Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts.
Tamoghna ChattopadhyayNeha Ann JoshySaket S OzarkarKetaki BuwaYixue FengEmily LaltooSophia I ThomopoulosJulio E VillalonHimanshu JoshiGanesan VenkatasubramanianJohn P JohnPaul M ThompsonPublished in: bioRxiv : the preprint server for biology (2024)
Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort). We first benchmark our models by predicting "brain age" - the task of predicting a person's chronological age from their MRI scan and proceed to AD classification. We also evaluate the benefit of using a 3D CycleGAN approach to harmonize the imaging datasets before training the CNN models. Our experiments show that classification performance improves after harmonization in most cases, as well as better performance for dMRI as input.
Keyphrases
- deep learning
- convolutional neural network
- contrast enhanced
- diffusion weighted
- magnetic resonance imaging
- computed tomography
- machine learning
- artificial intelligence
- magnetic resonance
- diffusion weighted imaging
- resting state
- white matter
- mild cognitive impairment
- functional connectivity
- mass spectrometry
- cognitive impairment
- resistance training
- multiple sclerosis
- dual energy
- rna seq
- body composition
- high intensity
- genome wide association study