Weighted graph regularized sparse brain network construction for MCI identification

Yu, Renping; Qiao, Liyan; Chen, Mingming; Lee, Seong Whan; Xuan, Fu‐Zhen; Shen, Dinggang

doi:10.1016/j.patcog.2019.01.015

Cited by 58 publications

(31 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where α, β, and γ are positive scalars weight the corresponding terms in (7).

can be rewritten as tr ( WLW T ), where L = D − G (Xu et al, 2015 ; Yu et al, 2019 ). Thus, we transform the objective function as…”

Section: Proposed Methodsmentioning

confidence: 99%

Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy

Zhu

Yang²,

Xu³

et al. 2021

Front. Neurosci.

View full text Add to dashboard Cite

Brain network analysis has been proved to be one of the most effective methods in brain disease diagnosis. In order to construct discriminative brain networks and improve the performance of disease diagnosis, many machine learning–based methods have been proposed. Recent studies show that combining functional and structural brain networks is more effective than using only single modality data. However, in the most of existing multi-modal brain network analysis methods, it is a common strategy that constructs functional and structural network separately, which is difficult to embed complementary information of different modalities of brain network. To address this issue, we propose a unified brain network construction algorithm, which jointly learns both functional and structural data and effectively face the connectivity and node features for improving classification. First, we conduct space alignment and brain network construction under a unified framework, and then build the correlation model among all brain regions with functional data by low-rank representation so that the global brain region correlation can be captured. Simultaneously, the local manifold with structural data is embedded into this model to preserve the local structural information. Second, the PageRank algorithm is adaptively used to evaluate the significance of different brain regions, in which the interaction of multiple brain regions is considered. Finally, a multi-kernel strategy is utilized to solve the data heterogeneity problem and merge the connectivity as well as node information for classification. We apply the proposed method to the diagnosis of epilepsy, and the experimental results show that our method can achieve a promising performance.

show abstract

“…where α, β, and γ are positive scalars weight the corresponding terms in (7).

can be rewritten as tr ( WLW T ), where L = D − G (Xu et al, 2015 ; Yu et al, 2019 ). Thus, we transform the objective function as…”

Section: Proposed Methodsmentioning

confidence: 99%

Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy

Zhu

Yang²,

Xu³

et al. 2021

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Unfortunately, it is hard to reveal informative patterns by a direct comparison of the fMRI time series between different subjects, since the fMRI signals are arbitrarily scaled and have no unit (Jenkinson and Chappell, 2018 ). In contrast, brain functional network (BFN), as a measure of the relative relationship between the fMRI time series, can provide a more reliable way of exploring the inherent organization of the brain (Liu et al, 2015 ; Yu et al, 2019 ), and has been used in identifying neurological or psychiatric disorders (Stam, 2014 ), including autism spectrum disorder (Weikai et al, 2017 ), major depressive disorder (Greicius et al, 2007 ), obsessive compulsive disorder (Admon et al, 2012 ), Alzheimer's disease (AD) (Jin et al, 2010 ; Shi et al, 2017 ), and its early stage, namely mild cognitive impairment (MCI) (Yu et al, 2017 ; Li et al, 2019b ), to name a few.…”

Section: Introductionmentioning

confidence: 99%

Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification

Chen

Zhang

et al. 2021

Front. Aging Neurosci.

Self Cite

View full text Add to dashboard Cite

Brain functional network (BFN) analysis is becoming a crucial way to explore the inherent organized pattern of the brain and reveal potential biomarkers for diagnosing neurological or psychological disorders. In so doing, a well-estimated BFN is of great concern. In practice, however, noises or artifacts involved in the observed data (i.e., fMRI time series in this paper) generally lead to a poor estimation of BFN, and thus a complex preprocessing pipeline is often used to improve the quality of the data prior to BFN estimation. One of the popular preprocessing steps is data-scrubbing that aims at removing “bad” volumes from the fMRI time series according to the amplitude of the head motion. Despite its helpfulness in general, this traditional scrubbing scheme cannot guarantee that the removed volumes are necessarily unhelpful, since such a step is fully independent to the subsequent BFN estimation task. Moreover, the removal of volumes would reduce the statistical power, and different numbers of volumes are generally scrubbed for different subjects, resulting in an inconsistency or bias in the estimated BFNs. To address these issues, we develop a new learning framework that conducts BFN estimation and data-scrubbing simultaneously by an alternating optimization algorithm. The newly developed algorithm adaptively weights volumes (instead of removing them directly) for the task of BFN estimation. As a result, the proposed method can not only reduce the difficulty of threshold selection involved in the traditional scrubbing scheme, but also provide a more flexible framework that scrubs the data in the subsequent FBN estimation model. Finally, we validate the proposed method by identifying subjects with mild cognitive impairment (MCI) from normal controls based on the estimated BFNs, achieving an 80.22% classification accuracy, which significantly improves the baseline methods.

show abstract

“…A higher λ forces more links in W to be zeros (no connection). In the toolbox, for all the SR‐based methods (including SR), an additional step is conducted to make the network symmetric, similar to that used in aHOFC via W ← ( W + W ′ )/2 (Yu et al, ; Zhang, Zhang, et al, ). Of note, another symmetrization method

w_{ij}^{*} \leftarrow italicsign ((), w_{italicij}) \sqrt{w_{ij} w_{ji}}

can also be used (Peng, Wang, Zhou, & Zhu, ).…”

Section: Methodsmentioning

confidence: 99%

A toolbox for brain network construction and classification (BrainNetClass)

Zhou

Chen

Zhang

et al. 2020

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

A B S T R A C TBrain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson's correlation-based functional connectivity. The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (Brain-NetClass) to the field to promote more advanced brain network construction methods. It comprises various brain network construction methods, including some state-of-the-art methods that were recently developed to capture more complex interactions among brain regions along with connectome feature extraction, reduction, parameter optimization towards network-based individualized classification. BrainNetClass is a MATLAB-based, open-source, cross-platform toolbox with graphical user-friendly interfaces for cognitive and clinical neuroscientists to perform rigorous computer-aided diagnosis with interpretable result presentations even though they do not possess neuroimage computing and machine learning knowledge. We demonstrate the implementations of this toolbox on real datasets. BrainNetClass (v1.0) can be downloaded from https://github.com/zzstefan/BrainNetClass.

show abstract

Weighted graph regularized sparse brain network construction for MCI identification

Cited by 58 publications

References 71 publications

Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy

Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy

Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification

A toolbox for brain network construction and classification (BrainNetClass)

Contact Info

Product

Resources

About