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Translational Advances in Neurodegenerative Dementias, Second Edition
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Laboratory of Experimental Neuropsychophysiology, Non-Invasive Brain Stimulation Unit, Clinical and Behavioral Neurology Department, IRCCS Fondazione Santa Lucia, Rome, Italy
Unit of Clinical Neurology, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
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Translational Advances in Neurodegenerative Dementias, Second Edition

Abstract submission deadline
30 June 2026
Manuscript submission deadline
30 September 2026
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Topic Information

Dear Colleagues,

Neurodegenerative dementias encompass a heterogeneous group of chronic conditions marked by progressive cognitive decline, ultimately leading to loss of autonomy and, eventually, death. The prevalence of these disorders is rapidly rising, posing severe social, economic, and healthcare challenges. Despite extensive research efforts, their pathophysiological mechanisms remain elusive. Furthermore, diagnosis and prognostic assessment are particularly complex due to their phenotypic heterogeneity. This Topic aims to bridge translational gaps by gathering the latest advances across multiple disciplines, including neurobiology, clinical neurology, neuroimaging, chronobiology, and innovative therapeutic strategies. Recent breakthroughs in genetics, neuropathology, neurophysiology, and non-invasive brain stimulation have provided novel insights into disease mechanisms and potential interventions. Sleep and circadian rhythm disturbances are also gaining recognition as key factors influencing disease progression. By integrating original research and comprehensive reviews from diverse yet complementary fields, this Topic seeks to provide a state-of-the-art overview of neurodegenerative dementias, fostering interdisciplinary collaboration to refine diagnostics and accelerate therapeutic innovation.

Dr. Francesco Di Lorenzo
Dr. Annibale Antonioni
Topic Editors

Keywords

  • neurodegenerative diseases
  • Alzheimer's disease (AD)
  • frontotemporal dementia (FTD)
  • Lewy body dementia (LBD)
  • prion diseases
  • noninvasive brain stimulation techniques (NIBS)
  • chronobiology
  • sleep
  • biomarkers

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Brain Sciences
brainsci 		2.8 5.6 2011 16.2 Days CHF 2200 Submit
Clocks & Sleep
clockssleep 		2.1 4.2 2019 37 Days CHF 1600 Submit
Neurology International
neurolint 		3.0 4.8 2009 21.4 Days CHF 1800 Submit
NeuroSci
neurosci 		2.0 - 2020 27.1 Days CHF 1200 Submit

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Published Papers (1 paper)

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21 pages, 1561 KiB  
Article
A Multimodal Multi-Stage Deep Learning Model for the Diagnosis of Alzheimer’s Disease Using EEG Measurements
by Tuan Vo, Ali K. Ibrahim and Hanqi Zhuang
Neurol. Int. 2025, 17(6), 91; https://doi.org/10.3390/neurolint17060091 - 13 Jun 2025
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Abstract
Background/Objectives: Alzheimer’s disease (AD) is a progressively debilitating neurodegenerative disorder characterized by the accumulation of abnormal proteins, such as amyloid-beta plaques and tau tangles, leading to disruptions in memory storage and neuronal degeneration. Despite its portability, non-invasiveness, and cost-effectiveness, electroencephalography (EEG) as a [...] Read more.
Background/Objectives: Alzheimer’s disease (AD) is a progressively debilitating neurodegenerative disorder characterized by the accumulation of abnormal proteins, such as amyloid-beta plaques and tau tangles, leading to disruptions in memory storage and neuronal degeneration. Despite its portability, non-invasiveness, and cost-effectiveness, electroencephalography (EEG) as a diagnostic tool for AD faces challenges due to its susceptibility to noise and the complexity involved in the analysis. Methods: This study introduces a novel methodology employing three distinct stages for data-driven AD diagnosis: signal pre-processing, frame-level classification, and subject-level classification. At the frame level, convolutional neural networks (CNNs) are employed to extract features from spectrograms, scalograms, and Hilbert spectra. These features undergo fusion and are then fed into another CNN for feature selection and subsequent frame-level classification. After each frame for a subject is classified, a procedure is devised to determine if the subject has AD or not. Results: The proposed model demonstrates commendable performance, achieving over 80% accuracy, 82.5% sensitivity, and 81.3% specificity in distinguishing AD patients from healthy individuals at the subject level. Conclusions: This performance enables early and accurate diagnosis with significant clinical implications, offering substantial benefits over the existing methods through reduced misdiagnosis rates and improved patient outcomes, potentially revolutionizing AD screening and diagnostic practices. However, the model’s efficacy diminishes when presented with data from frontotemporal dementia (FTD) patients, emphasizing the need for further model refinement to address the intricate nuances associated with the simultaneous detection of various neurodegenerative disorders alongside AD. Full article
(This article belongs to the Topic Translational Advances in Neurodegenerative Dementias, Second Edition)
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