Effect of interictal epileptiform discharges on EEG-based functional connectivity networks

2019 ◽  
Author(s):  
Derek K. Hu ◽  
Daniel W. Shrey ◽  
Beth A. Lopour
Keyword(s):  
Functional Connectivity ◽  
Network Structure ◽  
Infantile Spasms ◽  
List Type ◽  
Interictal Spikes ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Network Strength ◽  
The Impact ◽  
Connectivity Strength

AbstractObjectiveFunctional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis.MethodsWe introduced simulated IEDs to sleep EEG recordings of eight healthy controls and analyzed the effect of IED amplitude and rate on the FCN. We then generated FCNs based on epochs with and without IEDs and compared them to the analogous FCNs from eight subjects with infantile spasms (IS), based on 1,340 visually marked IEDs. Differences in network structure and strength were assessed.ResultsIEDs in IS subjects caused increased connectivity strength but no change in network structure. In controls, simulated IEDs with physiological amplitudes and rates did not alter network strength or structure.ConclusionsIncreases in connectivity strength in IS subjects are not artifacts caused by the interictal spike waveform and may be related to the underlying pathophysiology of IS.SignificanceDynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy.HighlightsInfantile spasms subjects exhibit broadly increased connectivity strength during interictal spikesFunctional connectivity network structure is unaltered by interictal spikes in infantile spasmsSimulated spikes in healthy control EEG did not alter network strength or structure

scholarly journals The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

2020 ◽  
Author(s):  
Stuart Oldham ◽  
Aurina Arnatkevic̆iūtė ◽  
Robert E. Smith ◽  
Jeggan Tiego ◽  
Mark A. Bellgrove ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Motion Correction ◽  
Healthy Adult ◽  
Structural Connectivity ◽  
Head Motion ◽  
List Type ◽  
Edge Weighting ◽  
Quantitative Metrics ◽  
The Impact ◽  
Connectivity Strength

AbstractHead motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 252), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction.HighlightsWe assess how motion affects structural connectivity in 240 preprocessing pipelinesMotion contamination of structural connectivity depends on preprocessing choicesAdvanced motion correction tools reduce motion confoundsFA edge weighting is more susceptible to motion effects than streamline count

scholarly journals Benchmarking common preprocessing strategies in early childhood functional connectivity MRI

2020 ◽  
Author(s):  
Kirk Graff ◽  
Ryann Tansey ◽  
Amanda Ip ◽  
Christiane Rohr ◽  
Dennis Dimond ◽  
...  
Keyword(s):  
Early Childhood ◽  
Functional Connectivity ◽  
Young Children ◽  
Noise Removal ◽  
Head Motion ◽  
List Type ◽  
Unique Challenge ◽  
Global Signal ◽  
Passive Viewing ◽  
The Impact

AbstractFunctional connectivity magnetic resonance imaging (FC-MRI) has been widely used to investigate neurodevelopment. However, FC-MRI is vulnerable to head motion, which is associated with age and distorts FC estimates. Numerous preprocessing strategies have been developed to mitigate confounds, each with advantages and drawbacks. Preprocessing strategies for FC-MRI have typically been validated and compared using resting state data from adults. However, FC-MRI in young children presents a unique challenge due to relatively high head motion and a growing use of passive viewing paradigms to mitigate motion. This highlights a need to compare processing choices in pediatric samples. To this end, we leveraged longitudinal, passive viewing fMRI data collected from 4 to 8-year-old children. We systematically investigated combinations of widely used and debated preprocessing strategies, namely global signal regression, volume censoring, ICA-AROMA, and bandpass filtering. We implemented commonly used metrics of noise removal (i.e. quality control-functional connectivity), metrics sensitive to individual differences (i.e. connectome fingerprinting), and, because data was collected during a passive viewing task, we also assessed the impact on stimulus-evoked responses (i.e. intersubject correlations; ISC). We found that the most efficacious pipeline included censoring, global signal regression, bandpass filtering, and head motion parameter regression. Despite the drawbacks of noise-mitigation steps, our findings show benefits for both noise removal and information retention in a high-motion early childhood sample.Highlights- We evaluated 27 preprocessing pipelines in passive viewing data from young children- Pipelines were evaluated on noise-removed and information retained- Pipelines that included censoring and GSR outperformed alternatives across benchmarks- For high-motion scans, preprocessing choices substantially alter connectomes

scholarly journals Effect of interictal epileptiform discharges on EEG-based functional connectivity networks

2020 ◽  
Vol 131 (5) ◽  
pp. 1087-1098
Author(s):  
Derek K. Hu ◽  
Andrew Mower ◽  
Daniel W. Shrey ◽  
Beth A. Lopour
Keyword(s):  
Functional Connectivity ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges

scholarly journals Improved mapping of interictal epileptiform discharges with EEG-fMRI and voxel-wise functional connectivity analysis

Epilepsia ◽  
2014 ◽  
Vol 55 (9) ◽  
pp. 1380-1388 ◽  
Author(s):  
Junjie V. Liu ◽  
Erik J. Kobylarz ◽  
Terrance M. Darcey ◽  
Zhengang Lu ◽  
Yu-Chien Wu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Connectivity Analysis ◽  
Functional Connectivity Analysis ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges

scholarly journals Fluctuations in functional connectivity associated with interictal epileptiform discharges (IEDs) in intracranial EEG

2021 ◽  
Author(s):  
Jennifer Stiso ◽  
Lorenzo Caciagli ◽  
Peter Hadar ◽  
Kathryn A Davis ◽  
Timothy H Lucas ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Epileptiform Activity ◽  
Lasso Regression ◽  
Intracranial Eeg ◽  
Small Perturbations ◽  
Frequency Bands ◽  
Global Synchrony ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Starting Point

All epilepsies are defined by a propensity for recurrent seizures, characterized by hypersynchronous electrographic activity. Understanding this overarching property would be advanced by a thorough quantification of how the global synchrony of the epileptic brain responds to small perturbations that do not trigger seizures. Here, we leverage analysis of transient focal bursts of epileptiform activity, termed interictal epileptiform discharges (IEDs), to characterize this response. Specifically, we use a group of 145 participants implanted with intracranial EEG (iEEG) electrodes to quantify changes in 5 functional connectivity measures associated with three properties of IEDs: their presence, spread, and number. We perform this analysis in 5 frequency bands in order to contextualize our findings in relation to ongoing neural processes at different spatial and temporal scales. We find that, across frequency bands, both the presence and spread of IEDs tend to lead to independent increases of functional connectivity, but only in functional connectivity measures influenced by the amplitude, rather than the phase, of a signal. We find that these increases are not explained by simple subgroups of connections, such as the weakest connections in the brain, or only connections within the seizure onset zone. Evaluating patterns of similarity across different bands and measure combinations, we find that the presence of IEDs impacts high frequencies (gamma and high gamma)and low frequencies (theta, alpha, and beta) differently, although responses within each group are similar. Using grouped LASSO regression, we identify which individual-level features explain differences in functional connectivity changes associated with IEDs. While no single feature robustly explains observed differences, the most consistently included predictor across bands and measures is the anatomical locus of IEDs. Overall, this work provides compelling evidence for increases in global synchrony associated with IEDs, and delivers a thorough exploration of different functional connectivity measures, frequency bands, and IED properties. These observations show a disruption of several types of ongoing neural dynamics associated with IEDs. Additionally, we provide a starting point for future models of how small perturbations affect neural systems and how those systems support the hypersynchrony seen in epilepsy.

Epileptic Discharge Related Functional Connectivity Within and Between Networks in Benign Epilepsy with Centrotemporal Spikes

2017 ◽  
Vol 27 (07) ◽  
pp. 1750018 ◽  
Author(s):  
Rong Li ◽  
Gong-Jun Ji ◽  
Yangyang Yu ◽  
Yang Yu ◽  
Mei-Ping Ding ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Epileptic Activity ◽  
Epilepsy Syndrome ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Motor Network ◽  
Dorsal Attention Network ◽  
Intrinsic Connectivity Networks ◽  
Positive Correlations ◽  
Benign Epilepsy

Benign epilepsy with centrotemporal spikes (BECTS) is a common childhood epilepsy syndrome associated with abnormalities in neurocognitive domains, particularly during interictal epileptiform discharges (IEDs). Here, we investigated the effects of IEDs on brain’s intrinsic connectivity networks in 43 BECTS patients and 28 matched healthy controls (HCs). Patients were further divided into IED and non-IED subgroups based on simultaneous EEG-fMRI recordings. Functional connectivity within and between five networks, corresponding to seizure origination and cognitive processes, were analyzed to measure IED effects. We found that patients exhibited increased connectivity within the auditory network (AN) and the somato-motor network (SMN), and decreased connectivity within the basal ganglia network and the dorsal attention network, suggesting that both transient and chronic seizure activity may disturb normal network organization. The IED group showed decreased functional connectivity within the default mode network (DMN) compared with the non-IED group and HCs, implying that the DMN was selectively impaired during epileptiform discharges associated with altered self-referential cognitive functions. Moreover, the IED group exhibited increased positive correlations between the AN and the SMN, which suggests a possible excessive influence of centrotemporal spiking on information processing in the auditory system. The association between epileptic activity and network dysfunctions highlights their importance in investigating the pathological mechanism underlying BECTS.

scholarly journals Individual differences in functional connectivity during naturalistic viewing conditions

2016 ◽  
Author(s):  
Tamara Vanderwal ◽  
Jeffrey Eilbott ◽  
Emily S. Finn ◽  
R. Cameron Craddock ◽  
Adam Turnbull ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Identification Algorithm ◽  
List Type ◽  
Dynamic Stimuli ◽  
Individual Level ◽  
Eyes Open ◽  
The Individual ◽  
The Impact ◽  
Work First

AbstractNaturalistic viewing paradigms such as movies have been shown to reduce participant head motion and improve arousal during fMRI scanning relative to task-free rest, and have been used to study both functional connectivity and task-evoked BOLD-signal changes. These task-evoked changes result in cortical activity that is synchronized across subjects and involves large areas of the cortex, and it is unclear whether individual differences in functional connectivity are enhanced or diminished under such naturalistic conditions. This work first aims to characterize variability in functional connectivity (FC) across two distinct movie conditions and eyes-open rest (n=34 healthy adults, 2 scan sessions each). At the whole-brain level, we found that movies have higher intra- and inter-subject correlations in cluster-wise FC relative to rest. The anatomical distribution of inter-subject variability was similar across conditions, with higher variability occurring at the lateral prefrontal lobes and temporoparietal junctions. Second, we used an unsupervised test-retest matching (or “finger-printin”) algorithm that identifies individual subjects from within a group based on functional connectivity patterns, quantifying the accuracy of the algorithm across the three conditions. We also evaluated the impact of parcellation resolution, scan duration, and number of edges on observed inter-individual differences. The movies and resting state all enabled identification of individual subjects based on FC matrices, with accuracies between 62 and 100%. Overall, pairings involving movies outperformed rest, and the more social and faster-paced movie attained 100% accuracy. When the parcellation resolution, scan duration and number of edges used were increased, accuracies improved across conditions, and the pattern of movies>rest was preserved. These results suggest that using dynamic stimuli such as movies enhances the detection of FC patterns that are distinct at the individual level.HighlightsIntra- and inter-subject FC correlations are compared across rest and movies.Movies outperformed rest in an unsupervised identification algorithm based on FC.Movies outperform rest regardless of parcellation, scan length, or number of edges.Watching movies enhances the detection of individual differences in FC.

scholarly journals Human interictal epileptiform discharges are traveling waves reflecting ictal self-organization

2021 ◽  
Author(s):  
Elliot H Smith ◽  
Jyun-You Liou ◽  
Edward M. Merricks ◽  
Tyler S Davis ◽  
Kyle Thomson ◽  
...  
Keyword(s):  
Traveling Waves ◽  
Microelectrode Arrays ◽  
Interictal Spikes ◽  
Spatial Properties ◽  
Human Epilepsy ◽  
Spatial Features ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Patients With Epilepsy ◽  
The Brain

AbstractInterictal epileptiform discharges (IEDs), also known as interictal spikes, are large intermittent electrophysiological events observed between seizures in patients with epilepsy. While seizures are infrequent and unpredictable, IEDs are far more common, often occurring several times per minute. Yet despite the abundance of IEDs, it remains unknown how they relate to seizures. To better understand this relationship, we examined multi-day recordings of 96-channel microelectrode arrays implanted in human epilepsy patients. These recordings—spanning single cell action potentials to population field potentials—allowed us to study the microscale spatiotemporal organization of over 45,000 IEDs across 10 participants from 2 surgical centers. These recordings showed that the majority of IEDs propagate across neocortex as traveling waves. While all of these traveling wave distributions exhibited a predominant, consistent direction, the majority also exhibited a second, auxiliary, direction. Clustering the IED distributions revealed that their predominant and auxiliary distributions were antipodal, mimicking the spatial microstructure of seizure discharges (SDs) that we have previously reported. We thus compared spatial features of IED sub-distributions to those for SDs, showing a correspondence between ictal and interictal spatial properties in participants whose microelectrode arrays were recruited into the seizure from adjacent cortical tissue. These results reveal fundamental relationships between IEDs and seizures and suggest how IEDs could be used to infer spatial features of seizures.One Sentence SummaryEpileptiform electrical events occurring between human seizures propagate across the brain in directions that reflect the self-organizing structure of seizures.

scholarly journals Extinction models of robustness for weighted ecological networks

2017 ◽  
Author(s):  
Miranda S. Bane ◽  
Michael J. O. Pocock ◽  
Richard James
Keyword(s):  
Network Structure ◽  
Structural Heterogeneity ◽  
Ecological Networks ◽  
List Type ◽  
Bipartite Networks ◽  
Opposing Effects ◽  
The Impact ◽  
Plant Pollinator ◽  
Binary Networks ◽  
New Framework

AbstractAnalysis of ecological networks is a valuable approach to understanding the vulnerability of systems to environmental change. The tolerance of ecological networks to co-extinctions, resulting from sequences of primary extinctions, is a widely-used tool for modelling network ‘robustness’. Previously, these ‘extinction models’ have been developed for and applied mostly to binary networks and recently used to predict cascades of co-extinctions in plant-pollinator networks. There is a need for robustness models that can make the most of the weighted data available and most importantly there is a need to understand how the structure of a network affects its robustness.Here, we developed a framework of extinction models for bipartite ecological networks (specifically plant-pollinator networks). In previous models co-extinctions occurred when nodes lost all their links, but by relaxing this rule (according to a set threshold) our models can be applied to binary and weighted networks, and can permit structurally correlated extinctions, i.e. the potential for avalanches of extinctions. We tested how the average and the range of robustness values is impacted by network structure and the impact of structurally-correlated extinctions sampling non-uniformly from the distribution of random extinction sequences.We found that the way that structurally-correlated extinctions are modelled impacts the results; our two ecologically-plausible models produce opposing effects which shows the importance of understanding the model. We found that when applying the models to networks with weighted interactions, the effects are amplified and the variation in robustness increases. Variation in robustness is a key feature of these extinction models and is driven by the structural heterogeneity (i.e. the skewness of the degree distribution) of nodes (specifically, plant nodes) in the network.Our new framework of models enables us to calculate robustness with weighted, as well as binary, bipartite networks, and to make direct comparisons between models and between networks. This allows us to differentiate effects of the model and of the data (network structure) which is vital for those making ecological inferences from robustness models. The models can be applied to mutualistic and antagonistic networks, and can be extended to food webs.

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