Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification

Li, Yang; Liu, Jingyu; Gao, Xinqiang; Jie, Biao; Kim, Minjeong; Yap, Pew Thian; Wee, Chong Yaw; Shen, Dinggang

doi:10.1016/j.media.2018.11.006

Cited by 72 publications

(41 citation statements)

References 77 publications

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“…It provides a novel approach for revealing altered brain network patterns (delEtoile and Adeli, 2017; Khazaee et al, 2017;Filippi et al, 2018). Given the large numbers of network features in the brain connectome, the Student's t-test (Qiao et al, 2016;Li W. et al, 2019) and sparse methods such as least absolute shrinkage and selection operator (LASSO) have been applied to select the critical features of brain networks (Wee et al, 2014;Li Y. et al, 2019). Nodal graph metrics naturally have a group topology (i.e., a node corresponds to a group of node-graph theoretical attributes).…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Combination of Information in the Functional Brain Connectome for Discrimination of Mild Cognitive Impairment and Analyses of Altered Brain Patterns

Mei

et al. 2020

Front. Aging Neurosci.

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Mild cognitive impairment (MCI) is often considered a critical time window for predicting early conversion to Alzheimer's disease (AD). Brain functional connectome data (i.e., functional connections, global and nodal graph metrics) based on resting-state functional magnetic resonance imaging (rs-fMRI) provides numerous information about brain networks and has been used to discriminate normal controls (NCs) from subjects with MCI. In this paper, Student's t-tests and group-least absolute shrinkage and selection operator (group-LASSO) were used to extract functional connections with significant differences and the most discriminative network nodes, respectively. Based on group-LASSO, the middle temporal, inferior temporal, lingual, posterior cingulate, and middle frontal gyri were the most predominant brain regions for nodal observation in MCI patients. Nodal graph metrics (within-module degree, participation coefficient, and degree centrality) showed the maximum discriminative ability. To effectively combine the multipattern information, we employed the multiple kernel learning support vector machine (MKL-SVM). Combined with functional connectome information, the MKL-SVM achieved a good classification performance (area under the receiving operating characteristic curve = 0.9728). Additionally, the altered brain connectome pattern revealed that functional connectivity was generally decreased in the whole-brain network, whereas graph theory topological attributes of some special nodes in the brain network were increased in MCI patients. Our findings demonstrate that optimal feature selection and combination of all connectome features (i.e., functional connections, global and nodal graph metrics) can achieve good performance in discriminating NCs from MCI subjects. Thus, the combination of functional connections and global and nodal graph metrics of brain networks can predict the occurrence of MCI and contribute to the early clinical diagnosis of AD.

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Section: Introductionmentioning

confidence: 99%

Feature Selection and Combination of Information in the Functional Brain Connectome for Discrimination of Mild Cognitive Impairment and Analyses of Altered Brain Patterns

Mei

et al. 2020

Front. Aging Neurosci.

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“…To address these issues, machine learning approaches combining feature selection and classifier have been applied for early and accurate diagnosis of AD. For the multimodal properties of brain connectome, least absolute shrinkage and selection operator (LASSO) ( Wee et al, 2014 ; Li et al, 2019 ) and Student’s t -test ( Qiao et al, 2016 ) were used to identify the predominant features of the brain network. Considering that a brain node has a group of nodal graph metrics, the modified group-LASSO method is considered to be more suitable for feature selection of nodal graph metrics ( Liu et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View

Tao

et al. 2020

Front. Neurosci.

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Subjective cognitive decline (SCD) is considered the earliest preclinical stage of Alzheimer’s disease (AD) that precedes mild cognitive impairment (MCI). Effective and accurate diagnosis of SCD is crucial for early detection of and timely intervention in AD. In this study, brain functional connectome (i.e., functional connections and graph theory metrics) based on the resting-state functional magnetic resonance imaging (rs-fMRI) provided multiple information about brain networks and has been used to distinguish individuals with SCD from normal controls (NCs). The consensus connections and the discriminative nodal graph metrics selected by group least absolute shrinkage and selection operator (LASSO) mainly distributed in the prefrontal and frontal cortices and the subcortical regions corresponded to default mode network (DMN) and frontoparietal task control network. Nodal efficiency and nodal shortest path showed the most significant discriminative ability among the selected nodal graph metrics. Furthermore, the comparison results of topological attributes suggested that the brain network integration function was weakened and network segregation function was enhanced in SCD patients. Moreover, the combination of brain connectome information based on multiple kernel-support vector machine (MK-SVM) achieved the best classification performance with 83.33% accuracy, 90.00% sensitivity, and an area under the curve (AUC) of 0.927. The findings of this study provided a new perspective to combine machine learning methods with exploration of brain pathophysiological mechanisms in SCD and offered potential neuroimaging biomarkers for diagnosis of early-stage AD.

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“…More importantly, how to properly handle the variations between different views requires a smart information exchange strategy between different phases. While how to efficiently integrate information from multi-modalities has been widely studied [3,6,16], the direction on learning multi-phase information has been rarely explored, especially for tumor detection and segmentation purposes.…”

Section: Introductionmentioning

confidence: 99%

Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation

Zhou

Zhang

et al. 2019

Lecture Notes in Computer Science

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Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of pancreatic disease. In this study, we aim at enhancing PDAC automatic segmentation by integrating multi-phase information (i.e., arterial phase and venous phase). To this end, we present Hyper-Pairing Network (HPN), a 3D fully convolution neural network which effectively integrates information from different phases. The proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange. Additionally, a pairing loss is added to encourage the commonality between high-level feature representations of different phases. Compared to prior arts which use single phase data, HPN reports a significant improvement up to 7.73% (from 56.21% to 63.94%) in terms of DSC.

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Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification

Cited by 72 publications

References 77 publications

Feature Selection and Combination of Information in the Functional Brain Connectome for Discrimination of Mild Cognitive Impairment and Analyses of Altered Brain Patterns

Feature Selection and Combination of Information in the Functional Brain Connectome for Discrimination of Mild Cognitive Impairment and Analyses of Altered Brain Patterns

Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View

Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation

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