Graph-based vulnerability assessment of resting-state functional brain networks in full-term neonates

Network disruption during early brain development can result in long-term cognitive impairments. In this study, we investigated rich-club organization in resting-state functional brain networks in full-term neonates using a multiscale connectivity analysis. We further identified the most influential nodes, also called spreaders, having higher impacts on the flow of information throughout the network. The network vulnerability to damage to rich-club (RC) connectivity within and between resting-state networks was also assessed using a graph-based vulnerability analysis. Our results revealed a rich club organization and small-world topology for resting-state functional brain networks in full term neonates, regardless of the network size. Interconnected mostly through short-range connections, functional rich-club hubs were confined to sensory-motor, cognitive-attention-salience (CAS), default mode, and language-auditory networks with an average cross-scale overlap of 36%, 20%, 15% and 12%, respectively. The majority of the functional hubs also showed high spreading potential, except for several non-RC spreaders within CAS and temporal networks. The functional networks exhibited high vulnerability to loss of RC nodes within sensorimotor cortices, resulting in a significant increase and decrease in network segregation and integration, respectively. The network vulnerability to damage to RC nodes within the language-auditory, cognitive-attention-salience, and default mode networks was also significant but relatively less prominent. Our findings suggest that the network integration in neonates can be highly compromised by damage to RC connectivity due to brain immaturity

Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Images

Since 2019, the global dissemination of the Coronavirus and its novel strains has resulted in a surge of new infections. The use of X-ray and computed tomography (CT) imaging techniques is critical in diagnosing and managing COVID-19. Incorporating artificial intelligence (AI) into the field of medical imaging is a powerful combination that can provide valuable support to healthcare professionals.This paper focuses on the methodological approach of using machine learning (ML) to enhance medical imaging for COVID-19 diagnosis.For example, deep learning can accurately distinguish lesions from other parts of the lung without human intervention in a matter of minutes.Moreover, ML can enhance performance efficiency by assisting radiologists in making more precise clinical decisions, such as detecting and distinguishing Covid-19 from different respiratory infections and segmenting infections in CT and X-ray images, even when the lesions have varying sizes and shapes.This article critically assesses machine learning methodologies utilized for the segmentation, classification, and detection of Covid-19 within CT and X-ray images, which are commonly employed tools in clinical and hospital settings to represent the lung in various aspects and extensive detail.There is a widespread expectation that this technology will continue to hold a central position within the healthcare sector, driving further progress in the management of the pandemic

Brain Dynamics and Connectivity from Birth through Adolescence

The human brain as a complex dynamic system undergoes significant structural and functional changes from birth to adulthood to engender neurocognitive functions [...

Sleep Spindle Characteristics in Obstructive Sleep Apnea Syndrome (OSAS)

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 in the study. All participants underwent a whole night polysomnography. Additionally, those with OSAS were divided into mild, moderate and severe cases of OSAS. Results: 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, post-hoc 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. Conclusion: 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

Détection et classification spatiotemporelle automatique d'évènements EEG pour l'analyse de sources d'activité cérébrale chez le nouveau-né et l'enfant

Les nouveau-nés, particulièrement les prématurés présentent d'importants risques de dommages cérébraux et d'incapacité cognitive à vie. Concernant les prématurés, les pathologies neurologiques sont souvent accompagnées des manifestations épileptiques. Ces nouveau-nés peuvent être affectés dans d'autres domaines dont la coordination, la cognition et le comportement. L'EEG est un outil non-invasif permettant de mesurer l'activité électrique du cerveau. Dans cette the se, nous avons développé des outils pour identifier des événements normaux et pathologiques de l'EEG chez les nouveau-nés et les enfants. Nous nous sommes plus particulièrement intéressés à la détection (i) des crises, en employant les éléments spécifiques de l'EEG du nouveau-né, dépendants de l'âge, (ii) les états épileptiques de cerveau et (iii) les événements de courte durée comme la pointe et la pointe-onde pour chaque état. Nous avons caractérisé des événements EEG en extrayant un ensemble de caractéristiques contextuelles afin de les classifier. Puis la localisation des générateurs cérébraux a été trouvée et suivie en groupant spatialement des dipôles équivalents des événements EEG dans différents états du cerveau. Les résultats montrent de bonnes sensibilités et sélectivités avec de faibles taux de fausse détection chez les nouveaux-nés et les enfants.AMIENS-BU Santé (800212102) / SudocSudocFranceF

Assessing the effects of head modelling errors and measurement noise on EEG source localization accuracy in preterm newborns: A single‐subject study

International audienceThe accuracy of electroencephalogram (EEG) source localization is compromised because of head modelling errors. In this study, we investigated the effect of inaccuracy in the conductivity of head tissues and head model structural deficiencies on the accuracy of EEG source analysis in premature neonates. A series of EEG forward and inverse simulations was performed by introducing structural deficiencies into the reference head models to generate test models, which were then used to investigate head modelling errors caused by cerebrospinal fluid (CSF) exclusion, lack of grey matter (GM)-white matter (WM) distinction, fontanel exclusion and inaccuracy in skull conductivity. The modelling errors were computed between forward and inverse solutions obtained using the reference and test models generated for each deficiency. Our results showed that the exclusion of CSF from the head model had a strong widespread effect on the accuracy of the EEG source localization with position errors lower than 4.17 mm. The GM and WM distinction also caused strong localization errors (up to 3.5 mm). The exclusion of fontanels from the head model also strongly affected the accuracy of the EEG source localization for sources located beneath the fontanels with a maximum localization error of 4.37 mm. Similarly, inaccuracies in the skull conductivity caused errors in EEG forward and inverse modelling in sources beneath cranial bones. Our results indicate that the accuracy of EEG source imaging in premature neonates can be largely improved by using head models, which include not only the brain, skull and scalp but also the CSF, GM, WM and fontanels

P300 Component Modulation During a Go/Nogo Task in Healthy Children

ABSTRACT Introduction: Several differences in the P300 component are observed when responses must be executed or inhibited in the Go/Nogo task. However, few studies were established by using well-controlled task with respect to the preparatory processing and stimulus probability. In the present study, we examined the peak amplitude and latency of Go-P300 (P300 evoked by visual Go stimuli) and Nogo-P300 (P300 evoked by visual Nogo stimuli) component in healthy children. Methods: High resolution EEG data were recorded from 13 children (7-11 years old) during a cued equiprobable Go/Nogo task. The P300 component was measured at frontal (F3, Fz, F4) and parietal (P3, Pz, P4) regions in response to both Go and Nogo stimuli. Data were analyzes using a three-way repeated measures ANOVA.Results: These children displayed higher P300 amplitude in the Go relative to Nogo condition at parietal region. In addition, decrease in P300 latency was observed at the frontal in comparison to parietal region.Discussion: The results might suggest that the P300 is related to different processes or arise from different generators in execution and inhibition conditions

P300 Component Modulation During a Go/Nogo Task in Healthy Children

ABSTRACT Introduction: Several differences in the P300 component are observed when responses must be executed or inhibited in the Go/Nogo task. However, few studies were established by using well-controlled task with respect to the preparatory processing and stimulus probability. In the present study, we examined the peak amplitude and latency of Go-P300 (P300 evoked by visual Go stimuli) and Nogo-P300 (P300 evoked by visual Nogo stimuli) component in healthy children. Methods: High resolution EEG data were recorded from 13 children (7-11 years old) during a cued equiprobable Go/Nogo task. The P300 component was measured at frontal (F3, Fz, F4) and parietal (P3, Pz, P4) regions in response to both Go and Nogo stimuli. Data were analyzes using a three-way repeated measures ANOVA.Results: These children displayed higher P300 amplitude in the Go relative to Nogo condition at parietal region. In addition, decrease in P300 latency was observed at the frontal in comparison to parietal region.Discussion: The results might suggest that the P300 is related to different processes or arise from different generators in execution and inhibition conditions

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

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

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

AbstractBenign 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