Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas
© 2021 Kumar et al. The visual cortex of the mouse brain can be divided into ten or more areas that each contain complete or partial retinotopic maps of the contralateral visual field. It is generally assumed that these areas represent discrete processing regions. In contrast to the conventional inp...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021
|
| Online Access: | https://hdl.handle.net/1721.1/133673 |
| _version_ | 1826193312862699520 |
|---|---|
| author | Kumar, Mari Ganesh Hu, Ming Ramanujan, Aadhirai Sur, Mriganka Murthy, Hema A |
| author_facet | Kumar, Mari Ganesh Hu, Ming Ramanujan, Aadhirai Sur, Mriganka Murthy, Hema A |
| author_sort | Kumar, Mari Ganesh |
| collection | MIT |
| description | © 2021 Kumar et al. The visual cortex of the mouse brain can be divided into ten or more areas that each contain complete or partial retinotopic maps of the contralateral visual field. It is generally assumed that these areas represent discrete processing regions. In contrast to the conventional input-output characterizations of neuronal responses to standard visual stimuli, here we asked whether six of the core visual areas have responses that are functionally distinct from each other for a given visual stimulus set, by applying machine learning techniques to distinguish the areas based on their activity patterns. Visual areas defined by retinotopic mapping were examined using supervised classifiers applied to responses elicited by a range of stimuli. Using two distinct datasets obtained using wide-field and two-photon imaging, we show that the area labels predicted by the classifiers were highly consistent with the labels obtained using retinotopy. Furthermore, the classifiers were able to model the boundaries of visual areas using resting state cortical responses obtained without any overt stimulus, in both datasets. With the wide-field dataset, clustering neuronal responses using a constrained semi-supervised classifier showed graceful degradation of accuracy. The results suggest that responses from visual cortical areas can be classified effectively using datadriven models. These responses likely reflect unique circuits within each area that give rise to activity with stronger intra-areal than inter-areal correlations, and their responses to controlled visual stimuli across trials drive higher areal classification accuracy than resting state responses. |
| first_indexed | 2024-09-23T09:37:18Z |
| format | Article |
| id | mit-1721.1/133673 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:37:18Z |
| publishDate | 2021 |
| publisher | Public Library of Science (PLoS) |
| record_format | dspace |
| spelling | mit-1721.1/1336732021-10-28T04:42:47Z Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas Kumar, Mari Ganesh Hu, Ming Ramanujan, Aadhirai Sur, Mriganka Murthy, Hema A © 2021 Kumar et al. The visual cortex of the mouse brain can be divided into ten or more areas that each contain complete or partial retinotopic maps of the contralateral visual field. It is generally assumed that these areas represent discrete processing regions. In contrast to the conventional input-output characterizations of neuronal responses to standard visual stimuli, here we asked whether six of the core visual areas have responses that are functionally distinct from each other for a given visual stimulus set, by applying machine learning techniques to distinguish the areas based on their activity patterns. Visual areas defined by retinotopic mapping were examined using supervised classifiers applied to responses elicited by a range of stimuli. Using two distinct datasets obtained using wide-field and two-photon imaging, we show that the area labels predicted by the classifiers were highly consistent with the labels obtained using retinotopy. Furthermore, the classifiers were able to model the boundaries of visual areas using resting state cortical responses obtained without any overt stimulus, in both datasets. With the wide-field dataset, clustering neuronal responses using a constrained semi-supervised classifier showed graceful degradation of accuracy. The results suggest that responses from visual cortical areas can be classified effectively using datadriven models. These responses likely reflect unique circuits within each area that give rise to activity with stronger intra-areal than inter-areal correlations, and their responses to controlled visual stimuli across trials drive higher areal classification accuracy than resting state responses. 2021-10-27T19:54:06Z 2021-10-27T19:54:06Z 2021 2021-03-18T14:09:24Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/133673 en 10.1371/JOURNAL.PCBI.1008548 PLoS Computational Biology Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science (PLoS) PLoS |
| spellingShingle | Kumar, Mari Ganesh Hu, Ming Ramanujan, Aadhirai Sur, Mriganka Murthy, Hema A Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas |
| title | Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas |
| title_full | Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas |
| title_fullStr | Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas |
| title_full_unstemmed | Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas |
| title_short | Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas |
| title_sort | functional parcellation of mouse visual cortex using statistical techniques reveals response dependent clustering of cortical processing areas |
| url | https://hdl.handle.net/1721.1/133673 |
| work_keys_str_mv | AT kumarmariganesh functionalparcellationofmousevisualcortexusingstatisticaltechniquesrevealsresponsedependentclusteringofcorticalprocessingareas AT huming functionalparcellationofmousevisualcortexusingstatisticaltechniquesrevealsresponsedependentclusteringofcorticalprocessingareas AT ramanujanaadhirai functionalparcellationofmousevisualcortexusingstatisticaltechniquesrevealsresponsedependentclusteringofcorticalprocessingareas AT surmriganka functionalparcellationofmousevisualcortexusingstatisticaltechniquesrevealsresponsedependentclusteringofcorticalprocessingareas AT murthyhemaa functionalparcellationofmousevisualcortexusingstatisticaltechniquesrevealsresponsedependentclusteringofcorticalprocessingareas |