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The brain's energy budget can be non-invasively assessed with different imaging modalities such as functional MRI (fMRI) and PET (fPET), which are sensitive to oxygen and glucose demands, respectively. The introduction of hybrid PET/MRI systems further enables the simultaneous acquisition of these parameters. Although a recently developed method offers the quantification of task-specific changes in glucose metabolism (CMRGlu) in a single measurement, direct comparison of the two imaging modalities is still difficult because of the different temporal resolutions. Thus, we optimized the protocol and systematically assessed shortened task durations of fPET to approach that of fMRI. Twenty healthy subjects (9 male) underwent one measurement on a hybrid PET/MRI scanner. During the scan, tasks were completed in four blocks for fMRI (4 × 30 s blocks) and fPET: participants tapped the fingers of their right hand repeatedly to the thumb while watching videos of landscapes. For fPET, subjects were randomly assigned to groups of n = 5 with varying task durations of 10, 5, 2 and 1 min, where task durations were kept constant within a measurement. The radiolabeled glucose analogue [18F]FDG was administered as 20% bolus plus constant infusion. The bolus increases the signal-to-noise ratio and leaves sufficient activity to detect task-related effects but poses additional challenges due to a discontinuity in the tracer uptake. First, three approaches to remove task effects from the baseline term were evaluated: (1) multimodal, based on the individual fMRI analysis, (2) atlas-based by removing presumably activated regions and (3) model-based by fitting the baseline with exponential functions. Second, we investigated the need to capture the arterial input function peak with automatic blood sampling for the quantification of CMRGlu. We finally compared the task-specific activation obtained from fPET and fMRI qualitatively and statistically. CMRGlu quantified only with manual arterial samples showed a strong correlation to that obtained with automatic sampling (r = 0.9996). The multimodal baseline definition was superior to the other tested approaches in terms of residuals (p < 0.001). Significant task-specific changes in CMRGlu were found in the primary visual and motor cortices (tM1 = 18.7 and tV1 = 18.3). Significant changes of fMRI activation were found in the same areas (tM1 = 16.0 and tV1 = 17.6) but additionally in the supplementary motor area, ipsilateral motor cortex and secondary visual cortex. Post-hoc t-tests showed strongest effects for task durations of 5 and 2 min (all p < 0.05 FWE corrected), whereas 1 min exhibited pronounced unspecific activation. Percent signal change (PSC) was higher for CMRGlu (∼18%–27%) compared to fMRI (∼2%). No significant association between PSC of task-specific CMRGlu and fMRI was found (r = 0.26). Using a bolus plus constant infusion protocol, the necessary task duration for reliable quantification of task-specific CMRGlu could be reduced to 5 and 2 min, therefore, approaching that of fMRI. Important for valid quantification is a correct baseline definition, which was ideal when task-relevant voxels were determined with fMRI. The absence of a correlation and the different activation pattern between fPET and fMRI suggest that glucose metabolism and oxygen demand capture complementary aspects of energy demands. •Quantification of task-specific CMRGlu with 20% bolus plus constant infusion.•Functional PET task durations down to 1 min were evaluated.•Active primary regions overlap between BOLD and CMRGlu.•No significant correlation between BOLD and CMRGlu.

Two-photon calcium imaging and electron microscopy were used to explore the relationship between structure and function in mouse primary visual cortex, showing that layer 2/3 neurons are connected in subnetworks, that pyramidal neurons with similar orientation selectivity preferentially form synapses with each other, and that neurons with similar orientation tuning form larger synapses; this study exemplifies functional connectomics as a powerful method for studying the organizational logic of cortical networks. The connectomics of excitatory cortical networks To explore the relationship between structure and function in cortical networks, Wei-Chung Allen Lee and colleagues combined two-photon calcium imaging and electron microscopy in mouse primary visual cortex. They found that layer 2/3 neurons are organized into subnetworks, that pyramidal neurons with similar orientation selectivity preferentially form synapses with each other, and that neurons with similar orientation tuning form larger synapses. This study exemplifies functional connectomics as a powerful method for studying the organizational logic of cortical networks. Circuits in the cerebral cortex consist of thousands of neurons connected by millions of synapses. A precise understanding of these local networks requires relating circuit activity with the underlying network structure. For pyramidal cells in superficial mouse visual cortex (V1), a consensus is emerging that neurons with similar visual response properties excite each other 1 , 2 , 3 , 4 , 5 , but the anatomical basis of this recurrent synaptic network is unknown. Here we combined physiological imaging and large-scale electron microscopy to study an excitatory network in V1. We found that layer 2/3 neurons organized into subnetworks defined by anatomical connectivity, with more connections within than between groups. More specifically, we found that pyramidal neurons with similar orientation selectivity preferentially formed synapses with each other, despite the fact that axons and dendrites of all orientation selectivities pass near (<5 μm) each other with roughly equal probability. Therefore, we predict that mechanisms of functionally specific connectivity take place at the length scale of spines. Neurons with similar orientation tuning formed larger synapses, potentially enhancing the net effect of synaptic specificity. With the ability to study thousands of connections in a single circuit, functional connectomics is proving a powerful method to uncover the organizational logic of cortical networks.

The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex. Single-cell transcriptomics of more than 20,000 cells from two functionally distinct areas of the mouse neocortex identifies 133 transcriptomic types, and provides a foundation for understanding the diversity of cortical cell types.

Sensory processing in the neocortex requires both feedforward and feedback information flow between cortical areas 1 . In feedback processing, higher-level representations provide contextual information to lower levels, and facilitate perceptual functions such as contour integration and figure–ground segmentation 2 , 3 . However, we have limited understanding of the circuit and cellular mechanisms that mediate feedback influence. Here we use long-range all-optical connectivity mapping in mice to show that feedback influence from the lateromedial higher visual area (LM) to the primary visual cortex (V1) is spatially organized. When the source and target of feedback represent the same area of visual space, feedback is relatively suppressive. By contrast, when the source is offset from the target in visual space, feedback is relatively facilitating. Two-photon calcium imaging data show that this facilitating feedback is nonlinearly integrated in the apical tuft dendrites of V1 pyramidal neurons: retinotopically offset (surround) visual stimuli drive local dendritic calcium signals indicative of regenerative events, and two-photon optogenetic activation of LM neurons projecting to identified feedback-recipient spines in V1 can drive similar branch-specific local calcium signals. Our results show how neocortical feedback connectivity and nonlinear dendritic integration can together form a substrate to support both predictive and cooperative contextual interactions. Feedback influence from a higher visual area to primary visual cortex in mice engages nonlinear dendritic integration.

How is it that groups of neurons dispersed through the brain interact to generate complex behaviors? Three papers in this issue present brain-scale studies of neuronal activity and dynamics (see the Perspective by Huk and Hart). Allen et al. found that in thirsty mice, there is widespread neural activity related to stimuli that elicit licking and drinking. Individual neurons encoded task-specific responses, but every brain area contained neurons with different types of response. Optogenetic stimulation of thirst-sensing neurons in one area of the brain reinstated drinking and neuronal activity across the brain that previously signaled thirst. Gründemann et al. investigated the activity of mouse basal amygdala neurons in relation to behavior during different tasks. Two ensembles of neurons showed orthogonal activity during exploratory and nonexploratory behaviors, possibly reflecting different levels of anxiety experienced in these areas. Stringer et al. analyzed spontaneous neuronal firing, finding that neurons in the primary visual cortex encoded both visual information and motor activity related to facial movements. The variability of neuronal responses to visual stimuli in the primary visual area is mainly related to arousal and reflects the encoding of latent behavioral states. Science , this issue p. eaav3932 , p. eaav8736 , p. eaav7893 ; see also p. 236 Neurons in the primary visual cortex encode both visual information and motor activity. Neuronal populations in sensory cortex produce variable responses to sensory stimuli and exhibit intricate spontaneous activity even without external sensory input. Cortical variability and spontaneous activity have been variously proposed to represent random noise, recall of prior experience, or encoding of ongoing behavioral and cognitive variables. Recording more than 10,000 neurons in mouse visual cortex, we observed that spontaneous activity reliably encoded a high-dimensional latent state, which was partially related to the mouse’s ongoing behavior and was represented not just in visual cortex but also across the forebrain. Sensory inputs did not interrupt this ongoing signal but added onto it a representation of external stimuli in orthogonal dimensions. Thus, visual cortical population activity, despite its apparently noisy structure, reliably encodes an orthogonal fusion of sensory and multidimensional behavioral information.

Cognitive control guides behaviour by controlling what, when, and how information is represented in the brain 1 . For example, attention controls sensory processing; top-down signals from prefrontal and parietal cortex strengthen the representation of task-relevant stimuli 2 – 4 . A similar ‘selection’ mechanism is thought to control the representations held ‘in mind’—in working memory 5 – 10 . Here we show that shared neural mechanisms underlie the selection of items from working memory and attention to sensory stimuli. We trained rhesus monkeys to switch between two tasks, either selecting one item from a set of items held in working memory or attending to one stimulus from a set of visual stimuli. Neural recordings showed that similar representations in prefrontal cortex encoded the control of both selection and attention, suggesting that prefrontal cortex acts as a domain-general controller. By contrast, both attention and selection were represented independently in parietal and visual cortex. Both selection and attention facilitated behaviour by enhancing and transforming the representation of the selected memory or attended stimulus. Specifically, during the selection task, memory items were initially represented in independent subspaces of neural activity in prefrontal cortex. Selecting an item caused its representation to transform from its own subspace to a new subspace used to guide behaviour. A similar transformation occurred for attention. Our results suggest that prefrontal cortex controls cognition by dynamically transforming representations to control what and when cognitive computations are engaged. The prefrontal cortex in monkeys controls working memory in a similar way to attention, by selectively transforming the representations of remembered items.

Brain regions are often topographically connected: nearby locations within one brain area connect with nearby locations in another area. Mapping these connection topographies, or ‘connectopies’ in short, is crucial for understanding how information is processed in the brain. Here, we propose principled, fully data-driven methods for mapping connectopies using functional magnetic resonance imaging (fMRI) data acquired at rest by combining spectral embedding of voxel-wise connectivity ‘fingerprints’ with a novel approach to spatial statistical inference. We apply the approach in human primary motor and visual cortex, and show that it can trace biologically plausible, overlapping connectopies in individual subjects that follow these regions' somatotopic and retinotopic maps. As a generic mechanism to perform inference over connectopies, the new spatial statistics approach enables rigorous statistical testing of hypotheses regarding the fine-grained spatial profile of functional connectivity and whether that profile is different between subjects or between experimental conditions. The combined framework offers a fundamental alternative to existing approaches to investigating functional connectivity in the brain, from voxel- or seed-pair wise characterizations of functional association, towards a full, multivariate characterization of spatial topography. •We propose methods for mapping individualised connectopies using resting-state fMRI.•These methods include a spatial statistics approach for inference over connectopies.•The approach can tease apart overlapping connectopies that coexist within an area.•We demonstrate the methods in somatotopic and retinotopic cortex.

Humans are remarkably efficient at categorizing natural scenes. In fact, scene categories can be decoded from functional MRI (fMRI) data throughout the ventral visual cortex, including the primary visual cortex, the parahippocampal place area (PPA), and the retrosplenial cortex (RSC). Here we ask whether, and where, we can still decode scene category if we reduce the scenes to mere lines. We collected fMRI data while participants viewed photographs and line drawings of beaches, city streets, forests, highways, mountains, and offices. Despite the marked difference in scene statistics, we were able to decode scene category from fMRI data for line drawings just as well as from activity for color photographs, in primary visual cortex through PPA and RSC. Even more remarkably, in PPA and RSC, error patterns for decoding from line drawings were very similar to those from color photographs. These data suggest that, in these regions, the information used to distinguish scene category is similar for line drawings and photographs. To determine the relative contributions of local and global structure to the human ability to categorize scenes, we selectively removed long or short contours from the line drawings. In a category-matching task, participants performed significantly worse when long contours were removed than when short contours were removed. We conclude that global scene structure, which is preserved in line drawings, plays an integral part in representing scene categories.

The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically 1 . However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset—part of the Allen Brain Observatory 2 —that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas 3 . We find that four classical hierarchical measures—response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale—are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas. A large, open dataset containing parallel recordings from six visual cortical and two thalamic areas of the mouse brain is presented, from which the relative timing of activity in response to visual stimuli and behaviour is used to construct a hierarchy scheme that corresponds to anatomical connectivity data.