Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits.

Area of Science:

• Neuroscience
• Quantum Computing
• Machine Learning

Background:

• Brain morphology changes with age, making brain age estimation crucial for identifying atypical patterns.
• Predicting gender from neuroimaging data provides insights into neurological differences between sexes.
• Structural Magnetic Resonance Imaging (sMRI) is a key tool for analyzing brain morphology.

Purpose of the Study:

• To compare the performance of classical machine learning models against a variational quantum circuit for brain age estimation and gender prediction.
• To evaluate these models using both combined and distinct neuroimaging datasets.
• To assess the potential of quantum machine learning in neuroscience applications.

Main Methods:

• Utilized structural Magnetic Resonance Imaging (sMRI) data from 1157 participants (ages 14-89).
• Compared six classical machine learning models with a variational quantum circuit (VQC).
• Evaluated model performance on combined and benchmark sub-datasets using training and testing splits.

Main Results:

• The variational quantum circuit model generally outperformed classical models in brain age estimation and gender classification on the combined dataset.
• The VQC approach showed superior performance on benchmark sub-datasets compared to previous studies using the same data.
• Results indicate comparable effectiveness between VQC and classical algorithms, with potential for reduced error.

Conclusions:

• Variational quantum algorithms show significant potential for brain age and gender prediction tasks.
• Quantum machine learning offers a promising avenue for enhancing neuroimaging analysis and understanding brain health.
• This study highlights the efficacy of VQC in analyzing complex neuroimaging data for clinical and research applications.