How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease

Abstract

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.

Can discourse processing performance serve as an early marker of Alzheimer’s disease and mild cognitive impairment? A systematic review of text comprehension

“…However, there have been efforts in the past few years to simplify the procedure and to develop standardized measures for discourse analysis (Dalton et al 2019 ). Additionally, recent advances in computational linguistics are promising, with major components of the analyses being automatized, making the process less time consuming and less error-prone (Aluisio et al 2016 ; Clarke et al 2020 ).…”

Artificial Intelligence and Its Application to Minimal Hepatic Encephalopathy Diagnosis

“…Moreover, speech and handwriting data can be acquired at low cost and without any distress to the patient. Speech has proven to be a suitable modality for diagnosing Alzheimer's disease [65,66] and Parkinson's disease Countless convolutional neural network architectures can be used, but they often share similar common concepts as convolutional, pooling, and dense layers. ResNet50 [60], EfficientNet [61], and VGG16 [62] are all considered the state-of-art.…”

“…Moreover, speech and handwriting data can be acquired at low cost and without any distress to the patient. Speech has proven to be a suitable modality for diagnosing Alzheimer's disease [65,66] and Parkinson's disease [67,68]. In the case of AD, the accuracy of the diagnosis depends on the type of speech features used and computational algorithm employed.…”

Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach

“…Linguistic measures elicited from speech are one type of behavioral indicator that have proved to be useful in predicting cognitive abilities. To this end, studies have considered the use of linguistic measures from transcribed speeches of healthy subjects, or those with different degrees of cognitive impairment in structured clinical assessments [ 18 ]. In fact, it has been shown that language markers predict normal and pathological cognitive functioning [ 19 ].…”