On the relationship between slow cortical potentials and BOLD signal changes in humans

Khader, Patrick H.; Schicke, Tobias; Röder, Brigitte; Rösler, Frank

doi:10.1016/j.ijpsycho.2007.05.018

Cited by 91 publications

(71 citation statements)

References 53 publications

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“…Whereas nonneuronal factors have been shown to contribute to spontaneous BOLD signal variance (21)(22)(23), these studies and the present work suggest an important neural origin of these signals. Furthermore, numerous early EEG studies have shown that the negative shift of SCP occurs in response to various task demands much in the same way as the BOLD signal activation does [for a recent review, see the article by Khader et al (24)]. Hence, SCP may be a fundamental neural basis of the BOLD signal-a basis for the spontaneous BOLD fluctuations and task-evoked BOLD responses alike.…”

Section: Discussionmentioning

confidence: 99%

Electrophysiological correlates of the brain's intrinsic large-scale functional architecture

He¹,

Snyder

Zempel

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

638

536

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Spontaneous fluctuations in the blood-oxygen-level-dependent (BOLD) signals demonstrate consistent temporal correlations within large-scale brain networks associated with different functions. The neurophysiological correlates of this phenomenon remain elusive. Here, we show in humans that the slow cortical potentials recorded by electrocorticography demonstrate a correlation structure similar to that of spontaneous BOLD fluctuations across wakefulness, slow-wave sleep, and rapid-eye-movement sleep. Gamma frequency power also showed a similar correlation structure but only during wakefulness and rapid-eye-movement sleep. Our results provide an important bridge between the largescale brain networks readily revealed by spontaneous BOLD signals and their underlying neurophysiology.electrocorticography ͉ fMRI ͉ functional connectivity ͉ human ͉ sleep S pontaneous slow (Ͻ0.1 Hz) fluctuations in the blood-oxygenlevel-dependent (BOLD) signals of functional magnetic resonance imaging (fMRI) appear to reflect a fundamental aspect of the brain's organization (1, 2). These fluctuations are temporally covariant within large-scale functional brain networks, such as those associated with sensorimotor (1), language (3), attention (4), and executive (5) functions as well as the ''default network'' (6). These covariant relations (i.e., correlation structures) of spontaneous BOLD signals exist during restful waking (1, 3-6), task performance (3, 7), sleep (8), and even general anesthesia (2). Furthermore, their integrity appears to be essential to normal brain function (7). However, in contrast to evoked BOLD responses (9-12), the electrophysiological basis of these spontaneous covariant BOLD fluctuations is unknown. Here, we investigated this question in five patients with intractable epilepsy undergoing evaluation with surgically implanted grids of subdural electrodes. Each patient underwent about a week of continuous videomonitored electrocorticography (ECoG) for the purpose of determining the epileptic focus before surgical resection.The present analyses were based on ECoG data recorded in three distinct arousal states: (i) extended awake periods during which patients were in bed or seated, typically watching TV, eating, or engaged in social interactions; (ii) slow-wave sleep (SWS); and (iii) rapid-eye-movement (REM) sleep. Representative ECoG data are shown in supporting information (SI) Fig. S1. Resting-state (maintaining visual fixation) BOLD fMRI was acquired in a separate session either before or after surgical intervention. Patient information and data details are included in Table S1. In what follows, we present analyses using four different strategies to compare the correlation structures of BOLD and ECoG signals. Results Correlation Structures of Spontaneous BOLD Signal and Slow CorticalPotential. In the first three analyses, we focused on the sensorimotor network, because the ECoG electrodes provided adequate coverage of the sensorimotor network in all presently studied patients but much poorer coverage of the other ...

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Section: Discussionmentioning

confidence: 99%

Electrophysiological correlates of the brain's intrinsic large-scale functional architecture

He¹,

Snyder

Zempel

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

638

536

View full text Add to dashboard Cite

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“…There has been an active effort to ascertain the electrical correlates of the fMRI BOLD signal [for summaries of this work from different perspectives, see the studies by Raichle and Mintun (2006), Khader et al (2008), Logothetis (2008)]. The conclusion is that the fMRI BOLD signal is best correlated with local field potentials (LFPs).…”

Section: The Neurophysiology Of Boldmentioning

confidence: 99%

“…These electrical potentials vary so slowly that they are sometimes called direct current (DC) potentials. ISFs are much less often recorded because of the amplifier requirements and concerns about artifacts (Khader et al, 2008). The ␦ frequency band and the ISFs have been combined and referred to as slow cortical potentials or SCPs (Rockstroh et al, 1989).…”

Section: The Neurophysiology Of Boldmentioning

confidence: 99%

A Paradigm Shift in Functional Brain Imaging

Raichle¹

2009

J. Neurosci.

247

182

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“…The SCP training trials have been related with a large number of cognitive functions, motor performance of the brain and many neurological disorders in literature. Ergenoglu et al worked with the positivity and negativity SCP trials to getting a connection between the SCP trainings and P300 amplitude and experienced the P300 amplitudes of trials with the negative SCPs are significantly higher in comparison with the positive SCPs at Cz, Pz, Fz, P3, and P4 channels under normal and abnormal conditions of the brain [12], Khader et al investigated the relations between the SCP and Blood-oxygen-level dependent (BOLD) signal changes and proposed the similar topographical specificity of the SCP trials and the BOLD signal under cognitive experiments [16], Devrim et al investigated the detection of visual stimuli at sensory threshold using the SCP and the negative SCP trials have a better separation ability than the positive SCP trials at Oz, Pz, Cz, and Fz channels [6]. Kotchoubey et al analysed the influencing factors in epilepsy using twenty sessions SCP training and detected the positive trials are more important than the negative trials [7,8].…”

Section: Resultsmentioning

confidence: 99%

“…The SCP has been correlated with a large number of cognitive processes in a systematic and topographically ways and has been determinately utilized in psychophysiological experiments to dissociate cognitive functions and motor performance of the brain [14,15]. Ergenoglu et al worked on determining the relationship between SCP and P300 amplitude [12], Khader et al analysed the relations between the SCP and Blood-oxygen-level dependent (BOLD) signal changes [16], Devrim et al investigated the detection of visual stimuli at sensory threshold using the SCP [6], Kotchoubey et al used the SCP training in the research on epilepsy with analysis of influencing factors [7,8]. Strehl et al used functional magnetic resonance imaging and the BOLD signal in the SCP to reduce epileptic seizure frequency [17], Siniatchkin et al evaluated the analysis of migraine [9], Schneider et al determined the efficiency of the SCP training in psychiatric patients with alcohol dependency [10], Cosch et al associated the SCP with the eventrelated potentials such as object, spatial, and verbal information [4], Hinterberger et al suggested a robust and steady communication method between computer and brain for amyotrophic lateral sclerosis patients using the SCP and studied on developing a tough translation device [3], Pham et al developed an auditory brain-computer stimuli for paralysed patients using the SCP [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke

Altan

Kutlu

Allahverdi

2016

International Journal of Applied Mathematics, Electronics and Computers

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An electroencephalogram (EEG) is an electrical activity which is recorded from the scalp over the sensorimotor cortex during vigilance or sleeping conditions of subjects. It can be used to detect potential problems associated with brain disorders. The aim of this study is assessing the clinical usefulness of EEG which is recorded from slow cortical potentials (SCP) training in stroke patients using Deep belief network (DBN) which has a greedy layer wise training using Restricted Boltzmann Machines based unsupervised weight and bias evaluation and neural network based supervised training. EEGs are recorded during eight SCP neurofeedback sessions from two stroke patients with a sampling rate of 256 Hz. All EEGs are filtered with a low pass filter. Hilbert-Huang Transform is applied to the trails and various numbers of Instinct Mode Functions (IMFs) are obtained. High order statistics and standard statistics are extracted from IMFs to create the dataset. The proposed DBN-based brain activity classification has discriminated positivity and negativity tasks in stroke patients and has achieved high rates of 90.30%, 96.58%, and 91.15%, for sensitivity, selectivity, and accuracy, respectively.

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On the relationship between slow cortical potentials and BOLD signal changes in humans

Cited by 91 publications

References 53 publications

Electrophysiological correlates of the brain's intrinsic large-scale functional architecture

Electrophysiological correlates of the brain's intrinsic large-scale functional architecture

A Paradigm Shift in Functional Brain Imaging

Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke

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