Mapping function in the human brain with magnetoencephalography, anatomical magnetic resonance imaging, and functional magnetic resonance imaging

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.

A resting-state network for novelty: Similar involvement of a global network under rest and task conditions

Neuroimaging research provides converging evidence in support of functional networks active under rest conditions. While these networks are typically locally-distributed, a globally-distributed resting-state network (gRSN) was recently identified. The gRSN component is characterized by a scalp topography similar to that of the widely-studied P3 component of the event related potential, thought to represent the brain's response to novelty. In this study, we investigate similarities between the neural generators underlying these two networks to test the hypothesis that the gRSN is a resting-state network for novelty. By using the second-order blind identification (SOBI) algorithm, which works with temporal information, we show that (1) a resting-state component resembling the topography of the P3 can be recovered in all participants; (2) this gRSN component can be modeled with a set of ECDs with high goodness of fit; (3) ECD locations of the gRSN correspond to a network of globally-distributed brain structures overlapping heavily with the networking underlying the P3; and, (4) structures underlying these two networks are similarly involved during task and resting-state conditions. We interpret this as evidence in support of a resting-state network for detection and response to novelty.

Investigation of fMRI Analysis Method to Visualize the Difference in the Brain Activation Timing

RATIONALE AND OBJECTIVES

In general functional magnetic resonance imaging (fMRI) analysis, the task onset time of the statistical model is typically set according to the timing of stimulation. In this study, using a high temporal resolution fMRI data, we examined the way of dynamically visualizing the difference in the activation timing between the brain activation areas by analyzing the task onset time of the statistical model shifted from the actual stimulation timing.

MATERIALS AND METHODS

fMRI data with high temporal resolution was acquired using 3 T magnetic resonance imaging for 10 right-handed healthy volunteers. While being scanned, the volunteers completed a task that comprised two sets of a rest and right hand grip movement task. Statistical Parametric Mapping 12 (SPM12) software was used to analyze fMRI data. After preprocessing, statistical analyses were performed by shifting the task onset time on the statistical model by about 1 second forward or backward from the actual stimulation timing. Activation maps of multiple time phases were then created.

RESULTS

Activity was observed to the left of the primary motor area and the supplementary motor area and to the right of the cerebellum (familywise error rate, P < .05). In the right hand grip movement, the primary motor area and the supplementary motor area were activated from 1.12 to 4.48 seconds earlier than the cerebellum.

CONCLUSIONS

Using this analysis method, we visualized the differences in activation timings of different areas of the brain.

A systematic review investigating the relationship of electroencephalography and magnetoencephalography measurements with sensorimotor upper limb impairments after stroke

BACKGROUND

Predicting sensorimotor upper limb outcome receives continued attention in stroke. Neurophysiological measures by electroencephalography (EEG) and magnetoencephalography (MEG) could increase the accuracy of predicting sensorimotor upper limb recovery.

NEW METHOD

The aim of this systematic review was to summarize the current evidence for EEG/MEG-based measures to index neural activity after stroke and the relationship between abnormal neural activity and sensorimotor upper limb impairment. Relevant papers from databases EMBASE, CINHAL, MEDLINE and pubMED were identified. Methodological quality of selected studies was assessed with the Modified Downs and Black form. Data collected was reported descriptively.

RESULTS

Seventeen papers were included; 13 used EEG and 4 used MEG applications. Findings showed that: (a) the presence of somatosensory evoked potentials in the acute stage are related to better outcome of upper limb motor impairment from 10 weeks to 6 months post-stroke; (b) an interhemispheric imbalance of cortical oscillatory signals associated with upper limb impairment; and (c) predictive models including beta oscillatory cortical signal factors with corticospinal integrity and clinical measures could enhance upper limb motor prognosis.

COMPARING WITH EXISTING METHOD

The combination of neurological biomarkers with clinical measures results in higher statistical power than using neurological biomarkers alone when predicting motor recovery in stroke.

CONCLUSIONS

Alterations in neural activity by means of EEG and MEG are demonstrated from the early post-stroke stage onwards, and related to sensorimotor upper limb impairment. Future work exploring cortical oscillatory signals in the acute stage could provide further insight about prediction of upper limb sensorimotor recovery.

Cortical layers, rhythms and BOLD signals

This review investigates how laminar fMRI can complement insights into brain function derived from the study of rhythmic neuronal synchronization. Neuronal synchronization in various frequency bands plays an important role in neuronal communication between brain areas, and it does so on the backbone of layer-specific interareal anatomical projections. Feedforward projections originate predominantly in supragranular cortical layers and terminate in layer 4, and this pattern is reflected in inter-laminar and interareal directed gamma-band influences. Thus, gamma-band synchronization likely subserves feedforward signaling. By contrast, anatomical feedback projections originate predominantly in infragranular layers and terminate outside layer 4, and this pattern is reflected in inter-laminar and interareal directed alpha- and/or beta-band influences. Thus, alpha-beta band synchronization likely subserves feedback signaling. Furthermore, these rhythms explain part of the BOLD signal, with independent contributions of alpha-beta and gamma. These findings suggest that laminar fMRI can provide us with a potentially useful method to test some of the predictions derived from the study of neuronal synchronization. We review central findings regarding the role of layer-specific neuronal synchronization for brain function, and regarding the link between neuronal synchronization and the BOLD signal. We discuss the role that laminar fMRI could play by comparing it to invasive and non-invasive electrophysiological recordings. Compared to direct electrophysiological recordings, this method provides a metric of neuronal activity that is slow and indirect, but that is uniquely non-invasive and layer-specific with potentially whole brain coverage.

Neuronal synchrony and the relation between the blood-oxygen-level dependent response and the local field potential

The most widespread measures of human brain activity are the blood-oxygen-level dependent (BOLD) signal and surface field potential. Prior studies report a variety of relationships between these signals. To develop an understanding of how to interpret these signals and the relationship between them, we developed a model of (a) neuronal population responses and (b) transformations from neuronal responses into the functional magnetic resonance imaging (fMRI) BOLD signal and electrocorticographic (ECoG) field potential. Rather than seeking a transformation between the two measures directly, this approach interprets each measure with respect to the underlying neuronal population responses. This model accounts for the relationship between BOLD and ECoG data from human visual cortex in V1, V2, and V3, with the model predictions and data matching in three ways: across stimuli, the BOLD amplitude and ECoG broadband power were positively correlated, the BOLD amplitude and alpha power (8-13 Hz) were negatively correlated, and the BOLD amplitude and narrowband gamma power (30-80 Hz) were uncorrelated. The two measures provide complementary information about human brain activity, and we infer that features of the field potential that are uncorrelated with BOLD arise largely from changes in synchrony, rather than level, of neuronal activity.

Changes in functional magnetic resonance imaging with Yogic meditation: A pilot study

Background:

The neural substrates of Yogic meditation are not well understood. Meditation is theorized to be a conscious mental process that induces a set of complex physiological changes within the areas of the brain termed as the “relaxation response.”

Aims and objective:

Pilot data of a functional magnetic resonance imaging (fMRI) study is presented to observe and understand the selective activations of designated brain regions during meditation.

Material and methods:

Four trained healthy Patanjali Yoga practitioners in their mid-60s participated in this prototype interventional study. A three-part 1-min block design alternating between meditation (test) and relaxation (control) phase with an imaginary visual fixation and auditory stimulation was used.

Result and observation:

The fMRI images revealed strong activation in the right prefrontal regions during the visual and auditory fixation meditation phases compared to no activations during the relaxation phase. A comparison between the visual and auditory fixations revealed shifts within the prefrontal and temporal regions. In addition, activation in occipital and temporal regions was observed during the meditation phase. Occipital lobe activation was more apparent during visual meditation phase.

Conclusion:

It is concluded that specific fMRI brain activations are observed during different forms of Yogic meditation (visual and auditory phases). Occipital and prefrontal activation could be modulating the known neurophysiological and biological effects of meditation.

Incorporating priors for EEG source imaging and connectivity analysis

Electroencephalography source imaging (ESI) is a useful technique to localize the generators from a given scalp electric measurement and to investigate the temporal dynamics of the large-scale neural circuits. By introducing reasonable priors from other modalities, ESI reveals the most probable sources and communication structures at every moment in time. Here, we review the available priors from such techniques as magnetic resonance imaging (MRI), functional MRI (fMRI), and positron emission tomography (PET). The modality's specific contribution is analyzed from the perspective of source reconstruction. For spatial priors, EEG-correlated fMRI, temporally coherent networks (TCNs) and resting-state fMRI are systematically introduced in the ESI. Moreover, the fiber tracking (diffusion tensor imaging, DTI) and neuro-stimulation techniques (transcranial magnetic stimulation, TMS) are also introduced as the potential priors, which can help to draw inferences about the neuroelectric connectivity in the source space. We conclude that combining EEG source imaging with other complementary modalities is a promising approach toward the study of brain networks in cognitive and clinical neurosciences.

Magnetic resonance imaging markers for early diagnosis of Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by selective and progressive degeneration, as well as loss of dopaminergic neurons in the substantia nigra. In PD, approximately 60-70% of nigrostriatal neurons are degenerated and 80% of content of the striatal dopamine is reduced before the diagnosis can be established according to widely accepted clinical diagnostic criteria. This condition describes a stage of disease called “prodromal”, where non-motor symptoms, such as olfactory dysfunction, constipation, rapid eye movement behaviour disorder, depression, precede motor sign of PD. Detection of prodromal phase of PD is becoming an important goal for determining the prognosis and choosing a suitable treatment strategy. In this review, we present some non-invasive instrumental approaches that could be useful to identify patients in the prodromal phase of PD or in an early clinical phase, when the first motor symptoms begin to be apparent. Conventional magnetic resonance imaging (MRI) and advanced MRI techniques, such as magnetic resonance spectroscopy imaging, diffusion-weighted and diffusion tensor imaging and functional MRI, are useful to differentiate early PD with initial motor symptoms from atypical parkinsonian disorders, thus, making easier early diagnosis. Functional MRI and diffusion tensor imaging techniques can show abnormalities in the olfactory system in prodromal PD.

fMRI hemodynamics accurately reflects neuronal timing in the human brain measured by MEG

Neuronal activation sequence information is essential for understanding brain functions. Extracting such timing information from blood oxygenation level dependent (BOLD) fMRI is confounded by interregional neurovascular differences and poorly understood relations between BOLD and electrophysiological response delays. Here, we recorded whole-head BOLD fMRI at 100 ms resolution and magnetoencephalography (MEG) during a visuomotor reaction-time task. Both methods detected the same activation sequence across five regions, from visual towards motor cortices, with linearly correlated interregional BOLD and MEG response delays. The smallest significant interregional BOLD delay was 100 ms; all delays ≥400 ms were significant. Switching the order of external events reversed the sequence of BOLD activations, indicating that interregional neurovascular differences did not confound the results. This may open new avenues for using fMRI to follow rapid activation sequences in the brain.

Potential utility of resting-state magnetoencephalography as a biomarker of CNS abnormality in HIV disease

There is a lack of a neuroimaging biomarker for HIV-Associated Neurocognitive Disorder. We report magnetoencephalography (MEG) data from patients with HIV disease and risk-group appropriate controls that were collected to determine the MEG frequency profile during the resting state, and the stability of the profile over 24 weeks. 17 individuals (10 HIV+, 7 HIV-) completed detailed neurobehavioral evaluations and 10min of resting-state MEG acquisition with a 306-channel whole-head system. The entire evaluation and MEG measurement were repeated 24 weeks later. Relative MEG power in the delta (0-4Hz), theta (4-7Hz), alpha (8-12Hz), beta (12-30Hz) and low gamma (30-50Hz) bands was computed for 8 predefined sensor groups. The median stability of resting-state relative power over 24 weeks of follow-up was .80 with eyes closed, and .72 with eyes open. The relative gamma power in the right occipital (t(15)=1.99, p<.06, r=-.46) and right frontal (t(15)=2.15, p<.05, r=-.48) regions was associated with serostatus. The effect of age on delta power was greater in the seropositive subjects (r(2)=.51) than in the seronegative subjects (r(2)=.11). Individuals with high theta-to-gamma ratios tended to have lower cognitive test performance, regardless of serostatus. The stability of the wide-band MEG frequency profiles over 24 weeks supports the utility of MEG as a biomarker. The links between the MEG profile, serostatus, and cognition suggest further research on its potential in HAND is needed.

Complementary image analysis of diffusion tensor imaging and 3-dimensional t1-weighted imaging: white matter analysis in amyotrophic lateral sclerosis

INTRODUCTION

in order to obtain detailed information on disease-associated changes in the integrity of cerebral white matter (WM), complementary image analysis (CIA) was applied to patients with amyotrophic lateral sclerosis (ALS) and controls.

METHODS

both diffusion tensor imaging and T1-weighted 3-dimensional data were analyzed with respect to WM microstructure and T1 signal intensity alterations, respectively, in a sample of 19 ALS patients. Covariate information was added in the form of clinical parameters. All results were obtained in one common analysis software environment (Tensor Imaging and Fiber Tracking).

RESULTS

complementary analysis and display were performed for WM directionality and structure. Significant WM differences between ALS patients and controls were observed both in the motor system, that is, the bilateral corticospinal tracts, and in extramotor brain areas, in part correlating with clinical parameters. The performance of all analyses in one software environment enabled the synopsis of results obtained from various analyses.

DISCUSSION/CONCLUSION

within the application of CIA to a neurodegenerative disease for the whole brain-based analysis of WM alterations together with clinical characteristics, it could be demonstrated that ALS was associated with WM changes within and outside the motor system.

Magnetoencephalography as a putative biomarker for Alzheimer's disease

Alzheimer's Disease (AD) is the most common dementia in the elderly and is estimated to affect tens of millions of people worldwide. AD is believed to have a prodromal stage lasting ten or more years. While amyloid deposits, tau filaments, and loss of brain cells are characteristics of the disease, the loss of dendritic spines and of synapses predate such changes. Popular preclinical detection strategies mainly involve cerebrospinal fluid biomarkers, magnetic resonance imaging, metabolic PET scans, and amyloid imaging. One strategy missing from this list involves neurophysiological measures, which might be more sensitive to detect alterations in brain function. The Magnetoencephalography International Consortium of Alzheimer's Disease arose out of the need to advance the use of Magnetoencephalography (MEG), as a tool in AD and pre-AD research. This paper presents a framework for using MEG in dementia research, and for short-term research priorities.

Inferior alveolar and lingual nerve imaging

At present, there are no objective testing modalities available for evaluation of iatrogenic injury to the terminal branches of the trigeminal nerve, making such clinical diagnosis and management complicated for the oral and maxillofacial surgeon. Several imaging modalities can assist in the preoperative risk assessment of the trigeminal nerve as related to commonly performed procedures in the vicinity of the nerve, mostly third molar surgery. This article provides a review of all available imaging modalities and their clinical application relative to preoperative injury risk assessment of the inferior alveolar nerve and lingual nerve, and postinjury and postsurgical repair recovery status.

Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors

In this paper, we propose a novel symmetrical EEG/fMRI fusion method which combines EEG and fMRI by means of a common generative model. We use a total variation (TV) prior to model the spatial distribution of the cortical current responses and hemodynamic response functions, and utilize spatially adaptive temporal priors to model their temporal shapes. The spatial adaptivity of the prior model allows for adaptation to the local characteristics of the estimated responses and leads to high estimation performance for the cortical current distribution and the hemodynamic response functions. We utilize a Bayesian formulation with a variational Bayesian framework and obtain a fully automatic fusion algorithm. Simulations with synthetic data and experiments with real data from a multimodal study on face perception demonstrate the performance of the proposed method.

Selective averaging of rapidly presented individual trials using fMRI

A major limitation in conducting functional neuroimaging studies, particularly for cognitive experiments, has been the use of blocked task paradigms. Here we explored whether selective averaging techniques similar to those applied in event-related potential (ERP) experiments could be used to demonstrate functional magnetic resonance imaging (fMRI) responses to rapidly intermixed trials. In the first two experiments, we observed that for 1-sec trials of full-field visual checkerboard stimulation, the fMRI blood oxygenation level-dependent (BOLD) signal summated in a roughly linear fashion across successive trials even at very short (2 sec and 5 sec) intertrial intervals, although subtle departures from linearity were observed. In experiments 3 and 4, we observed that it is possible to obtain robust activation maps for rapidly presented randomly mixed trial types (left- and right-hemifield visual checkerboard stimulation) spaced as little as 2 sec apart. Taken collectively, these results suggest that selective averaging may enable fMRI experimental designs identical to those used in typical behavioral and ERP studies. The ability to analyze closely spaced single-trial, or event-related, signals provides for a class of experiments which cannot be conducted using blocked designs. Trial types can be randomly intermixed, and selective averaging based upon trial type and/or subject performance is possible.

EEG-fMRI Fusion of Paradigm-Free Activity Using Kalman Filtering

We address here the use of EEG and fMRI, and their combination, in order to estimate the full spatiotemporal patterns of activity on the cortical surface in the absence of any particular assumptions on this activity such as stimulation times. For handling such a high-dimension inverse problem, we propose the use of (1) a global forward model of how these measures are functions of the “neural activity” of a large number of sources distributed on the cortical surface, formalized as a dynamical system, and (2) adaptive filters, as a natural solution to solve this inverse problem iteratively along the temporal dimension. This estimation framework relies on realistic physiological models, uses EEG and fMRI in a symmetric manner, and takes into account both their temporal and spatial information. We use the Kalman filter and smoother to perform such an estimation on realistic artificial data and demonstrate that the algorithm can handle the high dimensionality of these data and that it succeeds in solving this inverse problem, combining efficiently the information provided by the two modalities (this information being naturally predominantly temporal for EEG and spatial for fMRI). It performs particularly well in reconstructing a random temporally and spatially smooth activity spread over the cortex. The Kalman filter and smoother show some limitations, however, which call for the development of more specific adaptive filters. First, they do not cope well with the strong nonlinearity in the model that is necessary for an adequate description of the relation between cortical electric activities and the metabolic demand responsible for fMRI signals. Second, they fail to estimate a sparse activity (i.e., presenting sharp peaks at specific locations and times). Finally their computational cost remains high. We use schematic examples to explain these limitations and propose further developments of our method to overcome them.

Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation

We propose a novel method, fMRI-Informed Regional Estimation (FIRE), which utilizes information from fMRI in E/MEG source reconstruction. FIRE takes advantage of the spatial alignment between the neural and the vascular activities, while allowing for substantial differences in their dynamics. Furthermore, with a region-based approach, FIRE estimates the model parameters for each region independently. Hence, it can be efficiently applied on a dense grid of source locations. The optimization procedure at the core of FIRE is related to the re-weighted minimum-norm algorithms. The weights in the proposed approach are computed from both the current source estimates and fMRI data, leading to robust estimates in the presence of silent sources in either fMRI or E/MEG measurements. We employ a Monte Carlo evaluation procedure to compare the proposed method to several other joint E/MEG-fMRI algorithms. Our results show that FIRE provides the best trade-off in estimation accuracy between the spatial and the temporal accuracy. Analysis using human E/MEG-fMRI data reveals that FIRE significantly reduces the ambiguities in source localization present in the minimum-norm estimates, and that it accurately captures activation timing in adjacent functional regions.

Relationship of the BOLD signal with VEP for ultrashort duration visual stimuli (0.1 to 5 ms) in humans

There is currently a great interest to combine electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to study brain function. Earlier studies have shown different EEG components to correlate well with the fMRI signal arguing for a complex relationship between both measurements. In this study, using separate EEG and fMRI measurements, we show that (1) 0.1 ms visual stimulation evokes detectable hemodynamic and visual-evoked potential (VEP) responses, (2) the negative VEP deflection at approximately 80 ms (N2) co-varies with stimulus duration/intensity such as with blood oxygenation level-dependent (BOLD) response; the positive deflection at approximately 120 ms (P2) does not, and (3) although the N2 VEP-BOLD relationship is approximately linear, deviation is evident at the limit of zero N2 VEP. The latter finding argues that, although EEG and fMRI measurements can co-vary, they reflect partially independent processes in the brain tissue. Finally, it is shown that the stimulus-induced impulse response function (IRF) at 0.1 ms and the intrinsic IRF during rest have different temporal dynamics, possibly due to predominance of neuromodulation during rest as compared with neurotransmission during stimulation. These results extend earlier findings regarding VEP-BOLD coupling and highlight the component- and context-dependency of the relationship between evoked potentials and hemodynamic responses.

New directions in clinical imaging of cortical dysplasias

Neuroimaging is essential in the work-up of patients with intractable epilepsy. In pediatric patients with medically refractory epilepsy, cortical dysplasias account for a large percentage of the epileptogenic substrate. Unfortunately, these are also the most subtle lesions to identify. For this reason, there has been ongoing interest in utilizing new advanced magnetic resonance imaging (MRI) techniques to improve the ability to identify, diagnose, characterize, and delineate cortical dysplasias. Technologic gains such as multichannel coils (32 phased array and beyond) and higher field strengths (3T, 7T, and greater) coupled with newer imaging sequences such as arterial spin labeling (ASL), susceptibility weighted imaging (SWI) and diffusion tensor/spectrum imaging (DTI/DSI) are likely to increase yield. Improved MRI techniques coupled with a multimodality approach including magnetoencephalography (MEG), positron emission tomography (PET), and other techniques will increase sensitivity and specificity for identifying cortical dysplasias.

Modeling direct effects of neural current on MRI

We investigate the effect of the magnetic field generated by neural activity on the magnitude and phase of the MRI signal in terms of a phenomenological parameter with the dimensions of length; it involves the product of the strength and duration of these currents. We obtain an analytic approximation to the MRI signal when the neuromagnetically induced phase is small inside the MRI voxel. The phase shift is the average of the MRI phase over the voxel, and therefore first order in that phase; and the reduction in the signal magnitude is one half the square of the standard deviation of the MRI phase, which is second order. The analytic approximation is compared with numerical simulations. For weak currents the agreement is excellent, and the magnitude change is generally much smaller than the phase shift. Using MEG data as a weak constraint on the current strength we find that for a net dipole moment of 10 nAm, a typical value for an evoked response, the reduction in the magnitude of the MRI signal is two parts in 10(5), and the maximum value of the overall phase shift is approximately 4 x 10(-3), obtained when the MRI voxel is displaced 2/3 the size of the neuronal activity. We also show signal changes over a large range of values of the net dipole moment. We compare these results with others in the literature. Our model overestimates the effect on the MRI signal.

Feature-based fusion of medical imaging data

The acquisition of multiple brain imaging types for a given study is a very common practice. There have been a number of approaches proposed for combining or fusing multitask or multimodal information. These can be roughly divided into those that attempt to study convergence of multimodal imaging, for example, how function and structure are related in the same region of the brain, and those that attempt to study the complementary nature of modalities, for example, utilizing temporal EEG information and spatial functional magnetic resonance imaging information. Within each of these categories, one can attempt data integration (the use of one imaging modality to improve the results of another) or true data fusion (in which multiple modalities are utilized to inform one another). We review both approaches and present a recent computational approach that first preprocesses the data to compute features of interest. The features are then analyzed in a multivariate manner using independent component analysis. We describe the approach in detail and provide examples of how it has been used for different fusion tasks. We also propose a method for selecting which combination of modalities provides the greatest value in discriminating groups. Finally, we summarize and describe future research topics.

Simultaneous recording of EEG and BOLD responses: A historical perspective

Electromagnetic fields as measured with electroencephalogram (EEG) are a direct consequence of neuronal activity and feature the same timescale as the underlying cognitive processes, while hemodynamic signals as measured with functional magnetic resonance imaging (fMRI) are related to the energy consumption of neuronal populations. It is obvious that a combination of both techniques is a very attractive aim in neuroscience, in order to achieve both high temporal and spatial resolution for the non-invasive study of cognitive brain function. During the last decade a number of research groups have taken up this challenge. Here, we review the development of the combined EEG-fMRI approach. We summarize the main data integration approaches developed to achieve such a combination, discuss the current state-of-the-art in this field and outline challenges for the future success of this promising approach.

Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC

A number of brain imaging techniques have been developed in order to investigate brain function and to develop diagnostic tools for various brain disorders. Each modality has strengths as well as weaknesses compared to the others. Recent work has explored how multiple modalities can be integrated effectively so that they complement one another while maintaining their individual strengths. Bayesian inference employing Markov Chain Monte Carlo (MCMC) techniques provides a straightforward way to combine disparate forms of information while dealing with the uncertainty in each. In this paper we introduce methods of Bayesian inference as a way to integrate different forms of brain imaging data in a probabilistic framework. We formulate Bayesian integration of magnetoencephalography (MEG) data and functional magnetic resonance imaging (fMRI) data by incorporating fMRI data into a spatial prior. The usefulness and feasibility of the method are verified through testing with both simulated and empirical data.

Surface-constrained volumetric brain registration using harmonic mappings

In order to compare anatomical and functional brain imaging data across subjects, the images must first be registered to a common coordinate system in which anatomical features are aligned. Intensity-based volume registration methods can align subcortical structures well, but the variability in sulcal folding patterns typically results in misalignment of the cortical surface. Conversely, surface-based registration using sulcal features can produce excellent cortical alignment but the mapping between brains is restricted to the cortical surface. Here we describe a method for volumetric registration that also produces an accurate one-to-one point correspondence between cortical surfaces. This is achieved by first parameterizing and aligning the cortical surfaces using sulcal landmarks. We then use a constrained harmonic mapping to extend this surface correspondence to the entire cortical volume. Finally, this mapping is refined using an intensity-based warp. We demonstrate the utility of the method by applying it to T1-weighted magnetic resonance images (MRIs). We evaluate the performance of our proposed method relative to existing methods that use only intensity information; for this comparison we compute the intersubject alignment of expert-labeled subcortical structures after registration.

[Contribution of the spectral analysis to the brain connectivity study by fMRI]

AIM

To validate, through functional magnetic resonance imaging (fMRI) from spectral analysis of time series during a visuomotor task, a model of functional connectivity mainly constituted by the pre-supplementary motor area (pre-SMA), the supplementary motor area proper (SMA-proper) and the primary motor cortex (M1).

MATERIALS AND METHODS

The paradigm that was tried out in young subjects (n=5) consisted of a preparation task of motor movement. We firstly proceeded with an estimate in the frequency domain of coherency coefficients and values of phase shift between these three areas. Secondly, the estimated coherency coefficients were integrated to a model of functional connectivity. Two interaction coefficients were calculated, one for the related M1 and pre-SMA regions, the other one for the related M1 and SMA-proper regions.

RESULTS AND CONCLUSION

Our results demonstrate hemodynamic activity that definitely occurred earlier in the pre-SMA area during the preparatory period of the task. In the same way, a more important interaction was found between M1 and pre-SMA areas, which corroborates the assumption of the prevalent role played by these two areas in the case of a preparation task of a motor movement. Thus, this study has allowed highlighting a functional dissociation between the two portions of the SMA.

Biophysical model for integrating neuronal activity, EEG, fMRI and metabolism

Our goal is to model the coupling between neuronal activity, cerebral metabolic rates of glucose and oxygen consumption, cerebral blood flow (CBF), electroencephalography (EEG) and blood oxygenation level-dependent (BOLD) responses. In order to accomplish this, two previous models are coupled: a metabolic/hemodynamic model (MHM) for a voxel, linking BOLD signals and neuronal activity, and a neural mass model describing the neuronal dynamics within a voxel and its interactions with voxels of the same area (short-range interactions) and other areas (long-range interactions). For coupling both models, we take as the input to the BOLD model, the number of active synapses within the voxel, that is, the average number of synapses that will receive an action potential within the time unit. This is obtained by considering the action potentials transmitted between neuronal populations within the voxel, as well as those arriving from other voxels. Simulations are carried out for testing the integrated model. Results show that realistic evoked potentials (EP) at electrodes on the scalp surface and the corresponding BOLD signals for each voxel are produced by the model. In another simulation, the alpha rhythm was reproduced and reasonable similarities with experimental data were obtained when calculating correlations between BOLD signals and the alpha power curve. The origin of negative BOLD responses and the characteristics of EEG, PET and BOLD signals in Alzheimer's disease were also studied.

Functional tissue pulsatility imaging of the brain during visual stimulation

Functional tissue pulsatility imaging is a new ultrasonic technique being developed to map brain function by measuring changes in tissue pulsatility as a result of changes in blood flow with neuronal activation. The technique is based in principle on plethysmography, an older, nonultrasound technology for measuring expansion of a whole limb or body part as a result of perfusion. Perfused tissue expands by a fraction of a percent early in each cardiac cycle when arterial inflow exceeds venous outflow, and it relaxes later in the cardiac cycle when venous drainage dominates. Tissue pulsatility imaging (TPI) uses tissue Doppler signal processing methods to measure this pulsatile "plethysmographic" signal from hundreds or thousands of sample volumes in an ultrasound image plane. A feasibility study was conducted to determine if TPI could be used to detect regional brain activation during a visual contrast-reversing checkerboard block paradigm study. During a study, ultrasound data were collected transcranially from the occipital lobe as a subject viewed alternating blocks of a reversing checkerboard (stimulus condition) and a static, gray screen (control condition). Multivariate analysis of variance was used to identify sample volumes with significantly different pulsatility waveforms during the control and stimulus blocks. In 7 of 14 studies, consistent regions of activation were detected from tissue around the major vessels perfusing the visual cortex.

Structural and functional magnetic resonance imaging in psychiatric disorders

OBJECTIVE

To report on recent advances in both structural and functional brain imaging studies in psychiatry and to highlight their importance for the field.

METHOD

We reviewed recently published articles dealing with such advances and abstracted them into a selective review of the field.

RESULTS

Some of the more important trends include integration of genetic information into research studies, use of novel quantitative image measurement techniques, studies of new subject populations, the use of pharmacologic probes in functional magnetic resonance imaging (fMRI) studies, the incorporation of elements of virtual reality into fMRI task stimuli, and the methodological innovation of hyperscanning.

CONCLUSIONS

A whole series of new approaches and techniques are resulting in rapid advances in neuroimaging in psychiatry. Several of these show the potential for clinical translation.

Dealing with mismatched fMRI activations in fMRI constrained EEG cortical source imaging: a simulation study assuming various mismatch types

Although fMRI constrained EEG source imaging could be a promising approach to enhancing both spatial and temporal resolutions of independent fMRI and EEG analyses, it has been frequently reported that a hard fMRI constraint may cause severe distortion or elimination of significant EEG sources when there are distinct mismatches between fMRI activations and EEG sources. If estimating actual EEG source locations is important and fMRI prior information is used as an auxiliary tool to enhance the concentration of widespread EEG source distributions, it is reasonable to weaken the fMRI constraint when significantly mismatched sources exist. The present study demonstrates that the mismatch problem may be partially solved by extending the prior fMRI activation regions based on the conventional source imaging results. A hard fMRI constraint is then applied when there is no distinct mismatch, while a weakened fMRI constraint is applied when there are significant mismatches. A preliminary simulation study assuming different types of mismatches such as fMRI invisible, extra, and discrepancy sources demonstrated that this approach can be a promising option to treat mismatched fMRI activations in fMRI constrained EEG source imaging.

A technique to consider mismatches between fMRI and EEG/MEG sources for fMRI-constrained EEG/MEG source imaging: a preliminary simulation study

fMRI-constrained EEG/MEG source imaging can be a powerful tool in studying human brain functions with enhanced spatial and temporal resolutions. Recent studies on the combination of fMRI and EEG/MEG have suggested that fMRI prior information could be readily implemented by simply imposing different weighting factors to cortical sources overlapping with the fMRI activations. It has been also reported, however, that such a hard constraint may cause severe distortions or elimination of meaningful EEG/MEG sources when there are distinct mismatches between the fMRI activations and the EEG/MEG sources. If one wants to obtain the actual EEG/MEG source locations and uses the fMRI prior information as just an auxiliary tool to enhance focality of the distributed EEG/MEG sources, it is reasonable to weaken the strength of fMRI constraint when severe mismatches between fMRI and EEG/MEG sources are observed. The present study suggests an efficient technique to automatically adjust the strength of fMRI constraint according to the mismatch level. The use of the proposed technique rarely affects the results of conventional fMRI-constrained EEG/MEG source imaging if no major mismatch between the two modalities is detected; while the new results become similar to those of typical EEG/MEG source imaging without fMRI constraint if the mismatch level is significant. A preliminary simulation study using realistic EEG signals demonstrated that the proposed technique can be a promising tool to selectively apply fMRI prior information to EEG/MEG source imaging.

Direct magnetic resonance detection of neuronal electrical activity

Present noninvasive neuroimaging methods measure neuronal activity indirectly, via either cerebrovascular changes or extracranial measurements of electrical/magnetic signals. Recent studies have shown evidence that MRI may be used to directly and noninvasively map electrical activity associated with human brain activation, but results are inconclusive. Here, we show that MRI can detect cortical electrical activity directly. We use organotypic rat-brain cultures in vitro that are spontaneously active in the absence of a cerebrovascular system. Single-voxel magnetic resonance (MR) measurements obtained at 7 T were highly correlated with multisite extracellular local field potential recordings of the same cultures before and after blockade of neuronal activity with tetrodotoxin. Similarly, for MR images obtained at 3 T, the MR signal changed solely in voxels containing the culture, thus allowing the spatial localization of the active neuronal tissue.

Brain imaging tools in neurosciences

In this chapter brain imaging tools in neurosciences are presented. These include a brief overview on single-photon emission tomography (SPET) and a detailed focus on positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). In addition, a critical discussion on the advantages and disadvantages of the three diagnostic systems is added. Furthermore, this article describes the image analysis tools from visual analysis over region-of-interest technique up to statistical parametric mapping, co-registration methods, and network analysis. It also compares the newly developed combined PET/CT scanner approach with established image fusion software approaches. There is rapid change: Better scanner qualities, new software packages and scanner concepts are on the road paved for an amply bright future in neurosciences.

Simultaneous EEG–fMRI

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

Vector projection of biomagnetic fields

Biomagnetic measurements are increasingly popular as functional imaging techniques for the non-invasive assessment of electrically active tissue. Although most currently available magnetometers utilise only one component of the vector magnetic field, some studies have suggested the possibility of obtaining additional information from recordings of the full magnetic field vector. Three projection techniques were applied to different biomagnetic signals for analysis of the three orthogonal components of the vector magnetic field. Vector magnetic fields obtained from fetal cardiac activity were projected into evenly spaced directions around a unit sphere. The vector magnetic field recorded from multiple intestinal current sources with independent temporal frequencies was then projected. Finally, an external reference signal from an invasive electrode was used to project the recorded vector magnetic fields due to gastric electrical activity. In each case, it was found that the information obtained by examination of the projected magnetic field vectors gave superior clinical insight to that obtained by analysis of any single magnetic field component.

Cortical generators of slow evoked responses elicited by spatial and nonspatial auditory working memory tasks

OBJECTIVE

Slow evoked responses have been extensively studied using electrophysiological and neuroimaging methods, but there is no consensus regarding their generators. We investigated the generators of the P3 and positive slow wave (PSW) in the evoked responses to probes recorded during auditory working memory tasks to find out whether there is dissociation between functional networks involved in the generation of the P3 and PSW and between spatial and nonspatial auditory processing within this time window.

METHODS

Whole-head magneto-(MEG) and electroencephalography (EEG); analysis of MEG data using minimum-norm current estimates.

RESULTS

The associative temporal, occipito-temporal and parietal areas contributed to the generation of the slow evoked responses. The temporal source increased while the occipito-temporal source diminished activity during transition from the P3 to PSW. The occipito-temporal generator of the P3 was activated more during the spatial than nonspatial task, and the left temporal generator of the PSW tended to be more strongly activated during the nonspatial task.

CONCLUSIONS

These findings indicate that partially distinct functional networks generate the P3 and PSW and provide evidence for segregation of spatial and nonspatial auditory information processing in associative areas beyond the supratemporal auditory cortex.

SIGNIFICANCE

The present results support the dual-stream model for auditory information processing.

Open magnetic and electric graphic analysis

The OMEGA software provides an analysis platform for user-independent, fast, and reproducible multimodal data analysis in one single software environment. Synergetic interactions pursued between the two functional imaging techniques fMRI and MEG use the morphological MRI recording as a basis for a common coordinate frame. In this way, direct interchange, comparison, and integration among the results of the different modalities have become feasible. The fMRI data analysis provides information about the localization of functional activity with low temporal resolution, whereas the MEG recording complements the corresponding time evolution with a high temporal resolution. The implementation of OMEGA allows the analyst to receive comprehensive MEG/fMRI results in a matter of minutes after the measurements have been completed. With OMEGA, the clinical researcher gets comprehensive information in a quick and standardized approach about the sites and the time course of neurological activation, which is useful for clinical applications and diagnostics.

Geodesic photogrammetry for localizing sensor positions in dense-array EEG

OBJECTIVE

An important goal for functional brain studies using EEG technology is to estimate the location of brain sources that produce the scalp-recorded signals. The accuracy of source estimates is dependent upon many variables, one of which is the accurate description of the scalp positions of the EEG sensors. The objective of the present research was to develop a photogrammatic method for sensor localization that is fast, accurate, and easy to use.

METHODS

With the novel photogrammetric method, multiple cameras were arranged in a geodesic array, and images of the sensors on the subject's head were acquired allowing for the reconstruction of the 3D sensor positions.

RESULTS

Data from the photogrammetric method were compared with data acquired with the conventional electromagnetic method. The accuracy of the photogrammatic method, quantified as RMS of the measured positions and the actual known positions, was similar (mean error = 1.27 mm) to the electromagnetic method (mean error = 1.02 mm), and both approximated the localization error of the calibration object (mean error = 0.56 mm).

CONCLUSIONS

Accurate determination of 3D sensor positions can be accomplished with minimal demands on the time of the subject and the experimenter using the photogrammetric method.

A framework for registration, statistical characterization and classification of cortically constrained functional imaging data

We present a framework for registering and analyzing functional neuroimaging data constrained to the cortical surface of the brain. We assume as input a set of labeled data points that lie on a set of parameterized topologically spherical surfaces that represent the cortical surfaces of multiple subjects. To perform analysis across subjects, we first co-register the coordinates from each surface to a cortical atlas using labeled sulcal maps as constraints. The registration minimizes a thin plate spline energy function on the deforming surface using covariant derivatives to solve the associated PDEs in the intrinsic geometry of the individual surface. The resulting warps are used to bring the functional data for multiple subjects into a common surface atlas coordinate system. We then present a novel method for performing statistical analysis of points on this atlas surface. We use the Green's function of the heat equation on the surface to model probability distributions and thus demonstrate the use of PDEs for statistical analysis in Riemannian manifolds. We describe methods for estimating the mean and variance of a set of points, such that the mean also lies in the manifold. We demonstrate the utility of this framework in the development of a maximum likelihood classifier for parcellation of somatosensory cortex in the atlas based on current dipole fits to MEG data, simulated to represent a somatotopic mapping of S1 sensory areas in multiple subjects.