Diagnosis Of Neurological Disorders Research Articles

Vectorial-based analysis of dual-tracer PET imaging: A proof of concept

BackgroundThe diagnosis of neurological diseases is complicated since they often share similar symptoms and occur in different severity levels. Imaging techniques such as PET molecular imaging are helpful for an early and accurate diagnosis and, staging allowing a noninvasive evaluation of the disease. The combination of two radioligands in the same patient could be valuable to achieve these diagnostic goals; nevertheless, the imaging data obtained with two radioligands is commonly interpreted independently. This novel approach to combine the PET data of two radiopharmaceuticals, separately acquired in the same subject, is to obtain new quantitative metrics. PET images of patients with Parkinson's disease (PD) and healthy controls (HC) were analyzed. Voxel-by-voxel uptake is compared by combining the imaging data. Dual-tracer PET imaging analysis was tested with [11C]DTBZ-[11C]Raclopride as proof of concept. ResultsThe new proposed metric based on a resultant vector is capable of efficiently discriminating healthy controls from PD patients (p < 0.0001) allowing the detection of slight changes in patients undergoing therapeutic approaches. Significant differences were found between HC and PD patients for the evaluated radiotracers. ConclusionsThe resultant vector appears to deliver useful information that could be helpful to evaluate PD patients under treatment and to improve differential diagnoses.

Plasma D-dimer levels are a biomarker for in-hospital complications and long-term mortality in patients with traumatic brain injury.

Traumatic brain injury (TBI) is a major health concern worldwide. D-dimer levels, commonly used in the diagnosis and treatment of neurological diseases, may be associated with adverse events in patients with TBI. However, the relationship between D-dimer levels, TBI-related in-hospital complications, and long-term mortality in patients with TBI has not been investigated. Here, examined whether elevated D-dimer levels facilitate the prediction of in-hospital complications and mortality in patients with TBI. Overall, 1,338 patients with TBI admitted to our institute between January 2016 and June 2022 were retrospectively examined. D-dimer levels were assessed within 24 h of admission, and propensity score matching was used to adjust for baseline characteristics. Among the in-hospital complications, high D-dimer levels were associated with electrolyte metabolism disorders, pulmonary infections, and intensive care unit admission (p < 0.05). Compared with patients with low (0.00-1.54 mg/L) D-dimer levels, the odds of long-term mortality were significantly higher in all other patients, including those with D-dimer levels between 1.55 mg/L and 6.35 mg/L (adjusted hazard ratio [aHR] 1.655, 95% CI 0.9632.843), 6.36 mg/L and 19.99 mg/L (aHR 2.38, 95% CI 1.416-4.000), and >20 mg/L (aHR 3.635, 95% CI 2.195-6.018; p < 0.001). D-dimer levels were positively correlated with the risk of death when the D-dimer level reached 6.82 mg/L. Overall, elevated D-dimer levels at admission were associated with adverse outcomes and may predict poor prognosis in patients with TBI. Our findings will aid in the acute diagnosis, classification, and management of TBI.

A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification

Epilepsy is one of the most severe non-communicable brain disorders associated with sudden attacks. Electroencephalography (EEG), a non-invasive technique, records brain activities, and these recordings are routinely used for the clinical evaluation of epilepsy. EEG signal analysis for seizure identification relies on expert manual examination, which is labour-intensive, time-consuming, and prone to human error. To overcome these limitations, researchers have proposed machine learning and deep learning approaches. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have shown significant results in automating seizure prediction, but due to complex gated mechanisms and the storage of excessive redundant information, these approaches face slow convergence and a low learning rate. The proposed modified GRU approach includes an improved update gate unit that adjusts the update gate based on the output of the reset gate. By decreasing the amount of superfluous data in the reset gate, convergence is speeded, which improves both learning efficiency and the accuracy of epilepsy seizure prediction. The performance of the proposed approach is verified on a publicly available epileptic EEG dataset collected from the University of California, Irvine machine learning repository (UCI) in terms of performance metrics such as accuracy, precision, recall, and F1 score when it comes to diagnosing epileptic seizures. The proposed modified GRU has obtained 98.84% accuracy, 96.9% precision, 97.1 recall, and 97% F1 score. The performance results are significant because they could enhance the diagnosis and treatment of neurological disorders, leading to better patient outcomes.

Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference.

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics.

On the origin of F-wave: involvement of central synaptic mechanisms.

Neurophysiological methods are used widely to gain information about motor neuron excitability and axon conduction in neurodegenerative diseases. The F-wave is a common biomarker used to test motor neuron properties in the diagnosis of neurological diseases. Although the origin of the F-wave is a subject of debate, the most widely accepted mechanism posits that the F-wave is generated by the backfiring of motor neurons stimulated antidromically from the periphery. In this study, we developed an ex vivo mouse sciatic nerve-attached spinal cord preparation with sensory axons severed. In this preparation, stimulation of the whole sciatic nerve or its tibial branch evoked responses with the electrophysiological signatures of F-waves. Manipulations of synaptic transmission by either removal of extracellular calcium or block of post-synaptic glutamate receptors abolished these responses. These results suggest that F-waves are mediated by spinal microcircuits activated by recurrent motor axon collaterals via glutamatergic synapses.

A Lightweight Low-dose PET Image Super-resolution Reconstruction Method based on Convolutional Neural Network.

PET imaging is one of the most widely used neurological disease screening and diagnosis techniques. Since PET involves the radiation and tolerance of different people, the improvement that has always been focused on is to cut down radiation, in the meantime, ensuring that the generated images with low-dose tracer and generated images with standard-dose tracer have the same details of images. We propose a lightweight low-dose PET super-resolution network (SRPET-Net) based on a convolutional neural network. In this research, We propose a method for accurately recovering highresolution (HR) PET images from low-resolution (LR) PET images. The network learns the details and structure of the image between low-dose PET images and standard-dose PET images and, afterward, reconstructs the PET image by the trained network model. The experiments indicate that the SRPET-Net can achieve a superior peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) values. Moreover, our method has less memory consumption and lower computational cost. In our follow-up work, the technology can be applied to medical imaging in many different directions.

B-284 Evaluating the Range of Serum Neurofiament Light Chain at Different Ages in Chinese

Abstract Background Neurofilament light chain (NFL) has recently been identified as a sensitive biomarker for diagnosis and prognosis in many neurological diseases. However, studies focused on NFL levels ranges in healthy individuals remain few, especially in the Chinese population. The clarification of the reference range among healthy individuals at different ages would be useful for further clinical utility. Objectives To evaluate the serum neurofilament light chain range at different ages in healthy Chinese and analyze the potential contributing factors for NFL levels. Methods Healthy individuals undergoing rigorous cognitive and neuroimaging detection were enrolled. Serum samples and plasma samples of different anticoagulants were collected for NFL levels detection by the ultra-sensitive Simoa HD-1 platform. The tendency of NFL levels to change with age and potential influence factors were analyzed. Results We enrolled three hundred and ninety-nine healthy participants. The positive correlation between serum NFL levels and age was observed and the regression formula was further established. There were no statistical differences between the &amp;lt;20 years group and the 20–29 years group (P = 0.2512), as well as the 60–69 years group and the 70–79 years group (P = 0.0528) and differences,were statistically significant among the remaining pairwise groups. We further observed the positive correlation between serum tNFL levels and age was 0.76 in total (P &amp;lt; 0.0001). The NFL levels between the serum and heparin plasma are statistically different and there was no statistical difference between the serum and plasma of other anticoagulants. Conclusions The age-related change pattern was uncovered, providing important assistance for the clinical use of NFL to monitor the potential neurological alterations for people of different ages.

Passive array micro-magnetic stimulation device based on multi-carrier wireless flexible control for magnetic neuromodulation.

Objective.The passive micro-magnetic stimulation (µMS) devices typically consist of an external transmitting coil and a single internal micro-coil, which enables a point-to-point energy supply from the external coil to the internal coil and the realization of magnetic neuromodulation via wireless energy transmission. The internal array of micro coils can achieve multi-target stimulation without movement, which improves the focus and effectiveness of magnetic stimulations. However, achieving a free selection of an appropriate external coil to deliver energy to a particular internal array of micro-coils for multiple stimulation targets has been challenging. To address this challenge, this study uses a multi-carrier modulation technique to transmit the energy of the external coil.Approach.In this study, a theoretical model of a multi-carrier resonant compensation network for the arrayµMS is established based on the principle of magnetically coupled resonance. The resonant frequency coupling parameter corresponding to each micro-coil of the arrayµMS is determined, and the magnetic field interference between the external coil and its non-resonant micro-coils is eliminated. Therefore, an effective magnetic stimulation threshold for a micro-coil corresponding to the target is determined, and wireless free control of the internal micro-coil array is achieved by using an external transmitting coil.Main results.The passiveµMS array model is designed using a multi-carrier wireless modulation method, and its synergistic modulation of the magnetic stimulation of synaptic plasticity long-term potentiation in multiple hippocampal regions is investigated using hippocampal isolated brain slices.Significance.The results presented in this study could provide theoretical and experimental bases for implantable micro-magnetic device-targeted therapy, introducing an efficient method for diagnosis and treatment of neurological diseases and providing innovative ideas for in-depth application of micro-magnetic stimulation in the neuroscience field.

Nesfatin-1: A Biomarker and Potential Therapeutic Target in Neurological Disorders.

Nesfatin-1 is a novel adipocytokine consisting of 82 amino acids with anorexic and anti-hyperglycemic properties. Further studies of nesfatin-1 have shown it to be closely associated with neurological disorders. Changes in nesfatin-1 levels are closely linked to the onset, progression and severity of neurological disorders. Nesfatin-1 may affect the development of neurological disorders and can indicate disease evolution and prognosis, thus informing the choice of treatment options. In addition, regulation of the expression or level of nesfatin-1 can improve the level of neuroinflammation, apoptosis, oxidative damage and other indicators. It is demonstrated that nesfatin-1 is involved in neuroprotection and may be a therapeutic target for neurological disorders. In this paper, we will also discuss the role of nesfatin-1 as a biomarker in neurological diseases and its potential mechanism of action in neurological diseases, providing new ideas for the diagnosis and treatment of neurological diseases.

A Machine Learning-Based Classification Method for Monitoring Alzheimer’s Disease Using Electromagnetic Radar Data

Alzheimer’s and Parkinson’s diseases are two neurodegenerative brain disorders affecting more than 50 million people globally. Early diagnosis and appropriate assessment of disease progression are critical for treatment and improving patients’ health. Currently, the diagnosis of these neurodegenerative diseases is based primarily on mental status exams and neuroimaging scans, which are costly, time-consuming, and sometimes erroneous. Novel, cost-effective, and precise diagnostic tools and techniques are, thus, urgently required, particularly for early detection and prediction. In the recent decade, electromagnetic imaging has evolved as a cost-effective and noninvasive alternative approach for studying brain diseases. These studies focus on wearable and portable devices and imaging algorithms. However, microwave imaging cannot detect minimal changes in the brain at early stages accurately due to its lower resolution. This article investigates machine learning (ML) techniques for the early diagnosis of acute neurological diseases, especially Alzheimer’s disease (AD). A machine-learning-based classification method is proposed. Simulations are performed on realistic numerical brain phantoms using the CST studio suite to get the scattered signals. A novel data augmentation method is proposed to generate synthetic data required for ML algorithms. A deep neural network-based autoencoder extracts features to train various ML algorithms. The classification results are compared with raw data and manual feature extraction. The study shows that the proposed ML-based method could be used to monitor AD at its early stages.

Application of nanodiamond in the diagnosis and treatment of neurological diseases

The blood-brain barrier has long been a barrier to the treatment of neurological diseases in the brain. It protects brain tissues from damage while limiting the access of drugs to the brain for drug delivery, so nanocarriers with smaller particle size are chosen to break the blood-brain barrier. This review summarizes the structural properties of nanodiamond, the interaction mechanism between nanodiamond and blood-brain barrier, and the application of nanodiamond as a carrier in the diagnosis and treatment of neurological diseases, which provides the theoretical basis for the subsequent diagnosis and treatment of neurological diseases.

An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME.

Electroencephalography (EEG) is a non-invasive method employed to discern human behaviors by monitoring the neurological responses during cognitive and motor tasks. Machine learning (ML) represents a promising tool for the recognition of human activities (HAR), and eXplainable artificial intelligence (XAI) can elucidate the role of EEG features in ML-based HAR models. The primary objective of this investigation is to investigate the feasibility of an EEG-based ML model for categorizing everyday activities, such as resting, motor, and cognitive tasks, and interpreting models clinically through XAI techniques to explicate the EEG features that contribute the most to different HAR states. The study involved an examination of 75 healthy individuals with no prior diagnosis of neurological disorders. EEG recordings were obtained during the resting state, as well as two motor control states (walking and working tasks), and a cognition state (reading task). Electrodes were placed in specific regions of the brain, including the frontal, central, temporal, and occipital lobes (Fz, C1, C2, T7, T8, Oz). Several ML models were trained using EEG data for activity recognition and LIME (Local Interpretable Model-Agnostic Explanations) was employed for interpreting clinically the most influential EEG spectral features in HAR models. The classification results of the HAR models, particularly the Random Forest and Gradient Boosting models, demonstrated outstanding performances in distinguishing the analyzed human activities. The ML models exhibited alignment with EEG spectral bands in the recognition of human activity, a finding supported by the XAI explanations. To sum up, incorporating eXplainable Artificial Intelligence (XAI) into Human Activity Recognition (HAR) studies may improve activity monitoring for patient recovery, motor imagery, the healthcare metaverse, and clinical virtual reality settings.

Deep learning framework for neurological diseases diagnosis through near-infrared eye video and time series imaging algorithms

Internet of Things (IoT) solutions have greatly evolved recently with the application of Artificial Intelligence (AI). Indeed, AI enriches the IoT with intelligence capabilities. In particular, AI methods are highly effective in the scope of the Internet of Medical Things (IoMT) for the applications requiring decision support for doctors. In this work, we propose a Deep Learning (DL) framework for the classification of Parkinson’s disease (PD) and Progressive Supranuclear Palsy (PSP). In contrast to the state-of-the-art solutions relying on only the saccade test for the classification of these neurodegenerative diseases, we collect a dataset while the subjects perform five exercises including saccade, spontaneous nystagmus, optokinetic nystagmus, pursuit, and gaze test. We then extract the pupil features (coordinates, area, minor axis, and major axis) using the image segmentation DL model and represent them as images using Gramian Angular Difference Field (GADF) time series imagining algorithm. The resultant images are supplied to the proposed disease-detection model for running the classification procedure. The best classification results were obtained for the optokinetic exercise with the accuracy of 96.9%, 90.8%, and 96.9% for the left, right, and both eyes, respectively.

A patient with neuropathy and ataxia: what do I have to consider?

An increasing number of peripheral neuro(no)pathies are identified as involving other components of the neurological system, particularly those that further impair balance. Here we aim to outline an evidence-based approach to the diagnosis of patients who present with a somatosensory disorder which also involves at least one other area of neurological impairment such as the vestibular, auditory, or cerebellar systems. Detailed objective investigation of patients who present with sensory impairment, particularly where the degree of imbalance is greater than would be expected, aids the accurate diagnosis of genetic, autoimmune, metabolic, and toxic neurological disease. Diagnosis and management of complex somatosensory disorders benefit from investigation which extends beyond the presenting sensory impairment.

Effective Human Motor Imagery Recognition via Segment Pool Based on One-Dimensional Convolutional Neural Network with Bidirectional Recurrent Attention Unit Network

Brain–computer interface (BCI) technology enables humans to interact with computers by collecting and decoding electroencephalogram (EEG) from the brain. For practical BCIs based on EEG, accurate recognition is crucial. However, existing methods often struggle to achieve a balance between accuracy and complexity. To overcome these challenges, we propose 1D convolutional neural networks with bidirectional recurrent attention unit network (1DCNN-BiRAU) based on a random segment recombination strategy (segment pool, SegPool). It has three main contributions. First, SegPool is proposed to increase training data diversity and reduce the impact of a single splicing method on model performance across different tasks. Second, it employs multiple 1D CNNs, including local and global models, to extract channel information with simplicity and efficiency. Third, BiRAU is introduced to learn temporal information and identify key features in time-series data, using forward–backward networks and an attention gate in the RAU. The experiments show that our model is effective and robust, achieving accuracy of 99.47% and 91.21% in binary classification at the individual and group levels, and 90.90% and 92.18% in four-category classification. Our model demonstrates promising results for recognizing human motor imagery and has the potential to be applied in practical scenarios such as brain–computer interfaces and neurological disorder diagnosis.

Direct multi-elemental analysis of cerebrospinal fluid samples by LA-ICP-MS employing an aerosol local extraction cryogenic ablation cell.

A novel method for direct high-throughput analysis of multi-elements in cerebrospinal fluid (CSF) samples by laser ablation inductively coupled plasma mass spectrometry with an aerosol local extraction cryogenic ablation cell (ALEC-LA-ICP-MS) was developed. Microliter-level CSF samples were frozen by a designed cryogenic ablation cell and directly analyzed by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) without time-consuming pretreatment. Compared with the precision obtained at room temperature (20℃), that obtained at low temperature (- 25℃) was significantly improved; the RSDs were reduced from 8.3% (Zn) to 32.6% (Mn) to 2.2% (Pb) to 6.5% (Mn) with six times parallel determination. To meet the analytical requirement of the micro-volume CSF samples, the laminar flow aerosol local extraction strategy was adopted to improve the transmission efficiency of aerosols, and the signal intensity was increased by four times compared with the standard commercial ablation cell. The standard solution with 0.4% bovine serum albumin (BSA) matrix was used as matrix-match external standard, and Rh was added into the samples as internal standard. The limits of detection (LODs) ranged from 0.17μg·L-1 (Mn) to 8.67μg·L-1 (Mg). Standard addition recovery experiments and the determination of CRM serum L-1 and L-2 were carried out to validate the accuracy of the method; all results indicated there were excellent accuracy and precision in the proposed method. The matrix-scanning function in the GeoLas software combined with themicrowell plate realizes the high-throughput automatic analysis. Twenty-four CSF samples from different patients were determined; the results showed that there might be a correlation between the metal elements in CSF and the diseases, which means that the proposed method has potential in the diagnosis of neurological diseases.

RTMS in mental health disorders.

Transcranial magnetic stimulation (TMS) is an innovative and non-invasive technique used in the diagnosis and treatment of psychiatric and neurological disorders. Repetitive TMS (rTMS) can modulate neuronal activity, neuroplasticity and arousal of the waking and sleeping brain, and, more generally, overall mental health. Numerous studies have examined the predictors of the efficacy of rTMS on clinical outcome variables in various psychiatric disorders. These predictors often encompass the stimulated brain region's location, electroencephalogram (EEG) activity patterns, potential morphological and neurophysiological anomalies, and individual patient's response to treatment. Most commonly, rTMS is used in awake patients with depression, catatonia, and tinnitus. Interestingly, rTMS has also shown promise in inducing slow-wave oscillations in insomnia patients, opening avenues for future research into the potential beneficial effects of these oscillations on reports of non-restorative sleep. Furthermore, neurophysiological measures emerge as potential, disease-specific biomarkers, aiding in predicting treatment response and monitoring post-treatment changes. The study posits the convergence of neurophysiological biomarkers and individually tailored rTMS treatments as a gateway to a new era in psychiatric care. The potential of rTMS to induce slow-wave activity also surfaces as a significant contribution to personalized treatment approaches. Further investigations are called for to validate the imaging and electrophysiological biomarkers associated with rTMS. In conclusion, the potential for rTMS to significantly redefine treatment strategies through personalized approaches could enhance the outcomes in neuropsychiatric disorders.

Genetic epilepsy-digging in for gold: a case series on diagnostic genetic testing in epilepsy

Developmental epileptic encephalopathy (DEE) is a group of ictal and interictal epileptiform disorders (clinical and encephalographic) associated with severe cognitive and behavioural impairments according to the classification and terminology criteria of the international league against epilepsy (ILAE). Less than half of the genetic etiologies have been identified, even though DEEs are genetically heterogenous. Hence, genetic diagnoses of childhood neurological disorders are important as well as challenging. This research aims at determining the diagnostic utility of genetic testing (whole exome sequencing) in heterogeneous group of childhood DEEs. Here, we present the case series of 5 children who have had at least 1 episode of seizure during their lifetime associated with developmental delay and a definite genetic etiology. In view of an unrecognised aetiology, parents were counselled for a genetic evaluation. Genetic test reports showed pathogenic gene variation for epilepsy. Even though intervention would be mostly be the same, genetic aetiology helped us in prognosticating and improving the family’s outlook towards the disease condition. Hence it is desirable to identify genetic variations in all possible childhood epilepsy cases as it has the potential to improve family planning, aid the prognosis, and start specific interventions and also helps to save time in selecting appropriate anti-epileptic drugs.

Mapping acute neuroinflammation in vivo with diffusion-MRI in rats given a systemic lipopolysaccharide challenge

It is becoming increasingly apparent that neuroinflammation plays a critical role in an array of neurological and psychiatric disorders. Recent studies have demonstrated the potential of diffusion MRI (dMRI) to characterize changes in microglial density and morphology associated with neuroinflammation, but these were conducted mostly ex vivo and/or in extreme, non-physiological animal models. Here, we build upon these studies by investigating the utility of well-established dMRI methods to detect neuroinflammation in vivo in a more clinically relevant animal model of sickness behavior. We show that diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) indicate widespread increases in diffusivity in the brains of rats given a systemic lipopolysaccharide challenge (n = 20) vs. vehicle-treated controls (n = 12). These diffusivity changes correlated with histologically measured changes in microglial morphology, confirming the sensitivity of dMRI to neuroinflammatory processes. This study marks a further step towards establishing a noninvasive indicator of neuroinflammation, which would greatly facilitate early diagnosis and treatment monitoring in various neurological and psychiatric diseases.

Mapping the branching pattern of the middle cerebral artery in the camel (Camelus dromedarius): a comprehensive anatomical analysis.

The complex branching structure of the middle cerebral artery serves as a crucial component in the blood supply to the cerebral cortex, playing a key role in sustaining brain function and overall neurological health in mammals. A thorough understanding of the branching structure of the middle cerebral artery is required for the advancement of veterinary medicine and neuroscience research. In this study, we provide the first comprehensive anatomical analysis of the branching structure of the middle cerebral artery (MCA) in the dromedary brain. To date, no study has examined the MCA branches in dromedaries. By examining 80 cerebral hemispheres from freshly slaughtered male dromedary camels aged 2-6 years, we aimed to explain the origin, course, and branching patterns of the MCA in the dromedary camel. Advanced casting techniques using colored latex, epoxy paint, and liquid plastic have been used to create precise renderings of the MCA structure. Our findings revealed that the MCA is the principal branch of the rostral cerebral artery and serves as the primary blood supply to the telencephalon in dromedaries. The main trunk of the MCA splits into several cortical branches, each supplying blood to a specific cerebral hemisphere. These branches comprise the rostral and caudal olfactory arteries; orbital artery; superior, middle, and inferior frontal arteries; rostral, middle, and caudal parietal arteries; and dorsal, middle, and ventral temporal arteries. This groundbreaking work considerably advances our understanding of the dromedary cerebrovascular system by providing insightful information on the anatomy and topography of the MCA. Our findings open new avenues for advancements in veterinary medicine and neuroscience research, with potential applications in the diagnosis and treatment of neurological disorders in dromedary camels. Furthermore, understanding the unique branching pattern of the MCA may have implications for comparative neuroanatomy and the evolution of cerebrovascular systems across species.