Neurophysiologie Clinique/Clinical Neurophysiology最新文献

Objectives

Aberrant movement-related cortical activity has been linked to impaired motor function in Parkinson's disease (PD). Dopaminergic drug treatment can restore these, but dosages and long-term treatment are limited by adverse side-effects. Effective non-pharmacological treatments could help reduce reliance on drugs. This experiment reports the first study of home-based electroencephalographic (EEG) neurofeedback training as a non-pharmacological candidate treatment for PD. Our primary aim was to test the feasibility of our EEG neurofeedback intervention in a home setting.

Methods

Sixteen people with PD received six home visits comprising symptomology self-reports, a standardised motor assessment, and a precision handgrip force production task while EEG was recorded (visits 1, 2 and 6); and 3 × 1-hr EEG neurofeedback training sessions to supress the EEG mu rhythm before initiating handgrip movements (visits 3 to 5).

Results

Participants successfully learned to self-regulate mu activity, and this appeared to expedite the initiation of precision movements (i.e., time to reach target handgrip force off-medication pre-intervention = 628 ms, off-medication post-intervention = 564 ms). There was no evidence of wider symptomology reduction (e.g., Movement Disorder Society Unified Parkinson's Disease Rating Scale Part III Motor Examination, off-medication pre-intervention = 29.00, off-medication post intervention = 30.07). Interviews indicated that the intervention was well-received.

Conclusion

Based on the significant effect of neurofeedback on movement-related cortical activity, positive qualitative reports from participants, and a suggestive benefit to movement initiation, we conclude that home-based neurofeedback for people with PD is a feasible and promising non-pharmacological treatment that warrants further research.

Objective

MScanFit motor unit number estimation (MUNE) is a sensitive method for detecting motor unit loss and has demonstrated high reproducibility in various settings. In this study, our aim was to assess the outputs of this method when the nerve conduction distance is increased.

Methods

MScanFit recordings were obtained from the abductor digiti minimi muscle of 20 healthy volunteers. To evaluate the effect of nerve conduction distance, the ulnar nerve was stimulated from the wrist and elbow respectively. Reproducibility of MUNE, compound muscle action potential (CMAP), and other motor unit parameters were assessed using intraclass correlation coefficients (ICCs).

Results

Motor unit numbers obtained from stimulation at the wrist and elbow did not significantly differ and exhibited strong consistency in the ICC test (120.3 ± 23.7 vs. 118.5 ± 27.9, p > 0.05, ICC: 0.88). Similar repeatability values were noted for other parameters. However, the Largest Unit (%) displayed notable variability between the two regions and exhibited a negative correlation with nerve conduction distance.

Conclusion

Our findings indicate that MScanFit can consistently calculate motor unit numbers and most of its outputs without substantial influence from nerve conduction distance. Exploring MScanFit's capabilities in various settings could enhance our understanding of its strengths and limitations for extensive use in clinical practice.

This study aimed to compare the diagnostic performance of visual assessment of electroencephalography (EEG) using the Grand Total EEG (GTE) score and quantitative EEG (QEEG) using spectral analysis in the context of cognitive impairment.

This was a retrospective study of patients with mild cognitive impairment, with (MCI+V) or without (MCI) vascular dysfunction, and patients with dementia including Alzheimer's disease, Lewy Body Dementia and vascular dementia.

The results showed that the GTE is a simple scoring system with some potential applications, but limited ability to distinguish between dementia subtypes, while spectral analysis appeared to be a powerful tool, but its clinical development requires the use of artificial intelligence tools.

Objective

This study aimed to explore the relationships between potential neurophysiological biomarkers and upper limb motor function recovery in stroke patients, specifically focusing on combining two neurophysiological markers: electroencephalography (EEG) and transcranial magnetic stimulation (TMS).

Methods

This cross-sectional study analyzed neurophysiological, clinical, and demographical data from 102 stroke patients from the DEFINE cohort. We searched for correlations of EEG and TMS measurements combined to build a prediction model for upper limb motor functionality, assessed by five outcomes, across five assessments: Fugl-Meyer Assessment (FMA), Handgrip Strength Test (HST), Finger Tapping Test (FTT), Nine-Hole Peg Test (9HPT), and Pinch Strength Test (PST).

Results

Our multivariate models agreed on a specific neural signature: higher EEG Theta/Alpha ratio in the frontal region of the lesioned hemisphere is associated with poorer motor outcomes, while increased MEP amplitude in the non-lesioned hemisphere correlates with improved motor function. These relationships are held across all five motor assessments, suggesting the potential of these neurophysiological measures as recovery biomarkers.

Conclusion

Our findings indicate a potential neural signature of brain compensation in which lower frequencies of EEG power are increased in the lesioned hemisphere, and lower corticospinal excitability is also increased in the non-lesioned hemisphere. We discuss the meaning of these findings in the context of motor recovery in stroke.

In patients with refractory epilepsy, the clinical interpretation of stereoelectroencephalographic (SEEG) signals is crucial to delineate the epileptogenic network that should be targeted by surgery. We propose a pipeline of patient-specific computational modeling of interictal epileptic activity to improve the definition of regions of interest. Comparison between the computationally defined regions of interest and the resected region confirmed the efficiency of the pipeline. This result suggests that computational modeling can be used to reconstruct signals and aid clinical interpretation.

Objectives

The Romberg test, undoubtedly a classical and well-established method in physical neurological assessment of patients with sensory ataxia, has long been suspected to be prone to several limitations. Here, we quantified upright stance before and after visual deprivation in a selected cohort of patients with pure sensory neuropathy.

Methods

Static balance was assessed in sensory neuropathy patients during quiet stance on a force platform under different visual and proprioceptive feedback conditions. Sural nerve neurography was employed to evaluate the severity of peripheral neuropathy. Conventional and advanced postural sway metrics were investigated to draw a quantitative analogy to the clinical Romberg test.

Results

Posturographic analyses showed that patients displayed Romberg and vestibular Romberg quotient values around 2, indicating an approximately twofold increase in body sway in the absence of vision. However, the diagnostic discrimination ability between patients and controls was only modest. Even less impactful were the diagnostic contributions of frequency domain and non-linear sway analyses. This was primarily attributed to the heightened body sway exhibited by patients with sensory neuropathy under 'eyes open' conditions, diminishing the contrast with the 'eyes closed' condition as assessed in the classical Romberg test.

Conclusion

We conclude that the Romberg test, even in its quantitative form with the aid of an apparatus, had an unsatisfactory classification value in terms of distinguishing patients from healthy controls. Instead, it should be interpreted within the comprehensive context of the broader neurological examination and the electrodiagnosis of peripheral nerve function.

Objective

The objective of this study was to develop artificial intelligence-based deep learning models and assess their potential utility and accuracy in diagnosing and predicting the future occurrence of diabetic distal sensorimotor polyneuropathy (DSPN) among individuals with type 2 diabetes mellitus (T2DM) and prediabetes.

Methods

In 394 patients (T2DM=300, Prediabetes=94), we developed a DSPN diagnostic and predictive model using Random Forest (RF)-based variable selection techniques, specifically incorporating the combined capabilities of the Clinical Toronto Neuropathy Score (TCNS) and nerve conduction study (NCS) to identify relevant variables. These important variables were then integrated into a deep learning framework comprising Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. To evaluate temporal predictive efficacy, patients were assessed at enrollment and one-year follow-up.

Results

RF-based variable selection identified key factors for diagnosing DSPN. Numbness scores, sensory test results (vibration), reflexes (knee, ankle), sural nerve attributes (sensory nerve action potential [SNAP] amplitude, nerve conduction velocity [NCV], latency), and peroneal/tibial motor NCV were candidate variables at baseline and over one year. Tibial compound motor action potential amplitudes were used for initial diagnosis, and ulnar SNAP amplitude for subsequent diagnoses. CNNs and LSTMs achieved impressive AUC values of 0.98 for DSPN diagnosis prediction, and 0.93 and 0.89 respectively for predicting the future occurrence of DSPN. RF techniques combined with two deep learning algorithms exhibited outstanding performance in diagnosing and predicting the future occurrence of DSPN. These algorithms have the potential to serve as surrogate measures, aiding clinicians in accurate diagnosis and future prediction of DSPN.

Background and objective

Motor function plays a critical role in everyday activities, from basic self-care tasks to complex activities that require precision and dexterity. This study was conducted to investigate the effects of transcranial direct current stimulation (tDCS) on grip strength and hand dexterity in healthy individuals.

Method

We conducted a double-blind randomized clinical trial with two groups of sham and active tDCS. The anode was fixed over the primary motor cortex area M1 on the C3 point. The primary outcome was hand grip strength measured by a dynamometer and the secondary outcomes were hand dexterity and assembly assessed by the Purdue Pegboard test. The tDCS program was administered at rest three and two times for the first and second week for a total of five sessions of 20 min each.

Results

There was no significant improvement in the mean difference in grip strength between the sham (N = 27) and active (N = 27) tDCS groups (1.7 vs. 2.3, Mann-Whitney U test, P = 0.869, d = 0.02). Participants who received active tDCS showed subtle improvements in right-hand dexterity (0.6 vs. 1.3, U test P = 0.017, d = 0.33) and overall manual dexterity (1.4 vs. 3.2, U test P = 0.023, d = 0.31) compared with the sham group. Other comparisons for hand dexterity and assembly (motor coordination and fine skills during the manipulation of small objects) between the two groups were not significant. We did not find any adverse effects of sham or active tDCS.

Conclusion

Our study showed a potential for clinical improvement in hand dexterity after five sessions of tDCS in healthy individuals.

Objectives

To assess the test-retest reliability of the corticokinematic coherence (CKC), an electrophysiological marker of proprioception, in children with cerebral palsy (CP).

Methods

Electroencephalography (EEG) signals from 15 children with unilateral or bilateral CP aged 23 to 53 months were recorded in two sessions 3 months apart using 128-channel EEG caps. During each session, children's fingers were moved at 2 Hz by an experimenter, in separate recordings for the more-affected (MA) and less-affected (LA) hands. The CKC was computed at the electrode and source levels, at movement frequency F0 (2 Hz) and its first harmonic F1 (4 Hz). A two-way mixed-effects model intraclass-correlation coefficient (ICC) was computed for the maximum CKC strength across electrodes at F0 and F1 obtained during the two sessions.

Results

ICC of the CKC strength acquired from LA and MA hands pooled together were respectively 0.51 (95% CI: 0.30–0.68) at F0 and 0.96 (95% CI: 0.93–0.98) at F1. The mean distances separating the CKC peaks in the source space at the two evaluation times were in the order of a centimeter.

Conclusion

CKC is a robust electrophysiologic marker to study the longitudinal changes in cortical processing of proprioceptive afferences in young children with CP.