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Applied Sciences
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Magnetic Resonance and Electromagnetic Evaluation of Brain Function in Health...
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Magnetic Resonance and Electromagnetic Evaluation of Brain Function in Health and Disease

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 November 2019) | Viewed by 2453

Special Issue Editor

Dr. Jeffrey David Lewine

E-Mail Website
Guest Editor
The Mind Research Network, Albuquerque, NM 87108, USA
Interests: autism; traumatic brain injury; post-traumatic stress disorder; tinnitus; epilepsy; neuro-plasticity

Special Issue Information

Dear Colleagues,

Recent technical advances in neuroimaging and electrophysiology are providing novel insights into the structural and functional connections between brain regions, which give rise to networks specialized for processing different types of information and for controlling motor, cognitive, and emotional outputs. This Special Issue will focus on the latest research using MRI (fMRI and MRS), EEG, and MEG methods to examine brain function in health and disease, with an emphasis on neurodegenerative and psychiatric conditions, including, but not limited to, epilepsy, Alzheimer’s disease, multiple sclerosis, traumatic brain injury, autism, PTSD, and schizophrenia. It is planned that the Issue will include introductory tutorial articles on each of the major imaging modalities, plus articles applying imaging to specific clinical conditions. Of particular interest are methods for examining the functional connectivity patterns that may be altered by disease.

Dr. Jeffrey David Lewine
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • functional MRI
  • MR spectroscopy
  • EEG
  • magnetoencephalography
  • epilepsy
  • dementia
  • MS
  • TBI
  • autism
  • PTSD
  • Schizophrenia

Published Papers (1 paper)

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Research

12 pages, 2259 KiB  
Article
Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare
by Lingyun Jiang, Kai Qiao, Linyuan Wang, Chi Zhang, Jian Chen, Lei Zeng, Haibing Bu and Bin Yan
Appl. Sci. 2019, 9(22), 4749; https://doi.org/10.3390/app9224749 - 7 Nov 2019
Cited by 2 | Viewed by 2232
Abstract
Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with [...] Read more.
Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space. Full article
(This article belongs to the Special Issue Magnetic Resonance and Electromagnetic Evaluation of Brain Function in Health and Disease)
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