Academic Journal
NeuroLens: A Holistic Visual Analytics System for Exploring Brain Networks Across Scales.
| Title: | NeuroLens: A Holistic Visual Analytics System for Exploring Brain Networks Across Scales. |
|---|---|
| Authors: | Zhang W, Tao J |
| Source: | IEEE transactions on visualization and computer graphics [IEEE Trans Vis Comput Graph] 2026 Sep; Vol. 32 (9), pp. 7714-7728. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: IEEE Computer Society Country of Publication: United States NLM ID: 9891704 Publication Model: Print Cited Medium: Internet ISSN: 1941-0506 (Electronic) Linking ISSN: 10772626 NLM ISO Abbreviation: IEEE Trans Vis Comput Graph Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York, NY : IEEE Computer Society, c1995- |
| MeSH Terms: | Brain*/diagnostic imaging , Brain*/physiology , Nerve Net*/diagnostic imaging , Nerve Net*/physiology , Image Processing, Computer-Assisted*/methods , Computer Graphics*, Diffusion Tensor Imaging/methods ; Brain Mapping/methods ; Humans ; Graph Neural Networks ; Algorithms |
| Abstract: | Identifying biomarkers from human brain networks is critical in early detection of neurological disorders and understanding disease mechanisms. Existing visual analytics approaches show a remarkable ability to assist experts in discovering and validating biomarkers through exploration. However, these approaches often focus only on the diffusion features of fiber bundles and evaluate individual bundles and regions separately. This may hinder their ability to accurately represent the complex brain network for investigation from various perspectives. In this paper, we present NeuroLens, a visual analytics system that integrates comprehensive information across multiple levels, including fiber bundles, local regions and entire brain networks. Specifically, to model the bundles more precisely, we enhance the features by incorporating the joint distribution of geometric features. The bundle information is further aggregated to form representations at the region and the brain level using an attention-based graph neural network. The region-level representation describes complex structures involving multiple bundles, and the brain-level representation enables comparisons between subjects and groups. The NeuroLens interface enables comparative exploration of this multi-level and multi-faceted information. To verify the findings during exploration, NeuroLens leverages the large language model to query related information from existing literature. We collaborate with domain experts to examine the effectiveness of NeuroLens. Their exploration, findings, and feedback are discussed. |
| Entry Date(s): | Date Created: 20260608 Date Completed: 20260731 Latest Revision: 20260929 |
| Update Code: | 20260930 |
| DOI: | 10.1109/TVCG.2026.3701404 |
| PMID: | 42258687 |
| Database: | MEDLINE |
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| Items | – Name: Title Label: Title Group: Ti Data: NeuroLens: A Holistic Visual Analytics System for Exploring Brain Networks Across Scales. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Zhang+W%22">Zhang W</searchLink><br /><searchLink fieldCode="AU" term="%22Tao+J%22">Tao J</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%229891704%22">IEEE transactions on visualization and computer graphics</searchLink> [IEEE Trans Vis Comput Graph] 2026 Sep; Vol. 32 (9), pp. 7714-7728. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22IEEE+Computer+Society%22">IEEE Computer Society </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>9891704 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>1941-0506 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2210772626%22">10772626 </searchLink><i>NLM ISO Abbreviation: </i>IEEE Trans Vis Comput Graph <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Original Publication</i>: New York, NY : IEEE Computer Society, c1995- – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Brain%22">Brain*</searchLink>/<searchLink fieldCode="MM" term="%22Brain+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Brain%22">Brain*</searchLink>/<searchLink fieldCode="MM" term="%22Brain+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Nerve+Net%22">Nerve Net*</searchLink>/<searchLink fieldCode="MM" term="%22Nerve+Net+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Nerve+Net%22">Nerve Net*</searchLink>/<searchLink fieldCode="MM" term="%22Nerve+Net+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted%22">Image Processing, Computer-Assisted*</searchLink>/<searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Computer+Graphics%22">Computer Graphics*</searchLink><br /><searchLink fieldCode="MH" term="%22Diffusion+Tensor+Imaging%22">Diffusion Tensor Imaging</searchLink>/<searchLink fieldCode="MH" term="%22Diffusion+Tensor+Imaging+methods%22">methods</searchLink> ; <searchLink fieldCode="MH" term="%22Brain+Mapping%22">Brain Mapping</searchLink>/<searchLink fieldCode="MH" term="%22Brain+Mapping+methods%22">methods</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Graph+Neural+Networks%22">Graph Neural Networks</searchLink> ; <searchLink fieldCode="MH" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Identifying biomarkers from human brain networks is critical in early detection of neurological disorders and understanding disease mechanisms. Existing visual analytics approaches show a remarkable ability to assist experts in discovering and validating biomarkers through exploration. However, these approaches often focus only on the diffusion features of fiber bundles and evaluate individual bundles and regions separately. This may hinder their ability to accurately represent the complex brain network for investigation from various perspectives. In this paper, we present NeuroLens, a visual analytics system that integrates comprehensive information across multiple levels, including fiber bundles, local regions and entire brain networks. Specifically, to model the bundles more precisely, we enhance the features by incorporating the joint distribution of geometric features. The bundle information is further aggregated to form representations at the region and the brain level using an attention-based graph neural network. The region-level representation describes complex structures involving multiple bundles, and the brain-level representation enables comparisons between subjects and groups. The NeuroLens interface enables comparative exploration of this multi-level and multi-faceted information. To verify the findings during exploration, NeuroLens leverages the large language model to query related information from existing literature. We collaborate with domain experts to examine the effectiveness of NeuroLens. Their exploration, findings, and feedback are discussed. – Name: DateEntry Label: Entry Date(s) Group: Date Data: <i>Date Created: </i>20260608 <i>Date Completed: </i>20260731 <i>Latest Revision: </i>20260929 – Name: DateUpdate Label: Update Code Group: Date Data: 20260930 – Name: DOI Label: DOI Group: ID Data: 10.1109/TVCG.2026.3701404 – Name: AN Label: PMID Group: ID Data: 42258687 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TVCG.2026.3701404 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 7714 Subjects: – SubjectFull: Diffusion Tensor Imaging methods Type: general – SubjectFull: Brain Mapping methods Type: general – SubjectFull: Humans Type: general – SubjectFull: Graph Neural Networks Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Brain diagnostic imaging Type: general – SubjectFull: Brain physiology Type: general – SubjectFull: Nerve Net diagnostic imaging Type: general – SubjectFull: Nerve Net physiology Type: general – SubjectFull: Image Processing, Computer-Assisted methods Type: general – SubjectFull: Computer Graphics Type: general Titles: – TitleFull: NeuroLens: A Holistic Visual Analytics System for Exploring Brain Networks Across Scales. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang W – PersonEntity: Name: NameFull: Tao J IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: 2026 Sep Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 1941-0506 Numbering: – Type: volume Value: 32 – Type: issue Value: 9 Titles: – TitleFull: IEEE transactions on visualization and computer graphics Type: main |
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