Anterior Cingulate Cortex (ACC) Pyramidal Neurons

Anterior Cingulate Cortex Pyramidal Neurons

<table class="infobox infobox-cell">
<tr>
<th class="infobox-header" colspan="2">Anterior Cingulate Cortex (ACC) Pyramidal Neurons</th>
</tr>
<tr>
<td class="label">Taxonomy</td>
<td>ID</td>
</tr>
<tr>
<td class="label">Cell Ontology (CL)</td>
<td>[CL:0000598](https://www.ebi.ac.uk/ols4/ontologies/cl/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FCL_0000598)</td>
</tr>
<tr>
<td class="label">Database</td>
<td>ID</td>
</tr>
<tr>
<td class="label">Cell Ontology</td>
<td>[CL:0000598](https://www.ebi.ac.uk/ols4/ontologies/cl/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FCL_0000598)</td>
</tr>
<tr>
<td class="label">Cell Type</td>
<td>Glutamatergic pyramidal neurons</td>
</tr>
<tr>
<td class="label">Layer</td>
<td>Predominantly layers 2/3 and 5</td>
</tr>
<tr>
<td class="label">Marker Genes</td>
<td>CTIP2 (BCL11B), SATB2, CUX1/CUX2, SLC17A7 (VGLUT1), TBR1</td>
</tr>
<tr>
<td class="label">Morphology</td>
<td>Classic pyramidal soma (15-25 μm), apical dendrite extending to layer 1, basal dendrites</td>
</tr>
<tr>
<td class="label">Brain Regions</td>
<td>Anterior cingulate cortex (Brodmann area 24/32), pregenual and subgenual ACC</td>
</tr>
<tr>
<td class="label">Gene</td>
<td>Expression</td>
</tr>
<tr>
<td class="label">CTIP2 (BCL11B)</td>
<td>High</td>
</tr>
<tr>
<td class="label">SATB2</td>
<td>High</td>
</tr>
<tr>
<td class="l

...

Anterior Cingulate Cortex Pyramidal Neurons

<table class="infobox infobox-cell">
<tr>
<th class="infobox-header" colspan="2">Anterior Cingulate Cortex (ACC) Pyramidal Neurons</th>
</tr>
<tr>
<td class="label">Taxonomy</td>
<td>ID</td>
</tr>
<tr>
<td class="label">Cell Ontology (CL)</td>
<td>[CL:0000598](https://www.ebi.ac.uk/ols4/ontologies/cl/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FCL_0000598)</td>
</tr>
<tr>
<td class="label">Database</td>
<td>ID</td>
</tr>
<tr>
<td class="label">Cell Ontology</td>
<td>[CL:0000598](https://www.ebi.ac.uk/ols4/ontologies/cl/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FCL_0000598)</td>
</tr>
<tr>
<td class="label">Cell Type</td>
<td>Glutamatergic pyramidal neurons</td>
</tr>
<tr>
<td class="label">Layer</td>
<td>Predominantly layers 2/3 and 5</td>
</tr>
<tr>
<td class="label">Marker Genes</td>
<td>CTIP2 (BCL11B), SATB2, CUX1/CUX2, SLC17A7 (VGLUT1), TBR1</td>
</tr>
<tr>
<td class="label">Morphology</td>
<td>Classic pyramidal soma (15-25 μm), apical dendrite extending to layer 1, basal dendrites</td>
</tr>
<tr>
<td class="label">Brain Regions</td>
<td>Anterior cingulate cortex (Brodmann area 24/32), pregenual and subgenual ACC</td>
</tr>
<tr>
<td class="label">Gene</td>
<td>Expression</td>
</tr>
<tr>
<td class="label">CTIP2 (BCL11B)</td>
<td>High</td>
</tr>
<tr>
<td class="label">SATB2</td>
<td>High</td>
</tr>
<tr>
<td class="label">SLC17A7 (VGLUT1)</td>
<td>High</td>
</tr>
<tr>
<td class="label">FOXP2</td>
<td>Moderate</td>
</tr>
<tr>
<td class="label">NRG1</td>
<td>Moderate</td>
</tr>
<tr>
<td class="label">RELN</td>
<td>Low-Moderate</td>
</tr>
<tr>
<td class="label">Target</td>
<td>Strategy</td>
</tr>
<tr>
<td class="label">Glutamatergic signaling</td>
<td>NMDA receptor modulators</td>
</tr>
<tr>
<td class="label">Neuroinflammation</td>
<td>Microglial activation modulators</td>
</tr>
<tr>
<td class="label">Network connectivity</td>
<td>Transcranial magnetic stimulation</td>
</tr>
<tr>
<td class="label">Tau pathology</td>
<td>Anti-tau immunotherapies</td>
</tr>
</table>

Introduction

Anterior Cingulate Cortex (Acc) Pyramidal Neurons is an important component in the neurobiology of neurodegenerative diseases. This page provides detailed information about its structure, function, and role in disease processes.

Overview

This page provides comprehensive information about ACC Pyramidal Neurons, including its structure, normal function in the nervous system, and its role in neurodegenerative diseases. [@shenhav2013]

The anterior cingulate cortex (ACC) pyramidal neurons are layer 2/3 and layer 5 glutamatergic neurons in the ACC, a critical node in the salience network and emotion regulation circuits. These neurons play essential roles in cognitive control, pain perception, decision-making, and emotional processing. [@holroyd2012]

<!-- taxonomy-enrichment --> [@devinsky1995]

<!-- multi-taxonomy-enrichment -->

Multi-Taxonomy Classification

Taxonomy Database Cross-References

Morphology & Electrophysiology

• Morphology: pyramidal neuron (source: Cell Ontology)
• Morphology can be inferred from Cell Ontology classification

PanglaoDB Marker Cross-References

• Unknown (PanglaoDB):

External Database Links

• [Cell Ontology (CL:0000598)](https://www.ebi.ac.uk/ols4/ontologies/cl/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FCL_0000598)
• [OBO Foundry (CL:0000598)](http://purl.obolibrary.org/obo/CL_0000598)
• [Allen Brain Cell Atlas](https://portal.brain-map.org/atlases-and-data/bkp/abc-atlas)
• [CellxGene Census](https://cellxgene.cziscience.com/)
• [Human Cell Atlas](https://www.humancellatlas.org/)
• [PanglaoDB](https://panglaodb.se/)

Taxonomy & Classification

PanglaoDB Marker Cross-References

• Unknown (PanglaoDB):

External Database Links

• [Cell Ontology (CL:0000598)](https://www.ebi.ac.uk/ols4/ontologies/cl/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FCL_0000598)
• [OBO Foundry (CL:0000598)](http://purl.obolibrary.org/obo/CL_0000598)
• [Allen Brain Cell Atlas](https://portal.brain-map.org/atlases-and-data/bkp/abc-atlas)
• [CellxGene Census](https://cellxgene.cziscience.com/)
• [PanglaoDB](https://panglaodb.se/)

Morphology and Markers

Normal Function

The ACC is part of the salience network and participates in:

• Cognitive Control: Error detection, conflict monitoring, and task set maintenance
• Pain Processing: Emotional and affective dimensions of pain
• Emotion Regulation: Processing of negative emotions, fear, and anxiety
• Decision Making: Value-based choices, reward prediction error signaling
• Social Cognition: Theory of mind, empathy, social pain

ACC pyramidal neurons project to:

• Prefrontal cortex (dorsolateral and ventromedial)
• Orbitofrontal cortex
• Amygdala (via indirect pathways)
• Periaqueductal gray
• Spinal cord (pain modulation)

Vulnerability in Disease

Alzheimer's Disease

ACC is among the early regions showing amyloid deposition and hypometabolism in AD. ACC pyramidal neurons exhibit:

• Early tau pathology spreading from entorhinal cortex
• Hypometabolism detectable via PET even before clinical symptoms
• Dysfunctional connectivity in the salience network
• Accumulation of p-tau in dendrites and soma

Parkinson's Disease

• ACC shows reduced dopamine innervation in PD
• Impaired error prediction signaling
• Connection to non-motor symptoms including depression and anxiety

Frontotemporal Dementia

• ACC degeneration is prominent in behavioral variant FTD
• Layer 5 pyramidal neuron loss
• Early disruption of salience network connectivity

Other Conditions

• Depression: ACC hypermetabolism, altered glutamatergic signaling
• Chronic Pain: Structural and functional alterations in ACC neurons
• Schizophrenia: Reduced ACC volume and pyramidal neuron density

Transcriptomic Profile

Key differentially expressed genes in ACC pyramidal neurons:

Therapeutic Implications

Key Publications

Zhou J et al. (2020). "Molecular profiling of human anterior cingulate neurons." Cell Reports 33(2): 108108.

Deller T et al. (2019). "The anatomical and functional organization of the ACC." Brain Structure and Function 224(1): 1-21.

Seeley WW et al. (2009). "Neurodegenerative diseases target large-scale human brain networks." Neuron 62(1): 42-52.

• [Alzheimer's Disease](/diseases/alzheimers-disease)
• [Parkinson's Disease](/diseases/parkinsons-disease)
• [Frontotemporal Dementia](/diseases/frontotemporal-dementia)
• Cortical Pyramidal Neurons
• Salience Network
• Tau Pathology Pathway

External Links

• [Allen Brain Atlas - ACC](https://portal.brain-map.org/atlases-and-data/rnaseq)
• [Human Connectome Project - ACC](https://www.humanconnectome.org/)
• [NINDS - Frontotemporal Dementia Information](https://www.ninds.nih.gov/Disorders/All-Disorders/Frontotemporal-Dementia-Information-Page)

Background

The study of Anterior Cingulate Cortex (Acc) Pyramidal Neurons has evolved significantly over the past decades. Research in this area has revealed important insights into the underlying mechanisms of neurodegeneration and continues to drive therapeutic development.

Historical context and key discoveries in this field have shaped our current understanding and will continue to guide future research directions.

Research Evidence

Dynamic functional connectivity measures are more reliable than stationary connectivity measures in attention networks

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dorsal attention network (DAN) Factor 3 (anterior DAN) obtained at rest significantly predicts alerting effect on Attention Network Test in both sessions (p=0.001 and p=0.037)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Fronto-parietal task control network (FPTC) Factor 3 predicts orienting effect at Session 1 (p=0.010)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

The relationship between DAN Factor 3 and alerting effect was present during both rest and task conditions

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Changes in dynamic connectivity factor scores between sessions correlated with changes in accuracy in Incongruent Flanker trials

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Higher dynamic connectivity (factor scores) was associated with larger alerting and orienting effects, possibly reflecting more effortful processing or rigidity in resource reallocation

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

No significant group differences in ICA-defined resting networks between PD and controls, suggesting subtle differences in early-stage PD

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dynamic connectivity factor structures are stable across rest and task states (Procrustes congruence 0.89-0.93 for DAN)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Individual differences in dynamic connectivity are reliable across scanner sessions but not invariant, and changes reflect behavioral changes

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Attention Network Test (ANT) behavioral performance measurement

PD participants showed slowed response latencies across all conditions. PD participants had significantly larger alerting effect (No Cue - Center Cue) compared to controls (PD: 47ms vs Controls: 28ms, p=0.025). No significant differences in orienting or executive effects between groups.

Model System: Human participants: 25 Parkinson disease (PD) patients and 21 healthy controls (ages 41-86)

Statistical Significance: p = 0.025 for alerting effect difference between groups

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

ICA analysis of resting-state networks

Identified dorsal attention network (DAN), salience network, and default mode network (DMN). No significant group differences found between PD and controls in these networks.

Model System: Human participants: 25 PD patients and 21 controls undergoing resting-state fMRI

Statistical Significance: No significant group differences (p > 0.05 after correction)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dynamic connectivity factor analysis

Extracted 4 factors for each network (DAN, FPTC, DMN). Factor structures were qualitatively similar to previous aging sample but explained less variance in this sample. Reliability of factor scores was higher than reliability of individual pairwise correlations.

Model System: Human participants: 25 PD and 21 controls during resting-state fMRI scans

Statistical Significance: DAN factor reliability 0.56-0.64, FPTC 0.35-0.69, DMN 0.57-0.78 (all p < 0.01 except FPTC Factor 4 p=0.01)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Reliability comparison: dynamic vs stationary connectivity

Dynamic connectivity measures are more reliable than stationary connectivity measures. Median reliability of factor scores higher than median reliability of pairwise correlations for DAN (p=0.020) and DMN (p=0.036). FPTC showed marginally significant difference (p=0.082).

Model System: Same 46 participants in resting-state fMRI

Statistical Significance: DAN: p=0.020, DMN: p=0.036, FPTC: p=0.082

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Prediction of alerting effect from resting-state dynamic connectivity

DAN Factor 3 (anterior DAN) significantly predicted alerting effect magnitude at both sessions (Session 1: p=0.001, R2=0.21; Session 2: p=0.037, R2=0.09). Effect remained significant after controlling for age. Group-by-factor interaction significant at Session 1 (p=0.002) but not Session 2.

Model System: 46 participants (25 PD, 21 controls) from resting-state scans to ANT performance

Statistical Significance: Session 1: t(44)=3.46, p=0.001; Session 2: t(44)=2.15, p=0.037; Group x Factor interaction Session 1: p=0.002

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Prediction of orienting effect from resting-state dynamic connectivity

FPTC Factor 3 predicted orienting effect at Session 1 (p=0.010) but not Session 2 (p=0.116). No significant group or group-by-factor interaction.

Model System: 46 participants from resting-state scans to ANT orienting effect

Statistical Significance: Session 1: t(44)=2.70, p=0.010; Session 2: t(44)=1.6, p=0.116

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Task-based dynamic connectivity analysis

DAN factor structure during task highly congruent with rest (Procrustes correlation 0.93 Session 1, 0.89 Session 2, p=0.001). DAN Factor 3 during tasks predicted alerting effect (Session 1: p=0.023, R2=0.11; Session 2: p=0.107). During tasks, DAN Factor 3 also negatively predicted orienting effect at Session 2 (p=0.013).

Model System: 46 participants during ANT task fMRI runs

Statistical Significance: DAN Factor 3: Session 1 p=0.023, Session 2 p=0.107; Orienting: Session 2 p=0.013

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Change in dynamic connectivity predicting behavioral change

Increase in DAN Factor 3 between sessions correlated with improvement in accuracy in Incongruent Flanker condition (r=0.37, p=0.011). Increase in FPTC Factor 3 correlated with improvement in Incongruent (r=0.39, p=0.007) and Center Cue conditions (r=0.32, p=0.027).

Model System: Longitudinal: Session 1 to Session 2 change in same 46 participants

Statistical Significance: DAN Factor 3: r(44)=0.37, p=0.011; FPTC Factor 3 Incongruent: r(44)=0.39, p=0.007; FPTC Factor 3 Center Cue: r(44)=0.32, p=0.027

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dynamic functional connectivity measures are more reliable than stationary connectivity measures in attention networks

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dorsal attention network (DAN) Factor 3 (anterior DAN) obtained at rest significantly predicts alerting effect on Attention Network Test in both sessions (p=0.001 and p=0.037)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Fronto-parietal task control network (FPTC) Factor 3 predicts orienting effect at Session 1 (p=0.010)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

The relationship between DAN Factor 3 and alerting effect was present during both rest and task conditions

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Changes in dynamic connectivity factor scores between sessions correlated with changes in accuracy in Incongruent Flanker trials

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Higher dynamic connectivity (factor scores) was associated with larger alerting and orienting effects, possibly reflecting more effortful processing or rigidity in resource reallocation

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

No significant group differences in ICA-defined resting networks between PD and controls, suggesting subtle differences in early-stage PD

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dynamic connectivity factor structures are stable across rest and task states (Procrustes congruence 0.89-0.93 for DAN)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Individual differences in dynamic connectivity are reliable across scanner sessions but not invariant, and changes reflect behavioral changes

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Attention Network Test (ANT) behavioral performance measurement

PD participants showed slowed response latencies across all conditions. PD participants had significantly larger alerting effect (No Cue - Center Cue) compared to controls (PD: 47ms vs Controls: 28ms, p=0.025). No significant differences in orienting or executive effects between groups.

Model System: Human participants: 25 Parkinson disease (PD) patients and 21 healthy controls (ages 41-86)

Statistical Significance: p = 0.025 for alerting effect difference between groups

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

ICA analysis of resting-state networks

Identified dorsal attention network (DAN), salience network, and default mode network (DMN). No significant group differences found between PD and controls in these networks.

Model System: Human participants: 25 PD patients and 21 controls undergoing resting-state fMRI

Statistical Significance: No significant group differences (p > 0.05 after correction)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dynamic connectivity factor analysis

Extracted 4 factors for each network (DAN, FPTC, DMN). Factor structures were qualitatively similar to previous aging sample but explained less variance in this sample. Reliability of factor scores was higher than reliability of individual pairwise correlations.

Model System: Human participants: 25 PD and 21 controls during resting-state fMRI scans

Statistical Significance: DAN factor reliability 0.56-0.64, FPTC 0.35-0.69, DMN 0.57-0.78 (all p < 0.01 except FPTC Factor 4 p=0.01)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Reliability comparison: dynamic vs stationary connectivity

Dynamic connectivity measures are more reliable than stationary connectivity measures. Median reliability of factor scores higher than median reliability of pairwise correlations for DAN (p=0.020) and DMN (p=0.036). FPTC showed marginally significant difference (p=0.082).

Model System: Same 46 participants in resting-state fMRI

Statistical Significance: DAN: p=0.020, DMN: p=0.036, FPTC: p=0.082

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Prediction of alerting effect from resting-state dynamic connectivity

DAN Factor 3 (anterior DAN) significantly predicted alerting effect magnitude at both sessions (Session 1: p=0.001, R2=0.21; Session 2: p=0.037, R2=0.09). Effect remained significant after controlling for age. Group-by-factor interaction significant at Session 1 (p=0.002) but not Session 2.

Model System: 46 participants (25 PD, 21 controls) from resting-state scans to ANT performance

Statistical Significance: Session 1: t(44)=3.46, p=0.001; Session 2: t(44)=2.15, p=0.037; Group x Factor interaction Session 1: p=0.002

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Prediction of orienting effect from resting-state dynamic connectivity

FPTC Factor 3 predicted orienting effect at Session 1 (p=0.010) but not Session 2 (p=0.116). No significant group or group-by-factor interaction.

Model System: 46 participants from resting-state scans to ANT orienting effect

Statistical Significance: Session 1: t(44)=2.70, p=0.010; Session 2: t(44)=1.6, p=0.116

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Task-based dynamic connectivity analysis

DAN factor structure during task highly congruent with rest (Procrustes correlation 0.93 Session 1, 0.89 Session 2, p=0.001). DAN Factor 3 during tasks predicted alerting effect (Session 1: p=0.023, R2=0.11; Session 2: p=0.107). During tasks, DAN Factor 3 also negatively predicted orienting effect at Session 2 (p=0.013).

Model System: 46 participants during ANT task fMRI runs

Statistical Significance: DAN Factor 3: Session 1 p=0.023, Session 2 p=0.107; Orienting: Session 2 p=0.013

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Change in dynamic connectivity predicting behavioral change

Increase in DAN Factor 3 between sessions correlated with improvement in accuracy in Incongruent Flanker condition (r=0.37, p=0.011). Increase in FPTC Factor 3 correlated with improvement in Incongruent (r=0.39, p=0.007) and Center Cue conditions (r=0.32, p=0.027).

Model System: Longitudinal: Session 1 to Session 2 change in same 46 participants

Statistical Significance: DAN Factor 3: r(44)=0.37, p=0.011; FPTC Factor 3 Incongruent: r(44)=0.39, p=0.007; FPTC Factor 3 Center Cue: r(44)=0.32, p=0.027

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

See Also

• [Principal Pars Compacta](/wiki/cell-types-principal-pars-compacta) — associated_with
• [Principal Pars Compacta](/wiki/cell-types-principal-pars-compacta) — expressed_in
• [Principal Pars Compacta](/wiki/cell-types-principal-pars-compacta) — inhibits
• [ADAM10 — A Disintegrin And Metalloproteinase Domain 10](/wiki/genes-adam10) — inhibits

Pathway Diagram

The following diagram shows the key molecular relationships involving Anterior Cingulate Cortex (ACC) Pyramidal Neurons discovered through SciDEX knowledge graph analysis: