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Life
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Quantitative MRI (qMRI) in Neurodegenerative Diseases: The Brain and Beyond
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Quantitative MRI (qMRI) in Neurodegenerative Diseases: The Brain and Beyond

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Radiobiology and Nuclear Medicine".

Deadline for manuscript submissions: closed (21 July 2023) | Viewed by 8099

Special Issue Editor

Dr. Yongsheng Chen

E-Mail Website
Guest Editor
Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
Interests: quantitative MRI; neuroimaging; monitoring biomarker; Parkinson’s disease; peripheral nerve diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last decade, quantitative magnetic resonance imaging (qMRI) has emerged for assessing a broad range of neurodegenerative diseases involving the central nervous system and the peripheral nervous system. Some of these include proton density, T1 and T2 mapping which are sensitive to the water content of the neuronal tissue; magnetization transfer to quantify macromolecules such as myelin and neuromelanin content; the use of diffusion metrics to assess tissue microstructural integrity; and quantitative susceptibility mapping and T2* mapping to assess neuronal iron deposition. However, these various qMRI techniques are yet to be translated to clinical practice, serving as diagnostic and monitoring biomarkers for neurodegenerative diseases.

For this Special Issue, we invite you to submit manuscripts addressing the state-of-the-art qMRI technical development; critical reviews of qMRI approaches and applications in the brain, spinal cord, and peripheral nerves; as well as preclinical and clinical research using qMRI in neurodegenerative disorders. Submissions addressing the role of qMRI measures in Parkinson’s disease, Alzheimer’s disease, and peripheral nerve diseases are highly encouraged.

Dr. Yongsheng Chen
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. Life 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 2600 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

  • quantitative MRI
  • neuroimaging
  • brain
  • spinal cord
  • peripheral nerves
  • Neurodegenerative diseases
  • Parkinson’s disease
  • Charcot–Marie–Tooth diseases
  • animal models

Published Papers (4 papers)

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Research

14 pages, 1337 KiB  
Article
Altered Grey Matter-Brain Healthcare Quotient: Interventions of Olfactory Training and Learning of Neuroplasticity
by Keita Watanabe, Keisuke Kokubun and Yoshinori Yamakawa
Life 2023, 13(3), 667; https://doi.org/10.3390/life13030667 - 28 Feb 2023
Cited by 1 | Viewed by 1958
Abstract
Recent studies revealed that grey matter (GM) changes due to various training and learning experiences, using magnetic resonance imaging. In this study, we investigate the effect of psychological characteristics and attitudes toward training and learning on GM changes. Ninety participants were recruited and [...] Read more.
Recent studies revealed that grey matter (GM) changes due to various training and learning experiences, using magnetic resonance imaging. In this study, we investigate the effect of psychological characteristics and attitudes toward training and learning on GM changes. Ninety participants were recruited and distributed into three groups: an olfactory training group that underwent 40 olfactory training sessions designed for odour classification tasks, a group classified for learning of neuroplasticity and brain healthcare using a TED Talk video and 28 daily brain healthcare messages, and a control group. Further, we assessed psychological characteristics, such as curiosity and personal growth initiatives. In the olfactory training group, we conducted a questionnaire survey on olfactory training regarding their interests and sense of accomplishment. In the olfactory training group, the GM change was significantly correlated with the sense of achievement and interest in training. The learning of neuroplasticity and brain healthcare group showed a significantly smaller 2-month GM decline than did the control group. The Curiosity and Exploration Inventory-II scores were significantly correlated with GM changes in both intervention groups only. In conclusion, our result suggested that training or learning with a sense of accomplishment, interest, and curiosity would lead to greater GM changes. Full article
(This article belongs to the Special Issue Quantitative MRI (qMRI) in Neurodegenerative Diseases: The Brain and Beyond)
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15 pages, 2177 KiB  
Article
The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction
by Yingwei Guo, Yingjian Yang, Mingming Wang, Yu Luo, Jia Guo, Fengqiu Cao, Jiaxi Lu, Xueqiang Zeng, Xiaoqiang Miao, Asim Zaman and Yan Kang
Life 2022, 12(11), 1847; https://doi.org/10.3390/life12111847 - 11 Nov 2022
Cited by 3 | Viewed by 1308
Abstract
Accurate and reliable outcome predictions can help evaluate the functional recovery of ischemic stroke patients and assist in making treatment plans. Given that recovery factors may be hidden in the whole-brain features, this study aims to validate the role of dynamic radiomics features [...] Read more.
Accurate and reliable outcome predictions can help evaluate the functional recovery of ischemic stroke patients and assist in making treatment plans. Given that recovery factors may be hidden in the whole-brain features, this study aims to validate the role of dynamic radiomics features (DRFs) in the whole brain, DRFs in local ischemic lesions, and their combination in predicting functional outcomes of ischemic stroke patients. First, the DRFs in the whole brain and the DRFs in local lesions of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) images are calculated. Second, the least absolute shrinkage and selection operator (Lasso) is used to generate four groups of DRFs, including the outstanding DRFs in the whole brain (Lasso (WB)), the outstanding DRFs in local lesions (Lasso (LL)), the combination of them (combined DRFs), and the outstanding DRFs in the combined DRFs (Lasso (combined)). Then, the performance of the four groups of DRFs is evaluated to predict the functional recovery in three months. As a result, Lasso (combined) in the four groups achieves the best AUC score of 0.971, which improves the score by 8.9% compared with Lasso (WB), and by 3.5% compared with Lasso (WB) and combined DRFs. In conclusion, the outstanding combined DRFs generated from the outstanding DRFs in the whole brain and local lesions can predict functional outcomes in ischemic stroke patients better than the single DRFs in the whole brain or local lesions. Full article
(This article belongs to the Special Issue Quantitative MRI (qMRI) in Neurodegenerative Diseases: The Brain and Beyond)
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15 pages, 1760 KiB  
Article
A Causal Analysis of the Effect of Age and Sex Differences on Brain Atrophy in the Elderly Brain
by Jaime Gómez-Ramírez, Miguel A. Fernández-Blázquez and Javier J. González-Rosa
Life 2022, 12(10), 1586; https://doi.org/10.3390/life12101586 - 12 Oct 2022
Cited by 2 | Viewed by 2160
Abstract
We studied how brain volume loss in old age is affected by age, the APOE gene, sex, and the level of education completed. The quantitative characterization of brain volume loss at an old age relative to a young age requires—at least in principle—two [...] Read more.
We studied how brain volume loss in old age is affected by age, the APOE gene, sex, and the level of education completed. The quantitative characterization of brain volume loss at an old age relative to a young age requires—at least in principle—two MRI scans, one performed at a young age and one at an old age. There is, however, a way to address this problem when having only one MRI scan obtained at an old age. We computed the total brain losses of elderly subjects as a ratio between the estimated brain volume and the estimated total intracranial volume. Magnetic resonance imaging (MRI) scans of 890 healthy subjects aged 70 to 85 years were assessed. A causal analysis of factors affecting brain atrophy was performed using probabilistic Bayesian modelling and the mathematics of causal inference. We found that both age and sex were causally related to brain atrophy, with women reaching an elderly age with a 1% larger brain volume relative to their intracranial volume than men. How the brain ages and the rationale for sex differences in brain volume losses during the adult lifespan are questions that need to be addressed with causal inference and empirical data. The graphical causal modelling presented here can be instrumental in understanding a puzzling scientific area of study—the biological aging of the brain. Full article
(This article belongs to the Special Issue Quantitative MRI (qMRI) in Neurodegenerative Diseases: The Brain and Beyond)
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11 pages, 2228 KiB  
Article
Cerebral Venous Oxygen Saturation in Hypoperfusion Regions May Become a New Imaging Indicator to Predict the Clinical Outcome of Stroke
by Fengqiu Cao, Mingming Wang, Shengyu Fan, Shanhua Han, Yingwei Guo, Asim Zaman, Jia Guo, Yu Luo and Yan Kang
Life 2022, 12(9), 1312; https://doi.org/10.3390/life12091312 - 26 Aug 2022
Cited by 1 | Viewed by 1932
Abstract
To automatically and quantitatively evaluate the venous oxygen saturation (SvO2) in cerebral ischemic tissues and explore its value in predicting prognosis. A retrospective study was conducted on 48 AIS patients hospitalized in our hospital from 2015–2018. Based on quantitative susceptibility mapping and perfusion-weighted [...] Read more.
To automatically and quantitatively evaluate the venous oxygen saturation (SvO2) in cerebral ischemic tissues and explore its value in predicting prognosis. A retrospective study was conducted on 48 AIS patients hospitalized in our hospital from 2015–2018. Based on quantitative susceptibility mapping and perfusion-weighted imaging, this paper measured the cerebral SvO2 in hypoperfusion tissues and its change after intraarterial rt-PA treatment. The cerebral SvO2 in different hypoperfusion regions between the favorable and unfavorable clinical outcome groups was analyzed using an independent t-test. Relationships between cerebral SvO2 and clinical scores were determined using the Pearson correlation coefficient. The receiver operating characteristic process was conducted to evaluate the accuracy of cerebral SvO2 in predicting unfavorable clinical outcomes. Cerebral SvO2 in hypoperfusion (Tmax > 4 and 6 s) was significantly different between the two groups at follow-up (p < 0.05). Cerebral SvO2 and its changes before and after treatment were negatively correlated with clinical scores. The positive predictive value, negative predictive value, accuracy, and area under the curve of the cerebral SvO2 were higher than those predicted by the ischemic core. Therefore, the cerebral SvO2 of hypoperfusion regions was a stronger imaging predictor of unfavorable clinical outcomes after stroke. Full article
(This article belongs to the Special Issue Quantitative MRI (qMRI) in Neurodegenerative Diseases: The Brain and Beyond)
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