Mapping causal interregional influences with concurrent TMS–fMRI

Transcranial magnetic stimulation (TMS) produces a direct causal effect on brain activity that can now be studied by new approaches that simultaneously combine TMS with neuroimaging methods, such as functional magnetic resonance imaging (fMRI). In this review we highlight recent concurrent TMS–fMRI studies that illustrate how this novel combined technique may provide unique insights into causal interactions among brain regions in humans. We show how fMRI can detect the spatial topography of local and remote TMS effects and how these may vary with psychological factors such as task-state. Concurrent TMS–fMRI may furthermore reveal how the brain adapts to so-called virtual lesions induced by TMS, and the distributed activity changes that may underlie the behavioural consequences often observed during cortical stimulation with TMS. We argue that combining TMS with neuroimaging techniques allows a further step in understanding the physiological underpinnings of TMS, as well as the neural correlated of TMS-evoked consequences on perception and behaviour. This can provide powerful new insights about causal interactions among brain regions in both health and disease that may ultimately lead to developing more efficient protocols for basic research and therapeutic TMS applications. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Combining TMS and EEG Offers New Prospects in Cognitive Neuroscience

The combination of brain stimulation by transcranial magnetic stimulation (TMS) with simultaneous electroencephalographic (EEG) imaging has become feasible due to recent technical developments. The TMS-EEG integration provides real-time information on cortical reactivity and connectivity through the analysis of TMS-evoked potentials (TEPs), and how functional activity links to behavior through the study of TMS-induced modulations thereof. It reveals how these effects vary as a function of neuronal state, differing between individuals and patient groups but also changing rapidly over time during task performance. This review discusses the wide range of possible TMS-EEG applications and what new information may be gained using this technique on the dynamics of brain functions, hierarchical organization, and cortical connectivity, as well as on TMS action per se. An advance in the understanding of these issues is timely and promises to have a substantial impact on many areas of clinical and basic neuroscience.     

EEG Signatures of Dynamic Functional Network Connectivity States

The human brain operates by dynamically modulating different neural populations to enable goal directed behavior. The synchrony or lack thereof between different brain regions is thought to correspond to observed functional connectivity dynamics in resting state brain imaging data. In a large sample of healthy human adult subjects and utilizing a sliding windowed correlation method on functional imaging data, earlier we demonstrated the presence of seven distinct functional connectivity states/patterns between different brain networks that reliably occur across time and subjects. Whether these connectivity states correspond to meaningful electrophysiological signatures was not clear. In this study, using a dataset with concurrent EEG and resting state functional imaging data acquired during eyes open and eyes closed states, we demonstrate the replicability of previous findings in an independent sample, and identify EEG spectral signatures associated with these functional network connectivity changes. Eyes open and eyes closed conditions show common and different connectivity patterns that are associated with distinct EEG spectral signatures. Certain connectivity states are more prevalent in the eyes open case and some occur only in eyes closed state. Both conditions exhibit a state of increased thalamocortical anticorrelation associated with reduced EEG spectral alpha power and increased delta and theta power possibly reflecting drowsiness. This state occurs more frequently in the eyes closed state. In summary, we find a link between dynamic connectivity in fMRI data and concurrently collected EEG data, including a large effect of vigilance on functional connectivity. As demonstrated with EEG and fMRI, the stationarity of connectivity cannot be assumed, even for relatively short periods.     

Connecting Mean Field Models of Neural Activity to EEG and fMRI Data

Progress in functional neuroimaging of the brain increasingly relies on the integration of data from complementary imaging modalities in order to improve spatiotemporal resolution and interpretability. However, the usefulness of merely statistical combinations is limited, since neural signal sources differ between modalities and are related non-trivially. We demonstrate here that a mean field model of brain activity can simultaneously predict EEG and fMRI BOLD with proper signal generation and expression. Simulations are shown using a realistic head model based on structural MRI, which includes both dense short-range background connectivity and long-range specific connectivity between brain regions. The distribution of modeled neural masses is comparable to the spatial resolution of fMRI BOLD, and the temporal resolution of the modeled dynamics, importantly including activity conduction, matches the fastest known EEG phenomena. The creation of a cortical mean field model with anatomically sound geometry, extensive connectivity, and proper signal expression is an important first step towards the model-based integration of multimodal neuroimages.     

Reliability and Validity of Functional Neuroimaging Techniques for Identifying Language-Critical Areas in Children and Adults

Advances in neuroimaging technologies over the last 15 years have prompted their relatively widespread use in the study of brain mechanisms supporting language function in children and adults. We reviewed reliability and external validity studies of 3 of the most common functional imaging methods, functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and positron emission tomography (PET). Although reliability and validity reports for fMRI are generally quite favorable, significant variability was found across studies with respect to methodology, preventing in some cases either the assessment of the reliability of individual datasets, or cross-study comparisons. Reliability and validity reports of MEG are strong, yet methodological questions regarding optimal modeling techniques remain. PET investigators report good concordance of language maps with data from more invasive brain mapping techniques, but its use of radioactive tracers and poorer spatial and temporal resolution make it the least optimal of the 3 methods for language mapping. Investigations of the cortical networks supporting language function during development and into adulthood should be viewed in the context of the validity and reliability of the methods used, with careful attention to details regarding the methodologies employed in the acquisition and analysis of statistical maps.     

Reliability and validity of functional neuroimaging techniques for identifying language-critical areas in children and adults

Advances in neuroimaging technologies over the last 15 years have prompted their relatively widespread use in the study of brain mechanisms supporting language function in children and adults. We reviewed reliability and external validity studies of 3 of the most common functional imaging methods, functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and positron emission tomography (PET). Although reliability and validity reports for fMRI are generally quite favorable, significant variability was found across studies with respect to methodology, preventing in some cases either the assessment of the reliability of individual datasets, or cross-study comparisons. Reliability and validity reports of MEG are strong, yet methodological questions regarding optimal modeling techniques remain. PET investigators report good concordance of language maps with data from more invasive brain mapping techniques, but its use of radioactive tracers and poorer spatial and temporal resolution make it the least optimal of the 3 methods for language mapping. Investigations of the cortical networks supporting language function during development and into adulthood should be viewed in the context of the validity and reliability of the methods used, with careful attention to details regarding the methodologies employed in the acquisition and analysis of statistical maps.     

正常脑老化中静息态功能磁共振成像功能连接的研究进展

【摘要】静息态功能磁共振成像（fMRI）作为一种不需要任务刺激就能呈现功能脑影像的技术手段,在临床上被广泛应 用。基于静息态fMRI的静息态功能连接（RSFC）,作为一种重要的计算机辅助分析法,能够度量不同脑区的脑功能连接 强度,对脑老化相关的神经科学领域的研究具有重要意义。本文介绍了功能连接的基本概念,总结了近年来脑老化相关 的人脑功能连接的研究成果,最后提出了该研究领域存在的问题及未来的研究方向。     

Funktionelle Konnektivit?t im Schlaf

This review focuses on the functional connectivity of the brain in the transition from wakefulness to slow-wave sleep. Functional connectivity is defined as temporal coherence between neural events, which can be evaluated in multiple frequency bands. We will focus on combined electroencephalography/functional magnetic resonance imaging (EEG/fMRI) measurements acquired during wakefulness, in light and deep sleep and particularly on ultraslow fluctuations in the fMRI signal. These fluctuations have a neuronal origin, represent spatial coherence in functionally related brain regions and may function as carrier waves for higher frequency fluctuations and oscillations. Various fMRI analyses, such as seed analyses, independent component analyses and whole-brain connectivity analyses, show local and global trends in functionally connectivity of the brain in the transition from wakefulness to sleep that may provide critical markers of sleep stages.     

Task-dependent changes in cortical excitability and effective connectivity: a combined TMS-EEG study

The brain's electrical response to transcranial magnetic stimulation (TMS) is known to be influenced by exogenous factors such as the frequency and intensity of stimulation and the orientation and positioning of the stimulating coil. Less understood, however, is the influence of endogenous neural factors, such as global brain state, on the TMS-evoked response (TMS-ER). In the present study, we explored how changes in behavioral state affect the TMS-ER by perturbing the superior parietal lobule (SPL) with single pulses of TMS and measuring consequent differences in the frequency, strength, and spatial spread of TMS-evoked currents during the delay period of a spatial short-term memory task and during a period of passive fixation. Results revealed that task performance increased the overall strength of electrical currents induced by TMS, increased the spatial spread of TMS-evoked activity to distal brain regions, and increased the ability of TMS to reset the phase of ongoing broadband cortical oscillations. By contrast, task performance had little effect on the dominant frequency of the TMS-ER, both locally and at distal brain areas. These findings contribute to a growing body of work using combined TMS and neuroimaging methods to explore task-dependent changes in the functional organization of cortical networks implicated in task performance.     

Multimodal Integration of fMRI and EEG Data for High Spatial and Temporal Resolution Analysis of Brain Networks

Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis (sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when presenting novel and salient stimuli, respectively, are related to the neuronal activation in large-scale networks, operating at different latencies and associated with different functional processes.     

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.     

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.     

痛觉的脑成像研究进展

痛觉是日常生活中最重要的感觉之一,痛觉的机理非常复杂.随着20世纪90年代各种脑成像技术的成熟,现在人们对于大脑的痛觉处理过程有了更好的理解.综述了痛觉脑成像的研究方法,包括电生理技术和脑功能成像技术,重点关注了这些技术在痛觉研究方面的相关成果.指出了解剖学上的痛觉处理机制.并分析了目前痛觉脑功能成像研究存在的缺陷和不足.     

动态因果模型在脑网络研究中的应用进展

大脑各功能区之间的有效连接是脑科学研究领域的一个重要内容.研究在不同情形下相关脑区之间有效连接所构成的大脑网络,对于全面理解大脑的功能机制,治疗各种与大脑相关疾病,开发脑功能具有重要意义.动态因果模型是一种分析大脑有效连接的优势方法.结合功能性磁共振成像、脑电、近红外脑功能成像等3种检测技术,综述动态因果模型的相关研究...     

Processing of Syntactic Information Monitored by Brain Surface Current Density Mapping Based on MEG

The cortical network subserving language processing is likely to exhibit a high spatial and temporal complexity. Studies using brain imaging methods, like fMRI or PET, succeeded in identifying a number of brain structures that seem to contribute to the processing of syntactic structures, while their dynamic interaction remains unclear due to the low temporal resolution of the methods. On the other hand, ERP studies have revealed a great deal of the temporal dimension of language processing without being able to provide more than very coarse information on the localisation of the underlying generators. MEG has a temporal resolution similar to EEG combined with a better spatial resolution. In this paper, Brain Surface Current Density (BSCD) mapping in a standard brain model was used to identify statistically significant differences between the activity of certain brain regions due to syntactically correct and incorrect auditory language input. The results show that the activity in the first 600 ms after violation onset is mainly concentrated in the temporal cortex and the adjacent frontal and parietal areas of both hemispheres. The statistical analysis reveals significantly different activity mainly in both frontal and temporal cortices. For longer latencies above 250 ms, the differential activity is more prominent in the right hemisphere. These findings confirm other recent results that suggest right hemisphere involvement in auditory language processing. One interpretation might be that right hemisphere regions play an important role in repair and re-analysis processes in order to free the specialised left hemisphere language areas for processing further input.     

Methodology for Combined TMS and EEG

The combination of transcranial magnetic stimulation (TMS) with simultaneous electroencephalography (EEG) provides us the possibility to non-invasively probe the brain’s excitability, time-resolved connectivity and instantaneous state. Early attempts to combine TMS and EEG suffered from the huge electromagnetic artifacts seen in EEG as a result of the electric field induced by the stimulus pulses. To deal with this problem, TMS-compatible EEG systems have been developed. However, even with amplifiers that are either immune to or recover quickly from the pulse, great challenges remain. Artifacts may arise from the movement of electrodes, from muscles activated by the pulse, from eye movements, from electrode polarization, or from brain responses evoked by the coil click. With careful precautions, many of these problems can be avoided. The remaining artifacts can be usually reduced by filtering, but control experiments are often needed to make sure that the measured signals actually originate in the brain. Several studies have shown the power of TMS–EEG by giving us valuable information about the excitability or connectivity of the brain.     

Instrumentation for the measurement of electric brain responses to transcranial magnetic stimulation

There is described a 60-channel EEG acquisition system designed for the recording of scalp-potential distributions starting just 2.5ms after individual transcranial magnetic stimulation (TMS) pulses. The amplifier comprises gain-control and sample-and-hold circuits to prevent large artefacts from magnetically induced voltages in the leads. The maximum amplitude of the stimulus artefact during the 2.5ms gating period is 1.7 μV, and 5 ms after the TMS pulse it is only 0.9 μV. It is also shown that mechanical forces to the electrodes under the stimulator coil are a potential source of artefacts, even though, with chlorided silver wire and Ag/AgCl-pellet electrodes, the artefact is smaller than 1 μV. The TMS-compatible multichannel EEG system makes it possible to locate TMS-evoked electric activity in the brain.     

Changes in resting connectivity with age: a simultaneous electroencephalogram and functional magnetic resonance imaging investigation

Resting fluctuations in the blood oxygenation level-dependent signal have attracted considerable interest for their sensitivity to pathological brain processes. However, these analyses are susceptible to confound by nonneural physiological factors such as vasculature, breathing, and head movement which is a concern when investigating elderly or pathological groups. Here, we used simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) (EEG/fMRI) to constrain the analysis of resting state networks (RSNs) and identify aging differences. Four of 26 RSNs showed fMRI and EEG/fMRI group differences; anterior default-mode network, left frontal-parietal network, bilateral middle frontal, and postcentral gyri. Seven RSNs showed only EEG/fMRI differences suggesting the combination of these 2 methods might be more sensitive to age-related neural changes than fMRI alone. Five RSNs showed only fMRI differences and might reflect nonneural group differences. Activity within some EEG/fMRI RSNs was better explained by neuropsychological measures (Mini Mental State Examination and Stroop) than age. These results support previous studies suggesting that age-related changes in specific RSNs are neural in origin, and show that changes in some RSNs relate better to elderly cognition than age.     

Human brain cortical correlates of short-latency afferent inhibition: a combined EEG-TMS study

When linking in time electrical stimulation of the peripheral nerve with transcranial magnetic stimulation (TMS), the excitability of the motor cortex can be modulated to evoke clear inhibition, as reflected by the amplitude decrement in the motor-evoked potentials (MEPs). This specific property, designated short-latency afferent inhibition (SAI), occurs when the nerve-TMS interstimulus interval (ISI) is approximately 25 ms and is considered to be a corticothalamic phenomenon. The aim of the present study was to use the electroencephalographic (EEG) responses to navigated-TMS coregistration to better characterize the neuronal circuits underlying SAI. The present experimental set included magnetic resonance imaging (MRI)-navigated TMS and 60-channel TMS-compatible EEG devices. TMS-evoked EEG responses and MEPs were analyzed in eight healthy volunteers; ISIs between median nerve and cortical stimulation were determined relative to the latency of the individual N20 component of the somatosensory-evoked potential (SEP) obtained after stimulation of the median nerve. ISIs from the latency of the N20 plus 3 ms and N20 plus 10 ms were investigated. In all experimental conditions, TMS-evoked EEG responses were characterized by a sequence of negative deflections peaking at approximately 7, 44, and 100 ms alternating with positive peaks at approximately 30, 60, and 180 ms post-TMS. Moreover, ISI N20+3 ms modulated both EEG-evoked activity and MEPs. In particular, it inhibited MEP amplitudes, attenuated cortical P60 and N100 responses, and induced motor cortex beta rhythm selective decrement of phase locking. The findings of the present experiment suggest the cortical origin of SAI that could result from the cortico-cortical activation of GABAergic-mediated inhibition onto the corticospinal neurons modulated by cholinergic activation able to reducing intralaminar inhibition and promoting intracolumnar inhibition.     

Two-Dimensional Temporal Clustering Analysis for Patients with Epilepsy: Detecting Epilepsy-Related Information in EEG-fMRI Concordant,Discordant and Spike-Less Patients

EEG acquired simultaneously with fMRI (EEG-fMRI) is a multimodal method that has shown promise in mapping the seizure onset zone in patients with focal epilepsy. However, there are many instances when this method is unsuccessful or not applicable, and other data driven fMRI methods may be utilized. One such method is the two-dimensional temporal clustering analysis (2dTCA). In this study we compared the classic EEG-fMRI and 2dTCA performance in mapping regions related to the seizure onset region in 18 focal epilepsy patients (12 presenting interictal epileptiform discharges (IEDs), during EEG-fMRI acquisition) with Engel I or II surgical outcome. Activation maps of both 2dTCA timing outputs (positive and negative histograms) and EEG detected IEDs were computed and compared to the region of epilepsy surgical resection. Patients were evaluated in three categories based on frequency of EEG detected spiking during the MRI. EEG-fMRI maps were concordant to the epilepsy region in 5/12 subjects, four with frequent IEDs on EEG. The 2dTCA was successful in mapping 13/18 patients including 3/6 with no IEDs detected (10/12 with IEDs detected). The epilepsy-related activities were successfully mapped by both methods in only 4/12 patients. This work suggests that the epilepsy-related information detected by each method may be different: while EEG-fMRI is more accurate in patients with high rather than lower numbers of EEG detected IEDs; 2dTCA can be useful in evaluating patients even when no concurrent EEG spikes are detected or EEG-fMRI is not effective. Therefore, our results support that 2dTCA might be an alternative for mapping epilepsy-related BOLD activity in negative EEG-fMRI (6/7 patients) and spike-less patients.