Deciphering the functional specialization of whole-brain spatiomolecular gradients in the adult brain
Open accessPublished version of a preprint
Abstract
Cortical arealization arises during neurodevelopment from the confluence of molecular gradients representing patterned expression of morphogens and transcription factors. However, whether similar gradients are maintained in the adult brain remains unknown. Here, we uncover three axes of topographic variation in gene expression in the adult human brain that specifically capture previously identified rostral-caudal, dorsal-ventral, and medial-lateral axes of early developmental patterning. The interaction of these spatiomolecular gradients i) accurately reconstructs the position of brain tissue samples, ii) delineates known functional territories, and iii) can model the topographical variation of diverse cortical features. The spatiomolecular gradients are distinct from canonical cortical axes differentiating the primary sensory cortex from the association cortex, but radiate in parallel with the axes traversed by local field potentials along the cortex. We replicate all three molecular gradients in three independent human datasets as well as two nonhuman primate datasets and find that each gradient shows a distinct developmental trajectory across the lifespan. The gradients are composed of several well-known transcription factors (e.g., PAX6 and SIX3 ), and a small set of genes shared across gradients are strongly enriched for multiple diseases. Together, these results provide insight into the developmental sculpting of functionally distinct brain regions, governed by three robust transcriptomic axes embedded within brain parenchyma.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · cited 1× · see citing works
“Transcriptomic information does not represent protein concentration with great fidelity (105), and relationships between gene sets and enriched terms do not entail a causal relationship between our gradients and biological processes or diseases.”
BackgroundLarge-scale automated synthesis of human functional neuroimaging data.
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011) · Nature Methods · doi:10.1038/nmeth.1635 · Cited by 3,686 · see citing works
Efficiency and Cost of Economical Brain Functional Networks
Achard, S., & Bullmore, E. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030017 · Cited by 2,553 · see citing works
Situating the default-mode network along a principal gradient of macroscale cortical organization
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., Eickhoff, S. B., Castellanos, F. X., Petrides, M., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608282113 · Cited by 2,321 · cited 5× · see citing works
“…gradients in the adult human cerebral cortex have been a topic of concerted study in recent years, where a sensorimotor–association axis has been proposed as a major feature of functional organization that can also explain variance in many aspects of cortical physiology and neurobiology (20, 21).”
BackgroundMethodInfluentialTranscriptional landscape of the prenatal human brain
Miller, J. A., Ding, S.-L., Sunkin, S. M., Smith, K. A., Ng, L., Szafer, A., Ebbert, A., Riley, Z. L., Royall, J. J., Aiona, K., et al. (2014) · Nature · doi:10.1038/nature13185 · Cited by 1,325 · see citing works
Kadoshima, T., Sakaguchi, H., Nakano, T., Soen, M., Ando, S., Eiraku, M., & Sasai, Y. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1315710110 · Cited by 999 · see citing works
A Hierarchy of Time-Scales and the Brain
Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000209 · Cited by 694 · see citing works
Canonical genetic signatures of the adult human brain
Hawrylycz, M., Miller, J. A., Menon, V., Feng, D., Dolbeare, T., Guillozet-Bongaarts, A. L., Jegga, A. G., Aronow, B. J., Lee, C.-K., Bernard, A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4171 · Cited by 663 · see citing works
Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons
Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A. (2016) · Science · doi:10.1126/science.aad7038 · Cited by 601 · see citing works
Roberts, J. A., Gollo, L. L., Abeysuriya, R. G., Roberts, G., Mitchell, P. B., Woolrich, M. W., & Breakspear, M. (2019) · Nature Communications · doi:10.1038/s41467-019-08999-0 · Cited by 264 · see citing works
Béna, F., Bruno, D. L., Eriksson, M., van Ravenswaaij‐Arts, C., Stark, Z., Dijkhuizen, T., Gerkes, E., Gimelli, S., Ganesamoorthy, D., Thuresson, A. C., et al. (2013) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32148 · Cited by 109 · see citing works
Fiber length profiling: a novel approach to structural brain organization
Bajada, C. J., Schreiber, J., & Caspers, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.070 · Cited by 55 · see citing works
Flagged first, then by citations
- Authors
- Vogel, J. W., Alexander-Bloch, A. F., Wagstyl, K., Bertolero, M. A., Markello, R. D., Pines, A., Sydnor, V. J., Diaz-Papkovich, A., Hansen, J. Y., Evans, A. C., Bernhardt, B., Misic, B., Satterthwaite, T. D., & Seidlitz, J.
- Affiliations
- Lund University (SE)
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Wellcome Centre for Human Neuroimaging (GB)
- University of Pennsylvania (US)
- Montreal Neurological Institute and Hospital (CA)
- McGill Genome Centre (CA)
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Children's Hospital of Philadelphia (US)
- Corresponding author
- Vogel, Jacob W.
- Satterthwaite, Theodore D.
- Seidlitz, Jakob
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2024-06-11
- Volume
- 121
- Issue
- 25
- Pages
- e2219137121-e2219137121
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC11194492
- OpenAlex
- W4399529545
- Cited by
- 34
- References
- 12
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 3 · 2023: 5 · 2022: 1
- FWCI
- 3.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- HHS | NIH | National Institute of Mental Health (T32MH019112)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Keywords
- adult human brain, dorsal-ventral axis, transcription factors, Pax6, Six3, functional brain regions, local field potentials, non-human primate brain
- MeSH terms
- PAX6 Transcription Factor, Adult, Animals, Brain, Female, Humans, Male, Nerve Tissue Proteins, Transcription Factors, Gene Expression Regulation, Developmental, Body Patterning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review