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Visual cortex is organized into discrete sub-regions or areas that are arranged into a hierarchy and serves different functions in the processing of visual information. In retinotopic maps of mouse cortex, there appear to be substantial mouse-to-mouse differences in visual area location, size and shape. Here we quantify the biological variation in the size, shape and locations of 11 visual areas in the mouse, after separating biological variation and measurement noise. We find that there is biological variation in the locations and sizes of visual areas.

To study the mechanisms of perception and cognition, neural measurements must be made during behavior. A goal of the is to map the activity of distinct cortical cell classes underlying visual and behavioral processing. Here we describe standardized methodology for training head-fixed mice on a visual change detection task, and we use our paradigm to characterize learning and behavior of five GCaMP6-expressing transgenic lines. We used automated training procedures to facilitate comparisons across mice. Training times varied, but most transgenic mice learned the behavioral task. Motivation levels also varied across mice. To compare mice in similar motivational states we subdivided sessions into over-, under-, and optimally motivated periods. When motivated, the pattern of perceptual decisions were highly correlated across transgenic lines, although overall performance (d-prime) was lower in one line labeling somatostatin inhibitory cells. These results provide important context for using these mice to map neural activity underlying perception and behavior.

Neurophysiological differentiation (ND), a measure of the number of distinct activity states that a neural population visits over a time interval, has been used as a correlate of meaningfulness or subjective perception of visual stimuli. ND has largely been studied in non-invasive human whole-brain recordings where spatial resolution is limited. However, it is likely that perception is supported by discrete neuronal populations rather than the whole brain. Therefore, here we use Neuropixels recordings from the mouse brain to characterize the ND metric across a wide range of temporal scales, within neural populations recorded at single-cell resolution in localized regions. Using the spiking activity of thousands of simultaneously recorded neurons spanning 6 visual cortical areas and the visual thalamus, we show that the ND of stimulus-evoked activity of the entire visual cortex is higher for naturalistic stimuli relative to artificial ones. This finding holds in most individual areas throughout the visual hierarchy. Moreover, for animals performing an image change detection task, ND of the entire visual cortex (though not individual areas) is higher for successful detection compared to failed trials, consistent with the assumed perception of the stimulus. Together, these results suggest that ND computed on cellular-level neural recordings is a useful tool highlighting cell populations that may be involved in subjective perception.

Visual behavior requires coordinated activity across hierarchically organized brain circuits. Understanding this complexity demands datasets that are both large-scale (sampling many areas) and dense (recording many neurons in each area). Here we present a database of spiking activity across the mouse visual system-including thalamus, cortex, and midbrain-while mice perform an image change detection task. Using Neuropixels probes, we record from >75,000 high-quality units in 54 mice, mapping area-, cortical layer-, and cell type-specific coding of sensory and motor information. Modulation by task-engagement increased across the thalamocortical hierarchy but was strongest in the midbrain. Novel images modulated cortical (but not thalamic) responses through delayed recurrent activity. Population decoding and optogenetics identified a critical decision window for change detection and revealed that mice use an adaptation-based rather than image-comparison strategy. This comprehensive resource provides a valuable substrate for understanding sensorimotor computations in neural networks.

Detecting novel stimuli in the environment is critical for learning and survival, yet the neural basis of novelty processing is not understood. To characterize cell type-specific novelty processing, we surveyed the activity of ~15,000 excitatory and inhibitory neurons in mice performing a visual task with novel and familiar stimuli. Clustering revealed a dozen functional neuron types defined by experience-dependent encoding. Vasoactive-intestinal-peptide (Vip) expressing inhibitory neurons were diverse, encoding novel stimuli, omissions of familiar stimuli, or behavioral features. Distinct Somatostatin (Sst) expressing inhibitory neurons encoded either familiar or novel stimuli. Subsets of excitatory neurons co-clustered with specific Vip or Sst subpopulations, while Sst and Vip inhibitory clusters were non-overlapping. This study establishes that novelty processing is mediated by diverse functional neuron types in the visual cortex.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Clustering analysis is now performed on all cell types together instead of each independently; A new figure describing the full open access dataset has been added (Figure 1); Supplementary Text describing the dataset and details of analysis methods has been added; Figures and text have been revised for overall clarity and ease of interpretation* https://allensdk.readthedocs.io/en/latest/visual_behavior_optical_physiology.html* https://doi.org/10.48324/dandi.000711/0.231121.1730

To understand the neural basis of behavior, it is essential to measure spiking dynamics across many interacting brain regions. While new technology, such as Neuropixels probes, facilitates multi-regional recordings, significant surgical and procedural hurdles remain for these experiments to achieve their full potential. Here, we describe a novel 3D-printed cranial implant for electrophysiological recordings from distributed areas of the mouse brain. The skull-shaped implant is designed with customizable insertion holes, allowing targeting of dozens of cortical and subcortical structures in single mice. We demonstrate the procedure's high success rate, implant biocompatibility, lack of adverse effects on behavior training, compatibility with optical imaging and optogenetics, and repeated high-quality Neuropixels recordings over multiple days. To showcase the scientific utility of this new methodology, we use multi-probe recordings to reveal how alpha rhythms organize spiking activity across visual and sensorimotor networks. Overall, this methodology enables powerful large-scale electrophysiological measurements for the study of distributed computation in the mouse brain.Competing Interest StatementThe authors have declared no competing interest.

Neurophysiological differentiation (ND), a metric that quantifies the number of distinct activity states that the brain or its part visits over a period of time, has been used as a correlate of meaningfulness or subjective perception of visual stimuli. ND has largely been studied in non-invasive human whole-brain recordings where spatial resolution is limited. However, it is likely that perception is supported by discrete populations of spiking neurons rather than the whole brain. Therefore, in this study, we use Neuropixels recordings from the mouse brain to characterize the ND metric within neural populations recorded at single-cell resolution in localized regions. Using the spiking activity of thousands of simultaneously recorded neurons spanning 6 visual cortical areas as well as the visual thalamus, we show that the ND of stimulus-evoked activity of the entire visual cortex is higher for naturalistic stimuli relative to artificial ones. This finding holds in most individual areas throughout the visual hierarchy as well. For animals performing an image change detection task, ND of the entire visual cortex (though not individual areas) is higher for successful detection compared to failed trials, consistent with the assumed perception of the stimulus. Analysis of spiking activity allows us to characterize the ND metric across a wide range of timescales from 10s of milliseconds to a few seconds. This analysis reveals that although ND of activity of single neurons is often maximized at an optimal timescale around 100 ms, the optimum shifts to under 5 ms for ND of neuronal ensembles. Finally, we find that the ND of activations in convolutional neural networks (CNNs) trained on an image classification task shows distinct trends relative to the mouse visual system: ND is often higher for less naturalistic stimuli and varies by orders of magnitude across the hierarchy, compared to modest variation in the mouse brain. Together, these results suggest that ND computed on cellular-level neural recordings can be a useful tool highlighting cell populations that may be involved in subjective perception. Advances in our understanding on neural coding has revealed that information about visual stimuli is represented across several brain regions. However, availability of information does not imply that it is necessarily utilized by the brain, much less that it is subjectively perceived. Since percepts originate in neural activity, distinct percepts must be associated with distinct ‘states’ of neural activity, at least within the brain region that supports the percepts. Thus, one approach developed in this direction is to quantify the number of distinct ‘states’ that the activity of the brain goes through, called neurophysiological differentiation (ND). ND of the entire brain has been shown to reflect subjective reports of visual stimulus meaningfulness. But what specific subpopulations within the brain could be supporting conscious perception, and what is the correct timescale on which states should be quantified? In this study, we analyze ND of spiking neural activity in the mouse visual cortex recorded using Neuropixels probes, allowing us to characterize the ND metric across a wide range of timescales all the way down from 5 ms to a few seconds. It also allows us to understand the ND of neural activity of different ensembles of neurons, from individual thalamic or cortical ensembles to those spanning across multiple visual areas in the mouse brain.

To study mechanisms of perception and cognition, neural measurements must be made during behavior. A goal of the Allen Brain Observatory is to map activity in distinct cortical cell classes during visual processing and behavior. Here we characterize learning and performance of five GCaMP6-expressing transgenic lines trained on a visual change detection task. We used automated training procedures to facilitate comparisons across mice. Training times varied, but most transgenic mice learned the task. Motivation levels also varied across mice. To compare mice in similar motivational states we subdivided sessions into over-, under-, and optimally motivated periods. When motivated, the pattern of perceptual decisions were highly correlated across transgenic lines, although overall d-prime was lower in one line labeling somatostatin inhibitory cells. These results provide important context for using these mice to map neural activity underlying perception and behavior.

Visual cortex is organized into discrete sub-regions or areas that are arranged into a hierarchy and serve different functions in the processing of visual information. In our previous work, we noted that retinotopic maps of cortical visual areas differed between mice, but did not quantify these differences or determine the relative contributions of biological variation and measurement noise. Here we quantify the biological variation in the size, shape and locations of 11 visual areas in the mouse. We find that there is substantial biological variation in the sizes of visual areas, with some visual areas varying in size by two-fold across the population of mice.

Despite the critical role of endothelial Wnt/β-catenin signaling during central nervous system (CNS) vascularization, how endothelial cells sense and respond to specific Wnt ligands and what aspects of the multistep process of intra-cerebral blood vessel morphogenesis are controlled by these angiogenic signals remain poorly understood. We addressed these questions at single-cell resolution in zebrafish embryos. We identify the GPI-anchored MMP inhibitor Reck and the adhesion GPCR Gpr124 as integral components of a Wnt7a/Wnt7b-specific signaling complex required for brain angiogenesis and dorsal root ganglia neurogenesis. We further show that this atypical Wnt/β-catenin signaling pathway selectively controls endothelial tip cell function and hence, that mosaic restoration of single wild-type tip cells in Wnt/β-catenin-deficient perineural vessels is sufficient to initiate the formation of CNS vessels. Our results identify molecular determinants of ligand specificity of Wnt/β-catenin signaling and provide evidence for organ-specific control of vascular invasion through tight modulation of tip cell function. Organs develop alongside the network of blood vessels that supply them with oxygen and nutrients. One way that new blood vessels grow is by sprouting out of the side of an existing vessel, via a process called angiogenesis. This process relies on signals that are received by the endothelial cells that line the inner wall of blood vessels, and that direct the cells to form a new ‘sprout’, consisting of tip and stalk cells. In the developing brain, the Wnt/β-catenin signaling pathway helps direct the formation of blood vessels. In this pathway, a member of a protein family called Wnt signals to specific proteins on the surface of the cells lining the blood vessels. Much effort has gone into uncovering the identity of these proteins, with many studies looking at blood vessel development in the brain of mouse embryos. In this study, Vanhollebeke et al. turned to zebrafish embryos to uncover new regulators of angiogenesis and define their roles during the multi-step process of blood vessel development in the brain. A variety of experimental techniques were used to alter and study the activity of different Wnt signaling pathway components. These experiments revealed that the Wnt7a and Wnt7b proteins signal to an endothelial cell membrane protein complex containing the proteins Gpr124 and Reck. Vanhollebeke et al. then created ‘mosaic’ zebrafish embryos, which contained two genetically distinct types of cells—cells that were missing one of the components of Wnt/β-catenin signaling pathway, and wild-type cells. Visualizing the growth of the vessels showed that all the new blood vessels that sprouted had normal tip cells. However, the cells in the stalk of the sprout could be either normal or missing a signaling protein. These findings demonstrate that Wnt/β-catenin signaling controls the pattern of blood vessel development in the brain by acting specifically on the invasive behaviors of the tip cells of new sprouts, a cellular mechanism that allows efficient organ-specific control of vascularization.