Recent Developments in Spatio-Temporal EEG Source Reconstruction Techniques

Kaur, Chamandeep; Singh, Preeti; Bisht, Amandeep; Joshi, Garima; Agrawal, Sunil

doi:10.1007/s11277-021-08960-9

Cited by 10 publications

(6 citation statements)

References 85 publications

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“…Even beyond this, the costs of scanner time for an fMRI study are estimated to be at least 10 times greater than conducting an EEG study (Luck, 2014;Slotnick, 2017). These practical considerations combined with recent advances in EEG source modeling (Andersen et al, 2020;Awan et al, 2019;Kaur et al, 2022;Samuelsson et al, 2020) illustrate the potential for highimpact research that may make it an exceptionally valuable tool for research into early indicators of disease risk.…”

Section: Introductionmentioning

confidence: 99%

“…Many combinations of location, orientation, and magnitude of neural sources could produce the same pattern of activity at the EEG sensors (Cohen, 2014; Grech et al, 2008). Despite this, advancements in source localization (e.g., effective parameters and algorithms for modeling electromagnetic properties of currents in the brain and improved realistic head models) have importantly advanced the opportunity for precise spatial estimation without sacrificing the temporal resolution of EEG (for review, see Awan et al, 2019; Kaur et al, 2022). Indeed, Torres and Beardsley (2019) recently demonstrated that concerns regarding cerebellar imaging with EEG were surmountable with current technology.…”

Section: Introductionmentioning

confidence: 99%

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Cerebellar EEG source localization reveals age‐related compensatory activity moderated by genetic risk for Alzheimer's disease

Paitel

Nielson

2023

Psychophysiology

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The apolipoprotein‐E (APOE) ε4 allele is the greatest genetic risk factor for late‐onset Alzheimer's disease (AD), but alone it is not sufficiently predictive. Because neuropathological changes associated with AD begin decades before cognitive symptoms, neuroimaging of healthy, cognitively intact ε4 carriers (ε4+) may enable early characterization of patterns associated with risk for future decline. Research in the cerebral cortex highlights a period of compensatory recruitment in elders and ε4+, which serves to maintain cognitive functioning. Yet, AD‐related changes may occur even earlier in the cerebellum. Advances in electroencephalography (EEG) source localization now allow effective modeling of cerebellar activity. Importantly, healthy aging and AD are associated with declines in both cerebellar functions and executive functioning (EF). However, it is not known whether cerebellar activity can detect pre‐symptomatic AD risk. Thus, the current study analyzed cerebellar EEG source localization during an EF‐dependent stop‐signal task (i.e., inhibitory control) in healthy, intact older adults (Mage = 80 years; 20 ε4+, 25 ε4−). Task performance was comparable between groups. Older age predicted greater activity in left crus II and lobule VIIb during the P300 window (i.e., performance evaluation), consistent with age‐related compensation. Age*ε4 moderations specifically showed that compensatory patterns were evident only in ε4−, suggesting that cerebellar compensatory resources may already be depleted in healthy ε4+ elders. Thus, the posterolateral cerebellum is sensitive to AD‐related neural deficits in healthy elders. Characterization of these patterns may be essential for the earliest possible detection of AD risk, which would enable critical early intervention prior to symptom onset.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cerebellar EEG source localization reveals age‐related compensatory activity moderated by genetic risk for Alzheimer's disease

Paitel

Nielson

2023

Psychophysiology

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“…This approach helps to estimate the most likely locations of neural sources responsible for the recorded EEG signals. Besides, this algorithm has been broadly applied in EEG studies, so its performance with this type of signals has been already demonstrated [35][36][37]. For more detailed information about sLORETA and its underlying principles, you can refer to [34].…”

Section: Source Inversionmentioning

confidence: 99%

Tracking EEG Network Dynamics through Transitions between Eyes-Closed, Eyes-Open, and Task States

Krukow,

Rodríguez-González,

Kopiś-Posiej

et al. 2024

Preprint

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Our study aimed to verify the possibilities of effectively applying chronnectomics methods to reconstruct the dynamic processes of network transition between three types of brain states, namely, eyes-closed rest, eyes-open rest, and a task state. The study involved dense EEG recordings and reconstruction of the source-level time-courses of the signals. Functional connectivity was measured using the phase lag index, and dynamic analyses concerned coupling strength and variability in alpha and beta frequencies. The results showed significant and dynamically specific transitions regarding processes of eyes opening and closing and during the eyes-closed-to-task transition in the alpha band. These observations considered a global dimension, default mode network, and central executive network. The decrease of connectivity strength and variability that accompanied eye-opening was a faster process than the synchronization increase during eye-opening, suggesting that these two transitions exhibit different reorganization times. While referring the obtained results to network studies, it was indicated that the scope of potential similarities and differences between rest and task-related networks depends on whether the resting state was recorded in eyes closed or open condition.

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“…This problem is severely ill-posed without a unique solution if no prior assumptions are given for brain activity. Nowadays, there are numerous inversion methods for source localization with diverse prior assumptions (Kaur et al, 2022). Only some of them take the temporal aspect of biopotential recordings into account in the modeling (Trujillo-Barreto et al, 2008;Ou et al, 2009;Dannhauer et al, 2013;Paz-Linares et al, 2017;Cui et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Standardized Kalman Filtering for Time Serial Source Localization of Simultaneous Subcortical and Cortical Brain Activity

Lahtinen,

Ronni,

Subramaniyam

et al. 2023

Preprint

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<p>In this article, we present a new Bayesian filtering method for non-invasive electroencephalography (EEG) that is capable of reconstructing spatiotemporal brain activity, including both cortical and weakly detectable deep components. Our approach improves upon the standard Bayesian recursive filtering method, also known as Kalman filtering, by introducing an additional step that re-weights the instantaneous reconstructions akin to the Standardized low-resolution brain electromagnetic tomography (sLORETA). This interim step helps to reduce any potential source depth biases, resulting in more accurate tracking of the dynamics of brain activity and better localization of dipolar components than distributed spatiotemporal Kalman filtering without any standardization or time-independent approaches, such as sLORETA. Our numerical simulations and reconstructions of real realistic somatosensory evoked potentials demonstrate that our proposed algorithmic framework can effectively track the time evolution and localize simultaneously both deep and superficial brain activity. Therefore, our method offers a promising solution for researchers and clinicians interested in investigating the dynamic behavior of brain activity.</p>

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Recent Developments in Spatio-Temporal EEG Source Reconstruction Techniques

Cited by 10 publications

References 85 publications

Cerebellar EEG source localization reveals age‐related compensatory activity moderated by genetic risk for Alzheimer's disease

Cerebellar EEG source localization reveals age‐related compensatory activity moderated by genetic risk for Alzheimer's disease

Tracking EEG Network Dynamics through Transitions between Eyes-Closed, Eyes-Open, and Task States

Standardized Kalman Filtering for Time Serial Source Localization of Simultaneous Subcortical and Cortical Brain Activity

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