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Acoustic, lexical, and syntactic information are simultaneously processed in the brain requiring complex strategies to distinguish their electrophysiological activity. Capitalizing on previous works that factor out acoustic information, we could concentrate on the lexical and syntactic contribution to language processing by testing competing statistical models. We exploited electroencephalographic recordings and compared different surprisal models selectively involving lexical information, part of speech, or syntactic structures in various combinations. Electroencephalographic responses were recorded in 32 participants during listening to affirmative active declarative sentences. We compared the activation corresponding to basic syntactic structures, such as noun phrases vs. verb phrases. Lexical and syntactic processing activates different frequency bands, partially different time windows, and different networks. Moreover, surprisal models based on part of speech inventory only do not explain well the electrophysiological data, while those including syntactic information do. By disentangling acoustic, lexical, and syntactic information, we demonstrated differential brain sensitivity to syntactic information. These results confirm and extend previous measures obtained with intracranial recordings, supporting our hypothesis that syntactic structures are crucial in neural language processing. This study provides a detailed understanding of how the brain processes syntactic information, highlighting the importance of syntactic surprisal in shaping neural responses during language comprehension., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Over the past years, different studies provided preliminary evidence that Disorganized Attachment (DA) may have dysregulatory and disintegrative effects on both autonomic arousal regulation and brain connectivity. However, despite the clinical relevance of this construct, few studies have investigated the specific alterations underlying DA using electroencephalography (EEG). Thus, the main aim of the current study was to investigate EEG microstate parameters of DA in a non-clinical sample (N = 50) before (pre) and after (post) the administration of the Adult Attachment Interview (AAI). Two EEG eyes-closed Resting State (RS) recordings were performed before and after the AAI, which was used for classifying the participants [i.e., Disorganized/Unresolved (D/U) or Organized/Resolved (O/R) individuals] and to trigger the attachment system. Microstates parameters (i.e., Mean Duration, Time Coverage and Occurrence) were extracted from each recording using Cartool software. EEG microstates clustering analysis revealed 6 different maps (labeled A, B, C, D, E, F) in both groups (i.e., D/U and O/R individuals) and in both conditions (i.e., pre-AAI and post-AAI). In the pre-AAI condition, compared to O/R individuals, D/U participants showed a shorter Mean Duration and Time Coverage of Map F; in the post-AAI condition, a significant reduction in the Mean Duration of Map E was also observed in D/U individuals. Finally, in the "within" statistical analysis (i.e., pre-AAI vs. post-AAI), only the D/U group exhibited a significant increase in Time Coverage of Map F after the AAI. Since these maps are associated with brain networks involved in emotional information processing and mentalization (i.e., Salience Network and Default Mode Network), our result might reflect the deficit in the ability to mentalize caregiver's interaction as well as the increased sensitivity to attachment-related stimuli typically observed in individuals with a D/U state of mind., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings., (© 2023. The Author(s).)

Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered 'dipolar' ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided., (Copyright © 2018. Published by Elsevier Inc.)

The coordinated recruitment of group of muscles through muscles synergies is known to simplify the control of movements. However, how and to what extent such control scheme is encoded at a cortical level is poorly understood. So far, electroencephalography (EEG) and electromyography (EMG) have been used, separately, to investigate the cortical regions of the human brain which may be involved in activating muscle synergies. Here we aim at extending these results by looking for a hierarchical relationship between cortical and muscular sources of activity (neuro-muscular synergies) with a unified analysis of independent components (IC) simultaneously extracted from both EEG and EMG signals. We show for the first time how the direct fusion of EEG and EMG signals to extract unified ICs (unICs) can overcome the limitations of previous approaches, i.e., the difficulty in linking neural with muscular activations, and the lack of reliability of separate preprocessing techniques. Our results show that unified ICs were physiologically meaningful components in agreement with previous works. UNICA (Unified Independent Component Analysis) can also be considered as a solution for estimating overcomplete ICA on EEG and EMG data. These findings are an important step towards an understanding of the cortical control of human muscles synergies, and may have important applications for understanding movement dysfunction and to develop novel approaches for brain-computer interfaces and neuroprostheses.

Priming is an implicit memory effect in which previous exposure to one stimulus influences the response to another stimulus. The main characteristic of priming is that it occurs without awareness. Priming takes place also when the physical attributes of previously studied and test stimuli do not match; in fact, it greatly refers to a general stimulus representation activated at encoding independently of the sensory modality engaged. Our aim was to evaluate whether, in a cross-modal word-stem completion task, negative priming scores could depend on inefficient word processing at study and therefore on an altered stimulus representation. Words were presented in the auditory modality, and word-stems to be completed in the visual modality. At study, we recorded auditory ERPs, and compared the P300 (attention/memory) and N400 (meaning processing) of individuals with positive and negative priming. Besides classical averaging-based ERPs analysis, we used an ICA-based method (ErpICASSO) to separate the potentials related to different processes contributing to ERPs. Classical analysis yielded significant difference between the two waves across the whole scalp. ErpICASSO allowed separating the novelty-related P3a and the top-down control-related P3b sub-components of P300. Specifically, in the component C3, the positive deflection identifiable as P3b, was significantly greater in the positive than in the negative priming group, while the late negative deflection corresponding to the parietal N400, was reduced in the positive priming group. In conclusion, inadequacy of specific processes at encoding, such as attention and/or meaning retrieval, could generate weak semantic representations, making words less accessible in subsequent implicit retrieval., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Independent Component Analysis (ICA) is a widely applied data-driven method for parsing brain and non-brain EEG source signals, mixed by volume conduction to the scalp electrodes, into a set of maximally temporally and often functionally independent components (ICs). Many ICs may be identified with a precise physiological or non-physiological origin. However, this process is hindered by partial instability in ICA results that can arise from noise in the data. Here we propose RELICA (RELiable ICA), a novel method to characterize IC reliability within subjects. RELICA first computes IC "dipolarity" a measure of physiological plausibility, plus a measure of IC consistency across multiple decompositions of bootstrap versions of the input data. RELICA then uses these two measures to visualize and cluster the separated ICs, providing a within-subject measure of IC reliability that does not involve checking for its occurrence across subjects. We demonstrate the use of RELICA on EEG data recorded from 14 subjects performing a working memory experiment and show that many brain and ocular artifact ICs are correctly classified as "stable" (highly repeatable across decompositions of bootstrapped versions of the input data). Many stable ICs appear to originate in the brain, while other stable ICs account for identifiable non-brain processes such as line noise. RELICA might be used with any linear blind source separation algorithm to reduce the risk of basing conclusions on unstable or physiologically un-interpretable component processes., (Copyright © 2014 Elsevier Inc. All rights reserved.)