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How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease
Clarke N, Foltz P, Garrard P.. Cortex; a journal devoted to the study of the nervous system and behavior, 2020-05-19
Topic: Amyloid, tau and blood-test diagnosis
Natural Language Processing (NLP) is an ever-growing field of computational science that aims to model natural human language. Combined with advances in machine learning, which learns patterns in data, it offers practical capabilities including automated language analysis. These approaches have garnered interest from clinical researchers seeking to understand the breakdown of language due to pathological changes in the brain, offering fast, replicable and objective methods. The study of Alzheimer's disease (AD), and preclinical Mild Cognitive Impairment (MCI), suggests that changes in discourse (connected speech or writing) may be key to early detection of disease. There is currently no disease-modifying treatment for AD, the leading cause of dementia in people over the age of 65, but detection of those at risk of developing the disease could help with the identification and testing of medications which can take effect before the underlying pathology has irreversibly spread. We outline important components of natural language, as well as NLP tools and approaches with which they can be extracted, analysed and used for disease identification and risk prediction. We review literature using these tools to model discourse across the spectrum of AD, including the contribution of machine learning approaches and Automatic Speech Recognition (ASR). We conclude that NLP and machine learning techniques are starting to greatly enhance research in the field, with measurable and quantifiable language components showing promise for early detection of disease, but there remain research and practical challenges for clinical implementation of these approaches. Challenges discussed include the availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability.
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APA 7th
Clarke, N., Foltz, P., & Garrard, P. (2020). How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease. Cortex; a journal devoted to the study of the nervous system and behavior. https://doi.org/10.1016/j.cortex.2020.05.001
Vancouver
Clarke N, Foltz P, Garrard P. How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease. Cortex; a journal devoted to the study of the nervous system and behavior. 2020. doi:10.1016/j.cortex.2020.05.001. PMID: 32622173.
BibTeX
@article{clarke2020-32622173,
author = {Clarke, N. and Foltz, P. and Garrard, P.},
title = {{How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease}},
journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
year = {2020},
doi = {10.1016/j.cortex.2020.05.001},
pmid = {32622173},
url = {https://doi.org/10.1016/j.cortex.2020.05.001}
}
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Key findings
- The study of Alzheimer's disease (AD), and preclinical Mild Cognitive Impairment (MCI), suggests that changes in discourse (connected speech or writing) may be key to early detection of disease.
- We conclude that NLP and machine learning techniques are starting to greatly enhance research in the field, with measurable and quantifiable language components showing promise for early detection of disease, but there remain research and practical challenges for clinical implementation of these approaches.
Why it matters
This review describes how natural language processing and machine learning can analyze connected speech or writing to help detect Alzheimer's disease and preclinical mild cognitive impairment early. It notes that measurable language components show promise for early detection but that challenges remain for clinical implementation, and it does not test any treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 41/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Cortex; a journal devoted to the study of the nervous system and behavior) |
| Journal's citation record | 2 / 4 | Cortex: journal h-index 202 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 68 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.5 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 3 / 6 | Cited 6.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 2 / 4 | Cited 27 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Peter Garrard: h-index 39 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Colorado Boulder: institution h-index 1,198 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32622173) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 41 / 100 | — |
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