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

Visual imagery of familiar people and places in category selective cortex

Visual imagery is a dynamic process recruiting a network of brain regions. We used electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) fusion to investigate the dynamics of category selective imagery in medial parietal cortex (MPC), ventral temporal cortex (VTC), and primary visual cortex (V1). Subjects attended separate EEG and fMRI sessions where they created mental images of personally familiar people and place stimuli. The fMRI contrast comparing people and place imagery replicated previous findings of category-selectivity in the medial parietal cortex. In addition, greater activity for places was found in the ventral and lateral place memory areas, the frontal eye fields, the inferior temporal sulcus, and the intraparietal sulcus. In contrast, greater activity for people was found in the fusiform face area and the right posterior superior temporal sulcus. Using multivariate decoding analysis in fMRI, we could decode individual stimuli within the preferred category in VTC. A more complex pattern emerged in MPC, which represented information that was not restricted to the preferred category. We were also able to decode category and individual stimuli in the EEG data. EEG-fMRI fusion indicated similar timings in MPC and VTC activity during imagery. However, in the VTC, fusion was higher in place selective regions during an early time window, and higher in face selective regions in a later time window. In contrast, fusion correlations in V1 occurred later during the imagery period, possibly reflecting the top-down progression of mental imagery from category-selective regions to primary visual cortex.

Application status and prospects of multimodal EEG-fMRI in HIV-associated neurocognitive disorders

HIV-associated neurocognitive disorders (HAND) are one of the common complications in people living with HIV (PLWH), which can affect their attention, working memory, and other related cognitive functions. With the widespread use of combination antiretroviral therapy (cART), the incidence of HAND has declined. However, HAND is still an important complication of HIV, which not only affects the quality of life of patients but also affects their adherence to HIV treatment. Its diagnosis mainly relies on neurocognitive tests, which have a certain degree of subjectivity, making it difficult to diagnose and classify HAND accurately, and there is an urgent need to explore more sensitive biomarkers. Multimodal brain imaging has seen a surge in recent years with simultaneous EEG-fMRI being at the forefront of cognitive multimodal neuroimaging. It is a complementary fusion technique that effectively combines the high spatial resolution of fMRI with the high temporal resolution of EEG, compensating for the shortcomings of a single technique and providing a new method for studying cognitive function. It is expected to reveal the underlying mechanisms of HAND and provide high spatiotemporal warning biomarkers of HAND, which will provide a new perspective for the early diagnosis and treatment of HAND and contribute to the improvement of patient prognosis.

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.

Probing the neural dynamics of musicians' and non-musicians' consonant/dissonant perception: Joint analyses of electrical encephalogram (EEG) and functional magnetic resonance imaging (fMRI)

The perception of two (or more) simultaneous musical notes, depending on their pitch interval(s), could be broadly categorized as consonant or dissonant. Previous literature has suggested that musicians and non-musicians adopt different strategies when discerning music intervals: while musicians rely on the frequency ratios between the two fundamental frequencies, such as "perfect fifth" (3:2) as consonant and "tritone" (45:32) as dissonant intervals; non-musicians may rely on the presence of 'roughness' or 'beats', generated by the difference of fundamental frequencies, as the key elements of 'dissonance'. The separate Event-Related Potential (ERP) differences in N1 and P2 along the midline electrodes provided evidence congruent with such 'separate reliances'. To replicate and to extend, in this study we reran the previous experiment, and separately collected fMRI data of the same protocol (with sparse sampling modifications). The behavioral and EEG results largely corresponded to our previous finding. The fMRI results, with the joint analyses by univariate, psycho-physiological interaction, and representational similarity analysis (RSA) approaches, further reinforce the involvement of central midline-related brain regions, such as ventromedial prefrontal and dorsal anterior cingulate cortex, in consonant/dissonance judgments. The final spatiotemporal searchlight RSA provided convincing evidence that the medial prefrontal cortex, along with the bilateral superior temporal cortex, is the joint locus of midline N1 and dorsal anterior cingulate cortex for the P2 effect (for musicians). Together, these analyses reaffirm that musicians rely more on experience-driven knowledge for consonance/dissonance perception; but also demonstrate the advantages of multiple analyses in constraining the findings from both EEG and fMRI.

Integrative approach of omics and imaging data to discover new insights for understanding brain diseases

Treatments that can completely resolve brain diseases have yet to be discovered. Omics is a novel technology that allows researchers to understand the molecular pathways underlying brain diseases. Multiple omics, including genomics, transcriptomics and proteomics, and brain imaging technologies, such as MRI, PET and EEG, have contributed to brain disease-related therapeutic target detection. However, new treatment discovery remains challenging. We focused on establishing brain multi-molecular maps using an integrative approach of omics and imaging to provide insights into brain disease diagnosis and treatment. This approach requires precise data collection using omics and imaging technologies, data processing and normalization. Incorporating a brain molecular map with the advanced technologies through artificial intelligence will help establish a system for brain disease diagnosis and treatment through regulation at the molecular level.

An advance in novel intelligent sensory technologies: From an implicit-tracking perspective of food perception

Food sensory evaluation mainly includes explicit and implicit measurement methods. Implicit measures of consumer perception are gaining significant attention in food sensory and consumer science as they provide effective, subconscious, objective analysis. A wide range of advanced technologies are now available for analyzing physiological and psychological responses, including facial analysis technology, neuroimaging technology, autonomic nervous system technology, and behavioral pattern measurement. However, researchers in the food field often lack systematic knowledge of these multidisciplinary technologies and struggle with interpreting their results. In order to bridge this gap, this review systematically describes the principles and highlights the applications in food sensory and consumer science of facial analysis technologies such as eye tracking, facial electromyography, and automatic facial expression analysis, as well as neuroimaging technologies like electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and functional near-infrared spectroscopy. Furthermore, we critically compare and discuss these advanced implicit techniques in the context of food sensory research and then accordingly propose prospects. Ultimately, we conclude that implicit measures should be complemented by traditional explicit measures to capture responses beyond preference. Facial analysis technologies offer a more objective reflection of sensory perception and attitudes toward food, whereas neuroimaging techniques provide valuable insight into the implicit physiological responses during food consumption. To enhance the interpretability and generalizability of implicit measurement results, further sensory studies are needed. Looking ahead, the combination of different methodological techniques in real-life situations holds promise for consumer sensory science in the field of food research.

Neurophysiological hallmarks of Huntington's disease progression: an EEG and fMRI connectivity study

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can provide corroborative data on neurophysiological alterations in Huntington's disease (HD). However, the alterations in EEG and fMRI resting-state functional connectivity (rsFC), as well as their interrelations, at different stages of HD remain insufficiently investigated. This study aimed to identify neurophysiological alterations in individuals with preclinical HD (preHD) and early manifest HD (EMHD) by analyzing EEG and fMRI rsFC and examining their interrelationships. We found significant differences in EEG power between preHD individuals and healthy controls (HC), with a decrease in power in a specific frequency range at the theta-alpha border and slow alpha activity. In EMHD patients, in addition to the decrease in power in the 7-9 Hz range, a reduction in power within the classic alpha band compared to HC was observed. The fMRI analysis revealed disrupted functional connectivity in various brain networks, particularly within frontal lobe, putamen-cortical, and cortico-cerebellar networks, in individuals with the HD mutation compared to HC. The analysis of the relationship between EEG and fMRI rsFC revealed an association between decreased alpha power, observed in individuals with EMHD, and increased connectivity in large-scale brain networks. These networks include putamen-cortical, DMN-related and cortico-hippocampal circuits. Overall, the findings suggest that EEG and fMRI provide valuable information for monitoring pathological processes during the development of HD. A decrease in inhibitory control within the putamen-cortical, DMN-related and cortico-hippocampal circuits, accompanied by a reduction in alpha and theta-alpha border oscillatory activity, could potentially contribute to cognitive decline in HD.

Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition

The recognition of inner speech, which could give a 'voice' to patients that have no ability to speak or move, is a challenge for brain-computer interfaces (BCIs). A shortcoming of the available datasets is that they do not combine modalities to increase the performance of inner speech recognition. Multimodal datasets of brain data enable the fusion of neuroimaging modalities with complimentary properties, such as the high spatial resolution of functional magnetic resonance imaging (fMRI) and the temporal resolution of electroencephalography (EEG), and therefore are promising for decoding inner speech. This paper presents the first publicly available bimodal dataset containing EEG and fMRI data acquired nonsimultaneously during inner-speech production. Data were obtained from four healthy, right-handed participants during an inner-speech task with words in either a social or numerical category. Each of the 8-word stimuli were assessed with 40 trials, resulting in 320 trials in each modality for each participant. The aim of this work is to provide a publicly available bimodal dataset on inner speech, contributing towards speech prostheses.

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.

EEG Microstate Analysis and the EEG Inverse Problem Solution as a Tool for Diagnosing Cognitive Dysfunctions in Individuals Who Have Had a Mild Form of COVID-19

The term "postcovid syndrome" is firmly entrenched in medical terminology, however, many aspects of its clinical manifestations are not well understood. The aim of this work was to find the causes of the development of cognitive dysfunctions in individuals who had a mild form of SARS-CoV-2 using high-density EEG technology and solving an inverse neurophysiological problem. A dynamic study was conducted of 38 people who had COVID-19 and returned to work. Neurophysiological studies were carried out using the EGI-GES-300 system (128 channels). The descriptive characteristics of electroencephalograms were built on the method of studying the spectral density of the EEG signal on the surface of the scalp, and the dynamic characteristics of the signal were studied by fixing EEG microstates, using the method of D. Lehmann and T. Koenig (2018). In the study, a relatively new diagnostic technique for studying cognitive impairments based on the analysis of EEG microstates was implemented, which made it possible to identify signs of functional restructuring of the neuronal macronetworks of the brain and trace the characteristic adaptation of a person during the period of convalescence. The results obtained made it possible to detect a violation of the implementation of the speech function, as a violation of the perception system (ventral information flow system), as well as the connection between the fields of Wernicke's center and Broca's center (dorsal information flow system), leading to the development of communicative dysfunctions that cause characteristic clinical symptoms due to impaired perception of new information and difficulties in implementing the solution. Thus, the survey showed that SARS-Co-V2 causes objective changes in the functional activity of the brain, which are manifested by the syndrome of cognitive dysfunction and require the development of more sensitive clinical tests than currently used.

Genetic association of apolipoprotein E genotype with EEG alpha rhythm slowing and functional brain network alterations during normal aging

The ε4 allele of the apolipoprotein E (APOE4+) genotype is a major genetic risk factor for Alzheimer’s disease (AD), but the mechanisms underlying its influence remain incompletely understood. The study aimed to investigate the possible effect of the APOE genotype on spontaneous electroencephalogram (EEG) alpha characteristics, resting-state functional MRI (fMRI) connectivity (rsFC) in large brain networks and the interrelation of alpha rhythm and rsFC characteristics in non-demented adults during aging. We examined the EEG alpha subband’s relative power, individual alpha peak frequency (IAPF), and fMRI rsFC in non-demented volunteers (age range 26–79 years) stratified by the APOE genotype. The presence of the APOE4+ genotype was associated with lower IAPF and lower relative power of the 11–13 Hz alpha subbands. The age related decrease in EEG IAPF was more pronounced in the APOE4+ carriers than in the APOE4+ non-carriers (APOE4-). The APOE4+ carriers had a stronger fMRI positive rsFC of the interhemispheric regions of the frontoparietal, lateral visual and salience networks than the APOE4– individuals. In contrast, the negative rsFC in the network between the left hippocampus and the right posterior parietal cortex was reduced in the APOE4+ carriers compared to the non-carriers. Alpha rhythm slowing was associated with the dysfunction of hippocampal networks. Our results show that in adults without dementia APOE4+ genotype is associated with alpha rhythm slowing and that this slowing is age-dependent. Our data suggest predominant alterations of inhibitory processes in large-scale brain network of non-demented APOE4+ carriers. Moreover, dysfunction of large-scale hippocampal network can influence APOE-related alpha rhythm vulnerability.

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.

On the Role of Neural Oscillations Across Timescales in Speech and Music Processing

This mini review is aimed at a clinician-scientist seeking to understand the role of oscillations in neural processing and their functional relevance in speech and music perception. We present an overview of neural oscillations, methods used to study them, and their functional relevance with respect to music processing, aging, hearing loss, and disorders affecting speech and language. We first review the oscillatory frequency bands and their associations with speech and music processing. Next we describe commonly used metrics for quantifying neural oscillations, briefly touching upon the still-debated mechanisms underpinning oscillatory alignment. Following this, we highlight key findings from research on neural oscillations in speech and music perception, as well as contributions of this work to our understanding of disordered perception in clinical populations. Finally, we conclude with a look toward the future of oscillatory research in speech and music perception, including promising methods and potential avenues for future work. We note that the intention of this mini review is not to systematically review all literature on cortical tracking of speech and music. Rather, we seek to provide the clinician-scientist with foundational information that can be used to evaluate and design research studies targeting the functional role of oscillations in speech and music processing in typical and clinical populations.

Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?

Simultaneous EEG-fMRI has developed into a mature measurement technique in the past 25 years. During this time considerable technical and analytical advances have been made, enabling valuable scientific contributions to a range of research fields. This review will begin with an introduction to the measurement principles involved in EEG and fMRI and the advantages of combining these methods. The challenges faced when combining the two techniques will then be considered. An overview of the leading application fields where EEG-fMRI has made a significant contribution to the scientific literature and emerging applications in EEG-fMRI research trends is then presented.