Transfer learning-trained convolutional neural networks identify novel MRI biomarkers of Alzheimer\u27s disease progression.

Introduction: Genome-wide association studies (GWAS) for late onset Alzheimer\u27s disease (AD) may miss genetic variants relevant for delineating disease stages when using clinically defined case/control as a phenotype due to its loose definition and heterogeneity. Methods: We use a transfer learning technique to train three-dimensional convolutional neural network (CNN) models based on structural magnetic resonance imaging (MRI) from the screening stage in the Alzheimer\u27s Disease Neuroimaging Initiative consortium to derive image features that reflect AD progression. Results: CNN-derived image phenotypes are significantly associated with fasting metabolites related to early lipid metabolic changes as well as insulin resistance and with genetic variants mapped to candidate genes enriched for amyloid beta degradation, tau phosphorylation, calcium ion binding-dependent synaptic loss, Discussion: This is the first attempt to show that non-invasive MRI biomarkers are linked to AD progression characteristics, reinforcing their use in early AD diagnosis and monitoring

Voxel-based statistical analysis and quantification of amyloid PET in the Japanese Alzheimer\u27s disease neuroimaging initiative (J-ADNI) multi-center study

Background: Amyloid PET plays a vital role in detecting the accumulation of in vivo amyloid-β (Aβ). The quantification of Aβ accumulation has been widely performed using the region of interest (ROI)-based mean cortical standardized uptake value ratio (mcSUVR). However, voxel-based statistical analysis has not been well studied. The purpose of this study was to examine the feasibility of analyzing amyloid PET scans by voxel-based statistical analysis. The results were then compared to those with the ROI-based mcSUVR. In total, 166 subjects who underwent 11C-PiB PET in the J-ADNI multi-center study were analyzed. Additionally, 18 Aβ-negative images were collected from other studies to form a normal database. The PET images were spatially normalized to the standard space using an adaptive template method without MRI. The mcSUVR was measured using a pre-defined ROI. Voxel- wise Z-scores within the ROI were calculated using the normal database, after which Z-score maps were generated. A receiver operating characteristic (ROC) analysis was performed to evaluate whether Z-sum (sum of the Z-score) and mcSUVR could be used to classify the scans into positive and negative using the central visual read as the reference standard. PET scans that were equivocal were regarded as positive. Results: Sensitivity and specificity were respectively 90.8% and 100% by Z-sum and 91.8% and 98.5% by mcSUVR. Most of the equivocal scans were subsequently classified by both Z-sum and mcSUVR as false negatives. Z-score maps correctly delineated abnormal Aβ accumulation over the same regions as the visual read. Conclusions: We examined the usefulness of voxel-based statistical analysis for amyloid PET. This method provides objective Z-score maps and Z-sum values, which were observed to be helpful as an adjunct to visual interpretation especially for cases with mild or limited Aβ accumulation. This approach could improve the Aβ detection sensitivity, reduce inter-reader variability, and allow for detailed monitoring of Aβ deposition