Brain age estimation using multi-feature-based networks
X Liu, I Beheshti, W Zheng, Y Li, S Li, Z Zhao… - Computers in Biology …, 2022 - Elsevier
Studying brain aging improves our understanding in differentiating typical and atypical
aging. Directly utilizing traditional morphological features for brain age estimation did not …
aging. Directly utilizing traditional morphological features for brain age estimation did not …
[HTML][HTML] Predicting brain age of healthy adults based on structural MRI parcellation using convolutional neural networks
H Jiang, N Lu, K Chen, L Yao, K Li, J Zhang… - Frontiers in …, 2020 - frontiersin.org
Structural magnetic resonance imaging (MRI) studies have demonstrated that the brain
undergoes age-related neuroanatomical changes not only regionally but also on the …
undergoes age-related neuroanatomical changes not only regionally but also on the …
Brain age prediction across the human lifespan using multimodal MRI data
Measuring differences between an individual's age and biological age with biological
information from the brain have the potential to provide biomarkers of clinically relevant …
information from the brain have the potential to provide biomarkers of clinically relevant …
[HTML][HTML] Brain age prediction: A comparison between machine learning models using brain morphometric data
Brain structural morphology varies over the aging trajectory, and the prediction of a person's
age using brain morphological features can help the detection of an abnormal aging …
age using brain morphological features can help the detection of an abnormal aging …
[HTML][HTML] Prediction of chronological age in healthy elderly subjects with machine learning from MRI brain segmentation and cortical parcellation
J Gómez-Ramírez, MA Fernández-Blázquez… - Brain Sciences, 2022 - mdpi.com
Normal aging is associated with changes in volumetric indices of brain atrophy. A
quantitative understanding of age-related brain changes can shed light on successful aging …
quantitative understanding of age-related brain changes can shed light on successful aging …
Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging
Numerous studies have established that estimated brain age constitutes a valuable
biomarker that is predictive of cognitive decline and various neurological diseases. In this …
biomarker that is predictive of cognitive decline and various neurological diseases. In this …
Decentralized brain age estimation using mri data
Recent studies have demonstrated that neuroimaging data can be used to estimate
biological brain age, as it captures information about the neuroanatomical and functional …
biological brain age, as it captures information about the neuroanatomical and functional …
Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan
Brain age estimated by machine learning from T1-weighted magnetic resonance images
(T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early …
(T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early …
Utilizing transfer learning of pre-trained AlexNet and relevance vector machine for regression for predicting healthy older adult's brain age from structural MRI
L Lin, G Zhang, J Wang, M Tian, S Wu - Multimedia Tools and Applications, 2021 - Springer
Discrepancies between the estimated brain age from brain structural MRI and the
chronological age have been associated with a broad spectrum of neurocognitive disorders …
chronological age have been associated with a broad spectrum of neurocognitive disorders …
Brain structure ages—A new biomarker for multi‐disease classification
Age is an important variable to describe the expected brain's anatomy status across the
normal aging trajectory. The deviation from that normative aging trajectory may provide …
normal aging trajectory. The deviation from that normative aging trajectory may provide …