A multimodal imaging approach to the evaluation of post-traumatic epilepsy

Object

Electroencephalography-functional magnetic resonance imaging (EEG-fMRI) coregistration and high-density EEG (hdEEG) can be combined to map noninvasively abnormal brain activation elicited by epileptic processes. By combining noninvasive imaging techniques in a multimodal approach, we sought to investigate pathophysiological mechanisms underlying epileptic activity in seven patients with severe traumatic brain injury.

Materials and methods

Standard EEG and fMRI data were acquired during a single scanning session. The EEG-fMRI data were analyzed using the general linear model and independent component analysis. Source localization of interictal epileptiform discharges (IEDs) was performed using 256-channel hdEEG. Blood oxygenation level dependent (BOLD) localizations were then compared to EEG source reconstruction.

Results

On hdEEG, focal source localization was detected in all seven patients; in six out of seven it was concordant with the expected epileptic activity as defined by EEG data and clinical evaluation; and in four out of seven in whom IEDs were recorded, BOLD signal changes were observed. These activities were partially concordant with the source localization.

Conclusion

Multimodal integration of EEG-fMRI and hdEEG combining two different methods to localize the same epileptic foci appears to be a promising tool to noninvasively map abnormal brain activation in patients with post-traumatic brain injury.     

In vivo functional connectome of human brainstem nuclei of the ascending arousal,autonomic, and motor systems by high spatial resolution 7-Tesla fMRI

Objective

Our aim was to map the in vivo human functional connectivity of several brainstem nuclei with the rest of the brain by using seed-based correlation of ultra-high magnetic field functional magnetic resonance imaging (fMRI) data.

Materials and methods

We used the recently developed template of 11 brainstem nuclei derived from multi-contrast structural MRI at 7 Tesla as seed regions to determine their connectivity to the rest of the brain. To achieve this, we used the increased contrast-to-noise ratio of 7-Tesla fMRI compared with 3 Tesla and time-efficient simultaneous multi-slice imaging to cover the brain with high spatial resolution (1.1-mm isotropic nominal resolution) while maintaining a short repetition time (2.5 s).

Results

The delineated Pearson’s correlation-based functional connectivity diagrams (connectomes) of 11 brainstem nuclei of the ascending arousal, motor, and autonomic systems from 12 controls are presented and discussed in the context of existing histology and animal work.

Conclusion

Considering that the investigated brainstem nuclei play a crucial role in several vital functions, the delineated preliminary connectomes might prove useful for future in vivo research and clinical studies of human brainstem function and pathology, including disorders of consciousness, sleep disorders, autonomic disorders, Parkinson’s disease, and other motor disorders.

     

Integration of EEG/MEG with MRI and fMRI.

This article discusses different approaches that have been proposed for multimodal neuroimaging, with special emphasis on the integration of electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance imaging (MRI), and functional MRI (fMRI). Some applications will be shown to illustrate the efficacy and importance of these techniques in clinical and neuroscience studies. Finally, some remaining challenges and problems in the multimodal integration will be discussed.     

A modified oddball paradigm for investigation of neural correlates of attention: a simultaneous ERP–fMRI study

Introduction

The objective of the presented study was to develop and evaluate a P300 experimental protocol for simultaneous registration of event-related potentials (ERPs) and functional MRI (fMRI) data with continuous imaging. It may be useful for investigating attention and working memory processes in specific populations, such as children and neuropsychiatric patients.

Materials and methods

Eleven children were investigated with simultaneous ERP–fMRI. To fulfill requirements of both BOLD and electroencephalographic signal registration, a modified oddball task was used. To verify the ERP–fMRI protocol we also performed a study outside the scanner using a typical two-stimuli oddball paradigm.

Results

Localization of the P300 component of ERPs partially corresponded with fMRI results in the frontal and parietal brain regions. FMRI activations were found in: middle frontal gyrus, insula, SMA, parietal lobule, thalamus, and cerebellum. Our modified oddball task provided ERP–fMRI results with high level of significance (EEG SNR = 35, fMRI p < 0.05–Bonf.). ERPs obtained in the scanner were comparable with those registered outside the scanner, although some differences in the amplitude were noticed, mainly in the N100 component.

Conclusion

In our opinion the presented paradigm may be successfully applied for simultaneous ERP–fMRI registration of neural correlates of attention in vulnerable populations.     

Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks

Graph theoretical analysis of structural and functional connectivity MRI data (ie. diffusion tractography or cortical volume correlation and resting-state or task-related (effective) fMRI, respectively) has provided new measures of human brain organization in vivo. The most striking discovery is that the whole-brain network exhibits “small-world” properties shared with many other complex systems (social, technological, information, biological). This topology allows a high efficiency at different spatial and temporal scale with a very low wiring and energy cost. Its modular organization also allows for a high level of adaptation. In addition, degree distribution of brain networks demonstrates highly connected hubs that are crucial for the whole-network functioning. Many of these hubs have been identified in regions previously defined as belonging to the default-mode network (potentially explaining the high basal metabolism of this network) and the attentional networks. This could explain the crucial role of these hub regions in physiology (task-related fMRI data) as well as in pathophysiology. Indeed, such topological definition provides a reliable framework for predicting behavioral consequences of focal or multifocal lesions such as stroke, tumors or multiple sclerosis. It also brings new insights into a better understanding of pathophysiology of many neurological or psychiatric diseases affecting specific local or global brain networks such as epilepsy, Alzheimer’s disease or schizophrenia. Graph theoretical analysis of connectivity MRI data provides an outstanding framework to merge anatomical and functional data in order to better understand brain pathologies.     

Functional magnetic resonance imaging of swine brain during change in thiopental anesthesia into EEG burst-suppression level--a preliminary study

Deepening anesthesia produces well known changes in electroencephalogram (EEG) and evoked potentials, differing in pathological and normal brain. Yet, it is not known how the T2*-weighted signal changes in the healthy brain during deepening anesthesia. We studied the effect of thiopental bolus on functional magnetic resonance imaging (fMRI) in the healthy brain using porcine model. In five pigs (2–3 months, 20–25 kg), the control bolus prior to fMRI resulted in a change into burst-suppression. After the recovery of continuous EEG, fMRI (4 min) was performed with a single bolus of thiopental (11.4-17.1 mg/kg) administered 1 min after the onset of imaging. This was repeated in four of five pigs. Positive (6-8%) or negative (-3 to -8%) signal intensity changes correlated to the thiopental bolus injection were seen in the group average fMRI response. Positive response was 1.6% and negative response 2.3% of the total brain region of interest (ROI) voxels. Responding voxels were distributed more prominently in the thalamic ROI (4.5%) than in the cortical ROI (2.2%). The group average of unthresholded voxel time courses showed that the net effect of thiopental bolus was a small (0.5%) but a clear positive change in the thalamic region, while variance changed in the global level. In conclusion, this study is the first to show that significant signal intensity changes occur in fMRI response during the sudden deepening of thiopental anesthesia. However, these responses are neither anatomically constant nor global in the healthy swine brain.     

Reproducibility of BOLD localization of interictal activity in patients with focal epilepsy: intrasession and intersession comparisons

Object  

Simultaneous EEG-fMRI recordings allow the identification of haemodynamic changes induced by neuronal activity during ictal or interictal epileptiform events (IEDs). We evaluated the reproducibility of continuous EEG-fMRI (cEEG-fMRI) in patients with focal epilepsy.     

薛定谔滤波结合阈值算法在核磁脑电梯度伪迹去噪的应用

基于功能磁共振(fMRI)同步采集的脑电图(EEG)，在使用平均模板相减法(AAS)预处理之后，仍存在梯度残留尖峰伪迹。需要更准确地去除残留尖峰，以减少基于频率的活动推断的干扰，降低时间序列之间的虚假相关性。本文针对EEG数据中尖峰伪迹的特性，先使用薛定谔滤波方法分解并识别包含尖峰的EEG数据，自动减去与EEG幅度相差较大的大部分尖峰成分，然后使用幅度阈值方法，通过逆补余误差定位与EEG幅度相当的残留尖峰，实现对尖峰伪迹的定位与去除。对于模拟信号，该方法得到的信号幅值误差(Er)较薛定谔滤波方法平均提高24.95%，信噪比(SNR)较薛定谔滤波方法提高27.13%；对于真实信号，本文方法得到皮尔逊相关系数明显小于另外4种方法，去噪效果较薛定谔滤波方法提升11.42%。无论是尖峰位于波形波谷，还是高频波动幅度与峰值相当的情况下，薛定谔滤波结合阈值算法较其他方法尖峰识别精度和去噪效果明显提高。此去噪方法为EEG-fMRI的融合研究提供了强有力的支持。     

A brief introduction to functional MRI.

IEEE Engineering in Medicine and Biology Magazine focuses on modern methods for the analysis of data from functional magnetic resonance imaging (fMRI) studies. Accordingly, the guest editors have seen fit to begin with a brief article on the history, mechanisms and methods behind fMRI. This is followed by the presentation of recent significant progress in paradigm design for fMRI as well as development of other methods for assessing the functional anatomy of the human brain, such as diffusion tensor imaging, for mapping white matter fiber tracts. Thus, the future appears to promise a more integrative approach to functional brain imaging, in which data from multiple modalities are entered into comprehensive analyses of brain function and connectivity.     

Accessory hardware for neuromuscular measurements during functional MRI experiments

Functional magnetic resonance imaging (fMRI) is increasingly being used for human sensorimotor function research. Few studies, however, have been able to acquire peripheral neuromuscular data (e.g. joint force and electromyograms [EMG]) online with fMRI measurements. The lack of muscle output information hinders interpretation of fMRI data and prevents investigators from designing more sophisticated experiments. We developed a data-acquisition system that can record force and EMG data simultaneously with fMRI signals. This system included three major components: a hydraulic, pressure transducer-based force measurement device, a well-shielded EMG-recording apparatus, and a visual feedback setup. The three components were integrated with a laptop computer equipped with data acquisition hardware and software. System evaluation experiments demonstrated that no significant mutual interference occurred between the MRI environment and the force-EMG data-acquisition system, i.e. the system can record relatively noise-free force and EMG signals while maintaining the quality of fMRI data. The system has enabled us to study human motor control function involving motor tasks such as handgrip and finger pinch that require precision control of force and EMG. This accessory equipment can facilitate fMRI investigations of human sensorimotor function.     

Towards human BCI applications based on cognitive brain systems: an investigation of neural signals recorded from the dorsolateral prefrontal cortex.

One of the critical issues in brain-computer interface (BCI) research is how to translate a person's intention into brain signals for controlling computer programs. The motor system is currently the primary focus, where signals are obtained during imagined motor responses. However, cognitive brain systems are also attractive candidates, in that they may be more amenable to conscious control, yielding better regulation of magnitude and duration of localized brain activity. We report on a proof of principle study for the potential use of a higher cognitive system for BCI, namely the working memory (WM) system. We show that mental calculation reliably activates the WM network as measured with functional magnetic resonance imaging (fMRI). Moreover, activity in the dorsolateral prefrontal cortex (DLPFC) indicates that this region is active for the duration of mental processing. This supports the notion that DLPFC can be activated, and remains active, at will. Further confirmation is obtained from a patient with an implanted electrode grid for diagnostic purposes, in that gamma power within DLPFC increases during mental calculation and remains elevated for the duration thereof. These results indicate that cortical regions involved in higher cognitive functions may serve as a readily self-controllable input for BCI applications. It also shows that fMRI is an effective tool for identifying function-specific foci in individual subjects for subsequent placement of cortical electrodes. The fact that electrocorticographic (ECoG) signal confirmed the functional localization of fMRI provides a strong argument for incorporating fMRI in BCI research.     

Opportunities and Challenges in MR-Compatible Robotics

This article gives an overview of the opportunities offered by a novel technique, the components of MR-compatible robotic systems, the history of MR-compatible robotics, and the main challenges and directions for future developments. Robotic interfaces can dynamically interact with humans performing movements and can be used to study neuromuscular adaptation. A haptic interface that could be used in conjunction with fMRI would enable neuroscientists to view and investigate the brain mechanisms involved in human motor control and related dysfunctions. This could become a critical tool in neuroscience and rehabilitation. It is concluded that with all robotic systems for medical applications, the community needs to demonstrate the ability of such systems in assisting surgeons and augmenting their performance.     

Accessory hardware for neuromuscular measurements during functional MRI experiments.

Functional magnetic resonance imaging (fMRI) is increasingly being used for human sensorimotor function research. Few studies, however, have been able to acquire peripheral neuromuscular data (e.g. joint force and electromyograms [EMG]) online with fMRI measurements. The lack of muscle output information hinders interpretation of fMRI data and prevents investigators from designing more sophisticated experiments. We developed a data-acquisition system that can record force and EMG data simultaneously with fMRI signals. This system included three major components: a hydraulic, pressure transducer-based force measurement device, a well-shielded EMG-recording apparatus, and a visual feedback setup. The three components were integrated with a laptop computer equipped with data acquisition hardware and software. System evaluation experiments demonstrated that no significant mutual interference occurred between the MRI environment and the force-EMG data-acquisition system, i.e. the system can record relatively noise-free force and EMG signals while maintaining the quality of fMRI data. The system has enabled us to study human motor control function involving motor tasks such as handgrip and finger pinch that require precision control of force and EMG. This accessory equipment can facilitate fMRI investigations of human sensorimotor function.     

Source analysis of lesional frontal-lobe epilepsy

Patients with frontal-lobe epilepsy comprise the second largest group undergoing epilepsy surgery. It has been reported that the difficulty in localizing the epileptogenic zone in these patients is due to the rapid spread of the epileptiform activity within the frontal lobe and to adjoining regions of the brain. We formulated the question of whether the functional localization of dynamic sources of interictal activity in patients with well-defined frontal lesions would yield clear evidence regarding both the topology of the primary sources in relation to the epileptogenic lesion and the pattern of spread of the epileptiform activity throughout the brain. In order to achieve this, we used high-resolution EEG recordings combined with MRI, and advanced source-reconstruction algorithms. In this article, the source-imaging procedures used will be discussed extensively, based on one exemplary patient with complex partial FLE     

Using nonlinear dynamic metric tools for characterizing brainstructures

The collective dynamic behavior of the neural mass of different brain structures can be assessed from electroencephalographic recordings with depth electrodes measurements at regular time intervals (EEG time series). In recent years, the cheery of nonlinear dynamics has developed methods for quantitative analysis of experimental time series. The aim of this article is to report a new attempt to characterize global brain dynamics through electrical activity using these nonlinear dynamical metric tools. In addition, the authors study the dependence of the metric magnitudes on brain structure. The methods employed in this work are independent of any modeling of brain activity. They rely solely on the analysis of data obtained from a single variable time series. The authors analyze the EEG signals from depth electrodes that intersect different brain anatomical structures in a patient with refractory epilepsy prone to surgical treatment. The electrical signal provided by this type of electrode guarantees a low noise signal     

脑电BCI系统的软硬件开发平台发展现状

脑-机接口系统(brain-computer interface,BCI)是一种将大脑活动信息直接转换为人工输出的系统,允许用户通过思维 活动直接控制外部设备。 脑电图技术(electroencephalogram,EEG)可以实时获取大脑活动产生的神经生理电信息,具有无创、低 廉、高时间分辨率等优点,是 BCI 获取大脑活动信息的主流方式之一。 脑电 BCI 系统具有脑电信号采集、处理和输出结果的功 能,能够诱发特征脑电,并控制外部设备,在康复、医疗诊断和神经科学研究等领域具有巨大的应用价值。 随着脑电 BCI 系统应 用需求不断增加,确保其快速高效地部署和应用的技术越来越重要。 结合近些年脑电 BCI 系统研究和应用,综合论述目前用于 开发脑电采集和编解码的硬件和软件平台的技术,分析归纳其当前现状与未来趋势,以促进开发脑电 BCI 系统软硬件平台的有 效发展。     

Functional brain imaging using EPI at 3 T

Functional magnetic resonance imaging (fMRI) is becoming an important tool in the mapping of brain activation. However there are two main concerns that need to be answered before functional imaging can be considered truly useful as a neurophysiological tool. The first is that the detected activation may be derived from large veins and, thus, be spatially separate from the underlying brain activity. The second is the incomplete understanding of the brain transfer function and its relation to brain activity, blood flow, and metabolism. This work contains initial results that will help address these points. Models of the brain vasculature predict that signal changes on SE (spin-echo) images are expected to be much smaller in magnitude but very accurate in localizing true areas of activation than on GE (gradient-echo) images which are susceptable to large veins. By comparing activation from SE and GE EPI at 3 T, we have shown that the regions of activation are spatially very similar, suggesting that GE activation is closely linked to the underlying brain activity. We have identified an experimental impulse response of the brain following 8-s visual stimulation. This impulse response can be used to successfully predict the frequency response obtained experimentally and its shape suggests a resonance phenomenon. This suggests the brain transfer function can be modeled from linear response theory corresponding to the inherent feedback control mechanisms of the brain homeostasis. Continuation of this early work will help to identify the links between fMRI signal change and underlying brain physiology.     

Three-Dimensional Source Imaging From Simultaneously Recorded ERP and BOLD-fMRI

We present the 3-D EEG source images reconstructed by using the minimum norm least square (MNLS) method in combination with the functional magnetic resonance imaging (fMRI) statistical parametric mapping. For a group of five normal subjects, electroencephalogram (EEG) and fMRI signals responding to the full-view checkerboard pattern-reversal visual stimulation were recorded simultaneously and separately. The electrical activities in V1/V2 and V5 were successfully imaged in the N75-P100-N145 and P100-N145 components, respectively. The present results demonstrate the merits of high-resolution spatiotemporal functional neuroimaging by integrating the simultaneously recorded fMRI and EEG data.     

Detecting evoked potentials with SVD- and ICA-based statistical models.

In the functional diagnostics of human sensor systems, the analysis of stimulus responses embedded in an electroencephalogram (EEG), e.g. evoked potentials (EPs), is of high relevance for an objective electrophysiological assessment. The aim of this work is to detect weak EPs from highly contaminated signal traces. In principle this can be done using methods of spatiotemporal signal processing, which simultaneously increase the weak SNR (signal-to-noise ratio). However, methods based on any a priori knowledge of spatial or temporal properties as well as the propagation speed and direction are not applicable. Models with adjustable signal properties similar to real cortical activity are necessary for the development and evaluation of new methods of spatiotemporal signal processing. A model is needed which can be used in forward- and inverse-projection calculations. This study aims to develop a signal generator of the background EEG activity with embedded EPs of fully adjustable signal parameters. The study also compares the results of modeled signal analysis by known methods for signal decomposition, SVD (singular value decomposition) and ICA (independent component analysis).     

Exploratory analysis of brain connectivity with ICA.

Covariance-based methods of exploration of functional connectivity of the brain from functional magnetic resonance imaging (fMRI) experiments, such as principal component analysis (PCA) and structural equation modeling (SEM), require a priori knowledge such as an anatomical model to infer functional connectivity. In this research, a hybrid method, combining independent component analysis (ICA) and SEM, which is capable of deriving functional connectivity in an exploratory manner without the need of a prior model is introduced. The spatial ICA (SICA) derives independent neural systems or sources involved in task-related brain activation, while an automated method based on the SEM finds the structure of the connectivity among the elements in independent neural systems. Unlike second-order approaches used in earlier studies, the task-related neural systems derived from the ICA provide brain connectivity in the complete statistical sense. The use and efficacy of this approach is illustrated on two fMRI datasets obtained from a visual task and a language reading task.