Dataset Information

Spatiotemporal signal space separation for regions of interest: Application for extracting neuromagnetic responses evoked by deep brain stimulation.

ABSTRACT: Magnetoencephalography (MEG) recordings are often contaminated by interference that can exceed the amplitude of physiological brain activity by several orders of magnitude. Furthermore, the activity of interference sources may spatially extend (known as source leakage) into the activity of brain signals of interest, resulting in source estimation inaccuracies. This problem is particularly apparent when using MEG to interrogate the effects of brain stimulation on large-scale cortical networks. In this technical report, we develop a novel denoising approach for suppressing the leakage of interference source activity into the activity representing a brain region of interest. This approach leverages spatial and temporal domain projectors for signal arising from prespecified anatomical regions of interest. We apply this denoising approach to reconstruct simulated evoked response topographies to deep brain stimulation (DBS) in a phantom recording. We highlight the advantages of our approach compared to the benchmark-spatiotemporal signal space separation-and show that it can more accurately reveal brain stimulation-evoked response topographies. Finally, we apply our method to MEG recordings from a single patient with Parkinson's disease, to reveal early cortical-evoked responses to DBS of the subthalamic nucleus.

SUBMITTER: Oswal A

PROVIDER: S-EPMC10826894 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Spatiotemporal signal space separation for regions of interest: Application for extracting neuromagnetic responses evoked by deep brain stimulation.

Oswal Ashwini A Abdi-Sargezeh Bahman B Sharma Abhinav A Özkurt Tolga Esat TE Taulu Samu S Sarangmat Nagaraja N Green Alexander L AL Litvak Vladimir V

Human brain mapping 20240201 2

Magnetoencephalography (MEG) recordings are often contaminated by interference that can exceed the amplitude of physiological brain activity by several orders of magnitude. Furthermore, the activity of interference sources may spatially extend (known as source leakage) into the activity of brain signals of interest, resulting in source estimation inaccuracies. This problem is particularly apparent when using MEG to interrogate the effects of brain stimulation on large-scale cortical networks. In ...[more]

PMID: 38339906

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Project description:Electrocardiography is the gold standard for electrical heartbeat activity, but offers no direct measurement of mechanical activity. Mechanical cardiac activity can be assessed non-invasively using, e.g., ballistocardiography and recently, medical radar has emerged as a contactless alternative modality. However, all modalities for measuring the mechanical cardiac activity are affected by respiratory movements, requiring a signal separation step before higher-level analysis can be performed. This paper adapts a non-linear filter for separating the respiratory and cardiac signal components of radar recordings. In addition, we present an adaptive algorithm for estimating the parameters for the non-linear filter. The novelty of our method lies in the combination of the non-linear signal separation method with a novel, adaptive parameter estimation method specifically designed for the non-linear signal separation method, eliminating the need for manual intervention and resulting in a fully adaptive algorithm. Using the two benchmark applications of (i) cardiac template extraction from radar and (ii) peak timing analysis, we demonstrate that the non-linear filter combined with adaptive parameter estimation delivers superior results compared to linear filtering. The results show that using locally projective adaptive signal separation (LoPASS), we are able to reduce the mean standard deviation of the cardiac template by at least a factor of 2 across all subjects. In addition, using LoPASS, 9 out of 10 subjects show significant (at a confidence level of 2.5%) correlation between the R-T-interval and the R-radar-interval, while using linear filters this ratio drops to 6 out of 10. Our analysis suggests that the improvement is due to better preservation of the cardiac signal morphology by the non-linear signal separation method. Hence, we expect that the non-linear signal separation method introduced in this paper will mostly benefit analysis methods investigating the cardiac radar signal morphology on a beat-to-beat basis.

Project description:ObjectiveWe aimed at clarifying the clinical significance of the responses evoked by human entorhinal cortex (EC) electrical stimulation by means of cortico-cortical evoked potentials (CCEPs).MethodsWe enrolled nine patients with medically intractable medial temporal lobe epilepsy who underwent invasive presurgical evaluations with subdural or depth electrodes. Single-pulse electrical stimulation was delivered to the EC and fusiform gyrus (FG), and their evoked potentials were compared. The correlation between the evoked potentials and Wechsler Memory Scale-Revised (WMS-R) score was analyzed to investigate whether memory circuit was involved in the generation of the evoked potentials.ResultsIn most electrodes placed on the neocortex, EC stimulation induced unique evoked potentials with positive polarity, termed as "widespread P1" (P1w). Compared with FG stimulation, P1w induced by EC stimulation were distinguished by their high occurrence rate, short peak latency (mean: 20.1 ms), small peak amplitude, and waveform uniformity among different recording sites. A stimulation of more posterior parts of the EC induced P1w with shorter latency and larger amplitude. P1w peak amplitude had a positive correlation (r = .69) with the visual memory score of the WMS-R. In one patient, with depth electrode implanted into the hippocampus, the giant evoked potentials were recorded in the electrodes of the anterior hippocampus and EC near the stimulus site.ConclusionsThe human EC electrical stimulation evoked the short-latency potentials in the broad neocortical regions. The origin of P1w remains unclear, although the limited evidence suggests that P1w is the far-field potential by the volume conduction of giant evoked potential from the EC itself and hippocampus. The significance of the present study is that those evoked potentials may be a potential biomarker of memory impairment in various neurological diseases, and we provided direct evidence for the functional subdivisions along the anterior-posterior axis in the human EC.

Dataset Information

Spatiotemporal signal space separation for regions of interest: Application for extracting neuromagnetic responses evoked by deep brain stimulation.

Publications

Spatiotemporal signal space separation for regions of interest: Application for extracting neuromagnetic responses evoked by deep brain stimulation.

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