Gray matter network differences between behavioral variant frontotemporal dementia and Alzheimer's disease

Area of Science:

• Neuroscience
• Neurology
• Medical Imaging

Background:

• Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are distinct neurodegenerative conditions.
• Understanding the specific neural network alterations in AD and bvFTD is crucial for accurate diagnosis and treatment.
• Gray matter (GM) network analysis offers a potential method to differentiate these diseases.

Purpose of the Study:

• To investigate if gray matter (GM) network disturbances are specific to Alzheimer's disease (AD) or behavioral variant frontotemporal dementia (bvFTD).
• To determine if these GM network alterations correlate with cognitive deficits.
• To use subjects with subjective cognitive decline as a reference group.

Main Methods:

• Comparative analysis of GM network properties (degree, connectivity density, clustering, path length, betweenness centrality, small world values) between AD, bvFTD, and subjective cognitive decline groups.
• Statistical analysis including ANOVA to compare network metrics between groups.
• Lasso logistic regression to identify specific anatomical areas and network features differentiating AD and bvFTD.

Main Results:

• Both AD and bvFTD patients exhibited lower network metrics (degree, connectivity density, clustering, path length, betweenness centrality, small world values) compared to controls.
• AD patients showed significantly lower connectivity density than bvFTD patients.
• Lasso regression identified 23 anatomical areas with distinct connectivity differences between bvFTD and AD, related to local GM volume, degree, and clustering.
• Lower clustering and degree values were specifically associated with poorer performance on cognitive tests (Mini-Mental State Examination and neuropsychological battery).

Conclusions:

• Gray matter network alterations are disease-specific in Alzheimer's disease and behavioral variant frontotemporal dementia.
• These alterations are significantly associated with the severity of cognitive deficits observed in patients.
• GM network analysis provides valuable insights for differentiating between AD and bvFTD and understanding their impact on cognition.