ArticleComplexity2019
Analyzing Knowledge Retrieval Impairments Associated with Alzheimer's Disease Using Network Analyses.
Article in Complexity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
17 citing papers in PubMed.
- Subcategory vs category fluency: Items and networks in healthy young adults and simulation with a large language model.Memory & cognition · 2026Article
- Free recall of semantically related words reveals similarity structure.Memory & cognition · 2026Article
- Leveraging relatedness-based measures in people with language disorders: A scoping review.Journal of neuropsychology · 2025Article
- Lexical retrieval in fluent and nonfluent aphasia: a network analysis of verbal fluency data.Frontiers in human neuroscience · 2025Article
- What Can Network Science Tell Us About Phonology and Language Processing?Topics in cognitive science · 2022Article
- Visualizing and Quantifying Longitudinal Changes in Verbal Fluency Using Recurrence Plots.Frontiers in aging neuroscience · 2022Article
- Knowledge Representations Derived From Semantic Fluency Data.Frontiers in psychology · 2022Review
- Trust-based partner switching among partitioned regions promotes cooperation in public goods game.PloS one · 2021Article
- A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma.PloS one · 2021Article
- Article
- Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort.Exploration of medicine · 2021Article
- Revealing the Role of Divergent Thinking and Fluid Intelligence in Children's Semantic Memory Organization.Journal of Intelligence · 2020Article
- SNAFU: The Semantic Network and Fluency Utility.Behavior research methods · 2020Article
- Heatmap centrality: A new measure to identify super-spreader nodes in scale-free networks.PloS one · 2020Article
- Article
- Forma mentis networks quantify crucial differences in STEM perception between students and experts.PloS one · 2019Article
- Cognitive Networks for Knowledge Modeling: A Gentle Introduction for Data- and Cognitive Scientists.Wiley interdisciplinary reviews. Cognitive scienceReview
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
A defining characteristic of Alzheimer's disease is difficulty in retrieving semantic memories, or memories encoding facts and knowledge. While it has been suggested that this impairment is caused by a degradation of the semantic store, the precise ways in which the semantic store is degraded are not well understood. Using a longitudinal corpus of semantic fluency data (listing of items in a category), we derive semantic network representations of patients with Alzheimer's disease and of healthy controls. We contrast our network-based approach with analyzing fluency data with the standard method of counting the total number of items and perseverations in fluency data. We find that the networks of Alzheimer's patients are more connected and that those connections are more randomly distributed than the connections in networks of healthy individuals. These results suggest that the semantic memory impairment of Alzheimer's patients can be modeled through the inclusion of spurious associations between unrelated concepts in the semantic store. We also find that information from our network analysis of fluency data improves prediction of patient diagnosis compared to traditional measures of the semantic fluency task.
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Registered trials
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