Influence of Resting-State Network on Lateralization of Functional Connectivity in Mesial Temporal Lobe Epilepsy

Su, Lili; An, Jie; Ma, Qingsong; Qiu, Shijun; Hu, Dewen

doi:10.3174/ajnr.a4346

Cited by 28 publications

(22 citation statements)

References 47 publications

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“…However, these modulating factors are not always methodologically controlled for in the studies that can have relatively large but heterogeneous samples. In a machine learning study, Su, An, Ma, Qiu, and Hu () investigated FC at rest in right TLE patients and matched healthy subjects to identify connections that distinguish the patients from the controls. Interestingly, their results showed reduced FC within the right hemisphere along with FC strengthening within the preserved left hemisphere, which was interpreted as a compensatory mechanism (Su et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…In a machine learning study, Su, An, Ma, Qiu, and Hu () investigated FC at rest in right TLE patients and matched healthy subjects to identify connections that distinguish the patients from the controls. Interestingly, their results showed reduced FC within the right hemisphere along with FC strengthening within the preserved left hemisphere, which was interpreted as a compensatory mechanism (Su et al, ). Current methods allow for the identification and description of networks in a remarkable complexity, there remains scope for a clearer explanatory understanding of how and importantly what these networks compute (Mill et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Hubs disruption in mesial temporal lobe epilepsy. A resting‐state fMRI study on a language‐and‐memory network

Roger

Pichat

Torlay

et al. 2019

Human Brain Mapping

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Mesial temporal lobe epilepsy (mTLE) affects the brain networks at several levels and patients suffering from mTLE experience cognitive impairment for language and memory. Considering the importance of language and memory reorganization in this condition, the present study explores changes of the embedded languageand-memory network (LMN) in terms of functional connectivity (FC) at rest, as measured with functional MRI. We also evaluate the cognitive efficiency of the reorganization, that is, whether or not the reorganizations support or allow the maintenance of optimal cognitive functioning despite the seizure-related damage.Data from 37 patients presenting unifocal mTLE were analyzed and compared to 48 healthy volunteers in terms of LMN-FC using two methods: pairwise correlations (region of interest [ROI]-to-ROI) and graph theory. The cognitive efficiency of the LMN-FC reorganization was measured using correlations between FC parameters and language and memory scores. Our findings revealed a large perturbation of the LMN hubs in patients. We observed a hyperconnectivity of limbic areas near the dysfunctional hippocampus and mainly a hypoconnectivity for several cortical regions remote from the dysfunctional hippocampus. The loss of FC was more important in left mTLE (L-mTLE) than in right (R-mTLE) patients. The LMN-FC reorganization may not be always compensatory and not always useful for patients as it may be associated with lower cognitive performance. We discuss the different connectivity patterns obtained and conclude that interpretation of FC changes in relation to neuropsychological scores is important to determine cognitive efficiency, suggesting the concept of "connectome" would gain to be associated with a "cognitome" concept. K E Y W O R D S brain plasticity, functional connectivity, graph theory, language, memory, mesial temporal lobe epilepsy, resting-state fMRI

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hubs disruption in mesial temporal lobe epilepsy. A resting‐state fMRI study on a language‐and‐memory network

Roger

Pichat

Torlay

et al. 2019

Human Brain Mapping

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“…Recent studies have established that TLE affects a distributed neural network, with widespread extratemporal effects, rather than having a single focal epileptogenic source [23][24][25][26]. Based on both structural and functional connectivity (FC) analyses, accumulating evidence suggests that brain-networks in TLE patients are pathologically altered [27][28][29][30]. Hsiao et al [31] investigated FC alterations in the default mode network (DMN) in TLE, using resting-state spike-free MEG recordings.…”

Section: Introductionmentioning

confidence: 99%

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

Chen

et al. 2018

Complexity

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Correct lateralization of temporal lobe epilepsy (TLE) is critical for improving surgical outcomes. As a relatively new noninvasive clinical recording system, magnetoencephalography (MEG) has rarely been applied for determining lateralization of unilateral TLE. Here we propose a framework for using resting-state brain-network features and support vector machine (SVM) for TLE lateralization based on MEG. We recruited 15 patients with left TLE, 15 patients with right TLE, and 15 age-and sex-matched healthy controls. The lateralization problem was then transferred into a series of binary classification problems, including left TLE versus healthy control, right TLE versus healthy control, and left TLE versus right TLE. Brain-network features were extracted for each participant using three network metrics (nodal degree, betweenness centrality, and nodal efficiency). A radial basis function kernel SVM (RBF-SVM) was employed as the classifier. The leave-one-subject-out cross-validation strategy was used to test the ability of this approach to overcome individual differences. The results revealed that the nodal degree performed best for left TLE versus healthy control and right TLE versus healthy control, with accuracy of 80.76% and 75.00%, respectively. Betweenness centrality performed best for left TLE versus right TLE with an accuracy of 88.10%. The proposed approach demonstrated that MEG is a good candidate for solving the lateralization problem in unilateral TLE using various brain-network features.

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“…This inherent network laterality can be altered by influence of various genetic and environmental factors, and this change reflects interhemispheric variation in perception processing. In several previous studies, this alteration was associated with the deterioration of auditory and psychologic symptoms in various neuropsychiatric conditions, such as epilepsy, cognitive impairment, and mild traumatic brain injury (3,4). Furthermore, some studies have reported that cognitive training effects the lateralization of intrinsic networks in healthy older adults and stroke patients (5,6).…”

Section: Introductionmentioning

confidence: 90%

“…In addition, sagittal structural three-dimensional (3D) T1-weighted images (T1WI) were acquired to create templates for anatomical brain image registration. Acquisition parameters were as follows: TR = 8.1 ms, TE = 3.7 ms, flip angle = 8°, FOV = 236 × 236 mm 2 , and voxel size = 1 × 1 × 1 mm 3 . Furthermore, T2-weighted axial images were acquired to screen for gross structural abnormalities in the temporal bones or brain.…”

Section: Acquisition Of Mrimentioning

confidence: 99%

Changes in the Laterality of Functional Connectivity Associated with Tinnitus: Resting-State fMRI Study

Shin

Ryu

Jahng

et al. 2019

Investig Magn Reson Imaging

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One of the suggested potential mechanisms of tinnitus is an alteration in perception in the neural auditory pathway. The aim of this study was to investigate the difference in laterality in functional connectivity between tinnitus patients and healthy controls using resting state functional MRI (rs-fMRI). Materials and Methods: Thirty-eight chronic tinnitus subjects and 45 age-matched healthy controls were enrolled in this study. Connectivity was investigated using independent component analysis, and the laterality index map was calculated based on auditory (AN) and dorsal attention (DAN), default mode (DMN), sensorimotor, salience (SalN), and visual networks (VNs). The laterality index (LI) of tinnitus subjects was compared with that of normal controls using region-of-interest (ROI) and voxelbased methods and a two-sample unpaired t-test. Pearson correlation was conducted to assess the associations between the LI in each network and clinical variables. Results: The AN and VN showed significant differences in LI between the two groups in ROI analysis (P < 0.05), and the tinnitus group had clusters with significantly decreased laterality of AN, SalN, and VN in voxel-based comparisons. The AN was positively correlated with tinnitus distress (tinnitus handicap inventory), and the SalN was negatively correlated with symptom duration (P < 0.05). Conclusion: The results of this study suggest that various functional networks related to psychological distress can be modified by tinnitus, and that this interrelation can present differently on the right and left sides, according to the dominance of the network.

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Influence of Resting-State Network on Lateralization of Functional Connectivity in Mesial Temporal Lobe Epilepsy

Cited by 28 publications

References 47 publications

Hubs disruption in mesial temporal lobe epilepsy. A resting‐state fMRI study on a language‐and‐memory network

Hubs disruption in mesial temporal lobe epilepsy. A resting‐state fMRI study on a language‐and‐memory network

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

Changes in the Laterality of Functional Connectivity Associated with Tinnitus: Resting-State fMRI Study

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