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Related Experiment Video

Updated: Jun 11, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

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Published on: December 15, 2023

Abnormal discourse in semantic dementia: a data-driven approach.

Peter Garrard¹, Richard Forsyth

¹University of Southampton School of Medicine, Division of Clinical Neurosciences, Southampton General Hospital, Southampton, UK. p.garrard@sgul.ac.uk

|July 9, 2010

Summary

This summary is machine-generated.

Semantic dementia (SD) patients exhibit distinct word usage in connected speech compared to controls. Principal component analysis (PCA) reveals differences in function, content, and generic word use, differentiating SD patients through an automated algorithm.

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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

Area of Science:

Neuroscience
Linguistics
Computational Linguistics

Background:

Semantic dementia (SD) is characterized by structural and content deficits in connected discourse.
Understanding variations in word usage is crucial for diagnosing and characterizing SD.

Purpose of the Study:

To differentiate word usage patterns in connected speech between patients with semantic dementia (SD) and healthy controls.
To identify specific linguistic features that distinguish SD patients' discourse using a data-driven approach.

Main Methods:

Principal Component Analysis (PCA) was applied to word frequencies from picture descriptions of 21 SD patients and 21 controls.
Analysis focused on 81 word types with ≥10 occurrences, correlating principal component (PC) values with semantic test performance.

Main Results:

The first two PCs accounted for 69% of the variance in word usage.
Controls primarily used function and content words, while SD patients used more pronouns, deictic, and generic words.
Single word meaning correlated with PC 1 (function words) but not PC 2 (content words).

Conclusions:

Automated, data-driven analysis of word usage effectively distinguishes connected speech in SD patients from controls.
Observed differences in word usage suggest potential independent variations in syntax and lexical content in SD.
PCA provides a rapid algorithm for identifying linguistic markers of semantic dementia.