Nikolaos Grigoriadis

Prof. Dr. Nikolaos Grigoriadis

Multiple Sclerosis Center, AHEPA, 2nd Neurological University Department, General Hospital of Thessaloniki, Aristotle University of Thessaloniki, Thessaloniki, Greece
orcid

Research Focus & Metrics

Academic Career Length

25Years (2002-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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27 May 2023 in Data
Verified
Journal Article
Open access
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AMAndreas Miltiadous
Andreas Miltiadous
Katerina D. TzimourtaKaterina D. Tzimourta
Katerina D. Tzimourta
Theodora AfrantouTheodora Afrantou
Theodora Afrantou
Panagiotis IoannidisPanagiotis Ioannidis
Panagiotis Ioannidis
NGNikolaos Grigoriadis
Nikolaos Grigoriadis
Alexandros T TzallasAlexandros T Tzallas
Alexandros T Tzallas
Recently, there has been a growing research interest in utilizing the electroencephalogram (EEG) as a non-invasive diagnostic tool for neurodegenerative diseases. This article provides a detailed description of a resting-state EEG dataset of individuals with Alzheimer’s disease and frontotemporal dementia, and healthy controls. The dataset was collected using a clinical EEG system with 19 scalp electrodes while participants were in a resting state with their eyes closed. The data collection process included rigorous quality control measures to ensure data accuracy and consistency. The dataset contains recordings of 36 Alzheimer’s patients, 23 frontotemporal dementia patients, and 29 healthy age-matched subjects. For each subject, the Mini-Mental State Examination score is reported. A monopolar montage was used to collect the signals. A raw and preprocessed EEG is included in the standard BIDS format. For the preprocessed signals, established methods such as artifact subspace reconstruction and an independent component analysis have been employed for denoising. The dataset has significant reuse potential since Alzheimer’s EEG Machine Learning studies are increasing in popularity and there is a lack of publicly available EEG datasets. The resting-state EEG data can be used to explore alterations in brain activity and connectivity in these conditions, and to develop new diagnostic and treatment approaches. Additionally, the dataset can be used to compare EEG characteristics between different types of dementia, which could provide insights into the underlying mechanisms of these conditions.

16 September 2015 in European Journal of Neurology
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Journal Article
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NGNikolaos Grigoriadis
Nikolaos Grigoriadis
Vincent van PeschVincent van Pesch
Vincent van Pesch

9 March 2022 in Nutrients
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Journal Article
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PSPanagiotis Stoiloudis
Panagiotis Stoiloudis
EKEvangelia Kesidou
Evangelia Kesidou
Christos BakirtzisChristos Bakirtzis
Christos Bakirtzis
SSStyliani-Aggeliki Sintila
Styliani-Aggeliki Sintila
NKNatalia Konstantinidou
Natalia Konstantinidou
NGNikolaos Grigoriadis
Nikolaos Grigoriadis
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS) characterized by inflammation and neurodegeneration. The most prominent clinical features include visual loss and sensorimotor symptoms and mainly affects those of young age. Some of the factors affecting its pathogenesis are genetic and/or environmental including viruses, smoking, obesity, and nutrition. Current research provides evidence that diet may influence MS onset, course, and quality of life of the patients. In this review, we address the role of nutrition on MS pathogenesis as well as dietary interventions that show promising beneficial results with respect to MS activity and progression. Investigation with large prospective clinical studies is required in order to thoroughly evaluate the role of diet in MS.

1 July 2011 in Experimental Neurology
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Journal Article
Closed access
NGNikolaos Grigoriadis
Nikolaos Grigoriadis
ALAthanasios Lourbopoulos
Athanasios Lourbopoulos
RLRoza Lagoudaki
Roza Lagoudaki
JFJosa M Frischer
Josa M Frischer
EPEleni Polyzoidou
Eleni Polyzoidou
HLHans Lassmann
Hans Lassmann

14 April 2019 in Brain Sciences
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Journal Article
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Katerina D. TzimourtaKaterina D. Tzimourta
Katerina D. Tzimourta
Nikolaos GiannakeasNikolaos Giannakeas
Nikolaos Giannakeas
Alexandros T TzallasAlexandros T Tzallas
Alexandros T Tzallas
LALoukas Astrakas
Loukas Astrakas
Theodora AfrantouTheodora Afrantou
Theodora Afrantou
Markos G. TsipourasMarkos G. Tsipouras
Markos G. Tsipouras
Alzheimer’s Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, β, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems.

14 April 2020 in Brain Sciences
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MBMarina Kleopatra Boziki
Marina Kleopatra Boziki
EKEvangelia Kesidou
Evangelia Kesidou
Paschalis TheotokisPaschalis Theotokis
Paschalis Theotokis
AMAlexios-Fotios Mentis
Alexios-Fotios Mentis
EKELENI KARAFOULIDOU
ELENI KARAFOULIDOU
NGNikolaos Grigoriadis
Nikolaos Grigoriadis
An increase of multiple sclerosis (MS) incidence has been reported during the last decade, and this may be connected to environmental factors. This review article aims to encapsulate the current advances targeting the study of the gut–brain axis, which mediates the communication between the central nervous system and the gut microbiome. Clinical data arising from many research studies, which have assessed the effects of administered disease-modifying treatments in MS patients to the gut microbiome, are also recapitulated.