Estimating age-related changes in in vivo cerebral magnetic resonance angiography using convolutional neural network

AbstractThe human subcortex comprises hundreds of unique structures. Subcortical functioning is crucial for behavior, and disrupted subcortical function is observed in common neurodegenerative diseases. Despite their importance, human subcortical structures continue to be difficult to study in vivo. Here, we zoom in on 17 prominent subcortical structures, by describing their approximate iron and myelin contents and thickness, and by providing detailed accounts of their age-related changes across the normal adult lifespan. The results provide compelling insights into the highly heterogeneous morphometry and intricate age-related variations of these structures. They also show that the locations of these structures shift across the lifespan, which is of direct relevance for the use of standard magnetic resonance imaging atlases. The results further our understanding of subcortical morphometry and neuroimaging properties, and of normal aging processes which ultimately can improve understanding of neurodegeneration.

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Explainable Convolutional Neural Network to Investigate the Age-Related Changes in Multi-Order Functional Connectivity

10.20944/preprints202110.0375.v1 ◽

2021 ◽

Author(s):

Sunghee Dong ◽

Yan Jin ◽

SuJin Bak ◽

Bumchul Yoon ◽

and Jichai Jeong

Keyword(s):

Neural Network ◽

Functional Connectivity ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

The Elderly ◽

Higher Order ◽

Potential Candidate ◽

Younger Adults ◽

Age Related ◽

Age Related Changes

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by current machine learning techniques because of a lack of its physiological understanding. To investigate the suitability of FC in BCI for the elderly, we propose the decoding of lower- and higher-order FCs using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. Seventeen younger (24.5±2.7 years) and twelve older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increases the classification accuracy by 88.3% compared to the filter-bank common spatial pattern (FBCSP). LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe depending on task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

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Age-Related Changes of the Human Crystalline Lens on High-Spatial Resolution Three-Dimensional T1-Weighted Brain Magnetic Resonance Images In Vivo

Investigative Opthalmology & Visual Science ◽

10.1167/iovs.61.14.7 ◽

2020 ◽

Vol 61 (14) ◽

pp. 7

Author(s):

Felix Streckenbach ◽

Oliver Stachs ◽

Sönke Langner ◽

Rudolf F. Guthoff ◽

Felix G. Meinel ◽

...

Keyword(s):

Magnetic Resonance ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Three Dimensional ◽

Crystalline Lens ◽

Magnetic Resonance Images ◽

Age Related ◽

Age Related Changes

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Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity

Electronics ◽

10.3390/electronics10233020 ◽

2021 ◽

Vol 10 (23) ◽

pp. 3020

Author(s):

Sunghee Dong ◽

Yan Jin ◽

SuJin Bak ◽

Bumchul Yoon ◽

Jichai Jeong

Keyword(s):

Neural Network ◽

Functional Connectivity ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

The Elderly ◽

Higher Order ◽

Potential Candidate ◽

Younger Adults ◽

Age Related ◽

Age Related Changes

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by the current machine learning techniques because of a lack of physiological understanding. To investigate the suitability of FC in BCIs for the elderly, we propose the decoding of lower- and higher-order FC using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. A total of 17 young adults 24.5±2.7 years and 12 older 72.5±3.2 years adults were recruited to perform tasks related to hand-force control with or without mental calculation. The CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increased the classification accuracy by 88.3% compared to the filter-bank common spatial pattern. The LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe, depending on the task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

Download Full-text