Publications by authors named "Ardalan Aarabi"

31 Publications

Disrupted Functional Rich-Club Organization of the Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder, a Resting-State EEG Study.

Brain Sci 2021 Jul 16;11(7). Epub 2021 Jul 16.

Laboratory of Functional Neuroscience and Pathologies (LNFP, EA 4559), University Research Center (CURS), University Hospital, 80054 Amiens, France.

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 (ADHD) and combined (ADHD) ADHD compared with typically developing children (TD) under the eyes-closed condition. EEG source analysis was performed by eLORETA in different frequency bands. The lagged phase synchronization (LPS) and graph theoretical metrics were then used to examine group differences in the topological properties and rich-club organization of functional networks. Compared with the TD children, the ADHD children were characterized by a widespread significant decrease in delta and beta LPS, as well as increased theta and alpha LPS in the left frontal and right occipital regions. The ADHD children displayed significant increases in LPS in the central, temporal and posterior areas. Both ADHD groups showed small-worldness properties with significant increases and decreases in the network degree in the θ and β bands, respectively. Both subtypes also displayed reduced levels of network segregation. Group differences in rich-club distribution were found in the central and posterior areas. Our findings suggest that resting-state EEG source connectivity analysis can better characterize alterations in the rich-club organization of functional brain networks in ADHD patients.
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http://dx.doi.org/10.3390/brainsci11070938DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305540PMC
July 2021

Morphological active contour model for automatic brain tumor extraction from multimodal magnetic resonance images.

J Neurosci Methods 2021 10 21;362:109296. Epub 2021 Jul 21.

Laboratory of Functional Neuroscience and Pathologies (LNFP EA4559), University Research Center (CURS), University Hospital, Amiens, France; Faculty of Medicine, University of Picardy Jules Verne, Amiens, France.

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 images. Brain tumors are first detected using k-means clustering. A morphological region-based active contour model is then used for tumor extraction using an initial contour defined based on the boundary of the detected brain tumor regions. The contour evolution for tumor extraction was performed using successive application of morphological operators. In our model, a Gaussian distribution was used to model local image intensities. The spatial correlation between neighboring voxels was also modeled using Markov random field.

Results: The proposed method was evaluated on BraTS 2013 dataset including patients with high-grade and low-grade tumors. In comparison with other active contour based methods, the proposed method yielded better performance on tumor segmentation with mean Dice similarity coefficients of 0.9179 ( ± 0.025) and 0.8910 ( ± 0.042) obtained on high-grade and low-grade tumors, respectively.

Conclusion: The proposed method achieved higher accuracies for brain tumor extraction in comparison to other contour-based methods.
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http://dx.doi.org/10.1016/j.jneumeth.2021.109296DOI Listing
October 2021

Resting state dynamic functional connectivity in children with attention deficit/hyperactivity disorder.

J Neural Eng 2021 08 16;18(4). Epub 2021 Aug 16.

Laboratory of Functional Neuroscience and Pathologies (LNFP EA4559), University Research Center (CURS), University Hospital, Amiens, France.

Attention deficit/hyperactivity disorder (ADHD) is characterized by inattention, hyperactivity and impulsivity. In this study, we investigated group differences in dynamic functional connectivity (dFC) between 113 children with inattentive (46 ADHD) and combined (67 ADHD) ADHD and 76 typically developing (TD) children using resting-state functional MRI data. For dynamic connectivity analysis, the data were first decomposed into 100 independent components, among which 88 were classified into eight well-known resting-state networks (RSNs). Three discrete FC states were then identified using k-means clustering and used to estimate transition probabilities between states in both patient and control groups using a hidden Markov model. Our results showed state-dependent alterations in intra and inter-network connectivity in both ADHD subtypes in comparison with TD. Spending less time than healthy controls in state 1, both ADHDand ADHDwere characterized with weaker intra-hemispheric connectivity with functional asymmetries. In this state, ADHDfurther showed weaker inter-hemispheric connectivity. The patients spent more time in state 2, exhibiting characteristic abnormalities in corticosubcortical and corticocerebellar connectivity. In state 3, a less frequently state observed across the ADHD and TD children, ADHDwas differentiated from ADHDby significant alterations in FC between bilateral temporal regions and other brain areas in comparison with TD. Across all three states, several strategic brain regions, mostly bilateral, exhibited significant alterations in both static functional connectivity (sFC) and dFC in the ADHD groups compared to TD, including inferior, middle and superior temporal gyri, middle frontal gyri, insula, anterior cingulum cortex, precuneus, calcarine, fusiform, superior motor area, and cerebellum. Our results show distributed abnormalities in sFC and dFC between different large-scale RSNs including cortical and subcortical regions in both ADHD subtypes compared to TD. Our findings show that the dynamic changes in brain FC can better explain the underlying pathophysiology of ADHD such as deficits in visual cognition, attention, memory and emotion processing, and cognitive and motor control.
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http://dx.doi.org/10.1088/1741-2552/ac16b3DOI Listing
August 2021

Effect of Multishell Diffusion MRI Acquisition Strategy and Parcellation Scale on Rich-Club Organization of Human Brain Structural Networks.

Diagnostics (Basel) 2021 May 27;11(6). Epub 2021 May 27.

Laboratory of Functional Neuroscience and Pathologies (LNFP), University Research Center (CURS), University Hospital, 80054 Amiens, France.

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 microstructures. The purpose of this study was to investigate the extent to which brain structural organization and network topology are affected by the choice of diffusion magnetic resonance imaging (MRI) acquisition strategy and parcellation scale. We performed graph-theoretical network analysis using DWI data from 35 Human Connectome Project subjects. Our study compared four single-shell (b = 1000, 3000, 5000, 10,000 s/mm) and multishell sampling schemes and six parcellation scales (68, 200, 400, 600, 800, 1000 nodes) using five graph metrics, including small-worldness, clustering coefficient, characteristic path length, modularity and global efficiency. Rich-club analysis was also performed to explore the rich-club organization of brain structural networks. Our results showed that the parcellation scale and imaging protocol have significant effects on the network attributes, with the parcellation scale having a substantially larger effect. Regardless of the parcellation scale, the brain structural networks exhibited a rich-club organization with similar cortical distributions across the parcellation scales involving at least 400 nodes. Compared to single b-value diffusion acquisitions, the deterministic tractography using multishell diffusion imaging data consisting of shells with b-values higher than 5000 s/mm resulted in significantly improved fiber-tracking results at the locations where fiber bundles cross each other. Brain structural networks constructed using the multishell acquisition scheme including high b-values also exhibited significantly shorter characteristic path lengths, higher global efficiency and lower modularity. Our results showed that both parcellation scale and sampling protocol can significantly impact the rich-club organization of brain structural networks. Therefore, caution should be taken concerning the reproducibility of connectivity results with regard to the parcellation scale and sampling scheme.
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http://dx.doi.org/10.3390/diagnostics11060970DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8227047PMC
May 2021

Multi-scale structural rich-club organization of the brain in full-term newborns: a combined DWI and fMRI study.

J Neural Eng 2021 05 25;18(4). Epub 2021 May 25.

Laboratory of Functional Neuroscience and Pathologies (LNFP), University Research Center (CURS), University Hospital, Amiens, France.

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-state fMRI data.A group independent component analysis was first performed to identify eight resting state networks (RSNs) used as functional modules. A groupwise whole-brain functional parcellation was also performed at five scales comprising 100-900 parcels. The distribution of RC nodes was then investigated within and between the RSNs. We further assessed the distribution of short and long-range RC, feeder and local connections across different parcellation scales.Sharing the scale-free characteristic of small-worldness, the neonatal structural brain networks exhibited an RC organization at different nodal scales (NSs). The subcortical, sensory-motor and default mode networks were found to be strongly involved in the RC organization of the structural brain networks, especially in the zones where the RSNs overlapped, with an average cross-scale proportion of 45.9%, 28.5% and 10.5%, respectively. A large proportion of the connector hubs were found to be RC members for the coarsest (73%) to finest (92%) NSs. Our results revealed a prominent involvement of cortico-subcortical and cortico-cerebellar white matter pathways in the RC organization of the neonatal brain. Regardless of the NS, the majority (more than 65.2%) of the inter-RSN connections were long distance RC or feeder with an average physical connection of 105.5 and 97.4 mm, respectively. Several key RC regions were identified, including the insula and cingulate gyri, middle and superior temporal gyri, hippocampus and parahippocampus, fusiform gyrus, precuneus, superior frontal and precentral gyri, calcarine fissure and lingual gyrus.Our results emphasize the importance of the multi-scale connectivity analysis in assessing the cross-scale reproducibility of the connectivity results concerning the global and local topological properties of the brain networks. Our findings may improve our understanding of the early brain development.
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http://dx.doi.org/10.1088/1741-2552/abfd46DOI Listing
May 2021

Sexual Dimorphisms and Asymmetries of the Thalamo-Cortical Pathways and Subcortical Grey Matter of Term Born Healthy Neonates: An Investigation with Diffusion Tensor MRI.

Diagnostics (Basel) 2021 Mar 20;11(3). Epub 2021 Mar 20.

Laboratory of Functional Neuroscience and Pathologies (LNFP), School of Medicine, University of Picardy Jules Verne, 80037 Amiens, France.

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-pallidus (GP). There was no significant difference between boys and girls in either the left or in the right hemispheric THC, TH, GP, CN and PT. In the combined group (boys + girls) significant left greater than right symmetry was observed in the THC (AD, RD and ADC), and TH (AD, ADC). Within the same group, we reported left greater than right asymmetry in the PT (FA), CN (RD and ADC). Different findings were recorded when we split the group of neonates by gender. Girls exhibited right > left AD, RD and ADC in the THC and left > right FA in the PT. In the group of boys, we observed right > left RD and ADC. We also reported left > right FA in the PT and left > right RD in the CN. These results provide insights into normal asymmetric development of sensory-motor networks within boys and girls.
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http://dx.doi.org/10.3390/diagnostics11030560DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003947PMC
March 2021

Sleep Spindle Characteristics in Obstructive Sleep Apnea Syndrome (OSAS).

Front Neurol 2021 25;12:598632. Epub 2021 Feb 25.

Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.

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. Thirty-one individuals with OSAS (mean age: 48.50 years) and 23 healthy controls took part in the study. All participants underwent a whole night polysomnography. Additionally, those with OSAS were divided into mild, moderate and severe cases of OSAS. For N2, sleep spindle density did not significantly differ between participants with and without OSAS, or among those with mild, moderate and severe OSAS. For N3, analyses revealed significantly higher spindle densities in healthy controls and individuals with mild OSAS than in those with moderate or severe OSAS. Last, in N2 a higher AHI was associated with a shorter sleep spindle duration. OSAS is associated with a significantly lower spindle density in N3 and a shorter spindle duration in N2. Our results also revealed that, in contrast to moderate and severe OSAS, the sleep spindle characteristics of individuals with mild OSAS were very similar to those of healthy controls.
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http://dx.doi.org/10.3389/fneur.2021.598632DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947924PMC
February 2021

Effect of structural complexities in head modeling on the accuracy of EEG source localization in neonates.

J Neural Eng 2020 10 9;17(5):056004. Epub 2020 Oct 9.

GRAMFC, Inserm U1105, University Research Center (CURS), CHU AMIENS - SITE SUD, Amiens, France.

Objective: Neonatal electroencephalography (EEG) source localization is highly prone to errors due to head modeling 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 source localization errors caused by cerebrospinal fluid (CSF) and fontanel exclusion and gray matter (GM)/white matter (WM) distinction using the finite element method.

Main Results: Our results showed that the exclusion of CSF from the head model could cause significant localization errors mostly for sources closer to the inner surface of the skull. With a less pronounced effect compared to the CSF exclusion, the discrimination between GM and WM also widely affected all sources, especially those located in deeper structures. The exclusion of the fontanels from the head model led to source localization errors for sources located in areas beneath the fontanels. Our finding clearly shows that the CSF inclusion and GM/WM distinction in EEG inverse modeling can substantially reduce EEG source localization errors. Moreover, fontanels should be included in neonatal head models, particularly in source localization applications, in which sources of interest are located beneath or in vicinity of fontanels.

Significance: Our findings have practical implications for a better understanding of the impact of head model complexities on the accuracy of EEG source localization in neonates.
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http://dx.doi.org/10.1088/1741-2552/abb994DOI Listing
October 2020

Cortical source analysis of resting state EEG data in children with attention deficit hyperactivity disorder.

Clin Neurophysiol 2020 09 23;131(9):2115-2130. Epub 2020 Jun 23.

Laboratory of Functional Neuroscience and Pathologies (LNFP), University Research Center, University Hospital, Amiens, France; Faculty of Medicine, University of Picardie Jules Verne, Amiens, France. Electronic address:

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 using resting state EEG recordings from 40 children (8-16 years) with combined and inattentive subtypes of ADHD and 41 age-matched healthy controls (HC). Group differences in EEG spectra and CSD were investigated at each scalp location, voxel and cortical region in delta, theta, alpha and beta bands. We also explored associations between topographic changes in EEG power and CSD and age.

Results: Compared to healthy controls, combined ADHD subtype was characterized with significantly increased diffuse theta/beta power ratios (TBR) with a widespread decrease in beta CSD. Inattentive ADHD subtype presented increased TBR in all brain regions except in posterior areas with a global increase in theta source power. In both ADHD and HC, older age groups showed significantly lower delta source power and TBR and higher alpha and beta source power than younger age groups. Compared to HC, ADHD was characterized with increases in theta fronto-central and temporal source power with increasing age.

Conclusions: Our results confirm that TBR can be used as a neurophysiological biomarker to differentiate ADHD from healthy children at both the source and sensor levels.

Significance: Our findings emphasize the importance of performing the source imaging analysis in order to better characterize age-related changes in resting-state EEG activity in ADHD and controls.
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http://dx.doi.org/10.1016/j.clinph.2020.05.028DOI Listing
September 2020

Neonatal EEG sleep stage classification based on deep learning and HMM.

J Neural Eng 2020 06 25;17(3):036031. Epub 2020 Jun 25.

Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran. Department of Electrical Engineering, Persian Gulf University, Bushehr, Iran.

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 performance was evaluated on quiet sleep (QS) and active sleep (AS) stages, each with two sub-states, using multichannel EEG data recorded from sixteen neonates with postmenstrual age of 38-40 weeks. A comprehensive set of linear and nonlinear features were extracted from thirty-second EEG segments. The feature space dimensionality was then reduced by using an evolutionary feature selection method called MGCACO (Modified Graph Clustering Ant Colony Optimization) based on the relevance and redundancy analysis. A bi-directional long-short time memory (BiLSTM) network was trained for sleep stage classification. The number of channels was optimized using the sequential forward selection method to reduce the spatial space. Finally, an HMM-based postprocessing stage was used to reduce false positives by incorporating the knowledge of transition probabilities between stages into the classification process. The method performance was evaluated using the K-fold (KFCV) and leave-one-out cross-validation (LOOCV) strategies.

Main Results: Using six-bipolar channels, our method achieved a mean kappa and an overall accuracy of 0.71-0.76 and 78.9%-82.4% using the KFCV and LOOCV strategies, respectively.

Significance: The presented automatic sleep stage scoring method can be used to study the neurodevelopmental process and to diagnose brain abnormalities in term neonates.
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http://dx.doi.org/10.1088/1741-2552/ab965aDOI Listing
June 2020

An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model.

J Neurosci Methods 2019 08 19;324:108320. Epub 2019 Jun 19.

Laboratory of Functional Neuroscience and Pathologies (LNFP, EA4559), University Research Center (CURS), CHU AMIENS - SITE SUD, Avenue Laënnec, Salouël 80420, France; Faculty of Medicine, University of Picardie Jules Verne, Amiens 80036, France. Electronic address:

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 first selected from a pool of features extracted from sleep EEG epochs by using a feature selection method based on the relevance and redundancy analysis. EEG segments were then classified using a random forest classifier. Finally, a Hidden Markov Model (HMM) was used to reduce false positives by incorporating knowledge of the temporal structure of transitions between sleep stages. We evaluated the proposed method using single-channel EEG signals from four public sleep EEG datasets scored according to R&K and AASM guidelines. We compared the performance of our method with existing methods using different cross validation strategies.

Results: Using a leave-one-out validation strategy, our method achieved overall accuracies in the range of (79.4-87.4%) and (77.6-80.4%) with Kappa values in the range of 0.7-0.85 for six-stage (R&K) and five-stage (AASM) classification, respectively. Our method showed a reduction in overall accuracy up to 8% using the cross-dataset validation strategy in comparison with the subject cross-validation method.

Comparison With Existing Method(s): Our method outperformed the existing methods for all multi-stage classification.

Conclusions: The proposed single-channel method can be used for robust and reliable sleep stage scoring with high accuracy and relatively low complexity required for real time applications.
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http://dx.doi.org/10.1016/j.jneumeth.2019.108320DOI Listing
August 2019

Seizure prediction in patients with focal hippocampal epilepsy.

Clin Neurophysiol 2017 07 12;128(7):1299-1307. Epub 2017 May 12.

Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA; Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN 55455, USA.

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 exponent, and nonlinear interdependence from segments of intracranial EEG.

Results: Our results showed an average sensitivity of 86.7% and 92.9%, an average false prediction rate of 0.126 and 0.096/h, and an average minimum prediction time of 14.3 and 33.3min, respectively, using seizure occurrence periods of 30 and 50min and a seizure prediction horizon of 10s. Two-third of the analyzed seizures showed significantly increased complexity in periods prior to the seizures in comparison with baseline. In four patients, strong bidirectional connectivities between epileptic contacts and the surrounding areas were observed. However, in five patients, unidirectional functional connectivities in preictal periods were observed from remote areas to epileptogenic zones.

Conclusions: Overall, preictal periods in patients with focal hippocampal epilepsy were characterized with patient-specific changes in univariate and bivariate nonlinear measures.

Significance: The spatio-temporal characterization of preictal periods may help to better understand the mechanism underlying seizure generation in patients with focal hippocampal epilepsy.
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http://dx.doi.org/10.1016/j.clinph.2017.04.026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5513720PMC
July 2017

Characterization and correction of the false-discovery rates in resting state connectivity using functional near-infrared spectroscopy.

J Biomed Opt 2017 05;22(5):55002

University of Pittsburgh, Departments of Radiology and Bioengineering, Clinical Science Translational Institute, and Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low levels of red to near-infrared light to measure changes in cerebral blood oxygenation. Spontaneous (resting state) functional connectivity (sFC) has become a critical tool for cognitive neuroscience for understanding task-independent neural networks, revealing pertinent details differentiating healthy from disordered brain function, and discovering fluctuations in the synchronization of interacting individuals during hyperscanning paradigms. Two of the main challenges to sFC-NIRS analysis are (i) the slow temporal structure of both systemic physiology and the response of blood vessels, which introduces false spurious correlations, and (ii) motion-related artifacts that result from movement of the fNIRS sensors on the participants’ head and can introduce non-normal and heavy-tailed noise structures. In this work, we systematically examine the false-discovery rates of several time- and frequency-domain metrics of functional connectivity for characterizing sFC-NIRS. Specifically, we detail the modifications to the statistical models of these methods needed to avoid high levels of false-discovery related to these two sources of noise in fNIRS. We compare these analysis procedures using both simulated and experimental resting-state fNIRS data. Our proposed robust correlation method has better performance in terms of being more reliable to the noise outliers due to the motion artifacts.
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http://dx.doi.org/10.1117/1.JBO.22.5.055002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5424771PMC
May 2017

Effect of confounding variables on hemodynamic response function estimation using averaging and deconvolution analysis: An event-related NIRS study.

Neuroimage 2017 07 25;155:25-49. Epub 2017 Apr 25.

GRAMFC-Inserm U1105, University Research Center (CURS), University Hospital, Amiens, 80054 France; EFSN Pediatric (Pediatric Nervous System Functional Investigation Unit), CHU AMIENS - SITE SUD, Amiens, France.

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) profile in event-related designs. In this study, we conducted a series of simulations using synthetic and real NIRS data to examine the effect of the main confounding factors, including event sequence timing parameters, different types of noise, signal-to-noise ratio (SNR), temporal autocorrelation and temporal filtering on the performance of these techniques in slow and rapid event-related designs. We also compared systematic errors in the estimates of the fitted HRF amplitude, latency and duration for both techniques. We further compared the performance of deconvolution methods based on Finite Impulse Response (FIR) basis functions and gamma basis sets. Our results demonstrate that DM was much less sensitive to confounding factors than CA. Event timing was the main parameter largely affecting the accuracy of CA. In slow event-related designs, deconvolution methods provided similar results to those obtained by CA. In rapid event-related designs, our results showed that DM outperformed CA for all SNR, especially above -5 dB regardless of the event sequence timing and the dynamics of background NIRS activity. Our results also show that periodic low-frequency systemic hemodynamic fluctuations as well as phase-locked noise can markedly obscure hemodynamic evoked responses. Temporal autocorrelation also affected the performance of both techniques by inducing distortions in the time profile of the estimated hemodynamic response with inflated t-statistics, especially at low SNRs. We also found that high-pass temporal filtering could substantially affect the performance of both techniques by removing the low-frequency components of HRF profiles. Our results emphasize the importance of characterization of event timing, background noise and SNR when estimating HRF profiles using CA and DM in event-related designs.
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http://dx.doi.org/10.1016/j.neuroimage.2017.04.048DOI Listing
July 2017

Hemodynamic Changes Associated with Interictal Spikes Induced by Acute Models of Focal Epilepsy in Rats: A Simultaneous Electrocorticography and Near-Infrared Spectroscopy Study.

Brain Topogr 2017 May 7;30(3):390-407. Epub 2017 Feb 7.

GRAMFC-Inserm U1105, University Research Center (CURS), CHU SITE SUD, avenue Laennec, 80054, Amiens, France.

Interictal spikes can be generated by blocking GABA 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 models of focal epilepsy. Simultaneous ElectroCorticoGraphy (ECoG) and Near-InfraRed Spectroscopy (NIRS) recordings were obtained in vivo from adult Sprague-Dawley rat brain during semi-periodic focal interictal spikes induced by local cortical application of low doses of Penicillin G (PG) and Bicuculline Methiodide (BM) as GABA deactivation agents. The Finite Impulse Response deconvolution technique was used to estimate the profile of hemodynamic changes in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations associated with interictal ECoG spikes in each rat. Our results show that, in both acute models of focal epilepsy, the hemodynamic changes associated with interictal spikes were characterized by pre-spike and post-spike primary NIRS responses, and recovery periods with slight differences in amplitude and latency. The pre-spike period starting at least 2 s prior to the onset of ECoG spikes was characterized by a significant decrease in HbO concomitant with an increase in HbR with respect to baseline. The post-spike primary NIRS response exhibited the expected changes described according to the classical view of neurovascular coupling, i.e., a significant increase in HbO and a significant decrease in HbR in response to interictal spikes. The recovery period was characterized by a decreased HbO signal and an increased HbR signal, followed by a return to baseline. Compared to the BM epilepsy model, the PG model was more stable and showed lower variability in the shape, amplitude and latency of the components of spike-related hemodynamic changes. Our findings support a prominent role for pre-spike hemodynamic changes in the initiation of interictal spikes. The mechanism of interactions between neuronal and vascular networks during the pre-spike period constitutes a complex process, resulting in increased sensitivity of the epileptogenic focus to induce neuronal spiking.
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http://dx.doi.org/10.1007/s10548-016-0541-zDOI Listing
May 2017

Characterization of the relative contributions from systemic physiological noise to whole-brain resting-state functional near-infrared spectroscopy data using single-channel independent component analysis.

Neurophotonics 2016 Apr 6;3(2):025004. Epub 2016 Jun 6.

University of Pittsburgh, Department of Radiology, 4200 Fifth Avenue, Pittsburgh, Pennsylvania 15260, United States; University of Pittsburgh, Department of Bioengineering, 4200 Fifth Avenue, Pittsburgh, Pennsylvania 15260, United States.

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 physiological noise to fNIRS signals during rest. Single-channel ICA is an underdetermined decomposition method, which separates a single time series into components containing nonredundant spectral information. Using scICA, fNIRS signals from a total of 17 subjects were decomposed into the constituent physiological components. The percentage contribution of the classes of physiology to the fNIRS signals including low-frequency (LF) fluctuations, respiration, and cardiac oscillations was estimated using spectral domain classification methods. Our results show that LF oscillations accounted for 40% to 55% of total power of both the oxy-Hb and deoxy-Hb signals. Respiration and its harmonics accounted for 10% to 30% of the power, and cardiac pulsations and cardio-respiratory components accounted for 10% to 30%. We describe this scICA method for decomposing fNIRS signals, which unlike other approaches to spatial covariance reduction is applicable to both single- or multiple-channel fNIRS signals and discuss how this approach allows functionally distinct sources of noise with disjoint spectral support to be separated from obscuring systemic physiology.
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http://dx.doi.org/10.1117/1.NPh.3.2.025004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4893204PMC
April 2016

Effects of uncertainty in head tissue conductivity and complexity on EEG forward modeling in neonates.

Hum Brain Mapp 2016 10 30;37(10):3604-22. Epub 2016 May 30.

GRAMFC, Inserm U1105, University Research Center, Department of Medicine, Amiens University Hospital, Amiens, France.

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). Using computer simulations, we investigated the effects of exclusion of CSF and fontanels, discrimination between GM and WM, and uncertainty in conductivity of neonatal head tissues on EEG forward modeling. We found that exclusion of CSF from the head model induced the strongest widespread effect on the EEG forward solution. Discrimination between GM and white matter also induced a strong widespread effect, but which was less intense than that of CSF exclusion. The results also showed that exclusion of the fontanels from the neonatal head model locally affected areas beneath the fontanels, but this effect was much less pronounced than those of exclusion of CSF and GM/WM discrimination. Changes in GM/WM conductivities by 25% with respect to reference values induced considerable effects in EEG forward solution, but this effect was more pronounced for GM conductivity. Similarly, changes in skull conductivity induced effects in the EEG forward modeling in areas covered by the cranial bones. The least intense effect on EEG was caused by changes in conductivity of the fontanels. Our findings clearly emphasize the impact of uncertainty in conductivity and deficiencies in head tissue compartments on modeling research and localization of brain electrical activity in neonates. Hum Brain Mapp 37:3604-3622, 2016. © 2016 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/hbm.23263DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6867251PMC
October 2016

EEG Resting State Functional Connectivity Analysis in Children with Benign Epilepsy with Centrotemporal Spikes.

Front Neurosci 2016 31;10:143. Epub 2016 Mar 31.

INSERM U 1105, CURS, Centre Hospitalier Universitaire Amiens-PicardieAmiens, France; INSERM U 1105, EFSN Pédiatriques, Centre Hospitalier Universitaire Amiens-PicardieAmiens, France.

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 Electromagnetic Tomography (eLORETA). We investigated FC between 84 Brodmann areas using lagged phase synchronization (LPS) in four frequency bands (δ, θ, α, and β). We further computed the network degree, clustering coefficient and efficiency. Compared to controls, patients displayed higher θ and α and lower β LPS values. In these frequency bands, patients were also characterized by less well ordered brain networks exhibiting higher global degrees and efficiencies and lower clustering coefficients. In the β band, patients exhibited reduced functional segregation and integration due to loss of both local and long-distance functional connections. These findings suggest that benign epileptic brain networks might be functionally disrupted due to their altered functional organization especially in the α and β frequency bands.
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http://dx.doi.org/10.3389/fnins.2016.00143DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4815534PMC
April 2016

EEG resting state analysis of cortical sources in patients with benign epilepsy with centrotemporal spikes.

Neuroimage Clin 2015 3;9:275-82. Epub 2015 Sep 3.

INSERM U 1105, CURS, CHU sud, Salouël, Av. Laennec, 80054 Amiens Cedex, France ; INSERM U 1105, EFSN Pédiatriques, CHU sud, Salouël, Av. Laennec, 80054 Amiens Cedex, France.

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-resolution scalp EEG data, we assessed statistical differences in spatial distributions of EEG power spectra and cortical sources of resting state EEG rhythms in five frequency bands: δ (0.5-3.5 Hz), θ (4-8 Hz), α (8.5-13 Hz), β1 (13.5-20 Hz) and β2 (20.5-30 Hz) under the eyes-closed resting state condition. To further investigate the impact of centrotemporal spikes on EEG spectra, we split the EEG data of the patient group into EEG portions with and without spikes. Source localization demonstrated the homogeneity of our population of BECTS patients with a common epileptic zone over the right centrotemporal region. Significant differences in terms of both spectral power and cortical source densities were observed between controls and patients. Patients were characterized by significantly increased relative power in θ, α, β1 and β2 bands in the right centrotemporal areas over the spike zone and in the right temporo-parieto-occipital junction. Furthermore, the relative power in all bands significantly decreased in the bilateral frontal and parieto-occipital areas of patients regardless of the presence or absence of spikes in EEG segments. However, the spectral differences between patients and controls were more pronounced in the presence of spikes. This observation emphasized the impact of benign epilepsy on cortical source power, especially in the right centrotemporal regions. Spectral changes in bilateral frontal and parieto-occipital areas may also suggest alterations in the default mode network in BECTS patients.
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http://dx.doi.org/10.1016/j.nicl.2015.08.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4576415PMC
July 2016

Functional Brain Dysfunction in Patients with Benign Childhood Epilepsy as Revealed by Graph Theory.

PLoS One 2015 2;10(10):e0139228. Epub 2015 Oct 2.

Institut National de la Santé et de la Recherche Médicale (INSERM U1105), Centre Universitaire de Recheche en Santé (CURS), University Hospital, Amiens, France; Explorations Fonctionnelles du Système Nerveux (EFSN) pédiatrique, University Hospital, Amiens, France.

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 young children under the age of 16 years. High-density EEG data were recorded from patients and controls in resting state with eyes closed. Data were preprocessed and spike and spike-free segments were selected for analysis. Phase locking value was calculated for all paired combinations of channels and for five frequency bands (δ, θ, α, β1 and β2). We computed the degree and small-world parameters--clustering coefficient (C) and path length (L)--and compared the two patient conditions to controls. A higher degree at epileptic zones during interictal epileptic spikes (IES) was observed in all frequency bands. Both patient conditions reduced connection at the occipital and right frontal regions close to the epileptic zone in the α band. The "small-world" features (high C and short L) were deviated in patients compared to controls. A changed from an ordered network in the δ band to a more randomly organized network in the α band was observed in patients compared to healthy controls. These findings show that the benign epileptic brain network is disrupted not only at the epileptic zone, but also in other brain regions especially frontal regions.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0139228PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4592214PMC
June 2016

Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach.

Clin Neurophysiol 2014 May 28;125(5):930-40. Epub 2013 Nov 28.

University of Minnesota, Minneapolis, MN 55455, USA. Electronic address:

Objectives: The aim of this study is to develop a model based seizure prediction method.

Methods: 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 the power spectral density of intracranial EEG signals and then integrated based on information obtained by investigating changes in the parameters prior to seizures. Twenty-one patients with medically intractable hippocampal and neocortical focal epilepsy were studied.

Results: Tuned to obtain maximum sensitivity, an average sensitivity of 87.07% and 92.6% with an average false prediction rate of 0.2 and 0.15/h were achieved using maximum seizure occurrence periods of 30 and 50 min and a minimum seizure prediction horizon of 10s, respectively. Under maximum specificity conditions, the system sensitivity decreased to 82.9% and 90.05% and the false prediction rates were reduced to 0.16 and 0.12/h using maximum seizure occurrence periods of 30 and 50 min, respectively.

Conclusions: The spatio-temporal changes in the parameters demonstrated patient-specific preictal signatures that could be used for seizure prediction.

Significance: The present findings suggest that the model-based approach may aid prediction of seizures.
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http://dx.doi.org/10.1016/j.clinph.2013.10.051DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3994166PMC
May 2014

Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS.

Biomed Opt Express 2013 17;4(8):1366-79. Epub 2013 Jul 17.

Department of Radiology, University of Pittsburgh, 4200 Fifth Avenue, Pittburgh, PA 15260, USA ; Department of Bioengineering, University of Pittsburgh, 4200 Fifth Avenue, Pittburgh, PA 15260, USA.

Systemic physiology and motion-induced artifacts represent two major sources of confounding noise in functional near infrared spectroscopy (fNIRS) imaging that can reduce the performance of analyses and inflate false positive rates (i.e., type I errors) of detecting evoked hemodynamic responses. In this work, we demonstrated a general algorithm for solving the general linear model (GLM) for both deconvolution (finite impulse response) and canonical regression models based on designing optimal pre-whitening filters using autoregressive models and employing iteratively reweighted least squares. We evaluated the performance of the new method by performing receiver operating characteristic (ROC) analyses using synthetic data, in which serial correlations, motion artifacts, and evoked responses were controlled via simulations, as well as using experimental data from children (3-5 years old) as a source baseline physiological noise and motion artifacts. The new method outperformed ordinary least squares (OLS) with no motion correction, wavelet based motion correction, or spline interpolation based motion correction in the presence of physiological and motion related noise. In the experimental data, false positive rates were as high as 37% when the estimated p-value was 0.05 for the OLS methods. The false positive rate was reduced to 5-9% with the proposed method. Overall, the method improves control of type I errors and increases performance when motion artifacts are present.
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http://dx.doi.org/10.1364/BOE.4.001366DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3756568PMC
September 2013

A rule-based seizure prediction method for focal neocortical epilepsy.

Clin Neurophysiol 2012 Jun 22;123(6):1111-22. Epub 2012 Feb 22.

University of Minnesota, Minneapolis, MN 55455, USA.

Objective: In the present study, we have developed a novel patient-specific rule-based seizure prediction system for focal neocortical epilepsy.

Methods: Five univariate measures including correlation dimension, correlation entropy, noise level, Lempel-Ziv complexity, and largest Lyapunov exponent as well as one bivariate measure, nonlinear interdependence, were extracted from non-overlapping 10-s segments of intracranial electroencephalogram (iEEG) data recorded using electrodes implanted deep in the brain and/or placed on the cortical surface. The spatio-temporal information was then integrated by using rules established based on patient-specific changes observed in the period prior to a seizure sample for each patient. The system was tested on 316 h of iEEG data containing 49 seizures recorded in 11 patients with medically intractable focal neocortical epilepsy.

Results: For seizure occurrence periods of 30 and 50 min our method showed an average sensitivity of 79.9% and 90.2% with an average false prediction rate of 0.17 and 0.11/h, respectively. In terms of sensitivity and false prediction rate, the system showed superiority to random and periodical predictors.

Conclusions: The nonlinear analysis of iEEG in the period prior to seizures revealed patient-specific spatio-temporal changes that were significantly different from those observed within baselines in the majority of the seizures analyzed in this study.

Significance: The present results suggest that the patient specific rule-based approach may become a potentially useful approach for predicting seizures prior to onset.
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http://dx.doi.org/10.1016/j.clinph.2012.01.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3361618PMC
June 2012

Seizure prediction in intracranial EEG: a patient-specific rule-based approach.

Annu Int Conf IEEE Eng Med Biol Soc 2011 ;2011:2566-9

Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.

In this study, we report our development of a patient-specific rule-based seizure prediction system. Five univariate and one bivariate nonlinear measures were extracted from non-overlapping 10-second segments of intracranial EEG (iEEG) data recorded using both depth electrodes in the brain and subdural electrodes over the cortical surface. Nonlinear features representing the specific characteristic properties of EEG signal were then integrated spatio-temporally in a way to predict to predict seizure with high sensitivity. The present system was tested on 58 hours of iEEG data containing ten seizures recorded in two patients with medically intractable focal epilepsy. Within a prediction horizon of 30 and 60 minutes, our method showed an average sensitivity of 90% and 96.5% with an average false prediction rate of 0.06/h and 0.055/h, respectively. The present results suggest that such a rule-based system can become potentially a useful approach for predicting seizures prior to onset.
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http://dx.doi.org/10.1109/IEMBS.2011.6090709DOI Listing
June 2012

Dynamic changes in quantitative electroencephalogram during continuous performance test in children with attention-deficit/hyperactivity disorder.

Int J Psychophysiol 2011 Sep 19;81(3):230-6. Epub 2011 Jul 19.

Department of Psychology, University of Tabriz, Tabriz, Iran.

To establish whether dynamic EEG changes in children with attention-deficit/hyperactivity disorder (ADHD) differ from those observed in controls, the authors investigated the effect of the continuous performance test (CPT) on delta, theta, alpha and beta frequency bands. High-resolution electroencephalography (EEG) was recorded during eyes-open resting and CPT performance in 16 right-handed children meeting the DSM-IV criteria for ADHD and 16 age-matched controls. Significant CPT vs. eyes-open differences in EEG activities was observed in children with ADHD. In particular, switching to CPT induced an alpha power increase in children with ADHD and an alpha power decrease in controls. This may reflect a primary deficit associated with cortical hypoarousal in ADHD. These EEG results agree with behavioral findings leading the authors to suggest that dynamic changes in neural network activities are impaired in children with ADHD.
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http://dx.doi.org/10.1016/j.ijpsycho.2011.06.016DOI Listing
September 2011

Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG.

Annu Int Conf IEEE Eng Med Biol Soc 2010 ;2010:3301-4

BRAIN Team at the Biomedical Signal Processing Laboratory, Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58202, USA.

In this paper, we present a method for epileptic seizure prediction from intracranial EEG recordings. We applied correlation dimension, a nonlinear dynamics based univariate characteristic measure for extracting features from EEG segments. Finally, we designed a fuzzy rule-based system for seizure prediction. The system is primarily designed based on expert's knowledge and reasoning. A spatial-temporal filtering method was used in accordance with the fuzzy rule-based inference system for issuing forecasting alarms. The system was evaluated on EEG data from 10 patients having 15 seizures.
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http://dx.doi.org/10.1109/IEMBS.2010.5627247DOI Listing
April 2011

Detection of EEG transients in neonates and older children using a system based on dynamic time-warping template matching and spatial dipole clustering.

Neuroimage 2009 Oct 30;48(1):50-62. Epub 2009 Jun 30.

GRAMFC, EFSN Péd., C.H.U. Nord, Place V. Pauchet, F-80054 Amiens, France.

We present a novel system for detecting electroencephalographic transient events in neonates and older children. The detection system consists of three major elements: (i) a preprocessing stage for filtering EEG and detecting artifacts, (ii) a hierarchical course-to-fine temporal event detection stage and (iii) a hierarchical course-to-fine spatial event selection stage to incorporate spatial contextual information for rejection of spurious events. The output consists of homogeneous EEG events and their corresponding dipole clusters. The system was evaluated on EEG signals recorded in four neonates and six older children. There was a high degree of correlation between system-detected and expert-marked events for all patients. Mean sensitivities of 84.9% and 91.9% and mean selectivities of 86.3% and 90.6% were obtained for the neonates and the older children, respectively. This tool is appropriate for the detection and selection of homogeneous EEG events prior to source localization. Quantitative spatial analysis of dipoles may facilitate the physician's assessment of patients' brain dysfunction.
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http://dx.doi.org/10.1016/j.neuroimage.2009.06.057DOI Listing
October 2009

Inverse coupling between respiratory and cardiac oscillators in a life-threatening event in a neonate.

Auton Neurosci 2008 Dec 11;143(1-2):79-82. Epub 2008 Sep 11.

GRAMFC, EFSN Pédiatrique, Pediatric Nervous System Functional Investigations Unit, CHU Amiens Nord, Amiens Cedex, France.

The authors report the case of a baby boy born at a gestational age of 32 weeks who experienced a life-threatening event triggered by vagal overactivity, associated with a transient phase of inverse coupling with a 1:1 phase ratio between ECG and respiration, resulting in respiratory arrest. This case report highlights the vital importance of coupling between cardiac and respiratory oscillators, especially in premature infants or neonates.
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http://dx.doi.org/10.1016/j.autneu.2008.07.012DOI Listing
December 2008

Does spatiotemporal synchronization of EEG change prior to absence seizures?

Brain Res 2008 Jan 26;1188:207-21. Epub 2007 Oct 26.

GRAMFC, EFSN Péd, CHU Nord, Place V. Pauchet, Amiens, France.

We applied linear and nonlinear synchronization measures to characterize the synchrony between cortical regions and detect cerebral epileptic states in scalp EEG recordings recorded prior and during typical absence seizures. An overall rapid increase in the synchronization level between different cerebral regions was observed during the ictal state. During the interictal state, the degree of interdependence between EEG channels was significantly less than that observed in the ictal state (p<0.05). In 63% of the 35 seizures analyzed, a preictal state was identified by a significant decrease in the synchronization level with respect to the interictal state. However, in 31% of the seizures, the synchronization level in the preictal state was higher than that of the interictal state. In the remaining 6% of the seizures, no significant changes were found in the synchronization values in the interictal state prior to the seizures onset. In all the seizures analyzed, the interchannel synchrony persisted in the postictal state with synchronization level significantly higher than that observed in the interictal state. This study supports the hypothesis of having a focal susceptibility of the cerebral cortex prior to absence seizures and further underlines that this susceptibility is reproducible and patient-specific.
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http://dx.doi.org/10.1016/j.brainres.2007.10.048DOI Listing
January 2008

A multistage knowledge-based system for EEG seizure detection in newborn infants.

Clin Neurophysiol 2007 Dec 1;118(12):2781-97. Epub 2007 Oct 1.

GRAMFC, EFSN Péd, CHU Nord, Place V Pauchet, 80054, Amiens, France.

Objective: Automatic seizure detection has attracted attention as a method to obtain valuable information concerning the duration, timing, and frequency of seizures. Methods currently used to detect EEG seizures in adults show high false detection rates in neonates because they lack information about specific age-dependent features of normal and pathological EEG and artifacts. This paper describes a novel multistage knowledge-based seizure detection system for newborn infants to identify and classify normal and pathological newborn EEGs as well as seizures with a reduced false detection rate.

Methods: We developed the system in a way to make comprehensive use of spatial and temporal contextual information obtained from multichannel EEGs. The system development consists of six major stages: (i) EEG data collection and bandpass filtering; (ii) automatic artifact detection; (iii) feature extraction from segments of non-seizure and seizure activities; (iv) feature selection via the relevance and redundancy analysis; (v) EEG classification and pattern recognition using a trained multilayer back-propagation neural network; and (v) knowledge-based decision-making to examine each of possible EEG patterns from a multi-channel perspective. The system was developed and tested with the EEG recordings of 10 newborns aged between 39 and 42 weeks.

Results: The overall sensitivity, selectivity, and average detection rate of the system were 74%, 70.1%, and 79.7%, respectively. The average false detection of 1.55/h was also achieved by the system with a feature reduction up to 80%.

Conclusions: The expert rule-based decision-making subsystem accompanying the classifier helped to reduce the false detection rate, reject a wide variety of artifacts, and discriminate various patterns of EEG.

Significance: This paper may serve as a guide for the selection of discriminative features to improve the accuracy of conventional seizure detection systems for routine clinical EEG interpretation and brain activity monitoring in newborns especially those hospitalized in the neonatal intensive care units.
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http://dx.doi.org/10.1016/j.clinph.2007.08.012DOI Listing
December 2007
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