
==== Front
Brain
Brain
brainj
Brain
0006-8950
1460-2156
Oxford University Press UK

38874456
10.1093/brain/awae189
awae189
Original Article
AcademicSubjects/MED00310
AcademicSubjects/SCI01870
The interictal suppression hypothesis is the dominant differentiator of seizure onset zones in focal epilepsy
https://orcid.org/0000-0002-2685-0762
Doss Derek J Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA

https://orcid.org/0000-0003-0702-4454
Shless Jared S Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA

https://orcid.org/0000-0003-0753-1720
Bick Sarah K Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA

https://orcid.org/0009-0002-9281-541X
Makhoul Ghassan S Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA

https://orcid.org/0000-0002-4216-8317
Negi Aarushi S Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA

https://orcid.org/0009-0006-6440-9245
Bibro Camden E Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA

https://orcid.org/0009-0004-8231-5782
Rashingkar Rohan Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA

https://orcid.org/0000-0002-0505-7511
Gummadavelli Abhijeet Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA

https://orcid.org/0000-0003-1541-9579
Chang Catie Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA

https://orcid.org/0000-0002-3537-4200
Gallagher Martin J Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA

https://orcid.org/0000-0002-4344-503X
Naftel Robert P Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA

https://orcid.org/0000-0002-3172-0102
Reddy Shilpa B Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA

https://orcid.org/0000-0003-1331-380X
Williams Roberson Shawniqua Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA

https://orcid.org/0000-0001-8263-2324
Morgan Victoria L Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA
Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA
Department of Radiology and Biomedical Imaging, Vanderbilt University Medical Center, Nashville, TN 37235, USA

https://orcid.org/0000-0002-9154-4315
Johnson Graham W Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA

https://orcid.org/0000-0001-8373-690X
Englot Dario J Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA
Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA
Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA
Department of Radiology and Biomedical Imaging, Vanderbilt University Medical Center, Nashville, TN 37235, USA

Correspondence to: Dario J. Englot, MD, PhD Department of Neurological Surgery, Vanderbilt University, 1500 21st Avenue South VAV 4340, Nashville, TN 37212, USA E-mail: dario.englot@vumc.org
Derek J Doss and Jared S Shless contributed equally to this work.

9 2024
14 6 2024
14 6 2024
147 9 30093017
20 10 2023
19 4 2024
16 5 2024
27 8 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.

Abstract

Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the interictal suppression hypothesis posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH.

Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8–13 Hz). Principal component analysis (PCA) was performed on each patient’s connectivity matrix. Each patient’s components were analysed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient.

A motif characteristic of the interictal suppression hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks.

We discovered that a potentially suppressive motif based on the interictal suppression hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment.

Successful surgical treatment of epilepsy traditionally relies on identification of seizure onset zones. Network analyses may improve identification, but it is unclear which motif is best. Here, Doss et al. show that the network motif suggested by the interictal suppression hypothesis is the most robust for identifying seizure onset zones.

See Lagarde et al. (https://doi.org/10.1093/brain/awae256) for a scientific commentary on this article.

connectomics
interictal suppression hypothesis
principal component analysis
stereo electroencephalography
machine learning
drug-resistant focal epilepsy
National Institutes of Health 10.13039/100000002 T32-EB001628, T32-EB021937, T32-GM007347, F31-NS131056, R01-NS112252, F31-NS120401, R00NS097618
==== Body
pmc See Lagarde et al. (https://doi.org/10.1093/brain/awae256) for a scientific commentary on this article.

Introduction

Epilepsy affects nearly 1% of the global population, and approximately 40% of patients suffer from seizures that are resistant to antiseizure medications.1-3 For patients with drug-resistant epilepsy (DRE), surgical treatment can result in reduction or elimination of seizures, but successful localization of the area where seizures originate, termed the seizure onset zone (SOZ), is often necessary.4-9 Localization of SOZs can be challenging, and patients undergo an extensive presurgical evaluation often including invasive recordings with stereo electroencephalography (SEEG) if the SOZ cannot be confidently localized non-invasively and if a multi-disciplinary conference determines the patient is a candidate.8-10 DRE has increasingly been conceptualized as a network disorder, in which the interactions between regions may better characterize and identify SOZs.11-14

Network studies with SEEG data have demonstrated substantial promise in the localization of SOZs.15-25 Recent work has outlined a potentially suppressive pattern of connectivity (‘motif’) for SOZs termed the interictal suppression hypothesis (ISH). Multiple groups have found that during resting state, there was increased inward connectivity towards SOZs and decreased outward connectivity from SOZs.23,26-28 These results suggest that when patients are not having a seizure, non-SOZs may inhibit SOZs, perhaps with the ultimate effect of suppressing seizure onset.28 However, it is unclear whether (i) this motif is the most robust motif for potential clinical localization of SOZs; (ii) the dominant motif differs across patients; or (iii) if there is another motif that hypothesis driven research has yet to uncover to localize SOZs. Thus, we seek to investigate these questions using unsupervised machine learning to detect connectivity motifs that may not have been tested before.

Materials and methods

Patient cohort and seizure designations

Eighty-one patients with DRE who were being considered for epilepsy surgery and thus underwent SEEG implantation surgery at Vanderbilt University Medical Center were included with no additional inclusion criteria (Table 1). Written and verbal consent was obtained. The diagnosis of DRE and the decision to pursue SEEG were determined by a multidisciplinary board including neurologists, neurosurgeons and neuropsychologists. The treating neurosurgeons planned electrode (Ad-Tech or PMT Corp.) trajectories according to standard clinical care at VUMC. Waypoint software (FHC, Bowdoin, ME) was used to plan and CRAnial Vault Explorer software (CRAVE; Vanderbilt University, Nashville, TN) was used to analyse the electrode trajectories as described in prior studies.23,28-30 Each patient had an average of 124.3 ± 31.4 (mean ± standard deviation) SEEG contacts implanted exclusively in grey matter. Contacts in white matter were excluded from our analysis.

Table 1 Principal component 1 and principal component 2+ cohort demographics

	Cohorts	P-values	
ISH PC 1	ISH PC 2+	
Demographics	
Sex	Male	30	5	P = 0.20	
Female	34	12	
Age of epilepsy onset, years	<18	26	7	P = 0.97	
	≥18	38	10	
Epilepsy type	Unilateral mTLE	22	6	P = 0.95	
Bilateral mTLE	12	1	P = 0.24	
Unilateral lTLE	9	1	P = 0.39	
Unilateral FLE	6	4	P = 0.14	
Unilateral PLE	2	2	P = 1.0	
Multifocal	13	3	P = 0.83	
Epilepsy duration, years	–	16.6 (12.1)	15.9 (11.9)	P = 0.84	
Age at SEEG recording, years	–	37.5 (12.2)	38.4 (11.9)	P = 0.78	
FBTCS present	Yes	46	15	P = 0.16	
No	18	2	
Bilateral SOZ	Yes	17	2	P = 0.20	
No	47	15	
SEEG node designations	SOZ	11.5 (9.4)	10.3 (8.9)	P = 0.65	
PZ	9.6 (10.8)	6.0 (7.3)	P = 0.19	
NIZ	70.8 (21.9)	45.1 (17.7)	*P = 2.7 × 10−5	
SEEG region designations	SOZ	2.0 (1.8)	2.1 (1.6)	P = 0.76	
PZ	1.8 (1.8)	1.5 (1.2)	P = 0.43	
NIZ	13.9 (4.5)	9.8 (3.8)	*P = 1.1 × 10−3	
Distance between SOZs and other nodes	–	47.8 (11.8)	40.1 (13.1)	P = 2.3 × 10−2	
Mesial temporal sclerosis on MRI	Yes	14	3	P = 0.70	
No	50	14	
Lesional	Yes	26	7	P = 0.97	
No	38	10	
Outcomes and surgical treatment	
Prior epilepsy surgery	Yes	14	3	P = 0.70	
No	50	14	
Epilepsy surgery type	Ablation/resection	27	12	P = 0.34	
Neuromodulation	20	3	
All outcomes	Favourable	22	8	P = 0.80	
Non-favourable	14	6	
Resection outcomes	Engel I	11	7	P = 0.64	
Engel II–IV	11	5	
RNS outcomes	Responder	11	2	P = 0.66	
Non-responder	3	1	
Counts are displayed for categorical variables. Continuous data are presented as mean (standard deviation). Statistical testing for categorical data was performed with chi-squared tests. Multiple comparisons were corrected with a Bonferroni–Holm correction. Responsive neurostimulation (RNS) responder rate is determined by patients reporting >50% seizure reduction at follow-up clinic visits. ‘Favourable outcome’ includes both surgical candidates with an Engel I outcome and RNS responders. FBTCS = focal-to-bilateral tonic-clonic seizures; FLE = frontal lobe epilepsy; ISH = interictal suppression hypothesis; lTLE = lateral temporal lobe epilepsy; mTLE = mesial temporal lobe epilepsy; NIZ = non-involved zone; PC = principal component; PLE = parietal lobe epilepsy; PZ = propagative zone; SEEG = stereoelectroencephalography; SOZ = seizure onset zone; TLE = temporal lobe epilepsy.

*Significant differences are highlighted in bold.

All patients underwent long-term video-SEEG in the epilepsy monitoring unit (EMU). Each bipole pair (node) was classified according to whether a board-certified epileptologist assigned each of their SEEG contacts as a SOZ, propagative zone (PZ) or non-involved zone (NIZ). SOZs are classified as contact(s) with observed seizure activity at the start of an electrographic seizure; PZs were classified as contacts in which seizure activity spreads to within 10 s of electrographic seizure onset but do not have seizure activity at seizure onset; NIZs belong to neither PZ nor SOZ designations.31 As indicated in our previous work, we blinded this review to any clinically assumed epilepsy subtype.28

Resting-State SEEG

Twenty minutes of resting-state SEEG data were collected for each patient. The resting state data were collected between 1 and 2 days after SEEG implantation, and the patients were instructed to close their eyes for the entire 20-min duration. The data were filtered with a high pass filter at 0.5 Hz, a low pass filter at 150 Hz and stopband filters between 59–61 Hz and 119–121 Hz. SEEG signals were referenced using a bipolar montage between all contacts in grey matter. Two adjacent bipolar channels are referred to as a ‘node’. The connectivity between two nodes is referred to as an ‘edge’.32 Connectivity was computed between all bipolar channels in grey matter, including bipolar channels that are located in the same anatomical structure. An additional analysis is presented in the Supplementary material that uses connectivity only between distinct anatomical regions to control for the possible confounding effect of distance on connectivity. Each patient’s 20-min resting-state recording was broken into ten 2-min epochs, across which partial directed coherence (PDC), a directed connectivity measure, was computed with the fieldtrip toolbox.33 These 2-min epochs were used to reduce the effect of outliers on the connectivity estimation. All 2-min epochs were analysed, even if spiking activity was present. It was then averaged across all of each patient’s 2-min epochs. PDC was chosen to compute connectivity as it is insensitive to volume conduction, and it only includes direct connections between nodes.34-36 All connectivity measures were computed in the delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–31 Hz), low gamma (31–81 Hz) and high gamma (81–150 Hz) frequency bands. Given that the alpha frequency band has been shown to peak during awake resting state, we present the analyses in the main manuscript in the alpha frequency band alone.22,37 However, all analyses were completed for all frequency bands, and the results are presented in the Supplementary material. A node’s inwards connectivity is defined as the average inwards PDC of edges from all other nodes. Likewise, a node’s outwards connectivity is the average outwards PDC of edges with respect to all other nodes in the network. Intra-patient z-scoring was also performed for each connectivity matrix, where the whole-matrix mean was subtracted and divided by the whole-matrix standard deviation.

Node level PCA and connectivity matrix reconstruction

Principal component analysis (PCA) was chosen to identify connectivity motifs as it identifies global components that explain the highest variance of the data.38 PCA was performed on each patient’s directed connectivity matrix using the singular value decomposition as implemented in MATLAB 2021b. The connectivity matrix was reoriented such that each node had an inward and an outward connectivity column, allowing for the PCA algorithm to treat both inward and outward connectivity as variables and the connectivity values to each other node as an observation (Supplementary Fig. 1). By computing a PCA on a per-patient basis rather than the entire cohort, electrophysiological features could be detected despite the different regions sampled for each patient. This approach is crucial to allow patient-specific features to be detected while also allowing for common features between patients to be identified. Each principal component was then ranked according to its explained variance; that is, the components with the highest explained variance were assigned lower-ranked numbers [e.g. ‘principal component 1’ (PC 1)].

This algorithm allows for simple analysis of principal component coefficients (i.e. loadings), weights and variance on a node level. The range of the coefficients was −1 to 1. Each patient’s noisiest n − 1 components were their lower-ranked components, allowing for seamless visualization of the components with the highest variance. Figure 1 highlights a high-level overview of this process, from SEEG implantation through data processing and analysis.

Figure 1 Data collection and principal component analysis schematic. All 81 patients were implanted with stereoelectroencephalography (SEEG) contacts. Each patient’s 2-min segment of resting-state SEEG data were fed into a principal component analysis (PCA) algorithm, providing coefficients and weights for all principal components. A whole-connectome connectivity analysis was then performed to find local and global connectivity motifs, which were then analysed with weights and coefficients. Based on the ranking of each principal component’s variance, weights and coefficients were used to reconstruct the original partial directed coherence data. Finally, a focused connectivity analysis was performed for common themes/features on reconstructed PCA data. Across the cohort, there was an average of 84.52 principal components per patient We then performed PCA-seeded connectivity analyses on this reconstructed data, allowing for an interpretation of the precise influence of each principal component on a patient’s larger connectivity profile.

To analyse the precise influence of the principal components on each patient’s complete connectivity profile, the connectivity matrix was reconstructed from all principal components, a single principal component and all principal components except for one. This allowed for the contribution of one principal component to the connectivity profile to be analysed and allowed for the comparison of the connectivity profile without the selected component to be analysed.

Feature extraction

The top five principal components were analysed for each patient. As each patient had a different number of bipole pairs and each patient had different samplings of anatomical regions, a two-step process was performed to identify connectivity patterns. The coefficients of the top five principal components were visually inspected to identify common connectivity themes of SOZs across all patients. Afterwards, a quantitative definition of the observed connectivity theme was created. For example, it was noted that SOZs had increased coefficients for inward strength and decreased coefficients for outward strength in one component across patients (Fig. 2). This component was quantitatively identified by finding the component with the greatest difference between inward strength coefficients of SOZs and outward strength coefficients of SOZs. The quantitatively identified component was then used for all analyses.

Figure 2 Principal component coefficient visualization and feature identification. (A and B) Visualization of the top five principal component (PC) coefficients for two sample patients (ranked by explained variance in the PC analysis). (C) Canonical interictal suppression hypothesis (ISH) components are outlined with a red box. Coefficient values are computed both inwards and outwards. That is, bipole pairs are repeated. PC 1, which is marked by a red rectangle, best exemplifies canonical suppressive behaviour for both patients, with high inwards and low outwards connectivity at nodes (bipole pairs), which are seizure onset zones (SOZs). This process may be repeated for nodes in specific regions or at specific SOZ designations to discover lobar or SOZ-driven patterns, respectively. While the high inward and low outward coefficients are present globally, this motif is only highly weighted for SOZs.

Analysis of components

Across patients in the analysis, a component containing a common pattern of high inward and low outward connectivity was identified. Considering that this pattern aligned with prior studies and hypotheses such as the ISH, we hereafter refer to this component as the ‘ISH component’.23,26-28 For the majority of the patients, this ISH component was found to be the top, highest-ranked principal component. For a smaller cohort, it was found to be the second or lower principal component. These cohorts are referred to as PC 1 and PC 2+, respectively. The explained variance was computed for each component across each of the PC 1 and PC 2+ cohorts, and variances were compared with a paired t-test. The ability of each component to separate SOZs and NIZs was determined by subtracting the average weight of the component for SOZs and the average weight of the component for NIZs. Furthermore, to test the robustness of each component in terms of SOZ, PZ and NIZ weight separation, we used a one-way ANOVA with post hoc group-wise comparisons with Tukey–Kramer multiple comparison corrections. Analyses that included several ANOVAs were corrected for multiple comparisons using the Bonferroni–Holm method.

Results

Coefficient values are suggestive of directed connectivity motifs

Visual inspection of principal component coefficients offers insight into underlying connectivity motifs (average principal components per patient = 84.52). Despite a PCA being run independently for each patient, a common electrographic pattern was noted. Every patient had a component with high inward and low outward connectivity, likely representing electrographic behaviour supportive of the ISH. Therefore, we have termed this the ‘ISH component’. An example of this pattern can be seen in Fig. 2. Interestingly, despite the diverse sampling present in the SEEG data from each patient, this observation remained. This qualitative visualization, which led to algorithmic analysis, suggests a paradigm that may be used to improve SOZ localization in patients with DRE.

The common ISH component differentiates SOZs in the majority of patients

From the qualitative analysis, a mathematical definition of the common component was created. The inward connectivity coefficients were averaged for all nodes designated as SOZs and the outward connectivity coefficients were averaged for these same nodes. The average inward coefficient was subtracted from the average outward coefficient, which best represented the ISH component.

It was discovered that the ISH component was the top component (PC 1) for most of the patients in this study (64/81; 79%). A much smaller cohort had the ISH component as a component other than the first (17/81; 21%). These cohorts are referred to as the PC 1 cohort and PC 2+ cohort throughout. Next, the ISH component was compared across the PC 1 and PC 2+ cohorts.

First, the explained variance of the ISH components was analysed for each cohort. This metric represents how much of the variability in the data can be explained by the specified component. It was found that the PC 1 cohort has a significantly higher explained variance than the PC 2+ cohort as seen in Fig. 3A (P = 2.14 × 10−9, confidence interval (CI) of difference = 6.14–11.27, two-sample t-test). Additionally, it was investigated whether the ISH component was weighted more strongly for SOZs versus NIZs between the cohorts. It was found that the PC 1 cohort had a significantly higher difference in SOZ and NIZ weights for the ISH component than the PC 2+ cohort as seen in Fig. 3B (P = 1.39 × 10−2, CI of difference = 1.51–6.07, two-sample t-test). This result suggests that even though the ISH component is present in both cohorts, the differentiating signal between SOZs and NIZs is stronger in the PC 1 cohort.

Figure 3 Weight and variance analysis of interictal suppression hypothesis (ISH) components. (A) For each ISH component, explained variance is plotted between the principal component (PC) 1 and PC 2+ cohorts (P = 2.14 × 10−9). (B) The difference in weights at seizure onset zone (SOZ) and non-involved zone (NIZ) nodes is plotted between both cohorts (P = 0.00139). The difference in weights is defined as the average SOZ weight of a component minus the average NIZ weight of the same component. These analyses suggest that the PC 1 cohort had a greater difference between SOZs and NIZs. In the violin plots, each dot represents a data point, the white circle represents the median value and the grey bar represents the 25th and 75th percentiles of these data. Significance testing was performed with a paired t-test: ***P < 0.001.

The two cohorts are clinically similar and electrographically different

Considering that these two different cohorts were present, it was investigated whether the cohorts represented clinically or demographically different groups of patients. Common variables such as sex, seizure types, intervention types, outcome and age of onset were compared. There were no significant differences in the demographic composition of either cohort (Table 1). This lack of any significant difference suggests that the PCA represents network variables outside of typical clinical variables; thus, it may add additional information to the surgical evaluation of epilepsy.

The ISH component in the PC 1 cohort identifies SOZs to a greater extent than the PC 2+ cohort

Given that the PCA analysis showed a difference in the PC 1 and PC 2+ sub-cohorts outside of clinical variables and demographics, it was investigated whether the ISH component demonstrated distinct signals for SOZs, PZs and NIZs in both sub-cohorts. The weights of the ISH component were averaged and compared. The weights of the ISH component for the PC 1 cohort were significantly different for SOZs, PZs and NIZs (P = 2.57 × 10−9, one-way ANOVA), as seen in Fig. 4A. Additionally, post hoc multiple comparisons showed that all SOZs were differentiated from PZs (P = 1.28 × 10−2, CI of differences = 0.39–4.06, two-sample t-test) and from NIZs (P = 9.59 × 10−9, CI of differences = 3.27–6.84). While the weights of the ISH component for the PC 2+ sub-cohort were significantly different across node types (P = 4.76 × 10−2), post hoc multiple comparisons showed that SOZs were only significantly differentiated from PZs (P = 0.045, CI of differences = 0.029–3.28, two-sample t-test), as seen in Fig. 4B. This suggests that while both cohorts demonstrate a similar ISH pattern, it is more ubiquitous within the PC 1 sub-cohort’s top principal component.

Figure 4 Weight of the interictal suppression hypothesis component for seizure onset zone, propagative zone and non-involved zone between the principal component (PC) 1 and PC 2+ cohorts. (A) Within the PC 1 sub-cohort, weights were computed for seizure onset zones (SOZs), propagative zones (PZs) and non-involved zones (NIZs); one-way ANOVA, P = 2.57 × 10−9 with post hoc multiple pairwise t-test comparisons significant for all groups. (B) This analysis was repeated for the PC 2+ sub-cohort (one-way ANOVA, P = 0.0476 with post hoc multiple pairwise t-test comparisons significant for SOZ-PZ). Post hoc paired t-test significance: *P < 0.05, **P < 0.01, ***P < 0.001.

Reciprocal strength describes performance of the PC 1 and PC 2+ cohorts

Considering that the ISH component signal discerns SOZs, PZs and NIZs, it was investigated whether a basic connectivity matrix when reconstructed from the ISH or other components would demonstrate a difference between SOZs, PZs and NIZs. As in previous work, the average inward strength was subtracted from the average outward strength for each node type (SOZ, PZ, NIZ).23,27,28 The contribution of the identified ISH components was isolated by (i) reconstructing the connectivity matrix from all components; (ii) reconstructing the connectivity matrix from the ISH component; and (iii) reconstructing the connectivity matrix from all components except the ISH component.

As expected, when the connectivity matrix was reconstructed from all components, SOZs, PZs and NIZs were significantly different in the full cohort (P = 6.04 × 10−12, one-way ANOVA), the PC 1 sub-cohort (P = 6.10 × 10−8) and the PC 2+ sub-cohort (P = 3.74 × 10−6). Post hoc multiple comparisons revealed that all three node types were significantly different in all connectivity matrix reconstructions (Fig. 5A). More interestingly, even when the connectivity matrix was reconstructed from only the ISH component, all node types were significantly different in the full cohort (P = 2.29 × 10−9) and the PC 1 sub-cohort (P = 2.69 × 10−7). Surprisingly, the PC 2+ cohort was also significantly different for SOZs, PZs and NIZs (P = 2.78 × 10−2), but post hoc multiple comparisons demonstrated that PZs were not differentiated from NIZs, which was different from the full cohort or the PC 1 cohort (Fig. 5B).

Figure 5 Alpha band reciprocal connectivity. (A) directed reciprocal alpha-band partial directed coherence (PDC) connectivity was evaluated for seizure onset zones (SOZs), propagative zones (PZs) and NIZs for the full 810-patient cohort (one-way ANOVA, P = 6.04 × 10−12), the principal component (PC) 1 cohort (one-way ANOVA, P = 6.10 × 10−8) and the PC 2+ cohort (one-way ANOVA, P = 3.74 × 10−6). (B) Using the same methods, reciprocal alpha-band connectivity was computed for each patient’s interictal suppression hypothesis (ISH) component in the full 81-patient cohort (one-way ANOVA, P = 2.29 × 10−9), the PC 1 cohort (one-way ANOVA, P = 2.69 × 10−7) and the PC 2+ cohort (one-way ANOVA, P = 2.78 × 10−2). (C) Using the same methods, reciprocal alpha-band connectivity was computed for all components other than the ISH component (i.e. excluding the ISH component) for the full 81-patient cohort (one-way ANOVA, P = 5.42 × 10−3), the PC 1 cohort (one-way ANOVA, P = 7.37 × 10−5) and the PC 2+ cohort (one-way ANOVA, P = 4.12 × 10−4). Significance levels for post hoc t-tests are shown: *P < 0.05, **P < 0.01, ***P < 0.001.

Finally, when the connectivity matrix was reconstructed from all components except the ISH component, the full cohort (P = 5.42 × 10−3), the PC 1 cohort (P = 7.37 × 10−5) and the PC 2+ cohort (P = 4.12 × 10−4) showed a significant difference between the node types (Fig. 5C). However, the PC 2+ cohort demonstrated significant differences in SOZ–PZ (P = 8.65 × 10−3) and SOZ–NIZ (P = 4.66 × 10−4), while the PC 1 cohort only demonstrated significant differences in SOZ–NIZ (P = 1.07 × 10−3) and PZ–NIZ (P = 1.22 × 10−4). This may suggest that the potential suppression in the SOZs for the PC 2+ cohort is not localized to the ISH component alone. Thus, these patients may represent a more complicated network with further patterns to localize SOZs better.

Discussion

Connectomics may improve identification of SOZs, but it is not clear which network motif best identifies SOZs.11-41 Our group and others have previously noted a common motif of suppression of SOZs by non-SOZs. This ISH posits that when a patient is not having a seizure, the SOZs are being suppressed by other brain regions. Some groups, on the other hand, suggest that SOZs have high outward connectivity and low inward connectivity. Our data driven approach in this study may help disentangle some of these questions. First, is the ISH connectivity motif of high-inward and low-outward connectivity the best motif for identifying clinically-defined SOZs? Second, is the best motif for identifying SOZs different across patients? Third, is there another motif that can augment the ISH in identifying SOZs? In this study we observed that the ISH motif identified SOZs across patients, the ISH motif was present in every patient, and no other common motif was discovered.

While the ISH motif was identified in every patient and was the first component for the vast majority of patients (PC 1 cohort), there existed a subset of patients in which the ISH motif was the second or later component (PC 2 cohort). Interestingly, these cohorts were not different in their demographics or outcomes (Table 1). The sampling was slightly different, with the PC 2 cohort exhibiting fewer sampled NIZ regions than the PC 1 cohort. Furthermore, when compared with the PC 1 cohort, the ISH component in the PC 2 cohort had lower explained variance, was weighted less for SOZs and did not significantly differentiate between SOZs/PZs/NIZs when the original connectivity data were reconstructed from the ISH component alone. Given that the ISH proposes that physiologically normal brain tissue ‘suppresses’ SOZs, the ISH component may be less defined in the PC 2 cohort as there are fewer observations of physiologically normal tissue. We believe that the cohorts diverge due to the PCA algorithm, as the ISH behaviour is similar in both the PC 1 and PC 2 cohorts when the entire connectivity matrix is used (Fig. 5A), but the behaviour is lost for only the PC 2 cohort when the connectivity matrix is reconstructed from the ISH component alone (Fig. 5B).

Across the field of focal epilepsy, several hypotheses have been suggested for the accurate localization of SOZs. High frequency oscillations (HFOs), which are EEG oscillations in the range of 80–250 Hz, have been shown to localize the SOZ well in several studies.42-45 Connectomics has also shown success in the localization of SOZs, with the sources and sinks hypothesis, effective inflow and the ISH emerging as leading hypotheses.23,26-28 All three of these hypotheses propose a similar idea: an imbalance between inward connectivity and outward connectivity, suggestive of SOZ suppression.

HFOs and the ISH have both demonstrated compelling results for the localization of SOZs, but why is it that SOZ localization can be performed relatively accurately with two divergent approaches? HFOs use activity of brain regions whereas the ISH uses relationships between brain regions. We propose that these are not divergent approaches but convergent ones. The frequency range of HFOs is similar to the high gamma frequency range in connectomics, the same frequency range that demonstrated compelling results for the interictal suppression of SOZs (Supplementary Figs 3–5). Additionally, we did not find a motif with high outward and low inward connectivity that other studies have reported. This may be due to the differential methodology used between the studies. Most studies of increased outward connectivity actually report an increased number of significant connections from the SOZ, whereas our study examines the strength of connections. Furthermore, the method of computing connectivity can influence these results. In this study, we used PDC, which is normalized column-wise, emphasizing inwards connectivity. Other methods, such as directed transfer function (DTF) are normalized row-wise, emphasizing outwards connectivity. The use of PDC likely increases the inwards connectivity; however, as ISH behaviour is defined as inwards–outwards connectivity, we expect the results to remain whether PDC or DTF is used.

This study serves an important purpose by allowing for the detection of motifs that differentiate SOZs from non-SOZs through a data driven approach. The minimization of human a priori assumptions to the pattern similarity measures allowed for independent differentiation of data across every patient. We believe that the fact that the ISH component was present in every patient and was the top component for 79% of the patients suggests that the possible suppression of SOZs is a robust physiological pattern of focal epilepsy.

This study has several limitations, including that it was a single-center study, and the sample size was 81 patients. Additionally, while an independent data-driven technique was used, qualitative interpretations were needed to develop the quantitative algorithm. This may introduce some bias into the analyses, but such interpretations are required to generalize findings from machine learning studies. Furthermore, we used all data obtained during the 20-min resting state acquisition regardless of whether sleep activity or interictal spiking activity was present in the electrographic data analysed. This is a possible confounder in our analysis, but we chose to analyse all data without manually processing epochs for spikes or sleep to increase the generalizability of our results to outside centres with minimal manual effort. Definitions of the SOZs, PZs and NIZs are also a limitation, since they are defined through manual review of electrographic recordings by epileptologists and are not quantitatively classified. Finally, while the most prominent component was identified, there are likely other network features to be uncovered, as suggested by the PC 2+ cohort. Such an analysis could lead to more accurate identification of SOZs for the surgical treatment of epilepsy.

We believe that these findings have three major implications. First, the ISH motif may be used to improve the identification of SOZs using interictal data. Second, patient specific networks may augment the ISH motif, but require more investigation. Third, the ISH may play a key role in the pathophysiology of focal epilepsy. The strength of the ISH motif in identifying SOZs across patients is unlikely due to chance. The ISH motif may represent a physiological adaptation to reduce seizure activity through suppression of the SOZ. While there are differences in each patient’s seizure network, which would be expected, the common theme of the ISH may allow for increased precision in the localization of SOZs. Future work investigating the ISH for localization of SOZs compared with other motifs should include an analysis of the effectiveness of each method.

Supplementary Material

awae189_Supplementary_Data

Data availability

Data are available upon reasonable request.

Funding

This work was funded by National Institutes of Health Grant Nos: T32-EB001628, T32-EB021937, T32-GM007347, F31-NS131056, R01-NS112252, F31-NS120401, R00-NS097618, R01-NS134625.

Competing interests

The authors report no competing interests.

Supplementary material

Supplementary material is available at Brain online.
==== Refs
References

1 Behr C , GoltzeneMA, KosmalskiG, HirschE, RyvlinP. Epidemiology of epilepsy. Rev Neurol (Paris). 2016;172 :27–36.26754036
2 Engel J . What can we do for people with drug-resistant epilepsy? Neurology. 2016;87 :2483–2489.27920283
3 Beghi E . The epidemiology of epilepsy. Neuroepidemiology. 2020;54 :185–191.31852003
4 Téllez-Zenteno JF , DharR, WiebeS. Long-term seizure outcomes following epilepsy surgery: A systematic review and meta-analysis. Brain. 2005;128 :1188–1198.15758038
5 Ryvlin P , CrossJH, RheimsS. Epilepsy surgery in children and adults. Lancet Neurol. 2014;13 :1114–1126.25316018
6 Consales A , CasciatoS, AsioliS, et al The surgical treatment of epilepsy. Neurol Sci. 2021;42 :2249–2260.33797619
7 Krucoff MO , ChanAY, HarwardSC, et al Rates and predictors of success and failure in repeat epilepsy surgery: A meta-analysis and systematic review. Epilepsia. 2017;58 :2133–2142.28994113
8 Englot DJ . A modern epilepsy surgery treatment algorithm: Incorporating traditional and emerging technologies. Epilepsy Behav. 2018;80 :68–74.29414561
9 Vakharia VN , DuncanJS, WittJA, ElgerCE, StabaR, EngelJJr. Getting the best outcomes from epilepsy surgery. Ann Neurol. 2018;83 :676–690.29534299
10 Baumgartner C , KorenJP, Britto-AriasM, ZocheL, PirkerS. Presurgical epilepsy evaluation and epilepsy surgery. [version 1; peer review: 2 approved]. F1000Res. 2019;8 (F1000 Faculty Rev) :1818.
11 Engel J Jr , ThompsonPM, SternJM, et al Connectomics and epilepsy. Curr Opin Neurol. 2013;26 :186–194.23406911
12 Scott RC , Menendez de la PridaL, MahoneyJM, KobowK, SankarR, de CurtisM. WONOEP APPRAISAL: The many facets of epilepsy networks. Epilepsia. 2018;59 :1475–1483.30009398
13 Davis KA , JirsaVK, SchevonCA. Wheels within wheels: Theory and practice of epileptic networks. Epilepsy Curr. 2021;21 :15357597211015663.
14 Sinha N , JohnsonGW, DavisKA, EnglotDJ. Integrating network neuroscience into epilepsy care: progress barriers, and next steps. Epilepsy Curr. 2022;22 :272–278.36285209
15 Mormann F , LehnertzK, DavidP, Elger CE. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D: Nonlinear Phenomena. 2000;144 :358–369.
16 Bettus G , WendlingF, GuyeM, et al Enhanced EEG functional connectivity in mesial temporal lobe epilepsy. Epilepsy Res. 2008;81 :58–68.18547787
17 Bettus G , RanjevaJP, WendlingF, et al Interictal functional connectivity of human epileptic networks assessed by intracerebral EEG and BOLD signal fluctuations. PLoS One. 2011;6 :e20071.21625517
18 Varotto G , TassiL, FranceschettiS, SpreaficoR, PanzicaF. Epileptogenic networks of type II focal cortical dysplasia: A stereo-EEG study. Neuroimage. 2012;61 :591–598.22510255
19 Antony AR , AlexopoulosAV, González-MartínezJA, et al Functional connectivity estimated from intracranial EEG predicts surgical outcome in intractable temporal lobe epilepsy. PLoS One. 2013;8 :e77916.24205027
20 Bartolomei F , BettusG, StamCJ, GuyeM. Interictal network properties in mesial temporal lobe epilepsy: A graph theoretical study from intracerebral recordings. Clin Neurophysiol. 2013;124 :2345–2353.23810635
21 Lagarde S , RoehriN, LambertI, et al Interictal stereotactic-EEG functional connectivity in refractory focal epilepsies. Brain. 2018;141 :2966–2980.30107499
22 Goodale SE , GonzálezHFJ, JohnsonGW, et al Resting-State SEEG may help localize epileptogenic brain regions. Neurosurgery. 2020;86 :792–801.31814011
23 Narasimhan S , KundasseryKB, GuptaK, et al Seizure-onset regions demonstrate high inward directed connectivity during resting-state: An SEEG study in focal epilepsy. Epilepsia. 2020;61 :2534–2544.32944945
24 Jiang H , KokkinosV, YeS, et al Interictal SEEG resting-state connectivity localizes the seizure onset zone and predicts seizure outcome. Adv Sci (Weinh). 2022;9 :e2200887.35545899
25 Paulo DL , WillsKE, JohnsonGW, et al SEEG functional connectivity measures to identify epileptogenic zones: stability medication influence, and recording condition. Neurology. 2022;98 :e2060–e2072.35338075
26 Vlachos I , KrishnanB, TreimanDM, TsakalisK, KugiumtzisD, IasemidisLD. The concept of effective inflow: Application to interictal localization of the epileptogenic focus from iEEG. IEEE Trans Biomed Eng. 2016;64 :2241–2252.28092511
27 Gunnarsdottir KM , LiA, SmithRJ, et al Source-sink connectivity: A novel interictal EEG marker for seizure localization. Brain. 2022;145 :3901–3915.36412516
28 Johnson GW , DossDJ, MorganVL, et al The interictal suppression hypothesis in focal epilepsy: Network-level supporting evidence. Brain. 2023:146 :2828–2845.36722219
29 D’Haese P-F , PallavaramS, LiR, et al CranialVault and its CRAVE tools: A clinical computer assistance system for deep brain stimulation (DBS) therapy. Med Image Anal. 2012;16 :744–753.20732828
30 Johnson GW , CaiLY, NarasimhanS, et al Temporal lobe epilepsy lateralisation and surgical outcome prediction using diffusion imaging. J Neurol Neurosurg Psychiatry. 2022;93 :599–608.35347079
31 Andrews JP , GummadavelliA, FarooqueP, et al Association of seizure spread with surgical failure in epilepsy. JAMA Neurol. 2019;76 :462–469.30508033
32 Barba C , CossuM, GuerriniR, et al Temporal lobe epilepsy surgery in children and adults: A multicenter study. Epilepsia. 2021;62 :128–142.33258120
33 Oostenveld R , FriesP, MarisE, SchoffelenJM. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. 2011;2011 :156869.21253357
34 Baccalá LA , SameshimaK. Partial directed coherence: A new concept in neural structure determination. Biol Cybern. 2001;84 :463–474.11417058
35 Bastos AM , SchoffelenJM. A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front Syst Neurosci. 2015;9 :175.26778976
36 Blinowska KJ . Review of the methods of determination of directed connectivity from multichannel data. Med Biol Eng Comput. 2011;49 :521–529.21298355
37 Hinkley LB , MarcoEJ, FindlayAM, et al The role of corpus callosum development in functional connectivity and cognitive processing 2012;PLoS One 7 :e39804.22870191
38 Wold S , EsbensenK, GeladiP. Principal component analysis. Chemometr Intell Lab Syst. 1987;2 (1–3 ):37–52.
39 Zijlmans M , ZweiphenningW, van KlinkN. Changing concepts in presurgical assessment for epilepsy surgery. Nat Rev Neurol. 2019;15 :594–606.31341275
40 Johnson GW , DossDJ, EnglotDJ. Network dysfunction in pre and postsurgical epilepsy: Connectomics as a tool and not a destination. Curr Opin Neurol. 2021;35 :196–201.
41 Piper RJ , RichardsonRM, WorrellG, et al Towards network-guided neuromodulation for epilepsy. Brain. 2022;145 :3347–3362.35771657
42 Bragin A , ModyI, WilsonCL, EngelJJr. Local generation of fast ripples in epileptic brain. J Neurosci. 2002;22 :2012–2021.11880532
43 Cimbalnik J , KucewiczMT, WorrellG. Interictal high-frequency oscillations in focal human epilepsy. Curr Opin Neurol. 2016;29 :175–181.26953850
44 Cimbalnik J , KlimesP, SladkyV, et al Multi-feature localization of epileptic foci from interictal, intracranial EEG. Clin Neurophysiol. 2019;130 :1945–1953.31465970
45 Worrell GA , GardnerAB, SteadSM, et al High-frequency oscillations in human temporal lobe: Simultaneous microwire and clinical macroelectrode recordings. Brain. 2008;131 (Pt 4 ):928–937.18263625
