Academic literature on the topic 'Mouse higher order visual cortical areas'

A characterization of the oscillatory activity in the cerebral cortex of the mouse was realized under ketamine anesthesia. Bilateral recordings were obtained from deep layers of primary visual, somatosensory, motor, and medial prefrontal cortex. A slow oscillatory activity consisting of up and down states was detected, the average frequency being 0.97 Hz in all areas. Different parameters of the oscillation were estimated across cortical areas, including duration of up and down states and their variability, speed of state transitions, and population firing rate. Similar values were obtained for all areas except for prefrontal cortex, which showed significant faster down-to-up state transitions, higher firing rate during up states, and more regular cycles. The wave propagation patterns in the anteroposterior axis in motor cortex and the mediolateral axis in visual cortex were studied with multielectrode recordings, yielding speed values between 8 and 93 mm/s. The firing of single units was analyzed with respect to the population activity. The most common pattern was that of neurons firing in >90% of the up states with 1–6 spikes. Finally, fast rhythms (beta, low gamma, and high gamma) were analyzed, all of them showing significantly larger power during up states than in down states. Prefrontal cortex exhibited significantly larger power in both beta and gamma bands (up to 1 order of magnitude larger in the case of high gamma) than the rest of the cortical areas. This study allows us to carry out interareal comparisons and provides a baseline to compare against cortical emerging activity from genetically altered animals.

Studies in the mouse retina have characterized the spatial distribution of an anisotropic ganglion cell and photoreceptor mosaic, which provides a solid foundation to study how the cortex pools from afferent parallel color channels. In particular, the mouse’s retinal mosaic exhibits a gradient of wavelength sensitivity along its dorsoventral axis. Cones at the ventral extreme mainly express S opsin, which is sensitive to ultraviolet (UV) wavelengths. Then, moving toward the retina’s dorsal extreme, there is a transition to M-opsin dominance. Here, we tested the hypothesis that the retina’s opsin gradient is recapitulated in cortical visual areas as a functional map of wavelength sensitivity. We first identified visual areas in each mouse by mapping retinotopy with intrinsic signal imaging (ISI). Next, we measured ISI responses to stimuli along different directions of the S- and M-color plane to quantify the magnitude of S and M input to each location of the retinotopic maps in five visual cortical areas (V1, AL, LM, PM, and RL). The results illustrate a significant change in the S:M-opsin input ratio along the axis of vertical retinotopy that is consistent with the gradient along the dorsoventral axis of the retina. In particular, V1 populations encoding the upper visual field responded to S-opsin contrast with 6.1-fold greater amplitude than to M-opsin contrast. V1 neurons encoding lower fields responded with 4.6-fold greater amplitude to M- than S-opsin contrast. The maps in V1 and higher visual areas (HVAs) underscore the significance of a wavelength sensitivity gradient for guiding the mouse’s behavior. NEW & NOTEWORTHY Two elements of this study are particularly novel. For one, it is the first to quantify cone inputs to mouse visual cortex; we have measured cone input in five visual areas. Next, it is the first study to identify a feature map in the mouse visual cortex that is based on well-characterized anisotropy of cones in the retina; we have identified maps of opsin selectivity in five visual areas.

Rapid adaptation dynamically alters sensory signals to account for recent experience. To understand how adaptation affects sensory processing and perception, we must determine how it impacts the diverse set of cortical and subcortical areas along the hierarchy of the mouse visual system. We find that rapid adaptation strongly impacts neurons in primary visual cortex, the higher visual areas, and the colliculus, consistent with its profound effects on behavior.

In this article, we review the anatomical inputs and outputs to the mouse primary visual cortex, area V1. Our survey of data from the Allen Institute Mouse Connectivity project indicates that mouse V1 is highly interconnected with both cortical and subcortical brain areas. This pattern of innervation allows for computations that depend on the state of the animal and on behavioral goals, which contrasts with simple feedforward, hierarchical models of visual processing. Thus, to have an accurate description of the function of V1 during mouse behavior, its involvement with the rest of the brain circuitry has to be considered. Finally, it remains an open question whether the primary visual cortex of higher mammals displays the same degree of sensorimotor integration in the early visual system.

Studies of area patterning of the neocortex have focused on primary areas, concluding that the primary visual area, V1, is specified by transcription factors (TFs) expressed by progenitors. Mechanisms that determine higher-order visual areas (VHO) and distinguish them from V1 are unknown. We demonstrated a requirement for thalamocortical axon (TCA) input by genetically deleting geniculocortical TCAs and showed that they drive differentiation of patterned gene expression that distinguishes V1 and VHO. Our findings suggest a multistage process for area patterning: TFs expressed by progenitors specify an occipital visual cortical field that differentiates into V1 and VHO; this latter phase requires geniculocortical TCA input to the nascent V1 that determines genetic distinctions between V1 and VHO for all layers and ultimately determines their area-specific functional properties.

The involvement of occipital cortex in sensory processing is not restricted solely to the visual modality. Tactile processing has been shown to modulate higher-order visual and multisensory integration areas in sighted as well as visually deprived subjects; however, the extent of involvement of early visual cortical areas remains unclear. To investigate this issue, we employed functional magnetic resonance imaging in normally sighted, briefly blindfolded subjects with well-defined visuotopic borders as they tactually explored and rated raised-dot patterns. Tactile task performance resulted in significant activation in primary visual cortex (V1) and deactivation of extrastriate cortical regions V2, V3, V3A, and hV4 with greater deactivation in dorsal subregions and higher visual areas. These results suggest that tactile processing affects occipital cortex via two distinct pathways: a suppressive top-down pathway descending through the visual cortical hierarchy and an excitatory pathway arising from outside the visual cortical hierarchy that drives area V1 directly.

Background: Postmortem studies of Alzheimer’s disease (AD) brains not only find amyloid-β (Aβ) and neurofibrillary tangles (NFT) in the primary and associative visual cortical areas, but also reveal a temporally successive sequence of AD pathology beginning in higher-order visual association areas, followed by involvement of lower-order visual processing regions with disease progression, and extending to primary visual cortex in late-stage disease. These findings suggest that neuronal loss associated with Aβ and NFT aggregation in these areas may alter not only the local neuronal activation but also visual neural network activity. Objective: Applying a novel method to identify the visual functional network and investigate the association of the network changes with disease progression. Methods: To investigate the effect of AD on the face-evoked visual-processing network, 8 severe AD (SAD) patients, 11 mild/moderate AD (MAD), and 26 healthy senior (HS) controls undertook a task-fMRI study of viewing face photos. Results: For the HS, the identified group-mean visual-processing network in the ventral pathway started from V1 and ended within the fusiform gyrus. In contrast, this network was disrupted and reduced in the AD patients in a disease-severity dependent manner: for the MAD patients, the network was disrupted and reduced mainly in the higher-order visual association areas; for the SAD patients, the network was nearly absent in the higher-order association areas, and disrupted and reduced in the lower-order areas. Conclusion: This finding is consistent with the current canonical view of the temporally successive sequence of AD pathology through visual cortical areas.

The extrastriate cortex of primates encompasses a substantial portion of the cerebral cortex and is devoted to the higher order processing of visual signals and their dispatch to other parts of the brain. A first step towards the understanding of the function of this cortical tissue is a description of the selectivities of the various neuronal populations for higher order aspects of the image. These selectivities present in the various extrastriate areas support many diverse representations of the scene before the subject. The list of the known selectivities includes that for pattern direction and speed gradients in middle temporal/V5 area; for heading in medial superior temporal visual area, dorsal part; for orientation of nonluminance contours in V2 and V4; for curved boundary fragments in V4 and shape parts in infero-temporal area (IT); and for curvature and orientation in depth from disparity in IT and CIP. The most common putative mechanism for generating such emergent selectivity is the pattern of excitatory and inhibitory linear inputs from the afferent area combined with nonlinear mechanisms in the afferent and receiving area.

Neural mechanisms of selective attention route behaviourally relevant information through brain networks for detailed processing. These attention mechanisms are classically viewed as being solely implemented in the cortex, relegating the thalamus to a passive relay of sensory information. However, this passive view of the thalamus is being revised in light of recent studies supporting an important role for the thalamus in selective attention. Evidence suggests that the first-order thalamic nucleus, the lateral geniculate nucleus, regulates the visual information transmitted from the retina to visual cortex, while the higher-order thalamic nucleus, the pulvinar, regulates information transmission between visual cortical areas, according to attentional demands. This chapter discusses how modulation of thalamic responses, switching the response mode of thalamic neurons, and changes in neural synchrony across thalamo-cortical networks contribute to selective attention.

A major aspect of the function of a thalamic relay is the nature of information being relayed. Thus, the function of the lateral geniculate nucleus can largely be described a relaying retinal information to cortex. That is, identification of the driver (i.e., information bearing) input to a thalamic relay largely defines that relay’s function. Identification of driving inputs to many thalamic nuclei reveal that there are two types: one that emanates from a subcortical source (e.g., retinal input to the lateral geniculate nucleus) and another that emanates from layer 5 of cortex (e.g., much or most of the pulvinar). The lateral geniculate nucleus is an exemplar of a first order thalamic relay, because it represents the first relay of a type of information (e.g., visual) to cortex, whereas the pulvinar is a higher order thalamic relay because it relays information already in cortex between cortical areas. We refer to the latter circuit as transthalamic. Examples of first order relays are the lateral geniculate nucleus (for vision), the ventral posterior nucleus (for somatosensation), and the ventral division of the medial geniculate nucleus (for hearing); the respective higher order relays are the pulvinar, posterior medial nucleus, and dorsal division of the medial geniculate nucleus. Other first and higher order thalamic relays are described, and the significance of the newly appreciated transthalamic pathways is discussed.

Background: Driving is a complex everyday activity that requires the use and integration of different cognitive and psychomotor functions, many of which are known to be affected when under the influence of cannabis (CNB). Given legal implications of drugged-driving and rapidly increasing use of CNB nationwide, there is an urgent need to better understand the effects of CNB on such functions in the context of driving. This longitudinal, double-blind placebo-controlled study investigated the effects of CNB on driving brain-behavior relationships in a controlled simulated environment using functional MRI (fMRI). Methods: N=26 frequent cannabis users were administered 0.5 grams of 13% THC or placebo flower cannabis via a Stortz+Bickel ‘Volcano’ vaporizer using paced inhalation, on separate days at least 1 week apart. On each study day, participants drove a virtual driving simulator (steering wheel, brake, gas pedal) inside an MRI scanner approximately 40 minutes post-dosing. Each fMRI driving session presented a naturalistic simulated environment that unobtrusively engaged drivers with scenarios that tested specific driving skills and response. There were three, approximately 10 min epochs where drivers engaged in task of lane keeping/weaving (LK), lead car following (CF), and safe overtaking (OT). fMRI data were prepared for analyses using the Human Connectome Project pipeline, then subjected to group independent component analysis (ICA) to isolate 50 spatially independent networks. 40 ICA networks were deemed valid and non-noisy. Network regions in these components were identified using 387 parcel locations, incorporating a cortical parcellation atlas (Glasser et al 2016) and detailed subcortical labels. A placebo minus high difference connectivity map was generated for each subject. A similar placebo minus high behavioral score was generated for each subject and then subjected to a principal component analysis (PCA) to reduce it to 8 orthogonal behavioral factors. Of the 8 driving behavior factors, two represented CF events (F1 and F5), three LK (F3, F4, and F8), and three OT (F2, F6, and F7). Driving behavior factors were evaluated for linear association with connectivity maps via FSL’s randomize (p<0.01 FWE-corrected significance). Results:Across all components examined, we found connectivity differences between placebo v high THC within right motion-sensitive visual cortex (parcel FST) (visual) and right superior temporal gyrus (social cognition) to positively correlate with LK driving performance. The strongest brain-behavior relationships were found for OT-related behavioral factors. Connectivity in left dorsolateral parcel a9-46v (cognitive flexibility) and right motor cortex parcel 3b (somatosensory) correlated negatively with F6 (OT). A left superior frontal parcel (higher order cognition/working memory) correlated negatively with F7 (OT) and finally R inferior frontal gyrus (response inhibition and reward deduction) correlated positively with F7 (OT). Conclusion: Our preliminary analyses yield a complex yet informative picture of key brain areas sensitive to acute CNB exposure on different driving behaviors using a simulated environment, further underscoring the impact of substance use on driving as a potential public safety issue.