Neuroscience and Molecular Sciences

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Life Sciences".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 6792

Special Issue Editors

Institute of Radiology, Südharz Hospital Nordhausen, Academic Hospital of Jena University Hospital, Friedrich-Schiller University of Jena, Dr.-Robert-Koch Street 39, 99734 Nordhausen, Germany
Interests: data science; epilepsy; Parkinson’s disease; light and fluorescent microscopy; shear-wave elastography; neuroradiology; functional neuroradiology; MR-spectroscopy
Dr. Alexandros Zoumpos
E-Mail Website
Co-Guest Editor
Institute of Radiology, Südharz Hospital Nordhausen, Academic Hospital of Jena University Hospital, Friedrich-Schiller University of Jena, Dr.-Robert-Koch street 39, 99734 Nordhausen, Germany
Interests: neuroscience

Special Issue Information

Dear Colleagues,

We, from the community of Symmetry, a peer-reviewed, interdisciplinary scientific journal of MDPI (IF Clarivate Analytics 2.94; https://www.mdpi.com/journal/symmetry), support the initiative of a Special Issue on neuroscience (https://www.mdpi.com/journal/symmetry/special_issues/Neuroscience_Molecular_Sciences), hosting both basic science (https://www.mdpi.com/2073-8994/14/9/1802) and clinical research (https://www.mdpi.com/2073-8994/14/5/943). We have joined forces from the fields of computer engineering, data science, deep learning, clinical neuroradiology, microscopy, and experimental neuroimmunology (https://www.mdpi.com/2073-8994/13/11/2168) in intensive discussions about big data collecting, sorting, and curating. All sides agree that well-curated public databases are a sine qua non for qualitative deep learning (DL) training.

Part of proper and responsible curation is GDPR-conforming anonymization, or, even better, the deidentification of imaging data, which totally disconnects useful information from a patient's identity. Neuroscientists working with brain imaging inevitably include facial details, which can be 3D reconstructed and, with the assistance of currently available artificial intelligence, precisely associated with a subject's identity by, for example, using their social account data. 

Elucidating this objective, we are thrilled to invite authors with backgrounds in #imageprocessing, #deidentification, #BrainMRI, #faceobscuring, #anonymization, #neuroimaging, and #publicdatabase to participate with an #originalresearch or #review contribution to our #neurosciences Special Issue.

https://www.mdpi.com/journal/symmetry/special_issues/Neuroscience_Molecular_Sciences.

Dr. Ismini E Papageorgiou
Guest Editor
Dr. Alexandros Zoumpos
Co-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. Symmetry 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 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

  • central neuronal system
  • peripheral neuronal system
  • protein structure
  • adial symmetry (tetramerism, pentamerism, etc.)
  • electrophysiology, spiking activity, oscillation
  • development
  • evolution
  • sequencing, transcriptome, proteomics
  • imaging
  • computer-aided design

Published Papers (4 papers)

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Editorial

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4 pages, 181 KiB  
Editorial
Neuroscience Scaffolded by Informatics: A Raging Interdisciplinary Field
Symmetry 2023, 15(1), 153; https://doi.org/10.3390/sym15010153 - 04 Jan 2023
Cited by 1 | Viewed by 709
Abstract
Following breakthrough achievements in molecular neurosciences, the current decade witnesses a trend toward interdisciplinary and multimodal development. Supplementation of neurosciences with tools from computer science solidifies previous knowledge and sets the ground for new research on “big data” and new hypothesis-free experimental models. [...] Read more.
Following breakthrough achievements in molecular neurosciences, the current decade witnesses a trend toward interdisciplinary and multimodal development. Supplementation of neurosciences with tools from computer science solidifies previous knowledge and sets the ground for new research on “big data” and new hypothesis-free experimental models. In this Special Issue, we set the focus on informatics-supported interdisciplinary neuroscience accomplishments symmetrically combining wet-lab and clinical routines. Video-tracking and automated mitosis detection in vitro, the macromolecular modeling of kinesin motion, and the unsupervised classification of the brain’s macrophage activation status share a common denominator: they are energized by machine and deep learning. Essential clinical neuroscience questions such as the estimated risk of brain aneurysm rupture and the surgical outcome of facial nerve transplantation are addressed in this issue as well. Precise and rapid evaluation of complex clinical data by deep learning and data mining dives deep to reveal symmetrical and asymmetrical features beyond the abilities of human perception or the limits of linear algebraic modeling. This editorial opts to motivate researchers from the wet lab, computer science, and clinical environments to join forces in reshaping scientific platforms, share and converge high-quality data on public platforms, and use informatics to facilitate interdisciplinary information exchange. Full article
(This article belongs to the Special Issue Neuroscience and Molecular Sciences)

Research

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14 pages, 2258 KiB  
Article
Smile Reanimation with Masseteric-to-Facial Nerve Transfer plus Cross-Face Nerve Grafting in Patients with Segmental Midface Paresis: 3D Retrospective Quantitative Evaluation
Symmetry 2022, 14(12), 2570; https://doi.org/10.3390/sym14122570 - 05 Dec 2022
Cited by 1 | Viewed by 1336
Abstract
Facial paresis involves functional and aesthetic problems with altered and asymmetric movement patterns. Surgical procedures and physical therapy can effectively reanimate the muscles. From our database, 10 patients (18–50 years) suffering from unilateral segmental midface paresis and rehabilitated by a masseteric-to-facial nerve transfer [...] Read more.
Facial paresis involves functional and aesthetic problems with altered and asymmetric movement patterns. Surgical procedures and physical therapy can effectively reanimate the muscles. From our database, 10 patients (18–50 years) suffering from unilateral segmental midface paresis and rehabilitated by a masseteric-to-facial nerve transfer combined with a cross-face facial nerve graft, followed by physical therapy, were retrospectively analyzed. Standardized labial movements were measured using an optoelectronic motion capture system. Maximum teeth clenching, spontaneous smiles, and lip protrusion (kiss movement) were detected before and after surgery (21 ± 13 months). Preoperatively, during the maximum smile, the paretic side moved less than the healthy one (23.2 vs. 28.7 mm; activation ratio 69%, asymmetry index 18%). Postoperatively, no differences in total mobility were found. The activity ratio and the asymmetry index differed significantly (without/with teeth clenching: ratio 65% vs. 92%, p = 0.016; asymmetry index 21% vs. 5%, p = 0.016). Postoperatively, the mobility of the spontaneous smiles significantly reduced (healthy side, 25.1 vs. 17.2 mm, p = 0.043; paretic side 16.8 vs. 12.2 mm, p = 0.043), without modifications of the activity ratio and asymmetry index. Postoperatively, the paretic side kiss movement was significantly reduced (27 vs. 19.9 mm, p = 0.028). Overall, the treatment contributed to balancing the displacements between the two sides of the face with more symmetric movements. Full article
(This article belongs to the Special Issue Neuroscience and Molecular Sciences)
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10 pages, 3047 KiB  
Article
Machine Learning for Rupture Risk Prediction of Intracranial Aneurysms: Challenging the PHASES Score in Geographically Constrained Areas
Symmetry 2022, 14(5), 943; https://doi.org/10.3390/sym14050943 - 05 May 2022
Cited by 4 | Viewed by 2142
Abstract
Intracranial aneurysms represent a potentially life-threatening condition and occur in 3–5% of the population. They are increasingly diagnosed due to the broad application of cranial magnetic resonance imaging and computed tomography in the context of headaches, vertigo, and other unspecific symptoms. For each [...] Read more.
Intracranial aneurysms represent a potentially life-threatening condition and occur in 3–5% of the population. They are increasingly diagnosed due to the broad application of cranial magnetic resonance imaging and computed tomography in the context of headaches, vertigo, and other unspecific symptoms. For each affected individual, it is utterly important to estimate the rupture risk of the respective aneurysm. However, clinically applied decision tools, such as the PHASES score, remain insufficient. Therefore, a machine learning approach assessing the rupture risk of intracranial aneurysms is proposed in our study. For training and evaluation of the algorithm, data from a single neurovascular center was used, comprising 446 aneurysms (221 ruptured, 225 unruptured). The machine learning model was then compared with the PHASES score and proved superior in accuracy (0.7825), F1-score (0.7975), sensitivity (0.8643), specificity (0.7022), positive predictive value (0.7403), negative predictive value (0.8404), and area under the curve (0.8639). The frequency distributions of the predicted rupture probabilities and the PHASES score were analyzed. A symmetry can be observed between the rupture probabilities, with a symmetry axis at 0.5. A feature importance analysis reveals that the body mass index, consumption of anticoagulants, and harboring vessel are regarded as the most important features when assessing the rupture risk. On the other hand, the size of the aneurysm, which is weighted most in the PHASES score, is regarded as less important. Based on our findings we discuss the potential role of the model for clinical practice in geographically confined aneurysm patients. Full article
(This article belongs to the Special Issue Neuroscience and Molecular Sciences)
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Other

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11 pages, 2464 KiB  
Commentary
Molecular Mechanism of Processive Stepping of Kinesin Motors
Symmetry 2021, 13(10), 1799; https://doi.org/10.3390/sym13101799 - 27 Sep 2021
Cited by 6 | Viewed by 1301
Abstract
Kinesin-1 is a motor protein that can step processively on microtubule by hydrolyzing ATP molecules, playing an essential role in intracellular transports. To better understand the mechanochemical coupling of the motor stepping cycle, numerous structural, biochemical, single molecule, theoretical modeling and numerical simulation [...] Read more.
Kinesin-1 is a motor protein that can step processively on microtubule by hydrolyzing ATP molecules, playing an essential role in intracellular transports. To better understand the mechanochemical coupling of the motor stepping cycle, numerous structural, biochemical, single molecule, theoretical modeling and numerical simulation studies have been undertaken for the kinesin-1 motor. Recently, a novel ultraresolution optical trapping method was employed to study the mechanics of the kinesin-1 motor and new results were supplemented to its stepping dynamics. In this commentary, the new single molecule results are explained well theoretically with one of the models presented in the literature for the mechanochemical coupling of the kinesin-1 motor. With the model, various prior experimental results for dynamics of different families of N-terminal kinesin motors have also been explained quantitatively. Full article
(This article belongs to the Special Issue Neuroscience and Molecular Sciences)
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