Ardalan Aarabi

Ardalan Aarabi
Université de Picardie Jules Verne | UPJV · LNFP, Laboratoire de neurosciences fonctionnelles et pathologies

PhD
Biomedical image and signal analysis, Computational modelling of brain dynamics, Brain imaging and connectivity analysis

About

112
Publications
7,014
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
1,745
Citations
Citations since 2016
58 Research Items
1281 Citations
2016201720182019202020212022050100150200250
2016201720182019202020212022050100150200250
2016201720182019202020212022050100150200250
2016201720182019202020212022050100150200250
Introduction
Associate Professor in Neuroscience at University of Picardie-Jules Verne (UPJV) School of Medicine ------ Academic Affiliations: Neuroscience & Biomedical Engineering ------ Current research interests: studying brain dynamics and function under pathological and physiological conditions in adults and children. exploring the biological significance of the brain networks through functional and structural connectivity analyses in adults and children

Publications

Publications (112)
Article
Background and objectives Although action slowing is the main cognitive impairment in stroke survivors, its mechanisms and determinants are still poorly understood. The objectives of the present study were to determine the mechanisms of post-stroke action slowing (using validated, highly specific simple reaction time (SRT) and tapping tests) and id...
Article
Background: Neurocognitive disorders (NCDs) are a part of the post-acute coronavirus disease (COVID-19) syndrome. No study has specifically evaluated NCDs in post-acute COVID-19 patients with cognitive complaints or their MRI determinants. Objective: To characterize NCDs in post-acute COVID-19 patients with cognitive complaints. The secondary ob...
Article
Introduction Bien que le ralentissement de l’action soit le trouble cognitif le plus fréquent après un accident vasculaire cérébral (AVC), ses mécanismes et déterminants lésionnels sont encore peu connus. Objectifs Cette étude vise à déterminer les mécanismes du ralentissement à l’aide de tests élémentaires, ainsi que ses déterminants lésionnels à...
Article
Introduction Les troubles neurocognitifs (TNC) entraînent des changements dans la structure et les fonctions du cerveau. Les troubles exécutifs ont une fréquence élevée et peuvent apparaître précocement, les composantes clefs étant l’altération de l’initiation et de la vitesse d’action. Objectifs L’objectif de cette étude était d’explorer l’associ...
Article
Introduction Les troubles neurocognitifs (TNC) font partie du syndrome post-« coronavirus disease » (COVID-19). Aucune étude n’a évalué spécifiquement les TNC chez les patients ayant une plainte cognitive post-COVID-19 ainsi que leurs déterminants IRM. Objectifs Notre objectif principal était de caractériser les TNC chez les patients ayant une pla...
Article
Introduction L’apathie concerne 1/3 des patients après un AVC. Malgré sa fréquence et ses conséquences, ses déterminants ne sont que partiellement définis. La difficulté consiste à différentier l’hypoactivité liée à l’apathie de celle secondaire à des comorbidités. Objectifs Nous avons cherché à examiner la prévalence de l’apathie, à identifier le...
Article
Full-text available
Abstract Cue-induced drug craving and disinhibition are two essential components of continued drug use and relapse in substance use disorders. While these phenomena develop and interact across time, the temporal dynamics of their underlying neural activity remain under-investigated. To explore these dynamics, an analysis of time-varying activation...
Article
Background: The present study aimed at determining the contributions of background disorders responsible for participation restriction as indexed by a structured interview for the modified Rankin scale (mRS-SI). Methods: A subset of 256 patients was assessed at 6 months after stroke using the National Institutes of Health Stroke Scale (NIHSS), g...
Article
Attention Deficit/Hyperactivity Disorder (ADHD) is characterized by inattention, hyperactivity and impulsivity. In this study, we investigated group differences in dynamic functional connectivity between 113 children with inattentive (46 ADHDI) and combined (67 ADHDC) ADHD and 76 typically developing (TD) children using resting state fMRI data. For...
Article
Full-text available
Growing evidence indicates that disruptions in the brain’s functional connectivity play an important role in the pathophysiology of ADHD. The present study investigates alterations in resting-state EEG source connectivity and rich-club organization in children with inattentive (ADHDI) and combined (ADHDC) ADHD compared with typically developing chi...
Preprint
Full-text available
Cue-induced drug craving and disinhibition are two essential components of continued drug use and relapse in substance use disorders. While these two phenomena develop and interact across time, the temporal dynamics of their underlying neural activity and their interaction remain under-investigated. To explore these dynamics, an analysis of time-va...
Article
Background Brain tumor extraction from magnetic resonance (MR) images is challenging due to variations in the location, shape, size and intensity of tumors. Manual delineation of brain tumors from MR images is time-consuming and prone to human errors. Method In this paper, we present a method for automatic tumor extraction from multimodal MR image...
Article
Full-text available
The majority of network studies of human brain structural connectivity are based on single-shell diffusion-weighted imaging (DWI) data. Recent advances in imaging hardware and software capabilities have made it possible to acquire multishell (b-values) high-quality data required for better characterization of white-matter crossing-fiber microstruct...
Article
Objective Our understanding of early brain development is limited due to rapid changes in white matter pathways after birth. In this study, we introduced a multi-scale cross-modal approach to investigate the rich club (RC) organization and topology of the structural brain networks in 40 healthy neonates using diffusion-weighted imaging and resting-...
Article
Full-text available
Diffusion-tensor-MRI was performed on 28 term born neonates. For each hemisphere, we quantified separately the axial and the radial diffusion (AD, RD), the apparent diffusion coefficient (ADC) and the fractional anisotropy (FA) of the thalamo-cortical pathway (THC) and four structures: thalamus (TH), putamen (PT), caudate nucleus (CN) and globus-pa...
Article
Full-text available
Background: We compared the density and duration of sleep spindles topographically in stage 2 and 3 of non-rapid eye movement sleep (N2 and N3) among adults diagnosed with Obstructive Sleep Apnea Syndrome (OSAS) and healthy controls. Materials and methods: Thirty-one individuals with OSAS (mean age: 48.50 years) and 23 healthy controls took part i...
Article
Objective: Neonatal EEG source localization is highly prone to errors due to head modelling deficiencies. In this study, we investigated the effect of head model complexities on the accuracy of EEG source localization in full term neonates using a realistic volume conductor head model. Approach: We performed numerical simulations to investigate...
Article
Full-text available
Brain extraction is one of the most important preprocessing steps in cerebral magnetic resonance (MR) image analysis. Brain extraction from neonatal MR images is particularly challenging due to significant differences in head size and shape between neonates and rapid changes in neonatal brain structure in the weeks and months after birth. In this w...
Article
Objective This study investigated age-dependent and subtype-related alterations in electroencephalography (EEG) power spectra and current source densities (CSD) in children with attention deficit and hyperactivity disorder (ADHD). Methods We performed spectral and cortical source (exact low-resolution electromagnetic tomography, eLORETA) analyses...
Article
Objective: Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve the accuracy of sleep stage classification in term neonates. Approach: The classification performan...
Article
6th Congress of the European-Academy-of-Neurology (EAN), ELECTR NETWORK, MAY 23-26, 2020
Article
Compared to adults, brain extraction from magnetic resonance (MR) images of newborn infants is particularly challenging due to their smaller brain size, which causes lower spatial resolution, lower tissue contrast and ambiguous tissue intensity distribution. In this work, a multi-atlas patch-based label fusion method is presented for automatic brai...
Article
The reliability assessment of connectivity measures and graph metrics is crucial for characterizing topological properties of resting-state brain networks that are intrinsic to the functioning of the brain and not biased by variability across subjects and data lengths. In this study, we investigated the effect of data length on the reliability and...
Article
Full-text available
Multilevel thresholding using Otsu or Kapur methods is widely used in the context of image segmentation. These methods select optimal thresholds in gray level images by maximizing between-class variance or entropy criterion. These methods become time consuming and less efficient with increasing number of thresholds. To increase the efficiency of th...
Article
Objective: Sleep stage scoring is essential for diagnosing sleep disorders. Visual scoring of sleep stages is very time-consuming and prone to human errors. In this work, we introduce an efficient approach to improve the accuracy of sleep stage scoring and classification for sleep analysis. Method: In this approach, a set of optimal features was...
Conference Paper
Full-text available
The goal of EEG source localization is to non-invasively localize active sources within the brain by first solving the so-called forward problem, in which the electric potentials produced by the active sources are computed at measurement sites on the scalp. For accurate EEG forward modeling, one requires a realistic head model reconstructed through...
Conference Paper
Graph theoretical approaches have proven to be very useful for characterizing the functional structure of brain networks using resting state fNIRS data. Nevertheless, it is still not clearly understood how data length and type of functional connectivity (FC) measures bias the topology of reconstructed functional brain networks. In this study, we in...
Article
Dimensionality reduction is an important preprocessing step to improve the performance of machine learning algorithms. Feature selection methods can efficiently speed up the learning process and improve the overall classification accuracy by reducing the computational complexity. Among the feature selection methods, multivariate methods are more ef...
Conference Paper
In this study, we investigated the effect of data length on the reliability and stability of three functional connectivity (FC) measures, Pearson’s correlation (PC), mutual information (MI) and partial correlation (PtC) for data lengths ranging from 0.5 to 4.5 min.
Article
Objective The accuracy of electroencephalography source reconstruction largely depends on the accuracy of head model, which, in turn, is dependent on the geometry and electrical properties of head tissues. Methods Based on a MR/CT coregistered images of one neonate we created a highly realistic 6 compartment head model. using a state of art finite...
Article
Full-text available
Interictal spikes can be generated by blocking GABAA receptor-mediated inhibition. The nature of the hemodynamic activities associated with interictal spikes in acute models of focal epilepsy based on GABA deactivation has not been determined. We analyzed systemic changes in hemodynamic signals associated with interictal spikes generated by acute m...
Article
Objective We evaluated the performance of our previously developed seizure prediction approach on thirty eight seizures from ten patients with focal hippocampal epilepsy. Methods The seizure prediction system was developed based on the extraction of correlation dimension, correlation entropy, noise level, Lempel-Ziv complexity, largest Lyapunov ex...
Article
Slow and rapid event-related designs are used in fMRI and functional near-infrared spectroscopy (fNIRS) experiments to temporally characterize the brain hemodynamic response to discrete events. Conventional averaging (CA) and the deconvolution method (DM) are the two techniques commonly used to estimate the Hemodynamic Response Function (HRF) profi...
Article
Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique used to measure changes in oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) in the brain. In this study, we present a decomposition approach based on single-channel independent component analysis (scICA) to investigate the contribution of phys...
Article
Full-text available
In this study, we investigated the impact of uncertainty in head tissue conductivities and inherent geometrical complexities including fontanels in neonates. Based on MR and CT coregistered images, we created a realistic neonatal head model consisting of scalp, skull, fontanels, cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM). Us...
Article
Full-text available
In this study, we investigated changes in functional connectivity (FC) of the brain networks in patients with benign epilepsy with centrotemporal spikes (BECTS) compared to healthy controls using high-density EEG data collected under eyes-closed resting state condition. EEG source reconstruction was performed with exact Low Resolution Electromagnet...
Article
Objectives There is increasing evidence that epilepsy might alter the brain connectivities. We investigated the changes in the brain connectivity in children with benign childhood epilepsy with centro-temporal spikes. Methods High density EEG data recorded from 8 patients and age-matched 9 controls with eyes closed. For controls five segments and...
Article
Objective In this study, we looked at the influence of temporal theta burst using the power asymmetry and connectivity analyzes on the cortical networks. Methods 16 preterms (27 ± 1.2 weeks) 8-channel EEG data were analyzed. We selected segments with bilateral (T3/T4) burst, unilateral burst from right (T4) and left (T3) hemispheres. We computed t...
Article
Full-text available
There is growing evidence that brain networks are altered in epileptic subjects. In this study, we investigated the functional connectivity and brain network properties of benign childhood epilepsy with centrotemporal spikes using graph theory. Benign childhood epilepsy with centrotemporal spikes is the most common form of idiopathic epilepsy in yo...
Article
Full-text available
Benign epilepsy with centrotemporal spikes (BECTS) is the most common idiopathic childhood epilepsy, which is often associated with developmental disorders in children. In the present study, we analyzed resting state EEG spectral changes in the sensor and source spaces in eight BECTS patients compared with nine age-matched controls. Using high-reso...
Conference Paper
Full-text available
Background / Purpose: Benign childhood epilepsy with centrotemporal spikes (BCECTS) is the most common idiopathic epileptic syndrome with prevalence of approximately 15% in children with seizures. Cognitive impairments in memory and language are often associated with BCECTS. Functional connectivity analysis may provide key information to better u...
Article
Question: Benign childhood epilepsy with centrotemporal spikes (BCECTS) is the most common idiopathic epileptic syndrome in childhood with characteristic EEG manifestations. In this study, we investigated the effect of interictal spikes on the resting state networks in children with BCECTS. Methods: Nine children with right centrotemporal spikes an...
Article
The aim of this study is to develop a model based seizure prediction method. A neural mass model was used to simulate the macro-scale dynamics of intracranial EEG data. The model was composed of pyramidal cells, excitatory and inhibitory interneurons described through state equations. Twelve model's parameters were estimated by fitting the model to...