Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI

Zhang, Tingting; Zhao, Zanzan; Zhang, Chao; Zhang, Junjun; Jin, Zhenlan; Li, Ling

doi:10.3389/fpsyt.2019.00572

Cited by 47 publications

(70 citation statements)

References 79 publications

(88 reference statements)

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“…In addition, we obtained close classification performance using different preprocessing methods (with/without GSR) and different brain regional parcellation schemes. Our classification accuracy is close to or even higher than the results of SZ ML studies [1,2,5,6,14,18] and other disease studies [9,19,44,51]. These results are consistent with a previous study which reported that the M3 method can obtain great classification accuracy [16].…”

Section: Discussionsupporting

confidence: 92%

“…Previous neuroimaging ML studies focused on a single modal image [ 50 , 51 ] or concatenated multimodal features into a longer feature vector [ 19 , 21 , 44 , 47 ]. Recent studies have shown that multimodal imaging using integrated information can significantly improve the classification accuracy [ 16 , 20 , 21 , 23 , 47 ].…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

Shi

Zhang

et al. 2021

Disease Markers

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Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

Shi

Zhang

et al. 2021

Disease Markers

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“…The combination of voxel and SPHARM features shows 88.75% accuracy, 83.10% sensitivity, and 91.58% specificity for an AD versus HC group with a linear kernel SVM, whereas, for EMCI versus LMCI group, the same combined features show 70.95% accuracy, 75.56% sensitivity, and 65.47% specificity with linear SVM. Likewise, another study by Zhang et al [ 19 ] used an SVM with nested cross-validation to distinguish the features into two groups to gain balanced results. The slow-5 frequency band shows 83.87% accuracy, 86.21% sensitivity, and 81.82% specificity for EMCI versus LMCI cohort by using the ADNI dataset.…”

Section: Resultsmentioning

confidence: 99%

“…For classification of NC versus AD patients, they also used the OASIS dataset and method with an accuracy of 74.75%, sensitivity of 96%, and specificity of 52.5%. Zang et al [ 19 ] utilized operative feature consequent from functional brain network of three frequency bands during resting states for the efficiency of the classification context to classify subjects with EMCI versus LMCI. Their approached method demonstrates that the functional network features chosen by the minimal redundancy maximal relevance (mRMR) algorithm improve the distinguishing between EMCI versus LMCI compared with others chosen by stationary selection (SS-LR) and Fisher score (FS) algorithms.…”

Section: Introductionmentioning

confidence: 99%

Classification of Alzheimer’s Disease and Mild Cognitive Impairment Based on Cortical and Subcortical Features from MRI T1 Brain Images Utilizing Four Different Types of Datasets

Toshkhujaev

Lee

Choi

et al. 2020

Journal of Healthcare Engineering

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Alzheimer’s disease (AD) is one of the most common neurodegenerative illnesses (dementia) among the elderly. Recently, researchers have developed a new method for the instinctive analysis of AD based on machine learning and its subfield, deep learning. Recent state-of-the-art techniques consider multimodal diagnosis, which has been shown to achieve high accuracy compared to a unimodal prognosis. Furthermore, many studies have used structural magnetic resonance imaging (MRI) to measure brain volumes and the volume of subregions, as well as to search for diffuse changes in white/gray matter in the brain. In this study, T1-weighted structural MRI was used for the early classification of AD. MRI results in high-intensity visible features, making preprocessing and segmentation easy. To use this image modality, we acquired four types of datasets from each dataset’s server. In this work, we downloaded 326 subjects from the National Research Center for Dementia homepage, 123 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) homepage, 121 subjects from the Alzheimer’s Disease Repository Without Borders homepage, and 131 subjects from the National Alzheimer’s Coordinating Center homepage. In our experiment, we used the multiatlas label propagation with expectation–maximization-based refinement segmentation method. We segmented the images into 138 anatomical morphometry images (in which 40 features belonged to subcortical volumes and the remaining 98 features belonged to cortical thickness). The entire dataset was split into a 70 : 30 (training and testing) ratio before classifying the data. A principal component analysis was used for dimensionality reduction. Then, the support vector machine radial basis function classifier was used for classification between two groups—AD versus health control (HC) and early mild cognitive impairment (MCI) (EMCI) versus late MCI (LMCI). The proposed method performed very well for all four types of dataset. For instance, for the AD versus HC group, the classifier achieved an area under curve (AUC) of more than 89% for each dataset. For the EMCI versus LMCI group, the classifier achieved an AUC of more than 80% for every dataset. Moreover, we also calculated Cohen kappa and Jaccard index statistical values for all datasets to evaluate the classification reliability. Finally, we compared our results with those of recently published state-of-the-art methods.

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“…Consequently, each subject has 32 RGB slices which can be seen in Supplementary Material . It is worth noting that some researches have reported that the temporal lobe may make an essential contribution during the early stage of MCI (Bi et al, 2018 ; Cui et al, 2018 ; Zhang et al, 2019 ), and the 32 slices, including the whole temporal lobe and other memory related regions, such as the hippocampus and callosum, were thus selected.…”

Section: Methodsmentioning

confidence: 99%

Identifying Early Mild Cognitive Impairment by Multi-Modality MRI-Based Deep Learning

Kang

Jiang

Huang

et al. 2020

Front. Aging Neurosci.

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Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI

Cited by 47 publications

References 79 publications

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

Classification of Alzheimer’s Disease and Mild Cognitive Impairment Based on Cortical and Subcortical Features from MRI T1 Brain Images Utilizing Four Different Types of Datasets

Identifying Early Mild Cognitive Impairment by Multi-Modality MRI-Based Deep Learning

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