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Brain Sciences
Special Issues
Biomarkers for Early Diagnosis of Dementia
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Biomarkers for Early Diagnosis of Dementia

A special issue of Brain Sciences (ISSN 2076-3425).

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 8406

Special Issue Editors

Prof. Dr. Notger G. Müller

E-Mail Website
Guest Editor
Research Group Degenerative and Chronic Diseases, Movement, Faculty of Health Sciences, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany
Interests: dementia; mild cognitive impairment; exercise; cognitive training; non-invasive brain stimulation; neuroimaging
Special Issues, Collections and Topics in MDPI journals
Dr. Marlen Schmicker

E-Mail Website
Guest Editor
Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Magdeburg, Germany
Interests: Cognitive Neuroscience; Neuroimaging; Memory; Cognitive Neuropsychology
Dr. Wenzel Glanz

E-Mail Website
Guest Editor
Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Magdeburg, Germany
Interests: Neurologe

Special Issue Information

Dear Colleagues,

Early (and accurate) diagnosis of dementia is essential both to patients and to research. For the patients, an early diagnosis can help them take control of their condition, plan for the future and live well with dementia. For research, early diagnosis has become a major issue since so many clinical trials with new, potentially disease-modifying drugs have failed in patients at later stages of disease, i.e. when the clinical symptoms of dementia are already interfering with everyday life. It is now widely believed that at this late stage the pathological brain changes and cognitive impairments are already irreversible. Hence, there is a strong need to detect signs of dementia (especially Alzheimer's) earlier, namely at the preclinical or prodromal stage when the typical symptoms are yet to emerge. This early detection is necessary in order for any upcoming disease modifying therapy or prevention approach to be effective. In order to account for this clinical need we have launched this Special Issue. The issue aims at identifying new methods for the early (i.e. sensitive) and accurate (i.e. specific) detection of dementias of all types. The methods can come from various fields, e.g. neuroimaging, biomarkers from blood or CSF, as well as new, more elaborate tests to detect early cognitive deficits. The cost-effectiveness and side effects of the suggested methods are also important factors that need to be considered.

Prof. Dr. Notger Müller
Dr. Marlen Schmicker
Dr. Wenzel Glanz
Guest Editors

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

  • dementia
  • diagnosis
  • biomarker
  • cognitive impairments
  • Alzheimer
  • neuroimaging

Published Papers (2 papers)

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Research

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34 pages, 8019 KiB  
Article
Computer-Aided Diagnosis System of Alzheimer’s Disease Based on Multimodal Fusion: Tissue Quantification Based on the Hybrid Fuzzy-Genetic-Possibilistic Model and Discriminative Classification Based on the SVDD Model
by Lilia Lazli, Mounir Boukadoum and Otmane Ait Mohamed
Brain Sci. 2019, 9(10), 289; https://doi.org/10.3390/brainsci9100289 - 22 Oct 2019
Cited by 22 | Viewed by 3859
Abstract
An improved computer-aided diagnosis (CAD) system is proposed for the early diagnosis of Alzheimer’s disease (AD) based on the fusion of anatomical (magnetic resonance imaging (MRI)) and functional (8F-fluorodeoxyglucose positron emission tomography (FDG-PET)) multimodal images, and which helps to address the [...] Read more.
An improved computer-aided diagnosis (CAD) system is proposed for the early diagnosis of Alzheimer’s disease (AD) based on the fusion of anatomical (magnetic resonance imaging (MRI)) and functional (8F-fluorodeoxyglucose positron emission tomography (FDG-PET)) multimodal images, and which helps to address the strong ambiguity or the uncertainty produced in brain images. The merit of this fusion is that it provides anatomical information for the accurate detection of pathological areas characterized in functional imaging by physiological abnormalities. First, quantification of brain tissue volumes is proposed based on a fusion scheme in three successive steps: modeling, fusion and decision. (1) Modeling which consists of three sub-steps: the initialization of the centroids of the tissue clusters by applying the Bias corrected Fuzzy C-Means (FCM) clustering algorithm. Then, the optimization of the initial partition is performed by running genetic algorithms. Finally, the creation of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) tissue maps by applying the Possibilistic FCM clustering algorithm. (2) Fusion using a possibilistic operator to merge the maps of the MRI and PET images highlighting redundancies and managing ambiguities. (3) Decision offering more representative anatomo-functional fusion images. Second, a support vector data description (SVDD) classifier is used that must reliably distinguish AD from normal aging and automatically detects outliers. The “divide and conquer” strategy is then used, which speeds up the SVDD process and reduces the load and cost of the calculating. The robustness of the tissue quantification process is proven against noise (20% level), partial volume effects and when inhomogeneities of spatial intensity are high. Thus, the superiority of the SVDD classifier over competing conventional systems is also demonstrated with the adoption of the 10-fold cross-validation approach for synthetic datasets (Alzheimer disease neuroimaging (ADNI) and Open Access Series of Imaging Studies (OASIS)) and real images. The percentage of classification in terms of accuracy, sensitivity, specificity and area under ROC curve was 93.65%, 90.08%, 92.75% and 97.3%; 91.46%, 92%, 91.78% and 96.7%; 85.09%, 86.41%, 84.92% and 94.6% in the case of the ADNI, OASIS and real images respectively. Full article
(This article belongs to the Special Issue Biomarkers for Early Diagnosis of Dementia)
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Review

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22 pages, 399 KiB  
Review
Biomarker-Based Signature of Alzheimer’s Disease in Pre-MCI Individuals
by Elena Chipi, Nicola Salvadori, Lucia Farotti and Lucilla Parnetti
Brain Sci. 2019, 9(9), 213; https://doi.org/10.3390/brainsci9090213 - 23 Aug 2019
Cited by 15 | Viewed by 4018
Abstract
Alzheimer’s disease (AD) pathology begins decades before the onset of clinical symptoms. It is recognized as a clinicobiological entity, being detectable in vivo independently of the clinical stage by means of pathophysiological biomarkers. Accordingly, neuropathological studies that were carried out on healthy elderly [...] Read more.
Alzheimer’s disease (AD) pathology begins decades before the onset of clinical symptoms. It is recognized as a clinicobiological entity, being detectable in vivo independently of the clinical stage by means of pathophysiological biomarkers. Accordingly, neuropathological studies that were carried out on healthy elderly subjects, with or without subjective experience of cognitive decline, reported evidence of AD pathology in a high proportion of cases. At present, mild cognitive impairment (MCI) represents the only clinically diagnosed pre-dementia stage. Several attempts have been carried out to detect AD as early as possible, when subtle cognitive alterations, still not fulfilling MCI criteria, appear. Importantly, pre-MCI individuals showing the positivity of pathophysiological AD biomarkers show a risk of progression similar to MCI patients. In view of successful treatment with disease modifying agents, in a clinical setting, a timely diagnosis is mandatory. In clinical routine, biomarkers assessment should be taken into consideration whenever a subject with subtle cognitive deficits (pre-MCI), who is aware of his/her decline, requests to know the cause of such disturbances. In this review, we report the available neuropsychological and biomarkers data that characterize the pre-MCI patients, thus proposing pre-MCI as the first clinical manifestation of AD. Full article
(This article belongs to the Special Issue Biomarkers for Early Diagnosis of Dementia)
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