Default mode network dynamics: An integrated neurocircuitry perspective on social dysfunction in human brain disorders.

Our intricate social brain is implicated in a range of brain disorders, where social dysfunction emerges as a common neuropsychiatric feature cutting across diagnostic boundaries. Understanding the neurocircuitry underlying social dysfunction and exploring avenues for its restoration could present a transformative and transdiagnostic approach to overcoming therapeutic challenges in these disorders. The brain's default mode network (DMN) plays a crucial role in social functioning and is implicated in various neuropsychiatric conditions. By thoroughly examining the current understanding of DMN functionality, we propose that the DMN integrates diverse social processes, and disruptions in brain communication at regional and network levels due to disease hinder the seamless integration of these social functionalities. Consequently, this leads to an altered balance between self-referential and attentional processes, alongside a compromised ability to adapt to social contexts and anticipate future social interactions. Looking ahead, we explore how adopting an integrated neurocircuitry perspective on social dysfunction could pave the way for innovative therapeutic approaches to address brain disorders.

Epileptic foci and networks in children with epilepsy after acute encephalopathy with biphasic seizures and late reduced diffusion.

BACKGROUND: Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) develops along with status epilepticus and widespread subcortical white matter edema. We aimed to evaluate the epileptic foci and networks in two patients with epilepsy after AESD using simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI). METHODS: Statistically significant blood oxygen level-dependent (BOLD) responses related to interictal epileptiform discharges (IEDs) were analyzed using an event-related design of hemodynamic response functions with multiple peaks. RESULTS: Patient 1 developed focal seizures at age 10 years, one year after AESD onset. Positive BOLD changes were observed in the bilateral frontotemporal lobes, left parietal lobe, and left insula. BOLD changes were also observed in the subcortical structures. Patient 2 developed epileptic spasms at age two years, one month after AESD onset. Following total corpus callosotomy (CC) at age three years, the epileptic spasms resolved, and neurodevelopmental improvement was observed. Before CC, positive BOLD changes were observed bilaterally in the frontotemporal lobes. BOLD changes were also observed in the subcortical structures. After CC, the positive BOLD changes were localized in the temporal lobe ipsilateral to the IEDs, and the negative BOLD changes were mainly in the cortex and subcortical structures of the hemisphere ipsilateral to IEDs. CONCLUSION: EEG-fMRI revealed multiple epileptic foci and extensive epileptic networks, including subcortical structures in two cases with post-AESD epilepsy. CC may be effective in disconnecting the bilaterally synchronous epileptic networks of epileptic spasms after AESD, and pre-and post-operative changes in EEG-fMRI may reflect improvements in epileptic symptoms.

Indications for a brain biopsy in neurological diseases of unknown etiology: The role of magnetic resonance imaging findings and liquid biopsy in yielding definitive pathological diagnoses.

Brain biopsies are often considered for patients who cannot be diagnosed with various laboratory test results. However, physicians tend to be hesitant regarding their application in possibly non-neoplastic brain diseases, due to the invasiveness and risks. The aim was to determine the indications for brain biopsies in cases of neurological diseases of unknown etiology. We retrospectively evaluated diagnostic accuracy, laboratory findings (including a liquid biopsy for malignant lymphoma), magnetic resonance imaging (MRI) characteristics and the post-treatment outcomes of patients undergoing brain biopsies for neurological diseases of unknown etiology. The data of patients who had undergone a brain biopsy during their admission to Niigata University Hospital, between 2011 and 2024, were reviewed. Moreover, the laboratory data and MRI findings between patients with definitive and nonspecific biopsy diagnoses were compared. Twenty-six patients underwent a brain biopsy, and a definitive diagnosis was obtained in 14 patients (53.8%). Even in cases where a nonspecific diagnosis was made, biopsy findings helped rule out malignancy and guide clinical diagnosis and treatment decisions. The liquid biopsy for malignant lymphoma was performed in eight patients, with one yielding a positive result, consistent with primary central nervous system lymphoma. The sensitivity and specificity of liquid biopsy were 0.5 and 1, respectively. Diffusely contrasted cortical lesions and the presence of mass effects on MRI, were significantly associated with a definitive diagnosis, compared to a nonspecific diagnosis. In conclusion, brain MRI and liquid biopsies can assist in determining the appropriate indications for brain biopsies in neurological diseases of unknown etiology.

Adaptive node feature extraction in graph-based neural networks for brain diseases diagnosis using self-supervised learning.

Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In particular, brain networks have gained prominence as they offer additional valuable insights by establishing connections between EEG signal channels. While brain connections are typically delineated by channel signal similarity, there lacks a consistent and reliable strategy for ascertaining node characteristics. Conventional node features such as temporal and frequency domain properties of EEG signals prove inadequate for capturing the extensive EEG information. In our investigation, we introduce a novel adaptive method for extracting node features from EEG signals utilizing a distinctive task-induced self-supervised learning technique. By amalgamating these extracted node features with fundamental edge features constructed using Pearson correlation coefficients, we showed that the proposed approach can function as a plug-in module that can be integrated to many common GNN networks (e.g., GCN, GraphSAGE, GAT) as a replacement of node feature selections module. Comprehensive experiments are then conducted to demonstrate the consistently superior performance and high generality of the proposed method over other feature selection methods in various of brain disorder prediction tasks, such as depression, schizophrenia, and Parkinson's disease. Furthermore, compared to other node features, our approach unveils profound spatial patterns through graph pooling and structural learning, shedding light on pivotal brain regions influencing various brain disorder prediction based on derived features.

Pediatric Cranial Ultrasound Revisited: A Comprehensive Review.

ABSTRACT: Cranial ultrasound (CUS) is an indispensable tool in the evaluation of intracranial pathology in premature and term neonates and older infants. Familiarity with standard cranial ultrasound techniques and parameters, normal anatomy, and commonly encountered abnormalities is crucial for providing appropriate care for these patients. This review provides a comprehensive overview of cranial ultrasound in clinical practice.

Evaluation of T2W FLAIR MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.

BACKGROUND: Artificial intelligence (AI) reconstruction techniques have the potential to improve image quality and decrease imaging time. However, these techniques must be assessed for safe and effective use in clinical practice. OBJECTIVE: To assess image quality and diagnostic confidence of AI reconstruction in the pediatric brain on fluid-attenuated inversion recovery (FLAIR) imaging. MATERIALS AND METHODS: This prospective, institutional review board (IRB)-approved study enrolled 50 pediatric patients (median age=12 years, Q1=10 years, Q3=14 years) undergoing clinical brain MRI. T2-weighted (T2W) FLAIR images were reconstructed by both standard clinical and AI reconstruction algorithms (strong denoising). Images were independently rated by two neuroradiologists on a dedicated research picture archiving and communication system (PACS) to indicate whether AI increased, decreased, or had no effect on image quality compared to standard reconstruction. Quantitative analysis of signal intensities was also performed to calculate apparent signal to noise (aSNR) and apparent contrast to noise (aCNR) ratios. RESULTS: AI reconstruction was better than standard in 99% (reader 1, 49/50; reader 2, 50/50) for overall image quality, 99% (reader 1, 49/50; reader 2, 50/50) for subjective SNR, and 98% (reader 1, 49/50; reader 2, 49/50) for diagnostic preference. Quantitative analysis revealed significantly higher gray matter aSNR (30.6±6.5), white matter aSNR (21.4±5.6), and gray-white matter aCNR (7.1±1.6) in AI-reconstructed images compared to standard reconstruction (18±2.7, 14.2±2.8, 4.4±0.8, p<0.001) respectively. CONCLUSION: We conclude that AI reconstruction improved T2W FLAIR image quality in most patients when compared with standard reconstruction in pediatric patients.

Fluorescent Probes for Disease Diagnosis.

The identification and detection of disease-related biomarkers is essential for early clinical diagnosis, evaluating disease progression, and for the development of therapeutics. Possessing the advantages of high sensitivity and selectivity, fluorescent probes have become effective tools for monitoring disease-related active molecules at the cellular level and in vivo. In this review, we describe current fluorescent probes designed for the detection and quantification of key bioactive molecules associated with common diseases, such as organ damage, inflammation, cancers, cardiovascular diseases, and brain disorders. We emphasize the strategies behind the design of fluorescent probes capable of disease biomarker detection and diagnosis and cover some aspects of combined diagnostic/therapeutic strategies based on regulating disease-related molecules. This review concludes with a discussion of the challenges and outlook for fluorescent probes, highlighting future avenues of research that should enable these probes to achieve accurate detection and identification of disease-related biomarkers for biomedical research and clinical applications.

Incidental findings in MRI of the brain.

This review investigates that there has been an increase in incidental brain MRI findings due to better technology and more scans. These unexpected, asymptomatic anomalies range from harmless to serious, requiring careful clinical and ethical handling. The prevalence of incidental findings with brain MRI is 4.2% and even higher when including white matter hyperintensities. There is a significant variation in this number dependent on the age of the person being scanned and the MRI quality.

GNB1 Encephalopathy: Clinical Case Report and Literature Review.

GNB1 encephalopathy is a rare genetic disease caused by pathogenic variants in the G Protein Subunit Beta 1 (GNB1) gene, with only around 68 cases documented worldwide. Although most cases had been caused by de novo germline mutations, in this case, the pathogenic variant was inherited from patient's mother, indicating an autosomal dominant inheritance pattern. The patient presented at 25 years of age with mild developmental delay and cognitive impairment, prominent generalized dystonia, and horizontal nystagmus which are all characterizing symptoms of GNB1 encephalopathy. Electroencephalography (EEG) showed no epileptiform patterns, and magnetic resonance imaging (MRI) revealed hypointensities in globus pallidus and dentate nucleus areas. The main theory for GNB1 encephalopathy pathogenesis is neuronal hyperexcitability caused by impaired ion channel regulation. Due to low specificity of symptoms, diagnosis relies on genetic testing. As there are no standardized GNB1 encephalopathy treatment guidelines, evaluation of different treatment options is based on anecdotal cases. Reviewing different treatment options, deep brain stimulation and intrathecal baclofen pump, as well as some other medications still in preclinical trials, seem to be the most promising.

Novel findings in a Swedish primary familial brain calcification cohort.

INTRODUCTION: Brain calcifications are frequent findings on imaging. In a small proportion of cases, these calcifications are associated with pathogenic gene variants, hence termed primary familial brain calcification (PFBC). The clinical penetrance is incomplete and phenotypic variability is substantial. This paper aims to characterize a Swedish PFBC cohort including 25 patients: 20 from seven families and five sporadic cases. METHODS: Longitudinal clinical assessment and CT imaging were conducted, abnormalities were assessed using the total calcification score (TCS). Genetic analyses, including a panel of six known PFBC genes, were performed in all index and sporadic cases. Additionally, three patients carrying a novel pathogenic copy number variant in SLC20A2 had their cerebrospinal fluid phosphate (CSF-Pi) levels measured. RESULTS: Among the 25 patients, the majority (76%) displayed varying symptoms during the initial assessment including motor (60%), psychiatric (40%), and/or cognitive abnormalities (24%). Clinical progression was observed in most patients (78.6%), but there was no significant difference in calcification between the first and second scans, with mean scores of 27.3 and 32.8, respectively. In three families and two sporadic cases, pathogenic genetic variants were identified, including a novel finding, in the SLC20A2 gene. In the three tested patients, the CSF-Pi levels were normal. CONCLUSIONS: This report demonstrates the variable expressivity seen in PFBC and includes a novel pathogenic variant in the SLC20A2 gene. In four families and three sporadic cases, no pathogenic variants were found, suggesting that new PFBC genes remain to be discovered.

Insights Into MRI Neuroimaging Patterns of COVID-19 in Children: A Retrospective Comprehensive Analysis.

RATIONALE AND OBJECTIVES: Neurological complications associated with coronavirus disease (COVID-19) have been reported in children; however, data on neuroimaging findings remain limited. This study aimed to comprehensively examine neuroimaging patterns of COVID-19 in children and their relationship with clinical outcomes. MATERIALS AND METHODS: This retrospective cross-sectional study involved reviewing the medical records and MRI scans of 95 children who developed new neurological symptoms within 2-4 weeks of clinical and laboratory confirmation of COVID-19. Patients were categorized into four groups based on guidelines approved by the Centers for Disease Control and Prevention (CDC). Initial brain/spinal MRI was performed. Images were reviewed by three blinded radiologists, and the findings were analyzed and categorized based on the observed patterns in the brain and spinal cord. Follow-up MRI was performed and analyzed to track lesion progression. RESULTS: Encephalopathy was the most common neurological symptom (50.5%). The most common initial MRI involvement patterns were non-confluent multifocal hyperintense white matter (WM) lesions (36.8%) and ischemia (18.9%). Most patients who underwent follow-up MRI (n = 56) showed complete resolution (69.9%); however, some patients developed encephalomalacia and myelomalacia (23.2% and 7.1%, respectively). Non-confluent hyperintense WM lesions were associated with good outcomes (45.9%, P = 0.014), whereas ischemia and hemorrhage were associated with poor outcomes (44.1%, P < 0.001). CONCLUSION: This study revealed diverse neuroimaging patterns in pediatric COVID-19 patients. Non-confluent WM lesions were associated with good outcomes, whereas ischemia and hemorrhage were associated with poorer prognoses. Understanding these patterns is crucial for their early detection, accurate diagnosis, and appropriate management.

Clinical characteristics and outcomes of COVID-19-associated encephalopathy in children.

Apart from the typical respiratory symptoms, coronavirus disease 2019 (COVID-19) also affects the central nervous system, leading to central disorders such as encephalopathy and encephalitis. However, knowledge of pediatric COVID-19-associated encephalopathy is limited, particularly regarding specific subtypes of encephalopathy. This study aimed to assess the features of COVID-19-associated encephalopathy/encephalitis in children. We retrospectively analyzed a single cohort of 13 hospitalized children with COVID-19-associated encephalopathy. The primary outcome was the descriptive analysis of the clinical characteristics, magnetic resonance imaging and electroencephalography findings, treatment progression, and outcomes. Thirteen children among a total of 275 (5%) children with confirmed COVID-19 developed associated encephalopathy/encephalitis (median age, 35 months; range, 3-138 months). Autoimmune encephalitis was present in six patients, acute necrotizing encephalopathy in three, epilepsy in three, and central nervous system small-vessel vasculitis in one patient. Eight (62%) children presented with seizures. Six (46%) children exhibited elevated blood inflammatory indicators, cerebrospinal fluid inflammatory indicators, or both. Two (15%) critically ill children presented with multi-organ damage. The magnetic resonance imaging findings varied according to the type of encephalopathy/encephalitis. Electroencephalography revealed a slow background rhythm in all 13 children, often accompanied by epileptic discharges. Three (23%) children with acute necrotizing encephalopathy had poor prognoses despite immunotherapy and other treatments. Ten (77%) children demonstrated good functional recovery without relapse. This study highlights COVID-19 as a new trigger of encephalopathy/encephalitis in children. Autoimmune encephalitis is common, while acute necrotizing encephalopathy can induce poor outcomes. These findings provide valuable insights into the impact of COVID-19 on children's brains.

Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

BACKGROUND AND PURPOSE: Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. MATERIALS AND METHODS: In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set. The data set encompassed studies with normal findings as well as those with pathologies, including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these, task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed the performance of each model. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each artificial intelligence model, along with the area under the ROC curve. RESULTS: Four teams processed 1177 studies. The median age of patients was 62 years, with an interquartile range of 33 years. Nineteen teams from various academic institutions registered for the competition. Of these, 4 teams submitted their final results. No commercial entities participated in the competition. For task 1, areas under the ROC curve ranged from 0.49 to 0.59. For task 2, two teams completed the task with area under the ROC curve values of 0.57 and 0.52. For task 3, teams had little-to-no agreement with the ground truth. CONCLUSIONS: To assess the performance of artificial intelligence models in real-world clinical scenarios, we analyzed their performance in the ASFNR Artificial Intelligence Competition. The first ASFNR Competition underscored the gap between expectation and reality; and the models largely fell short in their assessments. As the integration of artificial intelligence tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.