Literature DB >> 32765845

The correlation of everyday cognition test scores and the progression of Alzheimer's disease: a data analytics study.

Fadi Thabtah1, Robinson Spencer1, Yongsheng Ye1.   

Abstract

The process of diagnosing dementia conditions, especially Alzheimer's disease, and the cognitive tests that are involved in this process, are important areas of study. Everyday Cognition (ECog) is one test that can be used as part of Alzheimer's disease diagnosis to measure cognitive decline in different areas. In this study, we investigate two versions of the ECog test: the study partner reported version (ECogSP), and the patient reported version (ECogPT). We compare these, using statistical analysis and machine learning techniques, to create classification models to demonstrate the progression in ECog scores over time by using the Alzheimer's Disease Neuroimaging Initiative longitudinal data repository (ADNI); participants are classed with having normal cognition, mild cognitive impairment, or Alzheimer's disease. We found that participants who are diagnosed with Alzheimer's disease at baseline, or during a subsequent visit, tend to self-report consistent ECogPT scores over time indicating no change in cognitive ability. However, study partners tend to report higher and increasing ECogSP scores on behalf of participants in the same diagnosis category; this would indicate a degradation in the participant's cognitive ability over time, consistent with the progress of Alzheimer's disease. © Springer Nature Switzerland AG 2020.

Entities:  

Keywords:  ADNI; Alzheimer’s disease; Cognitive tests; Data analytics; Dementia; Everyday cognition; Longitudinal study; Machine learning

Year:  2020        PMID: 32765845      PMCID: PMC7378134          DOI: 10.1007/s13755-020-00114-8

Source DB:  PubMed          Journal:  Health Inf Sci Syst        ISSN: 2047-2501


  24 in total

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4.  The value of informant versus individual's complaints of memory impairment in early dementia.

Authors:  D B Carr; S Gray; J Baty; J C Morris
Journal:  Neurology       Date:  2000-12-12       Impact factor: 9.910

5.  Predicting Alzheimer's disease: neuropsychological tests, self-reports, and informant reports of cognitive difficulties.

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6.  Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade.

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Journal:  Lancet Neurol       Date:  2010-01       Impact factor: 44.182

7.  A new rating scale for Alzheimer's disease.

Authors:  W G Rosen; R C Mohs; K L Davis
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8.  MRI of hippocampal volume loss in early Alzheimer's disease in relation to ApoE genotype and biomarkers.

Authors:  N Schuff; N Woerner; L Boreta; T Kornfield; L M Shaw; J Q Trojanowski; P M Thompson; C R Jack; M W Weiner
Journal:  Brain       Date:  2009-02-27       Impact factor: 13.501

9.  Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease.

Authors:  Elaheh Moradi; Ilona Hallikainen; Tuomo Hänninen; Jussi Tohka
Journal:  Neuroimage Clin       Date:  2016-12-18       Impact factor: 4.881

10.  Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer's disease: implications for sequence of pathological events in Alzheimer's disease.

Authors:  Clifford R Jack; Val J Lowe; Stephen D Weigand; Heather J Wiste; Matthew L Senjem; David S Knopman; Maria M Shiung; Jeffrey L Gunter; Bradley F Boeve; Bradley J Kemp; Michael Weiner; Ronald C Petersen
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  2 in total

1.  Microstate feature fusion for distinguishing AD from MCI.

Authors:  Yupan Shi; Qinying Ma; Chunyu Feng; Mingwei Wang; Hualong Wang; Bing Li; Jiyu Fang; Shaochen Ma; Xin Guo; Tongliang Li
Journal:  Health Inf Sci Syst       Date:  2022-07-26

Review 2.  The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence.

Authors:  Anuschka Silva-Spínola; Inês Baldeiras; Joel P Arrais; Isabel Santana
Journal:  Biomedicines       Date:  2022-01-29
  2 in total

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