Search

Background: Alzheimer’s disease (AD) is a leading cause of dementia, and it is significantly influenced by the apolipoprotein E4 (APOE4) gene and gender. This study aimed to use machine learning (ML) algorithms to predict brain age and assess AD risk by considering the effects of the APOE4 genotype and gender. Methods: We collected brain volumetric MRI data and medical records from 1100 cognitively unimpaired individuals and 602 patients with AD. We applied three ML regression models—XGBoost, random forest (RF), and linear regression (LR)—to predict brain age. Additionally, we introduced two novel metrics, brain age difference (BAD) and integrated difference (ID), to evaluate the models’ performances and analyze the influences of the APOE4 genotype and gender on brain aging. Results: Patients with AD displayed significantly older brain ages compared to their chronological ages, with BADs ranging from 6.5 to 10 years. The RF model outperformed both XGBoost and LR in terms of accuracy, delivering higher ID values and more precise predictions. Comparing the APOE4 carriers with noncarriers, the models showed enhanced ID values and consistent brain age predictions, improving the overall performance. Gender-specific analyses indicated slight enhancements, with the models performing equally well for both genders. Conclusions: This study demonstrates that robust ML models for brain age prediction can play a crucial role in the early detection of AD risk through MRI brain structural imaging. The significant impact of the APOE4 genotype on brain aging and AD risk is also emphasized. These findings highlight the potential of ML models in assessing AD risk and suggest that utilizing AI for AD identification could enable earlier preventative interventions.

This article presents development of CMOS system-on-chip spectrometer processor for Spaceborne Earth Science, Planetary Science and Astronomy. The developed chip is intended for use in rotational emission spectroscopy observations from microwave to sub-millimeter wavelengths performed from Earth orbiting or deep space exploration spacecraft. The article first provides an overview of rotational spectroscopy for space sciences and highlights some of the important results achieved in Earth science, planetary science, and astronomy. The article then discusses the need for smaller and more compact spectrometer instruments across these three space science areas, which motivates this work. Then key considerations in development of spectrometer processors for space science are discussed including the significance including bandwidth, quantization efficiency and spectral channel shapes. The article then highlights detailed design of the developed spectrometer processor focusing on the many calibration techniques included to allow the chip to adapt to operations in space environments where large temperature ranges and high radiation levels are encountered. Presented measurements demonstrate the spectrometer processor operates up to 12.8 GS/s to provide up to 6.4 GHz of processing bandwidth with a frequency resolution of 8192 spectral channels and an analog resolution of 4 bits while consuming only 3960 mW of total DC power.