European cross-cultural neuropsychological test battery (CNTB) for the assessment of cognitive impairment in multiple sclerosis: Cognitive phenotyping and classification supported by machine learning techniques

Background

The European Cross-Cultural Neuropsychological Test Battery (CNTB) has been proposed as a comprehensive battery for cognitive assessment, reducing the potential impact of cultural variables. In this validation study, we aimed to evaluate the diagnostic capacity of CNTB for the assessment of participants with multiple sclerosis (pwMS) compared to the Neuronorma battery (NN) according to the International Classification of Cognitive Disorders in MS criteria, and to develop machine learning (ML) algorithms to improve the diagnostic capacity of CNTB and to select the most relevant tests.

Methods

Sixty pwMS and 60 healthy controls (HC) with no differences in sex, age, or years of education were enrolled. All participants completed the CNTB and pwMS were also examined with NN, depression, and fatigue scales. Impaired domains and cognitive phenotypes were defined following ICCoDiMS based on CNTB scores and compared to NN, according to -1SD and -1.5SD cutoff scores. To select the most relevant tests, random forest (RF) was performed for different binary classifications.

Results

PwMS showed a lower performance compared to HC with medium-large effect sizes, in episodic memory, executive function, attention, and processing speed, in accordance with their characteristic cognitive profile. There were no differences in impaired domains or cognitive phenotypes between CNTB and NN, highlighting the role of episodic memory, executive function, attention, and processing speed tests. The most relevant tests identified by RF were consistent with inter-group comparisons and allowed a better classification than SD cutoff scores.

Conclusion

CNTB is a valid test for cognitive diagnosis in pwMS, including key tests for the most frequently impaired cognitive domains in MS. The use of ML techniques may also be useful to improve diagnosis, especially in some tests with lower sensitivity.