Special Issue "Human Brain Dynamics: Latest Advances and Prospects"

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

Deadline for manuscript submissions: closed (15 October 2020).

Special Issue Editor

Dr. Stavros I. Dimitriadis
E-Mail Website
Guest Editor
Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
Interests: MEG/EEG/fMRI/dMRI/MRI; tractography; genetics; cognitive neuroscience; computational intelligence; network neuroscience; brain networks in healthy and disease conditions; neuroinformatics; genetic neuroimaging; connectomics; multimodal neuroimaging; biomarkers; Alzheimer’s disease; schizophrenia; mild traumatic brain injury

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 seeking to bring together researchers from machine learning and computational neuroscience and to stimulate collaboration between researchers 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 welcome submissions of original research papers 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 welcomed. The research work includes experimental studies using state-of-the-art in 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 diagnosis of patients with neurological and psychiatric brain disorders. Predictive models have been designed and employed on neuroimaging data to ask new questions, i.e., to uncover new aspects of cognitive organization. How machine learning is shaping cognitive neuroimaging? Experimentally computational or theoretical work including but not restricted to whole-brain neuroimaging human models is highly welcomed. The combination of brain neuroimaging (structural MRI, functional MRI and diffusion MRI) and genetic data increased our understanding of brain diseases. How neuroimaging genetics should 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 also one or all the following dimensions of research:

(1) Reliability and reproducibility of commonly used multimodal estimators across sites, scanners and in repeat scan sessions is of great importance. 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 going in the right direction for the future. The 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 the neurocognitive phenotyping across the lifespan and also in conditions that deviate from a normal trajectory like in mild cognitive impairment.

Dr. Stavros I. Dimitriadis
Guest Editor

Manuscript Submission Information

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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.

Keywords

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

Published Papers (16 papers)

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Editorial

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Editorial
Latest Advances in Human Brain Dynamics
Brain Sci. 2021, 11(11), 1476; https://doi.org/10.3390/brainsci11111476 - 08 Nov 2021
Viewed by 300
Abstract
It is paramount for every neuroscientist to understand the nature of emerging technologies and approaches in investigating functional brain dynamics [...] Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)

Research

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Article
Speech–Brain Frequency Entrainment of Dyslexia with and without Phonological Deficits
Brain Sci. 2020, 10(12), 920; https://doi.org/10.3390/brainsci10120920 - 28 Nov 2020
Cited by 3 | Viewed by 1055
Abstract
Developmental dyslexia is a cognitive disorder characterized by difficulties in linguistic processing. Our purpose is to distinguish subtypes of developmental dyslexia by the level of speech–EEG frequency entrainment (δ: 1–4; β: 12.5–22.5; γ1: 25–35; and γ2: 35–80 Hz) in word/pseudoword auditory discrimination. Depending [...] Read more.
Developmental dyslexia is a cognitive disorder characterized by difficulties in linguistic processing. Our purpose is to distinguish subtypes of developmental dyslexia by the level of speech–EEG frequency entrainment (δ: 1–4; β: 12.5–22.5; γ1: 25–35; and γ2: 35–80 Hz) in word/pseudoword auditory discrimination. Depending on the type of disabilities, dyslexics can divide into two subtypes—with less pronounced phonological deficits (NoPhoDys—visual dyslexia) and with more pronounced ones (PhoDys—phonological dyslexia). For correctly recognized stimuli, the δ-entrainment is significantly worse in dyslexic children compared to controls at a level of speech prosody and syllabic analysis. Controls and NoPhoDys show a stronger δ-entrainment in the left-hemispheric auditory cortex (AC), anterior temporal lobe (ATL), frontal, and motor cortices than PhoDys. Dyslexic subgroups concerning normolexics have a deficit of δ-entrainment in the left ATL, inferior frontal gyrus (IFG), and the right AC. PhoDys has higher δ-entrainment in the posterior part of adjacent STS regions than NoPhoDys. Insufficient low-frequency β changes over the IFG, the inferior parietal lobe of PhoDys compared to NoPhoDys correspond to their worse phonological short-term memory. Left-dominant 30 Hz-entrainment for normolexics to phonemic frequencies characterizes the right AC, adjacent regions to superior temporal sulcus of dyslexics. The pronounced 40 Hz-entrainment in PhoDys than the other groups suggest a hearing “reassembly” and a poor phonological working memory. Shifting up to higher-frequency γ-entrainment in the AC of NoPhoDys can lead to verbal memory deficits. Different patterns of cortical reorganization based on the left or right hemisphere lead to differential dyslexic profiles. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
Genome-Wide Scan for Five Brain Oscillatory Phenotypes Identifies a New QTL Associated with Theta EEG Band
Brain Sci. 2020, 10(11), 870; https://doi.org/10.3390/brainsci10110870 - 18 Nov 2020
Cited by 1 | Viewed by 1315
Abstract
Brain waves, measured by electroencephalography (EEG), are a powerful tool in the investigation of neurophysiological traits and a noninvasive and cost-effective alternative in the diagnostic of some neurological diseases. In order to identify novel Quantitative Trait Loci (QTLs) for brain wave relative power [...] Read more.
Brain waves, measured by electroencephalography (EEG), are a powerful tool in the investigation of neurophysiological traits and a noninvasive and cost-effective alternative in the diagnostic of some neurological diseases. In order to identify novel Quantitative Trait Loci (QTLs) for brain wave relative power (RP), we collected resting state EEG data in five frequency bands (δ, θ, α, β1, and β2) and genome-wide data in a cohort of 105 patients with late onset Alzheimer’s disease (LOAD), 41 individuals with mild cognitive impairment and 45 controls from Iberia, correcting for disease status. One novel association was found with an interesting candidate for a role in brain wave biology, CLEC16A (C-type lectin domain family 16), with a variant at this locus passing the adjusted genome-wide significance threshold after Bonferroni correction. This finding reinforces the importance of immune regulation in brain function. Additionally, at a significance cutoff value of 5 × 10−6, 18 independent association signals were detected. These signals comprise brain expression Quantitative Loci (eQTLs) in caudate basal ganglia, spinal cord, anterior cingulate cortex and hypothalamus, as well as chromatin interactions in adult and fetal cortex, neural progenitor cells and hippocampus. Moreover, in the set of genes showing signals of association with brain wave RP in our dataset, there is an overrepresentation of loci previously associated with neurological traits and pathologies, evidencing the pleiotropy of the genetic variation modulating brain function. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach
Brain Sci. 2020, 10(11), 777; https://doi.org/10.3390/brainsci10110777 - 25 Oct 2020
Cited by 4 | Viewed by 939
Abstract
Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of [...] Read more.
Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
Prediction of Human Inhibition Brain Function with Inter-Subject and Intra-Subject Variability
Brain Sci. 2020, 10(10), 726; https://doi.org/10.3390/brainsci10100726 - 13 Oct 2020
Cited by 3 | Viewed by 773
Abstract
The stop signal task has been used to quantify the human inhibitory control. The inter-subject and intra-subject variability was investigated under the inhibition of human response with a realistic environmental scenario. In present study, we used a battleground scenario where a sniper-scope picture [...] Read more.
The stop signal task has been used to quantify the human inhibitory control. The inter-subject and intra-subject variability was investigated under the inhibition of human response with a realistic environmental scenario. In present study, we used a battleground scenario where a sniper-scope picture was the background, a target picture was a go signal, and a nontarget picture was a stop signal. The task instructions were to respond on the target image and inhibit the response if a nontarget image appeared. This scenario produced a threatening situation and endorsed the evaluation of how subject’s response inhibition manifests in a real situation. In this study, 32 channels of electroencephalography (EEG) signals were collected from 20 participants during successful stop (response inhibition) and failed stop (response) trials. These EEG signals were used to predict two possible outcomes: successful stop or failed stop. The inter-subject variability (between-subjects) and intra-subject variability (within-subjects) affect the performance of participants in the classification system. The EEG signals of successful stop versus failed stop trials were classified using quadratic discriminant analysis (QDA) and linear discriminant analysis (LDA) (i.e., parametric) and K-nearest neighbor classifier (KNNC) and Parzen density-based (PARZEN) (i.e., nonparametric) under inter- and intra-subject variability. The EEG activities were found to increase during response inhibition in the frontal cortex (F3 and F4), presupplementary motor area (C3 and C4), parietal lobe (P3 and P4), and occipital (O1 and O2) lobe. Therefore, power spectral density (PSD) of EEG signals (1-50Hz) in F3, F4, C3, C4, P3, P4, O1, and O2 electrodes were measured in successful stop and failed stop trials. The PSD of the EEG signals was used as the feature input for the classifiers. Our proposed method shows an intra-subject classification accuracy of 97.61% for subject 15 with QDA classifier in C3 (left motor cortex) and an overall inter-subject classification accuracy of 71.66% ± 9.81% with the KNNC classifier in F3 (left frontal lobe). These results display how inter-subject and intra-subject variability affects the performance of the classification system. These findings can be used effectively to improve the psychopathology of attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), schizophrenia, and suicidality. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks
Brain Sci. 2020, 10(9), 657; https://doi.org/10.3390/brainsci10090657 - 22 Sep 2020
Cited by 6 | Viewed by 1077
Abstract
This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, [...] Read more.
This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on simulated EEGs obtained mixing source signals generated under different coupling conditions, showing its ability to retrieve source information dynamics from the scalp signals. Then, it was applied to investigate scalp and source brain connectivity in a group of children manifesting episodes of focal and generalized epilepsy; the analysis was performed on EEG signals lasting 5 s, collected in two consecutive windows preceding and one window following each ictal episode. Our results show that generalized seizures are associated with a significant decrease from pre-ictal to post-ictal periods of the information stored in the signals and of the information transferred among them, reflecting reduced self-predictability and causal connectivity at the level of both scalp and source brain dynamics. On the contrary, in the case of focal seizures the scalp EEG activity was not discriminated across conditions by any information measure, while source analysis revealed a tendency of the measures of information transfer to increase just before seizures and to decrease just after seizures. These results suggest that focal epileptic seizures are associated with a reorganization of the topology of EEG brain networks which is only visible analyzing connectivity among the brain sources. Our findings emphasize the importance of EEG modeling approaches able to deal with the adverse effects of volume conduction on brain connectivity analysis, and their potential relevance to the development of strategies for prediction and clinical treatment of epilepsy. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
Study on Representation Invariances of CNNs and Human Visual Information Processing Based on Data Augmentation
Brain Sci. 2020, 10(9), 602; https://doi.org/10.3390/brainsci10090602 - 02 Sep 2020
Cited by 1 | Viewed by 819
Abstract
Representation invariance plays a significant role in the performance of deep convolutional neural networks (CNNs) and human visual information processing in various complicated image-based tasks. However, there has been abounding confusion concerning the representation invariance mechanisms of the two sophisticated systems. To investigate [...] Read more.
Representation invariance plays a significant role in the performance of deep convolutional neural networks (CNNs) and human visual information processing in various complicated image-based tasks. However, there has been abounding confusion concerning the representation invariance mechanisms of the two sophisticated systems. To investigate their relationship under common conditions, we proposed a representation invariance analysis approach based on data augmentation technology. Firstly, the original image library was expanded by data augmentation. The representation invariances of CNNs and the ventral visual stream were then studied by comparing the similarities of the corresponding layer features of CNNs and the prediction performance of visual encoding models based on functional magnetic resonance imaging (fMRI) before and after data augmentation. Our experimental results suggest that the architecture of CNNs, combinations of convolutional and fully-connected layers, developed representation invariance of CNNs. Remarkably, we found representation invariance belongs to all successive stages of the ventral visual stream. Hence, the internal correlation between CNNs and the human visual system in representation invariance was revealed. Our study promotes the advancement of invariant representation of computer vision and deeper comprehension of the representation invariance mechanism of human visual information processing. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
Machine Learning Detects Pattern of Differences in Functional Magnetic Resonance Imaging (fMRI) Data between Chronic Fatigue Syndrome (CFS) and Gulf War Illness (GWI)
Brain Sci. 2020, 10(7), 456; https://doi.org/10.3390/brainsci10070456 - 17 Jul 2020
Cited by 3 | Viewed by 2407
Abstract
Background: Gulf War Illness (GWI) and Chronic Fatigue Syndrome (CFS) are two debilitating disorders that share similar symptoms of chronic pain, fatigue, and exertional exhaustion after exercise. Many physicians continue to believe that both are psychosomatic disorders and to date no underlying etiology [...] Read more.
Background: Gulf War Illness (GWI) and Chronic Fatigue Syndrome (CFS) are two debilitating disorders that share similar symptoms of chronic pain, fatigue, and exertional exhaustion after exercise. Many physicians continue to believe that both are psychosomatic disorders and to date no underlying etiology has been discovered. As such, uncovering objective biomarkers is important to lend credibility to criteria for diagnosis and to help differentiate the two disorders. Methods: We assessed cognitive differences in 80 subjects with GWI and 38 with CFS by comparing corresponding fMRI scans during 2-back working memory tasks before and after exercise to model brain activation during normal activity and after exertional exhaustion, respectively. Voxels were grouped by the count of total activity into the Automated Anatomical Labeling (AAL) atlas and used in an “ensemble” series of machine learning algorithms to assess if a multi-regional pattern of differences in the fMRI scans could be detected. Results: A K-Nearest Neighbor (70%/81%), Linear Support Vector Machine (SVM) (70%/77%), Decision Tree (82%/82%), Random Forest (77%/78%), AdaBoost (69%/81%), Naïve Bayes (74%/78%), Quadratic Discriminant Analysis (QDA) (73%/75%), Logistic Regression model (82%/82%), and Neural Net (76%/77%) were able to differentiate CFS from GWI before and after exercise with an average of 75% accuracy in predictions across all models before exercise and 79% after exercise. An iterative feature selection and removal process based on Recursive Feature Elimination (RFE) and Random Forest importance selected 30 regions before exercise and 33 regions after exercise that differentiated CFS from GWI across all models, and produced the ultimate best accuracies of 82% before exercise and 82% after exercise by Logistic Regression or Decision Tree by a single model, and 100% before and after exercise when selected by any six or more models. Differential activation on both days included the right anterior insula, left putamen, and bilateral orbital frontal, ventrolateral prefrontal cortex, superior, inferior, and precuneus (medial) parietal, and lateral temporal regions. Day 2 had the cerebellum, left supplementary motor area and bilateral pre- and post-central gyri. Changes between days included the right Rolandic operculum switching to the left on Day 2, and the bilateral midcingulum switching to the left anterior cingulum. Conclusion: We concluded that CFS and GWI are significantly differentiable using a pattern of fMRI activity based on an ensemble machine learning model. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
A Novel Connectome-based Electrophysiological Study of Subjective Cognitive Decline Related to Alzheimer’s Disease by Using Resting-state High-density EEG EGI GES 300
Brain Sci. 2020, 10(6), 392; https://doi.org/10.3390/brainsci10060392 - 19 Jun 2020
Cited by 5 | Viewed by 1883
Abstract
Aim: To investigate for the first time the brain network in the Alzheimer’s disease (AD) spectrum by implementing a high-density electroencephalography (HD-EEG - EGI GES 300) study with 256 channels in order to seek if the brain connectome can be effectively used to [...] Read more.
Aim: To investigate for the first time the brain network in the Alzheimer’s disease (AD) spectrum by implementing a high-density electroencephalography (HD-EEG - EGI GES 300) study with 256 channels in order to seek if the brain connectome can be effectively used to distinguish cognitive impairment in preclinical stages. Methods: Twenty participants with AD, 30 with mild cognitive impairment (MCI), 20 with subjective cognitive decline (SCD) and 22 healthy controls (HC) were examined with a detailed neuropsychological battery and 10 min resting state HD-EEG. We extracted correlation matrices by using Pearson correlation coefficients for each subject and constructed weighted undirected networks for calculating clustering coefficient (CC), strength (S) and betweenness centrality (BC) at global (256 electrodes) and local levels (29 parietal electrodes). Results: One-way ANOVA presented a statistically significant difference among the four groups at local level in CC [F (3, 88) = 4.76, p = 0.004] and S [F (3, 88) = 4.69, p = 0.004]. However, no statistically significant difference was found at a global level. According to the independent sample t-test, local CC was higher for HC [M (SD) = 0.79 (0.07)] compared with SCD [M (SD) = 0.72 (0.09)]; t (40) = 2.39, p = 0.02, MCI [M (SD) = 0.71 (0.09)]; t (50) = 0.41, p = 0.004 and AD [M (SD) = 0.68 (0.11)]; t (40) = 3.62, p = 0.001 as well, while BC showed an increase at a local level but a decrease at a global level as the disease progresses. These findings provide evidence that disruptions in brain networks in parietal organization may potentially represent a key factor in the ability to distinguish people at early stages of the AD continuum. Conclusions: The above findings reveal a dynamically disrupted network organization of preclinical stages, showing that SCD exhibits network disorganization with intermediate values between MCI and HC. Additionally, these pieces of evidence provide information on the usefulness of the 256 HD-EEG in network construction. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
Massive Data Management and Sharing Module for Connectome Reconstruction
Brain Sci. 2020, 10(5), 314; https://doi.org/10.3390/brainsci10050314 - 22 May 2020
Cited by 1 | Viewed by 1184
Abstract
Recently, with the rapid development of electron microscopy (EM) technology and the increasing demand of neuron circuit reconstruction, the scale of reconstruction data grows significantly. This brings many challenges, one of which is how to effectively manage large-scale data so that researchers can [...] Read more.
Recently, with the rapid development of electron microscopy (EM) technology and the increasing demand of neuron circuit reconstruction, the scale of reconstruction data grows significantly. This brings many challenges, one of which is how to effectively manage large-scale data so that researchers can mine valuable information. For this purpose, we developed a data management module equipped with two parts, a storage and retrieval module on the server-side and an image cache module on the client-side. On the server-side, Hadoop and HBase are introduced to resolve massive data storage and retrieval. The pyramid model is adopted to store electron microscope images, which represent multiresolution data of the image. A block storage method is proposed to store volume segmentation results. We design a spatial location-based retrieval method for fast obtaining images and segments by layers rapidly, which achieves a constant time complexity. On the client-side, a three-level image cache module is designed to reduce latency when acquiring data. Through theoretical analysis and practical tests, our tool shows excellent real-time performance when handling large-scale data. Additionally, the server-side can be used as a backend of other similar software or a public database to manage shared datasets, showing strong scalability. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
Structural Characteristic of the Arcuate Fasciculus in Patients with Fluent Aphasia Following Intracranial Hemorrhage: A Diffusion Tensor Tractography Study
Brain Sci. 2020, 10(5), 280; https://doi.org/10.3390/brainsci10050280 - 06 May 2020
Cited by 2 | Viewed by 1489
Abstract
This study investigated the relationship between the structural characteristics of the left arcuate fasciculus (AF) reconstructed using diffusion tensor image (DTI) and the type of fluent aphasia according to hemorrhage lesions in patients with fluent aphasia following intracranial hemorrhage (ICH). Five patients with [...] Read more.
This study investigated the relationship between the structural characteristics of the left arcuate fasciculus (AF) reconstructed using diffusion tensor image (DTI) and the type of fluent aphasia according to hemorrhage lesions in patients with fluent aphasia following intracranial hemorrhage (ICH). Five patients with fluent aphasia following ICH (three males, two females; mean age 55.0 years; range 47 to 60 years) and with sixteen age-matched heathy control subjects were involved in this study. The ICHs of patients 1 and 2 were located in the left parietal lobe and the left basal ganglia. ICHs were located in the left anterior temporal of patient 3, the left temporal lobe of patient 4, and the left frontal lobe of patient 5. We assessed patients’ language function using K-WAB (the Korean version of the Western Aphasia Battery) and reconstructed the AF using DTI. We measured DTI parameters including the fractional anisotropy (FA), tract volume (TV), fiber number (FN), and mean diffusivity (MD). All patients showed neural tract injury (the decrement of FA, TV, and FN and increment of MD). The left AFs in patients 1 and 2 were shifted from Broca’s and Wernicke’s territories. The destruction of Wernicke’s territory resulted in conduction or transcortical sensory aphasia in patients 3 and 4. The structural difference of the AF in patients following ICH in the left hemisphere was associated with various types of fluent aphasia. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
Optimal Interplay between Synaptic Strengths and Network Structure Enhances Activity Fluctuations and Information Propagation in Hierarchical Modular Networks
Brain Sci. 2020, 10(4), 228; https://doi.org/10.3390/brainsci10040228 - 10 Apr 2020
Cited by 2 | Viewed by 1803
Abstract
In network models of spiking neurons, the joint impact of network structure and synaptic parameters on activity propagation is still an open problem. Here, we use an information-theoretical approach to investigate activity propagation in spiking networks with a hierarchical modular topology. We observe [...] Read more.
In network models of spiking neurons, the joint impact of network structure and synaptic parameters on activity propagation is still an open problem. Here, we use an information-theoretical approach to investigate activity propagation in spiking networks with a hierarchical modular topology. We observe that optimized pairwise information propagation emerges due to the increase of either (i) the global synaptic strength parameter or (ii) the number of modules in the network, while the network size remains constant. At the population level, information propagation of activity among adjacent modules is enhanced as the number of modules increases until a maximum value is reached and then decreases, showing that there is an optimal interplay between synaptic strength and modularity for population information flow. This is in contrast to information propagation evaluated among pairs of neurons, which attains maximum value at the maximum values of these two parameter ranges. By examining the network behavior under the increase of synaptic strength and the number of modules, we find that these increases are associated with two different effects: (i) the increase of autocorrelations among individual neurons and (ii) the increase of cross-correlations among pairs of neurons. The second effect is associated with better information propagation in the network. Our results suggest roles that link topological features and synaptic strength levels to the transmission of information in cortical networks. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC
Brain Sci. 2020, 10(2), 116; https://doi.org/10.3390/brainsci10020116 - 20 Feb 2020
Cited by 6 | Viewed by 1419
Abstract
Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). [...] Read more.
Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
Breastfeeding Duration Is Associated with Regional, but Not Global, Differences in White Matter Tracts
Brain Sci. 2020, 10(1), 19; https://doi.org/10.3390/brainsci10010019 - 30 Dec 2019
Cited by 2 | Viewed by 1896
Abstract
Extended breastfeeding through infancy confers benefits on neurocognitive performance and intelligence tests, though few have examined the biological basis of these effects. To investigate correlations with breastfeeding, we examined the major white matter tracts in 4–8 year-old children using diffusion tensor imaging and [...] Read more.
Extended breastfeeding through infancy confers benefits on neurocognitive performance and intelligence tests, though few have examined the biological basis of these effects. To investigate correlations with breastfeeding, we examined the major white matter tracts in 4–8 year-old children using diffusion tensor imaging and volumetric measurements of the corpus callosum. We found a significant correlation between the duration of infant breastfeeding and fractional anisotropy scores in left-lateralized white matter tracts, including the left superior longitudinal fasciculus and left angular bundle, which is indicative of greater intrahemispheric connectivity. However, in contrast to expectations from earlier studies, no correlations were observed with corpus callosum size, and thus no correlations were observed when using such measures of global interhemispheric white matter connectivity development. These findings suggest a complex but significant positive association between breastfeeding duration and white matter connectivity, including in pathways known to be functionally relevant for reading and language development. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Article
Typical and Aberrant Functional Brain Flexibility: Lifespan Development and Aberrant Organization in Traumatic Brain Injury and Dyslexia
Brain Sci. 2019, 9(12), 380; https://doi.org/10.3390/brainsci9120380 - 16 Dec 2019
Cited by 3 | Viewed by 1622
Abstract
Intrinsic functional connectivity networks derived from different neuroimaging methods and connectivity estimators have revealed robust developmental trends linked to behavioural and cognitive maturation. The present study employed a dynamic functional connectivity approach to determine dominant intrinsic coupling modes in resting-state neuromagnetic data from [...] Read more.
Intrinsic functional connectivity networks derived from different neuroimaging methods and connectivity estimators have revealed robust developmental trends linked to behavioural and cognitive maturation. The present study employed a dynamic functional connectivity approach to determine dominant intrinsic coupling modes in resting-state neuromagnetic data from 178 healthy participants aged 8–60 years. Results revealed significant developmental trends in three types of dominant intra- and inter-hemispheric neuronal population interactions (amplitude envelope, phase coupling, and phase-amplitude synchronization) involving frontal, temporal, and parieto-occipital regions. Multi-class support vector machines achieved 89% correct classification of participants according to their chronological age using dynamic functional connectivity indices. Moreover, systematic temporal variability in functional connectivity profiles, which was used to empirically derive a composite flexibility index, displayed an inverse U-shaped curve among healthy participants. Lower flexibility values were found among age-matched children with reading disability and adults who had suffered mild traumatic brain injury. The importance of these results for normal and abnormal brain development are discussed in light of the recently proposed role of cross-frequency interactions in the fine-grained coordination of neuronal population activity. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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Review

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Review
Effects of Transcranial Electrical Stimulation on Human Auditory Processing and Behavior—A Review
Brain Sci. 2020, 10(8), 531; https://doi.org/10.3390/brainsci10080531 - 08 Aug 2020
Cited by 4 | Viewed by 1216
Abstract
Transcranial electrical stimulation (tES) can adjust the membrane potential by applying a weak current on the scalp to change the related nerve activity. In recent years, tES has proven its value in studying the neural processes involved in human behavior. The study of [...] Read more.
Transcranial electrical stimulation (tES) can adjust the membrane potential by applying a weak current on the scalp to change the related nerve activity. In recent years, tES has proven its value in studying the neural processes involved in human behavior. The study of central auditory processes focuses on the analysis of behavioral phenomena, including sound localization, auditory pattern recognition, and auditory discrimination. To our knowledge, studies on the application of tES in the field of hearing and the electrophysiological effects are limited. Therefore, we reviewed the neuromodulatory effect of tES on auditory processing, behavior, and cognitive function and have summarized the physiological effects of tES on the auditory cortex. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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