Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized Cerebral Blood Volume Measures
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911308766511104 |
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| author | Jomsky, Jordan Igwe, Kay C. Li, Zongyu Zhang, Yiren Lashley, Max Nuriel, Tal Laine, Andrew Guo, Jia |
| author_facet | Jomsky, Jordan Igwe, Kay C. Li, Zongyu Zhang, Yiren Lashley, Max Nuriel, Tal Laine, Andrew Guo, Jia |
| contents | Brain age gap estimation (BrainAGE) is a promising imaging-derived biomarker of neurobiological aging and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI (T1w), overlooking functional vascular changes that may precede tissue damage and cognitive decline. Artificial intelligence-generated cerebral blood volume (AICBV) maps, synthesized from non-contrast MRI, offer an alternative to contrast-enhanced perfusion imaging by capturing vascular information relevant to early neurodegeneration. We developed a multimodal BrainAGE framework that integrates brain age predictions using linear regression from two separate 3D VGG-based networks, one model trained on only structural T1w scans and one trained on only AICBV maps generated from a pre-trained 3D patch-based deep learning model. Each model was trained and validated on 2,851 scans from 13 open-source datasets and was evaluated for concordance with mild cognitive impairment (MCI) and Alzheimer's disease (AD) using ADNI subjects (n=1,233). The combined model achieved the most accurate brain age gap for cognitively normal (CN) controls, with a mean absolute error (MAE) of 3.95 years ($R^2$=0.943), outperforming models trained on T1w (MAE=4.10) or AICBV alone (MAE=4.49). Saliency maps revealed complementary modality contributions: T1w emphasized white matter and cortical atrophy, while AICBV highlighted vascular-rich and periventricular regions implicated in hypoperfusion and early cerebrovascular dysfunction, consistent with normal aging. Next, we observed that BrainAGE increased stepwise across diagnostic strata (CN < MCI < AD) and correlated with cognitive impairment (CDRSB r=0.403; MMSE r=-0.310). AICBV-based BrainAGE showed particularly strong separation between stable vs. progressive MCI (p=$1.47 \times 10^{-8}$), suggesting sensitivity to prodromal vascular changes that precede overt atrophy. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_01865 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized Cerebral Blood Volume Measures Jomsky, Jordan Igwe, Kay C. Li, Zongyu Zhang, Yiren Lashley, Max Nuriel, Tal Laine, Andrew Guo, Jia Image and Video Processing Machine Learning Brain age gap estimation (BrainAGE) is a promising imaging-derived biomarker of neurobiological aging and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI (T1w), overlooking functional vascular changes that may precede tissue damage and cognitive decline. Artificial intelligence-generated cerebral blood volume (AICBV) maps, synthesized from non-contrast MRI, offer an alternative to contrast-enhanced perfusion imaging by capturing vascular information relevant to early neurodegeneration. We developed a multimodal BrainAGE framework that integrates brain age predictions using linear regression from two separate 3D VGG-based networks, one model trained on only structural T1w scans and one trained on only AICBV maps generated from a pre-trained 3D patch-based deep learning model. Each model was trained and validated on 2,851 scans from 13 open-source datasets and was evaluated for concordance with mild cognitive impairment (MCI) and Alzheimer's disease (AD) using ADNI subjects (n=1,233). The combined model achieved the most accurate brain age gap for cognitively normal (CN) controls, with a mean absolute error (MAE) of 3.95 years ($R^2$=0.943), outperforming models trained on T1w (MAE=4.10) or AICBV alone (MAE=4.49). Saliency maps revealed complementary modality contributions: T1w emphasized white matter and cortical atrophy, while AICBV highlighted vascular-rich and periventricular regions implicated in hypoperfusion and early cerebrovascular dysfunction, consistent with normal aging. Next, we observed that BrainAGE increased stepwise across diagnostic strata (CN < MCI < AD) and correlated with cognitive impairment (CDRSB r=0.403; MMSE r=-0.310). AICBV-based BrainAGE showed particularly strong separation between stable vs. progressive MCI (p=$1.47 \times 10^{-8}$), suggesting sensitivity to prodromal vascular changes that precede overt atrophy. |
| title | Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized Cerebral Blood Volume Measures |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2412.01865 |