Brain-wide decoding of numbers and letters: Converging evidence from multivariate fMRI analysis and probabilistic meta-analysis.
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| Title: | Brain-wide decoding of numbers and letters: Converging evidence from multivariate fMRI analysis and probabilistic meta-analysis. |
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| Authors: | Liu R; Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA. Electronic address: rul23@stanford.edu., Chang H; Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA., El-Said D; Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA., Wassermann D; Inria Saclay Île-de-France, CEA, Université Paris-Saclay, Palaiseau, France., Zhang Y; Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA., Menon V; Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA; Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, USA; Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA; Graduate School of Education, Stanford University, Stanford, CA, USA. Electronic address: menon@stanford.edu. |
| Source: | Cortex; a journal devoted to the study of the nervous system and behavior [Cortex] 2025 Aug; Vol. 189, pp. 256-274. Date of Electronic Publication: 2025 Jun 13. |
| Publication Type: | Journal Article; Meta-Analysis |
| Language: | English |
| Journal Info: | Publisher: Masson Country of Publication: Italy NLM ID: 0100725 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1973-8102 (Electronic) Linking ISSN: 00109452 NLM ISO Abbreviation: Cortex Subsets: MEDLINE |
| Imprint Name(s): | Publication: Milan : Masson Original Publication: Varese. |
| MeSH Terms: | Brain*/physiology , Brain*/diagnostic imaging , Pattern Recognition, Visual*/physiology, Humans ; Magnetic Resonance Imaging/methods ; Brain Mapping/methods ; Male ; Female ; Adult ; Multivariate Analysis ; Young Adult |
| Abstract: | Previous studies exploring category-sensitive representations of numbers and letters have predominantly focused on individual brain regions. This study expands upon this research through computationally rigorous whole-brain neural decoding using Elastic Net (ND-EN), facilitating the analysis of neural patterns across the entire brain with greater precision. To establish the robustness and generalizability of our results, we also conducted innovative probabilistic meta-analyses of the extant functional neuroimaging literature. The investigation comprised both an active task, requiring participants to distinguish between numbers and letters, and a passive task where they simply viewed these symbols. ND-EN revealed that, during the active task, a distributed network-including the ventral temporal-occipital cortex, intraparietal sulcus, middle frontal gyrus, and insula-actively differentiated between numbers and letters. This distinction was not evident in the passive task, indicating that the task engagement level plays a crucial role in such neural differentiation. Further, regional neural representational similarity analyses within the ventral temporal-occipital cortex revealed similar activation patterns for numbers and letters, indicating a lack of differentiation in regions previously linked to these visual symbols. Thus, our findings indicate that category-sensitive representations of numbers and letters are not confined to isolated regions but involve a broader network of brain areas, and are modulated by task demands. Supporting these empirical findings, probabilistic meta-analyses conducted with NeuroLang and the Neurosynth database reinforced our observations. Together, the convergence of evidence from multivariate neural pattern analysis and meta-analysis advances our understanding of how numbers and letters are represented in the human brain. (Copyright © 2025 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest None. |
| References: | Front Neuroinform. 2011 Sep 06;5:17. (PMID: 21922006) Brain Struct Funct. 2023 Jan;228(1):293-304. (PMID: 36376522) Trends Cogn Sci. 2011 Jun;15(6):254-62. (PMID: 21592844) Annu Rev Neurosci. 2009;32:185-208. (PMID: 19400715) Neuropsychologia. 2016 Mar;83:257-273. (PMID: 26403660) Neuroimage. 2004 May;22(1):466-76. (PMID: 15110040) Sci Rep. 2017 Jul 21;7(1):6188. (PMID: 28733684) Neuroimage. 2017 Feb 1;146:376-394. (PMID: 27769786) Vision Res. 2001;41(10-11):1409-22. (PMID: 11322983) Front Syst Neurosci. 2008 Nov 24;2:4. (PMID: 19104670) Proc Natl Acad Sci U S A. 2004 May 11;101(19):7457-62. (PMID: 15123797) Neuron. 2018 Jul 25;99(2):257-273. (PMID: 30048614) Cereb Cortex. 2015 May;25(5):1252-64. (PMID: 24275831) Cogn Neuropsychol. 2011 May;28(3-4):251-75. (PMID: 22185237) Science. 2010 Oct 1;330(6000):97-101. (PMID: 20829453) Behav Res Methods. 2019 Feb;51(1):195-203. (PMID: 30734206) Neuroimage. 2004 Mar;21(3):829-39. (PMID: 15006649) Neuroimage. 2016 May 15;132:314-319. (PMID: 26940623) J Stat Softw. 2010;33(1):1-22. (PMID: 20808728) Neuron. 2007 Jan 18;53(2):293-305. (PMID: 17224409) Trends Cogn Sci. 2013 May;17(5):210-9. (PMID: 23608364) J Neurosci. 2013 Apr 17;33(16):6709-15. (PMID: 23595729) Neurosci Biobehav Rev. 2019 Aug;103:316-336. (PMID: 31082407) J Cogn Neurosci. 2021 Nov 5;33(12):2548-2558. (PMID: 34407190) iScience. 2024 Jan 06;27(2):108809. (PMID: 38303718) Neuroimage. 2011 Aug 1;57(3):1205-11. (PMID: 21620978) PLoS Comput Biol. 2017 Apr 24;13(4):e1005508. (PMID: 28437426) Neurosci Biobehav Rev. 2017 Jul;78:145-160. (PMID: 28467892) Cortex. 2011 Jan;47(1):2-13. (PMID: 19762013) J Neurosci. 2010 Jan 6;30(1):148-60. (PMID: 20053897) Neuropsychologia. 2011 Apr;49(5):1177-1186. (PMID: 21237182) Nat Neurosci. 2016 Aug 26;19(9):1192-3. (PMID: 27571199) Science. 2001 Aug 24;293(5534):1506-9. (PMID: 11520991) Neuroimage. 2011 Feb 1;54(3):2382-93. (PMID: 20946958) Proc Natl Acad Sci U S A. 2013 Jul 2;110(27):11187-92. (PMID: 23776242) Proc Natl Acad Sci U S A. 2018 Oct 16;115(42):E9981-E9990. (PMID: 30224475) Neuroimage. 2019 Jan 15;185:245-254. (PMID: 30342974) Cereb Cortex. 2010 Nov;20(11):2636-46. (PMID: 20154013) Neuropsychologia. 2006;44(14):2937-49. (PMID: 16920164) Proc Natl Acad Sci U S A. 2010 Jun 22;107(25):11163-70. (PMID: 20484679) Proc Natl Acad Sci U S A. 2011 Jun 14;108(24):9998-10003. (PMID: 21628569) Neuroimage. 2018 Oct 15;180(Pt A):4-18. (PMID: 28782682) Cereb Cortex. 2017 Jan 1;27(1):567-575. (PMID: 26503267) Dev Rev. 2021 Jun;60:. (PMID: 34108794) Hum Brain Mapp. 2015 May;36(5):1963-81. (PMID: 25628041) J Neurosci. 2016 Jan 06;36(1):88-97. (PMID: 26740652) Nat Rev Neurosci. 2008 Apr;9(4):278-91. (PMID: 18334999) Nat Commun. 2015 Aug 25;6:8151. (PMID: 26303198) Dev Cogn Neurosci. 2018 Apr;30:239-250. (PMID: 28844728) Nat Commun. 2019 Dec 6;10(1):5601. (PMID: 31811149) Nat Neurosci. 2014 Sep;17(9):1263-9. (PMID: 25129076) Front Hum Neurosci. 2014 Mar 20;8:88. (PMID: 24688462) Dev Sci. 2011 Sep;14(5):989-1001. (PMID: 21884315) Neuroimage. 2020 Jul 1;214:116716. (PMID: 32151762) Dev Sci. 2021 Nov;24(6):e13123. (PMID: 34060183) Neuroimage. 2018 Jul 15;175:188-200. (PMID: 29604456) Annu Rev Neurosci. 2014;37:435-56. (PMID: 25002277) Neuroimage. 2003 Aug;19(4):1492-500. (PMID: 12948705) Nat Neurosci. 2004 Jan;7(1):70-4. (PMID: 14647291) Proc Natl Acad Sci U S A. 2018 Aug 7;115(32):E7595-E7604. (PMID: 30038000) Neuroimage. 2014 Feb 15;87:311-22. (PMID: 24201011) Curr Biol. 2011 Aug 23;21(16):R618-21. (PMID: 21854998) Neuroimage. 2012 Mar;60(1):830-46. (PMID: 22178808) R Soc Open Sci. 2019 Oct 30;6(10):182067. (PMID: 31824678) J Neurosci. 2023 Apr 19;43(16):2950-2959. (PMID: 36922026) Nat Rev Neurosci. 2014 Aug;15(8):536-48. (PMID: 24962370) Nat Neurosci. 2020 Jul;23(7):788-799. (PMID: 32601411) Dev Cogn Neurosci. 2019 Dec;40:100719. (PMID: 31710975) Nat Methods. 2011 Jun 26;8(8):665-70. (PMID: 21706013) Neuron. 2001 Nov 20;32(4):737-45. (PMID: 11719212) |
| Grant Information: | R01 HD059205 United States HD NICHD NIH HHS; R01 MH084164 United States MH NIMH NIH HHS; R37 HD094623 United States HD NICHD NIH HHS |
| Contributed Indexing: | Keywords: Distributed neural representation; Multivariate decoding; Neural representational similarity; Quantitative meta-analysis; Ventral temporal-occipital cortex |
| Entry Date(s): | Date Created: 20250628 Date Completed: 20250810 Latest Revision: 20250812 |
| Update Code: | 20250812 |
| PubMed Central ID: | PMC12279025 |
| DOI: | 10.1016/j.cortex.2025.04.017 |
| PMID: | 40580696 |
| Database: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Previous studies exploring category-sensitive representations of numbers and letters have predominantly focused on individual brain regions. This study expands upon this research through computationally rigorous whole-brain neural decoding using Elastic Net (ND-EN), facilitating the analysis of neural patterns across the entire brain with greater precision. To establish the robustness and generalizability of our results, we also conducted innovative probabilistic meta-analyses of the extant functional neuroimaging literature. The investigation comprised both an active task, requiring participants to distinguish between numbers and letters, and a passive task where they simply viewed these symbols. ND-EN revealed that, during the active task, a distributed network-including the ventral temporal-occipital cortex, intraparietal sulcus, middle frontal gyrus, and insula-actively differentiated between numbers and letters. This distinction was not evident in the passive task, indicating that the task engagement level plays a crucial role in such neural differentiation. Further, regional neural representational similarity analyses within the ventral temporal-occipital cortex revealed similar activation patterns for numbers and letters, indicating a lack of differentiation in regions previously linked to these visual symbols. Thus, our findings indicate that category-sensitive representations of numbers and letters are not confined to isolated regions but involve a broader network of brain areas, and are modulated by task demands. Supporting these empirical findings, probabilistic meta-analyses conducted with NeuroLang and the Neurosynth database reinforced our observations. Together, the convergence of evidence from multivariate neural pattern analysis and meta-analysis advances our understanding of how numbers and letters are represented in the human brain.<br /> (Copyright © 2025 Elsevier Ltd. All rights reserved.) |
|---|---|
| ISSN: | 1973-8102 |
| DOI: | 10.1016/j.cortex.2025.04.017 |