Brain Abnormalities Research Articles

Abnormal brain entropy dynamics in ADHD

BackgroundBrain entropy (BEN) is a novel measure for irregularity and complexity of brain activities, which has been used to characterize abnormal brain activities in many brain disorders including attention-deficit/hyperactivity disorder (ADHD). While most research assumes BEN is stationary during scan sessions, the brain in resting state is also a highly dynamic system. The BEN dynamics in ADHD has not been explored. MethodsWe used a sliding window approach to derive the dynamical brain entropy (dBEN) from resting-state functional magnetic resonance imaging (rfMRI) dataset that includes 98 ADHD patients and 111 healthy controls (HCs). We identified 3 reoccurring BEN states. We tested whether the BEN dynamics differ between ADHD and HC, and whether they are associated with ADHD symptom severity. ResultsOne BEN states, characterized by low overall BEN and low within-state BEN located in SMN (sensorimotor network) and VN (visual network), its FW (fractional window) and MDT (mean dwell time) were increased in ADHD and positively correlated with ADHD severity; another state characterized by high overall BEN and low within-state BEN located in DMN (default mode network) and ECN (executive control network), its FW and MDT were decreased in ADHD and negatively correlated with ADHD severity. LimitationsThe window length of dBEN analysis can be further optimized to suit more datasets. The co-variation between dBEN and other dynamical brain metrics was not explored. ConclusionOur findings revealed abnormal BEN dynamics in ADHD, providing new insights into clinical diagnosis and neuropathology of ADHD.

Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma

Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the exploration of how PACG impacts the dynamic characteristics of functional brain networks. This study enrolled forty-four patients diagnosed with PACG and forty-four age, gender, and education level-matched healthy controls (HCs). The study employed Independent Component Analysis (ICA) techniques to extract resting-state networks (RSNs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. Subsequently, the RSNs was utilized as the basis for examining and comparing the functional connectivity variations within and between the two groups of resting-state networks. To further explore, a combination of sliding time window and k-means cluster analyses identified seven stable and repetitive dynamic functional network connectivity (dFNC) states. This approach facilitated the comparison of dynamic functional network connectivity and temporal metrics between PACG patients and HCs for each state. Subsequently, a support vector machine (SVM) model leveraging functional connectivity (FC) and FNC was applied to differentiate PACG patients from HCs. Our study underscores the presence of modified functional connectivity within large-scale brain networks and abnormalities in dynamic temporal metrics among PACG patients. By elucidating the impact of changes in large-scale brain networks on disease evolution, researchers may enhance the development of targeted therapies and interventions to preserve vision and cognitive function in PACG.

Parallel electrophysiological abnormalities due to COVID-19 infection and to Alzheimer's disease and related dementia.

Many coronavirus disease 2019 (COVID-19) positive individuals exhibit abnormal electroencephalographic (EEG) activity reflecting "brain fog" and mild cognitive impairments even months after the acute phase of infection. Resting-state EEG abnormalities include EEG slowing (reduced alpha rhythm; increased slow waves) and epileptiform activity. An expert panel conducted a systematic review to present compelling evidence that cognitive deficits due to COVID-19 and to Alzheimer's disease and related dementia (ADRD) are driven by overlapping pathologies and neurophysiological abnormalities. EEG abnormalities seen in COVID-19 patients resemble those observed in early stages of neurodegenerative diseases, particularly ADRD. It is proposed that similar EEG abnormalities in Long COVID and ADRD are due to parallel neuroinflammation, astrocyte reactivity, hypoxia, and neurovascular injury. These neurophysiological abnormalities underpinning cognitive decline in COVID-19 can be detected by routine EEG exams. Future research will explore the value of EEG monitoring of COVID-19 patients for predicting long-term outcomes and monitoring efficacy of therapeutic interventions. HIGHLIGHTS: Abnormal intrinsic electrophysiological brain activity, such as slowing of EEG, reduced alpha wave, and epileptiform are characteristic findings in COVID-19 patients. EEG abnormalities have the potential as neural biomarkers to identify neurological complications at the early stage of the disease, to assist clinical assessment, and to assess cognitive decline risk in Long COVID patients. Similar slowing of intrinsic brain activity to that of COVID-19 patients is typically seen in patients with mild cognitive impairments, ADRD. Evidence presented supports the idea that cognitive deficits in Long COVID and ADRD are driven by overlapping neurophysiological abnormalities resulting, at least in part, from neuroinflammatory mechanisms and astrocyte reactivity. Identifying common biological mechanisms in Long COVID-19 and ADRD can highlight critical pathologies underlying brain disorders and cognitive decline. It elucidates research questions regarding cognitive EEG and mild cognitive impairment in Long COVID that have not yet been adequately investigated.

42P Characterizing MRI brain abnormalities in X-linked myotubular myopathy

42P Characterizing MRI brain abnormalities in X-linked myotubular myopathy

T2 Hyperintensities in Children with Neurofibromatosis Type 1

Areas of increased signal intensity, known as T2 hyperintensities (T2Hs), observed on T2-weighted magnetic resonance imaging (MRI) scans, are linked to a spectrum of brain abnormalities in children with neurofibromatosis type 1 (NF1). Defining the radiological characteristics that distinguish non-neoplastic from neoplastic T2Hs in children with NF1 is crucial. Then, we could identify lesions that most likely to require oncologic surveillance. We conducted a single-center retrospective review of all available brain MRIs from 98 children with NF1 and 50 healthy pediatric controls. All T2Hs identified on MRI were characterized based on location, imaging features, presence of lesion related symptoms. Subsequently, all T2Hs were classified using newly established criteria and categorized into three distinct groups: low-risk tumor lesions, medium-risk tumor lesions, and high-risk tumor lesions. Lesions deemed to be high-risk will be recommended for surgical treatment. T2Hs were present in 61 (62.2%) individuals of the NF1 cohort. T2Hs were a highly sensitive (100%; 95% confidence interval [CI] 92.9%-100.0%) and specific (62.2%; 95% CI 51.9%-71.8%) marker for the diagnosis of NF1. In children aged 4-10, the detection rate of T2Hs is significantly higher than in children under 4 years old and those aged between 10 and 18(P<0.05). T2Hs were most frequently located in basal ganglia, cerebellar hemispheres, and brainstem. During the follow-up process, none of lesions categorized as low-risk or medium-risk tumor lesions progressed to high-risk tumor lesions. Seven patients had high-risk tumor lesions and underwent surgical treatment.The pathological assessment identified 5 cases of glioma among the 7 patients, along with 1 case of gliosis and 1 case of vascular dysplasia. Low-risk and medium-risk tumor lesions can both be classified as unidentified bright objects(UBOs).UBOs constituted the majority of T2Hs in children with NF1. High-risk tumor lesions should be considered as probable tumors.With the application of standardized radiologic criteria, a high prevalence of probable brain tumors will be identified in this at-risk population of children, which underscores the importance of vigilant and appropriate oncological surveillance to ensure timely detection and intervention for these tumors.

C-section and systemic inflammation synergize to disrupt the neonatal gut microbiota and brain development in a model of prematurity

Infants born very preterm (below 28 weeks of gestation) are at high risk of developing neurodevelopmental disorders, such as intellectual deficiency, autism spectrum disorders, and attention deficit. Preterm birth often occurs in the context of perinatal systemic inflammation due to chorioamnionitis and postnatal sepsis. In addition, C-section is often performed for very preterm neonates to avoid hypoxia during a vaginal delivery. We have developed and characterized a mouse model based on intraperitoneal injections of IL-1β between postnatal days one and five to reproduce perinatal systemic inflammation. This model replicates several neuropathological, brain imaging, and behavioral deficits observed in preterm infants. We hypothesized that C-sections could synergize with systemic inflammation to induce more severe brain abnormalities. We observed that C-sections significantly exacerbated the deleterious effects of IL-1β on reduced gut microbial diversity, increased levels of circulating peptidoglycans, abnormal microglia/macrophage reactivity, impaired myelination, and reduced functional connectivity in the brain relative to vaginal delivery plus intraperitoneal saline. These data demonstrate the deleterious synergistic effects of C-section and neonatal systemic inflammation on brain maldevelopment and malfunction, two conditions frequently observed in very preterm infants, who are at high risk of developing neurodevelopmental disorders.

Huperzine A suppresses absence seizures in the genetic absence epilepsy rat from Strasbourg (GAERS) model of genetic generalized epilepsy with absence seizures.

We evaluated huperzine A treatment in the Genetic Absence Epilepsy Rat from Strasbourg (GAERS) model of genetic generalized epilepsy (GGE) with absence seizures. Adult male GAERS (N = 15) were implanted with EEG recording electrodes 10 days before receiving study drug. Each animal received the following six treatments as a single, intraperitoneal dose, 7 days apart (in random order): huperzine A (0.3, 1.0, or 3.0 mg/kg), two periods of vehicle (0.9% NaCl), or ethosuximide (100 mg/kg) as a positive control. Electroencephalograms (EEGs) were acquired for 24 h before and after each treatment and analyzed for seizure activity during the 90-min period immediately post-treatment, including 30-min intervals at 30, 60, and 90 min. Additional analyses evaluated seizure activity over the 24-h post-treatment period using 60-min intervals at 6, 12, and 24 h. The cumulative 24-h periods before and after each administered treatment were also compared. Two-way ANOVA showed a treatment difference [F(91,182) = 3.592, p < 0.0001] on the number of seizures over the first 90-min post-treatment (primary outcome); Tukey's post hoc analyses showed that, compared to vehicle, huperzine A (3.0 mg/kg) significantly reduced seizures in the 30-min (p = 0.02) and 60-min (p = 0.001) intervals, and ethosuximide significantly reduced seizures at all measured time intervals except the 1-h blocks at 12 and 24 h. Huperzine A 3.0 mg/kg and ethosuximide significantly reduced seizures during the cumulative 24-h post-treatment period relative to pretreatment baseline. While huperzine A 3.0 mg/kg did not differ significantly from ethosuximide at any time point, the study was not designed to evaluate non-inferiority. The only adverse event after huperzine A or ethosuximide was mild, dose-dependent sedation. Huperzine A potently suppressed absence-like seizures in GAERS, albeit with a shorter duration of action relative to ethosuximide, showing promise for clinical efficacy in GGE. This study looked at how huperzine A affects seizures in rats with similar abnormal brain activity as seen in humans with absence epilepsy. Rats received different treatments, placebo (i.e., saline solution), huperzine A, and ethosuximide. Ethosuximide is considered a gold standard treatment for absence epilepsy. We recorded brain activity to measure seizures before and after each treatment. We found that huperzine A (3.0 mg/kg) reduced seizures soon after treatment, like ethosuximide. Both treatments appeared safe, causing only mild sleepiness. The study shows that huperzine A could be a good new treatment for a type of absence epilepsy.

Selective segmentation of brain abnormalities in colour MRI images using variational model

Early detection of brain abnormalities is vital for enhancing patient outcomes and survival rates. However, accurately identifying and segmenting these abnormalities from MRI images remains a persistent challenge. This study assesses the efficacy of the Selective Local Image Fitting (SLIF) model in segmenting brain abnormalities from colour MRI images and compares its performance with converted greyscale counterparts. The rationale behind this comparison stems from standard practice in image segmentation, where colour images are often converted to greyscale before the segmentation task. Converting the image might degrade data by diminishing its dimensions, potentially affecting segmentation computations. This study intends to evaluate the influence of colour information on segmentation accuracy and efficiency by directly assessing the SLIF model on both colour and converted greyscale images. Segmentation accuracy was evaluated using metrics such as the Dice Similarity Coefficient (DSC), Matthews Correlation Coefficient (MCC), and Intersection-over-Union (IoU). Efficiency was determined by measuring the average elapsed processing time. Experimental results demonstrate that colour MRI brain images outperform their converted greyscale counterparts in segmentation accuracy, as colour providing essential supplementary information for precise abnormality delineation. Despite a slight increase in average elapsed processing time for colour images, the enhanced accuracy justifies this trade-off. These findings emphasize the importance of colour MRI in enhancing diagnostic accuracy, especially in detecting brain abnormalities. This study can be extended in future work to evaluate the segmentation accuracy and efficiency of brain abnormalities in 3D colour and greyscale MRI images.

Determination of Mandibular Morphology in a Turkish Population With Bipolar I Disorder Using Panoramic Radiography.

The presence of specific craniofacial deviations may be evidence of abnormal early brain development in bipolar disorder (BD). This study aimed to compare mandibular morphology in patients with BD versus nonpsychiatric controls using panoramic radiography. A total of 215 recorded images, 70 patients with BD and 145 controls, were evaluated. Seven mandibular parameters of the ramus height (RH), condylar height (CH), gonial angle (GA), antegonial angle (AGA), antegonial notch depth (AND), ramal notch depth (RND), and bigonial width (BGW) were measured in a blinded manner. Group analysis was conducted separately for males and females. The univariate comparisons showed increased values in RH, CH, AND, RND, and BGW and decreased values in GA in males with BD compared with male controls. In addition, females with BD had higher RH, CH, RND, and BGW values in comparison to female controls. The differences were statistically significant. Data from multivariate analyses revealed that increases in RH and BGW values in both sexes were significant predictive variables that distinguished patients with BD from controls. Moreover, deeper AND was the most prominent predictor that distinguished male patients with BD from male controls. The identification of subtle changes in mandibular measurements is consistent with the notion of impaired neurodevelopment in BD.

Structural and functional brain markers of cognitive impairment in healthcare workers following mild SARS-CoV-2 infection during the original stream.

Severe acute respiratory syndrome coronavirus 2 infection often involves the nervous system, leading to cognitive dysfunctions, fatigue and many other neurological signs that are becoming increasingly recognized. Despite mild forms of the disease accounting for most cases worldwide, research on the pathophysiology driving mild coronavirus disease 2019 (COVID-19) has received little attention. In this respect, recent evidence has pointed out that around 30-40% of non-critical, mild-to-moderate severity COVID-19 survivors may display cognitive disturbances several months post-illness. Hence, the impact of COVID-19 on the brain structure and function, through potential neuropathological mechanisms underpinning cognitive alterations in post-mild COVID-19 infections, remains largely unexplored. This retrospective multicentre observational cohort study, entirely based on a healthcare worker sample (n = 65; 55% females, aged 21-61), investigated the cognitive status and the structural and functional brain integrity among non-hospitalized individuals who developed mild COVID-19 symptoms during the occurrence of severe acute respiratory syndrome coronavirus 2 variants Alpha to Delta, compared with healthy controls tested before the pandemic onset. All evaluations were performed at an average of 9-month follow-up post-infection period. Participants completed a comprehensive neuropsychological assessment and structural and functional MRI exams. Radiological inspection sought to detect the presence of white matter hyperintensities on axial fluid-attenuated inversion recovery images. Global and regional grey matter integrity assessment, analysing changes in grey matter volumes and cortical thinning, and functional connectivity alterations of resting-state brain networks were also conducted. Regression analyses tested the relationships between the presence of specific cognitive impairments and potential structural and functional brain findings. Our results revealed that clinical, cognitive screening and neuropsychological examinations were average between both groups, except for specific impairments related to executive functions in the mild COVID-19. Compared to healthy controls, mild COVID-19 subjects exhibited increased juxtacortical white matter hyperintensities, thalamic and occipital volume loss and diminished resting-state functional connectivity involving the left precuneus and cuneus in default-mode network and affecting the right angular gyrus and left precuneus in the dorsal attentional network. Reduced thalamic volume was the only variable selected in the final model explaining the observed executive function impairment in mild COVID-19. The presence of cognitive, structural and functional brain abnormalities over time suggests that the action of widespread neurovascular and inflammatory phenomena on the nervous system might also occur in mild forms following COVID-19 infection rather than permanent brain damage linked to the direct or indirect action of the virus. Our findings emphasize the need to pay attention to the long-term brain-related consequences of mild COVID-19 infections during the original stream.

Brain Tumor Detection using Improved Binomial Thresholding Segmentation and Sparse Bayesian Extreme Learning Machine Classification

People are dying these days from numerous deadliest diseases. One such illness is brain tumour, in which the unusual cells within the tumour quickly begin to damage the brain's healthy cells. Owing to this rapid growth, a person may pass away before the disease receives a correct diagnosis. Early disease detection is essential for any disease to help save the patient by providing them with better care. In a similar vein, a patient's life depends on early brain tumour detection. Brain tumour detection is an extremely challenging procedure that we would like to simplify in order to save time. The proposed model facilitates the quicker and more accurate identification of abnormal brain cells, leading to the early detection of brain tumours. In this work, an improved binomial thresholding-based segmentation (IBTBS) is introduced for segmentation purpose. From this segmented image, information theoretic based, wavelet transform (WT) based, and wavelet scattering transform (WST) based features are extracted. An optimization-based feature selection approach (OBFSA) is incorporated between feature selection and tumour classification in order to reduce the dimension of this retrieved feature. Finally, classification is performed using the Sparse Bayesian extreme learning machine (SBELM) classifier. The execution process of this proposed methodology takes an MRI image from the free accessible source. By computing and detecting four different parameters, the experimental analysis of the proposed approach displays the accuracy, specificity, and sensitivity values. This model can assist us in quickly diagnosing brain tumours, potentially saving the lives of patients.

Neuroimaging Findings From Cerebral Structure and Function in Coronary Artery Disease.

An increasing number of evidence suggests that bidirectional communication between the cardiovascular system and the central nervous system (CNS), known as the heart-brain interaction, is crucial in understanding the impact of coronary artery disease (CAD) on brain health. The multifactorial role of CAD in the brain involves processes such as inflammation, oxidative stress, neuronal activity, neuroendocrine imbalances, and reduced cerebral perfusion, leading to various cerebral abnormalities. The mechanisms underlying the relationship between CAD and brain injury are complex and involve parallel pathways in the CNS, endocrine system, and immune system. Although the exact mechanisms remain partially understood, neuroimaging techniques offer valuable insights into subtle cerebral abnormalities in CAD patients. Neuroimaging techniques, including assessment of neural function, brain metabolism, white matter microstructure, and brain volume, provide information on the evolving nature of CAD-related cerebral abnormalities over time. This review provides an overview of the pathophysiological mechanisms of CAD in the heart-brain interaction and summarizes recent neuroimaging studies utilizing multiparametric techniques to investigate brain abnormalities associated with CAD. The application of advanced neuroimaging, particularly functional, diffusion, and perfusion advanced techniques, offers high resolution, multiparametric capabilities, and high contrast, thereby allowing for the early detection of changes in brain structure and function, facilitating further exploration of the intricate relationship between CAD and brain health. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

Deep brain stimulation: a promising approach to revolutionize the treatment of pediatric epilepsy.

A variety of treatment modalities currently exist for epilepsy, a debilitating disorder. With the emergence of drug-resistant epilepsy, however, new options are being explored. Deep brain stimulation is a neuromodulation technique that can prove to be a ground-breaking treatment option for pediatric epilepsy. It employs a neurosurgical method in which electrodes are implanted within the brain that send impulses to control abnormal brain activity. Significant gaps exist in literature, thereby emphasizing the importance of further research in this promising approach.

Olfactory Bulb Volume and Asymmetry as Predictors of Executive Dysfunction in Adolescents with Congenital Heart Disease.

Common sequelae for patients with congenital heart disease (CHD) are neurodevelopmental disabilities including executive function, attention, and socio-emotional deficits. Although these are common diagnoses for patients with CHD, limited research has investigated the mechanistic underpinnings of these findings. Our previous research examined the association between abnormal respiratory ciliary motion and brain abnormalities in infants with CHD. Results suggested that abnormal ciliary motion correlated to a spectrum of subtle dysplasia, notably within the olfactory bulb (OB)1. Our current study investigates whether OB anomalies predict neurodevelopmental outcomes for pediatric patients with CHD. We hypothesize that adolescents with CHD who exhibit aberrant morphological measurements in the OB are more likely to suffer from executive functional deficits. A prospective, observational study of 54 CHD and 75 healthy subjects, ages 6-25 years old, was completed under the supervision of a senior pediatric neuroradiologist. T2 3D Space and T2 Blade 2MM MRI images were manually segmented to extract volumetric bilateral regions of the OB and cerebrospinal fluid (CSF) using ITK-SNAP. Imaging metrics were correlated to OB asymmetry, CSF to OB ratio, total CSF volume, total OB volume, and independent left and right CSF and OB volumes. Linear regression was used to evaluate MRI morphologic measurements with co-variates: CHD status, sex, MRI age, and segmenter. Executive function was determined by the Behavioral Rating Inventory of Executive Function (BRIEF) Parent Report and Delis-Kaplan Executive Function System (D-KEFS) for subjects ages 6-16. Cognition and olfactory function were measured with the NIH Toolbox Cognitive Battery and Odor Identification Test, respectively. No statistically significant results were reported between cohorts for asymmetry of OB, CSF to OB ratio, total CSF volume, total OB volume, nor between independent left and right CSF and OB volumes. Increased OB volume was associated with worse outcomes on the BRIEF Parent Report (p≤0.03). Asymmetry of OB predicted poorer executive functioning as reported by parents on the BRIEF (p≤0.05). Overall, the CHD cohort demonstrated worse scores on the BRIEF Parent Report compared to controls. Across groups, no significant association was reported for olfaction function measured by the NIH Toolbox Odor Identification Test on a limited subset of participants. As survival rates for CHD improve, there is an increased risk of long-term neurodevelopmental impairments. Our findings identify adolescents who are at risk for executive dysfunction, particularly those showing increased OB volume and/or asymmetry of the OB. This is particularly concerning for the CHD population with atypical OB morphology, who also exhibit significantly poorer outcomes on the BRIEF Parent Report and face a higher overall risk. Increased OB volume and OB asymmetry are olfactory-based biomarkers that may help identify at-risk CHD patients earlier, enabling more timely intervention and support.

Abnormal brain regional activity in acute thyrotoxic myopathy assessed by resting-state functional MRI.

The neurophysiological mechanisms underlying manifestations of bulbar paralysis in acute thyrotoxic myopathy (ATM) and the afflicted brain areas are unclear. We used resting-state functional magnetic resonance imaging (rs-fMRI) to evaluate the regional brain activities in patients with ATM. In total, 16 patients with ATM, 16 patients with hyperthyroidism without ATM, and 16 healthy controls underwent functional MRI scans. By calculating the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC), we assessed variations in resting-state cerebral activity. The correlation between the resting-state functional indexes and clinical assessments was also explored. Compared to the hyperthyroid patients, patients with ATM had stronger ReHo in the left precentral gyrus, reduced ReHo in the left orbitofrontal gyrus (OFG), and decreased FC in the left precentral gyri, left superior frontal gyrus (SFG), and left middle frontal gyrus (MFG). Patients with ATM showed reduced fALFF and ReHo in the right SFG and decreased ReHo in the bilateral supplementary motor area (SMA). A significantly decreased FC in the left SFG and left MFG, right precentral gyrus, and the orbital part of the right interior frontal gyrus was observed in patients with ATM compared to healthy controls. Additionally, fALFF and ReHo values were positively correlated with serum thyroid-related hormones and antibodies. The findings of rs-fMRI demonstrate that particular brain regions' functional activity was aberrant in individuals with ATM, especially in SFG area. This finding may help with better understanding of underlying pathophysiology of patients with ATM.

The function of Mef2c toward the development of excitatory and inhibitory cortical neurons.

Neurodevelopmental disorders (NDDs) are caused by abnormal brain development, leading to altered brain function and affecting cognition, learning, self-control, memory, and emotion. NDDs are often demarcated as discrete entities for diagnosis, but empirical evidence indicates that NDDs share a great deal of overlap, including genetics, core symptoms, and biomarkers. Many NDDs also share a primary sensitive period for disease, specifically the last trimester of pregnancy in humans, which corresponds to the neonatal period in mice. This period is notable for cortical circuit assembly, suggesting that deficits in the establishment of brain connectivity are likely a leading cause of brain dysfunction across different NDDs. Regulators of gene programs that underlie neurodevelopment represent a point of convergence for NDDs. Here, we review how the transcription factor MEF2C, a risk factor for various NDDs, impacts cortical development. Cortical activity requires a precise balance of various types of excitatory and inhibitory neuron types. We use MEF2C loss-of-function as a study case to illustrate how brain dysfunction and altered behavior may derive from the dysfunction of specific cortical circuits at specific developmental times.

Radiomic detection of abnormal brain regions in tuberous sclerosis complex.

Radiomics refers to the extraction of quantitative information from medical images and is most commonly utilized in oncology to provide ancillary information for solid tumor diagnosis, prognosis, and treatment response. The traditional radiomic pipeline involves segmentation of volumes of interest with comparison to normal brain. In other neurologic disorders, such as epilepsy, lesion delineation may be difficult or impossible due to poor anatomic definition, small size, and multifocal or diffuse distribution. Tuberous sclerosis complex (TSC) is a rare genetic disease in which brain magnetic resonance imaging (MRI) demonstrates multifocal abnormalities with variable imaging and epileptogenic features. The purpose of this study was to develop a radiomic workflow for identification of abnormal brain regions in TSC, using a whole-brain atlas-based approach with generation of heatmaps based on signal deviation from normal controls. This was a retrospective pilot study utilizing high-resolution whole-brain 3D FLAIR MRI datasets from retrospective enrollment of tuberous sclerosis complex (TSC) patients and normal controls. Subjects underwent MRI including high-resolution 3D FLAIR sequences. Preprocessing included skull stripping, coregistration, and intensity normalization. Using the Brainnetome and Harvard-Oxford atlases, brain regions were parcellated into 318 discrete regions. Expert neuroradiologists spatially labeled all tubers in TSC patients using ITK-SNAP. The pyradiomics toolbox was used to extract 88 radiomic features based on IBSI guidelines, comparing tuber-affected and non-tuber-affected parenchyma in TSC patients, as well as normal brain tissue in control patients. For model training and validation, regions with tubers from 20 TSC patients and 30 normal control subjects were randomly divided into two training sets (80%) and two validation sets (20%). Additional model testing was performed on a separate group of 20 healthy controls. LASSO (least absolute shrinkage and selection operator) was used to perform variable selection and regularization to identify regions containing tubers. Relevant radiomic features selected by LASSO were combined to produce a radiomic score ω, defined as the sum of squared differences from average control group values. Region-specific ω scores were converted to heat maps and spatially coregistered with brain MRI to reflect overall radiomic deviation from normal. The proposed radiomic workflow allows for quantification of deviation from normal in 318 regions of the brain with the use of a summative radiomic score ω. This score can be used to generate spatially registered heatmaps to identify brain regions with radiomic abnormalities. The pilot study of TSC showed radiomic scores ω that were statistically different in regions containing tubers from regions without tubers/normal brain (p<0.0001). Our model exhibits an AUC of 0.81 (95% confidence interval: 0.78-0.84) on the testing set, and the best threshold obtained on the training set, when applied to the testing set, allows us to identify regions with tubers with a specificity of 0.91 and a sensitivity of 0.60. We describe a whole-brain atlas-based radiomic approach to identify abnormal brain regions in TSC patients. This approach may be helpful for identifying specific regions of interest based on relatively greater signal deviation, particularly in clinical scenarios with numerous or poorly defined anatomic lesions.

Aggressive and Autoaggressive Behaviors in Patients with Autism Spectrum Disorder in Correlation with Middle Cerebral Artery Flow Velocity.

Background/Objectives The purpose of this study was to see whether there is a correlation between the behavior of autism spectrum disorder patients and brain abnormalities based on the velocity of blood flow in the MCA (middle cerebral artery). Methods: The use of HAP (High Altitude Protection) suits, which are used in aviation, to treat patients with ASD (autism spectrum disorder) causes significant changes in their functioning and physiological processes. These changes are not only noted in psychological tests but are observed in cerebral blood flow using transcranial Doppler ultrasound of the MCA. Results The results of this study made it possible to distinguish two groups with different flow velocities, which can be characterized as flows of less than 80 cm/s and flows of more than 80 cm/s. In addition, it was shown that in patients with elevated blood flow velocity, aggressive behaviors account for 86.96%, while self-aggressive behaviors account for 65.2%. On the other hand, in the case of patients with reduced flow velocity, i.e., less than 80 cm/s, the rate of aggressive behavior is 20% and that of self-aggressive behavior is 50%. The experiment showed that after therapy, there is a normalization of blood flow, which increased in the case of patients with a reduced flow rate below 80 cm/s and, in the case of elevated blood velocity after therapy, decreased towards normal levels. Conclusions The observed rate of normalization of flow velocities in the MCA translated into significant changes in the behavior and functioning of patients in the neurotypical direction, which was noticeable in the psychological tests conducted.

Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks

Early and precise detection of brain tumors is critical for improving clinical outcomes and patient quality of life. This research focused on developing an image classifier using convolutional neural networks (CNN) to detect brain tumors in magnetic resonance imaging (MRI). Brain tumors are a significant cause of morbidity and mortality worldwide, with approximately 300,000 new cases diagnosed annually. Magnetic resonance imaging (MRI) offers excellent spatial resolution and soft tissue contrast, making it indispensable for identifying brain abnormalities. However, accurate interpretation of MRI scans remains challenging, due to human subjectivity and variability in tumor appearance. This study employed CNNs, which have demonstrated exceptional performance in medical image analysis, to address these challenges. Various CNN architectures were implemented and evaluated to optimize brain tumor detection. The best model achieved an accuracy of 97.5%, sensitivity of 99.2%, and binary accuracy of 98.2%, surpassing previous studies. These results underscore the potential of deep learning techniques in clinical applications, significantly enhancing diagnostic accuracy and reliability.

Bridging Prenatal Diagnostics and AI: A Systematic Review and Meta-Analysis of the Efficacy of Advanced Algorithms in Identifying Congenital Fetal Abnormalities

Congenital fetal abnormalities have emerged as a major cause of infant mortality globally. About 3% of pregnancies are reported as fetal structural anomaly through ultrasound, ranging from minor defect to severe organ system anomalies. Our study aimed to evaluate effectiveness of Artificial intelligence (AI) algorithms in prediction of fetal heart and brain abnormalities by using meta-analysis approach. In this study, the “Reporting Items for Systematic Review and Meta-Analysis (PRISMA)" guidelines were applied for screening and selection of research articles. We searched the research articles according to research aims from Google scholar, PubMed, and Ovid MEDLINE. The data search was limited to January 2015 to May 2024. Two researchers independently screened the studies to detect research aim oriented studies. After screening of titles, those researchers assessed all abstract for eligibility. The risk bias of included studies was assessed by two researchers who used Cochrane library tool (version 5.4.0). Using the DerSimonian-Laird technique, a bivariate random effect model that generated SROC plots, we pooled test estimates. This method is particularly helpful in visualizing variability in the sensitivity and specificity across studies, accounting for the heterogeneity. This improves the reliability of the SROC plot, offering a clear summary of diagnostic performance Using the software RevMan 5.4, we carried out every statistical analysis, particularly heterogeneity through the Q test. About 243,456 screened fetus or pregnant women of 11-32 gestational weeks through 10 studies based on AI algorithms (machine learning, deep learning and AI diagnostic tool). The imaging protocols including ultrasound and MRI were used to take visuals of fetal brain and heart abnormalities in all of including studies. DL and ML algorithms provided high-performance diagnostic predictions, mostly include CNN models that assist in providing accurate diagnostic results with high sensitivity and specificity. The AI algorithm model showed a generally good accuracy scores for detection of brain and heart abnormalities and reference machine learning findings, with sensitivity ranging from 82 to 99% and specificity from 78 to 99%. Fig. 5's area under the SROC curve was 0.960. In contrast to the general detection, ML and DL seems to be more sensitive in diagnosing fetal brain and heart abnormalities. Our study provided the scientific evidence that AI models including deep learning and Machine learning are effective in generating highly reliable detection or classification results for diagnosis of fetal brain and heart anomalies. These strategies can help in reducing infant mortalities, improving treatment outcomes, and postpartum outcomes among women.