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Maxion A, Gaebler AJ, Röhrig R, Mathiak K, Zweerings J, Kutafina E. Spectral changes in electroencephalography linked to neuroactive medications: A computational pipeline for data mining and analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108319. [PMID: 39047578 DOI: 10.1016/j.cmpb.2024.108319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 07/02/2024] [Accepted: 07/05/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND AND OBJECTIVES The increasing amount of open-access medical data provides new opportunities to gain clinically relevant information without recruiting new patients. We developed an open-source computational pipeline, that utilizes the publicly available electroencephalographic (EEG) data of the Temple University Hospital to identify EEG profiles associated with the usage of neuroactive medications. It facilitates access to the data and ensures consistency in data processing and analysis, thus reducing the risk of errors and creating comparable and reproducible results. Using this pipeline, we analyze the influence of common neuroactive medications on brain activity. METHODS The pipeline is constructed using easily controlled modules. The user defines the medications of interest and comparison groups. The data is downloaded and preprocessed, spectral features are extracted, and statistical group comparison with visualization through a topographic EEG map is performed. The pipeline is adjustable to answer a variety of research questions. Here, the effects of carbamazepine and risperidone were statistically compared with control data and with other medications from the same classes (anticonvulsants and antipsychotics). RESULTS The comparison between carbamazepine and the control group showed an increase in absolute and relative power for delta and theta, and a decrease in relative power for alpha, beta, and gamma. Compared to antiseizure medications, carbamazepine showed an increase in alpha and theta for absolute powers, and for relative powers an increase in alpha and theta, and a decrease in gamma and delta. Risperidone compared with the control group showed a decrease in absolute and relative power for alpha and beta and an increase in theta for relative power. Compared to antipsychotic medications, risperidone showed a decrease in delta for absolute powers. These results show good agreement with state-of-the-art research. The database allows to create large groups for many different medications. Additionally, it provides a collection of records labeled as "normal" after expert assessment, which is convenient for the creation of control groups. CONCLUSIONS The pipeline allows fast testing of different hypotheses regarding links between medications and EEG spectrum through ecological usage of readily available data. It can be utilized to make informed decisions about the design of new clinical studies.
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Affiliation(s)
- Anna Maxion
- Research Group Neuroscience, Interdisciplinary Center for Clinical Research Within the Faculty of Medicine, RWTH Aachen University, Aachen, Germany.
| | - Arnim Johannes Gaebler
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany; Institute of Physiology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Rainer Röhrig
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Jana Zweerings
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Ekaterina Kutafina
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany; Institute for Biomedical Informatics, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
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Abbasi H, Davidson JO, Dhillon SK, Zhou KQ, Wassink G, Gunn AJ, Bennet L. Deep Learning for Generalized EEG Seizure Detection after Hypoxia-Ischemia-Preclinical Validation. Bioengineering (Basel) 2024; 11:217. [PMID: 38534490 DOI: 10.3390/bioengineering11030217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/12/2024] [Accepted: 02/23/2024] [Indexed: 03/28/2024] Open
Abstract
Brain maturity and many clinical treatments such as therapeutic hypothermia (TH) can significantly influence the morphology of neonatal EEG seizures after hypoxia-ischemia (HI), and so there is a need for generalized automatic seizure identification. This study validates efficacy of advanced deep-learning pattern classifiers based on a convolutional neural network (CNN) for seizure detection after HI in fetal sheep and determines the effects of maturation and brain cooling on their accuracy. The cohorts included HI-normothermia term (n = 7), HI-hypothermia term (n = 14), sham-normothermia term (n = 5), and HI-normothermia preterm (n = 14) groups, with a total of >17,300 h of recordings. Algorithms were trained and tested using leave-one-out cross-validation and k-fold cross-validation approaches. The accuracy of the term-trained seizure detectors was consistently excellent for HI-normothermia preterm data (accuracy = 99.5%, area under curve (AUC) = 99.2%). Conversely, when the HI-normothermia preterm data were used in training, the performance on HI-normothermia term and HI-hypothermia term data fell (accuracy = 98.6%, AUC = 96.5% and accuracy = 96.9%, AUC = 89.6%, respectively). Findings suggest that HI-normothermia preterm seizures do not contain all the spectral features seen at term. Nevertheless, an average 5-fold cross-validated accuracy of 99.7% (AUC = 99.4%) was achieved from all seizure detectors. This significant advancement highlights the reliability of the proposed deep-learning algorithms in identifying clinically translatable post-HI stereotypic seizures in 256Hz recordings, regardless of maturity and with minimal impact from hypothermia.
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Affiliation(s)
- Hamid Abbasi
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
- Auckland Bioengineering Institute (ABI), University of Auckland, Auckland 1010, New Zealand
| | - Joanne O Davidson
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Simerdeep K Dhillon
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Kelly Q Zhou
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Guido Wassink
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Alistair J Gunn
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Laura Bennet
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
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Dou Y, Xia J, Fu M, Cai Y, Meng X, Zhan Y. Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses. Neuroimage 2023; 284:120439. [PMID: 37939889 DOI: 10.1016/j.neuroimage.2023.120439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 10/01/2023] [Accepted: 10/31/2023] [Indexed: 11/10/2023] Open
Abstract
Stereoelectroencephalography (SEEG) offers unique neural data from in-depth brain structures with fine temporal resolutions to better investigate the origin of epileptic brain activities. Although oscillatory patterns from different frequency bands and functional connectivity computed from the SEEG datasets are employed to study the epileptic zones, direct electrical stimulation-evoked electrophysiological recordings of synaptic responses, namely cortical-cortical evoked potentials (CCEPs), from the same SEEG electrodes are not explored for the localization of epileptic zones. Here we proposed a two-stream model with unsupervised learning and graph convolutional network tailored to the SEEG and CCEP datasets in individual patients to perform localization of epileptic zones. We compared our localization results with the clinically marked electrode sites determined for surgical resections. Our model had good classification capability when compared to other state-of-the-art methods. Furthermore, based on our prediction results we performed group-level brain-area mapping analysis for temporal, frontal and parietal epilepsy patients and found that epileptic and non-epileptic brain networks were distinct in patients with different types of focal epilepsy. Our unsupervised data-driven model provides personalized localization analysis for the epileptic zones. The epileptic and non-epileptic brain areas disclosed by the prediction model provide novel insights into the network-level pathological characteristics of epilepsy.
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Affiliation(s)
- Yonglin Dou
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jing Xia
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Mengmeng Fu
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Yunpeng Cai
- Institute of Advanced Computing and Digital Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xianghong Meng
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China.
| | - Yang Zhan
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China.
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Gombolay GY, Gopalan N, Bernasconi A, Nabbout R, Megerian JT, Siegel B, Hallman-Cooper J, Bhalla S, Gombolay MC. Review of Machine Learning and Artificial Intelligence (ML/AI) for the Pediatric Neurologist. Pediatr Neurol 2023; 141:42-51. [PMID: 36773406 PMCID: PMC10040433 DOI: 10.1016/j.pediatrneurol.2023.01.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/03/2023] [Accepted: 01/09/2023] [Indexed: 01/15/2023]
Abstract
Artificial intelligence (AI) and a popular branch of AI known as machine learning (ML) are increasingly being utilized in medicine and to inform medical research. This review provides an overview of AI and ML (AI/ML), including definitions of common terms. We discuss the history of AI and provide instances of how AI/ML can be applied to pediatric neurology. Examples include imaging in neuro-oncology, autism diagnosis, diagnosis from charts, epilepsy, cerebral palsy, and neonatal neurology. Topics such as supervised learning, unsupervised learning, and reinforcement learning are discussed.
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Affiliation(s)
- Grace Y Gombolay
- Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia.
| | - Nakul Gopalan
- Georgia Institute of Technology, Interactive Computing, Atlanta, Georgia
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, UK
| | - Rima Nabbout
- Department of Pediatric Neurology, Necker Enfants Malades Hospital, Reference Centre for Rare Epilepsies and Member of the ERN EpiCARE, Imagine Institute UMR1163, Paris Descartes University, Paris, France
| | - Jonathan T Megerian
- Department of Pediatrics, CHOC Children's, Irvine School of Medicine, University of California, Orange, California
| | - Benjamin Siegel
- Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia
| | - Jamika Hallman-Cooper
- Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia
| | - Sonam Bhalla
- Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia
| | - Matthew C Gombolay
- Georgia Institute of Technology, Interactive Computing, Atlanta, Georgia
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