Obsessive-Compulsive Disorder (OCD) is a hereditary mental illness, and unaffected first-degree relative (UFDR) is also at high risk. This study constructed a framework based on traditional machine learning and deep learning methods to identify OCD and UFDR. Specifically, we propose Multiple Spatial Similarity-Aware Learning (MSSL) models to construct Brain Functional Connectivity Networks (BFCNs) and use Diffusion Structure-Aware Graph Convolutional Network (DSAGCN) for feature learning. Two regularization terms are added to the MSSL model, one is to limit the similarity of functional connectivity of adjacent brain regions, and the other is to constrain the similarity of signals in adjacent brain regions. In this strategy, redundant information can be removed while constructing the interrelationships of various brain regions, which can reflect the interrelationships between brain regions.The graph diffusion is introduced to the DSAGCN model, which can not only solve the edge noise problem caused by the graph structure constructed by the custom adjacency matrix but also enrich the node features by using the position information of the nodes on the reconstructed graph structure.This framework has been validated on our dataset collected from local hospitals, and the experimental results show that our proposed method outperforms the state-of-the-art.

Deep learning techniques were widely adopted in various scenarios as a service. However, they are found naturally exposed to adversarial attacks. Such imperceptible-perturbation-based attacks can cause severe damage in nowaday authentication systems that adopt DNNs as the core, such as fingerprint liveness detection systems, face recognition systems, etc. This paper avoids improving the model's robustness and realizes the defense against adversarial attacks based on denoising and reconstruction. Our proposed method can be viewed as a two-step defense framework. The first step denoises the input adversarial example, then reconstructing the sample to close to the original clean image and help the target model output the original label. The proposed method is evaluated using six kinds of state-of-art adversarial attacks, including the adaptive attacks, which are known as the strongest attacks.We also specifically focus on demonstrating the effectiveness of our proposed work in Finance Authentication systems as a real-life case study. Experimental results reveal that our method is more robust than the previous super-resolution-only defense in respect of attaining a higher averaging accuracy over clean and distorted samples. To the best of our knowledge, it's the first work that reveals a comprehensive defense framework against adversarial attacks over Finance Authentication systems.

Obsessive-compulsive disorder (OCD) is characterized by uncontrollable repetitive actions thought to rely on abnormalities within fundamental instrumental learning systems. We investigated cognitive and computational mechanisms underlying Pavlovian biases on instrumental behavior in both clinical OCD patients and healthy controls using a Pavlovian-Instrumental Transfer (PIT) task. PIT is typically evidenced by increased responding in the presence of a positive (previously rewarded) Pavlovian cue, and reduced responding in the presence of a negative cue. Thirty OCD patients and thirty-one healthy controls completed the Pavlovian Instrumental Transfer test, which included instrumental training, Pavlovian training for positive, negative and neutral cues, and a PIT phase in which participants performed the instrumental task in the presence of the Pavlovian cues. Modified Rescorla-Wagner models were fitted to trial-by-trial data of participants to estimate underlying computational mechanism and quantify individual differences during training and transfer stages. Bayesian hierarchical methods were used to estimate free parameters and compare the models. Behavioral and computational results indicated a weaker Pavlovian influence on instrumental behavior in OCD patients than in HC, especially for negative Pavlovian cues. Our results contrast with the increased PIT effects reported for another set of disorders characterized by compulsivity, substance use disorders, in which PIT is enhanced. A possible reason for the reduced PIT in OCD may be impairment in using the contextual information provided by the cues to appropriately adjust behavior, especially when inhibiting responding when a negative cue is present. This study provides deeper insight into our understanding of deficits in OCD from the perspective of Pavlovian influences on instrumental behavior and may have implications for OCD treatment modalities focused on reducing compulsive behaviors.