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Owen TW, Janiukstyte V, Hall GR, Chowdhury FA, Diehl B, McEvoy A, Miserocchi A, de Tisi J, Duncan JS, Rugg-Gunn F, Wang Y, Taylor PN. Interictal magnetoencephalography abnormalities to guide intracranial electrode implantation and predict surgical outcome. Brain Commun 2023; 5:fcad292. [PMID: 37953844 PMCID: PMC10636564 DOI: 10.1093/braincomms/fcad292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/24/2023] [Accepted: 10/24/2023] [Indexed: 11/14/2023] Open
Abstract
Intracranial EEG is the gold standard technique for epileptogenic zone localization but requires a preconceived hypothesis of the location of the epileptogenic tissue. This placement is guided by qualitative interpretations of seizure semiology, MRI, EEG and other imaging modalities, such as magnetoencephalography. Quantitative abnormality mapping using magnetoencephalography has recently been shown to have potential clinical value. We hypothesized that if quantifiable magnetoencephalography abnormalities were sampled by intracranial EEG, then patients' post-resection seizure outcome may be better. Thirty-two individuals with refractory neocortical epilepsy underwent magnetoencephalography and subsequent intracranial EEG recordings as part of presurgical evaluation. Eyes-closed resting-state interictal magnetoencephalography band power abnormality maps were derived from 70 healthy controls as a normative baseline. Magnetoencephalography abnormality maps were compared to intracranial EEG electrode implantation, with the spatial overlap of intracranial EEG electrode placement and cerebral magnetoencephalography abnormalities recorded. Finally, we assessed if the implantation of electrodes in abnormal tissue and subsequent resection of the strongest abnormalities determined by magnetoencephalography and intracranial EEG corresponded to surgical success. We used the area under the receiver operating characteristic curve as a measure of effect size. Intracranial electrodes were implanted in brain tissue with the most abnormal magnetoencephalography findings-in individuals that were seizure-free postoperatively (T = 3.9, P = 0.001) but not in those who did not become seizure-free. The overlap between magnetoencephalography abnormalities and electrode placement distinguished surgical outcome groups moderately well (area under the receiver operating characteristic curve = 0.68). In isolation, the resection of the strongest abnormalities as defined by magnetoencephalography and intracranial EEG separated surgical outcome groups well, area under the receiver operating characteristic curve = 0.71 and area under the receiver operating characteristic curve = 0.74, respectively. A model incorporating all three features separated surgical outcome groups best (area under the receiver operating characteristic curve = 0.80). Intracranial EEG is a key tool to delineate the epileptogenic zone and help render individuals seizure-free postoperatively. We showed that data-driven abnormality maps derived from resting-state magnetoencephalography recordings demonstrate clinical value and may help guide electrode placement in individuals with neocortical epilepsy. Additionally, our predictive model of postoperative seizure freedom, which leverages both magnetoencephalography and intracranial EEG recordings, could aid patient counselling of expected outcome.
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Affiliation(s)
- Thomas W Owen
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Vytene Janiukstyte
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Gerard R Hall
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Fahmida A Chowdhury
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Beate Diehl
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Andrew McEvoy
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Anna Miserocchi
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
- NIHR University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - John S Duncan
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
- NIHR University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Fergus Rugg-Gunn
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Peter N Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
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Owen TW, Janiukstyte V, Hall GR, Horsley JJ, McEvoy A, Miserocchi A, de Tisi J, Duncan JS, Rugg‐Gunn F, Wang Y, Taylor PN. Identifying epileptogenic abnormalities through spatial clustering of MEG interictal band power. Epilepsia Open 2023; 8:1151-1156. [PMID: 37254660 PMCID: PMC10472397 DOI: 10.1002/epi4.12767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 05/22/2023] [Indexed: 06/01/2023] Open
Abstract
Successful epilepsy surgery depends on localizing and resecting cerebral abnormalities and networks that generate seizures. Abnormalities, however, may be widely distributed across multiple discontiguous areas. We propose spatially constrained clusters as candidate areas for further investigation and potential resection. We quantified the spatial overlap between the abnormality cluster and subsequent resection, hypothesizing a greater overlap in seizure-free patients. Thirty-four individuals with refractory focal epilepsy underwent pre-surgical resting-state interictal magnetoencephalography (MEG) recording. Fourteen individuals were totally seizure-free (ILAE 1) after surgery and 20 continued to have some seizures post-operatively (ILAE 2+). Band power abnormality maps were derived using controls as a baseline. Patient abnormalities were spatially clustered using the k-means algorithm. The tissue within the cluster containing the most abnormal region was compared with the resection volume using the dice score. The proposed abnormality cluster overlapped with the resection in 71% of ILAE 1 patients. Conversely, an overlap only occurred in 15% of ILAE 2+ patients. This effect discriminated outcome groups well (AUC = 0.82). Our novel approach identifies clusters of spatially similar tissue with high abnormality. This is clinically valuable, providing (a) a data-driven framework to validate current hypotheses of the epileptogenic zone localization or (b) to guide further investigation.
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Affiliation(s)
- Thomas W. Owen
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Vytene Janiukstyte
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Gerard R. Hall
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Jonathan J. Horsley
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Andrew McEvoy
- UCL Queen Square Institute of NeurologyLondonUK
- National Hospital for Neurology & NeurosurgeryLondonUK
| | - Anna Miserocchi
- UCL Queen Square Institute of NeurologyLondonUK
- National Hospital for Neurology & NeurosurgeryLondonUK
| | - Jane de Tisi
- UCL Queen Square Institute of NeurologyLondonUK
- National Hospital for Neurology & NeurosurgeryLondonUK
- NIHR University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | - John S. Duncan
- UCL Queen Square Institute of NeurologyLondonUK
- National Hospital for Neurology & NeurosurgeryLondonUK
- NIHR University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | - Fergus Rugg‐Gunn
- UCL Queen Square Institute of NeurologyLondonUK
- National Hospital for Neurology & NeurosurgeryLondonUK
| | - Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle upon TyneUK
- UCL Queen Square Institute of NeurologyLondonUK
- National Hospital for Neurology & NeurosurgeryLondonUK
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Peter N. Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle upon TyneUK
- UCL Queen Square Institute of NeurologyLondonUK
- National Hospital for Neurology & NeurosurgeryLondonUK
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
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Janiukstyte V, Owen TW, Chaudhary UJ, Diehl B, Lemieux L, Duncan JS, de Tisi J, Wang Y, Taylor PN. Normative brain mapping using scalp EEG and potential clinical application. Sci Rep 2023; 13:13442. [PMID: 37596291 PMCID: PMC10439201 DOI: 10.1038/s41598-023-39700-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 07/29/2023] [Indexed: 08/20/2023] Open
Abstract
A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.
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Affiliation(s)
- Vytene Janiukstyte
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK
| | - Thomas W Owen
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK
| | - Umair J Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, NE2 4HH, UK
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Peter N Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK.
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, NE2 4HH, UK.
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.
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Janiukstyte V, Owen TW, Chaudhary UJ, Diehl B, Lemieux L, Duncan JS, de Tisi J, Wang Y, Taylor PN. Normative brain mapping using scalp EEG and potential clinical application. ArXiv 2023:arXiv:2304.03204v1. [PMID: 37064533 PMCID: PMC10104182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.
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Affiliation(s)
- Vytene Janiukstyte
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom, NE4 5DG
| | - Thomas W Owen
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom, NE4 5DG
| | - Umair J Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom, NE4 5DG
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom, NE2 4HH
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom, NE4 5DG
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom, NE2 4HH
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
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Owen TW, Schroeder GM, Janiukstyte V, Hall GR, McEvoy A, Miserocchi A, de Tisi J, Duncan JS, Rugg‐Gunn F, Wang Y, Taylor PN. MEG abnormalities and mechanisms of surgical failure in neocortical epilepsy. Epilepsia 2023; 64:692-704. [PMID: 36617392 PMCID: PMC10952279 DOI: 10.1111/epi.17503] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Epilepsy surgery fails to achieve seizure freedom in 30%-40% of cases. It is not fully understood why some surgeries are unsuccessful. By comparing interictal magnetoencephalography (MEG) band power from patient data to normative maps, which describe healthy spatial and population variability, we identify patient-specific abnormalities relating to surgical failure. We propose three mechanisms contributing to poor surgical outcome: (1) not resecting the epileptogenic abnormalities (mislocalization), (2) failing to remove all epileptogenic abnormalities (partial resection), and (3) insufficiently impacting the overall cortical abnormality. Herein we develop markers of these mechanisms, validating them against patient outcomes. METHODS Resting-state MEG recordings were acquired for 70 healthy controls and 32 patients with refractory neocortical epilepsy. Relative band-power spatial maps were computed using source-localized recordings. Patient and region-specific band-power abnormalities were estimated as the maximum absolute z-score across five frequency bands using healthy data as a baseline. Resected regions were identified using postoperative magnetic resonance imaging (MRI). We hypothesized that our mechanistically interpretable markers would discriminate patients with and without postoperative seizure freedom. RESULTS Our markers discriminated surgical outcome groups (abnormalities not targeted: area under the curve [AUC] = 0.80, p = .003; partial resection of epileptogenic zone: AUC = 0.68, p = .053; and insufficient cortical abnormality impact: AUC = 0.64, p = .096). Furthermore, 95% of those patients who were not seizure-free had markers of surgical failure for at least one of the three proposed mechanisms. In contrast, of those patients without markers for any mechanism, 80% were ultimately seizure-free. SIGNIFICANCE The mapping of abnormalities across the brain is important for a wide range of neurological conditions. Here we have demonstrated that interictal MEG band-power mapping has merit for the localization of pathology and improving our mechanistic understanding of epilepsy. Our markers for mechanisms of surgical failure could be used in the future to construct predictive models of surgical outcome, aiding clinical teams during patient pre-surgical evaluations.
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Affiliation(s)
- Thomas W. Owen
- Computational Neurology, Neuroscience & Psychiatry Lab, ICOS Group, School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Gabrielle M. Schroeder
- Computational Neurology, Neuroscience & Psychiatry Lab, ICOS Group, School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Vytene Janiukstyte
- Computational Neurology, Neuroscience & Psychiatry Lab, ICOS Group, School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Gerard R. Hall
- Computational Neurology, Neuroscience & Psychiatry Lab, ICOS Group, School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | | | | | | | | | | | - Yujiang Wang
- Computational Neurology, Neuroscience & Psychiatry Lab, ICOS Group, School of ComputingNewcastle UniversityNewcastle upon TyneUK
- UCL Queen Square Institute of NeurologyLondonUK
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Peter N. Taylor
- Computational Neurology, Neuroscience & Psychiatry Lab, ICOS Group, School of ComputingNewcastle UniversityNewcastle upon TyneUK
- UCL Queen Square Institute of NeurologyLondonUK
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
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Lopez SM, Aksman LM, Oxtoby NP, Vos SB, Rao J, Kaestner E, Alhusaini S, Alvim M, Bender B, Bernasconi A, Bernasconi N, Bernhardt B, Bonilha L, Caciagli L, Caldairou B, Caligiuri ME, Calvet A, Cendes F, Concha L, Conde‐Blanco E, Davoodi‐Bojd E, de Bézenac C, Delanty N, Desmond PM, Devinsky O, Domin M, Duncan JS, Focke NK, Foley S, Fortunato F, Galovic M, Gambardella A, Gleichgerrcht E, Guerrini R, Hamandi K, Ives‐Deliperi V, Jackson GD, Jahanshad N, Keller SS, Kochunov P, Kotikalapudi R, Kreilkamp BAK, Labate A, Larivière S, Lenge M, Lui E, Malpas C, Martin P, Mascalchi M, Medland SE, Meletti S, Morita‐Sherman ME, Owen TW, Richardson M, Riva A, Rüber T, Sinclair B, Soltanian‐Zadeh H, Stein DJ, Striano P, Taylor P, Thomopoulos SI, Thompson PM, Tondelli M, Vaudano AE, Vivash L, Wang Y, Weber B, Whelan CD, Wiest R, Winston GP, Yasuda CL, McDonald CR, Alexander D, Sisodiya SM, Altmann A. Event-based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data. Epilepsia 2022; 63:2081-2095. [PMID: 35656586 PMCID: PMC9540015 DOI: 10.1111/epi.17316] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/01/2022] [Accepted: 06/01/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features. METHODS We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1-weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1625 healthy controls from 25 centers. Features with a moderate case-control effect size (Cohen d ≥ .5) were used to train an event-based model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. RESULTS In MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10-16 ), age at onset (ρ = -.18, p = 9.82 × 10-7 ), and ASM resistance (area under the curve = .59, p = .043, Mann-Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE-HS with mild or nondetectable abnormality on T1W MRI. SIGNIFICANCE From cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features.
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Affiliation(s)
- Seymour M. Lopez
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Leon M. Aksman
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Neil P. Oxtoby
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Sjoerd B. Vos
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Neuroradiological Academic Unit, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Jun Rao
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Erik Kaestner
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Saud Alhusaini
- Department of NeurologyAlpert Medical School of Brown UniversityProvidenceRhode IslandUSA
- Department of Molecular and Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland
| | - Marina Alvim
- Department of Neurology and Neuroimaging LaboratoryUniversity of CampinasCampinasBrazil
| | - Benjamin Bender
- Department of Radiology, Diagnostic and Interventional NeuroradiologyUniversity Hospital TübingenTübingenGermany
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Boris Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuebecCanada
| | | | - Lorenzo Caciagli
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuebecCanada
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Benoit Caldairou
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | - Angels Calvet
- Magnetic Resonance Image Core FacilityAugust Pi i Sunyer Biomedical Research Institute, University of BarcelonaBarcelonaSpain
| | - Fernando Cendes
- Department of Neurology and Neuroimaging LaboratoryUniversity of CampinasCampinasBrazil
| | - Luis Concha
- Institute of NeurobiologyNational Autonomous University of MexicoQuerétaroMexico
| | - Estefania Conde‐Blanco
- Epilepsy Program, Neurology DepartmentHospital Clinic of BarcelonaBarcelonaSpain
- August Pi i Sunyer Biomedical Research InstituteBarcelonaSpain
| | | | - Christophe de Bézenac
- Department of Pharmacology and TherapeuticsInstitute of Systems, Molecular and Integrative Biology, University of LiverpoolLiverpoolUK
| | - Norman Delanty
- Department of Molecular and Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland
- FutureNeuro SFI Research Centre for Rare and Chronic Neurological DiseasesDublinIreland
| | - Patricia M. Desmond
- Department of Radiology, Royal Melbourne HospitalUniversity of MelbourneMelbourneVictoriaAustralia
| | - Orrin Devinsky
- New York University Grossman School of MedicineNew YorkNew YorkUSA
| | - Martin Domin
- Functional Imaging Unit, Department of Diagnostic Radiology and NeuroradiologyGreifswald University MedicineGreifswaldGermany
| | - John S. Duncan
- Department of NeurologyEmory UniversityAtlantaUSA
- Chalfont Centre for EpilepsyChalfont St PeterUK
| | - Niels K. Focke
- Department of NeurologyUniversity Medical CenterGöttingenGermany
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Francesco Fortunato
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | - Marian Galovic
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- Department of NeurologyUniversity Hospital ZurichZurichSwitzerland
| | - Antonio Gambardella
- Neuroscience Research Center, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | | | - Renzo Guerrini
- Neuroscience DepartmentUniversity of FlorenceFlorenceItaly
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
- Wales Epilepsy Unit, Department of NeurologyUniversity Hospital of WalesCardiffUK
| | | | - Graeme D. Jackson
- Florey Institute of Neuroscience and Mental Health, Austin CampusHeidelbergVictoriaAustralia
- University of MelbourneParkvilleVictoriaAustralia
- Department of NeurologyAustin HealthHeidelbergVictoriaAustralia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Simon S. Keller
- Institute of Systems, Molecular and Integrative BiologyUniversity of LiverpoolLiverpoolUK
| | - Peter Kochunov
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Raviteja Kotikalapudi
- Department of Radiology, Diagnostic and Interventional NeuroradiologyUniversity Hospital TübingenTübingenGermany
- Department of Clinical NeurophysiologyUniversity Hospital GöttingenGöttingenGermany
- Department of Neurology and EpileptologyHertie Institute for Clinical Brain Research, University of TübingenTübingenGermany
| | - Barbara A. K. Kreilkamp
- Institute of Systems, Molecular and Integrative BiologyUniversity of LiverpoolLiverpoolUK
- Clinical NeurophysiologyUniversity Medical Center GöttingenGöttingenGermany
| | - Angelo Labate
- Neuroscience Research Center, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuebecCanada
| | - Matteo Lenge
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and LaboratoriesA. Meyer Children's Hospital, University of FlorenceFlorenceItaly
- Functional and Epilepsy Neurosurgery Unit, Neurosurgery DepartmentA. Meyer Children's Hospital, University of FlorenceFlorenceItaly
| | - Elaine Lui
- Department of Radiology, Royal Melbourne HospitalUniversity of MelbourneMelbourneVictoriaAustralia
| | - Charles Malpas
- Department of NeurologyRoyal Melbourne HospitalMelbourneVictoriaAustralia
- Department of Medicine, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaAustralia
| | - Pascal Martin
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Mario Mascalchi
- Mario Serio Department of Clinical and Experimental Medical SciencesUniversity of FlorenceFlorenceItaly
| | - Sarah E. Medland
- Psychiatric GeneticsQIMR Berghofer Medical Research InstituteBrisbaneQueenslandAustralia
| | - Stefano Meletti
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Neurology Unit, OCB HospitalModena University HospitalModenaItaly
| | - Marcia E. Morita‐Sherman
- Department of NeurologyUniversity of CampinasCampinasBrazil
- Cleveland Clinic Neurological InstituteClevelandOhioUSA
| | - Thomas W. Owen
- School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | | | - Antonella Riva
- Giannina Gaslini Institute, Scientific Institute for Research and Health CareGenoaItaly
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child HealthUniversity of GenoaGenoaItaly
| | - Theodor Rüber
- Department of EpileptologyUniversity Hospital BonnBonnGermany
| | - Ben Sinclair
- Department of Neuroscience, Central Clinical School, Alfred HospitalMonash UniversityMelbourneVictoriaAustralia
- Departments of Medicine and Radiology, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaAustralia
| | - Hamid Soltanian‐Zadeh
- Radiology and Research AdministrationHenry Ford Health SystemDetroitMichiganUSA
- School of Electrical and Computer EngineeringCollege of Engineering, University of TehranTehranIran
| | - Dan J. Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Pasquale Striano
- Giannina Gaslini Institute, Scientific Institute for Research and Health CareGenoaItaly
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child HealthUniversity of GenoaGenoaItaly
| | - Peter N. Taylor
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Manuela Tondelli
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Primary Care DepartmentLocal Health Authority of ModenaModenaItaly
| | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Neurology Unit, OCB HospitalModena University HospitalModenaItaly
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Alfred HospitalMonash UniversityMelbourneVictoriaAustralia
- Departments of Medicine and Radiology, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaAustralia
| | - Yujiang Wang
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition ResearchUniversity of BonnBonnGermany
| | - Christopher D. Whelan
- Department of Molecular and Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland
| | - Roland Wiest
- Support Center for Advanced NeuroimagingUniversity Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of BernBernSwitzerland
| | - Gavin P. Winston
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- Chalfont Centre for EpilepsyChalfont St PeterUK
- Department of Medicine, Division of NeurologyQueen's UniversityKingstonOntarioCanada
| | - Clarissa Lin Yasuda
- Department of Neurology and Neuroimaging LaboratoryUniversity of CampinasCampinasBrazil
| | - Carrie R. McDonald
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Sanjay M. Sisodiya
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- Chalfont Centre for EpilepsyChalfont St PeterUK
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
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Taylor PN, Papasavvas CA, Owen TW, Schroeder GM, Hutchings FE, Chowdhury FA, Diehl B, Duncan JS, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Wang Y. Normative brain mapping of interictal intracranial EEG to localize epileptogenic tissue. Brain 2022; 145:939-949. [PMID: 35075485 PMCID: PMC9050535 DOI: 10.1093/brain/awab380] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 08/19/2021] [Accepted: 09/03/2021] [Indexed: 11/14/2022] Open
Abstract
The identification of abnormal electrographic activity is important in a wide range of neurological disorders, including epilepsy for localizing epileptogenic tissue. However, this identification may be challenging during non-seizure (interictal) periods, especially if abnormalities are subtle compared to the repertoire of possible healthy brain dynamics. Here, we investigate if such interictal abnormalities become more salient by quantitatively accounting for the range of healthy brain dynamics in a location-specific manner. To this end, we constructed a normative map of brain dynamics, in terms of relative band power, from interictal intracranial recordings from 234 participants (21 598 electrode contacts). We then compared interictal recordings from 62 patients with epilepsy to the normative map to identify abnormal regions. We proposed that if the most abnormal regions were spared by surgery, then patients would be more likely to experience continued seizures postoperatively. We first confirmed that the spatial variations of band power in the normative map across brain regions were consistent with healthy variations reported in the literature. Second, when accounting for the normative variations, regions that were spared by surgery were more abnormal than those resected only in patients with persistent postoperative seizures (t = -3.6, P = 0.0003), confirming our hypothesis. Third, we found that this effect discriminated patient outcomes (area under curve 0.75 P = 0.0003). Normative mapping is a well-established practice in neuroscientific research. Our study suggests that this approach is feasible to detect interictal abnormalities in intracranial EEG, and of potential clinical value to identify pathological tissue in epilepsy. Finally, we make our normative intracranial map publicly available to facilitate future investigations in epilepsy and beyond.
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Affiliation(s)
- Peter N Taylor
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Christoforos A Papasavvas
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Thomas W Owen
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Gabrielle M Schroeder
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Frances E Hutchings
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Fahmida A Chowdhury
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Beate Diehl
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - John S Duncan
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Andrew W McEvoy
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Anna Miserocchi
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Sjoerd B Vos
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Matthew C Walker
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Yujiang Wang
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
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Owen TW, de Tisi J, Vos SB, Winston GP, Duncan JS, Wang Y, Taylor PN. Multivariate white matter alterations are associated with epilepsy duration. Eur J Neurosci 2021; 53:2788-2803. [PMID: 33222308 PMCID: PMC8246988 DOI: 10.1111/ejn.15055] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 11/12/2020] [Accepted: 11/15/2020] [Indexed: 01/08/2023]
Abstract
Previous studies investigating associations between white matter alterations and duration of temporal lobe epilepsy (TLE) have shown differing results, and were typically limited to univariate analyses of tracts in isolation. In this study, we apply a multivariate measure (the Mahalanobis distance), which captures the distinct ways white matter may differ in individual patients, and relate this to epilepsy duration. Diffusion MRI, from a cohort of 94 subjects (28 healthy controls, 33 left-TLE and 33 right-TLE), was used to assess the association between tract fractional anisotropy (FA) and epilepsy duration. Using ten white matter tracts, we analysed associations using the traditional univariate analysis (z-scores) and a complementary multivariate approach (Mahalanobis distance), incorporating multiple white matter tracts into a single unified analysis. For patients with right-TLE, FA was not significantly associated with epilepsy duration for any tract studied in isolation. For patients with left-TLE, the FA of two limbic tracts (ipsilateral fornix, contralateral cingulum gyrus) were significantly negatively associated with epilepsy duration (Bonferonni corrected p < .05). Using a multivariate approach we found significant ipsilateral positive associations with duration in both left, and right-TLE cohorts (left-TLE: Spearman's ρ = 0.487, right-TLE: Spearman's ρ = 0.422). Extrapolating our multivariate results to duration equals zero (i.e., at onset) we found no significant difference between patients and controls. Associations using the multivariate approach were more robust than univariate methods. The multivariate Mahalanobis distance measure provides non-overlapping and more robust results than traditional univariate analyses. Future studies should consider adopting both frameworks into their analysis in order to ascertain a more complete understanding of epilepsy progression, regardless of laterality.
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Affiliation(s)
- Thomas W. Owen
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Jane de Tisi
- NIHR University College London Hospitals Biomedical Research CentreUCL Institute of NeurologyQueen SquareLondonUK
| | - Sjoerd B. Vos
- Centre for Medical Image ComputingUniversity College LondonLondonUK
- Epilepsy Society MRI UnitChalfont St PeterUK
- Neuroradiological Academic UnitUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Gavin P. Winston
- NIHR University College London Hospitals Biomedical Research CentreUCL Institute of NeurologyQueen SquareLondonUK
- Epilepsy Society MRI UnitChalfont St PeterUK
- Department of MedicineDivision of NeurologyQueen's UniversityKingstonCanada
| | - John S Duncan
- NIHR University College London Hospitals Biomedical Research CentreUCL Institute of NeurologyQueen SquareLondonUK
- Epilepsy Society MRI UnitChalfont St PeterUK
| | - Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of ComputingNewcastle UniversityNewcastle upon TyneUK
- NIHR University College London Hospitals Biomedical Research CentreUCL Institute of NeurologyQueen SquareLondonUK
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Peter N. Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of ComputingNewcastle UniversityNewcastle upon TyneUK
- NIHR University College London Hospitals Biomedical Research CentreUCL Institute of NeurologyQueen SquareLondonUK
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
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Carmon J, Heege J, Necus JH, Owen TW, Pipa G, Kaiser M, Taylor PN, Wang Y. Reliability and comparability of human brain structural covariance networks. Neuroimage 2020; 220:117104. [PMID: 32621973 DOI: 10.1016/j.neuroimage.2020.117104] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 05/01/2020] [Accepted: 06/25/2020] [Indexed: 12/11/2022] Open
Abstract
Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects, as well as the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurfer versions. In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults. These differences persist after univariate site correction, they are exacerbated by low sample sizes, and they are most pronounced when using average cortical thickness as a morphological measure. Down-stream graph theoretic analyses further show statistically significant differences. In terms of reliability, substantial differences were also found when comparing repeated scan sessions of the same subjects, image resolutions, and even FreeSurfer versions of the same image. We could further estimate the relative measurement error and showed that it is largest when using cortical thickness as a morphological measure. Using simulated data, we argue that cortical thickness is least reliable because of larger relative measurement errors. Practically, we make the following recommendations (1) combining subjects across sites into one group should be avoided, particularly if sites differ in image resolutions, subject demographics, or preprocessing steps; (2) surface area and volume should be preferred as morphological measures over cortical thickness; (3) a large number of subjects (n≫30 for the Desikan-Killiany parcellation) should be used to estimate structural covariance; (4) measurement error should be assessed where repeated measurements are available; (5) if combining sites is critical, univariate (per ROI) site-correction is insufficient, but error covariance (between ROIs) should be explicitly measured and modelled.
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Affiliation(s)
- Jona Carmon
- Institute of Cognitive Science, Osnabrueck University, Osnabrueck, Germany
| | - Jil Heege
- Humboldt University Berlin, Berlin, Germany
| | - Joe H Necus
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Thomas W Owen
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Gordon Pipa
- Institute of Cognitive Science, Osnabrueck University, Osnabrueck, Germany
| | - Marcus Kaiser
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; Institute of Neurology, University College London, UK
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; Institute of Neurology, University College London, UK.
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Owen TW, Al-Kaysi RO, Bardeen CJ, Cheng Q. Microgravimetric immunosensor for direct detection of aerosolized influenza A virus particles. Sens Actuators B Chem 2007; 126:691-699. [PMID: 32288239 PMCID: PMC7127275 DOI: 10.1016/j.snb.2007.04.028] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2006] [Revised: 04/11/2007] [Accepted: 04/13/2007] [Indexed: 05/09/2023]
Abstract
The development and characterization of a quartz crystal microbalance (QCM) sensor for the direct detection of aerosolized influenza A virions is reported. Self-assembled monolayers (SAMs) of mercaptoundecanoic acid (MUA) are formed on QCM gold electrodes to provide a surface amenable for the immobilization of anti-influenza A antibodies using NHS/EDC coupling chemistry. The surface-bound antibody provides a selective and specific sensing interface for the capture of influenza virions. A nebulizer is used to create aerosolized samples and is directly connected to a chamber housing the antibody-modified crystal ("immunochip"). Upon exposure to the aerosolized virus, the interaction between the antibody and virus leads to a dampening of the oscillation frequency of the quartz crystal. The magnitude of frequency change is directly related to virus concentration. Control experiments using aerosols from chicken egg allantoic fluid and an anti-murine antibody based immunosensor confirm that the observed signal originates from specific viral binding on the chip surface. Step-by-step surface modification of MUA assembly, antibody attachment, and antibody-virus interaction are characterized by atomic force microscopy (AFM) imaging analysis. Using the S/N = 3 principle, the limit of detection is estimated to be 4 virus particles/mL. The high sensitivity and real-time sensing scheme presented here can play an important role in the public health arena by offering a new analytical tool for identifying bio-contaminated areas and assisting in timely patient diagnosis.
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Affiliation(s)
| | | | | | - Quan Cheng
- Corresponding author. Tel.: +1 951 827 2702; fax: +1 951 827 4713.
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