Fully Hyperbolic Graph Convolutional Neural Networks for Age Prediction with Multi-Modal Brain Data
Characterizing age-related alterations in MEG brain networks holds great promise in understanding aging trajectories and revealing aberrant patterns of neurodegenerative disorders, such as Alzheimer’s disease. In this study, we utilize a Fully Hyperbolic Neural Network (FHNN) to embed functional bra...
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| 其他作者: | |
| 格式: | Thesis |
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Massachusetts Institute of Technology
2024
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| 在线阅读: | https://hdl.handle.net/1721.1/153908 |
| _version_ | 1826211320230313984 |
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| author | Ramirez, Hugo |
| author2 | Katz, Boris |
| author_facet | Katz, Boris Ramirez, Hugo |
| author_sort | Ramirez, Hugo |
| collection | MIT |
| description | Characterizing age-related alterations in MEG brain networks holds great promise in understanding aging trajectories and revealing aberrant patterns of neurodegenerative disorders, such as Alzheimer’s disease. In this study, we utilize a Fully Hyperbolic Neural Network (FHNN) to embed functional brain connectivity graphs, derived from magnetoencephalography (MEG) data, into low dimensions on a Lorentz Hyperboloid model for hyperbolic space. Using these embeddings, we aim to detect changes in the intrinsic hierarchy of functional subnetworks across time as well as predict age for patients across multiple decades. We use the hyperbolic embedding pipeline in tandem with multimodal MEG and MRI data to create embeddings from the Cam-CAN (Cambridge Centre for Ageing and Neuroscience) dataset for the downstream task of brain age prediction in healthy patients to better understand how brain connectivity structure impacts brain aging trends. Our hyperbolic MEG brain network embedding framework effectively transforms high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations, facilitating structural brain hierarchy analysis across age, as well as age prediction. Our versatile embedding pipeline allows for the ready implementation of other downstream tasks like clustering and classification. This constitutes a novel way of studying connectivity alterations in brain networks. |
| first_indexed | 2024-09-23T15:04:03Z |
| format | Thesis |
| id | mit-1721.1/153908 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:04:03Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1539082024-03-22T03:15:14Z Fully Hyperbolic Graph Convolutional Neural Networks for Age Prediction with Multi-Modal Brain Data Ramirez, Hugo Katz, Boris Pantazis, Dimitrios Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Characterizing age-related alterations in MEG brain networks holds great promise in understanding aging trajectories and revealing aberrant patterns of neurodegenerative disorders, such as Alzheimer’s disease. In this study, we utilize a Fully Hyperbolic Neural Network (FHNN) to embed functional brain connectivity graphs, derived from magnetoencephalography (MEG) data, into low dimensions on a Lorentz Hyperboloid model for hyperbolic space. Using these embeddings, we aim to detect changes in the intrinsic hierarchy of functional subnetworks across time as well as predict age for patients across multiple decades. We use the hyperbolic embedding pipeline in tandem with multimodal MEG and MRI data to create embeddings from the Cam-CAN (Cambridge Centre for Ageing and Neuroscience) dataset for the downstream task of brain age prediction in healthy patients to better understand how brain connectivity structure impacts brain aging trends. Our hyperbolic MEG brain network embedding framework effectively transforms high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations, facilitating structural brain hierarchy analysis across age, as well as age prediction. Our versatile embedding pipeline allows for the ready implementation of other downstream tasks like clustering and classification. This constitutes a novel way of studying connectivity alterations in brain networks. M.Eng. 2024-03-21T19:15:16Z 2024-03-21T19:15:16Z 2024-02 2024-03-04T16:38:18.503Z Thesis https://hdl.handle.net/1721.1/153908 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Ramirez, Hugo Fully Hyperbolic Graph Convolutional Neural Networks for Age Prediction with Multi-Modal Brain Data |
| title | Fully Hyperbolic Graph Convolutional Neural Networks for Age Prediction with Multi-Modal Brain Data |
| title_full | Fully Hyperbolic Graph Convolutional Neural Networks for Age Prediction with Multi-Modal Brain Data |
| title_fullStr | Fully Hyperbolic Graph Convolutional Neural Networks for Age Prediction with Multi-Modal Brain Data |
| title_full_unstemmed | Fully Hyperbolic Graph Convolutional Neural Networks for Age Prediction with Multi-Modal Brain Data |
| title_short | Fully Hyperbolic Graph Convolutional Neural Networks for Age Prediction with Multi-Modal Brain Data |
| title_sort | fully hyperbolic graph convolutional neural networks for age prediction with multi modal brain data |
| url | https://hdl.handle.net/1721.1/153908 |
| work_keys_str_mv | AT ramirezhugo fullyhyperbolicgraphconvolutionalneuralnetworksforagepredictionwithmultimodalbraindata |