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

Cognitive Enhancers: Cholinesterase Inhibitors and NMDA Receptor Antagonists01:30

Cognitive Enhancers: Cholinesterase Inhibitors and NMDA Receptor Antagonists

95
Cognitive enhancers, also known as "smart drugs," are substances used to enhance memory, mental alertness, and concentration. These can be natural or synthetic and improve cognition in conditions like Alzheimer's disease (AD) and other neurodegenerative diseases. Some common examples include caffeine, amphetamines, methylphenidate, modafinil, arecoline, donepezil, vortioxetine, and piracetam. These enhancers work on the principle of synaptic plasticity and altered circuit function.
95

You might also read

Related Articles

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

Sort by
Same author

A Knowledge-Guided Weight Optimization Method Based on Augmented Lagrangian for Active Suspension Preview Control.

IEEE transactions on cybernetics·2026
Same author

From symptom tracking to prevention - A transformer-based dynamic model for predicting mild cognitive impairment risk in older adults with depression: A longitudinal study based on CHARLS and CLHLS.

Medicine·2026
Same author

Comparative effects of different fish meal sources on growth, immune response, antioxidant capacity, gut barrier function, and gut microbiota in rice field eel (Monopterus albus).

Fish & shellfish immunology·2026
Same author

Earthworm Powder Mitigates Soybean Meal-Induced Growth Inhibition in Rice Field Eel (<i>Monopterus albus</i>) by Regulating Appetite and Improving Intestinal Health.

Biology·2026
Same author

Glyphosate promotes calcium oxalate crystal-induced renal injury by modulating the PI3K/Akt-mediated mechanism.

Ecotoxicology and environmental safety·2026
Same author

Human-machine shared driving for vehicle collision avoidance based on Hamilton-Jacobi reachability.

Accident; analysis and prevention·2026

Related Experiment Video

Updated: May 29, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K

Leveraging transformer models to predict cognitive impairment: accuracy, efficiency, and interpretability.

Kai Ma1, Junzhi Zhang2, Xinhang Huang3

  • 1College of Culture and Health Communication, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR, China.

BMC Public Health
|February 7, 2025
PubMed
Summary

This study introduces an enhanced Transformer model for predicting mild cognitive impairment (MCI) with over 90% accuracy. The model effectively handles mixed data types, outperforming traditional methods for early neurodegenerative disease detection.

Keywords:
Alzheimer’s disease (AD)Machine learningMild cognitive impairment (MCI)Neurodegenerative disease predictionTransformer model

More Related Videos

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

7.4K
Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
10:13

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

Published on: February 14, 2014

13.6K

Related Experiment Videos

Last Updated: May 29, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K
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

7.4K
Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
10:13

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

Published on: February 14, 2014

13.6K

Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning for Medical Diagnosis
  • Computational Neuroscience

Background:

  • Mild cognitive impairment (MCI) is a precursor to dementia, necessitating early detection for effective intervention.
  • Traditional predictive models often struggle with the heterogeneity of clinical data.
  • The CHARLS dataset provides a rich resource for studying cognitive decline in aging populations.

Purpose of the Study:

  • To develop an enhanced Transformer model for predicting MCI.
  • To effectively integrate and process mixed data types (categorical and continuous) from the CHARLS dataset.
  • To improve the accuracy and efficiency of cognitive decline prediction.

Main Methods:

  • Utilized a Transformer model with multi-head attention (4 heads) to process integrated categorical and continuous data.
  • Employed separate embedding layers for categorical features and feed-forward networks for continuous features.
  • Compared model performance against Support Vector Machine (SVM) and XGBoost, training for 150 epochs with RMSProp and a cosine annealing scheduler.

Main Results:

  • The Transformer model achieved over 90% accuracy with a Mean Absolute Error (MAE) tolerance of 3.5, surpassing SVM and XGBoost.
  • Demonstrated rapid model convergence, with training loss stabilizing within 20 epochs.
  • An attention heatmap visualized feature importance, confirming the model's ability to identify key predictive variables.

Conclusions:

  • The enhanced Transformer model provides superior accuracy and efficiency for predicting cognitive decline compared to conventional methods.
  • The model's capability to handle diverse data types and its interpretability via attention mechanisms offer a promising approach for early detection of neurodegenerative diseases.
  • This approach has the potential to significantly enhance clinical decision-making and guide timely interventions for cognitive health.