Synthesising and Rationalising Descriptors of Presurgical EEG-fMRI Maps in Epilepsy
Poster No:
281
Submission Type:
Abstract Submission
Authors:
Bohan Zhang1, Boyuan Song1, Louis Lemieux1
Institutions:
1University College London, London, London
First Author:
Co-Author(s):
Introduction:
Concurrent EEG-fMRI of epileptiform activity has provided great opportunities to study focal epilepsy, particularly in patients with drug-resistant epilepsy considered for surgical treatment. However, its clinical value remains uncertain, as reflected in the large variety of schemes employed to assess the technique's capability to localise the sources of epilepsy. Specifically, given the common finding of epileptic activity-related fMRI maps showing multiple regions of significant BOLD change, often in regions considered remote from presumed epileptogenic regions, investigators have attempted to discover the clinical relevance of various features of the EEG-fMRI data and resulting maps in relation to other, independent, electro-clinical data: 'Concordance' schemes. Our aim is to propose and illustrate a methodology to summarise and rationalise the concordance schemes employed in the interictal EEG-fMRI literature.
Methods:
A review of the literature on interictal resting-state EEG-fMRI in patients with focal epilepsy resulted in the identification of forty-seven original research articles. We analysed the descriptions of the schemes used in those investigations to assess the concordance between the maps of epileptic activity-related BOLD changes and localization of the sources of epileptic activity (irritative, epileptogenic or seizure onset zone, depending on the study; whether confirmed or presumed) to identify a set of binary criteria, e.g. whether a concordance scheme assigned a special status to the cluster containing the global statistical maximum or not. The concordance schemes, represented as multidimensional binary vectors, were then grouped into communities using the brainconn function 'modularity_und' (undirected modularity; Newman, 2006) and cosine similarity as a metric. The resulting groupings were then assessed by publication authorship and chronology, and a most representative scheme identified for each community as the one most similar to the community's average.
Results:
Thirty-one binary criteria were identified (Table 1).
The criterion 'Statistical significance of the BOLD clusters' (EEG-fMRI category) was the only one considered in all schemes. The second and third most employed criteria are 'Categorised concordance levels' (44/47 studies; Interpretation category) and 'Lobar or sub-lobar agreement' (40/47; Interpretation category) respectively. The three least adopted criteria were all in the Independent Localisation category: 'Use of only non-simultaneous invasive EEG technique' (3/47), 'Non-simultaneous non-invasive EEG only' (4/47); and 'Non-simultaneous interictal EEG only' (5/47).
Based on binarised criteria representation, only two out of 47 concordance schemes were found to be identical.
Three communities of concordance schemes were identified (Table 2) consisting of 15, 23 and 9 schemes respectively. The three most employed criteria for each community were as follows:
C1: 'Statistical significance of the BOLD clusters' (15/15), 'Based on EEG findings only' (15/15) and 'Categorised concordance levels' (13/15);
C2: 'Statistical significance of the BOLD clusters' (15/15), 'Considers spatial overlap (within/outside)' (23/23) and 'Categorised concordance levels' (23/23);
C3: 'Statistical significance of the BOLD clusters' (9/9), 'Global statistical maximum considered' (9/9) and 'Lobar or sub-lobar agreement' (9/9).
The representative concordance schemes for each community are highlighted in Table 2.
We also note a good correspondence between communities and senior authorship, with two communities (C1 and C3) characterised by J Gotman as the most frequent senior author and the other (C2) by L Lemieux, with some overlap in C2.
The criterion 'Statistical significance of the BOLD clusters' (EEG-fMRI category) was the only one considered in all schemes. The second and third most employed criteria are 'Categorised concordance levels' (44/47 studies; Interpretation category) and 'Lobar or sub-lobar agreement' (40/47; Interpretation category) respectively. The three least adopted criteria were all in the Independent Localisation category: 'Use of only non-simultaneous invasive EEG technique' (3/47), 'Non-simultaneous non-invasive EEG only' (4/47); and 'Non-simultaneous interictal EEG only' (5/47).
Based on binarised criteria representation, only two out of 47 concordance schemes were found to be identical.
Three communities of concordance schemes were identified (Table 2) consisting of 15, 23 and 9 schemes respectively. The three most employed criteria for each community were as follows:
C1: 'Statistical significance of the BOLD clusters' (15/15), 'Based on EEG findings only' (15/15) and 'Categorised concordance levels' (13/15);
C2: 'Statistical significance of the BOLD clusters' (15/15), 'Considers spatial overlap (within/outside)' (23/23) and 'Categorised concordance levels' (23/23);
C3: 'Statistical significance of the BOLD clusters' (9/9), 'Global statistical maximum considered' (9/9) and 'Lobar or sub-lobar agreement' (9/9).
The representative concordance schemes for each community are highlighted in Table 2.
We also note a good correspondence between communities and senior authorship, with two communities (C1 and C3) characterised by J Gotman as the most frequent senior author and the other (C2) by L Lemieux, with some overlap in C2.
·Table 1. Concordance Scheme Binarised Criteria Found in the Literature. Thirty-one criteria were found by analysing the descriptions found in 47 research articles, and grouped in 3 categories.
·Table 2. Found Communities. Forty-seven EEG-fMRI research articles grouped into three concordance scheme communities, with the most representative articles highlighted for each community.
Conclusions:
A new approach has been proposed to conduct comparison of pre-surgical imaging concordance schemes. This study highlights the potential for more standardised concordance schemes with the potential for improving, objectivity, reproducibility and comparability.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Other Methods
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Data analysis
Electroencephaolography (EEG)
Epilepsy
FUNCTIONAL MRI
Machine Learning
Source Localization
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
Provide references using APA citation style.
Al-Asmi, A, etal. 2003. Epilepsia, 44(10): 1328-39
An, D, etal. 2013. Epilepsia, 54(12): 2184-94
Bagshaw, AP, etal. 2004. Hum Br. Mapp, 22(3): 179-92
Benar, CG, etal. 2002. Neuroim, 17(3): 1182-92.
Benar, CG, etal. 2006. Neuroim, 30(4): 1161-70
Caballero-Gaudes, C, etal 2013. Neuroim, 68: 248-62
Cai, Z, etal. 2023. Hum Br. Mapp, 44(17): 5982-6000
Centeno, M, etal. 2016. PLoS One, 11(2): e0149048
Centeno, M, etal. 2017. Ann Neurol, 82(2): 278-287
Chaudhary, UJ, etal. 2012. Neuroim, 61(4): 1383-93
Chaudhary, UJ, 2021. Front Neurol, 12: 693504
Coan, AC, etal. 2016. J Neurol Neurosurg Psychiatry, 87(6): 642-9
Ebrahimzadeh, E, etal. 2019. Comput Methods Programs Biomed, 177: 231-241
Ebrahimzadeh, E, etal. 2019. J Neurosci Methods, 322: 34-49
Gholipour, T, etal. 2011. Epilepsia, 52(3): 433-42
Grouiller, F, etal. 2011. Brain, 134(10): 2867-86
Grouiller, F, etal. 2015. Eur J Nucl Med Mol Imaging, 42(7): 1133-43
Grova, C, etal. 2008. Neuroim, 39(2): 755-74
Hao, Y, etal. 2018. Neuroim Clin, 17: 962-975
Hauf, M, etal. 2012. AJNR Am J Neuroradiol, 33(9): 1818-24
Hawco, CS, etal. 2007. Neuroim, 35(4): 1450-8
Heers, M, etal. 2014. Hum Br. Mapp, 35(9): 4396-414
Ito, Y, etal. 2021. J Neurosurg, 134(3): 1027-1036
Jager, V, etal. 2015. PLoS One, 10(10): e0140537
Khoo, HM, etal. 2017. Epilepsia, 58(5): 811-823
Kobayashi, E, etal. 2006. Brain, 129(2): 366-74
Koupparis, A, etal. 2021. Neurology, 97(15): e1523-e1536
Lemieux, L, etal. 2008. Hum Br. Mapp, 29(3): 329-45
Markoula, S, etal. 2018. Seizure, 61: 30-37
Maziero, D, etal. 2018. Br. Topogr, 31(2): 322-336
Mirandola, L, etal. 2021. Front Neurol, 12: 746468
Pedreira, C, etal. 2014. Neuroim, 99: 461-76
Pesaresi, I, etal. 2011. MAGMA, 24(5): 285-96
Pittau, F, etal. 2013. Br. Topogr, 26(4): 627-40
Rollings, DT, etal. 2016. Clin Neurophysiol, 127(1): 245-253
Salek-Haddadi, A, etal. 2006. Br. Res, 1088(1): 148-66
Sharma, NK, etal. 2019. Neuroim, 184: 981-992
Su, J, etal. 2020. Br. Topogr, 33(4): 545-557
Urriola, J, etal. 2022. Epilepsy Research, 188: 107039
van Houdt, PJ, etal. 2010. Magn Reson Imaging, 28(8): 1078-86
Vulliemoz, S, etal. 2009. Neuroim, 46(3): 834-43
Vulliemoz, S, etal. 2010. Neuroim, 49(4): 3219-29
Vulliemoz, S, etal. 2011. Neuroim, 54(1): 182-90
Wilson, W, etal. 2024, Brain, 147(12):4157-4168
Yamazoe, T, etal. 2024. Clin Neurophysiol, 2019. 130(4): 429-438
Zijlmans, M, etal. 2007. Brain, 130(9): 2343-53