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Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

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Published on: January 26, 2024

Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019.

Pedro F Da Costa^1,2, Jessica Dafflon¹, Walter H L Pinaya³

¹Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.

Frontiers in Psychiatry

|December 21, 2020

Summary

This summary is machine-generated.

This study developed a machine learning model to predict brain age, achieving high accuracy. The approach highlights the effectiveness of shallow methods in analyzing neuroimaging data for brain health assessment.

Keywords:

brain-age genetic algorithm linear models shallow machine learning support vector machine

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

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

Published on: January 11, 2020

Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Brain structure and cognitive function change with age.
Individual differences in brain aging rates are linked to neurological and psychiatric diseases.
Neuroimaging biomarkers are crucial for understanding brain health variability.

Purpose of the Study:

To develop accurate brain-age prediction models.
To identify patterns in neuroimaging data associated with brain aging.
To improve the development of neuroimaging biomarkers for brain condition.

Main Methods:

Utilized an ensemble of shallow machine learning methods, including Support Vector Regression and Decision Tree-based regressors.
Combined voxel-based and surface-based morphometric data, including normalized brain volume maps (gray matter, white matter) and cortical features (thickness, volume, mean curvature).
Employed genetic algorithms and grid search for hyperparameter tuning.

Main Results:

Achieved a mean absolute error of 3.7597 years in the Predictive Analytics Competition (PAC) 2019.
Secured a top 10 position in the competition.
Demonstrated the potential of shallow machine learning methods in brain-age prediction.

Conclusions:

Shallow machine learning methods offer a viable approach for accurate brain-age prediction.
Ensemble models combining diverse morphometric data can enhance prediction accuracy.
This work contributes to the development of advanced neuroimaging biomarkers for brain health.