المؤلفون: Nicklas Linz, Philipp Müller, Insa Kröger, Frans R.J. Verhey, Inez H.G.B. Ramakers, Radia Zeghari, Alexandra König, Johannes Tröger, Hali Lindsay

المصدر: RANLP

مصطلحات موضوعية: Computer science, business.industry, computer.software_genre, Variety (linguistics), language.human_language, Test (assessment), Task (project management), German, language, Verbal fluency test, Screening tool, Artificial intelligence, Cognitive impairment, business, computer, Word (computer architecture), Natural language processing

الوصف: The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds. State-of-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences. While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance. In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity. To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings. In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls=66, MCI=66), we show that our multilingual approaches clearly improve over single-language baselines.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::6f46b5be971094a6d07989b35a6bbbb1Test
https://doi.org/10.26615/978-954-452-072-4_095Test

المؤلفون: Jessica Peter, Nicklas Linz, Johannes Tröger, Hali Lindsay, Jutta Kray, Mario Magued Mina, Stefan Klöppel

مصطلحات موضوعية: medicine.medical_specialty, Neuropsychological Tests, Audiology, behavioral disciplines and activities, 050105 experimental psychology, Task (project management), Executive Function, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Semantic memory, Verbal fluency test, Cognitive Dysfunction, 0501 psychology and cognitive sciences, In patient, Latency (engineering), Control (linguistics), Set (psychology), Point (typography), General Neuroscience, 05 social sciences, Semantics, Psychiatry and Mental health, Clinical Psychology, 150 Psychologie, Neurology (clinical), Psychology, 610 Medizin und Gesundheit, 030217 neurology & neurosurgery

الوصف: Objective:Semantic verbal fluency (SVF) tasks require individuals to name items from a specified category within a fixed time. An impaired SVF performance is well documented in patients with amnestic Mild Cognitive Impairment (aMCI). The two leading theoretical views suggest either loss of semantic knowledge or impaired executive control to be responsible.Method:We assessed SVF 3 times on 2 consecutive days in 29 healthy controls (HC) and 29 patients with aMCI with the aim to answer the question which of the two views holds true.Results:When doing the task for the first time, patients with aMCI produced fewer and more common words with a shorter mean response latency. When tested repeatedly, only healthy volunteers increased performance. Likewise, only the performance of HC indicated two distinct retrieval processes: a prompt retrieval of readily available items at the beginning of the task and an active search through semantic space towards the end. With repeated assessment, the pool of readily available items became larger in HC, but not patients with aMCI.Conclusion:The production of fewer and more common words in aMCI points to a smaller search set and supports the loss of semantic knowledge view. The failure to improve performance as well as the lack of distinct retrieval processes point to an additional impairment in executive control. Our data did not clearly favour one theoretical view over the other, but rather indicates that the impairment of patients with aMCI in SVF is due to a combination of both.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::96ce4e29fbe653cb195d5c036779cfb4Test

المؤلفون: Dimitrios Kokkinakis, Nicklas Linz, Philippe Robert, Frank Rudzicz, Jan Alexandersson, Bai Li, Alexandra König, Kathleen C. Fraser, Kristina Lundholm Fors

المصدر: NAACL-HLT (1)

مصطلحات موضوعية: Domain adaptation, Training set, business.industry, Computer science, computer.software_genre, medicine.disease, Task (project management), 030507 speech-language pathology & audiology, 03 medical and health sciences, 0302 clinical medicine, medicine, Dementia, Language modelling, Language model, Artificial intelligence, 0305 other medical science, business, Set (psychology), computer, 030217 neurology & neurosurgery, Natural language processing

الوصف: There is growing evidence that changes in speech and language may be early markers of dementia, but much of the previous NLP work in this area has been limited by the size of the available datasets. Here, we compare several methods of domain adaptation to augment a small French dataset of picture descriptions (n = 57) with a much larger English dataset (n = 550), for the task of automatically distinguishing participants with dementia from controls. The first challenge is to identify a set of features that transfer across languages; in addition to previously used features based on information units, we introduce a new set of features to model the order in which information units are produced by dementia patients and controls. These concept-based language model features improve classification performance in both English and French separately, and the best result (AUC = 0.89) is achieved using the multilingual training set with a combination of information and language model features.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::f36e527a739665c9382b8af3ae241d45Test
https://doi.org/10.18653/v1/n19-1367Test

المؤلفون: Marie Eckerström, Jan Alexandersson, Kristina Lundholm Fors, Nicklas Linz, Dimitrios Kokkinakis, Hali Lindsay

المصدر: Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology.

مصطلحات موضوعية: Vocabulary, medicine.medical_specialty, medicine.diagnostic_test, media_common.quotation_subject, Word count, Neuropsychology, Audiology, Task (project management), Interval (music), medicine, Verbal fluency test, Neuropsychological assessment, Psychology, Word (group theory), media_common

الوصف: The Semantic Verbal Fluency (SVF) task is a classical neuropsychological assessment where persons are asked to produce words belonging to a semantic category (e.g., animals) in a given time. This paper introduces a novel method of temporal analysis for SVF tasks utilizing time intervals and applies it to a corpus of elderly Swedish subjects (mild cognitive impairment, subjective cognitive impairment and healthy controls). A general decline in word count and lexical frequency over the course of the task is revealed, as well as an increase in word transition times. Persons with subjective cognitive impairment had a higher word count during the last intervals, but produced words of the same lexical frequencies. Persons with MCI had a steeper decline in both word count and lexical frequencies during the third interval. Additional correlations with neuropsychological scores suggest these findings are linked to a person’s overall vocabulary size and processing speed, respectively. Classification results improved when adding the novel features (AUC=0.72), supporting their diagnostic value.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::0c8dbc3f3e0001bd0c156473ff5c3548Test
https://doi.org/10.18653/v1/w19-3012Test

المؤلفون: P. Robert, Alexandra König, Nicklas Linz, Johannes Tröger, Jan Alexandersson

المصدر: French Journal of Psychiatry. 1:S76

مصطلحات موضوعية: Computer science, business.industry, Word error rate, Cognition, computer.software_genre, Task (project management), Classifier (linguistics), Semantic memory, Verbal fluency test, Artificial intelligence, Cognitive decline, business, computer, Neurocognitive, Natural language processing

الوصف: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI) and seem to be highly sensitive to cognitive changes [1]. In this task, participants name as many items of a semantic category under a time constraint. SVF can be considered as a multifactorial task, comprising both semantic memory retrieval and executive control processes. However, clinicians measure task performance manually only by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic speech analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. Furthermore, we examined the technologic feasibility of automatically assessing the SVF task – via a telephone-based solution for potential remote frontline prescreening of cognitive impairments [2]. We will present SVF data which was collected from 95 older people with MCI (n=47), Alzheimer’s or related dementias (ADRD; n=24) and healthy controls (HC; n=24). Speech was recorded through a mobile tablet device using the built-in microphone. All data was annotated manually and automatically with the named words, clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI and ADRD. Afterwards to simulate telephone conditions, the recordings were downsampled to a lower sampling rate. Automatically extracted clusters and switches were highly correlated (r=0.9) with manually established values, and performed as well on the classification task separating healthy controls from persons with Alzheimer’s (AUC=0.939) and MCI (AUC=0.758) [3]. The downsampled quality obtained similar encouraging results. We observe a relatively low word error rate of 33% despite phone-quality speech samples. The automated classification pipeline performs equally well compared to the classifier trained on manual transcriptions of the same speech data. Our results indicate SVF as a prime candidate for inclusion into an automated telephone-screening system and that it is possible to automate fine-grained analyses of this task for the assessment of cognitive decline.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::f570a363489a16ac7a6174acf5b1a066Test
https://doi.org/10.1016/s2590-2415Test(19)30183-7

المؤلفون: Nicklas Linz, Philipp Müller, Johannes Tröger, Mario Magued Mina, Alexandra König, Hali Lindsay, Radia Zeghari

المصدر: Scopus-Elsevier

مصطلحات موضوعية: Dissociation (neuropsychology), Point (typography), Semantic similarity, medicine, Dementia, Verbal fluency test, Cognition, medicine.disease, Psychology, Neurocognitive, Cognitive psychology, Task (project management)

الوصف: Effective management of dementia hinges on timely detection and precise diagnosis of the underlying cause of the syndrome at an early mild cognitive impairment (MCI) stage. Verbal fluency tasks are among the most often applied tests for early dementia detection due to their efficiency and ease of use. In these tasks, participants are asked to produce as many words as possible belonging to either a semantic category (SVF task) or a phonemic category (PVF task). Even though both SVF and PVF share neurocognitive function profiles, the PVF is typically believed to be less sensitive to measure MCI-related cognitive impairment and recent research on fine-grained automatic evaluation of VF tasks has mainly focused on the SVF. Contrary to this belief, we show that by applying state-of-the-art semantic and phonemic distance metrics in automatic analysis of PVF word productions, in-depth conclusions about production strategy of MCI patients are possible. Our results reveal a dissociation between semantically- and phonemically-guided search processes in the PVF. Specifically, we show that subjects with MCI rely less on semantic- and more on phonemic processes to guide their word production as compared to healthy controls (HC). We further show that semantic similarity-based features improve automatic MCI versus HC classification by 29% over previous approaches for the PVF. As such, these results point towards the yet underexplored utility of the PVF for in-depth assessment of cognition in MCI.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d92a821e7c7405359b9ae5c9660a7379Test
http://www.scopus.com/inward/record.url?eid=2-s2.0-85123183333&partnerID=MN8TOARSTest