Article

From a deep learning model back to the brain - inferring morphological markers and their relation to aging

2019-10-15

Abstract excerpt

We present a Deep Learning framework for the prediction of chronological age from structural MRI scans. Previous findings associate an overestimation of brain age with neurodegenerative diseases and higher mortality rates. However, the importance of brain age prediction goes beyond serving as biomarkers for neurological disorders. Specifically, utilizing convolutional neural network (CNN) analysis to identify brai...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 858f6b27-ae0a-5bba-a5b0-077cea2c408e
DOI: 10.1101/803742

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

From a deep learning model back to the brain - inferring morphological markers and their relation to agingDOI 10.1101/803742Read

Brain age prediction of healthy subjects on anatomic MRI with deep learning: going beyond with an "explainable AI" mindsetSemantic proximity Read

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker.Semantic proximity Read

Brain-age prediction: a systematic comparison of machine learning workflowsSemantic proximity Read

Regional brain aging patterns reveal disease-specific pathways of neurodegenerationSemantic proximity Read

Predicting brain-age from multimodal imaging data captures cognitive impairmentSemantic proximity Read

Brain Age Prediction: Deep Models Need a Hand to GeneralizeSemantic proximity Read

Extensive T1-weighted MRI Preprocessing Improves Generalizability of Deep Brain Age Prediction Models <sup>⋆</sup>Semantic proximity Read

Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometrySemantic proximity Read

Deep neural networks learn general and clinically relevant representations of the ageing brainSemantic proximity Read

Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging.Semantic proximity Read

Optimizing Artificial Neural Network Models to Predict Brain-Age from Functional MRISemantic proximity Read

Using deep learning to predict brain age from brain magnetic resonance images and cognitive tests reveals that anatomical and functional brain aging are phenotypically and genetically distinctSemantic proximity Read