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Background: Irreversible neurological dysfunction (IND) is an adverse event after cervical spinal cord injury (CSCI). However, there is still a shortage of objective criteria for the early prediction of neurological function. We aimed to screen independent predictors of IND and use these findings to construct a nomogram that could predict the development of neurological function in CSCI patients., Methods: Patients with CSCI attending the Affiliated Hospital of Southwest Medical University between January 2014 and March 2021 were included in this study. We divided the patients into two groups: reversible neurological dysfunction (RND) and IND. The independent predictors of IND in CSCI patients were screened using the regularization technique to construct a nomogram, which was finally converted into an online calculator. Concordance index (C-index), calibration curves analysis and decision curve analysis (DCA) evaluated the model's discrimination, calibration, and clinical applicability. We tested the nomogram in an external validation cohort and performed internal validation using the bootstrap method., Results: We enrolled 193 individuals with CSCI in this study, including IND (n = 75) and RND (n = 118). Six features, including age, American spinal injury association Impairment Scale (AIS) grade, signal of spinal cord (SC), maximum canal compromise (MCC), intramedullary lesion length (IMLL), and specialized institution-based rehabilitation (SIBR), were included in the model. The C-index of 0.882 from the training set and its externally validated value of 0.827 demonstrated the model's prediction accuracy. Meanwhile, the model has satisfactory actual consistency and clinical applicability, verified in the calibration curve and DCA., Conclusion: We constructed a prediction model based on six clinical and MRI features that can be used to assess the probability of developing IND in patients with CSCI., (© 2023. The Author(s).)

Background: Dynamic-contrast-enhanced (DCE) MRI data acquired using gradient echo based sequences is affected by errors in flip angle (FA) due to transmit B 1 inhomogeneity (B 1 inh). The purpose of the study was to evaluate the effect of B 1 inh on quantitative analysis of DCE-MRI data of human brain tumor patients and to evaluate the clinical significance of B 1 inh correction of perfusion parameters (PPs) on tumor grading., Methods: An MRI study was conducted on 35 glioma patients at 3T. The patients had histologically confirmed glioma with 23 high-grade (HG) and 12 low-grade (LG). Data for B 1 -mapping, T 1 -mapping and DCE-MRI were acquired. Relative B 1 maps (B 1rel ) were generated using the saturated-double-angle method. T 1 -maps were computed using the variable flip-angle method. Post-processing was performed for conversion of signal-intensity time (S(t)) curve to concentration-time (C(t)) curve followed by tracer kinetic analysis (K trans , Ve, Vp, Kep) and first pass analysis (CBV, CBF) using the general tracer-kinetic model. DCE-MRI data was analyzed without and with B 1 inh correction and errors in PPs were computed. Receiver-operating-characteristic (ROC) analysis was performed on HG and LG patients. Simulations were carried out to understand the effect of B 1 inhomogeneity on DCE-MRI data analysis in a systematic way. S(t) curves mimicking those in tumor tissue, were generated and FA errors were introduced followed by error analysis of PPs. Dependence of FA-based errors on the concentration of contrast agent and on the duration of DCE-MRI data was also studied. Simulations were also done to obtain K trans of glioma patients at different B 1rel values and see whether grading is affected or not., Results: Current study shows that B 1rel value higher than nominal results in an overestimation of C(t) curves as well as derived PPs and vice versa. Moreover, at same B 1rel values, errors were large for larger values of C(t). Simulation results showed that grade of patients can change because of B 1 inh., Conclusions: B 1 inh in the human brain at 3T-MRI can introduce substantial errors in PPs derived from DCE-MRI data that might affect the accuracy of tumor grading, particularly for border zone cases. These errors can be mitigated using B 1 inh correction during DCE-MRI data analysis.

Background: Magnetic resonance imaging (MRI) has been widely used in studies evaluating the neuropathology of autism spectrum disorder (ASD). Studies are often limited, however, to higher functioning individuals with ASD. MRI studies of individuals with ASD and comorbid intellectual disability (ID) are lacking, due in part to the challenges of acquiring images without the use of sedation., Methods: Utilizing principles of applied behavior analysis (ABA), we developed a protocol for acquiring structural MRI scans in school-aged children with ASD and intellectual impairment. Board certified behavior analysts worked closely with each child and their parent(s), utilizing behavior change techniques such as pairing, shaping, desensitization, and positive reinforcement, through a series of mock scanner visits to prepare the child for the MRI scan. An objective, quantitative assessment of motion artifact in T1- and diffusion-weighted scans was implemented to ensure that high-quality images were acquired., Results: The sample consisted of 17 children with ASD who are participants in the UC Davis Autism Phenome Project, a longitudinal MRI study aimed at evaluating brain developmental trajectories from early to middle childhood. At the time of their initial scan (2-3.5 years), all 17 children had a diagnosis of ASD and development quotient (DQ) <70. At the time of the current scan (9-13 years), 13 participants continued to have IQs in the range of ID (mean IQ = 54.1, sd = 12.1), and four participants had IQs in the normal range (mean = 102.2, sd = 7.5). The success rate in acquiring T1-weighted images that met quality assurance for acceptable motion artifact was 100 %. The success rate for acquiring high-quality diffusion-weighted images was 94 %., Conclusions: By using principles of ABA in a research MRI setting, it is feasible to acquire high-quality images in school-aged children with ASD and intellectual impairment without the use of sedation. This is especially critical to ensure that ongoing longitudinal studies of brain development can extend from infancy and early childhood into middle childhood in children with ASD at all levels of functioning, including those with comorbid ID.

Background: Dynamic-contrast-enhanced (DCE) MRI data acquired using gradient echo based sequences is affected by errors in flip angle (FA) due to transmit B1 inhomogeneity (B1inh). The purpose of the study was to evaluate the effect of B1inh on quantitative analysis of DCE-MRI data of human brain tumor patients and to evaluate the clinical significance of B1inh correction of perfusion parameters (PPs) on tumor grading.Methods: An MRI study was conducted on 35 glioma patients at 3T. The patients had histologically confirmed glioma with 23 high-grade (HG) and 12 low-grade (LG). Data for B1-mapping, T1-mapping and DCE-MRI were acquired. Relative B1 maps (B1rel) were generated using the saturated-double-angle method. T1-maps were computed using the variable flip-angle method. Post-processing was performed for conversion of signal-intensity time (S(t)) curve to concentration-time (C(t)) curve followed by tracer kinetic analysis (Ktrans, Ve, Vp, Kep) and first pass analysis (CBV, CBF) using the general tracer-kinetic model. DCE-MRI data was analyzed without and with B1inh correction and errors in PPs were computed. Receiver-operating-characteristic (ROC) analysis was performed on HG and LG patients. Simulations were carried out to understand the effect of B1 inhomogeneity on DCE-MRI data analysis in a systematic way. S(t) curves mimicking those in tumor tissue, were generated and FA errors were introduced followed by error analysis of PPs. Dependence of FA-based errors on the concentration of contrast agent and on the duration of DCE-MRI data was also studied. Simulations were also done to obtain Ktrans of glioma patients at different B1rel values and see whether grading is affected or not.Results: Current study shows that B1rel value higher than nominal results in an overestimation of C(t) curves as well as derived PPs and vice versa. Moreover, at same B1rel values, errors were large for larger values of C(t). Simulation results showed that grade of patients can change because of B1inh.Conclusions: B1inh in the human brain at 3T-MRI can introduce substantial errors in PPs derived from DCE-MRI data that might affect the accuracy of tumor grading, particularly for border zone cases. These errors can be mitigated using B1inh correction during DCE-MRI data analysis. [ABSTRACT FROM AUTHOR]

Background: Because of superior soft tissue contrast, the use of magnetic resonance imaging (MRI) as a complement to computed tomography (CT) in the target definition procedure for radiotherapy is increasing. To keep the workflow simple and cost effective and to reduce patient dose, it is natural to strive for a treatment planning procedure based entirely on MRI. In the present study, we investigate the dose calculation accuracy for different treatment regions when using bulk density assignments on MRI data and compare it to treatment planning that uses CT data. Methods: MR and CT data were collected retrospectively for 40 patients with prostate, lung, head and neck, or brain cancers. Comparisons were made between calculations on CT data with and without inhomogeneity corrections and on MRI or CT data with bulk density assignments. The bulk densities were assigned using manual segmentation of tissue, bone, lung, and air cavities. Results: The deviations between calculations on CT data with inhomogeneity correction and on bulk density assigned MR data were small. The maximum difference in the number of monitor units required to reach the prescribed dose was 1.6%. This result also includes effects of possible geometrical distortions. Conclusions: The dose calculation accuracy at the investigated treatment sites is not significantly compromised when using MRI data when adequate bulk density assignments are made. With respect to treatment planning, MRI can replace CT in all steps of the treatment workflow, reducing the radiation exposure to the patient, removing any systematic registration errors that may occur when combining MR and CT, and decreasing time and cost for the extra CT investigation. [ABSTRACT FROM AUTHOR]

Background: Based upon the acquainted loss of dopaminergic neurons in the substantia nigra in Parkinson's disease (PD), we hypothesised changes in magnetic resonance imaging signal intensities of the basal ganglia to be useful as an additional technical tool in the diagnostic work-up., Methods: Region-of-interest analyses (substantia nigra and globus pallidus internus) of T2-weighted scans were performed in seventy subjects with PD, 170 age- and gender-matched controls and 38 patients with an atypical form of neurodegenerative Parkinsonian syndrome (N = 11 multisystem atrophy, N = 22 progressive supranuclear palsy, N = 5 corticobasal syndrome)., Results: In patients with PD, significant changes in signal intensities within the substantia nigra were observed compared to controls at p < 0.001. For the globus pallidus internus, signal alterations in PD and progressive supranuclear palsy were found to be significant (p < 0.001) if compared to controls. Furthermore, signal changes of substantia nigra correlated with signal intensities of globus pallidus internus in the ipsilateral hemisphere in both groups. Sensitivity was 86% and specificity was 90% for the combined analysis of substantia nigra and globus pallidus internus in the complete patient sample versus controls., Conclusions: Signal alterations of substantia nigra and globus pallidus internus in routine magnetic resonance imaging were useful to distinguish patients with PD from controls. In addition, signal changes in globus pallidus internus could be used to differentiate progressive supranuclear palsy patients from controls. These analyses have the potential to serve as an additional non-invasive technical tool to support the individual differential diagnosis of PD.

Background: Although Monte Carlo simulations of light propagation in full segmented three-dimensional MRI based anatomical models of the human head have been reported in many articles. To our knowledge, there is no patient-oriented simulation for individualized calibration with NIRS measurement. Thus, we offer an approach for brain modeling based on image segmentation process with in vivo MRI T1 three-dimensional image to investigate the individualized calibration for NIRS measurement with Monte Carlo simulation., Methods: In this study, an individualized brain is modeled based on in vivo MRI 3D image as five layers structure. The behavior of photon migration was studied for this individualized brain detections based on three-dimensional time-resolved Monte Carlo algorithm. During the Monte Carlo iteration, all photon paths were traced with various source-detector separations for characterization of brain structure to provide helpful information for individualized design of NIRS system., Results: Our results indicate that the patient-oriented simulation can provide significant characteristics on the optimal choice of source-detector separation within 3.3 cm of individualized design in this case. Significant distortions were observed around the cerebral cortex folding. The spatial sensitivity profile penetrated deeper to the brain in the case of expanded CSF. This finding suggests that the optical method may provide not only functional signal from brain activation but also structural information of brain atrophy with the expanded CSF layer. The proposed modeling method also provides multi-wavelength for NIRS simulation to approach the practical NIRS measurement., Conclusions: In this study, the three-dimensional time-resolved brain modeling method approaches the realistic human brain that provides useful information for NIRS systematic design and calibration for individualized case with prior MRI data.

During the past two decades, the advent of functional magnetic resonance imaging (fMRI) has fundamentally changed our understanding of brain-behavior relationships. However, the data from any one study add only incrementally to the big picture. This fact raises important questions about the dominant practice of performing studies in isolation. To what extent are the findings from any single study reproducible? Are researchers who lack the resources to conduct a fMRI study being needlessly excluded? Is pre-existing fMRI data being used effectively to train new students in the field? Here, we will argue that greater sharing and synthesis of raw fMRI data among researchers would make the answers to all of these questions more favorable to scientific discovery than they are today and that such sharing is an important next step for advancing the field of cognitive neuroscience.

Background: Structural Magnetic Resonance Imaging (sMRI) of the brain is employed in the assessment of a wide range of neuropsychiatric disorders. In order to improve statistical power in such studies it is desirable to pool scanning resources from multiple centres. The CaliBrain project was designed to provide for an assessment of scanner differences at three centres in Scotland, and to assess the practicality of pooling scans from multiple-centres., Methods: We scanned healthy subjects twice on each of the 3 scanners in the CaliBrain project with T1-weighted sequences. The tissue classifier supplied within the Statistical Parametric Mapping (SPM5) application was used to map the grey and white tissue for each scan. We were thus able to assess within scanner variability and between scanner differences. We have sought to correct for between scanner differences by adjusting the probability mappings of tissue occupancy (tissue priors) used in SPM5 for tissue classification. The adjustment procedure resulted in separate sets of tissue priors being developed for each scanner and we refer to these as scanner specific priors., Results: Voxel Based Morphometry (VBM) analyses and metric tests indicated that the use of scanner specific priors reduced tissue classification differences between scanners. However, the metric results also demonstrated that the between scanner differences were not reduced to the level of within scanner variability, the ideal for scanner harmonisation., Conclusion: Our results indicate the development of scanner specific priors for SPM can assist in pooling of scan resources from different research centres. This can facilitate improvements in the statistical power of quantitative brain imaging studies.

Background: Myocardial motion is an important observable for the assessment of heart condition. Accurate estimates of ventricular (LV) wall motion are required for quantifying myocardial deformation and assessing local tissue function and viability. Harmonic Phase (HARP) analysis was developed for measuring regional LV motion using tagged magnetic resonance imaging (tMRI) data. With current computer-aided postprocessing tools including HARP analysis, large motions experienced by myocardial tissue are, however, often intractable to measure. This paper addresses this issue and provides a solution to make such measurements possible., Methods: To improve the estimation performance of large cardiac motions while analyzing tMRI data sets, we propose a two-step solution. The first step involves constructing a model to describe average systolic motion of the LV wall within a subject group. The second step involves time-reversal of the model applied as a spatial coordinate transformation to digitally relax the contracted LV wall in the experimental data of a single subject to the beginning of systole. Cardiac tMRI scans were performed on four healthy rats and used for developing the forward LV model. Algorithms were implemented for preprocessing the tMRI data, optimizing the model parameters and performing the HARP analysis. Slices from the midventricular level were then analyzed for all systolic phases., Results: The time-reversal operation derived from the LV model accounted for the bulk portion of the myocardial motion, which was the average motion experienced within the overall subject population. In analyzing the individual tMRI data sets, removing this average with the time-reversal operation left small magnitude residual motion unique to the case. This remaining residual portion of the motion was estimated robustly using the HARP analysis., Conclusion: Utilizing a combination of the forward LV model and its time reversal improves the performance of motion estimation in evaluating the cardiac function.

Background: Hepatocellular carcinoma (HCC) is a common liver malignancy with limited treatment options. Previous studies expressed the potential synergy of sorafenib and NK cell immunotherapy as a promising approach against HCC. MRI is commonly used to assess response of HCC to therapy. However, traditional MRI-based metrics for treatment efficacy are inadequate for capturing complex changes in the tumor microenvironment, especially with immunotherapy. In this study, we investigated potent MRI radiomics analysis to non-invasively assess early responses to combined sorafenib and NK cell therapy in a HCC rat model, aiming to predict multiple treatment outcomes and optimize HCC treatment evaluations. Methods: Sprague Dawley (SD) rats underwent tumor implantation with the N1-S1 cell line. Tumor progression and treatment efficacy were assessed using MRI following NK cell immunotherapy and sorafenib administration. Radiomics features were extracted, processed, and selected from both T1w and T2w MRI images. The quantitative models were developed to predict treatment outcomes and their performances were evaluated with area under the receiver operating characteristic (AUROC) curve. Additionally, multivariable linear regression models were constructed to determine the correlation between MRI radiomics and histology, aiming for a noninvasive evaluation of tumor biomarkers. These models were evaluated using root-mean-squared-error (RMSE) and the Spearman correlation coefficient. Results: A total of 743 radiomics features were extracted from T1w and T2w MRI data separately. Subsequently, a feature selection process was conducted to identify a subset of five features for modeling. For therapeutic prediction, four classification models were developed. Support vector machine (SVM) model, utilizing combined T1w + T2w MRI data, achieved 96% accuracy and an AUROC of 1.00 in differentiating the control and treatment groups. For multi-class treatment outcome prediction, Linear regression model attained 85% accuracy and an AUC of 0.93. Histological analysis showed that combination therapy of NK cell and sorafenib had the lowest tumor cell viability and the highest NK cell activity. Correlation analyses between MRI features and histological biomarkers indicated robust relationships (r = 0.94). Conclusions: Our study underscored the significant potential of texture-based MRI imaging features in the early assessment of multiple HCC treatment outcomes. [ABSTRACT FROM AUTHOR]

Background: Somatic Symptom Disorder (SSD) is a condition often linked to excessive health anxiety and somatic symptoms. In recent years, studies have found associations between the cerebellum and various mental illnesses, including SSD. However, the microstructure of cerebellar subregions in SSD using diffusion magnetic resonance imaging has not been fully defined. Methods: This is a cross-sectional study, that included 30 SSD patients and 30 age- and gender-matched healthy controls to investigate the microstructure of the cerebellum using diffusion magnetic resonance imaging. SSD diagnosis followed DSM-5 criteria, excluding major psychiatric comorbidities, while healthy controls underwent rigorous screening to exclude psychiatric or neurological histories. Clinical evaluations utilized standardized scales to assess depressive, anxiety, and cognitive symptoms. MRI data were acquired using a 3T Siemens Prisma scanner, including T1-weighted and diffusion-weighted imaging (30 directions, b = 1000/2000 s/mm²). Multi-compartment diffusion magnetic resonance imaging metrics from free water elimination diffusion tensor imaging and neurite orientation dispersion and density imaging were used to observe microstructural changes in the cerebellum's white matter and gray matter subregions in SSD patients. Results: Compared to the control group, patients with SSD exhibited significant alterations in white matter microstructure. These changes were characterized by increased free water-eliminated fractional anisotropy and neurite density index, as well as decreased free water-eliminated mean diffusivity and radial diffusivity. Furthermore, the cerebellum displayed varying microstructural changes across 26 gray matter subregions. These changes included reduced mean diffusivity, free water-eliminated axial diffusivity, and free water-eliminated radial diffusivity, alongside increased neurite density index and orientation dispersion index. Importantly, the study identified significant correlations between these microstructural changes and clinical symptoms. Specifically, Vermis X and the left lobule VIIb showed significant associations with both depression and anxiety scores. Conclusions: The findings suggest greater neurite density and enhanced diffusion restriction in the cerebellum of patients with SSD, which may indicate possible adaptive changes associated with chronic stress. [ABSTRACT FROM AUTHOR]

Background: To develop and validate a model that integrates clinical data, deep learning radiomics, and radiomic features to predict high-risk patients for cage subsidence (CS) after lumbar fusion. Methods: This study analyzed preoperative CT and MRI data from 305 patients undergoing lumbar fusion surgery from three centers. Using a deep learning model based on 3D vision transformations, the data were divided the dataset into training (n = 214), validation (n = 61), and test (n = 30) groups. Feature selection was performed using LASSO regression, followed by the development of a logistic regression model. The predictive ability of the model was assessed using various machine learning algorithms, and a combined clinical model was also established. Results: Ultimately, 11 traditional radiomic features, 5 deep learning radiomic features, and 1 clinical feature were selected. The combined model demonstrated strong predictive performance, with area under the curve (AUC) values of 0.941, 0.832, and 0.935 for the training, validation, and test groups, respectively. Notably, our model outperformed predictions made by two experienced surgeons. Conclusions: This study developed a robust predictive model that integrates clinical features and imaging data to identify high-risk patients for CS following lumbar fusion. This model has the potential to improve clinical decision-making and reduce the need for revision surgeries, easing the burden on healthcare systems. [ABSTRACT FROM AUTHOR]

Objectives: To quantitatively assess the differences in parameters of dynamic contrast-enhanced MRI (DCE–MRI) in HER2-zero, HER2-low, or HER2-positive tumors, and to build optimal model for early prediction of HER2-low breast cancer (BC). Materials and methods: Clinical and DCE–MRI data from 220 BC patients receiving neoadjuvant chemotherapy (NACT) were retrospectively analyzed. Quantitative and semi-quantitative DCE–MRI parameters were compared in the HER2-zero, HER2-low, or HER2-positive groups before and after early NACT. Empirical models were developed to predict HER2-low BC using logistic regression analysis and receiver operating characteristic (ROC) analysis. Results: Patients of HER2-low BC have a lower pCR rate compared with HER2-zero and HER2-positive (17.9% vs. 10.4% vs. 29.5%, p < 0.001), predominantly in the HR (hormone receptor) negative group (22.2% vs. 7.7% vs. 40.5%, p < 0.001). Before NACT, HER2-low BC exhibited higher Kep, Ktrans, Washin, and lower TME intratumoral perfusion characteristics, and higher Kep and lower TME in peritumoral region compared to HER2-zero and HER2-positive BC patients. Notably, after early NACT, changes in intratumoral perfusion (Kep) and in peritumoral perfusion (Ktrans, Washin) were more pronounced in the HER2-low group compared to HER2-zero and HER2-positive group. The ROC curves (AUC) for the pre-NACT intratumoral, peritumoral, and combined perfusion models were 0.675(95% CI 0.600–0.750), 0.661(95% CI 0.585–0.738), 0.731(95% CI 0.660–0.802). The combined pre-and-post-NACT perfusion model further improved predictive performance accordingly, with AUCs of 0.764 (95% 0.637–0.865), 0.795 (95% CI 0.711–0.878), 0.850 (95% CI 0.774–0.926). Conclusions: The study revealed perfusion heterogeneity between different HER2 statuses and identified the best imaging model as a non-invasive tool to predict HER2-low BC, which can help pre-treatment clinical decision-making. Highlights: In the NACT cohort, the pathological complete response (pCR) rates were notably lower in patients with HER2-low breast cancer compared to patients with other HER2 statuses, particularly within the HR(-) subtype. HER2-low breast cancers exhibit distinct perfusion characteristics on quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE–MRI). The combined baseline and early NACT perfusion model is expected to early predict HER2-low breast cancer. [ABSTRACT FROM AUTHOR]

Background: Recent studies indicate that patients with first-episode drug-naïve (FEDN) and recurrent major depressive disorder (R-MDD) exhibit distinct atrophy patterns in the hippocampal subregions along the proximal-distal axis. However, it remains unclear whether such differences occur along the long axis and how they may relate to specific genes. Methods: In the present study, we analyzed T1-weighted images from 421 patients (FEDN: n = 232; R-MDD: n = 189) and 544 normal controls (NC) as part of the REST-meta-MDD consortium. Additionally, transcriptome maps and structural Magnetic Resonance Imaging (MRI) data of six donated brains were obtained from the Allen Human Brain Atlas (AHBA). We first identified changes in gray matter volume (GMV) within the hippocampus of both FEDN and R-MDD patients and then integrated these findings with AHBA transcriptome data to investigate the genes associated with hippocampal GMV changes. Results: Compared to NC, FEDN patients displayed reduced GMV in the left hippocampal tail, whereas R-MDD patients exhibited decreased GMV in the bilateral hippocampal body and increased GMV in the bilateral hippocampal tail. Further analysis revealed that expression levels of SYTL2 positively correlated with GMV changes in the hippocampus of FEDN patients, while SORCS3 and SLIT2 positively correlated with those in R-MDD. Conclusions: Our results suggest that GMV alterations in hippocampal subfields along the long axis differ between FEDN and R-MDD, reflecting progressive hippocampal deterioration with prolonged depression, potentially supported by the expression of specific genes. These findings offer valuable insights into the distinct neural and genetic mechanisms underlying FEDN and R-MDD, which may aid in the development of more targeted and effective treatment strategies for MDD subtypes. [ABSTRACT FROM AUTHOR]

Background: Vaginal childbirth is one of the main risk factors for pelvic floor dysfunction. Magnetic resonance imaging (MRI) can facilitate quantitative evaluation of the morphology and function of the pelvic floor in static and dynamic environments. The objective of this study was to investigate the changes in pelvic floor morphology and function in primigravida women before pregnancy (BP) and after vaginal delivery. Methods: Primigravida women underwent pelvic floor MRI scans BP, at 3 months postpartum (3mPP), and at 6 months postpartum (6mPP). Various pelvic floor MRI data were measured, including the obturator internus muscle (OIM) area, anterior pelvic area, puborectalis muscle thickness (PRT), levator plate angle (LPA), iliococcygeal angle, bladder–pubococcygeal line (B-PCL), uterus–pubococcygeal line (U-PCL), puborectal hiatus line, muscular pelvic floor relaxation line, levator hiatus area, urethral mobility, bladder neck descent, and cervix descent. Results: In the resting state, the OIM area and PRT decreased whereas the anterior pelvic area increased from BP to 3mPP. During the straining maneuver, all parameters except U-PCL and urethral mobility showed statistically significant differences (P < 0.05). The OIM area and PRT increased whereas the anterior pelvic area decreased from 3mPP to 6mPP. During the straining maneuver, B-PCL, bladder neck descent, levator hiatus area, and LPA showed statistically significant differences (P < 0.05). In the resting state, the OIM area and PRT decreased whereas the anterior pelvic area increased from BP to 6mPP. During the straining maneuver, B-PCL, muscular pelvic floor relaxation line, and bladder neck descent showed statistically significant differences (P < 0.05). Conclusion: Vaginal delivery can cause pelvic floor injury that may gradually recover over time. However, the injury does not fully recover to the pre-pregnancy level within 6mPP. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer (PCa) is definitively diagnosed by systematic prostate biopsy (SBx) with 13 cores. This method, however, can increase the risk of urinary retention, infection and bleeding due to the excessive number of biopsy cores. Methods: We retrospectively analyzed 622 patients who underwent SBx with prostate multiparametric MRI (mpMRI) from two centers between January 2014 to June 2022. The MRI data were collected to manually segment Regions of Interest (ROI) of the tumor layer by layer. ROI reconstructions were fused to form outline of the volume of interest (VOI), which were exported and applied to subsequent extraction of radiomics features. The t-tests, Mann-Whitney U-tests and chi-squared tests were performed to evaluate the significance of features. The logistic regression was used for calculating the PCa risk score (PCS). The PCS model was trained to optimize the SBx core number, utilizing both mpMRI radiomics and clinical features. Results: The predicted number of SBx cores was determined by PCS model. Optimal core numbers of SBx for PCS subgroups 1–5 were calculated as 13, 10, 8, 6, and 6, respectively. Accuracies of predicted core numbers were high: 100%, 95.8%, 91.7%, 90.6%, and 92.7% for PCS subgroups 1–5. Optimized SBx reduced core rate by 41.9%. Leakage rates for PCa and clinically significant PCa were 8.2% and 3.4%, respectively. The optimized SBx also demonstrated high accuracy on the validation set. Conclusion: The optimization PCS model described in this study could therefore effectively reduce the number of systematic biopsy cores obtained from patients with high PCS, especially for biopsy cores far away from suspicious lesions. This method can enhance patient experience without reducing tumor detection rate. [ABSTRACT FROM AUTHOR]

Objective: This study was to develop a multi-parametric MRI radiomics model to predict preoperative Ki-67 status. Materials and methods: A total of 120 patients with pathologically confirmed breast cancer were retrospectively enrolled and randomly divided into a training set (n = 84) and a validation set (n = 36). Radiomic features were derived from both the intratumoral and peritumoral regions, extending 5 mm from the tumor boundary, using magnetic resonance imaging (MRI). The MRI sequences employed included T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE) imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. The T-test and the Least Absolute Shrinkage and Selection Operator Cross-Validation (LASSO CV) were conducted for feature selection. Modelintra, modelperi, modelintra+peri were established by eleven supervised machine learning (ML) algorithms to predict the expression status of Ki-67 in breast cancer and were verified by the validation groups. The model's performance was evaluated by employing metrics such as the area under the curve (AUC), accuracy, sensitivity, and specificity. Results: The features of intratumor, peritumor, intratumor + peritumor were extracted 851, 851 and 1702 samples respectively, 14, 23 and 35 features were selected by LASSO. ML algorithms based on modelintra and modelperi consistently yield AUCs that are below 80% in the validation set. Hower, Logistic regression (LR) and linear discriminant analysis (LDA) based on modelintra+peri demonstrated significant advantages over other algorithms, achieving AUCs of 0.92 and 0.98, accuracies of 0.94 and 0.97, sensitivities of 1 and 0.96, and specificities of 0.85 and 1 respectively in the validation set. Conclusion: The integrated intra- and peritumoral radiomics model, developed using multiparametric MRI data and machine learning classifiers, exhibits significant predictive power for Ki-67 expression levels. This model could facilitate personalized clinical treatment strategies for individuals diagnosed with breast cancer (BC). Clinical trial number: Not applicable. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is characterized by difficulties in social interaction, communication challenges, and repetitive behaviors. Despite extensive research, the molecular mechanisms underlying these neurodevelopmental abnormalities remain elusive. We integrated microscale brain gene expression data with macroscale MRI data from 1829 participants, including individuals with ASD and typically developing controls, from the autism brain imaging data exchange I and II. Using fractal dimension as an index for quantifying cortical complexity, we identified significant regional alterations in ASD, within the left temporoparietal, left peripheral visual, right central visual, left somatomotor (including the insula), and left ventral attention networks. Partial least squares regression analysis revealed gene sets associated with these cortical complexity changes, enriched for biological functions related to synaptic transmission, synaptic plasticity, mitochondrial dysfunction, and chromatin organization. Cell-specific analyses, protein–protein interaction network analysis and gene temporal expression profiling further elucidated the dynamic molecular landscape associated with these alterations. These findings indicate that ASD-related alterations in cortical complexity are closely linked to specific genetic pathways. The combined analysis of neuroimaging and transcriptomic data enhances our understanding of how genetic factors contribute to brain structural changes in ASD. [ABSTRACT FROM AUTHOR]

Background: It is known that open wedge high tibial osteotomy (OWHTO) may lead to progression of patellofemoral degeneration due to descent of the patellar height. However, the difference in patellofemoral joint (PFJ) loads with normal daily activity between uniplane and biplane osteotomies is unclear. The purpose of this study was to reveal the differences in PFJ biomechanics between uniplane and biplane OWHTO using finite element analysis (FEA). Methods: In this study, a finite element model of the knee joint was established using computed tomography (CT) and magnetic resonance imaging (MRI) data from a healthy volunteer, and a 10° varus deformity of the proximal tibia was simulated. Under the guidance of experienced orthopedic surgeons, simulations of both uniplane and biplane open wedge high tibial osteotomy procedures were conducted. The maximum stress and contact area of the PFJ at knee flexion angles of 90°, 60°, 30°, and 0° during sitting-to-standing and walking were measured in the three finite element models (normal knee joint model, uniplane OWHTO model, and biplane OWHTO model). Results: In all models, the peak value of von-mises stress (VMS) occurred at 90 degrees of knee flexion. At 90 degrees of knee flexion, the biplane OWHTO model exhibited the highest PFJ stress, measuring 9.664 MPa. As the knee joint extended from 90 degrees of flexion to 0 degrees of extension, the PFJ stress gradually decreased in all three models. The decrease was most pronounced in the uniplane OWHTO model, although it remained higher than in the normal model. Conclusion: Uniplane OWHTO would induce lower contact stress and larger contact area on the patellofemoral surface than biplane OWHTO during walking and sitting-to-standing, which may cause less mechanical pain and secondary damage to the articular cartilage. Therefore, uniplane OWHTO might be a better option for patients with anterior knee pain and/or PFJ degeneration. [ABSTRACT FROM AUTHOR]

The article presents a study that investigates a method for brain modeling based on image segmentation process with in vitro MRI T1 three-dimensional image to examine the individualized calibration for NIRS measurement with Monte Carlo simulation. All photon paths in Monte Carlo iteration were traced with various source detector separations. It is found that the patient-oriented simulation can provide significant characteristics on the optimal choice of source-detector separation.

Background: Limited studies of multiple sclerosis (MS) exist whereby magnetic resonance imaging (MRI) of the brain with consistent imaging protocols occurs at the same time points as collection of healthy lifestyle measures. The aim of this study was to test the feasibility, acceptability and preliminary efficacy of acquiring MRI data as an objective, diagnostic and prognostic marker of MS, at the same time point as brain-healthy lifestyle measures including diet. Methods: Participants living with relapsing remitting MS partook in one structural MRI scanning session of the brain, completed two online 24-hour dietary recalls and demographic and self-reported lifestyle questionnaires (e.g. self-reported disability, comorbidities, physical activity, smoking status, body mass index (BMI), stress). Measures of central tenancy and level of dispersion were calculated for feasibility and acceptability of the research protocols. Lesion count was determined by one radiologist and volumetric analyses by a data analysis pipeline based on FreeSurfer software suite. Correlations between white matter lesion count, whole brain volume analyses and lifestyle measures were assessed using Spearman's rank-order correlation coefficient. Results: Thirteen female participants were included in the study: eligibility rate 90.6% (29/32), recruitment rate 46.9% (15/32) and compliance rate 87% (13/15). The mean time to complete all required tasks, including MRI acquisition was 115.86 minutes ( ± 23.04), over 4 days. Conversion to usual dietary intake was limited by the small sample. There was one strong, negative correlation between BMI and brain volume (rs = −0.643, p = 0.018) and one strong, positive correlation between physical activity and brain volume (rs = 0.670, p = 0.012) that were both statistically significant. Conclusions: Acquiring MRI brain scans at the same time point as lifestyle profiles in adults with MS is both feasible and accepted among adult females living with MS. Quantification of volumetric MRI data support further investigations using semi-automated pipelines among people living with MS, with pre-processing steps identified to increase automated feasibility. This protocol may be used to determine relationships between elements of a brain-healthy lifestyle, including dietary intake, and measures of disease burden and brain health, as assessed by T1-weighted and T2-weighted lesion count and whole brain volume, in an adequately powered sample. Trial registration: The study protocol was retrospectively registered in the Australia New Zealand Clinical Trials Registry (ACTRN12624000296538). [ABSTRACT FROM AUTHOR]

Objective: Through resting state functional magnetic resonance imaging (rs-fMRI) we evaluate the spontaneous brain activity changes of maintenance hemodialysis (MHD) patients with restless legs syndrome (RSL) and analyzed the imaging features and related mechanisms of RLS in patients with MHD. Method: We select 27 MHD patients with RLS and 27 patients without RSL matched by age, gender, cognitive function. Both groups underwent neuropsychological tests and MRI scans. MRI data analysis was performed to obtain and compare the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) values, which were mALFF, mfALFF, and mReHo. Clinical data were collected and compared. Differentiated indicators and RLS scores conduct Pearson correlation analysis. Result: Compared with the MHD-nRLS group, the MHD-RLS group showed significantly lower mALFF values in the left precentral, right precentral gyrus, and right postcentral gyrus, lower mfALFF values in the left precentral gyrus, right precentral gyrus, left calcarine fissure, left lingual gyrus, left postcentral gyrus, and right postcentral gyrus, and lower mReHo values in the left precentral gyrus, right precentral gyrus, left calcarine fissure, left lingual gyrus, left postcentral gyrus, and right postcentral gyrus, and right postcentral gyrus (P < 0.05). The MHD-RLS group exhibited lower hemoglobin levels (P = 0.001), higher total iron-binding capacity levels (P = 0.011), and higher folic acid levels (P = 0.022). The above indicators were correlated with RLS scores using Pearson correlation analysis, and it was found that the mfALFF value of the right precentral gyrus and the right postcentral gyrus, and the mReHo values of the right precentral gyrus and right postcentral gyrus were negatively correlated with the RLS score (r = -0.567, P = 0.002;r = -0.705, P < 0.001;r = -0.414, P = 0.032; r = -0.410, P = 0.034), and the hemoglobin concentration was negatively correlated with the RLS scores (r = -0.394, P = 0.042). Conclusion: Patients with MHD-RLS exhibit abnormal spontaneous brain activity in the right precentral gyrus and right postcentral gyrus within the sensorimotor network, along with lower hemoglobin levels, which may be associated with the pathogenesis and severity of MHD-RLS. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's Disease is a neurodegenerative condition leading to irreversible and progressive brain damage, with possible features such as structural atrophy. Effective precision diagnosis is crucial for slowing disease progression and reducing the incidence rate and morbidity. Traditional computer-aided diagnostic methods using structural MRI data often focus on capturing such features but face challenges, like overfitting with 3D image analysis and insufficient feature capture with 2D slices, potentially missing multi-planar information, and the complementary nature of features across different orientations. Methods: The study introduces MHAGuideNet, a classification method incorporating a guidance network utilizing multi-head attention. The model utilizes a pre-trained 3D convolutional neural network to direct the feature extraction of multi-planar 2D slices, specifically targeting the detection of features like structural atrophy. Additionally, a hybrid 2D slice-level network combining 2D CNN and 2D Swin Transformer is employed to capture the interrelations between the atrophy in different brain structures associated with Alzheimer's Disease. Results: The proposed MHAGuideNet is tested using two datasets: the ADNI and OASIS datasets. The model achieves an accuracy of 97.58%, specificity of 99.89%, F1 score of 93.98%, and AUC of 99.31% on the ADNI test dataset, demonstrating superior performance in distinguishing between Alzheimer's Disease and cognitively normal subjects. Furthermore, testing on the independent OASIA test dataset yields an accuracy of 96.02%, demonstrating the model's robust performance across different datasets. Conclusion: MHAGuideNet shows great promise as an effective tool for the computer-aided diagnosis of Alzheimer's Disease. Within the guidance of information from the 3D pre-trained CNN, the ability to leverage multi-planar information and capture subtle brain changes, including the interrelations between different structural atrophies, underscores its potential for clinical application. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairments are prevalent across mood disorders and psychosis spectrum disorders, but there is a lack of real-life-like cognitive training programmes. Fully immersive virtual reality has the potential to ensure motivating and engaging cognitive training directly relevant to patients' daily lives. We will examine the effect of a 4-week, intensive virtual reality-based cognitive remediation programme involving daily life challenges on cognition and daily life functioning in patients with mood disorders or psychosis spectrum disorders and explore the neuronal underpinnings of potential treatment efficacy. Methods: The trial has a randomized, controlled, double-blinded, parallel-group design. We will include 66 symptomatically stable outpatients with mood disorders or psychosis spectrum disorders aged 18–55 years with objective and subjective cognitive impairment. Assessments encompassing a virtual reality test of daily life cognitive skills, neuropsychological testing, measures of daily life functioning, symptom ratings, questionnaires on subjective cognitive complaints, and quality of life are carried out at baseline, after the end of 4 weeks of treatment and at a 3-month follow-up after treatment completion. Functional magnetic resonance imaging scans are performed at baseline and at the end of treatment. The primary outcome is a broad cognitive composite score comprising five subtasks on a novel ecologically valid virtual reality test of daily life cognitive functions. Two complete data sets for 54 patients will provide a power of 80% to detect a clinically relevant between-group difference in the primary outcome. Behavioural data will be analysed using linear mixed models in SPSS, while MRI data will be analysed with the FMRIB Expert Analysis Tool (FEAT). Treatment-related changes in neural activity from baseline to end of treatment will be investigated for the dorsal prefrontal cortex and hippocampus as the regions of interest. Discussion: The results will provide insight into whether virtual reality-based cognitive remediation has beneficial effects on cognition and functioning in symptomatically stable patients with mood disorders or psychosis spectrum disorders, which can aid future treatment development. Trial registration: ClinicalTrials.gov NCT06038955. Registered on September 15, 2023. [ABSTRACT FROM AUTHOR]

Background: The spatial layout of large-scale functional brain networks exhibits considerable inter-individual variability, especially in the association cortex. Research has demonstrated a link between early socioeconomic status (SES) and variations in both brain structure and function, which are further associated with cognitive and mental health outcomes. However, the extent to which SES is associated with individual differences in personalized functional network topography during childhood remains largely unexplored. Methods: We used a machine learning approach—spatially regularized non-negative matrix factorization (NMF)—to delineate 17 personalized functional networks in children aged 9–10 years, utilizing high-quality functional MRI data from 6001 participants in the Adolescent Brain Cognitive Development study. Partial least square regression approach with repeated random twofold cross-validation was used to evaluate the association between the multivariate pattern of functional network topography and three SES factors, including family income-to-needs ratio, parental education, and neighborhood disadvantage. Results: We found that individual variations in personalized functional network topography aligned with the hierarchical sensorimotor-association axis across the cortex. Furthermore, we observed that functional network topography significantly predicted the three SES factors from unseen individuals. The associations between functional topography and SES factors were also hierarchically organized along the sensorimotor-association cortical axis, exhibiting stronger positive associations in the higher-order association cortex. Additionally, we have made the personalized functional networks publicly accessible. Conclusions: These results offer insights into how SES influences neurodevelopment through personalized functional neuroanatomy in childhood, highlighting the cortex-wide, hierarchically organized plasticity of the functional networks in response to diverse SES backgrounds. [ABSTRACT FROM AUTHOR]

Background: Past research has highlighted that bipolar I disorder is associated with significant changes in brain structure and function. Notably, the manifestation and progression of bipolar I disorder have been known to differ between males and females. However, the relationship between sex-related differences and bipolar I disorder diagnosis affecting these changes was not fully understood. This study aimed to investigate the sex-by-diagnosis interactions concerning the structural and functional features of the brain in individuals with bipolar I disorder. Methods: Both structural and functional MRI data were obtained from 105 individuals with bipolar I disorder (36 males and 69 females) and 210 healthy controls (72 males and 138 females). Voxel-wise analyses of gray matter volume and functional connectivity were conducted using a general linear regression model. This model included age, sex, diagnosis, and a sex-by-diagnosis interaction as predictors to explore potential sex-related differences in the brain features of participants with bipolar I disorder. Results: The gray matter volume analysis revealed significant sex-by-diagnosis interactions in six brain regions: the left caudate (p < 0.001), left thalamus (p < 0.001), right caudate (p = 0.003), right thalamus (p < 0.001), left anterior cingulate gyrus (p = 0.022), and left middle/posterior cingulate gyrus (p = 0.015). Using these regions as seeds, we detected a significant sex-by-diagnosis interaction in the functional connectivity alteration between the left thalamus and right angular gyrus (p = 0.019). Conclusions: Our findings revealed a noteworthy sex-by-diagnosis interaction, with male individuals with bipolar I disorder displaying larger gray matter volume and altered functional connectivity in the limbic system compared to female individuals with bipolar I disorder and healthy participants. These results hint at potential sex-related differences in the pathophysiology of the limbic system in bipolar I disorder, which may have significant implications for understanding the underlying mechanisms in bipolar I disorder. Our findings could contribute to developing more personalized treatment approaches for individuals with bipolar I disorder. [ABSTRACT FROM AUTHOR]

Background: Childhood trauma (CT) is a major risk factor for psychiatric disorders. Emotional and cognitive functions are often affected in many psychiatric conditions, and these functions are mediated by the limbic system. However, previous research has primarily focused on patient populations. Therefore, we aim to examine the impact of CT on the limbic brain structure in healthy individuals. Methods: We enrolled 48 individuals in health, evenly split into two groups: 24 healthy participants with CT (HP-CT) and 24 healthy participants without CT (HP-nCT). They underwent scale assessments and MRI data acquisition. Comparisons between the two groups were performed after subcortical subregion volume segmentation using FreeSufer. Lastly, we examined correlations between volume changes and scale scores. Results: We found that HP-CT group had smaller volumes in several subregions of the hippocampus, amygdala, and cortical limbic structures, including the subiculum (Sub) head and body, cornu ammonis (CA)1 head, molecular layer (ML) head, granule cell layer of the dentate gyrus (GC-ML-DG) body, CA4 body, fimbria, hippocampus-amygdala transition area (HATA), whole hippocampus head and body, whole hippocampus, basal nucleus (Ba), accessory basal nucleus (AB), cortico-amygdaloid transition area (CAT), paralaminar nucleus (PL) of the left hemisphere; and hippocampal tail, presubiculum (PreSub) body, and basal forebrain of the right hemisphere. Volume changes in the CA4 body and GC-ML-DG body were correlated with sexual abuse. Changes in the volume of the right basal forebrain were linked to emotional neglect. However, these findings were not significant after correction for multiple comparisons. Conclusion: CT impacts multiple structures of the limbic system, including the hippocampus, and amygdala. This also suggests that region-specific changes within the limbic system can serve as clinical biomarkers supporting cross-diagnostic psychiatric illnesses. [ABSTRACT FROM AUTHOR]

Introduction: The delineation of organs-at-risk and lymph node areas is a crucial step in radiotherapy, but it is time-consuming and associated with substantial user-dependent variability in contouring. Artificial intelligence (AI) appears to be the solution to facilitate and standardize this work. The objective of this study is to compare eight available AI software programs in terms of technical aspects and accuracy for contouring organs-at-risk and lymph node areas with current international contouring recommendations. Material and methods: From January–July 2023, we performed a blinded study of the contour scoring of the organs-at-risk and lymph node areas by eight self-contouring AI programs by 20 radiation oncologists. It was a single-center study conducted in radiation department at the Lorraine Cancer Institute. A qualitative analysis of technical characteristics of the different AI programs was also performed. Three adults (two women and one man) and three children (one girl and two boys) provided six whole-body anonymized CT scans, along with two other adult brain MRI scans. Using a scoring scale from 1 to 3 (best score), radiation oncologists blindly assessed the quality of contouring of organs-at-risk and lymph node areas of all scans and MRI data by the eight AI programs. We have chosen to define the threshold of an average score equal to or greater than 2 to characterize a high-performing AI software, meaning an AI with minimal to moderate corrections but usable in clinical routine. Results: For adults CT scans: There were two AI programs for which the overall average quality score (that is, all areas tested for OARs and lymph nodes) was higher than 2.0: Limbus (overall average score = 2.03 (0.16)) and MVision (overall average score = 2.13 (0.19)). If we only consider OARs for adults, only Limbus, Therapanacea, MVision and Radformation have an average score above 2. For children CT scan, MVision was the only program to have a average score higher than 2 with overall average score = 2.07 (0.19). If we only consider OARs for children, only Limbus and MVision have an average score above 2. For brain MRIs: TheraPanacea was the only program with an average score over 2, for both brain delineation (2.75 (0.35)) and OARs (2.09 (0.19)). The comparative analysis of the technical aspects highlights the similarities and differences between the software. There is no difference in between senior radiation oncologist and residents for OARs contouring. Conclusion: For adult CT-scan, two AI programs on the market, MVision and Limbus, delineate most OARs and lymph nodes areas that are useful in clinical routine. For children CT-scan, only one IA, MVision, program is efficient. For adult brain MRI, Therapancea,only one AI program is efficient. Trial registration: CNIL-MR0004 Number HDH434. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to identify risk factors contributing to the early onset of patellofemoral osteoarthritis (PFOA) within the first two years following anterior cruciate ligament reconstruction (ACLR) using a hamstring tendon autograft. Methods: Participants aged 18 to 40 who had undergone ACLR within the past two years were included in this study, along with a control group of healthy volunteers. Magnetic resonance imaging (MRI) data were obtained preoperatively, at two years postoperatively, and from the control group. T-tests were used to assess differences in patellofemoral alignment (PA) and trochlear morphology (TM) between the pre- and post-ACLR patients and healthy controls. The incidence of PFOA was recorded, and associations between PA, TM, and clinical parameters were evaluated in patients with and without PFOA. Logistic regression analysis was conducted to identify potential risk factors for PFOA development. Results: A total of 177 patients, with a mean follow-up period of 22.17 ± 5.09 months and a mean age of 26.4 ± 5.6 years, were included in the study. Following ACL injury, significant alterations in patellar tilt angle (PTA), tuberositas tibae-trochlear groove distance (TT-TG), Insall-Salvati ratio (ISR), and static anterior tibial translation (SATT) were observed compared to the control group. Postoperatively, deviations in PTA and SATT remained significant when compared to healthy controls. Of the 177 patients, 68 (38.42%) developed early-onset PFOA. Factors associated with the early onset of PFOA included age at the time of surgery, the interval between injury and surgery, PTA, bisect offset (BO), sulcus angle (SA), thigh circumference, SATT, and partial meniscectomy. Conclusion: Significant differences in PTA, TT-TG, ISR, and SATT were identified between patients who underwent ACLR and healthy controls. Postoperatively, there was no correction in PTA or SATT, which remained significantly altered. Factors such as age at the time of surgery, PTA, BO, SA, ISR, SATT, thigh circumference, partial meniscectomy, and the time interval between injury and surgery were associated with the early onset of PFOA within two years post-ACLR. These findings may aid in the prevention of PFOA by identifying individuals at higher risk for early development. [ABSTRACT FROM AUTHOR]

Background: For over three decades, the concomitance of cortical neurodegeneration and white matter hyperintensities (WMH) has sparked discussions about their coupled temporal dynamics. Longitudinal studies supporting this hypothesis nonetheless remain scarce. Methods: We applied global and regional bivariate latent growth curve modelling to determine the extent to which WMH and cortical thickness were interrelated over a four-year period. For this purpose, we leveraged longitudinal MRI data from 451 cognitively unimpaired participants (DELCODE; median age 69.71 [IQR 65.51, 75.50] years; 52.32% female). Participants underwent MRI sessions annually over a four-year period (1815 sessions in total, with roughly four MRI sessions per participant). We adjusted all models for demographics and cardiovascular risk. Results: Our findings were three-fold. First, larger WMH volumes were linked to lower cortical thickness (σ = -0.165, SE = 0.047, Z = -3.515, P < 0.001). Second, individuals with higher WMH volumes experienced more rapid cortical thinning (σ = -0.226, SE = 0.093, Z = -2.443, P = 0.007), particularly in temporal, cingulate, and insular regions. Similarly, those with lower initial cortical thickness had faster WMH progression (σ = -0.141, SE = 0.060, Z = -2.336, P = 0.009), with this effect being most pronounced in temporal, cingulate, and insular cortices. Third, faster WMH progression was associated with accelerated cortical thinning (σ = -0.239, SE = 0.139, Z = -1.710, P = 0.044), particularly in frontal, occipital, and insular cortical regions. Conclusions: Our study suggests that cortical thinning and WMH progression could be mutually reinforcing rather than parallel, unrelated processes, which become entangled before cognitive deficits are detectable. Trial registration: German Clinical Trials Register (DRKS00007966, 04/05/2015). [ABSTRACT FROM AUTHOR]

Purpose: (1) to investigate the consecutive changes in hip muscle volume in patients with femoroacetabular impingement syndrome (FAIS) during the initial postoperative period, and (2) to determine the potential effect of the early changes in hip muscle volume on clinical outcomes. Methods: Data between March 2021 and March 2022 was reviewed. Patients diagnosed with FAIS based on clinical symptoms and radiographic findings, and undergoing hip arthroscopic treatment were included. Exclusion criteria were incomplete MRI data, prior history of hip surgery, and concomitant hip conditions including hip osteoarthritis with a Tönnis grade > 1, avascular necrosis, Legg-Calvé-Perthes disease, osteoid osteoma, synovial chondromatosis, pigmented villonodular synovitis, and developmental dysplasia of the hip (DDH). MRI was performed preoperatively and 3, 6, 12-month postoperatively. Cross-sectional area (CSA) of hip muscles including rectus femoris (RF), iliocapsularis (IC), iliopsoas (IP), gluteus medius/minimus complex (G-med/min), and gluteus maximus (G-max) were collected on MRI. The CSA was corrected by body surface area (BSA). Preoperative and a minimum of 2-year postoperative patient-reported outcome (PRO) scores including Visual Analog pain Scale (VAS), modified Harris Hip Score (mHHS), and international Hip Outcome Tool, 12-component form (iHOT-12) were collected. A multivariate linear regression model was built to determine the influence of the potential factors on postoperative PROs. Results: A total of 76 patients were included in the study. Compared to the preoperative level, decreased volume of RF and G-max, and increased IC/RF ratio were observed at postoperative 3 months (all with P <.05). Both G-med/min and G-max presented decreased volume at postoperative 6 months (all with P <.05). G-med/min presented decreased volume (P =.001) at postoperative 12 months. Changes in RF at postoperative 3 months and 12 months were positively related to improvement of iHOT-12 (Beta = 0.371, P =.012 and Beta = 0.330, P =.026, respectively). Changes in IC at postoperative 6-month was positively related to improvement of mHHS (Beta = 0.367, P =.027) and iHOT-12 (Beta = 0.315, P =.044). Conclusion: During the initial first year following arthroscopic treatment for FAIS, decreased volume of the RF and gluteal muscles was observed. Early changes in volume of RF and IC were positively correlated to the improvement of minimum 2-year PROs. Level of evidence: Level IV; case series. [ABSTRACT FROM AUTHOR]

Background: To construct and compare merged models integrating clinical factors, MRI-based radiomics features and deep learning (DL) models for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). Methods: Totally 197 patients with LARC administered surgical resection after nCRT were assigned to cohort 1 (training and test sets); meanwhile, 52 cases were assigned to cohort 2 as a validation set. Radscore and DL models were established for predicting pCR applying pre- and post-nCRT MRI data, respectively. Different merged models integrating clinical factors, Radscore and DL model were constituted. Their predictive performances were validated and compared by receiver operating characteristic (ROC) and decision curve analyses (DCA). Results: Merged models were established integrating selected clinical factors, Radscore and DL model for pCR prediction. The areas under the ROC curves (AUCs) of the pre-nCRT merged model were 0.834 (95% CI: 0.737–0.931) and 0.742 (95% CI: 0.650–0.834) in test and validation sets, respectively. The AUCs of the post-nCRT merged model were 0.746 (95% CI: 0.636–0.856) and 0.737 (95% CI: 0.646–0.828) in test and validation sets, respectively. DCA showed that the pretreatment algorithm could yield enhanced clinically benefit than the post-nCRT approach. Conclusions: The pre-nCRT merged model including clinical factors, Radscore and DL model constitutes an effective non-invasive tool for pCR prediction in LARC. [ABSTRACT FROM AUTHOR]

Background: Using neuroimaging techniques, growing evidence has suggested that the choroid plexus (CP) volume is enlarged in multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Notably, the CP has been suggested to play an important role in inflammation-induced CNS damage under disease conditions. However, to our knowledge, no study has investigated the relationships between peripheral inflammation and CP volume in sporadic ALS patients. Thus, in this study, we aimed to verify CP enlargement and explore its association with peripheral inflammation in vivo in independent ALS cohorts. Methods: Based on structural MRI data, CP volume was measured using Gaussian mixture models and further manually corrected in two independent cohorts of sporadic ALS patients and healthy controls (HCs). Serum inflammatory protein levels were measured using a novel high-sensitivity Olink proximity extension assay (PEA) technique. Xtreme gradient boosting (XGBoost) was used to explore the contribution of peripheral inflammatory factors to CP enlargement. Then, partial correlation analyses were performed. Results: CP volumes were significantly higher in ALS patients than in HCs in the independent cohorts. Compared with HCs, serum levels of CRP, IL-6, CXCL10, and 35 other inflammatory factors were significantly increased in ALS patients. Using the XGBoost approach, we established a model-based importance of features, and the top three predictors of CP volume in ALS patients were CRP, IL-6, and CXCL10 (with gains of 0.24, 0.18, and 0.15, respectively). Correlation analyses revealed that CRP, IL-6, and CXCL10 were significantly associated with CP volume in ALS patients (r = 0.462 ∼ 0.636, p < 0.001). Conclusion: Our study is the first to reveal a consistent and replicable contribution of peripheral inflammation to CP enlargement in vivo in sporadic ALS patients. Given that CP enlargement has been recently detected in other brain diseases, these findings should consider extending to other disease conditions with a peripheral inflammatory component. [ABSTRACT FROM AUTHOR]

Background: Working memory (WM), a core component of executive functions, relies on a dedicated brain system that maintains and stores information in the short term. While extensive neuroimaging research has identified a distributed set of neural substrates relevant to WM, their underlying molecular mechanisms remain enigmatic. This study investigated the neural correlates of WM as well as their underlying molecular mechanisms. Results: Our voxel-wise analyses of resting-state functional MRI data from 502 healthy young adults showed that better WM performance (higher accuracy and shorter reaction time of the 3-back task) was associated with lower functional connectivity density (FCD) in the left inferior temporal gyrus and higher FCD in the left anterior cingulate cortex. A combination of transcriptome-neuroimaging spatial correlation and the ensemble-based gene category enrichment analysis revealed that the identified neural correlates of WM were associated with expression of diverse gene categories involving important cortical components and their biological processes as well as sodium channels. Cross-region spatial correlation analyses demonstrated significant associations between the neural correlates of WM and a range of neurotransmitters including dopamine, glutamate, serotonin, and acetylcholine. Conclusions: These findings may help to shed light on the molecular mechanisms underlying the neural correlates of WM. [ABSTRACT FROM AUTHOR]

Background: The accumulation of amyloid-β (Aβ) peptide in the brain is a hallmark of Alzheimer's disease (AD), occurring years before symptom onset. Current methods for quantifying in vivo amyloid load involve invasive or costly procedures, limiting accessibility. Early detection of amyloid positivity in non-demented individuals is crucial for aiding early AD diagnosis and for initiating anti-amyloid immunotherapies at early stages. This study aimed to develop and validate predictive models to identify brain amyloid positivity in non-demented patients, using routinely collected clinical data. Methods: Predictive models for amyloid positivity were developed using data from 853 non-demented participants in the MEMENTO cohort. Amyloid levels were measured potentially repeatedly during study course through Positron Emision Tomography or CerebroSpinal Fluid analysis. The probability of amyloid positivity was modelled using mixed-effects logistic regression. Predictors included demographic information, cognitive assessments, visual brain MRI evaluations of hippocampal atrophy and lobar microbleeds, AD-related blood biomarkers (Aβ42/40 and P-tau181), and ApoE4 status. Models were subjected to internal cross-validation and external validation using data from the Amsterdam Dementia Cohort. Performance also was evaluated in a subsample that met the main criteria of the Appropriate Use Recommendations (AUR) for lecanemab. Results: The most effective model incorporated demographic data, cognitive assessments, ApoE status, and AD-related blood biomarkers, achieving AUCs of 0.82 [95%CI 0.81-0.82] in MEMENTO sample and 0.90 [95%CI 0.86-0.94] in the external validation sample. This model significantly outperformed a reference model based solely on demographic and cognitive data, with an AUC difference in MEMENTO of 0.10 [95%CI 0.10-0.11]. A similar model without ApoE genotype achieved comparable discriminatory performance. MRI markers did not improve model performance. Performances in AUR of lecanemab subsample were comparable. Conclusion: A predictive model integrating demographic, cognitive, and blood biomarker data offers a promising method to help identify amyloid status in non-demented patients. ApoE genotype and brain MRI data were not necessary for strong discriminatory ability, suggesting that ApoE genotyping may be deferred when assessing the risk-benefit ratio of immunotherapies in amyloid-positive patients who desire treatment. The integration of this model into clinical practice could reduce the need for lumbar puncture or PET examinations to confirm amyloid status. [ABSTRACT FROM AUTHOR]

Background: Puberty depicts a period of profound and multifactorial changes ranging from social to biological factors. While brain development in youths has been studied mostly from an age perspective, recent evidence suggests that pubertal measures may be more sensitive to study adolescent neurodevelopment, however, studies on pubertal timing in relation to brain development are still scarce. Methods: We investigated if pre- vs. post-menarche status can be classified using machine learning on cortical and subcortical structural magnetic resonance imaging (MRI) data from strictly age-matched adolescent females from the Adolescent Brain Cognitive Development (ABCD) cohort. For comparison of the identified menarche-related patterns to age-related patterns of neurodevelopment, we trained a brain age prediction model on data from the Philadelphia Neurodevelopmental Cohort and applied it to the same ABCD data, yielding differences between predicted and chronological age referred to as brain age gaps. We tested the sensitivity of both these frameworks to measures of pubertal maturation, specifically age at menarche and puberty status. Results: The machine learning model achieved moderate but statistically significant accuracy in the menarche classification task, yielding for each subject a class probability ranging from 0 (pre-) to 1 (post- menarche). Comparison to brain age predictions revealed shared and distinct patterns of neurodevelopment captured by both approaches. Continuous menarche class probabilities were positively associated with brain age gaps, but only the menarche class probabilities—not the brain age gaps—were associated with age at menarche. Conclusions: This study demonstrates the use of a machine learning model to classify menarche status from structural MRI data while accounting for age-related neurodevelopment. Given its sensitivity towards measures of puberty timing, our work suggests that menarche class probabilities may be developed toward an objective brain-based marker of pubertal development. Highlights: We classified pre- vs. post-menarche status in adolescent females from structural brain imaging data We compared class probabilities to brain-age predictions to disentangle puberty- vs. age-related patterns of brain development The derived continuous brain-based menarche class probabilities captured shared but also unique variations of adolescent neurodevelopment, and were associated with pubertal timing and status Plain language summary: Puberty is a period of substantial changes in the life of youths, and these include profound brain changes. Most studies have investigated age related changes in brain development, recent work however suggests that looking at brain development through the lens of pubertal development can provide additional insights beyond age effects. We here analyzed brain imaging data from a group of same-aged adolescent girls from the Adolescent Brain Cognitive Development study. Our goal was to investigate if we could determine from brain images whether a girl had started her menstrual period (menarche) or not, and we used machine learning to classify between them. This machine learning model does not just return a "yes/no" decision, but also returns a number between 0 and 1 indicating a probability to be pre- (0) or post- (1) menarche. To rule out that our approach only maps age-related development, we selected a strictly age-matched sample of girls and compared our classification model to a brain age model trained on independent individuals. Our model classified between pre- and post-menarche with moderate accuracy. The obtained class probability was partly related to age-related brain development, but only the probability was significantly associated with pubertal timing (age at menarche). In summary, our study uses a machine learning model to estimate whether a girl has reached menarche based on her brain structure. This approach offers new insights into the connection between puberty and brain development and might serve as an objective way to assess pubertal timing from imaging data. [ABSTRACT FROM AUTHOR]

Purpose: Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. Methods: Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. Results: After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71–0.84, 95% CI) based on T1-weighted images, 0.79 (0.72–0.84, 95% CI) for T2-weighted images, 0.88 (0.83–0.92, 95% CI) for CE-T1 images, and 0.88 (0.83–0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66–0.78, 95% CI), 0.84 (0.78–0.89, 95% CI) based on T2-WI, 0.74 (0.67–0.80, 95% CI) for CE-T1, and 0.81 (0.76–0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. Conclusions: CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment. [ABSTRACT FROM AUTHOR]

Background: Augmented reality (AR), a form of 3D imaging technology, has been preliminarily applied in tumor surgery of the head and spine, both are rigid bodies. However, there is a lack of research evaluating the clinical value of AR in tumor surgery of the brachial plexus, a non-rigid body, where the anatomical position varies with patient posture. Methods: Prior to surgery in 8 patients diagnosed with brachial plexus tumors, conventional MRI scans were performed to obtain conventional 2D MRI images. The MRI data were then differentiated automatically and converted into AR-based 3D models. After point-to-point relocation and registration, the 3D models were projected onto the patient's body using a head-mounted display for navigation. To evaluate the clinical value of AR-based 3D models compared to the conventional 2D MRI images, 2 senior hand surgeons completed questionnaires on the evaluation of anatomical structures (tumor, arteries, veins, nerves, bones, and muscles), ranging from 1 (strongly disagree) to 5 (strongly agree). Results: Surgeons rated AR-based 3D models as superior to conventional MRI images for all anatomical structures, including tumors. Furthermore, AR-based 3D models were preferred for preoperative planning and intraoperative navigation, demonstrating their added value. The mean positional error between the 3D models and intraoperative findings was approximately 1 cm. Conclusions: This study evaluated, for the first time, the clinical value of an AR-based 3D navigation system in preoperative planning and intraoperative navigation for brachial plexus tumor surgery. By providing more direct spatial visualization, compared with conventional 2D MRI images, this 3D navigation system significantly improved the clinical accuracy and safety of tumor surgery in non-rigid bodies. [ABSTRACT FROM AUTHOR]