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Cortical recycling in high-level visual cortex during childhood development

Nature Human Behaviour volume 5, pages 1686–1697 (2021)Cite this article

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Abstract

Human ventral temporal cortex contains category-selective regions that respond preferentially to ecologically relevant categories such as faces, bodies, places and words and that are causally involved in the perception of these categories. How do these regions develop during childhood? We used functional magnetic resonance imaging to measure longitudinal development of category selectivity in school-age children over 1 to 5 years. We discovered that, from young childhood to the teens, face- and word-selective regions in ventral temporal cortex expand and become more category selective, but limb-selective regions shrink and lose their preference for limbs. Critically, as a child develops, increases in face and word selectivity are directly linked to decreases in limb selectivity, revealing that during childhood, limb selectivity in ventral temporal cortex is repurposed into word and face selectivity. These data provide evidence for cortical recycling during childhood development. This has important implications for understanding typical as well as atypical brain development and necessitates a rethinking of how cortical function develops during childhood.

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Fig. 1: Developmental increases and decreases in category-selective activation in lateral VTC.
Fig. 2: Development of category-selective ROIs.
Fig. 3: Age-related increases in word and face selectivities parallel decreases in limb selectivity in the developing regions.
Fig. 4: Developmental changes in word, face and limb selectivities are linked.
Fig. 5: In emerging category-selective ROIs, selectivity flips from preference for limbs at age 5 years to preference for either words or faces, respectively, later in childhood.

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Data availability

The data required to generate the main figures are available in the GitHub repository (https://github.com/VPNL/corticalRecycling). Due to the large size of the raw data, it will be made available from the corresponding author upon request.

Code availability

Code is available at https://github.com/VPNL/corticalRecycling.

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Acknowledgements

The authors thank L. Villalobos, E. Y. Hwang, S. Huskins, A. Fitisemanu, and P. Eykamp for manually editing grey–white matter brain segmentations, B. Jeska, M. Barnett, C. Estrada and N. Lopez-Alvarez for help with data collection, R. Hinds for help with data entry and management. Funding was provided by a fellowship from the German National Academic Foundation NO 1448/1-1 (M.N.), NIH grant 2RO1 EY 022318 (K.G.-S.), NIH training grant 5T32EY020485 (V.S.N.), NSF Graduate Research Development Program DGE-114747 (J.G.) and Ruth L. Kirschstein National Research Service Award F31EY027201 (J.G.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Psychology, Stanford University, Stanford, CA, USA

    Marisa Nordt, Vaidehi S. Natu, Alex A. Rezai, Dawn Finzi, Holly Kular & Kalanit Grill-Spector

  2. Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA

    Jesse Gomez

  3. Neurosciences Program, Stanford University, Stanford, CA, USA

    Kalanit Grill-Spector

  4. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

    Kalanit Grill-Spector

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Contributions

M.N. collected data, developed and coded the analysis pipeline, analysed the data and wrote the manuscript. V.S.N. and J.G. designed the experiment, collected data and contributed to the manuscript. A.A.R. collected the data, contributed to data analysis and contributed to the manuscript. D.F. and H.K. collected the data and contributed to the manuscript. K.G.-S. designed the experiment, contributed to the analysis pipeline and data analyses and wrote the manuscript.

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Correspondence to Kalanit Grill-Spector.

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Peer review information Nature Human Behaviour thanks Marius Peelen and Frank Tong for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Control analyses examining developmental increases and decreases in category-selective activation in lateral VTC.

LMM slopes indicating change in category-selective activation volume per month (n = 128 sessions, 29 children). Error bars: 95% CI. If the CI does not cross the y = 0 line, this indicates that the slope is significantly different than 0 (before FDR-correction). (a) Slopes for the age predictor for models in which adult faces, child faces, limbs, and words are excluded as control categories from the contrast. No effects survive FDR-correction. (b) Slopes for the age predictor for models including both age and time series signal-to-noise ratio (tSNR) as predictors. Significant development after FDR-correction (p < 0.05) is indicated by asterisks. (c) Slopes for the main analysis (filled bars), the contrast control (open bars with gray outline) and the tSNR control (open bars with red outline) are overlaid to illustrate the changes in effect size across the different analyses. Full statistics in Supplementary Tables 3-4,6-7. Related to Fig. 1.

Extended Data Fig. 2 Control analyses examining functional changes underlying the development of category-selective ROIs.

Left panel: Colored bars: Slopes of LMMs indicating changes in selectivity by age for all 10 categories in emerging and waning ROIs. Open bars with gray outline: LMM slopes for contrasts in which adult faces, child faces, limbs, and words are excluded as control categories in contrasts. Error bars: 95% CI. If the CI does not cross the y = 0 line, this indicates that the slope is significantly different than 0 (before FDR-correction). Asterisks: significant development after FDR-correction (p < 0.05) for colored bars, circles: significant after FDR-correction for open bars. Right panel: Response amplitudes for 5–9-year-olds and 13–17-year-olds. Lighter colors indicate younger ages. One functional session per child is included per boxplot. Boxplots show the 75% and 25% percentiles (colored areas) and median (horizontal lines). Whiskers extend to the most extreme data points not considered outliers (minimum, maximum). Crosses: outliers (values more than 1.5 times the interquartile range away from the bottom or top of the box). Black diamonds: LMM prediction for the response at the mean age of each age group. (a) Left emerging pOTS-words. Left panel: n = 24 (112 sessions). (b) Left waning OTS-limbs. n = 26, 122 sessions. (c) Left emerging pFus-faces. n = 22, 105 sessions. (d) Right waning OTS-limbs. n = 21, 100 sessions. (e) Right emerging pFus-faces. n = 21, 96 sessions. Full statistics in Supplementary Tables 9-10. As we observed a significant decrease in limb-selectivity in emerging parts of word- and face-selective regions and word-, face- and limb-selective regions neighbor, we tested if emerging parts of word- and face-selective regions overlap with waning parts of the limb-selective regions. However, the overlap between the developing parts of the ROIs (difference between initial and end ROIs) assessed by the dice coefficient (DC) was small (overlap between developing parts of OTS-limbs and pFus-faces, left: DC = 0.025 ± 0.01 (mean ± SD), n = 22; right: DC = 0.026 ± 0.01, n = 18; overlap between developing parts of left OTS-limbs and pOTS-words: DC = 0.006±SD = 0.004, n = 21). Related to Fig. 3.

Extended Data Fig. 3 Developmental changes in word-, face-, and limb-selectivity are also linked in the right hemisphere.

(a) Limb-selectivity vs face- and word-selectivity in the waning right OTS-limbs. (b) Face-selectivity vs. limb- and word-selectivity in the emerging right pFus faces. Left: Model prediction for 5–9-year-olds and 13–17-year-olds for the selectivity that defines the ROI as a function of the selectivity to the other two variables. Middle: Individual participant data visualized in 3D. In each panel the variable on the z-axis is related to the x- and y-variables. LMM βs, 95%-CIs, t-values, df, and p-values are shown on top. Full statistics are reported in Supplementary Table 11. Orange arrows: Individual child data. Blue arrows: LMM, same as left panel. Right: Rotated version of the plots in the middle column to increase visibility of changes along the horizontal axes. Related to Fig. 4.

Extended Data Fig. 4 Pairwise preferences of the ROI-defining category in the right hemisphere.

In each plot we show the pairwise preference of the ROI-defining category vs. each of the other two developing categories as a function of age. Thin lines: individual participant data showing the pairwise preference from the initial to end session. Gray line: LMM prediction of pairwise preference based on data from all sessions. Shaded gray: 95%-CI. LMM results (intercept: βintercept and slope: βage (rate of change in preference, t/month) and their significance are reported under each panel. (a) Waning right OTS-limbs (n = 21 participants, n = 100 sessions). Left: Limbs vs faces. Right: Limbs vs words. (b) Emerging right pFus-faces (n = 21 participants, n = 96 sessions). Left: Faces vs limbs. Right: Faces vs words. Related to Fig. 5. Statistics in Supplementary Table 15.

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Nordt, M., Gomez, J., Natu, V.S. et al. Cortical recycling in high-level visual cortex during childhood development. Nat Hum Behav 5, 1686–1697 (2021). https://doi.org/10.1038/s41562-021-01141-5

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