<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>
<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.
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.
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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<sup>[2]</sup> 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.
<sup>[3]</sup> Minoshima S, Giordani B, Berent S, et al. Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. Ann Neurol. 1997.
<sup>[4]</sup> Johnson KA, Fox NC, Sperling RA, Klunk WE. Brain imaging in Alzheimer disease. Cold Spring Harb Perspect Med. 2012.
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<sup>[8]</sup> Kuhl BA, Szczepanik AM. Memory retrieval: Brain systems. In: Seli P, et al., editors. Cognitive Psychology. 2019.
The following diagram shows the key molecular relationships involving Posterior Cingulate Cortex (PCC) Pyramidal Neurons discovered through SciDEX knowledge graph analysis: