Graph-theory based degree centrality combined with machine learning algorithms can predict response to treatment with antiepileptic medications in children with epilepsy |
| |
| Institution: | 1. Department of Pediatrics, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China;2. Department of Pediatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China;3. Department of Radiology, Yanan University Affiliated Hospital, China;4. Department of Radiology, Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, China;5. Department of Internal Medicine, The Second Affiliated Hospital of Shandong Traditional Chinese Medicine University, Jinan, China;6. Department of Pediatrics, The First Affiliated Hospital of Shandong First Medical University, Jinan, China;7. Department of Cardiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, China;1. University of Maryland, Baltimore County, Baltimore, MD 21250, USA;2. University of Maryland School of Medicine, Baltimore, MD 21201, USA;3. The Mind Research Network, Albuquerque, NM 87106, USA;4. Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA;1. Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China;2. Department of Psychology, Southwest University, Chongqing, China;3. Department of Psychiatry, University of Connecticut Health Center, Farmington, USA;1. Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Chieti, Italy;2. Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, via Álvaro del Portillo, 21, 00128, Rome, Italy;3. Department of Neurology and Psychiatry, “Sapienza” University of Rome, Italy;4. Institute for Advanced Biomedical Technologies (ITAB), G. d’Annunzio University of Chieti-Pescara, Chieti, Italy;5. Department of Systems Medicine, Neuroscience, University of Rome Tor Vergata, Rome, Italy;6. Neurorehabilitation Department, IRCCS Salvatore Maugeri Foundation, Institute of Milan, Milan, Italy.;1. School of Medicine, Nankai University, Tianjin, China;2. Department of Radiology, Chinese PLA General Hospital, Beijing, China;3. Department of Radiology, The Six Medical Center, Chinese PLA General Hospital, Beijing, China;4. Department of Neurology, Chinese PLA General Hospital, Beijing, China;1. Department of Pediatric Surgery and Neurosurgery, McGovern Medical School at UTHealth, Houston, Texas, USA;2. Department of Diagnostic and Interventional Imaging, McGovern Medical School at UTHealth, Houston, Texas, USA;3. Department of Pediatric Neurology, McGovern Medical School at UTHealth, Houston, Texas, USA;4. Biostatistics and Epidemiology Research Design Core, Institute for Clinical and Translational Sciences, McGovern Medical School at UTHealth, Houston, Texas, USA;5. Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri, USA;6. Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA;1. Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea;2. Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea |
| |
| Abstract: | Background and purposeThe purpose of the current study is to detect changes of graph-theory-based degree centrality (DC) and their relationship with the clinical treatment effects of anti-epileptic drugs (AEDs) for patients with childhood absence epilepsy (CAE) using resting-state functional MRI (RS-fMRI).MethodsRS-fMRI data from 35 CAE patients were collected and compared with findings from 35 age and gender matched healthy controls (HCs). The patients were treated with AEDs for 46.03 weeks before undergoing a second RS-fMRI scan.ResultsCAE children at baseline showed increased DC in thalamus, postcentral and precentral and reduced DC in medial frontal cortex, superior frontal cortex, middle temporal cortex, angular and precuneus. However, those abnormalities showed a clear renormalization after AEDs treatments. We then explored the viability of graph-theory-based degree centrality to accurately classify effectiveness to AEDs. Support Vector Machine analysis using leave-one-out cross-validation achieved a correct classification rate of 84.22% sensitivity 78.76%, specificity 89.65%, and area under the receiver operating characteristic curve (AUC) 0.96] for differentiating effective subjects from ineffective subjects. Brain areas that contributed most to the classification model were mainly located within the right thalamus, bilateral middle temporal gyrus, right medial frontal gyrus, right inferior frontal gyrus, left precuneus, bilateral angular right precentral and left postcentral. Furthermore, the DC change within the bilateral angular are positively correlated with the symptom improvements after AEDs treatment.ConclusionThese findings suggest that graph-theory-based measures, such as DC, combined with machine-learning algorithms, can provide crucial insights into pathophysiological mechanisms and the effectiveness of AEDs. |
| |
| Keywords: | Childhood absence epilepsy Resting-state functional MRI Degree centrality Support Vector Machine analysis Anti-epileptic drugs |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
