Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging

Zhou, Baiwan; An, Dongmei; Xiao, Fenglai; Niu, Running; Li, Wenbin; Li, Wei; Tong, Xin; Kemp, Graham J.; Zhou, Dong; Gong, Qiyong; Lei, Du

doi:10.1007/s11684-019-0718-4

Cited by 35 publications

(36 citation statements)

References 55 publications

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“…Most previous studies have extracted features based on the brain atlas, which only extracted the mean values of the metrics (such as ALFF, fractional anisotropy, mean diffusivity, regional homogeneity, functional connectivity, voxel-mirrored homotopic connectivity, etc.) in the ROI defined by the brain atlas (Dai et al, 2012 ; Cui et al, 2016 ; Ding et al, 2017 ; Tang et al, 2017 ; Sun et al, 2018 ; Zhou et al, 2020 ). In our study, we extracted not only the mean ALFF values in the predefined ROIs but also other histogram features, including the minimum, maximum, range, standard deviation, variance, median, skewness, kurtosis, 10th percentile, and the 90th percentile, which could more comprehensively reflect ALFF information in the predefined ROIs.…”

Section: Discussionmentioning

confidence: 99%

“…Radiomics has now been widely used in the study of neuropsychological diseases, their diagnosis, and their neurological mechanism (Sun et al, 2018 ; Huang K. et al, 2019 ; Mo et al, 2019 ; Wang et al, 2020 ). Histogram analysis is the most commonly used radiomic feature extraction method, which is widely used in neuroimaging research (Cui et al, 2016 ; Sun et al, 2018 ; Huang K. et al, 2019 ; Zhou et al, 2020 ). To our knowledge, there is no existing study that has used histogram analysis to diagnose PD.…”

Section: Introductionmentioning

confidence: 99%

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Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Shi¹,

Zhang²,

Wang³

et al. 2021

Front. Aging Neurosci.

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This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson’s disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Shi¹,

Zhang²,

Wang³

et al. 2021

Front. Aging Neurosci.

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“…This finding is in accordance with the results reported in previous studies. 36 Zhou et al conducted a multimodal machine learning approach based on functional and structural neuroimaging measures, functional MRI, and T1-weighted imaging, respectively. They achieved an accuracy of 78% by functional data and 79% by structural data for patients with temporal lobe epilepsy, and the highest accuracy of 84% was obtained when all functional and structural measures were combined.…”

Section: Discussionmentioning

confidence: 99%

“…They achieved an accuracy of 78% by functional data and 79% by structural data for patients with temporal lobe epilepsy, and the highest accuracy of 84% was obtained when all functional and structural measures were combined. 36 Therefore, combining multimodal measures could serve as a promising tool for improving the classification of patients with focal epilepsy from healthy controls. Furthermore, our findings suggest a potential role for connectomic biomarkers to differentiate patients with focal epilepsy from those with healthy controls.…”

Section: Discussionmentioning

confidence: 99%

Identification of focal epilepsy by diffusion tensor imaging using machine learning

Lee

Kim

et al. 2021

Acta Neurol Scand

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Brain magnetic resonance imaging (MRI) plays a pivotal role in the evaluation and management of patients with focal epilepsy. 1 Of the several modalities of the brain MRI, diffusion tensor imaging (DTI) can provide quantification of intuitive measurements of axonal fibers, revealing microstructural changes in the brain. 2 Prior DTI studies in patients with epilepsy using conventional DTI measures, such as fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), have revealed impaired white matter microstructure of the brain, especially epileptogenic lesion. 2 In addition, more recently, DTI can be also used for the evaluation

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“…Thus social support can help in optimizing the health of patients. [130] introduced a new scope in discriminating mTLE from NC with an increasing accuracy.…”

Section: ) Ml-based Approaches In Epilepsy Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging

Cited by 35 publications

References 55 publications

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Identification of focal epilepsy by diffusion tensor imaging using machine learning

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

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