Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

Afnan, Jawata; von, Ellenrieder Nicolás; Lina, Jean‐Marc; Pellegrino, Giovanni; Arcara, Giorgio; Cai, Zhengchen; Hedrich, Tanguy; Chifaou, Abdallah; Khajehpour, Hassan; Frauscher, Birgit; Gotman, Jean; Grova, Christophe

doi:10.1016/j.neuroimage.2023.120158

Cited by 9 publications

(13 citation statements)

References 79 publications

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“…Although this study provided initial insights on the time-frequency modulation occurring in the brain during long-term recognition of auditory sequences, further research employing MEG and additional tools such as stereo-electroencephalography (SEEG) is needed to expand our experimental design and conduct cross-frequency coupling analysis, testing whether gamma-theta coupling is connected to long-term recognition of auditory sequences. This would also provide supporting evidence of the quality of the source reconstruction performed in this study since SEEG data is less sensitive to source leakage artifacts than MEG source reconstruction algorithms 67 .…”

Section: Discussionsupporting

confidence: 68%

“…Before computing the weights, the orientations were reduced to one using the singular value decomposition algorithm on the matrix multiplication reported in equation (4). This procedure is widely adopted to simplify the beamforming output 67,68 . Here, l represents the leadfield model with the three orientations, while L is the resolved one-orientation model that was utilized in (3).…”

Section: Methodsmentioning

confidence: 99%

Section: Source Reconstructionmentioning

confidence: 99%

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Spatiotemporal brain hierarchies of auditory memory recognition and predictive coding

Bonetti

Fernández-Rubio

Carlomagno

et al. 2022

Preprint

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Humans extract information from the key features of the physical word, namely space and time. However, while much is known about the neural processing of visuospatial patterns, there is little information on the hierarchical brain mechanisms underlying conscious recognition of auditory sequences extended over time and their associated prediction errors. Using magnetoencephalography (MEG), we studied the brain activity of 83 participants during recognition of original and varied auditory sequences. Results showed a network comprising auditory, ventro-medial prefrontal and inferior temporal cortices, hippocampus, and medial cingulate gyrus underlying sequence processing. The hierarchical pathway from auditory cortex to hippocampus and ventro-medial prefrontal cortex was associated with both originals and variations, but its temporal dynamics crucially differed. Recognition of original versus varied sequences elicited stronger gamma power. Our results complement the knowledge on neural processing of visuospatial patterns by providing the hierarchical brain mechanisms underlying auditory, temporal sequence recognition.

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Section: Discussionsupporting

confidence: 68%

Section: Methodsmentioning

confidence: 99%

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Spatiotemporal brain hierarchies of auditory memory recognition and predictive coding

Bonetti

Fernández-Rubio

Carlomagno

et al. 2022

Preprint

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“…The mechanisms sustaining these phenomena are manyfold and they involve structural damage 104 , cortical plasticity 105,106 and intracranial epileptiform abnormalities which appear as slow activity on the surface due to the conductance properties of skin and skull 103,107,108 . Simultaneous invasive and non-invasive recordings may be beneficial to parametrize the influence of these components on the exponent-derived estimation of E/I 11 .…”

Section: Limitationsmentioning

confidence: 99%

“…Despite its biological and clinical relevance, E/I balance is rarely directly investigated in epilepsy, most likely because its estimation is difficult and time-consuming. Fortunately, recent developments offer a streamlined assessment of E/I balance, leveraging properties of spontaneous electromagnetic signals which can be easily measured with magneto- or electroencephalography at high spatial and temporal resolution, providing extensive brain coverage with potential translations in a clinical setting 11–14,15 (p2) . Amongst multiple signal features, the exponent of the aperiodic component of the M/EEG signal power spectrum provides information related to system E/I balance 16–18 .…”

Section: Introductionmentioning

confidence: 99%

Excitation/Inhibition balance relates to cognitive function and gene expression in Temporal Lobe Epilepsy: an hdEEG assessment with aperiodic exponent

Duma,

Cuozzo,

Wilson

et al. 2024

Preprint

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Patients with epilepsy are characterized by a dysregulation of excitation-inhibition balance (E/I). The assessment of E/I may inform clinicians during the diagnosis and therapy management, even though it is rarely performed. An accessible measure of the E/I of the brain represents a clinically relevant feature. Here we exploited the exponent of the aperiodic component of the power spectrum of EEG signal as a noninvasive and cost-effective proxy of the E/I balance. We recorded resting-state activity with high-density EEG from 65 patients with temporal lobe epilepsy (TLE) and 35 controls. We extracted the exponent of the aperiodic fit of the power spectrum from source-reconstructed EEG and tested differences between TLE and controls. Spearman correlation was performed between the exponent and clinical variables (age of onset, epilepsy duration and neuropsychology) and cortical expression of epilepsy-related genes derived from Human Allen Brain Atlas. Patients with TLE showed a significantly larger exponent, corresponding to an inhibition directed E/I balance, in bilateral frontal and temporal regions. Lower E/I in the left entorhinal, and bilateral dorsolateral prefrontal cortices corresponded to a lower performance of short term verbal memory. Limited to TLE, we detected a significant correlation between the exponent and the cortical expression of GABRA1, GRIN2A, GABRD, GABRG2, KCNA2and PDYN. EEG aperiodic exponent maps the E/I balance non-invasively in patients with epilepsy and reveals a tight relationship between altered E/I patterns, cognition and genetics.

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EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy

Afnan,

Cai,

Lina

et al. 2024

Human Brain Mapping

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Electro/Magneto‐EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth‐weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high‐density EEG (HD‐EEG) simulations of epileptic activity and actual MEG/HD‐EEG recordings from patients with focal epilepsy. We incorporated depth‐weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD‐EEG involving a wide range of spatial extents and signal‐to‐noise ratio (SNR) levels, before investigating EMSI on clinical HD‐EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth‐weighted cMEM and depth‐weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth‐weighted cMEM improved the localization when compared to cMEM and depth‐weighted MNE, whereas depth‐weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD‐EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth‐weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD‐EEG and clinical MEG and HD‐EEG for epilepsy patients.

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Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

Cited by 9 publications

References 79 publications

Spatiotemporal brain hierarchies of auditory memory recognition and predictive coding

Spatiotemporal brain hierarchies of auditory memory recognition and predictive coding

Excitation/Inhibition balance relates to cognitive function and gene expression in Temporal Lobe Epilepsy: an hdEEG assessment with aperiodic exponent

EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy

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