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Brain Sciences
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Cortical Connectivity Pattern: Neuroimaging Advances with MRI
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Cortical Connectivity Pattern: Neuroimaging Advances with MRI

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

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 11016

Special Issue Editors

Prof. Dr. Xi-Nian Zuo

E-Mail Website
Guest Editor
IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
Interests: population neuroscience; developmental neuroimaging; network neuroscience
Prof. Dr. Hui-Jie Li

E-Mail Website
Guest Editor
Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
Interests: brain aging and intervention; mental health; cognitive neuroscience

Special Issue Information

Dear Colleagues,

In recent years, advances in functional magnetic resonance imaging (fMRI) have made mapping brain connectivity possible. While existing studies mostly investigate the spatial patterns of human cortical connectivity, temporal or spatiotemporal integrative patterns of connectivity have been overlooked.
In this Special Issue, we aim to present various aspects of cortical connectivity in terms of their temporal or spatiotemporal patterns at different scales.
Innovative research is expected to improve understanding of the mechanisms involved in brain and brain–mind associations through multimodal fMRI technologies.
Authors are invited to submit cutting-edge research and reviews that address a broad range of topics related to cortical connectivity patterns (CCP), including the following: CCP methodology (preferable that is reproducible), CCP across different stages of the human lifespan, CCP under clinical conditions, and other individual differences in CCP.

Prof. Dr. Xi-Nian Zuo
Prof. Dr. Hui-Jie Li
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 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

  • brain connectivity
  • development
  • aging
  • brain disorder
  • connectome
  • reproducibility

Published Papers (5 papers)

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Research

25 pages, 3509 KiB  
Article
Noninvasive Characterization of Functional Pathways in Layer-Specific Microcircuits of the Human Brain Using 7T fMRI
by Gopikrishna Deshpande and Yun Wang
Brain Sci. 2022, 12(10), 1361; https://doi.org/10.3390/brainsci12101361 - 07 Oct 2022
Cited by 1 | Viewed by 1863
Abstract
Layer-specific cortical microcircuits have been explored through invasive animal studies, yet it is not possible to reliably characterize them functionally and noninvasively in the human brain. However, recent advances in ultra-high-field functional magnetic resonance imaging (fMRI) have made it feasible to reasonably resolve [...] Read more.
Layer-specific cortical microcircuits have been explored through invasive animal studies, yet it is not possible to reliably characterize them functionally and noninvasively in the human brain. However, recent advances in ultra-high-field functional magnetic resonance imaging (fMRI) have made it feasible to reasonably resolve layer-specific fMRI signals with sub-millimeter resolution. Here, we propose an experimental and analytical framework that enables the noninvasive functional characterization of layer-specific cortical microcircuits. Specifically, we illustrate this framework by characterizing layer-specific functional pathways in the corticogeniculate network of the human visual system by obtaining sub-millimeter fMRI at 7T using a task which engages the magnocellular pathway between the lateral geniculate nucleus (LGN) and the primary visual cortex. Our results demonstrate that: (i) center-surround inhibition in magnocellular neurons within LGN is detectable using localized fMRI responses; (ii) feedforward (LGN → layers VI/IV, layer IV → layer VI) and feedback (layer VI → LGN) functional pathways, known to exist from invasive animal studies, can be inferred using dynamic directional connectivity models of fMRI and could potentially explain the mechanism underlying center-surround inhibition as well as gain control by layer VI in the human visual system. Our framework is domain-neutral and could potentially be employed to investigate the layer-specific cortical microcircuits in other systems related to cognition, memory and language. Full article
(This article belongs to the Special Issue Cortical Connectivity Pattern: Neuroimaging Advances with MRI)
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12 pages, 2125 KiB  
Article
Identification of Young High-Functioning Autism Individuals Based on Functional Connectome Using Graph Isomorphism Network: A Pilot Study
by Sihong Yang, Dezhi Jin, Jun Liu and Ye He
Brain Sci. 2022, 12(7), 883; https://doi.org/10.3390/brainsci12070883 - 05 Jul 2022
Cited by 4 | Viewed by 2086
Abstract
Accumulated studies have determined the changes in functional connectivity in autism spectrum disorder (ASD) and spurred the application of machine learning for classifying ASD. Graph Neural Network provides a new method for network analysis in brain disorders to identify the underlying network features [...] Read more.
Accumulated studies have determined the changes in functional connectivity in autism spectrum disorder (ASD) and spurred the application of machine learning for classifying ASD. Graph Neural Network provides a new method for network analysis in brain disorders to identify the underlying network features associated with functional deficits. Here, we proposed an improved model of Graph Isomorphism Network (GIN) that implements the Weisfeiler-Lehman (WL) graph isomorphism test to learn the graph features while taking into account the importance of each node in the classification to improve the interpretability of the algorithm. We applied the proposed method on multisite datasets of resting-state functional connectome from Autism Brain Imaging Data Exchange (ABIDE) after stringent quality control. The proposed method outperformed other commonly used classification methods on five different evaluation metrics. We also identified salient ROIs in visual and frontoparietal control networks, which could provide potential neuroimaging biomarkers for ASD identification. Full article
(This article belongs to the Special Issue Cortical Connectivity Pattern: Neuroimaging Advances with MRI)
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9 pages, 1345 KiB  
Article
Topologic Efficiency Abnormalities of the Connectome in Asymptomatic Patients with Leukoaraiosis
by Shun Yao, Hong-Ying Zhang, Ren Wang, Ding-Sheng Cheng and Jing Ye
Brain Sci. 2022, 12(6), 784; https://doi.org/10.3390/brainsci12060784 - 15 Jun 2022
Viewed by 1450
Abstract
Leukoaraiosis (LA) is commonly found in aging healthy people but its pathophysiological mechanism is not entirely known. Furthermore, there is still a lack of effective pathological biomarkers that can be used to identify the early stage of LA. Our aim was to investigate [...] Read more.
Leukoaraiosis (LA) is commonly found in aging healthy people but its pathophysiological mechanism is not entirely known. Furthermore, there is still a lack of effective pathological biomarkers that can be used to identify the early stage of LA. Our aim was to investigate the white matter structural network in asymptomatic patients with the early stage of LA. Tractography data of 35 asymptomatic patients and 20 matched healthy controls (HCs) based on diffusion kurtosis imaging (DKI) were analysed by using graph theory approaches and tract-based spatial statistics (TBSS). Diffusion parameters measured within the ALAs and HCs were compared. Decreased clustering coefficient and local efficiency values of the overall topological white matter network were observed in the ALAs compared with those of the HCs. Participants in the asymptomatic group also had lower nodal efficiency in the left triangular part of the inferior frontal gyrus, left parahippocampal gyrus, right calcarine fissure and surrounding cortex, right temporal pole of the superior temporal gyrus and left middle temporal gyrus compared to the ALAs. Moreover, similar hub distributions were found within participants in the two groups. In this study, our data demonstrated a topologic efficiency abnormalities of the structural network in asymptomatic patients with leukoaraiosis. The structural connectome provides potential connectome-based measures that may be helpful for detecting leukoaraiosis before clinical symptoms evolve. Full article
(This article belongs to the Special Issue Cortical Connectivity Pattern: Neuroimaging Advances with MRI)
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14 pages, 4612 KiB  
Article
A Macaque Brain Extraction Model Based on U-Net Combined with Residual Structure
by Qianshan Wang, Hong Fei, Saddam Naji Abdu Nasher, Xiaoluan Xia and Haifang Li
Brain Sci. 2022, 12(2), 260; https://doi.org/10.3390/brainsci12020260 - 12 Feb 2022
Cited by 7 | Viewed by 2405
Abstract
Accurately extracting brain tissue is a critical and primary step in brain neuroimaging research. Due to the differences in brain size and structure between humans and nonhuman primates, the performance of the existing tools for brain tissue extraction, working on macaque brain MRI, [...] Read more.
Accurately extracting brain tissue is a critical and primary step in brain neuroimaging research. Due to the differences in brain size and structure between humans and nonhuman primates, the performance of the existing tools for brain tissue extraction, working on macaque brain MRI, is constrained. A new transfer learning training strategy was utilized to address the limitations, such as insufficient training data and unsatisfactory model generalization ability, when deep neural networks processing the limited samples of macaque magnetic resonance imaging(MRI). First, the project combines two human brain MRI data modes to pre-train the neural network, in order to achieve faster training and more accurate brain extraction. Then, a residual network structure in the U-Net model was added, in order to propose a ResTLU-Net model that aims to improve the generalization ability of multiple research sites data. The results demonstrated that the ResTLU-Net, combined with the proposed transfer learning strategy, achieved comparable accuracy for the macaque brain MRI extraction tasks on different macaque brain MRI volumes that were produced by various medical centers. The mean Dice of the ResTLU-Net was 95.81% (no need for denoise and recorrect), and the method required only approximately 30–60 s for one extraction task on an NVIDIA 1660S GPU. Full article
(This article belongs to the Special Issue Cortical Connectivity Pattern: Neuroimaging Advances with MRI)
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14 pages, 3395 KiB  
Article
Localizing Spectral Interactions in the Resting State Network Using the Hilbert–Huang Transform
by Ai-Ling Hsu, Chia-Wei Li, Pengmin Qin, Men-Tzung Lo and Changwei W. Wu
Brain Sci. 2022, 12(2), 140; https://doi.org/10.3390/brainsci12020140 - 21 Jan 2022
Cited by 2 | Viewed by 2404
Abstract
Brain synchronizations are orchestrated from neuronal oscillations through frequency interactions, such as the alpha rhythm during relaxation. Nevertheless, how the intrinsic interaction forges functional integrity across brain segregations remains elusive, thereby motivating recent studies to localize frequency interactions of resting-state fMRI (rs-fMRI). To [...] Read more.
Brain synchronizations are orchestrated from neuronal oscillations through frequency interactions, such as the alpha rhythm during relaxation. Nevertheless, how the intrinsic interaction forges functional integrity across brain segregations remains elusive, thereby motivating recent studies to localize frequency interactions of resting-state fMRI (rs-fMRI). To this point, we aim to unveil the fMRI-based spectral interactions using the time-frequency (TF) analysis; however, Fourier-based TF analyses impose restrictions on revealing frequency interactions given the limited time points in fMRI signals. Instead of using the Fourier-based wavelet analysis to identify the fMRI frequency of interests, we employed the Hilbert–Huang transform (HHT) for probing the specific frequency contribution to the functional integration, called ensemble spectral interaction (ESI). By simulating data with time-variant frequency changes, we demonstrated the Hilbert TF maps with high spectro-temporal resolution and full accessibility in comparison with the wavelet TF maps. By detecting amplitude-to-amplitude frequency couplings (AAC) across brain regions, we elucidated the ESI disparity between the eye-closed (EC) and eye-open (EO) conditions in rs-fMRI. In the visual network, the strength of the spectral interaction within 0.03–0.04 Hz was amplified in EC compared with that in EO condition, whereas a canonical connectivity analysis did not present differences between conditions. Collectively, leveraging from the instantaneous frequency of HHT, we firstly addressed the ESI technique to map the fMRI-based functional connectivity in a brand-new AAC perspective. The ESI possesses potential in elucidating the functional connectivity at specific frequency bins, thereby providing additional diagnostic merits for future clinical neuroscience. Full article
(This article belongs to the Special Issue Cortical Connectivity Pattern: Neuroimaging Advances with MRI)
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