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Brain Sciences
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Predictive Medicine in Neuropathology
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Predictive Medicine in Neuropathology

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

Deadline for manuscript submissions: closed (25 April 2020) | Viewed by 3812

Special Issue Editor

Dr. Cassie S. Mitchell

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA
Interests: predictive medicine, systems neuropathology, machine learning, computational neuroscience, big data, Alzheimer's Disease, Amyotrophic Lateral Sclerosis

Special Issue Information

Dear Colleagues,

Predictive medicine had its roots in oncology with the advent of high-throughput arrays and omics, which have enabled personalized customization of chemotherapies. However, neuroscience has always prided itself as a foundational field in the application of computational approaches. Predictive medicine is a relatively newer thrust for neuropathology (e.g., pathology, disease or trauma resulting in abnormal neurological function). However, predictive medicine for neuropathology is making great strides in both technological construction of brand-new algorithms and methods, as well as applications of existing machine learning, network science, biostatistics, and systems engineering techniques to improve neuropathology research and clinical care. Predictive medicine is being used to provide diagnostic, prognostic, therapeutic or rehabilitative decision support for neurologic patient care; such research focuses on identifying unique features, or patient phenotypes, that enable custom-tailored, personalized predictions. Other examples of predictive medicine in neuropathology include preclinical or clinical “big data” exploratory analyses to better understand multiscalar, multifactorial etiology that plagues currently intractable neurological disease. From personalized models, population models, network models, and beyond, predictive medicine is the crystal ball for identifying new neuropathological causes, expediting the development of new neurologic disease treatments, and optimizing neurologic patient care.

Dr. Cassie S. Mitchell
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. 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

  • Personalized
  • Medicine
  • Predictive medicine
  • Machine learning
  • Computational model
  • Biostatistics, systems biology
  • Big data, network theory
  • Clinical decision support

Published Papers (1 paper)

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Research

16 pages, 1824 KiB  
Article
Dynamical Role of Pivotal Brain Regions in Parkinson Symptomatology Uncovered with Deep Learning
by Alex A. Nguyen, Pedro D. Maia, Xiao Gao, Pablo F. Damasceno and Ashish Raj
Brain Sci. 2020, 10(2), 73; https://doi.org/10.3390/brainsci10020073 - 30 Jan 2020
Cited by 5 | Viewed by 3559
Abstract
Background: The release of a broad, longitudinal anatomical dataset by the Parkinson’s Progression Markers Initiative promoted a surge of machine-learning studies aimed at predicting disease onset and progression. However, the excessive number of features used in these models often conceals their relationship to [...] Read more.
Background: The release of a broad, longitudinal anatomical dataset by the Parkinson’s Progression Markers Initiative promoted a surge of machine-learning studies aimed at predicting disease onset and progression. However, the excessive number of features used in these models often conceals their relationship to the Parkinsonian symptomatology. Objectives: The aim of this study is two-fold: (i) to predict future motor and cognitive impairments up to four years from brain features acquired at baseline; and (ii) to interpret the role of pivotal brain regions responsible for different symptoms from a neurological viewpoint. Methods: We test several deep-learning neural network configurations, and report our best results obtained with an autoencoder deep-learning model, run on a 5-fold cross-validation set. Comparison with Existing Methods: Our approach improves upon results from standard regression and others. It also includes neuroimaging biomarkers as features. Results: The relative contributions of pivotal brain regions to each impairment change over time, suggesting a dynamical reordering of culprits as the disease progresses. Specifically, the Putamen is initially the most critical region accounting for the overall cognitive state, only being surpassed by the Substantia Nigra in later years. The Pallidum is the first region to influence motor scores, followed by the parahippocampal and ambient gyri, and the anterior orbital gyrus. Conclusions: While the causal link between regional brain atrophy and Parkinson symptomatology is poorly understood, our methods demonstrate that the contributions of pivotal regions to cognitive and motor impairments are more dynamical than generally appreciated. Full article
(This article belongs to the Special Issue Predictive Medicine in Neuropathology)
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