Unveiling Key Features: A Comparative Study of Machine Learning Models for Alzheimer\u27s Detection

This thesis rigorously evaluates the application of an array of natural language processing (NLP) techniques and machine learning models to identify linguistic signatures indicative of dementia, as sourced from the DementiaBank Pitt corpus. Utilizing a binary classification paradigm, this study meticulously integrates sophisticated embedding methods—including Doc2Vec, Word2Vec, GloVe, and BERT—with traditional machine learning algorithms such as Random Forest, Multinomial Naïve Bayes, ADA boost, KNN classifier, and Logistic Regression, alongside deep learning architectures like LSTM, Bi-LSTM, and CNN-LSTM. The efficacy of these methodologies is evaluated based on their capacity to differentiate between transcribed speech impacted by dementia and that from control subjects. To enhance interpretability, this research also employs feature importance analysis through LIME, SHAP, permutation importance, and integrated gradients, shedding light on the variables most instrumental in driving model predictions. The results of this comprehensive analysis not only illuminate the robust potential of these combined NLP and machine learning approaches in the context of medical screening but also contribute additional valuable insights to the field of NLP and dementia screening specifically

Evaluating the Role of Gender in Dementia-Related Language Deficiencies

Typically, about 60% of dementia patients are women. Researchers have historically dismissed this imbalance as a result of the life expectancy for women being longer, and since age is the primary risk factor associated with dementia, and women’s longer lifespan equates to a higher percentage of the dementia patient population (Mielke, 2018). While the exact cause of dementia is unknown, researchers and clinicians have historically treated male and female populations the same, asserting that there is no significant difference between the two sexes in regards to detecting dementia. The present study aims to address this potential gap in dementia research, where newer research (as recent as 2018) also demands for differences in gender to be addressed in this field. In the present study the Pitt Corpus from DementiaBank, to attempt to find significant results in how men and women with dementia utilize language. A statistical analysis was performed using linear regression and ANOVA models, which found significant interactions between sex and linguistic features. This same data was used to train and test machine learning models in attempts to categorize utterances from both sexes accurately. Logistic regression, Naive Bayes, and SVM models were used on various forms of TF-IDF vectors, with logistic regression performing at the highest accuracy at 56%. The implication of these results aligns with the hypothesis of this study, that there is a significant difference between the linguistic markers of both sexes

The Linguistic and Cultural Aspects of Neuropsychological Assessment in People with Dementia

Objectives: Given the public health crisis that Alzheimer's disease (AD) has become (Naylor et al., 2012),neuropsychological assessment tools that provide timely and accurate identification of cognitive decline in older adults have gained increasing focus in the scientific literature. Accurate evaluation of cognitive function and early identification of cognitive changes are paramount to understanding the disease course of AD and improving effective treatments and patients' quality of life. To this end, language offers a cognitive neuropsychological approach to identifying cognitive decline in the early stages of AD. Moreover, it represents a multi-dimensional variable that may influence the neuropsychological test performance of older adults due to its potential contribution to cognitive reserve. Therefore, the present thesis aims at combining two aspects of language to explore its potential in the early detection of AD and its association with neuropsychological test performance in older adults and cross-cultural neuropsychology. Study 1 assessed the currently available studies to explore whether discourse processing, particularly macro-structural discourse comprehension, offers a novel approach to neuropsychological testing in distinguishing normal cognitive aging from AD pathology-related decline. Study 2 evaluated the results of the studies that examined the impact of bilingualism on neuropsychological test performance in monolingual and bilingual older adults to inform the neuropsychological evaluation of these groups in clinical practice. Study 3 investigated the influence of bilingualism and its associated factors, namely, cultural background and acculturation, on cognitive screening tests in three clinically diagnosed AD patient groups to identify a cross-culturally/linguistically appropriate measure of cognition. Method: Data of Study 1 and Study 2 were based on the original research studies published in English investigating discourse comprehension and bilingualism in healthy older adults, individuals with mild cognitive impairment (MCI), and AD. A literature search focusing on these topics with participant groups aged 60 years and over was conducted in PubMed, Web of Science, and PsycINFO databases. Study 1 included eight articles consisting of studies only with cross sectional designs. Study 2 was comprised of twenty-seven articles, of which sixteen articles had cross-sectional designs. On the other hand, Study 3 was original research based on a cross sectional design targeting culturally/linguistically diverse patients diagnosed with AD. Specifically, the study sample consisted of Turkish immigrant (n=21) and monolingual, non-immigrant German (n=20) and Turkish (n=24) patients with AD. All participants were administered the Mini-Mental State Examination (MMSE), Rowland Universal Dementia Assessment Scale (RUDAS), a dementia severity rating scale, and a self-report measure of depression. Additionally, self-report measures of bilingualism and acculturation were conducted with Turkish-immigrant participants with AD. Results: Study 1 revealed that people with AD and MCI have significant deficits in discourse comprehension, which are not observed in cognitively normal older adults of any age. On five of six discourse comprehension measures, groups with AD were significantly worse than healthy older adults, with one measure yielding mixed findings. Furthermore, compared to the cognitively healthy groups, individuals with MCI showed significant performance deficits in discourse comprehension measures similar to those with AD. Study 2 indicated better performance for bilingual older adults on executive function tests when compared to their monolingual counterparts. On the other hand, bilinguals were found to perform poorer than monolinguals on tests assessing the language domain. However, these findings did not remain robust when the impact of bilingualism on test performance was investigated longitudinally. Lastly, Study 3 provided further evidence on the linguistic and educational bias of the MMSE when employed in culturally and linguistically diverse individuals with AD. Bilingualism was linked to better performance on the MMSE in the Turkish immigrant group. German patients with AD obtained higher scores on this test than the other two groups. Furthermore, RUDAS was shown to be a better alternative for assessing global cognition in German and Turkish individuals with AD. Conclusion: The macro-structural discourse comprehension assessment paradigm has shown promising results in identifying the preclinical stages of AD. Further research on this paradigm may help develop a diagnostic tool with a clinical value that can be utilized for differential diagnosis, predicting conversion from MCI to dementia in research and clinical settings. On the other hand, another aspect of linguistic skills, namely, the evaluation of research on the link between bilingualism and neuropsychological test performance, did not provide definitive answers to the question of bilingual advantages and disadvantages addressed in the second study due to methodological challenges in the field. However, it identified a comprehensive and critical list of clinically and empirically relevant bilingualism-associated variables which may guide future research and neuropsychological practice. In light of the Study 2 findings, Study 3 filled an important gap in the literature by exploring cultural, demographic, and immigration related factors that may influence neuropsychological testing experiences in Germany. The study findings may help the field of cross-cultural neuropsychology serve culturally and linguistically diverse populations more efficiently. Overall, the present thesis contributed to the literature by highlighting the importance and potential of linguistic abilities in the clinical diagnosis and neuropsychological evaluation of individuals with dementia

Using Healthcare Data in Embedded Pragmatic Clinical Trials among People Living with Dementia and Their Caregivers: State of the Art

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/156003/1/jgs16617_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/156003/2/jgs16617.pd

AUTOMATED INTERPRETATION OF THE BACKGROUND EEG USING FUZZY LOGIC

A new framework is described for managing uncertainty and for deahng with artefact corruption to introduce objectivity in the interpretation of the electroencephalogram (EEG). Conventionally, EEG interpretation is time consuming and subjective, and is known to show significant inter- and intra-personnel variation. A need thus exists to automate the interpretation of the EEG to provide a more consistent and efficient assessment. However, automated analysis of EEGs by computers is complicated by two major factors. The difficulty of adequately capturing in machine form, the skills and subjective expertise of the experienced electroencephalbgrapher, and the lack of a reliable means of dealing with the range of EEG artefacts (signal contamination). In this thesis, a new framework is described which introduces objectivity in two important outcomes of clinical evaluation of the EEG, namely, the clinical factual report and the clinical 'conclusion', by capturing the subjective expertise of the electroencephalographer and dealing with the problem of artefact corruption. The framework is separated into two stages .to assist piecewise optimisation and to cater for different requirements. The first stage, 'quantitative analysis', relies on novel digital signal processing algorithms and cluster analysis techniques to reduce data and identify and describe background activities in the EEG. To deal with artefact corruption, an artefact removal strategy, based on new reUable techniques for artefact identification is used to ensure that artefact-free activities only are used in the analysis. The outcome is a quantitative analysis, which efficiently describes the background activity in the record, and can support future clinical investigations in neurophysiology. In clinical practice, many of the EEG features are described by the clinicians in natural language terms, such as very high, extremely irregular, somewhat abnormal etc. The second stage of the framework, 'qualitative analysis', captures the subjectivity and linguistic uncertainty expressed.by the clinical experts, using novel, intelligent models, based on fuzzy logic, to provide an analysis closely comparable to the clinical interpretation made in practice. The outcome of this stage is an EEG report with qualitative descriptions to complement the quantitative analysis. The system was evaluated using EEG records from 1 patient with Alzheimer's disease and 2 age-matched normal controls for the factual report, and 3 patients with Alzheimer's disease and 7 age-matched nonnal controls for the 'conclusion'. Good agreement was found between factual reports produced by the system and factual reports produced by qualified clinicians. Further, the 'conclusion' produced by the system achieved 100% discrimination between the two subject groups. After a thorough evaluation, the system should significantly aid the process of EEG interpretation and diagnosis

Diagnostic and prognostic role of semantic processing in preclinical Alzheimer's disease

Relatively spared during most of the timeline of normal aging, semantic memory shows a subtle yet measurable decline even during the pre-clinical stage of Alzheimer's disease. This decline is thought to reflect early neurofibrillary changes and impairment is detectable using tests of language relying on lexical-semantic abilities. A promising approach is the characterization of semantic parameters such as typicality and age of acquisition of words, and propositional density from verbal output. Seminal research like the Nun Study or the analysis of the linguistic decline of famous writers and politicians later diagnosed with Alzheimer's disease supports the early diagnostic value of semantic processing and semantic memory. Moreover, measures of these skills may play an important role for the prognosis of patients with mild cognitive impairment

Laughter as a paradigm of socio-emotional signal processing in dementia

Laughter is a fundamental communicative signal in our relations with other people and is used to convey a diverse repertoire of social and emotional information. It is therefore potentially a useful probe of impaired socio-emotional signal processing in neurodegenerative diseases. Here we investigated the cognitive and affective processing of laughter in forty-seven patients representing all major syndromes of frontotemporal dementia, a disease spectrum characterised by severe socio-emotional dysfunction (twenty-two with behavioural variant frontotemporal dementia, twelve with semantic variant primary progressive aphasia, thirteen with nonfluent-agrammatic variant primary progressive aphasia), in relation to fifteen patients with typical amnestic Alzheimer’s disease and twenty healthy age-matched individuals. We assessed cognitive labelling (identification) and valence rating (affective evaluation) of samples of spontaneous (mirthful and hostile) and volitional (posed) laughter versus two auditory control conditions (a synthetic laughter-like stimulus and spoken numbers). Neuroanatomical associations of laughter processing were assessed using voxel-based morphometry of patients’ brain MR images. While all dementia syndromes were associated with impaired identification of laughter subtypes relative to healthy controls, this was significantly more severe overall in frontotemporal dementia than in Alzheimer’s disease and particularly in the behavioural and semantic variants, which also showed abnormal affective evaluation of laughter. Over the patient cohort, laughter identification accuracy was correlated with measures of daily-life socio-emotional functioning. Certain striking syndromic signatures emerged, including enhanced liking for hostile laughter in behavioural variant frontotemporal dementia, impaired processing of synthetic laughter in the nonfluent-agrammatic variant (consistent with a generic complex auditory perceptual deficit) and enhanced liking for numbers (‘numerophilia’) in the semantic variant. Across the patient cohort, overall laughter identification accuracy correlated with regional grey matter in a core network encompassing inferior frontal and cingulo-insular cortices; and more specific correlates of laughter identification accuracy were delineated in cortical regions mediating affective disambiguation (identification of hostile and posed laughter in orbitofrontal cortex) and authenticity (social intent) decoding (identification of mirthful and posed laughter in anteromedial prefrontal cortex) (all p<0.05 after correction for multiple voxel-wise comparisons over the whole brain). These findings reveal a rich diversity of cognitive and affective laughter phenotypes in canonical dementia syndromes and suggest that laughter is an informative probe of neural mechanisms underpinning socio-emotional dysfunction in neurodegenerative disease

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

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