Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas
| Autor: | Mari Ganesh Kumar, Mriganka Sur, Ming Hu, Aadhirai Ramanujan, Hema A. Murthy |
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| Rok vydání: | 2020 |
| Předmět: |
Male
Visual perception genetic structures Computer science Vision Physiology Visual System Sensory Physiology Normal Distribution Social Sciences Linear Discriminant Analysis Machine Learning Mice Mathematical and Statistical Techniques Animal Cells Medicine and Health Sciences Contrast (vision) Cluster Analysis Psychology Biology (General) media_common Visual Cortex Neurons Brain Mapping Principal Component Analysis Ecology Statistics Brain Sensory Systems Visual field medicine.anatomical_structure Computational Theory and Mathematics Modeling and Simulation Physical Sciences Female Sensory Perception Anatomy Cellular Types Research Article Computer and Information Sciences QH301-705.5 media_common.quotation_subject Mice Transgenic Stimulus (physiology) Research and Analysis Methods Retina Cellular and Molecular Neuroscience Prosencephalon Artificial Intelligence Support Vector Machines Genetics medicine Animals Visual Pathways Statistical Methods Cluster analysis Molecular Biology Ecology Evolution Behavior and Systematics Artificial Neural Networks Computational Neuroscience Photons Models Statistical Resting state fMRI business.industry Cognitive Psychology Biology and Life Sciences Computational Biology Pattern recognition Cell Biology Visual cortex Retinotopy Cellular Neuroscience Multivariate Analysis Cognitive Science Perception Artificial intelligence Visual Fields business Photic Stimulation Mathematics Neuroscience |
| Zdroj: | PLoS Computational Biology PLoS Computational Biology, Vol 17, Iss 2, p e1008548 (2021) |
| ISSN: | 1553-7358 |
| Popis: | 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 data-driven 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. Author summary The visual cortex has a prominent role in the processing of visual information by the brain. Previous work has segmented the mouse visual cortex into different areas based on the organization of retinotopic maps. Here, we collect responses of the visual cortex to various types of stimuli and ask if we could discover unique clusters from this dataset using machine learning methods. The retinotopy based area borders are used as ground truth to compare the performance of our clustering algorithms. We show our results on two datasets, one collected by the authors using wide-field imaging and another a publicly available dataset collected using two-photon imaging. The proposed supervised approach is able to predict the area labels accurately using neuronal responses to various visual stimuli. Following up on these results using visual stimuli, we hypothesized that each area of the mouse brain has unique responses that can be used to classify the area independently of stimuli. Experiments using resting state responses, without any overt stimulus, confirm this hypothesis. Such activity-based segmentation of the mouse visual cortex suggests that large-scale imaging combined with a machine learning algorithm may enable new insights into the functional organization of the visual cortex in mice and other species. |
| Databáze: | OpenAIRE |
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