Biology subjects

Popuri, K.

Publications and source records attributed to Popuri, K..

2 recordsLinked to original sources

bioRxiv2022

Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration

Neuroimaging-based brain-age estimation via machine learning has emerged as an important new approach for studying brain aging. The difference between ones estimated brain age and chronological age, the brain age gap (BAG), has been proposed as an Alzheimers Disease (AD) biomarker. However, most past studies on the BAG have been cross-sectional. Identifying how an individuals BAG temporal pattern changes over time would enable improved prediction of clinical outcome based on neurophysiological changes and better understanding of AD progression. To fill this gap, our study conducted predictive modeling using large neuroimaging data with up to 8 years of follow-up to examine the temporal patterns of the BAGs trajectory and how it varies by subject-level characteristics and disease status. To the best of our knowledge, this is the first effort to take a longitudinal approach to investigate the pattern and rate of change in BAG over time in individuals who progress from mild cognitive impairment (MCI) to clinical AD. Combining multimodal imaging data in a support vector regression model to estimate brain age yielded improved performance than single modality. Multilevel modeling results showed the BAG followed a linear increasing trajectory with a significantly faster rate in individuals with MCI who progressed to AD compared to cognitively normal or MCI individuals who did not progress. The dynamic changes in the BAG during AD progression were further moderated by gender and APO{varepsilon}4 carriership. Findings demonstrate the BAG as a potential biomarker for understanding individual specific temporal patterns related to AD progression.

neuroscience↗

bioRxiv2019

Transfer Learning for Predicting Conversion from Mild Cognitive Impairment to Dementia of Alzheimer Type based on 3D-Convolutional Neural Network

Dementia of Alzheimers Type (DAT) is associated with a devastating and irreversible cognitive decline. As a pharmacological intervention has not yet been developed to reverse disease progression, preventive medicine will play a crucial role for patient care and treatment planning. However, predicting which patients will progress to DAT is difficult as patients with Mild Cognitive Impairment (MCI) could either convert to DAT (MCI-C) or not (MCI-NC). In this paper, we develop a deep learning model to address the heterogeneous nature of DAT development. Structural magnetic resonance imaging was utilized as a single biomarker, and a three-dimensional convolutional neural network (3D-CNN) was developed. The 3D-CNN was trained using transfer learning from the classification of Normal Control and DAT scans at the source task. This was applied to the target task of classifying MCI-C and MCI-NC scans. The model results in 82.4% classification accuracy, which outperforms current models in the field. Furthermore, by implementing an occlusion map approach, we visualize key brain regions that significantly contribute to the prediction of MCI-C and MCI-NC. Results show the hippocampus, amygdala, cerebellum, and pons regions as significant to prediction, which are consistent with current understanding of disease. Finally, the models prediction value is significantly correlated with rates of change in clinical assessment scores, indicating the model is able to predict an individual patients future cognitive decline. This information, in conjunction with the identified anatomical features, will aid in building a personalized therapeutic strategy for individuals with MCI. This model could also be useful for selection of participants for clinical trials.

bioinformatics↗