Group Similarity Constraint Functional Brain Network Estimation for Mild Congititive Impairment Classification

AbstractFunctional brain network (FBN) provides an effective biomarker for understanding brain activation patterns, which also improve the diagnostic criteria for neurodegenerative diseases or the information transmission of brain. Unfortunately, despite its efficiency, FBN still suffers several challenges for accurately estimate the biological meaningful or discriminative FBNs, under the poor quality of functional magnetic resonance imaging (fMRI) data as well as the limited understanding of human brain. Hence, there still a motivation to alleviate those issues above, it is currently still an open field to discover. In this paper, a novel FBN estimation model based on group similarity constraints is proposed. In particular, we extend the FBN estimation model to the tensor form and incorporate the trace-norm regularizer for formulating the group similarity constraint. In order to verify the proposed method, we conduct experiments on identifying Mild Cognitive Impairments (MCIs) from normal controls (NCs) based on the estimated FBNs. The experimental results illustrated that the proposed method can construct a more discriminative brain network. Consequently, we achieved an 91.97% classification accuracy which outperforms the baseline methods. The post hoc analysis further shown more biologically meaningful functional brain connections obtained by our proposed method.

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Group Similarity Constraint Functional Brain Network Estimation for Mild Cognitive Impairment Classification

Frontiers in Neuroscience ◽

10.3389/fnins.2020.00165 ◽

2020 ◽

Vol 14 ◽

Cited By ~ 2

Author(s):

Xin Gao ◽

Xiaowen Xu ◽

Xuyun Hua ◽

Peijun Wang ◽

Weikai Li ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Brain Network ◽

Functional Brain ◽

Functional Brain Network ◽

Network Estimation ◽

Group Similarity

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Functional Brain Network Estimation with Time Series Self-scrubbing

10.1101/191262 ◽

2017 ◽

Author(s):

Weikai Li ◽

Lishan Qiao ◽

Zhengxia Wang ◽

Dinggang Shen

Keyword(s):

Time Series ◽

Data Quality ◽

Latent Variable ◽

Estimation Method ◽

Brain Network ◽

Functional Brain ◽

Functional Brain Network ◽

Data Points ◽

Network Estimation

AbstractFunctional brain network (FBN) has been becoming an increasingly important measurement for exploring the cerebral working mechanism and mining informative biomarkers for assisting diagnosis of some neurodegenerative disorders. Despite its potential performance in discovering the valuable patterns hidden in the brains, the estimated FBNs are often heavily influenced by the quality of the observed data (e.g., BOLD signal series). In practice, a preprocessing pipeline is usually employed for improving the data quality prior to the FBN estimation; but, even so, some data points in the time series are still not clean enough, possibly including original artifacts (e.g., micro head motion), non-resting functional disturbing (e.g., mind-wandering), and new “noises” caused by the preprocessing pipeline per se. Therefore, not all data points in the time series can contribute to the subsequent FBN estimation. To address this issue, in this paper, we propose a novel FBN estimation method by introducing a latent variable as an indicator of the data quality, and develop an alternating optimization algorithm for scrubbing the data and estimating FBN simultaneously in a single framework. As a result, we can obtain more accurate FBNs with the self-scrubbing data. To illustrate the effectiveness of the proposed method, we conduct experiments on two publicly available datasets to identify mild cognitive impairment (MCI) patients from normal control (NC) subjects based on the estimated FBNs. Experimental results show that the proposed FBN modelling method can achieve higher classification accuracy, significantly outperforming the baseline methods.

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