A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement

The neural circuit mechanism for auditory responses in the mediodorsal thalamic nucleus of awake mice

The mediodorsal thalamic nucleus (MD) forms neural circuits with various brain regions, including the prefrontal cortex (PFC), the reticular thalamic nucleus (TRN), and the midbrain reticular nucleus (MRN). However, the specific roles and underlying mechanisms in auditory information processing remain unclear. Here, we perform multi-channel electrophysiological recordings in awake mice to investigate the response patterns of the MD to auditory stimuli, as well as the regulatory effects of PFC, MRN, and TRN inputs. We identify two distinct types of sound-evoked responses. The Phasic-response features a transient burst firing to the stimulus with short latency, rapidly adapting to baseline and corresponding to the onset fluctuation of the local field potential. The Sustained-response is marked by prolonged firing with longer latency and is accompanied by persistent enhancement of oscillatory power following stimulus offset. The response patterns of MD neurons remain consistent across different types of auditory stimuli. Optogenetic inactivation of the MRN suppresses both response types in the MD. The Sustained-response is attenuated by PFC inactivation but enhanced by TRN inactivation, while the Phasic-response remains unaffected by inactivation of either the PFC or TRN. Our findings expand the understanding of the MD's role in sound information integration and auditory cognitive regulation.

Motor learning refines thalamic influence on motor cortex

The primary motor cortex (M1) is central for the learning and execution of dexterous motor skills1-3, and its superficial layer (layers 2 and 3; hereafter, L2/3) is a key locus of learning-related plasticity1,4-6. It remains unknown how motor learning shapes the way in which upstream regions activate M1 circuits to execute learned movements. Here, using longitudinal axonal imaging of the main inputs to M1 L2/3 in mice, we show that the motor thalamus is the key input source that encodes learned movements in experts (animals trained for two weeks). We then use optogenetics to identify the subset of M1 L2/3 neurons that are strongly driven by thalamic inputs before and after learning. We find that the thalamic influence on M1 changes with learning, such that the motor thalamus preferentially activates the M1 neurons that encode learned movements in experts. Inactivation of the thalamic inputs to M1 in experts impairs learned movements. Our study shows that motor learning reshapes the thalamic influence on M1 to enable the reliable execution of learned movements.

Developmental overproduction of cortical superficial neurons impairs adult auditory cortical processing

While evolutionary cortical expansion is thought to underlie the evolution of human cognitive capabilities, excessive developmental expansion can lead to megalencephaly, often found in neurodevelopmental disorders. Still, little is known about how the overproduction of cortical neurons during development affects cortical processing and behavior in later life. Here we show that developmental overproduction of cortical superficial neurons impairs auditory processing in adult mice. We applied XAV939 to overproduce cortical superficial excitatory neurons during development. XAV939-treated adult mice exhibited auditory behavioral deficits and abnormal auditory cortical processing. Furthermore, we found fewer functional monosynaptic connections between cortical putative excitatory neurons. Altogether, our results suggest that abnormal auditory cortical processing contributes to the atypical auditory detectability in XAV939-treated mice. Although the expansion of cortical size is evolutionarily advantageous, an abnormal expansion during development can result in detrimental effects on cortical processing and perceptual behavior in adulthood.

Neuronal responses in the human primary motor cortex coincide with the subjective onset of movement intention in brain-machine interface-mediated actions

Self-initiated behavior is accompanied by the experience of intending our actions. Here, we leverage the unique opportunity to examine the full intentional chain-from intention to action to environmental effects-in a tetraplegic person outfitted with a primary motor cortex (M1) brain-machine interface (BMI) generating real hand movements via neuromuscular electrical stimulation (NMES). This combined BMI-NMES approach allowed us to selectively manipulate each element of the intentional chain (intention, action, effect) while probing subjective experience and performing extra-cellular recordings in human M1. Behaviorally, we reveal a novel form of intentional binding: motor intentions are reflected in a perceived temporal attraction between the onset of intentions and that of actions. Neurally, we demonstrate that evoked spiking activity in M1 largely coincides in time with the onset of the experience of intention and that M1 spike counts and the onset of subjective intention may co-vary on a trial-by-trial basis. Further, population-level dynamics, as indexed by a decoder instantiating movement, reflect intention-action temporal binding. The results fill a significant knowledge gap by relating human spiking activity in M1 with the onset of subjective intention and complement prior human intracranial work examining pre-motor and parietal areas.

DREDge: robust motion correction for high-density extracellular recordings across species

High-density microelectrode arrays have opened new possibilities for systems neuroscience, but brain motion relative to the array poses challenges for downstream analyses. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from action potential data, DREDge enables automated, high-temporal-resolution motion tracking in local field potential data. In human intraoperative recordings, DREDge's local field potential-based tracking reliably recovered evoked potentials and single-unit spike sorting. In recordings of deep probe insertions in nonhuman primates, DREDge tracked motion across centimeters of tissue and several brain regions while mapping single-unit electrophysiological features. DREDge reliably improved motion correction in acute mouse recordings, especially in those made with a recent ultrahigh-density probe. Applying DREDge to recordings from chronic implantations in mice yielded stable motion tracking despite changes in neural activity between experimental sessions. These advances enable automated, scalable registration of electrophysiological data across species, probes and drift types, providing a foundation for downstream analyses of these rich datasets.

The Computational Bottleneck of Basal Ganglia Output (and What to Do About it)

What the basal ganglia do is an oft-asked question; answers range from the selection of actions to the specification of movement to the estimation of time. Here, I argue that how the basal ganglia do what they do is a less-asked but equally important question. I show that the output regions of the basal ganglia create a stringent computational bottleneck, both structurally, because they have far fewer neurons than do their target regions, and dynamically, because of their tonic, inhibitory output. My proposed solution to this bottleneck is that the activity of an output neuron is setting the weight of a basis function, a function defined by that neuron's synaptic contacts. I illustrate how this may work in practice, allowing basal ganglia output to shift cortical dynamics and control eye movements via the superior colliculus. This solution can account for troubling issues in our understanding of the basal ganglia: why we see output neurons increasing their activity during behavior, rather than only decreasing as predicted by theories based on disinhibition, and why the output of the basal ganglia seems to have so many codes squashed into such a tiny region of the brain.

Dissociable roles of distinct thalamic circuits in learning reaches to spatial targets

Reaching movements are critical for survival, and are learned and controlled by distributed motor networks. Even though the thalamus is a highly interconnected node in these networks, its role in learning and controlling reaches remains underexplored. We report dissociable roles of two thalamic forelimb circuits coursing through parafascicular (Pf) and ventroanterior/ventrolateral (VAL) nuclei in refining reaches to a spatial target. Using 2-photon calcium imaging as mice learn directional reaches, we observe high reach-related activity from both circuits early in learning, which decreases with learning. Pf activity encodes reach direction early in learning, more so than VAL. Consistently, bilateral lesions of Pf before training impairs refinement of reach direction. Pre-training lesions of VAL does not affect reach direction, but increases reach speed and target overshoot. Lesions of either nucleus after training does not affect the execution of learned reaches. These findings reveal different thalamic circuits governing distinct aspects of learned reaches.

Galvanic vestibular stimulation to rehabilitate postural instability in Parkinson's disease

BACKGROUND

 Galvanic vestibular stimulation (GVS) is a non-invasive technique employed to rehabilitate balance by delivering low-intensity, short-duration electrical stimulation to the mastoid bones, effectively activating the vestibulospinal tract.

OBJECTIVE

 To evaluate the effects of GVS on balance in patients with Parkinson's disease (PD) and postural instability.

METHODS

 In this clinical study, 25 PD patients with postural instability in the ON phase (best effect of dopaminergic medication) underwent GVS. Balance was assessed using the Berg Balance Scale (BBS), the Timed Up and Go (TUG) test, and posturography on a force platform. Electrical current intensity was progressively increased between the mastoids, starting at 1.0 mA and reaching 3.5 mA by the 6th session, with this level maintained until the 8th session. Stimulation duration began at 9 minutes in the 1st session, increased to 30 minutes by the 3rd session, and was sustained through the 8th session.

RESULTS

 A blinded comparison of pre- and post-GVS evaluations demonstrated significant improvements in BBS (p = 0.00001) and TUG (p = 0.00003) scores. Posturography showed an increase in the stability limit area (p = 0.026) and the general balance index (p = 0.001).

CONCLUSION

 In the therapeutic management of postural instability in PD, GVS emerges as a promising complementary strategy for enhancing balance. Further research is needed to determine whether these improvements persist after GVS cessation.

REGISTRATION OF CLINICAL TRIAL

 https://ensaiosclinicos.gov.br/rg/RBR-22j8728.

Acting together: Cortex and striatum specify movement kinematics

In this issue of Neuron, Park et al.1 show that striatal activity is necessary for the specification of movement kinematics using a novel reach-to-pull task for mice. Through simultaneous cortical and subcortical recordings and manipulations, they demonstrate that motor cortex and striatum conjointly specify parameters necessary for shaping flexible, goal-directed actions.

Ramping dynamics in the frontal cortex unfold over multiple timescales during motor planning

Plans are formulated and refined throughout the period leading up to their execution, ensuring that the appropriate behaviors are enacted at the appropriate times. Although existing evidence suggests that memory circuits convey the passage of time through diverse neuronal responses, it remains unclear whether the neural circuits involved in planning exhibit analogous temporal dynamics. Using publicly available data, we analyzed how activity in the mouse frontal motor cortex evolves during motor planning. Individual neurons exhibited diverse ramping activity throughout a delay interval that preceded a planned movement. The collective activity of these neurons was useful for making temporal predictions that became increasingly precise as the movement time approached. This temporal diversity gave rise to a spectrum of encoding patterns, ranging from stable to dynamic representations of the upcoming movement. Our results indicate that ramping activity unfolds over multiple timescales during motor planning, suggesting a shared mechanism in the brain for processing temporal information related to both memories from the past and plans for the future. NEW & NOTEWORTHY Neuronal responses in the cortex are diverse, but the nature and functional consequences of this diversity remain ambiguous. We identified a specific pattern of temporal heterogeneity in the mouse frontal motor cortex, whereby the firing of different neurons ramps up at varying speeds before the execution of a movement. Our decoding analyses reveal that this heterogeneity in ramping dynamics enables precise and reliable encoding of movement plans and time across various timescales.

Secondary motor cortex tracks decision value during the learning of a non-instructed task

Optimal decision-making depends on interconnected frontal brain regions, enabling animals to adapt decisions based on internal states, experiences, and contexts. The secondary motor cortex (M2) is key in adaptive behaviors in expert rodents, particularly in encoding decision values guiding complex probabilistic tasks. However, its role in deterministic tasks during initial learning remains uncertain. Here, we describe a self-initiated deterministic task requiring mice to use their forepaws to make choices without guiding cues. Our findings reveal that spontaneous decisions follow a "race" model between actions, which uncovers underlying decision values. We use in vivo microscopy and modeling to show that M2 neurons in male mice exhibit persistent activity-encoding decision values that predict action-selection probabilities. Optogenetic inhibition of the M2 reduces the reversal performance and alters the decision value. Additionally, updates in decision values determine the rate at which learning is reversed. These results highlight the use of decision values by the M2 to adapt choice during initial learning without instructive cues.

A brainstem map of orofacial rhythms

Rhythmic orofacial movements, such as eating, drinking, or vocalization, are controlled by distinct premotor oscillator networks in the brainstem. Orofacial movements must be coordinated with rhythmic breathing to avoid aspiration and because they share muscles. Understanding how brainstem circuits coordinate rhythmic motor programs requires neurophysiological measurements in behaving animals. We used Neuropixels probe recordings to map brainstem neural activity related to breathing, licking, and swallowing in mice drinking water. Breathing and licking rhythms were tightly coordinated and phase-locked, whereas intermittent swallowing paused breathing and licking. Multiple clusters of neurons, each recruited during different orofacial rhythms, delineated a lingual premotor network in the intermediate nucleus of the reticular formation (IRN). Local optogenetic perturbation experiments identified a region in the IRN where constant stimulation can drive sustained rhythmic licking, consistent with a central pattern generator for licking. Stimulation to artificially induce licking showed that coupled brainstem oscillators autonomously coordinated licking and breathing. The brainstem oscillators were further patterned by descending inputs at moments of licking initiation. Our results reveal the logic governing interactions of orofacial rhythms during behavior and outline their neural circuit dynamics, providing a model for dissecting multi-oscillator systems controlling rhythmic motor programs.

Cortical and subcortical gray matter volume and cognitive impairment in Parkinson's disease

INTRODUCTION

This study investigated the cortical and subcortical gray matter volume (GMV) and cognitive impairment (CI) in patients with Parkinson's disease (PD).

METHODS

In this study, T1-weighted magnetic resonance imaging of the cortex and subcortex was conducted on 92 individuals diagnosed with PD and 92 healthy controls (HCs). PD patients were divided into three groups: PD with normal cognition (PD-NC, n = 21), PD with mild CI (PD-MCI, n = 43), and PD with severe CI (PD-SCI, n = 28). Differences in GMV were analyzed using voxel-based morphometry (VBM). Statistical analysis was conducted using SPSS 26.

RESULTS

Compared to the HCs, the PD-NC group exhibited reduced GMV in the right middle frontal gyrus (RMFG), right precentral gyrus medial segment (RPGMS), left temporal pole, and right superior frontal gyrus medial segment (RSFGMS). In comparison to the HC and PD-NC groups, the PD-MCI and PD-SCI groups (respectively) demonstrated significant decreases in GMV in the right caudate, left hippocampus, left thalamus, RMFG, RPGMS, RSFGMS, and cerebellum (right crus I and left crus I). The regression analysis indicated that changes in the GMV of the frontal areas can predict cognitive test outcomes.

CONCLUSION

Compared to HCs, PD patients with CI presented significant volume reductions in the RC, LH, LT, RMFG, RPGMS, RSFGMS, and the right and left crus I regions. Consequently, as average GMV atrophy increased in the specified regions, PD patients exhibited more severe cognitive impairment than the HC group. This may be attributed to the initial pathological loss of frontal GMV (especially in the RMFG and RPGMS regions), which could subsequently lead to subcortical dysfunction.

Midbrain encodes sound detection behavior without auditory cortex

Hearing involves analyzing the physical attributes of sounds and integrating the results of this analysis with other sensory, cognitive, and motor variables in order to guide adaptive behavior. The auditory cortex is considered crucial for the integration of acoustic and contextual information and is thought to share the resulting representations with subcortical auditory structures via its vast descending projections. By imaging cellular activity in the corticorecipient shell of the inferior colliculus of mice engaged in a sound detection task, we show that the majority of neurons encode information beyond the physical attributes of the stimulus and that the animals' behavior can be decoded from the activity of those neurons with a high degree of accuracy. Surprisingly, this was also the case in mice in which auditory cortical input to the midbrain had been removed by bilateral cortical lesions. This illustrates that subcortical auditory structures have access to a wealth of non-acoustic information and can, independently of the auditory cortex, carry much richer neural representations than previously thought.

The Posterior Dominant Rhythm Remains Within Normal Limits in the Microgravity Environment

BACKGROUND

Electroencephalogram (EEG) biomarkers with adequate sensitivity and specificity to reflect the brain's health status can become indispensable for health monitoring during prolonged missions in space. The objective of our study was to assess whether the basic features of the posterior dominant rhythm (PDR) change under microgravity conditions compared to earth-based scalp EEG recordings.

METHODS

Three crew members during the 16-day AXIOM-1 mission to the International Space Station (ISS), underwent scalp EEG recordings before, during, and after the mission by means of a dry-electrode self-donning headgear designed to support long-term EEG recordings in space. Resting-state recordings were performed with eyes open and closed during relaxed wakefulness. The electrodes representative of EEG activity in each occipital lobe were used, and consecutive PDR oscillations were identified during periods of eye closure. In turn, cursor-based markers were placed at the negative peak of each sinusoidal wave of the PDR. Waveform averaging and time-frequency analysis were performed for all PDR samples for the respective pre-mission, mission, and post-mission EEGs.

RESULTS

No significant differences were found in the mean frequency of the PDR in any of the crew subjects between their EEG on the ISS and their pre- or post-mission EEG on ground level. The PDR oscillations varied over a ±1Hz standard deviation range. Similarly, no significant differences were found in PDR's power spectral density.

CONCLUSIONS

Our study shows that the spectral features of the PDR remain within normal limits in a short exposure to the microgravity environment, with its frequency manifesting within an acceptable ±1 Hz variation from the pre-mission mean. Further investigations for EEG features and markers reflecting the human brain neurophysiology during space missions are required.

Preconfigured cortico-thalamic neural dynamics constrain movement-associated thalamic activity

Neural preconfigured activity patterns (nPAPs), conceptualized as organized activity parcellated into groups of neurons, have been proposed as building blocks for cognitive and sensory processing. However, their existence and function in motor networks have been scarcely studied. Here, we explore the possibility that nPAPs are present in the motor thalamus (VL/VM) and their potential contribution to motor-related activity. To this end, we developed a preparation where VL/VM multiunitary activity could be robustly recorded in mouse behavior evoked by primary motor cortex (M1) optogenetic stimulation and forelimb movements. VL/VM-evoked activity was organized as rigid stereotypical activity patterns at the single and population levels. These activity patterns were unable to dynamically adapt to different temporal architectures of M1 stimulation. Moreover, they were experience-independent, present in virtually all animals, and pairs of neurons with high correlations during M1-stimulation also presented higher correlations during spontaneous activity, confirming their preconfigured nature. Finally, subpopulations expressing specific M1-evoked patterns also displayed specific movement-related patterns. Our data demonstrate that the behaviorally related identity of specific neural subpopulations is tightly linked to nPAPs.

The Hippocampus Preorders Movements for Skilled Action Sequences

Plasticity in the subcortical motor basal ganglia-thalamo-cerebellar network plays a key role in the acquisition and control of long-term memory for new procedural skills, from the formation of population trajectories controlling trained motor skills in the striatum to the adaptation of sensorimotor maps in the cerebellum. However, recent findings demonstrate the involvement of a wider cortical and subcortical brain network in the consolidation and control of well-trained actions, including a brain region traditionally associated with declarative memory-the hippocampus. Here, we probe which role these subcortical areas play in skilled motor sequence control, from sequence feature selection during planning to their integration during sequence execution. An fMRI dataset (N = 24; 14 females) collected after participants learnt to produce four finger press sequences entirely from memory with high movement and timing accuracy over several days was examined for both changes in BOLD activity and their informational content in subcortical regions of interest. Although there was a widespread activity increase in effector-related striatal, thalamic, and cerebellar regions, in particular during sequence execution, the associated activity did not contain information on the motor sequence identity. In contrast, hippocampal activity increased during planning and predicted the order of the upcoming sequence of movements. Our findings suggest that the hippocampus preorders movements for skilled action sequences, thus contributing to the higher-order control of skilled movements that require flexible retrieval. These findings challenge the traditional taxonomy of episodic and procedural memory and carry implications for the rehabilitation of individuals with neurodegenerative disorders.

A distributed auditory network mediated by pontine central gray underlies ultra-fast awakening in response to alerting sounds

Sleeping animals can be woken up rapidly by external threat signals, which is an essential defense mechanism for survival. However, neuronal circuits underlying the fast transmission of sensory signals for this process remain unclear. Here, we report in mice that alerting sound can induce rapid awakening within hundreds of milliseconds and that glutamatergic neurons in the pontine central gray (PCG) play an important role in this process. These neurons exhibit higher sensitivity to auditory stimuli in sleep than wakefulness. Suppressing these neurons results in reduced sound-induced awakening and increased sleep in intrinsic sleep/wake cycles, whereas their activation induces ultra-fast awakening from sleep and accelerates awakening from anesthesia. Additionally, the sound-induced awakening can be attributed to the propagation of auditory signals from the PCG to multiple arousal-related regions, including the mediodorsal thalamus, lateral hypothalamus, and ventral tegmental area. Thus, the PCG serves as an essential distribution center to orchestrate a global auditory network to promote rapid awakening.

Hovering flight regulation of pigeon robots in laboratory and field

Compared to traditional bio-mimic robots, animal robots show superior locomotion, energy efficiency, and adaptability to complex environments but most remained in laboratory stage, needing further development for practical applications like exploration and inspection. Our pigeon robots validated in both laboratory and field, tested with an electrical stimulus unit (2-s duration, 0.5 ms pulse width, 80 Hz frequency). In a fixed stimulus procedure, hovering flight was conducted with 8 stimulus units applied every 2 s after flew over the trigger boundary. In a flexible procedure, stimulus was applied whenever they deviated from a virtual circle, with pulse width gains of 0.1 ms or 0.2 ms according to the trajectory angle. These optimized protocols achieved a success hovering rate of 87.5% and circle curvatures of 0.008 m-1-0.024 m-1, largely advancing the practical application of animal robots.

A line attractor encoding a persistent internal state requires neuropeptide signaling

Internal states drive survival behaviors, but their neural implementation is poorly understood. Recently, we identified a line attractor in the ventromedial hypothalamus (VMH) that represents a state of aggressiveness. Line attractors can be implemented by recurrent connectivity or neuromodulatory signaling, but evidence for the latter is scant. Here, we demonstrate that neuropeptidergic signaling is necessary for line attractor dynamics in this system by using cell-type-specific CRISPR-Cas9-based gene editing combined with single-cell calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1+ neurons that control aggression diminished attack, reduced persistent neural activity, and eliminated line attractor dynamics while only slightly reducing overall neural activity and sex- or behavior-specific tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor in mammals. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.

From Individual to Population: Circuit Organization of Pyramidal Tract and Intratelencephalic Neurons in Mouse Sensorimotor Cortex

The sensorimotor cortex participates in diverse functions with different reciprocally connected subregions and projection-defined pyramidal neuron types therein, while the fundamental organizational logic of its circuit elements at the single-cell level is still largely unclear. Here, using mouse Cre driver lines and high-resolution whole-brain imaging to selectively trace the axons and dendrites of cortical pyramidal tract (PT) and intratelencephalic (IT) neurons, we reconstructed the complete morphology of 1,023 pyramidal neurons and generated a projectome of 6 subregions within the sensorimotor cortex. Our morphological data revealed substantial hierarchical and layer differences in the axonal innervation patterns of pyramidal neurons. We found that neurons located in the medial motor cortex had more diverse projection patterns than those in the lateral motor and sensory cortices. The morphological characteristics of IT neurons in layer 5 were more complex than those in layer 2/3. Furthermore, the soma location and morphological characteristics of individual neurons exhibited topographic correspondence. Different subregions and layers were composed of different proportions of projection subtypes that innervate downstream areas differentially. While the axonal terminals of PT neuronal population in each cortical subregion were distributed in specific subdomains of the superior colliculus (SC) and zona incerta (ZI), single neurons selectively innervated a combination of these projection targets. Overall, our data provide a comprehensive list of projection types of pyramidal neurons in the sensorimotor cortex and begin to unveil the organizational principle of these projection types in different subregions and layers.

Learning and Control in Motor Cortex across Cell Types and Scales

The motor cortex is essential for controlling the flexible movements underlying complex behaviors. Behavioral flexibility involves the ability to integrate and refine new movements, thereby expanding an animal's repertoire. This review discusses recent strides in motor learning mechanisms across spatial and temporal scales, describing how neural networks are remodeled at the level of synapses, cell types, and circuits and across time as animals' learn new skills. It highlights how changes at each scale contribute to the evolving structure and function of neural circuits that accompanies the expansion and refinement of motor skills. We review new findings highlighted by advanced imaging techniques that have opened new vistas in optical physiology and neuroanatomy, revealing the complexity and adaptability of motor cortical circuits, crucial for learning and control. At the structural level, we explore the dynamic regulation of dendritic spines mediating corticocortical and thalamocortical inputs to the motor cortex. We delve into the role of perisynaptic astrocyte processes in maintaining synaptic stability during learning. We also examine the functional diversity among pyramidal neuron subtypes, their dendritic computations and unique contributions to single cell and network function. Further, we highlight how cortical activation is characterized by increased consistency and reduced strength as new movements are learned and how external inputs contribute to these changes. Finally, we consider the motor cortex's necessity as movements unfold over long time scales. These insights will continue to drive new research directions, enhancing our understanding of motor cortical circuit transformations that underpin behavioral changes expressed throughout an animal's life.

Brain-wide dynamics linking sensation to action during decision-making

Perceptual decisions rely on learned associations between sensory evidence and appropriate actions, involving the filtering and integration of relevant inputs to prepare and execute timely responses1,2. Despite the distributed nature of task-relevant representations3-10, it remains unclear how transformations between sensory input, evidence integration, motor planning and execution are orchestrated across brain areas and dimensions of neural activity. Here we addressed this question by recording brain-wide neural activity in mice learning to report changes in ambiguous visual input. After learning, evidence integration emerged across most brain areas in sparse neural populations that drive movement-preparatory activity. Visual responses evolved from transient activations in sensory areas to sustained representations in frontal-motor cortex, thalamus, basal ganglia, midbrain and cerebellum, enabling parallel evidence accumulation. In areas that accumulate evidence, shared population activity patterns encode visual evidence and movement preparation, distinct from movement-execution dynamics. Activity in movement-preparatory subspace is driven by neurons integrating evidence, which collapses at movement onset, allowing the integration process to reset. Across premotor regions, evidence-integration timescales were independent of intrinsic regional dynamics, and thus depended on task experience. In summary, learning aligns evidence accumulation to action preparation in activity dynamics across dozens of brain regions. This leads to highly distributed and parallelized sensorimotor transformations during decision-making. Our work unifies concepts from decision-making and motor control fields into a brain-wide framework for understanding how sensory evidence controls actions.

When preparation pays off

Computational principles shed light on why movement is preceded by preparatory activity within the neural networks that control muscles.

Encoding of 2D Self-Centered Plans and World-Centered Positions in the Rat Frontal Orienting Field

The neural mechanisms of motor planning have been extensively studied in rodents. Preparatory activity in the frontal cortex predicts upcoming choice, but limitations of typical tasks have made it challenging to determine whether the spatial information is in a self-centered direction reference frame or a world-centered position reference frame. Here, we trained male rats to make delayed visually guided orienting movements to six different directions, with four different target positions for each direction, which allowed us to disentangle direction versus position tuning in neural activity. We recorded single unit activity from the rat frontal orienting field (FOF) in the secondary motor cortex, a region involved in planning orienting movements. Population analyses revealed that the FOF encodes two separate 2D maps of space. First, a 2D map of the planned and ongoing movement in a self-centered direction reference frame. Second, a 2D map of the animal's current position on the port wall in a world-centered reference frame. Thus, preparatory activity in the FOF represents self-centered upcoming movement directions, but FOF neurons multiplex both self- and world-reference frame variables at the level of single neurons. Neural network model comparison supports the view that despite the presence of world-centered representations, the FOF receives the target information as self-centered input and generates self-centered planning signals.

The Neural and Computational Architecture of Feedback Dynamics in Mouse Cortex during Stimulus Report

Conscious reportability of visual input is associated with a bimodal neural response in the primary visual cortex (V1): an early-latency response coupled to stimulus features and a late-latency response coupled to stimulus report or detection. This late wave of activity, central to major theories of consciousness, is thought to be driven by the prefrontal cortex (PFC), responsible for "igniting" it. Here we analyzed two electrophysiological studies in mice performing different stimulus detection tasks and characterized neural activity profiles in three key cortical regions: V1, posterior parietal cortex (PPC), and PFC. We then developed a minimal network model, constrained by known connectivity between these regions, reproducing the spatiotemporal propagation of visual- and report-related activity. Remarkably, while PFC was indeed necessary to generate report-related activity in V1, this occurred only through the mediation of PPC. PPC, and not PFC, had the final veto in enabling the report-related late wave of V1 activity.

Gray Matter Volumes Mediate the Relationship Between Disease Duration and Balance Control Performance in Chronic Ankle Instability

The relationship between structural changes in the cerebral gray matter and diminished balance control performance in patients with chronic ankle instability (CAI) has remained unclear. This paper aimed to assess the difference in gray matter volume (GMV) between participants with CAI and healthy controls (HC) and to characterize the role of GMV in the relationship between disease duration and balance performance in CAI. 42 participants with CAI and 33 HC completed the structural brain MRI scans, one-legged standing test, and Y-balance test. Regional GMV was measured by applying voxel-based morphometry methods. The result showed that, compared with HC, participants with CAI exhibited lower GMV in multiple brain regions (familywise error [FWE] corrected p < 0.021). Within CAI only, but not in HC, lower GMV in the thalamus (β = -0.53, p = 0.003) and hippocampus (β = -0.57, p = 0.001) was associated with faster sway velocity of the center of pressure (CoP) in eyes closed condition (i.e., worse balance control performance). The GMV in the thalamus (percentage mediated [PM] = 32.02%; indirect effect β = 0.119, 95% CI = 0.003 to 0.282) and hippocampus (PM = 33.71%; indirect effect β = 0.122, 95% CI = 0.005 to 0.278) significantly mediated the association between the disease duration and balance performance. These findings suggest that the structural characteristics of the supraspinal elements is critical to the maintenance of balance control performance in individuals suffering from CAI, which deserve careful consideration in the management and rehabilitation programs in this population.

Age dictates brain functional connectivity and axonal integrity following repetitive mild traumatic brain injuries in mice

Traumatic brain injuries (TBI) present a major public health challenge, demanding an in-depth understanding of age-specific symptoms and risk factors. Aging not only significantly influences brain function and plasticity but also elevates the risk of hospitalizations and death following TBIs. Repetitive mild TBIs (rmTBI) compound these issues, resulting in cumulative and long-term brain damage in the brain. In this study, we investigate the impact of age on brain network changes and white matter properties following rmTBI by employing a multi-modal approach that integrates resting-state functional magnetic resonance imaging (rsfMRI), graph theory analysis, diffusion tensor imaging (DTI), and neurite orientation dispersion and density imaging (NODDI). Our hypothesis is that the effects of rmTBI are worsened in aged animals, with this group showing more pronounced alterations in brain connectivity and white matter structure. Utilizing the closed-head impact model of engineered rotational acceleration (CHIMERA) model, we conducted rmTBIs or sham (control) procedures on young (2.5-3-months-old) and aged (22-months-old) male and female mice to model high-risk groups. Functional and structural imaging unveiled age-related reductions in communication efficiency between brain regions, while injuries induced opposhigh-risking effects on the small-world index across age groups, influencing network segregation. Functional connectivity analysis also identified alterations in 79 out of 148 brain regions by age, treatment (sham vs. rmTBI), or their interaction. Injuries exerted pronounced effects on sensory integration areas, including insular and motor cortices. Age-related disruptions in white matter integrity were observed, indicating alterations in various diffusion directions (mean diffusivity, radial diffusivity, axial diffusivity, and fractional anisotropy) and density neurite properties (dispersion index, intracellular and isotropic volume fraction). Neuroinflammation, assessed through Iba-1 and GFAP markers, correlated with higher dispersion in the optic tract, suggesting a neuroinflammatory response in injured aged animals compared to sham aged. These findings offer insight into the interplay between age, injuries, and brain connectivity, shedding light on the long-term consequences of rmTBI.

Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules

Temporal rescaling of sequential neural activity has been observed in multiple brain areas during behaviors involving time estimation and motor execution at variable speeds. Temporally asymmetric Hebbian rules have been used in network models to learn and retrieve sequential activity, with characteristics that are qualitatively consistent with experimental observations. However, in these models sequential activity is retrieved at a fixed speed. Here, we investigate the effects of a heterogeneity of plasticity rules on network dynamics. In a model in which neurons differ by the degree of temporal symmetry of their plasticity rule, we find that retrieval speed can be controlled by varying external inputs to the network. Neurons with temporally symmetric plasticity rules act as brakes and tend to slow down the dynamics, while neurons with temporally asymmetric rules act as accelerators of the dynamics. We also find that such networks can naturally generate separate 'preparatory' and 'execution' activity patterns with appropriate external inputs.

Motor Cortex Latent Dynamics Encode Spatial and Temporal Arm Movement Parameters Independently

The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm movements are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics. Using a task where three male monkeys made a sequence of reaching movements to randomly placed targets, we show that the spatial and temporal parameters of arm movements are independently encoded in the low-dimensional trajectories of population activity in motor cortex: each movement's direction corresponds to a fixed neural trajectory through neural state space and its speed to how quickly that trajectory is traversed. Recurrent neural network models show that this coding allows independent control over the spatial and temporal parameters of movement by separate network parameters. Our results support a key prediction of the dynamical systems view of motor cortex, and also argue that not all parameters of movement are defined by different trajectories of population activity.

Slow ramping emerges from spontaneous fluctuations in spiking neural networks

The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ~2 s before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.

Amygdala and Cortex Relationships during Learning of a Sensory Discrimination Task

During learning of a sensory discrimination task, the cortical and subcortical regions display complex spatiotemporal dynamics. During learning, both the amygdala and cortex link stimulus information to its appropriate association, for example, a reward. In addition, both structures are also related to nonsensory parameters such as body movements and licking during the reward period. However, the emergence of the cortico-amygdala relationships during learning is largely unknown. To study this, we combined wide-field cortical imaging with fiber photometry to simultaneously record cortico-amygdala population dynamics as male mice learn a whisker-dependent go/no-go task. We were able to simultaneously record neuronal populations from the posterior cortex and either the basolateral amygdala (BLA) or central/medial amygdala (CEM). Prior to learning, the somatosensory and associative cortex responded during sensation, while amygdala areas did not show significant responses. As mice became experts, amygdala responses emerged early during the sensation period, increasing in the CEM, while decreasing in the BLA. Interestingly, amygdala and cortical responses were associated with task-related body movement, displaying significant responses ∼200 ms before movement initiation which led to licking for the reward. A correlation analysis between the cortex and amygdala revealed negative and positive correlation with the BLA and CEM, respectively, only in the expert case. These results imply that learning induces an involvement of the cortex and amygdala which may aid to link sensory stimuli with appropriate associations.

Assessing Cross-Contamination in Spike-Sorted Electrophysiology Data

Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike-sorting accuracy. One long-standing method of assessing the false discovery rate (FDR) of spike sorting-the rate at which spikes are assigned to the wrong cluster-has been the rate of interspike interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remains limited. Here, we describe an analytical solution that can be used to predict FDR from the ISI violation rate (ISIv). We test this model in silico through Monte Carlo simulation and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISIv and FDR is highly nonlinear, with additional dependencies on firing frequency, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets recorded in mice were found to range from 3.1 to 50.0%. We found that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike-sorting accuracy in a principled manner.

Environmental enrichment reduces restricted repetitive behavior by altering gray matter microstructure

Restricted, repetitive behaviors are common symptoms in neurodevelopmental disorders including autism spectrum disorder. Despite being associated with poor developmental outcomes, repetitive behaviors remain poorly understood and have limited treatment options. Environmental enrichment attenuates the development of repetitive behaviors, but the exact mechanisms remain obscure. Using the C58 mouse model of repetitive behavior, we performed diffusion tensor imaging to examine microstructural alterations associated with the development of repetitive behavior and its attenuation by environmental enrichment. The C57BL/6 mouse strain, which displays little or no repetitive behavior, was used as a control group. We observed widespread differences in diffusion metrics between C58 mice and C57BL/6 mice. In juvenile C58 mice, repetitive motor behavior displayed strong negative correlations with fractional anisotropy in multiple gray matter regions, whereas in young adult C58 mice, high repetitive motor behavior was most strongly associated with lower fractional anisotropy and higher radial diffusivity in the striatum. Environmental enrichment increased fractional anisotropy and axial diffusivity throughout gray matter regions in the brains of juvenile C58 mice and overlapped predominantly with cerebellar and sensory regions associated with repetitive behavior. Our results suggest environmental enrichment reduces repetitive behavior development by altering gray matter microstructure in the cerebellum, medial entorhinal cortex, and sensory processing regions in juvenile C58 mice. Under standard laboratory conditions, early pathology in these regions appears to contribute to later striatal and white matter dysfunction in adult C58 mice. Future studies should examine the role these regions play in the development of repetitive behavior and the relationship between sensory processing and cerebellar deficits and repetitive behavior.

Integrator dynamics in the cortico-basal ganglia loop underlie flexible motor timing

Flexible control of motor timing is crucial for behavior. Before volitional movement begins, the frontal cortex and striatum exhibit ramping spiking activity, with variable ramp slopes anticipating movement onsets. This activity in the cortico-basal ganglia loop may function as an adjustable 'timer,' triggering actions at the desired timing. However, because the frontal cortex and striatum share similar ramping dynamics and are both necessary for timing behaviors, distinguishing their individual roles in this timer function remains challenging. To address this, we conducted perturbation experiments combined with multi-regional electrophysiology in mice performing a flexible lick-timing task. Following transient silencing of the frontal cortex, cortical and striatal activity swiftly returned to pre-silencing levels and resumed ramping, leading to a shift in lick timing close to the silencing duration. Conversely, briefly inhibiting the striatum caused a gradual decrease in ramping activity in both regions, with ramping resuming from post-inhibition levels, shifting lick timing beyond the inhibition duration. Thus, inhibiting the frontal cortex and striatum effectively paused and rewound the timer, respectively. These findings suggest the striatum is a part of the network that temporally integrates input from the frontal cortex and generates ramping activity that regulates motor timing.

The mesopontine tegmentum in reward and aversion: From cellular heterogeneity to behaviour

The mesopontine tegmentum, comprising the pedunculopontine tegmentum (PPN) and the laterodorsal tegmentum (LDT), is intricately connected to various regions of the basal ganglia, motor systems, and limbic systems. The PPN and LDT can regulate the activity of different brain regions of these target systems, and in this way are in a privileged position to modulate motivated behaviours. Despite recent findings, the PPN and LDT have been largely overlooked in discussions about the neural circuits associated with reward and aversion. This review aims to provide a timely and comprehensive resource on past and current research, highlighting the PPN and LDT's connectivity and influence on basal ganglia and limbic, and motor systems. Seminal studies, including lesion, pharmacological, and optogenetic/chemogenetic approaches, demonstrate their critical roles in modulating reward/aversive behaviours. The review emphasizes the need for further investigation into the associated cellular mechanisms, in order to clarify their role in behaviour and contribution for different neuropsychiatric disorders.

Hippocampal-dependent navigation in head-fixed mice using a floating real-world environment

Head-fixation of mice enables high-resolution monitoring of neuronal activity coupled with precise control of environmental stimuli. Virtual reality can be used to emulate the visual experience of movement during head fixation, but a low inertia floating real-world environment (mobile homecage, MHC) has the potential to engage more sensory modalities and provide a richer experimental environment for complex behavioral tasks. However, it is not known whether mice react to this adapted environment in a similar manner to real environments, or whether the MHC can be used to implement validated, maze-based behavioral tasks. Here, we show that hippocampal place cell representations are intact in the MHC and that the system allows relatively long (20 min) whole-cell patch clamp recordings from dorsal CA1 pyramidal neurons, revealing sub-threshold membrane potential dynamics. Furthermore, mice learn the location of a liquid reward within an adapted T-maze guided by 2-dimensional spatial navigation cues and relearn the location when spatial contingencies are reversed. Bilateral infusions of scopolamine show that this learning is hippocampus-dependent and requires intact cholinergic signalling. Therefore, we characterize the MHC system as an experimental tool to study sub-threshold membrane potential dynamics that underpin complex navigation behaviors.

Motor cortex latent dynamics encode spatial and temporal arm movement parameters independently

The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm movements are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics. Using a task where monkeys made a sequence of reaching movements to randomly placed targets, we show that the spatial and temporal parameters of arm movements are independently encoded in the low-dimensional trajectories of population activity in motor cortex: Each movement's direction corresponds to a fixed neural trajectory through neural state space and its speed to how quickly that trajectory is traversed. Recurrent neural network models show this coding allows independent control over the spatial and temporal parameters of movement by separate network parameters. Our results support a key prediction of the dynamical systems view of motor cortex, but also argue that not all parameters of movement are defined by different trajectories of population activity.

Auditory neuroscience: Sounds make the face move

Animals including humans often react to sounds by involuntarily moving their face and body. A new study shows that facial movements provide a simple and reliable readout of a mouse's hearing ability that is more sensitive than traditional measurements.

Sound elicits stereotyped facial movements that provide a sensitive index of hearing abilities in mice

Sound elicits rapid movements of muscles in the face, ears, and eyes that protect the body from injury and trigger brain-wide internal state changes. Here, we performed quantitative facial videography from mice resting atop a piezoelectric force plate and observed that broadband sounds elicited rapid and stereotyped facial twitches. Facial motion energy (FME) adjacent to the whisker array was 30 dB more sensitive than the acoustic startle reflex and offered greater inter-trial and inter-animal reliability than sound-evoked pupil dilations or movement of other facial and body regions. FME tracked the low-frequency envelope of broadband sounds, providing a means to study behavioral discrimination of complex auditory stimuli, such as speech phonemes in noise. Approximately 25% of layer 5-6 units in the auditory cortex (ACtx) exhibited firing rate changes during facial movements. However, FME facilitation during ACtx photoinhibition indicated that sound-evoked facial movements were mediated by a midbrain pathway and modulated by descending corticofugal input. FME and auditory brainstem response (ABR) thresholds were closely aligned after noise-induced sensorineural hearing loss, yet FME growth slopes were disproportionately steep at spared frequencies, reflecting a central plasticity that matched commensurate changes in ABR wave 4. Sound-evoked facial movements were also hypersensitive in Ptchd1 knockout mice, highlighting the use of FME for identifying sensory hyper-reactivity phenotypes after adult-onset hyperacusis and inherited deficiencies in autism risk genes. These findings present a sensitive and integrative measure of hearing while also highlighting that even low-intensity broadband sounds can elicit a complex mixture of auditory, motor, and reafferent somatosensory neural activity.

Impact of Impedance Levels on Recording Quality in Flexible Neural Probes

Flexible neural probes are attractive emerging technologies for brain recording because they can effectively record signals with minimal risk of brain damage. Reducing the electrode impedance of the probe before recording is a common practice of many researchers. However, studies investigating the impact of low impedance levels on high-quality recordings using flexible neural probes are lacking. In this study, we electrodeposited Pt onto a commercial flexible polyimide neural probe and investigated the relationship between the impedance level and the recording quality. The probe was inserted into the brains of anesthetized mice. The electrical signals of neurons in the brain, specifically the ventral posteromedial nucleus of the thalamus, were recorded at impedance levels of 50, 250, 500 and 1000 kΩ at 1 kHz. The study results demonstrated that as the impedance decreased, the quality of the signal recordings did not consistently improve. This suggests that extreme lowering of the impedance may not always be advantageous in the context of flexible neural probes.

Brain mechanism of foraging: Reward-dependent synaptic plasticity versus neural integration of values

During foraging behavior, action values are persistently encoded in neural activity and updated depending on the history of choice outcomes. What is the neural mechanism for action value maintenance and updating? Here, we explore two contrasting network models: synaptic learning of action value versus neural integration. We show that both models can reproduce extant experimental data, but they yield distinct predictions about the underlying biological neural circuits. In particular, the neural integrator model but not the synaptic model requires that reward signals are mediated by neural pools selective for action alternatives and their projections are aligned with linear attractor axes in the valuation system. We demonstrate experimentally observable neural dynamical signatures and feasible perturbations to differentiate the two contrasting scenarios, suggesting that the synaptic model is a more robust candidate mechanism. Overall, this work provides a modeling framework to guide future experimental research on probabilistic foraging.

Input and Output Connections of the Crow Nidopallium Caudolaterale

The avian telencephalic structure nidopallium caudolaterale (NCL) functions as an analog to the mammalian prefrontal cortex. In crows, corvid songbirds, it plays a crucial role in higher cognitive and executive functions. These functions rely on the NCL's extensive telencephalic connections. However, systematic investigations into the brain-wide connectivity of the NCL in crows or other songbirds are lacking. Here, we studied its input and output connections by injecting retrograde and anterograde tracers into the carrion crow NCL. Our results, mapped onto a published carrion crow brain atlas, confirm NCL multisensory connections and extend prior pigeon findings by identifying a novel input from the hippocampal formation. Furthermore, we analyze crow NCL efferent projections to the arcopallium and report newly identified arcopallial neurons projecting bilaterally to the NCL. These findings help to clarify the role of the NCL as central executive hub in the corvid songbird brain.

Preparatory activity and the expansive null-space

The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.

Differential Formation of Motor Cortical Dynamics during Movement Preparation According to the Predictability of Go Timing

The motor cortex not only executes but also prepares movement, as motor cortical neurons exhibit preparatory activity that predicts upcoming movements. In movement preparation, animals adopt different strategies in response to uncertainties existing in nature such as the unknown timing of when a predator will attack-an environmental cue informing "go." However, how motor cortical neurons cope with such uncertainties is less understood. In this study, we aim to investigate whether and how preparatory activity is altered depending on the predictability of "go" timing. We analyze firing activities of the anterior lateral motor cortex in male mice during two auditory delayed-response tasks each with predictable or unpredictable go timing. When go timing is unpredictable, preparatory activities immediately reach and stay in a neural state capable of producing movement anytime to a sudden go cue. When go timing is predictable, preparation activity reaches the movement-producible state more gradually, to secure more accurate decisions. Surprisingly, this preparation process entails a longer reaction time. We find that as preparatory activity increases in accuracy, it takes longer for a neural state to transition from the end of preparation to the start of movement. Our results suggest that the motor cortex fine-tunes preparatory activity for more accurate movement using the predictability of go timing.

Identifying Interpretable Latent Factors with Sparse Component Analysis

In many neural populations, the computationally relevant signals are posited to be a set of 'latent factors' - signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans, and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations.

Brain-wide neural activity underlying memory-guided movement

Behavior relies on activity in structured neural circuits that are distributed across the brain, but most experiments probe neurons in a single area at a time. Using multiple Neuropixels probes, we recorded from multi-regional loops connected to the anterior lateral motor cortex (ALM), a circuit node mediating memory-guided directional licking. Neurons encoding sensory stimuli, choices, and actions were distributed across the brain. However, choice coding was concentrated in the ALM and subcortical areas receiving input from the ALM in an ALM-dependent manner. Diverse orofacial movements were encoded in the hindbrain; midbrain; and, to a lesser extent, forebrain. Choice signals were first detected in the ALM and the midbrain, followed by the thalamus and other brain areas. At movement initiation, choice-selective activity collapsed across the brain, followed by new activity patterns driving specific actions. Our experiments provide the foundation for neural circuit models of decision-making and movement initiation.

Age dictates brain functional connectivity and axonal integrity following repetitive mild traumatic brain injuries

Traumatic brain injuries (TBI) present a major public health challenge, demanding an in-depth understanding of age-specific signs and vulnerabilities. Aging not only significantly influences brain function and plasticity but also elevates the risk of hospitalizations and death following repetitive mild traumatic brain injuries (rmTBIs). In this study, we investigate the impact of age on brain network changes and white matter properties following rmTBI employing a multi-modal approach that integrates resting-state functional magnetic resonance imaging (rsfMRI), graph theory analysis, diffusion tensor imaging (DTI), and Neurite Orientation Dispersion and Density Imaging (NODDI). Utilizing the CHIMERA model, we conducted rmTBIs or sham (control) procedures on young (2.5-3 months old) and aged (22-month-old) male and female mice to model high risk groups. Functional and structural imaging unveiled age-related reductions in communication efficiency between brain regions, while injuries induced opposing effects on the small-world index across age groups, influencing network segregation. Functional connectivity analysis also identified alterations in 79 out of 148 brain regions by age, treatment (sham vs. rmTBI), or their interaction. Injuries exerted pronounced effects on sensory integration areas, including insular and motor cortices. Age-related disruptions in white matter integrity were observed, indicating alterations in various diffusion directions (mean, radial, axial diffusivity, fractional anisotropy) and density neurite properties (dispersion index, intracellular and isotropic volume fraction). Inflammation, assessed through Iba-1 and GFAP markers, correlated with higher dispersion in the optic tract, suggesting a neuroinflammatory response in aged animals. These findings provide a comprehensive understanding of the intricate interplay between age, injuries, and brain connectivity, shedding light on the long-term consequences of rmTBIs.

The dynamic causality brain network reflects whether the working memory is solidified

Working memory, which is foundational to higher cognitive function, is the "sketchpad of volitional control." Successful working memory is the inevitable outcome of the individual's active control and manipulation of thoughts and turning them into internal goals during which the causal brain processes information in real time. However, little is known about the dynamic causality among distributed brain regions behind thought control that underpins successful working memory. In our present study, given that correct responses and incorrect ones did not differ in either contralateral delay activity or alpha suppression, further rooting on the high-temporal-resolution EEG time-varying directed network analysis, we revealed that successful working memory depended on both much stronger top-down connections from the frontal to the temporal lobe and bottom-up linkages from the occipital to the temporal lobe, during the early maintenance period, as well as top-down flows from the frontal lobe to the central areas as the delay behavior approached. Additionally, the correlation between behavioral performance and casual interactions increased over time, especially as memory-guided delayed behavior approached. Notably, when using the network metrics as features, time-resolved multiple linear regression of overall behavioral accuracy was exactly achieved as delayed behavior approached. These results indicate that accurate memory depends on dynamic switching of causal network connections and shifting to more task-related patterns during which the appropriate intervention may help enhance memory.

Micropopulation mapping of the mouse parafascicular nucleus connections reveals diverse input-output motifs

Introduction

In primates, including humans, the centromedian/parafascicular (CM-Pf) complex is a key thalamic node of the basal ganglia system. Deep brain stimulation in CM-Pf has been applied for the treatment of motor disorders such as Parkinson's disease or Tourette syndrome. Rodents have become widely used models for the study of the cellular and genetic mechanisms of these and other motor disorders. However, the equivalence between the primate CM-Pf and the nucleus regarded as analogous in rodents (Parafascicular, Pf) remains unclear.

Methods

Here, we analyzed the neurochemical architecture and carried out a brain-wide mapping of the input-output motifs in the mouse Pf at micropopulation level using anterograde and retrograde labeling methods. Specifically, we mapped and quantified the sources of cortical and subcortical input to different Pf subregions, and mapped and compared the distribution and terminal structure of their axons.

Results

We found that projections to Pf arise predominantly (>75%) from the cerebral cortex, with an unusually strong (>45%) Layer 5b component, which is, in part, contralateral. The intermediate layers of the superior colliculus are the main subcortical input source to Pf. On its output side, Pf neuron axons predominantly innervate the striatum. In a sparser fashion, they innervate other basal ganglia nuclei, including the subthalamic nucleus (STN), and the cerebral cortex. Differences are evident between the lateral and medial portions of Pf, both in chemoarchitecture and in connectivity. Lateral Pf axons innervate territories of the striatum, STN and cortex involved in the sensorimotor control of different parts of the contralateral hemibody. In contrast, the mediodorsal portion of Pf innervates oculomotor-limbic territories in the above three structures.

Discussion

Our data thus indicate that the mouse Pf consists of several neurochemically and connectively distinct domains whose global organization bears a marked similarity to that described in the primate CM-Pf complex.