MRI with and without a high-density EEG cap--what makes the difference?

Transcranial direct current stimulation (tDCS) in depression induces structural plasticity

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique involving administration of well-tolerated electrical current to the brain through scalp electrodes. TDCS may improve symptoms in neuropsychiatric disorders, but mixed results from recent clinical trials underscore the need to demonstrate that tDCS can modulate clinically relevant brain systems over time in patients. Here, we analyzed longitudinal structural MRI data from a randomized, double-blind, parallel-design clinical trial in depression (NCT03556124, N = 59) to investigate whether serial tDCS individually targeted to the left dorso-lateral prefrontal cortex (DLPFC) can induce neurostructural changes. Significant (FWEc p < 0.05) treatment-related gray matter changes were observed with active high-definition (HD) tDCS relative to sham tDCS within the left DLPFC stimulation target. No changes were observed with active conventional tDCS. A follow-up analysis within individual treatment groups revealed significant gray matter increases with active HD-tDCS in brain regions functionally connected with the stimulation target, including the bilateral DLPFC, bilateral posterior cingulate cortex, subgenual anterior cingulate cortex, and the right hippocampus, thalamus and left caudate brain regions. Integrity of blinding was verified, no significant differences in stimulation-related discomfort were observed between treatment groups, and tDCS treatments were not augmented by any other adjunct treatments. Overall, these results demonstrate that serial HD-tDCS leads to neurostructural changes at a predetermined brain target in depression and suggest that such plasticity effects may propagate over brain networks.

Preservation of EEG spectral power features during simultaneous EEG-fMRI

Introduction

Electroencephalographic (EEG) data quality is severely compromised when recorded inside the magnetic resonance (MR) environment. Here we characterized the impact of the ballistocardiographic (BCG) artifact on resting-state EEG spectral properties and compared the effectiveness of seven common BCG correction methods to preserve EEG spectral features. We also assessed if these methods retained posterior alpha power reactivity to an eyes closure-opening (EC-EO) task and compared the results from EEG-informed fMRI analysis using different BCG correction approaches.

Method

Electroencephalographic data from 20 healthy young adults were recorded outside the MR environment and during simultaneous fMRI acquisition. The gradient artifact was effectively removed from EEG-fMRI acquisitions using Average Artifact Subtraction (AAS). The BCG artifact was corrected with seven methods: AAS, Optimal Basis Set (OBS), Independent Component Analysis (ICA), OBS followed by ICA, AAS followed by ICA, PROJIC-AAS and PROJIC-OBS. EEG signal preservation was assessed by comparing the spectral power of traditional frequency bands from the corrected rs-EEG-fMRI data with the data recorded outside the scanner. We then assessed the preservation of posterior alpha functional reactivity by computing the ratio between the EC and EO conditions during the EC-EO task. EEG-informed fMRI analysis of the EC-EO task was performed using alpha power-derived BOLD signal predictors obtained from the EEG signals corrected with different methods.

Results

The BCG artifact caused significant distortions (increased absolute power, altered relative power) across all frequency bands. Artifact residuals/signal losses were present after applying all correction methods. The EEG reactivity to the EC-EO task was better preserved with ICA-based correction approaches, particularly when using ICA feature extraction to isolate alpha power fluctuations, which allowed to accurately predict hemodynamic signal fluctuations during the EEG-informed fMRI analysis.

Discussion

Current software solutions for the BCG artifact problem offer limited efficiency to preserve the EEG spectral power properties using this particular EEG setup. The state-of-the-art approaches tested here can be further refined and should be combined with hardware implementations to better preserve EEG signal properties during simultaneous EEG-fMRI. Existing and novel BCG artifact correction methods should be validated by evaluating signal preservation of both ERPs and spontaneous EEG spectral power.

Combining magnetic resonance imaging with readout and/or perturbation of neural activity in animal models: Advantages and pitfalls

One of the main challenges in brain research is to link all aspects of brain function: on a cellular, systemic, and functional level. Multimodal neuroimaging methodology provides a continuously evolving platform. Being able to combine calcium imaging, optogenetics, electrophysiology, chemogenetics, and functional magnetic resonance imaging (fMRI) as part of the numerous efforts on brain functional mapping, we have a unique opportunity to better understand brain function. This review will focus on the developments in application of these tools within fMRI studies and highlight the challenges and choices neurosciences face when designing multimodal experiments.

Effects of Ketamine and Midazolam on Simultaneous EEG/fMRI Data During Working Memory Processes

Reliable measures of cognitive brain activity from functional neuroimaging techniques may provide early indications of efficacy in clinical trials. Functional magnetic resonance imaging and electroencephalography provide complementary spatiotemporal information and simultaneous recording of these two modalities can remove inter-session drug response and environment variability. We sought to assess the effects of ketamine and midazolam on simultaneous electrophysiological and hemodynamic recordings during working memory (WM) processes. Thirty participants were included in a placebo-controlled, three-way crossover design with ketamine and midazolam. Compared to placebo, ketamine administration attenuated theta power increases and alpha power decreases and midazolam attenuated low beta band decreases to increasing WM load. Additionally, ketamine caused larger blood-oxygen-dependent (BOLD) signal increases in the supplementary motor area and angular gyrus, and weaker deactivations of the default mode network (DMN), whereas no difference was found between midazolam and placebo. Ketamine administration caused positive temporal correlations between frontal-midline theta (fm-theta) power and the BOLD signal to disappear and attenuated negative correlations. However, the relationship between fm-theta and the BOLD signal from DMN areas was maintained in some participants during ketamine administration, as increasing theta strength was associated with stronger BOLD signal reductions in these areas. The presence of, and ability to manipulate, both positive and negative associations between the BOLD signal and fm-theta suggest the presence of multiple fm-theta components involved in WM processes, with ketamine administration disrupting one or more of these theta-linked WM strategies.

When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision.

The human mirror neuron system-A common neural basis for social cognition?

According to the theory of embodied simulation, mirror neurons (MN) in our brain's motor system are the neuronal basis of all social-cognitive processes. The assumption of such a mirroring process in humans could be supported by results showing that within one person the same region is involved in different social cognition tasks. We conducted an fMRI-study with 75 healthy participants who completed three tasks: imitation, empathy, and theory of mind. We analyzed the data using group conjunction analyses and individual shared voxel counts. Across tasks, across and within participants, we find common activation in inferior frontal gyrus, inferior parietal cortex, fusiform gyrus, posterior superior temporal sulcus, and amygdala. Our results provide evidence for a shared neural basis for different social-cognitive processes, indicating that interpersonal understanding might occur by embodied simulation.

Simultaneous EEG/fMRI recorded during ketamine infusion in patients with major depressive disorder

A single subanaesthetic dose of ketamine rapidly alleviates the symptoms of major depressive disorder (MDD). However, few studies have investigated the acute effects of ketamine on the BOLD pharmacological magnetic resonance imaging (phMRI) response and EEG spectra. In a randomised, double-blind, active placebo-controlled crossover trial, resting-state simultaneous EEG/fMRI was collected during infusion of ketamine or active placebo (remifentanil) in 30 participants with MDD. Montgomery-Asberg depression rating scale scores showed a significant antidepressant effect of ketamine compared to placebo (69% response rate). phMRI analyses showed BOLD signal increases in the anterior cingulate and medial prefrontal cortices and sensitivity of the decrease in subgenual anterior cingulate cortex (sgACC) BOLD signal to noise correction. EEG spectral analysis showed increased theta, high beta, low and high gamma power, and decreased delta, alpha, and low beta power with differing time-courses. Low beta and high gamma power time courses explained significant variance in the BOLD signal. Interestingly, the variance explained by high gamma power was significantly associated with non-response to ketamine, but significant associations were not found for other neurophysiological markers when noise correction was implemented. The results suggest that the decrease in sgACC BOLD signal is potentially noise and unrelated to ketamine's antidepressant effect, highlighting the importance of noise correction and multiple temporal regressors for phMRI analyses. The lack of effects significantly associated with antidepressant response suggests the phMRI methodology employed was unable to detect such effects, the effect sizes are relatively small, or that other processes, e.g. neural plasticity, underlie ketamine's antidepressant effect.

Temporal dynamics of the pharmacological MRI response to subanaesthetic ketamine in healthy volunteers: A simultaneous EEG/fMRI study

BACKGROUND

Pharmacological magnetic resonance imaging has been used to investigate the neural effects of subanaesthetic ketamine in healthy volunteers. However, the effect of ketamine has been modelled with a single time course and without consideration of physiological noise.

AIMS

This study aimed to investigate ketamine-induced alterations in resting neural activity using conventional pharmacological magnetic resonance imaging analysis techniques with physiological noise correction, and a novel analysis utilising simultaneously recorded electroencephalography data.

METHODS

Simultaneous electroencephalography/functional magnetic resonance imaging and physiological data were collected from 30 healthy male participants before and during a subanaesthetic intravenous ketamine infusion.

RESULTS

Consistent with previous literature, we show widespread cortical blood-oxygen-level dependent signal increases and decreased blood-oxygen-level dependent signals in the subgenual anterior cingulate cortex following ketamine. However, the latter effect was attenuated by the inclusion of motion regressors and physiological correction in the model. In a novel analysis, we modelled the pharmacological magnetic resonance imaging response with the power time series of seven electroencephalography frequency bands. This showed evidence for distinct temporal time courses of neural responses to ketamine. No electroencephalography power time series correlated with decreased blood-oxygen-level dependent signal in the subgenual anterior cingulate cortex.

CONCLUSIONS

We suggest the decrease in blood-oxygen-level dependent signals in the subgenual anterior cingulate cortex typically seen in the literature is the result of physiological noise, in particular cardiac pulsatility. Furthermore, modelling the pharmacological magnetic resonance imaging response with a single temporal model does not completely capture the full spectrum of neuronal dynamics. The use of electroencephalography regressors to model the response can increase confidence that the pharmacological magnetic resonance imaging is directly related to underlying neural activity.

Quantitative Analyses Help in Choosing Between Simultaneous vs. Separate EEG and fMRI

Simultaneous registration of scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is considered an attractive approach for studying brain function non-invasively. It combines the better spatial resolution of fMRI with the better temporal resolution of EEG, but comes at the cost of increased measurement artifact and the accompanying artifact preprocessing. This paper presents a study of the residual signal quality based on temporal signal to noise ratio (TSNR) for fMRI and fast Fourier transform (FFT) for EEG, after optimized conventional signal preprocessing. Measurements outside the magnetic resonance imaging scanner and inside the scanner prior to and during image acquisition were compared. For EEG, frequency and region dependent significant effects on FFT squared amplitudes were observed between separately vs. simultaneously recorded EEG and fMRI, with larger effects during image acquisition than without image acquisition inside the scanner bore. A graphical user interface was developed to aid in quality checking these measurements. For fMRI, separately recorded EEG-fMRI revealed relatively large areas with a significantly higher TSNR in right occipital and parietal regions and in the cingulum, compared to separately recorded EEG-fMRI. Simultaneously recorded EEG-fMRI showed significantly higher TSNR in inferior occipital cortex, diencephalon and brainstem, compared to separately recorded EEG-fMRI. Quantification of EEG and fMRI signals showed significant, but sometimes subtle, changes between separate compared to simultaneous EEG-fMRI measurements. To avoid interference with the experiment of interest in a simultaneous EEG-fMRI measurement, it seems warranted to perform a quantitative evaluation to ensure that there are no such uncorrectable effects present in regions or frequencies that are of special interest to the research question at hand.

Comparison of local spectral modulation, and temporal correlation, of simultaneously recorded EEG/fMRI signals during ketamine and midazolam sedation

RATIONALE AND OBJECTIVES

The identification of biomarkers of drug action can be supported by non-invasive brain imaging techniques, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), with simultaneous collection plausibly overcoming the limitations of either modality alone. Despite this, few studies have assessed the feasibility and utility of recording simultaneous EEG/fMRI in a drug study.

METHODS

We used simultaneous EEG/fMRI to assess the modulation of neural activity by ketamine and midazolam, in a placebo-controlled, single-blind, three-way cross-over design. Specifically, we analysed the sensitivity and direction of the spectral effects of each modality and the temporal correlations between the modulations of power of the common EEG bands and the blood-oxygen-level-dependent (BOLD) signal.

RESULTS AND CONCLUSIONS

Demonstrating feasibility, local spectral effects were similar to those found in previous non-simultaneous EEG and fMRI studies. Ketamine administration resulted in a widespread reduction of BOLD fractional amplitude of low frequency fluctuations (fALFF) and a diverse pattern of effects in the different EEG bands. Midazolam increased fALFF in occipital, parietal, and temporal areas, and frontal delta and beta EEG power. While EEG spectra were more sensitive to pharmacological modulations than the fALFF bands, there was no clear spatial relationship between the two modalities. Additionally, ketamine modulated the temporal correlation strengths between the theta EEG band and the BOLD signal, whereas midazolam altered temporal correlations with the alpha and beta bands. Taken together, these results demonstrate the utility of simultaneous recording: each modality provides unique insights, and combinatorial analyses elicit more information than separate recordings.

Constrained temporal parallel decomposition for EEG-fMRI fusion

OBJECTIVE

Multimodal neuroimaging has become a common practice in neuroscience research. Simultaneous EEG-fMRI is a popular multimodal recording approach due to the complementary spatiotemporal relationship between the two modalities. Several data fusion techniques have been proposed in the literature for EEG-fMRI fusion, including joint-ICA and parallel-ICA frameworks. Previous EEG-fMRI fusion approaches have used sensor-level EEG features. Recently, we introduced source-space ICA for EEG-MEG source reconstruction and component identification, which was shown to be a superior alternative to sensor-space ICA.

APPROACH

Here, we extend source-space ICA to the fusion of EEG-fMRI data. Additionally, we incorporate the use of a paradigm signal (constrained) and a lag-based signal decomposition approach to accommodate recent findings demonstrating the potentially variable lag structure between electrophysiological and BOLD signals. We evaluated this method on simulated concurrent EEG-fMRI during a boxcar task design, as well as real concurrent EEG-fMRI data from three participants performing an N-Back working memory task. The block diagram of the algorithm and corresponding source codes are provided.

MAIN RESULTS

Based on the results of the real working memory task, for all three subjects, one frontal theta component, and one right posterior alpha component had the highest contribution coefficients (~0.5) to the paradigm-related fused component. There were also two more alpha band components with contribution coefficients of 0.3. The highest contributing fMRI component (~0.8) was one known in the literature to be related to the attention network. The second fMRI component was related to the well-known default mode network, with a contribution coefficient of 0.3.

SIGNIFICANCE

The proposed EEG-fMRI fusion approach, is capable of estimating the brain maps of the EEG and fMRI for the fused components and account for the variable lag structure between electrophysiological and BOLD signals.

EEG-Informed fMRI: A Review of Data Analysis Methods

The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.

Safety and EEG data quality of concurrent high-density EEG and high-speed fMRI at 3 Tesla

PURPOSE

Concurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI). There is considerable interest in integrating recently developed high-speed fMRI methods with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality.

MATERIALS AND METHODS

The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors that were equipped with 64- and 256-channel MR-compatible EEG systems, respectively, and receive only array head coils. Data were collected in 11 healthy controls (3 males, age range 18-70 years) and 13 patients with epilepsy (8 males, age range 21-67 years). Three of the healthy controls were scanned with the 256-channel EEG system, the other subjects were scanned with the 64-channel EEG system. Scalp surface temperature, SNR in occipital cortex and head movement were measured with and without the EEG cap. The degree of artifacts and the ability to identify background activity was assessed by visual analysis by a trained expert in the 64 channel EEG data (7 healthy controls, 13 patients).

RESULTS

RF induced heating at the surface of the EEG electrodes during a 30-minute scan period with stable temperature prior to scanning did not exceed 1.0° C with either EEG system and any of the pulse sequences used in this study. There was no significant decrease in cortical SNR due to the presence of the EEG cap (p > 0.05). No significant differences in the visually analyzed EEG data quality were found between EEG recorded during high-speed fMRI and during conventional EPI (p = 0.78). Residual ballistocardiographic artifacts resulted in 58% of EEG data being rated as poor quality.

CONCLUSION

This study demonstrates that high-density EEG can be safely implemented in conjunction with high-speed fMRI and that high-speed fMRI does not adversely affect EEG data quality. However, the deterioration of the EEG quality due to residual ballistocardiographic artifacts remains a significant constraint for routine clinical applications of concurrent EEG-fMRI.

Age prediction on the basis of brain anatomical measures

In this study, we examined whether age can be predicted on the basis of different anatomical features obtained from a large sample of healthy subjects (n = 3,144). From this sample we obtained different anatomical feature sets: (1) 11 larger brain regions (including cortical volume, thickness, area, subcortical volume, cerebellar volume, etc.), (2) 148 cortical compartmental thickness measures, (3) 148 cortical compartmental area measures, (4) 148 cortical compartmental volume measures, and (5) a combination of the above-mentioned measures. With these anatomical feature sets, we predicted age using 6 statistical techniques (multiple linear regression, ridge regression, neural network, k-nearest neighbourhood, support vector machine, and random forest). We obtained very good age prediction accuracies, with the highest accuracy being R²  = 0.84 (prediction on the basis of a neural network and support vector machine approaches for the entire data set) and the lowest being R²  = 0.40 (prediction on the basis of a k-nearest neighborhood for cortical surface measures). Interestingly, the easy-to-calculate multiple linear regression approach with the 11 large brain compartments resulted in a very good prediction accuracy (R²  = 0.73), whereas the application of the neural network approach for this data set revealed very good age prediction accuracy (R²  = 0.83). Taken together, these results demonstrate that age can be predicted well on the basis of anatomical measures. The neural network approach turned out to be the approach with the best results. In addition, it was evident that good prediction accuracies can be achieved using a small but nevertheless age-representative dataset of brain features. Hum Brain Mapp 38:997-1008, 2017. © 2016 Wiley Periodicals, Inc.

Polymer thick film technology for improved simultaneous dEEG/MRI recording: Safety and MRI data quality

PURPOSE

To develop a 256-channel dense-array electroencephalography (dEEG) sensor net (the Ink-Net) using high-resistance polymer thick film (PTF) technology to improve safety and data quality during simultaneous dEEG/MRI.

METHODS

Heating safety was assessed with temperature measurements in an anthropomorphic head phantom during a 30-min, induced-heating scan at 7T. MRI quality assessment used B1 field mapping and functional MRI (fMRI) retinotopic scans in three humans at 3T. Performance of the 256-channel PTF Ink-Net was compared with a 256-channel MR-conditional copper-wired electroencephalography (EEG) net and to scans with no sensor net. A visual evoked potential paradigm assessed EEG quality within and outside the 3T scanner.

RESULTS

Phantom temperature measurements revealed nonsignificant heating (ISO 10974) in the presence of either EEG net. In human B1 field and fMRI scans, the Ink-Net showed greatly reduced cross-modal artifact and less signal degradation than the copper-wired net, and comparable quality to MRI without sensor net. Cross-modal ballistocardiogram artifact in the EEG was comparable for both nets.

CONCLUSION

High-resistance PTF technology can be effectively implemented in a 256-channel dEEG sensor net for MR conditional use at 7T and with significantly improved structural and fMRI data quality as assessed at 3T. Magn Reson Med 77:895-903, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Sleep reverts changes in human gray and white matter caused by wake-dependent training

Learning leads to rapid microstructural changes in gray (GM) and white (WM) matter. Do these changes continue to accumulate if task training continues, and can they be reverted by sleep? We addressed these questions by combining structural and diffusion weighted MRI and high-density EEG in 16 subjects studied during the physiological sleep/wake cycle, after 12 h and 24 h of intense practice in two different tasks, and after post-training sleep. Compared to baseline wake, 12 h of training led to a decline in cortical mean diffusivity. The decrease became even more significant after 24 h of task practice combined with sleep deprivation. Prolonged practice also resulted in decreased ventricular volume and increased GM and WM subcortical volumes. All changes reverted after recovery sleep. Moreover, these structural alterations predicted cognitive performance at the individual level, suggesting that sleep's ability to counteract performance deficits is linked to its effects on the brain microstructure. The cellular mechanisms that account for the structural effects of sleep are unknown, but they may be linked to its role in promoting the production of cerebrospinal fluid and the decrease in synapse size and strength, as well as to its recently discovered ability to enhance the extracellular space and the clearance of brain metabolites.