Petra Ritter

Rolandic alpha and beta EEG rhythms' strengths are inversely related to fMRI-BOLD signal in primary somatosensory and motor cortex

Similar to the posterior alpha rhythm, pericentral (Rolandic) EEG rhythms in the alpha and beta frequency range are referred to as “idle rhythms” indicating a “resting state” of the respective system. The precise function of these rhythms is not clear. We used simultaneous EEG‐fMRI during a bimanual motor task to localize brain areas involved in Rolandic alpha and beta EEG rhythms. The identification of these rhythms in the MR environment was achieved by a blind source separation algorithm. Rhythm “strength”, i.e. spectral power determined by wavelet analysis, inversely correlated most strongly with the fMRI‐BOLD signal in the postcentral cortex for the Rolandic alpha (mu) rhythm and in the precentral cortex for the Rolandic beta rhythm. FMRI correlates of Rolandic alpha and beta rhythms were distinct from those associated with the posterior “classical” alpha rhythm, which correlated inversely with the BOLD signal in the occipital cortex. An inverse correlation with the BOLD signal in the respective sensory area seems to be a general feature of “idle rhythms”. Hum Brain Mapp 2009.

Simultaneous EEG-fMRI

Acquisition of electroencephalogram (EEG) during functional magnetic resonance imaging (fMRI) provides an additional monitoring tool for the analysis of brain state fluctuations. The exploration of brain responses following inputs or in the context of state changes is crucial for a better understanding of the basic principles governing large-scale neuronal dynamics. State-of-the-art techniques allow EEG activity-from DC (direct current) up to high frequencies in the gamma range-to be acquired simultaneously with fMRI data. In the interleaved mode, spiking activities can also be assessed during concurrent fMRI. The utilization of fMRI evidence to better constrain solutions of the inverse problem of source localization of EEG activity is an exciting possibility. Nonetheless, this approach should be applied cautiously since the degree of overlap between underlying neuronal activity sources is variable and, for the most part, unknown. The ultimate goal is to make joint inferences about the activity, dynamics, and functions by exploiting complementary information from multimodal data sets.

Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity

The brain is widely assumed to be a paradigmatic example of a complex, self-organizing system. As such, it should exhibit the classic hallmarks of nonlinearity, multistability, and “nondiffusivity” (large coherent fluctuations). Surprisingly, at least at the very large scale of neocortical dynamics, there is little empirical evidence to support this, and hence most computational and methodological frameworks for healthy brain activity have proceeded very reasonably from a purely linear and diffusive perspective. By studying the temporal fluctuations of power in human resting-state electroencephalograms, we show that, although these simple properties may hold true at some temporal scales, there is strong evidence for bistability and nondiffusivity in key brain rhythms. Bistability is manifest as nonclassic bursting between high- and low-amplitude modes in the alpha rhythm. Nondiffusivity is expressed through the irregular appearance of high amplitude “extremal” events in beta rhythm power fluctuations. The statistical robustness of these observations was confirmed through comparison with Gaussian-rendered phase-randomized surrogate data. Although there is a good conceptual framework for understanding bistability in cortical dynamics, the implications of the extremal events challenge existing frameworks for understanding large-scale brain systems.

Spatial Attention Related SEP Amplitude Modulations Covary with BOLD Signal in S1—A Simultaneous EEG—fMRI Study

Recent studies investigating the influence of spatial-selective attention on primary somatosensory processing have produced inconsistent results. The aim of this study was to explore the influence of tactile spatial-selective attention on spatiotemporal aspects of evoked neuronal activity in the primary somatosensory cortex (S1). We employed simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) in 14 right-handed subjects during bilateral index finger Braille stimulation to investigate the relationship between attentional effects on somatosensory evoked potential (SEP) components and the blood oxygenation level-dependent (BOLD) signal. The 1st reliable EEG response following left tactile stimulation (P50) was significantly enhanced by spatial-selective attention, which has not been reported before. FMRI analysis revealed increased activity in contralateral S1. Remarkably, the effect of attention on the P50 component as well as long-latency SEP components starting at 190 ms for left stimuli correlated with attentional effects on the BOLD signal in contralateral S1. The implications are 2-fold: First, the correlation between early and long-latency SEP components and the BOLD effect suggest that spatial-selective attention enhances processing in S1 at 2 time points: During an early passage of the signal and during a later passage, probably via re-entrant feedback from higher cortical areas. Second, attentional modulations of the fast electrophysiological signals and the slow hemodynamic response are linearly related in S1.

Visual evoked potentials recovered from fMRI scan periods

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) may allow functional imaging of the brain at high temporal and spatial resolution. Artifacts generated in the EEG signal during MR acquisition, however, continue to pose a major challenge. Due to these artifacts, an interleaved modus has often been used for “evoked potential” experiments, i.e., only EEG signals recorded between MRI scan periods were assessed. An obvious disadvantage of this approach is the loss of a portion of the EEG information, which might be relevant for the specific scientific issue. In this study, continuous, simultaneous EEG‐fMRI measurements were carried out. Visual evoked potentials (VEPs) could be reconstructed reliably from periods during MR scanning and in between successive scans. No significant differences between both VEPs were detected. This indicates sufficient artifact removal as well as physiological correspondence of VEPs in both periods. Simultaneous continuous VEP‐fMRI recordings are thus shown to be feasible. Hum Brain Mapp, 2005.

High-frequency (600 Hz) population spikes in human EEG delineate thalamic and cortical fMRI activation sites

Functional magnetic resonance imaging (fMRI) measures neural activity indirectly via its slow vascular/metabolic consequences. At a temporal resolution on the order of seconds, fMRI does not reveal the real language of neurons, spelt out by fast electrical discharges (spikes) which occur on a time scale of milliseconds. In animal studies, these limitations have been addressed by adding invasive electrode measurements to fMRI. Here, we propose to circumvent this inverse problem of fMRI by deriving a noninvasive spike measure from recordings of ultrafast electroencephalography (EEG) signals during fMRI. We demonstrate how in response to median nerve stimulation 600 Hz oscillatory EEG signals can be measured reliably during fMRI. These high-frequency bursts (HFBs) are supposed to reflect population spikes in the thalamus and the somatosensory cortex, respectively. We show that distinct fMRI activations in these two generator structures can be attributed to spontaneous HFB fluctuations. Thus, our approach allowed the noninvasive identification of neural processes along the thalamocortical pathway unfolding at a millisecond time scale.

EEG Quality:The Image Acquisition Artefact

In this chapter, we focus on the artefacts that arise in the EEG during the fMRI acquisition process. Functional MRI using echo planar imaging (EPI) sequences involves the application of rapidly varying magnetic field gradients for spatial encoding of the MR signal and radiofrequency (RF) pulses for spin excitation (see the chapter “The Basics of Functional Magnetic Resonance Imaging”). Early in the implementation of EEG–fMRI, it was observed that the acquisition of an MR image results in complete obscuration of the physiological EEG (Ives et al. 1993; Allen et al. 2000). Electromagnetic induction in the circuit formed by the electrodes, leads, patient and amplifier exposed to a time-varying magnetic field causes an electromotive force. Artefacts induced in the EEG by the scanning process have a strong deterministic component, due to the preprogrammed nature of the RF and gradient switching sequence, and therefore artefact correction is generally considered a lesser problem than pulse-related artefacts (see the chapter “EEG Quality: Origin and Reduction of the EEG Cardiac-Related Artefact”). According to Faraday’s law of induction, the induced electromotive force is proportional to the time derivative of the magnetic flux (summation of the magnetic field perpendicular to the circuit plane over the area circuit), dΦ/dt, and can therefore reflect changes in the field (gradient switching, RF) or in the circuit geometry or position relative to the field due to body motion (Lemieux et al. 1997). Therefore, the combination of body motion with image acquisition artefacts can lead to random variations that represent a real challenge for artefact correction.

Inhibition and functional magnetic resonance imaging

Abstract This review summarizes our current knowledge on how inhibitory phenomena are reflected in the functional magnetic resonance imaging (fMRI) signal. It is well-established that activity-related changes of brain metabolism and blood flow are dominated by changes in synaptic activity. Both excitatory and inhibitory synaptic activities are associated with increased metabolic demands. The amount of energy consumption associated with inhibition vs. excitation is reflected in metabolism- and blood flow-related neuroimaging signals such as the blood oxygen level-dependent (BOLD) contrast; the relationship between the different “signals”, however, may not be linear. The influence on these signals depends on the number of active inhibiting synapses, the duration of inhibition and the degree of propagation within subsequent neuronal circuits. The relative influence of inhibition as compared to excitation on the metabolism/blood flow may also vary in different neuronal circuits. Inhibition leads to locally decreased discharge activity, which does not have a significant effect on the cerebral blood flow (CBF)/BOLD images. However, inhibition may also result in suppression of complex neuronal circuits, leading to a decrease in excitatory as well as inhibitory synaptic activity, and therewith, to a decreased metabolism and blood flow within those complex neuronal networks. The available fMRI data indicate that, depending on the abovementioned factors, inhibition may be reflected in positive, negative or no BOLD-signal at all. Thus, the BOLD-signal is ambiguous with respect to the underlying electrophysiological event. In the future, combining fMRI with electrophysiological methods will strengthen neuroimaging studies.

Background and evoked activity and their interaction in the human brain

Most functional neuroimaging studies have investigated brain activity evoked by certain types of stimulation or tasks. In recent years, resting brain activity and its influence on evoked activity has become accessible for investigation. However, despite numerous studies on background and evoked activities, either observed with vascular (functional magnetic resonance imaging, positron emission tomography, optical) or electrophysiological (electroencephalography, magnetoencephalography) or a combination of both methods, so far, there is no generally accepted view concerning both the precise meaning of background activity and its relationship to evoked activity. In this article, we give an overview of the current knowledge on this issue and we review recent studies examining the influence of ongoing activity on behavioral responses and the relationship between ongoing and evoked activity.

Bridging multiple scales in the human brain using computational modelling

Brain dynamics span multiple spatial and temporal scales, from fast spiking neurons to slow fluctuations over distributed areas. No single experimental method links data across scales. Here, we bridge this gap using The Virtual Brain connectome-based modelling platform to integrate multimodal data with biophysical models and support neurophysiological inference. Simulated cell populations were linked with subject-specific white-matter connectivity estimates and driven by electroencephalography-derived electric source activity. The models were fit to subject-specific resting-state functional magnetic resonance imaging data, and overfitting was excluded using 5-fold cross-validation. Further evaluation of the models show how balancing excitation with feedback inhibition generates an inverse relationship between α-rhythms and population firing on a faster time scale and resting-state network oscillations on a slower time scale. Lastly, large-scale interactions in the model lead to the emergence of scale-free power-law spectra. Our novel findings underscore the integrative role for computational modelling to complement empirical studies.