Mask-Guided Vision Transformer for Few-Shot Learning.

Learning with little data is challenging but often inevitable in various application scenarios where the labeled data are limited and costly. Recently, few-shot learning (FSL) gained increasing attention because of its generalizability of prior knowledge to new tasks that contain only a few samples. However, for data-intensive models such as vision transformer (ViT), current fine-tuning-based FSL approaches are inefficient in knowledge generalization and, thus, degenerate the downstream task performances. In this article, we propose a novel mask-guided ViT (MG-ViT) to achieve an effective and efficient FSL on the ViT model. The key idea is to apply a mask on image patches to screen out the task-irrelevant ones and to guide the ViT focusing on task-relevant and discriminative patches during FSL. Particularly, MG-ViT only introduces an additional mask operation and a residual connection, enabling the inheritance of parameters from pretrained ViT without any other cost. To optimally select representative few-shot samples, we also include an active learning-based sample selection method to further improve the generalizability of MG-ViT-based FSL. We evaluate the proposed MG-ViT on classification, object detection, and segmentation tasks using gradient-weighted class activation mapping (Grad-CAM) to generate masks. The experimental results show that the MG-ViT model significantly improves the performance and efficiency compared with general fine-tuning-based ViT and ResNet models, providing novel insights and a concrete approach toward generalizing data-intensive and large-scale deep learning models for FSL.

Brain Structural Connectivity Guided Vision Transformers for Identification of Functional Connectivity Characteristics in Preterm Neonates.

Preterm birth is the leading cause of death in children under five years old, and is associated with a wide sequence of complications in both short and long term. In view of rapid neurodevelopment during the neonatal period, preterm neonates may exhibit considerable functional alterations compared to term ones. However, the identified functional alterations in previous studies merely achieve moderate classification performance, while more accurate functional characteristics with satisfying discrimination ability for better diagnosis and therapeutic treatment is underexplored. To address this problem, we propose a novel brain structural connectivity (SC) guided Vision Transformer (SCG-ViT) to identify functional connectivity (FC) differences among three neonatal groups: preterm, preterm with early postnatal experience, and term. Particularly, inspired by the neuroscience-derived information, a novel patch token of SC/FC matrix is defined, and the SC matrix is then adopted as an effective mask into the ViT model to screen out input FC patch embeddings with weaker SC, and to focus on stronger ones for better classification and identification of FC differences among the three groups. The experimental results on multi-modal MRI data of 437 neonatal brains from publicly released Developing Human Connectome Project (dHCP) demonstrate that SCG-ViT achieves superior classification ability compared to baseline models, and successfully identifies holistically different FC patterns among the three groups. Moreover, these different FCs are significantly correlated with the differential gene expressions of the three groups. In summary, SCG-ViT provides a powerfully brain-guided pipeline of adopting large-scale and data-intensive deep learning models for medical imaging-based diagnosis.

A Unified and Biologically Plausible Relational Graph Representation of Vision Transformers.

Vision transformer (ViT) and its variants have achieved remarkable success in various tasks. The key characteristic of these ViT models is to adopt different aggregation strategies of spatial patch information within the artificial neural networks (ANNs). However, there is still a key lack of unified representation of different ViT architectures for systematic understanding and assessment of model representation performance. Moreover, how those well-performing ViT ANNs are similar to real biological neural networks (BNNs) is largely unexplored. To answer these fundamental questions, we, for the first time, propose a unified and biologically plausible relational graph representation of ViT models. Specifically, the proposed relational graph representation consists of two key subgraphs: an aggregation graph and an affine graph. The former considers ViT tokens as nodes and describes their spatial interaction, while the latter regards network channels as nodes and reflects the information communication between channels. Using this unified relational graph representation, we found that: 1) model performance was closely related to graph measures; 2) the proposed relational graph representation of ViT has high similarity with real BNNs; and 3) there was a further improvement in model performance when training with a superior model to constrain the aggregation graph.

Regularity and variability of functional brain connectivity characteristics between gyri and sulci under naturalistic stimulus.

The human cerebral cortex is folded into two fundamentally anatomical units: gyri and sulci. Previous studies have demonstrated the genetical, structural, and functional differences between gyri and sulci, providing a unique perspective for revealing the relationship among brain function, cognition, and behavior. While previous studies mainly focus on the functional differences between gyri and sulci under resting or task-evoked state, such characteristics under naturalistic stimulus (NS) which reflects real-world dynamic environments are largely unknown. To address this question, this study systematically investigates spatio-temporal functional connectivity (FC) characteristics between gyri and sulci under NS using a spatio-temporal graph convolutional network model. Based on the public Human Connectome Project dataset of 174 subjects with four different runs of both movie-watching NS and resting state 7T functional MRI data, we successfully identify unique FC features under NS, which are mainly involved in visual, auditory, emotional and cognitive control, and achieve high discriminative accuracy 93.06 % to resting state. Moreover, gyral regions as well as gyro-gyral connections consistently participate more as functional information exchange hubs than sulcal ones among these networks. This study provides novel insights into the functional brain mechanism under NS and lays a solid foundation for accurately mapping the brain anatomy-function relationship.

Characterizing functional brain networks via Spatio-Temporal Attention 4D Convolutional Neural Networks (STA-4DCNNs).

Characterizing individualized spatio-temporal patterns of functional brain networks (FBNs) via functional magnetic resonance imaging (fMRI) provides a foundation for understanding complex brain function. Although previous studies have achieved promising performances based on either shallow or deep learning models, there is still much space to improve the accuracy of spatio-temporal pattern characterization of FBNs by optimally integrating the four-dimensional (4D) features of fMRI. In this study, we introduce a novel Spatio-Temporal Attention 4D Convolutional Neural Network (STA-4DCNN) model to characterize individualized spatio-temporal patterns of FBNs. Particularly, STA-4DCNN is composed of two subnetworks, in which the first Spatial Attention 4D CNN (SA-4DCNN) models the spatio-temporal features of 4D fMRI data and then characterizes the spatial pattern of FBNs, and the second Temporal Guided Attention Network (T-GANet) further characterizes the temporal pattern of FBNs under the guidance of the spatial pattern together with 4D fMRI data. We evaluate the proposed STA-4DCNN on seven different task fMRI and one resting state fMRI datasets from the publicly released Human Connectome Project. The experimental results demonstrate that STA-4DCNN has superior ability and generalizability in characterizing individualized spatio-temporal patterns of FBNs when compared to other state-of-the-art models. We further apply STA-4DCNN on another independent ABIDE I resting state fMRI dataset including both autism spectrum disorder (ASD) and typical developing (TD) subjects, and successfully identify abnormal spatio-temporal patterns of FBNs in ASD compared to TD. In general, STA-4DCNN provides a powerful tool for FBN characterization and for clinical applications on brain disease characterization at the individual level.

Anatomy-Guided Spatio-Temporal Graph Convolutional Networks (AG-STGCNs) for Modeling Functional Connectivity Between Gyri and Sulci Across Multiple Task Domains.

The cerebral cortex is folded as gyri and sulci, which provide the foundation to unveil anatomo-functional relationship of brain. Previous studies have extensively demonstrated that gyri and sulci exhibit intrinsic functional difference, which is further supported by morphological, genetic, and structural evidences. Therefore, systematically investigating the gyro-sulcal (G-S) functional difference can help deeply understand the functional mechanism of brain. By integrating functional magnetic resonance imaging (fMRI) with advanced deep learning models, recent studies have unveiled the temporal difference in functional activity between gyri and sulci. However, the potential difference of functional connectivity, which represents functional dependency between gyri and sulci, is much unknown. Moreover, the regularity and variability of the G-S functional connectivity difference across multiple task domains remains to be explored. To address the two concerns, this study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on 830 subjects with seven different task-based and one resting state fMRI (rs-fMRI) datasets from the public Human Connectome Project (HCP), we consistently found that there are significant differences of functional connectivity between gyral and sulcal regions within task domains compared with resting state (RS). Furthermore, there is considerable variability of such functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors. Our study helps better understand the functional segregation of gyri and sulci within task domains as well as the anatomo-functional-behavioral relationship of the human brain.

Modeling spatio-temporal patterns of holistic functional brain networks via multi-head guided attention graph neural networks (Multi-Head GAGNNs).

Mounting evidence has demonstrated that complex brain function processes are realized by the interaction of holistic functional brain networks which are spatially distributed across specific brain regions in a temporally dynamic fashion. Therefore, modeling spatio-temporal patterns of holistic functional brain networks plays an important role in understanding brain function. Compared to traditional modeling methods such as principal component analysis, independent component analysis, and sparse coding, superior performance has been achieved by recent deep learning methodologies. However, there are still two limitations of existing deep learning approaches for functional brain network modeling. They either (1) merely modeled a single targeted network and ignored holistic ones at one time, or (2) underutilized both spatial and temporal features of fMRI during network modeling, and the spatial/temporal accuracy was thus not warranted. To address these limitations, we proposed a novel Multi-Head Guided Attention Graph Neural Network (Multi-Head GAGNN) to simultaneously model both spatial and temporal patterns of holistic functional brain networks. Specifically, a spatial Multi-Head Attention Graph U-Net was first adopted to model the spatial patterns of multiple brain networks, and a temporal Multi-Head Guided Attention Network was then introduced to model the corresponding temporal patterns under the guidance of modeled spatial patterns. Based on seven task fMRI datasets from the public Human Connectome Project and resting state fMRI datasets from the public Autism Brain Imaging Data Exchange I of 1448 subjects, the proposed Multi-Head GAGNN showed superior ability and generalizability in modeling both spatial and temporal patterns of holistic functional brain networks in individual brains compared to other state-of-the-art (SOTA) models. Furthermore, the modeled spatio-temporal patterns of functional brain networks via the proposed Multi-Head GAGNN can better predict the individual cognitive behavioral measures compared to the other SOTA models. This study provided a novel and powerful tool for brain function modeling as well as for understanding the brain-cognitive behavior associations.

Effectiveness and safety of different academic schools of traditional Chinese medicine in the treatment of obesity: A protocol for systematic review and meta-analysis.

BACKGROUND: Obesity is a global epidemic. Since 1975, the global obesity rate has almost tripled. Although many systematic reviews and clinical trials have shown that traditional Chinese medicine (TCM) can effectively treat obesity, the effectiveness and safety of different academic schools of TCM in treating obesity have not been systematically evaluated. METHODS: The retrieval language of this study was Chinese and English. From the date of creation of the following data to June 2023, the data of Medline, PubMed, Embase, Cochrane Science Network, China Biomedical Literature Database, Central Controlled Trial Registration Center, and China Science Journal Database were retrieved, respectively. This study included clinical randomized controlled trials related to the treatment of obesity by different academic schools of TCM. The main outcome measures were body mass index, waist circumference, hip circumference, waist hip ratio, body fat content, fasting blood glucose, glycosylated hemoglobin, and blood lipid level. In addition, we manually searched other resources, including reference lists of identified publications, conference articles, and gray literature. RESULTS: This study will provide a more diverse choice of treatment options. CONCLUSION: The purpose of this study is to summarize and evaluate the effectiveness and safety of different academic schools of TCM in improving and treating obese patients from clinical trials, so as to provide more options for obesity treatment.

Effectiveness and safety of acupuncture combined with hot compress in the treatment of obese adolescents with insulin resistance: A protocol for systematic review and meta-analysis.

BACKGROUND: In recent years, the incidence of obesity patients has become younger and younger, and adolescents are gradually becoming one of the groups with a high incidence of obesity. Although several systematic reviews and clinical trials suggest that acupuncture and warm compresses may be effective in the treatment of obesity, the effectiveness and safety of acupuncture combined with warm compresses in the treatment of obesity insulin resistance (IR) in adolescents have not been systematically reviewed. METHODS: The search language of this study is Chinese and English, and the data of Medline, PubMed, Embase, Cochrane Web of Science, China Biomedical Literature Database, Central Controlled Trial Registration Center, and China Scientific Journal Database were searched for this study respectively, from the date of creation of the above data to December 2022. Randomized controlled trials of acupuncture combined with warm compresses in adolescents with obese IR were included in this review. Main outcome measures were body mass index, waist circumference, hip circumference, waist-hip ratio, fasting blood glucose, glycosylated hemoglobin, IR index, body fat content, blood lipid level and blood pressure, etc. In addition, we manually retrieved other resources, including reference lists of identified publications, conference articles and gray literature. RESULTS: This study will provide more clinical treatment ideas and options for adolescent obese IR patients. CONCLUSION: The purpose of this study is to summarize and evaluate the efficacy and safety of acupuncture combined with hot compress in treating obesity IR in adolescents from clinical trials.