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Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.
In meditation practices that involve focused attention to a specific object, novice practitioners often experience moments of distraction (i.e., mind wandering). Previous studies have investigated the neural correlates of mind wandering during meditation practice through Electroencephalography (EEG) using linear metrics (e.g., oscillatory power). However, their results are not fully consistent. Since the brain is known to be a chaotic/nonlinear system, it is possible that linear metrics cannot fully capture complex dynamics present in the EEG signal. In this study, we assess whether nonlinear EEG signatures can be used to characterize mind wandering during breath focus meditation in novice practitioners. For that purpose, we adopted an experience sampling paradigm in which 25 participants were iteratively interrupted during meditation practice to report whether they were focusing on the breath or thinking about something else. We compared the complexity of EEG signals during mind wandering and breath focus states using three different algorithms: Higuchi's fractal dimension (HFD), Lempel-Ziv complexity (LZC), and Sample entropy (SampEn). Our results showed that EEG complexity was generally reduced during mind wandering relative to breath focus states. We conclude that EEG complexity metrics are appropriate to disentangle mind wandering from breath focus states in novice meditation practitioners, and therefore, they could be used in future EEG neurofeedback protocols to facilitate meditation practice.
Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.
Highlights
• A big dataset reveals age-related alterations in EEG biomarkers and cognition.
• Prominent decline of individual alpha peak frequency primarily in temporal lobes.
• A positive association between individual alpha peak frequency and working memory.
• Absence of age-related alpha power decline when controlling for 1/f decay of the PSD.
• Alpha power is negatively associated with the speed of processing in elderly sample.
Abstract
While many structural and biochemical changes in the brain have previously been associated with older age, findings concerning functional properties of neuronal networks, as reflected in their electrophysiological signatures, remain rather controversial. These discrepancies might arise due to several reasons, including diverse factors determining general spectral slowing in the alpha frequency range as well as amplitude mixing between the rhythmic and non-rhythmic parameters. We used a large dataset (N = 1703, mean age 70) to comprehensively investigate age-related alterations in multiple EEG biomarkers taking into account rhythmic and non-rhythmic activity and their individual contributions to cognitive performance. While we found strong evidence for an individual alpha peak frequency (IAF) decline in older age, we did not observe a significant relationship between theta power and age while controlling for IAF. Not only did IAF decline with age, but it was also positively associated with interference resolution in a working memory task primarily in the right and left temporal lobes suggesting its functional role in information sampling. Critically, we did not detect a significant relationship between alpha power and age when controlling for the 1/f spectral slope, while the latter one showed age-related alterations. These findings thus suggest that the entanglement of IAF slowing and power in the theta frequency range, as well as 1/f slope and alpha power measures, might explain inconsistencies reported previously in the literature. Finally, despite the absence of age-related alterations, alpha power was negatively associated with the speed of processing in the right frontal lobe while 1/f slope showed no consistent relationship to cognitive performance. Our results thus demonstrate that multiple electrophysiological features, as well as their interplay, should be considered for the comprehensive assessment of association between age, neuronal activity, and cognitive performance.
Based on Eysenck’s biopsychological trait theory, brain arousal has long been considered to explain individual differences in human personality. Yet, results from empirical studies remained inconclusive. However, most published results have been derived from small samples and, despite inherent limitations, EEG alpha power has usually served as an exclusive indicator for brain arousal. To overcome these problems, we here selected N = 468 individuals of the LIFE-Adult cohort and investigated the associations between the Big Five personality traits and brain arousal by using the validated EEG- and EOG-based analysis tool VIGALL. Our analyses revealed that participants who reported higher levels of extraversion and openness to experience, respectively, exhibited lower levels of brain arousal in the resting state. Bayesian and frequentist analysis results were especially convincing for openness to experience. Among the lower-order personality traits, we obtained the strongest evidence for neuroticism facet ‘impulsivity’ and reduced brain arousal. In line with this, both impulsivity and openness have previously been conceptualized as aspects of extraversion. We regard our findings as well in line with the postulations of Eysenck and consistent with the recently proposed ‘arousal regulation model’. Our results also agree with meta-analytically derived effect sizes in the field of individual differences research, highlighting the need for large (collaborative) studies.
Intention attribution in children and adolescents with autism spectrum disorder: an EEG study
(2021)
The ability to infer intentions from observed behavior and predict actions based on this inference, known as intention attribution (IA), has been hypothesized to be impaired in individuals with autism spectrum disorder (ASD). The underlying neural processes, however, have not been conclusively determined. The aim of this study was to examine the neural signature of IA in children and adolescents with ASD, and to elucidate potential links to contextual updating processes using electroencephalography. Results did not indicate that IA or early contextual updating was impaired in ASD. However, there was evidence of aberrant processing of expectation violations in ASD, particularly if the expectation was based on IA. Results are discussed within the context of impaired predictive coding in ASD.
Relationship between regional white matter hyperintensities and alpha oscillations in older adults
(2021)
Aging is associated with increased white matter hyperintensities (WMHs) and with alterations of alpha oscillations (7–13 Hz). However, a crucial question remains, whether changes in alpha oscillations relate to aging per se or whether this relationship is mediated by age-related neuropathology like WMHs. Using a large cohort of cognitively healthy older adults (N = 907, 60–80 years), we assessed relative alpha power, alpha peak frequency, and long-range temporal correlations from resting-state EEG. We further associated these parameters with voxel-wise WMHs from 3T MRI. We found that a higher prevalence of WMHs in the superior and posterior corona radiata as well as in the thalamic radiation was related to elevated alpha power, with the strongest association in the bilateral occipital cortex. In contrast, we observed no significant relation of the WMHs probability with alpha peak frequency and long-range temporal correlations. Finally, higher age was associated with elevated alpha power via total WMH volume. We suggest that an elevated alpha power is a consequence of WMHs affecting a spatial organization of alpha sources.
Transcranial alternating-current stimulation (tACS) in the frequency range of 1–100 Hz has come to be used routinely in electroencephalogram (EEG) studies of brain function through entrainment of neuronal oscillations. It turned out, however, to be highly non-trivial to remove the strong stimulation signal, including its harmonic and non-harmonic distortions, as well as various induced higher-order artifacts from the EEG data recorded during the stimulation. In this paper, we discuss some of the problems encountered and present methodological approaches aimed at overcoming them. To illustrate the mechanisms of artifact induction and the proposed removal strategies, we use data obtained with the help of a schematic demonstrator setup as well as human-subject data.
Relationship between regional white matter hyperintensities and alpha oscillations in older adults
(2021)
Aging is associated with increased white matter hyperintensities (WMHs) and with the alterations of alpha oscillations (7–13 Hz). However, a crucial question remains, whether changes in alpha oscillations relate to aging per se or whether this relationship is mediated by age-related neuropathology like WMHs. Using a large cohort of cognitively healthy older adults (N=907, 60-80 years), we assessed relative alpha power, alpha peak frequency, and long-range temporal correlations (LRTC) from resting-state EEG. We further associated these parameters with voxel-wise WMHs from 3T MRI. We found that a higher prevalence of WMHs in the superior and posterior corona radiata as well as in the thalamic radiation was related to elevated alpha power, with the strongest association in the bilateral occipital cortex. In contrast, we observed no significant relation of the WMHs probability with alpha peak frequency and LRTC. Finally, higher age was associated with elevated alpha power via total WMH volume. Although an increase in alpha oscillations due to WMH can have a compensatory nature, we rather suggest that an elevated alpha power is a consequence of WMH affecting a spatial organization of alpha sources.
How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, that is, the ease versus difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English and German) read isolated words, we encoded and decoded word vectors taken from the family of prediction-based Word2vec algorithms. We found that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.
Background: Obesity and depression are both associated with changes in sleep/wake regulation, with potential implications for individualized treatment especially in comorbid individuals suffering from both. However, the associations between obesity, depression, and subjective, questionnaire-based and objective, EEG-based measurements of sleepiness used to assess disturbed sleep/wake regulation in clinical practice are not well known.
Objectives: The study investigates associations between sleep/wake regulation measures based on self-reported subjective questionnaires and EEG-derived measurements of sleep/wake regulation patterns with depression and obesity and how/whether depression and/or obesity affect associations between such self-reported subjective questionnaires and EEG-derived measurements.
Methods: Healthy controls (HC, NHC = 66), normal-weighted depressed (DEP, NDEP = 16), non-depressed obese (OB, NOB = 68), and obese depressed patients (OBDEP, NOBDEP = 43) were included from the OBDEP (Obesity and Depression, University Leipzig, Germany) study. All subjects completed standardized questionnaires related to daytime sleepiness (ESS), sleep quality and sleep duration once as well as questionnaires related to situational sleepiness (KSS, SSS, VAS) before and after a 20 min resting state EEG in eyes-closed condition. EEG-based measurements of objective sleepiness were extracted by the VIGALL algorithm. Associations of subjective sleepiness with objective sleepiness and moderating effects of obesity, depression, and additional confounders were investigated by correlation analyses and regression analyses.
Results: Depressed and non-depressed subgroups differed significantly in most subjective sleepiness measures, while obese and non-obese subgroups only differed significantly in few. Objective sleepiness measures did not differ significantly between the subgroups. Moderating effects of obesity and/or depression on the associations between subjective and objective measures of sleepiness were rarely significant, but associations between subjective and objective measures of sleepiness in the depressed subgroup were systematically weaker when patients comorbidly suffered from obesity than when they did not.
Conclusion: This study provides some evidence that both depression and obesity can affect the association between objective and subjective sleepiness. If confirmed, this insight may have implications for individualized diagnosis and treatment approaches in comorbid depression and obesity.
EEG microstate periodicity explained by rotating phase patterns of resting-state alpha oscillations
(2020)
Spatio-temporal patterns in electroencephalography (EEG) can be described by microstate analysis, a discrete approximation of the continuous electric field patterns produced by the cerebral cortex. Resting-state EEG microstates are largely determined by alpha frequencies (8-12 Hz) and we recently demonstrated that microstates occur periodically with twice the alpha frequency.
To understand the origin of microstate periodicity, we analyzed the analytic amplitude and the analytic phase of resting-state alpha oscillations independently. In continuous EEG data we found rotating phase patterns organized around a small number of phase singularities which varied in number and location. The spatial rotation of phase patterns occurred with the underlying alpha frequency. Phase rotors coincided with periodic microstate motifs involving the four canonical microstate maps. The analytic amplitude showed no oscillatory behaviour and was almost static across time intervals of 1-2 alpha cycles, resulting in the global pattern of a standing wave.
In n=23 healthy adults, time-lagged mutual information analysis of microstate sequences derived from amplitude and phase signals of awake eyes-closed EEG records showed that only the phase component contributed to the periodicity of microstate sequences. Phase sequences showed mutual information peaks at multiples of 50 ms and the group average had a main peak at 100 ms (10 Hz), whereas amplitude sequences had a slow and monotonous information decay. This result was confirmed by an independent approach combining temporal principal component analysis (tPCA) and autocorrelation analysis.
We reproduced our observations in a generic model of EEG oscillations composed of coupled non-linear oscillators (Stuart-Landau model). Phase-amplitude dynamics similar to experimental EEG occurred when the oscillators underwent a supercritical Hopf bifurcation, a common feature of many computational models of the alpha rhythm.
These findings explain our previous description of periodic microstate recurrence and its relation to the time scale of alpha oscillations. Moreover, our results corroborate the predictions of computational models and connect experimentally observed EEG patterns to properties of critical oscillator networks.
Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations – typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to ‘explain away’ redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.
Relationship between regional white matter hyperintensities and alpha oscillations in older adults
(2020)
White matter hyperintensities (WMHs) in the cerebral white matter and attenuation of alpha oscillations (AO; 7–13 Hz) occur with the advancing age. However, a crucial question remains, whether changes in AO relate to aging per se or they rather reflect the impact of age-related neuropathology like WMHs. In this study, using a large cohort (N=907) of elderly participants (60-80 years), we assessed relative alpha power (AP), individual alpha peak frequency (IAPF) and long-range temporal correlations (LRTC) from resting-state EEG. We further associated these parameters with voxel-wise WMHs from 3T MRI. We found that higher prevalence of WMHs in the superior and posterior corona radiata was related to elevated relative AP, with strongest correlations in the bilateral occipital cortex, even after controlling for potential confounding factors. In contrast, we observed no significant relation of probability of WMH occurrence with IAPF and LRTC. We argue that the WMH-associated increase of AP reflects generalized and likely compensatory changes of AO leading to a larger number of synchronously recruited neurons.
Neural pattern similarity differentially relates to memory performance in younger and older adults
(2019)
Age-related memory decline is associated with changes in neural functioning, but little is known about how aging affects the quality of information representation in the brain. Whereas a long-standing hypothesis of the aging literature links cognitive impairments to less distinct neural representations in old age (“neural dedifferentiation”), memory studies have shown that overlapping neural representations of different studied items are beneficial for memory performance. In an electroencephalography (EEG) study, we addressed the question whether distinctiveness or similarity between patterns of neural activity supports memory differentially in younger and older adults. We analyzed between-item neural pattern similarity in 50 younger (19–27 years old) and 63 older (63–75 years old) male and female human adults who repeatedly studied and recalled scene–word associations using a mnemonic imagery strategy. We compared the similarity of spatiotemporal EEG frequency patterns during initial encoding in relation to subsequent recall performance. The within-person association between memory success and pattern similarity differed between age groups: For older adults, better memory performance was linked to higher similarity early in the encoding trials, whereas young adults benefited from lower similarity between earlier and later periods during encoding, which might reflect their better success in forming unique memorable mental images of the joint picture–word pairs. Our results advance the understanding of the representational properties that give rise to subsequent memory, as well as how these properties may change in the course of aging.
Post‐traumatic stress disorder (PTSD) is associated with a hypersensitivity to potential threat. This hypersensitivity manifests through differential patterns of emotional information processing and has been demonstrated in behavioral and neurophysiological experimental paradigms. However, the majority of research has been focused on adult patients with PTSD. To examine possible differences in underlying neurophysiological patterns for adolescent patients with PTSD after childhood sexual and/or physical abuse (CSA/CPA), ERP correlates of emotional word processing in 38 healthy participants and 40 adolescent participants with PTSD after experiencing CSA/CPA were studied. The experimental paradigm consisted of a passive reading task with neutral, positive (e.g., paradise), physically threatening (e.g., torment), and socially threatening (i.e., swearing, e.g., son of a bitch) words. A modulation of P3 amplitudes by emotional valence was found, with positive words inducing less elevated amplitudes over both groups. Interestingly, in later processing, the PTSD group showed augmented early late positive potential (LPP) amplitudes for socially threatening stimuli, while there were no modulations within the healthy control group. Also, region‐specific emotional modulations for anterior and posterior electrode clusters were found. For the anterior LPP, highest activations have been found for positive words, while socially and physically threatening words led to strongest modulations in the posterior LPP cluster. There were no modulations by group or emotional valence at the P1 and EPN stage. The findings suggest an enhanced conscious processing of socially threatening words in adolescent patients with PTSD after CSA/CPA, pointing to the importance of a disjoined examination of threat words in emotional processing research.
Dreams and psychosis share several important features regarding symptoms and underlying neurobiology, which is helpful in constructing a testable model of, for example, schizophrenia and delirium. The purpose of the present communication is to discuss two major concepts in dreaming and psychosis that have received much attention in the recent literature: insight and dissociation. Both phenomena are considered functions of higher order consciousness because they involve metacognition in the form of reflective thought and attempted control of negative emotional impact. Insight in dreams is a core criterion for lucid dreams. Lucid dreams are usually accompanied by attempts to control the dream plot and dissociative elements akin to depersonalization and derealization. These concepts are also relevant in psychotic illness. Whereas insightfulness can be considered innocuous in lucid dreaming and even advantageous in psychosis, the concept of dissociation is still unresolved. The present review compares correlates and functions of insight and dissociation in lucid dreaming and psychosis. This is helpful in understanding the two concepts with regard to psychological function as well as neurophysiology.
Human deep sleep is characterized by reduced sensory activity, responsiveness to stimuli, and conscious awareness. Given its ubiquity and reversible nature, it represents an attractive paradigm to study the neural changes which accompany the loss of consciousness in humans. In particular, the deepest stages of sleep can serve as an empirical test for the predictions of theoretical models relating the phenomenology of consciousness with underlying neural activity. A relatively recent shift of attention from the analysis of evoked responses toward spontaneous (or “resting state”) activity has taken place in the neuroimaging community, together with the development of tools suitable to study distributed functional interactions. In this review we focus on recent functional Magnetic Resonance Imaging (fMRI) studies of spontaneous activity during sleep and their relationship with theoretical models for human consciousness generation, considering the global workspace theory, the information integration theory, and the dynamical core hypothesis. We discuss the venues of research opened by these results, emphasizing the need to extend the analytic methodology in order to obtain a dynamical picture of how functional interactions change over time and how their evolution is modulated during different conscious states. Finally, we discuss the need to experimentally establish absent or reduced conscious content, even when studying the deepest sleep stages.
During meditation, practitioners are required to center their attention on a specific object for extended periods of time. When their thoughts get diverted, they learn to quickly disengage from the distracter. We hypothesized that learning to respond to the dual demand of engaging attention on specific objects and disengaging quickly from distracters enhances the efficiency by which meditation practitioners can allocate attention. We tested this hypothesis in a global-to-local task while measuring electroencephalographic activity from a group of eight highly trained Buddhist monks and nuns and a group of eight age and education matched controls with no previous meditation experience. Specifically, we investigated the effect of attentional training on the global precedence effect, i.e., faster detection of targets on a global than on a local level. We expected to find a reduced global precedence effect in meditation practitioners but not in controls, reflecting that meditators can more quickly disengage their attention from the dominant global level. Analysis of reaction times confirmed this prediction. To investigate the underlying changes in brain activity and their time course, we analyzed event-related potentials. Meditators showed an enhanced ability to select the respective target level, as reflected by enhanced processing of target level information. In contrast with control group, which showed a local target selection effect only in the P1 and a global target selection effect in the P3 component, meditators showed effects of local information processing in the P1, N2, and P3 and of global processing for the N1, N2, and P3. Thus, meditators seem to display enhanced depth of processing. In addition, meditation altered the uptake of information such that meditators selected target level information earlier in the processing sequence than controls. In a longitudinal experiment, we could replicate the behavioral effects, suggesting that meditation modulates attention already after a 4-day meditation retreat. Together, these results suggest that practicing meditation enhances the speed with which attention can be allocated and relocated, thus increasing the depth of information processing and reducing response latency.
We examined the neural signatures of stimulus features in visual working memory (WM) by integrating functional magnetic resonance imaging (fMRI) and event-related potential data recorded during mental manipulation of colors, rotation angles, and color–angle conjunctions. The N200, negative slow wave, and P3b were modulated by the information content of WM, and an fMRI-constrained source model revealed a progression in neural activity from posterior visual areas to higher order areas in the ventral and dorsal processing streams. Color processing was associated with activity in inferior frontal gyrus during encoding and retrieval, whereas angle processing involved right parietal regions during the delay interval. WM for color–angle conjunctions did not involve any additional neural processes. The finding that different patterns of brain activity underlie WM for color and spatial information is consistent with ideas that the ventral/dorsal “what/where” segregation of perceptual processing influences WM organization. The absence of characteristic signatures of conjunction-related brain activity, which was generally intermediate between the 2 single conditions, suggests that conjunction judgments are based on the coordinated activity of these 2 streams. Keywords: EEG, fMRI, source analysis, visual, working memory
Das ereigniskorrelierte Potential (EKP) P300 ist eines der am häufigsten untersuchten Potentiale des Elektroenzephalogramms (EEG). Wegen der bedeutsamen Rolle der P300 in der kognitiven Forschung mit gesunden Probanden und psychiatrischen Patienten kommt der Suche nach ihren neuronalen Generatoren ein hoher Stellenwert zu. Man geht im Allgemeinen davon aus, dass sie kein einheitliches Potential darstellt und von mehreren weit verstreuten Quellen generiert wird. Die Fragen nach der genauen Anzahl der P300-Subkomponenten, ihrer Lokalisierung sowie den ihnen zugrunde liegenden kognitiven Prozesse sind jedoch nach wie vor ungelöst. Die Zielsetzung der vorliegenden Arbeit war, die P300 mit Hilfe der Kombination vom EEG und der funktionalen Magnetresonanztomografie (fMRT) in ihre Subkomponenten zu untergliedern und deren Quellen zu lokalisieren. Zu diesem Zweck wurden drei kombinierte EEG/fMRT-Studien durchgeführt. Die ersten beiden Studien beinhalten eine abgewandelte Form des klassischen Oddballparadigmas. Bei der dritten Studie handelt es sich um ein Arbeitsgedächtnisexperiment. Durch die Verknüpfung der fMRT-Ergebnisse mit EKP-Daten aus den beiden Oddball-Experimenten konnten die neuronalen Quellen der zwei wichtigsten Subkomponenten der P300, der P3a und P3b, lokalisiert werden. Es konnte gezeigt werden, dass inferiore und posteriore parietale (IPL bzw. PPC) und inferior temporale (IT) Areale zur Entstehung der P3b beitrugen, während hauptsächlich die präzentralen Regionen (PrCS) die P3a generierten. Die Ergebnisse des Arbeitsgedächtnisexperiments bestätigten die P3b-Quellenlokalisierung der Oddball-Untersuchung mit einr Beteiligung von PPC und IT an der Generierung der P3b-Komponente. Das Arbeitsgedächtnisexperiment verdeutlichte aber auch, dass eine komplexere Abrufanforderung (mit langen Reaktionszeiten) zu einer anhaltenden Aktivität im PPC und einer späten Antwort im ventrolateralen präfrontalen Kortex (VLPFC) führte, die eine zweite P3b-Subkomponente generierten. Durch eine umfassende zeitlich-räumliche Trennung der neuronalen Aktivität beim Arbeitsgedächtnisabruf konnten darüber hinaus die einzelnen Stufen der beteiligten Informationsverarbeitungsprozesse (mentale Chronometrie) beschrieben werden. Diese Anwendung ging über die „reine“ Quellenlokalisation der P300-Komponenten hinaus. Die Ergebnisse zeigten frühe transiente Aktivierungen im IT, die sich zeitlich mit dem Beginn einer anhaltenden Aktivität im PPC überlappten. Darüber hinaus wurden eine späte transiente Aktivität im VLPFC und eine späte anhaltende Aktivität im medialen frontalen und motorischen Kortex (MFC bzw. MC) beobachtet. Es liegt nahe, dass diese neuronalen Signaturen einzelne Stufen kognitiver Aufgabenverarbeitungsschritte wie Reizevaluation (IT), Operationen am Gedächtnispuffer (PPC), aktiven Abruf (VLPFC) und Reaktionsorganisation (MFC und MC) reflektieren. Die vorgestellten Quellenmodelle zeigten übereinstimmend, dass mehrere kortikale Generatoren das P300-EKP erzeugen. Dabei trugen neben den erwarteten parietalen interessanterweise auch inferior temporale und inferior frontale Quellen zur P3b bei, während die P3a vor allem auf anterioren Generatoren im prämotorischen Kortex basierte. Diese Ergebnisse bestätigen teilweise die bisherigen Lokalisationsmodelle, die weitgehend auf neuropsychologischen und invasiven neurophysiologischen Befunden beruhen, widersprechen ihnen aber auch zum Teil, besonders was die Abwesenheit der postulierten präfrontalen und hippocampalen Beiträge zur P3a bzw. P3b betrifft.
Die Wahrnehmung von Objekten gelingt uns jeden Tag unzählige Male – zumeist rasend schnell und problemlos. Obwohl fast immer mehrere unserer Sinne gleichzeitig bei ihrer Wahrnehmung angesprochen werden, erscheinen uns diese Objekte dennoch als ganzheitlich und geschlossen. Für die neuronale Verarbeitung eines bellenden Hundes zum Beispiel empfängt die Großhirnrinde zumindest Eingangsdaten des Seh- und des Hörsystems. Sie werden auf getrennten Pfaden und in spezialisierten Arealen mit aufsteigender Komplexität analysiert. Dieses Funktionsprinzip der parallel verteilten Verarbeitung stellt die Wissenschaftler aber auch vor das so genannte »Bindungsproblem«: Wo und wie werden die Details wieder zu einem Ganzen – zu einer neuronalen Repräsentation – zusammengefügt? Am Institut für medizinische Psychologie der Universitätsklinik Frankfurt untersuchen Neurokognitionsforscher die crossmodale Objekterkennung mit einer Kombination modernster Verfahren der Hirnforschung und kommen dabei den Ver - arbeitungspfaden in der Großhirnrinde auf die Spur.
Nineteen-channel EEGs were recorded from the scalp surface of 30 healthy subjects (16 males and 14 females, mean age: 34 years, SD: 11.7 years) at rest and under trains of intermittent photic stimulation (IPS) at rates of 5, 10 and 20 Hz. Digitalized data were submitted to spectral analysis with fast fourier transformation providing the basis for the computation of global field power (GFP). For quantification, GFP values in the frequency ranges of 5, 10 and 20 Hz at rest were divided by the corresponding data obtained under IPS. All subjects showed a photic driving effect at each rate of stimulation. GFP data were normally distributed, whereas ratios from photic driving effect data showed no uniform behavior due to high interindividual variability. Suppression of alpha-power after IPS with 10 Hz was observed in about 70% of the volunteers. In contrast, ratios of alpha-power were unequivocal in all subjects: IPS at 20 Hz always led to a suppression of alpha-power. Dividing alpha-GFP with 20-Hz IPS by alpha-GFP at rest (R = a-GFPIPS/a-GFPrest) thus resulted in ratios lower than 1. We conclude that ratios from GFP data with 20-Hz IPS may provide a suitable paradigm for further investigations. Key words: EEG, Brain mapping, Intermittent photic stimulation, IPS, Global field power ratios