Cortical recruitment determines learning dynamics and strategy

The effects of different challenge-level balance tasks on stroke cortical responses and balance assessment using EEG

Previous studies have validated that different balance tasks induce different cortical responses, which are key indexes of balance assessment. Assessing balance is crucial for stroke survivors to prevent falls and improve rehabilitation outcomes. However, it was unclear whether these tasks may affect the balance assessment, particularly regarding the relationship between task difficulty and the corresponding cortical responses involved in balance control. Therefore, we sought to explore the effects of different challenge-level balance tasks on balance assessment. Eighteen participants with stroke and thirteen healthy individuals were recruited in this study. The EEG was collected during sitting, standing and perturbation tasks. The pairwise-derived Brain Symmetry Index (pdBSI), and Granger Causality (GC) were analyzed with a two-way (task ×group) RMANOVA. Finally, a multiple linear regression analysis was applied to predict the BBS score with the above parameters. We found a significant interaction effect on pdBSI and GC. In the frontal lobe, participants with stroke exhibited significantly higher pdBSI (standing: p=0.042, perturbation: p=0.013) and lower GC (standing: p<0.001, perturbation: p=0.028) compared to healthy controls. Similarly, in the parietal lobe, stroke survivors showed markedly higher pdBSI (standing: p = 0.006, perturbation: p=0.012) and lower GC (standing: p=0.030, perturbation: p=0.011). Finally, The Berg Balance Scale (BBS) scores could be reliably predicted using parietal BSI and frontal GC metrics recorded during standing (p<0.001, adjusted R²=0.938) and perturbation tasks (p=0.001, adjusted R²=0.644). It was discovered that the more challenging balance tasks better revealed the difference in the power distribution and the directional functional connection between groups. The pdBSI and GC during standing and perturbation tasks, could be used as biomarkers for stroke balance assessment.

A spatial code for temporal information is necessary for efficient sensory learning

The temporal structure of sensory inputs contains essential information for their interpretation. Sensory cortex represents these temporal cues through two codes: the temporal sequences of neuronal activity and the spatial patterns of neuronal firing rate. However, it is unknown which of these coexisting codes causally drives sensory decisions. To separate their contributions, we generated in the mouse auditory cortex optogenetically driven activity patterns differing exclusively along their temporal or spatial dimensions. Mice could rapidly learn to behaviorally discriminate spatial but not temporal patterns. Moreover, large-scale neuronal recordings across the auditory system revealed that the auditory cortex is the first region in which spatial patterns efficiently represent temporal cues on the timescale of several hundred milliseconds. This feature is shared by the deep layers of neural networks categorizing time-varying sounds. Therefore, the emergence of a spatial code for temporal sensory cues is a necessary condition to efficiently associate temporally structured stimuli with decisions.

Representational maps in the brain: concepts, approaches, and applications

Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.

All-optical interrogation of neural circuits in behaving mice

Recent advances combining two-photon calcium imaging and two-photon optogenetics with computer-generated holography now allow us to read and write the activity of large populations of neurons in vivo at cellular resolution and with high temporal resolution. Such 'all-optical' techniques enable experimenters to probe the effects of functionally defined neurons on neural circuit function and behavioral output with new levels of precision. This greatly increases flexibility, resolution, targeting specificity and throughput compared with alternative approaches based on electrophysiology and/or one-photon optogenetics and can interrogate larger and more densely labeled populations of neurons than current voltage imaging-based implementations. This protocol describes the experimental workflow for all-optical interrogation experiments in awake, behaving head-fixed mice. We describe modular procedures for the setup and calibration of an all-optical system (~3 h), the preparation of an indicator and opsin-expressing and task-performing animal (~3-6 weeks), the characterization of functional and photostimulation responses (~2 h per field of view) and the design and implementation of an all-optical experiment (achievable within the timescale of a normal behavioral experiment; ~3-5 h per field of view). We discuss optimizations for efficiently selecting and targeting neuronal ensembles for photostimulation sequences, as well as generating photostimulation response maps from the imaging data that can be used to examine the impact of photostimulation on the local circuit. We demonstrate the utility of this strategy in three brain areas by using different experimental setups. This approach can in principle be adapted to any brain area to probe functional connectivity in neural circuits and investigate the relationship between neural circuit activity and behavior.

Continuity within the somatosensory cortical map facilitates learning

The topographic organization is a prominent feature of sensory cortices, but its functional role remains controversial. Particularly, it is not well determined how integration of activity within a cortical area depends on its topography during sensory-guided behavior. Here, we train mice expressing channelrhodopsin in excitatory neurons to track a photostimulation bar that rotated smoothly over the topographic whisker representation of the primary somatosensory cortex. Mice learn to discriminate angular positions of the light bar to obtain a reward. They fail not only when the spatiotemporal continuity of the photostimulation is disrupted in this area but also when cortical areas displaying map discontinuities, such as the trunk and legs, or areas without topographic map, such as the posterior parietal cortex, are photostimulated. In contrast, when cortical topographic continuity enables to predict future sensory activation, mice demonstrate anticipation of reward availability. These findings could be helpful for optimizing feedback while designing cortical neuroprostheses.

Experienced entropy drives choice behavior in a boring decision-making task

Boredom has been defined as an aversive mental state that is induced by the disability to engage in satisfying activity, most often experienced in monotonous environments. However, current understanding of the situational factors inducing boredom and driving subsequent behavior remains incomplete. Here, we introduce a two-alternative forced-choice task coupled with sensory stimulation of different degrees of monotony. We find that human subjects develop a bias in decision-making, avoiding the more monotonous alternative that is correlated with self-reported state boredom. This finding was replicated in independent laboratory and online experiments and proved to be specific for the induction of boredom rather than curiosity. Furthermore, using theoretical modeling we show that the entropy in the sequence of individually experienced stimuli, a measure of information gain, serves as a major determinant to predict choice behavior in the task. With this, we underline the relevance of boredom for driving behavioral responses that ensure a lasting stream of information to the brain.

Engrams of Fast Learning

Fast learning designates the behavioral and neuronal mechanisms underlying the acquisition of a long-term memory trace after a unique and brief experience. As such it is opposed to incremental, slower reinforcement or procedural learning requiring repetitive training. This learning process, found in most animal species, exists in a large spectrum of natural behaviors, such as one-shot associative, spatial, or perceptual learning, and is a core principle of human episodic memory. We review here the neuronal and synaptic long-term changes associated with fast learning in mammals and discuss some hypotheses related to their underlying mechanisms. We first describe the variety of behavioral paradigms used to test fast learning memories: those preferentially involve a single and brief (from few hundred milliseconds to few minutes) exposures to salient stimuli, sufficient to trigger a long-lasting memory trace and new adaptive responses. We then focus on neuronal activity patterns observed during fast learning and the emergence of long-term selective responses, before documenting the physiological correlates of fast learning. In the search for the engrams of fast learning, a growing body of evidence highlights long-term changes in gene expression, structural, intrinsic, and synaptic plasticities. Finally, we discuss the potential role of the sparse and bursting nature of neuronal activity observed during the fast learning, especially in the induction plasticity mechanisms leading to the rapid establishment of long-term synaptic modifications. We conclude with more theoretical perspectives on network dynamics that could enable fast learning, with an overview of some theoretical approaches in cognitive neuroscience and artificial intelligence.

Deep imaging in the brainstem reveals functional heterogeneity in V2a neurons controlling locomotion

V2a neurons are a genetically defined cell class that forms a major excitatory descending pathway from the brainstem reticular formation to the spinal cord. Their activation has been linked to the termination of locomotor activity based on broad optogenetic manipulations. However, because of the difficulties involved in accessing brainstem structures for in vivo cell type-specific recordings, V2a neuron function has never been directly observed during natural behaviors. Here, we imaged the activity of V2a neurons using micro-endoscopy in freely moving mice. We find that as many as half of the V2a neurons are excited at locomotion arrest and with low reliability. Other V2a neurons are inhibited at locomotor arrests and/or activated during other behaviors such as locomotion initiation or stationary grooming. Our results establish that V2a neurons not only drive stops as suggested by bulk optogenetics but also are stratified into subpopulations that likely contribute to diverse motor patterns.

How many neurons are sufficient for perception of cortical activity?

Many theories of brain function propose that activity in sparse subsets of neurons underlies perception and action. To place a lower bound on the amount of neural activity that can be perceived, we used an all-optical approach to drive behaviour with targeted two-photon optogenetic activation of small ensembles of L2/3 pyramidal neurons in mouse barrel cortex while simultaneously recording local network activity with two-photon calcium imaging. By precisely titrating the number of neurons stimulated, we demonstrate that the lower bound for perception of cortical activity is ~14 pyramidal neurons. We find a steep sigmoidal relationship between the number of activated neurons and behaviour, saturating at only ~37 neurons, and show this relationship can shift with learning. Furthermore, activation of ensembles is balanced by inhibition of neighbouring neurons. This surprising perceptual sensitivity in the face of potent network suppression supports the sparse coding hypothesis, and suggests that cortical perception balances a trade-off between minimizing the impact of noise while efficiently detecting relevant signals.

An optically transparent multi-electrode array for combined electrophysiology and optophysiology at the mesoscopic scale

OBJECTIVE

A number of tissue penetrating opto-electrodes to simultaneously record and optogenetically influence brain activity have been developed. For experiments at the surface of the brain, such as electrocorticogram (ECoG) recordings and surface optogenetics, fewer devices have been described and no device has found widespread adoption for neuroscientific experiments. One issue slowing adoption is the complexity and fragility of existing devices, typically based on transparent electrode materials like graphene and indium-tin oxide (ITO). We focused here on improving existing processes based on metal traces and polyimide (PI), which produce more robust and cost-effective devices, to develop a multi-electrode array for optophysiology.

APPROACH

The most widely used substrate material for surface electrodes, PI, has seen little use for optophysiologicalμECoG/ECoG arrays. This is due to its lack of transparency at optogenetically relevant short wavelengths. Here we use very thin layers of PI in combination with chrome-gold-platinum electrodes to achieve the necessary substrate transparency and high mechanical flexibility in a device that still rejects light artifacts well.

MAIN RESULTS

The manufactured surface arrays have a thickness of only 6.5 µm, resulting in 80% transparency for blue light. We demonstrate immunity against opto-electric artifacts, long term stability and biocompatibility as well as suitability for optical voltage imaging. The biocompatible arrays are capable of recording stable ECoGs over months without any measurable degradation and can be used to map the tonotopic organization of the curved rodent auditory cortex.

SIGNIFICANCE

Our novel probes combine proven materials and processing steps to create optically near-transparent electrode arrays with superior longevity. In contrast to previous opto-electrodes, our probes are simple to manufacture, robust, offer long-term stability, and are a practical engineering solution for optophysiological experiments not requiring transparency of the electrode sites themselves.