. 33:1470–89 [Google Scholar]
  82. Paus T. 1996. Location and function of the human frontal eye-field: a selective review. Neuropsychologia 34:475–83 [Google Scholar]
  83. Pearson K. 1904. Report on certain enteric fever inoculation statistics. Br. Med. J. 3:1243–46 [Google Scholar]
  84. Picard N, Strick PL. 1996. Motor areas of the medial wall: a review of their location and functional activation. Cereb. Cortex 6:342–53 [Google Scholar]
  85. Poldrack RA. 2006. Can cognitive processes be inferred from neuroimaging data?. Trends Cogn. Sci. 10:59–63 [Google Scholar]
  86. Posner M, Petersen S, Fox PT, Raichle M. 1988. Localization of cognitive operations in the human brain. Science 240:1627–31 [Google Scholar]
  87. Postuma RB, Dagher A. 2006. Basal ganglia functional connectivity based on a meta-analysis of 126 positron emission tomography and functional magnetic resonance imaging publications. Cereb. Cortex 16:1508–21 [Google Scholar]
  88. Price CJ, Friston KJ. 2005. Functional ontologies for cognition: the systematic definition of structure and function. Cogn. Neuropsychol. 22:262–75 [Google Scholar]
  89. Price LR, Laird AR, Fox PT. 2009. Modeling dynamic functional neuroimaging data using structural equation modeling. Struct. Equ. Modeling 16:147–62 [Google Scholar]
  90. Ragland JD, Laird AR, Ranganath C, Blumenfeld RS, Gonzales SM, Glahn DC. 2009. Prefrontal activation deficits during episodic memory in schizophrenia. Am. J. Psychiatry 166:863–74 [Google Scholar]
  91. Robinson JL, Laird AR, Glahn DC, Blangero J, Sanghera MK. et al. 2012. The functional connectivity of the human caudate: an application of meta-analytic connectivity modeling with behavioral filtering. NeuroImage 60:117–29Introduced meta-analytic connectivity modeling with behavioral filtering, i.e., sorting connected networks by their functional meta-data. [Google Scholar]
  92. 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. 31:173–84 [Google Scholar]
  93. Rottschy C, Caspers S, Roski C, Reetz K, Dogan I. et al. 2012. Differentiated parietal connectivity of frontal regions for “what” and “where” memory. Brain Struct. Funct. 218:1551–67 [Google Scholar]
  94. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM. et al. 2009. Correspondence of the brain's functional architecture during activation and rest. Proc. Natl. Acad. Sci. USA 106:13040–45Presented the first ICA meta-analysis and demonstrated similarity of ICA networks for BrainMap and resting-state fMRI data sets. [Google Scholar]
  95. Sporns O, Honey CJ, Kötter R. 2007. Identification and classification of hubs in brain networks. PLoS ONE 2:e1049 [Google Scholar]
  96. Spreng RN, Mar RA, Kim AS. 2009. The common neural basis of autobiographical memory, prospection, navigation, theory of mind and the default mode: a quantitative meta-analysis. J. Cogn. Neurosci. 21:489–510 [Google Scholar]
  97. Stephan KI, Tittgemeyer M, Knosche TR, Moran RJ, Friston KJ. 2009. Tractography based priors for dynamic causal models. NeuroImage 47:1628–38 [Google Scholar]
  98. Talairach J, Szikla G, Tournoux P, Prosalentis A, Bordas-Ferrier M. 1967. Atlas d'Anatomie Stéréotaxique du Télencépahle Paris: Masson
  99. Talairach J, Tournoux P. 1988. Co-Planar Stereotaxic Atlas of the Human Brain New York: Thieme
  100. Toga AW. 1998. Brain Warping San Diego: Academic
  101. Toro R, Fox PT, Paus T. 2008. Functional coactivation map of the human brain. Cereb. Cortex 18:2553–59Presented the first large-scale (whole-database) application of coactivation-based meta-analysis. [Google Scholar]
  102. Torta DM, Costa T, Duca S, Fox PT, Cauda F. 2013. Parcellation of the cingulate cortex at rest and during tasks: a meta-analytic clustering and experimental study. Front. Hum. Neurosci. 7:275 [Google Scholar]
  103. Tulving E, Kapur S, Craik FI, Moscovitch M, Huole S. 1994. Hemispheric encoding/retrieval asymmetry in episodic memory: positron emission tomography findings. Proc. Natl. Acad. Sci. USA 91:2016–20 [Google Scholar]
  104. Turkeltaub PE, Eden GF, Jones KM, Zeffiro TA. 2002. Meta-analysis of the functional neuroanatomy of single-word reading: method and validation. NeuroImage 16:765–80Introduced the activation likelihood estimation method of coordinate-based meta-analysis. [Google Scholar]
  105. Turkeltaub PE, Eickhoff SB, Laird AR, Fox M, Wiener M, Fox PT. 2012. Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses. Hum. Brain Mapp. 33:1–13 [Google Scholar]
  106. Turner JA, Laird AR. 2011. The cognitive paradigm ontology: design and application. Neuroinformatics 10:57–66 [Google Scholar]
  107. Wager TD, Phan KL, Liberzon I, Taylor SF. 2003. Valence, gender, and lateralization of functional brain anatomy in emotion: a meta-analysis of findings in neuroimaging. NeuroImage 19:513–31 [Google Scholar]
  108. Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. 2011. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8:8665–70 [Google Scholar]
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Meta-Analysis in Human Neuroimaging: Computational Modeling of Large-Scale Databases
Annual Review of Neuroscience 37, 409 (2014); https://doi.org/10.1146/annurev-neuro-062012-170320
/content/journals/10.1146/annurev-neuro-062012-170320
/content/journals/10.1146/annurev-neuro-062012-170320
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