Neurophysiologie Clinique/Clinical Neurophysiology最新文献

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.

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

Altered somatosensory processing in the posterior insula may play a role in chronic pain development and contribute to Parkinson disease (PD)-related pain. Posterior-superior insula (PSI) repetitive transcranial magnetic stimulation (rTMS) has been demonstrated to have analgesic effects among patients with some chronic pain conditions. This study aimed at assessing the efficacy of PSI-rTMS for treating PD-related pain.

Methods

This was a double-blinded, randomized, sham-controlled, parallel-arm trial (NCT03504748). People with PD (PwP)-related chronic pain underwent five daily PSI-rTMS sessions for a week, followed by once weekly maintenance stimulations for seven weeks. rTMS was delivered at 10 Hz and 80% of the resting motor threshold. The primary outcome was a ≥ 30% pain intensity reduction at 8 weeks compared to baseline. Functionality, mood, cognitive, motor status, and somatosensory thresholds were also assessed.

Results

Twenty-five patients were enrolled. Mean age was 55.2 ± 9.5 years-old, and 56% were female. Nociceptive pain accounted for 60%, and neuropathic and nociplastic for 20% each. No significant difference was found for 30% pain reduction response rates between active (42.7%) and sham groups (14.6%, p = 0.26). Secondary clinical outcomes and sensory thresholds also did not differ significantly. In a post hoc analysis, PwP with nociceptive pain sub-type experienced more pain relief after active (85.7%) compared to sham PSI-rTMS (25%, p = 0.032).

Conclusion

Our preliminary results suggest that different types of PD-related pain may respond differently to treatment, and therefore people with PD may benefit from having PD-related pain well characterized in research trials and in clinical practice.

Vagus nerve stimulation (VNS) is an effective neuromodulatory treatment for patients with drug resistant epilepsy who cannot undergo curative surgical resection. Safety information states that the use of radiofrequency ablation devices may damage the VNS generator and leads. However, documented cases are scarce. This 62-year-old patient with bitemporal lobe epilepsy treated with VNS underwent radiofrequency ablation of an atrial fibrillation without any perioperative or postoperative complications.

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.

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.

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.