Posterior Cingulate Cortex (PCC) Pyramidal Neurons

Posterior Cingulate Cortex (PCC) Pyramidal Neurons

Introduction

<table class="infobox infobox-cell">
<tr>
<th class="infobox-header" colspan="2">Posterior Cingulate Cortex (PCC) Pyramidal Neurons</th>
</tr>
<tr>
<td class="label">Category</td>
<td>Cell Types</td>
</tr>
<tr>
<td class="label">Brain Region</td>
<td>Limbic Cortex, Posterior Cingulate</td>
</tr>
<tr>
<td class="label">Neurotransmitter</td>
<td>Glutamate (excitatory)</td>
</tr>
<tr>
<td class="label">Cell Type</td>
<td>Pyramidal neuron</td>
</tr>
<tr>
<td class="label">Associated Diseases</td>
<td>Alzheimer's Disease, FTD, Schizophrenia, Depression</td>
</tr>
<tr>
<td class="label">Approach</td>
<td>Target</td>
</tr>
<tr>
<td class="label">Anti-amyloid antibodies</td>
<td>[Aβ](/proteins/amyloid-beta) plaques</td>
</tr>
<tr>
<td class="label">Brain stimulation</td>
<td>PCC activity</td>
</tr>
<tr>
<td class="label">Memory training</td>
<td>PCC engagement</td>
</tr>
<tr>
<td class="label">Lifestyle modification</td>
<td>DMN function</td>
</tr>
<tr>
<td class="label">Subregion</td>
<td>Function</td>
</tr>
<tr>
<td class="label">Dorsal PCC</td>
<td>Attention, salience</td>
</tr>
<tr>
<td class="label">Ventral PCC</td>
<td>Memory, DMN</td>
</tr>
<tr>
<td class="label">PCC-ACC</td>
<td>Self-referential</td>
</tr>
</table>

Posterior Cingulate Cortex (PCC) Pyramidal Neurons

Introduction

<table class="infobox infobox-cell">
<tr>
<th class="infobox-header" colspan="2">Posterior Cingulate Cortex (PCC) Pyramidal Neurons</th>
</tr>
<tr>
<td class="label">Category</td>
<td>Cell Types</td>
</tr>
<tr>
<td class="label">Brain Region</td>
<td>Limbic Cortex, Posterior Cingulate</td>
</tr>
<tr>
<td class="label">Neurotransmitter</td>
<td>Glutamate (excitatory)</td>
</tr>
<tr>
<td class="label">Cell Type</td>
<td>Pyramidal neuron</td>
</tr>
<tr>
<td class="label">Associated Diseases</td>
<td>Alzheimer's Disease, FTD, Schizophrenia, Depression</td>
</tr>
<tr>
<td class="label">Approach</td>
<td>Target</td>
</tr>
<tr>
<td class="label">Anti-amyloid antibodies</td>
<td>[Aβ](/proteins/amyloid-beta) plaques</td>
</tr>
<tr>
<td class="label">Brain stimulation</td>
<td>PCC activity</td>
</tr>
<tr>
<td class="label">Memory training</td>
<td>PCC engagement</td>
</tr>
<tr>
<td class="label">Lifestyle modification</td>
<td>DMN function</td>
</tr>
<tr>
<td class="label">Subregion</td>
<td>Function</td>
</tr>
<tr>
<td class="label">Dorsal PCC</td>
<td>Attention, salience</td>
</tr>
<tr>
<td class="label">Ventral PCC</td>
<td>Memory, DMN</td>
</tr>
<tr>
<td class="label">PCC-ACC</td>
<td>Self-referential</td>
</tr>
</table>

Posterior Cingulate Cortex (Pcc) 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.

The Posterior Cingulate [Cortex](/brain-regions/cortex) (PCC) is a key hub of the default mode network (DMN) and is critically vulnerable in Alzheimer's disease. PCC pyramidal [neurons](/entities/neurons) support self-referential processing, memory retrieval, and spatial orientation.

Overview

Morphology and Markers

• Soma: Large pyramidal neurons (25-35 μm)
• Dendrites: Extensive apical dendrites reaching layer I, dense basal arborization
• Axon: Long-range projections to [hippocampus](/brain-regions/hippocampus), precuneus, and prefrontal cortex
• Key Markers:
• RORα (RORA) - transcription factor
• CaBP (Ca-binding protein)
• NADPH-diaphorase
• Type 1 cannabinoid receptor (CB1)
• CTIP2 (BCL11B)

Normal Function

Default Mode Network Hub:

• Resting state
• Self-referential thinking
• Episodic memory retrieval
• Mental time travel
• Future planning

Key Functions:

• Integrates hippocampal-cortical memory traces
• Supports retrieval of autobiographical memories
• Links past experiences to present context

• Egocentric and allocentric spatial processing
• Scene recognition and navigation
• Virtual navigation tasks activate PCC

• Processes emotional valence of memories
• Connects limbic system to cortical networks
• Supports emotional regulation

• Strong connections to precuneus, medial prefrontal cortex
• Hippocampal formation inputs
• Reciprocal connections with parietal cortex

Electrophysiology:

• Resting membrane potential: ~-65 mV
• Firing pattern: Regular spiking, bursting
• Intrinsic oscillations: Strong theta and gamma coupling

Disease Vulnerability

Alzheimer's Disease

• Early hypometabolism: PCC shows early hypometabolism in PET studies
• Amyloid deposition: Heavy amyloid plaque accumulation in PCC
• [Tau](/proteins/tau) pathology: Neurofibrillary tangles in PCC layers III and V
• Atrophy: PCC atrophy is a hallmark of early AD
• Clinical correlation: PCC hypometabolism predicts cognitive decline
• Biomarker: FDG-PET of PCC is diagnostic for AD

Frontotemporal Dementia

• Different pattern: Less affected than ACC in behavioral variant FTD
• Semantic variant: More involved in semantic FTD

Psychiatric Disorders

• Depression: PCC hyperconnectivity in major depressive disorder
• Schizophrenia: Altered PCC activity during memory tasks
• Anxiety: PCC dysfunction relates to rumination

Molecular Pathways

Amyloid-Tau Interaction in PCC:

Gene Expression:

• DMN markers: RORA, BCL11B, CUX1
• Metabolic genes: High mitochondrial density
• Vulnerability factors: High [Aβ](/proteins/amyloid-beta) accumulation, calcium dysregulation

Therapeutic Implications

PCC Subregions

See Also

• [Anterior Cingulate Cortex (ACC) Pyramidal Neurons](/cell-types/anterior-cingulate-cortex-pyramidal-neurons)
• [Retrosplenial Cortex (RSC) Neurons](/retrosplenial-cortex-(rsc)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)-neurons)
• [Precuneus Neurons](/cell-types/precuneus-neurons)
• [Default Mode Network](/mechanisms/default-mode-network)
• [Alzheimer's Disease](/diseases/alzheimers-disease)
• [Brain Regions: Cingulate Cortex](/brain-regions/cingulate-cortex)

Background

The study of Posterior Cingulate Cortex (Pcc) 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.

External Links

• [PubMed](https://pubmed.ncbi.nlm.nih.gov/) - Biomedical literature
• [Alzheimer's Disease Neuroimaging Initiative](https://adni.loni.usc.edu/) - Research data
• [Allen Brain Atlas](https://brain-map.org/) - Brain gene expression data

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](/diseases/parkinsons-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)

References

^[1] Buckner RL, Andrews-Hanna JR, Schacter DF. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008.

^[2] Fransson P, Marrelec G. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis. Neuroimage. 2008.

^[3] Minoshima S, Giordani B, Berent S, et al. Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. Ann Neurol. 1997.

^[4] Johnson KA, Fox NC, Sperling RA, Klunk WE. Brain imaging in Alzheimer disease. Cold Spring Harb Perspect Med. 2012.

^[5] Zhou J, Greicius MD, Gennatas ED, et al. Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease. Brain. 2010.

^[6] Ranganath C, Ritchey M. Two cortical systems for memory-guided behaviour. Nat Rev Neurosci. 2012.

^[7] Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition and disease. Brain. 2014.

^[8] Kuhl BA, Szczepanik AM. Memory retrieval: Brain systems. In: Seli P, et al., editors. Cognitive Psychology. 2019.

Pathway Diagram

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