Development and arealization of the cerebral cortex
Open accessAbstract
Adult cortical areas consist of specialized cell types and circuits that support unique higher-order cognitive functions. How this regional diversity develops from an initially uniform neuroepithelium has been the subject of decades of seminal research, and emerging technologies, including single-cell transcriptomics, provide a new perspective on area-specific molecular diversity. Here, we review the early developmental processes that underlie cortical arealization, including both cortex intrinsic and extrinsic mechanisms as embodied by the protomap and protocortex hypotheses, respectively. We propose an integrated model of serial homology whereby intrinsic genetic programs and local factors establish early transcriptomic differences between excitatory neurons destined to give rise to broad "proto-regions," and activity-dependent mechanisms lead to progressive refinement and formation of sharp boundaries between functional areas. Finally, we explore the potential of these basic developmental processes to inform our understanding of the emergence of functional neural networks and circuit abnormalities in neurodevelopmental disorders.
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The set of cell type-defining transcription factors in a progenitor or daughter cell can change with time, so different neuronal types emanate from the same progenitor in a precisely timed birth order.
“The set of cell type-defining transcription factors in a progenitor or a daughter cell can change with time, such that different neuronal types emanate from the same progenitor in a precisely timed “birth order”.”
From What is a cell type and how to define it? (2022) · cites this paper as Cadwell et al., 2019
Opposing morphogen gradients establish the basic plan for cortex (anterior-posterior) or spinal cord (dorsal-ventral) patterning and provide instructive signals for expression of complementary transcription factor activators and repressors, which define distinct neural progenitor domains.
“First, opposing morphogen gradients establish the basic plan for cortex (anterior-posterior) or spinal cord (dorsal-ventral) patterning and provide instructive signals for the expression of complementary sets of transcription factor activators and repressors, which in turn define distinct neural progenitor domains within cortex or spinal cord.”
From What is a cell type and how to define it? (2022) · cites this paper as Cadwell et al., 2019
Later in development, the same neural progenitors also generate non-neuronal cell types such as astrocytes and oligodendrocytes.
“Later in development, the same neural progenitors also generate non-neuronal cell types such as astrocytes and oligodendrocytes.”
From What is a cell type and how to define it? (2022) · cites this paper as Cadwell et al., 2019
Extensive historical studies of mouse cortex and spinal cord cell types have uncovered several common principles.
“The generation and patterning of mouse cortex and spinal cord cell types are two example systems where extensive historical studies have uncovered several common principles (Cadwell et al., 2019; Catela et al., 2015; Jessell, 2000; O’Leary et al., 2007; Osseward and Pfaff, 2019; Sagner and Briscoe, 2019).”
From What is a cell type and how to define it? (2022) · cites this paper as Cadwell et al., 2019
Specific sets of cell adhesion molecules provide guidance cues for axon path finding and synapse formation, leading to assembly of region- and cell type-specific local and global circuit networks.
“Third, specific sets of cell adhesion molecules provide guidance cues for axon path finding and synapse formation, leading to the assembly of region- and cell type-specific local and global circuit networks.”
From What is a cell type and how to define it? (2022) · cites this paper as Cadwell et al., 2019
Driven by transcription factor networks, each type of neural progenitor generates a series of neuronal cell types.
“Second, driven by the transcription factor network, each type of neural progenitors generates a series of neuronal cell types.”
From What is a cell type and how to define it? (2022) · cites this paper as Cadwell et al., 2019
Area specialization is likely a major contributing factor to the diversity of functions supported by cortex.
“This area specialization is likely a major contributing factor to the diversity of functions supported by cortex”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Cadwell et al., 2019
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- Authors
- Cadwell, C. R., Bhaduri, A., Mostajo-Radji, M. A., Keefe, M. G., & Nowakowski, T. J.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Nowakowski, Tomasz J.
- ORCID
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 103
- Issue
- 6
- Pages
- 980-1004
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC9245854
- OpenAlex
- W2976579916
- Cited by
- 408
- References
- 370
- Citations by year
- 2026: 53 · 2025: 76 · 2024: 68 · 2023: 68 · 2022: 63 · 2021: 45 · 2020: 32 · 2019: 2
- FWCI
- 4.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- William K. Bowes, Jr. Foundation
- Topics
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Keywords
- cortical development, serial homology, excitatory neurons, single-cell transcriptomics, neurodevelopmental disorders
- MeSH terms
- Deep Learning, Animals, Cerebral Cortex, Humans, Interneurons, Neural Inhibition, Neurons, Thalamus, Gene Expression Regulation, Developmental, Neurogenesis, Single-Cell Analysis
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.
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