Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Correction: "Not That I've Become Exceptional, But I'm Able to Make Myself Understood Better": Impact of Speech and Language Therapy on Everyday Communication in People with Primary Progressive Aphasia and Their Carers.

Neurology and therapy·2026
Same author

Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic and Critical Review on Methodological Approaches to Glioma Segmentation and Classification Through Multiparametric MRI.

Brain sciences·2026
Same author

The influence of bilingualism on the assessment and treatment of an Italian-English speaker with the logopenic variant of PPA.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026
Same author

"Not That I've Become Exceptional, But I'm Able to Make Myself Understood Better": Impact of Speech and Language Therapy on Everyday Communication in People with Primary Progressive Aphasia and Their Carers.

Neurology and therapy·2026
Same author

Rehabilitation After Severe Traumatic Brain Injury with Acute Symptomatic Seizure: Neurofeedback and Motor Therapy in a 6-Month Follow-Up Case Study.

Neurology international·2026
Same author

Respiratory Biofeedback Training as an Adjunct Intervention in Pulmonary Rehabilitation for Late-Stage COPD: A Pilot Trial.

Applied psychophysiology and biofeedback·2026

Related Experiment Video

Updated: Mar 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A

Petronilla Battista1, Christian Salvatore1, Isabella Castiglioni1

  • 1Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Milano, Italy.

Behavioural Neurology
|March 4, 2017
PubMed
Summary

Machine learning can help classify Alzheimer's disease (AD) patients using fewer neuropsychological tests. Key tests like ADAS-Cog and FAQ show promise in identifying cognitive impairment severity.

More Related Videos

Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

6.4K
Assessment of Spontaneous Alternation, Novel Object Recognition and Limb Clasping in Transgenic Mouse Models of Amyloid-β and Tau Neuropathology
10:02

Assessment of Spontaneous Alternation, Novel Object Recognition and Limb Clasping in Transgenic Mouse Models of Amyloid-β and Tau Neuropathology

Published on: May 28, 2017

28.2K

Related Experiment Videos

Last Updated: Mar 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K
Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

6.4K
Assessment of Spontaneous Alternation, Novel Object Recognition and Limb Clasping in Transgenic Mouse Models of Amyloid-β and Tau Neuropathology
10:02

Assessment of Spontaneous Alternation, Novel Object Recognition and Limb Clasping in Transgenic Mouse Models of Amyloid-β and Tau Neuropathology

Published on: May 28, 2017

28.2K

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Gerontology

Background:

  • Alzheimer's disease (AD) significantly impacts cognitive, behavioral, and functional states, affecting daily life quality.
  • Neuropsychological assessments are vital for detecting AD-related changes but often involve subjectivity.
  • Existing clinical measures for AD classification and diagnosis still contain a degree of subjectivity.

Purpose of the Study:

  • To evaluate machine learning's potential in quantifying Alzheimer's disease progression.
  • To optimize or reduce the number of neuropsychological tests required for AD patient classification.
  • To enable early detection of cognitive impairment using automated methods.

Main Methods:

  • Utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
  • Investigated twelve advanced neuropsychological tests for automatic classification of impairment levels (none, mild, severe) based on CDR scores.
  • Included cognitive domain and subdomain measures as features for analysis.

Main Results:

  • Identified specific tests as frequent top predictors for automatic AD classification: Logical Memory (LM), Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog), Auditory Verbal Learning Test (AVLT), and Functional Activities Questionnaire (FAQ).
  • Highlighted the significant contribution of ADAS-Cog memory subdomains (immediate and delayed recall) and FAQ financial competency measures.
  • Demonstrated machine learning's capability in classifying AD severity with potential for test reduction.

Conclusions:

  • Machine learning models can effectively classify Alzheimer's disease severity using a subset of neuropsychological tests.
  • Specific tests, notably ADAS-Cog and FAQ, are crucial for accurate automated classification.
  • This approach offers a more objective and potentially streamlined method for AD diagnosis and monitoring.