Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics.
Modeling the network topology of the human brain within the mesoscale has become an increasing focus within the neuroscientific community due to its variation across diverse cognitive processes, in the presence of neuropsychiatric disease or injury, and over the lifespan. Much research has been done...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-11-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012524 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850225005538836480 |
|---|---|
| author | Alexandria M Jensen Peter DeWitt Brianne M Bettcher Julia Wrobel Katerina Kechris Debashis Ghosh |
| author_facet | Alexandria M Jensen Peter DeWitt Brianne M Bettcher Julia Wrobel Katerina Kechris Debashis Ghosh |
| author_sort | Alexandria M Jensen |
| collection | DOAJ |
| description | Modeling the network topology of the human brain within the mesoscale has become an increasing focus within the neuroscientific community due to its variation across diverse cognitive processes, in the presence of neuropsychiatric disease or injury, and over the lifespan. Much research has been done on the creation of algorithms to detect these mesoscopic structures, called communities or modules, but less has been done to conduct inference on these structures. The literature on analysis of these community detection algorithms has focused on comparing them within the same subject. These approaches, however, either do not accomodate a more general association between community structure and an outcome or cannot accommodate additional covariates that may confound the association of interest. We propose a semiparametric kernel machine regression model for either a continuous or binary outcome, where covariate effects are modeled parametrically and brain connectivity measures are measured nonparametrically. By incorporating notions of similarity between network community structures into a kernel distance function, the high-dimensional feature space of brain networks, defined on input pairs, can be generalized to non-linear spaces, allowing for a wider class of distance-based algorithms. We evaluate our proposed methodology on both simulated and real datasets. |
| format | Article |
| id | doaj-art-3c4c2aa1c67a407dbb92e147612290eb |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-3c4c2aa1c67a407dbb92e147612290eb2025-08-20T02:05:29ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-11-012011e101252410.1371/journal.pcbi.1012524Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics.Alexandria M JensenPeter DeWittBrianne M BettcherJulia WrobelKaterina KechrisDebashis GhoshModeling the network topology of the human brain within the mesoscale has become an increasing focus within the neuroscientific community due to its variation across diverse cognitive processes, in the presence of neuropsychiatric disease or injury, and over the lifespan. Much research has been done on the creation of algorithms to detect these mesoscopic structures, called communities or modules, but less has been done to conduct inference on these structures. The literature on analysis of these community detection algorithms has focused on comparing them within the same subject. These approaches, however, either do not accomodate a more general association between community structure and an outcome or cannot accommodate additional covariates that may confound the association of interest. We propose a semiparametric kernel machine regression model for either a continuous or binary outcome, where covariate effects are modeled parametrically and brain connectivity measures are measured nonparametrically. By incorporating notions of similarity between network community structures into a kernel distance function, the high-dimensional feature space of brain networks, defined on input pairs, can be generalized to non-linear spaces, allowing for a wider class of distance-based algorithms. We evaluate our proposed methodology on both simulated and real datasets.https://doi.org/10.1371/journal.pcbi.1012524 |
| spellingShingle | Alexandria M Jensen Peter DeWitt Brianne M Bettcher Julia Wrobel Katerina Kechris Debashis Ghosh Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics. PLoS Computational Biology |
| title | Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics. |
| title_full | Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics. |
| title_fullStr | Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics. |
| title_full_unstemmed | Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics. |
| title_short | Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics. |
| title_sort | kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics |
| url | https://doi.org/10.1371/journal.pcbi.1012524 |
| work_keys_str_mv | AT alexandriamjensen kernelmachinetestsofassociationusingextrinsicandintrinsicclusterevaluationmetrics AT peterdewitt kernelmachinetestsofassociationusingextrinsicandintrinsicclusterevaluationmetrics AT briannembettcher kernelmachinetestsofassociationusingextrinsicandintrinsicclusterevaluationmetrics AT juliawrobel kernelmachinetestsofassociationusingextrinsicandintrinsicclusterevaluationmetrics AT katerinakechris kernelmachinetestsofassociationusingextrinsicandintrinsicclusterevaluationmetrics AT debashisghosh kernelmachinetestsofassociationusingextrinsicandintrinsicclusterevaluationmetrics |