Biology subjects

Bagic, A.

Publications and source records attributed to Bagic, A..

3 recordsLinked to original sources

bioRxiv2022

Personalized Deep Learning based Source Imaging Framework Improves the Imaging of Epileptic Sources from MEG Interictal Spikes

Electromagnetic source imaging (ESI) has been widely used to image brain activities for research and clinical applications from MEG and EEG. It is a challenging task due to the ill-posedness of the problem and the complexity of modeling the underlying brain dynamics. Deep learning has gained attention in the ESI field for its ability to model complex distributions and has successfully demonstrated improved imaging performance for ESI. In this work, we investigated the capability of imaging epileptic sources from MEG interictal spikes using deep learning-based source imaging framework (DeepSIF). A generic DeepSIF model was first trained with a generic head model using a template MRI. A fine-tuning procedure was proposed to introduce personalized head model information into the neural network for a personalized DeepSIF model. Two models were evaluated and compared in extensive computer simulations. The MEG-DeepSIF approach was further rigorously validated for imaging epileptogenic regions from interictal spike recordings in focal epilepsy patients. We demonstrated that DeepSIF can be successfully applied to MEG recordings and the additional fine-tuning step for personalized DeepSIF can alleviate the impact of head model variations and further improve the performance significantly. In a cohort of 29 drug-resistant focal epilepsy patients, the personalized DeepSIF model provided a sublobar concordance of 93%, sublobar sensitivity of 77% and specificity of 99%, respectively. When compared to the seizure-onset-zone defined by intracranial recordings, the localization error is 15.78 {+/-} 5.54 mm; and when compared with resection volume in seizure free patients, the spatial dispersion is 8.19 {+/-} 8.14 mm. DeepSIF enables an accurate and robust imaging of spatiotemporal brain dynamics from MEG recordings, suggesting its unique value to neuroscience research and clinical applications.

bioengineering↗

bioRxiv2022

Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy

Resting state electromagnetic recordings have been analyzed in epilepsy patients aiding presurgical evaluation. However, it has been rarely explored how pathological networks can be separated and thus used for epileptogenic focus localization purpose. We proposed here a resting state EEG/MEG analysis framework, to disentangle brain functional networks represented by electrophysiological oscillations. Firstly, by using an Embedded Hidden Markov Model (EHMM), we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. After that, functional connectivity analysis along with graph theory were applied on the extracted brain states to quantify the network features of the extracted brain states, and we determine the source location of pathological states based on these features. The EHMM model was rigorously evaluated using computer simulations. Our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We than validated the entire framework as compared with clinical ground truth in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings. We segmented the resting state MEG recordings into a few brain states with diverse connectivity patterns and extracted pathological brain states by applying graph theory on the constructed functional networks. We showed reasonable localization results using the extracted pathological brain states in 6/10 patients, as compared to the invasive clinical findings. The framework can serve as an objective tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings. It promises to aid presurgical evaluation guiding intracranial EEG electrodes implantation.

neuroscience↗

bioRxiv2021

Imaging the Extent and Location of Spatiotemporally Distributed Epileptiform Sources from MEG Measurements

Non-invasive MEG/EEG source imaging provides valuable information about the epileptogenic brain areas which can be used to aid presurgical planning in focal epilepsy patients suffering from drug-resistant seizures. However, the source extent estimation for electrophysiological source imaging remains to be a challenge and is usually largely dependent on subjective choice. Our recently developed algorithm, fast spatiotemporal iteratively reweighted edge sparsity minimization (FAST-IRES) strategy, has been shown to objectively estimate extended sources from EEG recording, while it has not been applied to MEG recordings. In this work, through extensive numerical experiments and real data analysis in a group of focal drug-resistant epilepsy patients interictal spikes, we demonstrated the ability of FAST-IRES algorithm to image the location and extent of underlying epilepsy sources from MEG measurements. Our results indicate the merits of FAST-IRES in imaging the location and extent of epilepsy sources for pre-surgical evaluation from MEG measurements.

bioengineering↗