Localization of Broca's area using verb generation tasks in the MEG: Validation against fMRI

Functional MRI (fMRI) is routinely used to non-invasively localize language areas. Magnetoencephalography (MEG) is being explored as an alternative technique. MEG tasks to localize receptive language are well established although there are no standardized tasks to localize expressive language areas. We developed two expressive language tasks for MEG and validated their localizations against fMRI data. Ten right-handed adolescents (μ = 17.5 years) were tested with fMRI and MEG on two tasks: verb generation to pictures and verb generation to words. MEG and fMRI data were normalized and overlaid. The number of overlapping voxels activated in fMRI and MEG were counted for each subject, for each task, at different thresholding levels. For picture verb generation, there was 100% concordance between MEG and fMRI lateralization, and for word verb generation, there was 75% concordance. A count showed 79.6% overlap of voxels activated by both MEG and fMRI for picture verb generation and 50.2% overlap for word verb generation. The percentage overlap decreased with increasingly stringent activation thresholds. Our novel MEG expressive language tasks successfully identified neural regions involved in language production and showed high concordance with fMRI laterality. Percentage overlap of activated voxels was also high when validated against fMRI, but showed task-specific and threshold-related effects. The high concordance and high percentage overlap between fMRI and MEG activations confirm the validity of our new MEG task. Furthermore, the higher concordance from the picture verb generation task suggests that this is a promising task for use in the young clinical population.     

Integrated MEG/fMRI Model Validated Using Real Auditory Data

The main objective of this paper is to present methods and results for the estimation of parameters of our proposed integrated magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) model. We use real auditory MEG and fMRI datasets from 7 normal subjects to estimate the parameters of the model. The MEG and fMRI data were acquired at different times, but the stimulus profile was the same for both techniques. We use independent component analysis (ICA) to extract activation-related signal from the MEG data. The stimulus-correlated ICA component is used to estimate MEG parameters of the model. The temporal and spatial information of the fMRI datasets are used to estimate fMRI parameters of the model. The estimated parameters have reasonable means and standard deviations for all subjects. Goodness of fit of the real data to our model shows the possibility of using the proposed model to simulate realistic datasets for evaluation of integrated MEG/fMRI analysis methods.     

Breast vibro-acoustography: initial experience in benign lesions

Background

Functional magnetic resonance imaging (fMRI) analysis is commonly done with cross-correlation analysis (CCA) and the General Linear Model (GLM). Both CCA and GLM techniques, however, typically perform calculations on a per-voxel basis and do not consider relationships neighboring voxels may have. Clustered voxel analyses have then been developed to improve fMRI signal detections by taking advantages of relationships of neighboring voxels. Mean-shift clustering (MSC) is another technique which takes into account properties of neighboring voxels and can be considered for enhancing fMRI activation detection.

Methods

This study examines the adoption of MSC to fMRI analysis. MSC was applied to a Statistical Parameter Image generated with the CCA technique on both simulated and real fMRI data. The MSC technique was then compared with CCA and CCA plus cluster analysis. A range of kernel sizes were used to examine how the technique behaves.

Results

Receiver Operating Characteristic curves shows an improvement over CCA and Cluster analysis. False positive rates are lower with the proposed technique. MSC allows the use of a low intensity threshold and also does not require the use of a cluster size threshold, which improves detection of weak activations and highly focused activations.

Conclusion

The proposed technique shows improved activation detection for both simulated and real Blood Oxygen Level Dependent fMRI data. More detailed studies are required to further develop the proposed technique.     

Magnetoencephalography: in search of neural processes for visual motion information

Magnetoencephalography (MEG) has become a standard approach to the investigation of human brain functions. This review starts with a brief review of the human visual system and studies on visual motion detection mechanisms is followed by the presentation of MEG studies that have contributed to the field. Emphasis is placed on the fact that because the neural activities measured in functional magnetic resonance imaging (fMRI) differ substantially from those measured in MEG--fMRI data cannot be used directly to estimate MEG signal sources. The basic ideas behind the methods of signal processing and analyses generally used in MEG studies are described and theoretical considerations of the neural mechanisms determining MEG response latency and amplitude changes are discussed. Here, scalar fields theory is proposed to explain MEG responses to incoherent motions, and the ways in which detection of complex motions such as transparency, rotation and expansion can be explained by this theory are also presented. Relationships between human behavioral reaction time and MEG response latency suggest a new concept underlying the reasons why humans are late in detecting slow motion.     

fMRI responses in medial frontal cortex that depend on the temporal frequency of visual input

Functional networks in the human brain have been investigated using electrophysiological methods (EEG/MEG, LFP, and MUA) and steady-state paradigms that apply periodic luminance or contrast modulation to drive cortical networks. We have used this approach with fMRI to characterize a cortical network driven by a checkerboard reversing at a fixed frequency. We found that the fMRI signals in voxels located in occipital cortex were increased by checkerboard reversal at frequencies ranging from 3 to 14 Hz. In contrast, the response of a cluster of voxels centered on basal medial frontal cortex depended strongly on the reversal frequency, consistently exhibiting a peak in the response for specific reversal frequencies between 3 and 5 Hz in each subject. The fMRI signals at the frontal voxels were positively correlated indicating a homogeneous cluster. Some of the occipital voxels were positively correlated to the frontal voxels apparently forming a large-scale functional network. Other occipital voxels were negatively correlated to the frontal voxels, suggesting a functionally distinct network. The results provide preliminary fMRI evidence that during visual stimulation, input frequency can be varied to engage different functional networks.     

Auditory processing that leads to conscious perception: a unique window to central auditory processing opened by the mismatch negativity and related responses

In this review, we will present a model of brain events leading to conscious perception in audition. This represents an updated version of Näätänen's previous model of automatic and attentive central auditory processing. This revised model is mainly based on the mismatch negativity (MMN) and N1 indices of automatic processing, the processing negativity (PN) index of selective attention, and their magnetoencephalographic (MEG) and functional magnetic resonance imaging (fMRI) equivalents. Special attention is paid to determining the neural processes that might underlie conscious perception and the borderline between automatic and attention‐dependent processes in audition.     

基于正则化方法的加权最小模估计在脑磁源成像中的应用

脑磁图 ( m agnetoencephalography,MEG)逆问题的研究 ,根据点源和分布源两种源模型 ,可分为偶极子定位和磁源成像两大类求逆方法。采用非参数的分布源模型 ,MEG逆问题转化为一个病态的欠定方程组的求解。本文系统地阐述了结合 Tikhonov正则技术的加权最小模磁源重建方法 ,着重介绍了深度归一化算法、低分辨率脑电磁断层成像技术、局部欠定系统解法、选择性最小模方法 ,此外还从广义的加权最小模估计角度对最大熵重建方法 ,融合其它脑功能成像技术的方法以及最大后验概率估计方法加以解释和分析。不同的磁源成像方法目的都是通过引入合适的约束条件 ,从算法公式本身及神经细胞活动的特性中加以修正 ,减少逆问题的不适定程度 ,因此均可认为是使用正则方法来约束解空间 ,从而获得与测量磁场数据相拟合的并具有神经生理学和解剖学意义下的最合理的解。基于正则化技术的加权最小模估计是 MEG逆问题研究中最早开展、并已被广泛应用的磁源分布图像重建方法 ,本文给出了一个较为完整的理论发展框架     

人脑对握力刺激响应特征的数值计算分析方法

为研究人脑对握力刺激的响应特征,提出一种新的数值计算分析方法：结合独立成分分析和云模型,对握力刺激脑响应特征进行数值计算。采集10名健康受试者不同握力任务下的功能磁共振（fMRI）数据并进行预处理,应用独立成分分析获取不同握力刺激条件下的脑激活区域位置和大小,然后通过云模型计算脑激活区域内的数据分布特征。结果表明,握力刺激的脑激活区域主要分布在对侧大脑Brodmann 2、3、4、6区和同侧小脑,并且随着握力强度的增加,中央前回、中央后回等激活区域增大(激活簇体素个数分别为4 075、4 218、4 965);在不同握力刺激条件下,激活区域的任务态与非任务态间的期望、熵、超熵（Ex、En、He）均有明显的统计学差异,Ex（P<0001）和En(P<0.005)增大,He(P<0.005)减小;不同握力刺激间三个参数的差异不明显,并且非激活区域内任务状态与非任务状态间的期望、熵、超熵均无统计学差异。该方法可为不同任务下大脑激活区域的数据分布特征研究提供一种新的分析手段。     

Blood oxygenation level‐dependent functional MRI signal turbulence caused by ultrahigh spatial resolution: numerical simulation and theoretical explanation

High‐spatial‐resolution functional MRI (fMRI) can enhance image contrast and improve spatial specificity for brain activity mapping. As the voxel size is reduced, an irregular magnetic fieldmap will emerge as a result of less local averaging, and will lead to abnormal fMRI signal evolution with respect to the image acquisition TE. In this article, we report this signal turbulence phenomenon observed in simulations of ultrahigh‐spatial‐resolution blood oxygenation level‐dependent (BOLD) fMRI (voxel size of less than 50 × 50 × 50 µm³). We present a four‐level coarse‐to‐fine multiresolution BOLD fMRI signal simulation. Based on the statistical histogram of an intravoxel fieldmap, we reformulate the intravoxel dephasing summation (a form of Riemann sum) into a new formula that is a discrete Fourier transformation of the intravoxel fieldmap histogram (a form of Lebesgue sum). We interpret the BOLD signal formation by relating its magnitude (phase) to the even (odd) symmetry of the fieldmap histogram. Based on multiresolution BOLD signal simulation, we find that the signal turbulence mainly emerges at the vessel boundary, and that there are only a few voxels (less than 10%) in an ultrahigh‐resolution image that reveal turbulence in the form of sparse point noise. Our simulation also shows that, for typical human brain imaging of the cerebral cortex with millimeter resolution, TE < 30 ms and B₀ = 3 T, we are unlikely to observe BOLD signal turbulence. Overall, the main causes of voxel signal turbulence include a high spatial resolution, high field, long TE and large vessel. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Post-movement beta rebound abnormality as indicator of mirror neuron system dysfunction in autistic spectrum disorder: An MEG study

The mu rhythm is regarded as a physiological indicator of the human mirror neuron system (MNS). The dysfunctional MNS hypothesis in patients with autistic spectrum disorder (ASD) has often been tested using EEG and MEG, targeting mu rhythm suppression during action observation/execution, although with controversial results. We explored neural activity related to the MNS in patients with ASD, focusing on power increase in the beta frequency band after observation and execution of movements, known as post-movement beta rebound (PMBR). Multiple source beamformer (MSBF) and BrainVoyager QX were used for MEG source imaging and statistical group analysis, respectively. Seven patients with ASD and ten normal subjects participated in this study. During the MEG recordings, the subjects were asked to observe and later execute object-related hand actions performed by an experimenter. We found that both groups exhibited pronounced PMBR exceeding 20% when observing and executing actions with a similar topographic distribution of maximal activity. However, significantly reduced PMBR was found only during the observation condition in the patients relative to controls in cortical regions within the MNS, namely the sensorimotor area, premotor cortex and superior temporal gyrus. Reduced PMBR during the observation condition was also found in the medial prefrontal cortex. These results support the notion of a dysfunctional execution/observation matching system related to MNS impairment in patients with ASD, and the feasibility of using MEG to detect neural activity, in particular PMBR abnormalities, as an index of MNS dysfunction during performance of motor or cognitive tasks.     

Evidence for interhemispheric processing of inputs from the hands in human S2 and PV

In the present investigation, we identified cortical areas involved in the integration of bimanual inputs in human somatosensory cortex. Using functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG), we compared the responses to unilateral versus bilateral stimulation in anterior parietal cortex and areas in the Sylvian fissure of the contralateral hemisphere. The extent of fMRI activation on the upper bank of the Sylvian fissure, in the second somatosensory (S2) and the parietal ventral (PV) areas, was significantly larger for bilateral stimulation than for unilateral stimulation. Using MEG, we were able to describe the latency of response in S1 and S2/PV to unilateral and bilateral stimulation. The MEG response had three components under both stimulus conditions. An early peak in S1 at 40 ms, a middle peak in S2/PV at 80-160 ms, and three late peaks in S2/PV at 250-420 ms. There was an increase in magnetic field strength in S2/PV to bilateral stimulation at 300-400 ms post stimulus. The fMRI results indicate that, as in monkeys, S2/PV receives inputs from both the contralateral and ipsilateral hand. The MEG data suggest that information is processed serially from S1 to S2. The very late response in S2/PV indicates that extensive intrahemispheric processing occurs before information is transferred to the opposite hemisphere. The neural substrate for the increased activation and field strength at long latencies during bilateral stimulation can be accounted for in three ways. Under bilateral stimulus conditions, more neurons may be active, neuronal firing rate may increase, and/or neural activity may be more synchronous.     

MEG inversion using spherical head model combined with brain-shaped head model

INTRODUCTION　　 Neural currentsources in the human brain produce external magnetic fieldsthatcan be measured noninvasively by using superconducting quantum interferencedevices(SQUID s) .This technique is called Magnetoencephalography(MEG)〔1〕that is becoming a promising technique for brain function research and diagnosis ofsome brain disease〔2〕.In many cases the neuromagnetic sources can be modeled aseveral current dipoles that are appropriate for interpreting postsynaptic potentia…     

Age Distribution of MEG Spontaneous Theta Activity in Healthy Subjects

No HeadingSummary: This study investigates the possible relevance of distribution and age variation of spontaneous theta activity (4–8 Hz) in normal subjects using magnetoencephalography (MEG) recordings. Spontaneous theta was recorded with a 151-channel MEG in healthy subjects; moreover, in a group of 10 subjects, simultaneous MEG-EEG was recorded in order to compare the two methods. Theta was divided in two sub-bands: TA (4–6 Hz) and TB (6–8 Hz). The pre-processed data were transformed into the frequency domain by Fast Fourier Transform (FFT)-based software by subdividing the data in epochs of 5 sec, on which FFT amplitudes are computed. Moreover, on all trials a simple model of a single electric current embedded in a spherically symmetric conductor was fitted automatically to the magnetic fields and projected onto an averaged MRI. The results obtained show that FFT-based theta power spectrum was distributed in adults with the highest power over the posterior parietal and occipital areas with TB dominance. The dipole analysis resulted in a mid-sagittal distribution, though the youngest group displayed theta dipoles fitting more posteriorly respect to the adults and the elderly. These results suggest that spontaneous theta activity is a diffuse and pervasive rhythm which shows some different topographical distribution among the age groups. Whether the prevalent posterior distribution of theta is the expression of distinct networks or the outcome of complex dynamics are questions of possible relevance in the organization of higher order processes.     

Investigation of geometrical and scoring grid resolution for Monte Carlo dose calculations for IMRT

Monte Carlo based treatment planning of two different patient groups treated with step-and-shoot IMRT (head-and-neck and lung treatments) with different CT resolutions and scoring methods is performed to determine the effect of geometrical and scoring voxel sizes on DVHs and calculation times. Dose scoring is performed in two different ways: directly into geometrical voxels (or in a number of grouped geometrical voxels) or into scoring voxels defined by a separate scoring grid superimposed on the geometrical grid. For the head-and-neck cancer patients, more than 2% difference is noted in the right optical nerve when using voxel dimensions of 4 x 4 x 4 mm3 compared to the reference calculation with 1 x 1 x 2 mm3 voxel dimensions. For the lung cancer patients, 2% difference is noted in the spinal cord when using voxel dimensions of 4 x 4 x 10 mm3 compared to the 1 x 1 x 5 mm3 calculation. An independent scoring grid introduces several advantages. In cases where a relatively high geometrical resolution is required and where the scoring resolution is less important, the number of scoring voxels can be limited while maintaining a high geometrical resolution. This can be achieved either by grouping several geometrical voxels together into scoring voxels or by superimposing a separate scoring grid of spherical voxels with a user-defined radius on the geometrical grid. For the studied lung cancer cases, both methods produce accurate results and introduce a speed increase by a factor of 10-36. In cases where a low geometrical resolution is allowed, but where a high scoring resolution is required, superimposing a separate scoring grid on the geometrical grid allows a reduction in geometrical voxels while maintaining a high scoring resolution. For the studied head-and-neck cancer cases, calculations performed with a geometrical resolution of 2 x 2 x 2 mm3 and a separate scoring grid containing spherical scoring voxels with a radius of 2 mm produce accurate results and introduce a speed increase by a factor of 13. The scoring grid provides an additional degree of freedom for limiting calculation time and memory requirements by selecting optimized scoring and geometrical voxel dimensions in an independent way.     

脑磁图在耐药性癫(癎)术前定位中的价值及影响因素探讨

目的:评价脑磁图(MEG)在耐药性癫癎术前定位中的价值并探讨影响其定位的因素。方法:在47例术前均行MEG,VEEG,MRI检查的耐药性癫癎病例中,以术中皮层脑电图(ECoG)为金标准,比较VEEG和MEG定位准确性,并作影响MEG和VEEG定位的logistic多因素分析。结果:47例颞叶癫癎中,MEG与ECoG完全吻合32例(68％),部分吻合8例(17％);VEEG与ECoG完全吻合18例(38％),部分吻合23例(49％)。MEG与ECoG完全吻合病例明显多于MEG与VEEG吻合例(P <0．05)。MEG定位的多因素分析显示发作频率对MEG定位有影响(P<0．05)。结论:MEG的应用有助于致癎灶的准确定位和手术方式的选择,在难治性颞叶癫癎的术前定位中有重要的临床应用价值。     

A comparison of the mean signal change method and the voxel count method to evaluate the sensitivity of individual variability in visuospatial performance

This study compared the mean signal change method and the voxel count method in evaluating the sensitivity of individual variability in visuospatial performance using functional Magnetic Resonance Imaging (fMRI). Sixteen right-handed male college students (mean age 23.2 years) participated in this study as subjects. Functional brain images were scanned with a 3T MRI single-shot EPI method during a visuospatial task. No correlation was found between visuospatial performance and the number of activated voxels in the activated brain areas. Significant positive correlations, however, were found between visuospatial performance and the mean signal changes of activated voxels in the parietal, frontal and other areas. In conclusion, the mean signal change is more sensitive to individual variability in visuospatial performance than the number of activated voxels.     

MRI-guided Thermal Ablation Therapy: Model and Parameter Estimates to Predict Cell Death from MR Thermometry Images

Solid tumors and other pathologies can be treated using laser thermal ablation under interventional magnetic resonance imaging (iMRI) guidance. A model was developed to predict cell death from magnetic resonance (MR) thermometry measurements based on the temperature–time history, and validated using in vivo rabbit brain data. To align post-ablation T2-weighted spin-echo MR lesion images to gradient-echo MR images, from which temperature is derived, a registration method was used that aligned fiducials placed near the thermal lesion. The outer boundary of the hyperintense rim in the post-ablation MR lesion image was used as the boundary for cell death, as verified from histology. Model parameters were simultaneously estimated using an iterative optimization algorithm applied to every interesting voxel in 328 images from multiple experiments having various temperature histories. For a necrotic region of 766 voxels across all lesions, the model provided a voxel specificity and sensitivity of 98.1 and 78.5%, respectively. Mislabeled voxels were typically within one voxel from the segmented necrotic boundary with median distances of 0.77 and 0.22 mm for false positives (FP) and false negatives (FN), respectively. As compared to the critical temperature cell death model and the generalized Arrhenius model, our model typically predicted fewer FP and FN. This is good evidence that iMRI temperature maps can be used with our model to predict therapeutic regions in real-time during treatment.     

A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG

The spherical head model has been used in magnetoencephalography (MEG) as a simple forward model for calculating the external magnetic fields resulting from neural activity. For more realistic head shapes, the boundary element method (BEM) or similar numerical methods are used, but at greatly increased computational cost. We introduce a sensor-weighted overlapping-sphere (OS) head model for rapid calculation of more realistic head shapes. The volume currents associated with primary neural activity are used to fit spherical head models for each individual MEG sensor such that the head is more realistically modelled as a set of overlapping spheres, rather than a single sphere. To assist in the evaluation of this OS model with BEM and other head models, we also introduce a novel comparison technique that is based on a generalized eigenvalue decomposition and accounts for the presence of noise in the MEG data. With this technique we can examine the worst possible errors for thousands of dipole locations in a realistic brain volume. We test the traditional single-sphere model, three-shell and single-shell BEM, and the new OS model. The results show that the OS model has accuracy similar to the BEM but is orders of magnitude faster to compute.     

Current densities in a 2 mm resolution anatomically realistic model of the body induced by low frequency electric fields

Current density distributions in a fine resolution (2 mm) anatomically realistic voxel model of the human body have been calculated for uniform, low frequency vertically aligned electric fields for a body grounded and isolated from 50 Hz to 10 MHz. The voxel phantom NORMAN is used which has a height of 1.76 m and a mass of 73 kg. There are 8.3 million voxels in the body differentiated into 37 tissue types. Both finite-difference potential and time-domain methods were used. Results are presented for the current density averaged over 1 cm2 in muscle, heart, brain and retina. Electric field values required to reach the NRPB and ICNIRP basic restrictions on current density are derived and compared with the external field guidelines from these standards.