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  "access_rights": "",
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  "author": "Alin Banka",
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  "author": "Alin Banka",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [],
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  "citation": [],
            
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "defined_in": "https://doi.org/10.48550/arXiv.2009.11553",
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  "defined_in": "https://doi.org/10.48550/arXiv.2009.11553",
            
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  "doi": "10.57702/09l1pa8w",
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  "doi": "10.57702/09l1pa8w",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-02",
            
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  "doi_publisher": "TIB",
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  "doi_publisher": "TIB",
            
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  "domain": "https://service.tib.eu/ldmservice",
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  "domain": "https://service.tib.eu/ldmservice",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Inis Buzi",
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      "extra_author": "Inis Buzi",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Islem Rekik",
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      "extra_author": "Islem Rekik",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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    }
            
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  ],
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  ],
            
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  "groups": [
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  "groups": [
            
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Brain HyperConnectome",
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      "display_name": "Brain HyperConnectome",
            
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      "image_display_url": "",
            
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      "name": "brain-hyperconnectome",
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      "name": "brain-hyperconnectome",
            
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      "title": "Brain HyperConnectome"
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      "title": "Brain HyperConnectome"
            
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    },
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    },
            
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Multi-View Brain Networks",
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      "display_name": "Multi-View Brain Networks",
            
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      "image_display_url": "",
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      "image_display_url": "",
            
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      "name": "multi-view-brain-networks",
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      "name": "multi-view-brain-networks",
            
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      "title": "Multi-View Brain Networks"
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      "title": "Multi-View Brain Networks"
            
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    }
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    }
            
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  ],
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  "isopen": false,
            
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  "landing_page": "http://github.com/basiralab/HCAE",
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  "landing_page": "http://github.com/basiralab/HCAE",
            
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  "license_title": null,
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  "license_title": null,
            
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  "link_orkg": "",
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  "link_orkg": "",
            
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  "metadata_created": "2024-12-02T21:33:10.538466",
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  "metadata_modified": "2024-12-02T21:33:10.538471",
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  "metadata_modified": "2024-12-02T21:33:11.147659",
            
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  "name": "multi-view-brain-hyperconnectome",
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  "name": "multi-view-brain-hyperconnectome",
            
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  "notes": "Graph embedding is a powerful method to represent graph 
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  "notes": "Graph embedding is a powerful method to represent graph 
            
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neurological data (e.g., brain connectomes) in a low dimensional space 
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neurological data (e.g., brain connectomes) in a low dimensional space 
            
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for brain connectivity mapping, prediction and classi\ufb01cation. 
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for brain connectivity mapping, prediction and classi\ufb01cation. 
            
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However, existing embedding algorithms have two major limitations. 
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However, existing embedding algorithms have two major limitations. 
            
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First, they primarily focus on preserving one-to-one topological 
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First, they primarily focus on preserving one-to-one topological 
            
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relationships between nodes (i.e., regions of interest (ROIs) in a 
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relationships between nodes (i.e., regions of interest (ROIs) in a 
            
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connectome), but they have mostly ignored many-to-many relationships 
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connectome), but they have mostly ignored many-to-many relationships 
            
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(i.e., set to set), which can be captured using a hyperconnectome 
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(i.e., set to set), which can be captured using a hyperconnectome 
            
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structure. Second, existing graph embedding techniques cannot be 
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structure. Second, existing graph embedding techniques cannot be 
            
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easily adapted to multi-view graph data with heterogeneous 
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easily adapted to multi-view graph data with heterogeneous 
            
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distributions. In this paper, while cross-pollinating adversarial deep 
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distributions. In this paper, while cross-pollinating adversarial deep 
            
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learning with hypergraph theory, we aim to jointly learn deep latent 
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learning with hypergraph theory, we aim to jointly learn deep latent 
            
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embeddings of subject-speci\ufb01c multi-view brain graphs to 
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embeddings of subject-speci\ufb01c multi-view brain graphs to 
            
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eventually disentangle di\ufb00erent brain states such as 
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eventually disentangle di\ufb00erent brain states such as 
            
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Alzheimer\u2019s disease (AD) versus mild cognitive impairment (MCI). 
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Alzheimer\u2019s disease (AD) versus mild cognitive impairment (MCI). 
            
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First, we propose a new simple strategy to build a hyperconnectome for 
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First, we propose a new simple strategy to build a hyperconnectome for 
            
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each brain view based on nearest neighbour algorithm to preserve the 
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each brain view based on nearest neighbour algorithm to preserve the 
            
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con-nectivities across pairs of ROIs. Second, we design a 
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con-nectivities across pairs of ROIs. Second, we design a 
            
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hyperconnectome autoencoder (HCAE) framework which operates directly 
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hyperconnectome autoencoder (HCAE) framework which operates directly 
            
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on the multi-view hyperconnectomes based on hypergraph convolutional 
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on the multi-view hyperconnectomes based on hypergraph convolutional 
            
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layers to better capture the many-to-many relationships between brain 
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layers to better capture the many-to-many relationships between brain 
            
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regions (i.e., graph nodes). For each subject, we further regularize 
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regions (i.e., graph nodes). For each subject, we further regularize 
            
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the hyper-graph autoencoding by adversarial regularization to align 
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the hyper-graph autoencoding by adversarial regularization to align 
            
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the distribution of the learned hyperconnectome embeddings with the 
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the distribution of the learned hyperconnectome embeddings with the 
            
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original hyperconnectome distribution. We formalize our 
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original hyperconnectome distribution. We formalize our 
            
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hyperconnectome embedding within a geometric deep learning framework 
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hyperconnectome embedding within a geometric deep learning framework 
            
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to optimize for a given subject, thereby designing an individual-based 
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to optimize for a given subject, thereby designing an individual-based 
            
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learning framework.",
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learning framework.",
            
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      "name": "brain hyperconnectome",
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      "name": "brain hyperconnectome",
            
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    {
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      "id": "2838e443-12b3-4f93-8bc3-2d486c8ee5ee",
            
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      "name": "brain state classi\ufb01cation",
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      "name": "brain state classi\ufb01cation",
            
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    },
            
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    {
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      "display_name": "geometric hyperconnectome autoencoder",
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      "display_name": "geometric hyperconnectome autoencoder",
            
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      "id": "2c96681a-4bab-4bfd-b5f9-a123a6eb9620",
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      "id": "2c96681a-4bab-4bfd-b5f9-a123a6eb9620",
            
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      "name": "geometric hyperconnectome autoencoder",
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      "name": "geometric hyperconnectome autoencoder",
            
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      "state": "active",
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      "state": "active",
            
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  ],
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  ],
            
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  "title": "Multi-View Brain HyperConnectome",
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  "title": "Multi-View Brain HyperConnectome",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}