Biology subjects
Publications and source records attributed to Stirman, J. N..
Withdrawal StatementThe authors have withdrawn this manuscript due to a duplicate posting of manuscript number BIORXIV/2018/469114. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The correct preprint can be found at doi: 10.1101/469114
The posterior parietal cortex (PPC) in mice has various functions including multisensory integration1-3, vision-guided behaviors4-6, working memory7-13, and posture control14,15. However, an integrated understanding of these functions and their cortical localizations in and around the PPC and higher visual areas (HVAs) has not been completely elucidated. Here we simultaneously imaged the activity of thousands of neurons within a 3 x 3 mm2 field-of-view, including eight cortical areas around the PPC, during behavior with a two-photon mesoscope16. Mice performed both a vision-guided task and a choice history-dependent task, and the imaging results revealed distinct, localized, behavior-related functions of two medial PPC areas. Neurons in the anteromedial (AM) HVA responded to both vision and choice information, and thus AM is a locus of association between these channels. By contrast, the anterior (A) HVA stores choice history with sequential dynamics and represents posture. Mesoscale correlation analysis on the intertrial variability of neuronal activity demonstrated that neurons in AM exhibited diverse, area-dependent interactions, while neurons in area A shared fluctuations with the primary somatosensory area. Pairwise interareal interactions among neurons were precisely predicted by the anatomical input correlations, with the exception of some global interactions. Thus, the medial PPC has two distinct modules, areas AM and A, with each having distinctive modes of cortical communication. These medial PPC modules can serve separate higher-order functions: area AM for multisensory and cognitive integration with locally processed signals and area A for transmission of information including posture, movement, and working memory.
Mice have a constellation of higher visual areas, but their functional specializations are unclear. Here, we used a data-driven approach to examine neuronal representations of complex visual stimuli across mouse higher visual areas, measured using large field-of-view two-photon calcium imaging. Using specialized stimuli, we found higher fidelity representations of texture in area LM, compared to area AL. Complementarily, we found higher fidelity representations of motion in area AL, compared to area LM. We also observed this segregation of information in response to naturalistic videos. Finally, we explored how popular models of visual cortical neurons could produce the segregated representations of texture and motion we observed. These selective representations could aid in behaviors such as visually guided navigation.
Understanding brain function requires monitoring local and global brain dynamics. Two-photon imaging of the brain across mesoscopic scales has presented trade-offs between imaging area and acquisition speed. We describe a flexible cellular resolution two-photon microscope capable of simultaneous video rate acquisition of four independently targetable brain regions spanning an approximate five-millimeter field of view. With this system, we demonstrate the ability to measure calcium activity across mouse sensorimotor cortex at behaviorally relevant timescales.
Imaging the activity of neurons that are widely distributed across brain regions deep in scattering tissue at high speed remains challenging. Here, we introduce an open-source system with Dual Independent Enhanced Scan Engines for Large Field-of-view Two-Photon imaging (Diesel2p). Combining novel optical design, adaptive optics, and temporal multiplexing, the system offers subcellular resolution over a large field-of-view ([~] 25 mm2) with independent scan engines. We demonstrate the flexibility and various use cases of this system for calcium imaging of neurons in the living brain.
Among brain areas, axonal projections carry channels of information that can be mixed to varying degrees. Here, we assess the rules for the network consisting of the primary visual cortex and higher visual areas (V1-HVA) in mice. We use large field-of-view two-photon calcium imaging to measure correlated variability (i.e., noise correlations, NCs) among thousands of neurons, forming over a million unique pairs, distributed across multiple cortical areas simultaneously. The amplitude of NCs is proportional to functional connectivity in the network, and we find that they are robust, reproducible statistical measures, and are remarkably similar across stimuli, thus providing effective constraints to network models. We used these NCs to measure the statistics of functional connectivity among tuning classes of neurons in V1 and HVAs. Using a data-driven clustering approach, we identify approximately 60 distinct tuning classes found in V1 and HVAs. We find that NCs are higher between neurons from the same tuning class, both within and across cortical areas. Thus, in the V1-HVA network, mixing of channels is avoided. Instead, distinct channels of visual information are broadcast within and across cortical areas, at both the micron and millimeter length scales. This principle for the functional organization and correlation structure at the individual neuron level across multiple cortical areas can inform and constrain computational theories of neocortical networks.
Mice use vision to navigate and avoid predators in natural environments. However, the spatial resolution of mouse vision is poor compared to primates, and mice lack a fovea. Thus, it is unclear how well mice can discriminate ethologically relevant scenes. Here, we examined natural scene discrimination in mice using an automated touch-screen system. We estimated the discrimination difficulty using the computational metric structural similarity (SSIM), and constructed psychometric curves. However, the performance of each mouse was better predicted by the population mean than SSIM. This high inter-mouse agreement indicates that mice use common and robust strategies to discriminate natural scenes. We tested several other image metrics to find an alternative to SSIM for predicting discrimination performance. We found that a simple, primary visual cortex (V1)-inspired model predicted mouse performance with fidelity approaching the inter-mouse agreement. The model involved convolving the images with Gabor filters, and its performance varied with the orientation of the Gabor filter. This orientation dependence was driven by the stimuli, rather than an innate biological feature. Together, these results indicate that mice are adept at discriminating natural scenes, and their performance is well predicted by simple models of V1 processing.