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Riederer F, Beiersdorf J, Lang C, Pirker-Kees A, Klein A, Scutelnic A, Platho-Elwischger K, Baumgartner C, Dreier JP, Schankin C. Signatures of migraine aura in high-density-EEG. Clin Neurophysiol 2024; 160:113-120. [PMID: 38422969 DOI: 10.1016/j.clinph.2024.01.008] [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: 10/20/2023] [Revised: 12/17/2023] [Accepted: 01/04/2024] [Indexed: 03/02/2024]
Abstract
OBJECTIVE Cortical spreading depolarization is highly conserved among the species. It is easily detectable in direct cortical surface recordings and has been recorded in the cortex of humans with severe neurological disease. It is considered the pathophysiological correlate of human migraine aura, but direct electrophysiological evidence is still missing. As signatures of cortical spreading depolarization have been recognized in scalp EEG, we investigated typical spontaneous migraine aura, using full band high-density EEG (HD-EEG). METHODS In this prospective study, patients with migraine with aura were investigated during spontaneous migraine aura and interictally. Time compressed HD-EEG were analyzed for the presence of cortical spreading depolarization characterized by (a) slow potential changes below 0.05 Hz, (b) suppression of faster activity from 0.5 Hz - 45 Hz (c) spreading of these changes to neighboring regions during the aura phase. Further, topographical changes in alpha-power spectral density (8-14 Hz) during aura were analyzed. RESULTS In total, 26 HD-EEGs were recorded in patients with migraine with aura, thereof 10 HD-EEGs during aura. Eight HD-EEGs were recorded in the same subject. During aura, no slow potentials were recorded, but alpha-power was significantly decreased in parieto-occipito-temporal location on the hemisphere contralateral to visual aura, lasting into the headache phase. Interictal alpha-power in patients with migraine with aura did not differ significantly from age- and sex-matched healthy controls. CONCLUSIONS Unequivocal signatures of spreading depolarization were not recorded with EEG on the intact scalp in migraine. The decrease in alpha-power contralateral to predominant visual symptoms is consistent with focal depression of spontaneous brain activity as a consequence of cortical spreading depolarization but is not specific thereof. SIGNIFICANCE Cortical spreading depolarization is relevant in migraine, other paroxysmal neurological disorders and neurointensive care.
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Affiliation(s)
- Franz Riederer
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; University of Zurich, Medical Faculty, Zurich, Switzerland.
| | - Johannes Beiersdorf
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology
| | - Clemens Lang
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology; Department of Neurology, Clinic Hietzing, Vienna, Austria
| | - Agnes Pirker-Kees
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology; Department of Neurology, Clinic Hietzing, Vienna, Austria
| | - Antonia Klein
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Adrian Scutelnic
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Kirsten Platho-Elwischger
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology; Department of Neurology, Clinic Hietzing, Vienna, Austria
| | - Christoph Baumgartner
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology; Department of Neurology, Clinic Hietzing, Vienna, Austria
| | - Jens P Dreier
- Department of Neurology and Experimental Neurology Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph Schankin
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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Niddam DM, Lai KL, Hsiao YT, Wang YF, Wang SJ. Grey matter structure within the visual networks in migraine with aura: multivariate and univariate analyses. Cephalalgia 2024; 44:3331024231222637. [PMID: 38170950 DOI: 10.1177/03331024231222637] [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: 01/05/2024]
Abstract
BACKGROUND The visual cortex is involved in the generation of migraine aura. Voxel-based multivariate analyses applied to this region may provide complementary information about aura mechanisms relative to the commonly used mass-univariate analyses. METHODS Structural images constrained within the functional resting-state visual networks were obtained in migraine patients with (n = 50) and without (n = 50) visual aura and healthy controls (n = 50). The masked images entered a multivariate analysis in which Gaussian process classification was used to generate pairwise models. Generalizability was assessed by five-fold cross-validation and non-parametric permutation tests were used to estimate significance levels. A univariate voxel-based morphometry analysis was also performed. RESULTS A multivariate pattern of grey matter voxels within the ventral medial visual network contained significant information related to the diagnosis of migraine with visual aura (aura vs. healthy controls: classification accuracy = 78%, p < 0.001; area under the curve = 0.84, p < 0.001; migraine with aura vs. without aura: classification accuracy = 71%, p < 0.001; area under the curve = 0.73, p < 0.003). Furthermore, patients with visual aura exhibited increased grey matter volume in the medial occipital cortex compared to the two other groups. CONCLUSIONS Migraine with visual aura is characterized by multivariate and univariate patterns of grey matter changes within the medial occipital cortex that have discriminative power and may reflect pathological mechanisms.
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Affiliation(s)
- David M Niddam
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Kuan-Lin Lai
- Department of Neurology, The Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Clinical Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Ting Hsiao
- Department of Neurology, The Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yen-Feng Wang
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, The Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shuu-Jiun Wang
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, The Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, 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 J, Lee DA, Lee HJ, Park KM. Multilayer network changes in patients with migraine. Brain Behav 2023; 13:e3316. [PMID: 37941321 PMCID: PMC10726869 DOI: 10.1002/brb3.3316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/10/2023] Open
Abstract
INTRODUCTION To investigate changes in the multilayer network in patients with migraine compared to healthy controls. METHODS This study enrolled 82 patients with newly diagnosed migraine without aura and 53 healthy controls. Brain magnetic resonance imaging (MRI) was conducted using a 3-tesla MRI scanner, including three-dimensional T1-weighted and diffusion tensor imaging (DTI). A gray matter layer matrix was created with a morphometric similarity network using T1-weighted imaging and the FreeSurfer program. A white matter layer matrix was also created with structural connectivity using the DTI studio (DSI) program. A multilayer network analysis was then performed by applying graph theory using the BRAPH program. RESULTS Significant changes were observed in the multilayer network at the global level in patients with migraines compared to the healthy controls. The multilayer modularity (0.177 vs. 0.160, p = .0005) and average multiplex participation (0.934 vs. 0.924, p = .002) were higher in patients with migraines than in the healthy controls. In contrast, the average multilayer clustering coefficient (0.406 vs. 0.461, p = .0005), average overlapping strength (56.061 vs. 61.676, p = .0005), and average weighted multiplex participation (0.847 vs. 0.878, p = .0005) were lower in patients with migraine than in the healthy controls. In addition, several regions showed significant changes in the multilayer network at the nodal level, including multiplex participation, multilayer clustering coefficients, overlapping strengths, and weighted multiplex participation. CONCLUSION This study demonstrated significant changes in the multilayer network in patients with migraines compared to healthy controls. This could aid an understanding of the complex brain network in patients with migraine and may be associated with the pathophysiology of migraines. Patients with migraine show multilayer network changes in widespreading brain regions compared to healthy controls, and specific brain areas seem to play a hub role for pathophysiology of the migraine.
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Affiliation(s)
- Jinseung Kim
- Department of Family Medicine, Busan Paik Hospital, Inje University College of Medicine, Busan, South Korea
| | - Dong Ah Lee
- Departments of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea
| | - Ho-Joon Lee
- Departments of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea
| | - Kang Min Park
- Departments of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea
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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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Mitrović K, Petrušić I, Radojičić A, Daković M, Savić A. Migraine with aura detection and subtype classification using machine learning algorithms and morphometric magnetic resonance imaging data. Front Neurol 2023; 14:1106612. [PMID: 37441607 PMCID: PMC10333052 DOI: 10.3389/fneur.2023.1106612] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 05/22/2023] [Indexed: 07/15/2023] Open
Abstract
Introduction Migraine with aura (MwA) is a neurological condition manifested in moderate to severe headaches associated with transient visual and somatosensory symptoms, as well as higher cortical dysfunctions. Considering that about 5% of the world's population suffers from this condition and manifestation could be abundant and characterized by various symptoms, it is of great importance to focus on finding new and advanced techniques for the detection of different phenotypes, which in turn, can allow better diagnosis, classification, and biomarker validation, resulting in tailored treatments of MwA patients. Methods This research aimed to test different machine learning techniques to distinguish healthy people from those suffering from MwA, as well as people with simple MwA and those experiencing complex MwA. Magnetic resonance imaging (MRI) post-processed data (cortical thickness, cortical surface area, cortical volume, cortical mean Gaussian curvature, and cortical folding index) was collected from 78 subjects [46 MwA patients (22 simple MwA and 24 complex MwA) and 32 healthy controls] with 340 different features used for the algorithm training. Results The results show that an algorithm based on post-processed MRI data yields a high classification accuracy (97%) of MwA patients and precise distinction between simple MwA and complex MwA with an accuracy of 98%. Additionally, the sets of features relevant to the classification were identified. The feature importance ranking indicates the thickness of the left temporal pole, right lingual gyrus, and left pars opercularis as the most prominent markers for MwA classification, while the thickness of left pericalcarine gyrus and left pars opercularis are proposed as the two most important features for the simple and complex MwA classification. Discussion This method shows significant potential in the validation of MwA diagnosis and subtype classification, which can tackle and challenge the current treatments of MwA.
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Affiliation(s)
- Katarina Mitrović
- Department of Information Technologies, Faculty of Technical Sciences in Čačak, University of Kragujevac, Čačak, Serbia
| | - Igor Petrušić
- Laboratory for Advanced Analysis of Neuroimages, Faculty of Physical Chemistry, University of Belgrade, Belgrade, Serbia
| | - Aleksandra Radojičić
- Headache Center, Neurology Clinic, Clinical Center of Serbia, Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Marko Daković
- Laboratory for Advanced Analysis of Neuroimages, Faculty of Physical Chemistry, University of Belgrade, Belgrade, Serbia
| | - Andrej Savić
- Science and Research Centre, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia
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