Weighted Random Support Vector Machine Clusters Analysis of Resting-State fMRI in Mild Cognitive Impairment

Bi, Xia-an; Xu, Qian; Luo, Xianhao; Sun, Qi; Wang, Zhigang

doi:10.3389/fpsyt.2018.00340

Cited by 12 publications

(6 citation statements)

References 70 publications

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“…Since Cortes and Vapnik proposed the Support Vector Machine (SVM) technique, 29 it has been widely used in brain science research to solve the binary classification problem 27,30–38 . The decision function of SVM is shown as follows:

Y (x_{k}) = sign \{{false\sum}_{i = 1}^{K} α_{i} y_{i} . K (x_{k}, x_{i}) + b\}

where Ƙ represents the kernel function.…”

Section: Methodsmentioning

confidence: 99%

Stage detection of mild cognitive impairment via fMRI using Hilbert Huang transform based classification framework

Shi

Liu

2020

Medical Physics

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This work aims to establish a classification framework for the diagnosis of mild cognitive impairment (MCI) at different stages (early MCI and late MCI) through direct analysis of restingstate functional magnetic resonance imaging (rs-fMRI) signals and using the accuracy (total correct rate), specificity (correct rate of late MCI) and sensitivity (correct rate of early MCI) to validate its classification performance. Methods: All fMR images of subjects were parcellated into 116 regions of interest (ROIs) by applying the Anatomical Automatic Labeling (AAL) template, and the average rs-fMRI signals of each ROI were extracted. The Hilbert-Huang transform (HHT) was introduced into the framework to decompose each rs-fMRI signal into a series of intrinsic mode functions (IMFs) and to analyze these nonstationary and nonlinear time-series from the perspective of multiresolution. After obtaining the instantaneous frequencies and amplitudes of all IMFs of a signal, the Hilbert weighted frequencies (HWFs) were calculated and combined into a vector as the feature of the corresponding ROI. Support Vector Machine (SVM) was implemented to classify MCI at different stages. We used the independent two-sample t-test as the feature selection method and measured the classification performance through the leave-one-out cross-validation (LOOCV) method. Results: Results on 77 early MCI (eMCI) and 64 late MCI (lMCI) with baseline rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) yielded 87.94% classification accuracy. Some of the brain regions with significant differences found by previous studies have been confirmed in this work. We found that HWF characteristics exhibited a significant downward trend in all cerebellar regions. The rs-fMRI signals in differential brain regions have not changed completely, but only altered in some narrow frequency bands. The analysis results showed that during the progress of MCI, the main changes of rs-fMRI were concentrated in IMF3, while IMFs with other indexes also contained HWF features with high SVM weights, such as Orbitofrontal superior frontal gyrus in IMF2, Insula in IMF4, and Lobule Ⅲ of vermis in IMF5, indicating that other IMFs provide important information for the diagnosis of MCI as well. Conclusions: This work confirmed the classification ability of HHT-based classification framework in classification of at different stages of MCI. Through the analysis, we found that during the progress of MCI the main changes of rs-fMRI were concentrated in IMF3, and HWF characteristics showed a significant downward trend in all cerebellar regions.

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Y (x_{k}) = sign \{{false\sum}_{i = 1}^{K} α_{i} y_{i} . K (x_{k}, x_{i}) + b\}

where Ƙ represents the kernel function.…”

Section: Methodsmentioning

confidence: 99%

Stage detection of mild cognitive impairment via fMRI using Hilbert Huang transform based classification framework

Shi

Liu

2020

Medical Physics

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“…The PreCG, which overlaps with the SMA, is involved in producing and controlling the movement (Cai et al, 2017). Abnormalities in the PreCG may affect learning and memorizing and show sluggish behavior; a positive correlation between the damage of this region and dysfunction of verbal short-term memory was reported in MCI (Bi et al, 2018;Sakurai et al, 2018). Increased blood oxygen level-dependent signal activation in the PreCG in MCI after daily supplementation for 16 weeks was reported by a clinical study that investigated the curative effect of blueberry supplementation (Boespflug et al, 2018).…”

Section: Specific Imaging Abnormal Changes In Snmentioning

confidence: 99%

Functional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis

Song

Chen

et al. 2021

Front. Aging Neurosci.

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Background Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Amnestic MCI (aMCI) and non-amnestic MCI are the two subtypes of MCI with the former having a higher risk for progressing to Alzheimer's disease (AD). Compared with healthy elderly adults, individuals with MCI have specific functional alterations in the salience network (SN). However, no consistent results are documenting these changes. This meta-analysis aimed to investigate the specific functional alterations in the SN in MCI and aMCI.Methods: We systematically searched PubMed, Embase, and Web of Science for scientific neuroimaging literature based on three research methods, namely, functional connectivity (FC), regional homogeneity (ReHo), and the amplitude of low-frequency fluctuation or fractional amplitude of low-frequency fluctuation (ALFF/fALFF). Then, we conducted the coordinate-based meta-analysis by using the activation likelihood estimation algorithm.Results: In total, 30 functional neuroimaging studies were included. After extracting the data and analyzing it, we obtained specific changes in some brain regions in the SN including decreased ALFF/fALFF in the left superior temporal gyrus, the insula, the precentral gyrus, and the precuneus in MCI and aMCI; increased FC in the thalamus, the caudate, the superior temporal gyrus, the insula, and the cingulate gyrus in MCI; and decreased ReHo in the anterior cingulate gyrus in aMCI. In addition, as to FC, interactions of the SN with other networks including the default mode network and the executive control network were also observed mainly in the middle frontal gyrus and superior frontal gyrus in MCI and inferior frontal gyrus in aMCI.Conclusions: Specific functional alternations in the SN and interactions of the SN with other networks in MCI could be useful as potential imaging biomarkers for MCI or aMCI. Meanwhile, it provided a new insight in predicting the progression of health to MCI or aMCI and novel targets for proper intervention to delay the progression.Systematic Review Registration: [PROSPERO], identifier [No. CRD42020216259].

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“…After the model was fitted, we evaluated the importance of the input variables on the model. To enhance the precision of predicting risk factors, we utilized the score to assess the influence of each input feature of the models, and take the intersection of both conditions and obtain the top-performing accessions as the important features [ 43 , 44 ]. Subsequently, we established the generalized linear mixed model by using the statsmodels.api Python package to further analyze the important features and obtain the final influencing factors.…”

Section: Methodsmentioning

confidence: 99%

Association between two-component systems gene mutation and Mycobacterium tuberculosis transmission revealed by whole genome sequencing

Li,

Kong,

et al. 2023

BMC Genomics

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Background Two-component systems (TCSs) assume a pivotal function in Mycobacterium tuberculosis (M.tuberculosis) growth. However, the exact regulatory mechanism of this system needs to be elucidated, and only a few studies have investigated the effect of gene mutations within TCSs on M.tuberculosis transmission. This research explored the relationship between TCSs gene mutation and the global transmission of (M.tuberculosis). Results A total of 13531 M.tuberculosis strains were enrolled in the study. Most of the M.tuberculosis strains belonged to lineage4 (n=6497,48.0%), followed by lineage2 (n=5136,38.0%). Our results showed that a total of 36 single nucleotide polymorphisms (SNPs) were positively correlated with clustering of lineage2, such as Rv0758 (phoR, C820G), Rv1747(T1102C), and Rv1057(C1168T). A total of 30 SNPs showed positive correlation with clustering of lineage4, such as phoR(C182A, C1184G, C662T, T758G), Rv3764c (tcrY, G1151T), and Rv1747 C20T. A total of 19 SNPs were positively correlated with cross-country transmission of lineage2, such as phoR A575C, Rv1028c (kdpD, G383T, G1246C), and Rv1057 G817T. A total of 41 SNPs were positively correlated with cross-country transmission of lineage4, such as phoR(T758G, T327G, C284G), kdpD(G1755A, G625C), Rv1057 C980T, and Rv1747 T373G. Conclusions Our study identified that SNPs in genes of two-component systems were related to the transmission of M. tuberculosis. This finding adds another layer of complexity to M. tuberculosis virulence and provides insight into future research that will help to elucidate a novel mechanism of M. tuberculosis pathogenicity.

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Weighted Random Support Vector Machine Clusters Analysis of Resting-State fMRI in Mild Cognitive Impairment

Cited by 12 publications

References 70 publications

Stage detection of mild cognitive impairment via fMRI using Hilbert Huang transform based classification framework

Stage detection of mild cognitive impairment via fMRI using Hilbert Huang transform based classification framework

Functional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis

Association between two-component systems gene mutation and Mycobacterium tuberculosis transmission revealed by whole genome sequencing

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