Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data

The present study aims at finding the optimal inverse solution for the bioelectromagnetic inverse problem in the absence of reliable a priori information about the generating sources. Three approaches to tackle this problem are compared theoretically: the maximum-likelihood approach, the minimum norm approach, and the resolution optimization approach. It is shown that in all three of these frameworks, it is possible to make use of the same kind of a priori information if available, and the same solutions are obtained if the same a priori information is implemented. In particular, they all yield the minimum norm pseudoinverse (MNP) in the complete absence of such information. This indicates that the properties of the MNP, and in particular, its limitations like the inability to localize sources in depth, are not specific to this method but are fundamental limitations of the recording modalities. The minimum norm solution provides the amount of information that is actually present in the data themselves, and is therefore optimally suited to investigate the general resolution and accuracy limits of EEG and MEG measurement configurations. Furthermore, this strongly suggests that the classical minimum norm solution is a valuable method whenever no reliable a priori information about source generators is available, that is, when complex cognitive tasks are employed or when very noisy data (e.g., single-trial data) are analyzed. For that purpose, an efficient and practical implementation of this method will be suggested and illustrated with simulations using a realistic head geometry.     

Decoding word and category-specific spatiotemporal representations from MEG and EEG

The organization and localization of lexico-semantic information in the brain has been debated for many years. Specifically, lesion and imaging studies have attempted to map the brain areas representing living versus nonliving objects, however, results remain variable. This may be due, in part, to the fact that the univariate statistical mapping analyses used to detect these brain areas are typically insensitive to subtle, but widespread, effects. Decoding techniques, on the other hand, allow for a powerful multivariate analysis of multichannel neural data. In this study, we utilize machine-learning algorithms to first demonstrate that semantic category, as well as individual words, can be decoded from EEG and MEG recordings of subjects performing a language task. Mean accuracies of 76% (chance=50%) and 83% (chance=20%) were obtained for the decoding of living vs. nonliving category or individual words respectively. Furthermore, we utilize this decoding analysis to demonstrate that the representations of words and semantic category are highly distributed both spatially and temporally. In particular, bilateral anterior temporal, bilateral inferior frontal, and left inferior temporal-occipital sensors are most important for discrimination. Successful intersubject and intermodality decoding shows that semantic representations between stimulus modalities and individuals are reasonably consistent. These results suggest that both word and category-specific information are present in extracranially recorded neural activity and that these representations may be more distributed, both spatially and temporally, than previous studies suggest.     

Bayesian model averaging in EEG/MEG imaging

In this paper, the Bayesian Theory is used to formulate the Inverse Problem (IP) of the EEG/MEG. This formulation offers a comparison framework for the wide range of inverse methods available and allows us to address the problem of model uncertainty that arises when dealing with different solutions for a single data. In this case, each model is defined by the set of assumptions of the inverse method used, as well as by the functional dependence between the data and the Primary Current Density (PCD) inside the brain. The key point is that the Bayesian Theory not only provides for posterior estimates of the parameters of interest (the PCD) for a given model, but also gives the possibility of finding posterior expected utilities unconditional on the models assumed. In the present work, this is achieved by considering a third level of inference that has been systematically omitted by previous Bayesian formulations of the IP. This level is known as Bayesian model averaging (BMA). The new approach is illustrated in the case of considering different anatomical constraints for solving the IP of the EEG in the frequency domain. This methodology allows us to address two of the main problems that affect linear inverse solutions (LIS): (a) the existence of ghost sources and (b) the tendency to underestimate deep activity. Both simulated and real experimental data are used to demonstrate the capabilities of the BMA approach, and some of the results are compared with the solutions obtained using the popular low-resolution electromagnetic tomography (LORETA) and its anatomically constraint version (cLORETA).     

Source analysis of interictal spikes in polymicrogyria: loss of relevant cortical fissures requires simultaneous EEG to avoid MEG misinterpretation

PURPOSE: Multiple source analysis of interictal EEG and MEG spikes was used to identify irritative zones in polymicrogyria (PMG). Spike onset times and source localization were compared between both modalities. PMG is characterized by a marked loss of deep cortical fissures. Hence, differences between EEG and MEG were expected since MEG signals are predominantly generated from tangentially orientated neurons in fissures. PATIENTS: We studied 7 children and young adults (age 7.5 to 19 years) with localization-related epilepsy and unilateral polymicrogyria (PMG) as defined from anatomical MRI. METHODS: 122-channel whole-head MEG and 32-channel EEG were recorded simultaneously for 25 to 40 min. Using the BESA program, interictal spikes were identified visually and used as templates to search for similar spatio-temporal spike patterns throughout the recording. Detected similar spikes (r > 0.85) were averaged, high-pass filtered (5 Hz) to enhance spike onset, and subjected to multiple spatio-temporal source analysis. Source localization was visualized by superposition on T1-weighted MRI and compared to the lesion. RESULTS: Nine spike types were identified in seven patients (2 types in 2 patients). Eight out of nine EEG sources and seven MEG sources modeling spike onset were localized within the visible lesion. EEG spike onset preceded MEG significantly in two spike types by 19 and 25 ms. This was related to radial onset activity in EEG while MEG localized propagated activity. In one case, the earliest MEG spike activity was localized to the normal hemisphere while the preceding radial EEG onset activity was localized within the lesion. Distances between EEG and MEG onset sources varied markedly between 9 and 51 mm in the eight spike types with concordant lateralization. CONCLUSION: Interictal irritative zones were localized within the lesion in PMG comparable to other malformations, e.g., FCD. Discrepancies in MEG and EEG were related to the lack of deep fissures in PMG. In two cases, MEG was blind to the onset of radial interictal spike activity and localized propagated spike activity. In two other cases, MEG localized to the more peripheral parts of the irritative zone. Simultaneous EEG recordings with MEG and multiple source analysis are required to avoid problems of MEG interpretation.     

A MEG investigation of somatosensory processing in the rhesus monkey

The use of minimally and non-invasive neuroimaging methods in animal models has sharply increased over the past decade. Such studies have enhanced understanding of the neural basis of the physical signals quantified by these tools, and have addressed an assortment of fundamental and otherwise intractable questions in neurobiology. To date, these studies have almost exclusively utilized positron-emission tomography or variants of magnetic resonance based imaging. These methods provide largely indirect measures of brain activity and are strongly reliant on intact vasculature and normal blood-flow, which is known to be compromised in many clinical conditions. The current study provides the first demonstration of whole-head magnetoencephalography (MEG), a non-invasive and direct measure of neuronal activity, in a rhesus monkey, and in the process supplies the initial data on systems-level dynamics in somatosensory cortices. An adult rhesus monkey underwent three separate studies of tactile stimulation on the pad of the right second or fifth digit as whole-head MEG data were acquired. The neural generators of the primary neuromagnetic components were localized using an equivalent-current-dipole model. Second digit stimulation produced an initial cortical response peaking  16 ms after stimulus onset in the contralateral somatosensory cortices, with a later response at  96 ms in an overlapping or nearby neural area with a roughly orthogonal orientation. Stimulation of the fifth digit produced similar results, the main exception being a substantially weaker later response. We believe the 16 ms response is likely the monkey homologue of the human M50 response, as both are the earliest cortical response and localize to the contralateral primary somatosensory area. Thus, these data suggest that mechanoreception in nonhuman primates operates substantially faster than that in adult humans. More broadly, these results demonstrate that it is feasible to use current human whole-head MEG instrumentation to record neuromagnetic responses in adult rhesus monkeys. Nonhuman primate models of human disease provide the closest phylogenetic link to humans. The present, non-invasive imaging study could promote exciting translational integration of invasive animal studies and non-invasive human studies, allowing experimentally induced deficits and pharmacological treatments to be interpreted in light of resulting brain network interactions.     

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (-50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICA-decomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed non-artifact processes themselves.     

Headache-related circuits and high frequencies evaluated by EEG,MRI, PET as potential biomarkers to differentiate chronic and episodic migraine: Evidence from a systematic review

BackgroundThe diagnosis of migraine is mainly clinical and self-reported, which makes additional examinations unnecessary in most cases. Migraine can be subtyped into chronic (CM) and episodic (EM). Despite the very high prevalence of migraine, there are no evidence-based guidelines for differentiating between these subtypes other than the number of days of migraine headache per month. Thus, we consider it timely to perform a systematic review to search for physiological evidence from functional activity (as opposed to anatomical structure) for the differentiation between CM and EM, as well as potential functional biomarkers. For this purpose, Web of Science (WoS), Scopus, and PubMed databases were screened.FindingsAmong the 24 studies included in this review, most of them (22) reported statistically significant differences between the groups of CM and EM. This finding is consistent regardless of brain activity acquisition modality, ictal stage, and recording condition for a wide variety of analyses. That speaks for a supramodal and domain-general differences between CM and EM that goes beyond a differentiation based on the days of migraine per month. Together, the reviewed studies demonstrates that electro- and magneto-physiological brain activity (M/EEG), as well as neurovascular and metabolic recordings from functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), show characteristic patterns that allow to differentiate between CM and EM groups.ConclusionsAlthough a clear brain activity-based biomarker has not yet been identified to distinguish these subtypes of migraine, research is approaching headache specialists to a migraine diagnosis based not only on symptoms and signs reported by patients. Future studies based on M/EEG should pay special attention to the brain activity in medium and fast frequency bands, mainly the beta band. On the other hand, fMRI and PET studies should focus on neural circuits and regions related to pain and emotional processing.     

Cortical evidence of the perceptual backward masking effect on /l/ and /r/ sounds from a following vowel in Japanese speakers

We examined the influence of stimulus duration of foreign consonant vowel stimuli on the MMNm (magnetic counter part of mismatch negativity). In Experiment 1, /ra/ and /la/ stimuli were synthesized and subjects were native Japanese speakers who are known to have difficulty discriminating the stimuli. "Short" duration stimuli were terminated in the middle of the consonant-to-vowel transition (110 ms). They were nevertheless clearly identifiable by English speakers. A clear MMNm was observed only for short-duration stimuli but not for untruncated long-duration (150-ms) stimuli. We suggest that the diminished MMNm for longer duration stimuli result from more effective masking by the longer vowel part. In Experiment 2 we examined this hypothesis by presenting only the third formant (F3) component of the original stimuli, since the acoustic difference between /la/ and /ra/ is most evident in the third formant, whereas F1 and F2 play a major role in vowel perception. If the MMNm effect depends on the acoustic property of F3, a stimulus duration effect comparable to that found with the original /la/ and /ra/ stimuli might be expected. However, if the effect is attributable to the masking effect from the vowel, no influence of stimulus duration would be expected, since neither stimulus contains F1 and F2 components. In fact, the results showed that the "F3 only" stimuli did not show a duration effect; MMNm was always elicited independent of stimulus duration. The MMN stimulus duration effect is thus suggested to come from the backward masking of foreign consonants by subsequent vowels.     

Towards source volume estimation of interictal spikes in focal epilepsy using magnetoencephalography

Interictal spikes are a hallmark of cortical epileptogenicity; their spatial distribution in the cortex defines the so-called ‘irritative’ zone or spiking volume (SV). Delineating the SV precisely is a challenge during the presurgical evaluation of patients with epilepsy. Magnetoencephalography (MEG) recordings enable determination of the brain sources of epileptic spikes using source localization procedures. Most previous clinical MEG studies have relied on dipole modeling of epileptic spikes, which does not permit a volumetric estimation of the spiking cortex.In the present study, we propose a new source modeling procedure, Volumetric Imaging of Epileptic Spikes (VIES). In VIES, the SV is identified as the 3D region where sources of the high frequency activities (> 20 Hz) associated with epileptic spikes are distributed. We localized these sources using a beamforming approach (DICS, Dynamic Imaging of Coherent Neural Sources). To determine the optimal parameters and accuracy of the method, we compared the SV obtained by VIES with the SV defined by the invasive gold standard, intracranial stereotactic EEG recordings (SEEG), in 21 patients with focal epilepsy. Using rigorous validation criteria based on the exact anatomical location of SEEG contacts, we found that the overall sensitivity of VIES for detecting spiking SEEG contacts was 76% and its specificity for correctly identifying non-spiking SEEG contacts was 67%, indicating a good agreement between VIES and SEEG. Moreover, we found that classical dipole clustering was not informative in 9/21 patients, while VIES enable to delineate the SV in all patients. For the 12 patients having a SV delineated both with VIES and dipole clustering, VIES method had higher sensitivity and lower specificity. This proof-of-concept study shows that VIES is a promising approach to non-invasive estimation of the SV in focal epilepsy.     

多模态磁共振成像对新生儿缺血缺氧性脑病诊断及预后的研究进展

MRI现已成为新生儿缺血缺氧性脑病(hypoxic-ischemic encephalopathy,HIE)的必备检查方式,但常规MRI检查技术对于早期发现HIE的诊断敏感性较低,与常规MRI相比,多模态MRI成像技术,如弥散加权成像(diffusion weighted imaging,DWI)、弥散张量成像(dif...     

Mode and site of acupuncture modulation in the human brain: 3D (124-ch) EEG power spectrum mapping and source imaging

This study determined: (a) if acupuncture stimulation at a traditional site might modulate ongoing EEG as compared with stimulation of a control site; (b) if high-frequency vs. low-frequency stimulation could exert differential effects of acupuncture; (c) if the observed effects of acupuncture were specific to certain EEG bands; and (d) if the acupuncture effect could be isolated at a specific scalp field, with its putative underlying intracranial source. Twelve healthy male volunteers (age range 22-35) participated in two experimental sessions separated by 1 week, which involved transcutaneous acupoint stimulation at selected acupoint (Li 4, HeGu) vs. a mock point at the fourth interosseous muscle area on the left hand in high (HF: 100 Hz) vs. low-frequency (LF: 2 Hz) stimulation by counter-balanced order. 124-ch EEG data were used to analyze the Delta, Theta, Alpha-1, Alpha-2, Beta, and Gamma bands. The absolute EEG powers (muv2) at focal maxima across three stages (baseline, stimulation, post) were examined by two-way (condition, stage) repeated measures ANOVA. The activity of the Theta power significantly decreased (P = 0.02), compared with control during HF but not LF stimulation at acupoint stimulation, however, there was no study effect at the mock point. A decreased Theta EEG power was prominent at the frontal midline sites (FCz, Fz) and the contralateral right hemisphere front site (FCC2h). In contrast, the Theta power of low-frequency stimulation showed an increase from the baseline as those in both controlled mock point stimulations. The observed high-frequency acupoint stimulation effects of Theta EEG were only present during, but not after, simulation. The topographic Theta activity was tentatively identified to originate from the intracranial current source in cingulate cortex, likely ACC. It is likely that short-term cortical plasticity occurs during high-frequency but not low-frequency stimulation at the HeGu point, but not mock point. We suggest that HeGu acupuncture stimulation modulates limbic cingulum by a frequency modulation mode, which then may damp nociceptive processing in the brain.     

婴儿痉挛的预后与EEG和头颅CT结果的关系

DuringJanuary１９９７～December２００１，４７infantswithspasmwererecruited．Follow－uprangedfrom１８monthsto５years．Weanalyzedtherelationshipbetweenelectroencephalogram（EEG），headcomputerized（CT）anddistalprognosis．Hereisthereport．１Subjectandmethod１．１Subject４７infantsenteredthestudyincluding２６maleand２１female．１４infantsaged～６months，２６aged～１２months，４aged～１８months，３aged～２４months．Courseofdiseaserangedfrom２daysto１６months．Followupranged１８yearsto５years（…     

MEG/EEG sources of the 170-ms response to faces are co-localized in the fusiform gyrus

The 170-ms electrophysiological processing stage (N170 in EEG, M170 in MEG) is considered an important computational step in face processing. Hence its neuronal sources have been modelled in several studies. The current study aimed to specify the relation of the dipolar sources underlying N170 and M170. Whole head EEG and MEG were measured simultaneously during the presentation of unfamiliar faces. An Independent Component Analysis (ICA) was applied to the data prior to localization. N170 and M170 were then modelled with a pair of dipoles in a four-shell ellipse (EEG)/homogeneous sphere (MEG) arranged symmetrically across midline. The dipole locations were projected onto the individual structural MR brain images. Dipoles were localized in fusiform gyri in ten out of eleven individuals for EEG and in seven out of eleven for MEG. N170 and M170 were co-localized in the fusiform gyrus in six individuals. The ICA shifted some of the single-subject dipoles up from cerebellum to fusiform gyrus mainly due to the removal of cardiac activity. The group mean dipole locations were also found in posterior fusiform gyri, and did not differ significantly between EEG and MEG. The result was replicated in a repeated measurement 3 months later.     

脑性瘫痪儿童脑电图纺锤波变化分析

目的了解脑性瘫痪(CerebralPalsy,CP)儿童脑电图纺锤波变化与临床表现关系。方法检查179例CP儿童脑电图并对其中有纺锤波变化者进行分析。结果CP儿童纺锤波变化中缺失占72％,且双侧痉挛型纺锤波双侧缺失占83％。结论儿童纺锤波的变化可作为观察CP儿童脑器质性损害的指征之一。     

睡眠中发作症状的脑电图特征及其与睡眠分期的关系

目的探讨睡眠中发作症状的脑电图特征及其与睡眠分期的关系。方法统计分析2012年9月至2014年9月收治的86例睡眠中发作症状患者的临床资料。结果夜发性额叶癫痫患者的痫样波检出率57.1%(12/21)显著高于睡眠肌阵挛、夜惊症、梦游症、梦魇患者9.7%(3/31)、18.8%(3/16)、0、0(P0.05);86例患者中,NREMⅠ期、NREMⅡ期是睡眠肌阵挛集中发生的时期,NREMⅢ期、NREMⅣ期是夜惊症、梦游症集中发生的时期,REM期是梦魇集中发生的时期,NREMⅠ期、NREMⅡ期是夜发性额叶癫痫主要发生的时期,其次为NREMⅢ期、NREMⅣ期,最后为REM期。结论不同睡眠中发作症状的脑电图特征差异显著,和睡眠分期关系密切,临床可以依据脑电图特征对睡眠中发作症状患者的疾病类型进行诊断,从而为及时准确地治疗和改善患者预后提供良好的前提条件。     

Combining early post-resuscitation EEG and HRV features improves the prognostic performance in cardiac arrest model of rats

Objective

Early and reliable prediction of neurological outcome remains a challenge for comatose survivors of cardiac arrest (CA). The purpose of this study was to evaluate the predictive ability of EEG, heart rate variability (HRV) features and the combination of them for outcome prognostication in CA model of rats.

DWI和最小ADC值鉴别诊断儿童颅内间变性室管膜瘤与室管膜瘤

目的 探讨DWI和最小ADC值鉴别诊断儿童颅内间变性室管膜瘤与室管膜瘤的价值。方法 回顾性分析22例经手术及病理证实的儿童颅内间变性室管膜瘤及室管膜瘤的MRI资料,其中室管膜瘤8例、间变性室管膜瘤14例,所有患儿术前均接受MR平扫、增强和DWI检查。测量肿瘤实质部分的最小ADC值,比较两者之间的差异,并以ROC曲线分析其诊断效能。结果 间变性室管膜瘤最小ADC值为（0.579±0.245）×10^-3 mm²/s,室管膜瘤最小ADC值为（0.943±0.128）×10^-3 mm²/s,差异有统计学意义（t=3.881,P=0.001）。ROC曲线结果显示,AUC为0.884（P=0.003）,以最小ADC值=0.695×10^-3 mm²/s为临界值,鉴别诊断2种肿瘤的敏感度为100%,特异度为71.4%。结论 最小ADC值在儿童颅内间变性室管膜瘤与室管膜瘤的鉴别诊断中具有较高临床应用价值,可作为常规MR检查的有效补充。