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Brain Sciences
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Alzheimer’s Disease: Symptoms, Early Diagnosis, Treatment, and Prevention
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Alzheimer’s Disease: Symptoms, Early Diagnosis, Treatment, and Prevention

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurodegenerative Diseases".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 2470

Special Issue Editor

Dr. Vasileios Papaliagkas

E-Mail Website
Guest Editor
Department of Biomedical Sciences, Alexander Campus, International Hellenic University, P.O. Box 141, Sindos, 57400 Thessaloniki, Greece
Interests: Alzheimer’s disease; clinical neurophysiology; mild cognitive impairment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease, characterized by progressive memory impairment, and functional deterioration, and it is believed to affect over 55 million people worldwide. Despite significant advances in neuroscience and clinical practice, early diagnosis and effective therapeutic strategies remain limited. 

This Special Issue, “Alzheimer’s Disease: Symptoms, Early Diagnosis, Treatment, and Prevention”, welcomes original articles, reviews, and case reports that address the multifactorial nature of AD. We invite contributions that include clinical and neuropathological characterization, advances in neuroimaging, neurophysiology, and other biomarkers, and innovative diagnostic tools for early detection. Equally emphasized are pharmacological and non-pharmacological interventions, lifestyle and dietary approaches, and preventive strategies aimed at delaying onset or mitigating progression. 

Dr. Vasileios Papaliagkas
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 250 words) can be sent to the Editorial Office for assessment.

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

  • Alzheimer’s disease
  • mild cognitive impairment
  • clinical neurophysiology
  • biomarkers
  • diagnosis
  • prevention

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Published Papers (3 papers)

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Research

14 pages, 4003 KB  
Article
Integrated Analysis of Cerebral Small Vessel Disease and Facial Soft-Tissue Markers in the Alzheimer’s Disease Continuum
by Caterina Bernetti, Gianfranco Di Gennaro, Roberta Roberti, Milena Ricci, Francesco Pipitone, Marta Profilo, Francesco Motolese, Rosalinda Calandrelli, Fabio Pilato, Vincenzo Di Lazzaro, Bruno Beomonte Zobel and Carlo Augusto Mallio
Brain Sci. 2026, 16(4), 403; https://doi.org/10.3390/brainsci16040403 - 9 Apr 2026
Viewed by 481
Abstract
Objective: To investigate the integrated relationship between Cerebral Small Vessel Disease (CSVD) markers and quantitative facial soft-tissue measurements in Alzheimer’s disease (AD) continuum, utilizing peripheral muscle health as a potential biomarker for systemic frailty and neurodegeneration. Methods: Retrospective analysis of 3T brain MRI [...] Read more.
Objective: To investigate the integrated relationship between Cerebral Small Vessel Disease (CSVD) markers and quantitative facial soft-tissue measurements in Alzheimer’s disease (AD) continuum, utilizing peripheral muscle health as a potential biomarker for systemic frailty and neurodegeneration. Methods: Retrospective analysis of 3T brain MRI data from 67 patients (AD, N = 45; Mild Cognitive Impairment [MCI], N = 22). CSVD markers were assessed using STRIVE and standardized scales (Fazekas, Potter). Facial soft-tissue metrics, including masseter and tongue volume, temporal muscle thickness (TMT), and fat infiltration (Mercuri Scale), were quantified via semi-automatic segmentation on T1-weighted sequences. Group comparisons (AD vs. MCI) used regression models adjusted for age and sex. The overall central–peripheral relationship was explored via Canonical Correlation Analysis (CCA). Results: The AD group showed a highly significant cognitive decline (MMSE: 23.2 ± 4.1 vs. 28.2 ± 1.4, p < 0.0001). Centrally, the presence of PVSs in the mesencephalic region was the most robust predictor for AD (p = 0.003). Peripherally, average masseter muscle volume was significantly lower in the AD group (p = 0.0273), and masseter fat infiltration was significantly higher (p = 0.025), supporting localized sarcopenia. The CCA demonstrated a statistically significant positive multivariate relationship (r = 0.51, Roy’s Largest Root p = 0.015) between a higher combined CSVD burden and a worse soft tissue profile across the cohort. Conclusions: Quantitative indices of facial soft tissues, particularly masseter muscle volume and quality, reflect systemic frailty and cognitive deterioration along the AD continuum. The strong central–peripheral correlation suggests that sarcopenia and CSVD are interconnected manifestations of a shared pathobiological process. These easily measurable facial markers could serve as valuable, non-invasive peripheral biomarkers, complementing traditional neuroimaging risk stratification in AD. Full article
(This article belongs to the Special Issue Alzheimer’s Disease: Symptoms, Early Diagnosis, Treatment, and Prevention)
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15 pages, 1115 KB  
Article
Alzheimer’s Disease Classification Using Population-Referenced Brain Volumetric Percentiles
by Jae Hyuk Shim and Hyeon-Man Baek
Brain Sci. 2026, 16(3), 334; https://doi.org/10.3390/brainsci16030334 - 20 Mar 2026
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Abstract
Background/Objectives: Translating brain volumetric biomarkers to individual-level Alzheimer’s disease (AD) diagnosis remains challenging due to difficulty interpreting raw volumes without longitudinal monitoring or matched controls. We tested a classification model using population-referenced volumetric percentiles to distinguish AD from cognitively normal (CN) subjects [...] Read more.
Background/Objectives: Translating brain volumetric biomarkers to individual-level Alzheimer’s disease (AD) diagnosis remains challenging due to difficulty interpreting raw volumes without longitudinal monitoring or matched controls. We tested a classification model using population-referenced volumetric percentiles to distinguish AD from cognitively normal (CN) subjects and evaluated its generalization across independent cohorts. Methods: Brain volumes from 95 regions were extracted using an automated segmentation pipeline and converted to age and sex adjusted percentiles using a reference population (N = 1833). A logistic regression classifier was trained on ADNI subjects (N = 873; AD = 183, CN = 690) split into training (60%), validation (20%), and test (20%) sets. The model was evaluated on two independent validation datasets: the held-out ADNI validation set and an external Korean cohort (N = 72; AD = 36, CN = 36) acquired with different scanner protocols and demographic characteristics. Results: The model achieved excellent discrimination across all evaluation sets: ADNI validation (AUC = 0.963, accuracy = 90.3%), ADNI test (AUC = 0.960, accuracy = 89.7%), and Korean external validation (AUC = 0.981, accuracy = 87.5%). The minimal validation gap (0.018) demonstrated robust generalization. Positive coefficients for ventricular regions reflected AD-associated atrophy patterns, while negative coefficients for medial temporal structures indicated their contribution within multivariate patterns distinguishing AD from normal aging. Conclusions: Population-referenced brain volumetric percentiles enable accurate AD classification with robust generalization across populations and scanner protocols. By contextualizing individual brain structure relative to normative populations while accounting for age and sex, this approach demonstrates potential for clinical translation as an accessible neuroimaging-based diagnostic tool. Full article
(This article belongs to the Special Issue Alzheimer’s Disease: Symptoms, Early Diagnosis, Treatment, and Prevention)
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15 pages, 242 KB  
Article
Factors Associated with the Social Behaviour of People with Alzheimer’s Dementia: A Video Observation Study
by Jasmine Shaw, Fern Rodgers, Deniz Eda Kavustu, Yuding Wang, Sarah Assaad, Gill Livingston and Andrew Sommerlad
Brain Sci. 2025, 15(11), 1205; https://doi.org/10.3390/brainsci15111205 - 8 Nov 2025
Viewed by 855
Abstract
Background/Objectives: People with Alzheimer’s dementia (AD) experience distressing changes in social behaviour. However, little is understood about whether social behaviour is associated with support provided by, or familiarity with, conversation partners. We aimed to explore the association between support provided by, and familiarity [...] Read more.
Background/Objectives: People with Alzheimer’s dementia (AD) experience distressing changes in social behaviour. However, little is understood about whether social behaviour is associated with support provided by, or familiarity with, conversation partners. We aimed to explore the association between support provided by, and familiarity with, conversation partners and the social behaviour of people with mild AD during conversation. Method: We designed an exploratory within-subjects study wherein conversations between 19 participants with mild AD and a familiar informant, followed by an unfamiliar researcher, were video-recorded and double-rated using two measures of social behaviour (Social Observation Inventory and Measure of Participation in Conversation—Dementia), and one measure of support from the conversation partner (Measure of Support in Conversation—Dementia). Multilevel linear regression with within-subject clusters was used to explore adjusted associations between support and familiarity and social behaviour. Results: Greater support in conversation was associated with more appropriate participation in social conversation of participants with AD. In fully adjusted models, every 1-point increase in MSC-D score was associated with a 0.29 (95% CI: 0.14 to 0.44) increase in MPC-D score and a 1.59 (95% CI: 0.87 to 2.32) increase in SOI score. Familiarity with the conversation partner was not associated with the social behaviour of the participants with AD. Conclusions: We found evidence for an association between social behaviour in AD and support provided by unimpaired conversation partners, but the numbers were small, and this should be interpreted cautiously. Future research should continue this hypothetical lead to expand our understanding of how support and familiarity influence social behaviour to inform potential interventions. Full article
(This article belongs to the Special Issue Alzheimer’s Disease: Symptoms, Early Diagnosis, Treatment, and Prevention)
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