Discrimination of Tourette Syndrome Based on the Spatial Patterns of the Resting–State EEG Network

Duan, Keyi; Wu, Qian; Liao, Yuanyuan; Si, Yajing; Bore, Joyce Chelangat; Li, Fali; Tao, Qin; Lin, Li; Wei, Lei; Hu, Xudong; Yao, Dezhong; Pei, Changfu; Zhang, Tao; Huang, Lin; Xu, Peng

doi:10.1007/s10548-020-00801-5

Cited by 13 publications

(8 citation statements)

References 38 publications

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“…One study looked at monozygotic twins with TS and found the more severely affected twin to have more fronto-central theta activity [ 106 ]. Another study has also shown that there is reduced long range connectivity between the frontal and temporal/occipital/parietal lobes in patients with TS compared to healthy controls, which mirrors results in fMRI studies [ 107 , 108 ]. It is hypothesized that dysrhythmic thalamo-cortical oscillations may lead to a loss of neuronal control over movements and lead to tics [ 109 ].…”

Section: Epidemiologysupporting

confidence: 62%

Emerging therapies and recent advances for Tourette syndrome

Chou

Agin‐Liebes

Kuo

2023

Heliyon

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Section: Epidemiologysupporting

confidence: 62%

Emerging therapies and recent advances for Tourette syndrome

Chou

Agin‐Liebes

Kuo

2023

Heliyon

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“…As clarified in previous studies, the increasing pairs of SPN filters might facilitate the classification of situations of interest [44,45]; for example, different pairs of SPN features (i.e., 1 pair, 2 pairs, and 3 pairs) have been used to achieve the classification of psychogenic nonepileptic seizures from epilepsy, and 3 pairs of SPN features achieved the highest classification accuracy [45]. Consistent with the protocols used in previous studies [46,47], in our present study, three pairs of SPN filters were accordingly adopted to achieve the classification of responders and non-responders. In particular, for a 21 × 21 adjacency matrix, M, each SPN filter was a 21-length vector, and therefore, three pairs of SPN filters comprised a 21×6 matrix.…”

Section: ) Discrimination Of Responders From Non-responders Based On ...supporting

confidence: 58%

Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder

Peng

Huang

Chen

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively

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“…To fill in this gap, we had additionally developed a new feature extraction approach for brain network, namely, spatial pattern of the network (SPN), to extract spatial pattern features contained in a given brain network [43], [44]. As proved in the previous studies [45], [46], SPN advantages in estimating the corresponding spatial network filters by enhancing the nodes that have strong connectivity with large values, while compressing those having weak connections with rather small values (closing to zero) [39].…”

Section: Prediction Analysismentioning

confidence: 99%

Predicting the Symptom Severity in Autism Spectrum Disorder Based on EEG Metrics

Zhang

Chen

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Autism spectrum disorder (ASD) is associated with the impaired integrating and segregating of related information that is expanded within the large-scale brain network. The varying ASD symptom severities have been explored, relying on their behaviors and related brain activity, but how to effectively predict ASD symptom severity needs further exploration. In this study, we aim to investigate whether the ASD symptom severity could be predicted with electroencephalography (EEG) metrics. Based on a publicly available dataset, the EEG brain networks were constructed, and four types of EEG metrics were calculated. Then, we statistically compared the brain network differences among ASD children with varying severities, i.e., high/low autism diagnostic observation schedule (A-DOS) scores, as well as with the typically developing (T-D) children. Thereafter, the EEG metrics were utilized to validate whether they could facilitate the prediction of the ASD symptom severity. The results demonstrated that both high-and low-scoring ASD children showed the decreased long-range frontal-occipital connectivity, increased anterior frontal connectivity and altered network properties. Furthermore, we found that the four types of EEG metrics are significantly correlated with the ADOS scores, and their combination can serve as the features to effectively predict the ASD symptom severity. The current findings will expand our knowledge of network dysfunction in ASD children and provide new EEG metrics for predicting the symptom severity.

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Discrimination of Tourette Syndrome Based on the Spatial Patterns of the Resting–State EEG Network

Cited by 13 publications

References 38 publications

Emerging therapies and recent advances for Tourette syndrome

Emerging therapies and recent advances for Tourette syndrome

Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder

Predicting the Symptom Severity in Autism Spectrum Disorder Based on EEG Metrics

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