Numerical and Experimental Analysis of Radiofrequency-Induced Heating Versus Lead Conductivity During EEG-MRI at 3 T.

This study investigates radiofrequency (RF)-induced heating in a head model with a 256-channel electroencephalogram (EEG) cap during magnetic resonance imaging (MRI). Nine computational models were implemented each with different EEG lead electrical conductivity, ranging from 1 to 5.8 × 107 S/m. The peak values of specific absorption rate (SAR) averaged over different volumes were calculated for each lead conductivity. Experimental measurements were also performed at 3-T MRI with a Gracilaria Lichenoides (GL) phantom with and without a low-conductive EEG lead cap ("InkNet"). The simulation results showed that SAR was a nonlinear function of the EEG lead conductivity. The experimental results were in line with the numerical simulations. Specifically, there was a ΔT of 1.7 °C in the GL phantom without leads compared to ΔT of 1.8 °C calculated with the simulations. Additionally, there was a ΔT of 1.5 °C in the GL phantom with the InkNet compared to a ΔT of 1.7 °C in the simulations with a cap of similar conductivity. The results showed that SAR is affected by specific location, number of electrodes, and the volume of tissue considered. As such, SAR averaged over the whole head, or even SAR averaged over volumes of 1 or 0.1 g, may conceal significant heating effects and local analysis of RF heating (in terms of peak SAR and temperature) is needed.

Polymer thick film technology for improved simultaneous dEEG/MRI recording: Safety and MRI data quality.

PURPOSE: To develop a 256-channel dense-array electroencephalography (dEEG) sensor net (the Ink-Net) using high-resistance polymer thick film (PTF) technology to improve safety and data quality during simultaneous dEEG/MRI. METHODS: Heating safety was assessed with temperature measurements in an anthropomorphic head phantom during a 30-min, induced-heating scan at 7T. MRI quality assessment used B1 field mapping and functional MRI (fMRI) retinotopic scans in three humans at 3T. Performance of the 256-channel PTF Ink-Net was compared with a 256-channel MR-conditional copper-wired electroencephalography (EEG) net and to scans with no sensor net. A visual evoked potential paradigm assessed EEG quality within and outside the 3T scanner. RESULTS: Phantom temperature measurements revealed nonsignificant heating (ISO 10974) in the presence of either EEG net. In human B1 field and fMRI scans, the Ink-Net showed greatly reduced cross-modal artifact and less signal degradation than the copper-wired net, and comparable quality to MRI without sensor net. Cross-modal ballistocardiogram artifact in the EEG was comparable for both nets. CONCLUSION: High-resistance PTF technology can be effectively implemented in a 256-channel dEEG sensor net for MR conditional use at 7T and with significantly improved structural and fMRI data quality as assessed at 3T. Magn Reson Med 77:895-903, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

An improved artifacts removal method for high dimensional EEG.

BACKGROUND: Multiple noncephalic electrical sources superpose with brain signals in the recorded EEG. Blind source separation (BSS) methods such as independent component analysis (ICA) have been shown to separate noncephalic artifacts as unique components. However, robust and objective identification of artifact components remains a challenge in practice. In addition, with high dimensional data, ICA requires a large number of observations for stable solutions. Moreover, using signals from long recordings to provide the large observation set might violate the stationarity assumption of ICA due to signal changes over time. NEW METHOD: Instead of decomposing all channels simultaneously, subsets of channels are randomly selected and decomposed with ICA. With reduced dimensionality of the subsets, much less amount of data is required to derive stable components. To characterize each independent component, an artifact relevance index (ARI) is calculated by template matching each component with a model of the artifact. Automatic artifact identification is then implemented based on the statistical distribution of ARI of the numerous components generated. RESULTS: The proposed permutation resampling for identification matching (PRIM) method effectively removed eye blink artifacts from both simulated and real EEG. COMPARISON WITH EXISTING METHOD: The average topomap correlation coefficient between the cleaned EEG and the ground truth is 0.89±0.01 for PRIM, compared with 0.64±0.05 for conventional ICA based method. The average relative root-mean-square error is 0.40±0.01 for PRIM, compared with 0.66±0.10 for conventional method. CONCLUSIONS: The proposed method overcame limitations of conventional ICA based method and succeeded in removing eye blink artifacts automatically.

Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression.

Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI.

EEG source localization: Sensor density and head surface coverage.

BACKGROUND: The accuracy of EEG source localization depends on a sufficient sampling of the surface potential field, an accurate conducting volume estimation (head model), and a suitable and well-understood inverse technique. The goal of the present study is to examine the effect of sampling density and coverage on the ability to accurately localize sources, using common linear inverse weight techniques, at different depths. Several inverse methods are examined, using the popular head conductivity. NEW METHOD: Simulation studies were employed to examine the effect of spatial sampling of the potential field at the head surface, in terms of sensor density and coverage of the inferior and superior head regions. In addition, the effects of sensor density and coverage are investigated in the source localization of epileptiform EEG. RESULTS: Greater sensor density improves source localization accuracy. Moreover, across all sampling density and inverse methods, adding samples on the inferior surface improves the accuracy of source estimates at all depths. COMPARISON WITH EXISTING METHODS: More accurate source localization of EEG data can be achieved with high spatial sampling of the head surface electrodes. CONCLUSIONS: The most accurate source localization is obtained when the voltage surface is densely sampled over both the superior and inferior surfaces.

Critical periods for the neurodevelopmental processes of externalizing and internalizing.

Research on neurobiological development is providing insight into the nature and mechanisms of human neural plasticity. These mechanisms appear to support two different forms of developmental learning. One form of learning could be described as externalizing, in which neural representations are highly responsive to environmental influences, as the child typically operates under a mode of hedonic approach. A second form of learning supports internalizing, in which motive control separates attention and self-regulation from the immediate influences of the context, particularly when the child faces conditions of avoidance and threat. The dorsal cortical networks of externalizing are organized around dorsal limbic (cingulate, septal, lateral hypothalamic, hippocampal, and ventral striatal) circuits. In contrast, the ventral cortical networks of internalizing are organized around ventral limbic (anterior temporal and orbital cortex, extended amygdala, dorsal striatal, and mediodorsal thalamic) circuits. These dual divisions of the limbic system in turn self-regulate their arousal levels through different brain stem and forebrain neuromodulator projection systems, with dorsal corticolimbic networks regulated strongly by locus coeruleus norepinephrine and brain stem raphe nucleus serotonin projection systems, and ventral corticolimbic networks regulated by ventral tegmental dopamine and forebrain acetylcholine projections. Because the arousal control systems appear to regulate specific properties of neural plasticity in development, an analysis of these systems explains differences between externalizing and internalizing at multiple levels of neural and psychological self-regulation. In neuroscience, the concept of critical periods has been applied to times when experience is essential for the maturation of sensory systems. In a more general neuropsychological analysis, certain periods of the child's development require successful self-regulation through the differential capacities for externalizing and internalizing.

Exploring age-related changes in dynamical non-stationarity in electroencephalographic signals during early adolescence.

Dynamics of brain signals such as electroencephalogram (EEG) can be characterized as a sequence of quasi-stable patterns. Such patterns in the brain signals can be associated with coordinated neural oscillations, which can be modeled by non-linear systems. Further, these patterns can be quantified through dynamical non-stationarity based on detection of qualitative changes in the state of the systems underlying the observed brain signals. This study explored age-related changes in dynamical non-stationarity of the brain signals recorded at rest, longitudinally with 128-channel EEG during early adolescence (10 to 13 years of age, 56 participants). Dynamical non-stationarity was analyzed based on segmentation of the time series with subsequent grouping of the segments into clusters with similar dynamics. Age-related changes in dynamical non-stationarity were described in terms of the number of stationary states and the duration of the stationary segments. We found that the EEG signal became more non-stationary with age. Specifically, the number of states increased whereas the mean duration of the stationary segment decreased with age. These two effects had global and parieto-occipital distribution, respectively, with the later effect being most dominant in the alpha (around 10 Hz) frequency band.

Learning and the development of contexts for action.

Neurophysiological evidence from animal studies suggests that frontal corticolimbic systems support early stages of learning, whereas later stages involve context representation formed in hippocampus and posterior cingulate cortex. In dense-array EEG studies of human learning, we observed brain activity in medial prefrontal cortex (the medial frontal negativity or MFN) was not only observed in early stages, but, surprisingly, continued to increase as learning progressed. In the present study we investigated this finding by examining MFN amplitude as participants learned an arbitrary associative learning task over three sessions. On the fourth session the same task with new stimuli was presented to assess changes in MFN amplitude. The results showed that MFN amplitude continued to increase with practice over the first three sessions, in contrast to P3 amplitudes. Even when participants were presented with new stimuli in session 4, MFN amplitude was larger than that observed in the first session. Furthermore, MFN activity from the third session predicted learning rate in the fourth session. The results point to an interaction between early and late stages in which learning results in corticolimbic consolidation of cognitive context models that facilitate new learning in similar contexts.

Maturation of EEG power spectra in early adolescence: a longitudinal study.

This study investigated the fine-grained development of the EEG power spectra in early adolescence, and the extent to which it is reflected in changes in peak frequency. It also sought to determine whether sex differences in the EEG power spectra reflect differential patterns of maturation. A group of 56 adolescents were tested at age 10 years and then at two further time-points approximately 18 months apart. The EEG was recorded during both eyes-closed and eyes-open conditions and Fourier transformed to provide estimates of absolute and relative spectral power at 0.5 Hz intervals from 0.5 to 40 Hz. The peak alpha frequency for each individual at each time-point was also determined for relative spectral power. Partial Least Squares (PLS) analysis was used to determine the combination of electrodes and frequencies that showed developmental change, or differed between the sexes. As a function of age, absolute delta and theta frequencies power decreased, and relative alpha2 and beta frequencies increased, replicating the standard findings of a decrease in lower, and increase in higher, frequencies with age. A small but significant increase in peak alpha frequency with age was detected. Moreover PLS analysis performed with individual alpha frequencies aligned to 10 Hz suggested that the age-related increase seen in alpha2 relative power was driven by changes in the peak frequency. Although males demonstrated higher alpha power than females, there were no sex differences in peak frequency, suggesting that there may be more to sex differences in EEG power than simply different rates of maturation between the two sexes.

Frontolimbic activity and cognitive bias in major depression.

In order to explore neural activity that accompanies cognitive bias in mood disorders, the authors had clinically depressed and nondepressed controls complete a self-evaluation procedure in which they indicated whether trait words were self-descriptive. Dense-array (256-channel) electroencephalography was recorded. Greater depression and low Positive Affect were associated with decreased endorsement of favorable (Good) traits, and greater anxiety and high Negative Affect were associated with increased endorsement of unfavorable (Bad) traits. For controls, the event-related potential (ERP) showed an enhanced visual N1 for trials in which Bad traits were endorsed. For depressed participants, this N1 was attenuated, specifically for these endorsed Bad trials. A similar pattern was observed in the P2-medial frontal negativity (P2-MFN) complex, with controls showing an enhanced MFN to the endorsed Bad words, while depressed participants showed an attenuated or absent medial frontal response on these items specifically. Distributed linear-inverse source analysis of the ERP localized the N1 effect to the inferotemporal-occipital cortex and the medial frontal effect to the dorsal anterior cingulate cortex. The altered ERP responses in depressed participants may provide clues to the neurophysiological processes associated with negatively biased cognition and self-evaluation in clinical depression.

Age-related changes in transient and oscillatory brain responses to auditory stimulation during early adolescence.

Maturational changes in the capacity to process quickly the temporal envelope of sound have been linked to language abilities in typically developing individuals. As part of a longitudinal study of brain maturation and cognitive development during adolescence, we employed dense-array EEG and spatiotemporal source analysis to characterize maturational changes in the timing of brain responses to temporal variations in sound. We found significant changes in the brain responses compared longitudinally at two time points in early adolescence, namely 10 years (65 subjects) and 11.5 years (60 of the 65 subjects), as well as large differences between adults, studied with the same protocol (Poulsen, Picton & Paus, 2007), and the children at 10 and 11.5 years of age. The transient auditory evoked potential to tone onset showed decreases in the latency of vertex and T-complex components, and a highly significant increase in the amplitude of the N1 wave with increasing age. The auditory steady state response to a 40-Hz frequency-modulated tone increased in amplitude with increasing age. The peak frequency of the envelope-following response to sweeps of amplitude-modulated white noise also increased significantly with increasing age. These results indicate persistent maturation of the cortical mechanisms for auditory processing from childhood into middle adulthood.

Neural mechanisms of resistance to peer influence in early adolescence.

During the shift from a parent-dependent child to a fully autonomous adult, peers take on a significant role in shaping the adolescent's behavior. Peer-derived influences are not always positive, however. Here, we explore neural correlates of interindividual differences in the probability of resisting peer influence in early adolescence. Using functional magnetic resonance imaging, we found striking differences between 10-year-old children with high and low resistance to peer influence in their brain activity during observation of angry hand movements and angry facial expressions: compared with subjects with low resistance to peer influence, individuals with high resistance showed a highly coordinated brain activity in neural systems underlying perception of action and decision making. These findings suggest that the probability of resisting peer influence depends on neural interactions during observation of emotion-laden actions.

Age-related changes in transient and oscillatory brain responses to auditory stimulation in healthy adults 19-45 years old.

The capacity of the human cerebral cortex to track fast temporal changes in auditory stimuli is related to the development of language in children and to deficits in speech perception in the elderly. Although maturation of temporal processing in children and its deterioration in the elderly has been investigated previously, little is known about naturally occurring changes in auditory temporal processing between these limits. The present study examined age-related (19-45 years) changes in 3 electrophysiological measures of auditory processing: 1) the late transient auditory evoked potentials to tone onset, 2) the auditory steady-state response (ASSR) to a 40-Hz frequency-modulated tone, and 3) the envelope following response (EFR) to sweeps of amplitude-modulated white noise from 10 to 100 Hz. With increasing age, the latency of the auditory P1-N1 complex decreased, the oscillatory (ASSR) response became larger and more stable, and the resonant peak of the EFR increased from 38 Hz at 19 years to 46 Hz at 45 years. Source analysis localized these changes to the auditory regions of the temporal lobe. These results indicate persistent adaptation of cortical auditory processes into middle adulthood. We speculate that experience-driven myelination and/or refinement of inhibitory circuits may underlie these changes.

Dynamics of task sets: evidence from dense-array event-related potentials.

Prior research suggests that task sets facilitate coherent, goal-directed behavior by providing an internal, contextual frame that biases selection toward context-relevant stimulus attributes and responses. Questions about how task sets are engaged, maintained, and shifted have recently become a major focus of research on executive control processes. We employed dense-array (128-channel) event-related potential (ERP) methodology to examine the dynamics of brain systems engaged during the preparation and implementation of task switching. The EEG was recorded while participants performed letter and digit judgments to pseudorandomly-ordered, univalent (#3, A%) and bivalent (G5) stimulus trials, with the appropriate task cued by a colored rectangle presented 450 ms before target onset. Results revealed spatial and temporal variations in brain activity that could be related to preparatory processes common to both switch and repeat trials, switch-specific control processes engaged to reconfigure and maintain task set under conflict, and visual priming benefits of task repetition. Despite extensive practice and improvement, both behavioral and ERP results indicated that subjects maintained high levels of executive control processing with extended task engagement. The patterns of ERP activity obtained in the present study fit well with functional neuroanatomical models of self-regulation of action. The frontopolar and right-lateralized frontal switch effects obtained in the present study are consistent with the role of these regions in adapting to changing contextual contingencies. In contrast, the centroparietal P3b and N384 effects related to the contextual ambiguity of bivalent trials are consistent with the context monitoring and updating functions associated with the posterior cingulate learning circuit.

Frontolimbic response to negative feedback in clinical depression.

Functional neuroimaging suggests that limbic regions of the medial frontal cortex may be abnormally active in individuals with depression. These regions, including the anterior cingulate cortex, are engaged in both action regulation, such as monitoring errors and conflict, and affect regulation, such as responding to pain. The authors examined whether clinically depressed subjects would show abnormal sensitivity of frontolimbic networks as they evaluated negative feedback. Depressed subjects and matched control subjects performed a video game in the laboratory as a 256-channel EEG was recorded. Speed of performance on each trial was graded with a feedback signal of A, C, or F. By 350 ms after the feedback signal, depressed subjects showed a larger medial frontal negativity for all feedback compared with control subjects with a particularly striking response to the F grade. This response was strongest for moderately depressed subjects and was attenuated for subjects who were more severely depressed. Localization analyses suggested that negative feedback engaged sources in the anterior cingulate and insular cortices. These results suggest that moderate depression may sensitize limbic networks to respond strongly to aversive events.

Electrophysiological responses to errors and feedback in the process of action regulation.

The anterior cingulate cortex (ACC) is believed to be involved in the executive control of actions, such as in monitoring conflicting response demands, detecting errors, and evaluating the emotional significance of events. In this study, participants performed a task in which evaluative feedback was delayed, so that it was irrelevant to immediate response control but retained its emotional value as a performance indicator. We found that a medial frontal feedback-related negativity similar to the error-related negativity (ERN) tracked affective response to the feedback and predicted subsequent performance. Source analysis of the feedback-related negativity and ERN revealed a common dorsomedial ACC source and a rostromedial ACC source specific to the ERN. The oscillatory nature of these sources provides further evidence that the ERN reflects ongoing theta activity generated in the mediofrontal regions. These results suggest that action regulation by the cingulate gyrus may require the entrainment of multiple structures of the Papez corticolimbic circuit.