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: 16 November 2020.

Special Issue Editors

Prof. Dr. Manousos A. Klados
Website
Guest Editor
Department of Psychology, CITY College, International Faculty of the University of Sheffield 24 Pr. Koromila st., 546 22, Thessaloniki, Greece
Prof. Dr. Vivas Ana

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
Website
Guest Editor
Department of Molecular Medicine, “Sapienza” University of Rome, 00185 Rome, Italy
Interests: brain-computer interface; EEG; machine learning; neuroscience; mental states; human factors
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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. Pietro Aricò
Dr. Manousos A. Klados
Prof. Dr. Vivas Ana
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 1600 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 (1 paper)

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Research

Open AccessArticle
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 1
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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