Frontiers in Neurology最新文献

Background: Decision-making in epilepsy surgery is strongly connected to the interpretation of the intracranial EEG (iEEG). Although deep learning approaches have demonstrated efficiency in processing extracranial EEG, few studies have addressed iEEG seizure detection, in part due to the small number of seizures per patient typically available from intracranial investigations. This study aims to evaluate the efficiency of deep learning methodology in detecting iEEG seizures using a large dataset of ictal patterns collected from epilepsy patients implanted with a responsive neurostimulation system (RNS). Methods: Five thousand two hundred and twenty-six ictal events were collected from 22 patients implanted with RNS. A convolutional neural network (CNN) architecture was created to provide personalized seizure annotations for each patient. Accuracy of seizure identification was tested in two scenarios: patients with seizures occurring following a period of chronic recording (scenario 1) and patients with seizures occurring immediately following implantation (scenario 2). The accuracy of the CNN in identifying RNS-recorded iEEG ictal patterns was evaluated against human neurophysiology expertise. Statistical performance was assessed via the area-under-precision-recall curve (AUPRC). Results: In scenario 1, the CNN achieved a maximum mean binary classification AUPRC of 0.84 ± 0.19 (95%CI, 0.72-0.93) and mean regression accuracy of 6.3 ± 1.0 s (95%CI, 4.3-8.5 s) at 30 seed samples. In scenario 2, maximum mean AUPRC was 0.80 ± 0.19 (95%CI, 0.68-0.91) and mean regression accuracy was 6.3 ± 0.9 s (95%CI, 4.8-8.3 s) at 20 seed samples. We obtained near-maximum accuracies at seed size of 10 in both scenarios. CNN classification failures can be explained by ictal electro-decrements, brief seizures, single-channel ictal patterns, highly concentrated interictal activity, changes in the sleep-wake cycle, and progressive modulation of electrographic ictal features. Conclusions: We developed a deep learning neural network that performs personalized detection of RNS-derived ictal patterns with expert-level accuracy. These results suggest the potential for automated techniques to significantly improve the management of closed-loop brain stimulation, including during the initial period of recording when the device is otherwise naïve to a given patient's seizures.

Background: Cervical dystonia is the most common form of focal dystonia. The frequency and pattern of degenerative changes of the cervical spine in patients with cervical dystonia and their relation to clinical symptoms remain unclear as no direct comparison to healthy controls has been performed yet. Here, we used magnetic resonance imaging (MRI) to investigate (1) whether structural abnormalities of the cervical spine are more common in patients with cervical dystonia compared to age-matched healthy controls, (2) if there are clinical predictors for abnormalities on MRI, and (3) to calculate the inter-rater reliability of the respective radiological scales. Methods: Twenty-five consecutive patients with cervical dystonia and 20 age-matched healthy controls were included in the study. MRI scans of the cervical spine were analyzed separately by three experienced raters blinded to clinical information, applying different MRI rating scales. Structural abnormalities were compared between groups for upper, middle, and lower cervical spine segments. The associations between scores differentiating both groups and clinical parameters were assessed in dystonia patients. Additionally, inter-rater reliability of the MRI scales was calculated. Results: Comparing structural abnormalities, we found minor differences in the middle cervical spine, indicated by a higher MRI total score in patients but no significant correlation between clinical parameters and MRI changes. Inter-rater reliability was satisfying for most of the MRI rating scales. Conclusion: Our results do not provide evidence for a role of MRI of the cervical spine in the routine work-up of patients with cervical dystonia in the absence of specific clinical signs or symptoms.

A decrease in glutamate in the medial prefrontal cortex (mPFC) has been extensively found in animal models of chronic pain. Given that the mPFC is implicated in emotional appraisal, cognition and extinction of fear, could a potential decrease in glutamate be associated with increased pessimistic thinking, fear and worry symptoms commonly found in people with chronic pain? To clarify this question, 19 chronic pain subjects and 19 age- and gender-matched control subjects without pain underwent magnetic resonance spectroscopy. Both groups also completed the Temperament and Character, the Beck Depression and the State Anxiety Inventories to measure levels of harm avoidance, depression, and anxiety, respectively. People with chronic pain had significantly higher scores in harm avoidance, depression and anxiety compared to control subjects without pain. High levels of harm avoidance are characterized by excessive worry, pessimism, fear, doubt and fatigue. Individuals with chronic pain showed a significant decrease in mPFC glutamate levels compared to control subjects without pain. In people with chronic pain mPFC glutamate levels were significantly negatively correlated with harm avoidance scores. This means that the lower the concentration of glutamate in the mPFC, the greater the total scores of harm avoidance. High scores are associated with fearfulness, pessimism, and fatigue-proneness. We suggest that chronic pain, particularly the stress-induced release of glucocorticoids, induces changes in glutamate transmission in the mPFC, thereby influencing cognitive, and emotional processing. Thus, in people with chronic pain, regulation of fear, worry, negative thinking and fatigue is impaired.

We describe the case of one patient with pure sporadic hemiplegic migraine (SHM) with a novel ATP1A2 gene variant and a large patent foramen ovale (PFO) with atrial septal aneurysm. In hemiplegic migraine (HM), the relationship between incomplete penetrance, environmental triggers, and phenotypic expression is underdetermined. A genetic evaluation of the proband was requested for the HM associated genes and extended to the members of his family. Genetic analysis revealed a never described before ATP1A2 gene mutation, inherited by his father, who never experienced motor aura but only typical visual aura. The proband-but not his father-was also affected by a large PFO with atrial septal aneurysm. SHM patient showed a marked reduction in motor aura episodes per year in the 12 months following the PFO percutaneous closure, followed by a complete remission from attacks at least in the following 24 months. We speculated that as well as incomplete penetrance of the novel mutation and natural history of the disease, an additional pathological condition such as the PFO could contribute to the phenotypical expression in this case of HM.

Guam parkinsonism-dementia complex (G-PDC) is an enigmatic neurodegenerative disease that is endemic to the Pacific island of Guam. G-PDC patients are clinically characterized by progressive cognitive impairment and parkinsonism. Neuropathologically, G-PDC is characterized by abundant neurofibrillary tangles, which are composed of hyperphosphorylated tau, marked deposition of 43-kDa TAR DNA-binding protein, and neuronal loss. Although both genetic and environmental factors have been implicated, the etiology and pathogenesis of G-PDC remain unknown. Recent neuropathological studies have provided new clues about the pathomechanisms involved in G-PDC. For example, deposition of abnormal components of the protein quality control system in brains of G-PDC patients indicates a role for proteostasis imbalance in the disease. This opens up promising avenues for new research on G-PDC and could have important implications for the study of other neurodegenerative disorders.