Wernicke's Encephalopathy With MRI Findings Despite Coadministration of Thiamine and Glucose.

Wernicke's encephalopathy (WE) is a prominent neurologic manifestation of thiamine (vitamin B1) deficiency. While often linked to alcoholism, it can also arise from various causes, including malabsorption, inadequate dietary intake, increased metabolic requirement, and among dialysis patients. Here, we present a case of altered mental status from acute metabolic encephalopathy attributed to sepsis, acute kidney injury (AKI), and hypoglycemia. WE was overlooked in the early hospitalization course due to the daily administration of thiamine. However, the patient's cognitive decline persisted despite the improvement of sepsis and AKI. Subsequent brain MRI revealed thalamic T2 signal intensity changes, suggesting either a past infarction or WE. Implementing an empirical regimen of high-dose thiamine resulted in the patient's rapid cognitive recovery. This therapeutic strategy was integrated into the management of her sepsis and AKI, leading to her full recovery and subsequent hospital discharge without complications.

Urgent Direct Access to Diagnostic Services for General Practitioners: Bridging the Gap in Cancer Diagnosis.

Urgent direct access to diagnostic services for general practitioners (GPs) is a new pathway to capture any cancer diagnoses that may have been missed due to vague symptom presentations. Hence, GPs should look out for the key symptoms mentioned by NHS England that should prompt urgent direct access referrals for chest X-ray (CXR), computed tomography (CT) chest, MRI brain, ultrasound (US) abdomen and pelvis, and CT abdomen and pelvis. By implementing this approach, we can significantly reduce the time to diagnosis, while minimizing the number of visits to GP and specialist appointments prior to initiating investigations. However, the use of this pathway can only improve if access to diagnostic scans is improved. This needs to be done by ensuring all GPs in the country have access to directly request MRI brains, CT chest, abdomen, and pelvis. Further research into the impact of the urgent direct access pathway as well as investigating the number of GPs without access to these vital diagnostic services is required to fully improve and measure the progress of this referral pathway.

Enhancing Alzheimer's Disease Classification with Transfer Learning: Finetuning a Pre-trained Algorithm.

OBJECTIVE: The increasing longevity of the population has made Alzheimer's disease (AD) a significant public health concern. However, the challenge of accurately distinguishing different disease stages due to limited variability within the same stage and the potential for errors in manual classification highlights the need for more precise approaches to classifying AD stages. In the field of deep learning, the ResNet50V2 model stands as a testament to its exceptional capabilities in image classification tasks. MATERIALS: The dataset employed in this study was sourced from Kaggle and consisted of 6400 MRI images that were meticulously collected and rigorously verified to assure their precision. The selection of images was conducted with great attention to detail, drawing from a diverse array of sources. METHODS: This study focuses on harnessing the potential of this model for AD classification, a task that relies on extracting disease-specific features. Furthermore, to achieve this, a multi-class classification methodology is employed, using transfer learning and fine-tuning of layers to adapt the pre-trained ResNet50V2 model for AD classification. Notably, the impact of various input layer sizes on model performance is investigated, meticulously striking a balance between capacity and computational efficiency. The optimal fine-tuning strategy is determined by counting layers within convolution blocks and selectively unfreezing and training individual layers after a designated layer index, ensuring consistency and reproducibility. Custom classification layers, dynamic learning rate reduction, and extensive visualization techniques are incorporated. RESULTS: The model's performance is evaluated using accuracy, AUC, precision, recall, F1-score, and ROC curves. The comprehensive analysis reveals the model's ability to discriminate between AD stages. Visualization through confusion matrices aided in understanding model behavior. The rounded predicted labels enhanced practical utility. CONCLUSION: This approach combined empirical research and iterative refinement, resulting in enhanced accuracy and reliability in AD classification. Our model holds promise for real-world applications, achieving an accuracy of 96.18%, showcasing the potential of deep learning in addressing complex medical challenges.

Long term survival in patients with classic infantile Pompe disease reveals a spectrum with progressive brain abnormalities and changes in cognitive functioning.

The aim of this longitudinal cohort study, is to provide more insight into the pattern of brain abnormalities, and possible consequences for cognitive functioning, in patients with classic infantile Pompe disease. We included 19 classic infantile Pompe patients (median age last assessment 8.9 years, range 1.5-22.5 years; 5/19 CRIM negative), treated with ERT. Using MR imaging of the brain (T1, T2, and FLAIR acquisitions), we classified progression of brain abnormalities on a 12-point rating scale at multiple time points throughout follow-up. Additionally we noted specific white matter patterns and examined atrophy. Cognitive development was studied using Wechsler IQ assessments obtained by certified neuropsychologists. The association between age and cognitive functioning, and MRI ratings and cognitive functioning was assessed by linear regression models. All but one patient developed brain abnormalities. The abnormalities progressed in a similar pattern throughout the brain, with early involvement of periventricular white matter, later followed by subcortical white matter, gray matter structures, and juxtacortical U-fibers. We found a significant decline (p < 0.01), with increasing age for full scale IQ, performance IQ and processing speed, but not for verbal IQ (p = 0.17). Each point increment in the 12-point MRI rating scale was associated with a significant decline (3.1-6.0 points) in all the IQ index scores (p < 0.05). The majority of long-term surviving patients in our cohort develop incremental brain MRI abnormalities and decline in cognitive functioning. This highlights the need for new therapies that can cross the blood-brain barrier in order to treat this CNS phenotype.

Long-term effects of SARS-CoV-2 infection in patients with and without chemosensory disorders at disease onset: a psychophysical and magnetic resonance imaging exploratory study.

A preserved sense of smell and taste allows us to understand many environmental "messages" and results in meaningfully improvements to quality of life. With the COVID-19 pandemic, it became clear how important these senses are for social and nutritional status and catapulted this niche chemosensory research area towards widespread interest. In the current exploratory work, we assessed two groups of post-COVID-19 patients who reported having had (Group 1) or not (Group 2) a smell/taste impairment at the disease onset. The aim was to compare them using validated smell and taste tests as well as with brain magnetic resonance imaging volumetric analysis. Normative data were used for smell scores comparison and a pool of healthy subjects, recruited before the pandemic, served as controls for taste scores. The majority of patients in both groups showed an olfactory impairment, which was more severe in Group 1 (median UPSIT scores: 24.5 Group 1 vs 31.0 Group 2, p = 0.008), particularly among women (p = 0.014). No significant differences emerged comparing taste scores between Group 1 and Group 2, but dysgeusia was only present in Group 1 patients. However, for taste scores, a significant difference was found between Group 1 and controls (p = 0.005). No MRI anatomical abnormalities emerged in any patients while brain volumetric analysis suggested a significant difference among groups for the right caudate nucleus (p = 0.028), although this was not retained following Benjamini-Hochberg correction. This exploratory study could add new information in COVID-19 chemosensory long-lasting impairment and address future investigations on the post-COVID-19 patients' research.

Therapeutic Time Window With DWI-ADC (Diffusion-Weighted Imaging-Apparent Diffusion Coefficient) Match and Periodic Discharges for Status Epilepticus.

A man in his 70s with alcoholic dementia was admitted for acute, prolonged impaired consciousness. Blood and cerebrospinal fluid findings were unremarkable. Brain MRI revealed multiple high-signal cortical regions. Following diazepam and levetiracetam administration, electroencephalography (EEG) revealed <1 Hz lateralized periodic discharges, indicating that the seizures were ceasing. The periodic discharges had disappeared during the gradual recovery process by day 10; however, cortical arterial spin labeling findings persisted only in regions exhibiting cytotoxic edema. Without additional anti-seizure medication, no seizure recurred, but cognitive dysfunction remained. He was transferred to a rehabilitation hospital with the continued oral administration of levetiracetam at 1,000 mg/day. DWI-ADC (diffusion-weighted imaging-apparent diffusion coefficient) match may suggest an indication of a missed suitable treatment window for seizures.

Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions.

The work presented in this paper is in the area of brain tumor detection. We propose a fast detection system with 3D MRI scans of Flair modality. It performs 2 functions, predicting the gray level distribution and location distribution of the pixels in the tumor regions and generating tumor masks with pixel-wise precision. To facilitate 3D data analysis and processing, we introduce a 2D histogram presentation encompassing the gray-level distribution and pixel-location distribution of a 3D object. In the proposed system, specific 2D histograms highlighting tumor-related features are established by exploiting the left-right asymmetry of a brain structure. A modulation function, generated from the input data of each patient case, is applied to the 2D histograms to transform them into coarsely or finely predicted distributions of tumor pixels. The prediction result helps to identify/remove tumor-free slices. The prediction and removal operations are performed to the axial, coronal and sagittal slice series of a brain image, transforming it into a 3D minimum bounding box of its tumor region. The bounding box is utilized to finalize the prediction and generate a 3D tumor mask. The proposed system has been tested extensively with the data of more than 1200 patient cases in BraTS2018∼2021 datasets. The test results demonstrate that the predicted 2D histograms resemble closely the true ones. The system delivers also very good tumor detection results, comparable to those of state-of-the-art CNN systems with mono-modality inputs. They are reproducible and obtained at an extremely low computation cost and without need for training.

Clinico-Radiological Correlation of Weber's Syndrome.

Weber's syndrome, named after Hermann Weber, is characterized by midbrain lesions often caused by strokes, resulting in ipsilateral third nerve palsy, including ptosis and pupillary abnormalities, and contralateral hemiplegia. We discuss a case of a 35-year-old lady with cognitive impairment, right hemiparesis, diplopia, left eye ptosis, and lateral eye deviation. MRI of the brain with contrast suggested an acute infarct in the left-sided paramedian region of the midbrain. The oculomotor nucleus and cerebral peduncle were both affected by an abrupt left-sided paramedian midbrain stroke. The participation of particular midbrain nuclei together with symptoms including drooping eyelids, diplopia, and limb paralysis suggested Weber's syndrome. An MRI study of the brain is the modality of choice in suspected stroke cases and is more sensitive when it comes to the brainstem lesions. A comprehensive neurological examination with a clinical diagnosis of Weber's syndrome before radiological investigations is of great help for localizing brain stem lesions and thus aids in early diagnosis and treatment.

Cranial Conundrum: An Unusual Case of an Epidermoid Cyst in the Prepontine Cistern.

We present a fascinating case of a patient who suffered from persistent headaches for three months due to an epidermoid cyst located in the prepontine cistern. Epidermoid cysts are a very uncommon type of intracranial tumor, known for their slow growth and gradual onset of neurological symptoms. In this particular case, our patient, a 35-year-old, experienced a headache that was accompanied by dizziness, photophobia, and pain when moving their eyes. Further imaging revealed a cystic lesion in the prepontine cistern, which had a mass effect on the pons. After confirming the lesion was likely an epidermoid cyst through an MRI, the patient underwent surgery to have it removed. We hope to highlight the rarity of this type of tumor and its unique features when viewed through imaging.

An Incidental Finding of Idiopathic Hypertrophic Pachymeningitis: A Case Report.

In this case report, we discuss and explore the clinical, laboratory, and imaging findings, as well as the treatment options and follow-up measures, in an 83-year-old patient with idiopathic hypertrophic pachymeningitis (IHP), a rare disorder characterized by fibrosing, hypertrophic inflammation that thickens the dura mater. An 83-year-old female with a medical history of hypertension and hyperlipidemia presented with speech arrest and was taken to the emergency department, where she received a stroke code, a CT scan, and an MRI. The MRI results showed a temporal lobe meningioma and a pan-cranial pachymeningitis encasing the entire brain and cerebellum and extending into the upper cervical spine. Multiple unsuccessful attempts at a lumbar puncture were made, so a dural biopsy specimen was obtained, which revealed no malignant process. A cerebral spinal fluid specimen (CSF) from the biopsy showed minimal white blood cells (WBCs) which ruled out infection. Idiopathic hypertrophic pachymeningitis was the given diagnosis based on the apparent MRI findings. The patient was treated in the hospital for four days with IV methylprednisolone and discharged on oral methylprednisolone for four to six weeks.

Voxel representation of brain images inpainting via Regional Pixel Semantic Network and pyramidal attention AE - Quantile differential mechanism model.

Medical image inpainting holds significant importance in enhancing the quality of medical images by restoring missing areas, thereby rendering them suitable for diagnostic purposes. While several techniques have been previously proposed for medical image inpainting, they are not suitable for distorted images containing metallic implants due to their limited consideration of known shaped masking. To overcome this limitation, a novel Vectorized Box Interpolation with Arbitrary Auto-Rand Augment Masking technique has been proposed which involves scaling and vectorizing images to expand their details and generating asymmetrically shaped masking in an automatic random format. One of the challenging tasks in this regard is the precise detection of lost regions, which is addressed through the introduction of the Regional Pixel Semantic Network. This technique employs the locally shared features (LSF) based region sensing with FCN (fully convolutional network) segmentation, which performs automatic segmentation based on neighboring pixel local dependency and regional features to determine the location of masked regions. During the reconstruction of missing parts, a significant challenge posed is the inability to recognize proximity in encoding owing to the generation of shadow-like regions on the feature map. To address this issue, a novel Multilayered DRC Regularized Pyramidal Attention AE Model has been proposed which employs dilated convolution with coherent pyramidal attention for feature extraction and improves image resolution using a Laplacian convolutional layer. Moreover, the realness of the generated image is determined using the Quantile Differential Mechanism model, where in the Quantile Differential Partial Convolutional Discriminator utilizes the hyperbolic tangent activation function in the partial convolutional layer to calculate recognition accuracy. As a result, the proposed method achieves high percentages for accuracy (98 %), precision (97 %), sensitivity (96 %), recall (95 %), and F-measure (96 %) thereby outperforming existing methods. Overall, this proposed method effectively handles distorted images with metallic implants, accurately detects lost regions, and improves the reconstructed image quality.

Apport de l'IRM multimodale dans la prise en charge des tumeurs encéphaliques chez l'adulte à Abidjan / Contribution of multimodal MRI in the management of brain tumors in adults in Abidjan

Dans le monde, le nombre de nouveaux cas de tumeurs encéphaliques était de 14 millions en 2014 avec environ 8 millions de personnes décédées. Objectif: déterminer l'efficacité diagnostique de l'IRM multimodale dans la prise en charge des tumeurs encéphaliques dans notre contexte. Matériels et Méthodes: Etude rétrospective à visée descriptive, multicentrique sur une période de 2 ans allant de février 2017 à mars 2019, dans 2 services de radiologie et 2 services de Neurochirurgie, à Abidjan. Nous nous sommes intéressés àtous les patients ayant réalisé une IRM multimodale pour suspicion d'un processus tumoral encéphalique, et qui ont eu une exérèse chirurgicale suivie d'un examen anatomopathologique de la pièce opératoire. Les examens d'IRM multimodale encéphaliques ont été réalisés selon un protocole standard sur des appareils de 1,5 Tesla, et lus par des radiologues séniors. Nous avons confronté les résultats anatomopathologiques aux diagnostics posés à l'imagerie IRM multimodale.Résultats: Ont été retenu 18 dossiers. On notait une prédominance masculine avec un sex-ratio de 1,57. L'âge moyen était de 42,8 ans avec des extrêmes de 20ans et 61ans et un écart type de 10,75.La corrélation diagnostique entre l'examen anatomopathologique et l'IRM a été de6 sur 7 cas, 4 sur 5 cas, 2 sur 2 cas, 2 sur 2 cas et 1 sur 1 cas respectivement pour le méningiome, le gliome, les métastases, l'adénome hypophysaire, le lymphome cérébral primitif.Conclusion:Les tumeurs encéphaliques dans notre étude, étaient variéeset concernaient la population jeune. Les motifs d'erreur diagnostique étaient essentiellement en rapport avec la variété de présentations des tumeurs à l'IRM et l'expérience récente de nos radiologues sur l'utilisation efficace des séquences avancées.

Worldwide, the number of new cases of brain tumors was 14 million in 2014 with approximately 8 million people dying.Objective: determine the diagnostic effectiveness of multimodal MRI in the management of brain tumors in our context.Methods: Retrospective study with a descriptive aim, multicenter over a period of 2 years from February 2017 to March 2019, in 2 radiology departments and 2 neurosurgery departments, in Abidjan. We were interested in all patients who had a multimodal MRI for suspicion of a brain tumor process, and who had a surgical excision followed by an pathological examination of the surgical specimen. The multimodal brain MRI examinations were carried out according to a standard protocol on 1.5 Tesla devices, and read by seniorradiologists. We compared the pathological results with the diagnoses made on multimodal MRI imaging.Results:18 files were retained. There was a male predominance with a sex ratio of 1.57. The average age was 42.8 years with extremes of 20 and 61 years and a standard deviation of 10.75. The diagnostic correlation between the pathological examination and MRI was 6 out of 7 cases, 4 out of 5 cases, 2 out of 2 cases, 2 out of 2 cases and 1 out of 1 case respectively for meningioma, glioma, metastases, pituitary adenoma, primary cerebral lymphoma.Conclusion: The brain tumors in our study were varied and concerned the young population. The reasons for diagnostic error were mainly related to the variety of tumor presentations on MRI and the recent experience of our radiologists on the effective use of advanced sequences.

Rate of Missed Intracranial Colloid Cysts on MRI and CT.

BACKGROUND AND PURPOSE: Intracranial colloid cysts of the third ventricle are rare; however, they may be symptomatic. They can create a mass effect on the foramina of Monro, resulting in acute hydrocephalus. Colloid cysts are detectable on CT and MRI but are commonly missed. In this paper, we investigate the rate of missed colloid cysts on MRI and/or CT imaging within our multihospital metropolitan medical group. MATERIALS AND METHODS: A retrospective, institutional review board-approved search of the network-wide picture archiving and communication system (PACS) from January 1, 2010, to October 31, 2020, was performed to identify reports including a "colloid cyst" in MRI brain or CT head imaging. Results without imaging and/or surgical confirmation of intracranial colloid cysts were excluded, rendering 229 cases. A PACS review of these cases was performed by two neuroradiologists to determine instances where the cyst had previously been imaged but not diagnosed on either CT or MRI. RESULTS: Two hundred twenty-nine subjects had confirmed colloid cysts through imaging and/or surgical reports. Of these, 46 had prior imaging depicting a colloid cyst either on CT and/or MRI without mention on the interpretative report, resulting in a non-detection rate of 20.1%. CONCLUSION: Intracranial colloid cysts can be missed at a considerably high rate, which is concerning given their clinically unpredictable nature and ability to cause significant morbidity and mortality. As such, it is important to take a proactive approach to searching for these cysts as part of a regular imaging search pattern and to continue to determine new methods of increasing detection sensitivity.

A cerebral lymphoma mimicking a meningioma: case report.

Introduction: Cerebral lymphoma is a rare and aggressive brain tumor. It accounts for 1% of all non-Hodgkin's lymphomas (NHL) and 2% of all brain tumors. Untreated brain lymphoma has a very poor prognosis, with an overall life expectancy of around 1.5 months. Case presentation: The authors report the case of a 35-year-old patient, with no previous pathological history, who presented for 3 weeks with deafness and recently aggravated otalgia. In MRI, brain imaging revealed a formation initially suggestive of an aggressive meningioma, and the histological study of the operative specimen was in favor of a diffuse large-cell non-germ-center B NHL. Clinical discussion: Primary central nervous system lymphoma is an extra-nodal NHL localized to the brain, meninges, spinal cord, and eyes. In 90% of cases, these are diffuse large B-cell lymphomas, the other types being poorly characterized low-grade lymphomas, T-cell lymphomas, and Burkitt's lymphomas. MRI with gadolinium contrast is the gold standard for diagnosis which enhancement is homogeneous and well-limited, frequently associated with perilesional vascular edema. In T2-weighted sequences, there is a weak signal with restricted diffusion on diffusion-weighted imaging. The management of brain lymphoma is currently based on chemotherapy with high-dose methotrexate combined with the other agents, mainly rituximab. Conclusion: Cerebral lymphoma remains a non-negligible entity of central nervous system tumors, which can be confused with several other tumors, mainly glial and meningioma.

MRI Findings of Desmoplastic Infantile Ganglioglioma: A Case Report and Analysis.

Desmoplastic infantile gangliogliomas (DIG) are rare intracranial tumors that predominantly affect children. They are characterized by a mixture of glial and neuronal components interspersed with abundant fibrous stroma and are typically located on the surface of the cerebral hemispheres. In this case report, we present a seven-year-old male child with a late presentation of DIG, which is typically diagnosed between zero and 60 months of age. We discuss the MRI findings, clinical symptoms, and differential diagnosis of DIG in patients with this central nervous system tumor.

Identification of optimal value of magnetic resonance planimetry and the parkinsonism index for the diagnosis of Parkinson's disease and progressive supranuclear palsy.

Objectives: Parkinson's disease (PD) and progressive supranuclear palsy (PSP) are neurodegenerative conditions that have overlapping clinical and imaging features, thus making it difficult to distinguish and diagnose PSP from PD. Therefore, in this study, we aimed to investigate the optimal value of magnetic resonance planimetry and the parkinsonism index to differentiate between PSP and PD. Methods: In this retrospective study, we recruited a total of 84 patients (27 patients with PSP, 27 patients with PD and 27 normal controls) who underwent MRI brain examinations. For each subject, we calculated the corpus callosum area, midbrain area, pons area, middle cerebellar peduncle (MCP) width and superior cerebellar peduncle (SCP) width on MRI brain images. We also calculated the pons to midbrain area (P/M) ratio, MCP/SCP ratio and magnetic resonance parkinsonism index (MRPI). Results: Receiver operating characteristic curve (ROC) analysis was used to identify the diagnostic value of each biomarker. MRPI had a sensitivity of 70.4%, a specificity of 88.9%, and a diagnostic accuracy of 79.6% with an optimum cut off of 24.3 for differentiating PSP from PD. P/M ratio had a sensitivity of 74.1%, a specificity of 77.8%, and a diagnostic accuracy of 75.9% with an optimal cutoff of 24.3 for differentiating PSP from PD. The MCP/SCP ratio had a sensitivity of 66.7%, a specificity of 77.8%, and an accuracy of 72.2% with an optimal cut off of 4.65 for differentiating PSP from PD. Conclusions: The study revealed that MRPI and P/M ratio are accurate markers for differentiating PSP from PD. The optimal cut-off values derived from our study can help in the early diagnosis of PD.

Carboxyhaemoglobin levels in infants with hypoxic ischaemic encephalopathy.

OBJECTIVES: Hypoxic ischaemic encephalopathy (HIE) is associated with oxidative stress. A potential marker of oxidative damage is carboxyhaemoglobin (COHb) which is the product of the reaction between carbon monoxide and haemoglobin and is routinely assessed on blood gas analysis. Our objective was to test the hypothesis that higher COHb levels would be associated with worse outcomes in infants treated for HIE. METHODS: A retrospective, observational study was performed of all infants who received whole body hypothermia for HIE at a tertiary neonatal intensive care unit between January 2018 and August 2021. For each participating infant, the highest COHb level per day was recorded for days one, three and five after birth. RESULTS: During the study period, 67 infants with a median (IQR) gestational age of 40 (38-41) weeks underwent therapeutic hypothermia for HIE. The median (IQR) COHb level on day three was higher in infants without electroencephalographic seizures (1.4 [1.1-1.4] %) compared with infants with seizures (1.1 [0.9-1.3] %, p=0.024). The median (IQR) COHb on day five was higher in infants without MRI brain abnormalities (1.4 [1.2-1.7] %) compared with infants with MRI abnormalities (1.2 [1.0-1.4] %, p=0.032). The COHb level was not significantly different between the nine infants who died compared to the infants who survived. CONCLUSIONS: COHb levels were higher in infants with HIE without seizures and in those with normal MRI brain examinations. We suggest that carbon monoxide has a potential protective role in HIE.

Acute Disseminated Encephalomyelitis in a Six-Year-Old Child: A Case Report.

Acute disseminated encephalomyelitis (ADEM) is a rare autoimmune demyelinating disorder that primarily affects the central nervous system (CNS). It is characterized by an acute inflammatory response targeting the myelin sheath surrounding nerve fibers in the brain and spinal cord. The exact mechanism of ADEM is not fully understood, but it is believed to involve an abnormal immune response that leads to the activation of immune cells and subsequent inflammation within the CNS. This immune-mediated attack results in the destruction of myelin, impairing the transmission of nerve signals and causing a wide range of neurological symptoms. This is a case of a six-year-old girl with no notable medical history presented with complaints of a fever and headache for the last month, in addition to difficulty walking for 20 days and speaking for 14 days. On CNS examination, the right upper and lower limbs' power was reduced, and the Babinski sign was seen in both lower limbs. Both sides of the triceps and knee showed increased reflexes, whereas both sides of the ankle showed decreased reflexes. Magnetic resonance imaging (MRI) showed multiple T1 hypointensities and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) hyperintensities in the subcortical white matter of the bilateral frontal and parietal lobes, bilateral cerebellar peduncles, corpus callosum, pons, and midbrain. Our case report aims to raise awareness and aid in the early recognition of ADEM because prompt recognition, accurate diagnosis, and appropriate management are essential to minimizing neurological damage and promoting favorable outcomes in affected individuals.

An Efficient Ensemble Approach for Alzheimer's Disease Detection Using an Adaptive Synthetic Technique and Deep Learning.

Alzheimer's disease is an incurable neurological disorder that leads to a gradual decline in cognitive abilities, but early detection can significantly mitigate symptoms. The automatic diagnosis of Alzheimer's disease is more important due to the shortage of expert medical staff, because it reduces the burden on medical staff and enhances the results of diagnosis. A detailed analysis of specific brain disorder tissues is required to accurately diagnose the disease via segmented magnetic resonance imaging (MRI). Several studies have used the traditional machine-learning approaches to diagnose the disease from MRI, but manual extracted features are more complex, time-consuming, and require a huge amount of involvement from expert medical staff. The traditional approach does not provide an accurate diagnosis. Deep learning has automatic extraction features and optimizes the training process. The Magnetic Resonance Imaging (MRI) Alzheimer's disease dataset consists of four classes: mild demented (896 images), moderate demented (64 images), non-demented (3200 images), and very mild demented (2240 images). The dataset is highly imbalanced. Therefore, we used the adaptive synthetic oversampling technique to address this issue. After applying this technique, the dataset was balanced. The ensemble of VGG16 and EfficientNet was used to detect Alzheimer's disease on both imbalanced and balanced datasets to validate the performance of the models. The proposed method combined the predictions of multiple models to make an ensemble model that learned complex and nuanced patterns from the data. The input and output of both models were concatenated to make an ensemble model and then added to other layers to make a more robust model. In this study, we proposed an ensemble of EfficientNet-B2 and VGG-16 to diagnose the disease at an early stage with the highest accuracy. Experiments were performed on two publicly available datasets. The experimental results showed that the proposed method achieved 97.35% accuracy and 99.64% AUC for multiclass datasets and 97.09% accuracy and 99.59% AUC for binary-class datasets. We evaluated that the proposed method was extremely efficient and provided superior performance on both datasets as compared to previous methods.

The impact of early adversity and education on genetic and brain morphological predictors of cognitive ability.

Cognitive ability is a strong predictor of occupational achievement, quality of life and physical health. While variation in cognition is strongly heritable and has been robustly associated with early environment and brain morphology, little is known about how these factors combine and interact to explain this variation in cognition. To address this, we modelled the relationship between common genetic variation, grey matter volume, early life adversity and education and cognitive ability in a UK Biobank sample of N = 5237 individuals using structural equation modelling. We tested the hypotheses that total grey matter volume would mediate the association between genetic variation and cognitive ability, and that early life adversity and educational attainment would moderate this relationship. Common genetic variation, grey matter volume and early life adversity were each significant predictors in the model, explaining ~15% of variation in cognitive ability. Contrary to our hypothesis, grey matter volume did not mediate the relation between genetic variation and cognition performance. Neither did early life adversity or educational attainment moderate this relation, although educational attainment was observed to moderate the relationship between grey matter volume and cognitive performance. We interpret these findings in terms of the modest explanatory value of currently estimated polygenic scores accounting for variation in cognitive performance (~5%), making potential mediating and moderating variables difficult to confirm.