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Stubberud A, Langseth H, Nachev P, Matharu MS, Tronvik E. Artificial intelligence and headache. Cephalalgia 2024; 44:3331024241268290. [PMID: 39099427 DOI: 10.1177/03331024241268290] [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] [Indexed: 08/06/2024]
Abstract
BACKGROUND AND METHODS In this narrative review, we introduce key artificial intelligence (AI) and machine learning (ML) concepts, aimed at headache clinicians and researchers. Thereafter, we thoroughly review the use of AI in headache, based on a comprehensive literature search across PubMed, Embase and IEEExplore. Finally, we discuss limitations, as well as ethical and political perspectives. RESULTS We identified six main research topics. First, natural language processing can be used to effectively extract and systematize unstructured headache research data, such as from electronic health records. Second, the most common application of ML is for classification of headache disorders, typically based on clinical record data, or neuroimaging data, with accuracies ranging from around 60% to well over 90%. Third, ML is used for prediction of headache disease trajectories. Fourth, ML shows promise in forecasting of headaches using self-reported data such as triggers and premonitory symptoms, data from wearable sensors and external data. Fifth and sixth, ML can be used for prediction of treatment responses and inference of treatment effects, respectively, aiming to optimize and individualize headache management. CONCLUSIONS The potential uses of AI and ML in headache are broad, but, at present, many studies suffer from poor reporting and lack out-of-sample evaluation, and most models are not validated in a clinical setting.
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Affiliation(s)
- Anker Stubberud
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Helge Langseth
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Computer Science, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Parashkev Nachev
- High Dimensional Neurology Group, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Manjit S Matharu
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Headache and Facial Pain Group, UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK
| | - Erling Tronvik
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology and Clinical Neurophysiology, Neuroclinic, StOlav University Hospital, Trondheim, Norway
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Wei HL, Yu YS, Wang MY, Zhou GP, Li J, Zhang H, Zhou Z. Exploring potential neuroimaging biomarkers for the response to non-steroidal anti-inflammatory drugs in episodic migraine. J Headache Pain 2024; 25:104. [PMID: 38902598 PMCID: PMC11191194 DOI: 10.1186/s10194-024-01812-4] [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: 05/14/2024] [Accepted: 06/13/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND Non-steroidal anti-inflammatory drugs (NSAIDs) are considered first-line medications for acute migraine attacks. However, the response exhibits considerable variability among individuals. Thus, this study aimed to explore a machine learning model based on the percentage of amplitude oscillations (PerAF) and gray matter volume (GMV) to predict the response to NSAIDs in migraine treatment. METHODS Propensity score matching was adopted to match patients having migraine with response and nonresponse to NSAIDs, ensuring consistency in clinical characteristics and migraine-related features. Multimodal magnetic resonance imaging was employed to extract PerAF and GMV, followed by feature selection using the least absolute shrinkage and selection operator regression and recursive feature elimination algorithms. Multiple predictive models were constructed and the final model with the smallest predictive residuals was chosen. The model performance was evaluated using the area under the receiver operating characteristic (ROCAUC) curve, area under the precision-recall curve (PRAUC), balance accuracy (BACC), sensitivity, F1 score, positive predictive value (PPV), and negative predictive value (NPV). External validation was performed using a public database. Then, correlation analysis was performed between the neuroimaging predictors and clinical features in migraine. RESULTS One hundred eighteen patients with migraine (59 responders and 59 non-responders) were enrolled. Six features (PerAF of left insula and left transverse temporal gyrus; and GMV of right superior frontal gyrus, left postcentral gyrus, right postcentral gyrus, and left precuneus) were observed. The random forest model with the lowest predictive residuals was selected and model metrics (ROCAUC, PRAUC, BACC, sensitivity, F1 score, PPV, and NPV) in the training and testing groups were 0.982, 0.983, 0.927, 0.976, 0.930, 0.889, and 0.973; and 0.711, 0.648, 0.639, 0.667,0.649, 0.632, and 0.647, respectively. The model metrics of external validation were 0.631, 0.651, 0.611, 0.808, 0.656, 0.553, and 0.706. Additionally, a significant positive correlation was found between the GMV of the left precuneus and attack time in non-responders. CONCLUSIONS Our findings suggest the potential of multimodal neuroimaging features in predicting the efficacy of NSAIDs in migraine treatment and provide novel insights into the neural mechanisms underlying migraine and its optimized treatment strategy.
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Affiliation(s)
- Heng-Le Wei
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, No.169, Hushan Road, Nanjing, China
| | - Yu-Sheng Yu
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, No.169, Hushan Road, Nanjing, China
| | - Meng-Yao Wang
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, No.169, Hushan Road, Nanjing, China
| | - Gang-Ping Zhou
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, No.169, Hushan Road, Nanjing, China
| | - Junrong Li
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, China.
| | - Hong Zhang
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, No.169, Hushan Road, Nanjing, China.
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
- Department of Radiology, Nanjing Drum Tower Hospital, Nanjing, China.
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Abumalloh RA, Nilashi M, Samad S, Ahmadi H, Alghamdi A, Alrizq M, Alyami S. Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review. Ageing Res Rev 2024; 96:102285. [PMID: 38554785 DOI: 10.1016/j.arr.2024.102285] [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: 08/03/2023] [Revised: 03/20/2024] [Accepted: 03/24/2024] [Indexed: 04/02/2024]
Abstract
Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.
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Affiliation(s)
- Rabab Ali Abumalloh
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar
| | - Mehrbakhsh Nilashi
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam; School of Computer Science, Duy Tan University, Da Nang, Vietnam; UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, Cheras, Kuala Lumpur 56000, Malaysia; Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, Penang 11800, Malaysia.
| | - Sarminah Samad
- Faculty of Business, UNITAR International University, Tierra Crest, Jalan SS6/3, Petaling Jaya, Selangor 47301, Malaysia
| | - Hossein Ahmadi
- Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth PL4 8AA, UK
| | - Abdullah Alghamdi
- Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia; AI Lab, Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia
| | - Mesfer Alrizq
- Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia; AI Lab, Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia
| | - Sultan Alyami
- AI Lab, Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia; Computer Science Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
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Khan L, Shahreen M, Qazi A, Jamil Ahmed Shah S, Hussain S, Chang HT. Migraine headache (MH) classification using machine learning methods with data augmentation. Sci Rep 2024; 14:5180. [PMID: 38431729 PMCID: PMC10908834 DOI: 10.1038/s41598-024-55874-0] [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] [Received: 05/26/2023] [Accepted: 02/28/2024] [Indexed: 03/05/2024] Open
Abstract
Migraine headache, a prevalent and intricate neurovascular disease, presents significant challenges in its clinical identification. Existing techniques that use subjective pain intensity measures are insufficiently accurate to make a reliable diagnosis. Even though headaches are a common condition with poor diagnostic specificity, they have a significant negative influence on the brain, body, and general human function. In this era of deeply intertwined health and technology, machine learning (ML) has emerged as a crucial force in transforming every aspect of healthcare, utilizing advanced facilities ML has shown groundbreaking achievements related to developing classification and automatic predictors. With this, deep learning models, in particular, have proven effective in solving complex problems spanning computer vision and data analytics. Consequently, the integration of ML in healthcare has become vital, especially in developing countries where limited medical resources and lack of awareness prevail, the urgent need to forecast and categorize migraines using artificial intelligence (AI) becomes even more crucial. By training these models on a publicly available dataset, with and without data augmentation. This study focuses on leveraging state-of-the-art ML algorithms, including support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DST), and deep neural networks (DNN), to predict and classify various types of migraines. The proposed models with data augmentations were trained to classify seven various types of migraine. The proposed models with data augmentations were trained to classify seven various types of migraine. The revealed results show that DNN, SVM, KNN, DST, and RF achieved an accuracy of 99.66%, 94.60%, 97.10%, 88.20%, and 98.50% respectively with data augmentation highlighting the transformative potential of AI in enhancing migraine diagnosis.
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Affiliation(s)
- Lal Khan
- Department of Computer Science, Ibadat International University Islamabad Pakpattan Campus, Pakpattan, Pakistan
| | - Moudasra Shahreen
- Department of Computer Science, Mir Chakar Khan Rind University, Sibi, Pakistan
| | - Atika Qazi
- Centre for Lifelong Learning, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei Darussalam
| | | | - Sabir Hussain
- Department of Agriculture, Mir Chakar Khan Rind University, Sibi, Pakistan
| | - Hsien-Tsung Chang
- Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan.
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
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Mitrović K, Savić AM, Radojičić A, Daković M, Petrušić I. Machine learning approach for Migraine Aura Complexity Score prediction based on magnetic resonance imaging data. J Headache Pain 2023; 24:169. [PMID: 38105182 PMCID: PMC10726649 DOI: 10.1186/s10194-023-01704-z] [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: 10/19/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND Previous studies have developed the Migraine Aura Complexity Score (MACS) system. MACS shows great potential in studying the complexity of migraine with aura (MwA) pathophysiology especially when implemented in neuroimaging studies. The use of sophisticated machine learning (ML) algorithms, together with deep profiling of MwA, could bring new knowledge in this field. We aimed to test several ML algorithms to study the potential of structural cortical features for predicting the MACS and therefore gain a better insight into MwA pathophysiology. METHODS The data set used in this research consists of 340 MRI features collected from 40 MwA patients. Average MACS score was obtained for each subject. Feature selection for ML models was performed using several approaches, including a correlation test and a wrapper feature selection methodology. Regression was performed with the Support Vector Machine (SVM), Linear Regression, and Radial Basis Function network. RESULTS SVM achieved a 0.89 coefficient of determination score with a wrapper feature selection. The results suggest a set of cortical features, located mostly in the parietal and temporal lobes, that show changes in MwA patients depending on aura complexity. CONCLUSIONS The SVM algorithm demonstrated the best potential in average MACS prediction when using a wrapper feature selection methodology. The proposed method achieved promising results in determining MwA complexity, which can provide a basis for future MwA studies and the development of MwA diagnosis and treatment.
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Affiliation(s)
- Katarina Mitrović
- Department of Information Technologies, Faculty of Technical Sciences Čačak, University of Kragujevac, 65 Svetog Save, Čačak, 32000, Serbia.
| | - Andrej M Savić
- Science and Research Centre, University of Belgrade - School of Electrical Engineering, University of Belgrade, 73 Bulevar kralja Aleksandra, Belgrade, 11000, Serbia
| | - Aleksandra Radojičić
- Headache Center, Neurology Clinic, University Clinical Centre of Serbia, 6 dr Subotića starijeg, Belgrade, 11000, Serbia
- Faculty of Medicine, University of Belgrade, 8 dr Subotića starijeg, Belgrade, 11000, Serbia
| | - Marko Daković
- Laboratory for Advanced Analysis of Neuroimages, Faculty of Physical Chemistry, University of Belgrade, 12-16 Studentski trg, Belgrade, 11000, Serbia
| | - Igor Petrušić
- Laboratory for Advanced Analysis of Neuroimages, Faculty of Physical Chemistry, University of Belgrade, 12-16 Studentski trg, Belgrade, 11000, Serbia
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Kim SJ, Yang K, Kim D. Quantitative electroencephalography as a potential biomarker in migraine. Brain Behav 2023; 13:e3282. [PMID: 37815172 PMCID: PMC10726885 DOI: 10.1002/brb3.3282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/26/2023] [Accepted: 09/28/2023] [Indexed: 10/11/2023] Open
Abstract
OBJECTIVE The aim of this study was to investigate the utility of quantitative electroencephalography (QEEG) as a diagnostic tool for migraine and as an indicator of treatment response by comparing QEEG characteristics between migraine patients and controls, and monitoring changes in these characteristics alongside clinical symptoms in response to treatment BACKGROUND: We hypothesized that patients with migraine exhibit distinctive characteristics in QEEG measurements, which could be used as potential diagnostic biomarkers and as a tool for monitoring treatment response. METHODS A total of 720 patients were included in the study, comprising 619 patients with migraine and 101 subjects as a control group. QEEG measurements were analyzed for absolute power across specific frequency bands: delta wave (0.5-4 Hz), theta wave (4-8 Hz), alpha wave (8-12 Hz), beta wave (12-25 Hz), and high beta wave (25-30 Hz). The absolute power was normalized against a normative dataset from NeuroGuide, with electrodes being highlighted for significance if they exceeded 1.96. Clinical symptoms were also monitored for correlation with QEEG changes. RESULTS Our analysis showed that patients with migraine exhibited significantly higher absolute power across all frequencies, most markedly within the high beta frequency range. When considering electrodes with z-scores exceeding the threshold of 1.96 in the high beta range, a significant association with migraine diagnosis was observed (per 1 electrode increase, OR 1.06; 95% CI 1.01-1.11; p = .012). Moreover, pre- and posttreatment changes in QEEG measurements corresponded with changes in clinical symptoms. CONCLUSION Patients with migraine have distinctive QEEG measurements, particularly regarding absolute power and the number of electrodes that surpassed the z-score threshold in high beta wave activity. These findings suggest the potential of QEEG as a diagnostic biomarker and as a tool for monitoring treatment response in migraine patients, warranting further large-scale studies for confirmation and expansion.
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Affiliation(s)
- Suk Jae Kim
- Samsung Smart Neurology ClinicCheonanChungcheongnam‐doSouth Korea
| | - Kyungjin Yang
- PE Research Lab, SK Hynix Inc.IcheonGyeonggi‐doSouth Korea
| | - Daeyoung Kim
- Department of NeurologyChungnam National University College of Medicine, Chungnam National University HospitalDaejeonSouth Korea
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Hsiao FJ, Chen WT, Wu YT, Pan LLH, Wang YF, Chen SP, Lai KL, Coppola G, Wang SJ. Characteristic oscillatory brain networks for predicting patients with chronic migraine. J Headache Pain 2023; 24:139. [PMID: 37848845 PMCID: PMC10583316 DOI: 10.1186/s10194-023-01677-z] [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] [Received: 09/05/2023] [Accepted: 10/05/2023] [Indexed: 10/19/2023] Open
Abstract
To determine specific resting-state network patterns underlying alterations in chronic migraine, we employed oscillatory connectivity and machine learning techniques to distinguish patients with chronic migraine from healthy controls and patients with other pain disorders. This cross-sectional study included 350 participants (70 healthy controls, 100 patients with chronic migraine, 40 patients with chronic migraine with comorbid fibromyalgia, 35 patients with fibromyalgia, 30 patients with chronic tension-type headache, and 75 patients with episodic migraine). We collected resting-state magnetoencephalographic data for analysis. Source-based oscillatory connectivity within each network, including the pain-related network, default mode network, sensorimotor network, visual network, and insula to default mode network, was examined to determine intrinsic connectivity across a frequency range of 1-40 Hz. Features were extracted to establish and validate classification models constructed using machine learning algorithms. The findings indicated that oscillatory connectivity revealed brain network abnormalities in patients with chronic migraine compared with healthy controls, and that oscillatory connectivity exhibited distinct patterns between various pain disorders. After the incorporation of network features, the best classification model demonstrated excellent performance in distinguishing patients with chronic migraine from healthy controls, achieving high accuracy on both training and testing datasets (accuracy > 92.6% and area under the curve > 0.93). Moreover, in validation tests, classification models exhibited high accuracy in discriminating patients with chronic migraine from all other groups of patients (accuracy > 75.7% and area under the curve > 0.8). In conclusion, oscillatory synchrony within the pain-related network and default mode network corresponded to altered neurophysiological processes in patients with chronic migraine. Thus, these networks can serve as pivotal signatures in the model for identifying patients with chronic migraine, providing reliable and generalisable results. This approach may facilitate the objective and individualised diagnosis of migraine.
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Affiliation(s)
- Fu-Jung Hsiao
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Wei-Ta Chen
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, 11217, Taiwan
- Department of Neurology, Keelung Hospital, Ministry of Health and Welfare, Keelung, Taiwan
| | - Yu-Te Wu
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Li-Ling Hope Pan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen-Feng Wang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, 11217, Taiwan
| | - Shih-Pin Chen
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, 11217, Taiwan
| | - Kuan-Lin Lai
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, 11217, Taiwan
| | - Gianluca Coppola
- Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Polo Pontino, Latina, Italy
| | - Shuu-Jiun Wang
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, 11217, Taiwan.
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Riederer F, Beiersdorf J, Scutelnic A, Schankin CJ. Migraine Aura-Catch Me If You Can with EEG and MRI-A Narrative Review. Diagnostics (Basel) 2023; 13:2844. [PMID: 37685382 PMCID: PMC10486733 DOI: 10.3390/diagnostics13172844] [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: 07/20/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Roughly one-third of migraine patients suffer from migraine with aura, characterized by transient focal neurological symptoms or signs such as visual disturbance, sensory abnormalities, speech problems, or paresis in association with the headache attack. Migraine with aura is associated with an increased risk for stroke, epilepsy, and with anxiety disorder. Diagnosis of migraine with aura sometimes requires exclusion of secondary causes if neurological deficits present for the first time or are atypical. It was the aim of this review to summarize EEG an MRI findings during migraine aura in the context of pathophysiological concepts. This is a narrative review based on a systematic literature search. During visual auras, EEG showed no consistent abnormalities related to aura, although transient focal slowing in occipital regions has been observed in quantitative studies. In contrast, in familial hemiplegic migraine (FHM) and migraine with brain stem aura, significant EEG abnormalities have been described consistently, including slowing over the affected hemisphere or bilaterally or suppression of EEG activity. Epileptiform potentials in FHM are most likely attributable to associated epilepsy. The initial perfusion change during migraine aura is probably a short lasting hyperperfusion. Subsequently, perfusion MRI has consistently demonstrated cerebral hypoperfusion usually not restricted to one vascular territory, sometimes associated with vasoconstriction of peripheral arteries, particularly in pediatric patients, and rebound hyperperfusion in later phases. An emerging potential MRI signature of migraine aura is the appearance of dilated veins in susceptibility-weighted imaging, which may point towards the cortical regions related to aura symptoms ("index vein"). Conclusions: Cortical spreading depression (CSD) cannot be directly visualized but there are probable consequences thereof that can be captured Non-invasive detection of CSD is probably very challenging in migraine. Future perspectives will be elaborated based on the studies summarized.
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Affiliation(s)
- Franz Riederer
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, CH 3010 Bern, Switzerland (C.J.S.)
- Department of Neurology, University Hospital Zurich, Medical Faculty, University of Zurich, CH 8091 Zurich, Switzerland
| | - Johannes Beiersdorf
- Karl Landsteiner Institute for Clinical Epilepsy Reserach and Cognitive Neurology, AT 1130 Vienna, Austria;
| | - Adrian Scutelnic
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, CH 3010 Bern, Switzerland (C.J.S.)
| | - Christoph J. Schankin
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, CH 3010 Bern, Switzerland (C.J.S.)
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Alpay B, Cimen B, Akaydin E, Bolay H, Sara Y. Levcromakalim provokes an acute rapid-onset migraine-like phenotype without inducing cortical spreading depolarization. J Headache Pain 2023; 24:93. [PMID: 37488480 PMCID: PMC10367339 DOI: 10.1186/s10194-023-01627-9] [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: 06/06/2023] [Accepted: 07/06/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND Migraine headache attacks and accompanying sensory augmentation can be induced by several agents including levcromakalim (LVC), that is also capable of provoking aura-like symptoms in migraineurs. We investigated whether single LVC injection causes acute migraine-like phenotype in rats and induces/modulates cortical spreading depolarization (CSD), a rodent model of migraine aura. METHODS Wistar rats were administered LVC (1 mg/kg, i.p.) and compared to control (CTRL, vehicle, i.p.) and nitroglycerin (NTG, 10 mg/kg, i.p.) groups. Von Frey filaments were used to examine the periorbital and hind paw mechanical allodynia. Dark-light box (DLB), elevated plus maze (EPM), and open field arena (OFA) were used to evaluate light sensitivity and anxiety-related behaviors. The effects of LVC on CSD parameters, somatosensory evoked potentials, and baseline dural EEG (electroencephalography) were investigated. Possible CSD-induced c-fos expression was studied with Western Blot. Blood-brain barrier integrity in cortex was examined with Evans blue assay. RESULTS LVC and NTG administration robustly reduced periorbital mechanical thresholds in rats and induced anxiety-like behaviors and photophobia within 30 and 120 min, respectively. LVC induced migraine-like phenotype recovered in 2 h while NTG group did not fully recover before 4 h. Both LVC and NTG did not provoke DC (direct current) shift, EEG alterations or cortical c-fos expression characteristic to CSD. LVC did not induce de novo CSD and affect KCl (potassium chloride)-induced CSD parameters except for an increase in propagation failure. However, NTG significantly increased both CSD susceptibility and propagation failure. Somatosensory evoked potential (SSEP) configurations were not altered in both LVC and NTG groups, but SSEP latencies were prolonged after CSD. Acute LVC or NTG injection did not increase cortical BBB permeability. CONCLUSIONS Single LVC administration induced the fastest manifestation and recovery of acute migraine-like phenotype which was not mediated by CSD waves in the cerebral cortex. We suppose LVC triggered rapid-onset migraine-like symptoms are probably related to functional alterations in the trigeminal nociceptive system and K+ channel opening properties of LVC. Understanding the neurobiological mechanisms of this nociceptive window, may provide a novel target in migraine treatment.
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Affiliation(s)
- Berkay Alpay
- Department of Medical Pharmacology, Faculty of Medicine, Hacettepe University, Sihhiye, Ankara, Türkiye
- Neuroscience and Neurotechnology Excellence Joint Application and Research Center (NÖROM), Ankara, Türkiye
| | - Bariscan Cimen
- Department of Medical Pharmacology, Faculty of Medicine, Hacettepe University, Sihhiye, Ankara, Türkiye
- Neuroscience and Neurotechnology Excellence Joint Application and Research Center (NÖROM), Ankara, Türkiye
| | - Elif Akaydin
- Department of Medical Pharmacology, Faculty of Medicine, Hacettepe University, Sihhiye, Ankara, Türkiye
- Neuroscience and Neurotechnology Excellence Joint Application and Research Center (NÖROM), Ankara, Türkiye
| | - Hayrunnisa Bolay
- Neuroscience and Neurotechnology Excellence Joint Application and Research Center (NÖROM), Ankara, Türkiye.
- Department of Neurology and Algology, Faculty of Medicine, Gazi University, Besevler, Ankara, Türkiye.
| | - Yildirim Sara
- Department of Medical Pharmacology, Faculty of Medicine, Hacettepe University, Sihhiye, Ankara, Türkiye
- Neuroscience and Neurotechnology Excellence Joint Application and Research Center (NÖROM), Ankara, Türkiye
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Zhuravlev M, Novikov M, Parsamyan R, Selskii A, Runnova A. The Objective Assessment of Event-Related Potentials: An Influence of Chronic Pain on ERP Parameters. Neurosci Bull 2023; 39:1105-1116. [PMID: 36813952 PMCID: PMC10313590 DOI: 10.1007/s12264-023-01035-8] [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: 04/06/2022] [Accepted: 10/07/2022] [Indexed: 02/24/2023] Open
Abstract
The article presents an original method for the automatic assessment of the quality of event-related potentials (ERPs), based on the calculation of the coefficient ε, which describes the compliance of recorded ERPs with some statistically significant parameters. This method was used to analyze the neuropsychological EEG monitoring of patients suffering from migraines. The frequency of migraine attacks was correlated with the spatial distribution of the coefficients ε, calculated for EEG channels. More than 15 migraine attacks per month was accompanied by an increase in calculated values in the occipital region. Patients with infrequent migraines exhibited maximum quality in the frontal areas. The automatic analysis of spatial maps of the coefficient ε demonstrated a statistically significant difference between the two analyzed groups with different means of migraine attack numbers per month.
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Affiliation(s)
- Maksim Zhuravlev
- Coordinating Center for Fundamental Research, National Medical Research Center for Therapy and Preventive Medicine, Moscow, 101000, Russia.
- Institute of Physics, Saratov State University, Saratov, 410012, Russia.
| | - Mikhail Novikov
- Department of Fundamental Research in Neurocardiology, Institute of Cardiological Research, Saratov State Medical University, Saratov, 410012, Russia
| | - Ruzanna Parsamyan
- Department of Fundamental Research in Neurocardiology, Institute of Cardiological Research, Saratov State Medical University, Saratov, 410012, Russia
| | - Anton Selskii
- Institute of Physics, Saratov State University, Saratov, 410012, Russia
- Department of Fundamental Research in Neurocardiology, Institute of Cardiological Research, Saratov State Medical University, Saratov, 410012, Russia
| | - Anastasiya Runnova
- Coordinating Center for Fundamental Research, National Medical Research Center for Therapy and Preventive Medicine, Moscow, 101000, Russia
- Department of Fundamental Research in Neurocardiology, Institute of Cardiological Research, Saratov State Medical University, Saratov, 410012, Russia
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11
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Hsiao FJ, Chen WT, Pan LLH, Liu HY, Wang YF, Chen SP, Lai KL, Coppola G, Wang SJ. Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning. J Headache Pain 2022; 23:130. [PMID: 36192689 PMCID: PMC9531441 DOI: 10.1186/s10194-022-01500-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/21/2022] [Indexed: 11/10/2022] Open
Abstract
To identify and validate the neural signatures of resting-state oscillatory connectivity for chronic migraine (CM), we used machine learning techniques to classify patients with CM from healthy controls (HC) and patients with other pain disorders. The cross-sectional study obtained resting-state magnetoencephalographic data from 240 participants (70 HC, 100 CM, 35 episodic migraine [EM], and 35 fibromyalgia [FM]). Source-based oscillatory connectivity of relevant cortical regions was calculated to determine intrinsic connectivity at 1–40 Hz. A classification model that employed a support vector machine was developed using the magnetoencephalographic data to assess the reliability and generalizability of CM identification. In the findings, the discriminative features that differentiate CM from HC were principally observed from the functional interactions between salience, sensorimotor, and part of the default mode networks. The classification model with these features exhibited excellent performance in distinguishing patients with CM from HC (accuracy ≥ 86.8%, area under the curve (AUC) ≥ 0.9) and from those with EM (accuracy: 94.5%, AUC: 0.96). The model also achieved high performance (accuracy: 89.1%, AUC: 0.91) in classifying CM from other pain disorders (FM in this study). These resting-state magnetoencephalographic electrophysiological features yield oscillatory connectivity to identify patients with CM from those with a different type of migraine and pain disorder, with adequate reliability and generalizability.
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Affiliation(s)
- Fu-Jung Hsiao
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Wei-Ta Chen
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan. .,School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan. .,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, Taiwan, 11217. .,Department of Neurology, Keelung Hospital, Ministry of Health and Welfare, Keelung, Taiwan.
| | - Li-Ling Hope Pan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hung-Yu Liu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, Taiwan, 11217
| | - Yen-Feng Wang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, Taiwan, 11217
| | - Shih-Pin Chen
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, Taiwan, 11217
| | - Kuan-Lin Lai
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, Taiwan, 11217
| | - Gianluca Coppola
- Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome Polo Pontino, Latina, Italy
| | - Shuu-Jiun Wang
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, Taiwan, 11217
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12
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Shin U, Ding C, Zhu B, Vyza Y, Trouillet A, Revol ECM, Lacour SP, Shoaran M. NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC. IEEE JOURNAL OF SOLID-STATE CIRCUITS 2022; 57:3243-3257. [PMID: 36744006 PMCID: PMC9897226 DOI: 10.1109/jssc.2022.3204508] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information for accurate disease-state detection, existing systems have low channel counts and poor scalability, which could limit their therapeutic efficacy. This work presents a highly scalable and versatile closed-loop neural interface SoC that can overcome these limitations. A 256-channel time-division multiplexed (TDM) front-end with a two-step fast-settling mixed-signal DC servo loop (DSL) is proposed to record high-spatial-resolution neural activity and perform channel-selective brain-state inference. A tree-structured neural network (NeuralTree) classification processor extracts a rich set of neural biomarkers in a patient- and disease-specific manner. Trained with an energy-aware learning algorithm, the NeuralTree classifier detects the symptoms of underlying disorders (e.g., epilepsy and movement disorders) at an optimal energy-accuracy trade-off. A 16-channel high-voltage (HV) compliant neurostimulator closes the therapeutic loop by delivering charge-balanced biphasic current pulses to the brain. The proposed SoC was fabricated in 65nm CMOS and achieved a 0.227μJ/class energy efficiency in a compact area of 0.014mm2/channel. The SoC was extensively verified on human electroencephalography (EEG) and intracranial EEG (iEEG) epilepsy datasets, obtaining 95.6%/94% sensitivity and 96.8%/96.9% specificity, respectively. In-vivo neural recordings using soft μECoG arrays and multi-domain biomarker extraction were further performed on a rat model of epilepsy. In addition, for the first time in literature, on-chip classification of rest-state tremor in Parkinson's disease (PD) from human local field potentials (LFPs) was demonstrated.
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Affiliation(s)
- Uisub Shin
- Institute of Electrical and Micro Engineering, EPFL, 1202 Geneva, Switzerland, and the School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Cong Ding
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Bingzhao Zhu
- Institute of Electrical and Micro Engineering, EPFL, 1202 Geneva, Switzerland, and the School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA
| | - Yashwanth Vyza
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Alix Trouillet
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Emilie C M Revol
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Stéphanie P Lacour
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Mahsa Shoaran
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
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13
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Abdulhussein MA, An X, Alsakaa AA, Ming D. Lack of habituation in migraine patients and Evoked Potential types: Analysis study from EEG signals. JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2022. [DOI: 10.1080/02522667.2022.2095958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
- Msallam Abbas Abdulhussein
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
- Faculty of Computer Science and Mathematics, Kufa University, Najaf, Iraq
| | - Xingwei An
- Tianjin International Joint Research Centre for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
| | - Akeel A. Alsakaa
- Department of Computer Science, University of Kerbala, Karbala, Iraq
| | - Dong Ming
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
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14
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Stubberud A, Gray R, Tronvik E, Matharu M, Nachev P. Machine prescription for chronic migraine. Brain Commun 2022; 4:fcac059. [PMID: 35528230 PMCID: PMC9070525 DOI: 10.1093/braincomms/fcac059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/30/2021] [Accepted: 03/08/2022] [Indexed: 11/12/2022] Open
Abstract
Responsive to treatment individually, chronic migraine remains strikingly resistant collectively, incurring the second-highest population burden of disability worldwide. A heterogeneity of responsiveness, requiring prolonged-currently heuristic-individual evaluation of available treatments, may reflect a diversity of causal mechanisms, or the failure to identify the most important, single causal factor. Distinguishing between these possibilities, now possible through the application of complex modelling to large-scale data, is critical to determine the optimal approach to identify new interventions in migraine and making the best use of existing ones. Examining a richly phenotyped cohort of 1446 consecutive unselected patients with chronic migraine, here we use causal multitask Gaussian process models to estimate individual treatment effects across 10 classes of preventatives. Such modelling enables us to quantify the accessibility of heterogeneous responsiveness to high-dimensional modelling, to infer the likely scale of the underlying causal diversity. We calculate the treatment effects in the overall population, and the conditional treatment effects among those modelled to respond and compare the true response rates between these two groups. Identifying a difference in response rates between the groups supports a diversity of causal mechanisms. Moreover, we propose a data-driven machine prescription policy, estimating the time-to-response when sequentially trialling preventatives by individualized treatment effects and comparing it to expert guideline sequences. All model performances are quantified out-of-sample. We identify significantly higher true response rates among individuals modelled to respond, compared with the overall population (mean difference of 0.034; 95% confidence interval 0.003-0.065; P = 0.033), supporting significant heterogeneity of responsiveness and diverse causal mechanisms. The machine prescription policy yields an estimated 35% reduction in time-to-response (3.750 months; 95% confidence interval 3.507-3.993; P < 0.0001) compared with expert guidelines, with no substantive increase in expense per patient. We conclude that the highly distributed mode of causation in chronic migraine necessitates high-dimensional modelling for optimal management. Machine prescription should be considered an essential clinical decision-support tool in the future management of chronic migraine.
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Affiliation(s)
- Anker Stubberud
- Headache and Facial Pain Group, UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK
- High Dimensional Neurology Group, UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Robert Gray
- High Dimensional Neurology Group, UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK
| | - Erling Tronvik
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology, St Olavs Hospital, Trondheim, Norway
| | - Manjit Matharu
- Headache and Facial Pain Group, UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK
| | - Parashkev Nachev
- High Dimensional Neurology Group, UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK
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15
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Yao L, Zhu B, Shoaran M. Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques. J Neural Eng 2022; 19. [DOI: 10.1088/1741-2552/ac4ed1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 01/25/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective: Accurate decoding of individual finger movements is crucial for advanced prosthetic control. In this work, we introduce the use of Riemannian-space features and temporal dynamics of electrocorticography (ECoG) signal combined with modern machine learning tools to improve the motor decoding accuracy at the level of individual fingers. Approach: We selected a set of informative biomarkers that correlated with finger movements and evaluated the performance of state-of-the-art machine learning algorithms on the BCI competition IV dataset (ECoG, three subjects) and a second ECoG dataset with a similar recording paradigm (Stanford, 9 subjects). We further explored the temporal concatenation of features to effectively capture the history of ECoG signal, which led to a significant improvement over single-epoch decoding in both classification (p<0.01) and regression tasks (p<0.01). Main results: Using feature concatenation and gradient boosted trees (the top-performing model), we achieved a classification accuracy of 77.0% in detecting individual finger movements (6-class task, including rest state), improving over the state-of-the-art conditional random fields (CRF) by 11.7% on the 3 BCI competition subjects. In continuous decoding of movement trajectory, our approach resulted in an average Pearson's correlation coefficient (r) of 0.537 across subjects and fingers, outperforming both the BCI competition winner and the state-of-the-art approach reported on the same dataset (CNN+LSTM). Furthermore, our proposed method features a low time complexity, with only <17.2s required for training and <50ms for inference. This enables about 250× speed-up in training compared to previously reported deep learning method with state-of-the-art performance. Significance: The proposed techniques enable fast, reliable, and high-performance prosthetic control through minimally-invasive cortical signals.
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16
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AIM in Clinical Neurophysiology and Electroencephalography (EEG). Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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17
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Zhu B, Shin U, Shoaran M. Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:877-897. [PMID: 34529573 PMCID: PMC8733782 DOI: 10.1109/tbcas.2021.3112756] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions. Neural prostheses capable of multi-channel neural recording, on-site signal processing, rapid symptom detection, and closed-loop stimulation are critical to enabling such novel treatments. However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence. In this paper, we first discuss both commercial and investigational closed-loop neuromodulation devices for brain disorders. Next, we review state-of-the-art neural prostheses with on-chip machine learning, focusing on application-specific integrated circuits (ASIC). System requirements, performance and hardware comparisons, design trade-offs, and hardware optimization techniques are discussed. To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability. Finally, we present several techniques to improve the key design metrics of tree-based on-chip classifiers, both in the context of ensemble methods and oblique structures. A novel Depth-Variant Tree Ensemble (DVTE) is proposed to reduce processing latency (e.g., by 2.5× on seizure detection task). We further develop a cost-aware learning approach to jointly optimize the power and latency metrics. We show that algorithm-hardware co-design enables the energy- and memory-optimized design of tree-based models, while preserving a high accuracy and low latency. Furthermore, we show that our proposed tree-based models feature a highly interpretable decision process that is essential for safety-critical applications such as closed-loop stimulation.
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18
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Yao L, Baker JL, Schiff ND, Purpura KP, Shoaran M. Predicting task performance from biomarkers of mental fatigue in global brain activity. J Neural Eng 2021; 18. [PMID: 33108778 PMCID: PMC8122624 DOI: 10.1088/1741-2552/abc529] [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: 06/17/2020] [Accepted: 10/27/2020] [Indexed: 11/23/2022]
Abstract
Objective. Detection and early prediction of mental fatigue (i.e. shifts in vigilance), could be used to adapt neuromodulation strategies to effectively treat patients suffering from brain injury and other indications with prominent chronic mental fatigue. Approach. In this study, we analyzed electrocorticography (ECoG) signals chronically recorded from two healthy non-human primates (NHP) as they performed a sustained attention task over extended periods of time. We employed a set of spectrotemporal and connectivity biomarkers of the ECoG signals to identify periods of mental fatigue and a gradient boosting classifier to predict performance, up to several seconds prior to the behavioral response. Main results. Wavelet entropy and the instantaneous amplitude and frequency were among the best single features across sessions in both NHPs. The classification performance using higher order spectral-temporal (HOST) features was significantly higher than that of conventional spectral power features in both NHPs. Across the 99 sessions analyzed, average F1 scores of 77.5%±8.2% and 91.2%±3.6%, and accuracy of 79.5%±8.9% and 87.6%±3.9 % for the classifier were obtained for each animal, respectively. Significance. Our results here demonstrate the feasibility of predicting performance and detecting periods of mental fatigue by analyzing ECoG signals, and that this general approach, in principle, could be used for closed-loop control of neuromodulation strategies.
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Affiliation(s)
- Lin Yao
- Frontiers Science Center for Brain&Brain-machine Integration, Zhejiang University, Hangzhou, Zhejiang 310000, People's Republic of China.,College of Computer Science, Zhejiang University, Hangzhou, Zhejiang 310000, People's Republic of China.,School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, United States of America
| | - Jonathan L Baker
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, United States of America
| | - Nicholas D Schiff
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, United States of America
| | - Keith P Purpura
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, United States of America
| | - Mahsa Shoaran
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, United States of America.,Institute of Electrical Engineering and Center for Neuroprosthetics, Swiss Federal Institute of Technology (EPFL), Geneva 1202, Switzerland
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19
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Somatosensory Gating Responses Are Associated with Prognosis in Patients with Migraine. Brain Sci 2021; 11:brainsci11020166. [PMID: 33525379 PMCID: PMC7911087 DOI: 10.3390/brainsci11020166] [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: 12/28/2020] [Revised: 01/19/2021] [Accepted: 01/25/2021] [Indexed: 12/27/2022] Open
Abstract
Sensory gating, a habituation-related but more basic protective mechanism against brain sensory overload, is altered in patients with migraine and linked to headache severity. This study investigated whether somatosensory (SI) gating responses determined 3-months treatment outcomes in patients with episodic migraine (EM) and chronic migraine (CM). A 306-channel magnetoencephalography (MEG) with paired-pulse stimulation paradigm was used to record their neuromagnetic responses. To calculate the peak amplitude and latency and compute the gating ratios (second vs. first amplitude), the first and second responses to the paired stimuli from the primary somatosensory cortex were obtained. All patients were assigned to subgroups labeled good or poor according to their headache frequency at baseline compared with at the third month of treatment. The gating ratio in the CM group (n = 37) was significantly different between those identified as good and poor (p = 0.009). In the EM group (n = 30), the latency in the second response differed by treatment outcomes (p = 0.007). In the receiver operating characteristic analysis, the areas under the curve for the CM and EM groups were 0.737 and 0.761, respectively. Somatosensory gating responses were associated with treatment outcomes in patients with migraine; future studies with large patient samples are warranted.
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20
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AIM in Clinical Neurophysiology and Electroencephalography (EEG). Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_257-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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21
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Pozo-Rosich P, Coppola G, Pascual J, Schwedt TJ. How does the brain change in chronic migraine? Developing disease biomarkers. Cephalalgia 2020; 41:613-630. [PMID: 33291995 DOI: 10.1177/0333102420974359] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND Validated chronic migraine biomarkers could improve diagnostic, prognostic, and predictive abilities for clinicians and researchers, as well as increase knowledge on migraine pathophysiology. OBJECTIVE The objective of this narrative review is to summarise and interpret the published literature regarding the current state of development of chronic migraine biomarkers. FINDINGS Data from functional and structural imaging, neurophysiological, and biochemical studies have been utilised towards the development of chronic migraine biomarkers. These biomarkers could contribute to chronic migraine classification/diagnosis, prognosticating patient outcomes, predicting response to treatment, and measuring treatment responses early after initiation. Results show promise for using measures of brain structure and function, evoked potentials, and sensory neuropeptide concentrations for the development of chronic migraine biomarkers, yet further optimisation and validation are still required. CONCLUSIONS Imaging, neurophysiological, and biochemical changes that occur with the progression from episodic to chronic migraine could be utilised for developing chronic migraine biomarkers that might assist with diagnosis, prognosticating individual patient outcomes, and predicting responses to migraine therapies. Ultimately, validated biomarkers could move us closer to being able to practice precision medicine in the field and thus improve patient care.
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Affiliation(s)
- Patricia Pozo-Rosich
- Headache Unit, Neurology Department, Hospital Universitari Vall d'Hebron, Barcelona, Spain.,Headache Research Group, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Gianluca Coppola
- Sapienza University of Rome Polo Pontino, Department of Medico-Surgical Sciences and Biotechnologies, Latina, Italy
| | - Julio Pascual
- University of Cantabria and Service of Neurology, University Hospital Marqués de Valdecilla and IDIVAL, Santander, Spain
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22
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Kwon J, Lee H, Cho S, Chung CS, Lee MJ, Park H. Machine learning-based automated classification of headache disorders using patient-reported questionnaires. Sci Rep 2020; 10:14062. [PMID: 32820214 PMCID: PMC7441379 DOI: 10.1038/s41598-020-70992-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 08/10/2020] [Indexed: 01/27/2023] Open
Abstract
Classification of headache disorders is dependent on a subjective self-report from patients and its interpretation by physicians. We aimed to apply objective data-driven machine learning approaches to analyze patient-reported symptoms and test the feasibility of the automated classification of headache disorders. The self-report data of 2162 patients were analyzed. Headache disorders were merged into five major entities. The patients were divided into training (n = 1286) and test (n = 876) cohorts. We trained a stacked classifier model with four layers of XGBoost classifiers. The first layer classified between migraine and others, the second layer classified between tension-type headache (TTH) and others, and the third layer classified between trigeminal autonomic cephalalgia (TAC) and others, and the fourth layer classified between epicranial and thunderclap headaches. Each layer selected different features from the self-reports by using least absolute shrinkage and selection operator. In the test cohort, our stacked classifier obtained accuracy of 81%, sensitivity of 88%, 69%, 65%, 53%, and 51%, and specificity of 95%, 55%, 46%, 48%, and 51% for migraine, TTH, TAC, epicranial headache, and thunderclap headaches, respectively. We showed that a machine-learning based approach is applicable in analyzing patient-reported questionnaires. Our result could serve as a baseline for future studies in headache research.
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Affiliation(s)
- Junmo Kwon
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, 16419, South Korea
| | - Hyebin Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, 16419, South Korea
| | - Soohyun Cho
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Chin-Sang Chung
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Mi Ji Lee
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea.
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, 16419, South Korea. .,School of Electronic and Electrical Engineering, Center for Neuroscience Imaging Research, Sungkyunkwan University, Suwon, 16419, South Korea.
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23
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Zhu B, Farivar M, Shoaran M. ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:692-704. [PMID: 32746347 DOI: 10.1109/tbcas.2020.3004544] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision trees to enable resource-efficient classification on a neural implant. By integrating model compression with probabilistic routing and implementing cost-aware learning, our proposed model could significantly reduce the memory and hardware cost compared to state-of-the-art models, while maintaining the classification accuracy. We trained the resource-efficient oblique tree with power-efficient regularization (ResOT-PE) on three neural classification tasks to evaluate the performance, memory, and hardware requirements. On seizure detection task, we were able to reduce the model size by 3.4× and the feature extraction cost by 14.6× compared to the ensemble of boosted trees, using the intracranial EEG from 10 epilepsy patients. In a second experiment, we tested the ResOT-PE model on tremor detection for Parkinson's disease, using the local field potentials from 12 patients implanted with a deep-brain stimulation (DBS) device. We achieved a comparable classification performance as the state-of-the-art boosted tree ensemble, while reducing the model size and feature extraction cost by 10.6× and 6.8×, respectively. We also tested on a 6-class finger movement detection task using ECoG recordings from 9 subjects, reducing the model size by 17.6× and feature computation cost by 5.1×. The proposed model can enable a low-power and memory-efficient implementation of classifiers for real-time neurological disease detection and motor decoding.
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The visual system as target of non-invasive brain stimulation for migraine treatment: Current insights and future challenges. PROGRESS IN BRAIN RESEARCH 2020. [PMID: 33008507 DOI: 10.1016/bs.pbr.2020.05.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
The visual network is crucially implicated in the pathophysiology of migraine. Several lines of evidence indicate that migraine is characterized by an altered visual cortex excitability both during and between attacks. Visual symptoms, the most common clinical manifestation of migraine aura, are likely the result of cortical spreading depression originating from the extrastriate area V3A. Photophobia, a clinical hallmark of migraine, is linked to an abnormal sensory processing of the thalamus which is converged with the non-image forming visual pathway. Finally, visual snow is an increasingly recognized persistent visual phenomenon in migraine, possibly caused by increased perception of subthreshold visual stimuli. Emerging research in non-invasive brain stimulation (NIBS) has vastly developed into a diversity of areas with promising potential. One of its clinical applications is the single-pulse transcranial magnetic stimulation (sTMS) applied over the occipital cortex which has been approved for treating migraine with aura, albeit limited evidence. Studies have also investigated other NIBS techniques, such as repetitive TMS (rTMS) and transcranial direct current stimulation (tDCS), for migraine prophylaxis but with conflicting results. As a dynamic brain disorder with widespread pathophysiology, targeting migraine with NIBS is challenging. Furthermore, unlike the motor cortex, evidence suggests that the visual cortex may be less plastic. Controversy exists as to whether the same fundamental principles of NIBS, based mainly on findings in the motor cortex, can be applied to the visual cortex. This review aims to explore existing literature surrounding NIBS studies on the visual system of migraine. We will first provide an overview highlighting the direct implication of the visual network in migraine. Next, we will focus on the rationale behind using NIBS for migraine treatment, including its effects on the visual cortex, and the shortcomings of currently available evidence. Finally, we propose a broader perspective of how novel approaches, the concept of brain networks and the integration of multimodal imaging with computational modeling, can help refine current NIBS methods, with the ultimate goal of optimizing a more individualized treatment for migraine.
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Ferroni P, Zanzotto FM, Scarpato N, Spila A, Fofi L, Egeo G, Rullo A, Palmirotta R, Barbanti P, Guadagni F. Machine learning approach to predict medication overuse in migraine patients. Comput Struct Biotechnol J 2020; 18:1487-1496. [PMID: 32637046 PMCID: PMC7327028 DOI: 10.1016/j.csbj.2020.06.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 05/19/2020] [Accepted: 06/05/2020] [Indexed: 11/23/2022] Open
Abstract
Machine learning (ML) is largely used to develop automatic predictors in migraine classification but automatic predictors for medication overuse (MO) in migraine are still in their infancy. Thus, to understand the benefits of ML in MO prediction, we explored an automated predictor to estimate MO risk in migraine. To achieve this objective, a study was designed to analyze the performance of a customized ML-based decision support system that combines support vector machines and Random Optimization (RO-MO). We used RO-MO to extract prognostic information from demographic, clinical and biochemical data. Using a dataset of 777 consecutive migraine patients we derived a set of predictors with discriminatory power for MO higher than that observed for baseline SVM. The best four were incorporated into the final RO-MO decision support system and risk evaluation on a five-level stratification was performed. ROC analysis resulted in a c-statistic of 0.83 with a sensitivity and specificity of 0.69 and 0.87, respectively, and an accuracy of 0.87 when MO was predicted by at least three RO-MO models. Logistic regression analysis confirmed that the derived RO-MO system could effectively predict MO with ORs of 5.7 and 21.0 for patients classified as probably (3 predictors positive), or definitely at risk of MO (4 predictors positive), respectively. In conclusion, a combination of ML and RO - taking into consideration clinical/biochemical features, drug exposure and lifestyle - might represent a valuable approach to MO prediction in migraine and holds the potential for improving model precision through weighting the relative importance of attributes.
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Key Words
- AI, Artificial Intelligence
- AUC, Area Under the Curve
- Artificial intelligence
- BMI, body mass index
- CI, Confidence Interval
- DBH 19-bp I/D polymorphism, Dopamine-Beta-Hydroxylase 19 bp insertion/deletion polymorphism
- DSS, Decision Support System
- Decision support systems
- ICT, Information and Communications Technology
- KELP, Kernel-based Learning Platform
- LRs, likelihood ratios
- MKL, Multiple Kernel Learning
- ML, Machine Learning
- MO, Medication Overuse
- Machine learning
- Medication overuse
- Migraine
- NSAID, nonsteroidal anti-inflammatory drugs
- PVI, Predictive Value Imputation
- RO, Random Optimization
- ROC, Receiver operating characteristic
- SE, Standard Error
- SVM, Support Vector Machine
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Affiliation(s)
- Patrizia Ferroni
- BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy
- Dept. of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, Via di Val Cannuta 247, 00166 Rome, Italy
| | - Fabio M. Zanzotto
- Department of Enterprise Engineering, University of Rome “Tor Vergata”, Viale Oxford 81, 00133 Rome, Italy
| | - Noemi Scarpato
- Dept. of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, Via di Val Cannuta 247, 00166 Rome, Italy
| | - Antonella Spila
- BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy
| | - Luisa Fofi
- Headache and Pain Unit, Dept. of Neurological, Motor and Sensorial Sciences, IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy
| | - Gabriella Egeo
- Headache and Pain Unit, Dept. of Neurological, Motor and Sensorial Sciences, IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy
| | - Alessandro Rullo
- Neatec S.p.A., Via Campi Flegrei, 34, 80078 Pozzuoli, Naples, Italy
| | - Raffaele Palmirotta
- Department of Biomedical Sciences & Human Oncology, University of Bari ‘Aldo Moro’, Bari, Italy
| | - Piero Barbanti
- Dept. of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, Via di Val Cannuta 247, 00166 Rome, Italy
- Headache and Pain Unit, Dept. of Neurological, Motor and Sensorial Sciences, IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy
| | - Fiorella Guadagni
- BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy
- Dept. of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, Via di Val Cannuta 247, 00166 Rome, Italy
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Yao L, Brown P, Shoaran M. Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering. Clin Neurophysiol 2020; 131:274-284. [PMID: 31744673 PMCID: PMC6927801 DOI: 10.1016/j.clinph.2019.09.021] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 07/25/2019] [Accepted: 09/10/2019] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Accurate and reliable detection of tremor onset in Parkinson's disease (PD) is critical to the success of adaptive deep brain stimulation (aDBS) therapy. Here, we investigated the potential use of feature engineering and machine learning methods for more accurate detection of rest tremor in PD. METHODS We analyzed the local field potential (LFP) recordings from the subthalamic nucleus region in 12 patients with PD (16 recordings). To explore the optimal biomarkers and the best performing classifier, the performance of state-of-the-art machine learning (ML) algorithms and various features of the subthalamic LFPs were compared. We further used a Kalman filtering technique in feature domain to reduce the false positive rate. RESULTS The Hjorth complexity showed a higher correlation with tremor, compared to other features in our study. In addition, by optimal selection of a maximum of five features with a sequential feature selection method and using the gradient boosted decision trees as the classifier, the system could achieve an average F1 score of up to 88.7% and a detection lead of 0.52 s. The use of Kalman filtering in feature space significantly improved the specificity by 17.0% (p = 0.002), thereby potentially reducing the unnecessary power dissipation of the conventional DBS system. CONCLUSION The use of relevant features combined with Kalman filtering and machine learning improves the accuracy of tremor detection during rest. SIGNIFICANCE The proposed method offers a potential solution for efficient on-demand stimulation for PD tremor.
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Affiliation(s)
- Lin Yao
- ECE Department, Cornell University, Ithaca, NY, USA.
| | - Peter Brown
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
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Marucco E, Lisicki M, Magis D. Electrophysiological Characteristics of the Migraine Brain: Current Knowledge and Perspectives. Curr Med Chem 2018; 26:6222-6235. [PMID: 29956611 DOI: 10.2174/0929867325666180627130811] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 02/16/2018] [Accepted: 03/27/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND Despite pain being its most prominent feature, migraine is primarily a disorder of sensory processing. Electrophysiology-based research in the field has consistently developed over the last fifty years. OBJECTIVE To summarize the current knowledge on the electrophysiological characteristics of the migraine brain, and discuss perspectives. METHODS We critically reviewed the literature on the topic to present and discuss articles selected on the basis of their significance and/or novelty. RESULTS Physiologic fluctuations within time, between-subject differences, and methodological issues account as major limitations of electrophysiological research in migraine. Nonetheless, several abnormalities revealed through different approaches have been described in the literature. Altogether, these results are compatible with an abnormal state of sensory processing. PERSPECTIVES The greatest contribution of electrophysiological testing in the future will most probably be the characterization of sub-groups of migraine patients sharing specific electrophysiological traits. This should serve as strategy towards personalized migraine treatment. Incorporation of novel methods of analysis would be worthwhile.
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Affiliation(s)
- Erica Marucco
- University of Liege - Headache Research Unit Liege, Liege, Belgium
| | - Marco Lisicki
- University of Liege - Headache Research Unit Liege, Liege, Belgium
| | - Delphine Magis
- Centre Hospitalier Universitaire de Liege - Headache Research Unit Liege, Liege, Belgium
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