Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI
Estimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and different neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2022.940922/full |
| _version_ | 1798002933226274816 |
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| author | Kushal Borkar Anusha Chaturvedi P. K. Vinod Raju Surampudi Bapi |
| author_facet | Kushal Borkar Anusha Chaturvedi P. K. Vinod Raju Surampudi Bapi |
| author_sort | Kushal Borkar |
| collection | DOAJ |
| description | Estimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and different neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain functions are also affected by “normal” brain aging. More information is needed on how functional connectivity relates to aging, particularly in the absence of neurodegenerative disorders. Resting-state fMRI enables us to investigate functional brain networks and can potentially help us understand the processes of development as well as aging in terms of how functional connectivity (FC) matures during the early years and declines during the late years. We propose models for estimation of the chronological age of a healthy person from the resting state brain activation (rsfMRI). In this work, we utilized a dataset (N = 638, age-range 20–88) comprising rsfMRI images from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository of a healthy population. We propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection, and an attention-based model for deep learning architecture for brain age assessment. We extracted features from the static functional connectivity (sFC) to predict the subject's age and classified them into different age groups (young, middle, middle, and old ages). To the best of our knowledge, a classification accuracy of 72.619 % and a mean absolute error of 6.797, and an r2 of 0.754 reported by our Ayu pipeline establish competitive benchmark results as compared to the state-of-the-art-approach. Furthermore, it is vital to identify how different functional regions of the brain are correlated. We also analyzed how functional regions contribute differently across ages by applying attention-based networks and integrated gradients. We obtained well-known resting-state networks using the attention model, which maps to within the default mode network, visual network, ventral attention network, limbic network, frontoparietal network, and somatosensory network connected to aging. Our analysis of fMRI data in healthy elderly Age groups revealed that dynamic FC tends to slow down and becomes less complex and more random with increasing age. |
| first_indexed | 2024-04-11T12:00:12Z |
| format | Article |
| id | doaj.art-daf69ab2aa2448b6bc7caf723b277946 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-11T12:00:12Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-daf69ab2aa2448b6bc7caf723b2779462022-12-22T04:24:53ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882022-09-011610.3389/fncom.2022.940922940922Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRIKushal BorkarAnusha ChaturvediP. K. VinodRaju Surampudi BapiEstimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and different neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain functions are also affected by “normal” brain aging. More information is needed on how functional connectivity relates to aging, particularly in the absence of neurodegenerative disorders. Resting-state fMRI enables us to investigate functional brain networks and can potentially help us understand the processes of development as well as aging in terms of how functional connectivity (FC) matures during the early years and declines during the late years. We propose models for estimation of the chronological age of a healthy person from the resting state brain activation (rsfMRI). In this work, we utilized a dataset (N = 638, age-range 20–88) comprising rsfMRI images from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository of a healthy population. We propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection, and an attention-based model for deep learning architecture for brain age assessment. We extracted features from the static functional connectivity (sFC) to predict the subject's age and classified them into different age groups (young, middle, middle, and old ages). To the best of our knowledge, a classification accuracy of 72.619 % and a mean absolute error of 6.797, and an r2 of 0.754 reported by our Ayu pipeline establish competitive benchmark results as compared to the state-of-the-art-approach. Furthermore, it is vital to identify how different functional regions of the brain are correlated. We also analyzed how functional regions contribute differently across ages by applying attention-based networks and integrated gradients. We obtained well-known resting-state networks using the attention model, which maps to within the default mode network, visual network, ventral attention network, limbic network, frontoparietal network, and somatosensory network connected to aging. Our analysis of fMRI data in healthy elderly Age groups revealed that dynamic FC tends to slow down and becomes less complex and more random with increasing age.https://www.frontiersin.org/articles/10.3389/fncom.2022.940922/fullrs-fMRIattentionstatic functional connectivity matrixage estimationinterpretabilityclassification |
| spellingShingle | Kushal Borkar Anusha Chaturvedi P. K. Vinod Raju Surampudi Bapi Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI Frontiers in Computational Neuroscience rs-fMRI attention static functional connectivity matrix age estimation interpretability classification |
| title | Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI |
| title_full | Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI |
| title_fullStr | Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI |
| title_full_unstemmed | Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI |
| title_short | Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI |
| title_sort | ayu characterization of healthy aging from neuroimaging data with deep learning and rsfmri |
| topic | rs-fMRI attention static functional connectivity matrix age estimation interpretability classification |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2022.940922/full |
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