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

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

1.9K
Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
1.9K
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

1.1K
Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Differentiation of human iPSCs into dopaminergic neurons: comparative analysis of 2D and 3D protocols for disease modeling and pharmacology.

Neuroscience applied·2026
Same author

Recommendations, guidelines, and best practice for the use of human induced pluripotent stem cells for neuropharmacological studies of neuropsychiatric disorders.

Neuroscience applied·2025
Same author

Cortisol-dependent impairment of dendrite plasticity in human dopaminergic neurons derived from hiPSCs is restored by ketamine: Relevance for major depressive disorders.

Neuroscience applied·2025
Same author

Optimizing Satellite Imagery Datasets for Enhanced Land/Water Segmentation.

Sensors (Basel, Switzerland)·2025
Same author

Gut Microbiome-Liver-Brain axis in Alcohol Use Disorder. The role of gut dysbiosis and stress in alcohol-related cognitive impairment progression: possible therapeutic approaches.

Neurobiology of stress·2025
Same author

Use of the SNOWED Dataset for Sentinel-2 Remote Sensing of Water Bodies: The Case of the Po River.

Sensors (Basel, Switzerland)·2024

Related Experiment Video

Updated: Mar 15, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K

A Machine Learning Pipeline for Prognostic Modeling of Alzheimer's Disease Using Multimodal Data.

Luisa De Palma1, Vito Ivano D'Alessandro1, Filippo Attivissimo1

  • 1Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

Predicting Alzheimer's disease (AD) progression is vital. Our study developed a data-driven pipeline using multimodal data, achieving high accuracy with minimal features for early intervention.

Keywords:
Alzheimer’s diseaseXGBoostfeatures selectionmultimodal biomarkersprognostic modeling

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

15.8K

Related Experiment Videos

Last Updated: Mar 15, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

15.8K

Area of Science:

  • Neuroscience
  • Biostatistics
  • Medical Informatics

Background:

  • Accurate prediction of Alzheimer's disease (AD) progression is critical for timely intervention.
  • Existing prognostic models often struggle with heterogeneous multimodal data and missing values.

Purpose of the Study:

  • To develop a robust, data-driven survival analysis pipeline for predicting time-to-progression from cognitively normal (CN) and mild cognitive impairment (MCI) to AD.
  • To integrate diverse biomarkers including cognitive, clinical, neuroimaging (MRI/PET), and biospecimen data.

Main Methods:

  • Utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, a multi-center platform for multimodal data integration.
  • Developed a harmonized preprocessing pipeline to ensure robustness against cross-site and cross-instrument variability.
  • Employed eXtreme Gradient Boosting (XGBoost) for survival analysis, capable of natively handling missing data.

Main Results:

  • Achieved a high concordance index (C-index) of 0.92 with 13 features and 0.90 with only 4 features.
  • Demonstrated strong predictive performance through multi-domain feature integration and transparent feature selection.
  • Highlighted the prognostic value of underexplored biomarkers, such as lipid metabolites.

Conclusions:

  • Multi-domain feature integration is crucial for accurate prognostic modeling in Alzheimer's disease.
  • A minimal set of well-selected features can yield high predictive accuracy.
  • Incorporating novel biomarkers like lipid metabolites enhances the predictive power of Alzheimer's progression models.