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Analyzing Knowledge Retrieval Impairments Associated with Alzheimer's Disease Using Network Analyses.

Jeffrey C Zemla¹, Joseph L Austerweil¹

¹Department of Psychology, University of Wisconsin-Madison, USA.

Complexity

|July 26, 2019

Summary

Alzheimer's disease impairs semantic memory retrieval. Network analysis reveals Alzheimer's patients have more random semantic connections, suggesting spurious associations contribute to memory loss and improve diagnostic prediction.

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Area of Science:

Neuroscience
Cognitive Psychology
Computational Linguistics

Background:

Alzheimer's disease (AD) is characterized by semantic memory deficits.
The exact mechanisms of semantic store degradation in AD remain unclear.

Purpose of the Study:

To model semantic memory impairment in Alzheimer's disease using network analysis.
To compare network-based analysis with traditional semantic fluency measures for diagnostic prediction.

Main Methods:

Longitudinal semantic fluency data from AD patients and healthy controls were collected.
Semantic network representations were derived from fluency data.
Network properties were contrasted between patient groups and with traditional metrics (item count, perseverations).

Main Results:

Alzheimer's patient networks exhibited increased connectivity and randomness compared to controls.
Network analysis improved diagnostic prediction accuracy over traditional methods.
Findings suggest spurious associations contribute to semantic memory deficits in AD.

Conclusions:

Semantic memory impairment in Alzheimer's disease can be modeled by increased spurious associations.
Network analysis of semantic fluency data offers a more sensitive diagnostic tool for AD.