Electrophysiological Imaging of Brain Activity and Connectivity—Challenges and Opportunities

He, Bin; Yang, Lin; Wilke, Christopher; Yuan, Han

doi:10.1109/tbme.2011.2139210

Cited by 250 publications

(103 citation statements)

References 114 publications

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“…2012; Wu et al . 2012) have also been investigated from noninvasive measurements during interictal (He et al, 1987; Hämäläinen et al 1993; Baillet et al 2001; Lantz et al ., 2003; Baumgartner & Pataraia, 2006; Koessler et al ., 2010; Holmes et al ., 2010; Wang et al ., 2010; He et al ., 2011; Lai et al ., 2011) and ictal stages (Ding et al ., 2007; Yang et al ., 2011; Lu et al ., 2012) by solving the EEG/MEG inverse source imaging problem. While such noninvasive imaging techniques have improved substantially over the past decades, source imaging approaches in general are still relatively insensitive toward deep brain structures.…”

Section: Introductionmentioning

confidence: 99%

Lateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI

Zhang

Brinkmann

et al. 2015

Clinical Neurophysiology

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Objective The aim was to develop a method for the purpose of localizing epilepsy related hemodynamic foci for patients suffering intractable focal epilepsy using task-free fMRI alone. Methods We studied three groups of subjects: patients with intractable focal epilepsy, healthy volunteers performing motor tasks, and healthy volunteers in resting state. We performed spatial independent component analysis (ICA) on the fMRI alone data and developed a set of IC selection criteria to identify epilepsy related ICs. The method was then tested in the two healthy groups. Results In seven out of the nine surgery patients, identified ICs were concordant with surgical resection. Our results were also consistent with presurgical evaluation of the remaining one patient without surgery and may explain why she was not suitable for resection treatment. In the motor task study of ten healthy subjects, our method revealed components with concordant spatial and temporal features as expected from the unilateral motor tasks. In the resting state study of seven healthy subjects, the method successfully rejected all components in four out of seven subjects as non-epilepsy related components. Conclusion These results suggest the lateralization and localization value of fMRI alone in presurgical evaluation for patients with intractable unilateral focal epilepsy. Significance The proposed method is noninvasive in nature and easy to implement. It has the potential to be incorporated in current presurgical workup for treating intractable focal epilepsy patients.

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Section: Introductionmentioning

confidence: 99%

Lateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI

Zhang

Brinkmann

et al. 2015

Clinical Neurophysiology

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“…While in the past the localization of brain activity was the main focus of neuroimaging, connectivity analysis is nowadays considered crucial for understanding brain function in neuroscience as well as in clinical research (Bassett and Gazzaniga, 2011;Castellanos et al, 2011;He et al, 2011;Palva and Palva, 2012;Schnitzler and Gross, 2005;Schoffelen and Gross, 2009). The notion of brain connectivity encompasses at least three major concepts: effective, functional, and structural connectivity (Friston, 1994;Horwitz, 2003).…”

Section: Introductionmentioning

confidence: 99%

Influence of the head model on EEG and MEG source connectivity analyses

et al. 2015

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The results of brain connectivity analysis using reconstructed source time courses derived from EEG and MEG data depend on a number of algorithmic choices. While previous studies have investigated the influence of the choice of source estimation method or connectivity measure, the effects of the head modeling errors or simplifications have not been studied sufficiently. In the present simulation study, we investigated the influence of particular properties of the head model on the reconstructed source time courses as well as on source connectivity analysis in EEG and MEG. Therefore, we constructed a realistic head model and applied the finite element method to solve the EEG and MEG forward problems. We considered the distinction between white and gray matter, the distinction between compact and spongy bone, the inclusion of a cerebrospinal fluid (CSF) compartment, and the reduction to a simple 3-layer model comprising only the skin, skull, and brain. Source time courses were reconstructed using a beamforming approach and the source connectivity was estimated by the imaginary coherence (ICoh) and the generalized partial directed coherence (GPDC). Our results show that in both EEG and MEG, neglecting the white and gray matter distinction or the CSF causes considerable errors in reconstructed source time courses and connectivity analysis, while the distinction between spongy and compact bone is just of minor relevance, provided that an adequate skull conductivity value is used. Large inverse and connectivity errors are found in the same regions that show large topography errors in the forward solution. Moreover, we demonstrate that the very conservative ICoh is relatively safe from the crosstalk effects caused by imperfect head models, as opposed to the GPDC.

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“…These EEG changes may be in the frequency domain but can also be found in connectivity. Indeed, functional electrophysiology has improved our knowledge of brain functions and dysfunctions and is now mature for use in imaging purposes (He et al 2011). Few works studied ECoG signal changes induced by brain tumours.…”

Section: Introductionmentioning

confidence: 99%

In Vivo Tumour Mapping Using Electrocorticography Alterations During Awake Brain Surgery: A Pilot Study

et al. 2016

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During awake brain surgery for tumour resection, in situ EEG recording (ECoG) is used to identify eloquent areas surrounding the tumour. We used the ECoG setup to record the electrical activity of cortical and subcortical tumours and then performed frequency and connectivity analyses in order to identify ECoG impairments and map tumours. We selected 16 patients with cortical (8) and subcortical (8) tumours undergoing awake brain surgery. For each patient, we computed the spectral content of tumoural and healthy areas in each frequency band. We computed connectivity of each electrode using connectivity markers (linear and non-linear correlations, phase-locking and coherence). We performed comparisons between healthy and tumour electrodes. The ECoG alterations were used to implement automated classification of the electrodes using clustering or neural network algorithms. ECoG alterations were used to image cortical tumours.Cortical tumours were found to profoundly alter all frequency contents (normalized and absolute power), with an increase in the δ activity and a decreases for the other bands (P < 0.05). Cortical tumour electrodes showed high level of connectivity compared to surrounding electrodes (all markers, P < 0.05). For subcortical tumours, a relative decrease in the γ1 band and in the alpha band in absolute amplitude (P < 0.05) were the only abnormalities. The neural network algorithm classification had a good performance: 93.6 % of the electrodes were classified adequately on a test subject. We found significant spectral and connectivity ECoG changes for cortical tumours, which allowed tumour recognition. Artificial neural algorithm pattern recognition seems promising for electrode classification in awake tumour surgery.

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Electrophysiological Imaging of Brain Activity and Connectivity—Challenges and Opportunities

Cited by 250 publications

References 114 publications

Lateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI

Lateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI

Influence of the head model on EEG and MEG source connectivity analyses

In Vivo Tumour Mapping Using Electrocorticography Alterations During Awake Brain Surgery: A Pilot Study

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