White Matter Integrity Supports BOLD Signal Variability and Cognitive Performance in the Aging Human Brain

Decline in cognitive performance in old age is linked to both suboptimal neural processing in grey matter (GM) and reduced integrity of white matter (WM), but the whole-brain structure-function-cognition associations remain poorly understood. Here we apply a novel measure of GM processing–moment-to-moment variability in the blood oxygenation level-dependent signal (SD_BOLD)—to study the associations between GM function during resting state, performance on four main cognitive domains (i.e., fluid intelligence, perceptual speed, episodic memory, vocabulary), and WM microstructural integrity in 91 healthy older adults (aged 60-80 years). We modeled the relations between whole-GM SD_BOLD with cognitive performance using multivariate partial least squares analysis. We found that greater SD_BOLD was associated with better fluid abilities and memory. Most of regions showing behaviorally relevant SD_BOLD (e.g., precuneus and insula) were localized to inter- or intra-network “hubs” that connect and integrate segregated functional domains in the brain. Our results suggest that optimal dynamic range of neural processing in hub regions may support cognitive operations that specifically rely on the most flexible neural processing and complex cross-talk between different brain networks. Finally, we demonstrated that older adults with greater WM integrity in all major WM tracts had also greater SD_BOLD and better performance on tests of memory and fluid abilities. We conclude that SD_BOLD is a promising functional neural correlate of individual differences in cognition in healthy older adults and is supported by overall WM integrity.     

Diminished Activation of Motor Working-Memory Networks in Parkinson's Disease

Parkinson''s disease (PD) is characterized by typical extrapyramidal motor features and increasingly recognized non-motor symptoms such as working memory (WM) deficits. Using functional magnetic resonance imaging (fMRI), we investigated differences in neuronal activation during a motor WM task in 23 non-demented PD patients and 23 age- and gender-matched healthy controls. Participants had to memorize and retype variably long visuo-spatial stimulus sequences after short or long delays (immediate or delayed serial recall). PD patients showed deficient WM performance compared to controls, which was accompanied by reduced encoding-related activation in WM-related regions. Mirroring slower motor initiation and execution, reduced activation in motor structures such as the basal ganglia and superior parietal cortex was detected for both immediate and delayed recall. Increased activation in limbic, parietal and cerebellar regions was found during delayed recall only. Increased load-related activation for delayed recall was found in the posterior midline and the cerebellum. Overall, our results demonstrate that impairment of WM in PD is primarily associated with a widespread reduction of task-relevant activation, whereas additional parietal, limbic and cerebellar regions become more activated relative to matched controls. While the reduced WM-related activity mirrors the deficient WM performance, the additional recruitment may point to either dysfunctional compensatory strategies or detrimental crosstalk from “default-mode” regions, contributing to the observed impairment.     

Visuospatial Working Memory in Toddlers with a History of Periventricular Leukomalacia: An EEG Narrow-Band Power Analysis

Background

Periventricular Leukomalacia (PVL) affects white matter, but grey matter injuries have also been reported, particularly in the dorsomedial nucleus and the cortex. Both structures have been related to working memory (WM) processes. The aim of this study was to compare behavioral performances and EEG power spectra during a visuospatial working memory task (VSWMT) of toddlers with a history of PVL and healthy toddlers.

Methodology/Principal Findings

A prospective, comparative study of WM was conducted in toddlers with a history of PVL and healthy toddlers. The task responses and the EEG narrow-band power spectra during a VSWMT were compared in both groups. The EEG absolute power was analyzed during the following three conditions: baseline, attention and WM retention. The number of correct responses was higher in the healthy group (20.5±5.0) compared to the PVL group (16.1±3.9) (p = 0.04). The healthy group had absolute power EEG increases (p≤0.05) during WM compared to the attention condition in the bilateral frontal and right temporal, parietal and occipital regions in frequencies ranging from 1.17 to 2.34 Hz and in the right temporal, parietal and occipital regions in frequencies ranging from 14.06 to 15.23 Hz. In contrast, the PVL group had absolute power increases (p≤0.05) in the bilateral fronto-parietal, left central and occipital regions in frequencies that ranged from 1.17 to 3.52 Hz and in the bilateral frontal and right temporal regions in frequencies ranging from 9.37 to 19.14 Hz.

Conclusions/Significance

This study provides evidence that PVL toddlers have visuospatial WM deficits and a very different pattern of absolute power increases compared to a healthy group of toddlers, with greater absolute power in the low frequency range and widespread neuronal networks in the WM retention phase.     

Sustained neural rhythms reveal endogenous oscillations supporting speech perception

Rhythmic sensory or electrical stimulation will produce rhythmic brain responses. These rhythmic responses are often interpreted as endogenous neural oscillations aligned (or “entrained”) to the stimulus rhythm. However, stimulus-aligned brain responses can also be explained as a sequence of evoked responses, which only appear regular due to the rhythmicity of the stimulus, without necessarily involving underlying neural oscillations. To distinguish evoked responses from true oscillatory activity, we tested whether rhythmic stimulation produces oscillatory responses which continue after the end of the stimulus. Such sustained effects provide evidence for true involvement of neural oscillations. In Experiment 1, we found that rhythmic intelligible, but not unintelligible speech produces oscillatory responses in magnetoencephalography (MEG) which outlast the stimulus at parietal sensors. In Experiment 2, we found that transcranial alternating current stimulation (tACS) leads to rhythmic fluctuations in speech perception outcomes after the end of electrical stimulation. We further report that the phase relation between electroencephalography (EEG) responses and rhythmic intelligible speech can predict the tACS phase that leads to most accurate speech perception. Together, we provide fundamental results for several lines of research—including neural entrainment and tACS—and reveal endogenous neural oscillations as a key underlying principle for speech perception.

Just as a child on a swing continues to move after the pushing stops, this study reveals similar entrained rhythmic echoes in brain activity after hearing speech and electrical brain stimulation; perturbation with tACS shows that these brain oscillations help listeners to understand speech.     

MM-MDS: A Multidimensional Scaling Database with Similarity Ratings for 240 Object Categories from the Massive Memory Picture Database

Cognitive theories in visual attention and perception, categorization, and memory often critically rely on concepts of similarity among objects, and empirically require measures of “sameness” among their stimuli. For instance, a researcher may require similarity estimates among multiple exemplars of a target category in visual search, or targets and lures in recognition memory. Quantifying similarity, however, is challenging when everyday items are the desired stimulus set, particularly when researchers require several different pictures from the same category. In this article, we document a new multidimensional scaling database with similarity ratings for 240 categories, each containing color photographs of 16–17 exemplar objects. We collected similarity ratings using the spatial arrangement method. Reports include: the multidimensional scaling solutions for each category, up to five dimensions, stress and fit measures, coordinate locations for each stimulus, and two new classifications. For each picture, we categorized the item''s prototypicality, indexed by its proximity to other items in the space. We also classified pairs of images along a continuum of similarity, by assessing the overall arrangement of each MDS space. These similarity ratings will be useful to any researcher that wishes to control the similarity of experimental stimuli according to an objective quantification of “sameness.”     

Never Resting Brain: Simultaneous Representation of Two Alpha Related Processes in Humans

Brain activity is continuously modulated, even at “rest”. The alpha rhythm (8–12 Hz) has been known as the hallmark of the brain''s idle-state. However, it is still debated if the alpha rhythm reflects synchronization in a distributed network or focal generator and whether it occurs spontaneously or is driven by a stimulus. This EEG/fMRI study aimed to explore the source of alpha modulations and their distribution in the resting brain. By serendipity, while computing the individually defined power modulations of the alpha-band, two simultaneously occurring components of these modulations were found. An ‘induced alpha’ that was correlated with the paradigm (eyes open/ eyes closed), and a ‘spontaneous alpha’ that was on-going and unrelated to the paradigm. These alpha components when used as regressors for BOLD activation revealed two segregated activation maps: the ‘induced map’ included left lateral temporal cortical regions and the hippocampus; the ‘spontaneous map’ included prefrontal cortical regions and the thalamus. Our combined fMRI/EEG approach allowed to computationally untangle two parallel patterns of alpha modulations and underpin their anatomical basis in the human brain. These findings suggest that the human alpha rhythm represents at least two simultaneously occurring processes which characterize the ‘resting brain’; one is related to expected change in sensory information, while the other is endogenous and independent of stimulus change.     

Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity patterns and behavior that can be modeled, and suggest a unified setting in which diverse cognitive computations and mechanisms can be studied.     

Simultaneous EEG-fMRI during a Working Memory Task: Modulations in Low and High Frequency Bands

Background

EEG studies of working memory (WM) have demonstrated load dependent frequency band modulations. FMRI studies have localized load modulated activity to the dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (MPFC), and posterior parietal cortex (PPC). Recently, an EEG-fMRI study found that low frequency band (theta and alpha) activity negatively correlated with the BOLD signal during the retention phase of a WM task. However, the coupling of higher (beta and gamma) frequencies with the BOLD signal during WM is unknown.

Methodology

In 16 healthy adult subjects, we first investigated EEG-BOLD signal correlations for theta (5–7 Hz), alpha1 (8–10), alpha2 (10–12 Hz), beta1 (13–20), beta2 (20–30 Hz), and gamma (30–40 Hz) during the retention period of a WM task with set size 2 and 5. Secondly, we investigated whether load sensitive brain regions are characterised by effects that relate frequency bands to BOLD signals effects.

Principal Findings

We found negative theta-BOLD signal correlations in the MPFC, PPC, and cingulate cortex (ACC and PCC). For alpha1 positive correlations with the BOLD signal were found in ACC, MPFC, and PCC; negative correlations were observed in DLPFC, PPC, and inferior frontal gyrus (IFG). Negative alpha2-BOLD signal correlations were observed in parieto-occipital regions. Beta1-BOLD signal correlations were positive in ACC and negative in precentral and superior temporal gyrus. Beta2 and gamma showed only positive correlations with BOLD, e.g., in DLPFC, MPFC (gamma) and IFG (beta2/gamma). The load analysis revealed that theta and—with one exception—beta and gamma demonstrated exclusively positive load effects, while alpha1 showed only negative effects.

Conclusions

We conclude that the directions of EEG-BOLD signal correlations vary across brain regions and EEG frequency bands. In addition, some brain regions show both load sensitive BOLD and frequency band effects. Our data indicate that lower as well as higher frequency brain oscillations are linked to neurovascular processes during WM.     

Auditory Stimuli Mimicking Ambient Sounds Drive Temporal “Delta-Brushes” in Premature Infants

In the premature infant, somatosensory and visual stimuli trigger an immature electroencephalographic (EEG) pattern, “delta-brushes,” in the corresponding sensory cortical areas. Whether auditory stimuli evoke delta-brushes in the premature auditory cortex has not been reported. Here, responses to auditory stimuli were studied in 46 premature infants without neurologic risk aged 31 to 38 postmenstrual weeks (PMW) during routine EEG recording. Stimuli consisted of either low-volume technogenic “clicks” near the background noise level of the neonatal care unit, or a human voice at conversational sound level. Stimuli were administrated pseudo-randomly during quiet and active sleep. In another protocol, the cortical response to a composite stimulus (“click” and voice) was manually triggered during EEG hypoactive periods of quiet sleep. Cortical responses were analyzed by event detection, power frequency analysis and stimulus locked averaging. Before 34 PMW, both voice and “click” stimuli evoked cortical responses with similar frequency-power topographic characteristics, namely a temporal negative slow-wave and rapid oscillations similar to spontaneous delta-brushes. Responses to composite stimuli also showed a maximal frequency-power increase in temporal areas before 35 PMW. From 34 PMW the topography of responses in quiet sleep was different for “click” and voice stimuli: responses to “clicks” became diffuse but responses to voice remained limited to temporal areas. After the age of 35 PMW auditory evoked delta-brushes progressively disappeared and were replaced by a low amplitude response in the same location. Our data show that auditory stimuli mimicking ambient sounds efficiently evoke delta-brushes in temporal areas in the premature infant before 35 PMW. Along with findings in other sensory modalities (visual and somatosensory), these findings suggest that sensory driven delta-brushes represent a ubiquitous feature of the human sensory cortex during fetal stages and provide a potential test of functional cortical maturation during fetal development.     

Aging-related changes of EEG synchronization during a visual working memory task

Differences of EEG synchronization between normal old and young people during a working memory (WM) task were investigated. The synchronization likelihood (SL) is a novel method to assessed synchronization in multivariate time series for non-stationary systems. To evaluate this method to study the mechanisms of WM, we calculated the SL values in brain electrical activity for both resting state and task state. EEG signals were recorded from 14 young adults and 12 old adults during two different states, respectively. SL was used to measure EEG synchronization between 19 electrodes in delta, theta, alpha1, alpha2 and beta frequency bands. Bad task performance and significantly decreased EEG synchronization were found in old group compared to young group in alpha1, alpha2 and beta frequency bands during the WM task. Moreover, significantly decreased EEG synchronization in beta band in the elder was also detected during the resting state. The findings suggested that reduced EEG synchronization may be one of causes for WM capacity decline along with healthy aging.     

Schizophrenia Detection and Classification by Advanced Analysis of EEG Recordings Using a Single Electrode Approach

Electroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity. This paper concerns the diagnosis of schizophrenia using EEG, which currently suffers from several cardinal problems: it heavily depends on assumptions, conditions and prior knowledge regarding the patient. Additionally, the diagnostic experiments take hours, and the accuracy of the analysis is low or unreliable. This article presents the “TFFO” (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives. The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.     

Independent EEG sources are dipolar

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).     

A Generalized Framework for Quantifying the Dynamics of EEG Event-Related Desynchronization

Brains were built by evolution to react swiftly to environmental challenges. Thus, sensory stimuli must be processed ad hoc, i.e., independent—to a large extent—from the momentary brain state incidentally prevailing during stimulus occurrence. Accordingly, computational neuroscience strives to model the robust processing of stimuli in the presence of dynamical cortical states. A pivotal feature of ongoing brain activity is the regional predominance of EEG eigenrhythms, such as the occipital alpha or the pericentral mu rhythm, both peaking spectrally at 10 Hz. Here, we establish a novel generalized concept to measure event-related desynchronization (ERD), which allows one to model neural oscillatory dynamics also in the presence of dynamical cortical states. Specifically, we demonstrate that a somatosensory stimulus causes a stereotypic sequence of first an ERD and then an ensuing amplitude overshoot (event-related synchronization), which at a dynamical cortical state becomes evident only if the natural relaxation dynamics of unperturbed EEG rhythms is utilized as reference dynamics. Moreover, this computational approach also encompasses the more general notion of a “conditional ERD,” through which candidate explanatory variables can be scrutinized with regard to their possible impact on a particular oscillatory dynamics under study. Thus, the generalized ERD represents a powerful novel analysis tool for extending our understanding of inter-trial variability of evoked responses and therefore the robust processing of environmental stimuli.     

Exact neural mass model for synaptic-based working memory

A synaptic theory of Working Memory (WM) has been developed in the last decade as a possible alternative to the persistent spiking paradigm. In this context, we have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks encompassing realistic cellular mechanisms for short-term synaptic plasticity. This population model reproduces the macroscopic dynamics of the network in terms of the firing rate and the mean membrane potential. The latter quantity allows us to gain insight of the Local Field Potential and electroencephalographic signals measured during WM tasks to characterize the brain activity. More specifically synaptic facilitation and depression integrate each other to efficiently mimic WM operations via either synaptic reactivation or persistent activity. Memory access and loading are related to stimulus-locked transient oscillations followed by a steady-state activity in the β-γ band, thus resembling what is observed in the cortex during vibrotactile stimuli in humans and object recognition in monkeys. Memory juggling and competition emerge already by loading only two items. However more items can be stored in WM by considering neural architectures composed of multiple excitatory populations and a common inhibitory pool. Memory capacity depends strongly on the presentation rate of the items and it maximizes for an optimal frequency range. In particular we provide an analytic expression for the maximal memory capacity. Furthermore, the mean membrane potential turns out to be a suitable proxy to measure the memory load, analogously to event driven potentials in experiments on humans. Finally we show that the γ power increases with the number of loaded items, as reported in many experiments, while θ and β power reveal non monotonic behaviours. In particular, β and γ rhythms are crucially sustained by the inhibitory activity, while the θ rhythm is controlled by excitatory synapses.     

The Effects of Valence and Arousal on Associative Working Memory and Long-Term Memory

Background

Emotion can either facilitate or impair memory, depending on what, when and how memory is tested and whether the paradigm at hand is administered as a working memory (WM) or a long-term memory (LTM) task. Whereas emotionally arousing single stimuli are more likely to be remembered, memory for the relationship between two or more component parts (i.e., relational memory) appears to be worse in the presence of emotional stimuli, at least in some relational memory tasks. The current study investigated the effects of both valence (neutral vs. positive vs. negative) and arousal (low vs. high) in an inter-item WM binding and LTM task.

Methodology/Principal Findings

A five-pair delayed-match-to-sample (WM) task was administered. In each trial, study pairs consisted of one neutral picture and a second picture of which the emotional qualities (valence and arousal levels) were manipulated. These pairs had to be remembered across a delay interval of 10 seconds. This was followed by a probe phase in which five pairs were tested. After completion of this task, an unexpected single item LTM task as well as an LTM task for the pairs was assessed. As expected, emotional arousal impaired WM processing. This was reflected in lower accuracy for pairs consisting of high-arousal pictures compared to pairs with low-arousal pictures. A similar effect was found for the associative LTM task. However, the arousal effect was modulated by affective valence for the WM but not the LTM task; pairs with low-arousal negative pictures were not processed as well in the WM task. No significant differences were found for the single-item LTM task.

Conclusions/Significance

The present study provides additional evidence that processes during initial perception/encoding and post-encoding processes, the time interval between study and test and the interaction between valence and arousal might modulate the effects of “emotion” on associative memory.     

Dynamic construction of stimulus values in the ventromedial prefrontal cortex

Signals representing the value assigned to stimuli at the time of choice have been repeatedly observed in ventromedial prefrontal cortex (vmPFC). Yet it remains unknown how these value representations are computed from sensory and memory representations in more posterior brain regions. We used electroencephalography (EEG) while subjects evaluated appetitive and aversive food items to study how event-related responses modulated by stimulus value evolve over time. We found that value-related activity shifted from posterior to anterior, and from parietal to central to frontal sensors, across three major time windows after stimulus onset: 150-250 ms, 400-550 ms, and 700-800 ms. Exploratory localization of the EEG signal revealed a shifting network of activity moving from sensory and memory structures to areas associated with value coding, with stimulus value activity localized to vmPFC only from 400 ms onwards. Consistent with these results, functional connectivity analyses also showed a causal flow of information from temporal cortex to vmPFC. Thus, although value signals are present as early as 150 ms after stimulus onset, the value signals in vmPFC appear relatively late in the choice process, and seem to reflect the integration of incoming information from sensory and memory related regions.     

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN–2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.     

A comparative electrophysiological study of regulatory components of working memory in adults and seven- to eight-year-old children: An analysis of coherence of EEG rhythms

Coherence at the frequency of θ, α, and β EEG rhythms was analyzed in 14 adults and 23 children of 7–8 years old while they performed cognitive tasks requiring an involvement of working memory (WM). We used the pair matching paradigm in which subjects had to match a pair of stimuli shown in succession in the central visual field. The pairs of verbal and visuo-spatial stimuli were mixed together and presented in a pseudo random order. Each pair was preceded by a warning signal that did not specify a modality of upcoming stimuli. We analyzed EEG segments recorded (i) in the rest condition, (ii) prior to the first (reference) stimulus (maintenance of nonspecific voluntary attention), and (iii) prior to the second (test) stimulus (retention of information in WM). In the present study we focused on the regulatory functional components of WM, and therefore, the stimulus modality has not been taken into account. In adults, maintaining nonspecific voluntary attention was accompanied by an increase of the strength of θ-related functional coupling between medial areas of the frontal cortex and temporal cortical zones and by a strengthening of local β-related functional connectivity in the fronto-central areas of the cortex. In children, no such increase was found for θ rhythm; for β rhythm the increase was limited to several short-range functional links. In adults, the retention of information in WM was accompanied by the growth in α coherence in distant fronto-parietal links, predominantly in the right hemisphere, while in children information retention was accompanied by the growth of θ coherence in the inferio-temporal and parietal cortical regions. The results of the study point to a relative immaturity of the mechanisms of executive control of WM in children of 7–8 years old.     

A comparative electrophysiological study of regulatory components of working memory in adults and children of 7-8 years old. An analysis of coherence of EEG rhythms

Coherence at the frequency oftheta, alpha, and beta EEG rhythms was analyzed in 14 adults and 23 children of 7-8 years old while they performed cognitive tasks requiring an involvement of working memory (WM). We used the pair matching paradigm in which subjects had to match a pair of stimuli shown in succession in the central visual field. The pairs of verbal and visuo-spatial stimuli were mixed together and presented in a pseudo random order. Each pair was preceded by a warning signal that did not specify a modality of upcoming stimuli. We analyzed EEG segments recorded (i) in the rest condition, (ii) prior to the first (etalon) stimulus (maintenance of nonspecific voluntary attention), and (iii) prior to the second (test) stimulus (retention of information in WM). In the present study we focused on the regulatory functional components of WM, and therefore, the stimulus modality has not been taken into account. In adults, maintaining nonspecific voluntary attention was accompanied by an increase of the strength of theta-related functional coupling between medial areas of the frontal cortex and temporal cortical zones and by a strengthening of local beta-related functional connectivity in the anterior areas of the central cortex. In children, no such increase was found for theta-rhythm; for beta-rhythm the increase was limited to several short-range functional links. In adults, the retention of information in WM was accompanied by the growth in alpha coherence in distant fronto-parietal links, predominantly in the right hemisphere, while in children information retention was accompanied by the growth of theta-coherence in the inferio-temporal and parietal cortical regions. The results of the study point to a relative immaturity of the mechanisms of executive control of WM in children of 7-8 years old.     

Transmission of Facial Expressions of Emotion Co-Evolved with Their Efficient Decoding in the Brain: Behavioral and Brain Evidence

Competent social organisms will read the social signals of their peers. In primates, the face has evolved to transmit the organism''s internal emotional state. Adaptive action suggests that the brain of the receiver has co-evolved to efficiently decode expression signals. Here, we review and integrate the evidence for this hypothesis. With a computational approach, we co-examined facial expressions as signals for data transmission and the brain as receiver and decoder of these signals. First, we show in a model observer that facial expressions form a lowly correlated signal set. Second, using time-resolved EEG data, we show how the brain uses spatial frequency information impinging on the retina to decorrelate expression categories. Between 140 to 200 ms following stimulus onset, independently in the left and right hemispheres, an information processing mechanism starts locally with encoding the eye, irrespective of expression, followed by a zooming out to processing the entire face, followed by a zooming back in to diagnostic features (e.g. the opened eyes in “fear”, the mouth in “happy”). A model categorizer demonstrates that at 200 ms, the left and right brain have represented enough information to predict behavioral categorization performance.