Integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling.
Integrating multiscale, multimodal neuroimaging data is essential for a comprehensive understanding of neural circuits. However, this is challenging due to the inherent trade-offs between spatial coverage and resolution in each modality, necessitating a computational strategy that combines modality-...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012655 |
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| author | Jiyoung Kang Hae-Jeong Park |
| author_facet | Jiyoung Kang Hae-Jeong Park |
| author_sort | Jiyoung Kang |
| collection | DOAJ |
| description | Integrating multiscale, multimodal neuroimaging data is essential for a comprehensive understanding of neural circuits. However, this is challenging due to the inherent trade-offs between spatial coverage and resolution in each modality, necessitating a computational strategy that combines modality-specific information effectively. This study introduces a dynamic causal modeling (DCM) framework designed to address the challenge of combining partially observed, multiscale signals across a larger-scale neural circuit by employing a shared neural state model with modality-specific observation models. The proposed method achieves robust circuit inference by iteratively integrating parameter estimates from local microscale and global meso- or macroscale circuits, derived from signals across various scales and modalities. Parameters estimated from high-resolution data within specific regions inform global circuit estimation by constraining neural properties in unobserved regions, while large-scale circuit data help elucidate detailed local circuitry. Using a virtual ground truth system, we validated the method across diverse experimental settings, combining calcium imaging (CaI), voltage-sensitive dye imaging (VSDI), and blood-oxygen-level-dependent (BOLD) signals-each with distinct coverage and resolution. Our reciprocal and iterative parameter estimation approach markedly improves the accuracy of neural property and connectivity estimates compared to traditional one-step estimation methods. This iterative integration of local and global parameters presents a reliable approach to inferring extensive, complex neural circuits from partially observed, multimodal, and multiscale data, showcasing how information from different scales reciprocally enhances entire circuit parameter estimation. |
| format | Article |
| id | doaj-art-1e54a97932ed4f27a9473e2422337f6b |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-1e54a97932ed4f27a9473e2422337f6b2025-01-17T05:30:56ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-12-012012e101265510.1371/journal.pcbi.1012655Integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling.Jiyoung KangHae-Jeong ParkIntegrating multiscale, multimodal neuroimaging data is essential for a comprehensive understanding of neural circuits. However, this is challenging due to the inherent trade-offs between spatial coverage and resolution in each modality, necessitating a computational strategy that combines modality-specific information effectively. This study introduces a dynamic causal modeling (DCM) framework designed to address the challenge of combining partially observed, multiscale signals across a larger-scale neural circuit by employing a shared neural state model with modality-specific observation models. The proposed method achieves robust circuit inference by iteratively integrating parameter estimates from local microscale and global meso- or macroscale circuits, derived from signals across various scales and modalities. Parameters estimated from high-resolution data within specific regions inform global circuit estimation by constraining neural properties in unobserved regions, while large-scale circuit data help elucidate detailed local circuitry. Using a virtual ground truth system, we validated the method across diverse experimental settings, combining calcium imaging (CaI), voltage-sensitive dye imaging (VSDI), and blood-oxygen-level-dependent (BOLD) signals-each with distinct coverage and resolution. Our reciprocal and iterative parameter estimation approach markedly improves the accuracy of neural property and connectivity estimates compared to traditional one-step estimation methods. This iterative integration of local and global parameters presents a reliable approach to inferring extensive, complex neural circuits from partially observed, multimodal, and multiscale data, showcasing how information from different scales reciprocally enhances entire circuit parameter estimation.https://doi.org/10.1371/journal.pcbi.1012655 |
| spellingShingle | Jiyoung Kang Hae-Jeong Park Integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling. PLoS Computational Biology |
| title | Integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling. |
| title_full | Integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling. |
| title_fullStr | Integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling. |
| title_full_unstemmed | Integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling. |
| title_short | Integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling. |
| title_sort | integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling |
| url | https://doi.org/10.1371/journal.pcbi.1012655 |
| work_keys_str_mv | AT jiyoungkang integrationofpartiallyobservedmultimodalandmultiscaleneuralsignalsforestimatinganeuralcircuitusingdynamiccausalmodeling AT haejeongpark integrationofpartiallyobservedmultimodalandmultiscaleneuralsignalsforestimatinganeuralcircuitusingdynamiccausalmodeling |