Automated placement of stereotactic injections using a laser scan of the skull

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High-field Functional Magnetic Resonance Imaging of Vocalization Processing in Marmosets

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Vocalizations are behaviorally critical sounds, and this behavioral importance is reflected in the ascending auditory system, where conspecific vocalizations are increasingly over-represented at higher processing stages. Recent evidence suggests that, in macaques, this increasing selectivity for vocalizations might culminate in a cortical region that is densely populated by vocalization-preferring neurons. Such a region might be a critical node in the representation of vocal communication sounds, underlying the recognition of vocalization type, caller and social context. These results raise the questions of whether cortical specializations for vocalization processing exist in other species, their cortical location, and their relationship to the auditory processing hierarchy. To explore cortical specializations for vocalizations in another species, we performed high-field fMRI of the auditory cortex of a vocal New World primate, the common marmoset (Callithrix jacchus). Using a sparse imaging paradigm, we discovered a caudal-rostral gradient for the processing of conspecific vocalizations in marmoset auditory cortex, with regions of the anterior temporal lobe close to the temporal pole exhibiting the highest preference for vocalizations. These results demonstrate similar cortical specializations for vocalization processing in macaques and marmosets, suggesting that cortical specializations for vocal processing might have evolved before the lineages of these species diverged.

A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures

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Here we present a test-retest dataset of functional magnetic resonance imaging (fMRI) data acquired at rest. 22 participants were scanned during two sessions spaced one week apart. Each session includes two 1.5 mm isotropic whole-brain scans and one 0.75 mm isotropic scan of the prefrontal cortex, giving a total of six timepoints. Additionally, the dataset includes measures of mood, sustained attention, blood pressure, respiration, pulse, and the content of self-generated thoughts (mind wandering). This data enables the investigation of sources of both intra- and inter-session variability not only limited to physiological changes, but also including alterations in cognitive and affective states, at high spatial resolution. The dataset is accompanied by a detailed experimental protocol and source code of all stimuli used. A web-based repository for collecting and sharing unthresholded statistical maps of the human brain

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Here we present NeuroVault — a web based repository that allows researchers to store, share, visualise, and decode statistical maps of the human brain. NeuroVault is easy to use and employs modern web technologies to provide informative visualisation of data without the need to install additional software. In addition it leverages the power of the Neurosynth database to provide cognitive decoding of deposited maps. NeuroVault is also a resource for researchers interested in conducting meta- and coactivation analyses. All of the data is exposed through a public REST API enabling other services and tools to take advantage of it.

Selective corticostriatal plasticity during acquisition of an auditory discrimination task

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Perceptual decisions are based on the activity of sensory cortical neurons, but how organisms learn to transform this activity into appropriate actions remains unknown. Projections from the auditory cortex to the auditory striatum carry information that drives decisions in an auditory frequency discrimination task1. To assess the role of these projections in learning, we developed a Channelrhodopsin-2-based assay to selectively probe for synaptic plasticity associated with corticostriatal neurons representing different frequencies. Here we report that learning this auditory discrimination preferentially potentiates corticostriatal synapses from neurons representing either high or low frequencies, depending on reward contingencies. We observed frequency-dependent corticostriatal potentiation in vivo over the course of training, and in vitro in striatal brain slices. Our findings suggest a model in which selective potentiation of inputs representing different components of a sensory stimulus enables the learned transformation of sensory input into actions.

Explaining the hierarchy of visual representational geometries by remixing of features from many computational vision models

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Visual processing in cortex happens through a hierarchy of increasingly sophisticated representations. Here we explore a very wide range of model representations (29 models), testing their categorization performance (animate/inanimate) and their ability to account for the representational geometry of brain regions along the visual hierarchy (V1, V2, V3, V4, and LO). We also created new model instantiations (85 model instantiations in total) by reweighting and remixing of the model features. Reweighting and remixing was based on brain responses to an independent training set of 1750 images. We assessed the models with representational similarity analysis (RSA), which characterizes the geometry of a representation by a representational dissimilarity matrix (RDM). In this study, the RDM is either computed on the basis of the model features or on the basis of predicted voxel responses. Voxel responses are predicted by linear combinations of the model features. The model features are linearly remixed so as to best explain the voxel responses (as in voxel/population receptive-field modelling). This new approach of combining RSA with voxel receptive field modelling may help bridge the gap between the two methods. We found that early visual areas are best accounted for by a Gabor wavelet pyramid (GWP) model. The GWP implementations we used performed similarly with and without remixing, suggesting that the original features already approximate the representational space, obviating the need for remixing or reweighting. The lateral occipital region (LO), a higher visual representation, was best explained by the higher layers of a deep convolutional network (Krizhevsky et al., 2012). However, this model could explain the LO representation only after appropriate remixing of its feature set. Remixed RSA takes a step in an important direction, where each computational model representation is explored more broadly by considering not only its representational geometry, but the set of all geometries within reach of a linear transform. The exploration of many models and many brain areas may lead to a better understanding of the processing stages in the visual hierarchy, from low-level image representations in V1 to visuo-semantic representations in higher-level visual areas.

Quantitative differences in developmental profiles of spontaneous activity in cortical and hippocampal cultures

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Background: Neural circuits can spontaneously generate complex spatiotemporal firing patterns during development. This spontaneous activity is thought to help guide development of the nervous system. In this study, we had two aims. First, to characterise the changes in spontaneous activity in cultures of developing networks of either hippocampal or cortical neurons dissociated from mouse. Second, to assess whether there are any functional differences in the patterns of activity in hippocampal and cortical networks. Results: We used multielectrode arrays to record the development of spontaneous activity in cultured networks of either hippocampal or cortical neurons every two or three days for the first month after plating. Within a few days of culturing, networks exhibited spontaneous activity. This activity strengthened and then stabilised typically around 21 days in vitro. We quantified the activity patterns in hippocampal and cortical networks using eleven features. Three out of eleven features showed striking differences in activity between hippocampal and cortical networks. 1: Interburst intervals are less variable in spike trains from hippocampal cultures. 2: Hippocampal networks have higher correlations. 3: Hippocampal networks generate more robust theta bursting patterns. Machine learning techniques confirmed that these differences in patterning are sufficient to reliably classify recordings at any given age as either hippocampal or cortical networks. Conclusions: Although cultured networks of hippocampal and cortical networks both generate spontaneous activity that changes over time, at any given time we can reliably detect differences in the activity patterns. We anticipate that this quantitative framework could have applications in many areas, including neurotoxicity testing and for characterising phenotype of different mutant mice. All code and data relating to this report are freely available for others to use.