A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis

• URL: http://arxiv.org/abs/2109.01537v2
• Date: Sat, 23 Dec 2023 12:29:58 GMT
• Title: A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis
• Authors: Dimitris Gkoumas, Bo Wang, Adam Tsakalidis, Maria Wolters, Arkaitz Zubiaga, Matthew Purver and Maria Liakata
• Abstract summary: We introduce a novel fine-grained longitudinal multi-modal corpus collected from healthy controls and people with dementia. The corpus consists of spoken conversations, a subset of which are transcribed, as well as typed and written thoughts and associated extra-linguistic information.
• Score: 22.672055089496972
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dementia affects cognitive functions of adults, including memory, language, and behaviour. Standard diagnostic biomarkers such as MRI are costly, whilst neuropsychological tests suffer from sensitivity issues in detecting dementia onset. The analysis of speech and language has emerged as a promising and non-intrusive technology to diagnose and monitor dementia. Currently, most work in this direction ignores the multi-modal nature of human communication and interactive aspects of everyday conversational interaction. Moreover, most studies ignore changes in cognitive status over time due to the lack of consistent longitudinal data. Here we introduce a novel fine-grained longitudinal multi-modal corpus collected in a natural setting from healthy controls and people with dementia over two phases, each spanning 28 sessions. The corpus consists of spoken conversations, a subset of which are transcribed, as well as typed and written thoughts and associated extra-linguistic information such as pen strokes and keystrokes. We present the data collection process and describe the corpus in detail. Furthermore, we establish baselines for capturing longitudinal changes in language across different modalities for two cohorts, healthy controls and people with dementia, outlining future research directions enabled by the corpus.

Related papers

• Large Language Model-based FMRI Encoding of Language Functions for Subjects with Neurocognitive Disorder [53.575426835313536]
  This paper explores language-related functional changes in older NCD adults using LLM-based fMRI encoding and brain scores. We analyze the correlation between brain scores and cognitive scores at both whole-brain and language-related ROI levels. Our findings reveal that higher cognitive abilities correspond to better brain scores, with correlations peaking in the middle temporal gyrus.
  arXiv  Detail & Related papers  (2024-07-15T01:09:08Z)
• Selfsupervised learning for pathological speech detection [0.0]
  Speech production is susceptible to influence and disruption by various neurodegenerative pathological speech disorders. These disorders lead to pathological speech characterized by abnormal speech patterns and imprecise articulation. Unlike neurotypical speakers, patients with speech pathologies or impairments are unable to access various virtual assistants such as Alexa, Siri, etc.
  arXiv  Detail & Related papers  (2024-05-16T07:12:47Z)
• Syntactic Language Change in English and German: Metrics, Parsers, and Convergences [56.47832275431858]
  The current paper looks at diachronic trends in syntactic language change in both English and German, using corpora of parliamentary debates from the last c. 160 years. We base our observations on five dependencys, including the widely used Stanford Core as well as 4 newer alternatives. We show that changes in syntactic measures seem to be more frequent at the tails of sentence length distributions.
  arXiv  Detail & Related papers  (2024-02-18T11:46:16Z)
• Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia [18.964022118823532]
  We learn linguistic disorder patterns by making use of a moderately-sized pre-trained language model. We then use the probability estimates from the best model to construct digital linguistic markers. Our proposed linguistic disorder markers provide useful insights into gradual language impairment associated with disease progression.
  arXiv  Detail & Related papers  (2023-10-15T17:58:47Z)
• A Digital Language Coherence Marker for Monitoring Dementia [14.580879594539859]
  We propose methods to capture language coherence as a cost-effective, human-interpretable digital marker. We compare language coherence patterns between people with dementia and healthy controls. The coherence marker shows a significant difference between people with mild cognitive impairment, those with Alzheimer's Disease and healthy controls.
  arXiv  Detail & Related papers  (2023-10-14T17:10:19Z)
• Probing Brain Context-Sensitivity with Masked-Attention Generation [87.31930367845125]
  We use GPT-2 transformers to generate word embeddings that capture a fixed amount of contextual information. We then tested whether these embeddings could predict fMRI brain activity in humans listening to naturalistic text.
  arXiv  Detail & Related papers  (2023-05-23T09:36:21Z)
• Information-Restricted Neural Language Models Reveal Different Brain Regions' Sensitivity to Semantics, Syntax and Context [87.31930367845125]
  We trained a lexical language model, Glove, and a supra-lexical language model, GPT-2, on a text corpus. We then assessed to what extent these information-restricted models were able to predict the time-courses of fMRI signal of humans listening to naturalistic text. Our analyses show that, while most brain regions involved in language are sensitive to both syntactic and semantic variables, the relative magnitudes of these effects vary a lot across these regions.
  arXiv  Detail & Related papers  (2023-02-28T08:16:18Z)
• Deep learning reveals the common spectrum underlying multiple brain disorders in youth and elders from brain functional networks [53.257804915263165]
  Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions. Key evidence from neuroimaging data for pathological commonness remains unrevealed. We build a deep learning model, using multi-site functional magnetic resonance imaging data, for classifying 5 different brain disorders from healthy controls.
  arXiv  Detail & Related papers  (2023-02-23T09:22:05Z)
• Perception Point: Identifying Critical Learning Periods in Speech for Bilingual Networks [58.24134321728942]
  We compare and identify cognitive aspects on deep neural-based visual lip-reading models. We observe a strong correlation between these theories in cognitive psychology and our unique modeling.
  arXiv  Detail & Related papers  (2021-10-13T05:30:50Z)
• Predicting Early Indicators of Cognitive Decline from Verbal Utterances [2.387625146176821]
  Dementia is a group of irreversible, chronic, and progressive neurodegenerative disorders resulting in impaired memory, communication, and thought processes. We measure the feasibility of using the linguistic characteristics of verbal utterances elicited during neuropsychological exams to distinguish between elderly control groups, people with MCI, people diagnosed with possible Alzheimer's disease (AD), and probable AD. Our experiments show that a combination of contextual and psycholinguistic features extracted by a Support Vector Machine improved distinguishing the verbal utterances of elderly controls, people with MCI, possible AD, and probable AD.
  arXiv  Detail & Related papers  (2020-11-19T02:24:11Z)
• Identification of Dementia Using Audio Biomarkers [15.740689461116762]
  The objective of this work is to use speech processing and machine learning techniques to automatically identify the stage of dementia. Non-linguistic acoustic parameters are used for this purpose, making this a language independent approach. We analyze the contribution of various types of acoustic features such as spectral, temporal, cepstral their feature-level fusion and selection towards the identification of dementia stage.
  arXiv  Detail & Related papers  (2020-02-27T13:54:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.