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Breakthrough in Imaging-Guided Precision Medicine in Neurology
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Breakthrough in Imaging-Guided Precision Medicine in Neurology

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (25 April 2022) | Viewed by 4237

Special Issue Editor

Dr. Jinbin Xu

E-Mail Website
Guest Editor
Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
Interests: cancer; alzheimer disease; parkinson disease; central nervous disorders; postmortem biomarker validation and characterization; in vivo positron emission tomography; in vitro quantitative autoradiograp

Special Issue Information

Dear Colleagues,

The Journal of Personalized Medicine (JPM) Special Issue, “Breakthrough in Imaging-Guided Precision Medicine in Neurology”, has a broad research scope on the application of neuroimaging and the variety of different central nervous disorders. You are invited to submit research and review articles related to the following potential topics or any research communications in conjunction with acute or chronic neuroinflammation:

  • Neuroimaging of tau, amyloid-beta, and alpha-synuclein aggregation in the central nervous system;
  • Novel PET biomarkers for imaging brain damage, neuroinflammation, and disease progression of Alzheimer’s disease, Lewy body diseases (Parkinson’s disease, dementia with Lewy bodies, and Parkinson’s disease dementia), epilepsy, and other central nervous system disorders;
  • Neuroimaging and its application in the evaluation of interventions targeting neuroinflammation;
  • Multiple imaging modalities of neuroinflammation;
  • Animal models and their application for neuroimaging;
  • Epigenetics for validation of neuroimaging biomarkers;
  • Longitudinal and cross-sectional imaging analyses;
  • Imaging interactions of astrocytes/microglia with neurons;
  • Single-cell genetics, proteomics, and multi-omics of the rodent, nonhuman primate, human postmortem brain tissues and their application for neuroimaging.

We expect your submissions and look forward to working with you on publishing your research work.

Prof. Jinbin Xu
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. Journal of Personalized Medicine 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

  • Neuroimaging
  • Radiomics
  • Machine learning
  • Biomarkers
  • Precision medicine
  • Genetics
  • Personalized medicine
  • Neurology

Published Papers (2 papers)

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Research

15 pages, 2808 KiB  
Article
Classification of Huntington’s Disease Stage with Features Derived from Structural and Diffusion-Weighted Imaging
by Rui Lavrador, Filipa Júlio, Cristina Januário, Miguel Castelo-Branco and Gina Caetano
J. Pers. Med. 2022, 12(5), 704; https://doi.org/10.3390/jpm12050704 - 28 Apr 2022
Cited by 2 | Viewed by 1574
Abstract
The purpose of this study was to classify Huntington’s disease (HD) stage using support vector machines and measures derived from T1- and diffusion-weighted imaging. The effects of feature selection approach and combination of imaging modalities are assessed. Fourteen premanifest-HD individuals (Pre-HD; on average [...] Read more.
The purpose of this study was to classify Huntington’s disease (HD) stage using support vector machines and measures derived from T1- and diffusion-weighted imaging. The effects of feature selection approach and combination of imaging modalities are assessed. Fourteen premanifest-HD individuals (Pre-HD; on average > 20 years from estimated disease onset), eleven early-manifest HD (Early-HD) patients, and eighteen healthy controls (HC) participated in the study. We compared three feature selection approaches: (i) whole-brain segmented grey matter (GM; voxel-based measure) or fractional anisotropy (FA) values; (ii) GM or FA values from subcortical regions-of-interest (caudate, putamen, pallidum); and (iii) automated selection of GM or FA values with the algorithm Relief-F. We assessed single- and multi-kernel approaches to classify combined GM and FA measures. Significant classifications were achieved between Early-HD and Pre-HD or HC individuals (accuracy: generally, 85% to 95%), and between Pre-HD and controls for the feature FA of the caudate ROI (74% accuracy). The combination of GM and FA measures did not result in higher performances. We demonstrate evidence on the high sensitivity of FA for the classification of the earliest Pre-HD stages, and successful distinction between HD stages. Full article
(This article belongs to the Special Issue Breakthrough in Imaging-Guided Precision Medicine in Neurology)
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9 pages, 654 KiB  
Article
Automated Assessment of the Substantia Nigra Pars Compacta in Parkinson’s Disease: A Diffusion Tensor Imaging Study
by Niels Bergsland, Laura Pelizzari, Maria Marcella Laganá, Sonia Di Tella, Federica Rossetto, Raffaello Nemni, Mario Clerici and Francesca Baglio
J. Pers. Med. 2021, 11(11), 1235; https://doi.org/10.3390/jpm11111235 - 21 Nov 2021
Cited by 2 | Viewed by 2203
Abstract
The substantia nigra (SN) pars compacta (SNpc) and pars reticulata (SNpr) are differentially affected in Parkinson’s disease (PD). Separating the SNpc and SNpr is challenging with standard magnetic resonance imaging (MRI). Diffusion tensor imaging (DTI) allows for the characterization of SN microstructure in [...] Read more.
The substantia nigra (SN) pars compacta (SNpc) and pars reticulata (SNpr) are differentially affected in Parkinson’s disease (PD). Separating the SNpc and SNpr is challenging with standard magnetic resonance imaging (MRI). Diffusion tensor imaging (DTI) allows for the characterization of SN microstructure in a non-invasive manner. In this study, 29 PD patients and 28 healthy controls (HCs) were imaged with 1.5T MRI for DTI. Images were nonlinearly registered to standard space and SNpc and SNpr DTI parameters were measured. ANCOVA and receiver operator characteristic (ROC) analyses were performed. Clinical associations were assessed with Spearman correlations. Multiple corrections were controlled for false discovery rate. PD patients presented with significantly increased SNpc axial diffusivity (AD) (1.207 ± 0.068 versus 1.156 ± 0.045, p = 0.024), with ROC analysis yielding an under the curve of 0.736. Trends with Unified Parkinson’s Disease Rating Scale (UPDRS) III scores were identified for SNpc MD (rs = 0.449), AD (rs = 0.388), and radial diffusivity (rs = 0.391) (all p < 0.1). A trend between baseline SNpr MD and H&Y change (rs = 0.563, p = 0.081) over 2.9 years of follow-up was identified (n = 14). In conclusion, SN microstructure shows robust, clinically meaningful associations in PD. Full article
(This article belongs to the Special Issue Breakthrough in Imaging-Guided Precision Medicine in Neurology)
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