Silence Localization

Chamanzar, Alireza; Grover, Pulkit

doi:10.1109/ner.2019.8717188

Cited by 7 publications

(10 citation statements)

References 12 publications

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“…SilenceMap localizes the region of silence in two steps: (1) The first step finds a contiguous region of silence in a low-resolution source grid with the assumption that, at this resolution, the sources are uncorrelated across space. In this low-resolution grid, given that the sources are sufficiently separated, a reasonable approximation is to assume they have independent activity (see "Methods" for more details).…”

Section: Resultsmentioning

confidence: 99%

“…We adopt the term silences or regions of silence to refer to the areas of brain tissue with little or no neural activity. These regions reflect ischemic, necrotic, or lesional tissue, resected tissue (e.g., after epilepsy surgery), or tumors 1,2 . Dynamic regions of silence also arise in cortical spreading depolarizations (CSDs), which are slowly spreading waves of silences in the cerebral cortex [3][4][5] .…”

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confidence: 99%

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Neural silences can be localized rapidly using noninvasive scalp EEG

2021

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A rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

Neural silences can be localized rapidly using noninvasive scalp EEG

2021

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“…We define C f ull s as the source covariance matrix when there are no silent sources in the brain, and use it to measure the effect of region of silence on the power of each electrode. The source covariance matrix in the previous iteration (r − 1) is calculated as follows: 1) , and c s i j = 0, if i or j ∈ S (r−1) ,…”

Section: B Silencemap Without Baseline Recordingsmentioning

confidence: 99%

“…These regions reflect ischemic, necrotic, or lesional tissue (e.g., after ischemic stroke, traumatic brain injuries (TBIs), and intracranial hematoma), resected tissue (e.g. after epilepsy surgery), or tumors [1,2]. Dynamic regions of silence also arise in cortical spreading depolarizations (CSDs), which are slowly spreading waves of neural silences in the cerebral cortex [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…The localization of a region of silence, however, poses additional challenges. The most significant further challenge is in how background brain activity is treated: while it is usually grouped with noise in source localization 1 , it is of direct interest in silence localization where the goal is to distinguish normal brain activity from abnormal silences. Thus, in source localization paradigms applied to neuroscientific studies [12][13][14], as is also the case in the Event Related potential (ERP) paradigm [15,16], scalp EEG signals are averaged over event-related trials to average out background brain activity and noise, permitting the extraction of the signal activity that is consistent across trials.…”

Section: Introductionmentioning

confidence: 99%

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Neural silences can be localized rapidly using noninvasive scalp EEG

Chamanzar

Behrmann

Grover

2020

Preprint

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A rapid and cost-effective noninvasive tool to detect and characterize suppressed neural activity can be of significant benefit for the diagnosis and treatment of many disorders. We propose a novel algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural "silences", using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a novel hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric patients with lobectomy, using less than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53mm), as well for 70 different simulated regions of silence based on a real human head model (11±0.5mm vs. 54±2.2mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

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Noninvasive and reliable automated detection of spreading depolarization in severe traumatic brain injury using scalp EEG

Chamanzar,

Elmer,

Shutter

et al. 2023

Commun Med

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Background Spreading depolarizations (SDs) are a biomarker and a potentially treatable mechanism of worsening brain injury after traumatic brain injury (TBI). Noninvasive detection of SDs could transform critical care for brain injury patients but has remained elusive. Current methods to detect SDs are based on invasive intracranial recordings with limited spatial coverage. In this study, we establish the feasibility of automated SD detection through noninvasive scalp electroencephalography (EEG) for patients with severe TBI. Methods Building on our recent WAVEFRONT algorithm, we designed an automated SD detection method. This algorithm, with learnable parameters and improved velocity estimation, extracts and tracks propagating power depressions using low-density EEG. The dataset for testing our algorithm contains 700 total SDs in 12 severe TBI patients who underwent decompressive hemicraniectomy (DHC), labeled using ground-truth intracranial EEG recordings. We utilize simultaneously recorded, continuous, low-density (19 electrodes) scalp EEG signals, to quantify the detection accuracy of WAVEFRONT in terms of true positive rate (TPR), false positive rate (FPR), as well as the accuracy of estimating SD frequency. Results WAVEFRONT achieves the best average validation accuracy using Delta band EEG: 74% TPR with less than 1.5% FPR. Further, preliminary evidence suggests WAVEFRONT can estimate how frequently SDs may occur. Conclusions We establish the feasibility, and quantify the performance, of noninvasive SD detection after severe TBI using an automated algorithm. The algorithm, WAVEFRONT, can also potentially be used for diagnosis, monitoring, and tailoring treatments for worsening brain injury. Extension of these results to patients with intact skulls requires further study.

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Silence Localization

Cited by 7 publications

References 12 publications

Neural silences can be localized rapidly using noninvasive scalp EEG

Neural silences can be localized rapidly using noninvasive scalp EEG

Neural silences can be localized rapidly using noninvasive scalp EEG

Noninvasive and reliable automated detection of spreading depolarization in severe traumatic brain injury using scalp EEG

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