Resting‐state brain entropy in right temporal lobe epilepsy and its relationship with alertness

Zhou, Muhua; Jiang, Wenyu; Zhong, Dan; Zheng, Jinou

doi:10.1002/brb3.1446

Cited by 8 publications

(6 citation statements)

References 64 publications

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“…rITG (44). Consistently, another DTI-based study of 67 chronic left hemisphere stroke survivors revealed distinct damage in the ITG and MTG among individuals experiencing word comprehension difficulties, suggesting that these regions play an important role in the integration of auditory and conceptual processing (45).…”

Section: Discussionsupporting

confidence: 54%

“…In a previous study assessing alertness in unilateral patients with TLE, similar bilateral reductions in VMHC values in the MTG were reported in study subjects ( 28 ). Moreover, resting-state brain entropy analyses in patients with rTLE and the assessment of the relationship between these results, and alertness revealed synchronous alterations in the rMTG and rITG ( 44 ). Consistently, another DTI-based study of 67 chronic left hemisphere stroke survivors revealed distinct damage in the ITG and MTG among individuals experiencing word comprehension difficulties, suggesting that these regions play an important role in the integration of auditory and conceptual processing ( 45 ).…”

Section: Discussionmentioning

confidence: 98%

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Altered voxel-mirrored homotopic connectivity in right temporal lobe epilepsy as measured using resting-state fMRI and support vector machine analyses

Chu

Wei

et al. 2022

Front. Psychiatry

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BackgroundPrior reports revealed abnormalities in voxel-mirrored homotopic connectivity (VMHC) when analyzing neuroimaging data from patients with various psychiatric conditions, including temporal lobe epilepsy (TLE). Whether these VHMC changes can be leveraged to aid in the diagnosis of right TLE (rTLE), however, remains to be established. This study was thus developed to examine abnormal VMHC findings associated with rTLE to determine whether these changes can be used to guide rTLE diagnosis.MethodsThe resultant imaging data of resting-state functional MRI (rs-fMRI) analyses of 59 patients with rTLE and 60 normal control individuals were analyzed using VMHC and support vector machine (SVM) approaches.ResultsRelative to normal controls, patients with rTLE were found to exhibit decreased VMHC values in the bilateral superior and the middle temporal pole (STP and MTP), the bilateral middle and inferior temporal gyri (MTG and ITG), and the bilateral orbital portion of the inferior frontal gyrus (OrbIFG). These patients further exhibited increases in VMHC values in the bilateral precentral gyrus (PreCG), the postcentral gyrus (PoCG), and the supplemental motor area (SMA). The ROC curve of MTG VMHC values showed a great diagnostic efficacy in the diagnosis of rTLE with AUCs, sensitivity, specificity, and optimum cutoff values of 0.819, 0.831, 0.717, and 0.465. These findings highlight the value of the right middle temporal gyrus (rMTG) when differentiating between rTLE and control individuals, with a corresponding SVM analysis yielding respective accuracy, sensitivity, and specificity values of 70.59% (84/119), 78.33% (47/60), and 69.49% (41/59).ConclusionIn summary, patients with rTLE exhibit various forms of abnormal functional connectivity, and SVM analyses support the potential value of abnormal VMHC values as a neuroimaging biomarker that can aid in the diagnosis of this condition.

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Section: Discussionsupporting

confidence: 54%

Section: Discussionmentioning

confidence: 98%

Altered voxel-mirrored homotopic connectivity in right temporal lobe epilepsy as measured using resting-state fMRI and support vector machine analyses

Chu

Wei

et al. 2022

Front. Psychiatry

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“…This study has some limitations. Firstly, as obtaining a large dataset is difficult in neuroimaging, where data acquisition is costly, the sample size of this study is relatively limited, but compared with the sample sizes of previous intergroup comparison studies (Lei et al, 2017 ; Zhou M. et al, 2019 ) and some machine learning articles (Hou et al, 2016 ; Tang et al, 2017 ), the sample size of this study is relatively large. Previous studies (Sun et al, 2018 ; Huang K. et al, 2019 ) have shown that multimodal imaging and clinical indicators can improve the performance of machine learning models.…”

Section: Discussionmentioning

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 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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“…At present, the mTLE lesions are identified by certain biomarked brain regions including the insula, bilateral cingulate gyrus, and precuneus [4,9,44]. By considering the healthy people as a control, Zhou et al [45] found that the left middle temporal gyrus, right middle frontal gyrus, and the left anterior central gyrus were significantly different in temporal lobe epilepsy by the entropy values of the rfMRI data. In addition, by the anatomical connectivity between the left and right hemispheres in the temporal lobe epilepsy, a different connectivity pattern was observed in the cortical-limbic network and cerebellum [46].…”

Section: Functional Biomarker Of Tlementioning

confidence: 99%

Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere in Temporal Lobe Epilepsy Using Resting-State fMRI

Wang

Belkacem

et al. 2021

Journal of Healthcare Engineering

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The bottleneck associated with the validation of the parameters of the entropy model has limited the application of this model to modern functional imaging technologies such as the resting-state functional magnetic resonance imaging (rfMRI). In this study, an optimization algorithm that could choose the parameters of the multiscale entropy (MSE) model was developed, while the optimized effectiveness for localizing the epileptogenic hemisphere was validated through the classification rate with a supervised machine learning method. The rfMRI data of 20 mesial temporal lobe epilepsy patients with positive indicators (the indicators of epileptogenic hemisphere in clinic) in the hippocampal formation on either left or right hemisphere (equally divided into two groups) on the structural MRI were collected and preprocessed. Then, three parameters in the MSE model were statistically optimized by both receiver operating characteristic (ROC) curve and the area under the ROC curve value in the sensitivity analysis, and the intergroup significance of optimized entropy values was utilized to confirm the biomarked brain areas sensitive to the epileptogenic hemisphere. Finally, the optimized entropy values of these biomarked brain areas were regarded as the feature vectors input for a support vector machine to classify the epileptogenic hemisphere, and the classification effectiveness was cross-validated. Nine biomarked brain areas were confirmed by the optimized entropy values, including medial superior frontal gyrus and superior parietal gyrus ( p < .01). The mean classification accuracy was greater than 90%. It can be concluded that combination of the optimized MSE model with the machine learning model can accurately confirm the epileptogenic hemisphere by rfMRI. With the powerful information interaction capabilities of 5G communication, the epilepsy side-fixing algorithm that requires computing power can be integrated into a cloud platform. The demand side only needs to upload patient data to the service platform to realize the preoperative assessment of epilepsy.

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Resting‐state brain entropy in right temporal lobe epilepsy and its relationship with alertness

Cited by 8 publications

References 64 publications

Altered voxel-mirrored homotopic connectivity in right temporal lobe epilepsy as measured using resting-state fMRI and support vector machine analyses

Altered voxel-mirrored homotopic connectivity in right temporal lobe epilepsy as measured using resting-state fMRI and support vector machine analyses

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

Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere in Temporal Lobe Epilepsy Using Resting-State fMRI

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