MGRW-Transformer: Multigranularity Random Walk Transformer Model for Interpretable Learning.

Deep-learning models have been widely used in image recognition tasks due to their strong feature-learning ability. However, most of the current deep-learning models are "black box" systems that lack a semantic explanation of how they reached their conclusions. This makes it difficult to apply these methods to complex medical image recognition tasks. The vision transformer (ViT) model is the most commonly used deep-learning model with a self-attention mechanism that shows the region of influence as compared to traditional convolutional networks. Thus, ViT offers greater interpretability. However, medical images often contain lesions of variable size in different locations, which makes it difficult for a deep-learning model with a self-attention module to reach correct and explainable conclusions. We propose a multigranularity random walk transformer (MGRW-Transformer) model guided by an attention mechanism to find the regions that influence the recognition task. Our method divides the image into multiple subimage blocks and transfers them to the ViT module for classification. Simultaneously, the attention matrix output from the multiattention layer is fused with the multigranularity random walk module. Within the multigranularity random walk module, the segmented image blocks are used as nodes to construct an undirected graph using the attention node as a starting node and guiding the coarse-grained random walk. We appropriately divide the coarse blocks into finer ones to manage the computational cost and combine the results based on the importance of the discovered features. The result is that the model offers a semantic interpretation of the input image, a visualization of the interpretation, and insight into how the decision was reached. Experimental results show that our method improves classification performance with medical images while presenting an understandable interpretation for use by medical professionals.

Deep Multi-Modal Discriminative and Interpretability Network for Alzheimer's Disease Diagnosis.

Multi-modal fusion has become an important data analysis technology in Alzheimer's disease (AD) diagnosis, which is committed to effectively extract and utilize complementary information among different modalities. However, most of the existing fusion methods focus on pursuing common feature representation by transformation, and ignore discriminative structural information among samples. In addition, most fusion methods use high-order feature extraction, such as deep neural network, by which it is difficult to identify biomarkers. In this paper, we propose a novel method named deep multi-modal discriminative and interpretability network (DMDIN), which aligns samples in a discriminative common space and provides a new approach to identify significant brain regions (ROIs) in AD diagnosis. Specifically, we reconstruct each modality with a hierarchical representation through multilayer perceptron (MLP), and take advantage of the shared self-expression coefficients constrained by diagonal blocks to embed the structural information of inter-class and the intra-class. Further, the generalized canonical correlation analysis (GCCA) is adopted as a correlation constraint to generate a discriminative common space, in which samples of the same category gather while samples of different categories stay away. Finally, in order to enhance the interpretability of the deep learning model, we utilize knowledge distillation to reproduce coordinated representations and capture influence of brain regions in AD classification. Experiments show that the proposed method performs better than several state-of-the-art methods in AD diagnosis.

SD-CNN: A static-dynamic convolutional neural network for functional brain networks.

Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology of the brain network. dFCs, estimated by dividing rs-fMRI scans into a series of short sliding windows, are used to reveal time-varying changes in FC patterns. Currently, how to jointly use sFCs and dFCs to identify brain diseases under the framework of deep learning is still a hot issue. To this end, we propose a static-dynamic convolutional neural network for functional brain networks, which involves a static pathway and a dynamic pathway for taking full advantages of sFCs and dFCs. Specifically, the static pathway, using high-resolution convolution filters (i.e., convolution filters with a high number of channels) at a single adjacency matrix of sFCs, is performed to capture static FC patterns. The dynamic pathway, using low-resolution convolution filters at each adjacency matrix of dFCs, is performed to capture time-varying FC patterns. Two types of diffusion connections are used in this model for encouraging the transfer of information between the static pathway and the dynamic pathway, which can make the learned features more discriminative. Furthermore, a static and dynamic combination classifier is introduced to combine features from two pathways for identifying brain diseases. Experiments on two real datasets demonstrate the effectiveness and advantages of our proposed method.

A Novel Spark-Based Attribute Reduction and Neighborhood Classification for Rough Evidence.

Neighborhood classification (NEC) algorithms have been widely used to solve classification problems. Most traditional NEC algorithms employ the majority voting mechanism as the basis for final decision making. However, this mechanism hardly considers the spatial difference and label uncertainty of the neighborhood samples, which may increase the possibility of the misclassification. In addition, the traditional NEC algorithms need to load the entire data into memory at once, which is computationally inefficient when the size of the dataset is large. To address these problems, we propose a novel Spark-based attribute reduction and NEC for rough evidence in this article. Specifically, we first construct a multigranular sample space using the parallel undersampling method. Then, we evaluate the significance of attribute by neighborhood rough evidence decision error rate and remove the redundant attribute on different samples subspaces. Based on this attribute reduction algorithm, we design a parallel attribute reduction algorithm which is able to compute equivalence classes in parallel and parallelize the process of searching for candidate attributes. Finally, we introduce the rough evidence into the classification decision of traditional NEC algorithms and parallelize the classification decision process. Furthermore, the proposed algorithms are conducted in the Spark parallel computing framework. Experimental results on both small and large-scale datasets show that the proposed algorithms outperform the benchmarking algorithms in the classification accuracy and the computational efficiency.

Consistent connectome landscape mining for cross-site brain disease identification using functional MRI.

Many human brain disorders are associated with characteristic alterations in functional connectivity of the brain. A lot of efforts have been devoted to mining disease-related biomarkers for identifying patients with brain disorders from normal controls. However, previous studies show largely inconsistent findings due to variability across numerous study-specific factors such as heterogeneity across different preprocessing pipelines or the use of multi-site data. Also, existing methods usually employ human-engineered features (e.g., graph-theoretical measures) that may be less discriminate for disease identification. To this end, we propose a novel Connectome Landscape Modeling (CLM) method that can mine cross-site consistent connectome landscape and extract data-driven representation of functional connectivity networks for brain disorder identification. Specifically, with functional connectivity networks as input, the proposed CLM model aims to learn a weight matrix for joint cross-site consistent connectome landscape learning, network feature extraction, and disease identification. We impose the row-column overlap norm penalty on the network-based predictor to capture consistent connectome landscape across multiple sites. To capture site-specific patterns, we introduce an ℓ1-norm penalty in CLM. We develop an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve the proposed objective function. Experimental results on three real-world fMRI datasets demonstrate the potential use of our CLM in cross-site brain disorder analysis.

pH Regulates the Formation and Hatching of Cryptocaryon irritans Tomonts, Which Affects Cryptocaryoniasis Occurrence in Larimichthys crocea Aquaculture.

Cryptocaryon irritans are the main pathogens of white spot disease in marine teleost. However, the occurrence of cryptocaryoniasis is influenced by several abiotic factors including the pH. To explore the effect of pH on the life cycle of C. irritans (encystment, cleavage, and hatchability), protomonts and tomonts of C. irritans were incubated in seawater of 10 different pH levels (2-11). pH 8 was used as the control. The change in morphology and infectivity of theronts that hatched from tomonts against Larimichthys crocea were then recorded. We found that pH 6-9 had no significant effect on the encystment, cleavage, and hatching of the parasites. However, pH beyond this limit decreased the cleavage and hatching of the tomonts. Furthermore, extreme pH decreased the number of theronts hatched by each tomont and the pathogenicity of the theronts, but increased the aspect ratio of the theronts. Infectivity experiments further revealed that extreme pH significantly decreased the infectivity of C. irritans against L. crocea. In conclusion, the C. irritans can survive in pH of 5 to 10, but pH 6-9 is the optimal range for the reproduction and infectivity of C. irritans. However, extreme pH negatively affects these aspects. IMPORTANCECryptocaryon irritans is a ciliate parasite that causes "white spot disease" in marine teleosts. The disease outbreak is influenced by hosts and a range of abiotic factors, such as temperature, salinity, and pH. Studies have shown that change in pH of seawater affects the structure (diversity and abundance of marine organisms) of marine ecosystem. However, how pH affects the life cycle and survival of C. irritans, and how future ocean acidification will affect the occurrence of cryptocaryoniasis, are not well understood. In this study, we explored the effect of pH on the formation and hatching of C. irritans tomonts. The findings of this study provide the foundation of the environmental adaptation of C. irritans, the occurrence of cryptocaryoniasis, and better management of marine fish culture.

The complete chloroplast genome of Sauvagesia rhodoleuca, an endangered species endemic to China.

Sauvagesia rhodoleuca is an endangered and national key protected species of China, with limited natural distribution in Guangdong and Guangxi, Southern China. Here we reported the first complete chloroplast genome of S. rhodoleuca using genome skimming approach. The chloroplast genome is 157,300 bp in length, with a large single-copy region (LSC) of 86,021 bp and a small single-copy region (SSC) of 18,137 bp separated by a pair of inverted repeats (IRs) of 26,571 bp. It encodes 112 unique genes, including 80 protein-coding genes, 28 transfer RNA genes, and four ribosomal RNA genes. Phylogenetic analysis results strongly supported that S. rhodoleuca was closely related to Medusagyne oppositifolia.

Integration of metabolomic and transcriptomic analyses to characterize the influence of the gill metabolism of Nibea albiflora on the response to Cryptocaryon irritans infection.

The parasite Cryptocaryon irritans causes massive losses in the marine fish culture industry and is one of the most threatening pathogens affecting teleost species. The acute death of infected fish is primarily caused by the destruction of gill cells, resulting in osmotic imbalance and respiratory stress. C. irritans has wide host specificity; however, the yellow drum Nibea albiflora is highly resistant to this parasite. Metabolomic approaches in combination with transcriptomic analysis were used to characterize the host immune reaction and metabolic changes in yellow drum in response to C. irritans infection and to identify the key genes and compounds in the gills that have the strongest contribution to disease resistance. The yellow drum was challenged with theronts at a median death rate (2050 theronts per gram fish). The samples were collected from the gills 24 h and 72 h after the infection (hpi). The results of metabolomic analysis indicated that metabolites involved in energy metabolism were predominantly downregulated. In contrast, a compensatory increase in the expression of the genes involved in the citric acid cycle and glycolysis was detected 24 hpi. The suppression of metabolites was alleviated after feed intake recovery 72 hpi. The levels of amino acids were decreased, and the expression of aminoacyl-tRNA was increased. Additionally, elevated levels of arachidonic acid derivatives, primarily prostaglandins, were responsible for anti-inflammatory, osmotic, and hypoxia regulations. Purine metabolism was also involved in the immune response via generation of reactive oxygen species catalyzed by xanthine oxidase. A significant increase in the generation of retinoic acid, which could enhance mucosal adaptive immunity by stimulating the synthesis of antibodies and accelerating the restoration of epithelial integrity, was observed at 72 hpi. This result was consistent with high expression of the genes related to secreted immunoglobulin T 72 hpi. In conclusion, the present study comprehensively described the key compounds and genes related to C. irritans infection in yellow drum gills. Biomarkers that were significantly changed during the infection may represent future targets for nutritional intervention to enhance host immunity against C. irritans infection and to accelerate disease recovery.

Dual Attention Multi-Instance Deep Learning for Alzheimer's Disease Diagnosis With Structural MRI.

Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological disease diagnosis, which could reflect the variations of brain. However, due to the local brain atrophy, only a few regions in sMRI scans have obvious structural changes, which are highly correlative with pathological features. Hence, the key challenge of sMRI-based brain disease diagnosis is to enhance the identification of discriminative features. To address this issue, we propose a dual attention multi-instance deep learning network (DA-MIDL) for the early diagnosis of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI). Specifically, DA-MIDL consists of three primary components: 1) the Patch-Nets with spatial attention blocks for extracting discriminative features within each sMRI patch whilst enhancing the features of abnormally changed micro-structures in the cerebrum, 2) an attention multi-instance learning (MIL) pooling operation for balancing the relative contribution of each patch and yield a global different weighted representation for the whole brain structure, and 3) an attention-aware global classifier for further learning the integral features and making the AD-related classification decisions. Our proposed DA-MIDL model is evaluated on the baseline sMRI scans of 1689 subjects from two independent datasets (i.e., ADNI and AIBL). The experimental results show that our DA-MIDL model can identify discriminative pathological locations and achieve better classification performance in terms of accuracy and generalizability, compared with several state-of-the-art methods.

Modeling dynamic characteristics of brain functional connectivity networks using resting-state functional MRI.

Dynamic network analysis using resting-state functional magnetic resonance imaging (rs-fMRI) provides a great insight into fundamentally dynamic characteristics of human brains, thus providing an efficient solution to automated brain disease identification. Previous studies usually pay less attention to evolution of global network structures over time in each brain's rs-fMRI time series, and also treat network-based feature extraction and classifier training as two separate tasks. To address these issues, we propose a temporal dynamics learning (TDL) method for network-based brain disease identification using rs-fMRI time-series data, through which network feature extraction and classifier training are integrated into the unified framework. Specifically, we first partition rs-fMRI time series into a sequence of segments using overlapping sliding windows, and then construct longitudinally ordered functional connectivity networks. To model the global temporal evolution patterns of these successive networks, we introduce a group-fused Lasso regularizer in our TDL framework, while the specific network architecture is induced by an ℓ1-norm regularizer. Besides, we develop an efficient optimization algorithm to solve the proposed objective function via the Alternating Direction Method of Multipliers (ADMM). Compared with previous studies, the proposed TDL model can not only explicitly model the evolving connectivity patterns of global networks over time, but also capture unique characteristics of each network defined at each segment. We evaluate our TDL on three real autism spectrum disorder (ASD) datasets with rs-fMRI data, achieving superior results in ASD identification compared with several state-of-the-art methods.

The gill transcriptome reveals unique antimicrobial features that protect Nibea albiflora from Cryptocaryon irritans infection.

Cryptocaryonosis is the greatest threat to most teleost species among all parasitic diseases, causing mass loss to the marine aquaculture industry. Epidemiological investigation of teleost susceptibility to Cryptocaryon irritans infection revealed that yellow drum (Nibea albiflora) is highly resistant. In order to further understand the activation of the immune system in the gill, which is one of the main mucosal-associated lymphoid tissues and a target of parasites, transcriptome analysis of the yellow drum gill was performed. Gill samples were collected from fish challenged after 24 hr and 72 hr with theronts at a median death rate (2050 theronts per gram fish). Gene expression profiles showed that TLR5 was the only receptor that activated the downstream immune response. The infection activated complement cascade through alternative pathway and increased the expression of C5a anaphylatoxin chemotactic receptor 1. In addition, possible antimicrobial molecules, including lipoprotein and haptoglobin, which are responsible for trypanolysis in humans, were among the top significantly upregulated genes at 24 hr. After 72 hr, the expression of secreted immunoglobulin T-related genes was induced. These results suggested a rapid innate and adaptive immune response at the mucosal level. In conclusion, the results provide new perspectives on mucosal immune resistance in yellow drum against cryptocaryonosis and provide the possibility of mining resistance genes for future therapy.

A novel node-level structure embedding and alignment representation of structural networks for brain disease analysis.

Brain networks based on various neuroimaging technologies, such as diffusion tensor image (DTI) and functional magnetic resonance imaging (fMRI), have been widely applied to brain disease analysis. Currently, there are several node-level structural measures (e.g., local clustering coefficients and node degrees) for representing and analyzing brain networks since they usually can reflect the topological structure of brain regions. However, these measures typically describe specific types of structural information, ignoring important network properties (i.e., small structural changes) that could further improve the performance of brain network analysis. To overcome this problem, in this paper, we first define a novel node-level structure embedding and alignment (nSEA) representation to accurately characterize the node-level structural information of the brain network. Different from existing measures that characterize a specific type of structural properties with a single value, our proposed nSEA method can learn a vector representation for each node, thus contain richer structure information to capture small structural changes. Furthermore, we develop an nSEA representation based learning (nSEAL) framework for brain disease analysis. Specifically, we first perform structural embedding to calculate node vector representations for each brain network and then align vector representations of all brain networks into the common space for two group-level network analyses, including a statistical analysis and brain disease classifications. Experiment results on a real schizophrenia dataset demonstrate that our proposed method not only discover disease-related brain regions that could help to better understand the pathology of brain diseases, but also improve the classification performance of brain diseases, compared with state-of-the-art methods.

Attention-Diffusion-Bilinear Neural Network for Brain Network Analysis.

Brain network provides essential insights in diagnosing many brain disorders. Integrative analysis of multiple types of connectivity, e.g, functional connectivity (FC) and structural connectivity (SC), can take advantage of their complementary information and therefore may help to identify patients. However, traditional brain network methods usually focus on either FC or SC for describing node interactions and only consider the interaction between paired network nodes. To tackle this problem, in this paper, we propose an Attention-Diffusion-Bilinear Neural Network (ADB-NN) framework for brain network analysis, which is trained in an end-to-end manner. The proposed network seamlessly couples FC and SC to learn wider node interactions and generates a joint representation of FC and SC for diagnosis. Specifically, a brain network (graph) is first defined, where each node corresponding to a brain region is governed by the features of brain activities (i.e., FC) extracted from functional magnetic resonance imaging (fMRI), and the presence of edges is determined by neural fiber physical connections (i.e., SC) extracted from Diffusion Tensor Imaging (DTI). Based on this graph, we train two Attention-Diffusion-Bilinear (ADB) modules jointly. In each module, an attention model is utilized to automatically learn the strength of node interactions. This information further guides a diffusion process that generates new node representations by considering the influence from other nodes as well. After that, the second-order statistics of these node representations are extracted by bilinear pooling to form connectivity-based features for disease prediction. The two ADB modules correspond to the one-step and two-step diffusion, respectively. Experiments on a real epilepsy dataset demonstrate the effectiveness and advantages of our proposed method.

Coherent Pattern in Multi-Layer Brain Networks: Application to Epilepsy Identification.

Currently, how to conjointly fuse structural connectivity (SC) and functional connectivity (FC) for identifying brain diseases is a hot topic in the area of brain network analysis. Most of the existing works combine two types of connectivity in decision level, thus ignoring the underlying relationship between SC and FC. To solve this problem, in this paper, we model the brain network as the multi-layer network formed by the SC and FC, and then propose a coherent pattern to represent structural information of the multi-layer network for the brain disease identification. The proposed coherent pattern consists of a paired-subgraph extracted from the FC and SC within the same node-set. Compared with the previous methods, this coherent pattern not only describes the connectivity information of both SC and FC by subgraphs at each layer, but also reflects their intrinsic relationship by the co-occurrence pattern of the paired-subgraph. Based on this coherent pattern, we further develop a framework for identifying brain diseases. Specifically, we first construct multi-layer networks by using SC and FC for each subject and then mine coherent patterns that frequently appear in each group. Next, we select the discriminative coherent pattern from these frequent coherent patterns according to their frequency of occurrence. Finally, we construct a feature matrix for each subject based on the binary indicator vector and then use the support vector machine (SVM) as its classifier. Experimental results on real epilepsy datasets demonstrate that our method outperforms several state-of-the-art approaches in the tasks of brain disease classification.

Hierarchical Structured Sparse Learning for Schizophrenia Identification.

Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based schizophrenia (SZ) diagnosis. However, previous studies usually measure the fALFF within low-frequency fluctuation (from 0.01 to 0.08Hz), which cannot fully cover the complex neural activity pattern in the resting-state brain. In addition, existing studies usually ignore the fact that each specific frequency band can delineate the unique spontaneous fluctuations of neural activities in the brain. Accordingly, in this paper, we propose a novel hierarchical structured sparse learning method to sufficiently utilize the specificity and complementary structure information across four different frequency bands (from 0.01Hz to 0.25Hz) for SZ diagnosis. The proposed method can help preserve the partial group structures among multiple frequency bands and the specific characters in each frequency band. We further develop an efficient optimization algorithm to solve the proposed objective function. We validate the efficacy of our proposed method on a real SZ dataset. Also, to demonstrate the generality of the method, we apply our proposed method on a subset of Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results on both datasets demonstrate that our proposed method achieves promising performance in brain disease classification, compared with several state-of-the-art methods.

Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by a wide range of symptoms. Identifying biomarkers for accurate diagnosis is crucial for early intervention of ASD. While multi-site data increase sample size and statistical power, they suffer from inter-site heterogeneity. To address this issue, we propose a multi-site adaption framework via low-rank representation decomposition (maLRR) for ASD identification based on functional MRI (fMRI). The main idea is to determine a common low-rank representation for data from the multiple sites, aiming to reduce differences in data distributions. Treating one site as a target domain and the remaining sites as source domains, data from these domains are transformed (i.e., adapted) to a common space using low-rank representation. To reduce data heterogeneity between the target and source domains, data from the source domains are linearly represented in the common space by those from the target domain. We evaluated the proposed method on both synthetic and real multi-site fMRI data for ASD identification. The results suggest that our method yields superior performance over several state-of-the-art domain adaptation methods.

Hybrid Functional Brain Network With First-Order and Second-Order Information for Computer-Aided Diagnosis of Schizophrenia.

Brain functional connectivity network (BFCN) analysis has been widely used in the diagnosis of mental disorders, such as schizophrenia. In BFCN methods, brain network construction is one of the core tasks due to its great influence on the diagnosis result. Most of the existing BFCN construction methods only consider the first-order relationship existing in each pair of brain regions and ignore the useful high-order information, including multi-region correlation in the whole brain. Some early schizophrenia patients have subtle changes in brain function networks, which cannot be detected in conventional BFCN construction methods. It is well-known that the high-order method is usually more sensitive to the subtle changes in signal than the low-order method. To exploit high-order information among brain regions, we define the triplet correlation among three brain regions, and derive the second-order brain network based on the connectivity difference and ordinal information in each triplet. For making full use of the complementary information in different brain networks, we proposed a hybrid approach to fuse the first- and second-order brain networks. The proposed method is applied to identify the biomarkers of schizophrenia. The experimental results on six schizophrenia datasets (totally including 439 patients and 426 controls) show that the proposed method outperforms the existing brain network methods in the diagnosis of schizophrenia.

Discovering network phenotype between genetic risk factors and disease status via diagnosis-aligned multi-modality regression method in Alzheimer's disease.

MOTIVATION: Neuroimaging genetics is an emerging field to identify the associations between genetic variants [e.g. single-nucleotide polymorphisms (SNPs)] and quantitative traits (QTs) such as brain imaging phenotypes. However, most of the current studies focus only on the associations between brain structure imaging and genetic variants, while neglecting the connectivity information between brain regions. In addition, the brain itself is a complex network, and the higher-order interaction may contain useful information for the mechanistic understanding of diseases [i.e. Alzheimer's disease (AD)]. RESULTS: A general framework is proposed to exploit network voxel information and network connectivity information as intermediate traits that bridge genetic risk factors and disease status. Specifically, we first use the sparse representation (SR) model to build hyper-network to express the connectivity features of the brain. The network voxel node features and network connectivity edge features are extracted from the structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (fMRI), respectively. Second, a diagnosis-aligned multi-modality regression method is adopted to fully explore the relationships among modalities of different subjects, which can help further mine the relation between the risk genetics and brain network features. In experiments, all methods are tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results not only verify the effectiveness of our proposed framework but also discover some brain regions and connectivity features that are highly related to diseases. AVAILABILITY AND IMPLEMENTATION: The Matlab code is available at http://ibrain.nuaa.edu.cn/2018/list.htm.

Identifying Resting-State Multifrequency Biomarkers via Tree-Guided Group Sparse Learning for Schizophrenia Classification.

The fractional amplitude of low-frequency fluctuations (fALFF) has been widely used as potential clinical biomarkers for resting-state functional-magnetic-resonance-imaging-based schizophrenia diagnosis. How-ever, previous studies usually measure the fALFF with specific bands from 0.01 to 0.08 Hz, which cannot fully delineate the complex variations of spontaneous fluctuations in the resting-state brain. In addition, fALFF data are intrinsically constrained by the brain structure, but most of the traditional methods have not consider it in feature selection. For addressing these problems, we propose a model to classify schizophrenia in multifrequency bands with tree-guided group sparse learning. In detail, we first acquire the fALFF data in multifrequency bands (i.e., slow-5: 0.01-0.027 Hz, slow-4: 0.027-0.073 Hz, slow-3: 0.073-0.198 Hz, and slow-2: 0.198-0.25 Hz). Then, we divide the whole brain into different candidate patches and select those significant patches related to schizophrenia using random forest-based important score. Moreover, we use tree-structured sparse learning method for feature selection with the above patch spatial constraint. Finally, considering biomarkers from multifrequency bands can reflect complementary information among multiple-frequency bands, we adopt the multikernel learning method to combine features of multifrequency bands for classification. Our experimental results show that these biomarkers from multifrequency bands can achieve a classification accuracy of 91.1% on 17 schizophrenia patients and 17 healthy controls, further demonstrating that the multifrequency bands analysis can better account for classification of schizophrenia.

Ordinal Pattern: A New Descriptor for Brain Connectivity Networks.

Brain connectivity networks based on magnetic resonance imaging (MRI) or functional MRI (fMRI) data provide a straightforward way to quantify the structural or functional systems of the brain. Currently, there are several network descriptors developed for representing and analyzing brain connectivity networks. However, most of them are designed for unweighted networks, regardless of the valuable weight information of edges, or do not take advantage of the ordinal relationship of weighted edges (even though they are designed for weighted networks). In this paper, we propose a new network descriptor (i.e., ordinal pattern that contains a sequence of weighted edges) for brain connectivity network analysis. Compared with previous network properties, the proposed ordinal patterns cannot only take advantage of the weight information of edges but also explicitly model the ordinal relationship of weighted edges in brain connectivity networks. We further develop an ordinal pattern-based learning framework for brain disease diagnosis using resting-state fMRI data. Specifically, we first construct a set of brain functional connectivity networks, where each network is corresponding to a particular subject. We then develop an algorithm to identify ordinal patterns that frequently appear in brain connectivity networks of patients and normal controls. We further perform discriminative ordinal pattern selection and extract feature representations for subjects based on the selected ordinal patterns, followed by a learning model for automated brain disease diagnosis. Experimental results on both Alzheimer's Disease Neuroimaging Initiative and attention deficit hyperactivity disorder-200 data sets demonstrate that our method outperforms the several state-of-the-art approaches in the tasks of disease classification and clinical score regression.