Angular Gyrus plays an important role in the study of neurodegenerative diseases. This page provides comprehensive information about this topic, including its mechanisms, significance in disease processes, and therapeutic implications.
The angular gyrus (AG) is a region of the parietal lobe located in the posterior portion of the inferior parietal lobule, bordered by the supramarginal gyrus anteriorly and the occipital cortex posteriorly. This cortical area serves as a critical hub for multimodal integration, bridging auditory, visual, and somatosensory information to support higher-order cognitive functions including language, numerical processing, spatial awareness, and theory of mind. The angular gyrus is particularly vulnerable in Alzheimer's disease and shows early signs of dysfunction in neurodegenerative processes[@price2000][@butterworth1999].
<div class="infobox infobox-celltype">
<table>
<tr><th colspan="2" style="background:#e8f4f8; text-align:center; font-size:1.1em;">Angular Gyrus</th></tr>
<tr><td><strong>Brain Region</strong></td><td>Inferior Parietal Lobule</td></tr>
<tr><td><strong>Brodmann Area</strong></td><td>39</td></tr>
<tr><td><strong>Primary Function</strong></td><td>Multimodal Integration, Language, Numbers</td></tr>
<tr><td><strong>Key Connections</strong></td><td>STG → AG → SMG → Prefrontal Cortex</td></tr>
<tr><td><strong>Associated Diseases</strong></td><td>AD, Gerstmann Syndrome, Acalculia</td></tr>
</table>
</div>
Angular Gyrus plays an important role in the study of neurodegenerative diseases. This page provides comprehensive information about this topic, including its mechanisms, significance in disease processes, and therapeutic implications.
The angular gyrus (AG) is a region of the parietal lobe located in the posterior portion of the inferior parietal lobule, bordered by the supramarginal gyrus anteriorly and the occipital cortex posteriorly. This cortical area serves as a critical hub for multimodal integration, bridging auditory, visual, and somatosensory information to support higher-order cognitive functions including language, numerical processing, spatial awareness, and theory of mind. The angular gyrus is particularly vulnerable in Alzheimer's disease and shows early signs of dysfunction in neurodegenerative processes[@price2000][@butterworth1999].
<div class="infobox infobox-celltype">
<table>
<tr><th colspan="2" style="background:#e8f4f8; text-align:center; font-size:1.1em;">Angular Gyrus</th></tr>
<tr><td><strong>Brain Region</strong></td><td>Inferior Parietal Lobule</td></tr>
<tr><td><strong>Brodmann Area</strong></td><td>39</td></tr>
<tr><td><strong>Primary Function</strong></td><td>Multimodal Integration, Language, Numbers</td></tr>
<tr><td><strong>Key Connections</strong></td><td>STG → AG → SMG → Prefrontal Cortex</td></tr>
<tr><td><strong>Associated Diseases</strong></td><td>AD, Gerstmann Syndrome, Acalculia</td></tr>
</table>
</div>
The angular gyrus occupies the posterior aspect of the inferior parietal lobule:
The angular gyrus (Brodmann area 39) is characterized by:
The angular gyrus forms a critical node in the language network:
The angular gyrus integrates information from multiple sensory modalities:
Visual-Auditory Integration:
The angular gyrus supports multiple aspects of language:
Reading:
The angular gyrus is central to number processing:
Basic Numeracy:
The angular gyrus contributes to social cognitive processes:
Theory of Mind:
The angular gyrus receives inputs from:
Major outputs project to:
The angular gyrus serves as a hub in multiple networks:
The angular gyrus shows early and significant involvement in AD:
Damage to the angular gyrus produces the classic tetrad:
Primary Progressive Aphasia:
Angular Gyrus plays an important role in the study of neurodegenerative diseases. This page provides comprehensive information about this topic, including its mechanisms, significance in disease processes, and therapeutic implications.
The study of Angular Gyrus 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 (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)