Deep Brain Stimulation and Hypoxemic Perinatal Encephalopathy: State of Art and Perspectives.

Life (Basel)

Unité "Pathologies Cérébrales Résistantes", Department of Neurosurgery, Montpellier University Hospital, 34295 Montpellier, France.

Published: May 2021

Cerebral palsy (CP) is a heterogeneous group of non-progressive syndromes with lots of clinical variations due to the extent of brain damages and etiologies. CP is majorly defined by dystonia and spasticity. The treatment of acquired dystonia in CP is very difficult. Many pharmacological treatments have been tried and surgical treatment consists of deep brain stimulation (continuous electrical neuromodulation) of internal globus pallidus (GPi). A peculiar cause of CP is neonatal encephalopathy due to an anoxic event in the perinatal period. Many studies showed an improvement of dystonia in CP patients with bilateral GPi DBS. However, it remains a variability in the range of 1% to 50%. Published case-series concerned mainly small population with a majority of adult patients. Selection of patients according to the clinical pattern, to the brain lesions observed on classical imaging and to DTI is the key of a high success rate of DBS in children with perinatal hypoxemic encephalopathy. Only a large retrospective study with a high number of patients in a homogeneous pediatric population with a long-term follow-up or a prospective multicenter trial investigation could answer with a high degree of certitude of the real interest of this therapeutic in children with hypoxemic perinatal encephalopathy.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8227328PMC
http://dx.doi.org/10.3390/life11060481DOI Listing

Publication Analysis

Top Keywords

deep brain
8
brain stimulation
8
hypoxemic perinatal
8
perinatal encephalopathy
8
stimulation hypoxemic
4
perinatal
4
encephalopathy
4
encephalopathy state
4
state art
4
art perspectives
4

Similar Publications

Systematic Review of EEG-Based Imagined Speech Classification Methods.

Sensors (Basel)

December 2024

Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain-computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance.

View Article and Find Full Text PDF

In recent years, the application of AI has expanded rapidly across various fields. However, it has faced challenges in establishing a foothold in medicine, particularly in invasive medical procedures. Medical algorithms and devices must meet strict regulatory standards before they can be approved for use on humans.

View Article and Find Full Text PDF

One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs.

View Article and Find Full Text PDF

Adaptive Memory-Augmented Unfolding Network for Compressed Sensing.

Sensors (Basel)

December 2024

School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Deep unfolding networks (DUNs) have attracted growing attention in compressed sensing (CS) due to their good interpretability and high performance. However, many DUNs often improve the reconstruction effect at the price of a large number of parameters and have the problem of feature information loss during iteration. This paper proposes a novel adaptive memory-augmented unfolding network for compressed sensing (AMAUN-CS).

View Article and Find Full Text PDF

Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep-wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!