High Temporal Resolution Of EEG Research Articles

Cognitive penetrability of scene representations based on horizontal image disparities

The structure of natural scenes is signaled by many visual cues. Principal amongst them are the binocular disparities created by the laterally separated viewpoints of the two eyes. Disparity cues are believed to be processed hierarchically, first in terms of local measurements of absolute disparity and second in terms of more global measurements of relative disparity that allow extraction of the depth structure of a scene. Psychophysical and oculomotor studies have suggested that relative disparities are particularly relevant to perception, whilst absolute disparities are not. Here, we compare neural responses to stimuli that isolate the absolute disparity cue with stimuli that contain additional relative disparity cues, using the high temporal resolution of EEG to determine the temporal order of absolute and relative disparity processing. By varying the observers’ task, we assess the extent to which each cue is cognitively penetrable. We find that absolute disparity is extracted before relative disparity, and that task effects arise only at or after the extraction of relative disparity. Our results indicate a hierarchy of disparity processing stages leading to the formation of a proto-object representation upon which higher cognitive processes can act.

Sparse algorithms for EEG source localization

Source localization using EEG is important in diagnosing various physiological and psychiatric diseases related to the brain. The high temporal resolution of EEG helps medical professionals assess the internal physiology of the brain in a more informative way. The internal sources are obtained from EEG by an inversion process. The number of sources in the brain outnumbers the number of measurements. In this article, a comprehensive review of the state-of-the-art sparse source localization methods in this field is presented. A recently developed method, certainty-based-reduced-sparse-solution (CARSS), is implemented and is examined. A vast comparative study is performed using a sixty-four-channel setup involving two source spaces. The first source space has 5004 sources and the other has 2004 sources. Four test cases with one, three, five, and seven simulated active sources are considered. Two noise levels are also being added to the noiseless data. The CARSS is also evaluated. The results are examined. A real EEG study is also attempted. Graphical Abstract.

Dynamic Electrode-to-Image (DETI) mapping reveals the human brain’s spatiotemporal code of visual information

A number of neuroimaging techniques have been employed to understand how visual information is transformed along the visual pathway. Although each technique has spatial and temporal limitations, they can each provide important insights into the visual code. While the BOLD signal of fMRI can be quite informative, the visual code is not static and this can be obscured by fMRI’s poor temporal resolution. In this study, we leveraged the high temporal resolution of EEG to develop an encoding technique based on the distribution of responses generated by a population of real-world scenes. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. Our analyses of the mapping results revealed that scenes undergo a series of nonuniform transformations that prioritize different spatial frequencies at different regions of scenes over time. This mapping technique offers a potential avenue for future studies to explore how dynamic feedforward and recurrent processes inform and refine high-level representations of our visual world.

Patient-Clinician Brain Response During Clinical Encounter and Pain Treatment.

The patient-clinician relationship is known to significantly affect the pain experience, as empathy, mutual trust and therapeutic alliance can significantly modulate pain perception and influence clinical therapy outcomes. The aim of the present study was to use an EEG hyperscanning setup to identify brain and behavioral mechanisms supporting the patient-clinician relationship while this clinical dyad is engaged in a therapeutic interaction. Our previous study applied fMRI hyperscanning to investigate whether brain concordance is linked with analgesia experienced by a patient while undergoing treatment by the clinician. In this current hyperscanning project we investigated similar outcomes for the patient-clinician dyad exploiting the high temporal resolution of EEG and the possibility to acquire the signals while patients and clinicians were present in the same room and engaged in a face-to-face interaction under an experimentally-controlled therapeutic context. Advanced source localization methods allowed for integration of spatial and spectral information in order to assess brain correlates of therapeutic alliance and pain perception in different clinical interaction contexts. Preliminary results showed that both behavioral and brain responses across the patient-clinician dyad were significantly affected by the interaction style.Clinical Relevance- The context of a clinical intervention can significantly impact the treatment of chronic pain. Effective therapeutic alliance, based on empathy, mutual trust, and warmth can improve treatment adherence and clinical outcomes. A deeper scientific understanding of the brain and behavioral mechanisms underlying an optimal patient-clinician interaction may lead to improved quality of clinical care and physician training, as well as better understanding of the social aspects of the biopsychosocial model mediating analgesia in chronic pain patients.

Characterizing the neural signature of face processing in Williams syndrome via multivariate pattern analysis and event related potentials

Face recognition ability is often reported to be a relative strength in Williams syndrome (WS). Yet methodological issues associated with the supporting research, and evidence that atypical face processing mechanisms may drive outcomes ‘in the typical range’, challenge these simplistic characterisations of this important social ability. Detailed investigations of face processing abilities in WS both at a behavioural and neural level provide critical insights. Here, we behaviourally characterised face recognition ability in 18 individuals with WS comparatively to typically developing children and adult control groups. A subset of 11 participants with WS as well as chronologically age matched typical adults further took part in an EEG task where they were asked to attentively view a series of upright and inverted faces and houses. State-of-the-art multivariate pattern analysis (MVPA) was used alongside standard ERP analysis to obtain a detailed characterisation of the neural profile associated with 1) viewing faces as an overall category (by examining neural activity associated with upright faces and houses), and to 2) the canonical upright configuration of a face, critically associated with expertise in typical development and often linked with holistic processing (upright and inverted faces). Our results show that while face recognition ability is not on average at a chronological age-appropriate level in individuals with WS, it nonetheless appears to be a relative strength within their cognitive profile. Furthermore, all participants with WS revealed a differential pattern of neural activity to faces compared to objects, showing a distinct response to faces as a category, as well as a differential neural pattern for upright vs. inverted faces. Nonetheless, an atypical profile of face orientation classification was found in WS, suggesting that this group differs from typical individuals in their face processing mechanisms. Through this innovative application of MVPA, alongside the high temporal resolution of EEG, we provide important new insights into the neural processing of faces in WS.

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns.

This paper presents a methodology for the recording and analysis of cortical visual evoked potentials (CVEPs) in response to various visual stimuli using 128-channel high-density electroencephalography (EEG). The specific aim of the described stimuli and analyses is to examine whether it is feasible to replicate previously reported CVEP morphological patterns elicited by an apparent motion stimulus, designed to simultaneously stimulate both ventral and dorsal central visual networks, using object and motion stimuli designed to separately stimulate ventral and dorsal visual cortical networks. Four visual paradigms are presented: 1. Randomized visual objects with consistent temporal presentation. 2. Randomized visual objects with inconsistent temporal presentation (or jitter). 3. Visual motion via a radial field of coherent central dot motion without jitter. 4. Visual motion via a radial field of coherent central dot motion with jitter. These four paradigms are presented in a pseudo-randomized order for each participant. Jitter is introduced in order to view how possible anticipatory-related effects may affect the morphology of the object-onset and motion-onset CVEP response. EEG data analyses are described in detail, including steps of data exportation from and importation to signal processing platforms, bad channel identificationand removal, artifact rejection, averaging, and categorization of average CVEP morphological pattern type based upon latency ranges of component peaks. Representative data show that the methodological approach is indeed sensitive in eliciting differential object-onset and motion-onset CVEP morphological patterns and may, therefore, be useful in addressing the larger research aim. Given the high temporal resolution of EEG and the possible application of high-density EEG in source localization analyses, this protocol is ideal for the investigation of distinct CVEP morphological patterns and the underlying neural mechanisms which generate these differential responses.

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

Multimodal Functional and Structural Brain Connectivity Analysis in Autism: A Preliminary Integrated Approach With EEG, fMRI, and DTI

This paper proposes a novel approach of integrating different neuroimaging techniques to characterize an autistic brain. Different techniques like EEG, fMRI and DTI have traditionally been used to find biomarkers for autism, but there have been very few attempts for a combined or multimodal approach of EEG, fMRI and DTI to understand the neurobiological basis of autism spectrum disorder (ASD). Here, we explore how the structural brain network correlate with the functional brain network, such that the information encompassed by these two could be uncovered only by using the latter. In this paper, source localization from EEG using independent component analysis (ICA) and dipole fitting has been applied first, followed by selecting those dipoles that are closest to the active regions identified with fMRI. This allows translating the high temporal resolution of EEG to estimate time varying connectivity at the spatial source level. Our analysis shows that the estimated functional connectivity between two active regions can be correlated with the physical properties of the structure obtained from DTI analysis. This constitutes a first step towards opening the possibility of using pervasive EEG to monitor the long-term impact of ASD treatment without the need for frequent expensive fMRI or DTI investigations.

EEG source estimation accuracy in presence of simulated cortical lesions.

Methods to reconstruct the neuroelectrical activity in the brain source space can be used to improve the spatial resolution of scalp-recorded EEG and to estimate the locations of electrical sources in the brain. This procedure can improve the investigation of the functional organization of the human brain, exploiting the high temporal resolution of EEG to follow the temporal dynamics of information processing. As for today, the uncertainties about the effects of inhomogeneities due to brain lesions preclude the adoption of EEG functional mapping on patients with lesioned brain. The aim of this work is to quantify the accuracy of a distributed source localization method in recovering extended sources of activated cortex when cortical lesions of different dimensions are introduced in simulated data. For this purpose, EEG source-distributed activity estimated from real data was modified including silent lesion areas. Then, for each simulated lesion, forward and inverse calculations were carried out to localize the produced scalp activity and the reconstructed cortical activity. Finally, the error induced in the reconstruction by the presence of the lesion was computed and analyzed in relation to the number of electrodes and to the size of the simulated lesion. Results returned values of global error in the whole cortex and of error in the non-lesioned area which are strongly dependent on the number of recorded scalp sensors, as they increase when a lower spatial sampling is performed on the scalp (64 versus 32 EEG channels). For increasing spatial sampling frequencies, the accuracy of the source reconstruction improves and even the presence of small lesions induces significantly higher error levels with respect to the lesion-free condition.

Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI

Objective. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. Approach. To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. Main results. The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (<35 Hz) frequency ranges. MBRLAF is computationally more demanding than RLAF, but highly effective in all EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. Significance. In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI.HighlightsWe present a new and reusable reference layer cap prototype for simultaneous EEG-fMRIWe introduce new algorithms for reducing EEG artifacts due to simultaneous fMRIThe algorithms combine a reference layer and adaptive filteringSeveral evaluation criteria suggest superior effectivity in terms of artifact reductionWe demonstrate that physiological EEG components are preserved

Large-Scale Functional Networks Identified from Resting-State EEG Using Spatial ICA.

Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1) An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2) A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.

Simultaneous Electroencephalography and Functional Magnetic Resonance Imaging and the Identification of Epileptic Networks in Children.

EEG/fMRI takes advantage of the high temporal resolution of EEG in combination with the high spatial resolution of fMRI. These features make it particularly applicable to the study of epilepsy in which the event duration (e.g., interictal epileptiform discharges) is short, typically less than 200 milliseconds. Interictal or ictal discharges can be identified on EEG and be used for source localization in fMRI analyses. The acquisition of simultaneous EEG/fMRI involves the use of specialized EEG hardware that is safe in the MR environment and comfortable to the participant. Advanced data analysis approaches such as independent component analysis conducted alone or sometimes combined with other, e.g., Granger Causality or "sliding window" analyses are currently thought to be most appropriate for EEG/fMRI data. These approaches make it possible to identify networks of brain regions associated with ictal and/or interictal events allowing examination of the mechanisms critical for generation and propagation through these networks. After initial evaluation in adults, EEG/fMRI has been applied to the examination of the pediatric epilepsy syndromes including Childhood Absence Epilepsy, Benign Epilepsy with Centrotemporal Spikes (BECTS), Dravet Syndrome, and Lennox-Gastaut Syndrome. Results of EEG/fMRI studies suggest that the hemodynamic response measured by fMRI may have a different shape in response to epileptic events compared to the response to external stimuli; this may be especially true in the developing brain. Thus, the main goal of this review is to provide an overview of the pediatric applications of EEG/fMRI and its associated findings up until this point.

Comparison of BCG artifact removal methods for evoked responses in simultaneous EEG–fMRI

Simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has gained attention due to the complimentary properties of the two imaging modalities. Their combined recording enables the study of brain function while taking advantage of the high temporal resolution of EEG and high spatial resolution of fMRI. However EEG data recorded inside the MR scanner is significantly contaminated by two main sources of artifacts: MR gradient artifacts and ballistocardiogram (BCG) artifacts. Most existing removal approaches for these artifacts fall into two main categories: average artifact subtraction (AAS) and optimal basis selection (OBS). While these techniques can improve the data quality significantly, highly effective removal of artifacts – particularly the BCG artifact – from the data is still lacking.Here, we compared two of the most commonly used algorithms for BCG artifact removal (OBS and AAS) based on the estimated signal-to-noise ratio (SNR) of auditory and visual evoked responses recorded during fMRI acquisition. We also further compared optimization of OBS for groups, and at the individual subject and run level. The results suggest that performance of the OBS algorithm can be significantly improved by choosing the optimum number of principal components. Furthermore, optimizing the number of principal components at the individual participant and run level results in significant improvements in the SNR of evoked responses compared to group optimization.

SSVEP signatures of binocular rivalry during simultaneous EEG and fMRI

BackgroundBinocular rivalry is a perceptual phenomenon that arises when two incompatible images are presented separately, one to each eye, and the observer experiences involuntary perceptual alternations between the two images. If the two images are flickering at two distinct frequencies, electroencephalography (EEG) can be used to track the frequency-tagged steady-state visually evoked potential (SSVEP) driven by each image as they compete for awareness, providing an objective measure of the subjective perceptual state. This spontaneous alternation in perceptual dominance is believed to be driven by neural processes across widespread regions in the brain, but the real-time mechanisms of these processes remain unclear. New MethodThe goal of this study was to determine the feasibility of investigating binocular rivalry using a simultaneous EEG-fMRI approach in order to leverage the high temporal resolution of EEG with the high spatial resolution of fMRI. ResultsWe have developed novel techniques for artifact removal and signal optimization for the rivalry-related SSVEP data collected simultaneously during fMRI. Comparison with Existing MethodsOur methods address several significant technical challenges of recording SSVEP data in the noisy fMRI environment, and enabled us to successfully reconstruct SSVEP signatures of rivalry in a group of healthy human subjects. ConclusionFurther development and application of these techniques will enable more comprehensive integration of EEG and fMRI data collected simultaneously and could have significant implications for EEG-fMRI studies of brain activity in general.

The Dynamics of Proactive and Reactive Cognitive Control Processes in the Human Brain

In this study, we leveraged the high temporal resolution of EEG to examine the neural mechanisms underlying the flexible regulation of cognitive control that unfolds over different timescales. We measured behavioral and neural effects of color-word incongruency, as different groups of participants performed three different versions of color-word Stroop tasks in which the relative timing of the color and word features varied from trial to trial. For this purpose, we used a standard Stroop color identification task with equal congruent-to-incongruent proportions (50%/50%), along with two versions of the "Reverse Stroop" word identification tasks, for which we manipulated the incongruency proportion (50%/50% and 80%/20%). Two canonical ERP markers of neural processing of stimulus incongruency, the frontocentral negative polarity incongruency wave (NINC) and the late positive component (LPC), were evoked across the various conditions. Results indicated that color-word incongruency interacted with the relative feature timing, producing greater neural and behavioral effects when the task-irrelevant stimulus preceded the target, but still significant effects when it followed. Additionally, both behavioral and neural incongruency effects were reduced by nearly half in the word identification task (Reverse Stroop 50/50) relative to the color identification task (Stroop 50/50), with these effects essentially fully recovering when incongruent trials appeared only infrequently (Reverse Stroop 80/20). Across the conditions, NINC amplitudes closely paralleled RTs, indicating this component is sensitive to the overall level of stimulus conflict. In contrast, LPC amplitudes were largest with infrequent incongruent trials, suggesting a possible readjustment role when proactive control is reduced. These findings thus unveil distinct control mechanisms that unfold over time in response to conflicting stimulus input under different contexts.

Fast fMRI provides high statistical power in the analysis of epileptic networks

EEG-fMRI is a unique method to combine the high temporal resolution of EEG with the high spatial resolution of MRI to study generators of intrinsic brain signals such as sleep grapho-elements or epileptic spikes. While the standard EPI sequence in fMRI experiments has a temporal resolution of around 2.5–3s a newly established fast fMRI sequence called MREG (Magnetic-Resonance-Encephalography) provides a temporal resolution of around 100ms. This technical novelty promises to improve statistics, facilitate correction of physiological artifacts and improve the understanding of epileptic networks in fMRI. The present study compares simultaneous EEG-EPI and EEG-MREG analyzing epileptic spikes to determine the yield of fast MRI in the analysis of intrinsic brain signals.Patients with frequent interictal spikes (>3/20min) underwent EEG-MREG and EEG-EPI (3T, 20min each, voxel size 3×3×3mm, EPI TR=2.61s, MREG TR=0.1s). Timings of the spikes were used in an event-related analysis to generate activation maps of t-statistics. (FMRISTAT, |t|>3.5, cluster size: 7 voxels, p<0.05 corrected). For both sequences, the amplitude and location of significant BOLD activations were compared with the spike topography.13 patients were recorded and 33 different spike types could be analyzed. Peak T-values were significantly higher in MREG than in EPI (p<0.0001). Positive BOLD effects correlating with the spike topography were found in 8/29 spike types using the EPI and in 22/33 spikes types using the MREG sequence. Negative BOLD responses in the default mode network could be observed in 3/29 spike types with the EPI and in 19/33 with the MREG sequence. With the latter method, BOLD changes were observed even when few spikes occurred during the investigation.Simultaneous EEG-MREG thus is possible with good EEG quality and shows higher sensitivity in regard to the localization of spike-related BOLD responses than EEG-EPI. The development of new methods of analysis for this sequence such as modeling of physiological noise, temporal analysis of the BOLD signal and defining appropriate thresholds is required to fully profit from its high temporal resolution.

Imaging the Sleep Deprived Brain: A Brief Review

Functional magnetic resonance imaging (fMRI) is a highly versatile tool used to study neurobehavioral alterations associated with sleep deprivation (SD). Task-related fMRI is the most widely used technique and an impressive list of cognitive domains has been evaluated using this technique (Table 1). fMRI measures relative change in blood oxygenation level dependant (BOLD) signal in capillaries and venules adjacent to neuronal clusters whose firing rate and consequently, synaptic potentials are modulated by task performance. An increase in MR signal occurs as a result of a relatively disproportionate elevation in blood flow relative to oxygen consumption in response to sensory stimulation and/or task performance. In addition to task-related activation, the evaluation of task-related deactivation where signal changes fall below baseline levels can be evaluated. Blood oxygenation level dependant imaging measures relative changes in blood flow, but does not ascertain absolute blood flow. Quantification of blood flow may occasionally be useful, for example, to study time-on-task effects, and other phenomena whose observation requires signal stability over several minutes as opposed to several seconds. Such measurements can be obtained using a variety of arterial spin labeling (ASL) techniques that have different levels of precision. A disadvantage of ASL is its inferior signal to noise ratio relative to BOLD imaging. Additionally, the requirement for block sampling also makes it impossible to perform event-related designs that are important in separating out trials where the subject may have been asleep. The evaluation of functional connectivity, conducted by assessing signal covariation in pairs of regions, or by determining the extent to which signal in a ‘target’ region interacts with that of a ‘seed’ region according to state/task context provides additional characterization of altered physiology. The latter method, known as psychophysiological Interaction-PPI has been applied in studies evaluating selective attention, the processing of emotional pictures as well as executive function/working memory. In addition to fMRI studies designed to evaluate signal changes in response to task performance, it may be informative to evaluate ‘resting-state’ activity or intrinsic functional connectivity. This refers to the identification of regions showing synchronous low frequency oscillations (0.1-0.01 Hz) in BOLD signal that are not time locked to task performance or sensory stimulation. Studies of this type in sleeping individuals have shown changes in connectivity within the default mode network (DMN) alluded to earlier. The first study evaluating resting state networks in the setting of SD found selective reductions in DMN functional connectivity and reduced anti-correlation with low frequency oscillations in the ‘task-positive’ network (Fig. 1). Analyses of resting state data hold promise of being informative of alterations in brain function without requiring motivated performance on the part of a participant. Related to functional connectivity, MRI in the form of diffusion tensor imaging (DTI) can be used to evaluate white matter connectivity. Strangely, only one study to date has used DTI to study sleep deprived individuals. Combining electroencephalography (EEG) and fMRI in SD or sleep related studies is primarily motivated by the need to monitor sleep stage although the high temporal resolution of EEG is also well suited to study transient phenomena like lapsing. The neural correlates of spindles and slow waves have been studied using this technique. In addition, fMRI guided repetitive transcranial magnetic stimulation has been used Received June 12, 2013 Revised June 18, 2013 Accepted June 18, 2013

Functional brain network organisation of children between 2 and 5 years derived from reconstructed activity of cortical sources of high-density EEG recordings

There is increasing interest in applying connectivity analysis to brain measures (Rubinov and Sporns, 2010), but most studies have relied on fMRI, which substantially limits the participant groups and numbers that can be studied. High-density EEG recordings offer a comparatively inexpensive easy-to-use alternative, but require channel-level connectivity analysis which currently lacks a common analytic framework and is very limited in spatial resolution. To address this problem, we have developed a new technique for studies of network development that overcomes the spatial constraint and obtains functional networks of cortical areas by using EEG source reconstruction with age-matched average MRI templates (He et al., 1999). In contrast to previously reported channel-level analysis, this approach provides information about the cortical areas most likely to be involved in the network as well as their functional relationship (Babiloni et al., 2005; De Vico Fallani et al., 2007).In this study, we applied source reconstruction with age-matched templates to task-free high-density EEG recordings in typically-developing children between 2 and 6years of age (O'Reilly, 2012). Graph theory was then applied to the association strengths of 68 cortical regions of interest based on the Desikan–Killiany atlas. We found linear increases of mean node degree, mean clustering coefficient and maximum betweenness centrality between 2years and 6years of age. Characteristic path length was negatively correlated with age. The correlation of the network measures with age indicates network development towards more closely integrated networks similar to reports from other imaging modalities (Fair et al., 2008; Power et al., 2010). We also applied eigenvalue decomposition to obtain functional modules (Clayden et al., 2013). Connection strength within these modules did not change with age, and the modules resembled hub networks previously described for MRI (Hagmann et al., 2010; Power et al., 2010). The high temporal resolution of EEG additionally allowed us to distinguish between frequency bands potentially reflecting dynamic coupling between different neural oscillators. Generally, network parameters were similar for networks based on different frequency bands, but frequency band did emerge as a significant factor for clustering coefficient and characteristic path length. In conclusion, the current analysis shows that source reconstruction of high-density EEG recordings with appropriate head models offers a valuable tool for estimating network parameters in studies of brain development. The findings replicate the pattern of closer functional integration over development described for other imaging modalities (Fair et al., 2008; Power et al., 2010).

Human Brain Mapping based on COLD Signal Hemodynamic Response and Electrical Neuroimaging

To understand Working of Human Brain measurements related to the brain function are required. These measurements should be possibly non-invasive. Brain should be disturbed as less as possible during the measurement. Integration of various modalities plays a vital role in understanding the cognitive and the behavioral changes in the human brain. It is an important source of converging evidence about specific aspects of neural functions and dysfunctions under certain pathological conditions. Focal changes in cortical blood flow are tightly coupled with the changes in neuronal activity. This constitutes the option to map the hemodynamic response and infer principles of the cortical processing, even of complex tasks. The very high temporal resolution of EEG and good spatial resolution by NIRS make this concurrent measurement unique to study the spatio-temporal dynamics of large scale neuronal networks in the human brain. Such integration of two techniques will help to overcome the limitations of a specific method. Such as insensitivity of electroencephalogram (EEG) to unsynchronized neural events or lack of near infrared spectroscopy (NIRS) to low metabolic demand. A combination of EEG and NIRS will be more informative than the two separate analyses in both modalities.