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Purpose: Myotonic dystrophy type 1 is the most common muscular dystrophy in adults. Although brain involvement is well recognized, the relationship between cortical motor control and voluntary movement has not been sufficiently explored. This study aims at assessing magnetoencephalographic (MEG) rhythms at oscillatory and connectivity levels to map central motor control., Methods: Magnetoencephalographic data were acquired from healthy subjects and five myotonic dystrophy type 1 subjects during resting state and foot movement. Resting state EEG band power, event-related desynchronization/synchronization, functional connectivity, and network features (node strength and betweenness centrality) were estimated. A statistical comparison of these indexes between the two groups was run; a linear correlation between event-related desynchronization and motor performance was obtained., Results: Myotonic dystrophy type 1 subjects showed higher theta power over central motor regions and lower beta power over frontal areas, with a decrease of beta node strength over the dominant hemisphere and an increase of betweenness centrality over the vertex. Foot movement in the most impaired myotonic dystrophy type 1 subjects was inefficient in evoking event-related desynchronization. In less severely impaired participants, dominant foot movement was related to a bilateral sensorimotor event-related desynchronization., Conclusions: Results provide proof of a central dysfunction of movement. Identification of neurophysiological motor patterns in myotonic dystrophy type 1 could provide a guide for tailored therapy.

Background: Bilateral arm training (BAT) has shown promise in expediting progress toward upper limb recovery in chronic stroke patients, but its neural correlates are poorly understood., Objective: To evaluate changes in upper limb function and EEG power after a robot-assisted BAT in chronic stroke patients., Methods: In a within-subject design, seven right-handed chronic stroke patients with upper limb paresis received 21 sessions (3 days/week) of the robot-assisted BAT. The outcomes were changes in score on the upper limb section of the Fugl-Meyer assessment (FM), Motricity Index (MI), and Modified Ashworth Scale (MAS) evaluated at the baseline (T 0 ), posttraining (T 1 ), and 1-month follow-up (T 2 ). Event-related desynchronization/synchronization were calculated in the upper alpha and the beta frequency ranges., Results: Significant improvement in all outcomes was measured over the course of the study. Changes in FM were significant at T 2 , and in MAS at T 1 and T 2 . After training, desynchronization on the ipsilesional sensorimotor areas increased during passive and active movement, as compared with T 0 ., Conclusions: A repetitive robotic-assisted BAT program may improve upper limb motor function and reduce spasticity in the chronically impaired paretic arm. Effects on spasticity were associated with EEG changes over the ipsilesional sensorimotor network.

Passiveand imagined limbmovements induce changes in cerebral oscillatory activity. Central modulatory effects play a role in plastic changes, and are of uttermost importance in rehabilitation. This has extensively been studied for upper limb, but less is known for lower limb. The aim of this study is to investigate the topographical distribution of event-related desynchronization/synchronization(ERD/ERS) and task-relatedcoherence during a robot-assisted and a motor imagery task of lower limb in healthy subjects to inform rehabilitation paradigms. 32-channels electroencephalogram (EEG) was recorded in twenty-one healthy right footed and handed subjects during a robot-assisted single-joint cyclic right ankle movement performed by the BTS ANYMOV robotic hospital bed. Data were acquired with a block protocol for passive and imagined movement at a frequency of 0.2 Hz. ERD/ERS and task related coherence were calculated in alpha1 (8-10 Hz), alpha2 (10.5-12.5 Hz) and beta (13-30 Hz) frequency ranges. During passive movement, alpha2 rhythm desynchronized overC3 and ipsilateral frontal areas (F4, FC2, FC6); betaERD was detected over the bilateral motor areas (Cz, C3, C4). During motor imagery, a significant desynchronization was evident for alpha1 over contralateral sensorimotor cortex (C3), for alpha2 over bilateral motor areas (C3 and C4), and for beta over central scalp areas. Task-related coherence decreased during passive movement in alpha2 band between contralateral central area (C3, CP5, CP1, P3) and ipsilateral frontal area (F8, FC6, T8); beta band coherence decreased between C3-C4 electrodes, and increased between C3-Cz. These data contribute to the understanding of oscillatory activity and functional neuronal interactions during lower limb robot-assisted motor performance. The final output of this line of research is to inform the design and development of neurorehabilitation protocols.

Functional connectivity estimates the temporal synchrony among functionally homogeneous brain regions based on the assessment of the dynamics of topologically localized neurophysiological responses. The aim of this study was to investigate task-related changes in brain activity and functional connectivity by applying different methods namely event-related desynchronization (ERD), coherence, and graph-theoretical analysis to electroencephalographic (EEG) recordings, for comparing their respective descriptive power and complementarity. As it is well known, ERD provides an estimate of differences in power spectral densities between active (or task) and rest conditions, functional connectivity allows assessing the level of synchronization between the signals recorded at different scalp locations and graph analysis enables the estimation of the functional network features and topology. EEG activity was recorded on 10 subjects during left/right arm movements. The theta, alpha, and beta bands were considered. Conventional analysis showed a significant ERD in both alpha and beta bands over the sensorimotor cortex during the left arm movement and in beta band during the right arm movement, besides identifying the regions involved in the task, as it was expected. On the other hand, connectivity assessment highlighted that stronger connections are those that involved the motor regions for which graph analysis revealed reduced accessibility and an increased centrality during the movement. Jointly, the last two methods allow identifying the cortical areas that are functionally related in the active condition as well as the topological organization of the functional network. Results support the hypothesis that network analysis brings complementary knowledge with respect to established approaches for modeling motor-induced functional connectivity and could be profitably exploited in clinical contexts., (© EEG and Clinical Neuroscience Society (ECNS) 2015.)

The combined use of electroencephalography (EEG) and functional magnetic resonance imaging (EEG-fMRI) in epilepsy allows the noninvasive hemodynamic characterization of epileptic discharge-related neuronal activations. The aim of this study was to investigate pathophysiologic mechanisms underlying epileptic activity by exploring the spatial and temporal distribution of fMRI signal modifications during seizure in a single patient with posttraumatic epilepsy. EEG and fMRI data were acquired during two scanning sessions: a spontaneous critical episode was observed during the first, and interictal events were recorded during the second. The EEG-fMRI data were analyzed using the general linear model (GLM). Blood oxygenation level-dependent (BOLD) localization derived from the preictal and artifact-free postictal phase was concordant with the BOLD localization of the interictal epileptiform discharges identified in the second session, pointing to a left perilesional mesiofrontal area. Of note, BOLD signal modifications were already visible several seconds before seizure onset. In brief, BOLD activations from the preictal, postictal, and interictal epileptiform discharge analysis appear to be concordant with the clinically driven localization hypothesis, whereas a widespread network of activations is detected during the ictal phase in a partial seizure., (© EEG and Clinical Neuroscience Society (ECNS) 2014.)

A better understanding of cortical modifications related to movement preparation and execution after robot-assisted training could aid in refining rehabilitation therapy protocols for stroke patients. Electroencephalography (EEG) modifications of cortical activity in healthy subjects were evaluated using time-frequency event-related EEG and task-related coherence (TRCoh). Twenty-one channel EEG was recorded in eight subjects during protocols of active, passive, and imagined movements. The subjects performed robot-assisted tasks using the Bi-Manu-Track robot-assisted arm trainer. We applied time-frequency event-related synchronization/desynchronization (ERS/ERD) and TRCoh approaches to investigate where movement-related decreases in power were localized and to study the functional relationships between areas. Our results showed ERD of sensorimotor (SM) area over the contralateral side before the movement and bilateral ERD during execution of the movement. ERD during passive movements was similar in topography to that observed during voluntary movements, but without pre-movement components. No significant difference in time course ERD was observed among the three types of movement over the two SM areas. The TRCoh topography was similar for active and imagined movement; before passive movement, the frontal regions were uncoupled from the SM regions and did not contribute to task performance. This study suggests new perspectives for the evaluation of brain oscillatory activity and the neurological assessment of motor performance by means of quantitative EEG to better understand the planning and execution of movement.

Purpose: To assess the applicability of arterial spin labeling (ASL) in comparison to blood-oxygenation-level-dependent (BOLD) contrast fMRI in detecting brain activations elicited by active and passive hand movements., Materials and Methods: A block design for ASL and BOLD fMRI was applied in 8 healthy subjects using active and passive hand tasks. Data analyses were performed at individual and group level, comparing both the different movements and the performance of the two techniques., Results: Group analyses showed involvement of the same areas during both tasks, as the contralateral sensorimotor cortex, supplementary motor area, cerebellum, inferior parietal lobes, thalamus. ASL detected smaller activation volumes than BOLD, but the areas had a high degree of colocalization. Few significant differences (P < 0.05) were found when the two tasks were compared for the number of activated voxels, coordinates of center of mass, and CBF estimates. Considering together all the areas, the mean %BOLD change was 0.79 ± 0.27 and 0.73 ± 0.24 for the active and passive movements respectively, while the mean %CBF changes were 34.1 ± 8.9 and 27.1 ± 14.8., Conclusion: Our findings confirm passive and active tasks are strongly coupled, supporting the importance of passive tasks as a diagnostic tool in the clinical setting. ASL fMRI proved suitable for functional mapping and quantifying CBF changes, making it a promising technique for patient cohort applications., (© 2013 Wiley Periodicals, Inc.)

Integrating electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies enables to non-invasively investigate human brain function and to find the direct correlation of these two important measures of brain activity. Presurgical evaluation of patients with epilepsy is one of the areas where EEG and fMRI integration has considerable clinical relevance for localizing the brain regions generating interictal epileptiform activity. The conventional analysis of EEG-fMRI data is based on the visual identification of the interictal epileptiform discharges (IEDs) on scalp EEG. The convolution of these EEG events, represented as stick functions, with a model of the fMRI response, i.e. the hemodynamic response function, provides the regressor for general linear model (GLM) analysis of fMRI data. However, the conventional analysis is not automatic and suffers of some subjectivity in IEDs classification. Here, we present an easy-to-use and automatic approach for combined EEG-fMRI analysis able to improve IEDs identification based on Independent Component Analysis and wavelet analysis. EEG signal due to IED is reconstructed and its wavelet power is used as a regressor in GLM. The method was validated on simulated data and then applied on real data set consisting of 2 normal subjects and 5 patients with partial epilepsy. In all continuous EEG-fMRI recording sessions a good quality EEG was obtained allowing the detection of spontaneous IEDs and the analysis of the related BOLD activation. The main clinical finding in EEG-fMRI studies of patients with partial epilepsy is that focal interictal slow-wave activity was invariably associated with increased focal BOLD responses in a spatially related brain area. Our study extends current knowledge on epileptic foci localization and confirms previous reports suggesting that BOLD activation associated with slow activity might have a role in localizing the epileptogenic region even in the absence of clear interictal spikes., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Electroencephalography combined with functional magnetic resonance imaging (EEG-fMRI) identifies blood oxygenation level dependent (BOLD) signal changes associated with physiological and pathological EEG events. In this study we used EEG-fMRI to determine the possible correlation between topographical movement related EEG changes in brain oscillatory activity recorded from EEG electrodes over the scalp and fMRI cortical responses in motor areas during finger movement. Thirty-two channels of EEG were recorded in 12 subjects during eyes-closed condition inside a three T magnetic resonance (MR) scanner using an MR-compatible EEG recording system. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. For EEG data analysis we used a time-frequency approach to measure time by varying the energy in a signal at a given frequency band by the convolution of the EEG signal with a wavelet family in the alpha and beta bands. The correlation between the BOLD signal associated with the EEG regressor provides that sensory motor region is a source of the EEG. We conclude that combined EEG-fMRI can be used to investigate movement-related oscillations of the human brain inside an MRI scanner and wavelet analysis adds further details on the EEG changes. The movement-related changes in the EEG signals are useful to identify the brain activation sources responsible for BOLD-signal changes.

Passive electrical stimulation activates various human somatosensory cortical systems including the contralateral primary somatosensory area (SI), bilateral secondary somatosensory area (SII) and bilateral insula. The effect of stimulation frequency on blood oxygenation level-dependent (BOLD) activity remains unclear. We acquired 3-T functional magnetic resonance imaging (fMRI) in eight healthy volunteers during electrical median nerve stimulation at frequencies of 1, 3 and 10 Hz. During stimulation BOLD signal changes showed activation in the contralateral SI, bilateral SII and bilateral insula. Results of fMRI analysis showed that these areas were progressively active with the increase of rate of stimulation. As a major finding, the contralateral SI showed an increase of peak of BOLD activation from 1 to 3 Hz but reached a plateau during 10-Hz stimulation. Our finding is of interest for basic research and for clinical applications in subjects unable to perform cognitive tasks in the fMRI scanner.

Purpose: We present two patients who developed neurogenic stuttering after long COVID-19 related to SARS-CoV-2 infection. Methods and Results: Both patients experienced both physical (e.g., fatigue) and cognitive difficulties, which led to impaired function of attention, lexical retrieval, and memory consolidation. Both patients had new-onset stuttering-like speech dysfluencies: Blocks and repetitions were especially evident at the initial part of words and sentences, sometimes accompanied by effortful and associated movements (e.g., facial grimaces and oro-facial movements). Neuropsychological evaluations confirmed the presence of difficulties in cognitive tasks, while neurophysiological evaluations (i.e., electroencephalography) suggested the presence of "slowed" patterns of brain activity. Neurogenic stuttering and cognitive difficulties were evident for 4-5 months after negativization of SARS-CoV-2 nasopharyngeal swab, with gradual improvement and near-to-complete recovery. Conclusions: It is now evident that SARS-CoV-2 infection may significantly involve the central nervous system, also resulting in severe and long-term consequences, even if the precise mechanisms are still unknown. In the present report, long COVID-19 resulted in neurogenic stuttering, as the likely consequence of a "slowed" metabolism of (pre)frontal and sensorimotor brain regions (as suggested by the present and previous clinical evidence). As a consequence, the pathophysiological mechanisms related to the appearance of neurogenic stuttering have been hypothesized, which help to better understand the broader and possible neurological consequences of COVID-19.