Special Issue "Network Neuroscience: Brain Networks in the Field of Affective, Cognitive and Personality Neuroscience"

A special issue of Brain Sciences (ISSN 2076-3425).

Deadline for manuscript submissions: closed (10 April 2021).

Special Issue Editors

Prof. Dr. Manousos A. Klados
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Guest Editor
Department of Psychology, CITY College, International Faculty of the University of Sheffield 24 Pr. Koromila st., 546 22 Thessaloniki, Greece
Interests: brain networks; EEG; machine learning; personality neuroscience; mathematical anxiety; BCI
Prof. Dr. Ana Vivas
E-Mail
Guest Editor
Department of Psychology, CITY College, International Faculty of the University of Sheffield 24 Pr. Koromila st., 546 22, Thessaloniki, Greece
Dr. Pietro Aricò
E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues,

In the last few years, network neuroscience has emerged as an interdisciplinary field of extensive research, aiming to understand the role of human brain networks during affective and cognitive processing, as in the explanation of more subtle phenotypic characteristics, like personality. Network neuroscience shifted our mind-set from unimodal (activation/deactivation of certain regions) to multimodal models (collaboration between different brain regions), which seem to be closer to the brain’s complexity. In the last years, this became even more meaningful, considering the increase of brain regions that can be simultaneously recorded either by different neuroimaging modalities like electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI).

Despite the major advances in the field of network neuroscience, research on its application to increase our understanding of emotions and cognitive functioning, as well as in individual differences is still scarce. This is mainly due to the limitations of current computational approaches, as to their application in enough neuroimaging data (Big Data) to safely answer biologically inspired neuroscientific questions.

The current Special Issue will cover state-of-the-art research in network neuroscience focused on computational approaches, as well as on their applications in the fields of affective, cognitive, and personality neuroscience, using both neurophysiological (EEG, MEG, etc.) and neuroimaging (fMRI, DWI, etc.) modalities, with the main aim to expand our current knowledge and produce new theoretical frameworks.

Topics may include, but are not limited to, the following:

  • Computational approaches in estimating the functional and/or structural connectivity
  • Graph theoretical models in network neuroscience
  • Multi-scale and multi-graph models of the human brain
  • Brain Networks in the field of the affective/cognitive/personality neuroscience
  • Brain networks in brain–computer interface-based applications

Dr. Manousos A. Klados
Prof. Dr. Ana Vivas
Dr. Pietro Aricò
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

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Article
Joint Analysis of Eye Blinks and Brain Activity to Investigate Attentional Demand during a Visual Search Task
Brain Sci. 2021, 11(5), 562; https://doi.org/10.3390/brainsci11050562 - 28 Apr 2021
Cited by 1 | Viewed by 591
Abstract
In several fields, the need for a joint analysis of brain activity and eye activity to investigate the association between brain mechanisms and manifest behavior has been felt. In this work, two levels of attentional demand, elicited through a conjunction search task, have [...] Read more.
In several fields, the need for a joint analysis of brain activity and eye activity to investigate the association between brain mechanisms and manifest behavior has been felt. In this work, two levels of attentional demand, elicited through a conjunction search task, have been modelled in terms of eye blinks, brain activity, and brain network features. Moreover, the association between endogenous neural mechanisms underlying attentional demand and eye blinks, without imposing a time-locked structure to the analysis, has been investigated. The analysis revealed statistically significant spatial and spectral modulations of the recorded brain activity according to the different levels of attentional demand, and a significant reduction in the number of eye blinks when a higher amount of attentional investment was required. Besides, the integration of information coming from high-density electroencephalography (EEG), brain source localization, and connectivity estimation allowed us to merge spectral and causal information between brain areas, characterizing a comprehensive model of neurophysiological processes behind attentional demand. The analysis of the association between eye and brain-related parameters revealed a statistically significant high correlation (R > 0.7) of eye blink rate with anterofrontal brain activity at 8 Hz, centroparietal brain activity at 12 Hz, and a significant moderate correlation with the participation of right Intra Parietal Sulcus in alpha band (R = −0.62). Due to these findings, this work suggests the possibility of using eye blinks measured from one sensor placed on the forehead as an unobtrusive measure correlating with neural mechanisms underpinning attentional demand. Full article
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Article
The Development of Brain Network in Males with Autism Spectrum Disorders from Childhood to Adolescence: Evidence from fNIRS Study
Brain Sci. 2021, 11(1), 120; https://doi.org/10.3390/brainsci11010120 - 18 Jan 2021
Viewed by 1021
Abstract
In the current study, functional near-infrared spectroscopy (fNIRS) was used to collect resting-state signals from 77 males with autism spectrum disorders (ASD, age: 6~16.25) and 40 typically developing (TD) males (age: 6~16.58) in the theory-of-mind (ToM) network. The graph theory analysis was used [...] Read more.
In the current study, functional near-infrared spectroscopy (fNIRS) was used to collect resting-state signals from 77 males with autism spectrum disorders (ASD, age: 6~16.25) and 40 typically developing (TD) males (age: 6~16.58) in the theory-of-mind (ToM) network. The graph theory analysis was used to obtain the brain network properties in ToM network, and the multiple regression analysis demonstrated that males with ASD showed a comparable global network topology, and a similar age-related decrease in the medial prefrontal cortex area (mPFC) compared to TD individuals. Nevertheless, participants with ASD showed U-shaped trajectories of nodal metrics of right temporo-parietal junction (TPJ), and an age-related decrease in the left middle frontal gyrus (MFG), while trajectories of TD participants were opposite. The nodal metrics of the right TPJ was negatively associated with the social deficits of ASD, while the nodal metrics of the left MFG was negatively associated with the communication deficits of ASD. Current findings suggested a distinct developmental trajectory of the ToM network in males with ASD from childhood to adolescence. Full article
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Article
Automatic Recognition of Personality Profiles Using EEG Functional Connectivity during Emotional Processing
Brain Sci. 2020, 10(5), 278; https://doi.org/10.3390/brainsci10050278 - 03 May 2020
Cited by 3 | Viewed by 1917
Abstract
Personality is the characteristic set of an individual’s behavioral and emotional patterns that evolve from biological and environmental factors. The recognition of personality profiles is crucial in making human–computer interaction (HCI) applications realistic, more focused, and user friendly. The ability to recognize personality [...] Read more.
Personality is the characteristic set of an individual’s behavioral and emotional patterns that evolve from biological and environmental factors. The recognition of personality profiles is crucial in making human–computer interaction (HCI) applications realistic, more focused, and user friendly. The ability to recognize personality using neuroscientific data underpins the neurobiological basis of personality. This paper aims to automatically recognize personality, combining scalp electroencephalogram (EEG) and machine learning techniques. As the resting state EEG has not so far been proven efficient for predicting personality, we used EEG recordings elicited during emotion processing. This study was based on data from the AMIGOS dataset reflecting the response of 37 healthy participants. Brain networks and graph theoretical parameters were extracted from cleaned EEG signals, while each trait score was dichotomized into low- and high-level using the k-means algorithm. A feature selection algorithm was used afterwards to reduce the feature-set size to the best 10 features to describe each trait separately. Support vector machines (SVM) were finally employed to classify each instance. Our method achieved a classification accuracy of 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, 83.8% for neuroticism, and 73% for openness. Full article
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Review

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Review
The Multilayer Network Approach in the Study of Personality Neuroscience
Brain Sci. 2020, 10(12), 915; https://doi.org/10.3390/brainsci10120915 - 27 Nov 2020
Cited by 3 | Viewed by 1219
Abstract
It has long been understood that a multitude of biological systems, from genetics, to brain networks, to psychological factors, all play a role in personality. Understanding how these systems interact with each other to form both relatively stable patterns of behaviour, cognition and [...] Read more.
It has long been understood that a multitude of biological systems, from genetics, to brain networks, to psychological factors, all play a role in personality. Understanding how these systems interact with each other to form both relatively stable patterns of behaviour, cognition and emotion, but also vast individual differences and psychiatric disorders, however, requires new methodological insight. This article explores a way in which to integrate multiple levels of personality simultaneously, with particular focus on its neural and psychological constituents. It does so first by reviewing the current methodology of studies used to relate the two levels, where psychological traits, often defined with a latent variable model are used as higher-level concepts to identify the neural correlates of personality (NCPs). This is known as a top-down approach, which though useful in revealing correlations, is not able to include the fine-grained interactions that occur at both levels. As an alternative, we discuss the use of a novel complex system approach known as a multilayer network, a technique that has recently proved successful in revealing veracious interactions between networks at more than one level. The benefits of the multilayer approach to the study of personality neuroscience follow from its well-founded theoretical basis in network science. Its predictive and descriptive power may surpass that of statistical top-down and latent variable models alone, potentially allowing the discernment of more complete descriptions of individual differences, and psychiatric and neurological changes that accompany disease. Though in its infancy, and subject to a number of methodological unknowns, we argue that the multilayer network approach may contribute to an understanding of personality as a complex system comprised of interrelated psychological and neural features. Full article
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