TY - GEN
T1 - Topological Analysis of Seizure-Induced Changes in Brain Hierarchy Through Effective Connectivity
AU - El-Yaagoubi, Anass B.
AU - Chung, Moo K.
AU - Ombao, Hernando
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Traditional Topological Data Analysis (TDA) methods, such as Persistent Homology (PH), rely on distance measures (e.g., cross-correlation, partial correlation, coherence, and partial coherence) that are symmetric by definition. While useful for studying topological patterns in functional brain connectivity, the main limitation of these methods is their inability to capture the directional dynamics - which are crucial for understanding effective brain connectivity. We propose the Causality-Based Topological Ranking (CBTR) method, which integrates Causal Inference (CI) to assess effective brain connectivity with Hodge Decomposition (HD) to rank brain regions based on their mutual influence. Our simulations confirm that the CBTR method accurately and consistently identifies hierarchical structures in multivariate time series data. Moreover, this method effectively identifies brain regions showing the most significant interaction changes with other regions during seizures using electroencephalogram (EEG) data. These results provide novel insights into the brain’s hierarchical organization and illuminate the impact of seizures on its dynamics.
AB - Traditional Topological Data Analysis (TDA) methods, such as Persistent Homology (PH), rely on distance measures (e.g., cross-correlation, partial correlation, coherence, and partial coherence) that are symmetric by definition. While useful for studying topological patterns in functional brain connectivity, the main limitation of these methods is their inability to capture the directional dynamics - which are crucial for understanding effective brain connectivity. We propose the Causality-Based Topological Ranking (CBTR) method, which integrates Causal Inference (CI) to assess effective brain connectivity with Hodge Decomposition (HD) to rank brain regions based on their mutual influence. Our simulations confirm that the CBTR method accurately and consistently identifies hierarchical structures in multivariate time series data. Moreover, this method effectively identifies brain regions showing the most significant interaction changes with other regions during seizures using electroencephalogram (EEG) data. These results provide novel insights into the brain’s hierarchical organization and illuminate the impact of seizures on its dynamics.
KW - Effective Brain Connectivity
KW - Hodge Decomposition
KW - Seizure EEG Data
KW - Time Series Analysis
KW - Topological Data Analysis
UR - http://www.scopus.com/inward/record.url?scp=85207653300&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-73967-5_13
DO - 10.1007/978-3-031-73967-5_13
M3 - Conference contribution
AN - SCOPUS:85207653300
SN - 9783031739668
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 134
EP - 145
BT - Topology- and Graph-Informed Imaging Informatics - 1st International Workshop, TGI3 2024, Held in Conjunction with MICCAI 2024, Proceedings
A2 - Chen, Chao
A2 - Singh, Yash
A2 - Hu, Xiaoling
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st Workshop on Topology- and Graph- Informed Imaging Informatics, TGI3 2024, held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024
Y2 - 10 October 2024 through 10 October 2024
ER -