Magnetic Resonance Imaging Analysis Research Articles

Intra voxel analysis in magnetic resonance imaging via deep learning

AbstractMagnetic Resonance Imaging (MRI) is a useful diagnostic method for producing anatomical images of the body. It differentiates tissues thanks to the measure of their proton density and relaxation times and . Since pathological tissues often present altered , and with respect to the physiological ones, MRI is largely used for the detection of large number of conditions. In addition, MRI scan presents an effective resolution able to detect very small anatomical elements, making the imaging system well suitable in contexts like prevention and early diagnosis. Nevertheless, system resolution imposes pathological volume to be larger than the voxel dimension. Since in some pathologies and conditions it could be very helpful to find tissues even smaller than the voxel dimension, this manuscript proposes an algorithm able to analyze voxel content and detect which one presents more than one tissue in its volume (i.e., which one is heterogeneous). More in detail, a machine learning algorithm is proposed, able to highlight, in the MR image, which pixel corresponds to an heterogeneous voxel. Method shows to be promising in combining good results and near real‐time processing in both simulated and real scenario.

The role of visual rating and automated brain volumetry in early detection and differential diagnosis of Alzheimer's disease.

Medial temporal lobe atrophy (MTA) is a diagnostic marker for mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the accuracy of quantitative MTA (QMTA) in diagnosing early AD is unclear. This study aimed to investigate the accuracy of QMTA and its related components (inferior lateral ventricle [ILV] and hippocampus) with MTA in the early diagnosis of MCI and AD. This study included four groups: normal (NC), MCI stable (MCIs), MCI converted to AD (MCIs), and mild AD (M-AD) groups. Magnetic resonance image analysis software was used to quantify the hippocampus, ILV, and QMTA. MTA was rated by two experienced neurologists. Receiver operating characteristic area under the curve (AUC) analysis was performed to compare their capability in differentiating AD from NC and MCI, and optimal thresholds were determined using the Youden index. QMTA distinguished M-AD from NC and MCI with higher diagnostic accuracy than MTA, hippocampus, and ILV (AUCNC = 0.976, AUCMCI = 0.836, AUCMCIs = 0.894, AUCMCIc = 0.730). The diagnostic accuracy of QMTA was superior to that of MTA, the hippocampus, and ILV in differentiating MCI from AD. The diagnostic accuracy of QMTA was found to remain the best across age, sex, and pathological subgroups analyzed. The sensitivity (92.45%) and specificity (90.64%) were higher in this study when a cutoff value of 0.635 was chosen for QMTA. QMTA may be a better choice than the MTA scale or the associated quantitative components alone in identifying AD patients and MCI individuals with higher progression risk.

Multi-class classification of brain tumour magnetic resonance images using multi-branch network with inception block and five-fold cross validation deep learning framework

The expertise of radiologists plays a pivotal role in the intricate task of diagnosing brain tumors. However, the escalating number of patients has rendered traditional diagnostic methods increasingly formidable and expensive. Consequently, researchers have diligently delved into the realm of medical image analysis to seek more efficient and precise approaches for the detection and classification of brain malignancies. In recent times, the surge in interest surrounding Deep Learning (DL) techniques has been notable, particularly in the development of automated systems with the capability to swiftly and reliably categorize brain tumors. The essence of this study revolves around the endeavor to confront the technical challenges inherent in this domain, with a specific focus on the intricate task of classifying brain tumors through the analysis of magnetic resonance images (MRI). The crux of the proposed model for brain tumor classification rests upon the foundation of Convolutional Neural Networks (CNNs), further fortified by a multi-branch network enriched with an inception block, elegantly incorporating a five-fold cross-validation methodology. This comprehensive approach was meticulously calibrated and evaluated using the Br35H dataset, a repository encompassing 395 images devoid of tumors, 826 images depicting gliomas, 822 portraying meningiomas, and 827 capturing pituitary tumors. This research diligently concentrates on discerning among three tumor types, including scenarios where no tumors are present, and the outcomes are meticulously juxtaposed against an array of cutting-edge classification models, such as the Support Vector Classifier (SVC), Decision Tree Classifier, Logistic Regression, and Gaussian Naive Bayes. To provide a thorough assessment of the proposed model's prowess, a battery of performance metrics was subjected to intense scrutiny, spanning accuracy, loss, and variance. The meticulous analysis culminated in the revelation that the CNN with Multi-Branch Network and Inception block exhibited unparalleled excellence, achieving an awe-inspiring accuracy rate of 99.30% and a scarcely perceptible variance of 0.0005 in its classification of the four distinct tumor classes, firmly establishing its supremacy over the existing state-of-the-art models in the field.

Physical Activity Attenuates Brain Irradiation-Associated Skeletal Muscle Damage in the Rat

Background: Radiotherapy for brain tumors increases patient survival. Nonetheless, side effects are increasingly reported such as cognitive deficits and fatigue. The etiology of fatigue remains poorly described. Our hypothesis is that the abscopal effects of radiotherapy on skeletal muscle may be involved in fatigue. The present study aims to assess the impact of brain irradiation on skeletal muscles, its relationships with fatigue and to analyze whether physical activity could counteract brain radiation-induced side effects. Methods: Adult Wistar rats were randomly distributed between 4 groups: control (CTL), irradiated (IR), non-irradiated with physical activity (PA) and irradiated with physical activity (IR+PA). Irradiated rats were exposed to a whole-brain irradiation (WBI) of 30 Gy (3×10 Gy). Rats subjected to PA underwent sessions of running on a treadmill, 3 times/week during 6 months. The effects of WBI on muscles were evaluated by complementary approaches: behavioral tests (fatigue, locomotion activity), magnetic resonance imaging (MRI) and histological analyses. Results: Irradiated rats displayed a significant fatigue and a reduced locomotor activity at short term compared to the CTL group which were attenuated with PA at 6 months after whole-brain irradiation. The IR rat's gastrocnemius mass decreased compared to CTL rats which was reversed by physical activity at 14 days after WBI. Multiparametric MRI imaging of the skeletal muscle highlighted an alteration of the fiber organization in IR rats as demonstrated by a significant decrease of the mean diffusivity in the gastrocnemius at short term. Alteration of fibers was confirmed by histological analyses: the number of type I fibers was decreased while that of type IIa fibers was increased in irradiated animals but not in IR+PA group. Conclusions: The data show that whole-brain irradiation induces skeletal muscle damage which are attenuated by PA. This muscle damage may explain, at least in part, the fatigue of patients treated with radiotherapy.

Photobac derived from bacteriochlorophyll-a shows potential for treating brain tumor in animal models by photodynamic therapy with desired pharmacokinetics and limited toxicity in rats and dogs

Photobac is a near infrared photosensitizer (PS) derived from naturally occurring bacteriochlorophyll- a, with a potential for treating a variety of cancer types (U87, F98 and C6 tumor cells in vitro). The main objective of the studies presented herein was to evaluate the efficacy, toxicity and pharmacokinetic profile of Photobac in animals (mice, rats and dogs) and submit these results to the United States Food and Drug Administration (US FDA) for its approval to initiate Phase I human clinical trials of glioblastoma, a deadly cancer disease with no long term cure. The photodynamic therapy (PDT) efficacy of Photobac was evaluated in mice subcutaneously implanted with U87 tumors, and in rats bearing C6 tumors implanted in brain. In both tumor types, the Photobac-PDT was quite effective. The long-term cure in rats was monitored by magnetic resonance imaging (MRI) and histopathology analysis. A detailed pharmacology, pharmacokinetics and toxicokinetic study of Photobac was investigated in both non-GLP and GLP facilities at variable doses following the US FDA parameters. Safety Pharmacology studies suggest that there is no phototoxicity, cerebral or retinal toxicity with Photobac. No metabolites of Photobac were observed following incubation in rat, dog, mini-pig and human hepatocytes. Based on current biological data, Photobac-IND received the approval for Phase-I human clinical trials to treat Glioblastoma (brain cancer), which is currently underway at our institute. Photobac has also received an orphan drug status from the US FDA, because of its potential for treating Glioblastoma as no effective treatment is currently available for this deadly disease.

MRI-based radiomics signature: a potential imaging biomarker for prediction of microvascular invasion in combined hepatocellular-cholangiocarcinoma.

To investigate the potential of radiomics analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in preoperatively predicting microvascular invasion (MVI) in patients with combined hepatocellular-cholangiocarcinoma (cHCC-CC) before surgery. A cohort of 91 patients with histologically confirmed cHCC-CC who underwent preoperative liver DCE-MRI were enrolled and divided into a training cohort (27 MVI-positive and 37 MVI-negative) and a validation cohort (11 MVI-positive and 16 MVI-negative). Clinical characteristics and MR features of the patients were evaluated. Radiomics features were extracted from DCE-MRI, and a radiomics signature was built using the least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort. Prediction performance of the developed radiomics signature was evaluated by utilizing the receiver operating characteristic (ROC) analysis. Larger tumor size and higher Radscore were associated with the presence of MVI in the training cohort (p = 0.026 and < 0.001, respectively), and theses findings were also confirmed in the validation cohort (p = 0.040 and 0.001, respectively). The developed radiomics signature, composed of 4 stable radiomics features, showed high prediction performance in both the training cohort (AUC = 0.866, 95% CI 0.757-0.938, p < 0.001) and validation cohort (AUC = 0.841, 95% CI 0.650-0.952, p < 0.001). The radiomics signature developed from DCE-MRI can be a reliable imaging biomarker to preoperatively predict MVI in cHCC-CC.

Aberrant functional and causal connectivity of the amygdala in herpes zoster and post-herpetic neuralgia patients.

Resting-state functional magnetic resonance imaging (rs-fMRI) and Granger causality analysis (GCA) were used to observe the characteristics of amygdala and whole-brain effect connections in patients with herpes zoster (HZ) and post-herpetic neuralgia (PHN) and to determine their relationship with clinical features. Rs-fMRI scans were performed on 50 HZ; 50 PHN; and 50 age-, sex- and education-year-matched healthy controls (HCs). Bilateral amygdala subregions were used as seeds for functional connectivity (FC). GCA was used to analyze the effective connection of brain regions that were significantly different among groups. Then, the correlation between FC, and GCA values and clinical indices was investigated. PHN had impaired FC between the amygdala subregion with the putamen, cortex, anterior cingulate cortex (ACC) to HCs and reduced FC of medial amygdala (MeA) with the parieto-occipital lobe and motor cortex to HZ; HZ had reduced FC of the lateral amygdala (LA) with the insula to HCs. GCA values from the bilateral LA to the bilateral ACC, left MeA to the bilateral ACC and left putamen, and right ACC to the bilateral MeA were reduced in PHN patients compared to HCs. Compared with HCs, the GCA values from the left MeA to the left ACC and right putamen were reduced in HZ. The GCA values from the amygdala subregion to the ACC were positively correlated with HAMA or HAMD scores in PHN. PHN showed reduced FC between the amygdala subregions and cortico-putamen and decreased effective connectivity from the amygdala subregion to the ACC and putamen. HZ and PHN patients had significant changes in effective connectivity in brain regions, including diverse functional areas emanating from and projecting to the amygdala. The current findings will provide a new perspective for understanding the neuropathophysiological mechanism HZ and PHN.

Cardiac MRI Radiomics: review

A study of foreign and domestic literature devoted to the application of texture analysis of magnetic resonance images of the heart was performed. The analysis included publications selected by key words and their combinations: cardiac magnetic resonance imaging (MRI), myocarditis, myocardial infarction, cardiomyopathy, radiomics, and texture analysis. Radiomics and texture analysis, as a new and rapidly developing direction of in-depth analysis of digital medical images, is developing, the number of publications on this topic is growing every year and the topic is not losing its relevance. Radiomics is a promising method of image analysis that aims to improve the diagnosis and prognosis of diseases by extracting a large number of quantitative features that can be missed by the human eye in the visual analysis of images. Radiomics biomarkers derived by extracting data from magnetic resonance images of the heart could be a valuable tool for assessing myocardial viability, myocardial lesions in myocarditis and cardiomyopathies.

Morphological analysis of three-dimensional MR images of patellofemoral joints in asymptomatic subjects

The existing methods for analyzing patellofemoral (PF) osteoarthritis (OA) are limited. Our purpose was to clarify the frequency, localization, and morphological progression of PFOA by observing three-dimensional (3D) magnetic resonance (MR) images from a cohort population. The subjects were 561 patients aged 30–79 years from the Kanagawa Knee Study who had not visited a hospital for more than three consecutive months for knee symptoms. MR images of the PF joints, separated into the medial and lateral types, were presented in order of the highest to lowest patella cartilage area ratios. Cartilage defects in the patella were detected in 37 subjects (6.6%). Medial lesions (4.6%) were significantly more frequent than lateral lesions (2.0%) (p < 0.01). For both medial and lateral lesions, the patellar cartilage defects were divided into confined and unconfined types. The 3D MR images of the PF joint showed that the patellar cartilage defect occurred along each ridge of the femoral trochlea. The 3D MR images revealed a 6.6% prevalence of patellar cartilage defects, higher in the medial than lateral regions. The 3D MR images can easily determine PF morphology and cartilage defect location, making them useful in understanding the pathophysiology and etiology of PFOA.

Artificial intelligence to analyze magnetic resonance imaging in rheumatology

Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.

Intravenous infusion of cardiac progenitor cells in animal models of single ventricular physiology.

The goal of this study was to identify the practical applications of intravenous cell therapy for single-ventricle physiology (SVP) by establishing experimental SVP models. An SVP with a three-stage palliation was constructed in an acute swine model without cardiopulmonary bypass. A modified Blalock-Taussig (MBT) shunt was created using an aortopulmonary shunt with the superior and inferior venae cavae (SVC and IVC, respectively) connected to the left atrium (n = 10). A bidirectional cavopulmonary shunt (BCPS) was constructed using a graft between the IVC and the left atrium with an SVC cavopulmonary connection (n = 10). The SVC and the IVC were connected to the pulmonary artery to establish a total cavopulmonary connection (TCPC, n = 10). The survival times of half of the animal models were studied. The other half and the biventricular sham control (n = 5) were injected intravenously with cardiosphere-derived cells (CDCs), and the cardiac retention of CDCs was assessed after 2 h. All SVP models died within 20 h. Perioperative mortality was higher in the BCPS group because of lower oxygen saturation (P < 0.001). Cardiac retention of intravenously delivered CDCs, as detected by magnetic resonance imaging and histologic analysis, was significantly higher in the modified Blalock-Taussig and BCPS groups than in the TCPC group (P < 0.01). Without the total right heart exclusion, stage-specific SVP models can be functionally constructed in pigs with stable outcomes. Intravenous CDC injections may be applicable in patients with SVP before TCPC completion, given that the initial lung trafficking is efficiently bypassed and sufficient systemic blood flow is supplied from the single ventricle.

The potential positive effect of periodontal treatment on brain function activity using functional magnetic resonance imaging analysis

Background/purposeThere are reports on the relationship between periodontal treatment and the whole body. The purpose of the present study was to investigate the effect of periodontal initial treatment on brain function activity by improving periodontal tissue and the occlusal status of subjects with periodontitis. Materials and methodsThe subjects were 13 patients with periodontitis. Following the patient's informed written consent, the periodontal initial treatment provided to the patient included tooth brushing instruction, scaling and root planning, however, occlusal adjustment was not performed at this stage. Periodontal examination, occlusal force examination and fMRI results were also evaluated at the initial and the reevaluation examinations. ResultsAfter the periodontal initial treatment had been performed, periodontal tissue had significantly improved. In addition, cerebral blood flow in the insula and primary motor cortex was also improved, as confirmed by fMRI. ConclusionThis result suggests that the periodontal ligament has recovered and the periodontal ligament neuron have been further subjected to clenching in the insula.

A Predictive Model to Identify the Effects of Transcutaneous Sacral Nerve Stimulation With Pelvic Floor Exercises in Fecal Incontinence After Surgery for Anorectal Malformation.

Although the combination of transcutaneous sacral nerve stimulation (tSNS) and pelvic floor exercises (PFEs) has shown significant effectiveness in treating fecal incontinence (FI) after surgery for congenital anorectal malformation (CARM), not all patients achieve satisfactory continence. Therefore, identifying which individuals will benefit from this method is crucial. A prospective cohort study enrolled 92 children with FI. All patients underwent tSNS with PFE treatment, and an improved outcome was defined as a Wexner score ≤4. A predictive model to identify the effects of tSNS with PFEs in FI was developed based on the analysis of magnetic resonance imaging and high-resolution anorectal manometry with area under the receiver-operating characteristic curve to evaluate the predictive value of external anal sphincter (EAS) thickness index and anal squeezing pressure (ASP). tSNS with PFEs improved outcomes in 72 patients and led to poor outcomes in 20 (4 had their rectums deviate from the puborectalis muscle center or puborectal muscle ruptures while 16 lacked EAS with a lower ASP). The areas under the receiver-operating characteristic curve for EAS thickness index and ASP in predicting the effects of tSNS with PFEs were 0.915 (95% confidence interval 0.846-0.983, P = 0.000) and 0.886 (95% confidence interval 0.819-0.952, P = 0.000), respectively. By applying cutoff values of 0.076 for EAS thickness index and 21.95 mm Hg for ASP, tSNS with PFEs was found to be ineffective. tSNS with PFEs is effective for most patients with FI after CARM surgery, except when the rectum deviates from the puborectal muscle center, puborectal muscle rupture occurs, or EAS is absent with a low ASP.

Learning Common and Task-Specific Radiomic Features via Graph Regularized NMF for the Joint Prediction of Multiple Clinical Indicators in Breast Cancer.

Assessments of multiple clinical indicators based on radiomic analysis of magnetic resonance imaging (MRI) are beneficial to the diagnosis, prognosis and treatment of breast cancer patients. Many machine learning methods have been designed to jointly predict multiple indicators for more accurate assessments while using original clinical labels directly without considering the noisy and redundant information among them. To this end, we propose a multilabel learning method based on label space dimensionality reduction (LSDR), which learns common and task-specific features via graph regularized nonnegative matrix factorization (CTFGNMF) for the joint prediction of multiple indicators in breast cancer. A nonnegative matrix factorization (NMF) is adopted to map original clinical labels to a low-dimensional latent space. The latent labels are employed to exploit task correlations by using a least square loss function with [Formula: see text]-norm regularization to identify common features, which help to improve the generalization performance of correlated tasks. Furthermore, task-specific features were retained by a multitask regression formulation to increase the discrimination power for different tasks. Common and task-specific features are incorporated by dynamic graph Laplacian regularization into a unified model to learn complementary features. Then, a multilabel classification is built to predict multiple clinical indicators including human epidermal growth factor receptor 2 (HER2), Ki-67, and histological grade. Experimental results show that CTFGNMF achieves AUCs of 0.823, 0.691 and 0.776 in the three indicator predictions, outperforming other counterparts that consider only task-independent features or common features. It indicates CTFGNMF is a promising application for multiple classification tasks in breast cancer.

Size and Morphology of the Anterior and Posterior Cruciate Ligaments at Different Pediatric Age Intervals: An MRI Analysis.

The incidence of anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) tears in children and adolescents has increased over the past decade, with increasing numbers of ACL and PCL reconstructions in this patient population. To evaluate the size and morphology of both the ACL and the PCL by magnetic resonance imaging (MRI) in different pediatric age groups. Cross-sectional study; Level of evidence, 3. MRI examinations of 127 knees (67 female, 60 male; aged 0-18 years) were analyzed retrospectively. The cohort was split into 6 age subgroups, 1 subgroup for every 3 years (minimum 8 patients per subgroup). The following parameters were measured by 2 independent raters at 2 different time points: ACL length, anteroposterior and mediolateral ACL width, sagittal and coronal ACL inclination, inclination of the intercondylar notch, bicondylar width, notch width, coronal ACL and PCL width, PCL length, and sagittal width of the lateral femoral condyle. The following indices, areas, and volumes were calculated: sagittal width of the lateral femoral condyle/PCL length, ACL area and volume, notch width index, ACL width/notch width, PCL width/notch width, ACL width/bicondylar width, and PCL width/bicondylar width. A correlation analysis was performed for patient age, height, weight, and body mass index (BMI). ACL length was between 18 and 37 mm, and ACL width was between 4 and 6 mm. PCL length ranged between 27 and 43 mm, while PCL width was between 7 and 9 mm. Growth of the cruciate ligaments was the most pronounced between the ages of 4 and 12 years. Correlations with size and weight were strong, while BMI correlated slightly with the measurements. Measurements in female patients were slightly larger than in their male counterparts between the ages of 0 and 6 years, while male patients tended to have larger values starting from ages 7 to 9 years. These values were significantly larger in male patients from the ages of 16 to 18 years (P < .05). This study provides normative data on the morphology of pediatric anatomic features in the knee as a basis for age-appropriate and individualized surgical care of ACL and PCL injuries in children and adolescents.

TCT-366 Hyperoxemic Supersaturated Oxygen Therapy in Patients With Acute Anterior Myocardial Infarction Reduces Infarct Size and Microvascular Obstruction: A Cardiac Magnetic Resonance Imaging Analysis

TCT-366 Hyperoxemic Supersaturated Oxygen Therapy in Patients With Acute Anterior Myocardial Infarction Reduces Infarct Size and Microvascular Obstruction: A Cardiac Magnetic Resonance Imaging Analysis

Neural representation of a one-week delay in remembering information after production and self-generated elaboration encoding strategy

Many studies have confirmed the memory enhancement effect of production, generation and elaboration which can be effective after only one encoding. It is also known that greater memory enhancement effects can be obtained by combining multiple memory strategies during encoding. This study aimed to investigate whether the combination of production and self-generated elaboration enhances memory performance compared with production or generation alone. A total of 23 undergraduate and graduate students participated in this study. In the functional magnetic resonance imaging analysis, we explored the neural representation of remembering information after production and self-generated elaboration strategy. We set four encoding strategy conditions: (1) Read Silent (read without production), (2) Read Aloud (only production), (3) Add Silent (self-generated elaboration without production), (4) Add Aloud (production and self-generated elaboration). The retrieval performance and brain activity while retrieving the learned sentences after a one-week delay were examined. The behavioral results showed that the highest memory performance was for sentences encoded in Add Aloud. The interaction between production and self-generated elaboration was statistically significant. These results suggest that the memory enhancement effect of combining production and self-generated elaboration is not a simple addition nor synergistic facilitation effect. The imaging results showed that the following areas were related to the retrieval of the target encoded in the add aloud condition: the area related to integration of internal and external information (precuneus), area related to information rich stimuli (lateral occipital lobe), area related to self-involvement and inference of others' feelings (MPFC), area related to seen imagery (retrosplenial region) and area related to adjustment of movement (cerebellum). These results suggest that with an encoding strategy that combines production and self-generated elaboration, integrated auditory input of vocalizations and generated images, visual images of the scene, self-relevance, inference of other's feeling, movement by moving mouth are stored with the target and enhanced memory performance of AA.

Risk Assessment of Maladaptive Behaviors in Adolescents: Nutrition, Screen Time, Prenatal Exposure, Childhood Adversities - Adolescent Brain Cognitive Development Study

PurposeWe aimed to identify significant contributing factors to the risk of maladaptive behaviors, such as alcohol use disorder or obesity, in children. To achieve this, we utilized the extensive adolescent brain cognitive development data set, which encompasses a wide range of environmental, social, and nutritional factors. MethodsWe divided our sample into equal sets (test, validation; n = 3,415 each). On exploratory factor analysis, six factor domains were identified as most significant (fat/sugar intake, screen time, and prenatal alcohol exposure, parental aggressiveness, hyperactivity, family violence, parental education, and family income) and used to stratify the children into low- (n = 975), medium- (n = 967), high- (n = 977) risk groups. Regression models were used to analyze the relationship between identified risk groups, and differences in reward sensitivity, and behavioral problems at 2-year follow-up. ResultsThe functional magnetic resonance imaging analyses showed reduced activation in several brain regions during reward or loss anticipation in high/medium-risk (vs. low-risk) children on a monetary incentive delay task. High-risk children exhibited heightened middle frontal cortex activity when receiving large rewards. They also displayed increased impulsive and motivated reward-seeking behaviors, along with behavioral problems. These findings replicated in our validation set, and a negative correlation between middle frontal cortexthickness and impulsivity behavior was observed in high-risk children. DiscussionOur findings show altered reward function and increased impulsiveness in high-risk adolescents. This study has implications for early risk identification and the development of prevention strategies for maladaptive behaviors in children, particularly those at high risk.

Focused Ultrasound for Blood-Brain Barrier Opening and Delivery of Anti-PD1 in Diffuse Midline Gliomas

Diffuse midline glioma with H3K27 mutation is a fatal pediatric brain tumor, most commonly arising in the brainstem. This tumor remains universally fatal, despite a multitude of clinical trials, with a median overall survival of only 9-12 months. While immune-checkpoint inhibitors (ICIs) have transformed the treatment landscape of multiple solid tumors, delivery past the blood brain barrier (BBB) remains challenging. Programmed cell death protein 1 (PD1) is an immune checkpoint protein expressed on the surface of activated T cells; interaction with its ligand, PDL1, is tumor-protective, dampening T cell response. Recent phase I clinical trials have shown that ICIs targeting proteins along the PD1/PDL1 axis are well tolerated in patients with DMG; however, efficacy remains low. The blood-brain barrier (BBB) poses a major challenge to the efficacious delivery of therapeutic agents with large molecular size, such as anti-PD1. We hypothesize that BBB opening (BBBO) using focused ultrasound (FUS), a form of non-ionizing acoustic radiation, can enhance delivery and efficacy of anti-PD1 for treatment of DMG. We established a syngeneic mouse DMG model with intracranial injection of cell line 4423 (PDGFB+, H3.3K27M, p53-/-). Magnetic resonance imaging (MRI) was utilized to evaluate BBBO and tumor progression. We measured delivery of anti-PD1 after BBBO using Western Blot and 3D in vivo optical fluorescent imaging/CT (OI/CT) of Cy7 labeled anti-PD1. We demonstrate that delivery of anti-PD1 can be enhanced over 3.5-fold after reversible BBBO with FUS and concurrent microbubble administration. OI/CT revealed enhanced real-time antibody distribution peritumorally. Furthermore, we demonstrate that combined treatment of FUS and anti-PD1 led to benefit in local control of tumor growth using volumetric analysis of MRI. Preliminary survival studies suggest a positive trend for overall survival. Our results support that FUS-mediated BBBO can increase treatment efficacy of anti-PD1 in a DMG murine model, due to improved targeted delivery to the tumoral region after systemic antibody administration. We consider these findings strong rationale for further investigation of the therapeutic effects of combinatorial treatment using FUS-mediated BBBO and ICIs for the treatment of DMG.

Neurovascular coupling dysfunction in high myopia patients: Evidence from a multi-modal magnetic resonance imaging analysis

Background and purposeTo investigate neurovascular coupling dysfunction in high myopia (HM) patients. Materials and methodsA total of 37 HM patients and 36 healthy controls were included in this study. Degree centrality (DC), regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) maps were employed to represent neuronal activity. Cerebral blood perfusion was characterized by cerebral blood flow (CBF). The correlation coefficient was calculated to reflect the relationship between neuronal activity and cerebral blood perfusion. Pearson partial correlation analysis was utilized to evaluate the association between HM dysfunction and clinical indicators. ResultsHM patients exhibited significant alterations in neurovascular coupling across 37 brain regions compared to healthy controls. The brain regions with marked changes varied among the four neurovascular coupling patterns, including the middle frontal gyrus, superior occipital gyrus, middle occipital gyrus, and fusiform gyrus. Additionally, the superior frontal gyrus orbital part, medial superior frontal gyrus, inferior occipital gyrus, and dorsolateral superior frontal gyrus displayed significant changes in three coupling patterns. In HM patients, the ReHo-CBF changes in the inferior frontal gyrus orbital part were positively correlated with best-corrected visual acuity (BCVA) and refractive diopter changes. Similarly, the ALFF-CBF changes in the inferior frontal gyrus orbital part showed a positive correlation with refractive diopter changes. ReHo-CBF and ALFF-CBF alterations in the paracentral lobule were positively correlated with BCVA and refractive diopter changes. ConclusionOur findings underscore the abnormal alterations in neurovascular coupling across multiple brain regions in HM patients. These results suggest that neurovascular dysfunction in HM patients may be associated with an aberrant visual regulation mechanism.