Visualizing and Quantifying Longitudinal Changes in Verbal Fluency Using Recurrence Plots.

Area of Science:

• Neuroscience
• Cognitive Psychology
• Data Science

Background:

• Verbal fluency tasks assess language and memory retrieval.
• Longitudinal studies of verbal fluency are increasing, but quantitative analysis methods are limited.
• Existing methods often overlook word sequence and identity, focusing only on unique word count.

Purpose of the Study:

• To introduce novel quantitative methods for analyzing longitudinal verbal fluency data.
• To develop a new metric, the "distance from diagonal" (DfD), incorporating word sequence and list transitions.
• To demonstrate the utility of recurrence analysis for visualizing and analyzing verbal fluency data.

Main Methods:

• Applied recurrence analysis and the novel DfD metric to longitudinal semantic fluency data.
• Utilized a dataset comprising individuals with Alzheimer's disease and age-matched controls.
• Incorporated analysis of word recall order, repetitions, and out-of-category intrusions.

Main Results:

• The DfD metric significantly differentiated between Alzheimer's disease patients and healthy controls.
• DfD complements existing metrics for improved diagnostic prediction.
• Visualization methods effectively displayed aggregate group-level recall patterns.

Conclusions:

• The DfD metric and recurrence analysis offer powerful tools for understanding verbal fluency changes.
• These methods enhance the analysis of semantic fluency, capturing nuances beyond simple word counts.
• The approach has potential applications in diagnosing and understanding deficits in various neurological and neuropsychiatric disorders.