Inference-based statistical network analysis uncovers star-like brain functional architectures for internalizing psychopathology in children

To improve the statistical power for imaging biomarker detection, we propose a latent variable-based statistical network analysis (LatentSNA) that combines brain functional connectivity with internalizing psychopathology, implementing network science in a generative statistical process to preserve the neurologically meaningful network topology in the adolescents and children population. The developed inference-focused generative Bayesian framework (1) addresses the lack of power and inflated Type II errors in current analytic approaches when detecting imaging biomarkers, (2) allows unbiased estimation of biomarkers' influence on behavior variants, (3) quantifies the uncertainty and evaluates the likelihood of the estimated biomarker effects against chance and (4) ultimately improves brain-behavior prediction in novel samples and the clinical utilities of neuroimaging findings. We collectively model multi-state functional networks with multivariate internalizing profiles for 5,000 to 7,000 children in the Adolescent Brain Cognitive Development (ABCD) study with sufficiently accurate prediction of both children internalizing traits and functional connectivity, and substantially improved our ability to explain the individual internalizing differences compared with current approaches. We successfully uncover large, coherent star-like brain functional architectures associated with children's internalizing psychopathology across multiple functional systems and establish them as unique fingerprints for childhood internalization

Bayesian pathway analysis over brain network mediators for survival data

Technological advancements in noninvasive imaging facilitate the construction of whole brain interconnected networks, known as brain connectivity. Existing approaches to analyze brain connectivity frequently disaggregate the entire network into a vector of unique edges or summary measures, leading to a substantial loss of information. Motivated by the need to explore the effect mechanism among genetic exposure, brain connectivity and time to disease onset, we propose an integrative Bayesian framework to model the effect pathway between each of these components while quantifying the mediating role of brain networks. To accommodate the biological architectures of brain connectivity constructed along white matter fiber tracts, we develop a structural modeling framework that includes a symmetric matrix-variate accelerated failure time model and a symmetric matrix response regression to characterize the effect paths. We further impose within-graph sparsity and between-graph shrinkage to identify informative network configurations and eliminate the interference of noisy components. Extensive simulations confirm the superiority of our method compared with existing alternatives. By applying the proposed method to the landmark Alzheimer's Disease Neuroimaging Initiative study, we obtain neurobiologically plausible insights that may inform future intervention strategies

Optimal Spatial-Temporal Triangulation for Bearing-Only Cooperative Motion Estimation

Vision-based cooperative motion estimation is an important problem for many multi-robot systems such as cooperative aerial target pursuit. This problem can be formulated as bearing-only cooperative motion estimation, where the visual measurement is modeled as a bearing vector pointing from the camera to the target. The conventional approaches for bearing-only cooperative estimation are mainly based on the framework distributed Kalman filtering (DKF). In this paper, we propose a new optimal bearing-only cooperative estimation algorithm, named spatial-temporal triangulation, based on the method of distributed recursive least squares, which provides a more flexible framework for designing distributed estimators than DKF. The design of the algorithm fully incorporates all the available information and the specific triangulation geometric constraint. As a result, the algorithm has superior estimation performance than the state-of-the-art DKF algorithms in terms of both accuracy and convergence speed as verified by numerical simulation. We rigorously prove the exponential convergence of the proposed algorithm. Moreover, to verify the effectiveness of the proposed algorithm under practical challenging conditions, we develop a vision-based cooperative aerial target pursuit system, which is the first of such fully autonomous systems so far to the best of our knowledge

Polygenic mediation analysis of Alzheimer's disease implicated intermediate amyloid imaging phenotypes

Mediation models have been employed in the study of brain disorders to detect the underlying mechanisms between genetic variants and diagnostic outcomes implicitly mediated by intermediate imaging biomarkers. However, the statistical power is influenced by the modest effects of individual genetic variants on both diagnostic and imaging phenotypes and the limited sample sizes ofimaging genetic cohorts. In this study, we propose a polygenic mediation analysis that comprises a polygenic risk score (PRS) to aggregate genetic effects ofa set ofcandidate variants and then explore the implicit effect ofimaging phenotypes between the PRS and disease status. We applied our proposed method to an amyloid imaging genetic study of Alzheimer's disease (AD), identified multiple imaging mediators linking PRS with AD, and further demonstrated the promise of the PRS on mediator detection over individual variants alone

Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness

Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies

Neurometabolic and structural alterations of medial septum and hippocampal CA1 in a model of post-operative sleep fragmentation in aged mice: a study combining 1H-MRS and DTI

Post-operative sleep disturbance is a common feature of elderly surgical patients, and sleep fragmentation (SF) is closely related to post-operative cognitive dysfunction (POCD). SF is characterized by sleep interruption, increased number of awakenings and sleep structure destruction, similar to obstructive sleep apnea (OSA). Research shows that sleep interruption can change neurotransmitter metabolism and structural connectivity in sleep and cognitive brain regions, of which the medial septum and hippocampal CA1 are key brain regions connecting sleep and cognitive processes. Proton magnetic resonance spectroscopy (1H-MRS) is a non-invasive method for the evaluation of neurometabolic abnormalities. Diffusion tensor imaging (DTI) realizes the observation of structural integrity and connectivity of brain regions of interest in vivo. However, it is unclear whether post-operative SF induces harmful changes in neurotransmitters and structures of the key brain regions and their contribution to POCD. In this study, we evaluated the effects of post-operative SF on neurotransmitter metabolism and structural integrity of medial septum and hippocampal CA1 in aged C57BL/6J male mice. The animals received a 24-h SF procedure after isoflurane anesthesia and right carotid artery exposure surgery. 1H-MRS results showed after post-operative SF, the glutamate (Glu)/creatine (Cr) and glutamate + glutamine (Glx)/Cr ratios increased in the medial septum and hippocampal CA1, while the NAA/Cr ratio decreased in the hippocampal CA1. DTI results showed post-operative SF decreased the fractional anisotropy (FA) of white matter fibers in the hippocampal CA1, while the medial septum was not affected. Moreover, post-operative SF aggravated subsequent Y-maze and novel object recognition performances accompanied by abnormal enhancement of glutamatergic metabolism signal. This study suggests that 24-h SF induces hyperglutamate metabolism level and microstructural connectivity damage in sleep and cognitive brain regions in aged mice, which may be involved in the pathophysiological process of POCD