Reconstruction and localization of auditory sources from intracerebral SEEG using independent component analysis

López‐Madrona, Víctor J.; Villalon, Samuel Médina; Velmurugan, J; Semeux-Bernier, Aurore; Garnier, Élodie; Badier, Jean‐Michel; Schön, Daniele

doi:10.1016/j.neuroimage.2023.119905

Cited by 4 publications

(10 citation statements)

References 57 publications

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“…This study is a proof of concept, showing that it is possible to define the EZN from an undersampled SEEG, using a combination of ICA and source localization 10 . The results of a second SEEG implantation served as ground truth to validate our strategy.…”

Section: Discussionmentioning

confidence: 90%

“…Source localization was performed on the component topographies 10 (Fig. 2 top right) using Brainstorm 20 .…”

Section: Methodsmentioning

confidence: 99%

“…We have recently demonstrated that combining ICA and source localization on SEEG signals potentially allows identifying sources that are remote from the sensors 10 . However, the use of source localization on SEEG signals is still scarce 8 – 12 , even though it has the potential to estimate brain activity of the regions that were not sampled and thus to extend the field of view of SEEG.…”

Section: Introductionmentioning

confidence: 99%

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Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy

Medina Villalon,

Makhalova,

López-Madrona

et al. 2024

Sci Rep

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Stereoelectroencephalography is a powerful intracerebral EEG recording method for the presurgical evaluation of epilepsy. It consists in implanting depth electrodes in the patient’s brain to record electrical activity and map the epileptogenic zone, which should be resected to render the patient seizure-free. Stereoelectroencephalography has high spatial accuracy and signal-to-noise ratio but remains limited in the coverage of the explored brain regions. Thus, the implantation might provide a suboptimal sampling of epileptogenic regions. We investigate the potential of improving a suboptimal stereoelectroencephalography recording by performing source localization on stereoelectroencephalography signals. We propose combining independent component analysis, connectivity measures to identify components of interest, and distributed source modelling. This approach was tested on two patients with two implantations each, the first failing to characterize the epileptogenic zone and the second giving a better diagnosis. We demonstrate that ictal and interictal source localization performed on the first stereoelectroencephalography recordings matches the findings of the second stereo-EEG exploration. Our findings suggest that independent component analysis followed by source localization on the topographies of interest is a promising method for retrieving the epileptogenic zone in case of suboptimal implantation.

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Section: Discussionmentioning

confidence: 90%

“…Source localization was performed on the component topographies 10 (Fig. 2 top right) using Brainstorm 20 .…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy

Medina Villalon,

Makhalova,

López-Madrona

et al. 2024

Sci Rep

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show abstract

“…It has been shown that ICA is an optimal methodology to disentangle overlapping sources with microelectrodes in rodents ( Makarov et al, 2010 ; Herreras et al, 2022 ) and close and distant sources in humans ( Michelmann et al, 2018 ; López-Madrona et al, 2023 ). However, its efficacy to separate close sources may be limited in macroelectrodes.…”

Section: Discussionmentioning

confidence: 99%

“…Although the identification of intracerebral sources with ICA has so far been restricted to animal studies, it has been proposed as an optimal solution to remove the electrical reference ( Hu et al, 2007 ; Whitmore and Lin, 2016 ), or for rereferencing intracerebral EEG data and identifying neural generators in LFP recordings ( Michelmann et al, 2018 ). Moreover, ICA has been recently proposed as a method to localize and reconstruct remote sources nonsampled with the intracerebral electrodes ( López-Madrona et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Early Hippocampal Dynamics during Recognition Memory with Independent Component Analysis

López-Madrona,

Trébuchon,

Mindruta

et al. 2024

eNeuro

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The hippocampus is generally considered to have relatively late involvement in recognition memory, its main electrophysiological signature being between 400 and 800 ms after stimulus onset. However, most electrophysiological studies have analyzed the hippocampus as a single responsive area, selecting only a single-site signal exhibiting the strongest effect in terms of amplitude. These classical approaches may not capture all the dynamics of this structure, hindering the contribution of other hippocampal sources that are not located in the vicinity of the selected site. We combined intracerebral electroencephalogram recordings from epileptic patients with independent component analysis (ICA) during a recognition memory task involving the recognition of old and new images. We identified two sources with different responses emerging from the hippocampus: a fast one (maximal amplitude at ∼250 ms) that could not be directly identified from raw recordings, and a later one, peaking at ∼400 ms. The earliest component presented different amplitudes between old and new items in 6 out of 10 patients. The latter component had different delays for each condition, with a faster activation (∼290 ms after stimulus onset) for recognized items. We hypothesize that both sources represent two steps of hippocampal recognition memory, the faster reflecting the input from other structures and the latter the hippocampal internal processing. Recognized images evoking early activations would facilitate neural computation in the hippocampus, accelerating memory retrieval of complementary information. Overall, our results suggest that hippocampal activity is composed by several sources with an early activation related to recognition memory.Significance StatementIn the human memory circuit, the hippocampus is considered as a structure with relatively late activation, associated to the retrieval of elaborate memories. However, in most electrophysiological studies, it is characterized by the activity of a single contact, which may not represent the entire dynamics of this structure. Here, we combined intracerebral recordings with independent component analysis to separate the activity from two different neural sources generated in the hippocampus. We analyzed the responses of both sources during the recognition of old and new images. Our results reveal new dynamics associated to different neuronal sources within the hippocampus, with recognition memory occurring much faster than previously reported.

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Progress of independent component analysis and its recent application in spectroscopy quantitative analysis

Li,

Zhang,

Bian

et al. 2024

Microchemical Journal

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Reconstruction and localization of auditory sources from intracerebral SEEG using independent component analysis

Cited by 4 publications

References 57 publications

Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy

Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy

Identification of Early Hippocampal Dynamics during Recognition Memory with Independent Component Analysis

Progress of independent component analysis and its recent application in spectroscopy quantitative analysis

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