Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (528 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
The basic cellular units of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the nervous system.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Neuron; Nerve Cells; Cell, Nerve; Cells, Nerve; Nerve Cell.
Where it fits in the MeSH classification
- Broader topics
- Nervous System; Cells
- Narrower topics
- Adrenergic Neurons; Cholinergic Neurons; Dendrites; Dopaminergic Neurons; GABAergic Neurons; Grid Cells; Interneurons; Mirror Neurons; Nerve Fibers; Neural Analyzers; Neurofibrils; Neurons, Afferent; Neurons, Efferent; Neuropil
- See also
- Neural Conduction
Related concepts
Research landscape
PubMed indexes 627,165 records tagged with the MeSH heading “Neurons” (counts retrieved 2026-10-29). 86,361 of them (13.8%) were published from 2020 onward, and the busiest period so far is through 1999 with 231,157 records.
Reviews account for 63,194 records (10.1%) and meta-analyses for 317. Randomized controlled trials number 1,637 (0.3%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 231,157 |
| 2000-2009 | 149,407 |
| 2010-2019 | 160,240 |
| 2020 onward | 86,361 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 63,194 | 10.1% |
| Meta-analyses | 317 | 0.1% |
| Randomized controlled trials | 1,637 | 0.3% |
Method: PubMed query "Neurons"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in neuroscience
Neuroscience draws on tools with different strengths: cellular and animal work can manipulate circuits directly, while human imaging and electrophysiology observe the living brain at lower resolution. Small imaging studies are prone to false positives, and findings in animals do not always carry over to people. Clinical neurology and psychiatry rely on trials and cohort studies whose outcomes are often symptom scales. Check the species, the sample size, how the outcome was measured and whether results were replicated.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Leucine-rich repeat kinase 2 impairs the release sites of Parkinson's disease vulnerable dopamine axons | 2026 | Research article Design not recorded in structured metadata | 5 | cc by PMID 42660892 · DOI · PMC13522591 |
| A massively parallel CRISPR-based screening platform for modifiers of neuronal depolarization | 2026 | Research article Design not recorded in structured metadata | 4 | cc by PMID 42669708 · DOI · PMC13526836 |
| Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations | 2026 | Research article Design not recorded in structured metadata | 4 | cc by PMID 42665582 · DOI · PMC13524952 |
Leucine-rich repeat kinase 2 impairs the release sites of Parkinson's disease vulnerable dopamine axons [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Parkinson's disease (PD) is defined pathologically by loss of dopamine-producing neurons in the substantia nigra pars compacta (SNc).
- Closing excerpt
- Together, these findings highlight a subtype-specific, cell-autonomous mechanism by which pathogenic LRRK2 impairs PD-vulnerable nigrostriatal synapses and provide a framework for therapeutic strategies targeting early synaptic deficits in PD.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (156 words)
Parkinson's disease (PD) is defined pathologically by loss of dopamine-producing neurons in the substantia nigra pars compacta (SNc). Yet synaptic dysfunction emerges much earlier, making it essential to define the mechanisms that drive early nigrostriatal deregulation. In the SNc, molecularly distinct dopamine neuron subtypes show differential susceptibility to PD. Here, we used intersectional genetic mouse models to determine how the PD-linked kinase LRRK2 affects vulnerable dopamine subtypes. Immunofluorescence and proximity-labeling proteomics revealed enriched LRRK2 expression in vulnerable dopamine neuron subclusters. High-resolution imaging showed that pathogenic LRRK2 disrupts presynaptic release-site organization in vulnerable dopamine axons, leading to reduced spontaneous and evoked striatal dopamine release in vivo. Proteomic analyses further showed that mutant LRRK2 increases phosphorylation of RAB3 proteins, impairing their interaction with the active-zone effectors RIM1 and RIM2. Together, these findings highlight a subtype-specific, cell-autonomous mechanism by which pathogenic LRRK2 impairs PD-vulnerable nigrostriatal synapses and provide a framework for therapeutic strategies targeting early synaptic deficits in PD.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42660892 · DOI · PMC13522591
A massively parallel CRISPR-based screening platform for modifiers of neuronal depolarization [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Understanding the complex interplay between gene expression and neuronal activity is crucial for unraveling the molecular mechanisms underlying cognitive function and neurological disorders.
- Closing excerpt
- This CRISPRi-based screening platform offers a versatile tool to uncover molecular mechanisms controlling neuronal function in health and disease.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (124 words)
Understanding the complex interplay between gene expression and neuronal activity is crucial for unraveling the molecular mechanisms underlying cognitive function and neurological disorders. Here, we developed pooled screens using CRISPR interference (CRISPRi) and the fluorescent calcium integrator CaMPARI2 to evaluate genetic modifiers of neuronal depolarization. Using this screening method, we evaluated 1343 genes for their effect on depolarization in a human iPSC-derived neuron model, revealing potential links to neurodegenerative and neurodevelopmental disorders. These genes include known regulators of neuronal excitability, such as TARPs and ion channels, as well as genes associated with autism spectrum disorder and Alzheimer's disease not previously described to affect neuronal depolarization. This CRISPRi-based screening platform offers a versatile tool to uncover molecular mechanisms controlling neuronal function in health and disease.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42669708 · DOI · PMC13526836
Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Accumulation of abnormal tau protein into neurofibrillary tangles (NFTs) is a pathologic hallmark of Alzheimer disease (AD).
- Closing excerpt
- We openly release this multi-institution deep-learning pipeline to provide detailed NFT spatial distribution and morphology analysis capability at a scale otherwise infeasible by manual assessment.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (248 words)
Accumulation of abnormal tau protein into neurofibrillary tangles (NFTs) is a pathologic hallmark of Alzheimer disease (AD). Accurate detection of NFTs in tissue samples can reveal relationships with clinical, demographic, and genetic features through deep phenotyping. However, expert manual analysis is time-consuming, subject to observer variability, and cannot handle the data amounts generated by modern imaging. We present a scalable, open-source, deep-learning approach to quantify NFT burden in digital whole slide images (WSIs) of post-mortem human brain tissue. To achieve this, we developed a method to generate detailed NFT boundaries directly from single-point-per-NFT annotations. We then trained a semantic segmentation model on 45 annotated 2400 μm by 1200 μm regions of interest (ROIs) selected from 15 unique temporal cortex WSIs of AD cases from three institutions (University of California (UC)-Davis, UC-San Diego, and Columbia University). Segmenting NFTs at the single-pixel level, the model achieved an area under the receiver operating characteristic of 0.832 and an F1 of 0.527 (196-fold over random) on a held-out test set of 664 NFTs from 20 ROIs (7 WSIs). We compared this to deep object detection, which achieved comparable but coarser-grained performance that was 60% faster. The segmentation and object detection models correlated well with expert semi-quantitative scores at the whole-slide level (Spearman's rho ρ = 0.654 (p = 6.50e-5) and ρ = 0.513 (p = 3.18e-3), respectively). We openly release this multi-institution deep-learning pipeline to provide detailed NFT spatial distribution and morphology analysis capability at a scale otherwise infeasible by manual assessment.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42665582 · DOI · PMC13524952
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article The nature of dopamine dysfunction in schizophrenia and what this means for treatment [4]
- Research article Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology [5]
- Research article Gray matter volume abnormalities in ADHD: voxel-based meta-analysis exploring the effects of age and stimulant medication [6]
- Research article Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology [7]
- Research article An interactive meta-analysis of MRI biomarkers of myelin [8]
- Research article A systematic review and meta-analysis of the prevalence of small fiber pathology in fibromyalgia: Implications for a new paradigm in fibromyalgia etiopathogenesis [9]
References
- Chen C, He Q, Tombesi G, Napier E, Jaconelli M, Moreno-Ramos OA, et al.. Leucine-rich repeat kinase 2 impairs the release sites of Parkinson's disease vulnerable dopamine axons. . 2026. PMID 42660892 · DOI · PMC13522591
- Boggess SC, Gandhi V, Tsai MC, Marzette E, Teyssier N, Chou JY, et al.. A massively parallel CRISPR-based screening platform for modifiers of neuronal depolarization. . 2026. PMID 42669708 · DOI · PMC13526836
- Ghandian S, Albarghouthi L, Nava K, Sharma SRR, Minaud L, Beckett L, et al.. Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations. . 2026. PMID 42665582 · DOI · PMC13524952
- Howes OD, Kambeitz J, Kim E, Stahl D, Slifstein M, Abi-Dargham A, et al.. The nature of dopamine dysfunction in schizophrenia and what this means for treatment. Arch Gen Psychiatry. 2012. PMID 22474070 · DOI · PMC3730746
- van Rheenen W, van der Spek RAA, Bakker MK, van Vugt JJFA, Hop PJ, Zwamborn RAJ, et al.. Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology. Nat Genet. 2021. PMID 34873335 · DOI · PMC8648564
- Nakao T, Radua J, Rubia K, Mataix-Cols D. Gray matter volume abnormalities in ADHD: voxel-based meta-analysis exploring the effects of age and stimulant medication. Am J Psychiatry. 2011. PMID 21865529 · DOI
- Lazari A, Lipp I. Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology. Neuroimage. 2021. PMID 33524576 · DOI · PMC8063174
- Mancini M, Karakuzu A, Cohen-Adad J, Cercignani M, Nichols TE, Stikov N. An interactive meta-analysis of MRI biomarkers of myelin. Elife. 2020. PMID 33084576 · DOI · PMC7647401
- Grayston R, Czanner G, Elhadd K, Goebel A, Frank B, Üçeyler N, et al.. A systematic review and meta-analysis of the prevalence of small fiber pathology in fibromyalgia: Implications for a new paradigm in fibromyalgia etiopathogenesis. Semin Arthritis Rheum. 2019. PMID 30314675 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.