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International Journal of Developmental... Jul 2024Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a diverse profile of cognitive functions. Heterogeneity is observed among both baseline and comorbid... (Review)
Review
OBJECTIVES
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a diverse profile of cognitive functions. Heterogeneity is observed among both baseline and comorbid features concerning the diversity of neuropathology in autism. Symptoms vary depending on the developmental stage, level of severity, or comorbidity with other medical or psychiatric diagnoses such as intellectual disability, epilepsy, and anxiety disorders.
METHOD
The neurodiversity movement does not face variations in neurological and cognitive development in ASD as deficits but as normal non-pathological human variations. Thus, ASD is not identified as a neurocognitive pathological disorder that deviates from the typical, but as a neuro-individuality, a normal manifestation of a neurobiological variation within the population.
RESULTS
In this light, neurodiversity is described as equivalent to any other human variation, such as ethnicity, gender, or sexual orientation. This review will provide insights about the neurodiversity approach in children and adults with ASD. Using a neurodiversity approach can be helpful when working with children who have autism spectrum disorder (ASD).
DISCUSSION
This method acknowledges and values the various ways that people with ASD interact with one another and experience the world in order to embrace the neurodiversity approach when working with children with ASD.
PubMed: 38953464
DOI: 10.1002/jdn.10356 -
Minerva Surgery Jul 2024
PubMed: 38953423
DOI: 10.23736/S2724-5691.24.10407-8 -
Epilepsy & Behavior Reports 2024Functional neurological disorder (FND) is a common neurologic disorder associated with many comorbid symptoms including fatigue, pain, headache, and orthostasis. These... (Review)
Review
Functional neurological disorder (FND) is a common neurologic disorder associated with many comorbid symptoms including fatigue, pain, headache, and orthostasis. These concurrent symptoms lead patients to accumulate multiple diagnoses comorbid with FND, including fibromyalgia, chronic fatigue syndrome, postural orthostatic tachycardia syndrome, persistent post-concussive symptoms, and chronic pain. The role of physical activity and exercise has not been evaluated in FND populations, though has been studied in certain comorbid conditions. In this traditional narrative literature review, we highlight some existing literature on physical activity in FND, then look to comorbid disorders to highlight the therapeutic potential of physical activity. We then consider abnormalities in the autonomic nervous system (ANS) as a potential pathophysiological explanation for symptoms in FND and comorbid disorders and postulate how physical activity and exercise may provide benefit via autonomic regulation.
PubMed: 38953100
DOI: 10.1016/j.ebr.2024.100682 -
Epilepsy & Behavior Reports 2024We undertook a survey among epileptologists in China to explore their attitudes toward physical exercise and sports for persons with epilepsy (PWEs). A total of 288...
We undertook a survey among epileptologists in China to explore their attitudes toward physical exercise and sports for persons with epilepsy (PWEs). A total of 288 epileptologists participated. Most recognized the potential benefits of physical exercise and sports for PWEs, including improved cognitive function (74.6 %), alleviation of mental disorders (73.2 %), and enhanced quality of life (83.8 %). Epileptologists overwhelmingly agreed on the importance of discussing and encouraging physical exercise and sports for PWEs (97.4 % and 95.2 %, respectively). Before engagement in physical exercise and sports, most epileptologists considered that the duration of seizure-free status could be shorter if the seizures were typically focal, non-motor, or without impaired awareness (p < 0.05). There was consensus (99.1 %) on the need to grade the risk of related activities. Opinions were divided regarding the use of health certificates for restricting PWEs (favored by 63.2 %). The majority (93.9 %) called for an expert consensus or clinical guidelines in China. In conclusion, epileptologists in China generally demonstrate a positive attitude toward physical exercise and sports for PWEs. Both benefits and risks of these activities have generally been acknowledged. It is recommended to prioritize activities with lower risks and higher benefits. However, the recommendations for PWEs with a lower likelihood of recurrence and less risky seizure types can be more liberal. Urgent development of normative guidance from governmental and professional bodies is warranted.
PubMed: 38953099
DOI: 10.1016/j.ebr.2024.100685 -
Epilepsy & Behavior Reports 2024In this patient, now 42 years old, genetic generalized epilepsy (juvenile myoclonic epilepsy) manifested itself at the age of 13. At the age of 39, she experienced a...
In this patient, now 42 years old, genetic generalized epilepsy (juvenile myoclonic epilepsy) manifested itself at the age of 13. At the age of 39, she experienced a status episode with prolonged ICU treatment. She was left with a left-sided hippocampal sclerosis and probably focal seizures. In addition, since the age of 24, the patient also experiences functional seizures on the background of a borderline personality disorder. While generalized epileptic seizures could be controlled with antiseizure medication (ASM), the patient was multiple times admitted to Emergency Departments for her functional seizures with subsequent intensive care treatments, including intubation. As a complication, the patient developed critical illness polyneuropathy and myopathy, resulting in wheelchair dependence. Additionally, she acquired a complex regional pain syndrome after extravasation of ASM. The report demonstrates the uncommon development of hippocampal sclerosis after a generalized tonic-clonic status epilepticus and the poor treatability of functional seizures as compared to generalized and focal seizures.
PubMed: 38953098
DOI: 10.1016/j.ebr.2024.100684 -
Frontiers in Immunology 2024Antiglycine receptor (anti-GlyR) antibody mediates multiple immune-related diseases. This study aimed to summarize the clinical features to enhance our understanding of... (Review)
Review
BACKGROUND AND OBJECTIVES
Antiglycine receptor (anti-GlyR) antibody mediates multiple immune-related diseases. This study aimed to summarize the clinical features to enhance our understanding of anti-GlyR antibody-related disease.
METHODS
By collecting clinical information from admitted patients positive for glycine receptor (GlyR) antibody, the clinical characteristics of a new patient positive for GlyR antibody were reported in this study. To obtain additional information regarding anti-GlyR antibody-linked illness, clinical data and findings on both newly reported instances in this study and previously published cases were merged and analyzed.
RESULTS
A new case of anti-GlyR antibody-related progressive encephalomyelitis with rigidity and myoclonus (PERM) was identified in this study. A 20-year-old man with only positive cerebrospinal fluid anti-GlyR antibody had a good prognosis with first-line immunotherapy. The literature review indicated that the common clinical manifestations of anti-GlyR antibody-related disease included PERM or stiff-person syndrome (SPS) (n = 179, 50.1%), epileptic seizure (n = 94, 26.3%), and other neurological disorders (n = 84, 24.5%). Other neurological issues included demyelination, inflammation, cerebellar ataxia and movement disorders, encephalitis, acute psychosis, cognitive impairment or dementia, celiac disease, Parkinson's disease, neuropathic pain and allodynia, steroid-responsive deafness, hemiballism/tics, laryngeal dystonia, and generalized weakness included respiratory muscles. The group of PERM/SPS exhibited a better response to immunotherapy than others.
CONCLUSIONS
The findings suggest the presence of multiple clinical phenotypes in anti-GlyR antibody-related disease. Common clinical phenotypes include PERM, SPS, epileptic seizure, and paraneoplastic disease. Patients with RERM/SPS respond well to immunotherapy.
Topics: Humans; Male; Receptors, Glycine; Autoantibodies; Young Adult; Encephalomyelitis; Muscle Rigidity; Myoclonus; Stiff-Person Syndrome; Adult
PubMed: 38953026
DOI: 10.3389/fimmu.2024.1387591 -
Network Neuroscience (Cambridge, Mass.) 2024Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have...
Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.
PubMed: 38952816
DOI: 10.1162/netn_a_00362 -
Network Neuroscience (Cambridge, Mass.) 2024Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In...
Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome on a patient-by-patient basis, we developed a framework of individualized computational models that combines epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). ESSES parameters were fitted in a retrospective study ( = 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES reproduced the iEEG-recorded seizures, and significantly better so for patients with good (seizure-free, SF) than bad (nonseizure-free, NSF) outcome. We illustrate here the clinical applicability of ESSES with a ( = 34) with a blind setting (to the resection strategy and surgical outcome) that emulated presurgical conditions. By setting the model parameters in the retrospective study, ESSES could be applied also to patients without iEEG data. ESSES could predict the chances of good outcome after resection by finding patient-specific model-based optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan overlapped more with the model-based optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated with a fully independent cohort and without the need for iEEG recordings.
PubMed: 38952815
DOI: 10.1162/netn_a_00361 -
Network Neuroscience (Cambridge, Mass.) 2024This study delves into functional brain-heart interplay (BHI) dynamics during interictal periods before and after seizure events in focal epilepsy. Our analysis focuses...
This study delves into functional brain-heart interplay (BHI) dynamics during interictal periods before and after seizure events in focal epilepsy. Our analysis focuses on elucidating the causal interaction between cortical and autonomic nervous system (ANS) oscillations, employing electroencephalography and heart rate variability series. The dataset for this investigation comprises 47 seizure events from 14 independent subjects, obtained from the publicly available Siena Dataset. Our findings reveal an impaired brain-heart axis especially in the heart-to-brain functional direction. This is particularly evident in bottom-up oscillations originating from sympathovagal activity during the transition between preictal and postictal periods. These results indicate a pivotal role of the ANS in epilepsy dynamics. Notably, the brain-to-heart information flow targeting cardiac oscillations in the low-frequency band does not display significant changes. However, there are noteworthy changes in cortical oscillations, primarily originating in central regions, influencing heartbeat oscillations in the high-frequency band. Our study conceptualizes seizures as a state of hyperexcitability and a network disease affecting both cortical and peripheral neural dynamics. Our results pave the way for a deeper understanding of BHI in epilepsy, which holds promise for the development of advanced diagnostic and therapeutic approaches also based on bodily neural activity for individuals living with epilepsy.
PubMed: 38952812
DOI: 10.1162/netn_a_00367 -
Frontiers in Computational Neuroscience 2024Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the...
Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88-100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches.
PubMed: 38952709
DOI: 10.3389/fncom.2024.1415967