Extensions of Granger Causality Calculations on Brain Networks for Efficient and Accurate Seizure Focus Identification via iEEGs

Current epilepsy surgery planning protocol determines the seizure onset zone (SOZ) through resource-intensive, invasive monitoring of ictal events. Recently, we have reported that Granger Causality (GC) maps produced from analysis of interictal iEEG recordings have potential in revealing SOZ. In this study, we investigate GC maps’ network connectivity patterns to determine possible clinical correlation with patients’ SOZ and resection zone (RZ). While building understanding of interictal network topography and its relationship to the RZ/SOZ, we identify algorithmic tools with potential applications in epilepsy surgery planning. These graph algorithms are retrospectively tested on data from 25 patients and compared to the neurologist-determined SOZ and surgical RZ, viewed as sources of truth. Centrality algorithms yielded statistically significant RZ rank order sums for 16 of 24 patients with RZ data, representing an improvement from prior algorithms. While SOZ results remained largely the same, this study validates the applicability of graph algorithms to RZ/SOZ detection, opening the door to further exploration of iEEG datasets. Furthermore, this study offers previously inaccessible insights into the relationship between interictal brain connectivity patterns and epileptic brain networks, utilizing the overall topology of the graphs as well as data on edge weights and quantity of edges contained in GC maps.

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Group-Level Progressive Alterations in Brain Connectivity Patterns Revealed by Diffusion-Tensor Brain Networks across Severity Stages in Alzheimer’s Disease

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2017.00215 ◽

2017 ◽

Vol 9 ◽

Cited By ~ 14

Author(s):

Javier Rasero ◽

Carmen Alonso-Montes ◽

Ibai Diez ◽

Laiene Olabarrieta-Landa ◽

Lakhdar Remaki ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Diffusion Tensor ◽

Brain Connectivity ◽

Brain Networks ◽

Group Level ◽

Connectivity Patterns

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Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33011198 ◽

2019 ◽

Vol 33 ◽

pp. 1198-1205 ◽

Cited By ~ 3

Author(s):

Mingliang Wang ◽

Jiashuang Huang ◽

Mingxia Liu ◽

Daoqiang Zhang

Keyword(s):

Network Analysis ◽

Human Brain ◽

Brain Connectivity ◽

Brain Networks ◽

Disease Diagnosis ◽

Brain Network ◽

Brain Disease ◽

Brain Diseases ◽

Central Position ◽

Connectivity Patterns

Brain network analysis can help reveal the pathological basis of neurological disorders and facilitate automated diagnosis of brain diseases, by exploring connectivity patterns in the human brain. Effectively representing the brain network has always been the fundamental task of computeraided brain network analysis. Previous studies typically utilize human-engineered features to represent brain connectivity networks, but these features may not be well coordinated with subsequent classifiers. Besides, brain networks are often equipped with multiple hubs (i.e., nodes occupying a central position in the overall organization of a network), providing essential clues to describe connectivity patterns. However, existing studies often fail to explore such hubs from brain connectivity networks. To address these two issues, we propose a Connectivity Network analysis method with discriminative Hub Detection (CNHD) for brain disease diagnosis using functional magnetic resonance imaging (fMRI) data. Specifically, we incorporate both feature extraction of brain networks and network-based classification into a unified model, while discriminative hubs can be automatically identified from data via ℓ1-norm and ℓ2,1-norm regularizers. The proposed CNHD method is evaluated on three real-world schizophrenia datasets with fMRI scans. Experimental results demonstrate that our method not only outperforms several state-of-the-art approaches in disease diagnosis, but also is effective in automatically identifying disease-related network hubs in the human brain.

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fMRI Analysis of Brain Network Connectivity during Adolescence. Knowledge of Normotypic Brain Connectivity Patterns May Further Understanding of Aberrant Processes that Cause Psychiatric Illnesses

eNeuro ◽

10.1523/eneuro.0275-18.2018 ◽

2018 ◽

Vol 5 (4) ◽

pp. ENEURO.0275-18.2018

Author(s):

Rosalind S.E. Carney

Keyword(s):

Brain Connectivity ◽

Network Connectivity ◽

Brain Network ◽

Psychiatric Illnesses ◽

Connectivity Patterns ◽

Brain Network Connectivity ◽

Fmri Analysis

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Granger Causality Analysis of Interictal iEEG Predicts Seizure Focus and Ultimate Resection

Neurosurgery ◽

10.1093/neuros/nyx195 ◽

2017 ◽

Vol 82 (1) ◽

pp. 99-109 ◽

Cited By ~ 13

Author(s):

Eun-Hyoung Park ◽

Joseph R Madsen

Keyword(s):

Granger Causality ◽

Epilepsy Surgery ◽

Statistical Approach ◽

Rank Order ◽

Causality Analysis ◽

Active Electrode ◽

Seizure Focus ◽

Gc Analysis ◽

Granger Causality Analysis ◽

Ictal Activity

Abstract BACKGROUND A critical conceptual step in epilepsy surgery is to locate the causal region of seizures. In practice, the causal region may be inferred from the set of electrodes showing early ictal activity. There would be advantages in deriving information about causal regions from interictal data as well. We applied Granger's statistical approach to baseline interictal data to calculate causal interactions. We hypothesized that maps of the Granger causality network (or GC maps) from interictal data might inform about the seizure network, and set out to see if “causality” in the Granger sense correlated with surgical targets. OBJECTIVE To determine whether interictal baseline data could produce GC maps, and whether the regions of high GC would statistically resemble the topography of the ictally active electrode (IAE) set and resection. METHODS Twenty-minute interictal baselines obtained from 25 consecutive patients were analyzed. The “GC maps” were quantitatively compared to conventionally constructed surgical plans, by using rank order and Cartesian distance statistics. RESULTS In 16 of 25 cases, the interictal GC rankings of the electrodes in the IAE set were lower than predicted by chance (P < .05). The aggregate probability of such a match by chance alone is very small (P < 10−20) suggesting that interictal GC maps correlated with ictal networks. The distance of the highest GC electrode to the IAE set and to the resection averaged 4 and 6 mm (Wilcoxon P < .001). CONCLUSION GC analysis has the potential to help localize ictal networks from interictal data.

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Estimating brain connectivity patterns from EEG: Performance of Granger Causality, PDC and PSI on simulated data

Neuroscience Letters ◽

10.1016/j.neulet.2011.05.189 ◽

2011 ◽

Vol 500 ◽

pp. e42

Author(s):

Stefan Haufe ◽

Vadim Nikulin ◽

Guido Nolte

Keyword(s):

Granger Causality ◽

Brain Connectivity ◽

Simulated Data ◽

Connectivity Patterns

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Studying functional brain networks from dry electrode EEG set during music and resting states in neurodevelopment disorder

10.1101/759738 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ekansh Sareen ◽

Anubha Gupta ◽

Rohit Verma ◽

G. Krishnaveni Achary ◽

Blessin Varkey

Keyword(s):

Brain Connectivity ◽

Brain Networks ◽

Network Connectivity ◽

Electrode System ◽

Social Impairment ◽

Functional Brain ◽

Dry Electrode ◽

Functional Brain Networks ◽

Research Findings ◽

System Data

AbstractThere has been an emerging interest in the study of functional brain networks in cognitive neuroscience in order to better understand brain responses to different stimuli. Such studies can help in understanding brain connectivity alterations that arise in neurodevelopmental disorders such as intellectual disability (ID). This research contributes to this body of knowledge by studying alterations in brain connectivity in ID compared to the typically developing controls (TDC). Electroencephalography (EEG) data of subjects with ID and TDC is collected through limited channel dry electrode system. Data was analyzed for the auditory and rest state processing along with the study of intra-network connectivity of the brain via clustering coefficients. Research findings indicate evidences for links between the sensory deficits and social impairment in ID individuals.

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Neuroplasticity Within and between Functional Brain Networks in Mental Training Based on Long-Term Meditation

Brain Sciences ◽

10.3390/brainsci11081086 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1086

Author(s):

Roberto Guidotti ◽

Cosimo Del Gratta ◽

Mauro Gianni Perrucci ◽

Gian Luca Romani ◽

Antonino Raffone

Keyword(s):

Large Scale ◽

Brain Connectivity ◽

Brain Networks ◽

Connectivity Pattern ◽

Mental Training ◽

Functional Brain ◽

Connectivity Patterns ◽

Open Monitoring ◽

The Impact

(1) The effects of intensive mental training based on meditation on the functional and structural organization of the human brain have been addressed by several neuroscientific studies. However, how large-scale connectivity patterns are affected by long-term practice of the main forms of meditation, Focused Attention (FA) and Open Monitoring (OM), as well as by aging, has not yet been elucidated. (2) Using functional Magnetic Resonance Imaging (fMRI) and multivariate pattern analysis, we investigated the impact of meditation expertise and age on functional connectivity patterns in large-scale brain networks during different meditation styles in long-term meditators. (3) The results show that fMRI connectivity patterns in multiple key brain networks can differentially predict the meditation expertise and age of long-term meditators. Expertise-predictive patterns are differently affected by FA and OM, while age-predictive patterns are not influenced by the meditation form. The FA meditation connectivity pattern modulated by expertise included nodes and connections implicated in focusing, sustaining and monitoring attention, while OM patterns included nodes associated with cognitive control and emotion regulation. (4) The study highlights a long-term effect of meditation practice on multivariate patterns of functional brain connectivity and suggests that meditation expertise is associated with specific neuroplastic changes in connectivity patterns within and between multiple brain networks.

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The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

Cerebral Cortex Communications ◽

10.1093/texcom/tgaa088 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Camille Fauchon ◽

David Meunier ◽

Isabelle Faillenot ◽

Florence B Pomares ◽

Hélène Bastuji ◽

...

Keyword(s):

Information Integration ◽

Functional Organization ◽

Brain Connectivity ◽

Brain Networks ◽

Modular Organization ◽

Noxious Heat ◽

Intracranial Eeg ◽

Brain Connectome ◽

Posterior Parietal ◽

The Brain

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

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