Magnetic Resonance Imaging Analysis Research Articles

The clinical value of applying diffusion-weighted imaging combined with T2-weighted imaging to assess diagnostic performance of muscularis propria invasion in mid-to-high rectal cancer.

This study aimed to evaluate the diagnostic performance of combining diffusion-weighted imaging (DWI) with T2-weighted imaging (T2WI) for detecting muscularis propria invasion in rectal cancer. We conducted a retrospective analysis of MR images from 76 patients with pathologically confirmed rectal cancer between January 2018 and June 2022. Patients were categorized into invasion and non-invasion groups. the present of muscularis propria invasion. The examination regimen included T2WI, dynamic enhanced scanning and DWI.The apparent diffusion coefficient (ADC) values from DWI were compared between the groups, and the diagnostic performance of combining ADC with T2WI was assessed. There were differences in ADCmean, ADCmsi, and ADCmin values between the non-invasion group and theinvasion group, and the t values were 3.949, 2.221 and 2.978, respectively. The P values were 0.000, 0.029 and 0.004, respectively. Using an ADCmean threshold of 1.07 × 10- 3 mm²/s, the sensitivity and specificity for detecting muscularis propria invasion were 82.22% and 61.29%, respectively, with an overall diagnostic accuracy of 89.5%. For ADCmsi, with a threshold of 0.996 × 10- 3 mm²/s, the sensitivity and specificity were 62.75% and 73.33%, respectively, yielding a diagnostic accuracy of 82.9%. The ADCmin, with a threshold of 0.854 × 10- 3 mm²/s, demonstrated sensitivity and specificity of 78.43% and 66.67%, respectively, with a diagnostic accuracy of 85.5%. T2WI combined ADC value has a good predictive value for judging the degree of musculature invasion of medium-to-high rectal cancer, while ADC mean has a higher comprehensive diagnostic efficiency. Our finding may provide reference for increasing accuracy in mid-to-high rectal cancer diagnosis.

Ninerafaxstat in the Treatment of Diabetic Cardiomyopathy and Nonobstructive Hypertrophic Cardiomyopathy.

Ninerafaxstat is a novel mitotrope under investigation in 2 large clinical trials: IMPROVE-DiCE (a phase IIa trial investigating ninerafaxstat) and IMPROVE-hypertrophic cardiomyopathy (HCM). IMPROVE-DiCE is a single-center, open-label, phase 2a trial investigating the effectiveness of ninerafaxstat in diabetic cardiomyopathy. Ninerafaxstat significantly improved phosphocreatine/adenosine triphosphate median by 32% (P < 0.01) and reduced myocardial triglyceride content by 34% (P = 0.026). Magnetic resonance imaging (MRI) analysis showed improved left ventricular peak circumferential diastolic strain rate by 15% (P < 0.047) and peak left ventricular filling rate by 11% (P < 0.05). Pyruvate dehydrogenase flux was increased in 7 of 9 patients (P = 0.08), consistent with improved glucose utilization. IMPROVE-HCM (ninerafaxstat safe, effective for nonobstructive hypertrophic cardiomyopathy patients) is a phase 2, multicenter, randomized controlled and double-blinded study. From baseline to 12 weeks, ninerafaxstat was associated with a significantly improved ventilatory efficiency slope compared with placebo (P = 0.006). In a post hoc analysis with 35 patients with baseline Kansas City Cardiomyopathy Questionnaire score ≤80, changes in ventilatory efficiency slope favored ninerafaxstat versus placebo (P = 0.02). Left atrial size, a surrogate marker of diastolic dysfunction, was significantly decreased in patients on ninerafaxstat versus placebo (P = 0.01). These findings support a larger phase 3 study in symptomatic nonobstructive HCM patients to further investigate ninerafaxstat. Several drugs that also improve glucose utilization including fatty acid oxidation inhibitors, carnitine palmitoyltransferase I inhibitors, and glucagon-like peptide-1 receptor agonists are presently under investigation in clinical trials.

Mapping Functional Connectivity Signatures of Pharmacoresistant Focal Cortical Dysplasia-Related Epilepsy.

To determine common network alterations in focal cortical dysplasia pharmacoresistant epilepsy (FCD-PRE) using functional connectivity analysis of resting-state functional magnetic resonance imaging (rsfMRI). This is a retrospective imaging cohort from Children's National Hospital (Washington, DC, USA) from January, 2011 to January, 2022. Patients with 3-T MRI-confirmed FCD-PRE underwent rsfMRI as part of routine clinical care. Patients were included if they were age 5-22 years at the time of the scan, and had a minimum of 18 months of follow-up. Healthy, typically-developing controls were included from Children's National Hospital (n = 16) and matched from Human Connectome Project-Development public dataset (n = 100). A total of 42 FCD-PRE patients (20 M:22 F, aged 14.2 ± 4.1 years) and 116 healthy controls (56 M:60 F, aged 13.7 ± 3.3 years) with rsfMRI were included. Seed-based functional connectivity maps were generated for each FCD, and each seed was used to generate a patient-specific z-scored connectivity map on 116 controls. FCD-PRE patients had mutual altered connectivity in regions of dorsal attention, default mode, and control networks. Functional connectivity was diminished within the FCD dominant functional network, as well as in homotopic regions. Cluster specific connectivity patterns varied by pathological subtype. Higher FCD connectivity to the limbic network was associated with increased odds of Engel I outcome. This study demonstrates diminished functional connectivity patterns in FCD-PRE, which may represent a neuromarker for the disease, independent of FCD location, involving the dorsal attention, default mode, and control functional networks. Higher connectivity to the limbic network is associated with a seizure-free outcome. Future multicenter, prospective studies are needed to allow for much earlier detection of signatures of treatment-resistant epilepsy. ANN NEUROL 2024.

ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance Imaging

Background and Objectives:Artificial intelligence (AI) is revolutionizing Magnetic Resonance Imaging (MRI) along the acquisition and processing chain. Advanced AI frameworks have been applied in various successive tasks, such as image reconstruction, quantitative parameter map estimation, and image segmentation. However, existing frameworks are often designed to perform tasks independently of each other or are focused on specific models or single datasets, limiting generalization. This work introduces the Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC), a novel open-source toolbox that streamlines AI applications for accelerated MRI reconstruction and analysis. ATOMMIC implements several tasks using deep learning (DL) models and enables MultiTask Learning (MTL) to perform related tasks in an integrated manner, targeting generalization in the MRI domain. Methods:We conducted a comprehensive literature review and analyzed 12,479 GitHub repositories to assess the current landscape of AI frameworks for MRI. Subsequently, we demonstrate how ATOMMIC standardizes workflows and improves data interoperability, enabling effective benchmarking of various DL models across MRI tasks and datasets. To showcase ATOMMIC’s capabilities, we evaluated twenty-five DL models on eight publicly available datasets, focusing on accelerated MRI reconstruction, segmentation, quantitative parameter map estimation, and joint accelerated MRI reconstruction and segmentation using MTL. Results:ATOMMIC’s high-performance training and testing capabilities, utilizing multiple GPUs and mixed precision support, enable efficient benchmarking of multiple models across various tasks. The framework’s modular architecture implements each task through a collection of data loaders, models, loss functions, evaluation metrics, and pre-processing transformations, facilitating seamless integration of new tasks, datasets, and models. Our findings demonstrate that ATOMMIC supports MTL for multiple MRI tasks with harmonized complex-valued and real-valued data support while maintaining active development and documentation. Task-specific evaluations demonstrate that physics-based models outperform other approaches in reconstructing highly accelerated acquisitions. These high-quality reconstruction models also show superior accuracy in estimating quantitative parameter maps. Furthermore, when combining high-performing reconstruction models with robust segmentation networks through MTL, performance is improved in both tasks. Conclusions:ATOMMIC advances MRI reconstruction and analysis by leveraging MTL and ensuring consistency across tasks, models, and datasets. This comprehensive framework serves as a versatile platform for researchers to use existing AI methods and develop new approaches in medical imaging.

Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field

Deep learning (DL) has been applied to glioblastoma (GBM) magnetic resonance imaging (MRI) assessment for tumor segmentation and inference of molecular, diagnostic, and prognostic information. We comprehensively overviewed the currently available DL applications, critically examining the limitations that hinder their broader adoption in clinical practice and molecular research. Technical limitations to the routine application of DL include the qualitative heterogeneity of MRI, related to different machinery and protocols, and the absence of informative sequences, possibly compensated by artificial image synthesis. Moreover, taking advantage from the available benchmarks of MRI, algorithms should be trained on large amounts of data. Additionally, the segmentation of postoperative imaging should be further addressed to limit the inaccuracies previously observed for this task. Indeed, molecular information has been promisingly integrated in the most recent DL tools, providing useful prognostic and therapeutic information. Finally, ethical concerns should be carefully addressed and standardized to allow for data protection. DL has provided reliable results for GBM assessment concerning MRI analysis and segmentation, but the routine clinical application is still limited. The current limitations could be prospectively addressed, giving particular attention to data collection, introducing new technical advancements, and carefully regulating ethical issues.

Non-invasive estimation of cerebrospinal fluid pressure in idiopathic intracranial hypertension: magnetic resonance imaging analysis of optic nerve and eyeball.

Invasive methods such as lumbar puncture and intraventricular catheters are commonly used to measure intracranial pressure (ICP). This study aims to develop quantitative and non-invasive techniques to measure ICP in patients with Idiopathic Intracranial Hypertension (IIH) using magnetic resonance imaging (MRI) findings. MRI data obtained from 50 patients with IIH and 30 age- and sex- matched controls were analyzed and optic nerve sheath diameter (ONSD), eyeball transverse diameter (ETD) and optic nerve diameter (OND) were measured. ONSD, ONSD/ETD and OND/ONSD indexes were calculated according to different ONSD measurement distances. Correlations of MRI findings with ICP were calculated. Sensitivity and specificity of all methods were analyzed. ONSD and ONSD/ETD index at 3mm and 10mm behind the eyeball were significantly higher (p < 0.001) and OND/ONSD index at 3mm behind the eyeball was significantly lower (p < 0.001) in the IIH group. The ONSD/ETD index at 3mm had the highest area under the curve (AUC) value (0.898) with a cut-off of 0.27mm (82% sensitivity and 91.67% specificity) for predicting high cerebrospinal fluid (CSF) pressure, followed by ONSD measurements at 3mm (AUC = 0.886) with a cut-off of 6.17mm (83% sensitivity and 86.67% specificity). The OND/ONSD index at 3mm posterior to the eyeball decreased significantly as ICP increased, and the strength of the relationship was moderate (p < 0.001; r = -0.358). ONSD and ONSD/ETD index measured on MRI sequences are potentially useful in detecting elevated ICP. The OND/ONSD index correlates with CSF pressure and these techniques may be helpful in diagnosing IIH.

Probing the neural dynamics of musicians’ and non-musicians’ consonant/dissonant perception: Joint analyses of electrical encephalogram (EEG) and functional magnetic resonance imaging (fMRI)

The perception of two (or more) simultaneous musical notes, depending on their pitch interval(s), could be broadly categorized as consonant or dissonant. Previous literature has suggested that musicians and non-musicians adopt different strategies when discerning music intervals: while musicians rely on the frequency ratios between the two fundamental frequencies, such as “perfect fifth” (3:2) as consonant and “tritone” (45:32) as dissonant intervals; non-musicians may rely on the presence of ‘roughness’ or ‘beats’, generated by the difference of fundamental frequencies, as the key elements of ‘dissonance’. The separate Event-Related Potential (ERP) differences in N1 and P2 along the midline electrodes provided evidence congruent with such ‘separate reliances’. To replicate and to extend, in this study we reran the previous experiment, and separately collected fMRI data of the same protocol (with sparse sampling modifications). The behavioral and EEG results largely corresponded to our previous finding. The fMRI results, with the joint analyses by univariate, psycho-physiological interaction, and representational similarity analysis (RSA) approaches, further reinforce the involvement of central midline-related brain regions, such as ventromedial prefrontal and dorsal anterior cingulate cortex, in consonant/dissonance judgments. The final spatiotemporal searchlight RSA provided convincing evidence that the medial prefrontal cortex, along with the bilateral superior temporal cortex, is the joint locus of midline N1 and dorsal anterior cingulate cortex for the P2 effect (for musicians). Together, these analyses reaffirm that musicians rely more on experience-driven knowledge for consonance/dissonance perception; but also demonstrate the advantages of multiple analyses in constraining the findings from both EEG and fMRI.

Neural mechanisms of credit assignment for delayed outcomes during contingent learning.

Adaptive behavior in complex environments critically relies on the ability to appropriately link specific choices or actions to their outcomes. However, the neural mechanisms that support the ability to credit only those past choices believed to have caused the observed outcomes remain unclear. Here, we leverage multivariate pattern analyses of functional magnetic resonance imaging (fMRI) data and an adaptive learning task to shed light on the underlying neural mechanisms of such specific credit assignment. We find that the lateral orbitofrontal cortex (lOFC) and hippocampus (HC) code for the causal choice identity when credit needs to be assigned for choices that are separated from outcomes by a long delay, even when this delayed transition is punctuated by interim decisions. Further, we show when interim decisions must be made, learning is additionally supported by lateral frontopolar cortex (FPl). Our results indicate that FPl holds previous causal choices in a "pending" state until a relevant outcome is observed, and the fidelity of these representations predicts the fidelity of subsequent causal choice representations in lOFC and HC during credit assignment. Together, these results highlight the importance of the timely reinstatement of specific causes in lOFC and HC in learning choice-outcome relationships when delays and choices intervene, a critical component of real-world learning and decision making.

Advanced neuroimaging of the trigeminal nerve and the whole brain in trigeminal neuralgia: a systematic review.

For trigeminal neuralgia (TN), a major role of imaging is to identify the causes, but recent studies demonstrated structural and microstructural changes in the affected nerve. Moreover, an increasing number of studies have reported central nervous system involvement in TN. In this systematic review, recent quantitative magnetic resonance imaging (MRI) studies of the trigeminal nerve and the brain in patients with TN were compiled, organized, and discussed, particularly emphasizing the possible background mechanisms and the interpretation of the results. A systematic search of quantitative MRI studies of the trigeminal nerve and the brain in patients with TN was conducted using PubMed. We included the studies of the primary TN published during 2013 to 2023, conducted for the assessment of the structural and microstructural analysis of the trigeminal nerve, and the structural, diffusion, and functional MRI analysis of the brain. Quantitative MRI studies of the affected trigeminal nerves and the trigeminal pathway demonstrated structural/microstructural alterations and treatment-related changes, which differentiated responders from nonresponders. Quantitative analysis of the brain revealed changes in the brain areas associated with pain processing/modulation and emotional networks. Studies of the affected nerve demonstrated evidence of demyelination and axonal damage, compatible with pathological findings, and have shown its potential value as a tool to assess treatment outcomes. Quantitative MRI has also revealed the possibility of dynamic microstructural, structural, and functional neuronal plasticity of the brain. Further studies are needed to understand these complex mechanisms of neuronal plasticity and to achieve a consensus on the clinical use of quantitative MRI in TN.

Micron-resolution fiber mapping in histology independent of sample preparation.

Detailed knowledge of the brain's nerve fiber network is crucial for understanding its function in health and disease. However, mapping fibers with high resolution remains prohibitive in most histological sections because state-of-the-art techniques are incompatible with their preparation. Here, we present a micron-resolution light-scattering-based technique that reveals intricate fiber networks independent of sample preparation for extended fields of view. We uncover fiber structures in both label-free and stained, paraffin-embedded and deparaffinized, newly-prepared and archived, animal and human brain tissues - including whole-brain sections from the BigBrain atlas. We identify altered microstructures in demyelination and hippocampal neurodegeneration, and show key advantages over diffusion magnetic resonance imaging, polarization microscopy, and structure tensor analysis. We also reveal structures in non-brain tissues - including muscle, bone, and blood vessels. Our cost-effective, versatile technique enables studies of intricate fiber networks in any type of histological tissue section, offering a new dimension to neuroscientific and biomedical research.

40 Hz light flickering facilitates the glymphatic flow via adenosine signaling in mice

The glymphatic-lymphatic system is increasingly recognized as fundamental for the homeostasis of the brain milieu since it defines cerebral spinal fluid flow in the brain parenchyma and eliminates metabolic waste. Animal and human studies have uncovered several important physiological factors regulating the glymphatic system including sleep, aquaporin-4, and hemodynamic factors. Yet, our understanding of the modulation of the glymphatic system is limited, which has hindered the development of glymphatic-based treatment for aging and neurodegenerative disorders. Here, we present the evidence from fluorescence tracing, two-photon recording, and dynamic contrast-enhanced magnetic resonance imaging analyses that 40 Hz light flickering enhanced glymphatic influx and efflux independently of anesthesia and sleep, an effect attributed to increased astrocytic aquaporin-4 polarization and enhanced vasomotion. Adenosine-A2A receptor (A2AR) signaling emerged as the neurochemical underpinning of 40 Hz flickering-induced enhancement of glymphatic flow, based on increased cerebrofluid adenosine levels, the abolishment of enhanced glymphatic flow by pharmacological or genetic inactivation of equilibrative nucleotide transporters-2 or of A2AR, and by the physical and functional A2AR–aquaporin-4 interaction in astrocytes. These findings establish 40 Hz light flickering as a novel non-invasive strategy of enhanced glymphatic flow, with translational potential to relieve brain disorders.

Difference in Laterality of the Dorsal Striatum in Schizoaffective Disorder.

Recent research has demonstrated that the dorsal striatum is directly associated with the integration of cognitive, sensory-motor, and motivational/emotional data. Disruptions in the corticostriatal circuit have been implicated in the pathophysiology of psychosis. The dorsal striatum was reported to show lateralized pathology in psychotic disorders. In this study, we aimed to analyze the laterality of the dorsal striatum with texture analysis of T2-weighted magnetic resonance imaging (MRI) images from schizoaffective disorder (SAD) patients. Twenty SAD patients, met the inclusion criteria and had available cranial MRI data were assigned as the patient group. Twenty healthy individuals were determined as the control group. Texture analysis values were obtained from striatum region of interests (ROI) generated from T2-weighted MRI images. Data are presented as mean and standard deviation. The suitability of the data for normal distribution was analyzed with the Kolmogorov-Smirnov test. Analysis of variance (ANOVA) test (Post Hoc TUKEY) was employed to compare the group data based on test findings. There was no significant difference between the groups in terms of gender and age. There were differences in the values of texture analysis parameters of both caudate and putamen nuclei in comparison to controls. We identified differences in the left dorsal striatum nuclei in SAD. The differences in the putamen were more and more pronounced than in the caudate. Texture analyses suggest that the left dorsal striatum nuclei may be different in SAD patients. Further studies are needed to determine the pathophysiology of SAD and how it may affect disease treatment.

The effects of musicality on brain network topology in the context of Alzheimer’s disease and memory decline

Abstract Music’s role in modulating brain structure, particularly in neurodegenerative contexts such as Alzheimer’s Disease (AD), has been increasingly recognized. While previous studies have hinted at the potential neuroplastic benefits of musical engagement and training, the mechanisms through which music impacts structural connectivity in neurodegenerative pathways remain underexplored. We aimed to examine the impact of music perception skills, active musical engagement, and musical training on structural connectivity in areas relating to memory, emotion, and learning in individuals with worsening memory impairment, investigating the potential neuroplastic effects of music. Employing diffusion tensor imaging (DTI) based structural connectivity and graph theoretical analysis, we investigated brain topological features in 78 participants aged 42 to 85 with a range of memory impairments. Participants were assessed for musical training, engagement, and perception skills. The study analyzed regional and local network topological metrics to examine the influence of musical activities on graph metrics, while controlling for stages of objective memory impairment (SOMI) and diagnosis, separately. This study aimed to elucidate the effects of musical perception skills, active musical engagement, and musical training on structural connectivity within memory, emotion, and learning-related brain areas in individuals with varying degrees of memory impairment. We found enhanced structural connectivity of the right hippocampus and the right posterior cingulate cortex was associated with stronger local network metrics, such as clustering coefficient and betweenness centrality, with increased music perception skills like melody and beat perception. Musical training specifically impacted the clustering coefficient of the right hippocampus and the node degree of the right mid cingulate gyrus. Active musical engagement influenced the eigenvector centrality of the right hippocampus. Furthermore, musical training was associated with enhanced global metrics, such as global efficiency and characteristic path length. Our study integrates diffusion magnetic resonance imaging (MRI) and graph theoretical analysis to reveal significant effects of musical activities on structural connectivity in key brain regions. The results highlight the potential of musical activities to serve as a non-invasive modulatory tool for cognitive resilience, especially in memory impairment and neurodegeneration contexts. These insights contribute to the understanding of delaying AD onset and aiding early-stage patients through music-based interventions, emphasizing the importance of musical engagement in maintaining cognitive and brain health.

Sanguiin inhibits cerebral hemorrhage in rats by protecting the blood-brain barrier

IntroductionThe aim of the study was to observe the effect of Sanguiin on cerebral edema and behavior in a rat cerebral hemorrhage model.Material and methodsA rat collagenase-induced cerebral hemorrhage model was established to detect the effects of drugs on brain edema.ResultsThrough magnetic resonance imaging (MRI) analysis and brain weight content (BWC) determination, it was found that Sanguiin could significantly reduce the brain swelling index and BWC of the affected hemisphere after cerebral hemorrhage.ConclusionsSanguiin can significantly improve the neurological deficits in rats with cerebral hemorrhage, and down-regulate the expression of MMP-9 after cerebral hemorrhage, suggesting that Sanguiin has a certain protective effect on the blood-brain barrier after cerebral hemorrhage.

Whole-liver histogram analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging for predicting progression in patients with cirrhosis.

Liver cirrhosis, as the terminal phase of chronic liver disease fibrosis, is associated with high morbidity and mortality. Traditional methods for assessing liver function, such as clinical scoring systems, offer only a global evaluation and may not accurately reflect regional liver function variations. This study aimed at evaluating the diagnostic potential of whole-liver histogram analysis of gadobenate dimeglumine (Gd-BOPTA)-enhanced magnetic resonance imaging (MRI) for predicting the progression of cirrhosis. In this retrospective study, 265 consecutive patients with cirrhosis admitted to the Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University from August 2012 to September 2019 were enrolled. After the exclusion criteria were applied, 117 patients (84 males and 33 females) were divided into Child-Pugh A cirrhosis (n=43), Child-Pugh B cirrhosis (n=49), and Child-Pugh C cirrhosis (n=25). After correction for liver signal intensity with the spleen was completed, 19 histogram features of the whole liver were extracted and modeled to evaluate liver function, with the Child-Pugh class being incorporated as a clinical parameter. Receiver operating characteristic (ROC) curves were used to assess the diagnosis capability and determine the optimal cutoffs after a mean follow-up of 42.3±19.1 (range, 8-93) months. The association between significant histogram features and the cumulative incidence of hepatic insufficiency was analyzed with the adjusted Kaplan-Meier curve model. Among 117 patients (12%), 14 developed hepatic insufficiency through a period of follow-up. Five features, including the median (P<0.01), 90th percentile (P<0.01), root mean squared (P<0.01), mean (P<0.01), and 10th percentile (P<0.05), were significantly different between the groups with and without hepatic insufficiency according to the Kruskal-Wallis test; in the ROC curve analysis, the area under the curve (AUC) of these features was 0.723 [95% confidence interval (CI): 0.653-0.793], 0.722 (95% CI: 0.652-0.792), 0.722 (95% CI: 0.652-0.792), 0.721 (95% CI: 0.651-0.791), and 0.674 (95% CI: 0.600-0.748) after correction, respectively (all P values <0.05). Median, 90th percentile, root mean squared, and mean were found to be significant factors in predicting liver insufficiency. The adjusted Kaplan-Meier curves revealed that patients with a feature level less than the cutoff, as compared to those with a level above the cutoff, showed a statistically shorter progression-free survival and higher incidences of hepatic insufficiency for significant features of median (cutoff =26.001; 21.28% versus 5.71%; P=0.02), 90th percentile (cutoff =86.263; 20.41% versus 5.88%; P<0.01), root mean squared (cutoff =1,028.477; 19.15% versus 7.14%; P=0.049), and mean (cutoff =27.484; 19.15% versus 7.14%; P=0.049). Patients with a 10th percentile less than -39.811 also showed a higher cumulative incidence of hepatic insufficiency than did those with a value higher than the cutoff (0.18% versus 7.46%; P=0.22). Whole-liver histogram analysis of Gd-BOPTA-enhanced MRI may serve as a noninvasive analytical method to predict hepatic insufficiency in patients with cirrhosis.

A Multiview Brain Network Transformer Fusing Individualized Information for Autism Spectrum Disorder Diagnosis.

Functional connectivity (FC) networks, built from analyses of resting-state magnetic resonance imaging (rs-fMRI), serve as efficacious biomarkers for identifying Autism Spectrum Disorders (ASD) patients. Given the neurobiological heterogeneity across individuals and the unique presentation of ASD symptoms, the fusion of individualized information into diagnosis becomes essential. However, this aspect is overlooked in most methods. Furthermore, the existing methods typically focus on studying direct pairwise connections between brain ROIs, while disregarding interactions between indirectly connected neighbors. To overcome above challenges, we build common FC and individualized FC by tangent pearson embedding (TP) and common orthogonal basis extraction (COBE) respectively, and present a novel multiview brain transformer (MBT) aimed at effectively fusing common and indivinformation of subjects. MBT is mainly constructed by transformer layers with diffusion kernel (DK), fusion quality-inspired weighting module (FQW), similarity loss and orthonormal clustering fusion readout module (OCFRead). DK transformer can incorporate higher-order random walk methods to capture wider interactions among indirectly connected brain regions. FQW promotes adaptive fusion of features between views, and similarity loss and OCFRead are placed on the last layer to accomplish the ultimate integration of information. In our method, TP, DK and FQW modules all help to model wider connectivity in the brain that make up for the shortcomings of traditional methods. We conducted experiments on the public ABIDE dataset based on AAL and CC200 respectively. Our framework has shown promising results, outperforming state-of-the-art methods on both templates. This suggests its potential as a valuable approach for clinical ASD diagnosis.

Distributed midbrain responses signal the content of positive identity prediction errors.

Recent work across species has shown that midbrain dopamine neurons signal not only errors in the prediction of reward value but also in the prediction of value-neutral sensory features. To support learning of associative structures in downstream areas, identity prediction errors (iPEs) should signal specific information about the mis-predicted outcome. Here, we used pattern-based analysis of functional magnetic resonance imaging (fMRI) data acquired during reversal learning to characterize the information content of iPE responses in the human midbrain. We find that fMRI responses to value-neutral identity errors contain information about the identity of the unexpectedly received reward (positive iPE+) but not about the identity of theomitted reward (negative iPE-). Exploratory analyses revealed representations of iPE- in the dorsomedial prefrontal cortex. These results demonstrate that ensemble midbrain responses to value-neutral identity errors convey information about the identity of unexpectedly received outcomes, which could shape the formation of novel stimulus-outcome associations that constitute cognitive maps.

Patellofemoral instability in children and adolescents

Patellofemoral instability is acommon and clinically relevant disorder of multifactorial causes. Several concomitant problems such as genua valga, hyperlaxity, injuries or sports-related overuse may contribute to the development of instability and recurrent patellar dislocations. Athorough diagnosis is of paramount importance to delineate every contributing factor. This includes radiographic modalities and advanced imaging such as magnetic resonance imaging or torsional analyses. The authors recommend non-operative management (including physiotherapy, gait and proprioceptive training, orthoses) and, whenever non-operative measures fail, surgical patellar stabilization using, e.g. MPFL reconstruction.

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.

The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights. A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models. A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI. A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone. Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.

Radiological predictors of visual outcome in myelin oligodendrocyte glycoprotein-related optic neuritis

This study aimed to determine whether magnetic resonance imaging (MRI) biomarkers are associated with visual prognosis in myelin oligodendrocyte protein (MOG)-associated optic neuritis (MOG-ON). Cross-sectional analysis. Patients meeting 2023 international diagnostic criteria for MOG antibody-associated disease who were seen for first episodes of MOG-ON at three tertiary neuro-ophthalmology practices between January 2017 and July 2023 were enrolled. Patients who received less than 3 months of neuro-ophthalmic follow-up and did not demonstrate visual recovery (visual acuity [VA] ≥20/20 and visual field mean deviation [VFMD] >-5.0 dB) during this time were excluded. Patients received contrast-enhanced, fat-suppressed MRI of the brain and orbits within one month of symptom onset. The associations between radiological biomarkers and poor VA outcome (<20/40), incomplete VA recovery (<20/20), and poor VFMD outcome (VFMD <-5.0 dB) were assessed using multivariable logistic regression adjusting for time from symptom onset to treatment and nadir VA or VFMD. Radiological biomarkers included length of optic nerve enhancement (below vs. above 25%, 50%, and 75%); degree of orbital, canalicular, and intracranial or chiasmal optic nerve enhancement (mild vs. moderate-severe compared to the lacrimal gland); and absence vs. presence of optic nerve sheath enhancement on baseline T1-weighted MRI. A total of 129 eyes of 92 patients (median [IQR] age 37.0 [20.8-51.3], 65.2% female) were included. Poor VA outcome was seen in 6.2% of cases, incomplete VA recovery in 19.4%, and poor VFMD outcome in 16.9%. Compared to eyes with moderate-severe enhancement, eyes with mild orbital optic nerve enhancement were more likely to have poor VA outcome (OR 8.57; 95% CI [1.85, 51.14], P=0.009), incomplete VA recovery (OR 7.31, 95% CI [2.42, 25.47], P=0.001), and poor VFMD outcome (adjusting for time to treatment: OR 6.81, 95% CI [1.85, 28.98], P=0.005; adjusting for nadir VFMD: OR 11.65, 95% CI [1.60, 240.09], P=0.04). Lack of optic nerve sheath enhancement was additionally associated with incomplete VA recovery (OR 3.86, 95% CI [1.19, 12.85], P=0.02) compared to the presence of enhancement. These associations remained consistent in subgroup logistic regression analysis of MRIs performed before initiation of treatment but were not seen in pairwise analysis of MRIs performed after treatment. In eyes with first MOG-ON episodes, milder enhancement in the orbital optic nerve is associated with poorer VA and VF recovery. Prospective and mechanistic studies are needed to confirm the prognostic utility of MRI in MOG-ON.