Abstract
Regulation of gene expression drives protein interactions that govern synaptic wiring and neuronal activity. The resulting coordinated activity among neuronal populations supports complex psychological processes, yet how gene expression shapes cognition and emotion remains unknown. Here, we directly bridge the microscale and macroscale by mapping gene expression patterns to functional activation patterns across the cortical sheet. Applying unsupervised learning to the Allen Human Brain Atlas and Neurosynth databases, we identify a ventromedial–dorsolateral gradient of gene assemblies that separate affective and perceptual domains. This topographic molecular-psychological signature reflects the hierarchical organization of the neocortex, including systematic variations in cell type, myeloarchitecture, laminar differentiation and intrinsic network affiliation. In addition, this molecular-psychological signature strengthens over neurodevelopment and can be replicated in two independent repositories. Collectively, our results reveal spatially covarying transcriptomic and cognitive architectures, highlighting the influence that molecular mechanisms exert on psychological processes.
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Data availability
The AHBA is available at https://human.brain-map.org/static/download. The Neurosynth database is available at https://neurosynth.org/. The HCP database is available at https://db.humanconnectome.org/data/projects/HCP_1200. The BrainSpan database is available at https://www.brainspan.org/static/download.html. Processed data as used in this report are available at https://github.com/netneurolab/hansen_genescognition. BrainMap data are available upon reasonable request to B.M.
Code availability
Code used to conduct the reported analyses is available at https://github.com/netneurolab/hansen_genescognition.
References
Betzel, R. F. & Bassett, D. S. Multi-scale brain networks. NeuroImage 160, 73–83 (2017).
Tripathy, S. J. et al. Transcriptomic correlates of neuron electrophysiological diversity. PLoS Comput. Biol. 13, e1005814 (2017).
Cadwell, C. R. et al. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat. Biotech. 34, 199–203 (2016).
Lee, K. F. H., Soares, C., Thivierge, J.-P. & Béïque, J.-C. Correlated synaptic inputs drive dendritic calcium amplification and cooperative plasticity during clustered synapse development. Neuron 89, 784–799 (2016).
Frémaux, N. & Gerstner, W. Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules. Front. Neural Circuits 9, 85 (2016).
Blankenship, A. G. & Feller, M. B. Mechanisms underlying spontaneous patterned activity in developing neural circuits. Nat. Rev. Neurosci. 11, 18–29 (2010).
Calhoon, G. G. & Tye, K. M. Resolving the neural circuits of anxiety. Nat. Neurosci. 18, 1394–1404 (2015).
Suárez, L. E., Markello, R. D., Betzel, R. F. & Misic, B.Linking structure and function in macroscale brain networks. Trends Cogn. Sci. 24, 302–315 (2020).
Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).
Damoiseaux, J. S. et al. Consistent resting-state networks across healthy subjects. Proc. Natl Acad. Sci. USA 103, 13848–13853 (2006).
Richiardi, J. et al. Correlated gene expression supports synchronous activity in brain networks. Science 348, 1241–1244 (2015).
Fulcher, B. D. & Fornito, A. A transcriptional signature of hub connectivity in the mouse connectome. Proc. Natl Acad. Sci. USA 113, 1435–1440 (2016).
Fulcher, B. D., Murray, J. D., Zerbi, V. & Wang, X.-J. Multimodal gradients across mouse cortex. Proc. Natl Acad. Sci. USA 116, 4689–4695 (2019).
Betzel, R. F. et al. Structural, geometric and genetic factors predict interregional brain connectivity patterns probed by electrocorticography. Nat. Biomed. Eng. 3, 902–916 (2019).
Vértes, P. E. et al. Gene transcription profiles associated with intra-modular and inter-modular hubs in human fMRI networks. Phil. Trans. R. Soc. Lond. B Biol. Sci. 371, 735–769 (2016).
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. & Wager, T. D.Large-scale automated synthesis of human functional neuroimaging data. Nat. Meth. 8, 665–670 (2011).
Fox, P. T. & Lancaster, J. L. Mapping context and content: the BrainMap model. Nat. Rev. Neurosci. 3, 319–321 (2002).
Gorgolewski, K. J. et al. NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Front. Neuroinform. 9, 8 (2015).
Dockès, J. et al. NeuroQuery, comprehensive meta-analysis of human brain mapping. eLife 9, e53385 (2020).
Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).
Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18, 1832–1844 (2015).
Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).
Seidlitz, J. et al. Transcriptomic and cellular decoding of regional brain vulnerability to neurodevelopmental disorders. Nat. Commun. 11, 3358 (2020).
Whitaker, K. J. et al. Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome. Proc. Natl Acad. Sci. USA 113, 9105–9110 (2016).
Váša, F. et al. Conservative and disruptive modes of adolescent change in human brain functional connectivity. Proc. Natl Acad. Sci. USA 117, 3248–3253 (2020).
Burt, J. B. et al. Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography. Nat. Neurosci. 21, 1251–1259 (2018).
Arnatkevičiūtė, A., Fulcher, B. D. & Fornito, A. A practical guide to linking brain-wide gene expression and neuroimaging data. NeuroImage 189, 353–367 (2019).
Fox, A. S., Chang, L. J., Gorgolewski, K. J. & Yarkoni, T. Bridging psychology and genetics using large-scale spatial analysis of neuroimaging and neurogenetic data. Preprint at bioRxiv https://doi.org/10.1101/012310 (2014).
Poldrack, R. A. et al. The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. Front. Neuroinform. 5, 17 (2011).
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980 (2006).
Cammoun, L. et al. Mapping the human connectome at multiple scales with diffusion spectrum MRI. J. Neurosci. Methods 203, 386–397 (2012).
McIntosh, A. R., Bookstein, F. L., Haxby, J. V. & Grady, C. L. Spatial pattern analysis of functional brain images using partial least squares. NeuroImage 3, 143–157 (1996).
Krishnan, A., Williams, L. J., McIntosh, A. R. & Abdi, H. Partial least squares (PLS) methods for neuroimaging: a tutorial and review. NeuroImage 56, 455–475 (2011).
McIntosh, A. R. & Mišić, B. Multivariate statistical analyses for neuroimaging data. Annu. Rev. Psychol. 64, 499–525 (2013).
Vázquez-Rodríguez, B. et al. Gradients of structure–function tethering across neocortex. Proc. Natl Acad. Sci. USA 116, 21219–21227 (2019).
Markello, R. & Misic, B. Comparing spatially-constrained null models for parcellated brain maps. Preprint at bioRxiv https://doi.org/10.1101/2020.08.13.249797 (2020).
Burt, J. B., Helmer, M., Shinn, M., Anticevic, A. & Murray, J. D.Generative modeling of brain maps with spatial autocorrelation. NeuroImage 220, 117038 (2020).
Fulcher, B. D, Arnatkevičiūtė, A. & Fornito, A. Overcoming bias in gene-set enrichment analyses of brain-wide transcriptomic data. Preprint at bioRxiv https://doi.org/10.1101/2020.04.24.058958 (2020).
Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).
Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).
Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).
Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).
Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).
McKenzie, A. T. et al. Brain cell type specific gene expression and co-expression network architectures. Sci. Rep. 8, 8868 (2018).
Huntenburg, J. M., Bazin, P.-L. & Margulies, D. S. Large-scale gradients in human cortical organization. Trends Cogn. Sci. 22, 21–31 (2018).
Wang, X.-J.Macroscopic gradients of synaptic excitation and inhibition in the neocortex. Nat. Rev. Neurosci. 21, 169–178 (2020).
Glasser, M. F. & Van Essen, D. C. Mapping human cortical areas in vivo based on myelin content as revealed by T1-and T2-weighted MRI. J. Neurosci. 31, 11597–11616 (2011).
Wagstyl, K., Ronan, L., Goodyer, I. M. & Fletcher, P. C. Cortical thickness gradients in structural hierarchies. NeuroImage 111, 241–250 (2015).
Huntenburg, J. M. et al. A systematic relationship between functional connectivity and intracortical myelin in the human cerebral cortex. Cereb. Cortex 27, 981–997 (2017).
Jones, E. G. & Powell, T. P. S. An anatomical study of converging sensory pathways within the cerebral cortex of the monkey. Brain 93, 793–820 (1970).
Mesulam, M.-M. From sensation to cognition. Brain 121, 1013–1052 (1998).
Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl Acad. Sci. USA 113, 12574–12579 (2016).
Coifman, R. R. et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc. Natl Acad. Sci. USA 102, 7426–7431 (2005).
Van Der Maaten, L., Postma, E. & Van den Herik, J. Dimensionality reduction: a comparative. J. Mach. Learn. Res. 10, 66–71 (2009).
Von Economo, C. & Koskinas, G. N. Die Cytoarchitektonik der Hirnrinde des Erwachsenen Menschen (Springer, 1925).
Mesulam, M.-M. Principles of Behavioral and Cognitive Neurology 2nd edn (Oxford Univ. Press, 2000).
Paquola, C. et al. Microstructural and functional gradients are increasingly dissociated in transmodal cortices. PLoS Biol. 17, e3000284 (2019).
Fox, P. T. et al. BrainMap taxonomy of experimental design: description and evaluation. Hum. Brain Mapp. 25, 185–198 (2005).
Vanasse, T. J. et al. BrainMap VBM: an environment for structural meta-analysis. Hum. Brain Mapp. 39, 3308–3325 (2018).
Laird, A. R., Lancaster, J. J. & Fox, P. T. BrainMap. Neuroinformatics 3, 65–77 (2005).
Alexander-Bloch, A. F. et al. Imaging local genetic influences on cortical folding. Proc. Natl Acad. Sci. USA 117, 7430–7436 (2020).
Forest, M. et al. Gene networks show associations with seed region connectivity. Hum. Brain Mapp. 38, 3126–3140 (2017).
Krienen, F. M., Yeo, B. T. T., Ge, T., Buckner, R. L. & Sherwood, C. C. Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain. Proc. Natl Acad. Sci. USA 113, E469–E478 (2016).
Buckner, R. L. & Krienen, F. M. The evolution of distributed association networks in the human brain. Trends Cogn. Sci. 17, 648–665 (2013).
Mueller, S. et al. Individual variability in functional connectivity architecture of the human brain. Neuron 77, 586–595 (2013).
Jo, Y., Faskowitz, J., Esfahlani, F. Z., Sporns, O. & Betzel, R. F. Subject identification using edge-centric functional connectivity. Preprint at bioRxiv https://doi.org/10.1101/2020.09.13.291898 (2020).
Vogel, J. W. et al. A molecular gradient along the longitudinal axis of the human hippocampus informs large-scale behavioral systems. Nat. Commun. 11, 960 (2020).
Mišić, B. & Sporns, O. From regions to connections and networks: new bridges between brain and behavior. Curr. Opin. Neurobiol. 40, 1–7 (2016).
Kirschner, M. et al. Latent clinical-anatomical dimensions of schizophrenia. Schizophr. Bull. 46, 1426–1438 (2020).
Amor, S., Puentes, F., Baker, D. & Van Der Valk, P. Inflammation in neurodegenerative diseases. Immunology 129, 154–169 (2010).
Shafiei, G. et al. Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture. Biol. Psychiatry 87, 727–735 (2020).
Bush, G., Luu, P. & Posner, M. I. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn. Sci. 4, 215–222 (2000).
Goodkind, M. et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72, 305–315 (2015).
Patania, A. et al. Topological gene expression networks recapitulate brain anatomy and function. Network Neurosci. 3, 744–762 (2019).
Miller, J. A. et al. Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-seq. BMC Genomics 15, 154 (2014).
Alexander-Bloch, A. F. et al. On testing for spatial correspondence between maps of human brain structure and function. NeuroImage 178, 540–551 (2018).
Eckart, C. & Young, G. The approximation of one matrix by another of lower rank. Psychometrika 1, 211–218 (1936).
Kovacevic, N., Abdi, H., Beaton, D. & McIntosh, A. R. in New Perspectives in Partial Least Squares and Related Methods 159–170 (Springer, 2013).
Helmer, M. et al. On stability of canonical correlation analysis and partial least squares with application to brain–behavior associations. Preprint at bioRxiv https://doi.org/10.1101/2020.08.25.265546 (2020).
Hotelling, H. The most predictable criterion. J. Educ. Psychol. 26, 139–142 (1935).
McIntosh, A. R. & Lobaugh, N. J. Partial least squares analysis of neuroimaging data: applications and advances. NeuroImage 23, S250–S263 (2004).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Essen, D. C. Van et al. The WU-Minn Human Connectome Project: an overview. NeuroImage 80, 62–79 (2013).
Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105–124 (2013).
Werling, D. M. et al. Whole-genome and RNA sequencing reveal variation and transcriptomic coordination in the developing human prefrontal cortex. Cell Rep. 31, 107489 (2020).
Acknowledgements
We thank V. Bazinet, L. Suarez, G. Shafiei, B. Vazquez-Rodriguez and Z.-Q. Liu for comments and suggestions on the manuscript. This research was undertaken thanks in part to funding from the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative. B.M. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant RGPIN 017-04265) and Canada Research Chairs Program. J.W.V. was funded by NIH grant T32MH019112. D.B. was supported by the Healthy Brains for Healthy Lives initiative (Canada First Research Excellence Fund), CIFAR Artificial Intelligence Chairs program (Canada Institute for Advanced Research), Google (Research Award) and NIH grant R01AG068563A. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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J.Y.H. and B.M. conceived of the study idea. J.Y.H. performed the formal analysis with contributions from R.D.M. R.D.M., J.W.V., J.S. and D.B. contributed data. J.Y.H. and B.M. wrote the manuscript with valuable revision by R.D.M., J.W.V., J.S. and D.B. B.M. was the project administrator.
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Hansen, J.Y., Markello, R.D., Vogel, J.W. et al. Mapping gene transcription and neurocognition across human neocortex. Nat Hum Behav 5, 1240–1250 (2021). https://doi.org/10.1038/s41562-021-01082-z
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DOI: https://doi.org/10.1038/s41562-021-01082-z
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