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

Huang, Jiashuang; Zhu, Qi; Hao, Xiaoke; Shi, Xiaomeng; Gao, Shuzhan; Xu, Xijia; Zhang, Daoqiang

doi:10.1109/jbhi.2018.2796588

Cited by 36 publications

(26 citation statements)

References 30 publications

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“…To verify the effectiveness of our proposed classification method, we compare some recently proposed methods for SZ classification using fMRI in the literature. Huang et al (2018) proposed a tree-guided group sparse learning method to select the most important information from FALFF data in four frequency bands and get a classification accuracy of 91.1% by using multi-kernel SVM. Cheng et al (2015) calculated only betweenness centrality measure to characterize the network.…”

Section: Comparison With Existing Classification Methodsmentioning

confidence: 99%

“…Nowadays, Magnetic resonance imaging technology has been widely used in various studies related to brain disease diagnosis (Nieuwenhuis et al, 2012;Liu et al, 2016Liu et al, , 2017bLiu et al, ,c, 2018aYang and Wang, 2018). Since SZ is reported to be a functional disease, functional magnetic resonance imaging (fMRI) is increasingly used to study brain dysfunction in patients with mental illness (Castro et al, 2011;Huang et al, 2018;Liu et al, 2018b;Moghimi et al, 2018;Chen et al, 2019). In addition, fMRI provides a database for functional analysis of these brain diseases owing to it's massive spatial and temporal information.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the number of neurobiological literatures using fMRI to study SZ disease has increased significantly. fMRI is usually applied to discover anomalous patterns present in activation maps [i.e., Regional Homogeneity (REHO), Amplitude of Low Frequency Fluctuations (ALFF), fractional Amplitude of Low Frequency Fluctuations (FALFF)] (Guo et al, 2014;Chyzhyk et al, 2015;Huang et al, 2018) of SZ. These activation maps are widely used as potential clinical biomarkers for the diagnosis of SZ.…”

Section: Introductionmentioning

confidence: 99%

“…These activation maps are widely used as potential clinical biomarkers for the diagnosis of SZ. For example, Huang et al (2018) used tree-guided group sparse learning method to perform feature selection on fALFF data in multi-frequency bands, and then used multi-kernel learning (MKL) method to achieve an accuracy of 91.10% on 34 subjects. Chyzhyk et al (2015) combined these activation maps by using extreme learning machines and successfully distinguished SZs from healthy controls (HCs).…”

Section: Introductionmentioning

confidence: 99%

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Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks

Xiang

Wang

Tan

et al. 2020

Front. Bioeng. Biotechnol.

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Schizophrenia (SZ) is a functional mental disorder that seriously affects the social life of patients. Therefore, accurate diagnosis of SZ has raised extensive attention of researchers. At present, study of brain network based on resting-state functional magnetic resonance imaging (rs-fMRI) has provided promising results for SZ identification by studying functional network alteration. However, previous studies based on brain network analysis are not very effective for SZ identification. Therefore, we propose an improved SZ identification method using multi-view graph measures of functional brain networks. Firstly, we construct an individual functional connectivity network based on Brainnetome atlas for each subject. Then, multi-view graph measures are calculated by the brain network analysis method as feature representations. Next, in order to consider the relationships between measures within the same brain region in feature selection, multi-view measures are grouped according to the corresponding regions and Sparse Group Lasso is applied to identify discriminative features based on this feature grouping structure. Finally, a support vector machine (SVM) classifier is employed to perform SZ identification task. To evaluate our proposed method, computational experiments are conducted on 145 subjects (71 schizophrenic patients and 74 healthy controls) using a leave-one-out cross-validation (LOOCV) scheme. The results show that our proposed method can obtain an accuracy of 93.10% for SZ identification. By comparison, our method is more effective for SZ identification than some existing methods.

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Section: Comparison With Existing Classification Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks

Xiang

Wang

Tan

et al. 2020

Front. Bioeng. Biotechnol.

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“…The MDD classification was performed by providing complementary information each other by two different types of clustering coefficients. Technically, integrating multi-features can improve the classification performance (Huang et al, 2019). As mentioned in Zhang et al (2011), kernel based feature combinations using multi-kernel learning provide more flexible feature fusion by estimating different weights of features from different modalities, which can provide better methods from different types of clustering coefficients (De Bie et al, 2007).…”

Section: Classification and Feature Validationmentioning

confidence: 99%

Hypernetwork Construction and Feature Fusion Analysis Based on Sparse Group Lasso Method on fMRI Dataset

Sun

et al. 2020

Front. Neurosci.

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Recent works have shown that the resting-state brain functional connectivity hypernetwork, where multiple nodes can be connected, are an effective technique for brain disease diagnosis and classification research. The lasso method was used to construct hypernetworks by solving sparse linear regression models in previous research. But, constructing a hypernetwork based on the lasso method simply selects a single variable, in that it lacks the ability to interpret the grouping effect. Considering the group structure problem, the previous study proposed to create a hypernetwork based on the elastic net and the group lasso methods, and the results showed that the former method had the best classification performance. However, the highly correlated variables selected by the elastic net method were not necessarily in the active set in the group. Therefore, we extended our research to address this issue. Herein, we propose a new method that introduces the sparse group lasso method to improve the construction of the hypernetwork by solving the group structure problem of the brain regions. We used the traditional lasso, group lasso method, and sparse group lasso method to construct a hypernetwork in patients with depression and normal subjects. Meanwhile, other clustering coefficients (clustering coefficients based on pairs of nodes) were also introduced to extract features with traditional clustering coefficients. Two types of features with significant differences obtained after feature selection were subjected to multi-kernel learning for feature fusion and classification using each method, respectively. The network topology results revealed differences among the three networks, where hypernetwork using the lasso method was the strictest; the group lasso, most lenient; and the sgLasso method, moderate. The network topology of the sparse group lasso method was similar to that of the group lasso method but different from the lasso method. The classification results show that the sparse group lasso method achieves the best classification accuracy by using multi-kernel learning, which indicates that better classification performance can be achieved when the group structure exists and is properly extended.

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Frequency‐specific coactivation patterns in resting‐state and their alterations in schizophrenia: An fMRI study

Yang

Zhang

Meng

et al. 2022

Human Brain Mapping

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The resting-state human brain is a dynamic system that shows frequency-dependent characteristics. Recent studies demonstrate that coactivation pattern (CAP) analysis can identify recurring brain states with similar coactivation configurations. However, it is unclear whether and how CAPs depend on the frequency bands. The current study investigated the spatial and temporal characteristics of CAPs in the four frequency sub-bands from slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz), to slow-2 (0.198-0.25 Hz), in addition to the typical lowfrequency range (0.01-0.08 Hz). In the healthy subjects, six CAP states were obtained at each frequency band in line with our prior study. Similar spatial patterns with the typical range were observed in slow-5, 4, and 3, but not in slow-2. While the frequency increased, all CAP states displayed shorter persistence, which caused more between-state transitions. Specifically, from slow-5 to slow-4, the coactivation not only changed significantly in distributed cortical networks, but also increased in the basal ganglia as well as the amygdala. Schizophrenia patients showed significant alteration in the persistence of CAPs of slow-5. Using leave-one-pair-out, hold-out and resampling validations, the highest classification accuracy (84%) was achieved by slow-4 among different frequency bands. In conclusion, our findings provide novel information about spatial and temporal characteristics of CAP states at different frequency bands, which contributes to a better understanding of the frequency aspect of biomarkers for schizophrenia and other disorders.

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

Cited by 36 publications

References 30 publications

Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks

Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks

Hypernetwork Construction and Feature Fusion Analysis Based on Sparse Group Lasso Method on fMRI Dataset

Frequency‐specific coactivation patterns in resting‐state and their alterations in schizophrenia: An fMRI study

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