Region Of Interest Research Articles

Baseline Frontoparietal Gray Matter Volume Predicts Executive Function Performance in Aging and Mild Cognitive Impairment at 24-Month Follow-Up.

Executive dysfunction in mild cognitive impairment (MCI) has been associated with gray matter atrophy. Prior studies have yielded limited insight into associations between gray matter volume and executive function in early and late amnestic MCI (aMCI). To examine the relative importance of predictors of executive function at 24 months and relationships between baseline regional gray matter volume and executive function performance at 24-month follow-up in non-demented older adults. 147 participants from the Alzheimer's Disease Neuroimaging Initiative (mean age = 70.6 years) completed brain magnetic resonance imaging and neuropsychological testing and were classified as cognitively normal (n = 49), early aMCI (n = 60), or late aMCI (n = 38). Analyses explored the importance of demographic, APOEɛ4, biomarker (p-tau/Aβ42, t-tau/Aβ42), and gray matter regions-of-interest (ROI) variables to 24-month executive function, whether ROIs predicted executive function, and whether relationships varied by baseline diagnostic status. Across all participants, baseline anterior cingulate cortex and superior parietal lobule volumes were the strongest predictors of 24-month executive function performance. In early aMCI, anterior cingulate cortex volume was the strongest predictor and demonstrated a significant interaction such that lower volume related to worse 24-month executive function in early aMCI. Educational attainment and inferior frontal gyrus volume were the strongest predictors of 24-month executive function performance for cognitively normal and late aMCI groups, respectively. Baseline frontoparietal gray matter regions were significant predictors of executive function performance in the context of aMCI and may identify those at risk of Alzheimer's disease. Anterior cingulate cortex volume may predict executive function performance in early aMCI.

SIPAS: A comprehensive susceptibility imaging process and analysis studio

Quantitative susceptibility mapping (QSM) is a rising MRI-based technology and quite a few QSM-related algorithms have been proposed to reconstruct maps of tissue susceptibility distribution from phase images. In this paper, we develop a comprehensive susceptibility imaging process and analysis studio (SIPAS) that can accomplish reliable QSM processing and offer a standardized evaluation system. Specifically, SIPAS integrates multiple methods for each step, enabling users to select algorithm combinations according to data conditions, and QSM maps could be evaluated by two aspects, including image quality indicators within all voxels and region-of-interest (ROI) analysis. Through a sophisticated design of user-friendly interfaces, the results of each procedure are able to be exhibited in axial, coronal, and sagittal views in real-time, meanwhile ROIs can be displayed in 3D rendering visualization. The accuracy and compatibility of SIPAS are demonstrated by experiments on multiple in vivo human brain datasets acquired from 3T, 5T, and 7T MRI scanners of different manufacturers. We also validate the QSM maps obtained by various algorithm combinations in SIPAS, among which the combination of iRSHARP and SFCR achieves the best results on its evaluation system. SIPAS is a comprehensive, sophisticated, and reliable toolkit that may prompt the QSM application in scientific research and clinical practice.

A lesion‐aware automated processing framework for clinical stroke magnetic resonance imaging

AbstractMagnetic resonance imaging (MRI) and MRI based computational modelling studies provide insights into severity and recovery of ischemic stroke patients. The presence of brain lesions, however, can heavily distort and impair state‐of‐the‐art processing frameworks due to abnormal intensity values and tissue distortions. In this study, we introduce and validate the novel “Lesion Aware automated Processing Pipeline (LeAPP).” LeAPP automatically processes clinical stroke MRI data while significantly reducing the impact of pathological artefacts on processing outputs such as structural and functional connectomes (SC, FC). This helps to advance the identification of biomarkers for recovery and mechanism‐based intervention planning using MRI as well as computational approaches. We extended existing frameworks, such as the Human Connectome Project (HCP) minimal processing pipeline, introducing correction steps, and implementing and modifying functional and diffusion processing to cope with MRI acquisition protocols more typical for the clinical context. A total of 51 participants (36 patients, 15 age‐matched controls) were processed across four time points for patients (3–5, 30–40, 85–95, 340–380 days after stroke onset) and one time point for controls. We validated performance using artificial lesioned brains (N = 81), derived from healthy brains and informed by real stroke lesions. The quality of reconstructing the ground truth was quantified on whole brain level and for lesion affected and unaffected regions‐of‐interest (ROIs) for brain parcellations and for SCs. Volume based agreement was evaluated using metrics such as dice coefficient, volume difference or ROI center‐of‐gravity distance while SC based agreement was defined as the difference in network metrics (e.g., node strength, clustering coefficient, or centrality). The observed deviations in reconstructed ground truth brain parcellations and structural connectomes from lesioned brains were significantly reduced for LeAPP, compared to the performance of existing pipelines such as HCP minimal processing pipeline. For instance, in the case of lesion affected ROIS we achieved a mean dice coefficient (where a value of one represents total agreement as defined by an exact overlap of both ROIs) of 0.81 with LeAPP, compared to 0.75 for HCP (p < .0001*). Additionally, the average measured ROI distance (where a value of zero represents no difference) for lesion ROIs was 0.87 for LeAPP in contrast to 1.7 for HCP (p < .0001*), indicating an overall superior performance of LeAPP. The pipeline generates standardized output files ready for brain network modelling for instance with The Virtual Brain software. This novel open‐source automated processing framework contributes to reproducible research and provides a robust framework for automated processing of clinical stroke MRI data, supporting the identification of brain network‐based biomarkers of stroke recovery.

Persistent brain metabolic impairment in long COVID patients with persistent clinical symptoms: a nine-month follow-up [18F]FDG-PET study.

A hypometabolic profile involving the limbic areas, brainstem and cerebellum has been identified in long COVID patients using [18F]fluorodeoxyglucose (FDG)-PET. This study was conducted to evaluate possible recovery of brain metabolism during the follow-up of patients with prolonged symptoms. Fifty-six adults with long COVID who underwent two brain [18F]FDG-PET scans in our department between May 2020 and October 2022 were retrospectively analysed, and compared to 51 healthy subjects. On average, PET1 was performed 7 months (range 3-17) after acute COVID-19 infection, and PET2 was performed 16 months (range 8-32) after acute infection, because of persistent severe or disabling symptoms, without significant clinical recovery. Whole-brain voxel-based analysis compared PET1 and PET2 from long COVID patients to scans from healthy subjects (p-voxel < 0.001 uncorrected, p-cluster < 0.05 FWE-corrected) and PET1 to PET2 (with the same threshold, and secondarily with a less constrained threshold of p-voxel < 0.005 uncorrected, p-cluster < 0.05 uncorrected). Additionally, a region-of-interest (ROI) semiquantitative anatomical approach was performed for the same comparisons (p < 0.05, corrected). PET1 and PET2 revealed voxel-based hypometabolisms consistent with the previously reported profile in the literature. This between-group analysis comparing PET1 and PET2 showed minor improvements in the pons and cerebellum (8.4 and 5.2%, respectively, only significant under the less constrained uncorrected p-threshold); for the pons, this improvement was correlated with the PET1-PET2 interval (r = 0.21, p < 0.05). Of the 14,068 hypometabolic voxels identified on PET1, 6,503 were also hypometabolic on PET2 (46%). Of the 7,732 hypometabolic voxels identified on PET2, 6,094 were also hypometabolic on PET1 (78%). The anatomical ROI analysis confirmed the brain hypometabolism involving limbic region, the pons and cerebellum at PET1 and PET2, without significant changes between PET1 and PET2. Subjects with persistent symptoms of long COVID exhibit durable deficits in brain metabolism, without progressive worsening.

An effectual recommendation model using hybrid learning models for early detection of Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of individuals worldwide, causing progressive cognitive decline. Early prediction and diagnosis the AD accurately is crucial for effective intervention and treatment. In this study, we propose a comprehensive framework for AD prediction using various techniques, including preprocessing and denoising with Multilayer Perceptron (MLP) and Ant Colony Optimization (ACO), segmentation using U-Net, and classification with Spatial Pyramid Pooling Network (SPPNet). Furthermore, we employ Convolutional Neural Network (CNN) with SPPNet for training and develop a chatbot for recommendation based on MRI data input. The preprocessing and denoising techniques play a vital role in enhancing the quality of the input data. MLP is utilized for preprocessing, where it effectively handles feature extraction and noise reduction. ACO is employed for denoising, optimizing the data to improve the signal-to-noise ratio, and enhancing the overall performance of subsequent stages. For accurate segmentation of brain regions, we employ the U-Net architecture, which has shown remarkable success in medical image segmentation tasks. U-Net effectively identifies the regions of interest, aiding in subsequent classification stages. The classification phase utilizes SPPNet, a deep learning model known for its ability to capture spatial information at multiple scales. SPPNet extracts features from segmented brain regions, enabling robust classification of AD and non-AD cases. To enhance the training process, we employ CNN with SPPNet, leveraging the power of convolutional layers to capture intricate patterns and improve predictive accuracy. The CNN-SPPNet model is trained on a large dataset of MRI scans, enabling it to learn complex representations and make accurate predictions. Hence the proposed work can be integrated with a chatbot that takes MRI data as input and provides recommendations based on the predicted AD probability. Experimental evaluation shows that the combination of preprocessing, denoising, segmentation, and classification offers a comprehensive solution for accurate and efficient AD diagnosis and management.

The amygdala volume moderates the relationship between childhood maltreatment and callous-unemotional traits in adolescents with conduct disorder.

CU traits, characterized by shallow affect, lack of fear, and absence of remorse, have been moderately associated with childhood maltreatment in a recent meta-analysis. However, the potential impact of brain structures remains undetermined. This paper examines the relationship between callous-unemotional (CU) traits, childhood maltreatment, and amygdala volumes. In this study, we used a region-of-interest (ROI) analysis to explore the interaction between the volumes of the amygdala, childhood maltreatment, and the manifestation of CU traits in adolescents diagnosed with conduct disorder (CD, N = 67), along with a comparison group of healthy-control youths (HCs, N = 89). The ROI analysis revealed no significant group differences in the bilateral amygdalar volumes. Significant positive correlation was discovered between all forms of child maltreatment (except for physical neglect) and CU traits across subjects. But the interaction of physical abuse and amygdala volumes was only significant within CD patients. Notably, a sensitivity analysis suggested that gender significantly influences these findings. These results contribute critical insights into the etiology of CU traits, emphasizing the need for customized clinical assessment tools and intervention strategies.

Longitudinal evaluation of cervical length and shear wave elastography in women with spontaneous preterm birth.

To evaluate longitudinal changes in cervical length (CL) and mean cervical shear wave elastography (CSWE) score in women with a singleton or twin pregnancy who undergo spontaneous preterm birth (sPTB) compared with those who deliver at term. This was a prospective longitudinal study of unselected women with a singleton or twin pregnancy attending a dedicated research clinic for screening for sPTB at four timepoints during pregnancy: 11 + 0 to 15 + 6 weeks, 16 + 0 to 20 + 6 weeks, 21 + 0 to 24 + 6 weeks and 28 + 0 to 32 + 6 weeks. At each visit, a transvaginal ultrasound scan was conducted to measure the CL and the CSWE scores in six regions of interest (ROI) (inner, middle and external parts of anterior and posterior cervical lips). The mean CSWE score from the six ROIs was calculated for analysis. Log10 transformation was applied to data to produce a Gaussian distribution prior to statistical analysis. A multilevel mixed-effects analysis was performed to compare longitudinally CL and CSWE between the sPTB and term-delivery groups. The final cohort consisted of 1264 women, including 1143 singleton pregnancies, of which 57 (5.0%) were complicated by sPTB, and 121 twin pregnancies, of which 33 (27.3%) were complicated by sPTB. Compared to those who delivered at term, women with sPTB had a lower CL across gestation when controlling for history of cervical surgery, number of fetuses, gestational age (GA) at cervical assessment and the interaction between GA at cervical assessment and sPTB (P < 0.001). Specifically, CL in the sPTB group was significantly lower at 21 + 0 to 24 + 6 weeks (P = 0.039) and 28 + 0 to 32 + 6 weeks (P < 0.001). Twin pregnancies had significantly greater CL throughout pregnancy compared with singleton pregnancies (regression coefficient, 0.01864; P < 0.001). After adjusting for maternal age, weight, height, body mass index and GA at cervical assessment, CSWE score in the sPTB group was significantly lower compared with that in the term-delivery group across gestation (P = 0.013). However, on analysis of individual visits, CSWE score in the sPTB group was significantly lower than that in the term-delivery group only at 11 + 0 to 15 + 6 weeks (P = 0.036). There was no difference in CSWE score between singleton and twin pregnancies throughout gestation (regression coefficient, -0.00128; P = 0.937). Women with sPTB have a shorter and softer cervix across gestation compared with those who deliver at term. A shorter cervix in the sPTB group is observed from the late second trimester onwards, while lower cervical stiffness in the sPTB group is observed primarily in the first trimester. CL is significantly lower in singleton pregnancies compared with twin pregnancies, while cervical stiffness does not differ between the two. Our findings indicate that the cervix tends to undergo a softening process prior to shortening in sPTB cases. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRI.

Facing the increasing worldwide prevalence of mental disorders, the symptom-based diagnostic criteria struggle to address the urgent public health concern due to the global shortfall in well-qualified professionals. Thanks to the recent advances in neuroimaging techniques, functional magnetic resonance imaging (fMRI) has surfaced as a new solution to characterize neuropathological biomarkers for detecting functional connectivity (FC) anomalies in mental disorders. However, the existing computer-aided diagnosis models for fMRI analysis suffer from unstable performance on large datasets. To address this issue, we propose an efficient multitask learning (MTL) framework for joint diagnosis of multiple mental disorders using resting-state fMRI data. A novel multiobjective evolutionary clustering algorithm is presented to group regions of interests (ROIs) into different clusters for FC pattern analysis. On the optimal clustering solution, the multicluster multigate mixture-of-expert model is used for the final classification by capturing the highly consistent feature patterns among related diagnostic tasks. Extensive simulation experiments demonstrate that the performance of the proposed framework is superior to that of the other state-of-the-art methods. Moreover, the potential for practical application of the framework is also validated in terms of limited computational resources, real-time analysis, and insufficient training data. The proposed model can identify the remarkable interpretative biomarkers associated with specific mental disorders for clinical interpretation analysis.

Effect of patient characteristics on aortic attenuation in iodinated contrast-enhanced Abdominopelvic CT: A retrospective study

IntroductionContrast Enhanced Computed Tomography (CECT) abdomen and pelvis is a common imaging procedure. Hospitals typically follow fixed protocols of contrast volume administration for triple-phase CECT abdomen and pelvis scans and have found that patients are either underdosed or overdosed with respect to their body habitus. The aim of the study was to correlate different patient characteristics such as Total body weight (TBW), Lean Body Mass (LBM), Body Mass Index (BMI), Body Surface Area (BSA) and Blood Volume (BV) with aortic enhancement in the arterial and portal venous phases for CECT Abdomen and pelvis. MethodsA total of 106 patients who underwent triple-phase CECT abdomen & pelvis were retrospectively studied. A circular region-of-interest (ROI) of 100 mm2 was positioned on descending aorta for unenhanced, arterial, and portal venous phases to measure the aortic enhancement in Hounsfield's units. Measure of contrast attenuation (ΔH) was calculated from the difference of CT values on unenhanced images and contrast images. Correlation analysis was performed to evaluate the relation of patient body characteristics with aortic enhancement. ResultsCorrelation analysis revealed that BMI exhibited the least correlation when compared to the other characteristics in both arterial (r = −0.3; p = 0.002) and portovenous phases (r = −0.35; p < 0.001) whereas TBW, LBW, BSA and BV reported moderate inverse correlations. BV was found to be the strongest of all characteristics under linear regression. ConclusionThe study supports the use of protocols that adjust contrast volume to either TBW, LBW, BSA, or BV for CT abdomen and pelvis scan. Implication of practiceThe right body parameter ensures optimal contrast enhancement, improving the visualization of anatomical structures and helps in adapting tailored contrast injection protocols.

Comparison of quantitative radiomorphometric predictors of healthy and MRONJ-affected bone using panoramic radiography and cone-beam CT.

To determine the most distinctive quantitative radiomorphometric parameter(s) for the detection of MRONJ-affected bone changes in panoramic radiography (PR) and cone-beam CT (CBCT). PR and sagittal CBCT slices of 24 MRONJ patients and 22 healthy controls were used for the measurements of mandibular cortical thickness (MCT), fractal dimension (FD), lacunarity, mean gray value (MGV), bone area fraction (BA/TA), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), trabecular number (Tb.N). MCT was measured in the mental foramen region. While FD and lacunarity were measured on mandibular trabecular and cortical regions-of-interest (ROIs), the remaining parameters were measured on trabecular ROIs. The independent samples t-test was used to compare the measurements between the MRONJ and control groups for both imaging modalities (P = .05). MCT was the only parameter that differentiated MRONJ-affected bone in both PR and CBCT (P < .05). None of the remaining parameters revealed any difference for MRONJ-affected bone in CBCT (P > .05). FD, lacunarity, MGV, BA/TA, and Tb.Sp could distinguish MRONJ-affected trabecular bone in PR (P < .05). The correspondent ROI for both imaging methods that was reliable for detecting MRONJ-affected bone was the trabecular bone distal to the mental foramen above the inferior alveolar canal (ROI-3). MCT is a reliable parameter for the discrimination of MRONJ-affected bone in both PR and CBCT images. PR may be used to detect MRONJ-affected trabecular bone using FD, lacunarity, MGV, BA/TA, and Tb.Sp measurements as well.

Microstructural changes of the white matter in systemic lupus erythematosus patients without neuropsychiatric symptoms: a multi-shell diffusion imaging study

BackgroundDiffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) provide more comprehensive and informative perspective on microstructural alterations of cerebral white matter (WM) than single-shell diffusion tensor imaging (DTI), especially in the detection of crossing fiber. However, studies on systemic lupus erythematosus patients without neuropsychiatric symptoms (non-NPSLE patients) using multi-shell diffusion imaging remain scarce.MethodsTotally 49 non-NPSLE patients and 41 age-, sex-, and education-matched healthy controls underwent multi-shell diffusion magnetic resonance imaging. Totally 10 diffusion metrics based on DKI (fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, mean kurtosis, axial kurtosis and radial kurtosis) and NODDI (neurite density index, orientation dispersion index and volume fraction of the isotropic diffusion compartment) were evaluated. Tract-based spatial statistics (TBSS) and atlas-based region-of-interest (ROI) analyses were performed to determine group differences in brain WM microstructure. The associations of multi-shell diffusion metrics with clinical indicators were determined for further investigation.ResultsTBSS analysis revealed reduced FA, AD and RK and increased ODI in the WM of non-NPSLE patients (P < 0.05, family-wise error corrected), and ODI showed the best discriminative ability. Atlas-based ROI analysis found increased ODI values in anterior thalamic radiation (ATR), inferior frontal-occipital fasciculus (IFOF), forceps major (F_major), forceps minor (F_minor) and uncinate fasciculus (UF) in non-NPSLE patients, and the right ATR showed the best discriminative ability. ODI in the F_major was positively correlated to C3.ConclusionThis study suggested that DKI and NODDI metrics can complementarily detect WM abnormalities in non-NPSLE patients and revealed ODI as a more sensitive and specific biomarker than DKI, guiding further understanding of the pathophysiological mechanism of normal-appearing WM injury in SLE.

Galaxies in the zone of avoidance: Misclassifications using machine learning tools

Context. Automated methods for classifying extragalactic objects in large surveys offer significant advantages compared to manual approaches in terms of efficiency and consistency. However, the existence of the Galactic disk raises additional concerns. These regions are known for high levels of interstellar extinction, star crowding, and limited data sets and studies. Aims. In this study, we explore the identification and classification of galaxies in the zone of avoidance (ZoA). In particular, we compare our results in the near-infrared (NIR) with X-ray data. Methods. We analyzed the appearance of objects in the Galactic disk classified as galaxies using a published machine-learning (ML) algorithm and make a comparison with the visually confirmed galaxies from the VVV NIRGC catalog. Results. Our analysis, which includes the visual inspection of all sources cataloged as galaxies throughout the Galactic disk using ML techniques reveals significant differences. Only four galaxies were found in both the NIR and X-ray data sets. Several specific regions of interest within the ZoA exhibit a high probability of being galaxies in X-ray data but closely resemble extended Galactic objects. Our results indicate the difficulty in using ML methods for galaxy classification in the ZoA, which is mainly due to the scarcity of information on galaxies behind the Galactic plane in the training set. They also highlight the importance of considering specific factors that are present to improve the reliability and accuracy of future studies in this challenging region.

#3063 MRI assessment of skin and muscle sodium (23Na) and fluid volume in haemodialysis patients

Abstract Background and Aims Haemodialysis (HD) is life sustaining for patients with end-stage kidney disease (ESKD). In healthy people, sodium balance is regulated by the kidneys; in ESKD this is achieved by sodium removal during HD. Recent evidence suggests non-osmotically stored sodium in the muscle and/or skin may be a critical factor impacting the development of hypertension and cardiovascular disease (CVD). Sodium (23Na) MRI allows skin and muscle sodium storage assessment and may provide a valuable tool in evaluating sodium storage in dialysis patients. Here, 23Na MRI is used to measure muscle and skin tissue sodium concentration (TSC) in HD participants. HD participants are scanned before and after a single haemodialysis session using 23Na MRI as well as 1H T2 relaxometry to study changes in fluid status. Method Data were collected on HD patients who underwent a pre-HD 23Na MRI calf scan, then had their usual dialysis session with a dialysate sodium of 137 mmol/L, followed by a repeat post-HD 23Na MRI scan. Patient dialysis vintage, residual renal function, and ultrafiltration volume, and blood measures pre- and post-HD were collected. 1H scans were acquired for localisation and muscle segmentation (mDIXON) along with T1 and T2 mapping. 23Na images were acquired for (TSC) quantification. Reference bottles (10, 20, 30 and 40 mmol/L NaCl) were placed above the leg to calibrate TSC muscle and skin maps. Regions-of-interest (ROIs) of each muscle group and the skin were manually segmented on mDIXON scans. In each muscle group the voxel-wise mode (to avoid influence of signals from vessels) of TSC and T2 was estimated. A paired-sample t-test was performed between metrics pre-HD and post-HD. Absolute measures of ΔTSC and ΔT2 were correlated with clinical measures. Results Ten people receiving HD were recruited (age 55-73 yrs, 5 M:5F). Figure 1 shows TSC maps of the HD patients pre-HD and post-HD, with a significant reduction (p &amp;lt; 0.05) in TSC in the extensor, peroneus and gastrocnemius muscles post-HD but skin sodium showed no detectable change. Post-HD TSC significantly positively correlated with post-HD Systolic Blood Pressure (SBP) (P &amp;lt; 0.004) but not with plasma sodium. 1H T2-maps (Fig. 2A) show a significant reduction (p &amp;lt; 0.01) in muscle T2 post-HD compared to pre-HD all muscle groups (Fig. 2B). There were no significant correlations between T2 and 23Na measures, or T2 and clinical measures. There was no significant detectable change in 1H T1. Conclusion Post-HD TSC values are consistent with published data from patients dialysed against a dialysate sodium of 137 mmol/L, and shows a significant correlation of post-HD TSC with post-HD SBP. 1H muscle T2 reduced post-HD, consistent with published data, as patients change from a pre-HD hypervolemia state closer to a post-HD to euvolemia state after fluid removal. There was no correlation of absolute or ΔTSC and 1H T2 and ΔT2 values, suggesting different mechanisms for equilibrium conditions of water and sodium ion concentrations. Future studies will study the effect of dialysate sodium on muscle TSC and mechanistic links to CVD, and include the use of a dedicated skin coil to study skin sodium alongside bioimpedance fluid measures.

Uncertainty-aware representation calibration for semi-supervised medical imaging segmentation

Semi-supervised learning methods aim to address the scarcity of pixel-level annotations in medical image segmentation. Previous approaches typically rely on filtering strategies to obtain pseudo-labels or implement consistency constraints for unlabeled images, which cannot robustly identify regions-of-interest (ROIs) with intricate semantic information. However, this often leads to uncertain predictions in semantically ambiguous areas and results in subpar segmentation outcomes. We observe that this issue stems from the dispersion of the representations of uncertain predictions among the boundaries of the semantically clustered representations of certain predictions. To this end, we propose a novel Uncertainty-Aware Representation Calibration (UA-RC) framework. This framework leverages an efficient uncertainty-aware criterion within a teacher–student SSL architecture to identify the representations of uncertain predictions. Then, UA-RC calibrates them via a semantic contrast paradigm by constructing positive prototypes and negative representations from certain predictions. Furthermore, UA-RC incorporates class-wise memory banks to store massive, diverse representations from training data, facilitating the calibration process and allowing for a better disentanglement of representations related to ROIs and backgrounds. Extensive experiments on four datasets, including Kvasir-SEG, ISIC-2018, BUL-2020, and ACDC, demonstrate the competitive edge of UA-RC over existing alternatives. Codes is available at https://github.com/Wu0409/UARC.

Analysis of functional connectivity changes in attention networks and default mode networks in patients with depression and insomnia.

Major Depressive Disorder (MDD) and Insomnia Disorder (ID) are prevalent psychiatric conditions often occurring concurrently, leading to substantial impairment in daily functioning. Understanding the neurobiological underpinnings of these disorders and their comorbidity is crucial for developing effective interventions. This study aims to analyze changes in functional connectivity within attention networks and default mode networks in patients with depression and insomnia. The functional connectivity alterations in individuals with MDD, ID, comorbid MDD and insomnia (iMDD), and healthy controls (HC) were assessed from a cohort of 174 participants. They underwent rs-fMRI scans, demographic assessments, and scale evaluations for depression and sleep quality. Functional connectivity analysis was conducted using region-of-interest (ROI) and whole-brain methods. The MDD and iMDD groups exhibited higher Hamilton Depression Scale (HAMD) scores compared to HC and ID groups (P < 0.001). Both ID and MDD groups displayed enhanced connectivity between the left and right orbital frontal cortex compared to HC (P < 0.05), while the iMDD group showed reduced connectivity compared to HC and ID groups (P < 0.05). In the left insula, reduced connectivity with the right medial superior frontal gyrus was observed across patient groups compared to HC (P < 0.05), with the iMDD group showing increased connectivity compared to MDD (P < 0.05). Moreover, alterations in functional connectivity between the left thalamus and left temporal pole were found in iMDD compared to HC and MDD (P < 0.05). Correlation analyses revealed associations between abnormal connectivity and symptom severity in MDD and ID groups. Our findings demonstrate distinct patterns of altered functional connectivity in individuals with MDD, ID, and iMDD compared to healthy controls. These findings contribute to a better understanding of the pathophysiology of depression and insomnia, which could be used as a reference for the diagnosis and treatments of these patients.

Improving Microstructural Estimation in Time-Dependent Diffusion MRI With a Bayesian Method.

Accurately fitting diffusion-time-dependent diffusion MRI (td-dMRI) models poses challenges due to complex and nonlinear formulas, signal noise, and limited clinical data acquisition. Introduce a Bayesian methodology to refine microstructural fitting within the IMPULSED (Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion) model and optimize the prior distribution within the Bayesian framework. Retrospective. Involving 69 pediatric patients (median age 6 years, interquartile range [IQR] 3-9 years, 61% male) with 41 low-grade and 28 high-grade gliomas, of which 76.8% were identified within the brainstem or cerebellum. 3 T, oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE). The Bayesian method's performance in fitting cell diameter ( ), intracellular volume fraction ( ), and extracellular diffusion coefficient ( ) was compared against the NLLS method, considering simulated and experimental data. The tumor region-of-interest (ROI) were manually delineated on the b0 images. The diagnostic performance in distinguishing high- and low-grade gliomas was assessed, and fitting accuracy was validated against H&E-stained pathology. T-test, receiver operating curve (ROC), area under the curve (AUC) and DeLong's test were conducted. Significance considered at P < 0.05. Bayesian methodology manifested increased accuracy with robust estimates in simulation (RMSE decreased by 29.6%, 40.9%, 13.6%, and STD decreased by 29.2%, 43.5%, and 24.0%, respectively for , , and compared to NLLS), indicating fewer outliers and reduced error. Diagnostic performance for tumor grade was similar in both methods, however, Bayesian method generated smoother microstructural maps (outliers ratio decreased by 45.3% ± 19.4%) and a marginal enhancement in correlation with H&E staining result (r = 0.721 for compared to r = 0.698 using NLLS, P = 0.5764). The proposed Bayesian method substantially enhances the accuracy and robustness of IMPULSED model estimation, suggesting its potential clinical utility in characterizing cellular microstructure. 3 TECHNICAL EFFICACY: Stage 1.

Single-Loop Sampling for Estimating Failure-Probability Upper-Bound Function

Under random-interval mixed uncertainties of structures, failure-probability upper-bound function (FPUBF), which varies with the distribution parameters of random inputs, can not only provide the influence of distribution parameters on the failure-probability upper bound (FPUB), but also contribute to decoupling a reliability-based design optimization model. Although FPUBF can be estimated by repeatedly evaluating FPUBs at different distribution parameter realizations, it suffers from unaffordable computational cost resulting from this double-loop framework. To address this issue, this paper proposes a single-loop sampling strategy (SL) to estimate FPUBF at arbitrary realizations in the interested distribution parameter region. Instead of the huge computational cost of a double-loop framework, the SL estimates the entire FPUBF only by one simulation analysis. Moreover, importance sampling (IS) variance reduction technique is introduced, and a single-loop IS probability density function (PDF), or SL-IS-PDF, is constructed to more efficiently estimate FPUBF by reducing the required size of the candidate sample pool. For approximating the optimal SL-IS-PDF and identifying the states of candidate samples efficiently, the double-loop adaptive Kriging model of performance function is introduced to further reduce the number of performance function evaluations. A numerical example and two composite structure examples are employed to verify the accuracy, efficiency, and feasibility of the proposed methods.

Disaggregating National Statistical Data to Assess the Sub-National SDG 6.4.2 Water Stress Indicator by Integrating Satellite Observations and Model Data

Ensuring the sustainable management of water and sanitation for all is the primary goal of Sustainable Development Goal 6 (SDG 6). SDG indicator 6.4.2 (level of water stress) is critical for monitoring the progress toward SDG 6. The assessment of the SDG indicator 6.4.2 is currently based on statistical data at the national scale, i.e., one value for one country, which cannot reflect spatial variability in water stress in a country. The lack of data at sub-national scales limits the assessment of water stress in sub-national regions. This study developed a method of disaggregating national statistical renewable water resources (TRWR) and freshwater withdrawals (TFWW) to estimate the SDG 6.4.2 water stress indicator at a sub-national scale by combining satellite remote sensing data and model simulated data. Remote sensing (RS)-based precipitation (P); the difference between precipitation and evapotranspiration (P-ET); and the difference between precipitation, evapotranspiration, terrestrial water storage change (P-ET-dS), and model-simulated naturized runoff and withdrawal water use were used as spatial and temporal surrogates to disaggregate the national-scale statistics of TRWR and TFWW to the grid scale. Gridded TRWR and TFWW can be used to calculate the water stress of any interest regions. Disaggregated TRWR, TFWW, and water stress estimation were validated at three different spatial scales, from major river basins and provinces to prefectures in China, by comparing the corresponding statistical data. The results show that the disaggregation for TRWR is generally better than for TFWW, and the overall accuracy for water stress estimation can reach up to 91%. The temporal evolution of disaggregated variables also showed good consistency with statistical time series data. The RS-based P-ET and P-ET-dS have great potential for disaggregating TRWR at different spatiotemporal scales, with no obvious differences with the results using the model simulation as a surrogate for the disaggregation of SDG indicator 6.4.2. The disaggregation accuracy can be further improved when the sub-regional statistical data of TRWR and TFWW are applied to the disaggregation approach.

Abstract PS05-02: PET Imaging of PARP Expression as a Biomarker of Response to Chemotherapy in Breast Cancer: A Nonrandomized Clinical Trial

Abstract Introduction: Poly–(adenosine diphosphate–ribose) polymerase (PARP) proteins are involved in double-stranded break repair, mediated through homologous recombination (HR) and non-homologous end joining (NHEJ). The majority of PARP activity in this context is attributed to PARP-1 (85-90%). PARP-1 binding to damaged DNA, catalytic activity, and subsequent release to allow access for other DNA repair proteins are necessary for efficient DNA repair. The critical function of PARP-1 in these processes makes this protein a unique potential biomarker of DNA repair capability. [18F]FluorThanatrace ([18F]FTT) is an 18F-labeled PARP inhibitor analog that binds to the same site as approved PARPi drugs. We hypothesized that the biologically relevant form of PARP in untreated breast cancer might provide an indication of functional tumor DNA repair capabilities and therefore predict chemotherapy response. Thus, this study aimed to determine whether a non-invasive quantitative measure of in vivo PARP expression correlated with response to chemotherapy in breast cancer. Methods: A single-arm prospective trial enriched for subjects with triple negative breast cancer (TNBC) was conducted at the University of Pennsylvania from May 2017 to March 2022 (NCT03083288 and NCT03846167). Inclusion criteria were primary breast cancer with tumor diameter of at least 1 cm on conventional imaging and willingness to undergo an [18F]FTT-PET scan prior to neoadjuvant chemotherapy. Participants provided written informed consent. [18F]FTT uptake in breast cancer was measured pre-therapy and tested for association with pathologic complete response (pCR). Subjects were scanned on an Ingenuity TF PET/CT (Philips Healthcare) following injection of [18F]FTT using a 20-minute static acquisition. Peak 1 cm3 average uptake (SUVpeak) were recorded from a spherical region-of-interest (ROI) covering the primary malignancy, guided by CT and prior imaging studies. All SUVs were partial volume corrected and normalized using muscle (NM). A Mann-Whitney test was used to determine if [18F]FTT uptake differed by pCR. Receiver operating characteristic (ROC) curves were constructed to test the performance of [18F]FTT uptake in predicting pCR. Statistical analyses utilized STATA v15.1 with significance based on a two-sided alpha level of ≤0.05. Participants: Twenty-six women with stage I-III breast cancer met inclusion criteria with age range of 29-74 years and self-reported race as Asian (1, 4%), Black (9, 37%) or White (16, 60%). One subject had bilateral tumors (ER+ stage IIA and HER2+ stage I) that were evaluated separately (27 total tumors). Breast tumors were pathologically confirmed and included 6 (22%) ER+/HER2-, 5 (19%) HER2+, and 16 (59%) TNBC. Germline sequencing was available for 21 participants. Results: Mean tumor diameter ranged from 15 to 91 mm (median 30 mm). A considerable range of [18F]FTT uptake was seen across subjects (SUVpeak/NM 0.66-6.5). [18F]FTT uptake was independent of subtype (ER+, HER2+, TN) (P=0.35), stage (P=0.39), and between mutations in genes associated with homologous recombination deficiency (i.e., BRCA1/2, PALB2) and wild-type (P=0.73). In the TNBC cohort, pre-treatment [18F]FTT uptake was higher in subjects who went on to have a pCR (n=16, P=0.05), while a trend toward higher uptake was seen when including all tumor types (n=27, P=0.11). ROC analysis of the value of [18F]FTT uptake for predicting pCR revealed an AUC of 0.79 and showed that a threshold SUV ratio &amp;lt; 2.47 predicted pCR in TNBC patients with a 100% specificity and 64% sensitivity. Conclusions: These early clinical results suggest a relationship between [18F]FTT uptake, a measure of PARP expression, and chemotherapy sensitivity in TNBC, warranting future studies to elucidate underlying mechanistic processes and potential clinical implications. Citation Format: Sarah Gitto, Anthony Young, Ira Bleiweiss, Amy Clark, Elizabeth McDonald. PET Imaging of PARP Expression as a Biomarker of Response to Chemotherapy in Breast Cancer: A Nonrandomized Clinical Trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS05-02.

Expanding the reach of the trans-middle cerebellar peduncle approach: pontine cavernous malformations, tissue transgression beyond the safe entry zone, and the invisible triangle.

In the authors' microsurgical experience, the trans-middle cerebellar peduncle (MCP) approach to the lateral and central pons has been the most common approach to brainstem cavernous malformations (BSCMs). This approach through a well-tolerated safe entry zone (SEZ) allows a wide vertical or posterior trajectory, reaching pontine lesions extending into the midbrain, medulla, and pontine tegmentum. Better understanding of the relationships among lesion location, surgical trajectory, and long-term clinical outcomes could determine areas of safe passage. A single-surgeon cohort study of all primary trans-MCP BSCM resections was conducted from July 1, 2017, to June 30, 2021. Preoperative and postoperative MR images were independently reviewed by 3 investigators blinded to the intervention, using a standardized rubric to define BSCM regions of interest (ROIs) involved with a lesion or microsurgical tract. Statistical testing, including the chi-square test with the Bonferroni correction, logistic regression, and structural equation modeling, was performed to analyze relationships between ROIs and clinical outcomes. Thirty-one patients underwent primary trans-MCP BSCM resection during the study period. The median age was 50 years (IQR 24-49 years); 19 (61%) patients were female, and 12 (39%) were male. Seven (23%) patients had familial cavernous malformation syndromes. The median follow-up was 9 months (range 6-37 months). At the last follow-up, composite neurological outcomes were favorable: 22 (71%) patients had 0 (n = 12, 39%) or 1 (n = 10, 32%) major persistent deficit, 5 patients (16%) had 2 deficits, 2 (7%) had 3 deficits, and 1 patient each (3%) had 4 or 6 deficits. Unfavorable composite outcomes were significantly associated with lesions (OR 7.14, p = 0.04) or surgical tracts (OR 12.18, p < 0.001) extending from the superior cerebellar peduncle (SCP) into the contralateral medial midbrain. The ipsilateral dorsal pons was the most frequently implicated ROI involving a surgical tract and the development of new postoperative deficits. This region involved the rhomboid pontine territory and transgression of the pontine tegmentum (OR 7.53, p < 0.001). Structural equation modeling supported medial midbrain and pontine tegmentum transgression as the primary drivers of morbidity. Trans-MCP resection is a safe and effective treatment for BSCMs, including lesions with marked superior or inferior ipsilateral extension. Two trajectories are associated with increased neurological risk: first, a superomedial trajectory to lesions extending into the midbrain that transgresses the SCP, its decussation, or both; and second, a posteromedial trajectory to lesions extending into the pontine tegmentum. The corticospinal tract, SCP, and pontine tegmentum form an invisible triangle within the pontine white matter tolerant of transgression. When the surgeon works within this triangle, most deep pontine BSCMs, including large lesions, those with contralateral or posterior extension, and others extending into the midbrain and medulla, can be resected safely with the trans-MCP approach.