Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

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

Neurobiology of aging - 1 Sept 2021

Ning Kaida, Duffy Ben A, Franklin Meredith, Matloff Will, Zhao Lu, Arzouni Nibal, Sun Fengzhu, Toga Arthur W

Abstract excerpt

To study genetic factors associated with brain aging, we first need to quantify brain aging. Statistical models have been created for estimating the apparent age of the brain, or predicted brain age (PBA), using imaging data. Recent studies have refined these models to obtain a more accurate PBA, but research has yet to demonstrate the scientific value of doing so. Here, we show that a more accurate PBA leads to...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Aged
Share to Aged, 80 and over

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Deep Learning

Share to Genome-Wide Association Study

Share to Life Style

Share to Neural Networks, Computer

Source

Open in PubMed Open full text

Identifiers

PMID: 34098431
DOI: 10.1016/j.neurobiolaging.2021.03.014
PMCID: PMC9004720

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging.PMID 34098431Read ↗

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 ↗

Genetic architecture of brain age and its casual relations with brain and mental disordersSemantic proximity Read ↗

Genetic architecture of brain age and its casual relations with brain and mental disordersSemantic proximity Read ↗

Genetic architecture of brain age and its causal relations with brain and mental disordersSemantic proximity Read ↗

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

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

The Vital Role of Central Executive Network in Brain Age: Evidence From Machine Learning and Transcriptional SignaturesSemantic proximity Read ↗

The Genetic Architecture of Multimodal Human Brain AgeSemantic proximity Read ↗

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

From a deep learning model back to the brain - inferring morphological markers and their relation to agingSemantic proximity Read ↗

Brain age prediction using deep learning uncovers associated sequence variants.Semantic proximity Read ↗

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

⌁Select a neighboring publication to make it the new centre.