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Related Experiment Video

Updated: Jun 23, 2026

Eye-Tracking Control to Assess Cognitive Functions in Patients with Amyotrophic Lateral Sclerosis
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Published on: October 13, 2016

ECAS-Based Neuropsychological Phenotyping in Amyotrophic Lateral Sclerosis: A Retrospective Study Comparing Different

Barbara Poletti1,2, Edoardo N Aiello3, Monica Consonni4

  • 1Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, Milan, Italy. b.poletti@auxologico.it.

Neurology and Therapy
|June 20, 2026
PubMed
Summary

Different algorithms using the Edinburgh Cognitive and Behavioural ALS Screen (ECAS) show moderate agreement but can lead to misclassification of amyotrophic lateral sclerosis (ALS) phenotypes, potentially affecting patient diagnosis and care.

Keywords:
Amyotrophic lateral sclerosisDiagnostic criteriaEdinburgh Cognitive and Behavioural ALS ScreenFrontotemporal degenerationNeuropsychology

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Area of Science:

  • Neuroscience
  • Neurology
  • Psychology

Background:

  • Amyotrophic lateral sclerosis (ALS) diagnosis requires neuropsychological phenotyping.
  • The Edinburgh Cognitive and Behavioural ALS Screen (ECAS) is a tool used for this assessment.
  • Discrepancies in ECAS-based classification algorithms may impact diagnostic accuracy.

Purpose of the Study:

  • To compare different algorithms based on the ECAS for classifying ALS patients' neuropsychological phenotypes.
  • To identify potential discrepancies among various ECAS-based classification systems.
  • To evaluate the impact of different algorithms on classifying ALS patients according to Strong et al.'s criteria.

Main Methods:

  • Retrospective analysis of ECAS-Cognitive and -Carer Interview (ECAS-C/-CI) scores from 901 ALS patients without dementia.
  • Classification of patients into cognitively and behaviourally normal (ALScbn), impaired (ALSci/bi/cbi), or Possible ALS-FTD using three ECAS-based algorithms (Abrahams', Poletti et al.'s, Subscale).
  • All algorithms utilized single-item ECAS-CI scores for behavioral classifications.

Main Results:

  • Classification agreement among the three algorithms was moderate to high (84-86%, Cohen's k=0.78-0.81).
  • Significant re-classifications occurred between "ALScbn" and "ALSci" categories (11-24%).
  • The most substantial disagreements (43%) were observed for the ALScbi category when comparing single-task (Poletti) to total-level (Abrahams) algorithms, with reclassifications into ALSbi or Possible ALS-FTD.

Conclusions:

  • Different ECAS-based algorithms for deriving Strong's phenotypes can lead to slight discrepancies.
  • These discrepancies may result in the under- or overestimation of a patient's neuropsychological classification.
  • Careful consideration of the algorithm used is necessary for accurate ALS phenotyping.