According to mechanistic theories of working memory (WM), information is retained as stimulus-dependent persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains a largely open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of several firing rate models of WM. The considered models have a metastable active regime, i.e., they demonstrate long-lasting transient post-stimulus firing rate elevation. We start from a single excitatory-inhibitory circuit and demonstrate that either gamma-band or noise input could stabilize the active regime, thus supporting WM retention. We then consider a system of two circuits with excitatory intercoupling. We find that fast coupling allows for better stabilization by common noise compared to independent noise and stronger amplification of this effect by in-phase gamma inputs compared to anti-phase inputs. Finally, we consider a multi-circuit system comprised of two clusters, each containing a group of circuits receiving a common noise input and a group of circuits receiving independent noise. Each cluster is associated with its own local gamma generator, so all its circuits receive gamma-band input in the same phase. We find that gamma-band input differentially stabilizes the activity of the “common-noise” groups compared to the “independent-noise” groups. If the inter-cluster connections are fast, this effect is more pronounced when the gamma-band input is delivered to the clusters in the same phase rather than in the anti-phase. Assuming that the common noise comes from a large-scale distributed WM representation, our results demonstrate that local gamma oscillations can stabilize the activity of the corresponding parts of this representation, with stronger effect for fast long-range connections and synchronized gamma oscillations.
Bilateral in-phase (IP) and anti-phase (AP) movements represent two fundamental modes of bilateral coordination that are essential for daily living. Although previous studies have shown that aging is behaviorally associated with decline in bilateral coordination, especially in AP movements, the underlying neural mechanisms remain unclear. Here, we use kinematic measurements and electroencephalography to compare motor performance of young and older adults executing bilateral IP and AP hand movements. On the behavioral level, inter-limb synchronization was reduced during AP movements compared to IP and this reduction was stronger in the older adults. On the neural level, we found interactions between group and condition for task-related power change in different frequency bands. The interaction was driven by smaller alpha power decreases over the non-dominant cortical motor area in young adults during IP movements and larger beta power decreases over the midline region in older adults during AP movements. In addition, the decrease in inter-limb synchronization during AP movements was predicted by stronger directional connectivity in the beta-band: an effect more pronounced in older adults. Our results therefore show that age-related differences in the two bilateral coordination modes are reflected on the neural level by differences in alpha and beta oscillatory power as well as interhemispheric directional connectivity.
The use of language as a universal tool for communication and interaction is the backbone of human society. General sociocultural milieu and specific contextual factors can strongly influence various aspects of linguistic experience, including language acquisition and use and the respective internal neurolinguistic processes. This is particularly relevant in the case of bilingualism, which encompasses a diverse set of linguistic experiences, greatly influenced by societal, cultural, educational, and personal factors. In this perspective piece, we focus on a specific type of linguistic experience: non-pathological first-language (L1) attrition—a phenomenon that is strongly tied to immersion in non-L1 environments. We present our view on what may be the essence of L1 attrition and suggest ways of examining it as a type of bilingual experience, in particular with relation to its neurocognitive bases.
The acquisition of new orthographic representations is a rapid and highly automatic process in monolingual readers. Our study extends existing research to biliterate populations, addressing the impact of phonological inconsistencies across native (L1) and second language (L2) alphabets during orthographic learning. Behavioral and EEG signals were collected from a group of 24 Russian-English biliterates via a reading-aloud task using familiar and novel words repeated across ten consecutive blocks in three Script conditions: (1) native Cyrillic, (2) non-native Roman, and (3) ambiguous (phonologically inconsistent graphemes shared by L1 and L2 alphabets). Linear mixed-effects modelling of both behavioral and ERP data revealed reliable Block x Lexicality x Script interactions, indicating that naming latencies and brain activity changed differently across training blocks for novel and familiar words and, importantly, depending on script presentation. Particularly, novel words presented in the ambiguous script showed longer naming latencies and slower reading automatization than those presented in L1 and L2 alphabets. Nonetheless, despite this interference, their naming latencies matched those of familiar words before the end of the training, suggesting the attribution of their representations in the reader's lexicon. The enhancement of early brain responses observed for these stimuli alongside their training confirmed the improvement in their orthographic analysis and lexical access. Critically, this pattern of results was not found for familiar, already represented words, which exhibited a suppression of their brain activity across repetitions. Overall, our results indicate that phonological inconsistency interferes with novel word encoding but it does not prevent efficient attribution of orthographic representations.
In bilingualism research, there is a rapidly growing interest towards potential neuroprotective mechanisms against age-related cognitive decline, supported by dual and multiple language use. In this brief review, we discuss existing evidence, which generally suggests that bilingualism may foster neuroplastic changes resulting in beneficial consequences for the brain both at the structural level and at the functional one during later stages of life. First, we outline the interplay between the neural function and the bilingual experience. We then propose how bilingual and multilingual experience may protect the mind and the brain from the age-related cognitive decline and its consequences. We continue by discussing the notions of cognitive and brain reserve and contextualize existing findings from bilingualism literature with regard to this newly proposed reserve framework. We highlight how bilingualism-induced neural and cognitive changes may pave the way for the development of the neural foundations of reserve: both at the neuroanatomical and at the cognitive levels. We conclude our review by proposing possible models of bilingualism-induced successful aging.
The acquisition of new orthographic representations is a rapid and accurate process in proficient monolingual readers. The present study used biliterate and bialphabetic population to address the impact of phonological inconsistencies across the native (L1) and second (L2) alphabets. Naming latencies were collected from 50 Russian–English biliterates through a reading-aloud task with familiar and novel word forms repeated across 10 blocks. There were three Script conditions: (1) native Cyrillic, (2) non-native Roman, and (3) Ambiguous (with graphically identical, but phonologically inconsistent graphemes shared by both alphabets). Our analysis revealed the main effect of Script on both reading and orthographic learning: naming latencies during training were longer for the ambiguous stimuli, particularly for the novel ones. Nonetheless, novel word forms in the ambiguous condition approached the latencies for the familiar words along the exposures, although this effect was faster in the phonologically consistent trials. Post-training tests revealed similarly successful performance patterns for previously familiar and newly trained forms, indicating successful rapid acquisition of the latter. Furthermore, we found the highest free recall rates for the ambiguous stimuli. Overall, our results indicate that phonological inconsistency initially interferes with the efficiency of novel word encoding. Nevertheless, it does not prevent efficient attribution of orthographic representations; instead, the knowledge of two distinct alphabets supports a more efficient learning and a better memory for ambiguous stimuli via enhancing their encoding and retrieval.
White matter makes up about fifty percent of the human brain. Maturation of white matter accompanies biological development and undergoes the most dramatic changes during childhood and adolescence. Despite the advances in neuroimaging techniques, controversy concerning spatial, and temporal patterns of myelination, as well as the degree to which the microstructural characteristics of white matter can vary in a healthy brain as a function of age, gender and cognitive abilities still exists. In a selective review we describe methods of assessing myelination and evaluate effects of age and gender in nine major fiber tracts, highlighting their role in higher-order cognitive functions. Our findings suggests that myelination indices vary by age, fiber tract, and hemisphere. Effects of gender were also identified, although some attribute differences to methodological factors or social and learning opportunities. Findings point to further directions of research that will improve our understanding of the complex myelination- behavior relation across development that may have implications for educational and clinical practice.
Background and Objectives: Cognitive reserve (CR) is meant to account for the mismatch between brain damage and cognitive decline or dementia. Generally, CR has been operationalized using proxy variables indicating exposure to enriching activities (activity-based CR). An alternative approach defines CR as residual variance in cognition, not explained by the brain status (residual-based CR). The aim of this study is to compare activity-based and residual-based CR measures in their association with cognitive trajectories and dementia. Furthermore, we seek to examine if the two measures modify the impact of brain integrity on cognitive trajectories and if they predict dementia incidence independent of brain status.
Methods: We used data on 430 older adults aged 60+ from the Swedish National Study on Aging and Care in Kungsholmen, followed for 12 years. Residual-based reserve was computed from a regression predicting episodic memory with a brain-integrity index incorporating six structural neuroimaging markers (white-matter hyperintensities volume, whole-brain gray matter volume, hippocampal volume, lateral ventricular volume, lacunes, and perivascular spaces), age, and sex. Activity-based reserve incorporated education, work complexity, social network, and leisure activities. Cognition was assessed with a composite of perceptual speed, semantic memory, letter-, and category fluency. Dementia was clinically diagnosed in accordance with DSM-IV criteria. Linear mixed models were used for cognitive change analyses. Interactions tested if reserve measures modified the association between brain-integrity and cognitive change. Cox proportional hazard models, adjusted for brain-integrity index, assessed dementia risk.
Results: Both reserve measures were associated with cognitive trajectories [β × time (top tertile, ref.: bottom tertile) = 0.013; 95% CI: –0.126, –0.004 (residual-based) and 0.011; 95% CI: –0.001, 0.024, (activity-based)]. Residual-based, but not activity-based reserve mitigated the impact of brain integrity on cognitive decline [β (top tertile × time × brain integrity) = –0.021; 95% CI: –0.043, 0.001] and predicted 12-year dementia incidence, after accounting for the brain-integrity status [HR (top tertile) = 0.23; 95% CI: 0.09, 0.58].
Interpretation: The operationalization of reserve based on residual cognitive performance may represent a more direct measure of CR than an activity-based approach. Ultimately, the two models of CR serve largely different aims. Accounting for brain integrity is essential in any model of reserve.
Abstract reasoning is associated with the ability to detect relations among objects, ideas, events. It underlies the understanding of other individuals’ thoughts and intentions. In natural settings, individuals have to infer relevant associations that have proven to be reliable or precise predictors. Salience theory suggests that the attribution of meaning to stimulus depends on their contingency, saliency, and relevance to adaptation. So far, subjective estimates of relevance have mostly been explored in motivation and implicit learning. Mechanisms underlying formation of associations in abstract thinking with regard to their subjective relevance, or salience, are not clear. Applying novel computational methods, we investigated relevance detection in categorization tasks in 17 healthy individuals. Two models of relevance detection were developed: a conventional one with nouns from the same semantic category, an aberrant one based on an insignificant common feature. Control condition introduced non-related words. The participants were to detect either a relevant principle or an insignificant feature to group presented words. In control condition they inferred that the stimuli were irrelevant to any grouping idea. Cross-frequency phase coupling analysis revealed statistically distinct patterns of synchronization representing search and decision in the models of normal and aberrant relevance detection. Significantly distinct frontotemporal functional networks with central and parietal components in the theta and alpha frequency bands may reflect differences in relevance detection.
One of the main methodological problems in evaluation of functional connectivity is the spatial leakage (SL) effect which occurs due to volume conduction and leads to false positives in coherence or phase-locking estimates. Several solutions have been already suggested, including the use of the imaginary part of coherency or cross-spectrum. Because these standard metrics are insensitive to zero-phase interactions, they prevent detection of false coupling, resulting from SL, but may underestimate true physiological interactions, characterized by close-to-zero phase lags. Due to the broad neurophysiological evidence, such interactions should not be excluded from consideration. The recently proposed method, referred as Phase Shift Invariant Imaging of Coherent Sources (PSIICOS), became the first implementation of the algorithm which reliably detects interactions for all the range of phase-lags by suppressing the power of SL subspace components of cross-spectrum. However, connectivity values obtained via PSIICOS are non-normalized by construction and depend on source power, so that uncoupled sources with high power profiles may become false positives. This limitation motivated us to develop a statistical test based on randomization of original time series or cross-spectrum in such a way that power distribution in source space is preserved, but phase interactions are eliminated. The generation of covariance matrices from Wishart distribution appeared to be the most reliable method, when applied to data from simulations. Thus, together with the proposed statistical test PSIICOS can be used as an effective instrument applicable to real EEG- or MEG-data in fundamental research or for clinical purposes.
We present an electrophysiological dataset collected from the amygdalae of nine participants attending a visual dynamic stimulation of emotional aversive content. The participants were patients affected by epilepsy who underwent preoperative invasive monitoring in the mesial temporal lobe. Participants were presented with dynamic visual sequences of fearful faces (aversive condition), interleaved with sequences of neutral landscapes (neutral condition). The dataset contains the simultaneous recording of intracranial EEG (iEEG) and neuronal spike times and waveforms, and localization information for iEEG electrodes. Participant characteristics and trial information are provided. We technically validated this dataset and provide here the spike sorting quality metrics and the spectra of iEEG signals. This dataset allows the investigation of amygdalar response to dynamic aversive stimuli at multiple spatial scales, from the macroscopic EEG to the neuronal firing in the human brain.
Objective: Brain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery. Approach: We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models. Main results: We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task. Significance: We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.
In this work, we motivate and present a novel compact CNN. For the architectures that combine the adaptation in both space and time, we describen a theoretically justified approach to interpreting the temporal and spatial weights. We apply the proposed architecture to Berlin BCI IV competition and our own datasets to decode electrocorticogram into finger kinematics. Without feature engineering our architecture delivers similar or better decoding accuracy as compared to the BCI competition winner. After training the network, we interpret the solution (spatial and temporal convolution weights) and extract physiologically meaningful patterns.
Research on conversational pragmatics demonstrates how interlocutors tailor the information they share depending on the audience. Previous research showed that, in informal contexts, speakers often provide several alternative answers, whereas in formal contexts they tend to give only a single answer; however, the psychological underpinnings of these effects remain obscure. To investigate this answer-selection process, we measured participants’ eye movements in different experimentally modeled social contexts. Participants answered general-knowledge questions by providing responses with either single (one) or plural (three) alternatives. Then, a formal (job interview) or informal (conversation with friends) context was presented and participants decided either to report or withdraw their responses after considering the given social context. Growth curve analysis on the eye movements indicates that the selected response option attracted more eye movements. There was a discrepancy between the answer selection likelihood and the proportion of fixations to the corresponding option – but only in the formal context. These findings support a more elaborate decision-making processes in formal contexts. They also suggest that eye movements do not necessarily accompany the options considered in the decision-making processes.
Intracranial stereoelectroencephalography (sEEG) provides unsurpassed sensitivity and specificity for human neurophysiology. However, functional mapping of brain functions has been limited because the implantations have sparse coverage and differ greatly across individuals. Here, we developed a distributed, anatomically realistic sEEG source-modeling approach for within- and between-subject analyses. In addition to intracranial event-related potentials (iERP), we estimated the sources of high broadband gamma activity (HBBG), a putative correlate of local neural firing. Our novel approach accounted for a significant portion of the variance of the sEEG measurements in leave-one-out cross-validation. After logarithmic transformations, the sensitivity and signal-to-noise ratio were linearly inversely related to the minimal distance between the brain location and electrode contacts (slope≈−3.6). The signa-to-noise ratio and sensitivity in the thalamus and brain stem were comparable to those locations at the vicinity of electrode contact implantation. The HGGB source estimates were remarkably consistent with analyses of intracranial-contact data. In conclusion, distributed sEEG source modeling provides a powerful neuroimaging tool, which facilitates anatomically-normalized functional mapping of human brain using both iERP and HBBG data.
Effectiveness of various emotion regulation (ER) strategies have received much attention in recent research. Among the most studied ER strategies are cognitive reappraisal and expressive suppression. However, the evidence of their effectiveness is controversial and depends on the measures used. The aim of the present study was to compare the effectiveness of cognitive reappraisal and expressive suppression strategies of ER via different measures such as self-report, facial expressions (zygomaticus major and corrugator supercilii electromyography), and physiological assessment (skin conductance response and heart rate deceleration). Participants were presented with intensely unpleasant or neutral pictures and performed ER tasks. We expected that the implementation of ER strategies would reduce negative emotions, and cognitive reappraisal would produce greater reduction in negative emotions compared to expressive suppression. Self-report data showed that reappraisal had a greater effect on the reduction of negative emotions compared to suppression. There was no difference between reappraisal and suppression assessed with skin conductance response and electromyography. Curiously, heart rate deceleration increased while participants tried to suppress their emotional expressions, which could reflect efforts exerted in the attempt to suppress. The ER strategies reduced negative emotions during the presentation of unpleasant pictures partially in skin conductance response and heart rate deceleration. Overall, reappraisal is more effective in changing subjective experience, whereas the physiological reactions do not differ substantially between the two ER strategies explored. We therefore recommend that the assessment of ER strategies in the laboratory should accommodate more than one type of measures to come to more reliable conclusions.
Human memory is prone to memory errors and distortion. Evidence from studies on cognitive functions in bilinguals indicates that they might be prone to different types of memory errors compared to monolinguals, however, the effect of language in false memories is still understudied. Source monitoring processes required for proper memory functioning, presumably, rely on inhibitory control, which is also heavily utilized by bilinguals. Morevoer, it is suggested that thinking in a second language leads to more systematic and deliberate reasoning. All these results lead to expect that bilinguals are more analytical when processing information in their second language overcoming some memory errors depending on the language of information. To test this hypothesis, we run a classical misinformation experiment with explicit source monitoring task with a sample of Russian-English bilinguals. The language of misinformation presentation did not affect the degree of the misinformation effect between Russian and English languages. Source monitoring demonstrated overall higher accuracy for attributions to the English source over the Russian source. Furthermore, analysis on incorrect source attributions showed that when participants misattributed the sources of false information (English or Russian narrative), they favored the Russian source over the not presented condition. Taking together these results imply that a high proficiency in the second language do not affect misinformation and that information processing and memory monitoring in bilinguals can differ depending on the language of the information, which seems lead to some memory errors and not the others.
This review aims at clarifying the concept of first language attrition by tracing its limits, identifying its phenomenological and contextual constraints, discussing controversies associated with its definition, and suggesting potential directions for future research. We start by reviewing different definitions of attrition as well as associated inconsistencies. We then discuss the underlying mechanisms of first language attrition and review available evidence supporting different background hypotheses. Finally, we attempt to provide the groundwork to build a unified theoretical framework allowing for generalizable results. To this end, we suggest the deployment of a rigorous neuroscientific approach, in search of neural markers of first language attrition in different linguistic domains, putting forward hypothetical experimental ways to identify attrition’s neural traces and formulating predictions for each of the proposed experimental paradigms.
Objective: The aim of the study was to show that short-lasting (90 s) transcranial alternating current stimulation (tACS) at 20 Hz delivered over the left primary motor cortex (M1) is able to change the shape of recruitment curve of the corticospinal pathway.
Methods: The corticospinal pathway was studied during tACS by means of the relationship between the intensity of transcranial magnetic stimulation (TMS) delivered over the left M1 and corresponding motor evoked potentials (MEPs) recorded from the right first dorsal interosseus muscle (FDI), in nine healthy subjects. In order to extract characteristics of the input–output relationship that have particular physiological relevance, data were fitted to the Boltzmann sigmoidal function by the Levenberg–Marquardt nonlinear, least mean squares algorithm.
Results: The β-rhythm tACS influenced the shape and parameters of the input–output relation, so that the initial segment of the conditioned curve (from threshold to 30% of maximum muscle size) diverged, while the subsequent segment converged to overlap the unconditioned control curve.
Discussion: β-rhythm tACS conditions only a definite subset of corticospinal elements influencing less than 30% of the entire motoneuronal pool. The fact that β-rhythm tACS mainly affects the most excitable motoneurons could explain the observed antikinetic effect of the tACS at β-rhythm applied in the motor regions.
Objective.Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements.Approach.We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop.Main results.When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances.Significance.We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems.
Interpretation of the neural networks architectures for decoding the signals of the brain usually reduced to the analysis of spatial and temporal weights. We propose a theoretically justified method of their interpretation within the simple architecture based on a priori knowledge of the subject area. This architecture is comparable in decoding quality to the winner of the BCI IV competition and allows for automatic engineering of physiologically meaningful features. To demonstrate the operation of the algorithm, we performed Monte Carlo simulations and received a significant improvement in the restoration of patterns for different noise levels and also investigated the relation between the decoding quality and patterns reconstruction fidelity.