A principled framework to assess the information-theoretic fitness of brain functional sub-circuits
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910539762892800 |
|---|---|
| author | Duong-Tran, Duy Nguyen, Nghi Mu, Shizhuo Chen, Jiong Bao, Jingxuan Xu, Frederick Garai, Sumita Cadena-Pico, Jose Kaplan, Alan David Chen, Tianlong Zhao, Yize Shen, Li Goñi, Joaquín |
| author_facet | Duong-Tran, Duy Nguyen, Nghi Mu, Shizhuo Chen, Jiong Bao, Jingxuan Xu, Frederick Garai, Sumita Cadena-Pico, Jose Kaplan, Alan David Chen, Tianlong Zhao, Yize Shen, Li Goñi, Joaquín |
| contents | In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods and provide insights for future research in individualized parcellations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18531 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A principled framework to assess the information-theoretic fitness of brain functional sub-circuits Duong-Tran, Duy Nguyen, Nghi Mu, Shizhuo Chen, Jiong Bao, Jingxuan Xu, Frederick Garai, Sumita Cadena-Pico, Jose Kaplan, Alan David Chen, Tianlong Zhao, Yize Shen, Li Goñi, Joaquín Neurons and Cognition In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods and provide insights for future research in individualized parcellations. |
| title | A principled framework to assess the information-theoretic fitness of brain functional sub-circuits |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2406.18531 |