Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in autism

Background: Autism spectrum disorders (ASD) are increasingly prevalent and have a significant impact on the lives of patients and their families. Currently, the diagnosis is determined by clinical judgment and no definitive physiological biomarker for ASD exists. Quantitative biomarkers obtainable from clinical neuroimaging data – such as the scalp electroencephalogram (EEG) - would provide an important aid to clinicians in the diagnosis of ASD. The interpretation of prior studies in this area has been limited by mixed results and the lack of validation procedures. Here we use retrospective clinical data from a well-characterized population of children with ASD to evaluate the rhythms and coupling patterns present in the EEG to develop and validate an electrophysiological biomarker of ASD. Methods: EEG data were acquired from a population of ASD (n = 27) and control (n = 55) children 4–8 years old. Data were divided into training (n = 13 ASD, n = 24 control) and validation (n = 14 ASD, n = 31 control) groups. Evaluation of spectral and functional network properties in the first group of patients motivated three biomarkers that were computed in the second group of age-matched patients for validation. Results: Three biomarkers of ASD were identified in the first patient group: (1) reduced posterior/anterior power ratio in the alpha frequency range (8–14 Hz), which we label the “peak alpha ratio”, (2) reduced global density in functional networks, and (3) a reduction in the mean connectivity strength of a subset of functional network edges. Of these three biomarkers, the first and third were validated in a second group of patients. Using the two validated biomarkers, we were able to classify ASD subjects with 83 % sensitivity and 68 % specificity in a post-hoc analysis. Conclusions: This study demonstrates that clinical EEG can provide quantitative biomarkers to assist diagnosis of autism. These results corroborate the general finding that ASD subjects have decreased alpha power gradients and network connectivities compared to control subjects. In addition, this study demonstrates the necessity of using statistical techniques to validate EEG biomarkers identified using exploratory methods. Electronic supplementary material The online version of this article (doi:10.1186/s12883-015-0355-8) contains supplementary material, which is available to authorized users

Physiological consequences of abnormal connectivity in a developmental epilepsy

Objective Many forms of epilepsy are associated with aberrant neuronal connections, but the relationship between such pathological connectivity and the underlying physiological predisposition to seizures is unclear. We sought to characterize the cortical excitability profile of a developmental form of epilepsy known to have structural and functional connectivity abnormalities. Methods We employed transcranial magnetic stimulation (TMS) with simultaneous electroencephalographic (EEG) recording in 8 patients with epilepsy from periventricular nodular heterotopia and matched healthy controls. We used connectivity imaging findings to guide TMS targeting and compared the evoked responses to single-pulse stimulation from different cortical regions. Results Heterotopia patients with active epilepsy demonstrated a relatively augmented late cortical response that was greater than that of matched controls. This abnormality was specific to cortical regions with connectivity to subcortical heterotopic gray matter. Topographic mapping of the late response differences showed distributed cortical networks that were not limited to the stimulation site, and source analysis in 1 subject revealed that the generator of abnormal TMS-evoked activity overlapped with the spike and seizure onset zone. Interpretation Our findings indicate that patients with epilepsy from gray matter heterotopia have altered cortical physiology consistent with hyperexcitability, and that this abnormality is specifically linked to the presence of aberrant connectivity. These results support the idea that TMS-EEG could be a useful biomarker in epilepsy in gray matter heterotopia, expand our understanding of circuit mechanisms of epileptogenesis, and have potential implications for therapeutic neuromodulation in similar epileptic conditions associated with deep lesions

Quantitative Analysis of Cell Migration Using Optical Flow

International audienceNeural crest cells exhibit dramatic migration behaviors as they populate their distant targets. Using a line of zebrafish expressing green fluorescent protein (sox10:EGFP) in neural crest cells we developed an assay to analyze and quantify cell migration as a population, and use it here to characterize in detail the subtle defects in cell migration caused by ethanol exposure during early development. The challenge was to quantify changes in the in vivo migration of all Sox10:EGFP expressing cells in the visual field of time-lapse movies. To perform this analysis we used an Optical Flow algorithm for motion detection and combined the analysis with a fit to an affine transformation. Through this analysis we detected and quantified significant differences in the cell migrations of Sox10:EGFP positive cranial neural crest populations in ethanol treated versus untreated embryos. Specifically, treatment affected migration by increasing the left-right asymmetry of the migrating cells and by altering the direction of cell movements. Thus, by applying this novel computational analysis, we were able to quantify the movements of populations of cells, allowing us to detect subtle changes in cell behaviors. Because cranial neural crest cells contribute to the formation of the frontal mass these subtle differences may underlie commonly observed facial asymmetries in normal human populations.Les cellules de la crête neurale peuvent présenter des comportements migratoires cliniquement dramatiques lors de leur migration distante. En utilisant un marqueur exprimant la protéine fluorescente verte (SOX10: EGFP) dans les cellules de la crête neurale, nous avons développé un test pour analyser et quantifier la migration cellulaire en tant que population, et caractériser en détail les défauts subtils dans la migration cellulaire causée par l'exposition à l'éthanol, au début du développement neural. Le défi était de quantifier les changements dans la migration in vivo de toutes les cellues de type Sox10:EGFP observable dans le champ visuel des films réalisés à très basse fréquence. Pour réaliser cette analyse, nous avons utilisé un algorithme de flux optique pour la détection de mouvement et combiné l'analyse avec un ajustement par une transformation affine. Grâce à cette analyse, nous avons détectés et quantifiés des différences notoires dans les migrations cellulaires de populations soumises à l'éthanol, au regard d'embryons non traités. Plus précisément, la migration est affectée par unec augmentation de l'asymétrie gauche-droite des cellules migrantes et une modification de la direction des mouvements cellulaires. En appliquant cette analyse computationnelle, nous avons été en mesure de quantifier les mouvements de populations de cellules, permettant de détecter des changements subtils dans les comportements cellulaires. Les cellules de la crête neurale crânienne contribuant à la formation de la masse cranienne antérieure, ces asymétries sous-tendent fréquemment des asymétries faciales observés dans les populations humaines concernées

Quantitative Analysis of Cell Migration Using Optical Flow

<div><p>Neural crest cells exhibit dramatic migration behaviors as they populate their distant targets. Using a line of zebrafish expressing green fluorescent protein (<i>sox10:EGFP</i>) in neural crest cells we developed an assay to analyze and quantify cell migration as a <i>population</i>, and use it here to characterize in detail the subtle defects in cell migration caused by ethanol exposure during early development. The challenge was to quantify changes in the <i>in vivo</i> migration of all Sox10:EGFP expressing cells in the visual field of time-lapse movies. To perform this analysis we used an Optical Flow algorithm for motion detection and combined the analysis with a fit to an affine transformation. Through this analysis we detected and quantified significant differences in the cell migrations of Sox10:EGFP positive cranial neural crest populations in ethanol treated versus untreated embryos. Specifically, treatment affected migration by increasing the left-right asymmetry of the migrating cells and by altering the direction of cell movements. Thus, by applying this novel computational analysis, we were able to quantify the movements of populations of cells, allowing us to detect subtle changes in cell behaviors. Because cranial neural crest cells contribute to the formation of the frontal mass these subtle differences may underlie commonly observed facial asymmetries in normal human populations.</p></div

Analysis of cell migration at the population level reveals EtOH induced differences in CNCC migration patterns.

<p>Representative examples of A) Control, embryos treated in B) 100 mM and C) 200 mM EtOH. 1–3) Video frames captured from time-lapse movies at beginning (0 h) middle (3 h) and end (6 h) of the recording period. 4) Cartoon of a 20–22 somite embryo, showing location of CNCC (green) at the end of migration. 5–7) Color-coded image showing the movement vectors detected by OF analysis. 8) Movement vectors summed for the complete six-hour sequence. The inset is the color scale representing direction and magnitude of movement. Color intensity represents movement magnitude and a given color hue represents the movement’s direction. Dorso-Frontal views: posterior to the top. Scale Bar = 50 µm. nt = neural tube, e = developing eye, op = olfactory placode. Anterior movements are toward the bottom of the page.</p

Optical Flow analyses of net cell movement.

<p>A, A’) Two consecutive frames of a time-lapse movie. B) Optical Flow analysis yields a vector field that is represented in a color-coded manner. As shown in the inset, color hue represents direction of movement and color intensity represents magnitude of movement. C) Polar histogram showing the distribution of the summed angles of motion vectors detected by OF. Blue and red bars depict binned movement in the left and right side of the field, respectively. Red and blue arrows represent the average vector of movement, and the green arrow is the average vector over the whole field. Scale Bar = 50 µm. Anterior movements are toward the bottom of the page.</p

Optical Flow averaged cell movements are different in EtOH treated vs. control groups.

<p>OF analysis were averaged and represented over time as net displacement reflecting: A) Posterior-Anterior cell movements over time (negative numbers in the Y-axis represent anterior cell movements) and B) Horizontal cell movements (medial and lateral) over time (negative numbers in the Y-axis represent movements towards the middle of the field). Lines show the sum of two sigmoid functions. n = 5 per group. a = anterior, p = posterior, L = lateral movements M = medial movements. Error bars = SEM.</p