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

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 19114

Special Issue Editor

Dr. Stavros I. Dimitriadis

E-Mail Website
Guest Editor
1. Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
2. Institut de Neurociències, University of Barcelona, Barcelona, Spain
3. Integrative Neuroimaging Lab, 55133 Thessaloniki, Macedonia, Greece
4. Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
Interests: multimodal neuroimaging; genetic neuroimaging; network neuroscience; biomarkers; reproducibility in neuroscience and neuroimaging analysis; biomedical signal processing; artificial intelligence; machine learning; Alzheimer’s disease; schizophrenia; traumatic brain injury; intervention protocols
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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 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 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.

Keywords

  • oscillations
  • brain connectivity
  • human brain dynamics
  • network neuroscience
  • whole-brain modeling
  • fMRI
  • magnetoencephalography
  • electroencephalography
  • diffusion MRI
  • brain networks
  • connectomics
  • biologically inspired models
  • fNIRS
  • tACS
  • tDCS

Published Papers (8 papers)

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Research

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15 pages, 1529 KiB  
Article
High Variability Periods in the EEG Distinguish Cognitive Brain States
by Dhanya Parameshwaran and Tara C. Thiagarajan
Brain Sci. 2023, 13(11), 1528; https://doi.org/10.3390/brainsci13111528 - 30 Oct 2023
Cited by 1 | Viewed by 862
Abstract
Objective: To describe a novel measure of EEG signal variability that distinguishes cognitive brain states. Method: We describe a novel characterization of amplitude variability in the EEG signal termed “High Variability Periods” or “HVPs”, defined as segments when the standard deviation of a [...] Read more.
Objective: To describe a novel measure of EEG signal variability that distinguishes cognitive brain states. Method: We describe a novel characterization of amplitude variability in the EEG signal termed “High Variability Periods” or “HVPs”, defined as segments when the standard deviation of a moving window is continuously higher than the quartile cutoff. We characterize the parameter space of the metric in terms of window size, overlap, and threshold to suggest ideal parameter choice and compare its performance as a discriminator of brain state to alternate single channel measures of variability such as entropy, complexity, harmonic regression fit, and spectral measures. Results: We show that the average HVP duration provides a substantially distinct view of the signal relative to alternate metrics of variability and, when used in combination with these metrics, significantly enhances the ability to predict whether an individual has their eyes open or closed and is performing a working memory and Raven’s pattern completion task. In addition, HVPs disappear under anesthesia and do not reappear in early periods of recovery. Conclusions: HVP metrics enhance the discrimination of various brain states and are fast to estimate. Significance: HVP metrics can provide an additional view of signal variability that has potential clinical application in the rapid discrimination of brain states. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects—2nd Edition)
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13 pages, 2134 KiB  
Article
A Mixed Visual Encoding Model Based on the Larger-Scale Receptive Field for Human Brain Activity
by Shuxiao Ma, Linyuan Wang, Panpan Chen, Ruoxi Qin, Libin Hou and Bin Yan
Brain Sci. 2022, 12(12), 1633; https://doi.org/10.3390/brainsci12121633 - 29 Nov 2022
Cited by 3 | Viewed by 1586
Abstract
Research on visual encoding models for functional magnetic resonance imaging derived from deep neural networks, especially CNN (e.g., VGG16), has been developed. However, CNNs typically use smaller kernel sizes (e.g., 3 × 3) for feature extraction in visual encoding models. Although the receptive [...] Read more.
Research on visual encoding models for functional magnetic resonance imaging derived from deep neural networks, especially CNN (e.g., VGG16), has been developed. However, CNNs typically use smaller kernel sizes (e.g., 3 × 3) for feature extraction in visual encoding models. Although the receptive field size of CNN can be enlarged by increasing the network depth or subsampling, it is limited by the small size of the convolution kernel, leading to an insufficient receptive field size. In biological research, the size of the neuronal population receptive field of high-level visual encoding regions is usually three to four times that of low-level visual encoding regions. Thus, CNNs with a larger receptive field size align with the biological findings. The RepLKNet model directly expands the convolution kernel size to obtain a larger-scale receptive field. Therefore, this paper proposes a mixed model to replace CNN for feature extraction in visual encoding models. The proposed model mixes RepLKNet and VGG so that the mixed model has a receptive field of different sizes to extract more feature information from the image. The experimental results indicate that the mixed model achieves better encoding performance in multiple regions of the visual cortex than the traditional convolutional model. Also, a larger-scale receptive field should be considered in building visual encoding models so that the convolution network can play a more significant role in visual representations. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects—2nd Edition)
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17 pages, 2262 KiB  
Article
Load-Dependent Prefrontal Cortex Activation Assessed by Continuous-Wave Near-Infrared Spectroscopy during Two Executive Tasks with Three Cognitive Loads in Young Adults
by Nounagnon Frutueux Agbangla, Michel Audiffren, Jean Pylouster and Cédric T. Albinet
Brain Sci. 2022, 12(11), 1462; https://doi.org/10.3390/brainsci12111462 - 28 Oct 2022
Cited by 2 | Viewed by 1575
Abstract
The present study examined the evolution of the behavioral performance, subjectively perceived difficulty, and hemodynamic activity of the prefrontal cortex as a function of cognitive load during two different cognitive tasks tapping executive functions. Additionally, it investigated the relationships between these behavioral, subjective, [...] Read more.
The present study examined the evolution of the behavioral performance, subjectively perceived difficulty, and hemodynamic activity of the prefrontal cortex as a function of cognitive load during two different cognitive tasks tapping executive functions. Additionally, it investigated the relationships between these behavioral, subjective, and neuroimaging data. Nineteen right-handed young adults (18–22 years) were scanned using continuous-wave functional near-infrared spectroscopy during the performance of n-back and random number generation tasks in three cognitive load conditions. Four emitter and four receptor optodes were fixed bilaterally over the ventrolateral and dorsolateral prefrontal cortices to record the hemodynamic changes. A self-reported scale measured the perceived difficulty. The findings of this study showed that an increasing cognitive load deteriorated the behavioral performance and increased the perceived difficulty. The hemodynamic activity increased parametrically for the three cognitive loads of the random number generation task and in a two-back and three-back compared to a one-back condition. In addition, the hemodynamic activity was specifically greater in the ventrolateral prefrontal cortex than in the dorsolateral prefrontal cortex for both cognitive tasks (random number generation and n-back tasks). Finally, the results highlighted some links between cerebral oxygenation and the behavioral performance, but not the subjectively perceived difficulty. Our results suggest that cognitive load affects the executive performance and perceived difficulty and that fNIRS can be used to specify the prefrontal cortex’s implications for executive tasks involving inhibition and working memory updating. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects—2nd Edition)
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16 pages, 4354 KiB  
Article
Odor Pleasantness Modulates Functional Connectivity in the Olfactory Hedonic Processing Network
by Veit Frederik Kepler, Manuel S. Seet, Junji Hamano, Mariana Saba, Nitish V. Thakor, Stavros I. Dimitriadis and Andrei Dragomir
Brain Sci. 2022, 12(10), 1408; https://doi.org/10.3390/brainsci12101408 - 19 Oct 2022
Viewed by 2343
Abstract
Olfactory hedonic evaluation is the primary dimension of olfactory perception and thus central to our sense of smell. It involves complex interactions between brain regions associated with sensory, affective and reward processing. Despite a recent increase in interest, several aspects of olfactory hedonic [...] Read more.
Olfactory hedonic evaluation is the primary dimension of olfactory perception and thus central to our sense of smell. It involves complex interactions between brain regions associated with sensory, affective and reward processing. Despite a recent increase in interest, several aspects of olfactory hedonic evaluation remain ambiguous: uncertainty surrounds the communication between, and interaction among, brain areas during hedonic evaluation of olfactory stimuli with different levels of pleasantness, as well as the corresponding supporting oscillatory mechanisms. In our study we investigated changes in functional interactions among brain areas in response to odor stimuli using electroencephalography (EEG). To this goal, functional connectivity networks were estimated based on phase synchronization between EEG signals using the weighted phase lag index (wPLI). Graph theoretic metrics were subsequently used to quantify the resulting changes in functional connectivity of relevant brain regions involved in olfactory hedonic evaluation. Our results indicate that odor stimuli of different hedonic values evoke significantly different interaction patterns among brain regions within the olfactory cortex, as well as in the anterior cingulate and orbitofrontal cortices. Furthermore, significant hemispheric laterality effects have been observed in the prefrontal and anterior cingulate cortices, specifically in the beta ((13–30) Hz) and gamma ((30–40) Hz) frequency bands. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects—2nd Edition)
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30 pages, 4846 KiB  
Article
Universal Lifespan Trajectories of Source-Space Information Flow Extracted from Resting-State MEG Data
by Stavros I. Dimitriadis
Brain Sci. 2022, 12(10), 1404; https://doi.org/10.3390/brainsci12101404 - 18 Oct 2022
Viewed by 1400
Abstract
Source activity was extracted from resting-state magnetoencephalography data of 103 subjects aged 18–60 years. The directionality of information flow was computed from the regional time courses using delay symbolic transfer entropy and phase entropy. The analysis yielded a dynamic source connectivity profile, disentangling [...] Read more.
Source activity was extracted from resting-state magnetoencephalography data of 103 subjects aged 18–60 years. The directionality of information flow was computed from the regional time courses using delay symbolic transfer entropy and phase entropy. The analysis yielded a dynamic source connectivity profile, disentangling the direction, strength, and time delay of the underlying causal interactions, producing independent time delays for cross-frequency amplitude-to-amplitude and phase-to-phase coupling. The computation of the dominant intrinsic coupling mode (DoCM) allowed me to estimate the probability distribution of the DoCM independently of phase and amplitude. The results support earlier observations of a posterior-to-anterior information flow for phase dynamics in {α1, α2, β, γ} and an opposite flow (anterior to posterior) in θ. Amplitude dynamics reveal posterior-to-anterior information flow in {α1, α2, γ}, a sensory-motor β-oriented pattern, and an anterior-to-posterior pattern in {δ, θ}. The DoCM between intra- and cross-frequency couplings (CFC) are reported here for the first time and independently for amplitude and phase; in both domains {δ, θ, α1}, frequencies are the main contributors to DoCM. Finally, a novel brain age index (BAI) is introduced, defined as the ratio of the probability distribution of inter- over intra-frequency couplings. This ratio shows a universal age trajectory: a rapid rise from the end of adolescence, reaching a peak in adulthood, and declining slowly thereafter. The universal pattern is seen in the BAI of each frequency studied and for both amplitude and phase domains. No such universal age dependence was previously reported. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects—2nd Edition)
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15 pages, 2244 KiB  
Article
High-Level Visual Encoding Model Framework with Hierarchical Ventral Stream-Optimized Neural Networks
by Wulue Xiao, Jingwei Li, Chi Zhang, Linyuan Wang, Panpan Chen, Ziya Yu, Li Tong and Bin Yan
Brain Sci. 2022, 12(8), 1101; https://doi.org/10.3390/brainsci12081101 - 19 Aug 2022
Viewed by 1610
Abstract
Visual encoding models based on deep neural networks (DNN) show good performance in predicting brain activity in low-level visual areas. However, due to the amount of neural data limitation, DNN-based visual encoding models are difficult to fit for high-level visual areas, resulting in [...] Read more.
Visual encoding models based on deep neural networks (DNN) show good performance in predicting brain activity in low-level visual areas. However, due to the amount of neural data limitation, DNN-based visual encoding models are difficult to fit for high-level visual areas, resulting in insufficient encoding performance. The ventral stream suggests that higher visual areas receive information from lower visual areas, which is not fully reflected in the current encoding models. In the present study, we propose a novel visual encoding model framework which uses the hierarchy of representations in the ventral stream to improve the model’s performance in high-level visual areas. Under the framework, we propose two categories of hierarchical encoding models from the voxel and the feature perspectives to realize the hierarchical representations. From the voxel perspective, we first constructed an encoding model for the low-level visual area (V1 or V2) and extracted the voxel space predicted by the model. Then we use the extracted voxel space of the low-level visual area to predict the voxel space of the high-level visual area (V4 or LO) via constructing a voxel-to-voxel model. From the feature perspective, the feature space of the first model is extracted to predict the voxel space of the high-level visual area. The experimental results show that two categories of hierarchical encoding models effectively improve the encoding performance in V4 and LO. In addition, the proportion of the best-encoded voxels for different models in V4 and LO show that our proposed models have obvious advantages in prediction accuracy. We find that the hierarchy of representations in the ventral stream has a positive effect on improving the performance of the existing model in high-level visual areas. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects—2nd Edition)
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16 pages, 1603 KiB  
Article
Effects of Hippocampal Sparing Radiotherapy on Brain Microstructure—A Diffusion Tensor Imaging Analysis
by Johannes G. Dinkel, Godehard Lahmer, Angelika Mennecke, Stefan W. Hock, Tanja Richter-Schmidinger, Rainer Fietkau, Luitpold Distel, Florian Putz, Arnd Dörfler and Manuel A. Schmidt
Brain Sci. 2022, 12(7), 879; https://doi.org/10.3390/brainsci12070879 - 04 Jul 2022
Cited by 3 | Viewed by 2087
Abstract
Hippocampal-sparing radiotherapy (HSR) is a promising approach to alleviate cognitive side effects following cranial radiotherapy. Microstructural brain changes after irradiation have been demonstrated using Diffusion Tensor Imaging (DTI). However, evidence is conflicting for certain parameters and anatomic structures. This study examines the effects [...] Read more.
Hippocampal-sparing radiotherapy (HSR) is a promising approach to alleviate cognitive side effects following cranial radiotherapy. Microstructural brain changes after irradiation have been demonstrated using Diffusion Tensor Imaging (DTI). However, evidence is conflicting for certain parameters and anatomic structures. This study examines the effects of radiation on white matter and hippocampal microstructure using DTI and evaluates whether these may be mitigated using HSR. A total of 35 tumor patients undergoing a prospective randomized controlled trial receiving either conventional or HSR underwent DTI before as well as 6, 12, 18, 24, and 30 (±3) months after radiotherapy. Fractional Anisotropy (FA), Mean Diffusivity (MD), Axial Diffusivity (AD), and Radial Diffusivity (RD) were measured in the hippocampus (CA), temporal, and frontal lobe white matter (TL, FL), and corpus callosum (CC). Longitudinal analysis was performed using linear mixed models. Analysis of the entire patient collective demonstrated an overall FACC decrease and RDCC increase compared to baseline in all follow-ups; ADCC decreased after 6 months, and MDCC increased after 12 months (p ≤ 0.001, 0.001, 0.007, 0.018). ADTL decreased after 24 and 30 months (p ≤ 0.004, 0.009). Hippocampal FA increased after 6 and 12 months, driven by a distinct increase in ADCA and MDCA, with RDCA not increasing until 30 months after radiotherapy (p ≤ 0.011, 0.039, 0.005, 0.040, 0.019). Mean radiation dose correlated positively with hippocampal FA (p < 0.001). These findings may indicate complex pathophysiological changes in cerebral microstructures after radiation, insufficiently explained by conventional DTI models. Hippocampal microstructure differed between patients undergoing HSR and conventional cranial radiotherapy after 6 months with a higher ADCA in the HSR subgroup (p ≤ 0.034). Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects—2nd Edition)
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Review

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14 pages, 1469 KiB  
Review
Modulating Brain Activity with Invasive Brain–Computer Interface: A Narrative Review
by Zhi-Ping Zhao, Chuang Nie, Cheng-Teng Jiang, Sheng-Hao Cao, Kai-Xi Tian, Shan Yu and Jian-Wen Gu
Brain Sci. 2023, 13(1), 134; https://doi.org/10.3390/brainsci13010134 - 12 Jan 2023
Cited by 9 | Viewed by 5809
Abstract
Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide [...] Read more.
Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide attention. In this review, we first introduce the concepts of neuronal signal decoding and encoding that are fundamental for information exchanges in BCI. Then, we review the history and recent advances in invasive BCI, particularly through studies using neural signals for controlling external devices on one hand, and modulating brain activity on the other hand. Specifically, regarding modulating brain activity, we focus on two types of techniques, applying electrical stimulation to cortical and deep brain tissues, respectively. Finally, we discuss the related ethical issues concerning the clinical application of this emerging technology. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects—2nd Edition)
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