Magnetic Resonance Imaging Data Research Articles

Visceral and subcutaneous adiposity and cardiovascular disease: Unravelling associations and prognostic value.

The distribution pattern of abdominal adiposity may help determine cardiovascular disease (CVD). Waist circumference (WC) is the most common but imprecise method for measuring abdominal adiposity, as it fails to differentiate between visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT). This study aimed to determine whether elevated VAT or ASAT provides greater prognostic value for CVD events compared to elevated WC in the general population using data from the UK Biobank. In this secondary analysis of UK Biobank study, 24 265 participants with available abdominal magnetic resonance imaging data were included. The primary outcome of the study was coronary heart disease (CHD), and secondary outcomes included stroke, heart failure (HF) and atrial fibrillation (AF). Cox regressions for VAT, ASAT and WC were examined in relation to the predefined outcomes on continuous scales using standard deviation (SD) changes and by categories of concordant and discordant values defined by medians. During a mean follow-up period of 12.9 ± 1.8 years, 2641 participants developed CVD events (1296 CHD, 165 stroke, 286 HF and 894 AF) Each 1 SD increase in VAT yielded a hazard ratio (HR) of 1.15 (95% confidence interval [CI]: 1.09-1.22) for CHD risk, whereas ASAT had a HR of 1.10 (95% CI: 1.04-1.18). Further adjustment for WC eliminated the association between ASAT and CHD risk, in contrast to the association between VAT and CHD risk, which remained almost unaffected. Discordant VAT above the median with WC below presented a HR of 1.43 (95% CI: 1.15-1.78) for CHD, compared with concordant VAT and WC below the median. Similar results were found for discordant WC above the median with VAT below, with a HR of 1.46 (95% CI: 1.18-1.81). In contrast, discordant ASAT above the median with WC below was not associated with an increased risk of CHD. Similarly, discordant ASAT above the median with VAT below was not associated with an increased risk of CHD. Additionally, there was no observed association between VAT or ASAT and the risks of stroke, HF or AF after further adjustment for WC. Additionally, there was no observed association between VAT or ASAT and the risks of stroke, HF or AF after further adjustment for WC. Incorporating VAT measurements alongside WC data improved the ability to identify individuals at high risk for CHD compared to using WC alone. Both VAT and WC proved to be more accurate indicators of CHD risk than ASAT. However, VAT alone did not fully account for the CHD risk associated with elevated WC levels. Neither VAT nor ASAT showed an association with the risk of stroke, HF and AF.

Conventional structural magnetic resonance imaging in differentiating chronic disorders of consciousness

BACKGROUND: Differential diagnosis of chronic disorders of consciousness remains one of the most difficult problems even for experienced clinicians. AIM: To evaluate the inter-expert consistency and capacity of the researcher-developed structural scale based on magnetic resonance imaging to differentiate chronic disorders of consciousness, named, DOC-MRIDS, on a larger sample of patients. MATERIALS AND METHODS: Sixty patients with a clinically stable status diagnosed with consciousness disorders (vegetative state, n=32; minimally conscious state, n=28) were enrolled. The revised coma recovery scale (CRS-R) was included in the clinical assessment. All patients underwent structural magnetic resonance imaging with 3.0-T Siemens scanners including T2 and T1 sequences. Structural changes were assessed using the DOC-MRIDS scale and included the following features: diffuse cortical atrophy, ventricular enlargement, gyri dilatation, leukoaraiosis, brainstem and/or thalamic degeneration, corpus callosum degeneration, and focal corpus callosum lesions. A total score was calculated. Magnetic resonance imaging data were analyzed by three neuroradiologists, and inter-observer agreement (Krippendorf’s alpha) was assessed. RESULTS: A high inter-examiner agreement of the DOC-MRIDS scale score was found, with α=0.806 (95% confidence interval 0.757–0.849). The vegetative state group had a higher DOC-MRIDS score than the minimally conscious state group (p 0.005). A negative correlation was obtained between CRS-R and DOC-MRIDS scale scores (ρ=–0.457, p 0.0001), individual clinical scale domains, and magnetic resonance imaging features. CONCLUSION: When assessing structural changes in patients with chronic consciousness disorders, the use of the DOC-MRIDS scale helps differentiate the type of such disorders with sufficient specificity, sensitivity, and inter-rater agreement. This scale can be used in clinical practice as an additional differential diagnostic tool.

A physics-informed deep learning framework for dynamic susceptibility contrast perfusion MRI.

Perfusion magnetic resonance imaging (MRI)s plays a central role in the diagnosis and monitoring of neurovascular or neurooncological disease. However, conventional processing techniques are limited in their ability to capture relevant characteristics of the perfusion dynamics and suffer from a lack ofstandardization. We propose a physics-informed deep learning framework which is capable of analyzing dynamic susceptibility contrast perfusion MRI data and recovering the dynamic tissue response with highaccuracy. The framework uses physics-informed neural networks (PINNs) to learn the voxel-wise TRF, which represents the dynamic response of the local vascular network to the contrast agent bolus. The network output is stabilized by total variation and elastic net regularization. Parameter maps of normalized cerebral blood flow (nCBF) and volume (nCBV) are then calculated from the predicted residue functions. The results are validated using extensive comparisons to values derived by conventional Tikhonov-regularized singular value decomposition (TiSVD), in silico simulations and an in vivo dataset of perfusion MRI exams of patients with high-gradegliomas. The simulation results demonstrate that PINN-derived residue functions show a high concordance with the true functions and that the calculated values of nCBF and nCBV converge towards the true values for higher contrast-to-noise ratios. In the in vivo dataset, we find high correlations between conventionally derived and PINN-predicted perfusion parameters (Pearson's rho for nCBF: and nCBV: ) and very high indices of image similarity (structural similarity index for nCBF: and for nCBV: ). PINNs can be used to analyze perfusion MRI data and stably recover the response functions of the local vasculature with highaccuracy.

Possibilities and limitations of magnetic resonance imaging in the diagnostics of endocervical adenocarcinomas

BACKGROUND: In recent decades, the incidence of cervical adenocarcinomas has increased from 5% to 20%. Endocervical adenocarcinomas are characterized by a more aggressive course and early metastasis. Owing to the difficulties in the cytological diagnosis of cervical adenocarcinoma, early radiation diagnostics and staging subsequently play a key role. Very few studies have examined the use of magnetic resonance imaging in diagnosing cervical adenocarcinomas. AIM: To determine the diagnostic informativeness of magnetic resonance imaging in the staging of cervical adenocarcinomas according to the T-criterion and assessing the depth of tumor invasion into the stroma of the cervix and clarify the semiotic signs of adenocarcinoma and features of tumor growth in the uterus. MATERIALS AND METHODS: In total, 123 patients diagnosed with cervical cancer (C53), who underwent diagnosis and treatment between 2020 and 2023, were examined. The examination results of 22 (18%) patients with cervical adenocarcinoma were analyzed. The average patient age was 56 years. A multiparametric magnetic resonance examination of the pelvic organs was performed on 22 patients using tomographs with a magnetic field strength of 1.5 T. Moreover, 14 (64%) patients underwent surgery including extirpation of the uterus and appendages with pelvic lymphadenectomy. The information value of magnetic resonance imaging was evaluated in 11 patients, whose first stage was surgical treatment. RESULTS: In this study, cervical adenocarcinoma was detected in 18% among all cases of cervical cancer. The information value of magnetic resonance imaging in assessing the local prevalence of endocervical adenocarcinoma according to the T-criterion was as follows (main value with the corresponding 95% confidence interval): sensitivity, 77.78% (39.99%–97.19%); specificity, 50.00% (1.26%–98.74%); positive predictive value, 87.50% (62.64%–96.69%); negative predictive value, 33.33% (7.30%–76.04%); and accuracy, 72.73% (39.03%–93.98%). The information value of magnetic resonance imaging in assessing the depth of tumor invasion into the cervical stroma was as follows: odds ratio, 3.500 (0.145%–84.694%); sensitivity, 85.7% (0.757%–0.993%); specificity, 33.3% (0.018%–0.0648%); positive predictive value, 75% (0.673%–0.883%); negative predictive value, 50% (0.027%–0.972%). CONCLUSIONS: The results of this study showed that magnetic resonance imaging is a good tool with high diagnostic informativeness in detecting endocervical cervical adenocarcinoma. The four macrostructures of tumor growth in endocervical adenocarcinoma identified during magnetic resonance imaging data analysis indicate locally aggressive tumor growth and a high frequency of endometrial dropouts. This finding will allow radiologists to structure a descriptive picture, including the verified cervical adenocarcinoma, to enhance methods of developing a treatment plan for the patient.

Magnetic resonance imaging in the evaluation of pectus excavatum

BACKGROUND: Magnetic resonance imaging is more often used to confirm the presence of pectus excavatum and assess compression changes in the heart at this level. AIM: To evaluate pectus excavatum preoperatively according to magnetic resonance imaging findings. MATERIALS AND METHODS: A retrospective evaluation of chest magnetic resonance imaging data of 38 patients (male, n=30; female, n=8) was performed. The average age was 19.9 years (±9 years).Cardiac magnetic resonance imaging was performed on a 1.5-T General Electric Optima MR450w GEM scanner with 2D-FIESTA-C pulse sequences, as well as functional assessment of the left and right ventricles. Parameters for surgical treatment of pectus excavatum were as follows: the Haller index, correction index, and sternum rotation angle. Statistical analysis of the relationship between the Haller index, correction index, and sternum rotation angle and ejection fraction of the right ventricle was conducted. A p-value 0.05 was considered significant. RESULTS: Moderate and severe pectus excavatum were found in 92% of the cases. No significant Pearson correlation was obtained between the Haller index and right ventricular ejection fraction (inspiratory and expiratory ejection fraction, p=0.777 and 0.798, respectively). The mean right ventricular ejection fraction was 46%. A correlation was noted between the Haller index and the correction index (p 0.05). The rotation angle of the sternum, which required modification of surgical intervention, was detected in 44.7% of patients. CONCLUSION: Magnetic resonance imaging is an informative diagnostic method for pectus excavatum pectus excavatum without radiation exposure and enables detailed preoperative assessment. A correlation was noted between the Haller index and the correction index (p 0.05). Magnetic resonance imaging revealed a decrease in the ejection fraction of the right ventricle.

Lower limb maltorsion and acetabular deformity in children and adolescents with X-linked hypophosphatemia.

X-linked hypophosphatemia (XLH) is a rare monogenetic skeletal disorder. Lower limb deformities contribute substantially to impaired gait quality and burden of disease in patients with XLH. Standardized data regarding onset and severity of lower limb maltorsion are unavailable. This study aimed to evaluate lower limb maltorsion using rotational magnetic resonance imaging (MRI) and computed tomography (CT). Rotational MRI and CT of children and adolescents with verified XLH were evaluated retrospectively. Femoral and tibial torsion, acetabular anteversion, and axial acetabular coverage were measured and compared with published age-adapted radiographic, clinical measurements and MRI data, respectively. Fifteen patients (mean age, 10.7 years) were included in the study. Decreased femoral torsion was observed in 47% (14/30 femora) and femoral retrotorsion in 17% (5/30 femora). Ten of 13 hips with coxa vara deformity presented with decreased femoral antetorsion. Reduced external tibial torsion manifested in 64% (18/28 tibiae). Abnormal axial femoral head coverage was present in 67% (20/30 hips), whereas 53% (16/30 hips) showed increased acetabular anteversion. Femoral and tibial torsional pathologies were found in children and adolescents with XLH. The occurrence of severe femoral retrotorsion in a 2-year-old child prior to ambulation raises questions regarding the biomechanical impact of gait on the development of torsional deformities in XLH.

Placental T2-weighted MRI-based radiomics-clinical nomogram to predict postpartum hemorrhage of placenta previa

Background Placenta previa is an obstetric complication related to severe maternal morbidity and mortality. Magnetic resonance imaging (MRI) can be used for the preoperative evaluation of postpartum hemorrhage. Purpose To investigate the value of MRI-based radiomics analysis in predicting postpartum hemorrhage among pregnant women with placenta previa. Material and Methods Preoperative T2-weighted MRI and related clinical data of 371 patients were retrospectively collected, and these patients were randomly allocated into two subsets: the training dataset (n = 260) and the validation dataset (n = 111). The logistic regression (LR) classifier was used for the development of the radiomics model and the calculation of the radiomics score (Radscore). Results A total of eight radiomics features and five clinical features were selected for model development. The area under the receiver operating characteristic curve (AUC) of the radiomics model in the training and validation datasets were 0.929 (95% confidence interval [CI] = 0.891–0.957) and 0.914 (95% CI = 0.846–0.959), respectively. Combined with clinical factors, nomograms demonstrated improved diagnostic efficacy, with an AUC of 0.968 (95% CI = 0.939–0.986) in the training dataset and 0.947 (95% CI = 0.888–0.981) in the validation dataset. Conclusion The MRI-based model has certain value in predicting postpartum hemorrhage in pregnant women with placenta previa.

Objective Determination of Site-of-Lesion in Auditory Neuropathy.

Auditory neuropathy (AN), a complex hearing disorder, presents challenges in diagnosis and management due to limitations of current diagnostic assessment. This study aims to determine whether diffusion-weighted magnetic resonance imaging (MRI) can be used to identify the site and severity of lesions in individuals with AN. This case-control study included 10 individuals with AN of different etiologies, 7 individuals with neurofibromatosis type 1 (NF1), 5 individuals with cochlear hearing loss, and 37 control participants. Participants were recruited through the University of Melbourne's Neuroaudiology Clinic and the Murdoch Children's Research Institute specialist outpatient clinics. Diffusion-weighted MRI data were collected for all participants and the auditory pathways were evaluated using the fixel-based analysis metric of apparent fiber density. Data on each participant's auditory function were also collected including hearing thresholds, otoacoustic emissions, auditory evoked potentials, and speech-in-noise perceptual ability. Analysis of diffusion-weighted MRI showed abnormal white matter fiber density in distinct locations within the auditory system depending on etiology. Compared with controls, individuals with AN due to perinatal oxygen deprivation showed no white matter abnormalities ( p > 0.05), those with a neurodegenerative conditions known/predicted to cause VIII cranial nerve axonopathy showed significantly lower white matter fiber density in the vestibulocochlear nerve ( p < 0.001), while participants with NF1 showed lower white matter fiber density in the auditory brainstem tracts ( p = 0.003). In addition, auditory behavioral measures of speech perception in noise and gap detection were correlated with fiber density results of the VIII nerve. Diffusion-weighted MRI reveals different patterns of anatomical abnormality within the auditory system depending on etiology. This technique has the potential to guide management recommendations for individuals with peripheral and central auditory pathway abnormality.

Comparison of different MRI-based unsupervised segmentation algorithms in predicting sarcopenia

PurposeTo compare the performance of MRI-based Gaussian mixture model (GMM), K-means clustering, and Otsu unsupervised algorithms in predicting sarcopenia and to develop a combined model by integrating clinical indicators. MethodsRetrospective analysis was conducted on clinical and lumbar MRI data from 118 patients diagnosed with sarcopenia and 222 patients without the sarcopenia. All patients were randomly divided into training and validation groups in a 7:3 ratio. Regions of interest (ROI), specifically the paravertebral muscles at the L3/4 intervertebral disc level, were delineated on axial T2-weighted images (T2WI). The Gaussian mixture model (GMM), K-means clustering, and Otsu’s thresholding algorithms were employed to automatically segment muscle and adipose tissues at both the cohort and case levels. Subsequently, the mean signal intensity, volumes, and percentages of these tissues were calculated and compared. Logistic regression analyses were conducted to construct models and identify independent predictors of sarcopenia. An combined model was developed by combining the optimal magnetic resonance imaging (MRI) model and clinical predictors. The performance of the constructed model was assessed using receiver operating characteristic (ROC) curve analysis. ResultsAge, BMI, and serum albumin were identified as independent clinical predictors of sarcopenia. The cohort-level GMM demonstrated the best predictive performance both in the training group (AUC=0.840) and validation group (AUC=0.800), while the predictive performance of the other models was lower than that of the clinical model both in the training and validation groups. After combining the cohort-level GMM with the independent clinical predictors, the AUC of the training and validation groups increased to 0.871 and 0.867, respectively. ConclusionThe cohort-level GMM shows potential in predicting sarcopenia, and the incorporation of independent clinical predictors further increased the performance.

Abnormal Dynamic Reconfiguration of Multilayer Temporal Networks in Patients with Bipolar Disorder.

Multilayer networks have been used to identify abnormal dynamic reconfiguration in bipolar disorder (BD). However, these studies ignore the differences in information interactions between adjacent layers when constructing multilayer networks, and the analysis of dynamic reconfiguration is not comprehensive enough; Methods: Resting-state functional magnetic resonance imaging data were collected from 46 BD patients and 54 normal controls. A multilayer temporal network was constructed for each subject, and inter-layer coupling of different nodes was considered using network similarity. The promiscuity, recruitment, and integration coefficients were calculated to quantify the different dynamic reconfigurations between the two groups; Results: The global inter-layer coupling, recruitment, and integration coefficients were significantly lower in BD patients. These results were further observed in the attention network and the limbic/paralimbic and subcortical network, reflecting reduced temporal stability, intra- and inter-subnetwork communication abilities in BD patients. The whole-brain promiscuity was increased in BD patients. The same results were observed in the somatosensory/motor and auditory network, reflecting more functional interactions; Conclusions: This study discovered abnormal dynamic interactions of BD from the perspective of dynamic reconfiguration, which can help to understand the pathological mechanisms of BD.

Reconciling opposing effects of emotion on relational memory: Behavioral, eye-tracking, and brain imaging investigations.

The effects of emotion on memory are wide-ranging and powerful, but they are not uniform. Although there is agreement that emotion enhances memory for individual items, how it influences memory for the associated contextual details (relational memory, RM) remains debated. The prevalent view suggests that emotion impairs RM, but there is also evidence that emotion enhances RM. To reconcile these diverging results, we carried out three studies incorporating the following features: (1) testing RM with increased specificity, distinguishing between subjective (recollection based) and objective (item-context match) RM accuracy, (2) accounting for emotion-attention interactions via eye-tracking and task manipulation, and (3) using stimuli with integrated item-context content. Challenging the prevalent view, we identified both enhancing and impairing effects. First, emotion enhanced subjective RM, separately and when confirmed by accurate objective RM. Second, emotion impaired objective RM through attention capturing, but it enhanced RM accuracy when attentional effects were statistically accounted for using eye-tracking data. Third, emotion also enhanced RM when participants were cued to focus on contextual details during encoding, likely by increasing item-context binding. Finally, functional magnetic resonance imaging data recorded from a subset of participants showed that emotional enhancement of RM was associated with increased activity in the medial temporal lobe (MTL) and ventrolateral prefrontal cortex, along with increased intra-MTL and ventrolateral prefrontal cortex-MTL functional connectivity. Overall, these findings reconcile evidence regarding opposing effects of emotion on RM and point to possible training interventions to increase RM specificity in healthy functioning, posttraumatic stress disorder, and aging, by promoting item-context binding and diminishing memory decontextualization. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.

Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in brain function and structure, overcoming the limitations of traditional univariate methods. To assess the reliability of neuroimaging-based biomarkers and the contribution of study characteristics in distinguishing individuals with schizophrenia spectrum disorder (SSD) from healthy controls (HCs), we conducted a systematic review of the studies that used multivariate pattern recognition for this objective. We systematically searched PubMed, Scopus, and Web of Science for studies on SSD classification using multivariate pattern analysis on magnetic resonance imaging data. We employed a bivariate random-effects meta-analytic model to explore the classification of sensitivity (SE) and specificity (SP) across studies while also evaluating the moderator effects of clinical and non-clinical variables. A total of 119 studies (with 12,723 patients with SSD and 13,196 HCs) were identified. The meta-analysis estimated a SE of 79.1% (95% confidence interval [CI], 77.1%-81.0%) and a SP of 80.0% (95% CI, 77.8%-82.0%). In particular, the Positive and Negative Syndrome Scale and the Global Assessment of Functioning scores, age, age of onset, duration of untreated psychosis, deep learning, algorithm type, features selection, and validation methods had significant effects on classification performance. Multivariate pattern analysis reliably identifies neuroimaging-based biomarkers of SSD, achieving ∼80% SE and SP. Despite clinical heterogeneity, discernible brain modifications effectively differentiate SSD from HCs. Classification performance depends on patient-related and methodological factors crucial for the development, validation, and application of prospective models in clinical settings.

Automated segmentation of midbrain nuclei using deep learning and multisequence MRI: a longitudinal study on iron accumulation with age

Abstract Elevated levels of brain iron, particularly within the basal ganglia, have been associated with cognitive and motor impairment in normal aging and neurodegenerative conditions. The subthalamic nucleus (STN), substantia nigra (SN), and red nucleus (RN), despite their high iron content and contribution to motor and cognitive processes, are less frequently studied. This oversight can largely be attributed to the challenges posed by in-vivo assessments of these small, deep-seated midbrain structures. We developed and validated an automated tool for the segmentation of the STN, SN, and RN. Multi-sequence magnetic resonance imaging (MRI) data including T1-weighted, FLAIR, Quantitative Susceptibility Mapping (QSM) and R2* alongside manual delineation on QSM images of 40 individuals, was used to train segmentation models based on nnU-Net deep-learning framework. A combination of QSM and FLAIR sequences was found to be optimal for structure segmentation (mean Dice scores of 0.84, 0.91 and 0.94 for STN, SN and RN, respectively). We next applied the automated segmentation method to an independent 3-year longitudinal dataset including 175 healthy adults (age range at baseline: 20-79 years old). Structural equation modelling was used to assess iron accumulation over time using age, sex, baseline iron and regional volume as factors of interest. Cross-sectionally, older age was linearly associated with higher iron load in SN and STN; the association was non-linear in RN. Longitudinally, results indicated significant iron accumulation in the STN (Mean increase = 0.02, p = 0.005) and SN (Mean increase = 0.035, p = 0.001), but not in the RN (Mean increase = 0.015, p = 0.2). Our findings demonstrated high performance of nnU-Net in automated segmentation, and advanced our understanding of iron accumulation in midbrain nuclei in aging.

Frontoparietal network topology as a neural marker of musical perceptual abilities

Why are some individuals more musical than others? Neither cognitive testing nor classical localizationist neuroscience alone can provide a complete answer. Here, we test how the interplay of brain network organization and cognitive function delivers graded perceptual abilities in a distinctively human capacity. We analyze multimodal magnetic resonance imaging, cognitive, and behavioral data from 200+ participants, focusing on a canonical working memory network encompassing prefrontal and posterior parietal regions. Using graph theory, we examine structural and functional frontoparietal network organization in relation to assessments of musical aptitude and experience. Results reveal a positive correlation between perceptual abilities and the integration efficiency of key frontoparietal regions. The linkage between functional networks and musical abilities is mediated by working memory processes, whereas structural networks influence these abilities through sensory integration. Our work lays the foundation for future investigations into the neurobiological roots of individual differences in musicality.

Breakdown of TMS evoked EEG signal propagation within the default mode network in Alzheimer’s disease

BackgroundThe neural activity of the Default Mode Network (DMN) is disrupted in patients with In Alzheimer’s disease (AD). ObjectivesWe used a novel multimodal approach to track neural signal propagation within the DMN in AD patients. MethodsTwenty mild to moderate AD patients were recruited. We used transcranial magnetic stimulation (TMS) pulses to probe with a millisecond time resolution the propagation of evoked electroencephalography (EEG) signal following the neural activation of the Precuneus (PC), which is a key hub area of the DMN. Moreover, functional and structural magnetic resonance imaging (MRI) data were collected to reconstruct individual features of the DMN. ResultsIn AD patients a probe TMS pulse applied over the PC evokes an increased local activity unmasking underlying hyperexcitability. In contrast, the EEG evoked neural signal did not propagate efficiently within the DMN showing a remarkable breakdown of signal propagation. fMRI and structural tractography showed that impaired signal propagation was related to the same connectivity matrices derived from DMN BOLD signal and transferred by specific white matter bundles forming the cingulum. These features were not detectable stimulating other areas (left dorsolateral prefrontal cortex) or for different networks (fronto-parietal network). Finally, connectivity breakdown was associated with cognitive impairment, as measured with the Clinical Dementia Rating Scale sum of boxes (CDR-SB). ConclusionsTMS-EEG in AD shows both local hyperexcitability and a lack of signal propagation within the DMN. These neurophysiological features also correlate with structural and cognitive attributes of the patients. SignificanceNeuronavigated TMS-EEG may be used as a novel neurophysiological biomarker of DMN connectivity in AD patients.

Identifying individual brain development using multimodality brain network

The cortical development of our brains is in a hierarchical manner and promotes the emergence of large-scale functional hierarchy. However, under interindividual heterogenicity, how the spatiotemporal features of brain networks reflect brain development and mental health remains unclear. Here we collect both resting-state electroencephalography and functional magnetic resonance imaging data from the Child Mind Institute Biobank to demonstrate that during brain growth, the global dynamic patterns of brain states become more active and the dominant networks shift from sensory to higher-level networks; the individual functional network patterns become more similar to that of adults and their spatial coupling tends to be invariable. Furthermore, the properties of multimodality brain networks are sufficiently robust to identify healthy brain age and mental disorders at specific ages. Therefore, multimodality brain networks provide new insights into the functional development of the brain and a more reliable and reasonable approach for age prediction and individual diagnosis.

Versatile and Tunable Performance of PVA/PAM Tridimensional Hydrogel Models for Tissues and Organs: Augmenting Realism in Advanced Surgical Training.

The increasing complexity and difficulty of surgical procedures have led to a rise in medical errors within clinical settings in recent years. Gastrointestinal diseases, in particular, present significant medical challenges and impose substantial economic burdens, underscoring the urgent need for experiential, high-fidelity gastrointestinal surgical training tools. This study leverages patient-specific computed tomography (CT) and magnetic resonance imaging (MRI) data, combined with 3D printed manufacturing, to develop hydrogel organ models with tunable performance and tissue-mimicking softness. These properties are achieved by regulating the freeze-thaw cycles, cross-linking agents, and the concentration of incorporated antibacterial nanoparticles in DN hydrogels. Through the application of indirect 3D printing and the "sacrificial material method", we successfully fabricate organ tissues such as the stomach, intestines, and blood vessels with high precision. In ex vivo surgical training demonstrations, these tissue-like soft hydrogels provide an effective platform for preoperative simulation and surgical training in digestive surgery, accommodating various surgical procedures and accurately simulating intraoperative bleeding. The development of advanced bionic organ models with specific and tunable characteristics based on DN hydrogels is poised to significantly advance surgical training, medical device testing, and the reform of medical education.

Inflammatory stimuli impact on cellular uptake and biodistribution of perfluorocarbon nanoemulsions.

Intravenously administered perfluorocarbon nanoemulsion (PFC) are taken up by phagocytic immune cells which enables the non-invasive visualization of inflammatory hot spots by combined 1H/19F magnetic resonance imaging (MRI). However, little is known about the influence of inflammatory stimuli on cellular uptake and biodistribution of PFCs. Here, we systematically investigated the impact of inflammation induced by subcutaneous implantation of Matrigel/lipopolysaccharide (Matrigel/LPS) or myocardial infarction (MI; 50 minutes ischemia reperfusion) on PFC-uptake and biodistribution in C57BL/6J mice. We detected strong 19F signals in Matrigel/LPS plugs and infarcted hearts, which were completely absent in controls. Cellular uptake of PFCs was increased in neutrophils isolated from the blood and Matrigel/LPS plugs, whereas uptake by monocytes was only slightly elevated. In contrast, MI caused only a moderate early increase of PFC-uptake in monocytes and neutrophils. Interestingly, the inflammatory model did also affect the biodistribution of the PFCs. The blood half-life of PFCs was slightly increased after Matrigel/LPS implantation, whereas it was reduced after MI. Compared to controls, the 19F signal of the liver was significantly stronger in Matrigel/LPS, but not in MI animals. Interestingly, stimulation of primary immune cells and RAW264.7 macrophages with LPS had no effect on PFC-uptake, whereas CRP-incubation elevated internalization of PFCs at least in RAW264.7 cells. In conclusion, we show that the cellular PFC-uptake can differ between individual inflammatory conditions. This is an important aspect that has to be considered for the proper interpretation of 1H/19F MRI data obtained from inflammatory hot spots.

Location of the Anatomic Footprint Centers of the Anterior Cruciate Ligament Determined by Quadrant Method on Three-Dimensional Magnetic Resonance Imaging.

The quadrant method is widely used to determine the femoral footprint center (FFC) on radiographs or computed tomography (CT) and can also describe the tibial footprint center (TFC). However, its application on three-dimensional (3D) magnetic resonance imaging (MRI) has been limited. This study aims to describe the ACL footprint center position on 3D MRI of healthy knees using the quadrant method. Proton density (PD) sequence 3D MRI was conducted on 45 intact knees, aged 18 to 45years. The centers of the ACL footprints were determined, and 2D simulated radiographic images were generated from the 3D MRI data. The quadrant method was then applied to calculate the positions of the footprint centers. The FFC was located at 31.6% in the deep-shallow (DS) direction and 31.3% in the high-low (HL) direction. The TFC was positioned at 45.1% in the mediolateral (ML) direction and 39.9% in the anteroposterior (AP) direction. The ACL footprint centers identified in this study were positioned similarly to previous studies, with the exception of the TFC in the ML direction, which was found to be more medial. This approach has the potential to enhance preoperative planning and intra-operative navigation in ACL reconstruction surgeries.

Abnormal resting-state functional connectivity of the right anterior cingulate cortex in chronic ketamine users and its correlation with cognitive impairments

BackgroundChronic ketamine use leads to cognitive impairments, however, the neural mechanisms underpinning these impairments are still unclear. AimsMany studies showed Anterior cingulate cortex (ACC)is strongly involved in cognition and drug addiction, as supported by our previous studies. The objective of this study was to assess the variations in resting-state functional connectivity (FC) changes in the right anterior cingulate cortex (ACC) of chronic ketamine users (CKUs) and their relationship with cognitive performance. MethodsThe study enrolled 28 chronic ketamine users (CKUs) and 30 healthy controls (HCs). Resting-state functional magnetic resonance imaging (fMRI) data were gathered from both groups. Cognitive functions were evaluated using the MATRICS Consensus Cognitive Battery (MCCB). ResultsCKUs demonstrated significantly poorer cognitive performance than HCs in various cognitive domains, including Visual Learning, Speed of Processing, Working Memory, and the composite score of MCCB. Group-level comparisons revealed that CKUs exhibited enhanced functional connectivity between the right ACC and the right postcentral gyrus (PCG) compared to HCs. There was a positive relationship between the connectivity of right ACC-PCG and reasoning and problem-solving score, but there was no significant association with the characteristics of ketamine use. ConclusionCKUs showed enhanced connectivity between the right ACC and the right PCG. This enhanced functional connectivity may indicate functional compensation for cognitive deficits in CKUs, especially for reasoning and problem-solving impairments in CKUs.