PPA: Principal parcellation analysis for brain connectomes and multiple traits
Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-08-01
|
| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923003658 |
| _version_ | 1797799073189724160 |
|---|---|
| author | Rongjie Liu Meng Li David B. Dunson |
| author_facet | Rongjie Liu Meng Li David B. Dunson |
| author_sort | Rongjie Liu |
| collection | DOAJ |
| description | Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data. |
| first_indexed | 2024-03-13T04:14:31Z |
| format | Article |
| id | doaj.art-4dd5e96e932a43ca86683435236dfcc2 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-03-13T04:14:31Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-4dd5e96e932a43ca86683435236dfcc22023-06-21T06:51:13ZengElsevierNeuroImage1095-95722023-08-01276120214PPA: Principal parcellation analysis for brain connectomes and multiple traitsRongjie Liu0Meng Li1David B. Dunson2Department of Statistics, Florida State University, Tallahassee, FL, USADepartment of Statistics, Rice University, Houston, TX, USA; Corresponding author.Department of Statistical Science, Duke University, Durham, NC, USAOur understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.http://www.sciencedirect.com/science/article/pii/S1053811923003658Brain networksBrain parcellationClusteringHuman connectome projectStructural connectomics |
| spellingShingle | Rongjie Liu Meng Li David B. Dunson PPA: Principal parcellation analysis for brain connectomes and multiple traits NeuroImage Brain networks Brain parcellation Clustering Human connectome project Structural connectomics |
| title | PPA: Principal parcellation analysis for brain connectomes and multiple traits |
| title_full | PPA: Principal parcellation analysis for brain connectomes and multiple traits |
| title_fullStr | PPA: Principal parcellation analysis for brain connectomes and multiple traits |
| title_full_unstemmed | PPA: Principal parcellation analysis for brain connectomes and multiple traits |
| title_short | PPA: Principal parcellation analysis for brain connectomes and multiple traits |
| title_sort | ppa principal parcellation analysis for brain connectomes and multiple traits |
| topic | Brain networks Brain parcellation Clustering Human connectome project Structural connectomics |
| url | http://www.sciencedirect.com/science/article/pii/S1053811923003658 |
| work_keys_str_mv | AT rongjieliu ppaprincipalparcellationanalysisforbrainconnectomesandmultipletraits AT mengli ppaprincipalparcellationanalysisforbrainconnectomesandmultipletraits AT davidbdunson ppaprincipalparcellationanalysisforbrainconnectomesandmultipletraits |