Emerging evidence indicates impairments in somatosensory function may be a major contributor to motor dysfunction associated with neurologic injury or disorders. However, the neuroanatomical substrates underlying the connection between aberrant sensory input and ineffective motor output are still under investigation. The primary somatosensory cortex (S1) plays a critical role in processing afferent somatosensory input and contributes to the integration of sensory and motor signals necessary for skilled movement. Neuroimaging and neurostimulation approaches provide unique opportunities to non-invasively study S1 structure and function including connectivity with other cortical regions. These research techniques have begun to illuminate casual contributions of abnormal S1 activity and connectivity to motor dysfunction and poorer recovery of motor function in neurologic patient populations. This review synthesizes recent evidence illustrating the role of S1 in motor control, motor learning and functional recovery with an emphasis on how information from these investigations may be exploited to inform stroke rehabilitation to reduce motor dysfunction and improve therapeutic outcomes.

Integration of sensory and motor information is one-step, among others, that underlies the successful production of goal-directed hand movements necessary for interacting with our environment. Disruption of sensorimotor integration is prevalent in many neurologic disorders, including stroke. In most stroke survivors, persistent paresis of the hand reduces function and overall quality of life. Current rehabilitative methods are based on neuroplastic principles to promote motor learning that focuses on regaining motor function lost due to paresis, but the sensory contributions to motor control and learning are often overlooked and currently understudied. There is a need to evaluate and understand the contribution of both sensory and motor function in the rehabilitation of skilled hand movements after stroke. Here, we will highlight the importance of integration of sensory and motor information to produce skilled hand movements in healthy individuals and individuals after stroke. We will then discuss how compromised sensorimotor integration influences relearning of skilled hand movements after stroke. Finally, we will propose an approach to target sensorimotor integration through manipulation of sensory input and motor output that may have therapeutic implications.

Background and Objectives Functional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. In this study, we examined the impact of brain age, a measure of neurobiological aging derived from whole-brain structural neuroimaging, on poststroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good vs poor outcomes. Methods We conducted a cross-sectional observational study using a multisite dataset of 3-dimensional brain structural MRIs and clinical measures from the ENIGMA Stroke Recovery. Brain age was calculated from 77 neuroanatomical features using a ridge regression model trained and validated on 4,314 healthy controls. We performed a 3-step mediation analysis with robust mixed-effects linear regression models to examine relationships between brain age, lesion damage, and stroke outcomes. We used propensity score matching and logistic regression to examine whether brain resilience predicts good vs poor outcomes in patients with matched lesion damage. Results We examined 963 patients across 38 cohorts. Greater lesion damage was associated with older brain age (β = 0.21; 95% CI 0.04–0.38, p = 0.015), which in turn was associated with poorer outcomes, both in the sensorimotor domain (β = −0.28; 95% CI −0.41 to −0.15, p < 0.001) and across multiple domains of function (β = −0.14; 95% CI −0.22 to −0.06, p < 0.001). Brain age mediated 15% of the impact of lesion damage on sensorimotor performance (95% CI 3%–58%, p = 0.01). Greater brain resilience explained why people have better outcomes, given matched lesion damage (odds ratio 1.04, 95% CI 1.01–1.08, p = 0.004). Discussion We provide evidence that younger brain age is associated with superior poststroke outcomes and modifies the impact of focal damage. The inclusion of imaging-based assessments of brain age and brain resilience may improve the prediction of poststroke outcomes compared with focal injury measures alone, opening new possibilities for potential therapeutic targets.

Background and Objectives It is difficult to predict poststroke outcome for individuals with severe motor impairment because both clinical tests and corticospinal tract (CST) microstructure may not reliably indicate severe motor impairment. Here, we test whether imaging biomarkers beyond the CST relate to severe upper limb (UL) impairment poststroke by evaluating white matter microstructure in the corpus callosum (CC). In an international, multisite hypothesis-generating observational study, we determined if (1) CST asymmetry index (CST-AI) can differentiate between individuals with mild-moderate and severe UL impairment and (2) CC biomarkers relate to UL impairment within individuals with severe impairment poststroke. We hypothesized that CST-AI would differentiate between mild-moderate and severe impairment, but CC microstructure would relate to motor outcome for individuals with severe UL impairment. Methods Seven cohorts with individual diffusion imaging and motor impairment (Fugl-Meyer Upper Limb) data were pooled. Hand-drawn regions-of-interest were used to seed probabilistic tractography for CST (ipsilesional/contralesional) and CC (prefrontal/premotor/motor/sensory/posterior) tracts. Our main imaging measure was mean fractional anisotropy. Linear mixed-effects regression explored relationships between candidate biomarkers and motor impairment, controlling for observations nested within cohorts, as well as age, sex, time poststroke, and lesion volume. Results Data from 110 individuals (30 with mild-moderate and 80 with severe motor impairment) were included. In the full sample, greater CST-AI (i.e., lower fractional anisotropy in the ipsilesional hemisphere, p < 0.001) and larger lesion volume (p = 0.139) were negatively related to impairment. In the severe subgroup, CST-AI was not reliably associated with impairment across models. Instead, lesion volume and CC microstructure explained impairment in the severe group beyond CST-AI (p9s < 0.010). Discussion Within a large cohort of individuals with severe UL impairment, CC microstructure related to motor outcome poststroke. Our findings demonstrate that CST microstructure does relate to UL outcome across the full range of motor impairment but was not reliably associated within the severe subgroup. Therefore, CC microstructure may provide a promising biomarker for severe UL outcome poststroke, which may advance our ability to predict recovery in individuals with severe motor impairment after stroke.

Cortical beta oscillations (13–30 Hz) reflect sensorimotor processing, but are not well understood in balance recovery. We hypothesized that sensorimotor cortical activity would increase under challenging balance conditions. We predicted greater beta power when balance was challenged, either by more difficult perturbations or by lower balance ability. In 19 young adults, we measured beta power over motor cortical areas (electroencephalography, Cz electrode) during three magnitudes of backward support -surface translations. Peak beta power was measured during early (50–150 ms), late (150–250 ms), and overall (0–400 ms) time bins, and wavelet-based analyses quantified the time course of evoked beta power. An ANOVA was used to compare peak beta power across perturbation magnitudes in each time bin. We further tested the association between perturbation-evoked beta power and individual balance ability measured in a challenging beam walking task. Beta power increased ~50 ms after perturbation, and to a greater extent in larger perturbations. Lower individual balance ability was associated with greater beta power in only the late (150–250 ms) time bin. These findings demonstrate greater sensorimotor cortical engagement under more challenging balance conditions, which may provide a biomarker for reduced automaticity in balance control that could be used in populations with neurological impairments.