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Dementia is an acquired, progressive syndrome characterized by a decline in multiple cognitive domains severe enough to impair daily functioning and reduce independence. Although memory loss is a central feature, the diagnosis requires additional deficits involving language, executive function, visuospatial skills, judgment, calculation, or abstract reasoning. These cognitive impairments reflect underlying neurodegenerative or vascular processes that gradually disrupt neuronal networks...
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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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Relating memory to functional performance in normal aging to dementia using hierarchical Bayesian cognitive

William R Shankle1, James P Pooley, Mark Steyvers

  • 1Department of Cognitive Sciences, UC Irvine, Irvine, CA, USA.

Alzheimer Disease and Associated Disorders
|March 13, 2012
PubMed
Summary
This summary is machine-generated.

Cognitive processing models accurately distinguished Alzheimer disease severity stages, translating memory and executive function into a continuous measure of functional decline. This approach links cognitive and functional data for better biomarker correlations.

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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Area of Science:

  • Cognitive neuroscience
  • Geriatric medicine
  • Biostatistics

Background:

  • Understanding the relationship between cognitive function and daily functional abilities is crucial for managing Alzheimer disease and related disorders.
  • Current assessment methods may not fully capture the nuanced progression of cognitive and functional decline.

Purpose of the Study:

  • To develop and validate a hierarchical Bayesian cognitive processing model to link cognitive performance with functional severity in Alzheimer disease.
  • To explore the utility of latent cognitive parameters in differentiating functional stages and creating a continuous measure of functional severity.

Main Methods:

  • A hierarchical Bayesian cognitive processing model was constructed, embedding a signal detection theory model of a recognition memory task (MCI Screen).
  • The model utilized latent parameters for discriminability (memory process) and response bias (executive function) to analyze data from 280 patients.
  • Data included 1514 assessments using the Functional Assessment Staging test (FAST) and the MCI Screen.

Main Results:

  • Observed recognition memory data did not differentiate the 6 FAST severity stages.
  • However, the latent cognitive parameters derived from the model successfully and completely separated these 6 FAST severity stages.
  • These latent parameters effectively transformed the ordinal FAST measure into a continuous measure reflecting the underlying continuum of functional severity.

Conclusions:

  • Hierarchical Bayesian cognitive processing models can accurately translate latent cognitive measures into continuous functional severity measures in clinical settings.
  • This approach successfully links cognitive processing (memory, executive function) with functional status, applicable to both individuals and patient groups.
  • The model's ability to bridge cognitive and functional data may facilitate more precise correlations with other biological markers, such as biomarkers.