Narrow Results

Sensing and perceiving involve enormous numbers of widely distributed dendritic and action potentials in cortex, before, during and after stimulus arrival but with differing spatiotemporal patterns. Stimulus-activated receptors drive cortical neurons directly (olfactory) or indirectly through thalamocortical relays. The driven activity induces hemisphere-wide, self-organized patterns of neural activity called wave packets. Three levels of brain function are hypothesized to mediate transition from sensation and perception. Microscopic activity expressed by action potentials is sensory. Macroscopic activity of the whole forebrain expressed by behavior is perceptual. Mesoscopic activity bridges the gap by the formation of wave packets. They form when sensory input destabilizes the primary receiving areas by local state transitions. The sensory-driven action potentials condense into mesoscopic wave packets like molecules forming raindrops from vapor. The condensation disks sustain 2D spatial patterns of phase and amplitude of carrier waves in the beta and gamma EEG. The AM patterns correlate not with features but with the context and value of sensory stimuli for the subjects, in a word, their meaning. The wave packets from all sensory areas are broadly transmitted through the forebrain. They induce the formation of macroscopic patterns that coalesce like scintillating pools over much and perhaps all of each hemisphere. The prediction is made for clinical testing that wave packets are precursor to states of awareness. They are not by themselves accessible to experience, as may be the macroscopic states initiated by global state transitions.

A recently developed quantitative model of cortical activity is used that permits data comparison with experiment using a quantitative and standardized means. The model incorporates properties of neurophysiology including axonal transmission delays, synapto-dendritic rates, range-dependent connectivities, excitatory and inhibitory neural populations, and intrathalamic, intracortical, corticocortical and corticothalamic pathways. This study tests the ability of the model to determine unique physiological properties in a number of different data sets varying in mean age and pathology. The model is used to fit individual electroencephalographic (EEG) spectra from post-traumatic stress disorder (PTSD), schizophrenia, first episode schizophrenia (FESz), attention deficit hyperactivity disorder (ADHD), and their age/sex matched controls. The results demonstrate that the model is able to distinguish each group in terms of a unique cluster of abnormal parameter deviations. The abnormal physiology inferred from these parameters is also consistent with known theoretical and experimental findings from each disorder. The model is also found to be sensitive to the effects of medication in the schizophrenia and FESz group, further supporting the validity of the model.

Over the last decade, an increasing number of research studies have focused on the construct of Emotional Intelligence (EI), which may be broadly defined as the capacity to perceive and regulate emotions in oneself as well as those of others. Researchers have generally adopted an organizational or management focus to the study of EI, however studies which adopt a more integrated perspective by combining psychological with biological measures, may help in further elucidating this relatively abstract construct. The first objective of this paper was to report on the psychometric properties of a brief, self-report measure of EI (Brain Resource Inventory for Emotional intelligence Factors or BRIEF), comprising internal emotional capacity (IEC), external emotional capacity (EEC) and self concept (SELF). Second, we further explored the validity of the measure by assessing the relationships between the BRIEF and variables considered relevant to the understanding of EI (including gender, age, personality, cognitive intelligence and resting state electroencephalography, EEG). The BRIEF possessed sound psychometric properties (internal consistency, r = 0.68 - 0.81; test-retest reliability, r = 0.92; construct validity with the Self Report Emotional Intelligence Test, r = 0.70). As hypothesized, females were found to score higher than males on EI. EI was associated more with personality than with cognitive ability, and EEG was found to explain a significant portion of the variance in EI scores. The finding that low EI is related to underarousal of the left-frontal cortex (increased theta EEG) is consistent with research on patients with depression, as well as attention deficit hyperactivity disorder. Although EI did not display age-related increases, this might relate to the exclusion of adolescents from our sample. In conclusion, examination of the way in which EI measures relate to a complementary range of psychological and biological measures may help to further elucidate this construct.

Early Life Stress (ELS) has been associated with a range of adverse outcomes in adults, including abnormalities in electrical brain activity [1], personality dimensions [40], increased vulnerability to substance abuse and depression [14]. The present study seeks to quantify these proposed effects in a large sample of non-clinical subjects. Data for the study was obtained from The Brain Resource International Database (six laboratories: two in USA, two in Europe, two in Australia). This study analyzed scalp electrophysiological data (EEG eyes open, closed and target auditory oddball data) and personality (NEO-FFI), history of addictive substance use and ELS) data that was acquired from 740 healthy volunteers. The ELS measures were collected via a self-report measure and covered a broad range of events from childhood sexual and physical abuse, to first-hand experience of traumatizing accidents and sustained domestic conflict [41]. Analysis of covariance, controlling for age and gender, compared EEG data from subjects exposed to ELS with those who were unexposed. ELS was associated with significantly decreased power across the EEG spectrum. The between group differences were strongest in the eyes closed paradigm, where subjects who experienced ELS showed significantly reduced beta (F_1,405 = 12.37, p = .000), theta (F_1,405 = 20.48, p = .000), alpha (F_1,405 = 9.65, p = .002) and delta power (F_1,450 = 36.22, p = .000). ELS exposed subjects also showed a significantly higher alpha peak frequency (F_1,405 = 6.39, p = .012) in the eyes closed paradigm. Analysis of covariance on ERP components revealed that subjects who experienced ELS had significantly decreased N2 amplitude (F_1,405 = 7.73, p = .006). Analyses of variance conducted on measures of personality revealed that subjects who experienced ELS had significantly higher levels of neuroticism (F_1,264 = 13.39, p = .000) and openness (F_1,264 = 17.11, p = .000), but lower levels of conscientiousness, than controls (F_1,264 = 4.08, p = .044). The number of ELS events experienced was shown to be a significant predictor of scores on the DASS questionnaire [27], which rates subjects on symptoms of depression (F_3,688 = 16.44, p = .000, R² = .07), anxiety (F_3,688 = 14.32, p = .000, R² = .06) and stress (F_3,688 = 20.02, p = .000, R² = .08). Each additional early life stressor was associated with an increase in these scores independent of age, gender and the type of stressor. Furthermore, the number of ELS experiences among smokers was also found to be a positive predictor of the nicotine dependency score (Faegstrom Test For Nicotine Dependence, [19]) (F_3,104 = 10.99, p = .000, R² = .24), independent of age, gender and type of stressor. In conclusion, we highlight the impact of a history of ELS showed significant effects on brain function (EEG and ERP activity), personality dimensions and nicotine dependence.

A framework for investigating information processing in cortico-thalamocortical (cortico-TC) networks is presented, that in part can be used to model and interpret individual changes in electroencephalographic spectra and event-related potentials such as those from the Brain Resource International Database. Scientific work covering neurophysiology, TC firing modes, and TC models are explored in the framework to explain how the brain might process complex information in a multistage process. It is proposed that the thalamus and the cortico-TC system have unique ionic properties and transmission delays (in humans), which are suited to the function of taking "snapshots" or samples of complex environmental stimuli, rather than continuous data streams. This leads to careful and sequential coordination of stimulus and response processes, and increases the probability of information transfer and the resulting information complexity in higher cortical regions. Given the scope of this framework, the multidimensional and standardized Brain Resource International Database provides a pertinent set of measures for both testing hypotheses generated from the model, and for fitting the model to experimental data to investigate mechanisms underlying information processing.

Fields of neural activity are seen in synchronized oscillations that are detected at mesoscopic scales in syntheses of multicellular recordings of action potentials and electroencephalograms (EEGs) over broad areas of cerebral cortex. The waves often have large-scale, highly textured spatial patterns of cortical activity, formed in the context of associative learning under classical and operant conditioning in rabbits. The patterns show spatial amplitude modulation of shared oscillations of carrier waves in the beta and gamma ranges of the EEG, with recurrence at frame rates in the alpha and theta ranges. The frames also show spatial phase modulation that is inconsistent with driving of the oscillations by focal pacemakers. The hypothesis is developed that the synchronization manifests continuous distributions of activity in cortical neuropil that modulate firings of selected neural networks embedded in the neuropil. Five interactive agencies have been postulated to explain the mechanism for the field synchrony: electric fields; magnetic fields; electromagnetic fields (radio waves); diffusion chemical gradients; and order parameters that control self-organization of large populations of neurons by widespread synaptic interaction constituting negative and positive feedback. Only the last interactive agency fits the data. The points are emphasized that these field patterns in frames require interactive neural dynamics that is modulated in respect to global operations mediating arousal, attention, selective emotional stance, wake, sleep, learn, habituate, dishabituate, etc., and that these operations require differing but complementary fields that form by massive parallel feed-forward architectures of brainstem neuromodulatory nuclei. An example is given using histamine of the neural discharges of brainstem nuclei that do not require fine spatiotemporal texturing of their firing; they operate by nonsynaptic release of neuromodulators that effect changes in background state, such that textured patterns of cortical activity can form and update in flexible adaptations of brains to their environments. These systems instantiate volume transmission by nonsynaptic diffusion transmission, in concert with the self-organization of the textured neural activity that supports cognition.

New treatments for Alzheimer's disease require early detection of cognitive decline. Most studies seeking to identify markers of early cognitive decline have focused on a limited number of measures. We sought to establish the profile of brain function measures which best define early neuropsychological decline. We compared subjects with subjective memory complaints to normative controls on a wide range of EEG derived measures, including a new measure of event-related spatio-temporal waves and biophysical modeling, which derives anatomical and physiological parameters based on subject's EEG measurements. Measures that distinguished the groups were then related to cognitive performance on a variety of learning and executive function tasks. The EEG measures include standard power measures, peak alpha frequency, EEG desynchronization to eyes-opening, and global phase synchrony. The most prominent differences in subjective memory complaint subjects were elevated alpha power and an increased number of spatio-temporal wave events. Higher alpha power and changes in wave activity related most strongly to a decline in verbal memory performance in subjects with subjective memory complaints, and also declines in maze performance and working memory reaction time. Interestingly, higher alpha power and wave activity were correlated with improved performance in reverse digit span in the subjective memory complaint group. The modeling results suggest that differences in the subjective memory complaint subjects were due to a decrease in cortical and thalamic inhibitory gains and slowed dendritic time-constants. The complementary profile that emerges from the variety of measures and analyses points to a nonlinear progression in electrophysiological changes from early neuropsychological decline to late-stage dementia, and electrophysiological changes in subjective memory complaint that vary in their relationships to a range of memory-related tasks.

Aims: Increasing age is the strongest risk factor for Alzheimer's disease (AD). Yet, departure from normal age-related decline for established markers of AD including memory, cognitive decline and brain function deficits, has not been quantified.

Methods: We examined the cross-sectional estimates of the "rate of decline" in cognitive performance and psychophysiological measures of brain function over age in AD, preclinical (subjective memory complaint-SMC, Mild Cognitive Impairment-MCI) and healthy groups. Correlations between memory performance and indices of brain function were also conducted.

Results: The rate of cognitive decline increased between groups: AD showed advanced decline, and SMC/MCI groups represented intermediate stages of decline relative to normal aging expectations. In AD, advanced EEG alterations (excessive slow-wave/reduced fast-wave EEG, decreased working memory P450 component) were observed over age, which were coupled with memory decline. By contrast, MCI group showed less severe cognitive changes but specific decreases in the working memory N300 component and slow-wave (delta) EEG, associated with decline in memory.

Discussion and Integrative Significance: While the cognitive data suggests a continuum of deterioration associated with increasing symptom severity across groups, integration with brain function measures points to possible distinct compensatory strategies in MCI and AD groups. An integrative approach offers the potential for objective markers of the critical turning point, with age as a potential factor, from mild memory problems to disease.

Aims: QEEG and neuropsychological tests were used to investigate the underlying neural processes in dyslexia.

Methods: A group of dyslexic children were compared with a matched control group from the Brain Resource International Database on measures of cognition and brain function (EEG and coherence).

Results: The dyslexic group showed increased slow activity (Delta and Theta) in the frontal and right temporal regions of the brain. Beta-1 was specifically increased at F7. EEG coherence was increased in the frontal, central and temporal regions for all frequency bands. There was a symmetric increase in coherence for the lower frequency bands (Delta and Theta) and a specific right-temporocentral increase in coherence for the higher frequency bands (Alpha and Beta). Significant correlations were observed between subtests such as Rapid Naming Letters, Articulation, Spelling and Phoneme Deletion and EEG coherence profiles.

Discussion: The results support the double-deficit theory of dyslexia and demonstrate that the differences between the dyslexia and control group might reflect compensatory mechanisms.

Integrative Significance: These findings point to a potential compensatory mechanism of brain function in dyslexia and helps to separate real dysfunction in dyslexia from acquired compensatory mechanisms.

Aims: This study investigated the relationship between electroencephalograph (EEG) power and basal metabolic rate (BMR) over the human lifespan, to better understand the mechanisms involved in the decline of neural activity with age.

Methods: Eyes-open EEG power was calculated in standard frequency bands and averaged across recording sites in 1831 healthy subjects aged 6 to 86 years, from the Brain Resource International Database. In a subset of 175 subjects, structural MRI scans were also undertaken to determine the role of grey matter. Cerebral metabolic rate (CMR) was estimated using two models of EEG power, based on: (1) normalization of BMR by total body mass, and (2) scaling by cortical grey matter.

Results: Regression analysis revealed a linear relationship between the CMR estimates and EEG power under both models. In the full sample, CMR explained 65% of the variance in delta power, and 53% of the variance in theta power over the age span.

Discussion: The results demonstrate that the large EEG signals in early childhood are associated with a higher BMR during that age.

Integrative Significance: The use of cross-modal measurements in this study highlights the utility of capturing data in an integrative framework to reveal fundamental physiological relationships.

Variable contributions of state and trait to the electroencephalographic (EEG) signal affect the stability over time of EEG measures, quite apart from other experimental uncertainties. The extent of intraindividual and interindividual variability is an important factor in determining the statistical, and hence possibly clinical significance of observed differences in the EEG. This study investigates the changes in classical quantitative EEG (qEEG) measures, as well as of parameters obtained by fitting frequency spectra to an existing continuum model of brain electrical activity. These parameters may have extra variability due to model selection and fitting. Besides estimating the levels of intraindividual and interindividual variability, we determined approximate time scales for change in qEEG measures and model parameters. This provides an estimate of the recording length needed to capture a given percentage of the total intraindividual variability. Also, if more precise time scales can be obtained in future, these may aid the characterization of physiological processes underlying various EEG measures. Heterogeneity of the subject group was constrained by testing only healthy males in a narrow age range (mean = 22.3 years, sd = 2.7). Eyes-closed EEGs of 32 subjects were recorded at weekly intervals over an approximately six-week period, of which 13 subjects were followed for a year. QEEG measures, computed from Cz spectra, were powers in five frequency bands, alpha peak frequency, and spectral entropy. Of these, theta, alpha, and beta band powers were most reproducible. Of the nine model parameters obtained by fitting model predictions to experiment, the most reproducible ones quantified the total power and the time delay between cortex and thalamus. About 95% of the maximum change in spectral parameters was reached within minutes of recording time, implying that repeat recordings are not necessary to capture the bulk of the variability in EEG spectra.

Recent functional magnetic resonance imaging studies demonstrate that increased task-related neural activity in parietal and frontal cortex during development and training is positively correlated with improved visuospatial working memory (vsWM) performance. Yet, the analysis of the corresponding underlying functional reorganization of the fronto-parietal network has received little attention. Here, we perform an integrative experimental and computational analysis to determine the effective balance between the superior frontal sulcus (SFS) and intraparietal sulcus (IPS) and their putative role(s) in protecting against distracters. To this end, we performed electroencephalographic (EEG) recordings during a vsWM task. We utilized a biophysically based computational cortical network model to analyze the effects of different neural changes in the underlying cortical networks on the directed transfer function (DTF) and spiking activity. Combining a DTF analysis of our EEG data with the DTF analysis of the computational model, a directed strong SFS → IPS network was revealed. Such a configuration offers protection against distracters, whereas the opposite is true for strong IPS → SFS connections. Our results therefore suggest that the previously demonstrated improvement of vsWM performance during development could be due to a shift in the control of the effective balance between the SFS–IPS networks.

Using a standardized database of EEG data, recorded during the habituation and oddball paradigms, changes in the auditory event-related potential (ERP) are demonstrated on the time scale of seconds and minutes. Based on previous research and a mathematical model of neural activity, neural mechanisms that could account for these changes are proposed. When the stimulus tones are not relevant to a task, N100 magnitude decreases substantially for the first repetition of a stimulus pattern and increases in response to a variant tone. It is argued these short-term changes are consistent with the hypothesis that there is a refractory period in the neural elements underlying the ERP. In the oddball paradigm, when the stimulus tones are task-relevant, the magnitudes of both N100 and P200 for backgrounds decrease over the entire six-minute recording session. It is argued that these changes are mediated by a decreasing arousal level, and consistent with this, a subject's electrodermal activity (EDA) is shown to reduce over the recording session. By fitting ERPs generated by a biophysical model of neural activity, it is shown that the changes in the background ERPs over the recording session can be reproduced by changing the strength of connections between populations of cortical neurons. For ERPs elicited by infrequent stimuli, there is no corresponding trend in the magnitudes of N100 or P300 components. The effects of stimuli serial order on ERPs are also assessed, showing that the N100 for background ERPs and the N100 and P300 for target ERPs increases as the probability, and expectancy, of receiving a task relevant stimulus increases. Cortical neuromodulation by acetylcholine (ACh) is proposed as a candidate mechanism to mediate the ERP changes associated with attention and arousal.

We consider electroencephalograms (EEGs) of healthy individuals and compare the properties of the brain functional networks found through two methods: unpartialized and partialized cross-correlations. The networks obtained by partial correlations are fundamentally different from those constructed through unpartial correlations in terms of graph metrics. In particular, they have completely different connection efficiency, clustering coefficient, assortativity, degree variability, and synchronization properties. Unpartial correlations are simple to compute and they can be easily applied to large-scale systems, yet they cannot prevent the prediction of non-direct edges. In contrast, partial correlations, which are often expensive to compute, reduce predicting such edges. We suggest combining these alternative methods in order to have complementary information on brain functional networks.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.

A new method for identification of stages in arithmetic task solving has been presented. Solving of addition tasks induced formation of EEG foci in delta–theta-band were formed in fronto-central and parietal regions of the left hemisphere and in parietal-temporal and frontal areas of right hemisphere. In multiplication tasks, delta–theta-foci were formed in frontal, central and parietal regions of the left hemisphere as well as in temporal areas of the right hemisphere. When solving addition tasks, the coherence asymmetry was increased in left hemisphere between frontal, parietal and temporal areas in delta–theta-band as well as between frontal and parietal regions in alpha-band. The neurophysiological mechanisms of complex addition and multiplication arithmetic task solving is discussed.

The characterization of human neural activity during imaginary movement tasks represent an important challenge in order to develop effective applications that allow the control of a machine. Yet methods based on brain network analysis of functional connectivity have been scarcely investigated. As a result we use graph theoretic methods to investigate the functional connectivity and brain network measures in order to characterize imagery hand movements in a set of healthy subjects. The results of the present study show that functional connectivity analysis and minimum spanning tree (MST) parameters allow to successfully discriminate between imagery hand movements (both right and left) and resting state conditions. In conclusion, this paper shows that brain network analysis of EEG functional connectivity could represent an efficient alternative to more classical local activation based approaches. Furthermore, it also suggests the shift toward methods based on the characterization of a limited set of fundamental functional connections that disclose salient network topological features.

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13–30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.

Besides the low-frequency electromagnetic body-processes measurable through the electroencephalography (EEG), electrocardiography (ECG), etc. there are processes that do not need external excitation, emitting light within or close to the visible spectra. Such ultraweak photon emission (UPE), also named biophoton emission, reflects the cellular (and body) oxidative status. Recently, a growing body of evidence shows that UPE may play an important role in the basic functioning of living cells. Moreover, interesting evidences are beginning to emerge that UPE may well play an important role in neuronal functions. In fact, biophotons are byproducts in cellular metabolism and produce false signals (e.g., retinal discrete dark noise) but on the other side neurons contain many light sensitive molecules that makes it hard to imagine how they might not be influenced by UPE, and thus UPE may carry informational contents. Here, we investigate UPE in the brain from different points of view such as experimental evidences, theoretical modeling, and physiological significance.

Brain is the command center for the body and contains a lot of information which can be extracted by using different non-invasive techniques. Electroencephalography (EEG), Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) are the most common neuroimaging techniques to elicit brain behavior. By using these techniques different activity patterns can be measured within the brain to decode the content of mental processes especially the visual and auditory content. This paper discusses the models and imaging techniques used in visual decoding to investigate the different conditions of brain along with recent advancements in brain decoding.

This paper concludes that it's not possible to extract all the information from the brain, however careful experimentation, interpretation and powerful statistical tools can be used with the neuroimaging techniques for better results.