Functional MRI Correlates of Resting-State Temporal Theta and Delta EEG Rhythms:

High-quality multimodal MRI with simultaneous EEG using conductive ink and polymer-thick film nets

Objective. Combining magnetic resonance imaging (MRI) and electroencephalography (EEG) provides a powerful tool for investigating brain function at varying spatial and temporal scales. Simultaneous acquisition of both modalities can provide unique information that a single modality alone cannot reveal. However, current simultaneous EEG-fMRI studies are limited to a small set of MRI sequences due to the image quality and safety limitations of commercially available MR-conditional EEG nets. We tested whether the Inknet2, a high-resistance polymer thick film based EEG net that uses conductive ink, could enable the acquisition of a variety of MR image modalities with minimal artifacts by reducing the radiofrequency-shielding caused by traditional MR-conditional nets.Approach. We first performed simulations to model the effect of the EEG nets on the magnetic field and image quality. We then performed phantom scans to test image quality with a conventional copper EEG net, with the new Inknet2, and without any EEG net. Finally, we scanned five human subjects at 3 Tesla (3 T) and three human subjects at 7 Tesla (7 T) with and without the Inknet2 to assess structural and functional MRI image quality.Main results. Across these simulations, phantom scans, and human studies, the Inknet2 induced fewer artifacts than the conventional net and produced image quality similar to scans with no net present.Significance. Our results demonstrate that high-quality structural and functional multimodal imaging across a variety of MRI pulse sequences at both 3 T and 7 T is achievable with an EEG net made with conductive ink and polymer thick film technology.

The effects of swLORETA Z-score neurofeedback for patients comorbid with major depressive disorder and anxiety symptoms

BACKGROUND

Patients with major depressive disorder (MDD) exhibit atypical brain activities in the frontal, temporal, and parietal lobes. The study aimed to investigate the effects of standardized weighted low-resolution electromagnetic tomography Z-score neurofeedback (swLZNFB) on symptoms of depression and anxiety, electroencephalography (EEG) parameters, and deep brain activities in patients with MDD.

METHOD

Forty-eight patients with MDD comorbid with anxiety symptoms were assigned to the swLZNFB group and the control group. Participants completed the Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI) and a 5-minute resting EEG at the pre-and post-tests. The swLZNFB group received ten sessions of one-hour treatment twice weekly. The control group received treatment as usual. The scores for BDI-II and BAI, number of EEG abnormalities, percentage of EEG abnormalities, and current source density (CSD) measured in the prefrontal cortex (PFC), anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and amygdala were compared at pre-and post-tests between the two groups.

RESULTS

There were decreased scores of BDI-II and BAI, number of EEG abnormalities, and percentage of EEG abnormalities at post-test compared with pre-test in the swLZNFB group, and lower scores of BDI-II and BAI at post-test in the swLZNFB group compared with the control group. Moreover, decreased CSD of beta1 and beta3 in the PFC, ACC, PCC, and amygdala at post-test compared to pre-test in the swLZNFB group.

LIMITATIONS

Not a randomized controlled trial.

CONCLUSION

Ten sessions of swLZNFB reduced clinical symptoms and atypical brain activities, it serves as a potential psychological intervention for patients with MDD.

Broadly applicable methods for the detection of artefacts in electroencephalography acquired simultaneously with hemodynamic recordings

Electroencephalography (EEG) data, acquired simultaneously with magnetic resonance imaging (MRI), must be corrected for artefacts related to MR gradient switches (GS) and the cardioballistic (CB) effect. Canonical approaches require additional signal acquisition for artefact detection (e.g., MR volume onsets, ECG), without which the EEG data would be rendered uncleanable from these artefacts.•We present two broadly applicable methods for artefact detection based on peak detection combined with temporal constraints with respect to periodicity directly from the EEG data itself; no additional signals are required. We validated the performance of our methods versus the two canonical approaches for detection of GS/CB artefact, respectively, on 26 healthy human EEG-functional MRI resting-state datasets. Utilising various performance metrics, we found our methods to perform as well as - and sometimes better than - the canonical standard approaches. With as little as one EEG channel recording, our methods can be applied to detect GS/CB artefacts in EEG data acquired simultaneously with MRI in the absence of MR volume onsets and/or an ECG recording. The detected artefact onsets can then be fed into the standard artefact correction software.

Time-frequency signatures evoked by single-pulse deep brain stimulation to the subcallosal cingulate

Precision targeting of specific white matter bundles that traverse the subcallosal cingulate (SCC) has been linked to efficacy of deep brain stimulation (DBS) for treatment resistant depression (TRD). Methods to confirm optimal target engagement in this heterogenous region are now critical to establish an objective treatment protocol. As yet unexamined are the time-frequency features of the SCC evoked potential (SCC-EP), including spectral power and phase-clustering. We examined these spectral features—evoked power and phase clustering—in a sample of TRD patients (n = 8) with implanted SCC stimulators. Electroencephalogram (EEG) was recorded during wakeful rest. Location of electrical stimulation in the SCC target region was the experimental manipulation. EEG was analyzed at the surface level with an average reference for a cluster of frontal sensors and at a time window identified by prior study (50–150 ms). Morlet wavelets generated indices of evoked power and inter-trial phase clustering. Enhanced phase clustering at theta frequency (4–7 Hz) was observed in every subject and was significantly correlated with SCC-EP magnitude, but only during left SCC stimulation. Stimulation to dorsal SCC evinced stronger phase clustering than ventral SCC. There was a weak correlation between phase clustering and white matter density. An increase in evoked delta power (2–4 Hz) was also coincident with SCC-EP, but was less consistent across participants. DBS evoked time-frequency features index mm-scale changes to the location of stimulation in the SCC target region and correlate with structural characteristics implicated in treatment optimization. Results also imply a shared generative mechanism (inter-trial phase clustering) between evoked potentials evinced by electrical stimulation and evoked potentials evinced by auditory/visual stimuli and behavioral tasks. Understanding how current injection impacts downstream cortical activity is essential to building new technologies that adapt treatment parameters to individual differences in neurophysiology.

Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (MRI) have long been used as tools to examine brain activity. Since both methods are very sensitive to changes of synaptic activity, simultaneous recording of EEG and fMRI can provide both high temporal and spatial resolution. Therefore, the two modalities are now integrated into a hybrid tool, EEG-fMRI, which encapsulates the useful properties of the two. Among other benefits, EEG-fMRI can contribute to a better understanding of brain connectivity and networks. This review lays its focus on the methodologies applied in performing EEG-fMRI studies, namely techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. We will investigate simultaneous resting-state and task-based EEG-fMRI studies and discuss their clinical and technological perspectives. Moreover, it is established that the brain regions affected by a task-based neural activity might not be limited to the regions in which they have been initiated. Advanced methods can help reveal the regions responsible for or affected by a developed neural network. Therefore, we have also looked into studies related to characterization of structure and dynamics of brain networks. The reviewed literature suggests that EEG-fMRI can provide valuable complementary information about brain neural networks and functions.

The relation between neuronal firing, local field potentials and hemodynamic activity in the human amygdala in response to aversive dynamic visual stimuli

The amygdala is a central part of networks of brain regions underlying perception and cognition, in particular related to processing of emotionally salient stimuli. Invasive electrophysiological and hemodynamic measurements are commonly used to evaluate functions of the human amygdala, but a comprehensive understanding of their relation is still lacking. Here, we aimed at investigating the link between fast and slow frequency amygdalar oscillations, neuronal firing and hemodynamic responses. To this aim, we recorded intracranial electroencephalography (iEEG), hemodynamic responses and single neuron activity from the amygdala of patients with epilepsy. Patients were presented with dynamic visual sequences of fearful faces (aversive condition), interleaved with sequences of neutral landscapes (neutral condition). Comparing responses to aversive versus neutral stimuli across participants, we observed enhanced high gamma power (HGP, >60 ​Hz) during the first 2 ​s of aversive sequence viewing, and reduced delta power (1-4 ​Hz) lasting up to 18 ​s. In 5 participants with implanted microwires, neuronal firing rates were enhanced following aversive stimuli, and exhibited positive correlation with HGP and hemodynamic responses. Our results show that high gamma power, neuronal firing and BOLD responses from the human amygdala are co-modulated. Our findings provide, for the first time, a comprehensive investigation of amygdalar responses to aversive stimuli, ranging from single-neuron spikes to local field potentials and hemodynamic responses.

Simultaneous EEG-fMRI for Functional Neurological Assessment

The increasing incidence of neurodegenerative and psychiatric diseases requires increasingly sophisticated tools for their diagnosis and monitoring. Clinical assessment takes advantage of objective parameters extracted by electroencephalogram and magnetic resonance imaging (MRI) among others, to support clinical management of neurological diseases. The complementarity of these two tools can be now emphasized by the possibility of integrating the two technologies in a hybrid solution, allowing simultaneous acquisition of the two signals by the novel EEG-fMRI technology. This review will focus on simultaneous EEG-fMRI technology and related early studies, dealing about issues related to the acquisition and processing of simultaneous signals, and including critical discussion about clinical and technological perspectives.

fMRI BOLD Correlates of EEG Independent Components: Spatial Correspondence With the Default Mode Network

Goal: We aimed to identify electroencephalographic (EEG) signal fluctuations within independent components (ICs) that correlate to spontaneous blood oxygenation level dependent (BOLD) activity in regions of the default mode network (DMN) during eyes-closed resting state. Methods: We analyzed simultaneously acquired EEG and functional magnetic resonance imaging (fMRI) eyes-closed resting state data in a convenience sample of 30 participants. IC analysis (ICA) was used to decompose the EEG time-series and common ICs were identified using data-driven IC clustering across subjects. The IC time courses were filtered into seven frequency bands, convolved with a hemeodynamic response function (HRF) and used to model spontaneous fMRI signal fluctuations across the brain. In parallel, group ICA analysis was used to decompose the fMRI signal into ICs from which the DMN was identified. Frequency and IC cluster associated hemeodynamic correlation maps obtained from the regression analysis were spatially correlated with the DMN. To investigate the reliability of our findings, the analyses were repeated with data collected from the same subjects 1 year later. Results: Our results indicate a relationship between power fluctuations in the delta, theta, beta and gamma frequency range and the DMN in different EEG ICs in our sample as shown by small to moderate spatial correlations at the first measurement (0.234 < |r| < 0.346, p < 0.0001). Furthermore, activity within an EEG component commonly identified as eye movements correlates with BOLD activity within regions of the DMN. In addition, we demonstrate that correlations between EEG ICs and the BOLD signal during rest are in part stable across time. Discussion: We show that ICA source separated EEG signals can be used to investigate electrophysiological correlates of the DMN. The relationship between the eye movement component and the DMN points to a behavioral association between DMN activity and the level of eye movement or the presence of neuronal activity in this component. Previous findings of an association between frontal midline theta activity and the DMN were replicated.