EEG-fMRI Research Articles

Brain state dynamics differ between eyes open and eyes closed rest.

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Mesial temporal lobe spiking reveals distinct patterns of blood oxygen level-dependent functional magnetic resonance imaging activation using simultaneous intracranial electroencephalography-functional magnetic resonance imaging.

Temporal lobe epilepsy (TLE) has a high probability of becoming drug resistant and is frequently considered for surgical intervention. However, 30% of TLE cases have nonlesional magnetic resonance imaging (MRI) scans, which is associated with worse surgical outcomes. Characterizing interactions between temporal and extratemporal structures in these patients may help understand these poor outcomes. Simultaneous intracranial electroencephalography-functional MRI (iEEG-fMRI) can measure the hemodynamic changes associated with interictal epileptiform discharges (IEDs) recorded directly from the brain. This study was designed to characterize the whole brain patterns of IED-associated fMRI activation recorded exclusively from the mesial temporal lobes of patients with nonlesional TLE. Eighteen patients with nonlesional TLE undergoing iEEG monitoring with mesial temporal IEDs underwent simultaneous iEEG-fMRI at 3 T. IEDs were marked, and statistically significant clusters of fMRI activation were identified. The locations of IED-associated fMRI activation for each patient were determined, and patients were grouped based on the location and pattern of fMRI activation. Two patterns of IED-associated fMRI activation emerged: primarily localized (n = 7), where activation was primarily located within the ipsilateral temporal lobe, and primarily diffuse (n = 11), where widespread bilateral extratemporal activation was detected. The primarily diffuse group reported significantly fewer focal to bilateral tonic-clonic seizures and had better postsurgical outcomes. Simultaneous iEEG-fMRI can measure the hemodynamic changes associated with focal IEDs not visible on scalp EEG, such as those arising from the mesial temporal lobe. Significant fMRI activation associated with these IEDs was observed in all patients. Two distinct patterns of IED-associated activation were seen: primarily localized to the ipsilateral temporal lobe and more widespread, bilateral activation. Patients with widespread IED associated-activation had fewer focal to bilateral tonic-clonic seizures and better postsurgical outcome, which may suggest a neuroprotective mechanism limiting the spread of ictal events.

Altered dynamic functional connectivity of motor cerebellum with sensorimotor network and default mode network in juvenile myoclonic epilepsy.

To investigate whether changes occur in the dynamic functional connectivity (dFC) of motor cerebellum with cerebral cortex in juvenile myoclonic epilepsy (JME). We adopted resting-state electroencephalography-functional magnetic resonance imaging (EEG-fMRI) and a sliding-window approach to explore the dFC of motor cerebellum with cortex in 36 JME patients compared with 30 and age-matched health controls (HCs). The motor cerebellum was divided into five lobules (I-V, VI, VIIb, VIIIa, and VIIIb). Additionally, correlation analyses were conducted between the variability of dFC and clinical variables in the Juvenile Myoclonic Epilepsy (JME) group, such as disease duration, age at disease onset, and frequency score of myoclonic seizures. Compared to HCs, the JME group presented increased dFC between the motor cerebellum with SMN and DMN. Specifically, connectivity between lobule VIIb and left precentral gyrus and right inferior parietal lobule (IPL); between lobule VIIIa and right inferior frontal gyrus (IFG) and left IPL; and between lobule VIIIb and left middle frontal gyrus (MFG), bilateral superior parietal gyrus (SPG), and left precuneus. In addition, within the JME group, the strength of dFC between lobule VIIIb and left precuneus was negatively (r = -0.424, p = 0.025, Bonferroni correction) related with the frequency score of myoclonic seizures. In patients with JME, there is a functional dysregulation between the motor cerebellum with DMN and SMN, and the variability of dynamic functional connectivity may be closely associated with the occurrence of motor symptoms in JME.

Simultaneous EEG-fMRI Investigation of Rhythm-Dependent Thalamo-Cortical Circuits Alteration in Schizophrenia.

Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional magnetic resonance imaging data, the effect of rhythm modulations on FC was explored using what we term "dynamic rhythm information." We also investigated the synergistic relationships among three networks under rhythm modulation conditions, where this relationship presents the coupling between two brain networks with other networks as the center by the rhythm modulation. This study found FC between the thalamus and cortical network regions was rhythm-specific. Further, the effects of the thalamus on the default mode network (DMN) and salience network (SN) were less similar under alpha rhythm modulation in schizophrenia patients than in controls ([Formula: see text]). However, the similarity between the effects of the central executive network (CEN) on the DMN and SN under gamma modulation was greater ([Formula: see text]), and the degree of coupling was negatively correlated with the duration of disease ([Formula: see text], [Formula: see text]). Moreover, schizophrenia patients exhibited less coupling with the thalamus as the center and greater coupling with the CEN as the center. These results indicate that modulations in dynamic rhythms might contribute to the disordered functional interactions seen in schizophrenia.

Electroencephalography-functional magnetic resonance imaging for clinical evaluation in focal epilepsy.

We aimed to evaluate the contribution of simultaneous recording of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) in the diagnosis of epilepsy syndrome, localization of the epileptogenic zone (EZ), and decision-making regarding surgical treatment. We performed a retrospective study to evaluate patients with focal epilepsy who underwent EEG-fMRI. Two evaluators assessed epilepsy syndrome, presumed focus, and surgical candidacy and defined confidence levels. They assessed these clinical characteristics first without EEG-fMRI and then including EEG-fMRI to assess how the results of EEG-fMRI changed the evaluations. We also determined how the clinical evaluation was affected by the concordance level between the blood oxygen level-dependent (BOLD) response and the presumed focus location, and by the confidence level of the BOLD response itself based on the t-value of the primary and secondary clusters. Fifty-one scans from 48 patients were included. The BOLD map affected 66.7% of the evaluations by altering evaluation items (epilepsy syndrome, presumed focus, or surgical candidacy) or their confidence levels. EEG-fMRI results increased the confidence levels of epilepsy syndrome, presumed focus, or surgical candidacy in 47.1% of patients but reduced clinical confidence in these features in 11.8%. More specifically, the confidence levels increased for epilepsy syndrome in 28.5%, identification of presumed focus in 33.9%, and determination of surgical candidacy in 29.4%. The BOLD signal confidence level, whether high or low, did not influence these clinical factors. Previous studies have emphasized the utility of EEG-fMRI for the localization of the epileptogenic zone. This study demonstrated the potential of EEG-fMRI to influence clinical confidence when determining epilepsy syndrome, the presumed epileptic focus, and surgical candidacy.

Prediction of the epileptic seizure through deep learning techniques using electroencephalography

<span>Electroencephalography (EEG) is a widely used and significant technique for aiding in epilepsy diagnosis and investigating the electrical patterns of the human brain. Due to the non-stationary nature of EEG signals, seizure patterns will vary across different recording sessions for individual patients. In this study, a new deep learning long short-term memory (LSTM) model is implemented for the detection of brain tumors and seizures. The process consists of four key steps: EEG signal pre-processing, preictal feature extraction, hyper optimization using grey wolf optimization (GWO), and LSTM-based classification. The evaluation makes use of long-term EEG recordings from the EEG and ABIDE fMRI datasets. By experimenting with various modules and layers of memory units, a pre-analysis is first conducted to determine the best LSTM network architecture. The LSTM model makes use of numerous retrieved features, including temporal and frequency domain information between EEG channels that were extracted before classification. The discovery of the implemented methodology revealed significant advantages in accurately predicting seizures while minimizing false alarms. The implemented LSTM method achieves a 99% accuracy rate, 98% precision, 99% recall, and 98% f1-measure which is better when compared with cross sub-pattern correlation-based principal component analysis (SUBXPCA) and gradient-boosting decision tree (GBDT) methods.</span>

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.

Comparison of Resting-State EEG Network Analyses With and Without Parallel MRI in Genetic Generalized Epilepsy

Genetic generalized epilepsy (GGE) is conceptualized as a brain disorder involving distributed bilateral networks. To study these networks, simultaneous EEG-fMRI measurements can be used. However, inside-MRI EEG suffers from strong MR-related artifacts; it is not established whether EEG-based metrics in EEG-fMRI resting-state measurements are suitable for the analysis of group differences at source-level. We evaluated the impact of the inside-MR measurement condition on statistical group comparisons of EEG on source-level power and functional connectivity in patients with GGE versus healthy controls. We studied the cross-modal spatial relation of statistical group differences in seed-based FC derived from EEG and parallel fMRI. We found a significant increase in power and a frequency-specific change in functional connectivity for the inside MR-scanner compared to the outside MR-scanner condition. For power, we found reduced group difference between GGE and controls both in terms of statistical significance as well as effect size. Group differences for ImCoh remained similar both in terms of statistical significance as well as effect size. We found increased seed-based FC for GGE patients from the thalamus to the precuneus cortex region in fMRI, and in the theta band of simultaneous EEG. Our findings suggest that the analysis of EEG functional connectivity based on ImCoh is suitable for MR-EEG, and that relative group difference in a comparison of patients with GGE against controls are preserved. Spatial correspondence of seed-based FC group differences between the two modalities was found for the thalamus.

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.

Functional Neuroimaging in Patients With Disorders of Consciousness: Caution Advised.

Functional Neuroimaging in Patients With Disorders of Consciousness: Caution Advised.

Physical behavior of PEDOT polymer electrode during magnetic resonance imaging and long-term test in the climate chamber

The PEDOT polymer electrode is a metal-free electrode, consisting of an acrylate (dental composite) and the conductive polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). The electrode is applied as gel onto the skin and cured with blue light for 10–20 s in order to achieve a conductive bond to the skin. The electrodes are used in combination with polymer cables consisting of a textile backbone and PEDOT:PSS. To test this new electrode and cable type under different conditions we designed two stress-tests: highly sensitive temperature recordings within a head phantom during Magnetic Resonance Imaging (MRI) and long-term stability inside a climate chamber with high humidity. To study the physical behavior inside the strong magnetic field (3 Tesla), the PEDOT polymer electrode was attached to an agarose head-phantom inside a magnetic resonance tomograph during an image sequence. MRI-safe temperature sensors were placed nearby in order to measure possible heating effects. In comparison to a metal cable, nearly no rise in temperature could be observed if the electrode was used in combination with a conductive textile cable. Furthermore, the electrode showed stable impedance values inside a climate chamber for 4 consecutive days. These results pave the way for testing the PEDOT polymer electrode as biosignal recording electrode during MRI, especially for cardio MRI and Electroencephalography in combination with functional MRI (EEG–fMRI).

Exploring Neural Mechanisms of Reward Processing Using Coupled Matrix Tensor Factorization: A Simultaneous EEG-fMRI Investigation.

It is crucial to understand the neural feedback mechanisms and the cognitive decision-making of the brain during the processing of rewards. Here, we report the first attempt for a simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) study in a gambling task by utilizing tensor decomposition. First, the single-subject EEG data are represented as a third-order spectrogram tensor to extract frequency features. Next, the EEG and fMRI data are jointly decomposed into a superposition of multiple sources characterized by space-time-frequency profiles using coupled matrix tensor factorization (CMTF). Finally, graph-structured clustering is used to select the most appropriate model according to four quantitative indices. The results clearly show that not only are the regions of interest (ROIs) found in other literature activated, but also the olfactory cortex and fusiform gyrus which are usually ignored. It is found that regions including the orbitofrontal cortex and insula are activated for both winning and losing stimuli. Meanwhile, regions such as the superior orbital frontal gyrus and anterior cingulate cortex are activated upon winning stimuli, whereas the inferior frontal gyrus, cingulate cortex, and medial superior frontal gyrus are activated upon losing stimuli. This work sheds light on the reward-processing progress, provides a deeper understanding of brain function, and opens a new avenue in the investigation of neurovascular coupling via CMTF.

Spatio-temporal dynamics of resting-state brain networks are associated with migraine disability

ObjectiveThe changes in resting-state functional networks and their correlations with clinical traits remain to be clarified in migraine. Here we aim to investigate the brain spatio-temporal dynamics of resting-state networks and their possible correlations with the clinical traits in migraine.MethodsTwenty Four migraine patients without aura and 26 healthy controls (HC) were enrolled. Each included subject underwent a resting-state EEG and echo planar imaging examination. The disability of migraine patients was evaluated by Migraine Disability Assessment (MIDAS). After data acquisition, EEG microstates (Ms) combining functional connectivity (FC) analysis based on Schafer 400-seven network atlas were performed. Then, the correlation between obtained parameters and clinical traits was investigated.ResultsCompared with HC group, the brain temporal dynamics depicted by microstates showed significantly increased activity in functional networks involving MsB and decreased activity in functional networks involving MsD; The spatial dynamics were featured by decreased intra-network FC within the executive control network( ECN) and inter-network FC between dorsal attention network (DAN) and ECN (P < 0.05); Moreover, correlation analysis showed that the MIDAS score was positively correlated with the coverage and duration of MsC, and negatively correlated with the occurrence of MsA; The FC within default mode network (DMN), and the inter-FC of ECN- visual network (VN), ECN- limbic network, VN-limbic network was negatively correlated with MIDAS. However, the FC of DMN-ECN was positively correlated with MIDAS; Furthermore, significant interactions between the temporal and spatial dynamics were also obtained.ConclusionsOur study confirmed the notion that altered spatio-temporal dynamics exist in migraine patients during resting-state. And the temporal dynamics, the spatial changes and the clinical traits such as migraine disability interact with each other. The spatio-temporal dynamics obtained from EEG microstate and fMRI FC analyses may be potential biomarkers for migraine and with a huge potential to change future clinical practice in migraine.

Increased connectivity of the anterior cingulate cortex is associated with the tendency to awakening during N2 sleep in patients with insomnia disorder.

To investigate the relationship between sleep transition dynamics and stage-specific functional connectivity (FC) of the anterior cingulate cortex (ACC) in patients with insomnia disorder (ID). Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) data from 37 patients with ID and 30 well-matched healthy controls (HCs) were recorded during wakefulness and different sleep stages and subsequently analyzed. A Markov chain model was used to estimate the transition probability between each stage. The FC between the ACC (set as the seed) and voxels across the whole brain was calculated. A linear mixed effect model was used to determine the group-by-stage interaction of the seed-based connectivity. The correlation between the sleep-stage transition probability and the ACC-based connectivity was explored. Patients with ID exhibited a higher likelihood of transitioning from N2 to wakefulness than HCs. A significant group-by-stage interaction of connectivity with the bilateral ACC was observed in the cerebellar, subcortical, and cortical regions. Moreover, a significant positive correlation was found in patients with ID between the transition probability from N2 to wakefulness and the FC of the ACC with the anterior cerebellum in N2 (r = 0.48). This exploratory analysis indicates that enhanced FC between the ACC and cerebellum represents a potential neural pathway underlying the greater likelihood of patients with ID waking during N2 sleep. These findings contribute to an emerging framework that reveals the link between sleep maintenance difficulty and ACC function, further highlighting the possibility that N2 sleep is a therapeutic target for meaningfully reducing sleep disruption.

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.

Electroencephalography-functional magnetic resonance imaging of epileptiform discharges: Noninvasive investigation of the whole brain.

Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) is a unique and noninvasive method for investigating epileptic activity. Interictal epileptiform discharge-related EEG-fMRI provides cortical and subcortical blood oxygen level-dependent (BOLD) signal changes specific to epileptic discharges. As a result, EEG-fMRI has revealed insights into generators and networks involved in epileptic activity in different types of epilepsy, demonstrating-for instance-the implication of the thalamus in human generalized spike and wave discharges and the role of the default mode network in absences and focal epilepsy, and has suggested a mechanism for the cortico-subcortical interactions in Lennox-Gastaut syndrome discharges. EEG-fMRI can find deep sources of epileptic activity not available to scalp EEG or magnetoencephalography, and provides critical new information to delineate the epileptic focus when considering surgical treatment or electrode implantation. In recent years, methodological advances, such as artifact removal and automatic detection of events, have rendered this method easier to implement, and its clinical potential has since been established by evidence of the impact of BOLD response on clinical decision-making and of the relationship between concordance of BOLD responses with extent of resection and surgical outcome. This review presents the recent developments in EEG-fMRI methodology and EEG-fMRI studies in different types of epileptic disorders as follows: EEG-fMRI acquisition, gradient and pulse artifact removal, statistical analysis, clinical applications, presurgical evaluation, altered physiological state in generalized genetic epilepsy, and pediatric EEG-fMRI studies.

Psychedelic Psychiatry

The last decade has seen a remarkable resurgence of interest in psychedelic drugs such as psilocybin (from magic mushrooms) LSD and DMT (dimethyl tryptamine – the active ingredient of ayahuasca). This has been driven by the discovery that these psychedelics all act agonists of 5-HT2A receptors. Human imaging studies have revealed this action leads to profound alterations in brain measures of activity particularly in terms of increased entropy of EEG MEG and fMRI signals and reduced within-network, but increased between-network, connectivity. In addition they all can increase synaptic growth and brain plasticity. These findings not only explain the subjective nature of the psychedelic experience but also have implications for the treatment of internalising disorders such as depression addiction anorexia and OCD that are characterised by increased within network connectivity especially of the default mode network. Subsequent trials, particularly of psilocybin, in these disorders has revealed significant clinical benefits from even just a single administration. A number of companies have now been set up to extend these discoveries with regulatory-level trials that could result in market authorisations within a few years. My talk will explore these brain mechanisms and clinical data and discuss the potential place of psychedelic medicine in the future of psychiatry.DisclosureI am an advisor to Compass pathways and Beckley Psytec two companies that are developing psychedelics for depression and other psychiatric indications. Several members of my research group receive support from these companies and also from Small Pharma.

Tracking of rigid head motion during MRI using anEEG system.

PurposeTo demonstrate a novel method for tracking of head movements during MRI using electroencephalography (EEG) hardware for recording signals induced by native imaging gradients.Theory and MethodsGradient switching during simultaneous EEG–fMRI induces distortions in EEG signals, which depend on subject head position and orientation. When EEG electrodes are interconnected with high‐impedance carbon wire loops, the induced voltages are linear combinations of the temporal gradient waveform derivatives. We introduce head tracking based on these signals (CapTrack) involving 3 steps: (1) phantom scanning is used to characterize the target sequence and a fast calibration sequence; (2) a linear relation between changes of induced signals and head pose is established using the calibration sequence; and (3) induced signals recorded during target sequence scanning are used for tracking and retrospective correction of head movement without prolonging the scan time of the target sequence. Performance of CapTrack is compared directly to interleaved navigators.ResultsHead‐pose tracking at 27.5 Hz during echo planar imaging (EPI) was demonstrated with close resemblance to rigid body alignment (mean absolute difference: [0.14 0.38 0.15]‐mm translation, [0.30 0.27 0.22]‐degree rotation). Retrospective correction of 3D gradient‐echo imaging shows an increase of average edge strength of 12%/−0.39% for instructed/uninstructed motion with CapTrack pose estimates, with a tracking interval of 1561 ms and high similarity to interleaved navigator estimates (mean absolute difference: [0.13 0.33 0.12] mm, [0.28 0.15 0.22] degrees).ConclusionMotion can be estimated from recordings of gradient switching with little or no sequence modification, optionally in real time at low computational burden and synchronized to image acquisition, using EEG equipment already found at many research institutions.

The neural correlates of amplitude of low-frequency fluctuation: a multimodal resting-state MEG and fMRI-EEG study.

The amplitude of low-frequency fluctuation (ALFF) describes the regional intensity of spontaneous blood-oxygen-level-dependent signal in resting-state functional magnetic resonance imaging (fMRI). How the fMRI-ALFF relates to the amplitude in electrophysiological signals remains unclear. We here aimed to investigate the neural correlates of fMRI-ALFF by comparing the spatial difference of amplitude between the eyes-closed (EC) and eyes-open (EO) states from fMRI and magnetoencephalography (MEG), respectively. By synthesizing MEG signal into amplitude-based envelope time course, we first investigated 2 types of amplitude in MEG, meaning the amplitude of neural activities from delta to gamma (i.e. MEG-amplitude) and the amplitude of their low-frequency modulation at the fMRI range (i.e. MEG-ALFF). We observed that the MEG-ALFF in EC was increased at parietal sensors, ranging from alpha to beta; whereas the MEG-amplitude in EC was increased at the occipital sensors in alpha. Source-level analysis revealed that the increased MEG-ALFF in the sensorimotor cortex overlapped with the most reliable EC-EO differences observed in fMRI at slow-3 (0.073-0.198Hz), and these differences were more significant after global mean standardization. Taken together, our results support that (i) the amplitude at 2 timescales in MEG reflect distinct physiological information and that (ii) the fMRI-ALFF may relate to the ALFF in neural activity.

Resting-state functional magnetic resonance imaging signal variations in aging: The role of neural activity.

Resting‐state functional magnetic resonance imaging (rs‐fMRI) has been extensively used to study brain aging, but the age effect on the frequency content of the rs‐fMRI signal has scarcely been examined. Moreover, the neuronal implications of such age effects and age–sex interaction remain unclear. In this study, we examined the effects of age and sex on the rs‐fMRI signal frequency using the Leipzig mind–brain–body data set. Over a frequency band of up to 0.3 Hz, we found that the rs‐fMRI fluctuation frequency is higher in the older adults, although the fluctuation amplitude is lower. The rs‐fMRI signal frequency is also higher in men than in women. Both age and sex effects on fMRI frequency vary with the frequency band examined but are not found in the frequency of physiological‐noise components. This higher rs‐fMRI frequency in older adults is not mediated by the electroencephalograph (EEG)‐frequency increase but a likely link between fMRI signal frequency and EEG entropy, which vary with age and sex. Additionally, in different rs‐fMRI frequency bands, the fMRI‐EEG amplitude ratio is higher in young adults. This is the first study to investigate the neuronal contribution to age and sex effects in the frequency dimension of the rs‐fMRI signal and may lead to the development of new, frequency‐based rs‐fMRI metrics. Our study demonstrates that Fourier analysis of the fMRI signal can reveal novel information about aging. Furthermore, fMRI and EEG signals reflect different aspects of age‐ and sex‐related brain differences, but the signal frequency and complexity, instead of amplitude, may hold their link.