Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN), salience network (SN) and central executive network (CEN)) are consistently altered in schizophrenia. However, similar changes have also been found in patients with major depressive disorder, prompting the question of specific triple network signatures for the two disorders. In this study, we proposed Supervised Convex Nonnegative Matrix Factorization (SCNMF) to extract distributed multi-modal brain patterns. These patterns distinguish schizophrenia and major depressive disorder in a latent low-dimensional space of the triple brain network. Specifically, 21 patients of schizophrenia and 25 patients of major depressive disorder were assessed by T1-weighted, diffusion-weighted, and resting-state functional MRIs. Individual structural and functional connectivity networks, based on pre-defined regions of the triple network were constructed, respectively. Afterwards, SCNMF was employed to extract the discriminative patterns. Experiments indicate that SCNMF allows extracting the low-rank discriminative patterns between the two disorders, achieving a classification accuracy of 82.6% based on the extracted functional and structural abnormalities with support vector machine. Experimental results show the specific brain patterns for schizophrenia and major depressive disorder that are multi-modal, complex, and distributed in the triple network. Parts of the prefrontal cortex including superior frontal gyri showed variation between patients with schizophrenia and major depression due to structural properties. In terms of functional properties, the middle cingulate cortex, inferior parietal lobule, and cingulate cortex were the most discriminative regions.
Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is exacerbated due to the ubiquity and complexity of Artificial Intelligence (AI)-based systems. This is the case of Reinforcement Learning (RL), where autonomous agents learn through trial-and-error how to find good solutions to a problem. Thus, the underlying decision-making criteria may become opaque to users that interact with the system and who may require explanations about the system’s reasoning. Available work for eXplainable Reinforcement Learning (XRL) offers different trade-offs: e.g. for runtime explanations, the approaches are model-specific or can only analyse results after-the-fact. Different from these approaches, this paper aims to provide an online model-agnostic approach for XRL towards trustworthy and understandable AI. We present ETeMoX, an architecture based on temporal models to keep track of the decision-making processes of RL systems. In cases where the resources are limited (e.g. storage capacity or time to response), the architecture also integrates complex event processing, an event-driven approach, for detecting matches to event patterns that need to be stored, instead of keeping the entire history. The approach is applied to a mobile communications case study that uses RL for its decision-making. In order to test the generalisability of our approach, three variants of the underlying RL algorithms are used: Q-Learning, SARSA and DQN. The encouraging results show that using the proposed configurable architecture, RL developers are able to obtain explanations about the evolution of a metric, relationships between metrics, and were able to track situations of interest happening over time windows.
Objectives: To investigate the relationship between maximum standardized uptake value (SUVmax) of 18F-FDG PET/CT and clinicopathological features of oral squamous cell carcinoma (OSCC), in order to formulate a better clinical guideline. Methods: In 104 patients with OSCC confirmed by pathology, there were 67 males and 37 females (age, 33–76 years; mean age, 56 years).18FDG, 18-fludeoxyglucose (18F-FDG) PET/CT manifestations and the clinicopathological features of the 104 patients were retrospectively analysed. Single-factor analysis and multiple regression analysis were conducted on possible factors influencing primary tumour SUVmax, including gender, age, smoking history, tumour location, tumour size, histological differentiation, TNM stage, T stage, N stage. Diagnostic performance of SUVmax for invading peri-tissue of OSCC was measured by the area under receiver operating characteristic curve, and sensitivity and specificity were determined at the Youdons index. Results: The single-analysis results showed that SUVmax was correlated with the histological differentiation, tumour size, TNM stage, T stage, N stage(p < 0.05), yet it was not correlated with gender, age, smoking history, tumour location (p > 0.05). Multivariate liner regression analysis showed that tumour size, TNM stage were influencing factors independent of primary tumour SUVmax (p < 0.05). Primary tumour SUVmax had predictive value for invading peri-tissue of OSCC. When the cutoff value was 7.98, the diagnostic efficiency was the highest, with the sensitivity being 90.0% and the specificity being 76.2%. Conclusions: OSCC 18F-FDG PET/CT SUVmax is higher among patients with larger tumour size, poorer stage, and that primary tumour SUVmax is of important significance in predicting invading peri-tissue.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.