Characterizing the hyper‐ and hypometabolism in temporal lobe epilepsy using multivariate machine learning-Reference-Cited by-同舟云学术

Characterizing the hyper‐ and hypometabolism in temporal lobe epilepsy using multivariate machine learning

Published:2021-09-09 Issue:11 Volume:99 Page:3035-3046
ISSN:0360-4012
Container-title:Journal of Neuroscience Research
language:en
Short-container-title:J Neurosci Res

Author:

Wu Dongyan¹,Yang Liyuan²,Gong Gaolang²,Zheng Yumin³,Jin Chaoling³,Qi Lei⁴,Li Yanran⁴,Wu Di⁴,Cui Zaixu⁵,He Xiaosong⁶,Ren Liankun⁷

Affiliation:

1. Department of Neurology China‐Japan Friendship Hospital Beijing China

2. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Beijing Normal University Beijing China

3. Department of Nuclear Medicine China‐Japan Friendship Hospital Beijing China

4. Department of Neurology Xuanwu Hospital, Capital Medical University Beijing China

5. Chinese Institute for Brain Research Beijing China

6. Department of Psychology University of Science and Technology of China Hefei China

7. Comprehensive Epilepsy Center of Beijing, The Beijing Key Laboratory of Neuromodulation, Department of Neurology Xuanwu HospitalCapital Medical University Beijing China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Cellular and Molecular Neuroscience

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jnr.24951

Reference46 articles.

1. Epilepsy duration impacts on brain glucose metabolism in temporal lobe epilepsy: Results of voxel-based mapping

2. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

3. Cortical thickness analysis in temporal lobe epilepsy: Reproducibility and relation to outcome

4. Subregional Mesiotemporal Network Topology Is Altered in Temporal Lobe Epilepsy

5. Graph-Theoretical Analysis Reveals Disrupted Small-World Organization of Cortical Thickness Correlation Networks in Temporal Lobe Epilepsy

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