Human Brain Dynamics: Latest Advances and Prospects—2nd Edition

Dear Colleagues,

In recent years, machine learning and artificial intelligence algorithms have been utilized to solve many fascinating problems in different fields of science, including neuroscience. In this Research Topic, we are aiming to unite researchers from machine learning and computational neuroscience and to stimulate collaboration between experts in these fields. More specifically, this collection of articles is intended to cover recent directions and activities in the field of machine learning, especially the recent paradigm of deep learning, in neuroscience dedicated to analysis, diagnosis, and modeling of the neural mechanisms of brain functions.

We are seeking original research papers covering topics from systems/cognitive and computational neuroscience to neuroimaging and neural signal processing. Original research and reviews, as well as theoretical work, methods, and modeling articles are welcome. The research work includes experimental studies using state-of-the-art electrophysiology and neuroimaging as well as experimentally based computational or theoretical work and biologically inspired neural networks.

Using different methodological techniques, from electrophysiological assessment to neuroimaging and electrophysiological recording, the aim of this Special Issue is to provide an overview of evidence illustrating the potentiality of the integration of machine learning with multimodal neuroimaging modalities as a common framework to design reliable biomarkers for the diagnosis of patients with neurological and psychiatric brain disorders. Predictive models have been designed and employed on neuroimaging data to ask new questions and uncover new aspects of cognitive organization; for example, how is machine learning shaping cognitive neuroimaging? Experimentally computational or theoretical work including, but not restricted to, whole-brain neuroimaging human models is highly welcome. The combination of brain neuroimaging (structural MRI, functional MRI, and diffusion MRI) and genetic data increased our understanding of brain diseases. How should neuroimaging genetics be combined with machine learning to increase the sensitivity and the certainty of the early diagnosis of brain diseases?

The Special Issue aims also to attract research articles that cover one or all of the following topics:

(1) The reliability and reproducibility of commonly used multimodal estimators across sites, scanners, and in repeat scan sessions. Quantify the range of variation of the reliability and reproducibility of these network metrics across imaging sites, scanners and in retest studies and develop novel metrics that improve the reliability and reproducibility of the findings are significant in neuroimaging. Further development of clinically oriented imaging markers in the field demands the access to big open datasets across sites. Data sharing, such as Consortium for Reliability and Reproducibility (CoRR: http://www.nature.com/sdata/collections/mri-reproducibility), Human Connectome Project (HCP: http://www.humanconnectome.org) and OpenFMRI (https://openfmri.org) is progressing in the right direction for the future. These data platforms will be used by researchers for evaluating the reliability of their novel metrics.

(2) We live in an era where neuroscientists have started collecting multi-modal datasets from thousands of individuals. Analysis of these open big multimodal neuroimaging datasets is a big challenge and raises the question of ‘how these datasets of unprecedented breadth will be analyzed?’. Non-parametric and generative models will be the main players in the statistical reasoning that will attempt to untangle the neurobiological knowledge from healthy and pathological brain measurements.

(3) It is of paramount importance to explore how multimodal neuroimaging patterns of activity and connectivity change across the lifespan. Discriminating age-related differences in brain structure, function, and cognition will inform us about neurocognitive phenotyping across the lifespan and also in conditions that deviate from a normal trajectory, such as in mild cognitive impairment.

We invite you read the Special Issue "Human Brain Dynamics: Latest Advances and Prospects" at https://www.mdpi.com/journal/brainsci/special_issues/Human_Brain_Dynamics

Dr. Stavros I. Dimitriadis
Guest Editor

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 submissions that pass pre-check are 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 250 words) can be sent to the Editorial Office for assessment.

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 2200 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.