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Stroke remains a leading cause of adult disability and the demand for stroke rehabilitation services is growing. Substantial advances are yet to be made in stroke rehabilitation practice to meet this demand and improve patient outcomes relative to current care. Several large intervention trials targeting motor recovery report that participants' motor performance improved, but to a similar extent for both the intervention and control groups in most trials. These neutral results might reflect an absence of additional benefit from the tested interventions or the many challenges of designing and doing large stroke rehabilitation trials. Strategies for improving trial quality include new approaches to the selection of patients, control interventions, and endpoint measures. Although stroke rehabilitation research strives for better trials, interventions, and outcomes, rehabilitation practices continue to help patients regain independence after stroke.

Progress has been made in understanding the genetic defects and the pathophysiology of this crippling motor neuron disease (commonly called Lou Gehrig’s disease). However, this information has not yet led to a successful intervention that alters the course of the disease.

In this phase 3 trial, among infants with spinal muscular atrophy, those who received nusinersen were more likely to achieve major motor milestones and less likely to need permanent assisted ventilation than those who underwent a sham procedure.

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations 1 , 2 , despite evidence for concentric functional zones 3 and maps of complex actions 4 . Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action 5 and physiological control 6 , arousal 7 , errors 8 and pain 9 . This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions 4 and connectivity to internal organs 10 such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate–isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement. Functional MRI studies across ages show that the classic homunculus of the motor cortex in humans is in fact discontinuous, alternating with action control-linked regions termed the somato-cognitive action network.

Brain–computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. So far, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping 1 – 5 or point-and-click typing with a computer cursor 6 , 7 . However, rapid sequences of highly dexterous behaviours, such as handwriting or touch typing, might enable faster rates of communication. Here we developed an intracortical BCI that decodes attempted handwriting movements from neural activity in the motor cortex and translates it to text in real time, using a recurrent neural network decoding approach. With this BCI, our study participant, whose hand was paralysed from spinal cord injury, achieved typing speeds of 90 characters per minute with 94.1% raw accuracy online, and greater than 99% accuracy offline with a general-purpose autocorrect. To our knowledge, these typing speeds exceed those reported for any other BCI, and are comparable to typical smartphone typing speeds of individuals in the age group of our participant (115 characters per minute) 8 . Finally, theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements. Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis. A brain–computer interface enables rapid communication through neural decoding of attempted handwriting movements in a person with paralysis.

Fatigue has been defined differently in the literature depending on the field of research. The inconsistent use of the term fatigue complicated scientific communication, thereby limiting progress towards a more in-depth understanding of the phenomenon. Therefore, Enoka and Duchateau (Med Sci Sports Exerc 48:2228–38, 2016, [ 3 ]) proposed a fatigue framework that distinguishes between trait fatigue (i.e., fatigue experienced by an individual over a longer period of time) and motor or cognitive task-induced state fatigue (i.e., self-reported disabling symptom derived from the two interdependent attributes performance fatigability and perceived fatigability). Thereby, performance fatigability describes a decrease in an objective performance measure, while perceived fatigability refers to the sensations that regulate the integrity of the performer. Although this framework served as a good starting point to unravel the psychophysiology of fatigue, several important aspects were not included and the interdependence of the mechanisms driving performance fatigability and perceived fatigability were not comprehensively discussed. Therefore, the present narrative review aimed to (1) update the fatigue framework suggested by Enoka and Duchateau (Med Sci Sports Exerc 48:2228–38, 2016, [ 3 ]) pertaining the taxonomy (i.e., cognitive performance fatigue and perceived cognitive fatigue were added) and important determinants that were not considered previously (e.g., effort perception, affective valence, self-regulation), (2) discuss the mechanisms underlying performance fatigue and perceived fatigue in response to motor and cognitive tasks as well as their interdependence, and (3) provide recommendations for future research on these interactions. We propose to define motor or cognitive task-induced state fatigue as a psychophysiological condition characterized by a decrease in motor or cognitive performance (i.e., motor or cognitive performance fatigue, respectively) and/or an increased perception of fatigue (i.e., perceived motor or cognitive fatigue). These dimensions are interdependent, hinge on different determinants, and depend on body homeostasis (e.g., wakefulness, core temperature) as well as several modulating factors (e.g., age, sex, diseases, characteristics of the motor or cognitive task). Consequently, there is no single factor primarily determining performance fatigue and perceived fatigue in response to motor or cognitive tasks. Instead, the relative weight of each determinant and their interaction are modulated by several factors.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals 1 . Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch–seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations. An examination of motor cortex in humans, marmosets and mice reveals a generally conserved cellular makeup that is likely to extend to many mammalian species, but also differences in gene expression, DNA methylation and chromatin state that lead to species-dependent specializations.

Implicit motor learning is considered to be particularly effective for learning sports-related motor skills. It should foster movement automaticity and thereby facilitate performance in multitasking and high-pressure environments. To scrutinize this hypothesis, we systematically reviewed all studies that compared the degree of automatization achieved (as indicated by dual-task performance) after implicit compared to explicit interventions for sports-related motor tasks. For this systematic review (CRD42016038249) conventional (MEDLINE, CENTRAL, Embase, PsycINFO, SportDiscus, Web of Science) and grey literature were searched. Two reviewers independently screened reports, extracted data, and performed risk of bias assessment. Implicit interventions of interest were analogy-, errorless-, dual-task-, and external focus learning. Data analysis involved descriptive synthesis of group comparisons on absolute motor dual-task (DT) performance, and motor DT performance relative to single-task motor performance (motor DTCs). Of the 4125 reports identified, we included 25 controlled trials that described 39 implicit-explicit group comparisons. Risk of bias was unclear across trials. Most comparisons did not show group differences. Some comparisons showed superior absolute motor DT performance (N = 2), superior motor DTCs (N = 4), or both (N = 3) for the implicit compared to the explicit group. The explicit group showed superior absolute motor DT performance in two comparisons. Most comparisons did not show group differences in automaticity. The remaining comparisons leaned more toward a greater degree of movement automaticity after implicit learning than explicit learning. However, due to an overall unclear risk of bias the strength of the evidence is level 3. Motor learning-specific guidelines for design and especially reporting are warranted to further strengthen the evidence and facilitate low-risk-of-bias trials.

Motor–cognitive training in Parkinson’s disease (PD) can positively affect gait and balance, but whether motor–cognitive (dual-task) performance improves is unknown. This meta-analysis, therefore, aimed to establish the current evidence on the effects of motor–cognitive training on dual-task performance in PD. Systematic searches were conducted in five databases and 11 studies with a total of 597 people (mean age: 68.9 years; mean PD duration: 6.8 years) were included. We found a mean difference in dual-task gait speed (0.12 m/s (95% CI 0.08, 0.17)), dual-task cadence (2.91 steps/min (95% CI 0.08, 5.73)), dual-task stride length (10.12 cm (95% CI 4.86, 15.38)) and dual-task cost on gait speed (− 8.75% (95% CI − 14.57, − 2.92)) in favor of motor–cognitive training compared to controls. The GRADE analysis revealed that the findings were based on high certainty evidence. Thus, we can for the first time systematically show that people with PD can improve their dual-task ability through motor–cognitive training.

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 responses 1 , 2 . Despite the distributed nature of task-relevant representations 3 – 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. Brain-wide recordings in mice show that learning leads to sensory evidence integration in many brain areas simultaneously, allowing sensory input to drive global movement preparatory dynamics, which collapse upon movement onset.