Computing Functional Brain Connectivity in Neurological Disorders: Efficient Processing and Retrieval of Electrophysiological Signal Data

Abstract

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.

Figure 1.

(A) Intracranial electrodes implanted in brain structures of an epilepsy patient and its electrode contacts are used to record high resolution EEG signal data. (B) The signal data are processed and analyzed to compute functional connectivity measures using methods such as nonlinear correlation coefficient measure, which provide correlation as well as directionality of seizure signals.

Figure 2.

The core components of the NIC framework for computing graph-based model of seizure networks

Figure 3.

A new signal data search and retrieval algorithm developed as part of the NIC framework to retrieve specific segment of signal data from CSF files corresponding to specific seizure events.

Figure 4.

Derivation of graph network model of three clinical events using SEEG data by leveraging the NIC-Index