English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep neural networks currently provide the most advanced and accurate machinelearning models to distinguish between structural MRI scans of subjects withAlzheimer's disease and healthy controls. Unfortunately, the subtle brainalterations captured by these models are difficult to interpret because of thecomplexity of these multi-layer and non-linear models. Several heatmap methodshave been proposed to address this issue and analyze the imaging patternsextracted from the deep neural networks, but no quantitative comparison betweenthese methods has been carried out so far. In this work, we explore thesequestions by deriving heatmaps from Convolutional Neural Networks (CNN) trainedusing T1 MRI scans of the ADNI data set, and by comparing these heatmaps withbrain maps corresponding to Support Vector Machines (SVM) coefficients. Threeprominent heatmap methods are studied: Layer-wise Relevance Propagation (LRP),Integrated Gradients (IG), and Guided Grad-CAM (GGC). Contrary to prior studieswhere the quality of heatmaps was visually or qualitatively assessed, weobtained precise quantitative measures by computing overlap with a ground-truthmap from a large meta-analysis that combined 77 voxel-based morphometry (VBM)studies independently from ADNI. Our results indicate that all three heatmapmethods were able to capture brain regions covering the meta-analysis map andachieved better results than SVM coefficients. Among them, IG produced theheatmaps with the best overlap with the independent meta-analysis.

Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies