ICBP90, an epigenetic regulator, induces DKK3 promoter methylation, promotes glioma progression, and reduces sensitivity to cis-platinum.

Glioma is the most common brain malignancy, characterized by high morbidity, high mortality, and treatment-resistance. Inverted CCAAT box Binding Protein of 90 kDa (ICBP90) has been reported to be involved in tumor progression and the maintenance of DNA methylation. Herein, we constructed ICBP90 over-expression and knockdown glioma cell lines, and found that ICBP90 knockdown inhibited glioma cell proliferation, migration, and invasion. ICBP90 silencing potentially enhanced cellular sensitivity to cis-platinum (DDP) and exacerbated DDP-induced pyroptosis, manifested by the elevated levels of gasdermin D-N-terminal and cleaved caspase 1; whereas, ICBP90 over-expression exhibited the opposite effects. Consistently, ICBP90 knockdown inhibited tumor growth in an in vivo mouse xenograft study using U251 cells stably expressing sh-ICBP90 and oe-ICBP90. Further experiments found that ICBP90 reduced the expression of Dickkopf 3 homolog (DKK3), a negative regulator of ß-catenin, by binding its promoter and inducing DNA methylation. ICBP90 knockdown prevented the nuclear translocation of ß-catenin and suppressed the expression of c-Myc and cyclin D1. Besides, DKK3 over-expression restored the effects of ICBP90 over-expression on cell proliferation, migration, invasion, and DDP sensitivity. Our findings suggest that ICBP90 inhibits the expression of DKK3 in glioma by maintaining DKK3 promoter methylation, thereby conducing to ICBP90-mediated carcinogenesis and drug insensitivity.

Vessel density mapping of small cerebral vessels on 3D high resolution black blood MRI.

Small cerebral blood vessels are largely inaccessible to existing clinical in vivo imaging technologies. This study aims to present a novel analysis pipeline for vessel density mapping of small cerebral blood vessels from high-resolution 3D black-blood MRI at 3T. Twenty-eight subjects (10 under 35 years old, 18 over 60 years old) were imaged with the T1-weighted turbo spin-echo with variable flip angles (T1w TSE-VFA) sequence optimized for black-blood small vessel imaging with iso-0.5 mm spatial resolution (interpolated from 0.51×0.51×0.64 mm3) at 3T. Hessian-based vessel segmentation methods (Jerman, Frangi and Sato filter) were evaluated by vessel landmarks and manual annotation of lenticulostriate arteries (LSAs). Using optimized vessel segmentation, large vessel pruning and non-linear registration, a semiautomatic pipeline was proposed for quantification of small vessel density across brain regions and further for localized detection of small vessel changes across populations. Voxel-level statistics was performed to compare vessel density between two age groups. Additionally, local vessel density of aged subjects was correlated with their corresponding gross cognitive and executive function (EF) scores using Montreal Cognitive Assessment (MoCA) and EF composite scores compiled with Item Response Theory (IRT). Jerman filter showed better performance for vessel segmentation than Frangi and Sato filter which was employed in our pipeline. Small cerebral blood vessels including small artery, arterioles, small veins, and venules on the order of a few hundred microns can be delineated using the proposed analysis pipeline on 3D black-blood MRI at 3T. The mean vessel density across brain regions was significantly higher in young subjects compared to aged subjects. In the aged subjects, localized vessel density was positively correlated with MoCA and IRT EF scores. The proposed pipeline is able to segment, quantify, and detect localized differences in vessel density of small cerebral blood vessels based on 3D high-resolution black-blood MRI. This framework may serve as a tool for localized detection of small vessel density changes in normal aging and cerebral small vessel disease.

Diffusion MRI harmonization via personalized template mapping.

One fundamental challenge in diffusion magnetic resonance imaging (dMRI) harmonization is to disentangle the contributions of scanner-related effects from the variable brain anatomy for the observed imaging signals. Conventional harmonization methods rely on establishing an atlas space to resolve anatomical variability and generate a unified inter-site mapping function. However, this approach is limited in accounting for the misalignment of neuroanatomy that still widely persists even after registration, especially in regions close to cortical boundaries. To overcome this challenge, we propose a personalized framework in this paper to more effectively address the confounding from the misalignment of neuroanatomy in dMRI harmonization. Instead of using a common template representing site-effects for all subjects, the main novelty of our method is the adaptive computation of personalized templates for both source and target scanning sites to estimate the inter-site mapping function. We integrate our method with the rotation invariant spherical harmonics (RISH) features to achieve the harmonization of dMRI signals between sites. In our experiments, the proposed approach is applied to harmonize the dMRI data acquired from two scanning platforms: Siemens Prisma and GE MR750 from the Adolescent Brain Cognitive Development dataset and compared with a state-of-the-art method based on RISH features. Our results indicate that the proposed harmonization framework achieves superior performance not only in reducing inter-site variations due to scanner differences but also in preserving sex-related biological variability in original cohorts. Moreover, we assess the impact of harmonization on the estimation of fiber orientation distributions and show the robustness of the personalized harmonization procedure in preserving the fiber orientation of original dMRI signals.

Diffusion Model-based FOD Restoration from High Distortion in dMRI.

Fiber orientation distributions (FODs) is a popular model to represent the diffusion MRI (dMRI) data. However, imaging artifacts such as susceptibility-induced distortion in dMRI can cause signal loss and lead to the corrupted reconstruction of FODs, which prohibits successful fiber tracking and connectivity analysis in affected brain regions such as the brain stem. Generative models, such as the diffusion models, have been successfully applied in various image restoration tasks. However, their application on FOD images poses unique challenges since FODs are 4-dimensional data represented by spherical harmonics (SPHARM) with the 4-th dimension exhibiting order-related dependency. In this paper, we propose a novel diffusion model for FOD restoration that can recover the signal loss caused by distortion artifacts. We use volume-order encoding to enhance the ability of the diffusion model to generate individual FOD volumes at all SPHARM orders. Moreover, we add cross-attention features extracted across all SPHARM orders in generating every individual FOD volume to capture the order-related dependency across FOD volumes. We also condition the diffusion model with low-distortion FODs surrounding high-distortion areas to maintain the geometric coherence of the generated FODs. We trained and tested our model using data from the UK Biobank (n = 1315). On a test set with ground truth (n = 43), we demonstrate the high accuracy of the generated FODs in terms of root mean square errors of FOD volumes and angular errors of FOD peaks. We also apply our method to a test set with large distortion in the brain stem area (n = 1172) and demonstrate the efficacy of our method in restoring the FOD integrity and, hence, greatly improving tractography performance in affected brain regions.

Pan-cancer analyses of immunogenic cell death-derived gene signatures: Potential biomarkers for prognosis and immunotherapy.

BACKGROUND: Immunogenic cell death (ICD) is a type of regulated cell death that is capable of initiating an adaptive immune response. Induction of ICD may be a potential treatment strategy, as it has been demonstrated to activate the tumor-specific immune response. AIMS: The biomarkers of ICD and their relationships with the tumor microenvironment, clinical features, and immunotherapy response are not fully understood in a clinical context. Therefore, we conducted pan-cancer analyses of ICD gene signatures across 33 cancer types from The Cancer Genome Atlas database. METHODS AND RESULTS: We identified key genes that had strong relationships with survival and the tumor microenvironment, contributing to a better understanding of the role of ICD genes in cancer therapy. In addition, we predicted therapeutic agents that target ICD genes and explored the potential mechanisms by which gemcitabine induce ICD. Moreover, we developed an ICD score based on the ICD genes and found it to be associated with patient prognosis, clinical features, tumor microenvironment, radiotherapy access, and immunotherapy response. A high ICD score was linked to the immune-hot phenotype, while a low ICD score was linked to the immune-cold phenotype. CONCLUSION: We uncovered the potential of ICD gene signatures as comprehensive biomarkers for ICD in pan-cancer. Our research provides novel insights into immuno-phenotypic assessment and cancer therapeutic strategies, which could help to broaden the application of immunotherapy to benefit more patients.

Postoperative radiotherapy does not improve survival in patients with Masaoka-Koga stage IIB thymomas: A propensity score matching study based on the SEER database.

This study, based on a population, explored the prognostic value of postoperative radiotherapy (PORT) for Masaoka-Koga IIB stage thymomas. Patients diagnosed with thymoma from 2004 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) database were included in the retrospective study. Through propensity score matching, the baseline characteristics of the patients were successfully matched to mitigate the selection bias of PORT. Survival rates and survival curves were compared between the PORT and non-PORT groups, with potential confounding factors addressed using a multivariate Cox regression model. In this study, 785 cases of IIB stage thymoma were included from the SEER database, and 303 patients were successfully matched between PORT and non-PORT groups through propensity score matching, with no significant differences in baseline characteristics. In the PORT and non-PORT groups, 10-year overall survival rates were 65.2% versus 59.6%, and cancer-specific survival rates were 87.0% vs. 84.4%, PORT did not yield statistically significant improvements in overall survival (P = .275) or cancer-specific survival (P = .336) for stage IIB thymomas. Based on the SEER database, the results of our study indicated that PORT does not confer a significant survival benefit for IIB stage thymomas.

Visual Cortical Thickness Increases with Prolonged Artificial Vision Restoration.

The Argus II retinal prosthesis restores visual perception to late blind patients. It has been shown that structural changes occur in the brain due to late-onset blindness, including cortical thinning in visual regions of the brain. Following vision restoration, it is not yet known whether these visual regions are reinvigorated and regain a normal cortical thickness or retain the diminished thickness from blindness. We evaluated the cortical thicknesses of ten Argus II Retinal Prostheses patients, ten blind patients, and thirteen sighted participants. The Argus II patients on average had a thicker left Cuneus Cortex and Lateral Occipital Cortex relative to the blind patients. The duration of the Argus II use (time since implant in active users) significantly partially correlated with thicker visual cortical regions in the left hemisphere. Furthermore, in the two case studies (scanned before and after implantation), the patient with longer device use (44.5 months) had an increase in the cortical thickness of visual regions, whereas the shorter-using patient did not (6.5 months). Finally, a third case, scanned at three time points post-implantation, showed an increase in cortical thickness in the Lateral Occipital Cortex between 43.5 and 57 months, which was maintained even after 3 years of disuse (106 months). Overall, the Argus II patients' cortical thickness was on average significantly rejuvenated in two higher visual regions and, patients using the implant for a longer duration had thicker visual regions. This research raises the possibility of structural plasticity reversing visual cortical atrophy in late-blind patients with prolonged vision restoration.

Flow-based Geometric Interpolation of Fiber Orientation Distribution Functions.

The fiber orientation distribution function (FOD) is an advanced model for high angular resolution diffusion MRI representing complex fiber geometry. However, the complicated mathematical structures of the FOD function pose challenges for FOD image processing tasks such as interpolation, which plays a critical role in the propagation of fiber tracts in tractography. In FOD-based tractography, linear interpolation is commonly used for numerical efficiency, but it is prone to generate false artificial information, leading to anatomically incorrect fiber tracts. To overcome this difficulty, we propose a flowbased and geometrically consistent interpolation framework that considers peak-wise rotations of FODs within the neighborhood of each location. Our method decomposes a FOD function into multiple components and uses a smooth vector field to model the flows of each peak in its neighborhood. To generate the interpolated result along the flow of each vector field, we develop a closed-form and efficient method to rotate FOD peaks in neighboring voxels and realize geometrically consistent interpolation of FOD components. By combining the interpolation results from each peak, we obtain the final interpolation of FODs. Experimental results on Human Connectome Project (HCP) data demonstrate that our method produces anatomically more meaningful FOD interpolations and significantly enhances tractography performance.

Uncovering Heterogeneity in Alzheimer's Disease from Graphical Modeling of the Tau Spatiotemporal Topography.

Growing evidence from post-mortem and in vivo studies have demonstrated the substantial variability of tau pathology spreading patterns in Alzheimer's disease(AD). Automated tools for characterizing the heterogeneity of tau pathology will enable a more accurate understanding of the disease and help the development of targeted treatment. In this paper, we propose a Reeb graph representation of tau pathology topography on cortical surfaces using tau PET imaging data. By comparing the spatial and temporal coherence of the Reeb graph representation across subjects, we can build a directed graph to represent the distribution of tau topography over a population, which naturally facilitates the discovery of spatiotemporal subtypes of tau pathology with graph-based clustering. In our experiments, we conducted extensive comparisons with state-of-the-art event-based model on synthetic and large-scale tau PET imaging data from ADNI3 and A4 studies. We demonstrated that our proposed method can more robustly achieve the subtyping of tau pathology with clear clinical significance and demonstrated superior generalization performance than event-based model.

Personalized Patch-based Normality Assessment of Brain Atrophy in Alzheimer's Disease.

Cortical thickness is an important biomarker associated with gray matter atrophy in neurodegenerative diseases. In order to conduct meaningful comparisons of cortical thickness between different subjects, it is imperative to establish correspondence among surface meshes. Conventional methods achieve this by projecting surface onto canonical domains such as the unit sphere or averaging feature values in anatomical regions of interest (ROIs). However, due to the natural variability in cortical topography, perfect anatomically meaningful one-to-one mapping can be hardly achieved and the practice of averaging leads to the loss of detailed information. For example, two subjects may have different number of gyral structures in the same region, and thus mapping can result in gyral/sulcal mismatch which introduces noise and averaging in detailed local information loss. Therefore, it is necessary to develop new method that can overcome these intrinsic problems to construct more meaningful comparison for atrophy detection. To address these limitations, we propose a novel personalized patch-based method to improve cortical thickness comparison across subjects. Our model segments the brain surface into patches based on gyral and sulcal structures to reduce mismatches in mapping method while still preserving detailed topological information which is potentially discarded in averaging. Moreover,the personalized templates for each patch account for the variability of folding patterns, as not all subjects are comparable. Finally, through normality assessment experiments, we demonstrate that our model performs better than standard spherical registration in detecting atrophy in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD).

Automated Mapping of Residual Distortion Severity in Diffusion MRI.

Susceptibility-induced distortion is a common artifact in diffusion MRI (dMRI), which deforms the dMRI locally and poses significant challenges in connectivity analysis. While various methods were proposed to correct the distortion, residual distortions often persist at varying degrees across brain regions and subjects. Generating a voxel-level residual distortion severity map can thus be a valuable tool to better inform downstream connectivity analysis. To fill this current gap in dMRI analysis, we propose a supervised deep-learning network to predict a severity map of residual distortion. The training process is supervised using the structural similarity index measure (SSIM) of the fiber orientation distribution (FOD) in two opposite phase encoding (PE) directions. Only b0 images and related outputs from the distortion correction methods are needed as inputs in the testing process. The proposed method is applicable in large-scale datasets such as the UK Biobank, Adolescent Brain Cognitive Development (ABCD), and other emerging studies that only have complete dMRI data in one PE direction but acquires b0 images in both PEs. In our experiments, we trained the proposed model using the Lifespan Human Connectome Project Aging (HCP-Aging) dataset (n=662) and apply the trained model to data (n=1330) from UK Biobank. Our results show low training, validation, and test errors, and the severity map correlates excellently with an FOD integrity measure in both HCP-Aging and UK Biobank data. The proposed method is also highly efficient and can generate the severity map in around 1 second for each subject.

FASSt : Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography.

Superficial white matter (SWM) plays an important role in functioning of the human brain, and it contains a large amount of cortico-cortical connections. However, the difficulties of generating complete and reliable U-fibers make SWM-related analysis lag behind relatively matured Deep white matter (DWM) analysis. With the aid of some newly proposed surface-based SWM tractography algorithms, we have developed a specialized SWM filtering method based on a symmetric variational autoencoder (VAE). In this work, we first demonstrate the advantage of the spherical representation and generate these spherical tracts using the triangular mesh and the registered spherical surface. We then introduce the Filtering via symmetric Autoencoder for Spherical Superficial White Matter tractography (FASSt) framework with a novel symmetric weights module to perform the filtering task in a latent space. We evaluate and compare our method with the state-of-the-art clustering-based method on diffusion MRI data from Human Connectome Project (HCP). The results show that our proposed method outperform these clustering methods and achieves excellent performance in groupwise consistency and topographic regularity.

Shape-Aware 3D Small Vessel Segmentation with Local Contrast Guided Attention.

The automated segmentation and analysis of small vessels from in vivo imaging data is an important task for many clinical applications. While current filtering and learning methods have achieved good performance on the segmentation of large vessels, they are sub-optimal for small vessel detection due to their apparent geometric irregularity and weak contrast given the relatively limited resolution of existing imaging techniques. In addition, for supervised learning approaches, the acquisition of accurate pixel-wise annotations in these small vascular regions heavily relies on skilled experts. In this work, we propose a novel self-supervised network to tackle these challenges and improve the detection of small vessels from 3D imaging data. First, our network maximizes a novel shape-aware flux-based measure to enhance the estimation of small vasculature with non-circular and irregular appearances. Then, we develop novel local contrast guided attention(LCA) and enhancement(LCE) modules to boost the vesselness responses of vascular regions of low contrast. In our experiments, we compare with four filtering-based methods and a state-of-the-art self-supervised deep learning method in multiple 3D datasets to demonstrate that our method achieves significant improvement in all datasets. Further analysis and ablation studies have also been performed to assess the contributions of various modules to the improved performance in 3D small vessel segmentation. Our code is available at https://github.com/dengchihwei/LCNetVesselSeg.

Personalized dMRI Harmonization on Cortical Surface.

The inter-site variability of diffusion magnetic resonance imaging (dMRI) hinders the aggregation of dMRI data from multiple centers. This necessitates dMRI harmonization for removing non-biological site-effects. Recently, the emergence of high-resolution dMRI data across various connectome imaging studies allows the large-scale analysis of cortical micro-structure. Existing harmonization methods, however, perform poorly in the harmonization of dMRI data in cortical areas because they rely on image registration methods to factor out anatomical variations, which have known difficulty in aligning cortical folding patterns. To overcome this fundamental challenge in dMRI harmonization, we propose a framework of personalized dMRI harmonization on the cortical surface to improve the dMRI harmonization of gray matter by adaptively estimating the inter-site harmonization mappings. In our experiments, we demonstrate the effectiveness of the proposed method by applying it to harmonize dMRI across the Human Connectome Project (HCP) and the Lifespan Human Connectome Projects in Development (HCPD) studies and achieved much better performance in comparison with conventional methods based on image registration.