Integrated Cognitive Assessment: Speed and Accuracy of Visual Processing as a Reliable Proxy to Cognitive Performance View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Seyed-Mahdi Khaligh-Razavi, Sina Habibi, Maryam Sadeghi, Haniye Marefat, Mahdiyeh Khanbagi, Seyed Massood Nabavi, Elham Sadeghi, Chris Kalafatis

ABSTRACT

Various mental disorders are accompanied by some degree of cognitive impairment. Particularly in neurodegenerative disorders, cognitive impairment is the phenotypical hallmark of the disease. Effective, accurate and timely cognitive assessment is key to early diagnosis of this family of mental disorders. Current standard-of-care techniques for cognitive assessment are primarily paper-based, and need to be administered by a healthcare professional; they are additionally language and education-dependent and typically suffer from a learning bias. These tests are thus not ideal for large-scale pro-active cognitive screening and disease progression monitoring. We developed the Integrated Cognitive Assessment (referred to as CGN_ICA), a 5-minute computerized cognitive assessment tool based on a rapid visual categorization task, in which a series of carefully selected natural images of varied difficulty are presented to participants. Overall 448 participants, across a wide age-range with different levels of education took the CGN_ICA test. We compared participants' CGN_ICA test results with a variety of standard pen-and-paper tests, such as Symbol Digit Modalities Test (SDMT) and Montreal Cognitive Assessment (MoCA), that are routinely used to assess cognitive performance. CGN_ICA had excellent test-retest reliability, showed convergent validity with the standard-of-care cognitive tests used here, and demonstrated to be suitable for micro-monitoring of cognitive performance. More... »

PAGES

1102

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-37709-x

DOI

http://dx.doi.org/10.1038/s41598-018-37709-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111771125

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30705371


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