Diagnosis Of Neurological Disorders Research Articles

Challenges to the Diagnosis of Functional Neurological Disorder: Feigning, Intentionality, and Responsibility.

The diagnosis of Functional Neurological Disorder (FND) requires differentiation from other neurologic diseases/syndromes, and from the comparatively rare diagnosis of feigning (Malingering and Factitious Disorder). Analyzing the process of diagnosing FND reveals a necessary element of presumption, which I propose underlies some of the uncertainty, discomfort, and stigma associated with this diagnosis. A conflict between the neurologist’s natural social cognition and professional judgement (cognitive dissonance) can be understood by applying a framework originally designed for the determination of moral responsibility. Understanding the source of this cognitive dissonance may effect its alleviation, and in turn, allow more compassionate treatment of patients with FND.

Why Mothers Avoid Breastfeeding?

Neurological diseases are widespread and serious global illnesses. Neurons highly rely on aerobic metabolism in mitochondria. Riboflavin receptor 3 (RFVT3) is a vital section of the mitochondrial network, which plays a key role in riboflavin homeostasis and the production of adenosine triphosphate (ATP). The abnormal expression of RFVT3 is closely associated with the occurrence and progression of multiple neurological diseases. Therefore, it is crucial to explore the dynamic expression of RFVT3 for the diagnosis and treatment of neurological diseases.

GNMF-based quadratic feature extraction in SSTFT domain for epileptic EEG detection

The EEG is an indispensable tool for clinical diagnosis of neurological disorders. The issue that how to correctly decoding the pathological information from EEG has been attracting a lot of interest. This paper creates a robust feature extraction scheme based on synchrosqueezing short-time Fourier transform (SSTFT) and graph regularized non-negative matrix factorization (GNMF), which is applied to epileptic EEG detection. The SSTFT is employed to analyze the EEG in time–frequency domain, followed by a GNMF decomposition that maps these components into a low-dimensional representation where latent clues are easily emphasized and learned, and finally the logarithmic energy, computed as features, is fed to a fuzzy K-Nearest Neighbor classifier for the purpose of discriminating seizure events. Our method is evaluated using two public databases including Bonn database and CHB-MIT database. Obtained results confirm the superiority of the proposed features by achieving classification accuracy of at least 98.0% for both short-term and long-term EEG recordings. It is expected to offer a new thought and perspective for the study and design of the automatic EEG monitoring system.

Synthesis, characterization, and application of gold nanostars for the identification of 2-Arachidonoyl glycerol

Background. The endocannabinoid system (ECS) is a complex of neurotransmitters in the central nervous system (CNS) and plays a key role in regulating cognitive and physiological processes. Also, 2-Arachidonoylglycerol (2-AG) is one of the imperative endocannabinoids with key roles in the CNS. It acts as a signaling lipid and activates the cannabinoid CB1 receptor. In addition, 2-AG is involved in a variety of physiological functions such as energy balance, emotion, pain sensation, cognition, and neuroinflammation. Hence, rapid and specific diagnosis of 2-AG is of great importance in medical neuroscience. Routine methods used in detection of 2-AG are not sensitive and specific. Therefore, the development of new methods in this area has been one of the most important research areas in recent years. Methods. Herein, an innovative immunosensor was developed for quantification of 2-AG. To this end, gold nanostars (GNSs) were synthesized and conjugated with a specific biotinylated antibody activated by EDC/NHS against 2-AG. The resultant bio-conjugate was immobilized on the gold electrode surface and used to detect and analyze 2-arachidonylglycerol antigen by cyclic voltammetry (CV), square wave voltammetry (SWV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy techniques. Results. For the first time, 2-AG protein was measured with an excellent linear range of 0.00048-1 ng/L and lower limit of quantification of 0.00048 ng/L by electroanalysis method. The designed immunosensor showed high sensitivity and specificity in the presence of interfering antigens, which proved its appropriate use in the diagnosis of neurological disorders. Conclusion. The remarkable achievement of the present study is the development of a new immunosensor for the sensitive and accurate detection of 2-AG. The fabricated sensor substrate showed good analytical results for the target analyte. Regardless of the appropriate sensitivity and selectivity of the designed biosensor, the stability of the sensor is low and it is recommended to use it immediately after preparation. Also, the immunosensor engineered in this research was very economical and had a very fast and good performance compared to the usual and traditional methods for detecting 2-AG. Practical Implications. For the first time, 2-AG protein was measured with an excellent linear range of 0.00048-1 ng/L and lower limit of quantification of 0.00048 ng/L by electro analysis method. The designed immunosensor showed high sensitivity and specificity in the presence of interfering antigens, which proved its appropriate use in the diagnosis of neurological disorders.

Better neural images by combining ultrahigh field strength MRI with innovative MRI sequences

Better MRI scanning technologies and protocols can provide insights into neurological disorders. In this review, we describe the basic concepts of MRI and, in the process, we convey to the reader the relevance of MRI as a high-resolution imaging modality of tissue structure and metabolism. We outline the main parameters for improving MRI resolution and sensitivity for the ultimate goal of optimizing the diagnosis of neurological diseases. A key to obtaining high-resolution images by MRI is the strength of the magnet’s external field strength (Bo). The higher the field strength, the better the signal-to-noise (SNR) of acquired signals. Hence, this results in improved sensitivity and resolution of the reconstructed images. This article recapitulates the advancement of MRI technology towards Ultra-High-Field Strength (UHF) apparatus and the consequent benefits in SNR. Other keys towards improving MRI images include deftly modifying the parameters of longitudinal magnetization relaxation time (T1), transverse magnetization relaxation time (T2), repetition times between radiofrequency (RF) pulses (TR), and the time of reading post-pulse (TE). Such parameters can be controlled through acquisition software associated with the MRI machines. The review profiles the cumulative efforts by researchers to complement UHF-MRI hardware with innovative RF pulse sequences protocols such as Diffusion Weighted Imaging (DWI), Pulse Gradient Spin Echo (PGSE), Oscillating Gradient Spin Echo (OGSE), Blood Oxygen Level Dependent (BOLD)-MRI and Arterial Spin Label (ASL)-MRI. Collectively, these advances in both MRI hardware and software have pushed the field to attain a mesoscopic level of resolution. Further enhancements in analyzing MRI images through Artificial Intelligence (AI) algorithms may advance resolutions beyond the mesoscopic stage and perhaps even toward the microscopic resolution of living tissue.

Multiplexed Electrochemical Device for the Detection of Biomarkers of Parkinson’s Disease Using 3D Printing

Neurotransmitters are chemical messengers that carry signals between cells. Imbalances in neurotransmitter levels have been linked to physical, psychotic, and neurodegenerative diseases such as Alzheimer's, Parkinson's, dementia, addiction, depression, and schizophrenia. Therefore, the rapid detection of neurotransmitters such as dopamine, acetylcholine, glutamate, serotine, and norepinephrine are essential for early diagnosis of various neurological disorders. This talk will focus on the development of a multiplexed electrochemical device using 3D printing for the simultaneous detection of biomarkers related to Parkinson’s disease. By combining the capabilities of stereolithographic (SLA) and fused-deposition modelling (FDM) 3D printing, we demonstrate the ability to fabricate and integrate potentiometric and voltametric sensors into the device. Importantly, we show that this approach is highly modifiable and scalable, providing a pathway towards the development of a full-panel diagnostic test for neurological disorders.

Assessing the mechanisms of brain plasticity by transcranial magnetic stimulation.

Transcranial magnetic stimulation (TMS) is a non-invasive technique for focal brain stimulation based on electromagnetic induction where a fluctuating magnetic field induces a small intracranial electric current in the brain. For more than 35 years, TMS has shown promise in the diagnosis and treatment of neurological and psychiatric disorders in adults. In this review, we provide a brief introduction to the TMS technique with a focus on repetitive TMS (rTMS) protocols, particularly theta-burst stimulation (TBS), and relevant rTMS-derived metrics of brain plasticity. We then discuss the TMS-EEG technique, the use of neuronavigation in TMS, the neural substrate of TBS measures of plasticity, the inter- and intraindividual variability of those measures, effects of age and genetic factors on TBS aftereffects, and then summarize alterations of TMS-TBS measures of plasticity in major neurological and psychiatric disorders including autism spectrum disorder, schizophrenia, depression, traumatic brain injury, Alzheimer's disease, and diabetes. Finally, we discuss the translational studies of TMS-TBS measures of plasticity and their therapeutic implications.

INTRIGUING LUNG FUNCTION TESTS IN NEUROMUSCULAR WEAKNESS

INTRIGUING LUNG FUNCTION TESTS IN NEUROMUSCULAR WEAKNESS

Effect of vitamin D on serum BDNF level in patients with hypothyroidism caused by autoimmune thyroiditis

Background. Hashimoto’s thyroiditis is a chronic autoimmune disease of the thyroid gland caused by an increase in thyroid antibodies. A growing body of research suggests that patients with Hashimoto’s thyroiditis may suffer from cognitive impairment. Therefore, it is important to identify risk factors that contribute to the development of cognitive impairment in patients with autoimmune thyroiditis to diagnose them early and treat. Given that the influence of thyroid hormones on the brain is undeniable, and their deficiency leads to a variety of neurological complications, early diagnosis and treatment of neurological disorders in thyroid diseases is crucial for preventing further progression of these complications. The purpose of the study is to determine the effect of vitamin D on the level of brain-derived neurotrophic factor (BDNF) in blood serum of patients with hypothyroidism caused by autoimmune thyroiditis. Materials and methods. Twenty-eight patients with hypothyroidism caused by autoimmune thyroiditis were included in the study. The serum level of BDNF was quantified using a solid-phase enzyme-linked immunosorbent assay with a highly sensitive Human BDNF ELISA Kit (Elabscience®, United States, Catalog No E-EL-H0010) on E.I.A. Reader Sirio S (Seac, Italy). Results. In our study, the administration of vitamin D and L-thyroxine led to a 7.4-fold increase in BDNF levels (p<0.001). In the group of patients who took only L-thyroxine, serum content of BDNF increased by 2.1 times. In patients who took cholecalciferol and L-thyroxine, the level of BDNF was 2.48 times higher compared to those who took only L-thyroxine (p<0.001). Conclusions. A positive effect of vitamin D on the serum level of BDNF was found in patients with hypothyroidism caused by autoimmune thyroiditis. Treatment of these individuals using additional administration of cholecalciferol on the background of L-thyroxine proved to be more effective compared to the administration of L-thyroxine alone (p<0.001).

Electrophysiological and functional signs of Guillain–Barré syndrome predicted by a multiscale neuromuscular computational model

Objective. The diagnosis of nerve disorders in humans has relied heavily on the measurement of electrical signals from nerves or muscles in response to electrical stimuli applied at appropriate locations on the body surface. The present study investigated the demyelinating subtype of Guillain–Barré syndrome using multiscale computational model simulations to verify how demyelination of peripheral axons may affect plantar flexion torque as well as the ongoing electromyogram (EMG) during voluntary isometric or isotonic contractions. Approach. Changes in axonal conduction velocities, mimicking those found in patients with the disease at different stages, were imposed on a multiscale computational neuromusculoskeletal model to simulate subjects performing unipodal plantar flexion force and position tasks. Main results. The simulated results indicated changes in the torque signal during the early phase of the disease while performing isotonic tasks, as well as in torque variability after partial conduction block while performing both isometric and isotonic tasks. Our results also indicated changes in the root mean square values and in the power spectrum of the soleus EMG signal as well as changes in the synchronization index computed from the firing times of the active motor units. All these quantitative changes in functional indicators suggest that the adoption of such additional measurements, such as torques and ongoing EMG, could be used with advantage in the diagnosis and be relevant in providing extra information for the neurologist about the level of the disease. Significance. Our findings enrich the knowledge of the possible ways demyelination affects force generation and position control during plantarflexion. Moreover, this work extends computational neuroscience to computational neurology and shows the potential of biologically compatible neuromuscular computational models in providing relevant quantitative signs that may be useful for diagnosis in the clinic, complementing the tools traditionally used in neurological electrodiagnosis.

Automatic detection of abnormal EEG signals using multiscale features with ensemble learning.

Electroencephalogram (EEG) is an economical and convenient auxiliary test to aid in the diagnosis and analysis of brain-related neurological diseases. In recent years, machine learning has shown great potential in clinical EEG abnormality detection. However, existing methods usually fail to consider the issue of feature redundancy when extracting the relevant EEG features. In addition, the importance of utilizing the patient age information in EEG detection is ignored. In this paper, a new framework is proposed for distinguishing an unknown EEG recording as either normal or abnormal by identifying different types of EEG-derived significant features. In the proposed framework, different hierarchical salient features are extracted using a time-wise multi-scale aggregation strategy, based on a selected group of statistical characteristics calculated from the optimum discrete wavelet transform coefficients. We also fuse the age information with multi-scale features for further improving discrimination. The integrated features are classified using three ensemble learning classifiers, CatBoost, LightGBM, and random forest. Experimental results show that our method with CatBoost classifier can yield superior performance vis-a-vis competing techniques, which indicates the great promise of our methodology in EEG pathology detection.

Multilayer Exponential Family Factor models for integrative analysis and learning disease progression.

Current diagnosis of neurological disorders often relies on late-stage clinical symptoms, which poses barriers to developing effective interventions at the premanifest stage. Recent research suggests that biomarkers and subtle changes in clinical markers may occur in a time-ordered fashion and can be used as indicators of early disease. In this article, we tackle the challenges to leverage multidomain markers to learn early disease progression of neurological disorders. We propose to integrate heterogeneous types of measures from multiple domains (e.g., discrete clinical symptoms, ordinal cognitive markers, continuous neuroimaging, and blood biomarkers) using a hierarchical Multilayer Exponential Family Factor (MEFF) model, where the observations follow exponential family distributions with lower-dimensional latent factors. The latent factors are decomposed into shared factors across multiple domains and domain-specific factors, where the shared factors provide robust information to perform extensive phenotyping and partition patients into clinically meaningful and biologically homogeneous subgroups. Domain-specific factors capture remaining unique variations for each domain. The MEFF model also captures nonlinear trajectory of disease progression and orders critical events of neurodegeneration measured by each marker. To overcome computational challenges, we fit our model by approximate inference techniques for large-scale data. We apply the developed method to Parkinson's Progression Markers Initiative data to integrate biological, clinical, and cognitive markers arising from heterogeneous distributions. The model learns lower-dimensional representations of Parkinson's disease (PD) and the temporal ordering of the neurodegeneration of PD.

Brain lipidomics: From functional landscape to clinical significance.

Lipids are crucial components of cellular function owing to their role in membrane formation, intercellular signaling, energy storage, and homeostasis maintenance. In the brain, lipid dysregulations have been associated with the etiology and progression of neurodegeneration and other neurological pathologies. Hence, brain lipids are emerging as important potential targets for the early diagnosis and prognosis of neurological diseases. This review aims to highlight the significance and usefulness of lipidomics in diagnosing and treating brain diseases. We explored lipid alterations associated with brain diseases, paying attention to organ-specific characteristics and the functions of brain lipids. As the recent advances in brain lipidomics would have been impossible without advances in analytical techniques, we provide up-to-date information on mass spectrometric approaches and integrative analysis with other omic approaches. Last, we present the potential applications of lipidomics combined with artificial intelligence techniques and interdisciplinary collaborative research for treating brain diseases with clinical heterogeneities.

Serum GFAP- reference interval and preanalytical properties in Danish adults.

Glial fibrillary acidic protein (GFAP) is a promising biomarker that could potentially contribute to diagnosis and prognosis in neurological diseases. The biomarker is approaching clinical use but the reference interval for serum GFAP remains to be established, and knowledge about the effect of preanalytical factors is also limited. Serum samples from 371 apparently healthy reference subjects, 21-90 years of age, were measured by a single-molecule array (Simoa) assay. Continuous reference intervals were modelled using non-parametric quantile regression and compared with traditional age-partitioned non-parametric reference intervals established according to the Clinical and Laboratory Standards Institute (CLSI) guideline C28-A3. The following preanalytical conditions were also examined: stability in whole blood at room temperature (RT), stability in serum at RT and-20°C, repeated freeze-thaw cycles, and haemolysis. The continuous reference interval showed good overall agreement with the traditional age-partitioned reference intervals of 25-136ng/L, 34-242ng/L, and 5-438ng/L for the age groups 20-39, 40-64, and 65-90 years, respectively. Both types of reference intervals showed increasing levels and variability of serum GFAP with age. In the preanalytical tests, the mean changes from baseline were 2.3% (95% CI:-2.4%, 6.9%) in whole blood after 9h at RT, 3.1% (95% CI:-4.5%, 10.7%) in serum after 7days at RT, 10.4% (95% CI:-6.0%, 26.8%) in serum after 133days at-20°C, and 10.4% (95% CI: 9.5%, 11.4%) after three freeze-thaw cycles. The study establishes age-dependent reference ranges for serum GFAP in adults and demonstrates overall good stability of the biomarker.

Transformed domain convolutional neural network for Alzheimer's disease diagnosis using structural MRI

Structural magnetic resonance imaging (sMRI) has become a prevalent and potent imaging modality for the computer-aided diagnosis (CAD) of neurological diseases like dementia. Recently, a handful of deep learning techniques such as convolutional neural networks (CNNs) have been proposed to diagnose Alzheimer's disease (AD) by learning the atrophy patterns available in sMRIs. Although CNN-based techniques have demonstrated superior performance and characteristics compared to conventional learning-based classifiers, their diagnostic performance still needs to be improved for reliable classification results. The drawback of current CNN-based approaches is the requirement to locate discriminative landmark (LM) locations by identifying regions of interest (ROIs) in sMRIs, thus the performance of the whole framework is highly influenced by the LM detection step. To overcome this issue, we propose a novel three-dimensional Jacobian domain convolutional neural network (JD-CNN) to diagnose AD subjects and achieve excellent classification performance without the involvement of the LM detection framework. We train the proposed JD-CNN model on the basis of features generated by transforming the sMRI from the spatial domain to the Jacobian domain. The proposed JD-CNN is evaluated on baseline T1-weighted sMRI data collected from 154 healthy control (HC) and 84 Alzheimer's disease (AD) subjects in the Alzheimer's disease neuroimaging initiative (ADNI) database. The proposed JD-CNN exhibits superior classification performance to previously reported state-of-the-art techniques.

Evidence for an Intrathecal Immunoglobulin Synthesis by Kappa Free Light Chains in Neurological Patients with an Isolated Band in Isoelectric Focusing.

The gold standard for detecting intrathecal immunoglobulin synthesis is the determination of the oligoclonal band (OCB) in the cerebrospinal fluid (CSF) using isoelectric focusing (IEF). Controversy still exists regarding the significance of an isolated band in the CSF. A highly promising alternative method for the assessment of intrathecal inflammation is the quantification of kappa free light chains (k-FLC). Our aim was to evaluate the clinical significance of quantitative k-FLC in patients with an isolated band in the CSF. Using the Human Kappa Freelite Mx Kit on a turbidimetric Optilite®, we quantified the k-FLCs in paired CSF and serum samples in 47 patients with a single band in IEF. We classified patients into 27× inflammatory neurological disorders (IND), 2× peripheral inflammatory neurological disorders (PIND), 9× non-inflammatory neurological disorders (NIND) and 9× symptomatic controls (SC) based on their medical diagnosis. k-FLC were below the lower measurement limit of the analyser (LML) in all SC and PIND, as well as in 8 out of 9 NIND and 11 IND. Only 1 NIND and 16 IND were above the LML, and of these, only 14 IND were above the upper discrimination limit (Qlim). A neuroinflammatory nature of the diseases can be indicated in many cases by positive k-FLC in patients with an isolated band in IEF. The measurement of k-FLC can support the diagnosis of neurological diseases if they are included in the routine work-up.

Brain disorder prediction with dynamic multivariate spatio-temporal features: Application to Alzheimer's disease and autism spectrum disorder.

The dynamic functional connectivity (dFC) in functional magnetic resonance imaging (fMRI) is beneficial for the analysis and diagnosis of neurological brain diseases. The dFCs between regions of interest (ROIs) are generally delineated by a specific template and clustered into multiple different states. However, these models inevitably fell into the model-driven self-contained system which ignored the diversity at spatial level and the dynamics at time level of the data. In this study, we proposed a spatial and time domain feature extraction approach for Alzheimer’s disease (AD) and autism spectrum disorder (ASD)-assisted diagnosis which exploited the dynamic connectivity among independent functional sub networks in brain. Briefly, independent sub networks were obtained by applying spatial independent component analysis (SICA) to the preprocessed fMRI data. Then, a sliding window approach was used to segment the time series of the spatial components. After that, the functional connections within the window were obtained sequentially. Finally, a temporal signal-sensitive long short-term memory (LSTM) network was used for classification. The experimental results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Autism Brain Imaging Data Exchange (ABIDE) datasets showed that the proposed method effectively predicted the disease at the early stage and outperformed the existing algorithms. The dFCs between the different components of the brain could be used as biomarkers for the diagnosis of diseases such as AD and ASD, providing a reliable basis for the study of brain connectomics.

Magnetic Nanoparticles: Current Advances in Nanomedicine, Drug Delivery and MRI

Magnetic nanoparticles (MNPs) have evolved tremendously during recent years, in part due to the rapid expansion of nanotechnology and to their active magnetic core with a high surface-to-volume ratio, while their surface functionalization opened the door to a plethora of drug, gene and bioactive molecule immobilization. Taming the high reactivity of the magnetic core was achieved by various functionalization techniques, producing MNPs tailored for the diagnosis and treatment of cardiovascular or neurological disease, tumors and cancer. Superparamagnetic iron oxide nanoparticles (SPIONs) are established at the core of drug-delivery systems and could act as efficient agents for MFH (magnetic fluid hyperthermia). Depending on the functionalization molecule and intrinsic morphological features, MNPs now cover a broad scope which the current review aims to overview. Considering the exponential expansion of the field, the current review will be limited to roughly the past three years.

NGS-driven molecular diagnosis of heterogeneous hereditary neurological disorders reveals novel and known variants in disease-causing genes.

Hereditary neurological disorders (HNDs) are a clinically and genetically heterogeneous group of disorders. These disorders arise from the impaired function of the central or peripheral nervous system due to aberrant electrical impulses. More than 600 various neurological disorders, exhibiting a wide spectrum of overlapping clinical presentations depending on the organ(s) involved, have been documented. Owing to this clinical heterogeneity, diagnosing these disorders has been a challenge for both clinicians and geneticists and a large number of patients are either misdiagnosed or remain entirely undiagnosed. Contribution of genetics to neurological disorders has been recognized since long; however, the complete picture of the underlying molecular bases are under-explored. The aim of this study was to accurately diagnose 11 unrelated Pakistani families with various HNDs deploying NGS as a first step approach. Using exome sequencing and gene panel sequencing, we successfully identified disease-causing genomic variants these families. We report four novel variants, one each in, ECEL1, NALCN, TBR1 and PIGP in four of the pedigrees. In the rest of the seven families, we found five previously reported pathogenic variants in POGZ, FA2H, PLA2G6 and CYP27A1. Of these, three families segregate a homozygous 18bp in-frame deletion of FA2H, indicating a likely founder mutation segregating in Pakistani population. Genotyping for this mutation can help low-cost population wide screening in the corresponding regions of the country. Our findings not only expand the existing repertoire of mutational spectrum underlying neurological disordersbut will also help in genetic testing of individuals with HNDs in other populations.

Sensing lysozyme fibrils by salicylaldimine substituted BODIPY dyes - A correlation with molecular structure

Quick and efficient detection of protein fibrils has enormous impact on the diagnosis and treatment of amyloid related neurological diseases. Among several methods, fluorescence based techniques have garnered most importance in the detection of amyloid fibrils due to its high sensitivity and extreme simplicity. Among other classes of molecular probes, BODIPY derivatives have been employed extensively for the detection of amyloid fibrils. However, there are very few studies on the relationship between the molecular structure of BODIPY dyes and their amyloid sensing activity. Here in a BODIPY based salicylaldimine Schiff base and its corresponding boron complex have been evaluated for their ability to sense amyloid fibrils from hen-egg white lysozyme using steady state and time-resolved spectroscopic techniques. Both dyes show fluorescence enhancement as well as increase in their excited state lifetime upon their binding with lysozyme fibrils. However, the BODIPY derivative which shows more emission enhancement in fibrillar solution has much lower affinity towards amyloid fibrils as compared to other derivative. This contrasting behaviour in the emission enhancement and binding affinity has been explained on the basis of differences in their photophysical properties in water and amyloid fibril originating from the difference in their molecular structure. Such correlation between the amyloid sensitivity and the molecular structure of the probe can open up a new strategy for designing new efficient amyloid probes.