Towards the utilization of EEG as a brain imaging tool

EEG-Informed fMRI: A Review of Data Analysis Methods

The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.

Disentangling Somatosensory Evoked Potentials of the Fingers: Limitations and Clinical Potential

In searching for clinical biomarkers of the somatosensory function, we studied reproducibility of somatosensory potentials (SEP) evoked by finger stimulation in healthy subjects. SEPs induced by electrical stimulation and especially after median nerve stimulation is a method widely used in the literature. It is unclear, however, if the EEG recordings after finger stimulation are reproducible within the same subject. We tested in five healthy subjects the consistency and reproducibility of responses through bootstrapping as well as test–retest recordings. We further evaluated the possibility to discriminate activity of different fingers both at electrode and at source level. The lack of consistency and reproducibility suggest responses to finger stimulation to be unreliable, even with reasonably high signal-to-noise ratio and adequate number of trials. At sources level, somatotopic arrangement of the fingers representation was only found in one of the subjects. Although finding distinct locations of the different fingers activation was possible, our protocol did not allow for non-overlapping dipole representations of the fingers. We conclude that despite its theoretical advantages, we cannot recommend the use of somatosensory potentials evoked by finger stimulation to extract clinical biomarkers.

Characterisation of ictal and interictal states of epilepsy: A system dynamic approach of principal dynamic modes analysis

Epilepsy is a brain disorder characterised by the recurrent and unpredictable interruptions of normal brain function, called epileptic seizures. The present study attempts to derive new diagnostic indices which may delineate between ictal and interictal states of epilepsy. To achieve this, the nonlinear modeling approach of global principal dynamic modes (PDMs) is adopted to examine the functional connectivity of the temporal and frontal lobes with the occipital brain segment using an ensemble of paediatric EEGs having the presence of epileptic seizure. The distinct spectral characteristics of global PDMs are found to be in line with the neural rhythms of brain dynamics. Moreover, we find that the linear trends of associated nonlinear functions (ANFs) associated with the 2nd and 4th global PDMs (representing delta, theta and alpha bands) of Fp1–F3 may differentiate between ictal and interictal states of epilepsy. These findings suggest that global PDMs and their associated ANFs may offer potential utility as diagnostic neural measures for ictal and interictal states of epilepsy.

Motor imagery learning across a sequence of trials in stroke patients

PURPOSE

In brain-computer interfaces (BCIs), electrical brain signals during motor imagery are utilized as commands connecting the brain to a computer. To use BCI in patients with stroke, unique brain signal changes should be characterized during motor imagery process. This study aimed to examine the trial-dependent motor-imagery-related activities in stroke patients.

METHODS

During the recording of electroencephalography (EEG) signals, 12 chronic stroke patients and 11 age-matched healthy controls performed motor imagery finger tapping at 1.3 sec intervals. Trial-dependent brain signal changes were assessed by analysis of the mu and beta bands.

RESULTS

Neuronal activity in healthy controls was observed over bilateral hemispheres at the mu and beta bands regardless of changes in the trials, whereas neuronal activity in stroke patients was mainly seen over the ipsilesional hemisphere at the beta band. With progression to repeated trials, healthy controls displayed a decrease in cortical activity in the contralateral hemisphere at the mu band and in bilateral hemispheres at the beta band. In contrast, stroke patients showed a decreasing trend in cortical activity only over the ipsilesional hemisphere at the beta band.

CONCLUSIONS

Trial-dependent changes during motor imagery learning presented in a different manner in stroke patients. Understanding motor imagery learning in stroke patients is crucial for enhancing the effectiveness of motor-imagery-based BCIs.

EEG synchronization measures predict epilepsy-related BOLD-fMRI fluctuations better than commonly used univariate metrics

OBJECTIVE

We hypothesize that the hypersynchronization associated with epileptic activity is best described by EEG synchronization measures, and propose to use these as predictors of epilepsy-related BOLD fluctuations.

METHODS

We computed the phase synchronization index (PSI) and global field synchronization (GFS), within two frequency bands, a broadband (1-45 Hz) and a narrower band focused on the presence of epileptic activity (3-10 Hz). The associated epileptic networks were compared with those obtained using conventional unitary regressors and two power-weighted metrics (total power and root mean square frequency), on nine simultaneous EEG-fMRI datasets from four epilepsy patients, exhibiting inter-ictal epileptiform discharges (IEDs).

RESULTS

The average PSI within 3-10 Hz achieved the best performance across several measures reflecting reliability in all datasets. The results were cross-validated through electrical source imaging of the IEDs. The applicability of PSI when no IEDs are recorded on the EEG was evaluated on three additional patients, yielding partially plausible networks in all cases.

CONCLUSIONS

Epileptic networks can be mapped based on the EEG PSI metric within an IED-specific frequency band, performing better than commonly used EEG metrics.

SIGNIFICANCE

This is the first study to investigate EEG synchronization measures as potential predictors of epilepsy-related BOLD fluctuations.

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data

Background: Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge. Methods: Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ≥50% decrease characterizing responders (N = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features. Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha₁/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha₂ being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 "concentration difficulty" scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the most important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha₂ and frontopolar alpha₁. Conclusions: These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, "biomarker"-based treatment approaches.

Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF)

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

Treatment in early psychosis with N-acetyl-cysteine for 6months improves low-level auditory processing: Pilot study

Sensory impairments constitute core dysfunctions in schizophrenia. In the auditory modality, impaired mismatch negativity (MMN) has been observed in chronic schizophrenia and may reflect N-methyl-d-aspartate (NMDA) hypo-function, consistent with models of schizophrenia based on oxidative stress. Moreover, a recent study demonstrated deficits in the N100 component of the auditory evoked potential (AEP) in early psychosis patients. Previous work has shown that add-on administration of the glutathione precursor N-acetyl-cysteine (NAC) improves the MMN and clinical symptoms in chronic schizophrenia. To date, it remains unknown whether NAC also improves general low-level auditory processing and if its efficacy would extend to early-phase psychosis. We addressed these issues with a randomized, double-blind study of a small sample (N=15) of early psychosis (EP) patients and 18 healthy controls from whom AEPs were recorded during an active, auditory oddball task. Patients were recorded twice: once prior to NAC/placebo administration and once after six months of treatment. The N100 component was significantly smaller in patients before NAC administration versus controls. Critically, NAC administration improved this AEP deficit. Source estimations revealed increased activity in the left temporo-parietal lobe in patients after NAC administration. Overall, the data from this pilot study, which call for replication in a larger sample, indicate that NAC improves low-level auditory processing in early psychosis.

Visual processing deficits in 22q11.2 Deletion Syndrome

Carriers of the rare 22q11.2 microdeletion present with a high percentage of positive and negative symptoms and a high genetic risk for schizophrenia. Visual processing impairments have been characterized in schizophrenia, but less so in 22q11.2 Deletion Syndrome (DS). Here, we focus on visual processing using high-density EEG and source imaging in 22q11.2DS participants (N = 25) and healthy controls (N = 26) with an illusory contour discrimination task.

Significant differences between groups emerged at early and late stages of visual processing. In 22q11.2DS, we first observed reduced amplitudes over occipital channels and reduced source activations within dorsal and ventral visual stream areas during the P1 (100–125 ms) and within ventral visual cortex during the N1 (150–170 ms) visual evoked components. During a later window implicated in visual completion (240–285 ms), we observed an increase in global amplitudes in 22q11.2DS. The increased surface amplitudes for illusory contours at this window were inversely correlated with positive subscales of prodromal symptoms in 22q11.2DS.

The reduced activity of ventral and dorsal visual areas during early stages points to an impairment in visual processing seen both in schizophrenia and 22q11.2DS. During intervals related to perceptual closure, the inverse correlation of high amplitudes with positive symptoms suggests that participants with 22q11.2DS who show an increased brain response to illusory contours during the relevant window for contour processing have less psychotic symptoms and might thus be at a reduced prodromal risk for schizophrenia.

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In schizophrenia, early visual processing is altered.

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22q11.2DS carriers have an increased risk for schizophrenia.

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Hd-EEG to investigate visual processing in an illusory contour task in 22q11.2DS.

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Occipital cortex activity is reduced in 22q11.2DS early in time.

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Both in 22q11.2DS and schizophrenia, early visual processing is impaired at P1.

The coupling of low-level auditory dysfunction and oxidative stress in psychosis patients

Patients diagnosed with schizophrenia often present with low-level sensory deficits. It is an open question whether there is a functional link between these deficits and the pathophysiology of the disease, e.g. oxidative stress and glutathione (GSH) metabolism dysregulation. Auditory evoked potentials (AEPs) were recorded from 21 psychosis disorder patients and 30 healthy controls performing an active, auditory oddball task. AEPs to standard sounds were analyzed within an electrical neuroimaging framework. A peripheral measure of participants' redox balance, the ratio of glutathione peroxidase and glutathione reductase activities (GPx/GR), was correlated with the AEP data. Patients displayed significantly decreased AEPs over the time window of the P50/N100 complex resulting from significantly weaker responses in the left temporo-parietal lobe. The GPx/GR ratio significantly correlated with patients' brain activity during the time window of the P50/N100 in the medial frontal lobe. We show for the first time a direct coupling between electrophysiological indices of AEPs and peripheral redox dysregulation in psychosis patients. This coupling is limited to stages of auditory processing that are impaired relative to healthy controls and suggests a link between biochemical and sensory dysfunction. The data highlight the potential of low-level sensory processing as a trait-marker of psychosis.

Face and gaze perception in borderline personality disorder: An electrical neuroimaging study

Humans are sensitive to gaze direction from early life, and gaze has social and affective values. Borderline personality disorder (BPD) is a clinical condition characterized by emotional dysregulation and enhanced sensitivity to affective and social cues. In this study we wanted to investigate the temporal-spatial dynamics of spontaneous gaze processing in BPD. We used a 2-back-working-memory task, in which neutral faces with direct and averted gaze were presented. Gaze was used as an emotional modulator of event-related-potentials to faces. High density EEG data were acquired in 19 females with BPD and 19 healthy women, and analyzed with a spatio-temporal microstates analysis approach. Independently of gaze direction, BPD patients showed altered N170 and P200 topographies for neutral faces. Source localization revealed that the anterior cingulate and other prefrontal regions were abnormally activated during the N170 component related to face encoding, while middle temporal deactivations were observed during the P200 component. Post-task affective ratings showed that BPD patients had difficulty to disambiguate neutral gaze. This study provides first evidence for an early neural bias toward neutral faces in BPD independent of gaze direction and also suggests the importance of considering basic aspects of social cognition in identifying biological risk factors of BPD.

Simultaneous Reality Filtering and Encoding of Thoughts: The Substrate for Distinguishing between Memories of Real Events and Imaginations?

Any thought, whether it refers to the present moment or reflects an imagination, is again encoded as a new memory trace. Orbitofrontal reality filtering (ORFi) denotes an on-line mechanism which verifies whether upcoming thoughts relate to ongoing reality or not. Its failure induces reality confusion with confabulations and disorientation. If the result of this process were simultaneously encoded, it would easily explain later distinction between memories relating to a past reality and memories relating to imagination, a faculty called reality monitoring. How the brain makes this distinction is unknown but much research suggests that it depends on processes active when information is encoded. Here we explored the precise timing between ORFi and encoding as well as interactions between the involved brain structures. We used high-density evoked potentials and two runs of a continuous recognition task (CRT) combining the challenges of ORFi and encoding. ORFi was measured by the ability to realize that stimuli appearing in the second run had not appeared in this run yet. Encoding was measured with immediately repeated stimuli, which has been previously shown to induce a signal emanating from the medial temporal lobe (MTL), which has a protective effect on the memory trace. We found that encoding, as measured with this task, sets in at about 210 ms after stimulus presentation, 35 ms before ORFi. Both processes end at about 330 ms. Both were characterized by increased coherence in the theta band in the MTL during encoding and in the orbitofrontal cortex (OFC) during ORFi. The study suggests a complex interaction between OFC and MTL allowing for thoughts to be re-encoded while they undergo ORFi. The combined influence of these two processes at 200-300 ms may leave a memory trace that allows for later effortless reality monitoring in most everyday situations.

Electrical Neuroimaging of Music Processing Reveals Mid-Latency Changes with Level of Musical Expertise

This original research focused on the effect of musical training intensity on cerebral and behavioral processing of complex music using high-density event-related potential (ERP) approaches. Recently we have been able to show progressive changes with training in gray and white matter, and higher order brain functioning using (f)MRI [(functional) Magnetic Resonance Imaging], as well as changes in musical and general cognitive functioning. The current study investigated the same population of non-musicians, amateur pianists and expert pianists using spatio-temporal ERP analysis, by means of microstate analysis, and ERP source imaging. The stimuli consisted of complex musical compositions containing three levels of transgression of musical syntax at closure that participants appraised. ERP waveforms, microstates and underlying brain sources revealed gradual differences according to musical expertise in a 300–500 ms window after the onset of the terminal chords of the pieces. Within this time-window, processing seemed to concern context-based memory updating, indicated by a P3b-like component or microstate for which underlying sources were localized in the right middle temporal gyrus, anterior cingulate and right parahippocampal areas. Given that the 3 expertise groups were carefully matched for demographic factors, these results provide evidence of the progressive impact of training on brain and behavior.

Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach

Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p values < 0.001. Furthermore, we found a significant reduction in alpha rhythm power from one stress level to another level, p values < 0.05. In comparison, results from self-reporting questionnaire NASA-TLX approach showed no significant differences between stress levels. In addition, we developed a discriminant analysis method based on multiclass support vector machine (SVM) with error-correcting output code (ECOC). Different stress levels were detected with an average classification accuracy of 94.79%. The lateral index (LI) results further showed dominant right prefrontal cortex (PFC) to mental stress (reduced alpha rhythm). The study demonstrated the feasibility of using EEG in classifying multilevel mental stress and reported alpha rhythm power at right prefrontal cortex as a suitable index.

ERP evidence of distinct processes underlying semantic facilitation and interference in word production

In everyday conversations, we take advantage of lexical-semantic contexts to facilitate speech production, but at the same time, we also have to reduce interference and inhibit semantic competitors. The blocked cyclic naming paradigm (BCNP) has been used to investigate such context effects. Typical results on production latencies showed semantic facilitation (or no effect) during the first presentation cycle, and interference emerging in subsequent cycles. Even if semantic contexts might be just as facilitative as interfering, previous BCNP studies focused on interference, which was interpreted as reflecting lemma selection and self-monitoring processes. Facilitation in the first cycle was rarely considered/analysed, although it potentially informs on word production to the same extent as interference. Here we contrasted the event-related potential (ERP) signatures of both semantic facilitation and interference in a BCNP. ERPs differed between homogeneous and heterogeneous blocks from about 365 msec post picture onset in the first cycle (facilitation) and in an earlier time-window (270 msec post picture onset) in the third cycle (interference). Three different analyses of the ERPs converge towards distinct processes underlying semantic facilitation and interference (post-lexical vs lexical respectively). The loci of semantic facilitation and interference are interpreted in the context of different theoretical frameworks of language production: the post-lexical locus of semantic facilitation involves interactive phonological-semantic processes and/or self-monitoring, whereas the lexical locus of semantic interference is in line with selection through increased lexical competition.

Evidence-based source modeling of nociceptive cortical responses: A direct comparison of scalp and intracranial activity in humans

BACKGROUND

Source modeling of EEG traditionally relies on interplay between physiological hypotheses and mathematical estimates. We propose to optimize the process by using evidence gathered from brain imaging and intracortical recordings.

METHODS

We recorded laser-evoked potentials in 18 healthy participants, using high-density EEG. Brain sources were modeled during the first second poststimulus, constraining their initial position to regions where nociceptive-related activity has been ascertained by intracranial EEG. These comprised the two posterior operculo-insular regions, primary sensorimotor, posterior parietal, anterior cingulate/supplementary motor (ACC/SMA), bilateral frontal/anterior insular, and posterior cingulate (PCC) cortices.

RESULTS

The model yielded an average goodness of fit of 91% for individual and 95.8% for grand-average data. When compared with intracranial recordings from 27 human subjects, no significant difference in peak latencies was observed between modeled and intracranial data for 5 of the 6 assessable regions. Morphological match was excellent for operculo-insular, frontal, ACC/SMA and PCC regions (cross-correlation > 0.7) and fair for sensori-motor and posterior parietal cortex (c-c ∼ 0.5).

CONCLUSIONS

Multiple overlapping activities evoked by nociceptive input can be disentangled from high-density scalp EEG guided by intracranial data. Modeled sources accurately described the timing and morphology of most activities recorded with intracranial electrodes, including those coinciding with the emergence of stimulus awareness. Hum Brain Mapp 38:6083-6095, 2017. © 2017 Wiley Periodicals, Inc.

Spatiotemporal brain dynamics supporting the immediate automatization of inhibitory control by implementation intentions

While cognitive interventions aiming at reinforcing intentional executive control of unwanted response showed only modest effects on impulse control disorders, the establishment of fast automatic, stimulus-driven inhibition of responses to specific events with implementation intention self-regulation strategies has proven to be an effective remediation approach. However, the neurocognitive mechanisms underlying implementation intentions remain largely unresolved. We addressed this question by comparing electrical neuroimaging analyses of event-related potentials recorded during a Go/NoGo task between groups of healthy participants receiving either standard or implementation intentions instructions on the inhibition stimuli. Inhibition performance improvements with implementation intentions were associated with a Group by Stimulus interaction 200–250 ms post-stimulus onset driven by a selective decrease in response to the inhibition stimuli within the left superior temporal gyrus, the right precuneus and the right temporo-parietal junction. We further observed that the implementation intentions group showed already at the beginning of the task the pattern of task-related functional activity reached after practice in the group having received standard instructions. We interpret our results in terms of an immediate establishment of an automatic, bottom-up form of inhibitory control by implementation intentions, supported by stimulus-driven retrieval of verbally encoded stimulus-response mapping rules, which in turn triggered inhibitory processes.

Dysfunctional gaze processing in bipolar disorder

Gaze conveys emotional information, and humans present sensitivity to its direction from the earliest days of life. Bipolar disorder is a disease characterized by fluctuating states of emotional and cognitive dysregulation. To explore the role of attentional control on face processing in bipolar patients (BP) we used gaze direction as an emotion modulation parameter in a two-back Working Memory (WM) task while high-density EEG data were acquired. Since gaze direction influences emotional attributions to faces with neutral expressions as well, we presented neutral faces with direct and averted gaze. Nineteen euthymic BP and a sample of age- and gender-matched controls were examined.

In BP we observed diminished P200 and augmented P300 evoked responses, differentially modulated by non-repeated or repeated faces, as well as by gaze direction. BP showed a reduced P200 amplitude, significantly stronger for faces with direct gaze than averted gaze. Source localization of P200 indicated decreased activity in sensory-motor regions and frontal areas suggestive of abnormal affective processing of neutral faces.

The present study provides neurophysiological evidence for abnormal gaze processing in BP and suggests dysfunctional processing of direct eye contact as a prominent characteristic of bipolar disorder.

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This ERP study identified abnormalities in gaze processing in bipolar patients.

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We observed functional anomalies in the P200 and P300 evoked responses.

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BP showed a strong suppression of the P200 for faces with direct gaze.

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Source localization indicated decreased activity in sensory-motor regions.

Auditory-visual integration modulates location-specific repetition suppression of auditory responses

Space is a dimension shared by different modalities, but at what stage spatial encoding is affected by multisensory processes is unclear. Early studies observed attenuation of N1/P2 auditory evoked responses following repetition of sounds from the same location. Here, we asked whether this effect is modulated by audiovisual interactions. In two experiments, using a repetition-suppression paradigm, we presented pairs of tones in free field, where the test stimulus was a tone presented at a fixed lateral location. Experiment 1 established a neural index of auditory spatial sensitivity, by comparing the degree of attenuation of the response to test stimuli when they were preceded by an adapter sound at the same location versus 30° or 60° away. We found that the degree of attenuation at the P2 latency was inversely related to the spatial distance between the test stimulus and the adapter stimulus. In Experiment 2, the adapter stimulus was a tone presented from the same location or a more medial location than the test stimulus. The adapter stimulus was accompanied by a simultaneous flash displayed orthogonally from one of the two locations. Sound-flash incongruence reduced accuracy in a same-different location discrimination task (i.e., the ventriloquism effect) and reduced the location-specific repetition-suppression at the P2 latency. Importantly, this multisensory effect included topographic modulations, indicative of changes in the relative contribution of underlying sources across conditions. Our findings suggest that the auditory response at the P2 latency is affected by spatially selective brain activity, which is affected crossmodally by visual information.

EEG Oscillations Are Modulated in Different Behavior-Related Networks during Rhythmic Finger Movements

Sequencing and timing of body movements are essential to perform motoric tasks. In this study, we investigate the temporal relation between cortical oscillations and human motor behavior (i.e., rhythmic finger movements). High-density EEG recordings were used for source imaging based on individual anatomy. We separated sustained and movement phase-related EEG source amplitudes based on the actual finger movements recorded by a data glove. Sustained amplitude modulations in the contralateral hand area show decrease for α (10-12 Hz) and β (18-24 Hz), but increase for high γ (60-80 Hz) frequencies during the entire movement period. Additionally, we found movement phase-related amplitudes, which resembled the flexion and extension sequence of the fingers. Especially for faster movement cadences, movement phase-related amplitudes included high β (24-30 Hz) frequencies in prefrontal areas. Interestingly, the spectral profiles and source patterns of movement phase-related amplitudes differed from sustained activities, suggesting that they represent different frequency-specific large-scale networks. First, networks were signified by the sustained element, which statically modulate their synchrony levels during continuous movements. These networks may upregulate neuronal excitability in brain regions specific to the limb, in this study the right hand area. Second, movement phase-related networks, which modulate their synchrony in relation to the movement sequence. We suggest that these frequency-specific networks are associated with distinct functions, including top-down control, sensorimotor prediction, and integration. The separation of different large-scale networks, we applied in this work, improves the interpretation of EEG sources in relation to human motor behavior.

SIGNIFICANCE STATEMENT

EEG recordings provide high temporal resolution suitable to relate cortical oscillations to actual movements. Investigating EEG sources during rhythmic finger movements, we distinguish sustained from movement phase-related amplitude modulations. We separate these two EEG source elements motivated by our previous findings in gait. Here, we found two types of large-scale networks, representing the right fingers in distinction from the time sequence of the movements. These findings suggest that EEG source amplitudes reconstructed in a cortical patch are the superposition of these simultaneously present network activities. Separating these frequency-specific networks is relevant for studying function and possible dysfunction of the cortical sensorimotor system in humans as well as to provide more advanced features for brain-computer interfaces.

The time-course of activation in the dorsal and ventral visual streams during landmark cueing and perceptual discrimination tasks

Different patterns of high density EEG activity were elicited by the same peripheral stimuli, in the context of Landmark Cueing and Perceptual Discrimination tasks. The C1 component of the visual event-related potential (ERP) at parietal - occipital electrode sites was larger in the Landmark Cueing task, and source localisation suggested greater activation in the superior parietal lobule (SPL) in this task, compared to the Perceptual Discrimination task, indicating stronger early recruitment of the dorsal visual stream. In the Perceptual Discrimination task, source localisation suggested widespread activation of the inferior temporal gyrus (ITG) and fusiform gyrus (FFG), structures associated with the ventral visual stream, during the early phase of the P1 ERP component. Moreover, during a later epoch (171-270ms after stimulus onset) increased temporal-occipital negativity, and stronger recruitment of ITG and FFG were observed in the Perceptual Discrimination task. These findings illuminate the contrasting functions of the dorsal and ventral visual streams, to support rapid shifts of attention in response to contextual landmarks, and conscious discrimination, respectively.

Automated long-term EEG analysis to localize the epileptogenic zone

Objective

We investigated the performance of automatic spike detection and subsequent electroencephalogram (EEG) source imaging to localize the epileptogenic zone (EZ) from long-term EEG recorded during video-EEG monitoring.

Methods

In 32 patients, spikes were automatically detected in the EEG and clustered according to their morphology. The two spike clusters with most single events in each patient were averaged and localized in the brain at the half-rising time and peak of the spike using EEG source imaging. On the basis of the distance from the sources to the resection and the known patient outcome after surgery, the performance of the automated EEG analysis to localize the EZ was quantified.

Results

In 28 out of the 32 patients, the automatically detected spike clusters corresponded with the reported interictal findings. The median distance to the resection in patients with Engel class I outcome was 6.5 and 15 mm for spike cluster 1 and 27 and 26 mm for cluster 2, at the peak and the half-rising time of the spike, respectively. Spike occurrence (cluster 1 vs. cluster 2) and spike timing (peak vs. half-rising) significantly influenced the distance to the resection (p < 0.05). For patients with Engel class II, III, and IV outcomes, the median distance increased to 36 and 36 mm for cluster 1. Localizing spike cluster 1 at the peak resulted in a sensitivity of 70% and specificity of 100%, positive prediction value (PPV) of 100%, and negative predictive value (NPV) of 53%. Including the results of spike cluster 2 led to an increased sensitivity of 79% NPV of 55% and diagnostic OR of 11.4, while the specificity dropped to 75% and the PPV to 90%.

Significance

We showed that automated analysis of long-term EEG recordings results in a high sensitivity and specificity to localize the epileptogenic focus.

Electrophysiological characterization of facilitation and interference in the picture-word interference paradigm

The picture-word interference paradigm is often used to investigate the processes underlying word production. In this paradigm, participants name pictures while ignoring distractor words. The aim of this study is to investigate the processes underlying this task and how/when they differ from those involved in simple picture naming. It examines the electrophysiological signature of general interference (longer response times with than without distractors) and facilitation (shorter response times for distractor-word stimuli overlapping in phonemes/orthography) effects. Mass univariate analyses are used to determine the temporal boundaries and spatial distribution of these effects without a priori restrictions in the time/space dimensions. Topographic pattern analyses complement this information by indicating whether (and when) the neural networks differ across conditions. Results suggest that the general interference effect has two loci, the grammatical encoding and the phonological encoding of the target word, with different neural networks involved in the two tasks during part of the grammatical encoding process. Furthermore, the electrophysiological signature of interference and facilitation effects in the time window of phonological encoding is highly similar, suggesting that the two effects could result from the same underlying mechanism. These findings are discussed in the light of existing accounts of interference and facilitation effects.

QEEG and LORETA in Teenagers With Conduct Disorder and Psychopathic Traits

BACKGROUND

Few studies have investigated the impact of the psychopathic traits on the EEG of teenagers with conduct disorder (CD). To date, there is no other research studying low-resolution brain electromagnetic tomography (LORETA) technique using quantitative EEG (QEEG) analysis in adolescents with CD and psychopathic traits.

OBJECTIVE

To find electrophysiological differences specifically related to the psychopathic traits. The current investigation compares the QEEG and the current source density measures between adolescents with CD and psychopathic traits and adolescents with CD without psychopathic traits.

METHODS

The resting EEG activity and LORETA for the EEG fast spectral bands were evaluated in 42 teenagers with CD, 25 with and 17 without psychopathic traits according to the Antisocial Process Screening Device. All adolescents were assessed using the DSM-IV-TR criteria. The EEG visual inspection characteristics and the use of frequency domain quantitative analysis techniques (narrow band spectral parameters) are described.

RESULTS

QEEG analysis showed a pattern of beta activity excess on the bilateral frontal-temporal regions and decreases of alpha band power on the left central-temporal and right frontal-central-temporal regions in the psychopathic traits group. Current source density calculated at 17.18 Hz showed an increase within fronto-temporo-striatal regions in the psychopathic relative to the nonpsychopathic traits group.

CONCLUSIONS

These findings indicate that QEEG analysis and techniques of source localization may reveal differences in brain electrical activity among teenagers with CD and psychopathic traits, which was not obvious to visual inspection. Taken together, these results suggest that abnormalities in a fronto-temporo-striatal network play a relevant role in the neurobiological basis of psychopathic behavior.

Markers of TMS-evoked visual conscious experience in a patient with altitudinal hemianopia

Transcranial magnetic stimulation (TMS) of the occipital and parietal cortices can induce phosphenes, i.e. visual sensations of light without light entering the eyes. In this paper, we adopted a TMS-EEG interactive co-registration approach with a patient (AM) showing altitudinal hemianopia. Occipital and parietal cortices in both hemispheres were stimulated while concurrently recording EEG signal. Results showed that, for all sites, neural activity differentially encoding for the presence vs. absence of a conscious experience could be found in a cluster of electrodes close to the stimulation site at an early (70ms) time-period after TMS. The present data indicate that both occipital and parietal sites are independent early gatekeepers of perceptual awareness, thus, in line with evidence in favor of early correlates of perceptual awareness. Moreover, these data support the valuable contribution of the TMS-EEG approach in patients with visual field defects to investigate the neural processes responsible for perceptual awareness.

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. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We introduce new algorithms for reducing EEG artifacts due to simultaneous fMRI The algorithms combine a reference layer and adaptive filtering Several evaluation criteria suggest superior effectivity in terms of artifact reduction We demonstrate that physiological EEG components are preserved.

Spatial Mnemonic Encoding: Theta Power Decreases and Medial Temporal Lobe BOLD Increases Co-Occur during the Usage of the Method of Loci

The method of loci is one, if not the most, efficient mnemonic encoding strategy. This spatial mnemonic combines the core cognitive processes commonly linked to medial temporal lobe (MTL) activity: spatial and associative memory processes. During such processes, fMRI studies consistently demonstrate MTL activity, while electrophysiological studies have emphasized the important role of theta oscillations (3-8 Hz) in the MTL. However, it is still unknown whether increases or decreases in theta power co-occur with increased BOLD signal in the MTL during memory encoding. To investigate this question, we recorded EEG and fMRI separately, while human participants used the spatial method of loci or the pegword method, a similarly associative but nonspatial mnemonic. The more effective spatial mnemonic induced a pronounced theta power decrease source localized to the left MTL compared with the nonspatial associative mnemonic strategy. This effect was mirrored by BOLD signal increases in the MTL. Successful encoding, irrespective of the strategy used, elicited decreases in left temporal theta power and increases in MTL BOLD activity. This pattern of results suggests a negative relationship between theta power and BOLD signal changes in the MTL during memory encoding and spatial processing. The findings extend the well known negative relation of alpha/beta oscillations and BOLD signals in the cortex to theta oscillations in the MTL.

Putting the Pieces Together in Gilles de la Tourette Syndrome: Exploring the Link Between Clinical Observations and the Biological Basis of Dysfunction

Gilles de la Tourette syndrome is a complex, idiopathic neuropsychiatric disorder whose pathophysiological mechanisms have yet to be elucidated. It is phenotypically heterogeneous and manifests more often than not with both motor and behavioral impairment, although tics are its clinical hallmark. Tics themselves present with a complex profile as they characteristically wax and wane and are often preceded by premonitory somatosensory sensations to which it is said a tic is the response. Highly comorbid with obsessive-compulsive disorder and attention deficit-hyperactivity disorder, it is purported to be an epigenetic, neurodevelopmental spectrum disorder with a complex genetic profile. It has a childhood onset, occurs disproportionately in males, and shows spontaneous symptomatic attenuation by adulthood in the majority of those afflicted. Although not fully understood, its neurobiological basis is linked to dysfunction in the cortico-basal ganglia-thalamo-cortical network. Treatment modalities for Tourette syndrome include behavioral, pharmacological and surgical interventions, but there is presently no cure for the disorder. For those severely affected, deep brain stimulation (DBS) has recently become a viable therapeutic option. A key factor to attaining optimal results from this surgery is target selection, a topic still under debate due to the complex clinical profile presented by GTS patients. Depending on its phenotypic expression and the most problematic aspect of the disorder for the individual, one of three brain regions is most commonly chosen for stimulation: the thalamus, globus pallidus, or nucleus accumbens. Neurophysiological analyses of intra- and post-operative human electrophysiological recordings from clinical DBS studies suggest a link between tic behavior and activity in both the thalamus and globus pallidus. In particular, chronic recordings from the thalamus have shown a correlation between symptomatology and (1) spectral activity in gamma band power and (2) theta/gamma cross frequency coherence. These results suggest gamma oscillations and theta/gamma cross correlation dynamics may serve as biomarkers for dysfunction. While acute and chronic recordings from human subjects undergoing DBS have provided better insight into tic genesis and the neuropathophysiological mechanisms underlying Tourette syndrome, these studies are still sparse and the field would greatly benefit from further investigations. This review reports data and discoveries of scientific and clinical relevance from a wide variety of methods and provides up-to-date information about our current understanding of the pathomechanisms underlying Tourette syndrome. It gives a comprehensive overview of the current state of knowledge and addresses open questions in the field.

Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification

The purpose of this study was to introduce an improved tool for automated classification of event-related potentials (ERPs) using spatiotemporally parcellated events incorporated into a functional brain network activation (BNA) analysis. The auditory oddball ERP paradigm was selected to demonstrate and evaluate the improved tool. Methods: The ERPs of each subject were decomposed into major dynamic spatiotemporal events. Then, a set of spatiotemporal events representing the group was generated by aligning and clustering the spatiotemporal events of all individual subjects. The temporal relationship between the common group events generated a network, which is the spatiotemporal reference BNA model. Scores were derived by comparing each subject's spatiotemporal events to the reference BNA model and were then entered into a support vector machine classifier to classify subjects into relevant subgroups. The reliability of the BNA scores (test-retest repeatability using intraclass correlation) and their utility as a classification tool were examined in the context of Target-Novel classification. Results: BNA intraclass correlation values of repeatability ranged between 0.51 and 0.82 for the known ERP components N100, P200, and P300. Classification accuracy was high when the trained data were validated on the same subjects for different visits (AUCs 0.93 and 0.95). The classification accuracy remained high for a test group recorded at a different clinical center with a different recording system (AUCs 0.81, 0.85 for 2 visits). Conclusion: The improved spatiotemporal BNA analysis demonstrates high classification accuracy. The BNA analysis method holds promise as a tool for diagnosis, follow-up and drug development associated with different neurological conditions.

Temporal Characteristics of EEG Microstates Mediate Trial-by-Trial Risk Taking

People seem to have difficulties when perceiving events whose outcome has no influence on the outcome of future events. This illusion that patterns exist where there are none may lead to adverse consequences, such as escalating losses in financial trading or gambling debt. Despite the enormous social consequences of these cognitive biases, however, their neural underpinnings are poorly understood. Attempts to investigate them have so far relied on evoked neural activity, whereas spontaneous brain activity has been treated as noise to be averaged out. Here, we focus on the spontaneous electroencephalographic (EEG) activity during inter-trial-intervals (ITI) in a sequential risky decision-making task. Using multilevel mediation analyses, our results show that the percentage of time covered by two EEG microstates (i.e., functional brain-states of coherent activity) mediate the influence of outcomes of prior decisions on subsequent risk taking on a trial-by-trial basis. The devised multilevel mediation analysis of the temporal characteristics of EEG microstates during ITI provides a new window into the neurobiology of decision making by bringing the spontaneous brain activity to the forefront of the analysis.

Early Left Parietal Activity Elicited by Direct Gaze: A High-Density EEG Study

Gaze is one of the most important cues for human communication and social interaction. In particular, gaze contact is the most primary form of social contact and it is thought to capture attention. A very early-differentiated brain response to direct versus averted gaze has been hypothesized. Here, we used high-density electroencephalography to test this hypothesis. Topographical analysis allowed us to uncover a very early topographic modulation (40-80 ms) of event-related responses to faces with direct as compared to averted gaze. This modulation was obtained only in the condition where intact broadband faces-as opposed to high-pass or low-pas filtered faces-were presented. Source estimation indicated that this early modulation involved the posterior parietal region, encompassing the left precuneus and inferior parietal lobule. This supports the idea that it reflected an early orienting response to direct versus averted gaze. Accordingly, in a follow-up behavioural experiment, we found faster response times to the direct gaze than to the averted gaze broadband faces. In addition, classical evoked potential analysis showed that the N170 peak amplitude was larger for averted gaze than for direct gaze. Taken together, these results suggest that direct gaze may be detected at a very early processing stage, involving a parallel route to the ventral occipito-temporal route of face perceptual analysis.

Seizure Onset Zone Localization from Ictal High-Density EEG in Refractory Focal Epilepsy

Epilepsy surgery is the most efficient treatment option for patients with refractory epilepsy. Before surgery, it is of utmost importance to accurately delineate the seizure onset zone (SOZ). Non-invasive EEG is the most used neuroimaging technique to diagnose epilepsy, but it is hard to localize the SOZ from EEG due to its low spatial resolution and because epilepsy is a network disease, with several brain regions becoming active during a seizure. In this work, we propose and validate an approach based on EEG source imaging (ESI) combined with functional connectivity analysis to overcome these problems. We considered both simulations and real data of patients. Ictal epochs of 204-channel EEG and subsets down to 32 channels were analyzed. ESI was done using realistic head models and LORETA was used as inverse technique. The connectivity pattern between the reconstructed sources was calculated, and the source with the highest number of outgoing connections was selected as SOZ. We compared this algorithm with a more straightforward approach, i.e. selecting the source with the highest power after ESI as the SOZ. We found that functional connectivity analysis estimated the SOZ consistently closer to the simulated EZ/RZ than localization based on maximal power. Performance, however, decreased when 128 electrodes or less were used, especially in the realistic data. The results show the added value of functional connectivity analysis for SOZ localization, when the EEG is obtained with a high-density setup. Next to this, the method can potentially be used as objective tool in clinical settings.

Overview of MEG

Magnetoencephalography (MEG) is a method to study electrical activity in the human brain by recording the neuromagnetic field outside the head. MEG, like electroencephalography (EEG), provides an excellent, millisecond-scale time resolution, and allows the estimation of the spatial distribution of the underlying activity, in favorable cases with a localization accuracy of a few millimeters. To detect the weak neuromagnetic signals, superconducting sensors, magnetically shielded rooms, and advanced signal processing techniques are used. The analysis and interpretation of MEG data typically involves comparisons between subject groups and experimental conditions using various spatial, temporal, and spectral measures of cortical activity and connectivity. The application of MEG to cognitive neuroscience studies is illustrated with studies of spoken language processing in subjects with normal and impaired reading ability. The mapping of spatiotemporal patterns of activity within networks of cortical areas can provide useful information about the functional architecture of the brain related to sensory and cognitive processing, including language, memory, attention, and perception.

Multimodal EEG Recordings, Psychometrics and Behavioural Analysis

High spatial and temporal resolution measurements of neuronal activity are preferably combined. In an overview on how this approach can take shape, multimodal electroencephalography (EEG) is treated in 2 main parts: by experiments without a task and in the experimentally cued working brain. It concentrates first on the alpha rhythm properties and next on data-driven search for patterns such as the default mode network. The high-resolution volumic distributions of neuronal metabolic indices result in distributed cortical regions and possibly relate to numerous nuclei, observable in a non-invasive manner in the central nervous system of humans. The second part deals with paradigms in which nowadays assessment of target-related networks can align level-dependent blood oxygenation, electrical responses and behaviour, taking the temporal resolution advantages of event-related potentials. Evidence-based electrical propagation in serial tasks during performance is now to a large extent attributed to interconnected pathways, particularly chronometry-dependent ones, throughout a chain including a dorsal stream, next ventral cortical areas taking the flow of information towards inferior temporal domains. The influence of aging is documented, and results of the first multimodal studies in neuropharmacology are consistent. Finally a scope on implementation of advanced clinical applications and personalized marker strategies in neuropsychiatry is indicated.

Directed Functional Brain Connectivity Based on EEG Source Imaging: Methodology and Application to Temporal Lobe Epilepsy

OBJECTIVE

The importance of functional brain connectivity to study physiological and pathological brain activity has been widely recognized. Here, we aimed to 1) review a methodological pipeline to investigate directed functional connectivity between brain regions using source signals derived from high-density EEG; 2) elaborate on some methodological challenges; and 3) apply this pipeline to temporal lobe epilepsy (TLE) patients and healthy controls to investigate directed functional connectivity differences in the theta and beta frequency bands during EEG epochs without visible pathological activity.

METHODS

The methodological pipeline includes: EEG acquisition and preprocessing, electrical-source imaging (ESI) using individual head models and distributed inverse solutions, parcellation of the gray matter in regions of interest, fixation of the dipole orientation for each region, computation of the spectral power in the source space, and directed functional connectivity estimation using Granger-causal modeling. We specifically analyzed how the signal-to-noise ratio (SNR) changes using different approaches for the dipole orientation fixation. We applied this pipeline to 20 left TLE patients, 20 right TLE patients, and 20 healthy controls.

RESULTS

Projecting each dipole to the predominant dipole orientation leads to a threefold SNR increase as compared to the norm of the dipoles. By comparing connectivity in TLE versus controls, we found significant frequency-specific outflow differences in physiologically plausible regions.

CONCLUSION

The results suggest that directed functional connectivity derived from ESI can help better understand frequency-specific resting-state network alterations underlying focal epilepsy.

SIGNIFICANCE

EEG-based directed functional connectivity could contribute to the search of new biomarkers of this disorder.

Mental stress assessment using simultaneous measurement of EEG and fNIRS

Previous studies reported mental stress as one of the major contributing factors leading to various diseases such as heart attack, depression and stroke. An accurate stress assessment method may thus be of importance to clinical intervention and disease prevention. We propose a joint independent component analysis (jICA) based approach to fuse simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) on the prefrontal cortex (PFC) as a means of stress assessment. For the purpose of this study, stress was induced by using an established mental arithmetic task under time pressure with negative feedback. The induction of mental stress was confirmed by salivary alpha amylase test. Experiment results showed that the proposed fusion of EEG and fNIRS measurements improves the classification accuracy of mental stress by +3.4% compared to EEG alone and +11% compared to fNIRS alone. Similar improvements were also observed in sensitivity and specificity of proposed approach over unimodal EEG/fNIRS. Our study suggests that combination of EEG (frontal alpha rhythm) and fNIRS (concentration change of oxygenated hemoglobin) could be a potential means to assess mental stress objectively.

Aberrant Development of Speech Processing in Young Children with Autism: New Insights from Neuroimaging Biomarkers

From the time of birth, a newborn is continuously exposed and naturally attracted to human voices, and as he grows, he becomes increasingly responsive to these speech stimuli, which are strong drivers for his language development and knowledge acquisition about the world. In contrast, young children with autism spectrum disorder (ASD) are often insensitive to human voices, failing to orient and respond to them. Failure to attend to speech in turn results in altered development of language and social-communication skills. Here, we review the critical role of orienting to speech in ASD, as well as the neural substrates of human voice processing. Recent functional neuroimaging and electroencephalography studies demonstrate that aberrant voice processing could be a promising marker to identify ASD very early on. With the advent of refined brain imaging methods, coupled with the possibility of screening infants and toddlers, predictive brain function biomarkers are actively being examined and are starting to emerge. Their timely identification might not only help to differentiate between phenotypes, but also guide the clinicians in setting up appropriate therapies, and better predicting or quantifying long-term outcome.

Identification of Interictal Epileptic Networks from Dense-EEG

Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical level from noninvasive recordings. In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: (i) the algorithm used in the solution of the EEG inverse problem and (ii) the method used in the estimation of the functional connectivity. We evaluate four inverse solutions algorithms (dSPM, wMNE, sLORETA and cMEM) and four connectivity measures (r ², h ², PLV, and MI) on data simulated from a combined biophysical/physiological model to generate realistic interictal epileptic spikes reflected in scalp EEG. We use a new network-based similarity index to compare between the network identified by each of the inverse/connectivity combination and the original network generated in the model. The method will be also applied on real data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focal epilepsy. In simulated data, results revealed that the selection of the inverse/connectivity combination has a significant impact on the identified networks. Results suggested that nonlinear methods (nonlinear correlation coefficient, phase synchronization and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The wMNE inverse solution showed higher performance than dSPM, cMEM and sLORETA. In real data, the combination (wMNE/PLV) led to a very good matching between the interictal epileptic network identified from noninvasive EEG recordings and the network obtained from connectivity analysis of intracerebral EEG recordings. These results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data.