BRICseq Bridges Brain-wide Interregional Connectivity to Neural Activity and Gene Expression in Single Animals.

Comprehensive analysis of neuronal networks requires brain-wide measurement of connectivity, activity, and gene expression. Although high-throughput methods are available for mapping brain-wide activity and transcriptomes, comparable methods for mapping region-to-region connectivity remain slow and expensive because they require averaging across hundreds of brains. Here we describe BRICseq (brain-wide individual animal connectome sequencing), which leverages DNA barcoding and sequencing to map connectivity from single individuals in a few weeks and at low cost. Applying BRICseq to the mouse neocortex, we find that region-to-region connectivity provides a simple bridge relating transcriptome to activity: the spatial expression patterns of a few genes predict region-to-region connectivity, and connectivity predicts activity correlations. We also exploited BRICseq to map the mutant BTBR mouse brain, which lacks a corpus callosum, and recapitulated its known connectopathies. BRICseq allows individual laboratories to compare how age, sex, environment, genetics, and species affect neuronal wiring and to integrate these with functional activity and gene expression.

Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data.

Widefield calcium imaging enables recording of large-scale neural activity across the mouse dorsal cortex. In order to examine the relationship of these neural signals to the resulting behavior, it is critical to demix the recordings into meaningful spatial and temporal components that can be mapped onto well-defined brain regions. However, no current tools satisfactorily extract the activity of the different brain regions in individual mice in a data-driven manner, while taking into account mouse-specific and preparation-specific differences. Here, we introduce Localized semi-Nonnegative Matrix Factorization (LocaNMF), a method that efficiently decomposes widefield video data and allows us to directly compare activity across multiple mice by outputting mouse-specific localized functional regions that are significantly more interpretable than more traditional decomposition techniques. Moreover, it provides a natural subspace to directly compare correlation maps and neural dynamics across different behaviors, mice, and experimental conditions, and enables identification of task- and movement-related brain regions.

Deviant Processing in the Primary Somatosensory Cortex.

Stimulus-specific adaptation (SSA) to repetitive stimulation has been proposed to separate behaviorally relevant features from a stream of continuous sensory information. However, the exact mechanisms giving rise to SSA and cortical deviance detection are not well understood. We therefore used an oddball paradigm and multicontact electrodes to characterize single-neuron and local field potential responses to various deviant stimuli across the rat somatosensory cortex. Changing different single-whisker stimulus features evoked robust SSA in individual cortical neurons over a wide range of stimulus repetition rates (0.25-80 Hz). Notably, SSA was weakest in the granular input layer and significantly stronger in the supra- and infragranular layers, suggesting that a major part of SSA is generated within cortex. Moreover, we found a small subset of neurons in the granular layer with a deviant-specific late response, occurring roughly 200 ms after stimulus offset. This late deviant response exhibited true-deviance detection properties that were not explained by depression of sensory inputs. Our results show that deviant responses are actively amplified within cortex and contain an additional late component that is sensitive for context-specific sensory deviations. This strongly implicates deviance detection as a feature of intracortical stimulus processing beyond simple sensory input depression.

Effects of neural synchrony on surface EEG.

It has long been assumed that the surface electroencephalography (EEG) signal depends on both the amplitude and spatial synchronization of underlying neural activity, though isolating their respective contribution remains elusive. To address this, we made simultaneous surface EEG measurements along with intracortical recordings of local field potentials (LFPs) in the primary visual cortex of behaving nonhuman primates. We found that trial-by-trial fluctuations in EEG power could be explained by a linear combination of LFP power and interelectrode temporal synchrony. This effect was observed in both stimulus and stimulus-free conditions and was particularly strong in the gamma range (30-100 Hz). Subsequently, we used pharmacological manipulations to show that neural synchrony can produce a positively modulated EEG signal even when the LFP signal is negatively modulated. Taken together, our results demonstrate that neural synchrony can modulate EEG signals independently of amplitude changes in neural activity. This finding has strong implications for the interpretation of EEG in basic and clinical research, and helps reconcile EEG response discrepancies observed in different modalities (e.g., EEG vs. functional magnetic resonance imaging) and different spatial scales (e.g., EEG vs. intracranial EEG).

An organic brain-inspired platform with neurotransmitter closed-loop control, actuation and reinforcement learning.

Organic neuromorphic platforms have recently received growing interest for the implementation and integration of artificial and hybrid neuronal networks. Here, achieving closed-loop and learning/training processes as in the human brain is still a major challenge especially exploiting time-dependent biosignalling such as neurotransmitter release. Here, we present an integrated organic platform capable of cooperating with standard silicon technologies, to achieve brain-inspired computing via adaptive synaptic potentiation and depression, in a closed-loop fashion. The microfabricated platform could be interfaced and control a robotic hand which ultimately was able to learn the grasping of differently sized objects, autonomously.

Novel two-alternative forced choice paradigm for bilateral vibrotactile whisker frequency discrimination in head-fixed mice and rats.

Rats and mice receive a constant bilateral stream of tactile information with their large mystacial vibrissae when navigating in their environment. In a two-alternative forced choice paradigm (2-AFC), head-fixed rats and mice learned to discriminate vibrotactile frequencies applied simultaneously to individual whiskers on the left and right sides of the snout. Mice and rats discriminated 90-Hz pulsatile stimuli from pulsatile stimuli with lower repetition frequencies (10-80 Hz) but with identical kinematic properties in each pulse. Psychometric curves displayed an average perceptual threshold of 50.6-Hz and 53.0-Hz frequency difference corresponding to Weber fractions of 0.56 and 0.58 in mice and rats, respectively. Both species performed >400 trials a day (>200 trials per session, 2 sessions/day), with a peak performance of >90% correct responses. In general, rats and mice trained in the identical task showed comparable psychometric curves. Behavioral readouts, such as reaction times, learning rates, trial omissions, and impulsivity, were also very similar in the two species. Furthermore, whisking of the animals before stimulus presentation reduced task performance. This behavioral paradigm, combined with whisker position tracking, allows precise stimulus control in the 2-AFC task for head-fixed rodents. It is compatible with state-of-the-art neurophysiological recording techniques, such as electrophysiology and two-photon imaging, and therefore represents a valuable framework for neurophysiological investigations of perceptual decision-making.

Validation of transparent and flexible neural implants for simultaneous electrophysiology, functional imaging, and optogenetics.

The combination of electrophysiology and neuroimaging methods allows the simultaneous measurement of electrical activity signals with calcium dynamics from single neurons to neuronal networks across distinct brain regions in vivo. While traditional electrophysiological techniques are limited by photo-induced artefacts and optical occlusion for neuroimaging, different types of transparent neural implants have been proposed to resolve these issues. However, reproducing proposed solutions is often challenging and it remains unclear which approach offers the best properties for long-term chronic multimodal recordings. We therefore created a streamlined fabrication process to produce, and directly compare, two types of transparent surface micro-electrocorticography (µECoG) implants: nano-mesh gold structures (m-µECoGs) versus a combination of solid gold interconnects and PEDOT:PSS-based electrodes (pp-µECoGs). Both implants allowed simultaneous multimodal recordings but pp-µECoGs offered the best overall electrical, electrochemical, and optical properties with negligible photo-induced artefacts to light wavelengths of interest. Showing functional chronic stability for up to four months, pp-µECoGs also allowed the simultaneous functional mapping of electrical and calcium neural signals upon visual and tactile stimuli during widefield imaging. Moreover, recordings during two-photon imaging showed no visible signal attenuation and enabled the correlation of network dynamics across brain regions to individual neurons located directly below the transparent electrical contacts.

Engaged decision-makers align spontaneous movements to stereotyped task demands.

Neural activity during sensory-guided decision-making is strongly modulated by animal movements. Although the impact of movements on neural activity is now well-documented, the relationship between these movements and behavioral performance remains unclear. To understand this relationship, we first tested whether the magnitude of animal movements (assessed with posture analysis of 28 individual body parts) was correlated with performance on a perceptual decision-making task. No strong relationship was present, suggesting that task performance is not affected by the magnitude of movements. We then tested if performance instead depends on movement timing and trajectory. We partitioned the movements into two groups: task-aligned movements that were well predicted by task events (such as the onset of the sensory stimulus or choice) and task independent movement (TIM) that occurred independently of task events. TIM had a reliable, inverse correlation with performance in head-restrained mice and freely moving rats. This argues that certain movements, defined by their timing and trajectories relative to task events, might indicate periods of engagement or disengagement in the task. To confirm this, we compared TIM to the latent behavioral states recovered by a hidden Markov model with Bernoulli generalized linear model observations (GLM-HMM) and found these, again, to be inversely correlated. Finally, we examined the impact of these behavioral states on neural activity measured with widefield calcium imaging. The engaged state was associated with widespread increased activity, particularly during the delay period. However, a linear encoding model could account for more overall variance in neural activity in the disengaged state. Our analyses demonstrate that this is likely because uninstructed movements had a greater impact on neural activity during disengagement. Taken together, these findings suggest that TIM is informative about the internal state of engagement, and that movements and state together have a major impact on neural activity.

Toward the Next Generation of Neural Iontronic Interfaces.

Neural interfaces are evolving at a rapid pace owing to advances in material science and fabrication, reduced cost of scalable complementary metal oxide semiconductor (CMOS) technologies, and highly interdisciplinary teams of researchers and engineers that span a large range from basic to applied and clinical sciences. This study outlines currently established technologies, defined as instruments and biological study systems that are routinely used in neuroscientific research. After identifying the shortcomings of current technologies, such as a lack of biocompatibility, topological optimization, low bandwidth, and lack of transparency, it maps out promising directions along which progress should be made to achieve the next generation of symbiotic and intelligent neural interfaces. Lastly, it proposes novel applications that can be achieved by these developments, ranging from the understanding and reproduction of synaptic learning to live-long multimodal measurements to monitor and treat various neuronal disorders.

Cortical glutamatergic projection neuron types contribute to distinct functional subnetworks.

The cellular basis of cerebral cortex functional architecture remains not well understood. A major challenge is to monitor and decipher neural network dynamics across broad cortical areas yet with projection-neuron-type resolution in real time during behavior. Combining genetic targeting and wide-field imaging, we monitored activity dynamics of subcortical-projecting (PTFezf2) and intratelencephalic-projecting (ITPlxnD1) types across dorsal cortex of mice during different brain states and behaviors. ITPlxnD1 and PTFezf2 neurons showed distinct activation patterns during wakeful resting, during spontaneous movements and upon sensory stimulation. Distinct ITPlxnD1 and PTFezf2 subnetworks were dynamically tuned to different sensorimotor components of a naturalistic feeding behavior, and optogenetic inhibition of ITsPlxnD1 and PTsFezf2 in subnetwork nodes disrupted distinct components of this behavior. Lastly, ITPlxnD1 and PTFezf2 projection patterns are consistent with their subnetwork activation patterns. Our results show that, in addition to the concept of columnar organization, dynamic areal and projection-neuron-type specific subnetworks are a key feature of cortical functional architecture linking microcircuit components with global brain networks.

Pyramidal cell types drive functionally distinct cortical activity patterns during decision-making.

Understanding how cortical circuits generate complex behavior requires investigating the cell types that comprise them. Functional differences across pyramidal neuron (PyN) types have been observed within cortical areas, but it is not known whether these local differences extend throughout the cortex, nor whether additional differences emerge when larger-scale dynamics are considered. We used genetic and retrograde labeling to target pyramidal tract, intratelencephalic and corticostriatal projection neurons and measured their cortex-wide activity. Each PyN type drove unique neural dynamics, both at the local and cortex-wide scales. Cortical activity and optogenetic inactivation during an auditory decision task revealed distinct functional roles. All PyNs in parietal cortex were recruited during perception of the auditory stimulus, but, surprisingly, pyramidal tract neurons had the largest causal role. In frontal cortex, all PyNs were required for accurate choices but showed distinct choice tuning. Our results reveal that rich, cell-type-specific cortical dynamics shape perceptual decisions.

A flexible Python-based touchscreen chamber for operant conditioning reveals improved visual perception of cardinal orientations in mice.

Natural scenes are composed of a wide range of edge angles and spatial frequencies, with a strong overrepresentation of vertical and horizontal edges. Correspondingly, many mammalian species are much better at discriminating these cardinal orientations compared to obliques. A potential reason for this increased performance could be an increased number of neurons in the visual cortex that are tuned to cardinal orientations, which is likely to be an adaptation to the natural scene statistics. Such biased angular tuning has recently been shown in the mouse primary visual cortex. However, it is still unknown if mice also show a perceptual dominance of cardinal orientations. Here, we describe the design of a novel custom-built touchscreen chamber that allows testing natural scene perception and orientation discrimination performance by applying different task designs. Using this chamber, we applied an iterative convergence towards orientation discrimination thresholds for cardinal or oblique orientations in different cohorts of mice. Surprisingly, the expert discrimination performance was similar for both groups but showed large inter-individual differences in performance and training time. To study the discrimination of cardinal and oblique stimuli in the same mice, we, therefore, applied, a different training regime where mice learned to discriminate cardinal and oblique gratings in parallel. Parallel training revealed a higher task performance for cardinal orientations in an early phase of the training. The performance for both orientations became similar after prolonged training, suggesting that learning permits equally high perceptual tuning towards oblique stimuli. In summary, our custom-built touchscreen chamber offers a flexible tool to test natural visual perception in rodents and revealed a training-induced increase in the perception of oblique gratings. The touchscreen chamber is entirely open-source, easy to build, and freely available to the scientific community to conduct visual or multimodal behavioral studies. It is also based on the FAIR principles for data management and sharing and could therefore serve as a catalyst for testing the perception of complex and natural visual stimuli across behavioral labs.

Minimally-invasive insertion strategy and in vivo evaluation of multi-shank flexible intracortical probes.

Chronically implanted neural probes are powerful tools to decode brain activity however, recording population and spiking activity over long periods remains a major challenge. Here, we designed and fabricated flexible intracortical Michigan-style arrays with a shank cross-section per electrode of 250 µm[Formula: see text] utilizing the polymer paryleneC with the goal to improve the immune acceptance. As flexible neural probes are unable to penetrate the brain due to the low buckling force threshold, a tissue-friendly insertion system was developed by reducing the effective shank length. The insertion strategy enabled the implantation of the four, bare, flexible shanks up to 2 mm into the mouse brain without increasing the implantation footprint and therefore, minimizing the acute trauma. In acute recordings from the mouse somatosensory cortex and the olfactory bulb, we demonstrated that the flexible probes were able to simultaneously detect local field potentials as well as single and multi-unit activity. Additionally, the flexible arrays outperformed stiff probes with respect to yield of single unit activity. Following the successful in vivo validation, we further improved the microfabrication towards a double-metal-layer process, and were able to double the number of electrodes per shank by keeping the shank width resulting in a cross-section per electrode of 118 µm[Formula: see text].

Chronic, cortex-wide imaging of specific cell populations during behavior.

Measurements of neuronal activity across brain areas are important for understanding the neural correlates of cognitive and motor processes such as attention, decision-making and action selection. However, techniques that allow cellular resolution measurements are expensive and require a high degree of technical expertise, which limits their broad use. Wide-field imaging of genetically encoded indicators is a high-throughput, cost-effective and flexible approach to measure activity of specific cell populations with high temporal resolution and a cortex-wide field of view. Here we outline our protocol for assembling a wide-field macroscope setup, performing surgery to prepare the intact skull and imaging neural activity chronically in behaving, transgenic mice. Further, we highlight a processing pipeline that leverages novel, cloud-based methods to analyze large-scale imaging datasets. The protocol targets laboratories that are seeking to build macroscopes, optimize surgical procedures for long-term chronic imaging and/or analyze cortex-wide neuronal recordings. The entire protocol, including steps for assembly and calibration of the macroscope, surgical preparation, imaging and data analysis, requires a total of 8 h. It is designed to be accessible to laboratories with limited expertise in imaging methods or interest in high-throughput imaging during behavior.

Harnessing behavioral diversity to understand neural computations for cognition.

With the increasing acquisition of large-scale neural recordings comes the challenge of inferring the computations they perform and understanding how these give rise to behavior. Here, we review emerging conceptual and technological advances that begin to address this challenge, garnering insights from both biological and artificial neural networks. We argue that neural data should be recorded during rich behavioral tasks, to model cognitive processes and estimate latent behavioral variables. Careful quantification of animal movements can also provide a more complete picture of how movements shape neural dynamics and reflect changes in brain state, such as arousal or stress. Artificial neural networks (ANNs) could serve as artificial model organisms to connect neural dynamics and rich behavioral data. ANNs have already begun to reveal how a wide range of different behaviors can be implemented, generating hypotheses about how observed neural activity might drive behavior and explaining diversity in behavioral strategies.

Single-trial neural dynamics are dominated by richly varied movements.

When experts are immersed in a task, do their brains prioritize task-related activity? Most efforts to understand neural activity during well-learned tasks focus on cognitive computations and task-related movements. We wondered whether task-performing animals explore a broader movement landscape and how this impacts neural activity. We characterized movements using video and other sensors and measured neural activity using widefield and two-photon imaging. Cortex-wide activity was dominated by movements, especially uninstructed movements not required for the task. Some uninstructed movements were aligned to trial events. Accounting for them revealed that neurons with similar trial-averaged activity often reflected utterly different combinations of cognitive and movement variables. Other movements occurred idiosyncratically, accounting for trial-by-trial fluctuations that are often considered 'noise'. This held true throughout task-learning and for extracellular Neuropixels recordings that included subcortical areas. Our observations argue that animals execute expert decisions while performing richly varied, uninstructed movements that profoundly shape neural activity.

Tactile frequency discrimination is enhanced by circumventing neocortical adaptation.

Neocortical responses typically adapt to repeated sensory stimulation, improving sensitivity to stimulus changes, but possibly also imposing limitations on perception. For example, it is unclear whether information about stimulus frequency is perturbed by adaptation or encoded by precise response timing. We addressed this question in rat barrel cortex by comparing performance in behavioral tasks with either whisker stimulation, which causes frequency-dependent adaptation, or optical activation of cortically expressed channelrhodopsin-2, which elicits non-adapting neural responses. Circumventing adaption by optical activation substantially improved cross-hemispheric discrimination of stimulus frequency. This improvement persisted when temporal precision of optically evoked spikes was reduced. We were able to replicate whisker-driven behavior only by applying adaptation rules mimicking sensory-evoked responses to optical stimuli. Conversely, in a change-detection task, animals performed better with whisker than optical stimulation. Our results directly demonstrate that sensory adaptation critically governs the perception of stimulus patterns, decreasing fidelity under steady-state conditions in favor of change detection.