Weighted and directed interactions in evolving large-scale epileptic brain networks

Abstract Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess—with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics—both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only – in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control.

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The connectome predicts resting state functional connectivity across the Drosophila brain

10.1101/2020.12.11.422105 ◽

2020 ◽

Cited By ~ 1

Author(s):

Maxwell H Turner ◽

Kevin Mann ◽

Thomas R. Clandinin

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Neural Circuit ◽

Brain Networks ◽

Brain Regions ◽

Central Complex ◽

Resting State Functional Connectivity ◽

Brain Structure And Function ◽

And Function

Connectomic datasets have emerged as invaluable tools for understanding neural circuits in many systems. What constraints does the connectome place on information processing and routing in a large scale neural circuit? For mesoscale brain networks, the relationship between cell and synaptic level connectivity and brain function is not well understood. Here, we use data from the Drosophila connectome in conjunction with whole-brain in vivo imaging to relate structural and functional connectivity in the central brain. We find that functional connectivity is strongly associated with the strength of both direct and indirect anatomical pathways. We also show that some brain regions, including the mushroom body and central complex, show considerably higher functional connectivity to other brain regions than is predicted based on their direct anatomical connections. We find several key topological similarities between mesoscale brain networks in flies and mammals, revealing conserved principles relating brain structure and function.

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On the structural connectivity of large-scale models of brain networks at cellular level

Scientific Reports ◽

10.1038/s41598-021-83759-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Giuseppe Giacopelli ◽

Domenico Tegolo ◽

Emiliano Spera ◽

Michele Migliore

Keyword(s):

Large Scale ◽

Brain Networks ◽

Structural Connectivity ◽

Network Models ◽

Brain Regions ◽

Cellular Level ◽

Scale Model ◽

Mathematical Framework ◽

Underlying Mechanisms ◽

Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

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Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

Scientific Reports ◽

10.1038/s41598-020-78899-7 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Rieke Fruengel ◽

Timo Bröhl ◽

Thorsten Rings ◽

Klaus Lehnertz

Keyword(s):

Large Scale ◽

Brain Networks ◽

Brain Regions ◽

Brain Network ◽

Theoretical Concept ◽

Global Network ◽

Time Resolved ◽

Functional Connections ◽

Node Centrality ◽

Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

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Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

Scientific Reports ◽

10.1038/s41598-020-80293-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Abhishek Uday Patil ◽

Sejal Ghate ◽

Deepa Madathil ◽

Ovid J. L. Tzeng ◽

Hsu-Wen Huang ◽

...

Keyword(s):

Cognitive Processes ◽

Large Scale ◽

Brain Networks ◽

Blood Oxygen Level Dependent ◽

Detection Algorithm ◽

Brain Regions ◽

Creative Cognition ◽

Network Flexibility ◽

Community Detection Algorithm ◽

Window Approach

AbstractCreative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.

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The Connectivity Index: An Effective Metric for Grading Epileptogenicity

10.1101/635664 ◽

2019 ◽

Author(s):

Qi Yan ◽

Nicolas Gaspard ◽

Hitten P Zaveri ◽

Hal Blumenfeld ◽

Lawrence J. Hirsch ◽

...

Keyword(s):

Average Distance ◽

Single Pulse ◽

Brain Regions ◽

Control Site ◽

Connectivity Index ◽

Evoked Responses ◽

Seizure Onset ◽

Seizure Onset Zone ◽

Site Of Stimulation ◽

Stimulation Of

AbstractObjectiveTo investigate the performance of a metric of functional connectivity to classify and grade the excitability of brain regions based on evoked potentials to single pulse electrical stimulation (SPES).MethodsPatients who received 1-Hz frequency stimulation between 2003 and 2014 at Yale at prospectively selected contacts were included. The stimulated contacts were classified as seizure onset zone (SOZ), highly irritative zone (IZp) or control. Response contacts were classified as seizure onset zone (SOZ), active interictal (IZp), quiet or other. The normalized number of responses was defined as the number of contacts with any evoked responses divided by the total number of recorded contacts, and the normalized distance is the ratio of the average distance between the site of stimulation and sites of evoked responses to the average distances between the site of stimulation and all other recording contacts. A new metric we labeled the connectivity index (CI) is defined as the product of the two values.Results57 stimulation-sessions in 22-patients were analyzed. The connectivity index (CI) of the SOZ was higher than control (median CI of 0.74 vs. 0.16, p = 0.0002). The evoked responses after stimulation of SOZ were seen at further distance compared to control (median normalized distance 0.96 vs. 0.62, p = 0.0005). It was 1.8 times more likely to record a response at SOZ than in non-epileptic contacts after stimulation of a control site. Habitual seizures were triggered in 27% of patients and 35 % of SOZ contacts (median stimulation intensity 4 mA) but in none of the control or IZp contacts. Non-SOZ contacts in multifocal or poor surgical outcome cases had a higher CI than non-SOZ contacts in those with localizable onsets (medians CI of 0.5 vs. 0.12, p = 0.04). There was a correlation between the stimulation current intensity and the normalized number of evoked responses (r = + 0.49, p 0.01) but not with distance (r = + 0.1, p 0.64)ConclusionsWe found enhanced connectivity when stimulating the SOZ compared to stimulating control contacts; responses were more distant as well. Habitual auras and seizures provoked by SPES were highly predictive of brain sites involved in seizure generation.

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Altered topology of large-scale structural brain networks in chronic stroke

Brain Communications ◽

10.1093/braincomms/fcz020 ◽

2019 ◽

Vol 1 (1) ◽

Cited By ~ 1

Author(s):

Bastian Cheng ◽

Eckhard Schlemm ◽

Robert Schulz ◽

Marlene Boenstrup ◽

Arnaud Messé ◽

...

Keyword(s):

Information Transfer ◽

Large Scale ◽

Neuronal Degeneration ◽

Brain Networks ◽

Brain Regions ◽

Local Network ◽

Structural Connectome ◽

The Impact ◽

Structural Brain ◽

Topological Changes

Abstract Beyond disruption of neuronal pathways, focal stroke lesions induce structural disintegration of distant, yet connected brain regions via retrograde neuronal degeneration. Stroke lesions alter functional brain connectivity and topology in large-scale brain networks. These changes are associated with the degree of clinical impairment and recovery. In contrast, changes of large scale, structural brain networks after stroke are less well reported. We therefore aimed to analyse the impact of focal lesions on the structural connectome after stroke based on data from diffusion-weighted imaging and probabilistic fibre tracking. In total, 17 patients (mean age 64.5 ± 8.4 years) with upper limb motor deficits in the chronic stage after stroke and 21 healthy participants (mean age 64.9 ± 10.3 years) were included. Clinical deficits were evaluated by grip strength and the upper extremity Fugl-Meyer assessment. We calculated global and local graph theoretical measures to characterize topological changes in the structural connectome. Results from our analysis demonstrated significant alterations of network topology in both ipsi- and contralesional, primarily unaffected, hemispheres after stroke. Global efficiency was significantly lower in stroke connectomes as an indicator of overall reduced capacity for information transfer between distant brain areas. Furthermore, topology of structural connectomes was shifted toward a higher degree of segregation as indicated by significantly higher values of global clustering and modularity. On a level of local network parameters, these effects were most pronounced in a subnetwork of cortico-subcortical brain regions involved in motor control. Structural changes were not significantly associated with clinical measures. We propose that the observed network changes in our patients are best explained by the disruption of inter- and intrahemispheric, long white matter fibre tracts connecting distant brain regions. Our results add novel insights on topological changes of structural large-scale brain networks in the ipsi- and contralesional hemisphere after stroke.

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Spatial variation in high-frequency oscillation rates and amplitudes in intracranial EEG

Neurology ◽

10.1212/wnl.0000000000004998 ◽

2018 ◽

Vol 90 (8) ◽

pp. e639-e646 ◽

Cited By ~ 23

Author(s):

Hari Guragain ◽

Jan Cimbalnik ◽

Matt Stead ◽

David M. Groppe ◽

Brent M. Berry ◽

...

Keyword(s):

High Frequency ◽

Anatomic Variation ◽

Brain Regions ◽

Brain Atlas ◽

Reproducible Research ◽

High Frequency Oscillation ◽

Seizure Onset ◽

Frequency Oscillation ◽

Intracranial Eeg ◽

Seizure Onset Zone

ObjectiveTo assess the variation in baseline and seizure onset zone interictal high-frequency oscillation (HFO) rates and amplitudes across different anatomic brain regions in a large cohort of patients.MethodsSeventy patients who had wide-bandwidth (5 kHz) intracranial EEG (iEEG) recordings during surgical evaluation for drug-resistant epilepsy between 2005 and 2014 who had high-resolution MRI and CT imaging were identified. Discrete HFOs were identified in 2-hour segments of high-quality interictal iEEG data with an automated detector. Electrode locations were determined by coregistering the patient's preoperative MRI with an X-ray CT scan acquired immediately after electrode implantation and correcting electrode locations for postimplant brain shift. The anatomic locations of electrodes were determined using the Desikan-Killiany brain atlas via FreeSurfer. HFO rates and mean amplitudes were measured in seizure onset zone (SOZ) and non-SOZ electrodes, as determined by the clinical iEEG seizure recordings. To promote reproducible research, imaging and iEEG data are made freely available (msel.mayo.edu).ResultsBaseline (non-SOZ) HFO rates and amplitudes vary significantly in different brain structures, and between homologous structures in left and right hemispheres. While HFO rates and amplitudes were significantly higher in SOZ than non-SOZ electrodes when analyzed regardless of contact location, SOZ and non-SOZ HFO rates and amplitudes were not separable in some lobes and structures (e.g., frontal and temporal neocortex).ConclusionsThe anatomic variation in SOZ and non-SOZ HFO rates and amplitudes suggests the need to assess interictal HFO activity relative to anatomically accurate normative standards when using HFOs for presurgical planning.

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Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

International Journal of Neural Systems ◽

10.1142/s0129065720500471 ◽

2020 ◽

Vol 30 (09) ◽

pp. 2050047

Author(s):

Lubin Wang ◽

Xianbin Li ◽

Yuyang Zhu ◽

Bei Lin ◽

Qijing Bo ◽

...

Keyword(s):

High Risk ◽

Functional Connectivity ◽

Symptom Severity ◽

Large Scale ◽

Brain Networks ◽

Brain Regions ◽

Support Vector ◽

Healthy Controls ◽

Intrinsic Functional Connectivity ◽

Ultra High Risk

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

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The seizure onset zone drives state-dependent epileptiform activity in susceptible brain regions

Clinical Neurophysiology ◽

10.1016/j.clinph.2019.05.032 ◽

2019 ◽

Vol 130 (9) ◽

pp. 1628-1641 ◽

Cited By ~ 2

Author(s):

Joshua M. Diamond ◽

Julio I. Chapeton ◽

William H. Theodore ◽

Sara K. Inati ◽

Kareem A. Zaghloul

Keyword(s):

Epileptiform Activity ◽

Brain Regions ◽

Seizure Onset ◽

State Dependent ◽

Seizure Onset Zone

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Stable high frequency background EEG activity distinguishes epileptic from healthy brain regions

Brain Communications ◽

10.1093/braincomms/fcaa107 ◽

2020 ◽

Vol 2 (2) ◽

Author(s):

Annika Minthe ◽

Wibke G Janzarik ◽

Daniel Lachner-Piza ◽

Peter Reinacher ◽

Andreas Schulze-Bonhage ◽

...

Keyword(s):

High Frequency ◽

Background Activity ◽

Sleep Stage ◽

Strong Association ◽

Brain Regions ◽

High Frequency Oscillation ◽

Seizure Onset ◽

High Frequency Oscillations ◽

Seizure Onset Zone ◽

Low Entropy

Abstract High-frequency oscillations are markers of epileptic tissue. Recently, different patterns of EEG background activity were described from which high-frequency oscillations occur: high-frequency oscillations with continuously oscillating background were found to be primarily physiological, those from quiet background were linked to epileptic tissue. It is unclear, whether these interactions remain stable over several days and during different sleep-wake stages. High-frequency oscillation patterns (oscillatory vs. quiet background) were analysed in 23 patients implanted with depth and subdural grid electrodes. Pattern scoring was performed on every channel in 10 s intervals in three separate day- and night-time EEG segments. An entropy value, measuring variability of patterns per channel, was calculated. A low entropy value indicated a stable occurrence of the same pattern in one channel, whereas a high value indicated pattern instability. Differences in pattern distribution and entropy were analysed for 143 280 10 s intervals with allocated patterns from inside and outside the seizure onset zone, different electrode types and brain regions. We found a strong association between high-frequency oscillations out of quiet background activity, and channels of the seizure onset zone (35.2% inside versus 9.7% outside the seizure onset zone, P < 0.001), no association was found for high-frequency oscillations from continuous oscillatory background (P = 0.563). The type of background activity remained stable over the same brain region over several days and was independent of sleep stage and recording technique. Stability of background activity was significantly higher in channels of the seizure onset zone (entropy mean value 0.56 ± 0.39 versus 0.64 ± 0.41; P < 0.001). This was especially true for the presumed epileptic high-frequency oscillations out of quiet background (0.57 ± 0.39 inside versus 0.72 ± 0.37 outside the seizure onset zone; P < 0.001). In contrast, presumed physiological high-frequency oscillations from continuous oscillatory backgrounds were significantly more stable outside the seizure onset zone (0.72 ± 0.45 versus 0.48 ± 0.53; P < 0.001). The overall low entropy values suggest that interactions between high-frequency oscillations and background activity are a stable phenomenon specific to the function of brain regions. High-frequency oscillations occurring from a quiet background are strongly linked to the seizure onset zone whereas high-frequency oscillations from an oscillatory background are not. Pattern stability suggests distinct underlying mechanisms. Analysing short time segments of high-frequency oscillations and background activity could help distinguishing epileptic from physiologically active brain regions.

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