Identification Of Neurological Disorders Research Articles

Retraction Note: Refining Parkinson’s neurological disorder identification through deep transfer learning

Retraction Note: Refining Parkinson’s neurological disorder identification through deep transfer learning

Brain Organoids: A Game-Changer for Drug Testing.

Neurological disorders are the second cause of death and the leading cause of disability worldwide. Unfortunately, no cure exists for these disorders, but the actual therapies are only able to ameliorate people's quality of life. Thus, there is an urgent need to test potential therapeutic approaches. Brain organoids are a possible valuable tool in the study of the brain, due to their ability to reproduce different brain regions and maturation stages; they can be used also as a tool for disease modelling and target identification of neurological disorders. Recently, brain organoids have been used in drug-screening processes, even if there are several limitations to overcome. This review focuses on the description of brain organoid development and drug-screening processes, discussing the advantages, challenges, and limitations of the use of organoids in modeling neurological diseases. We also highlighted the potential of testing novel therapeutic approaches. Finally, we examine the challenges and future directions to improve the drug-screening process.

Preserving specificity in federated graph learning for fMRI-based neurological disorder identification

Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive approach to examining abnormal brain connectivity associated with brain disorders. Graph neural network (GNN) gains popularity in fMRI representation learning and brain disorder analysis with powerful graph representation capabilities. Training a general GNN often necessitates a large-scale dataset from multiple imaging centers/sites, but centralizing multi-site data generally faces inherent challenges related to data privacy, security, and storage burden. Federated Learning (FL) enables collaborative model training without centralized multi-site fMRI data. Unfortunately, previous FL approaches for fMRI analysis often ignore site-specificity, including demographic factors such as age, gender, and education level. To this end, we propose a specificity-aware federated graph learning (SFGL) framework for rs-fMRI analysis and automated brain disorder identification, with a server and multiple clients/sites for federated model aggregation and prediction. At each client, our model consists of a shared and a personalized branch, where parameters of the shared branch are sent to the server while those of the personalized branch remain local. This can facilitate knowledge sharing among sites and also helps preserve site specificity. In the shared branch, we employ a spatio-temporal attention graph isomorphism network to learn dynamic fMRI representations. In the personalized branch, we integrate vectorized demographic information (i.e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics. Representations generated by the two branches are then fused for classification. Experimental results on two fMRI datasets with a total of 1218 subjects suggest that SFGL outperforms several state-of-the-art approaches.

Neurological Disorders Detection Based on Computer Brain Interface Using Centralized Blockchain with Intrusion System

A brain-computer interface (BCI) would afford real-time communication, pointedly refining the standard of lifespan, brain-to-internet (B2I) connection, and interaction between the external digital devices and the brain. This assistive technology invents information and transmission advancement patterns, like directly linking the brain and multimedia gadgets to the cyber world. This system will convert brain data to signals which is understandable by multimedia gadgets without physical intervention and exchanges human-related languages with external atmosphere control protocols. These progressive difficulties would limit security severely. Hence, the rate of ransomware, attacks, malware, and other types of vulnerabilities will be rising radically. On the other hand, the necessity to enhance conventional processes for investigating cyberenvironment security facets. This article presents a Neurological Disorders Detection based on Computer Brain Interface Using Centralized Blockchain with Intrusion System (NDDCBI-CBIS). The projected NDDCBI-CBIS technique focuses on the identification of neurological disorders and epileptic seizure detection. To attain this, the presented NDDCBI-CBIS technique pre-processes the biomedical signals. Next, to detect epileptic seizures, long short-term memory (LSTM) model is applied. The experimental evaluation of the NDDCBI-CBIS approach can be tested by making use of the medical dataset and the outcomes inferred from the enhanced outcomes of the NDDCBI-CBIS technique.

Detection of brain tumor using CNN and ML

An automated neurological disorder identification system that uses computer vision on magnetic resonance imaging to locate brain tumors (MRI). The most common and dangerous form of brain cancer is gliomas. Gliomas are tumors that, at their most advanced stage, result in a much shorter life span. Preparing for therapy is an important step in maintaining a better quality of life for oncology patients. Magnetic resonance imaging (MRI) is a technique for examining the structures and components of the human body as well as for medical diagnosis, determining the stage of disease, and monitoring without the use of ionizing radiation. The significant spatial and structural changeability of brain tumors complicates segmentation. As a result, an automatic and consistent segmentation technique is used, based on Convolutional Neural Networks (CNN). Because of the relatively low number of network weights, the use of small kernels enables the development of a deeper architecture, with such a positive effect on over-fitting. It also explores the use of intensity normalization as a pre-processing phase, which is not widely used in segmentation techniques based on the Convolution Neural Network, but has been shown to be efficient in segmenting brain tumors using Magnetic Resonance Imaging (MRI) in conjunction with data augmentation.

Evaluation of a screening tool for the identification of neurological disorders in rural Uganda

BackgroundNeurological disorders are common in sub-Saharan African, but accurate neuroepidemiologic data are lacking from the region. We assessed a neuroepidemiological screening tool in a rural Ugandan cohort with high HIV prevalence. MethodsParticipants were recruited from the Rakai Neurology Study in rural Rakai District, Uganda. A nurse administered the tool and a sociodemographic survey. 100 participants returned for validation examinations by a neurologist (validation cohort). The diagnostic utility and validity of the instrument were calculated and characteristics of those with and without neurological disorders compared. ResultsThe tool was administered to 392 participants, 48% female, 33% people with HIV, average age 35.1 ± 8.5 years. 33% of the study cohort screened positive for neurologic disorders. These participants were older [mean (SD): 38.3 (9.7) vs. 33.5 (7.1) years, p < 0.001], had a lower Karnofsky score [89.8 (8.4) vs. 93.9 (7.5), p < 0.001] and had a lower body mass index [21.8 (3.3) vs. 22.8 (3.7), p = 0.007] than those who screened negative. Amongst the validation cohort, 54% had a neurological abnormality of which 46% were symptomatic. The tool was 57% sensitive and 74% specific for detecting any neurological abnormality and 80% sensitive and 69% specific for symptomatic abnormalities. ConclusionsWe found a lower sensitivity and similar specificity for the screening tool compared with two previous studies. The lower validity in this study was likely due in part to the high percentage of asymptomatic neurological abnormalities detected. This screening tool will require further refinement and cultural contextualization before it can be widely implemented across new populations.

A Low-cost Webcam-based Eye Tracker and Saccade Measurement System

Eye movements are integrated with cognitive processes, which indeed make it a helpful research basis for the investigation of human practices. Eye movements can be deployed in discovering several cognitive processes of the brain. This research utilizes low-resolution webcam to develop an eye tracker and saccades measurement tool to extensively lower the gadgets expenses. A consistent algorithm is developed to suit the quality of the webcam using open-source software (Python) to record the time series of the eye location. Likewise, several algorithms are proposed to extract high-level eye movement saccadic measurements from the raw gaze outputs. A pilot study is performed on ten normal participants and Multiple Sclerosis (MS) patients. Experimental results demonstrate that the proposed system is quick, simple and efficient for eye tracking and saccade measurement. The developed tool can be used by clinicians and medical physicians for the diagnosis and identification of neurological disorders

RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work.

Acquisition of cervical control through high postures in patients with West Syndrome: a case report.

Introduction: early identification of neurological disorders is considered important in the assessment of risk factors and early treatment and subsequently in monitoring the child. Objective: report the lived experiences in a physiotherapy program based on high neuro‑evolutive postures in the development of the cervical and trunk control in a child diagnosed with West Syndrome, cerebral palsy and hydrocephalus. Methods: female child, chronological age of four years and five months, motor age less than three months. Kinetic and functional assessment was performed and the Gross Motor Function Measure (GMFM) and the Gross Motor Function Classification System (GMFCS) was used to characterize the subject. Results: there was an increase to 68.6% in the GMFM, maintaining the level of the GMFCS, improvement of cervical and trunk control in sitting and standing position and when were performed postural changes. Conclusion: the physiotherapeutic care proved to be beneficial in the gradual development of cervical and trunk motor control.

Identifying HIV-induced subgraph patterns in brain networks with side information.

Investigating brain connectivity networks for neurological disorder identification has attracted great interest in recent years, most of which focus on the graph representation alone. However, in addition to brain networks derived from the neuroimaging data, hundreds of clinical, immunologic, serologic, and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of subgraph selection from brain networks with side information guidance and propose a novel solution to find an optimal set of subgraph patterns for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph patterns by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view-guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.