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Lucas A, Revell A, Davis KA. Artificial intelligence in epilepsy - applications and pathways to the clinic. Nat Rev Neurol 2024; 20:319-336. [PMID: 38720105 DOI: 10.1038/s41582-024-00965-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 06/06/2024]
Abstract
Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew Revell
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
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Kazemzadeh K, Akhlaghdoust M, Zali A. Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery. Front Surg 2023; 10:1241923. [PMID: 37693641 PMCID: PMC10483402 DOI: 10.3389/fsurg.2023.1241923] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023] Open
Abstract
Neurosurgical practitioners undergo extensive and prolonged training to acquire diverse technical proficiencies, while neurosurgical procedures necessitate a substantial amount of pre-, post-, and intraoperative clinical data acquisition, making decisions, attention, and convalescence. The past decade witnessed an appreciable escalation in the significance of artificial intelligence (AI) in neurosurgery. AI holds significant potential in neurosurgery as it supplements the abilities of neurosurgeons to offer optimal interventional and non-interventional care to patients by improving prognostic and diagnostic outcomes in clinical therapy and assisting neurosurgeons in making decisions while surgical interventions to enhance patient outcomes. Other technologies including augmented reality, robotics, and virtual reality can assist and promote neurosurgical methods as well. Moreover, they play a significant role in generating, processing, as well as storing experimental and clinical data. Also, the usage of these technologies in neurosurgery is able to curtail the number of costs linked with surgical care and extend high-quality health care to a wider populace. This narrative review aims to integrate the results of articles that elucidate the role of the aforementioned technologies in neurosurgery.
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Affiliation(s)
- Kimia Kazemzadeh
- Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Meisam Akhlaghdoust
- Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- USERN Office, Functional Neurosurgery Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Zali
- Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- USERN Office, Functional Neurosurgery Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Jin C, Qi S, Yang L, Teng Y, Li C, Yao Y, Ruan X, Wei X. Abnormal functional connectivity density involvement in freezing of gait and its application for subtyping Parkinson's disease. Brain Imaging Behav 2023; 17:375-385. [PMID: 37243751 DOI: 10.1007/s11682-023-00765-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/19/2023] [Indexed: 05/29/2023]
Abstract
The pathophysiological mechanisms at work in Parkinson's disease (PD) patients with freezing of gait (FOG) remain poorly understood. Functional connectivity density (FCD) could provide an unbiased way to analyse connectivity across the brain. In this study, a total of 23 PD patients with FOG (PD FOG + patients), 26 PD patients without FOG (PD FOG- patients), and 22 healthy controls (HCs) were recruited, and their resting-state functional magnetic resonance imaging (rs-fMRI) images were collected. FCD mapping was first performed to identify differences between groups. Pearson correlation analysis was used to explore relationships between FCD values and the severity of FOG. Then, a machine learning model was employed to classify each pair of groups. PD FOG + patients showed significantly increased short-range FCD in the precuneus, cingulate gyrus, and fusiform gyrus and decreased long-range FCD in the frontal gyrus, temporal gyrus, and cingulate gyrus. Short-range FCD values in the middle temporal gyrus and inferior temporal gyrus were positively correlated with FOG questionnaire (FOGQ) scores, and long-range FCD values in the middle frontal gyrus were negatively correlated with FOGQ scores. Using FCD in abnormal regions as input, a support vector machine (SVM) classifier can achieve classification with good performance. The mean accuracy values were 0.895 (PD FOG + vs. HC), 0.966 (PD FOG- vs. HC), and 0.897 (PD FOG + vs. PD FOG-). This study demonstrates that PD FOG + patients showed altered short- and long-range FCD in several brain regions involved in action planning and control, motion processing, emotion, cognition, and object recognition.
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Affiliation(s)
- Chaoyang Jin
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Lei Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yueyang Teng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Chen Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA
| | - Xiuhang Ruan
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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4
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Transfer Learning from Healthy to Unhealthy Patients for the Automated Classification of Functional Brain Networks in fMRI. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Functional Magnetic Resonance Imaging (fMRI) is an essential tool for the pre-surgical planning of brain tumor removal, which allows the identification of functional brain networks to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rs-fMRI). This technique is not routinely available because of the necessity to have an expert reviewer who can manually identify each functional network. The lack of sufficient unhealthy data has so far hindered a data-driven approach based on machine learning tools for full automation of this clinical task. In this article, we investigate the possibility of such an approach via the transfer learning method from healthy control data to unhealthy patient data to boost the detection of functional brain networks in rs-fMRI data. The end-to-end deep learning model implemented in this article distinguishes seven principal functional brain networks using fMRI images. The best performance of a 75% correct recognition rate is obtained from the proposed deep learning architecture, which shows its superiority over other machine learning algorithms that were equally tested for this classification task. Based on this best reference model, we demonstrate the possibility of boosting the results of our algorithm with transfer learning from healthy patients to unhealthy patients. This application of the transfer learning technique opens interesting possibilities because healthy control subjects can be easily enrolled for fMRI data acquisition since it is non-invasive. Consequently, this process helps to compensate for the usual small cohort of unhealthy patient data. This transfer learning approach could be extended to other medical imaging modalities and pathology.
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Gautham BK, Mukherjee J, Narayanan M, Kenchaiah R, Mundlamuri RC, Asranna A, Lakshminarayanapuram VG, Bharath RD, Saini J, Nagaraj C, Mangalore S, Kulanthaivelu K, Sadashiva N, Mahadevan A, Rajan J, Kumar K, Arimappamagan A, Malla BR, Sinha S. Automated lateralization of temporal lobe epilepsy with cross frequency coupling using magnetoencephalography. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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6
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AIM in Neurology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Slinger G, Otte WM, Braun KPJ, van Diessen E. An updated systematic review and meta-analysis of brain network organization in focal epilepsy: Looking back and forth. Neurosci Biobehav Rev 2021; 132:211-223. [PMID: 34813826 DOI: 10.1016/j.neubiorev.2021.11.028] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 06/23/2021] [Accepted: 11/17/2021] [Indexed: 01/10/2023]
Abstract
Abnormalities of the brain network organization in focal epilepsy have been extensively quantified. However, the extent and directionality of abnormalities are highly variable and subtype insensitive. We conducted meta-analyses to obtain a more accurate and epilepsy type-specific quantification of the interictal global brain network organization in focal epilepsy. By using random-effects models, we estimated differences in average clustering coefficient, average path length, and modularity between patients with focal epilepsy and controls, based on 45 studies with a total sample size of 1,468 patients and 1,021 controls. Structural networks had a significant lower level of integration in patients with epilepsy as compared to controls, with a standardized mean difference of -0.334 (95 % confidence interval -0.631 to -0.038; p-value 0.027). Functional networks did not differ between patients and controls, except for the beta band clustering coefficient. Our meta-analyses show that differences in the brain network organization are not as well defined as individual studies often propose. We discuss potential pitfalls and suggestions to enhance the yield and clinical value of network studies.
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Affiliation(s)
- Geertruida Slinger
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
| | - Willem M Otte
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Kees P J Braun
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Eric van Diessen
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
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Abstract
Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. The last decade has seen a significant increase in the importance of artificial intelligence (AI) in neurosurgery. AI can provide a great promise in neurosurgery by complementing neurosurgeons' skills to provide the best possible interventional and noninterventional care for patients by enhancing diagnostic and prognostic outcomes in clinical treatment and help neurosurgeons with decision making during surgical interventions to improve patient outcomes. Furthermore, AI is playing a pivotal role in the production, processing and storage of clinical and experimental data. AI usage in neurosurgery can also reduce the costs associated with surgical care and provide high-quality healthcare to a broader population. Additionally, AI and neurosurgery can build a symbiotic relationship where AI helps to push the boundaries of neurosurgery, and neurosurgery can help AI to develop better and more robust algorithms. This review explores the role of AI in interventional and noninterventional aspects of neurosurgery during pre-, intra- and postoperative care, such as diagnosis, clinical decision making, surgical operation, prognosis, data acquisition, and research within the neurosurgical arena.
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Affiliation(s)
- Mohammad Mofatteh
- Sir William Dunn School of Pathology, Medical Sciences Division, University of Oxford, South Parks Road, Oxford OX1 3RE, United Kingdom
- Lincoln College, University of Oxford, Turl Street, Oxford OX1 3DR, United Kingdom
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Garcia-Cairasco N, Podolsky-Gondim G, Tejada J. Searching for a paradigm shift in the research on the epilepsies and associated neuropsychiatric comorbidities. From ancient historical knowledge to the challenge of contemporary systems complexity and emergent functions. Epilepsy Behav 2021; 121:107930. [PMID: 33836959 DOI: 10.1016/j.yebeh.2021.107930] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 03/06/2021] [Indexed: 10/21/2022]
Abstract
In this review, we will discuss in four scenarios our challenges to offer possible solutions for the puzzle associated with the epilepsies and neuropsychiatric comorbidities. We need to recognize that (1) since quite old times, human wisdom was linked to the plural (distinct global places/cultures) perception of the Universe we are in, with deep respect for earth and nature. Plural ancestral knowledge was added with the scientific methods; however, their joint efforts are the ideal scenario; (2) human behavior is not different than animal behavior, in essence the product of Darwinian natural selection; knowledge of animal and human behavior are complementary; (3) the expression of human behavior follows the same rules that complex systems with emergent properties, therefore, we can measure events in human, clinical, neurobiological situations with complexity systems' tools; (4) we can use the semiology of epilepsies and comorbidities, their neural substrates, and potential treatments (including experimental/computational modeling, neurosurgical interventions), as a source and collection of integrated big data to predict with them (e.g.: machine/deep learning) diagnosis/prognosis, individualized solutions (precision medicine), basic underlying mechanisms and molecular targets. Once the group of symptoms/signals (with a myriad of changing definitions and interpretations over time) and their specific sequences are determined, in epileptology research and clinical settings, the use of modern and contemporary techniques such as neuroanatomical maps, surface electroencephalogram and stereoelectroencephalography (SEEG) and imaging (MRI, BOLD, DTI, SPECT/PET), neuropsychological testing, among others, are auxiliary in the determination of the best electroclinical hypothesis, and help design a specific treatment, usually as the first attempt, with available pharmacological resources. On top of ancient knowledge, currently known and potentially new antiepileptic drugs, alternative treatments and mechanisms are usually produced as a consequence of the hard, multidisciplinary, and integrated studies of clinicians, surgeons, and basic scientists, all over the world. The existence of pharmacoresistant patients, calls for search of other solutions, being along the decades the surgeries the most common interventions, such as resective procedures (i.e., selective or standard lobectomy, lesionectomy), callosotomy, hemispherectomy and hemispherotomy, added by vagus nerve stimulation (VNS), deep brain stimulation (DBS), neuromodulation, and more recently focal minimal or noninvasive ablation. What is critical when we consider the pharmacoresistance aspect with the potential solution through surgery, is still the pursuit of localization-dependent regions (e.g.: epileptogenic zone (EZ)), in order to decide, no matter how sophisticated are the brain mapping tools (EEG and MRI), the size and location of the tissue to be removed. Mimicking the semiology and studying potential neural mechanisms and molecular targets - by means of experimental and computational modeling - are fundamental steps of the whole process. Concluding, with the conjunction of ancient knowledge, coupled to critical and creative contemporary, scientific (not dogmatic) clinical/surgical, and experimental/computational contributions, a better world and of improved quality of life can be offered to the people with epilepsy and neuropsychiatric comorbidities, who are still waiting (as well as the scientists) for a paradigm shift in epileptology, both in the Basic Science, Computational, Clinical, and Neurosurgical Arenas. This article is part of the Special Issue "NEWroscience 2018".
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Affiliation(s)
- Norberto Garcia-Cairasco
- Laboratório de Neurofisiologia e Neuroetologia Experimental, Departmento de Fisiologia, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto. Brazil; Departamento de Neurociências e Ciências do Comportamento, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil.
| | - Guilherme Podolsky-Gondim
- Departamento de Neurociências e Ciências do Comportamento, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil.
| | - Julian Tejada
- Departamento de Psicologia, Universidade Federal de Sergipe, Brazil.
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Sone D. Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy. Front Neurosci 2021; 15:699176. [PMID: 34385902 PMCID: PMC8353251 DOI: 10.3389/fnins.2021.699176] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/28/2021] [Indexed: 12/30/2022] Open
Abstract
It has been a clinically important, long-standing challenge to accurately localize epileptogenic focus in drug-resistant focal epilepsy because more intensive intervention to the detected focus, including resection neurosurgery, can provide significant seizure reduction. In addition to neurophysiological examinations, neuroimaging plays a crucial role in the detection of focus by providing morphological and neuroanatomical information. On the other hand, epileptogenic lesions in the brain may sometimes show only subtle or even invisible abnormalities on conventional MRI sequences, and thus, efforts have been made for better visualization and improved detection of the focus lesions. Recent advance in neuroimaging has been attracting attention because of the potentials to better visualize the epileptogenic lesions as well as provide novel information about the pathophysiology of epilepsy. While the progress of newer neuroimaging techniques, including the non-Gaussian diffusion model and arterial spin labeling, could non-invasively detect decreased neurite parameters or hypoperfusion within the focus lesions, advances in analytic technology may also provide usefulness for both focus detection and understanding of epilepsy. There has been an increasing number of clinical and experimental applications of machine learning and network analysis in the field of epilepsy. This review article will shed light on recent advances in neuroimaging for focal epilepsy, including both technical progress of images and newer analytical methodologies and discuss about the potential usefulness in clinical practice.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.,Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
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Sone D, Beheshti I. Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review. Front Neurosci 2021; 15:684825. [PMID: 34239413 PMCID: PMC8258163 DOI: 10.3389/fnins.2021.684825] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/31/2021] [Indexed: 12/13/2022] Open
Abstract
Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.,Department of Clinical and Experimental Epilepsy, University College London Institute of Neurology, London, United Kingdom
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
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Wang Y, Qin Y, Li H, Yao D, Sun B, Gong J, Dai Y, Wen C, Zhang L, Zhang C, Luo C, Zhu T. Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study. Front Neurosci 2021; 15:665578. [PMID: 34220426 PMCID: PMC8247769 DOI: 10.3389/fnins.2021.665578] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 05/26/2021] [Indexed: 01/14/2023] Open
Abstract
Although mounting neuroimaging studies have greatly improved our understanding of the neurobiological mechanism underlying internet addiction (IA), the results based on traditional group-level comparisons are insufficient in guiding individual clinical practice directly. Specific neuroimaging biomarkers are urgently needed for IA diagnosis and the evaluation of therapy efficacy. Therefore, this study aimed to develop support vector machine (SVM) models to identify IA and assess the efficacy of cognitive behavior therapy (CBT) based on unbiased functional connectivity density (FCD). Resting-state fMRI data were acquired from 27 individuals with IA before and after 8-week CBT sessions and 30 demographically matched healthy controls (HCs). The discriminative FCDs were computed as the features of the support vector classification (SVC) model to identify individuals with IA from HCs, and the changes in these discriminative FCDs after treatment were further used as features of the support vector regression (SVR) model to evaluate the efficacy of CBT. Based on the informative FCDs, our SVC model successfully differentiated individuals with IA from HCs with an accuracy of 82.5% and an area under the curve (AUC) of 0.91. Our SVR model successfully evaluated the efficacy of CBT using the FCD change ratio with a correlation efficient of 0.59. The brain regions contributing to IA classification and CBT efficacy assessment were the left inferior frontal cortex (IFC), middle frontal cortex (MFC) and angular gyrus (AG), the right premotor cortex (PMC) and middle cingulate cortex (MCC), and the bilateral cerebellum, orbitofrontal cortex (OFC) and superior frontal cortex (SFC). These findings confirmed the FCDs of hyperactive impulsive habit system, hypoactive reflecting system and sensitive interoceptive reward awareness system as potential neuroimaging biomarkers for IA, which might provide objective indexes for the diagnosis and efficacy evaluation of IA.
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Affiliation(s)
- Yang Wang
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yun Qin
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui Li
- School of Medicine, Chengdu University, Chengdu, China
| | - Dezhong Yao
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Sun
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jinnan Gong
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Yu Dai
- Department of Chinese Medicine, Chengdu Eighth People’s Hospital, Chengdu, China
| | - Chao Wen
- Department of Rehabilitation, Zigong Fifth People’s Hospital, Zigong, China
| | - Lingrui Zhang
- Department of Medicine, Leshan Vocational and Technical College, Leshan, China
| | - Chenchen Zhang
- Department of Rehabilitation, TCM Hospital of Longquanyi District, Chengdu, China
| | - Cheng Luo
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
| | - Tianmin Zhu
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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Panesar SS, Kliot M, Parrish R, Fernandez-Miranda J, Cagle Y, Britz GW. Promises and Perils of Artificial Intelligence in Neurosurgery. Neurosurgery 2020; 87:33-44. [PMID: 31748800 DOI: 10.1093/neuros/nyz471] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 08/28/2019] [Indexed: 11/13/2022] Open
Abstract
Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid and detailed analysis of the large quantities of clinical data generated in modern healthcare settings, at a level that is otherwise impossible by humans. Subsequently, AI may enhance clinical practice by pushing the limits of diagnostics, clinical decision making, and prognostication. Moreover, if combined with surgical robotics and other surgical adjuncts such as image guidance, AI may find its way into the operating room and permit more accurate interventions, with fewer errors. Despite the considerable hype surrounding the impending medical AI revolution, little has been written about potential downsides to increasing clinical automation. These may include both direct and indirect consequences. Directly, faulty, inadequately trained, or poorly understood algorithms may produce erroneous results, which may have wide-scale impact. Indirectly, increasing use of automation may exacerbate de-skilling of human physicians due to over-reliance, poor understanding, overconfidence, and lack of necessary vigilance of an automated clinical workflow. Many of these negative phenomena have already been witnessed in other industries that have already undergone, or are undergoing "automation revolutions," namely commercial aviation and the automotive industry. This narrative review explores the potential benefits and consequences of the anticipated medical AI revolution from a neurosurgical perspective.
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Affiliation(s)
- Sandip S Panesar
- Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas
| | - Michel Kliot
- Department of Neurosurgery, Stanford University, Stanford, California
| | - Rob Parrish
- Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas
| | | | - Yvonne Cagle
- NASA Ames Research Center, Mountain View, California
| | - Gavin W Britz
- Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas
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14
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Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach. Neurol Sci 2020; 42:2379-2390. [PMID: 33052576 DOI: 10.1007/s10072-020-04759-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 09/23/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE). METHODS Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE. RESULTS Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%. CONCLUSION Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.
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Roger E, Torlay L, Gardette J, Mosca C, Banjac S, Minotti L, Kahane P, Baciu M. A machine learning approach to explore cognitive signatures in patients with temporo-mesial epilepsy. Neuropsychologia 2020; 142:107455. [DOI: 10.1016/j.neuropsychologia.2020.107455] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 02/25/2020] [Accepted: 03/27/2020] [Indexed: 12/18/2022]
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16
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Foit NA, Bernasconi A, Bernasconi N. Functional Networks in Epilepsy Presurgical Evaluation. Neurosurg Clin N Am 2020; 31:395-405. [PMID: 32475488 DOI: 10.1016/j.nec.2020.03.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Continuing advancements in neuroimaging methodology allow for increasingly detailed in vivo characterization of structural and functional brain networks, leading to the recognition of epilepsy as a disorder of large-scale networks. In surgical candidates, analysis of functional networks has proved invaluable for the identification of eloquent brain areas, such as hemispherical language dominance. More recently, connectome-based biomarkers have demonstrated potential to further inform clinical decision making in drug-refractory epilepsy. This article summarizes current evidence on epilepsy as a network disorder, emphasizing potential benefits of network analysis techniques for preoperative assessments and resection planning.
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Affiliation(s)
- Niels Alexander Foit
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 Rue Université, Montreal, Quebec H3A 2B4, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 Rue Université, Montreal, Quebec H3A 2B4, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 Rue Université, Montreal, Quebec H3A 2B4, Canada.
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17
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Wang T, Liu H, Wang L, Ng ML, Li H, Yan N. Effect of Temporal Lobe Epilepsy on Auditory-motor Integration for Vocal Pitch Regulation: Evidence from Brain Functional Network Analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:3849-3853. [PMID: 31946713 DOI: 10.1109/embc.2019.8856902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Temporal lobe epilepsy (TLE) is a medically refractory focal epilepsy associated with structural deficits. Considerable evidence has revealed that patients with TLE also exhibit deficits in functional connectivity. According to previous research, patients with TLE exhibited decreased performance in speech sound perception and auditory-motor integration for voice control, which might be related to the compromised brain network connectivity. However, the specific nature of functional connectivity within and across brain regions remains largely unknown. To answer this question, we extended previous research from examining the topological properties of the entire brain network to the intra- and inter-regional communications of different brain regions. Patients with TLE and healthy controls were recruited to perform a pitch reflex task, during which electroencephalograph (EEG) data were acquired to construct graphical brain networks. Compared with healthy controls, inter-regional and cross-hemispheric connections were reduced in patients with TLE, whose functional networks were primarily composed of intra-regional connections. Significant differences in network parameters (betweenness centrality, modularity, and functional integration) as well as network hubs between the two groups further supported our findings that TLE is associated with alterations in functional connectivity during auditory-motor integration.
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18
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Senders JT, Arnaout O, Karhade AV, Dasenbrock HH, Gormley WB, Broekman ML, Smith TR. Natural and Artificial Intelligence in Neurosurgery: A Systematic Review. Neurosurgery 2019; 83:181-192. [PMID: 28945910 DOI: 10.1093/neuros/nyx384] [Citation(s) in RCA: 143] [Impact Index Per Article: 28.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Accepted: 08/11/2017] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed. OBJECTIVE To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as "natural intelligence." METHODS A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature. RESULTS Twenty-three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4-21%) and 0.14 (interquartile range 0.07-0.21), respectively. In 29 (58%) of the 50 outcome measures for which a P-value was provided or calculated, ML models outperformed clinical experts (P < .05). In 18 of 50 (36%), no difference was seen between ML and expert performance (P > .05), while in 3 of 50 (6%) clinical experts outperformed ML models (P < .05). All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group. CONCLUSION We conclude that ML models have the potential to augment the decision-making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting. Shifting from the preconceptions of a human-vs-machine to a human-and-machine paradigm could be essential to overcome these hurdles.
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Affiliation(s)
- Joeky T Senders
- Department of Neurosurgery, University Medical Center, Utrecht, the Netherlands.,Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Omar Arnaout
- Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.,Department of Neurological Surgery, Northwestern University School of Medicine, Chicago, Illinois
| | - Aditya V Karhade
- Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Hormuzdiyar H Dasenbrock
- Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - William B Gormley
- Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Marike L Broekman
- Department of Neurosurgery, University Medical Center, Utrecht, the Netherlands.,Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Timothy R Smith
- Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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19
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Bharath RD, Panda R, Raj J, Bhardwaj S, Sinha S, Chaitanya G, Raghavendra K, Mundlamuri RC, Arimappamagan A, Rao MB, Rajeshwaran J, Thennarasu K, Majumdar KK, Satishchandra P, Gandhi TK. Machine learning identifies "rsfMRI epilepsy networks" in temporal lobe epilepsy. Eur Radiol 2019; 29:3496-3505. [PMID: 30734849 DOI: 10.1007/s00330-019-5997-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Revised: 12/05/2018] [Accepted: 01/03/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE. METHODS Probabilistic independent component analysis (PICA) was applied to rsfMRI data from 132 subjects (42 TLE patients + 90 healthy controls) and 88 independent components (ICs) were obtained following standard procedures. Elastic net-selected features were used as inputs to support vector machine (SVM). The strengths of the top 10 networks were correlated with clinical features to obtain "rsfMRI epilepsy networks." RESULTS SVM could classify individuals with epilepsy with 97.5% accuracy (sensitivity = 100%, specificity = 94.4%). Ten networks with the highest ranking were found in the frontal, perisylvian, cingulo-insular, posterior-quadrant, thalamic, cerebello-thalamic, and temporo-thalamic regions. The posterior-quadrant, cerebello-thalamic, thalamic, medial-visual, and perisylvian networks revealed significant correlation (r > 0.40) with age at onset of seizures, the frequency of seizures, duration of illness, and a number of anti-epileptic drugs. CONCLUSIONS IC-derived rsfMRI networks contain epilepsy-related networks and machine learning methods are useful in identifying these networks in vivo. Increased network strength with disease progression in these "rsfMRI epilepsy networks" could reflect epileptogenesis in TLE. KEY POINTS • ICA of resting-state fMRI carries disease-specific information about epilepsy. • Machine learning can classify these components with 97.5% accuracy. • "Subject-specific epilepsy networks" could quantify "epileptogenesis" in vivo.
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Affiliation(s)
- Rose Dawn Bharath
- Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India.,Advance Brain Imaging Facility, Cognitive Neuroscience Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India
| | - Rajanikant Panda
- Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India.,Advance Brain Imaging Facility, Cognitive Neuroscience Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India.,Coma Science Group, GIGA-Consciousness, Universitè de Liège, Liège, Belgium
| | - Jeetu Raj
- Department of Computer Science, Indian Institute of Technology Delhi, New Delhi, Delhi, 110016, India
| | - Sujas Bhardwaj
- Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India.,Advance Brain Imaging Facility, Cognitive Neuroscience Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India.,Neurology, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India
| | - Sanjib Sinha
- Neurology, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India
| | - Ganne Chaitanya
- Neurology, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India.,Department of Neurology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Kenchaiah Raghavendra
- Neurology, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India
| | - Ravindranadh C Mundlamuri
- Neurology, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India
| | - Arivazhagan Arimappamagan
- Neurosurgery, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India
| | - Malla Bhaskara Rao
- Neurosurgery, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India
| | - Jamuna Rajeshwaran
- Neuropsychology, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India
| | - Kandavel Thennarasu
- Biostatistics, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India
| | - Kaushik K Majumdar
- Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore, Karnataka, 560059, India
| | - Parthasarthy Satishchandra
- Neurosurgery, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India
| | - Tapan K Gandhi
- Department of Electrical Engineering, Indian Institute of Technology Delhi, (IIT-D), New Delhi, Delhi, 110016, India.
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20
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Harary M, Smith TR, Gormley WB, Arnaout O. Letter: Big Data Research in Neurosurgery: A Critical Look at This Popular New Study Design. Neurosurgery 2018; 82:E186-E187. [PMID: 29618111 DOI: 10.1093/neuros/nyy085] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Maya Harary
- Department of Neurological Surgery Computational Neuroscience Outcomes Center Brigham and Women's Hospital Harvard Medical School Boston, Massachusetts
| | - Timothy R Smith
- Department of Neurological Surgery Computational Neuroscience Outcomes Center Brigham and Women's Hospital Harvard Medical School Boston, Massachusetts
| | - William B Gormley
- Department of Neurological Surgery Computational Neuroscience Outcomes Center Brigham and Women's Hospital Harvard Medical School Boston, Massachusetts
| | - Omar Arnaout
- Department of Neurological Surgery Computational Neuroscience Outcomes Center Brigham and Women's Hospital Harvard Medical School Boston, Massachusetts
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21
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Machine Learning Applications to Resting-State Functional MR Imaging Analysis. Neuroimaging Clin N Am 2017; 27:609-620. [DOI: 10.1016/j.nic.2017.06.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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22
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Verhoeven T, Coito A, Plomp G, Thomschewski A, Pittau F, Trinka E, Wiest R, Schaller K, Michel C, Seeck M, Dambre J, Vulliemoz S, van Mierlo P. Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes. NEUROIMAGE-CLINICAL 2017. [PMID: 29527470 PMCID: PMC5842753 DOI: 10.1016/j.nicl.2017.09.021] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Objective To diagnose and lateralise temporal lobe epilepsy (TLE) by building a classification system that uses directed functional connectivity patterns estimated during EEG periods without visible pathological activity. Methods Resting-state high-density EEG recording data from 20 left TLE patients, 20 right TLE patients and 35 healthy controls was used. Epochs without interictal spikes were selected. The cortical source activity was obtained for 82 regions of interest and whole-brain directed functional connectivity was estimated in the theta, alpha and beta frequency bands. These connectivity values were then used to build a classification system based on two two-class Random Forests classifiers: TLE vs healthy controls and left vs right TLE. Feature selection and classifier training were done in a leave-one-out procedure to compute the mean classification accuracy. Results The diagnosis and lateralization classifiers achieved a high accuracy (90.7% and 90.0% respectively), sensitivity (95.0% and 90.0% respectively) and specificity (85.7% and 90.0% respectively). The most important features for diagnosis were the outflows from left and right medial temporal lobe, and for lateralization the right anterior cingulate cortex. The interaction between features was important to achieve correct classification. Significance This is the first study to automatically diagnose and lateralise TLE based on EEG. The high accuracy achieved demonstrates the potential of directed functional connectivity estimated from EEG periods without visible pathological activity for helping in the diagnosis and lateralization of TLE. Both classifiers for TLE diagnosis and lateralization achieved high accuracy (90%). Outflow from left and right hippocampus was the most important for diagnosis. Outflow from the right ACC was the most important for lateralization. The interaction between features is important for a correct classification.
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Affiliation(s)
- Thibault Verhoeven
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium.
| | - Ana Coito
- Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Gijs Plomp
- Perceptual Networks Group, Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Aljoscha Thomschewski
- Department of Neurology, Paracelsus Medical University and Center for Cognitive Neuroscience, Salzburg, Austria
| | - Francesca Pittau
- Epilepsy Unit, Neurology Clinic, University Hospital of Geneva, Geneva, Switzerland
| | - Eugen Trinka
- Department of Neurology, Paracelsus Medical University and Center for Cognitive Neuroscience, Salzburg, Austria
| | - Roland Wiest
- Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Karl Schaller
- Neurosurgery Clinic, University Hospital Geneva, Geneva, Switzerland
| | - Christoph Michel
- Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Margitta Seeck
- Epilepsy Unit, Neurology Clinic, University Hospital of Geneva, Geneva, Switzerland
| | - Joni Dambre
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Serge Vulliemoz
- Epilepsy Unit, Neurology Clinic, University Hospital of Geneva, Geneva, Switzerland
| | - Pieter van Mierlo
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium; Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
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23
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Chiang S, Cassese A, Guindani M, Vannucci M, Yeh HJ, Haneef Z, Stern JM. Time-dependence of graph theory metrics in functional connectivity analysis. Neuroimage 2015; 125:601-615. [PMID: 26518632 DOI: 10.1016/j.neuroimage.2015.10.070] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Revised: 10/21/2015] [Accepted: 10/24/2015] [Indexed: 10/22/2022] Open
Abstract
Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations.
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Affiliation(s)
- Sharon Chiang
- Department of Statistics, Rice University, Houston, TX, USA.
| | - Alberto Cassese
- Department of Statistics, Rice University, Houston, TX, USA; Department of Biostatistics, University of Texas at MD Anderson Cancer Center, Houston, TX, USA; Department of Methodology and Statistics, Maastricht University, Maastricht, The Netherlands
| | - Michele Guindani
- Department of Statistics, Rice University, Houston, TX, USA; Department of Biostatistics, University of Texas at MD Anderson Cancer Center, Houston, TX, USA
| | | | - Hsiang J Yeh
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA; Neurology Care Line, Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - John M Stern
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
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24
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Kamiya K, Amemiya S, Suzuki Y, Kunii N, Kawai K, Mori H, Kunimatsu A, Saito N, Aoki S, Ohtomo K. Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy. Magn Reson Med Sci 2015; 15:121-9. [PMID: 26346404 DOI: 10.2463/mrms.2015-0027] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND AND PURPOSE We analyzed the ability of a machine learning approach that uses diffusion tensor imaging (DTI) structural connectomes to determine lateralization of epileptogenicity in temporal lobe epilepsy (TLE). MATERIALS AND METHODS We analyzed diffusion tensor and 3-dimensional (3D) T1-weighted images of 44 patients with TLE (right, 15, left, 29; mean age, 33.0 ± 11.6 years) and 14 age-matched controls. We constructed a whole brain structural connectome for each subject, calculated graph theoretical network measures, and used a support vector machine (SVM) for classification among 3 groups (right TLE versus controls, left TLE versus controls, and right TLE versus left TLE) following a feature reduction process with sparse linear regression. RESULTS In left TLE, we found a significant decrease in local efficiency and the clustering coefficient in several brain regions, including the left posterior cingulate gyrus, left cuneus, and both hippocampi. In right TLE, the right hippocampus showed reduced nodal degree, clustering coefficient, and local efficiency. With use of the leave-one-out cross-validation strategy, the SVM classifier achieved accuracy of 75.9 to 89.7% for right TLE versus controls, 74.4 to 86.0% for left TLE versus controls, and 72.7 to 86.4% for left TLE versus right TLE. CONCLUSION Machine learning of graph theoretical measures from the DTI structural connectome may give support to lateralization of the TLE focus. The present good discrimination between left and right TLE suggests that, with further refinement, the classifier should improve presurgical diagnostic confidence.
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25
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Pustina D, Avants B, Sperling M, Gorniak R, He X, Doucet G, Barnett P, Mintzer S, Sharan A, Tracy J. Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: A multimodal study. Neuroimage Clin 2015; 9:20-31. [PMID: 26288753 PMCID: PMC4536304 DOI: 10.1016/j.nicl.2015.07.010] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 07/11/2015] [Accepted: 07/19/2015] [Indexed: 01/09/2023]
Abstract
Pre-surgical evaluation of patients with temporal lobe epilepsy (TLE) relies on information obtained from multiple neuroimaging modalities. The relationship between modalities and their combined power in predicting the seizure focus is currently unknown. We investigated asymmetries from three different modalities, PET (glucose metabolism), MRI (cortical thickness), and diffusion tensor imaging (DTI; white matter anisotropy) in 28 left and 30 right TLE patients (LTLE and RTLE). Stepwise logistic regression models were built from each modality separately and from all three combined, while bootstrapped methods and split-sample validation verified the robustness of predictions. Among all multimodal asymmetries, three PET asymmetries formed the best predictive model (100% success in full sample, >95% success in split-sample validation). The combinations of PET with other modalities did not perform better than PET alone. Probabilistic classifications were obtained for new clinical cases, which showed correct lateralization for 7/7 new TLE patients (100%) and for 4/5 operated patients with discordant or non-informative PET reports (80%). Metabolism showed closer relationship with white matter in LTLE and closer relationship with gray matter in RTLE. Our data suggest that metabolism is a powerful modality that can predict seizure laterality with high accuracy, and offers high value for automated predictive models. The side of epileptogenic focus can affect the relationship of metabolism with brain structure. The data and tools necessary to obtain classifications for new TLE patients are made publicly available.
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Affiliation(s)
- Dorian Pustina
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Brian Avants
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Michael Sperling
- Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
| | - Richard Gorniak
- Department of Radiology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
| | - Xiaosong He
- Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
| | - Gaelle Doucet
- Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
| | - Paul Barnett
- Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
| | - Scott Mintzer
- Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
| | - Ashwini Sharan
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, USA
| | - Joseph Tracy
- Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
- Department of Radiology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
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26
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Haneef Z, Chiang S, Yeh HJ, Engel J, Stern JM. Functional connectivity homogeneity correlates with duration of temporal lobe epilepsy. Epilepsy Behav 2015; 46:227-33. [PMID: 25873437 PMCID: PMC4458387 DOI: 10.1016/j.yebeh.2015.01.025] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Revised: 01/19/2015] [Accepted: 01/21/2015] [Indexed: 10/23/2022]
Abstract
Temporal lobe epilepsy (TLE) is often associated with progressive changes to seizures, memory, and mood during its clinical course. However, the cerebral changes related to this progression are not well understood. Because the changes may be related to changes in brain networks, we used functional connectivity MRI (fcMRI) to determine whether brain network parameters relate to the duration of TLE. Graph theory-based analysis of the sites of reported regions of TLE abnormality was performed on resting-state fMRI data in 48 subjects: 24 controls, 13 patients with left TLE, and 11 patients with right TLE. Various network parameters were analyzed including betweenness centrality (BC), clustering coefficient (CC), path length (PL), small-world index (SWI), global efficiency (GE), connectivity strength (CS), and connectivity diversity (CD). These were compared for patients with TLE as a group, compared to controls, and for patients with left and right TLE separately. The association of changes in network parameters with epilepsy duration was also evaluated. We found that CC, CS, and CD decreased in subjects with TLE compared to control subjects. Analyzed according to epilepsy duration, patients with TLE showed a progressive reduction in CD. In conclusion, we found that several network parameters decreased in patients with TLE compared to controls, which suggested reduced connectivity in TLE. Reduction in CD associated with epilepsy duration suggests a homogenization of connections over time in TLE, indicating a reduction of the normal repertoire of stronger and weaker connections to other brain regions.
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Affiliation(s)
- Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA; Neurology Care Line, Michael E DeBakey VA Medical Center, Houston, TX, USA.
| | - Sharon Chiang
- Department of Statistics, Rice University, Houston, Texas, USA
| | - Hsiang J. Yeh
- Department of Neurology, University of California, Los Angeles, California, USA
| | - Jerome Engel
- Department of Neurology, University of California, Los Angeles, California, USA
| | - John M. Stern
- Department of Neurology, University of California, Los Angeles, California, USA
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27
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Caciagli L, Bernhardt BC, Hong SJ, Bernasconi A, Bernasconi N. Functional network alterations and their structural substrate in drug-resistant epilepsy. Front Neurosci 2014; 8:411. [PMID: 25565942 PMCID: PMC4263093 DOI: 10.3389/fnins.2014.00411] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Accepted: 11/24/2014] [Indexed: 12/24/2022] Open
Abstract
The advent of MRI has revolutionized the evaluation and management of drug-resistant epilepsy by allowing the detection of the lesion associated with the region that gives rise to seizures. Recent evidence indicates marked chronic alterations in the functional organization of lesional tissue and large-scale cortico-subcortical networks. In this review, we focus on recent methodological developments in functional MRI (fMRI) analysis techniques and their application to the two most common drug-resistant focal epilepsies, i.e., temporal lobe epilepsy related to mesial temporal sclerosis and extra-temporal lobe epilepsy related to focal cortical dysplasia. We put particular emphasis on methodological developments in the analysis of task-free or “resting-state” fMRI to probe the integrity of intrinsic networks on a regional, inter-regional, and connectome-wide level. In temporal lobe epilepsy, these techniques have revealed disrupted connectivity of the ipsilateral mesiotemporal lobe, together with contralateral compensatory reorganization and striking reconfigurations of large-scale networks. In cortical dysplasia, initial observations indicate functional alterations in lesional, peri-lesional, and remote neocortical regions. While future research is needed to critically evaluate the reliability, sensitivity, and specificity, fMRI mapping promises to lend distinct biomarkers for diagnosis, presurgical planning, and outcome prediction.
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Affiliation(s)
- Lorenzo Caciagli
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University Montreal, QC, Canada
| | - Boris C Bernhardt
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University Montreal, QC, Canada
| | - Seok-Jun Hong
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University Montreal, QC, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University Montreal, QC, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University Montreal, QC, Canada
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