Perspectives on Understanding Aberrant Brain Networks in Epilepsy

Sinha, Nishant; Joshi, Rasesh B.; Sandhu, Mani Ratnesh S.; Netoff, Theoden I.; Zaveri, Hitten P.; Lehnertz, Klaus

doi:10.3389/fnetp.2022.868092

Cited by 14 publications

(12 citation statements)

References 89 publications

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“…Multiple studies have reported changes that discriminate between the inter‐ictal and pre‐ictal state at various scales of neuronal organization from the local to the network level. This further provides evidence that epilepsy is a large‐scale brain network disease that requires novel approaches to improve understanding of the network's aberrant structure and function 9 . New approaches with minimally invasive and non‐invasive devices providing multimodal predictive biomarkers and conducted over prolonged periods are of interest for broader application.…”

Section: Introductionmentioning

confidence: 95%

“…Considering the brain as a complex network, with its structure and dynamics evolving in time, 43,44 has led to a paradigm shift in establishing epilepsy as a large-scale network disorder. 9,[45][46][47] Graph-theoretical concepts and methods 27 then enable improved characterizations of such networks. Early network-based seizure-prediction studies [48][49][50][51] have reported limited predictive performance from temporal changes of global network characteristics that assess the network's functional segregation, integration, or robustness.…”

Section: Seizure Forecastingmentioning

confidence: 99%

“…This further provides evidence that epilepsy is a large-scale brain network disease that requires novel approaches to improve understanding of the network's aberrant structure and function. 9 New approaches with minimally invasive and non-invasive devices providing multimodal predictive biomarkers and conducted over prolonged periods are of interest for broader application. These new Research, the Swiss National Science Foundation, UCB, FHC, the Wyss Center for bio-and neuro-engineering, the American Epilepsy Society (AES), the CURE epilepsy Foundation, Ripple neuro, Sintetica, DIXI medical, UNEEG medical and NeuroPace.…”

Section: Introductionmentioning

confidence: 99%

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Seizure forecasting: Where do we stand?

et al. 2023

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A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures—ICTALS 2022—convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long‐suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi‐day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large‐scale network disorder yielded novel perspectives on the pre‐ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure‐forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well‐being.

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Section: Introductionmentioning

confidence: 95%

Section: Seizure Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Seizure forecasting: Where do we stand?

et al. 2023

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“…Epilepsy has been identified increasingly as a network disorder, [11][12][13] and non-invasive imaging modalities, such as resting-state functional magnetic resonance imaging (rs-fMRI), have revealed both focal and global functional epileptic network abnormalities. [14][15][16] These network abnormalities can be used as biomarkers, helping to guide therapeutic decision-making non-invasively.…”

Section: Introductionmentioning

confidence: 99%

Resting state functional connectivity demonstrates increased segregation in bilateral temporal lobe epilepsy

et al. 2023

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Objective Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study was to leverage network neuroscience to better understand the interictal whole brain network of bilateral TLE (BiTLE). Methods In this study, using a multicenter resting state functional magnetic resonance imaging (rs‐fMRI) data set, we constructed whole‐brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting‐state, whole‐brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density vs each network metric were compared between groups. Finally, we derived a combined metric, which we term the “integration‐segregation axis,” by combining whole‐brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)–based dimensionality reduction. Results Compared to UTLE, BiTLE had decreased global efficiency (p = .031), and decreased whole brain average participation coefficient across a range of network densities (p = .019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p = .020). Differences in network properties separate BiTLE and UTLE along the integration‐segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration‐segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE. Significance Increased interictal whole‐brain network segregation, as measured by rs‐fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non‐invasively identifying this patient population prior to intracranial electroencephalography or device implantation.

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“…Epilepsy has been increasingly identified as a network disorder [11][12][13] . The study of brain connectomics has demonstrated the presence of large-scale network abnormalities even in focal epilepsies [14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

Resting State Functional Connectivity Demonstrates Increased Segregation in Bilateral Temporal Lobe Epilepsy

Lucas

Cornblath

Sinha

et al. 2022

Preprint

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Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe involvement during seizures. Bilateral TLE (BiTLE) remains understudied, likely due to its complex underlying pathophysiology and heterogeneous clinical presentation. In this study, using a multicenter resting state functional MRI (rs-fMRI) dataset, we constructed whole brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting-state, whole-brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities ranging from 0.10-0.50. Curves of network density versus each network metric were compared between groups. Finally, we derived a combined metric, which we term the integration-segregation axis, by combining whole brain average clustering coefficient and global efficiency curves and applying principal component analysis (PCA)-based dimensionality reduction. Compared to UTLE, BiTLE had decreased global efficiency (p=0.026), increased whole brain average clustering coefficient (p=0.035), and decreased whole brain average participation coefficient across a range of network densities (p=0.001). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p=0.016). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis: 68% of patients with BiTLE were identified within the high segregation region, while only 41% of the UTLE patients were identified in the same region (p=0.042). Along the integration-segregation axis, UTLE patients with poor surgical outcomes were more similar to BiTLE than those with good surgical outcomes (p=0.72). Increased interictal whole brain network segregation, as measured by rs-fMRI, is specific to BiTLE, and may assist in non-invasively identifying this patient population prior to intracranial EEG or device implantation.

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Perspectives on Understanding Aberrant Brain Networks in Epilepsy

Cited by 14 publications

References 89 publications

Seizure forecasting: Where do we stand?

Seizure forecasting: Where do we stand?

Resting state functional connectivity demonstrates increased segregation in bilateral temporal lobe epilepsy

Resting State Functional Connectivity Demonstrates Increased Segregation in Bilateral Temporal Lobe Epilepsy

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