Special Issue "Brain Imaging"

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: 20 July 2019

Special Issue Editors

Guest Editor
Prof. Dr. David Moratal

Department of Electronic Engineering & Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Cami de Vera, s/n, 46022 Valencia, Spain
Website | E-Mail
Interests: medical imaging; image-based biomarkers; magnetic resonance imaging (MRI); neuroimaging; machine learning; texture analysis; brain connectivity; functional MRI; multimodal image analysis
Guest Editor
Dr. Santiago Canals

Plasticity of Brain Networks, Cellular and Systems Neurobiology, Instituto de Neurociencias, 03550 San Juan de Alicante, Spain
Website | E-Mail
Interests: magnetic resonance imaging (MRI); neuroimaging; machine learning; brain connectivity; functional MRI; multimodal image analysis; network analysis

Special Issue Information

Dear Colleagues,

Brain imaging or neuroimaging refers to the use of non-invasive or minimally-invasive techniques to either directly or indirectly image the structure or function of the nervous system, being a powerful discipline within medicine, neuroscience, and psychology.

In this Special Issue, we intend to collect experiences of leading scientists and invite front-line researchers and authors to submit original research and review articles on neuroimaging. This Special Issue intends also to be a resource tool for people who are new to the world of brain imaging.

Potential topics of this Special Issue include, but are not limited to, the use of technology as well as novel image processing algorithms to image the nervous system by means of:

  • Head MRI
  • Tensor brain imaging
  • Functional MRI
  • Phase-contrast MRI
  • MR angiography
  • Multimodal imaging

Papers must present novel results, or the advancement of previously published data, and the matter should be dealt with scientific rigor.

Prof. Dr. David Moratal
Dr. Santiago Canals
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. Diagnostics is an international peer-reviewed open access quarterly 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 850 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

  • MRI
  • Biomarker
  • Image analysis
  • Medical imaging
  • Head MRI
  • Tensor brain imaging
  • fMRI
  • Functional MRI
  • Phase-contrast MRI
  • MR angiography
  • Multimodal imaging

Published Papers (4 papers)

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Research

Open AccessArticle Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder: A Developmental Resting-State fMRI Study
Diagnostics 2019, 9(1), 32; https://doi.org/10.3390/diagnostics9010032
Received: 23 January 2019 / Revised: 4 March 2019 / Accepted: 8 March 2019 / Published: 21 March 2019
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Abstract
Autism spectrum disorder (ASD) is a neurological and developmental disorder whose late diagnosis is based on subjective tests. In seeking for earlier diagnosis, we aimed to find objective biomarkers via analysis of resting-state functional MRI (rs-fMRI) images obtained from the Autism Brain Image [...] Read more.
Autism spectrum disorder (ASD) is a neurological and developmental disorder whose late diagnosis is based on subjective tests. In seeking for earlier diagnosis, we aimed to find objective biomarkers via analysis of resting-state functional MRI (rs-fMRI) images obtained from the Autism Brain Image Data Exchange (ABIDE) database. Thus, we estimated brain functional connectivity (FC) between pairs of regions as the statistical dependence between their neural-related blood-oxygen-level-dependent (BOLD) signals. We compared FC of individuals with ASD and healthy controls, matched by age and intelligence quotient (IQ), and split into three age groups (50 children, 98 adolescents, and 32 adults), from a developmental perspective. After estimating the correlation, we observed hypoconnectivities in children and adolescents with ASD between regions belonging to the default mode network (DMN). Concretely, in children, FC decreased between the left middle temporal gyrus and right frontal pole (p = 0.0080), and between the left orbitofrontal cortex and right superior frontal gyrus (p = 0.0144). In adolescents, this decrease was observed between bilateral postcentral gyri (p = 0.0012), and between the right precuneus and right middle temporal gyrus (p = 0.0236). These results help to gain a better understanding of the involved regions on autism and its connection with the affected superior cognitive brain functions. Full article
(This article belongs to the Special Issue Brain Imaging)
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Open AccessArticle Evaluation of the Performance of 18F-Fluorothymidine Positron Emission Tomography/Computed Tomography (18F-FLT-PET/CT) in Metastatic Brain Lesions
Diagnostics 2019, 9(1), 17; https://doi.org/10.3390/diagnostics9010017
Received: 24 December 2018 / Revised: 19 January 2019 / Accepted: 23 January 2019 / Published: 26 January 2019
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Abstract
18F-fluorothymidine (18F-FLT) is a radiolabeled thymidine analog that has been reported to help monitor tumor proliferation and has been studied in primary brain tumors; however, knowledge about 18F-FLT positron emission tomography/computed tomography (PET/CT) in metastatic brain lesions is limited. The purpose of this [...] Read more.
18F-fluorothymidine (18F-FLT) is a radiolabeled thymidine analog that has been reported to help monitor tumor proliferation and has been studied in primary brain tumors; however, knowledge about 18F-FLT positron emission tomography/computed tomography (PET/CT) in metastatic brain lesions is limited. The purpose of this study is to evaluate the performance of 18F-FLT-PET/CT in metastatic brain lesions. A total of 20 PET/CT examinations (33 lesions) were included in the study. Semiquantitative analysis was performed: standard uptake value (SUV) with the utilization of SUVmax, tumor-to-background ratio (T/B), SUVpeak, SUV1cm3, SUV0.5cm3, SUV50%, SUV75%, PV50% (volume × SUV50%), and PV75% (volume × SUV75%) were calculated. Sensitivity, specificity, and accuracy for each parameter were calculated. Optimal cutoff values for each parameter were obtained. Using a receiver operating characteristic (ROC) curve analysis, the optimal cutoff values of SUVmax, T/B, and SUVpeak for discriminating active from non-active lesions were found to be 0.615, 4.21, and 0.425, respectively. In an ROC curve analysis, the area under the curve (AUC) is higher for SUVmax (p-value 0.017) compared to the rest of the parameters, while using optimal cutoff T/B shows the highest sensitivity and accuracy. PVs (proliferation × volumes) did not show any significance in discriminating positive from negative lesions. 18F-FLT-PET/CT can detect active metastatic brain lesions and may be used as a complementary tool. Further investigation should be performed. Full article
(This article belongs to the Special Issue Brain Imaging)
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Open AccessArticle Evaluating Functional Connectivity Alterations in Autism Spectrum Disorder Using Network-Based Statistics
Diagnostics 2018, 8(3), 51; https://doi.org/10.3390/diagnostics8030051
Received: 18 June 2018 / Revised: 16 July 2018 / Accepted: 6 August 2018 / Published: 7 August 2018
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Abstract
The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of [...] Read more.
The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of 74 ASD subjects and a group of 82 typical-development (TD) subjects using functional magnetic resonance imaging (fMRI). We have used a network approach whereby the brain is divided into discrete regions or nodes that interact with each other through connections or edges. Functional brain networks were estimated using the Pearson’s correlation coefficient and compared by means of the Network-Based Statistic (NBS) method. The obtained results reveal a combination of both overconnectivity and underconnectivity, with the presence of networks in which the connectivity levels differ significantly between ASD and TD groups. The alterations mainly affect the temporal and frontal lobe, as well as the limbic system, especially those regions related with social interaction and emotion management functions. These results are concordant with the clinical profile of the disorder and can contribute to the elucidation of its neurological basis, encouraging the development of new clinical approaches. Full article
(This article belongs to the Special Issue Brain Imaging)
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Open AccessArticle ALTEA: A Software Tool for the Evaluation of New Biomarkers for Alzheimer’s Disease by Means of Textures Analysis on Magnetic Resonance Images
Diagnostics 2018, 8(3), 47; https://doi.org/10.3390/diagnostics8030047
Received: 29 May 2018 / Revised: 10 July 2018 / Accepted: 18 July 2018 / Published: 19 July 2018
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Abstract
The current criteria for diagnosing Alzheimer’s disease (AD) require the presence of relevant cognitive deficits, so the underlying neuropathological damage is important by the time the diagnosis is made. Therefore, the evaluation of new biomarkers to detect AD in its early stages has [...] Read more.
The current criteria for diagnosing Alzheimer’s disease (AD) require the presence of relevant cognitive deficits, so the underlying neuropathological damage is important by the time the diagnosis is made. Therefore, the evaluation of new biomarkers to detect AD in its early stages has become one of the main research focuses. The purpose of the present study was to evaluate a set of texture parameters as potential biomarkers of the disease. To this end, the ALTEA (ALzheimer TExture Analyzer) software tool was created to perform 2D and 3D texture analysis on magnetic resonance images. This intuitive tool was used to analyze textures of circular and spherical regions situated in the right and left hippocampi of a cohort of 105 patients: 35 AD patients, 35 patients with early mild cognitive impairment (EMCI) and 35 cognitively normal (CN) subjects. A total of 25 statistical texture parameters derived from the histogram, the Gray-Level Co-occurrence Matrix and the Gray-Level Run-Length Matrix, were extracted from each region and analyzed statistically to study their predictive capacity. Several textural parameters were statistically significant (p < 0.05) when differentiating AD subjects from CN and EMCI patients, which indicates that texture analysis could help to identify the presence of AD. Full article
(This article belongs to the Special Issue Brain Imaging)
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