Language Analytics for Assessing Brain Health: Cognitive Impairment, Depression and Pre-symptomatic Alzheimer’s Disease

Assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models

In this article, we developed an interview framework and natural language processing model for estimating cognitive function, based on an intake interview with psychologists in a hospital setting. The questionnaire consisted of 30 questions in five categories. To evaluate the developed interview items and the accuracy of the natural language processing model, we recruited participants with the approval of the University of Tokyo Hospital and obtained the cooperation of 29 participants (7 men and 22 women) aged 72-91 years. Based on the MMSE results, a multilevel classification model was created to classify the three groups, and a binary classification model to sort the two groups. For each of these models, we tested whether the accuracy would improve when text augmentation was performed. The accuracy in the multi-level classification results for the test data was 0.405 without augmentation and 0.991 with augmentation. The accuracy of the test data in the results of the binary classification without augmentation was 0.488 for the moderate dementia and mild dementia groups, 0.767 for the moderate dementia and MCI groups, and 0.700 for the mild dementia and MCI groups. In contrast, the accuracy of the test data in the augmented binary classification results was 0.972 for moderate dementia and mild dementia groups, 0.996 for moderate dementia and MCI groups, and 0.985 for mild dementia and MCI groups.

Number of ideas in spontaneous speech predicts cognitive impairment and frailty in community-dwelling older adults nine years later

OBJECTIVE

To investigate the associations between linguistic parameters in spontaneous speech at baseline and cognitive impairment and frailty nine years later.

METHODS

A prospective analysis was carried out on data of the Frailty in Brazilian Older People Study (FIBRA) Study, a population-based study on frailty. From a probabilistic sample of 384 individuals aged 65 and older at baseline (2008-2009), 124 aged 73 years and older at follow-up were selected, as they had scored above the cutoff values of cognitive screening for dementia adjusted by years of schooling at baseline and had answered to the question What is healthy aging and had no frailty at baseline. Verbal responses were submitted to content analysis and had its ideas and words counted. Number of ideas corresponded to the frequency of meaning categories and number of words to all identified significant textual elements in the text constituted by the sample answers to that question.

RESULTS

Multivariate logistic regression analyses, controlling for the effects of age, sex, and education, showed that individuals with a high number of ideas at baseline had lower chance of having cognitive impairment (OR = 0.39; 95% CI 0.22 - 0.69) and frailty (OR 0.66; 95% CI 0.44 - 0.99) nine years later than those with low number of ideas.

CONCLUSIONS

Higher number of ideas, but not number of words, in spontaneous speech seems to be associated to a more positive prognosis in mental and physical health nine years later. Linguistic markers may be used to predict cognitive impairment and frailty in older individuals.

Idea density in Japanese for the early detection of dementia based on narrative speech

Background

Idea density (ID), a natural language processing–based index, was developed to aid in the detection of dementia through the analysis of English narratives. However, it has not been applied to non-English languages due to the difficulties in translating grammatical concepts. In this study, we defined rules to count ideas in Japanese narratives based on a previous study and proposed a novel method to estimate ID in Japanese text using machine translation.

Materials

The study participants comprised 42 Japanese patients with dementia aged 69–98 years (mean: 84.95 years). We collected free narratives from the participants to build a speech corpus. The narratives of the patients were translated into English using three machine translation systems: Google Translate, Bing Translator, and Excite Translator. The ID in the translated text was then calculated using the Dependency-based Propositional ID (DEPID), an English ID scoring tool.

Results

The maximum correlation coefficient between ID calculated using DEPID-R-ADD (a modified DEPID method to calculate ID after removing vague sentences) and the Mini-Mental State Examination score was 0.473, indicating a moderate correlation.

Discussion

The results demonstrate the feasibility of machine translation-based ID measurement. We believe that the basic concept of this translation approach can be applied to other non-English languages.

Connected Speech in Neurodegenerative Language Disorders: A Review

Language assessment has a crucial role in the clinical diagnosis of several neurodegenerative diseases. The analysis of extended speech production is a precious source of information encompassing the phonetic, phonological, lexico-semantic, morpho-syntactic, and pragmatic levels of language organization. The knowledge about the distinctive linguistic variables identifying language deficits associated to different neurodegenerative diseases has progressively improved in the last years. However, the heterogeneity of such variables and of the way they are measured and classified limits any generalization and makes the comparison among studies difficult. Here we present an exhaustive review of the studies focusing on the linguistic variables derived from the analysis of connected speech samples, with the aim of characterizing the language disorders of the most prevalent neurodegenerative diseases, including primary progressive aphasia, Alzheimer's disease, movement disorders, and amyotrophic lateral sclerosis. A total of 61 studies have been included, considering only those reporting group analysis and comparisons with a group of healthy persons. This review first analyzes the differences in the tasks used to elicit connected speech, namely picture description, story narration, and interview, considering the possible different contributions to the assessment of different linguistic domains. This is followed by an analysis of the terminologies and of the methods of measurements of the variables, indicating the need for harmonization and standardization. The final section reviews the linguistic domains affected by each different neurodegenerative disease, indicating the variables most consistently impaired at each level and suggesting the key variables helping in the differential diagnosis among diseases. While a large amount of valuable information is already available, the review highlights the need of further work, including the development of automated methods, to take advantage of the richness of connected speech analysis for both research and clinical purposes.

Proactive screening for depression through metaphorical and automatic text analysis

OBJECTIVE

Proactive and automatic screening for depression is a challenge facing the public health system. This paper describes a system for addressing the above challenge.

MATERIALS AND METHOD

The system implementing the methodology--Pedesis--harvests the Web for metaphorical relations in which depression is embedded and extracts the relevant conceptual domains describing it. This information is used by human experts for the construction of a "depression lexicon". The lexicon is used to automatically evaluate the level of depression in texts or whether the text is dealing with depression as a topic.

RESULTS

Tested on three corpora of questions addressed to a mental health site the system provides 9% improvement in prediction whether the question is dealing with depression. Tested on a corpus of Blogs, the system provides 84.2% correct classification rate (p<.001) whether a post includes signs of depression. By comparing the system's prediction to the judgment of human experts we achieved an average 78% precision and 76% recall.

CONCLUSION

Depression can be automatically screened in texts and the mental health system may benefit from this screening ability.