Stéphanie Lefebvre PhD
Research Lab: Neural Plasticity and Neurorehabilitation Laboratory
Stéphanie Lefebvre is a Postdoctoral Researcher at the University of Southern California in the Neural Plasticity and Neurorehabilitation Laboratory. Prior to her position at USC, she completed a Bachelor’s Degree in Genetics and Biology from the University Claude Bernard of Lyon (France), a Master’s Degree in Biomedical Sciences from the University of Lausanne (Switzerland), and a PhD in Biomedical Sciences from the University Catholic of Louvain (Belgium): ‘Motor skill learning after stroke: exploration of neurophysiological mechanisms with functional neuroimaging and therapeutic modulation by non-invasive brain stimulation.’ She also completed a first Postdoctoral Fellowship at the University of Lille (France) and a second one at the CNRS, Lyon (France). Her main interests are mental health and brain plasticity, and specifically, how the brain reacts to changes in the environment during development or after a disease.
Doctor of Philosophy (PhD)
in Biomedical Sciences
2013 | University Catholic of Louvain, Belgium
Master of Science (MS)
in Biological Medicine
2008 | University of Lausanne, Switzerland
Bachelor of Science (BS)
in Biology and Genetics
2006 | University of Lyon, France
Juliano, J. M., Spicer, R. P., Vourvopoulos, A., Lefebvre, S., Jann, K., Ard, T., Santarnecchi, E., Krum, D. M., & Liew, S.-L. (2020). Embodiment Is related to better performance on a brain–computer interface in immersive virtual reality: A pilot study. Sensors, 20(4), 1204. https://doi.org/10.3390/s20041204 Show abstract
Electroencephalography (EEG)-based brain–computer interfaces (BCIs) for motor rehabilitation aim to “close the loop” between attempted motor commands and sensory feedback by providing supplemental information when individuals successfully achieve specific brain patterns. Existing EEG-based BCIs use various displays to provide feedback, ranging from displays considered more immersive (e.g., head-mounted display virtual reality (HMD-VR)) to displays considered less immersive (e.g., computer screens). However, it is not clear whether more immersive displays improve neurofeedback performance and whether there are individual performance differences in HMD-VR versus screen-based neurofeedback. In this pilot study, we compared neurofeedback performance in HMD-VR versus a computer screen in 12 healthy individuals and examined whether individual differences on two measures (i.e., presence, embodiment) were related to neurofeedback performance in either environment. We found that, while participants’ performance on the BCI was similar between display conditions, the participants’ reported levels of embodiment were significantly different. Specifically, participants experienced higher levels of embodiment in HMD-VR compared to a computer screen. We further found that reported levels of embodiment positively correlated with neurofeedback performance only in HMD-VR. Overall, these preliminary results suggest that embodiment may relate to better performance on EEG-based BCIs and that HMD-VR may increase embodiment compared to computer screens.
Keywords: brain–computer interface; neurofeedback; immersive virtual reality; head-mounted display; electroencephalography; presence; embodiment
Lefebvre, S., Jann, K., Schmiesing, A., Ito, K., Jog, M., Schweighofer, N., Wang, D. J., & Liew, S.-L. (2019). Differences in high-definition transcranial direct current stimulation over the motor hotspot versus the premotor cortex on motor network excitability. Scientific Reports, 9, 17605. https://doi.org/10.1038/s41598-019-53985-7 Show abstract
The effectiveness of transcranial direct current stimulation (tDCS) placed over the motor hotspot (thought to represent the primary motor cortex (M1)) to modulate motor network excitability is highly variable. The premotor cortex—particularly the dorsal premotor cortex (PMd)—may be a promising alternative target to reliably modulate motor excitability, as it influences motor control across multiple pathways, one independent of M1 and one with direct connections to M1. This double-blind, placebo-controlled preliminary study aimed to differentially excite motor and premotor regions using high-definition tDCS (HD-tDCS) with concurrent functional magnetic resonance imaging (fMRI). HD-tDCS applied over either the motor hotspot or the premotor cortex demonstrated high inter-individual variability in changes on cortical motor excitability. However, HD-tDCS over the premotor cortex led to a higher number of responders and greater changes in local fMRI-based complexity than HD-tDCS over the motor hotspot. Furthermore, an analysis of individual motor hotspot anatomical locations revealed that, in more than half of the participants, the motor hotspot is not located over anatomical M1 boundaries, despite using a canonical definition of the motor hotspot. This heterogeneity in stimulation site may contribute to the variability of tDCS results. Altogether, these preliminary findings provide new considerations to enhance tDCS reliability.
Vourvopoulos, A., Pardo, O. M., Lefebvre, S., Neureither, M., Saldana, D., Jahng, E., & Liew, S.-L. (2019). Effects of a brain-computer interface with virtual reality (VR) neurofeedback: A pilot study in chronic stroke patients. Frontiers in Human Neuroscience, 13, 210. https://doi.org/10.3389/fnhum.2019.00210 Show abstract
Rehabilitation for stroke patients with severe motor impairments is burdensome and demanding because most of the current rehabilitation options require some volitional movement to train the affected side. However, research has shown that survivors of severe stroke may receive modest benefits from action observation, virtual reality (VR), and brain-computer interfaces (BCIs). These approaches have shown some success in strengthening key motor pathways thought to support motor recovery after stroke. The purpose of this study was to combine the principles of action observation, VR, and BCI in a platform called REINVENT and assess its effects on four chronic stroke patients across different levels of motor impairment. REINVENT acquires post-stroke EEG signals that indicate an attempt to move and drives the movement of a virtual avatar arm, allowing patient-driven action observation neurofeedback in VR. In addition, synchronous electromyography (EMG) data were also captured to monitor overt muscle activity. Our results show that this EEG-based BCI can be used by stroke survivors across a wide range of motor disabilities. Finally, individual results suggest that patients with more severe motor impairments benefit the most from EEG-based neurofeedback, while patients with more mild impairments may benefit more from EMG-based feedback, harnessing existing sensorimotor pathways. Future research is needed to confirm these findings in a larger and more diverse population.
de Pierrefeu, A., Fovet, T., Hadj‐Selem, F., Löfstedt, T., Ciuciu, P., Lefebvre, S., Thomas, P., Lopes, R., Jardri, R., & Duchesnay, E. (2018). Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity. Human Brain Mapping, 39(4), 1777-1788. https://doi.org/10.1002/hbm.23953 Show abstract
Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time‐consuming. This article first proposes a machine‐learning algorithm to automatically identify resting‐state fMRI periods that precede hallucinations versus periods that do not. When applied to whole‐brain fMRI data, state‐of‐the‐art classification methods, such as support vector machines (SVM), yield dense solutions that are difficult to interpret. We proposed to extend the existing sparse classification methods by taking the spatial structure of brain images into account with structured sparsity using the total variation penalty. Based on this approach, we obtained reliable classifying performances associated with interpretable predictive patterns, composed of two clearly identifiable clusters in speech‐related brain regions. The variation in transition‐to‐hallucination functional patterns not only from one patient to another but also from one occurrence to the next (e.g., also depending on the sensory modalities involved) appeared to be the major difficulty when developing effective classifiers. Consequently, second, this article aimed to characterize the variability within the prehallucination patterns using an extension of principal component analysis with spatial constraints. The principal components (PCs) and the associated basis patterns shed light on the intrinsic structures of the variability present in the dataset. Such results are promising in the scope of innovative fMRI‐guided therapy for drug‐resistant hallucinations, such as fMRI‐based neurofeedback.
Liew, S.-L., Anglin, J. M., Banks, N. W., Sondag, M., Ito, K. L., Kim, H., Chan, J., Ito, J., Jung, C., Khoshab, N., Lefebvre, S., Nakamura, W., Saldana, D., Schmiesing, A., Tran, C., Vo, D., Ard, T., Heydari, P., Kim, B., Aziz-Zadeh, L., Cramer, S. C., Liu, J., Soekadar, S., Nordvik, J.-E., Westlye, L. T., Wang, J., Winstein, C., Yu, C., Ai, L., Koo, B., Craddock, R. C., Milham, M., Lakich, M., Pienta, A., & Stroud, A. (2018). A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific Data, 5, 180011. https://doi.org/10.1038/sdata.2018.11 Show abstract
Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.
Leroy, A., Foucher, J. R., Pins, D., Delmaire, C., Thomas, P., Roser, M. M., Lefebvre, S., Amad, A., Fovet, T., Jaafari, N., & Jardri, R. (2017). fMRI capture of auditory hallucinations: Validation of the two‐steps method. Human Brain Mapping, 38(10), 4966-4979. https://doi.org/10.1002/hbm.23707 Show abstract
Our purpose was to validate a reliable method to capture brain activity concomitant with hallucinatory events, which constitute frequent and disabling experiences in schizophrenia. Capturing hallucinations using functional magnetic resonance imaging (fMRI) remains very challenging. We previously developed a method based on a two‐steps strategy including (1) multivariate data‐driven analysis of per‐hallucinatory fMRI recording and (2) selection of the components of interest based on a post‐fMRI interview. However, two tests still need to be conducted to rule out critical pitfalls of conventional fMRI capture methods before this two‐steps strategy can be adopted in hallucination research: replication of these findings on an independent sample and assessment of the reliability of the hallucination‐related patterns at the subject level. To do so, we recruited a sample of 45 schizophrenia patients suffering from frequent hallucinations, 20 schizophrenia patients without hallucinations and 20 matched healthy volunteers; all participants underwent four different experiments. The main findings are (1) high accuracy in reporting unexpected sensory stimuli in an MRI setting; (2) good detection concordance between hypothesis‐driven and data‐driven analysis methods (as used in the two‐steps strategy) when controlled unexpected sensory stimuli are presented; (3) good agreement of the two‐steps method with the online button‐press approach to capture hallucinatory events; (4) high spatial consistency of hallucinatory‐related networks detected using the two‐steps method on two independent samples. By validating the two‐steps method, we advance toward the possible transfer of such technology to new image‐based therapies for hallucinations.
Lefebvre, S., & Liew, S.-L. (2017). Anatomical parameters of tDCS to modulate the motor system after stroke: A review. Frontiers in Neurology, 8, 29. https://doi.org/10.3389/fneur.2017.00029 Show abstract
Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method to modulate the local field potential in neural tissue and consequently, cortical excitability. As tDCS is relatively portable, affordable, and accessible, the applications of tDCS to probe brain-behavior connections have rapidly increased in the last 10 years. One of the most promising applications is the use of tDCS to modulate excitability in the motor cortex after stroke and promote motor recovery. However, the results of clinical studies implementing tDCS to modulate motor excitability have been highly variable, with some studies demonstrating that as many as 50% or more of patients fail to show a response to stimulation. Much effort has therefore been dedicated to understand the sources of variability affecting tDCS efficacy. Possible suspects include the placement of the electrodes, task parameters during stimulation, dosing (current amplitude, duration of stimulation, frequency of stimulation), individual states (e.g., anxiety, motivation, attention), and more. In this review, we first briefly review potential sources of variability specific to stroke motor recovery following tDCS. We then examine how the anatomical variability in tDCS placement [e.g., neural target(s) and montages employed] may alter the neuromodulatory effects that tDCS exerts on the post-stroke motor system.
Lefebvre, S., Dricot, L., Laloux, P., Desfontaines, P., Evrard, F., Peeters, A., Jamart, J., & Vandermeeren, Y. (2017). Increased functional connectivity one week after motor learning and tDCS in stroke patients. Neuroscience, 340, 424-435. https://doi.org/10.1016/j.neuroscience.2016.10.066 Show abstract
Recent studies using resting-state functional magnetic resonance imaging (rs-fMRI) demonstrated that changes in functional connectivity (FC) after stroke correlate with recovery. The aim of this study was to explore whether combining motor learning to dual transcranial direct current stimulation (dual-tDCS, applied over both primary motor cortices (M1)) modulated FC in stroke patients. Twenty-two chronic hemiparetic stroke patients participated in a baseline rs-fMRI session. One week later, dual-tDCS/sham was applied during motor skill learning (intervention session); one week later, the retention session started with the acquisition of a run of rs-fMRI imaging. The intervention + retention sessions were performed once with dual-tDCS and once with sham in a randomized, cross-over, placebo-controlled, double-blind design. A whole-brain independent component analysis based analysis of variance (ANOVA) demonstrated no changes between baseline and sham sessions in the somatomotor network, whereas a FC increase was observed one week after dual-tDCS compared to baseline (qFDR <0.05, t63 = 4.15). A seed-based analysis confirmed specific stimulation-driven changes within a network of motor and premotor regions in both hemispheres. At baseline and one week after sham, the strongest FC was observed between the M1 and dorsal premotor cortex (PMd) of the undamaged hemisphere. In contrast, one week after dual-tDCS, the strongest FC was found between the M1 and PMd of the damaged hemisphere. Thus, a single session of dual-tDCS combined with motor skill learning increases FC in the somatomotor network of chronic stroke patients for one week.
Lefebvre, S., Baille, G., Jardri, R., Plomhause, L., Szaffarczyk, S., Defebvre, L., Thomas, P., Delmaire, C., Pins, D., & Dujardin, K. (2016). Hallucinations and conscious access to visual inputs in Parkinson’s disease. Scientific Reports, 6, 36284. https://doi.org/10.1038/srep36284 Show abstract
The pathophysiology of visual hallucinations in Parkinson’s disease has yet to be characterized. Although stimulus-driven (“bottom-up”) processes are known to be impaired, the role of “top-down” processes remains to be determined. Distinguishing between conscious and non-conscious detections (i.e. access to consciousness) may be a valuable way of monitoring top-down processes. Conscious access to visual inputs was investigated to identify the neural substrates underlying susceptibility to hallucinations in Parkinson’s disease. Seventeen healthy controls, 18 Parkinson’s disease patients with minor visual hallucinations and 16 without were enrolled in the study. During functional magnetic resonance imaging, the participants performed a visual detection task. The detection threshold was significantly higher in each patient group than in healthy controls while the two groups of patients did not differ significantly. Compared with hallucination-free patients, patients with minor hallucinations displayed hyperactivation of prefrontal and right occipital cortices, and hypoactivation of the left cingulate, temporal and occipital cortices. During conscious access to visual inputs, the functional network in patients with visual hallucinations differed from that seen in patients without visual hallucinations. This suggests that the supremacy of top-down processes in visual information processing may enhance susceptibility to hallucinations in Parkinson’s disease.
Lefebvre, S., Demeulemeester, M., Leroy, A., Delmaire, C., Lopes, R., Pins, D., Thomas, P., & Jardri, R. (2016). Network dynamics during the different stages of hallucinations in schizophrenia. Human Brain Mapping, 37(7), 2571-2586. https://doi.org/10.1002/hbm.23197 Show abstract
The majority of patients with schizophrenia suffer from hallucinations. While the triple‐network model, which includes the default mode network (DMN), the central executive network (CEN) and the salience network (SAL), has recently been applied to schizophrenia, how this framework could explain the emergence of hallucinations remains unclear. Therefore, complementary brain regions that have been linked to hallucinations, such as the left hippocampus, should also be considered and added to this model. Accordingly, the present study explored the effective connectivity across these four components (i.e., the quadripartite model) during the different stages of hallucinations. Twenty‐five patients with schizophrenia participated in a single session of resting‐state functional magnetic resonance imaging to capture hallucinatory experiences. Based on the participants' self‐report of the psychosensory experiences that occurred during scanning, hallucinatory experiences were identified and divided into four stages: periods without hallucination (“OFF”), periods with hallucination (“ON”), transition periods between “OFF” and “ON”, and the extinction of the hallucinatory experience (“END”). Using stochastic dynamic causal modeling analysis, this study first confirmed that the SAL played a critical and causal role in switching between the CEN and the DMN in schizophrenia. In addition, effective connectivity within the quadripartite model depended on the hallucinatory stage. In particular, “ON” periods were linked to memory‐based sensory input from the hippocampus to the SAL, while “END” periods were associated with a takeover of the CEN in favor of a voluntary process. Finally, the pathophysiological and therapeutic implications of these findings are critically discussed.
Vandermeeren, Y., & Lefebvre, S. (2015). Combining motor learning and brain stimulation to enhance post-stroke neurorehabilitation. Neural Regeneration Research, 10(8), 1218-1220. https://doi.org/10.4103/1673-5374.158483 Show abstract
Worldwide, stroke is a leading cause of life-long disability resulting in dramatic restrictions in patient's independence and in a growing economic burden for the community. The majority of stroke survivors suffers from chronic sequels among which hemiparesis is one of the most debilitating. Despite quick progresses over the last 20 years, the impact of neurorehabilitation on post-stroke recovery remains unsatisfactory. Developing new ways to enhance neurorehabilitation could thus benefit to millions of patients. A better insight into the physiology of the normal motor system and the mechanisms driving post-stroke recovery and neural plasticity should permit to develop a new science of neurorehabilitation.
Lefebvre, S., Dricot, L., Laloux, P., Gradkowski, W., Desfontaines, P., Evrard, F., Peeters, A., Jamart, J., & Vandermeeren, Y. (2015). Neural substrates underlying motor skill learning in chronic hemiparetic stroke patients. Frontiers in Human Neuroscience, 9, 320. https://doi.org/10.3389/fnhum.2015.00320 Show abstract
Motor skill learning is critical in post-stroke motor recovery, but little is known about its underlying neural substrates. Recently, using a new visuomotor skill learning paradigm involving a speed/accuracy trade-off in healthy individuals we identified three subpopulations based on their behavioral trajectories: fitters (in whom improvement in speed or accuracy coincided with deterioration in the other parameter), shifters (in whom speed and/or accuracy improved without degradation of the other parameter), and non-learners. We aimed to identify the neural substrates underlying the first stages of motor skill learning in chronic hemiparetic stroke patients and to determine whether specific neural substrates were recruited in shifters versus fitters. During functional magnetic resonance imaging (fMRI), 23 patients learned the visuomotor skill with their paretic upper limb. In the whole-group analysis, correlation between activation and motor skill learning was restricted to the dorsal prefrontal cortex of the damaged hemisphere (DLPFCdamh: r = −0.82) and the dorsal premotor cortex (PMddamh: r = 0.70); the correlations was much lesser (−0.16 < r > 0.25) in the other regions of interest. In a subgroup analysis, significant activation was restricted to bilateral posterior parietal cortices of the fitters and did not correlate with motor skill learning. Conversely, in shifters significant activation occurred in the primary sensorimotor cortexdamh and supplementary motor areadamh and in bilateral PMd where activation changes correlated significantly with motor skill learning (r = 0.91). Finally, resting-state activity acquired before learning showed a higher functional connectivity in the salience network of shifters compared with fitters (qFDR < 0.05). These data suggest a neuroplastic compensatory reorganization of brain activity underlying the first stages of motor skill learning with the paretic upper limb in chronic hemiparetic stroke patients, with a key role of bilateral PMd.
Lefebvre, S., Dricot, L., Laloux, P., Gradkowski, W., Desfontaines, P., Evrard, F., Peeters, A., Jamart, J., & Vandermeeren, Y. (2015). Neural substrates underlying stimulation-enhanced motor skill learning after stroke. Brain, 138(1), 149–163. https://doi.org/10.1093/brain/awu336 Show abstract
Motor skill learning is one of the key components of motor function recovery after stroke, especially recovery driven by neurorehabilitation. Transcranial direct current stimulation can enhance neurorehabilitation and motor skill learning in stroke patients. However, the neural mechanisms underlying the retention of stimulation-enhanced motor skill learning involving a paretic upper limb have not been resolved. These neural substrates were explored by means of functional magnetic resonance imaging. Nineteen chronic hemiparetic stroke patients participated in a double-blind, cross-over randomized, sham-controlled experiment with two series. Each series consisted of two sessions: (i) an intervention session during which dual transcranial direct current stimulation or sham was applied during motor skill learning with the paretic upper limb; and (ii) an imaging session 1 week later, during which the patients performed the learned motor skill. The motor skill learning task, called the ‘circuit game’, involves a speed/accuracy trade-off and consists of moving a pointer controlled by a computer mouse along a complex circuit as quickly and accurately as possible. Relative to the sham series, dual transcranial direct current stimulation applied bilaterally over the primary motor cortex during motor skill learning with the paretic upper limb resulted in (i) enhanced online motor skill learning; (ii) enhanced 1-week retention; and (iii) superior transfer of performance improvement to an untrained task. The 1-week retention’s enhancement driven by the intervention was associated with a trend towards normalization of the brain activation pattern during performance of the learned motor skill relative to the sham series. A similar trend towards normalization relative to sham was observed during performance of a simple, untrained task without a speed/accuracy constraint, despite a lack of behavioural difference between the dual transcranial direct current stimulation and sham series. Finally, dual transcranial direct current stimulation applied during the first session enhanced continued learning with the paretic limb 1 week later, relative to the sham series. This lasting behavioural enhancement was associated with more efficient recruitment of the motor skill learning network, that is, focused activation on the motor-premotor areas in the damaged hemisphere, especially on the dorsal premotor cortex. Dual transcranial direct current stimulation applied during motor skill learning with a paretic upper limb resulted in prolonged shaping of brain activation, which supported behavioural enhancements in stroke patients.
Lefebvre, S., Thonnard, J.-L., Laloux, P., Peeters, A., Jamart, J., & Vandermeeren, Y. (2014). Single session of dual-tDCS transiently improves precision grip and dexterity of the paretic hand after stroke. Neurorehabilitation and Neural Repair, 28(2), 100-110. https://doi.org/10.1177/1545968313478485 Show abstract
Background. After stroke, deregulated interhemispheric interactions influence residual paretic hand function. Anodal or cathodal transcranial direct current stimulation (tDCS) can rebalance these abnormal interhemispheric interactions and improve motor function.
Objective. We explored whether dual-hemisphere tDCS (dual-tDCS) in participants with chronic stroke can improve fine hand motor function in 2 important aspects: precision grip and dexterity.
Methods. In all, 19 chronic hemiparetic individuals with mild to moderate impairment participated in a double-blind, randomized trial. During 2 separate cross-over sessions (real/sham), they performed 10 precision grip movements with a manipulandum and the Purdue Pegboard Test (PPT) before, during, immediately after, and 20 minutes after dual-tDCS applied simultaneously over the ipsilesional (anodal) and contralateral (cathodal) primary motor cortices.
Results. The precision grip performed with the paretic hand improved significantly 20 minutes after dual-tDCS, with reduction of the grip force/load force ratio by 7% and in the preloading phase duration by 18% when compared with sham. The dexterity of the paretic hand started improving during dual-tDCS and culminated 20 minutes after the end of dual-tDCS (PPT score +38% vs +5% after sham). The maximal improvements in precision grip and dexterity were observed 20 minutes after dual-tDCS. These improvements correlated negatively with residual hand function quantified with ABILHAND.
Conclusions. One bout of dual-tDCS improved the motor control of precision grip and digital dexterity beyond the time of stimulation. These results suggest that dual-tDCS should be tested in longer protocols for neurorehabilitation and with moderate to severely impaired patients. The precise timing of stimulation after stroke onset and associated training should be defined.
Vandermeeren, Y., Lefebvre, S., Desfontaines, P., & Laloux, P. (2013). Could dual-hemisphere transcranial direct current stimulation (tDCS) reduce spasticity after stroke? [Letter to the editor]. Acta Neurologica Belgica, 113(1), 87–89. https://doi.org/10.1007/s13760-012-0163-5
Lefebvre, S., Laloux, P., Peeters, A., Desfontaines, P., Jamart, J., & Vandermeeren, Y. (2013). Dual-tDCS enhances online motor skill learning and long-term retention in chronic stroke patients. Frontiers in Human Neuroscience, 6, 343. https://doi.org/10.3389/fnhum.2012.00343 Show abstract
Background. Since motor learning is a key component for stroke recovery, enhancing motor skill learning is a crucial challenge for neurorehabilitation. Transcranial direct current stimulation (tDCS) is a promising approach for improving motor learning. The aim of this trial was to test the hypothesis that dual-tDCS applied bilaterally over the primary motor cortices (M1) improves online motor skill learning with the paretic hand and its long-term retention.
Methods. Eighteen chronic stroke patients participated in a randomized, cross-over, placebo-controlled, double bind trial. During separate sessions, dual-tDCS or sham dual-tDCS was applied over 30 min while stroke patients learned a complex visuomotor skill with the paretic hand: using a computer mouse to move a pointer along a complex circuit as quickly and accurately as possible. A learning index involving the evolution of the speed/accuracy trade-off was calculated. Performance of the motor skill was measured at baseline, after intervention and 1 week later.
Results. After sham dual-tDCS, eight patients showed performance worsening. In contrast, dual-tDCS enhanced the amount and speed of online motor skill learning compared to sham (p < 0.001) in all patients; this superiority was maintained throughout the hour following. The speed/accuracy trade-off was shifted more consistently after dual-tDCS (n = 10) than after sham (n = 3). More importantly, 1 week later, online enhancement under dual-tDCS had translated into superior long-term retention (+44%) compared to sham (+4%). The improvement generalized to a new untrained circuit and to digital dexterity.
Conclusion. A single-session of dual-tDCS, applied while stroke patients trained with the paretic hand significantly enhanced online motor skill learning both quantitatively and qualitatively, leading to successful long-term retention and generalization. The combination of motor skill learning and dual-tDCS is promising for improving post-stroke neurorehabilitation.
Lefebvre, S., Jann, K., Schmiesing, A. N., Ito, K. L., Jog, M., Schweighofer, N., Wang, D. J., & Liew, S.-L. (2018, August). Concurrent HD-tDCS/fMRI study exploring changes in motor network physiology and complexity. Poster presented at the 2018 NYC Neuromodulation Conference & NANS Summer Series, New York, NY. Show abstract
Introduction. Transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) can be an efficient way to modulate cortical excitability and promote motor recovery following stroke(1). However, the effects of M1 tDCS on behavior have been highly variable across individuals. Here we explored whether another motor region that is less often affected by stroke (dorsal premotor cortex, PMd) could also modulate cortical excitability. We also examined motor network neural complexity changes following stimulation of either M1 or PMd using simultaneous fMRI.
Methods. Thirty healthy participants were randomized into 3 groups (over left M1, left PMd or sham (electrodes randomized over M1 or PMd)) in this double-blind study. tDCS (1mA) was applied concurrently during an fMRI session, using a Soterix MRI-compatible high-definition tDCS (HD-tDCS) system (4x1 configuration). Participants underwent 3 resting state runs (7 minutes each): pre, during and post the 7min HD-tDCS. Changes in neurophysiology were measured using motor evoked potentials (MEP) while motor network complexity was explored using a multiscale entropy (MSE) measure, which examines the variability of biological signals across a range of temporal scales(2). fMRI data were motion-realigned and noise-corrected using white-matter, CSF and motion-parameters as regressors. For MSE computations, we used a pattern matching threshold (r) of 0.5 and a pattern length (m) of 2. In total 20 coarse-sampled scales were investigated.
Results. Changes in cortical physiology were measured with a one-way ANOVA with 'Group' (M1, PMd, sham) as a factor. Group differences in cortical excitability (measured as an MEP ratio of post-tDCS/pre-tDCS) were marginally significantly different following HD-tDCS (F(1,27)=2.78, p=0.06; M1=1.56+-1.80, PMd=2.03+-1.03, Sham=0.74+-0.35). Post-hoc Tukey tests showed that cortical excitability in the PMd group was increased compared to sham (z=2.3, p=0.05). M1 versus sham was not statistically different (z=1.4, p=0.29).Changes in MSE were measured with repeated-measures ANOVAs with 'Time' (Pre, During, Post) and 'Group' as factors, performed for each of 4 regions of interest (left/right M1, left/right PMd). There was an MSE increase in the M1 group in the right PMd (F(2,54)=4.55, p=0.01; Post-hoc tests: M1-During>M1-Pre: t(54)=2.8; p=0.01; M1-Post>M1-Pre: t(54)=2.6, p=0.03) and an MSE increase in the PMd group in the left PMd (F(2,54)=6.032, p=0.004; Post-hoc tests: PMd-Post>PMd-Pre: t(54)=2.4, p=0.049).
Conclusion. This preliminary work suggests that both M1 and PMd HD-tDCS may modulate motor network neurophysiology and complexity, and that multiscale entropy may be a sensitive measurement of changes following noninvasive brain stimulation.
Stamatakis, J., Gonzalez, A., Caby, B., Lefebvre, S., Vandermeeren, Y., & Macq, B. (2012). Kinematic features of reach and grasp movements in stroke rehabilitation using accelerometers. In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2012) (Volume 1: BIOSIGNALS, pp. 199-205). https://doi.org/10.5220/0003711701990205 Show abstract
Rehabilitation is an essential process to recover impaired motor functions after stroke. Typically, visual marker-based systems such as the Codamotion are used, as kinematic analyses seem to be an excellent tool to quantify objectively the effects of rehabilitation processes. However, this solution remains expensive. A low-cost accelerometer-based system has been developed and its performances were compared to those of the Codamotion system, used as a gold standard. Thanks to a model for prediction and an error model Kalman filter, the recorded signals were broken up into gravity and dynamic accelerations components that were placed in a global frame and compared to the Codamotion signals. The vertical z-axis was well reconstructed and used as a basis for kinematic analyses. Different features expressing movement speed, control strategy or movement smoothness have been computed from both systems and compared. Despite the fact that some of them showed differences between both systems, the accelerometer-based system computed features with a discriminant power comparable to the ones derived from the Codamotion. In conclusion, this accelerometer-based system is a low-cost alternative to expensive visual marker-based systems that could be extensively used for rehabilitation processes in routine clinical practice or even at home.