scholarly journals Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

2021 ◽  
Vol 17 (2) ◽  
pp. e1008689
Author(s):  
Viktor Sip ◽  
Meysam Hashemi ◽  
Anirudh N. Vattikonda ◽  
Marmaduke M. Woodman ◽  
Huifang Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Epileptic Seizures ◽  
Brain Network ◽  
Temporal Organization ◽  
Data Driven ◽  
Patient Specific ◽  
Epileptogenic Zone ◽  
Surgery Outcome ◽  
Spatio Temporal

Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.

scholarly journals Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

2020 ◽  
Author(s):  
Viktor Sip ◽  
Meysam Hashemi ◽  
Anirudh N Vattikonda ◽  
Marmaduke Woodman ◽  
Huifang Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Epileptic Seizures ◽  
Brain Network ◽  
Temporal Organization ◽  
Data Driven ◽  
Patient Specific ◽  
Epileptogenic Zone ◽  
Success Rates ◽  
Surgery Outcome ◽  
Spatio Temporal

Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of fifty patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.

scholarly journals Early alterations of large-scale brain networks temporal dynamics in young children with autism

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Aurélie Bochet ◽  
Holger Franz Sperdin ◽  
Tonia Anahi Rihs ◽  
Nada Kojovic ◽  
Martina Franchini ◽  
...  
Keyword(s):  
Large Scale ◽  
Developmental Stages ◽  
Temporal Dynamics ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Brain Network ◽  
Clustering Methods ◽  
Early Developmental Stages ◽  
Spatio Temporal ◽  
Early Developmental

AbstractAutism spectrum disorders (ASD) are associated with disruption of large-scale brain network. Recently, we found that directed functional connectivity alterations of social brain networks are a core component of atypical brain development at early developmental stages in ASD. Here, we investigated the spatio-temporal dynamics of whole-brain neuronal networks at a subsecond scale in 113 toddlers and preschoolers (66 with ASD) using an EEG microstate approach. We first determined the predominant microstates using established clustering methods. We identified five predominant microstate (labeled as microstate classes A–E) with significant differences in the temporal dynamics of microstate class B between the groups in terms of increased appearance and prolonged duration. Using Markov chains, we found differences in the dynamic syntax between several maps in toddlers and preschoolers with ASD compared to their TD peers. Finally, exploratory analysis of brain–behavioral relationships within the ASD group suggested that the temporal dynamics of some maps were related to conditions comorbid to ASD during early developmental stages.

scholarly journals Identifying Public Perceptions toward Emerging Transportation Trends through Social Media-Based Interactions

2021 ◽  
Vol 1 (3) ◽  
pp. 794-813
Author(s):  
Md Rakibul Alam ◽  
Arif Mohaimin Sadri ◽  
Xia Jin
Keyword(s):  
Social Media ◽  
Built Environment ◽  
Large Scale ◽  
User Fees ◽  
Data Driven ◽  
Social Media Data ◽  
Shared Mobility ◽  
Vehicle Technology ◽  
Spatio Temporal ◽  
Media Data

The objective of this study is to mine and analyze large-scale social media data (rich spatio-temporal data unlike traditional surveys) and develop comparative infographics of emerging transportation trends and mobility indicators by adopting natural language processing and data-driven techniques. As such, first, around 13 million tweets for about 20 days (16 December 2019–4 January 2020) from North America were collected, and tweets closely aligned with emerging transportation and mobility trends (such as shared mobility, vehicle technology, built environment, user fees, telecommuting, and e-commerce) were identified. Data analytics captured spatio-temporal differences in social media user interactions and concerns about such trends, as well as topics of discussions formed through such interactions. California, Florida, Georgia, Illinois, New York are among the highly visible cities discussing such trends. Being positive overall, people carried more positive views on shared mobility, vehicle technology, telecommuting, and e-commerce, while being more negative on user fees, and the built environment. Ride-hailing, fuel efficiency, trip navigation, daily as well as shopping and recreational activities, gas price, tax, and product delivery were among the emergent topics. The social media data-driven framework would allow real-time monitoring of transportation trends by agencies, researchers, and professionals.

scholarly journals Novel biochemical, structural and systems insights into inflammatory signaling revealed by contextual interaction proteomics

2021 ◽  
Author(s):  
Rodolfo Ciuffa ◽  
Federico Uliana ◽  
Martin Mehnert ◽  
Cathy Marulli ◽  
Ari Satanowski ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Functional Organization ◽  
Temporal Organization ◽  
Protein Protein Interactions ◽  
Main Mode ◽  
Signaling Complex ◽  
Ppi Networks ◽  
Level Information ◽  
Spatio Temporal

Protein-protein interactions (PPI) represent the main mode of the proteome organization in the cell. In the last decade, several large-scale representations of PPI networks have captured generic aspects of the functional organization of network components, but mostly lack the context of cellular states. However, the generation of contextual representations of PPI networks is essential for structural and systems-level modeling of biological processes and remains an unsolved challenge. In this study we describe an integrated experimental/computational strategy to achieve a contextualized modeling of PPI. This strategy defines the composition, stoichiometry, spatio-temporal organization and cellular requirements for the formation of target assemblies. We used this approach to generate an integrated model of the formation principles and architecture of a large signalosome, the TNF-receptor signaling complex (TNF-RSC). Overall, we show that the integration of systems- and structure-level information provides a generic, largely unexplored link between the modular proteome and cellular function.

scholarly journals Quantifying the Spatio-Temporal Process of Township Urbanization: A Large-Scale Data-Driven Approach

2019 ◽  
Vol 8 (9) ◽  
pp. 389
Author(s):  
Xinliang Liu ◽  
Yi Wang ◽  
Yong Li ◽  
Jinshui Wu
Keyword(s):  
Large Scale ◽  
Driving Forces ◽  
Data Driven ◽  
Population Migration ◽  
Urbanization Process ◽  
Large Scale Data ◽  
Temporal Process ◽  
Data Driven Approach ◽  
Spatio Temporal ◽  
Scale Data

The integrated recognition of spatio-temporal characteristics (e.g., speed, interaction with surrounding areas, and driving forces) of urbanization facilitates regional comprehensive development. In this study, a large-scale data-driven approach was formed for exploring the township urbanization process. The approach integrated logistic models to quantify urbanization speed, partial triadic analysis to reveal dynamic relationships between rural population migration and urbanization, and random forest analysis to identify the response of urbanization to spatial driving forces. A typical subtropical town was chosen to verify the approach by quantifying the spatio-temporal process of township urbanization from 1933 to 2012. The results showed that (i) urbanization speed was well reflected by the changes of time-course areas of urban cores fitted by a four-parameter logistic equation (R2 = 0.95–1.00, p < 0.001), and the relatively fast and steady developing periods were also successfully predicted, respectively; (ii) the spatio-temporal sprawl of urban cores and their interactions with the surrounding rural residential areas were well revealed and implied that the town experienced different historically aggregating and splitting trajectories; and (iii) the key drivers (township merger, elevation and distance to roads, as well as population migration) were identified in the spatial sprawl of urban cores. Our findings proved that a comprehensive approach is powerful for quantifying the spatio-temporal characteristics of the urbanization process at the township level and emphasized the importance of applying long-term historical data when researching the urbanization process.

scholarly journals Contribution of Exploratory Methods to the Investigation of Extended Large-Scale Brain Networks in Functional MRI: Methodologies, Results, and Challenges

2008 ◽  
Vol 2008 ◽  
pp. 1-14 ◽  
Author(s):  
V. Perlbarg ◽  
G. Marrelec
Keyword(s):  
Large Scale ◽  
Blood Oxygen Level Dependent ◽  
Brain Network ◽  
Data Driven ◽  
Blood Oxygen ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Blood Oxygen Level ◽  
Functional Interactions ◽  
Efficient Exploration

A large-scale brain network can be defined as a set of segregated and integrated regions, that is, distant regions that share strong anatomical connections and functional interactions. Data-driven investigation of such networks has recently received a great deal of attention in blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). We here review the rationale for such an investigation, the methods used, the results obtained, and also discuss some issues that have to be faced for an efficient exploration.

scholarly journals Microstate EEGlab toolbox: An introductory guide

2018 ◽  
Author(s):  
Andreas Trier Poulsen ◽  
Andreas Pedroni ◽  
Nicolas Langer ◽  
Lars Kai Hansen
Keyword(s):  
Graphical User Interface ◽  
Large Scale ◽  
Brain Network ◽  
Network Activity ◽  
Eeg Signal ◽  
Widespread Application ◽  
New Methods ◽  
Temporal Features ◽  
Spatio Temporal ◽  
Microstate Analysis

AbstractEEG microstate analysis offers a sparse characterisation of the spatio-temporal features of large-scale brain network activity. However, despite the concept of microstates is straight-forward and offers various quantifications of the EEG signal with a relatively clear neurophysiological interpretation, a few important aspects about the currently applied methods are not readily comprehensible. Here we aim to increase the transparency about the methods to facilitate widespread application and reproducibility of EEG microstate analysis by introducing a new EEGlab toolbox for Matlab. EEGlab and the Microstate toolbox are open source, allowing the user to keep track of all details in every analysis step. The toolbox is specifically designed to facilitate the development of new methods. While the toolbox can be controlled with a graphical user interface (GUI), making it easier for newcomers to take their first steps in exploring the possibilities of microstate analysis, the Matlab framework allows advanced users to create scripts to automatise analysis for multiple subjects to avoid tediously repeating steps for every subject. This manuscript provides an overview of the most commonly applied microstate methods as well as a tutorial consisting of a comprehensive walk-through of the analysis of a small, publicly available dataset.

scholarly journals Computational modeling of seizure spread on a cortical surface

2020 ◽  
Author(s):  
Viktor Sip ◽  
Maxime Guye ◽  
Fabrice Bartolomei ◽  
Viktor Jirsa
Keyword(s):  
Large Scale ◽  
Model Building ◽  
Computational Study ◽  
Epileptic Seizures ◽  
Patient Specific ◽  
Clinical Settings ◽  
Real Surface ◽  
Cortical Surface ◽  
Specific Level ◽  
Realistic Geometry

AbstractIn the field of computational epilepsy, neural field models helped to understand some large-scale features of seizure dynamics. These insights however remain on general levels, without translation to the clinical settings via personalization of the model with the patient-specific structure. In particular, a link was suggested between epileptic seizures spreading across the cortical surface and the so-called theta-alpha activity (TAA) pattern seen on intracranial electrographic signals, yet this link was not demonstrated on a patient-specific level. Here we present a single patient computational study linking the seizure spreading across the patient-specific cortical surface with a specific instance of the TAA pattern recorded in the patient. Using the realistic geometry of the cortical surface we perform the simulations of seizure dynamics in The Virtual Brain platform, and we show that the simulated electrographic signals qualitatively agree with the recorded signals. Furthermore, the comparison with the simulations performed on surrogate surfaces reveals that the best quantitative fit is obtained for the real surface. The work illustrates how the patient-specific cortical geometry can be utilized in The Virtual Brain for personalized model building, and the importance of such approach.

scholarly journals Fault Network Reconstruction using Agglomerative Clustering: Applications to South Californian Seismicity

2020 ◽  
Author(s):  
Yavor Kamer ◽  
Guy Ouillon ◽  
Didier Sornette
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Network Reconstruction ◽  
Small Scale ◽  
Agglomerative Clustering ◽  
Computation Efficiency ◽  
Location Uncertainty ◽  
Spatio Temporal ◽  
Initial Sampling ◽  
Fault Network

Abstract. In this paper we introduce a method for fault network reconstruction based on the 3D spatial distribution of seismicity. One of the major drawbacks of statistical earthquake models is their inability to account for the highly anisotropic distribution of seismicity. Fault reconstruction has been proposed as a pattern recognition method aiming to extract this structural information from seismicity catalogs. Current methods start from simple large scale models and gradually increase the complexity trying to explain the small scale features. In contrast the method introduced here uses a bottom-up approach, that relies on initial sampling of the small scale features and reduction of this complexity by optimal local merging of substructures. First, we describe the implementation of the method through illustrative synthetic examples. We then apply the method to the probabilistic absolute hypocenter catalog KaKiOS-16, which contains three decades of South Californian seismicity. To reduce data size and increase computation efficiency, the new approach builds upon the previously introduced catalog condensation method that exploits the heterogeneity of the hypocenter uncertainties. We validate the obtained fault network through a pseudo prospective spatial forecast test and discuss possible improvements for future studies. The performance of the presented methodology attests the importance of the non-linear techniques used to quantify location uncertainty information, which is a crucial input for the large scale application of the method. We envision that the results of this study can be used to construct improved models for the spatio-temporal evolution of seismicity.

scholarly journals Identifying spatio-temporal seizure propagation patterns in epilepsy using Bayesian inference

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Anirudh N. Vattikonda ◽  
Meysam Hashemi ◽  
Viktor Sip ◽  
Marmaduke M. Woodman ◽  
Fabrice Bartolomei ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Synthetic Data ◽  
Patient Specific ◽  
Seizure Freedom ◽  
Epileptogenic Zone ◽  
Model Predictions ◽  
Seizure Propagation ◽  
Spatio Temporal ◽  
Eeg Recordings ◽  
Drug Resistant Epilepsy

AbstractFocal drug resistant epilepsy is a neurological disorder characterized by seizures caused by abnormal activity originating in one or more regions together called as epileptogenic zone. Treatment for such patients involves surgical resection of affected regions. Epileptogenic zone is typically identified using stereotactic EEG recordings from the electrodes implanted into the patient’s brain. Identifying the epileptogenic zone is a challenging problem due to the spatial sparsity of electrode implantation. We propose a probabilistic hierarchical model of seizure propagation patterns, based on a phenomenological model of seizure dynamics called Epileptor. Using Bayesian inference, the Epileptor model is optimized to build patient specific virtual models that best fit to the log power of intracranial recordings. First, accuracy of the model predictions and identifiability of the model are investigated using synthetic data. Then, model predictions are evaluated against a retrospective patient cohort of 25 patients with varying surgical outcomes. In the patients who are seizure free after surgery, model predictions showed good match with the clinical hypothesis. In patients where surgery failed to achieve seizure freedom model predictions showed a strong mismatch. Our results demonstrate that proposed probabilistic model could be a valuable tool to aid the clinicians in identifying the seizure focus.

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