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Diagnostics
Special Issues
Application of Translational Neuromodeling for Diagnostics in Neurological Disorders
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Application of Translational Neuromodeling for Diagnostics in Neurological Disorders

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2046

Special Issue Editor

Dr. Robert Fleischmann

E-Mail Website
Guest Editor
Department of Neurology, University Medicine Greifswald, Greifswald, Germany
Interests: neurology; neurophysiology; translational neuromodeling

Special Issue Information

Dear Colleagues,

Neurological disorders can be divided into those caused by structural damage to the peripheral or central nervous system, e.g., due to ischemia, haemorrhage, (neuro‑)inflammation or neoplasms, on the one hand. On the other hand, there is a number of disorders that are mainly caused by functional disturbances of neuronal networks, mostly in the central nervous system, e.g., disorders of consciousness (such as delirium), cognitive impairment/dementia, headache and movement disorders. These functional disorders typically lack biomarkers to support their diagnosis, guide treatment decisions, monitor disease activity and prognosticate the disease course. Consequently, medical decision making often relies on clinical judgement, experience and phenotypical presentation instead of biomarkers that better reflect the underlying pathomechanisms, i.e., the endotype of the disease. Translational neuromodeling (TN) is a neuroscientific discipline that can be key to meet the challenges of decision making in disorders that cannot be adequately characterised through the currently available methods in clinical diagnostics, such as (neuro‑)imaging, neurophysiological or fluid biomarkers. The aim of TN is to make technology and knowledge from basic neuroscientific research available for clinical diagnostics and the medical management of patients with neurological disorders. This includes, but is not limited to, electrophysiological (e.g., quantitative electroencephalography, evoked potential studies and magnetic encephalography) and functional imaging studies (e.g., functional magnetic resonance imaging and perfusion analyses), which may enable more objective detection, classification, monitoring and prognostication of neurological disorders that currently lack biomarkers.

Dr. Robert Fleischmann
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. Diagnostics 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 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

  • electrophysiology
  • electroencelography
  • evoked potentials
  • magnetic encephalography
  • neurophysiology
  • functional magnetic resonance imaging
  • biomarkers
  • precision medicine
  • diagnosis
  • decision making
  • artificial intelligence

Published Papers (2 papers)

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Research

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13 pages, 4169 KiB  
Article
Prediction of Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using Amyloid PET and Brain MR Imaging Data: A 48-Month Follow-Up Analysis of the Alzheimer’s Disease Neuroimaging Initiative Cohort
by Do-Hoon Kim, Minyoung Oh and Jae Seung Kim
Diagnostics 2023, 13(21), 3375; https://doi.org/10.3390/diagnostics13213375 - 02 Nov 2023
Viewed by 1001
Abstract
We developed a novel quantification method named “shape feature” by combining the features of amyloid positron emission tomography (PET) and brain magnetic resonance imaging (MRI) and evaluated its significance in predicting the conversion from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) in [...] Read more.
We developed a novel quantification method named “shape feature” by combining the features of amyloid positron emission tomography (PET) and brain magnetic resonance imaging (MRI) and evaluated its significance in predicting the conversion from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. From the ADNI database, 334 patients with MCI were included. The brain amyloid smoothing score (AV45_BASS) and brain atrophy index (MR_BAI) were calculated using the surface area and volume of the region of interest in AV45 PET and MRI. During the 48-month follow-up period, 108 (32.3%) patients converted from MCI to AD. Age, Mini-Mental State Examination (MMSE), cognitive subscale of the Alzheimer’s Disease Assessment Scale (ADAS-cog), apolipoprotein E (APOE), standardized uptake value ratio (SUVR), AV45_BASS, MR_BAI, and shape feature were significantly different between converters and non-converters. Univariate analysis showed that age, MMSE, ADAS-cog, APOE, SUVR, AV45_BASS, MR_BAI, and shape feature were correlated with the conversion to AD. In multivariate analyses, high shape feature, SUVR, and ADAS-cog values were associated with an increased risk of conversion to AD. In patients with MCI in the ADNI cohort, our quantification method was the strongest prognostic factor for predicting their conversion to AD. Full article
(This article belongs to the Special Issue Application of Translational Neuromodeling for Diagnostics in Neurological Disorders)
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Review

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35 pages, 12185 KiB  
Review
Tumor-like Lesions in Primary Angiitis of the Central Nervous System: The Role of Magnetic Resonance Imaging in Differential Diagnosis
by Marialuisa Zedde, Manuela Napoli, Claudio Moratti, Claudio Pavone, Lara Bonacini, Giovanna Di Cecco, Serena D’Aniello, Ilaria Grisendi, Federica Assenza, Grégoire Boulouis, Thanh N. Nguyen, Franco Valzania and Rosario Pascarella
Diagnostics 2024, 14(6), 618; https://doi.org/10.3390/diagnostics14060618 - 14 Mar 2024
Viewed by 659
Abstract
Primary Angiitis of the Central Nervous System (PACNS) is a rare disease and its diagnosis is a challenge for several reasons, including the lack of specificity of the main findings highlighted in the current diagnostic criteria. Among the neuroimaging pattern of PACNS, a [...] Read more.
Primary Angiitis of the Central Nervous System (PACNS) is a rare disease and its diagnosis is a challenge for several reasons, including the lack of specificity of the main findings highlighted in the current diagnostic criteria. Among the neuroimaging pattern of PACNS, a tumefactive form (t-PACNS) is a rare subtype and its differential diagnosis mainly relies on neuroimaging. Tumor-like mass lesions in the brain are a heterogeneous category including tumors (in particular, primary brain tumors such as glial tumors and lymphoma), inflammatory (e.g., t-PACNS, tumefactive demyelinating lesions, and neurosarcoidosis), and infectious diseases (e.g., neurotoxoplasmosis). In this review, the main features of t-PACNS are addressed and the main differential diagnoses from a neuroimaging perspective (mainly Magnetic Resonance Imaging—MRI—techniques) are described, including conventional and advanced MRI. Full article
(This article belongs to the Special Issue Application of Translational Neuromodeling for Diagnostics in Neurological Disorders)
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