Neural mechanisms of movement planning: motor cortex and beyond

Neural mechanisms underlying the improvement of gait disturbances in stroke patients through robot-assisted gait training based on QEEG and fNIRS: a randomized controlled study

BACKGROUND

Robot-assisted gait training is more effective in improving lower limb function and walking ability in stroke patients compared to conventional rehabilitation, but the neural mechanisms remain unclear. This study aims to explore the effects of robot-assisted gait training on lower limb motor dysfunction in stroke patients and its impact on neural activity in the motor cortex, providing objective evidence for clinical application.

METHODS

Forty-two stroke patients meeting the inclusion criteria were randomly assigned to either the experimental group receiving robot-assisted gait training or the control group receiving conventional overground walking training. Assessments were conducted at baseline and after four weeks of treatment. Primary outcome measures included cortical activation measured by functional near-infrared spectroscopy (fNIRS), power ratio index (PRI), and delta/alpha power ratio (DAR) measured by quantitative electroencephalography (QEEG), and their correlation with the Fugl-Meyer Assessment (FMA) for lower limb motor function. Secondary outcome measures included FMA and Functional Ambulation Category (FAC).

RESULTS

Data from 36 patients (18 in each group) after four weeks of treatment were analyzed. The fNIRS results indicated better activation in the premotor and supplementary motor cortices in the robot-assisted gait training group compared to the control group. QEEG analysis showed reduced PRI and DAR in the premotor, supplementary motor, and primary motor cortices in the robot-assisted gait training group, suggesting improved motor function recovery in stroke patients. Clinical scale analysis revealed superior motor function recovery in the robot-assisted gait training group compared to the control group.

CONCLUSIONS

Robot-assisted gait training significantly enhances activation in the primary motor cortex and supplementary motor area, potentially aiding stroke patients in recovering their ability to plan. PRI and DAR, particularly PRI, are valuable clinical indicators for assessing motor function recovery in stroke patients.

TRIAL REGISTRATION

Chinese Clinical Trial Registry (ChiCTR2200060668). Registered on June 6, 2022; https://www.chictr.org.cn/showproj.html?proj=171610 .

Brain volumes are related with motor skills at late childhood in children born extremely preterm

BACKGROUND

This study had three aims. First, we wanted to explore if there was difference in motor performance at 12 years of age in children born extremely preterm (EPT < 28 weeks of gestation) and at term. Our second aim was to study whether the volumes of motor networks and regions differed between those groups when they underwent brain scans at 10 years of age. Third, we investigated whether there were differences in the motor networks and regions of the brain in children born EPT who did or did not have motor impairment at 12 years of age.

METHODS

In a Swedish national study, a subgroup of 42 children born before 27 weeks and 25 term-born controls underwent MRI at age 10. A neuroradiologist performed MRI acquisitions, and analyses focused on brain regions associated with motor function. At age 12, motor function was assessed using the Movement Assessment Battery for Children - Second Edition (MABC-2), conducted by a licensed physiotherapist. Examiners were blinded to group status. Motor function and motor-related brain volumes were compared between the EPT and control group, and between children born EPT with and without motor impairments.

RESULTS

Findings revealed significantly reduced motor performance and smaller motor region volumes in EPT children compared to controls (p < 0.001). Among EPT children, those with motor impairment especially in aiming and catching, had notably smaller brain volume in the basal ganglia (mean difference:1.2 cm3, p = 0.049), cerebellum (mean difference:14.4 cm3, p < 0.001), motor execution (mean difference:3.7 cm3, p = 0.049) network and motor imagery network (mean difference 5.6 cm3, p = 0.049) than their EPT peers without such impairments. Cerebellar volume remained significant different between the groups when adjusting for birth weight and sex in a linear regression model, p = 0.02 (η2 = 0.17).

CONCLUSION

The results underscore the impact of extreme prematurity on motor function and brain structure, highlighting a specific link between reduced motor area volumes and impaired ball skills.

Corticothalamic neurons in motor cortex have a permissive role in motor execution

The primary motor cortex (M1) is a central hub for motor learning and execution. M1 is composed of heterogeneous cell types with varying relationships to movement. Here, we tagged active neurons at different stages of motor task performance in mice and characterized cell type composition. We identified corticothalamic neurons (M1CT) as consistently enriched with training progression. Using two-photon calcium imaging, we found that M1CT activity is largely suppressed during movement, and this negative correlation augments with training. Increasing M1CT activity through closed-loop optogenetic manipulations during forelimb movement significantly hinders execution, an effect that became stronger with training. Similar manipulations, however, had little effect on locomotion. In contrast, M1 corticospinal neurons positively correlate with movement, with an increase during training. We uncovered that M1CT neurons suppress corticospinal activity via feedforward inhibition, also scaling with training. These results identify a permissive role of corticothalamic neurons in movement execution through disinhibition of corticospinal neurons.

Cortical signatures linked to behavior quantitatively track arousal levels

While current arousal level assessments in patients with disorders of consciousness discriminate altered states of consciousness, there are significant limitations in characterizing the transition from one state to another or quantifying the frequent arousal level fluctuations observed in a patient. Here, we identified a repeated, temporally discrete, dynamical pattern evident in the recovery of consciousness from anesthesia and brain injury coma models in rodents. We prospectively validated these features we label "Arousal Units" (AU) in neonatal humans recovering from static hypoxic injuries and senior patients emerging from anesthesia indicating their generalizability. The AUs lawfully link changes in spectral power and breathing frequency and reliably associate with motor changes. Distinctive cortical patterns within AUs can be transformed into arousal indices, determining arousal levels. The reliability of these events is demonstrated across intact and brain-injured states and translates to the human brain; extracting these stereotyped dynamics could aid anesthesia monitoring, tracking coma recovery, and identifying cognitive motor dissociation.

A Heterogeneous Attractor Model for Neural Dynamical Mechanism of Movement Preparation

Preparatory activity is crucial for voluntary motor control, reducing reaction time and enhancing precision. To understand the neurodynamic mechanisms behind this, we construct a dynamical model within the motor cortex, which comprises coupled heterogeneous attractors to simulate delayed reaching tasks. This model replicates the neural activity patterns observed in the macaque motor cortex, within distinct attractor spaces for preparatory and executive activities. It can capture the transition from preparation to execution through shifts in an orthogonal subspace combined with a thresholding mechanism. Results show that the preparation duration modulates behavioral accuracy, with optimal preparation intervals enhancing performance. External inputs primarily shape the preparatory activity, while synaptic connections dominate execution. Our analysis of the network's multi-stable dynamics reveals that external inputs reshape the stable points of the heterogeneous attractor modules both before and after preparation, while synaptic strength affects dynamical stability and input sensitivity, allowing rapid and precise actions. Additionally, sensitivity to external perturbations decreases as preparatory time increases, emphasizing the importance of external inputs during preparation. Overall, this study provides insights into the neurodynamic mechanisms underlying the transition from motor preparation to execution and underscores the significance of preparatory activity for accurate motor control.

Implications of shared motor and perceptual activations on the sensorimotor cortex for neuroprosthetic decoding

Neuroprostheses can restore communicative ability to people with paralysis by decoding intended speech movements from the sensorimotor cortex (SMC). However, overlapping neural populations in the SMC are also engaged in visual and auditory processing. The nature of these shared motor and perceptual activations and their potential to interfere with decoding are particularly relevant questions for speech neuroprostheses, as reading and listening are essential daily functions. In two participants with vocal-tract paralysis and anarthria (ClinicalTrials.gov; NCT03698149 ), we developed an online electrocorticography (ECoG) based speech-decoding system that maintained accuracy and specificity to intended speech, even during common daily tasks like reading and listening. Offline, we studied the spectrotemporal characteristics and spatial distribution of reading, listening, and attempted-speech responses across our participants' ECoG arrays. Across participants, the speech-decoding system had zero false-positive activations during 63.2 minutes of attempted speech and perceptual tasks, maintaining accuracy and specificity to volitional speech attempts. Offline, though we observed shared neural populations that responded to attempted speech, listening, and reading, we found they leveraged different neural representations with differentiable spectrotemporal responses. Shared populations localized to the middle precentral gyrus and may have a distinct role in speech-motor planning. Potential neuroprosthesis users strongly desire reliable systems that will retain specificity to volitional speech attempts during daily use. These results demonstrate a decoding framework for speech neuroprostheses that maintains this specificity and further our understanding of shared perceptual and motor activity on the SMC.

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.

The prediction of auditory consequences of own and observed actions: a brain decoding multivariate pattern study

Evidence from the auditory domain suggests that sounds generated by self-performed as well as observed actions are processed differently compared to external sounds. This study aimed to investigate which brain regions are involved in the processing of auditory stimuli generated by actions, addressing the question of whether cerebellar forward models, which are supposed to predict the sensory consequences of self-performed actions, similarly underlie predictions for action observation. We measured brain activity with functional magnetic resonance imaging (fMRI) while participants elicited a sound via button press, observed another person performing this action, or listened to external sounds. By applying multivariate pattern analysis (MVPA), we found evidence for altered processing in the right auditory cortex for sounds following both self-performed and observed actions relative to external sounds. Evidence for the prediction of auditory action consequences was found in the bilateral cerebellum and the right supplementary motor area, but only for self-performed actions. Our results suggest that cerebellar forward models contribute to predictions of sensory consequences only for action performance. While predictions are also generated for action observation, the underlying mechanisms remain to be elucidated.

Association between the focus of attention and brain activation pattern during golf putting task in amateur and novice: A fNIRS study

PURPOSE

External focus of attention (FOA) has been shown to improve motor performance. However, recent research has found that the effectiveness of FOA is related to the level of expertise. Therefore, this study examined the effects of FOA on putting performance in golfers of different levels of expertise. The neural mechanisms behind FOA were explored in conjunction with fNIRS.

METHOD

A total of 30 participants, including 15 amateurs (Mage: 23.31(SD = 1.32)years; 15 males) and 15 novices (Mage: 22.69(SD = 1.55) years; 11 males; 4 females) were recruited. Participants completed EF and IF golf putting at a duration of 3s per time wearing fNIRS for 3 blocks of 30 s interspersed with 10-s rest blocks.

RESULT

Behavioral results showed a significant difference in the putting performance of the amateur group under the EF condition compared to the IF condition (P = 0.019), and relative to novices, the amateur group performed better under the EF condition (P = 0.003). fNIRS results revealed that the amateur group had higher activation levels in the right somatosensory association cortex (RSAC) and right motor cortex (RMC) under the IF condition. In contrast, for the novice group, higher activation levels were observed in the left prefrontal cortex and RMC under the EF condition.

CONCLUSIONS

Our results revealed SAC and MC over-activation in the amateur group under IF conditions with poor golf putting performance. Our findings suggest that the impairment of automated motor neural networks could be a possible mechanism by which IF affects motor performance with SAC and MC over-activation. Guiding novices to focus on task-related factors consciously could be a potential mechanism by which EF enhances motor performance.

Separating cognitive and motor processes in the behaving mouse

The cognitive processes supporting complex animal behavior are closely associated with movements responsible for critical processes, such as facial expressions or the active sampling of our environments. These movements are strongly related to neural activity across much of the brain and are often highly correlated with ongoing cognitive processes. A fundamental issue for understanding the neural signatures of cognition and movements is whether cognitive processes are separable from related movements or if they are driven by common neural mechanisms. Here we demonstrate how the separability of cognitive and motor processes can be assessed and, when separable, how the neural dynamics associated with each component can be isolated. We designed a behavioral task in mice that involves multiple cognitive processes, and we show that dynamics commonly taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using a novel approach for subspace decomposition, we find that they exhibit distinct dynamical trajectories and are encoded by largely separate populations of cells. Accurately isolating dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function.

Differed brain spontaneous neural activity between limb-onset and bulbar-onset amyotrophic lateral sclerosis patients

PURPOSE

To investigate the differences in brain spontaneous neural activity between limb-onset and bulbar-onset amyotrophic lateral sclerosis (ALS-L and ALS-B, respectively) patients using resting-state functional MRI (rs-fMRI) with amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo).

MATERIALS AND METHODS

The rs-fMRI data were collected from 41 ALS patients (11 ALS-B and 30 ALS-L) and 25 healthy controls (HC). ALFF and ReHo values were calculated, and group differences were assessed using one-way ANCOVA and two-sample t-tests. Correlation analyses with clinical measures were conducted. Support vector machine (SVM) analysis was performed to distinguish ALS subtypes.

RESULTS

Compared with ALS-L, ALS-B showed increased ALFF values in the right gyrus rectus/ orbital part of right middle frontal gyrus, orbital part of left middle frontal gyrus and left dorsolateral superior frontal gyrus/ left medial superior frontal gyrus and decreased ALFF values in the left superior occipital gyrus (FDR-corrected, P < 0.05). Both ALS subtypes demonstrated distinct ALFF alterations compared to HC. Differences in ReHo values were only found between ALS-B and HC. Correlation analyses revealed associations between ALFF in specific brain regions and ALS clinical scores. SVM analysis achieved an accuracy of 90.2 %, with an AUC of 0.909 in differentiating ALS-B and ALS-L.

CONCLUSION

ALS-B and ALS-L patients had distinct alterations in brain spontaneous neural activity, which could serve as potential biomarkers for accurately distinguishing these two subtypes. Our findings offer a new insight into the neural mechanism of ALS, underscoring the importance of personalized diagnostic approaches for this complex neurological disorder.

Redundant, weakly connected prefrontal hemispheres balance precision and capacity in spatial working memory

How the prefrontal hemispheres coordinate to adapt to spatial working memory (WM) demands remains an open question. Recently, two models have been proposed: A specialized model, where each hemisphere governs contralateral behavior, and a redundant model, where both hemispheres equally guide behavior in the full visual space. To explore these alternatives, we analyzed simultaneous bilateral prefrontal cortex recordings from three macaque monkeys performing a visuo-spatial WM task. Each hemisphere represented targets across the full visual field and equally predicted behavioral imprecisions. Furthermore, memory errors were weakly correlated between hemispheres, suggesting that redundant, weakly coupled prefrontal hemispheres support spatial WM. Attractor model simulations showed that the hemispheric redundancy improved precision in simple tasks, whereas weak inter-hemispheric coupling allowed for specialized hemispheres in complex tasks. This interhemispheric architecture reconciles previous findings thought to support distinct models into a unified architecture, providing a versatile interhemispheric architecture that adapts to varying cognitive demands.

Cortical changes associated with an anterior cruciate ligament injury may retrograde skilled kicking in football: preliminary EEG findings

Anterior cruciate ligament injuries (ACLi) impact football players substantially leading to performance declines and premature career endings. Emerging evidence suggests that ACLi should be viewed not merely as peripheral injuries but as complex conditions with neurophysiological aspects. The objective of the present study was to compare kicking performance and associated cortical activity between injured and healthy players. Ten reconstructed and 15 healthy players performed a kicking task. Kicking biomechanics were recorded using wearable inertial measurement unit sensors. Cortical activity was captured with a 64-electrode mobile electroencephalography. Multiscale entropy (MSE) analysis of biomechanics revealed increased variability in foot external rotation among injured players. Source-derived event-related spectral perturbations indicated significant differences in posterior alpha and frontal theta oscillations between the two groups. Furthermore, kick-related complexity of these regions as indexed by MSE was reduced in injured players at medium and coarse scales. Our findings suggest sensorimotor changes during kicking in injured players, which may necessitate compensatory strategies involving augmented attention at the cost of processing visuospatial information. This conflict may hinder the integration of task-relevant information across distributed networks. Our study provides preliminary insights into the neurophysiological implications of ACLi within football context and underscores the potential for prospective research.

Encoding of cerebellar dentate neuron activity during visual attention in rhesus macaques

The role of cerebellum in controlling eye movements is well established, but its contribution to more complex forms of visual behavior has remained elusive. To study cerebellar activity during visual attention we recorded extracellular activity of dentate nucleus (DN) neurons in two non-human primates (NHPs). NHPs were trained to read the direction indicated by a peripheral visual stimulus while maintaining fixation at the center, and report the direction of the cue by performing a saccadic eye movement into the same direction following a delay. We found that single-unit DN neurons modulated spiking activity over the entire time course of the task, and that their activity often bridged temporally separated intra-trial events, yet in a heterogeneous manner. To better understand the heterogeneous relationship between task structure, behavioral performance, and neural dynamics, we constructed a behavioral, an encoding, and a decoding model. Both NHPs showed different behavioral strategies, which influenced the performance. Activity of the DN neurons reflected the unique strategies, with the direction of the visual stimulus frequently being encoded long before an upcoming saccade. Moreover, the latency of the ramping activity of DN neurons following presentation of the visual stimulus was shorter in the better performing NHP. Labeling with the retrograde tracer Cholera Toxin B in the recording location in the DN indicated that these neurons predominantly receive inputs from Purkinje cells in the D1 and D2 zones of the lateral cerebellum as well as neurons of the principal olive and medial pons, all regions known to connect with neurons in the prefrontal cortex contributing to planning of saccades. Together, our results highlight that DN neurons can dynamically modulate their activity during a visual attention task, comprising not only sensorimotor but also cognitive attentional components.

Are there cortical somatotopic motor maps outside of the human precentral gyrus?

Human body movements are supported by a somatotopic map, primary motor cortex (M1), that is found along the precentral gyrus. Recent evidence has suggested two further motor maps that span the lateral occipitotemporal cortex (LOTC) and the precuneus. Confirmation of these maps is important, as they influence our understanding of the organization of motor behavior, for example by revealing how visual- and motor-related activity interact. However, evidence for these recently proposed maps is limited. We analyzed an open functional MRI (fMRI) dataset of 62 participants who performed 12 different body part movements. We analyzed the magnitude of responses evoked by movements with novel quantitative indices that test for maplike organization. We found strong evidence for bilateral somatotopic maps in precentral and postcentral gyri. In LOTC, we found much weaker responses to movement and little evidence of somatotopy. In the precuneus, we found only limited evidence for somatotopy. We also adopted a background connectivity approach to examine correlations between M1, LOTC, and the precuneus in the residual time series data. This revealed a ventral-posterior/dorsal-anterior distinction in the connectivity between precuneus and M1, favoring the head and arms, respectively. Posterior right hemisphere LOTC showed some evidence of preferential connectivity to arm-selective regions of M1. Overall, our results do not support the existence of a somatotopic motor map in LOTC but provide some support for a coarse map in the precuneus, especially as revealed in connectivity patterns. These findings help clarify the organization of human motor representations beyond the precentral gyrus.NEW & NOTEWORTHY We investigated previous claims about the existence of somatotopic motor maps in the human lateral occipitotemporal cortex (LOTC) and the precuneus, in comparison to known maps in the precentral and postcentral gyri. Consistent with previous findings, we identified clear somatotopic motor maps in the latter two regions. With multiple quantitative measures of activity and connectivity, however, we found no evidence for a map in the LOTC and limited evidence for a map in the precuneus.

A combinatorial neural code for long-term motor memory

Motor skill repertoire can be stably retained over long periods, but the neural mechanism that underlies stable memory storage remains poorly understood1-8. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of the lifespan of a mouse and show that learned actions are stably retained in combination with context, which protects existing memories from erasure during new motor learning. We established a continual learning paradigm in which mice learned to perform directional licking in different task contexts while we tracked motor cortex activity for up to six months using two-photon imaging. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories instead of modifying existing representations. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. Continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning.

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.

Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training

BACKGROUND

Neural activation induced by upper extremity robot-assisted training (UE-RAT) helps characterize adaptive changes in the brains of poststroke patients, revealing differences in recovery potential among patients. However, it remains unclear whether these task-related neural activities can effectively predict rehabilitation outcomes. In this study, we utilized functional near-infrared spectroscopy (fNIRS) to measure participants' neural activity profiles during resting and UE-RAT tasks and developed models via machine learning to verify whether task-related functional brain responses can predict the recovery of upper limb motor function.

METHODS

Cortical activation and brain network functional connectivity (FC) in brain regions such as the superior frontal cortex, premotor cortex, and primary motor cortex were measured using fNIRS in 82 subacute stroke patients in the resting state and during UE-RAT. The Fugl-Meyer Upper Extremity Assessment Scale (FMA-UE) was chosen as the index for assessing upper extremity motor function, and clinical information such as demographic and neurophysiological data was also collected. Robust features were screened in 100 randomly divided training sets using the least absolute shrinkage and selection operator (LASSO) method. Based on the selected robust features, machine learning algorithms were used to develop clinical models, fNIRS models, and combined models that integrated both clinical and fNIRS features. Finally, Shapley Additive Explanations (SHAP) was applied to interpret the prediction process and analyze key predictive factors.

RESULTS

Compared to the resting state, task-related FC is a more robust feature for modeling, with screening frequencies above 90%. The combined models built using artificial neural networks (ANNs) and support vector machines (SVMs) significantly outperformed the other algorithms, with an average AUC of 0.861 (± 0.087) for the ANN and an average correlation coefficient (r) of 0.860 (± 0.069) for the SVM. Furthermore, predictive factor analysis of the models revealed that FC measured during tasks is the most important factor for predicting upper limb motor function.

CONCLUSION

This study confirmed that UE-RAT-induced FC can serve as an important predictor of rehabilitation, especially when combined with clinical information, further enhancing the accuracy of model predictions. These findings provide new insights for the early prediction of patients' recovery potential, which may contribute to personalized rehabilitation decisions.

Muscle spindles provide flexible sensory feedback for movement sequences

Sensory feedback is essential for motor performance and must adapt to task demands. Muscle spindle afferents (MSAs) are a major primary source of feedback about movement, and their responses are readily modulated online by gain-controller fusimotor neurons and other mechanisms. They are therefore a powerful site for implementing flexible sensorimotor control. We recorded from MSAs innervating the jaw musculature during performance of a directed lick sequence task. Jaw MSAs encoded complex jaw-tongue kinematics. However, kinematic encoding alone accounted for less than half of MSA spiking variability. MSA coding of kinematics changed based on sequence progression (beginning, middle, or end of the sequence, or reward consumption), suggesting that MSAs are flexibly tuned across the task. Dynamic control of incoming feedback signals from MSAs may be a strategy for adaptable sensorimotor control during performance of complex behaviors.

The Impact of Hyperbaric Oxygen Therapy on Functional and Structural Plasticity in Rats With Spinal Cord Injury

INTRODUCTION

Spinal cord injury (SCI) can result in sensory and locomotor function loss below the injured segment. Hyperbaric oxygen therapy (HBOT) has been proven to alleviate SCI. This study aims to establish a reproducible rat model of SCI and investigate the impact of HBOT on alterations in brain neuronal activity and neuromotor function in this experimental rat SCI model using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

This is a prospective randomized controlled animal trial. A total number of 27 female SD rats were randomly divided into three groups: sham (n = 9), SCI (n = 9), and HBO (n = 9). rs-fMRI was utilized to assess regional homogeneity (ReHo) values and functional connectivity (FC) strength over the whole brain with the motor cortex as seeds. Correlation between neuroimaging characteristics and behavioral assessment was calculated. We examined Nissl body, NeuN, and caspase-3 expression in relevant brain regions.

RESULTS

Following SCI, reduced ReHo values were observed in the left primary somatosensory cortex, left striatum, right agranular insular cortex, and partial cortex in the limbic system, which was reversed after HBOT. HBOT could increase FC strength between the motor cortex and other brain regions, including the left secondary motor cortex, right basal forebrain region, bilateral primary somatosensory cortex, bilateral thalamus, and another partial cortex in the limbic system. BBB scale scores showed that HBOT promoted motor function recovery in SCI rats. The ReHo and FC values in all positive clusters were positively correlated with BBB scores. By histopathological analysis, our study found that HBOT could reduce apoptotic proteins, increase the number of neurons, and protect neuronal function in brain regions with significant ReHo and FC alteration in SCI rats.

CONCLUSION

This study reveals that HBOT facilitates functional and structural plasticity in the brain, contributing to the recovery of motor function in rats with SCI.

Whole-brain Mapping of Inputs and Outputs of Specific Orbitofrontal Cortical Neurons in Mice

The orbitofrontal cortex (ORB), a region crucial for stimulus-reward association, decision-making, and flexible behaviors, extensively connects with other brain areas. However, brain-wide inputs to projection-defined ORB neurons and the distribution of inhibitory neurons postsynaptic to neurons in specific ORB subregions remain poorly characterized. Here we mapped the inputs of five types of projection-specific ORB neurons and ORB outputs to two types of inhibitory neurons. We found that different projection-defined ORB neurons received inputs from similar cortical and thalamic regions, albeit with quantitative variations, particularly in somatomotor areas and medial groups of the dorsal thalamus. By counting parvalbumin (PV) or somatostatin (SST) interneurons innervated by neurons in specific ORB subregions, we found a higher fraction of PV neurons in sensory cortices and a higher fraction of SST neurons in subcortical regions targeted by medial ORB neurons. These results provide insights into understanding and investigating the function of specific ORB neurons.

Sex differences in prelimbic cortex calcium dynamics during stress and fear learning

In recent years, research has progressively increased the importance of considering sex differences in stress and fear memory studies. Many studies have traditionally focused on male subjects, potentially overlooking critical differences with females. Emerging evidence suggests that males and females can exhibit distinct behavioral and neurophysiological responses to stress and fear conditioning. These differences may be attributable to variations in hormone levels, brain structure, and neural circuitry, particularly in regions such as the prefrontal cortex (PFC). In the present study, we explored sex differences in prelimbic cortex (PL) calcium activity in animals submitted to immobilization stress (IMO), fear conditioning (FC), and fear extinction (FE). While no significant sex differences were found in behavioral responses, we did observe differences in several PL calcium activity parameters. To determine whether these results were related to behaviors beyond stress and fear memory, we conducted correlation studies between the movement of the animals and PL activity during IMO and freezing behavior during FC and FE. Our findings revealed a clear correlation between PL calcium activity with movement during stress exposure and freezing behavior, with no sex differences observed in these correlations. These results suggest a significant role for the PL in movement and locomotion, in addition to its involvement in fear-related processes. The inclusion of both female and male subjects is crucial for studies like this to fully understand the role of the PFC and other brain areas in stress and fear responses. Recognizing sex differences enhances our comprehension of brain function and can lead to more personalized and effective approaches in the study and treatment of stress and fear-related conditions.

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.

An intense electrical stimulus can elicit a StartReact effect but with decreased incidence and later onset of the startle reflex

Planned actions can be triggered involuntarily by a startling acoustic stimulus (SAS), resulting in very short reaction times (RT). This phenomenon, known as the StartReact effect, is thought to result from the startle-related activation of reticular structures. However, other sensory modalities also can elicit a reflexive startle response. Here, we assessed the effectiveness of an intense startling electric stimulus (SES) in eliciting the StartReact effect as compared to a SAS. We tested SES intensities at 15 and 25 times the perceptual threshold of each participant, as well as SAS intensities of 114 dB and 120 dB. The electrical stimulation electrodes were placed over short head of the biceps brachii on the arm not involved in the task. Intense electric and acoustic stimuli were presented on 20% of the trials in a simple RT paradigm requiring a targeted ballistic wrist extension movement. The proportion of trials showing short latency (≤ 120 ms) startle reflex-related activation in sternocleidomastoid was significantly lower on intense electrical stimulus trials compared to intense acoustic trials, and the startle response onset occurred significantly later on SES trials compared to SAS. However, when a startle reflex was observed, RTs related to the prepared movement were facilitated to a similar extent for both SES and SAS conditions, suggesting that the accelerated response latency associated with the StartReact effect is independent of stimulus type.

Observation of cerebral cortex activation during static balance task in sporting and non-sporting individuals: A cross sectional fNIRS study

INTRODUCTION

This study aimed to investigate the differences in cerebral cortex activation during a static balance task between sporting and non-sporting groups using functional near-infrared spectroscopy (fNIRS). The Clinical Test of Sensory Interaction on Balance (CTSIB) was employed to assess balance performance in both groups.

METHOD

The study involved 70 participants, assigned into 2 equal groups; the sporting (N = 35) and non sporting (N = 35) group. Hemodynamic changes measured as oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) concentrations, were recorded using a portable fNIRS system.

RESULTS

The results revealed significant differences in CTSIB scores between the sporting and non-sporting groups in five out of the six balance conditions. The sporting group showed superior balance performance compared to the non-sporting group. The fNIRS data showed activation patterns in various regions of interest (ROIs), including the occipito-parietal, prefrontal, and temporo-parietal regions. Differences in activation between the two groups were observed in the occipito-parietal and prefrontal cortex regions, indicating distinct neural responses during the balance task.

CONCLUSION

These findings suggest that regular participation in sports activities may contribute to improved balance control and associated changes in cerebral cortex activation. The activation patterns observed in different cortical areas provide valuable insights into the neural mechanisms underlying static balance and the potential effects of sporting activities on balance control. The research findings offer insights for targeted interventions aimed at improving balance in both athletes and non-athletes. These interventions have the potential to reduce the risk of falls and enhance overall physical performance, benefiting a diverse range of individuals.

Effect of spaceflight experience on human brain structure, microstructure, and function: systematic review of neuroimaging studies

Spaceflight-induced brain changes have been commonly reported in astronauts. The role of microgravity in the alteration of the brain structure, microstructure, and function can be tested with magnetic resonance imaging (MRI) techniques. Here, we aim to provide a comprehensive overview of Spaceflight studies exploring the potential role of brain alterations identified by MRI in astronauts. We conducted a search on PubMed, Web of Science, and Scopus to find neuroimaging correlates of spaceflight experience using MRI. A total of 20 studies (structural MRI n = 8, diffusion-based MRI n = 2, functional MRI n = 1, structural MRI and diffusion-weighted MRI n = 6, structural MRI and functional MRI n = 3) met our inclusion criteria. Overall, the studies showed that regardless of the MRI techniques, mission duration significantly impacts the human brain, prompting the inclusion of various brain regions as features in the analyses. After spaceflight, notable alterations were also observed in the superior occipital gyrus and the precentral gyrus which show alterations in connectivity and activation during spaceflight. The results provided highlight the alterations in brain structure after spaceflight, the unique patterns of brain remodeling, the challenges in drawing unified conclusions, and the impact of microgravity on intracranial cerebrospinal fluid volume.

The influence of traditional Chinese exercise on brain function compared with other sports: A meta-analysis on functional neuroimaging studies

Traditional Chinese Exercise (TCE) has been shown to improve quality of life, and functional magnetic resonance imaging (fMRI) is a highly used method for investigating its mechanism. However, there is currently a lack of systematic reviews and meta-analyses focusing on TCE-related brain changes. This study aims to fill this gap by conducting a meta-analysis on brain changes of TCE with fMRI technology. We searched relevant studies published until February 2024. Independent researchers conducted literature screening, quality assessment, and clinical and neuroimaging data extraction. Focis were filtered from eligible studies, and meta-analysis was performed using seed-based d mapping. Twenty-three studies involving 1182 participants were included in this study. The result found that longitudinal TCE increased brain activity in the left anterior cingulate gyri, right fusiform gyrus, right middle temporal gyrus, left middle occipital gyrus and left frontal superior compared with other exercises or healthcare. Subgroup analysis showed that the brain activity in the right superior frontal gyrus dorsolateral; right cortico-spinal projections; corpus callosum; right inferior network; right gyrus rectus; left middle occipital gyrus were decreased after TCE compared to other exercise among healthy participants. The right median cingulate gyri was increased after Baduanjin (one of the TCE) compared to other exercise; the left precentral gyrus activity was increased after Tai chi chuan (TCC) practice compared to other exercise. The brain activity in the right insula, right supplementary motor area, and left anterior thalamic were significantly increased after long-time TCC exercise. TCE effectively improved the cognitive level of the subjects. Among them, the MoCA score increased, but Memory Quotient was not improved. Research results indicate that TCE have specific neuromodulatory effects, and different TCE have different neuromodulatory patterns.

Network state changes in sensory thalamus represent learned outcomes

Thalamic brain areas play an important role in adaptive behaviors. Nevertheless, the population dynamics of thalamic relays during learning across sensory modalities remain unknown. Using a cross-modal sensory reward-associative learning paradigm combined with deep brain two-photon calcium imaging of large populations of auditory thalamus (medial geniculate body, MGB) neurons in male mice, we identified that MGB neurons are biased towards reward predictors independent of modality. Additionally, functional classes of MGB neurons aligned with distinct task periods and behavioral outcomes, both dependent and independent of sensory modality. During non-sensory delay periods, MGB ensembles developed coherent neuronal representation as well as distinct co-activity network states reflecting predicted task outcome. These results demonstrate flexible cross-modal ensemble coding in auditory thalamus during adaptive learning and highlight its importance in brain-wide cross-modal computations during complex behavior.

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.

Dynamics of directional motor tuning in the primate premotor and primary motor cortices during sensorimotor learning

Sensorimotor learning requires reorganization of neuronal activity in the premotor cortex (PM) and primary motor cortex (M1). To reveal PM- and M1-specific reorganization in a primate, we conducted calcium imaging in common marmosets while they learned a two-target reaching (pull/push) task after mastering a one-target reaching (pull) task. Throughout learning of the two-target reaching task, the dorsorostral PM (PMdr) showed peak activity earlier than the dorsocaudal PM (PMdc) and M1. During learning, the reaction time in pull trials increased and correlated strongly with the peak timing of PMdr activity. PMdr showed decreasing representation of newly introduced (push) movement, whereas PMdc and M1 maintained high representation of pull and push movements. Many task-related neurons in PMdc and M1 exhibited a strong preference to either movement direction. PMdc neurons dynamically switched their preferred direction depending on their performance in push trials in the early learning stage, whereas M1 neurons stably retained their preferred direction and high similarity of preferred direction between neighbors. These results suggest that in primate sensorimotor learning, dynamic directional motor tuning in PMdc converts the sensorimotor association formed in PMdr to the stable and specific motor representation of M1.

Neural underpinnings of fine motor skills under stress and anxiety: A review

This review offers a comprehensive examination of how stress and anxiety affect motor behavior, particularly focusing on fine motor skills and gait adaptability. We explore the role of several neurochemicals, including brain-derived neurotrophic factor (BDNF) and dopamine, in modulating neural plasticity and motor control under these affective states. The review highlights the importance of developing therapeutic strategies that enhance motor performance by leveraging the interactions between key neurochemicals. Additionally, we investigate the complex interplay between emotional-cognitive states and sensorimotor behaviors, showing how stress and anxiety disrupt neural integration, leading to impairments in skilled movements and negatively impacting quality of life. Synthesizing evidence from human and rodent studies, we provide a detailed understanding of the relationships among stress, anxiety, and motor behavior. Our findings reveal neurophysiological pathways, behavioral outcomes, and potential therapeutic targets, emphasizing the intricate connections between neurobiological mechanisms, environmental factors, and motor performance.

Classification of Mild Cognitive Impairment and Alzheimer's Disease Using Manual Motor Measures

INTRODUCTION

Manual motor problems have been reported in mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the specific aspects that are affected, their neuropathology, and potential value for classification modeling is unknown. The current study examined if multiple measures of motor strength, dexterity, and speed are affected in MCI and AD, related to AD biomarkers, and are able to classify MCI or AD.

METHODS

Fifty-three cognitively normal (CN), 33 amnestic MCI, and 28 AD subjects completed five manual motor measures: grip force, Trail Making Test A, spiral tracing, finger tapping, and a simulated feeding task. Analyses included (1) group differences in manual performance; (2) associations between manual function and AD biomarkers (PET amyloid β, hippocampal volume, and APOE ε4 alleles); and (3) group classification accuracy of manual motor function using machine learning.

RESULTS

Amnestic MCI and AD subjects exhibited slower psychomotor speed and AD subjects had weaker dominant hand grip strength than CN subjects. Performance on these measures was related to amyloid β deposition (both) and hippocampal volume (psychomotor speed only). Support vector classification well-discriminated control and AD subjects (area under the curve of 0.73 and 0.77, respectively) but poorly discriminated MCI from controls or AD.

CONCLUSION

Grip strength and spiral tracing appear preserved, while psychomotor speed is affected in amnestic MCI and AD. The association of motor performance with amyloid β deposition and atrophy could indicate that this is due to amyloid deposition in and atrophy of motor brain regions, which generally occurs later in the disease process. The promising discriminatory abilities of manual motor measures for AD emphasize their value alongside other cognitive and motor assessment outcomes in classification and prediction models, as well as potential enrichment of outcome variables in AD clinical trials.

Rule-based modulation of a sensorimotor transformation across cortical areas

Flexible responses to sensory stimuli based on changing rules are critical for adapting to a dynamic environment. However, it remains unclear how the brain encodes and uses rule information to guide behavior. Here, we made single-unit recordings while head-fixed mice performed a cross-modal sensory selection task where they switched between two rules: licking in response to tactile stimuli while rejecting visual stimuli, or vice versa. Along a cortical sensorimotor processing stream including the primary (S1) and secondary (S2) somatosensory areas, and the medial (MM) and anterolateral (ALM) motor areas, single-neuron activity distinguished between the two rules both prior to and in response to the tactile stimulus. We hypothesized that neural populations in these areas would show rule-dependent preparatory states, which would shape the subsequent sensory processing and behavior. This hypothesis was supported for the motor cortical areas (MM and ALM) by findings that (1) the current task rule could be decoded from pre-stimulus population activity; (2) neural subspaces containing the population activity differed between the two rules; and (3) optogenetic disruption of pre-stimulus states impaired task performance. Our findings indicate that flexible action selection in response to sensory input can occur via configuration of preparatory states in the motor cortex.

A combinatorial neural code for long-term motor memory

Motor skill repertoire can be stably retained over long periods, but the neural mechanism underlying stable memory storage remains poorly understood. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of a mouse's lifespan, and we show that learned actions are stably retained in motor memory in combination with context, which protects existing memories from erasure during new motor learning. We used automated home-cage training to establish a continual learning paradigm in which mice learned to perform directional licking in different task contexts. We combined this paradigm with chronic two-photon imaging of motor cortex activity for up to 6 months. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories while retaining the previous memories. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. At the same time, continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning. Learning in new contexts produces parallel new representations instead of modifying existing representations, thus protecting existing motor repertoire from erasure.

Effect of task-oriented training assisted by force feedback hand rehabilitation robot on finger grasping function in stroke patients with hemiplegia: a randomised controlled trial

BACKGROUND

Over 80% of patients with stroke experience finger grasping dysfunction, affecting independence in activities of daily living and quality of life. In routine training, task-oriented training is usually used for functional hand training, which may improve finger grasping performance after stroke, while augmented therapy may lead to a better treatment outcome. As a new technology-supported training, the hand rehabilitation robot provides opportunities to improve the therapeutic effect by increasing the training intensity. However, most hand rehabilitation robots commonly applied in clinics are based on a passive training mode and lack the sensory feedback function of fingers, which is not conducive to patients completing more accurate grasping movements. A force feedback hand rehabilitation robot can compensate for these defects. However, its clinical efficacy in patients with stroke remains unknown. This study aimed to investigate the effectiveness and added value of a force feedback hand rehabilitation robot combined with task-oriented training in stroke patients with hemiplegia.

METHODS

In this single-blinded randomised controlled trial, 44 stroke patients with hemiplegia were randomly divided into experimental (n = 22) and control (n = 22) groups. Both groups received 40 min/day of conventional upper limb rehabilitation training. The experimental group received 20 min/day of task-oriented training assisted by a force feedback rehabilitation robot, and the control group received 20 min/day of task-oriented training assisted by therapists. Training was provided for 4 weeks, 5 times/week. The Fugl-Meyer motor function assessment of the hand part (FMA-Hand), Action Research Arm Test (ARAT), grip strength, Modified Ashworth scale (MAS), range of motion (ROM), Brunnstrom recovery stages of the hand (BRS-H), and Barthel index (BI) were used to evaluate the effect of two groups before and after treatment.

RESULTS

Intra-group comparison: In both groups, the FMA-Hand, ARAT, grip strength, AROM, BRS-H, and BI scores after 4 weeks of treatment were significantly higher than those before treatment (p < 0.05), whereas there was no significant difference in finger flexor MAS scores before and after treatment (p > 0.05). Inter-group comparison: After 4 weeks of treatment, the experimental group's FMA-Hand total score, ARAT, grip strength, and AROM were significantly better than those of the control group (p < 0.05). However, there were no statistically significant differences in the scores of each sub-item of the FMA-Hand after Bonferroni correction (p > 0.007). In addition, there were no statistically significant differences in MAS, BRS-H, and BI scores (p > 0.05).

CONCLUSION

Hand performance improved in patients with stroke after 4 weeks of task-oriented training. The use of a force feedback hand rehabilitation robot to support task-oriented training showed additional value over conventional task-oriented training in stroke patients with hand dysfunction.

CLINICAL TRIAL REGISTRATION INFORMATION

NCT05841108.

Cholecystokinin facilitates motor skill learning by modulating neuroplasticity in the motor cortex

Cholecystokinin (CCK) is an essential modulator for neuroplasticity in sensory and emotional domains. Here, we investigated the role of CCK in motor learning using a single pellet reaching task in mice. Mice with a knockout of Cck gene (Cck-/-) or blockade of CCK-B receptor (CCKBR) showed defective motor learning ability; the success rate of retrieving reward remained at the baseline level compared to the wildtype mice with significantly increased success rate. We observed no long-term potentiation upon high-frequency stimulation in the motor cortex of Cck-/- mice, indicating a possible association between motor learning deficiency and neuroplasticity in the motor cortex. In vivo calcium imaging demonstrated that the deficiency of CCK signaling disrupted the refinement of population neuronal activity in the motor cortex during motor skill training. Anatomical tracing revealed direct projections from CCK-expressing neurons in the rhinal cortex to the motor cortex. Inactivation of the CCK neurons in the rhinal cortex that project to the motor cortex bilaterally using chemogenetic methods significantly suppressed motor learning, and intraperitoneal application of CCK4, a tetrapeptide CCK agonist, rescued the motor learning deficits of Cck-/- mice. In summary, our results suggest that CCK, which could be provided from the rhinal cortex, may surpport motor skill learning by modulating neuroplasticity in the motor cortex.

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.

Separating cognitive and motor processes in the behaving mouse

The cognitive processes supporting complex animal behavior are closely associated with ubiquitous movements responsible for our posture, facial expressions, ability to actively sample our sensory environments, and other critical processes. These movements are strongly related to neural activity across much of the brain and are often highly correlated with ongoing cognitive processes, making it challenging to dissociate the neural dynamics that support cognitive processes from those supporting related movements. In such cases, a critical issue is whether cognitive processes are separable from related movements, or if they are driven by common neural mechanisms. Here, we demonstrate how the separability of cognitive and motor processes can be assessed, and, when separable, how the neural dynamics associated with each component can be isolated. We establish a novel two-context behavioral task in mice that involves multiple cognitive processes and show that commonly observed dynamics taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using a novel approach for subspace decomposition, we find that they exhibit distinct dynamical trajectories. Further, properly accounting for movement revealed that largely separate populations of cells encode cognitive and motor variables, in contrast to the 'mixed selectivity' often reported. Accurately isolating the dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function and evaluating the function of the cell types of which neural circuits are composed.

Striatal dopamine release tracks the relationship between actions and their consequences

The acquisition and performance of goal-directed actions has long been argued to depend on the integration of glutamatergic inputs to the posterior dorsomedial striatum (pDMS) under the modulatory influence of dopamine. Nevertheless, relatively little is known about the dynamics of striatal dopamine during goal-directed actions. To investigate this, we chronically recorded dopamine release in the pDMS as rats acquired two actions for distinct outcomes as these action-outcome associations were incremented and then subsequently degraded or reversed. We found that bilateral dopamine release scaled with action value, whereas the lateralized dopamine signal, i.e., the difference in dopamine release ipsilaterally and contralaterally to the direction of the goal-directed action, reflected the strength of the action-outcome association independently of changes in movement. Our results establish, therefore, that striatal dopamine activity during goal-directed action reflects both bilateral moment-to-moment changes in action value and the long-term action-outcome association.

FABEL: Forecasting Animal Behavioral Events with Deep Learning-Based Computer Vision

Behavioral neuroscience aims to provide a connection between neural phenomena and emergent organism-level behaviors. This requires perturbing the nervous system and observing behavioral outcomes, and comparing observed post-perturbation behavior with predicted counterfactual behavior and therefore accurate behavioral forecasts. In this study we present FABEL, a deep learning method for forecasting future animal behaviors and locomotion trajectories from historical locomotion alone. We train an offline pose estimation network to predict animal body-part locations in behavioral video; then sequences of pose vectors are input to deep learning time-series forecasting models. Specifically, we train an LSTM network that predicts a future food interaction event in a specified time window, and a Temporal Fusion Transformer that predicts future trajectories of animal body-parts, which are then converted into probabilistic label forecasts. Importantly, accurate prediction of food interaction provides a basis for neurobehavioral intervention in the context of compulsive eating. We show promising results on forecasting tasks between 100 milliseconds and 5 seconds timescales. Because the model takes only behavioral video as input, it can be adapted to any behavioral task and does not require specific physiological readouts. Simultaneously, these deep learning models may serve as extensible modules that can accommodate diverse signals, such as in-vivo fluorescence imaging and electrophysiology, which may improve behavior forecasts and elucidate invervention targets for desired behavioral change.

Rule-based modulation of a sensorimotor transformation across cortical areas

Flexible responses to sensory stimuli based on changing rules are critical for adapting to a dynamic environment. However, it remains unclear how the brain encodes rule information and uses this information to guide behavioral responses to sensory stimuli. Here, we made single-unit recordings while head-fixed mice performed a cross-modal sensory selection task in which they switched between two rules in different blocks of trials: licking in response to tactile stimuli applied to a whisker while rejecting visual stimuli, or licking to visual stimuli while rejecting the tactile stimuli. Along a cortical sensorimotor processing stream including the primary (S1) and secondary (S2) somatosensory areas, and the medial (MM) and anterolateral (ALM) motor areas, the single-trial activity of individual neurons distinguished between the two rules both prior to and in response to the tactile stimulus. Variable rule-dependent responses to identical stimuli could in principle occur via appropriate configuration of pre-stimulus preparatory states of a neural population, which would shape the subsequent response. We hypothesized that neural populations in S1, S2, MM and ALM would show preparatory activity states that were set in a rule-dependent manner to cause processing of sensory information according to the current rule. This hypothesis was supported for the motor cortical areas by findings that (1) the current task rule could be decoded from pre-stimulus population activity in ALM and MM; (2) neural subspaces containing the population activity differed between the two rules; and (3) optogenetic disruption of pre-stimulus states within ALM and MM impaired task performance. Our findings indicate that flexible selection of an appropriate action in response to a sensory input can occur via configuration of preparatory states in the motor cortex.

Optimizing electrode placement for transcranial direct current stimulation in nonsuperficial cortical regions: a computational modeling study

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique for modulating neuronal excitability by sending a weak current through electrodes attached to the scalp. For decades, the conventional tDCS electrode for stimulating the superficial cortex has been widely reported. However, the investigation of the optimal electrode to effectively stimulate the nonsuperficial cortex is still lacking. In the current study, the optimal tDCS electrode montage that can deliver the maximum electric field to nonsuperficial cortical regions is investigated. Two finite element head models were used for computational simulation to determine the optimal montage for four different nonsuperficial regions: the left foot motor cortex, the left dorsomedial prefrontal cortex (dmPFC), the left medial orbitofrontal cortex (mOFC), and the primary visual cortex (V1). Our findings showed a good consistency in the optimal montage between two models, which led to the anode and cathode being attached to C4-C3 for the foot motor, F4-F3 for the dmPFC, Fp2-F7 for the mOFC, and Oz-Cz for V1. Our suggested montages are expected to enhance the overall effectiveness of stimulation of nonsuperficial cortical areas.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-023-00335-2.

The effects of mental fatigue on fine motor performance in humans and its neural network connectivity mechanism: a dart throwing study

While it is well known that mental fatigue impairs fine motor performance, the investigation into its neural basis remains scant. Here, we investigate the impact of mental fatigue on fine motor performance and explore its underlying neural network connectivity mechanisms. A total of 24 healthy male university students were recruited and randomly divided into two groups: a mental fatigue group (MF) and a control group (Control). Both groups completed 50 dart throws, while electroencephalography (EEG) data were collected. Following the Stroop intervention, participants in the MF group exhibited a decrease in Stroop task accuracy and throwing performance, and an increase in reaction time along with VAS and NASA scores. The EEG data during dart-throwing revealed that the network connectivity strength of theta oscillations in the frontal and left central regions was significantly higher in the MF group compared with the Control group, while the network connectivity strength of alpha oscillations in the left parietal region was significantly enhanced. The interregional connectivity within the theta and alpha rhythm bands, particularly in the frontal-central-parietal network connections, also showed a significant increase in the MF group. Mental fatigue impairs dart throwing performance and is accompanied by increased connectivity in alpha and theta.

Modeling and dissociation of intrinsic and input-driven neural population dynamics underlying behavior

Neural dynamics can reflect intrinsic dynamics or dynamic inputs, such as sensory inputs or inputs from other brain regions. To avoid misinterpreting temporally structured inputs as intrinsic dynamics, dynamical models of neural activity should account for measured inputs. However, incorporating measured inputs remains elusive in joint dynamical modeling of neural-behavioral data, which is important for studying neural computations of behavior. We first show how training dynamical models of neural activity while considering behavior but not input or input but not behavior may lead to misinterpretations. We then develop an analytical learning method for linear dynamical models that simultaneously accounts for neural activity, behavior, and measured inputs. The method provides the capability to prioritize the learning of intrinsic behaviorally relevant neural dynamics and dissociate them from both other intrinsic dynamics and measured input dynamics. In data from a simulated brain with fixed intrinsic dynamics that performs different tasks, the method correctly finds the same intrinsic dynamics regardless of the task while other methods can be influenced by the task. In neural datasets from three subjects performing two different motor tasks with task instruction sensory inputs, the method reveals low-dimensional intrinsic neural dynamics that are missed by other methods and are more predictive of behavior and/or neural activity. The method also uniquely finds that the intrinsic behaviorally relevant neural dynamics are largely similar across the different subjects and tasks, whereas the overall neural dynamics are not. These input-driven dynamical models of neural-behavioral data can uncover intrinsic dynamics that may otherwise be missed.

Roles of the cerebellar vermis in predictive postural controls against external disturbances

The central nervous system predictively controls posture against external disturbances; however, the detailed mechanisms remain unclear. We tested the hypothesis that the cerebellar vermis plays a substantial role in acquiring predictive postural control by using a standing task with floor disturbances in rats. The intact, lesioned, and sham groups of rats sequentially underwent 70 conditioned floor-tilting trials, and kinematics were recorded. Six days before these recordings, only the lesion group underwent focal suction surgery targeting vermal lobules IV-VIII. In the naïve stage of the sequential trials, the upright postures and fluctuations due to the disturbance were mostly consistent among the groups. Although the pattern of decrease in postural fluctuation due to learning corresponded among the groups, the learning rate estimated from the lumbar displacement was significantly lower in the lesion group than in the intact and sham groups. These results suggest that the cerebellar vermis contributes to predictive postural controls.

The Associative Thalamus: A Switchboard for Cortical Operations and a Promising Target for Schizophrenia

Schizophrenia is a brain disorder that profoundly perturbs cognitive processing. Despite the success in treating many of its symptoms, the field lacks effective methods to measure and address its impact on reasoning, inference, and decision making. Prefrontal cortical abnormalities have been well documented in schizophrenia, but additional dysfunction in the interactions between the prefrontal cortex and thalamus have recently been described. This dysfunction may be interpreted in light of parallel advances in neural circuit research based on nonhuman animals, which show critical thalamic roles in maintaining and switching prefrontal activity patterns in various cognitive tasks. Here, we review this basic literature and connect it to emerging innovations in clinical research. We highlight the value of focusing on associative thalamic structures not only to better understand the very nature of cognitive processing but also to leverage these circuits for diagnostic and therapeutic development in schizophrenia. We suggest that the time is right for building close bridges between basic thalamic research and its clinical translation, particularly in the domain of cognition and schizophrenia.

Task learning is subserved by a domain-general brain network

One of the most important human faculties is the ability to acquire not just new memories but the capacity to perform entirely new tasks. However, little is known about the brain mechanisms underlying the learning of novel tasks. Specifically, it is unclear to what extent learning of different tasks depends on domain-general and/or domain-specific brain mechanisms. Here human subjects (n = 45) learned to perform 6 new tasks while undergoing functional MRI. The different tasks required the engagement of perceptual, motor, and various cognitive processes related to attention, expectation, speed-accuracy tradeoff, and metacognition. We found that a bilateral frontoparietal network was more active during the initial compared with the later stages of task learning, and that this effect was stronger for task variants requiring more new learning. Critically, the same frontoparietal network was engaged by all 6 tasks, demonstrating its domain generality. Finally, although task learning decreased the overall activity in the frontoparietal network, it increased the connectivity strength between the different nodes of that network. These results demonstrate the existence of a domain-general brain network whose activity and connectivity reflect learning for a variety of new tasks, and thus may underlie the human capacity for acquiring new abilities.

Distributed and specific encoding of sensory, motor, and decision information in the mouse neocortex during goal-directed behavior

Goal-directed behaviors involve coordinated activity in many cortical areas, but whether the encoding of task variables is distributed across areas or is more specifically represented in distinct areas remains unclear. Here, we compared representations of sensory, motor, and decision information in the whisker primary somatosensory cortex, medial prefrontal cortex, and tongue-jaw primary motor cortex in mice trained to lick in response to a whisker stimulus with mice that were not taught this association. Irrespective of learning, properties of the sensory stimulus were best encoded in the sensory cortex, whereas fine movement kinematics were best represented in the motor cortex. However, movement initiation and the decision to lick in response to the whisker stimulus were represented in all three areas, with decision neurons in the medial prefrontal cortex being more selective, showing minimal sensory responses in miss trials and motor responses during spontaneous licks. Our results reconcile previous studies indicating highly specific vs. highly distributed sensorimotor processing.

Simultaneous, cortex-wide and cellular-resolution neuronal population dynamics reveal an unbounded scaling of dimensionality with neuron number

The brain's remarkable properties arise from collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, what would be the biological utility of such a redundant and metabolically costly encoding scheme and what is the appropriate resolution and scale of neural recording to understand brain function? Imaging the activity of one million neurons at cellular resolution and near-simultaneously across mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number. While half of the neural variance lies within sixteen behavior-related dimensions, we find this unbounded scaling of dimensionality to correspond to an ever-increasing number of internal variables without immediate behavioral correlates. The activity patterns underlying these higher dimensions are fine-grained and cortex-wide, highlighting that large-scale recording is required to uncover the full neural substrates of internal and potentially cognitive processes.

Cell type-specific connectome predicts distributed working memory activity in the mouse brain

Recent advances in connectomics and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the multiregional mouse brain for a cardinal cognitive function called working memory, the brain's ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for interareal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous attractor states, which are self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.