Sleep brain networks and sleep depth during somatosensory stimulation

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Magnetoencephalography in Stroke Recovery and Rehabilitation

Magnetoencephalography (MEG) is a non-invasive neurophysiological technique used to study the cerebral cortex. Currently, MEG is mainly used clinically to localize epileptic foci and eloquent brain areas in order to avoid damage during neurosurgery. MEG might, however, also be of help in monitoring stroke recovery and rehabilitation. This review focuses on experimental use of MEG in neurorehabilitation. MEG has been employed to detect early modifications in neuroplasticity and connectivity, but there is insufficient evidence as to whether these methods are sensitive enough to be used as a clinical diagnostic test. MEG has also been exploited to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface. In the current body of experimental research, MEG appears to be a powerful tool in neurorehabilitation, but it is necessary to produce new data to confirm its clinical utility

Memory Rehabilitation Strategies in Nonsurgical Temporal Lobe Epilepsy: A Review

open8siPeople with temporal lobe epilepsy (TLE) who have not undergone epilepsy surgery often complain of memory deficits. Cognitive re- habilitation is employed as a remedial intervention in clinical settings, but research is limited and findings concerning efficacy and the criteria for choosing different approaches have been inconsistent. We aimed to appraise existing evidence on memory rehabilitation in nonsurgical individuals with temporal lobe epilepsy and to ascertain the effectiveness of specific strategies. A scoping review was preferred given the het- erogeneous nature of the interventions. A comprehensive literature search using MEDLINE, EMBASE, CINAHL, AMED, Scholars Portal/ PSYCHinfo, Proceedings First, and ProQuest Dissertations and Theses identified articles published in English before February 2016. The search retrieved 372 abstracts. Of 25 eligible studies, six were included in the final review. None included pediatric populations. Strategies included cognitive training, external memory aids, brain training, and noninvasive brain stimulation. Selection criteria tended to be general. Overall, there was insufficient evidence to make definitive conclusions regarding the efficacy of traditional memory rehabilitation strategies, brain training, and noninvasive brain stimulation. The review suggests that cognitive rehabilitation in nonsurgical TLE is underresearched and that there is a need for a systematic evaluation in this population.embargoed_20180216DEL FELICE, Alessandra; Alderighi, Marzia; Martinato, Matteo; Grisafi, Davide; Bosco, Anna; Thompson, Pamela J.; Sander, Josemir W.; Masiero, StefanoDEL FELICE, Alessandra; Alderighi, Marzia; Martinato, Matteo; Grisafi, Davide; Bosco, Anna; Thompson, Pamela J.; Sander, Josemir W.; Masiero, Stefan

Short-wave diathermy in the clinical management of musculoskeletal disorders: a pilot observational study

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Exoskeleton Training Modulates Complexity in Movement Patterns and Cortical Activity in Able-Bodied Volunteers

Robot-aided gait training (RAGT) plays a crucial role in providing high-dose and high-intensity task-oriented physical therapy. The human-robot interaction during RAGT remains technically challenging. To achieve this aim, it is necessary to quantify how RAGT impacts brain activity and motor learning. This work quantifies the neuromuscular effect induced by a single RAGT session in healthy middle-aged individuals. Electromyographic (EMG) and motion (IMU) data were recorded and processed during walking trials before and after RAGT. Electroencephalographic (EEG) data were recorded during rest before and after the entire walking session. Linear and nonlinear analyses detected changes in the walking pattern, paralleled by a modulation of cortical activity in the motor, attentive, and visual cortices immediately after RAGT. Increases in alpha and beta EEG spectral power and pattern regularity of the EEG match the increased regularity of body oscillations in the frontal plane, and the loss of alternating muscle activation during the gait cycle, when walking after a RAGT session. These preliminary results improve the understanding of human-machine interaction mechanisms and motor learning and may contribute to more efficient exoskeleton development for assisted walking

Effect of lower limb exoskeleton on the modulation of neural activity and gait classification

: Neurorehabilitation with robotic devices requires a paradigm shift to enhance human-robot interaction. The coupling of robot assisted gait training (RAGT) with a brain-machine interface (BMI) represents an important step in this direction but requires better elucidation of the effect of RAGT on the user's neural modulation. Here, we investigated how different exoskeleton walking modes modify brain and muscular activity during exoskeleton assisted gait. We recorded electroencephalographic (EEG) and electromyographic (EMG) activity from ten able-bodied volunteers walking with an exoskeleton with three modes of user assistance (i.e., transparent, adaptive and full assistance) and during free overground gait. Results identified that exoskeleton walking (irrespective of the exoskeleton mode) induces a stronger modulation of central mid-line mu (8-13 Hz) and low-beta (14-20 Hz) rhythms compared to free overground walking. These modifications are accompanied by a significant re-organization of the EMG patterns in exoskeleton walking. On the other hand, we observed no significant differences in neural activity during exoskeleton walking with the different assistance levels. We subsequently implemented four gait classifiers based on deep neural networks trained on the EEG data during the different walking conditions. Our hypothesis was that exoskeleton modes could impact the creation of a BMI-driven RAGT. We demonstrated that all classifiers achieved an average accuracy of 84.13 ± 3.49% in classifying swing and stance phases on their respective datasets. In addition, we demonstrated that the classifier trained on the transparent mode exoskeleton data can classify gait phases during adaptive and full modes with an accuracy of 78.3 ± 4.8%, while the classifier trained on free overground walking data fails to classify the gait during exoskeleton walking (accuracy of 59.4 ± 11.8%). These findings provide important insights into the effect of robotic training on neural activity and contribute to the advancement of BMI technology for improving robotic gait rehabilitation therapy

Quantitative Characterization of Motor Control during Gait in Dravet Syndrome Using Wearable Sensors: A Preliminary Study

: Dravet syndrome (DS) is a rare and severe form of genetic epilepsy characterized by cognitive and behavioural impairments and progressive gait deterioration. The characterization of gait parameters in DS needs efficient, non-invasive quantification. The aim of the present study is to apply nonlinear indexes calculated from inertial measurements to describe the dynamics of DS gait. Twenty participants (7 M, age 9-33 years) diagnosed with DS were enrolled. Three wearable inertial measurement units (OPAL, Apdm, Portland, OR, USA; Miniwave, Cometa s.r.l., Italy) were attached to the lower back and ankles and 3D acceleration and angular velocity were acquired while participants walked back and forth along a straight path. Segmental kinematics were acquired by means of stereophotogrammetry (SMART, BTS). Community functioning data were collected using the functional independence measure (FIM). Mean velocity and step width were calculated from stereophotogrammetric data; fundamental frequency, harmonic ratio, recurrence quantification analysis, and multiscale entropy (τ = 1...6) indexes along anteroposterior (AP), mediolateral (ML), and vertical (V) axes were calculated from trunk acceleration. Results were compared to a reference age-matched control group (112 subjects, 6-25 years old). All nonlinear indexes show a disruption of the cyclic pattern of the centre of mass in the sagittal plane, quantitatively supporting the clinical observation of ataxic gait. Indexes in the ML direction were less altered, suggesting the efficacy of the compensatory strategy (widening the base of support). Nonlinear indexes correlated significantly with functional scores (i.e., FIM and speed), confirming their effectiveness in capturing clinically meaningful biomarkers of gait

Neurophysiological and BOLD signal uncoupling of giant somatosensory evoked potentials in progressive myoclonic epilepsy: a case-series study

In progressive myoclonic epilepsy (PME), a rare epileptic syndrome caused by a variety of genetic disorders, the combination of peripheral stimulation and functional magnetic resonance imaging (fMRI) can shed light on the mechanisms underlying cortical dysfunction. The aim of the study is to investigate sensorimotor network modifications in PME by assessing the relationship between neurophysiological findings and blood oxygen level dependent (BOLD) activation. Somatosensory-evoked potential (SSEP) obtained briefly before fMRI and BOLD activation during median-nerve electrical stimulation were recorded in four subjects with typical PME phenotype and compared with normative data. Giant scalp SSEPs with enlarger N20-P25 complex compared to normal data (mean amplitude of 26.2\u2009\ub1\u20098.2\u2009\u3bcV after right stimulation and 27.9\u2009\ub1\u20093.7\u2009\u3bcV after left stimulation) were detected. Statistical group analysis showed a reduced BOLD activation in response to median nerve stimulation in PMEs compared to controls over the sensorimotor (SM) areas and an increased response over subcortical regions (p\u2009\u20092.3, corrected). PMEs show dissociation between neurophysiological and BOLD findings of SSEPs (giant SSEP with reduced BOLD activation over SM). A direct pathway connecting a highly restricted area of the somatosensory cortex with the thalamus can be hypothesized to support the higher excitability of these areas