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Brain functional network connectivity is an important measure for characterizing changes in a variety of neurological disorders, for example Alzheimer’s Disease, Parkinson Disease, and Epilepsy. Epilepsy is a serious neurological disorder affecting more than 50 million persons worldwide with severe impact on the quality of life of patients and their family members due to recurrent seizures. More than 30% of epilepsy patients are refractive to pharmacotherapy and are considered for resection to disrupt epilepsy seizure networks. However, 20-50% of these patients continue to have seizures after surgery. Therefore, there is a critical need to gain new insights into the characteristics of epilepsy seizure networks involving one of more brain regions and accurately delineate epileptogenic zone as a target for surgery. Although there is growing availability of large volume of high resolution stereotactic electroencephalogram (SEEG) data recorded from intracranial electrodes during presurgical evaluation of patients, there are significant informatics challenges associated with processing and analyzing this large signal dataset for characterizing epilepsy seizure networks. In this paper, we describe the development and application of a high-performance indexing structure for efficient retrieval of large-scale SEEG signal data to compute seizure network patterns corresponding to brain functional connectivity networks. This novel Neuro-Integrative Connectivity (NIC) search and retrieval method has been developed by extending the red-black tree index model together with an efficient lookup algorithm. We systematically perform a comparative evaluation of the proposed NIC index using de-identified SEEG data from a patient with temporal lobe epilepsy to retrieve segments of signal data corresponding to multiple seizure events and demonstrate the significant advantages of the NIC index as compared to existing methods. This new NIC Index enables faster computation of brain functional connectivity measures in epilepsy patients for large-scale network analysis and potentially provide new insights into the organization as well as evolution of seizure networks in epilepsy patients.
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Scalable Signal Data Processing for Measuring Functional Connectivity in Epilepsy Neurological Disorder
The accurate characterization of how different brain structures interact in terms of both structural and functional networks is an area of active research in neuroscience. A better understanding of these interactions can potentially lead to targeted treatments and improved therapies for many neurological disorders, such as epilepsy, which alone affects over 65 million people worldwide. The study of functional connectivity networks in epilepsy, which is characterized by abnormalities in brain electrical activity, will help to provide new insights into the onset and progression of this complex neurological disorder. In this chapter, we discuss statistical signal processing techniques and their use in determining functional connectivity among brain regions exhibiting epileptic activity. We also discuss computational challenges associated with deriving functional connectivity measures from neurological Big Data, and we introduce our highly scalable signal processing pipeline for quantifying functional connectivity with the goal of addressing these challenges and potentially advancing understanding of the underlying mechanisms of epilepsy. This pipeline makes use of a novel signal data format that facilitates storing and retrieving data in a distributed computing environment. We conclude the chapter by describing our current activities and proposed plans for improving our computational pipeline, such as the inclusion of biomedical ontologies for semantic annotation in order to facilitate the integration and retrieval of signal data.
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- Award ID(s):
- 1636850
- PAR ID:
- 10110498
- Date Published:
- Journal Name:
- Signal Processing and Machine Learning for Biomedical Big Data (Book)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1201/9781351061223
