The Latent-Variable Structure of the Addenbrooke's Cognitive Examination-Revised

Investigating the Latent Structure of Executive Function in the Delis-Kaplan Executive Function System Using Cattell-Horn-Carroll Theory

OBJECTIVE

To replicate a seven-factor model previously reported for the Delis-Kaplan Executive Function System (D-KEFS).

METHOD

This study used the D-KEFS standardization sample including 1,750 non-clinical participants. Several seven-factor models previously reported for the D-KEFS were re-evaluated using confirmatory factor analysis (CFA). Previously published bi-factor models were also tested. These models were compared with a three-factor a priori model based on Cattell-Horn-Carroll (CHC) theory. Measurement invariance was examined across three age cohorts.

RESULTS

All previously reported models failed to converge when tested with CFA. None of the bi-factor models converged after large numbers of iterations, suggesting that bi-factor models are ill-suited to represent the D-KEFS scores as reported in the test manual. Although poor fit was initially observed for the three-factor CHC model, inspection of modification indices showed potential for improvement by including method effects via correlated residuals for scores derived from similar tests. The final CHC model showed good to excellent fit and strong metric measurement invariance across the three age cohorts with minor exceptions for a subset of Fluency parameters.

CONCLUSIONS

CHC theory extends to the D-KEFS, supporting findings from previous studies that executive functions can be integrated into CHC theory.

Proposal of neural network model for neurocognitive rehabilitation and its comparison with fuzzy expert system model

This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic.

The revised Addenbrooke's Cognitive Examination can facilitate differentiation of dementia with Lewy bodies from Alzheimer's disease

OBJECTIVES

Dementia with Lewy bodies (DLB) is a major cause of degenerative dementia, yet the diagnosis is often missed or mistaken for Alzheimer's disease (AD). We assessed whether the revised Addenbrooke's Cognitive Examination (ACE-R), a brief test for dementia, differentiates DLB from AD.

METHODS

We first compared baseline ACE-R performance in 76 individuals with DLB, 40 individuals with AD and 66 healthy controls. We then investigated the diagnostic accuracy of a simple standardised 'memory/visuospatial' ratio calculated from the ACE-R subscores. Finally, as a comparison a logistic regression machine learning algorithm was trained to classify between DLB and AD.

RESULTS

Individuals with AD had poorer memory (p = 0.001) and individuals with DLB had poorer visuospatial function (p = 0.005). Receiver operating characteristics curves confirmed that the ACE-R total score could differentiate dementia from non-dementia cases with 98% accuracy, but could not discriminate between dementia types (50%, or chance-level accuracy). However, a 'memory/visuospatial' ratio ≥1.1 differentiated DLB from AD with 82% sensitivity, 68% specificity and 77% mean accuracy. The machine learning classifier did not improve the overall diagnostic accuracy (74%) of the simple ACE-R subscores ratio.

CONCLUSIONS

The ACE-R-based 'memory/visuospatial' ratio, but not total score, demonstrates good clinical utility for the differential diagnosis of DLB from AD.