Higher-order functional brain networks and anterior cingulate glutamate + glutamine (Glx) in antipsychotic-naïve first episode psychosis patients.

Human connectome studies have provided abundant data consistent with the hypothesis that functional dysconnectivity is predominant in psychosis spectrum disorders. Converging lines of evidence also suggest an interaction between dorsal anterior cingulate cortex (dACC) cortical glutamate with higher-order functional brain networks (FC) such as the default mode (DMN), dorsal attention (DAN), and executive control networks (ECN) in healthy controls (HC) and this mechanism may be impaired in psychosis. Data from 70 antipsychotic-medication naïve first-episode psychosis (FEP) and 52 HC were analyzed. 3T Proton magnetic resonance spectroscopy (1H-MRS) data were acquired from a voxel in the dACC and assessed correlations (positive FC) and anticorrelations (negative FC) of the DMN, DAN, and ECN. We then performed regressions to assess associations between glutamate + glutamine (Glx) with positive and negative FC of these same networks and compared them between groups. We found alterations in positive and negative FC in all networks (HC > FEP). A relationship between dACC Glx and positive and negative FC was found in both groups, but when comparing these relationships between groups, we found contrasting associations between these variables in FEP patients compared to HC. We demonstrated that both positive and negative FC in three higher-order resting state networks are already altered in antipsychotic-naïve FEP, underscoring the importance of also considering anticorrelations for optimal characterization of large-scale functional brain networks as these represent biological processes as well. Our data also adds to the growing body of evidence supporting the role of dACC cortical Glx as a mechanism underlying alterations in functional brain network connectivity. Overall, the implications for these findings are imperative as this particular mechanism may differ in untreated or chronic psychotic patients; therefore, understanding this mechanism prior to treatment could better inform clinicians.Clinical trial registration: Trajectories of Treatment Response as Window into the Heterogeneity of Psychosis: A Longitudinal Multimodal Imaging Study, NCT03442101 . Glutamate, Brain Connectivity and Duration of Untreated Psychosis (DUP), NCT02034253 .

Fetal and neonatal outcomes of posterior fossa anomalies: a retrospective cohort study.

The primary aim of this study was to estimate the incidence of posterior fossa anomalies (PFA) and assess the associated outcomes in King Abdulaziz Medical City (KAMC), Riyadh. All fetuses diagnosed by prenatal ultrasound with PFA from 2017 to 2021 in KAMC were analyzed retrospectively. PFA included Dandy-Walker malformation (DWM), mega cisterna magna (MCM), Blake's pouch cyst (BPC), and isolated vermian hypoplasia (VH). The 65 cases of PFA were 41.5% DWM, 46.2% MCM, 10.8% VH, and 1.5% BPC. The annual incidence rates were 2.48, 2.64, 4.41, 8.75, and 1.71 per 1000 anatomy scans for 2017, 2018, 2019, 2020, and 2021, respectively. Infants with DWM appeared to have a higher proportion of associated central nervous system (CNS) abnormalities (70.4% vs. 39.5%; p-value = 0.014) and seizures than others (45% vs. 17.9%; p-value = 0.041). Ten patients with abnormal genetic testing showed a single gene mutation causing CNS abnormalities, including a pathogenic variant in MPL, C5orf42, ISPD, PDHA1, PNPLA8, JAM3, COL18A1, and a variant of uncertain significance in the PNPLA8 gene. Our result showed that the most common PFA is DWM and MCM. The autosomal recessive pathogenic mutation is the major cause of genetic disease in Saudi patients diagnosed with PFA.

Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas.

BACKGROUND: 1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI. METHODS: This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant. RESULTS: The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist. CONCLUSIONS: Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.

Impact of amyloid and tau positivity on longitudinal brain atrophy in cognitively normal individuals.

BACKGROUND: Individuals on the preclinical Alzheimer's continuum, particularly those with both amyloid and tau positivity (A + T +), display a rapid cognitive decline and elevated disease progression risk. However, limited studies exist on brain atrophy trajectories within this continuum over extended periods. METHODS: This study involved 367 ADNI participants grouped based on combinations of amyloid and tau statuses determined through cerebrospinal fluid tests. Using longitudinal MRI scans, brain atrophy was determined according to the whole brain, lateral ventricle, and hippocampal volumes and cortical thickness in AD-signature regions. Cognitive performance was evaluated with the Preclinical Alzheimer's Cognitive Composite (PACC). A generalized linear mixed-effects model was used to examine group × time interactions for these measures. In addition, progression risks to mild cognitive impairment (MCI) or dementia were compared among the groups using Cox proportional hazards models. RESULTS: A total of 367 participants (48 A + T + , 86 A + T - , 63 A - T + , and 170 A - T - ; mean age 73.8 years, mean follow-up 5.1 years, and 47.4% men) were included. For the lateral ventricle and PACC score, the A + T - and A + T + groups demonstrated statistically significantly greater volume expansion and cognitive decline over time than the A - T - group (lateral ventricle: ß = 0.757 cm3/year [95% confidence interval 0.463 to 1.050], P < .001 for A + T - , and ß = 0.889 cm3/year [0.523 to 1.255], P < .001 for A + T + ; PACC: ß = - 0.19 /year [- 0.36 to - 0.02], P = .029 for A + T - , and ß = - 0.59 /year [- 0.80 to - 0.37], P < .001 for A + T +). Notably, the A + T + group exhibited additional brain atrophy including the whole brain (ß = - 2.782 cm3/year [- 4.060 to - 1.504], P < .001), hippocampus (ß = - 0.057 cm3/year [- 0.085 to - 0.029], P < .001), and AD-signature regions (ß = - 0.02 mm/year [- 0.03 to - 0.01], P < .001). Cox proportional hazards models suggested an increased risk of progressing to MCI or dementia in the A + T + group versus the A - T - group (adjusted hazard ratio = 3.35 [1.76 to 6.39]). CONCLUSIONS: In cognitively normal individuals, A + T + compounds brain atrophy and cognitive deterioration, amplifying the likelihood of disease progression. Therapeutic interventions targeting A + T + individuals could be pivotal in curbing brain atrophy, cognitive decline, and disease progression.

Acute lumbar paraspinal compartment syndrome after radical cystectomy.

Lumbar paraspinal compartment syndrome (LPCS) is a rare diagnosis, seen in patients chronically after repeated lumbar trauma or acutely in a postoperative setting. Only a dozen cases are documented worldwide, and to date no clinical guidelines exist for the diagnosis nor the treatment.We describe the case of a 44-year-old man with excruciating lower back pain following a radical cystectomy. The postoperative laboratory values were compatible with acute rhabdomyolysis. The lumbar spine MRI showed necrosis of lumbosacral paraspinal muscles, making the diagnosis of acute LPCS. After seeking advice from different specialists, the conservative approach was chosen with combined pain treatment and physiotherapy. The patient is currently still disabled for some tasks and needs chronic pain medication.

Less is more: Smaller hippocampal subfield volumes predict greater improvements in posttraumatic stress disorder symptoms over 2 years.

Posttraumatic stress disorder (PTSD) is a heterogeneous disorder, and symptom severity varies over time. Neurobiological factors that predict PTSD symptoms and their chronicity remain unclear. This study investigated whether the volume of the hippocampus and its subfields, particularly cornu ammonis (CA) 1, CA3, and dentate gyrus, are associated with current PTSD symptoms and whether they predict PTSD symptom changes over 2 years. We examined clinical and structural magnetic resonance imaging measures from 252 trauma-exposed post-9/11 veterans (159 with Time 1 PTSD diagnosis) during assessments approximately 2 years apart. Automated hippocampal subfield segmentation was performed with FreeSurfer Version 7.1, producing 19 bilateral subfields. PTSD symptoms were measured at each assessment using the Clinician-Administered PTSD Scale-IV (CAPS). All models included total intracranial volume as a covariate. First, similar to previous reports, we showed that smaller overall hippocampal volume was associated with greater PTSD symptom severity at Time 1. Notably, when examining regions of interest (CA1, CA3, dentate gyrus), we found that smaller Time 1 hippocampal volumes in the bilateral CA1-body and CA2/3-body predicted decreased PTSD symptom severity at Time 2. These findings were not accounted for by combat exposure or treatment history. Additionally, both Time 1 CA1-body and CA2/3-body volume showed unique associations with changes in avoidance/numbing, but not with changes in reexperiencing or hyperarousal symptoms. This supports a more complex and nuanced relationship between hippocampal structure and PTSD symptoms, where during the posttrauma years bigger may not always mean better, and suggests that the CA1-body and CA2/3-body are important factors in the maintenance of PTSD symptoms. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Radio-anatomical evaluation of clinical and radiomic profile of multi-parametric magnetic resonance imaging of de novo glioblastoma multiforme.

BACKGROUND: Glioblastoma (GBM) is a fatal, fast-growing, and aggressive brain tumor arising from glial cells or their progenitors. It is a primary malignancy with a poor prognosis. The current study aims at evaluating the neuroradiological parameters of de novo GBM by analyzing the brain multi-parametric magnetic resonance imaging (mpMRI) scans acquired from a publicly available database analysis of the scans. METHODS: The dataset used was the mpMRI scans for de novo glioblastoma (GBM) patients from the University of Pennsylvania Health System, called the UPENN-GBM dataset. This was a collection from The Cancer Imaging Archive (TCIA), a part of the National Cancer Institute. The MRIs were reviewed by a single diagnostic radiologist, and the tumor parameters were recorded, wherein all recorded data was corroborated with the clinical findings. RESULTS: The study included a total of 58 subjects who were predominantly male (male:female ratio of 1.07:1). The mean age with SD was 58.49 (11.39) years. Mean survival days with SD were 347 (416.21) days. The left parietal lobe was the most commonly found tumor location with 11 (18.96%) patients. The mean intensity for T1, T2, and FLAIR with SD was 1.45E + 02 (20.42), 1.11E + 02 (17.61), and 141.64 (30.67), respectively (p = < 0.001). The tumor dimensions of anteroposterior, transverse, and craniocaudal gave a z-score (significance level = 0.05) of - 2.53 (p = 0.01), - 3.89 (p < 0.001), and 1.53 (p = 0.12), respectively. CONCLUSION: The current study takes a third-party database and reduces physician bias from interfering with study findings. Further prospective and retrospective studies are needed to provide conclusive data.

Machine learning and new insights for breast cancer diagnosis.

Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

[Establishment and evaluation of a rabbit model of frozen shoulder induced by persistent strain injuries and ice compression].

OBJECTIVE: To evaluate the rabbit modle of frozen shoulder induced by persistent strain injuries and ice compression. METHODS: Twelve clean, healthy male New Zealand rabbits with a mass of (2 500±500) g were selected and randomly divided into a blank group and a control group with 6 rabbits in each group. In the control group, the rabbits were modeled with persistent strain injuries and ice compression, the general conditions of the rabbits and the active and passive activities of the shoulder joint were observed and their body weights were recorded. MRI was performed on the affected shoulder joints at 6 d and 29 d after modelling to observe the fluid and soft tissue;HE staining was used to observe the morphology of the rabbit biceps longus tendon and the synovial membrane of the joint capsule;Masson staining was used to observe the fibrous deposits of the rabbit biceps longus tendon and the synovial membrane of the joint capsule, and the fibrous deposits were analysed semi-quantitatively by Image J software. RESULTS: Six days after the end of modeling, the active movement of the shoulder joints in the control group was limited, the passive movement was not significantly limited, and they walked with a limp;29 days after the end of the modeling, the active and passive movements of the shoulder joints in the model group were severely limited. Compared with the blank group (2.50±0.14) kg, the body weight of the model group (2.20±0.17) kg was significantly reduced(P<0.01). MRI showed that 6 days after modelling, the muscles around the shoulder joint were not smooth in shape, the joint capsule structure was narrowed and a large amount of fluid was seen in the joint cavity;29 days after modelling, the muscles around the shoulder joint were rough in shape, structure of the joint capsule was unclear and the fluid in the joint cavity was reduced compared with 6 days after modelling. Pathological staining showed that the long-headed biceps tendon fibres in the control group were disorganised, curled or even broken, and the synovial tissue of the joint capsule was heavily vascularised, with collagen fibre deposits and severe inflammatory cell infiltration. The fiber deposition of the long head of biceps brachii in the model group [(23.58±3.41)%, (27.56±3.70)%] and synovial tissue [(41.78±5.59)%, (62.19±7.54)%] were significantly higher than those in the blank group [(1.79±1.03) %, (1.29±0.63) %] at 7 and 30 days after modeling and synovial tissue fiber deposition [(8.15±3.61) %, (11.29±7.10) %], as shown by the semi-quantitative analysis of Masson staining results by Image J software. And the longer the time, the more severe the fibrosis (P<0.01). CONCLUSION: The behavioral, imaging and pathological findings showed that the rabbit frozen shoulder model with persistent strain injuries and ice compression is consistent with the clinical manifestations and pathogenesis of periarthritis, making it an ideal method for periarthritis research.

Multimodal workflows optimally predict response to repetitive transcranial magnetic stimulation in patients with schizophrenia: a multisite machine learning analysis.

The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structural MRI, clinical, sociodemographic, and genetic data using artificial intelligence. We developed and cross-validated rTMS response prediction models in patients with schizophrenia drawn from the multisite RESIS trial. The models incorporated pre-treatment sMRI, clinical, sociodemographic, and polygenic risk score (PRS) data. Patients were randomly assigned to receive active (N = 45) or sham (N = 47) rTMS treatment. The prediction target was individual response, defined as ≥20% reduction in pre-treatment negative symptom sum scores of the Positive and Negative Syndrome Scale. Our multimodal sequential prediction workflow achieved a balanced accuracy (BAC) of 94% (non-responders: 92%, responders: 95%) in the active-treated group and 50% in the sham-treated group. The clinical, clinical + PRS, and sMRI-based classifiers yielded BACs of 65%, 76%, and 80%, respectively. Apparent sadness, inability to feel, educational attainment PRS, and unemployment were most predictive of non-response in the clinical + PRS model, while grey matter density reductions in the default mode, limbic networks, and the cerebellum were most predictive in the sMRI model. Our sequential modelling approach provided superior predictive performance while minimising the diagnostic burden in the clinical setting. Predictive patterns suggest that rTMS responders may have higher levels of brain grey matter in the default mode and salience networks which increases their likelihood of profiting from plasticity-inducing brain stimulation methods, such as rTMS. The future clinical implementation of our models requires findings to be replicated at the international scale using stratified clinical trial designs.

Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy.

Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

Insights into the genetic architecture of cerebellar lobules derived from the UK Biobank.

In this work we endeavor to further understand the genetic architecture of the cerebellum by examining the genetic underpinnings of the different cerebellar lob(ul)es, identifying their genetic relation to cortical and subcortical regions, as well as to psychiatric disorders, as well as traces of their evolutionary trajectories. We confirm the moderate heritability of cerebellar volumes, and reveal genetic clustering and variability across their different substructures, which warranted a detailed analysis using this higher structural resolution. We replicated known genetic correlations with several subcortical volumes, and report new cortico-cerebellar genetic correlations, including negative genetic correlations between anterior cerebellar lobules and cingulate, and positive ones between lateral Crus I and lobule VI with cortical measures in the fusiform region. Heritability partitioning for evolutionary annotations highlighted that the vermis of Crus II has depleted heritability in genomic regions of "archaic introgression deserts", but no enrichment/depletion of heritability in any other cerebellar regions. Taken together, these findings reveal novel insights into the genetic underpinnings of the different cerebellar lobules.

Normative volumes and relaxation times at 3T during brain development.

While research has unveiled and quantified brain markers of abnormal neurodevelopment, clinicians still work with qualitative metrics for MRI brain investigation. The purpose of the current article is to bridge the knowledge gap between case-control cohort studies and individual patient care. Here, we provide a unique dataset of seventy-three 3-to-17 years-old healthy subjects acquired with a 6-minute MRI protocol encompassing T1 and T2 relaxation quantitative sequence that can be readily implemented in the clinical setting; MP2RAGE for T1 mapping and the prototype sequence GRAPPATINI for T2 mapping. White matter and grey matter volumes were automatically quantified. We further provide normative developmental curves based on these two imaging sequences; T1, T2 and volume normative ranges with respect to age were computed, for each ROI of a pediatric brain atlas. This open-source dataset provides normative values allowing to position individual patients acquired with the same protocol on the brain maturation curve and as such provides potentially useful quantitative biomarkers facilitating precise and personalized care.

Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks.

The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages. The first stage is responsible for tumor detection and classification by categorizing brain MRI images into normal, high-grade glioma (glioblastoma), and low-grade glioma. The uniqueness of the proposed network lies in its use of different levels of features, including local and global paths. The second stage is responsible for tumor segmentation, and skip connections and residual units are used during this step. Using 1800 images extracted from the BraTS 2017 dataset, the detection and classification stage was found to achieve a maximum accuracy of 99%. The segmentation stage was then evaluated using the Dice score, specificity, and sensitivity. The results showed that the suggested deep-learning-based system ranks highest among a variety of different strategies reported in the literature.

Investigating underlying brain structures and influence of mild and subjective cognitive impairment on dual-task performance in people with Parkinson's disease.

Cognitive impairment can affect dual-task abilities in Parkinson's disease (PD), but it remains unclear whether this is also driven by gray matter alterations across different cognitive classifications. Therefore, we investigated associations between dual-task performance during gait and functional mobility and gray matter alterations and explored whether these associations differed according to the degree of cognitive impairment. Participants with PD were classified according to their cognitive function with 22 as mild cognitive impairment (PD-MCI), 14 as subjective cognitive impairment (PD-SCI), and 20 as normal cognition (PD-NC). Multiple regression models associated dual-task absolute and interference values of gait speed, step-time variability, and reaction time, as well as dual-task absolute and difference values for Timed Up and Go (TUG) with PD cognitive classification. We repeated these regressions including the nucleus basalis of Meynert, dorsolateral prefrontal cortex, and hippocampus. We additionally explored whole-brain regressions with dual-task measures to identify dual-task-related regions. There was a trend that cerebellar alterations were associated with worse TUG dual-task in PD-SCI, but also with higher dual-task gait speed and higher dual-task step-time variability in PD-NC. After multiple comparison corrections, no effects of interest were significant. In summary, no clear set of variables associated with dual-task performance was found that distinguished between PD cognitive classifications in our cohort. Promising but non-significant trends, in particular regarding the TUG dual-task, do however warrant further investigation in future large-scale studies.

Association of uncertain significance genetic variants with myocardial mechanics and morphometrics in patients with nonischemic dilated cardiomyopathy.

BACKGROUND: Careful interpretation of the relation between phenotype changes of the heart and gene variants detected in dilated cardiomyopathy (DCM) is important for patient care and monitoring. OBJECTIVE: We sought to assess the association between cardiac-related genes and whole-heart myocardial mechanics or morphometrics in nonischemic dilated cardiomyopathy (NIDCM). METHODS: It was a prospective study consisting of patients with NIDCM. All patients were referred for genetic testing and a genetic analysis was performed using Illumina NextSeq 550 and a commercial gene capture panel of 233 genes (Systems Genomics, Cardiac-GeneSGKit®). It was analyzed whether there are significant differences in clinical, two-dimensional (2D) echocardiographic, and magnetic resonance imaging (MRI) parameters between patients with the genes variants and those without. 2D echocardiography and MRI were used to analyze myocardial mechanics and morphometrics. RESULTS: The study group consisted of 95 patients with NIDCM and the average age was 49.7 ± 10.5. All echocardiographic and MRI parameters of myocardial mechanics (left ventricular ejection fraction 28.4 ± 8.7 and 30.7 ± 11.2, respectively) were reduced and all values of cardiac chambers were increased (left ventricular end-diastolic diameter 64.5 ± 5.9 mm and 69.5 ± 10.7 mm, respectively) in this group. It was noticed that most cases of whole-heart myocardial mechanics and morphometrics differences between patients with and without gene variants were in the genes GATAD1, LOX, RASA1, KRAS, and KRIT1. These genes have not been previously linked to DCM. It has emerged that KRAS and KRIT1 genes were associated with worse whole-heart mechanics and enlargement of all heart chambers. GATAD1, LOX, and RASA1 genes variants showed an association with better cardiac function and morphometrics parameters. It might be that these variants alone do not influence disease development enough to be selective in human evolution. CONCLUSIONS: Combined variants in previously unreported genes related to DCM might play a significant role in affecting clinical, morphometrics, or myocardial mechanics parameters.

FLAMES overlaying anti-N-methyl-D-aspartate receptor encephalitis: a case report and literature review.

BACKGROUND: In recent years, simultaneous or sequential occurrence of MOG antibody disease and anti-NMDAR encephalitis in the same patient has been reported with increasing frequency. Scholars refer to the overlapping occurrence of these two disorders as MOG antibody disease and anti-NMDAR encephalitis overlap syndrome (MNOS). Cortical T2-weighted fluid-attenuated inversion recovery (FLAIR) -hyperintense lesions in anti-MOG-associated encephalitis with seizures (FLAMES) is a rare clinical phenotype of MOGAD in which cortical FLAIR high-signal lesions are unilateral, with little spread to the cortex and meninges bilaterally. Although cases of FLAMES have been consistently reported. However, to our knowledge, such cases of FLAMES combined with NMDARE are rare. CASE PRESENTATION: Here, we describe a case of FLAMES combined with anti-NMDARE. The patient was a young male, 29 years old, admitted to our hospital with isolated seizures, whose MRI showed unilateral thalamic and bilateral frontal and parietal leptomeningeal involvement. Since we were unaware of the possibility of bilateral meningo-cortical MOGAD manifestations, the case was initially diagnosed as viral encephalitis and was given antiviral therapy. The diagnosis was not clarified until anti-NMDAR-IgG and MOG-IgG positivity was detected in the cerebrospinal fluid and serum. The patient was then treated with high-dose corticosteroids and his symptoms responded well to the steroids. Therefore, this case expands the clinical spectrum of MNOS overlap syndrome. In addition, we describe the clinical features of MNOS by summarizing the existing literature and exploring the possible mechanisms of its immune response. CONCLUSIONS: Our case serves as a reminder to clinicians that when patients present with atypical clinical manifestations such as seizures, consideration should be given to MNOS and conduct testing for various relevant autoantibodies (including MOG abs) and viruses in both serum and cerebrospinal fluid, as it is easy to misdiagnose the disease as other CNS diseases, such as viral meningoencephalitis. This syndrome exhibits a high responsiveness to steroids, highlighting the critical importance of recognizing the clinical and neuroimaging features of this overlap syndrome for prompt diagnosis and treatment. Furthermore, it enriches the disease spectrum of MNOS.

MRCT and CT in the diagnosis of pediatric disease imaging: assessing imaging performance and clinical effects.

OBJECTIVE: This study focused on analyzing the clinical value and effect of magnetic resonance imaging plus computed tomography (MRCT) and CT in the clinical diagnosis of cerebral palsy in children. METHODS: From February 2021 to April 2023, 94 children diagnosed with cerebral palsy were selected from our hospital for study subjects. These patients were divided into CT and MRI groups, with CT examination given to the CT group and MRI examination given to the MRI group. The positive rate of the two examination methods in the diagnosis of cerebral palsy was compared, different imaging signs in two groups of children with cerebral palsy were compared, and the diagnostic test typing results between two groups were further analyzed. RESULTS: The diagnostic positivity rate of the children in the MRI group was 91.49%, which was significantly higher than that of the children in the CT group (70.21%) (P < 0.05). In both groups, encephalomalacia, bilateral frontal subdural effusions, and gray-white matter atrophy of the brain were the main signs, and the difference in the proportion of these three imaging signs between the two groups was not significant (P > 0.05). Differences between the two groups examined for cerebral palsy subtypes were not significant (P > 0.05). CONCLUSION: The positive rate of pediatric cerebral palsy examined by MRI is higher than that of CT diagnosis, but the clinic should organically combine the two to further improve the detection validity and accuracy.