Acta neurologica Belgica最新文献

Background: The hypothalamus is involved in cluster headache (CH) pathophysiology and is a hub for autonomic control. While cranial autonomic symptoms are prominent during attacks, other autonomic manifestations may be present in CH. This study aims to explore the autonomic nervous system (ANS) in patients with CH during remission period.

Methods: Cross-sectional study including 30 CH and 30 age- and sex-matched controls. We analysed time- and frequency-domain parameters of heart rate variability (HRV) and active orthostatic tests. To investigate the sympathetic nervous system, plasma norepinephrine (NE) levels were determined. All assessments were performed during remission period.

Results: All HRV parameters were lower in CH; the percentage of adjacent R-R intervals that differ by more than 50 milliseconds (pNN50) and standard deviation of normal-to-normal R-R intervals in 24 h (SDNN) were significantly lower in CH (pNN50, 31.0 [5.3-44.3] vs. 44.5 [25.8-58.5], p = 0.043; SDNN, 79.6 ± 42.6 vs. 99.6 ± 42.6, p = 0.004). All other time-domain parameters, including the root mean square of successive R-R differences (RMSSD) were lower in CH than in controls (RMSSD 59.5 ± 36.9 vs. 77.3 ± 39.4, p = 0.077). Compared to controls, mean HR was significantly higher in CH (64.2 [59.6-75.8] vs. 60.4 [57.3-62.7], p = 0.038). Supine and upright NE levels were significantly higher in CH, (supine 228.9 [161.6-324.1] vs. 209.9 [151.2-314.1], p = 0.015; standing 376.1 [264.6-527.8 vs. 327.4 [256.4-400.9], p = 0.019).

Conclusions: The present study indicates a significant decrease in HRV and an upward trend of plasmatic NE levels in CH during remission periods, suggesting an imbalance of the ANS in this state.

A 25-year-old patient, born in Eritrea who had lived for 7 years in Nigeria before he arrived in France 1 year ago, was referred for subacute and progressive tetraparesis evolving over 2 months. Examination revealed tetraparesis, impairment of sensory function below the C6 level and tetrapyramidal syndrome. Spine MRI showed increased T2 intensity at the C6 to T11 spinal cord levels consistent with a longitudinally extensive transverse myelitis. After gadolinium administration, a diffuse, nodular, and heterogeneous pattern of contrast enhancement was seen. Cerebrospinal fluid disclosed pleocytosis with 14% eosinophils, associated with an elevated protein level. Exhaustive analysis ruled out bacterial, viral, mycobacterial, or fungal infection. The diagnosis of spinal schistosomiasis was confirmed by detection of S. mansoni DNA in blood and cerebrospinal fluid by polymerase chain reaction (PCR). Treatment with praziquantel was initiated. The clinical course was favourable and follow-up spine MRI at 6 months disclosed a dramatic reduction in the T2 signal abnormality without contrast enhancement. Our case is notable because of the cervicothoracic involvement and because a post-therapeutic MRI was available, confirming the patient's favourable evolution.

Parkinson's Disease (PD) is a rapidly progressing neurodegenerative disorder that often presents neuropsychiatric symptoms, affecting millions globally, particularly within aging populations. Addressing the urgent need for early and accurate diagnosis, this study introduces ParkEnNET, a Majority Voting-Based Ensemble Transfer Learning Framework for early PD detection. Traditional deep learning models, although powerful, require large labeled datasets, extensive computational resources, and are prone to overfitting when applied to small, noisy medical datasets. To overcome these limitations, ParkEnNET leverages transfer learning, utilizing pretrained deep learning models to efficiently extract relevant features from limited MRI data. By integrating the strengths of multiple models through a majority voting ensemble strategy, ParkEnNET effectively handles challenges such as data variability, class imbalance, and imaging noise. The framework was validated both through internal testing and on an independent clinical dataset collected from Superspeciality Hospital Jammu, ensuring real-world generalizability. Experimental results demonstrated that ParkEnNET achieved a diagnostic accuracy of 98.23%, with a precision of 100.0%, recall of 95.24%, and an F1-score of 97.44%, outperforming all individual models including VGGNet, ResNet-50, and EfficientNet. These outcomes establish ParkEnNET as a promising diagnostic framework with strong performance on limited datasets, offering significant potential to enhance early clinical detection and timely intervention for Parkinson's Disease.