Fontanel Size from Birth to 24 Months of Age in Iranian Children

How to Cite This Article: Esmaeili Mo, Esmaeili Ma, Ghane Sharbaf F, Bokharaie Sh. Fontanel Size from Birth to 24 Months of Age in Iranian Children. Iran J Child Neurol. Autumn 2015;9(4):15-23.AbstractObjectiveDiagnosis of abnormal fontanel size, a potential clue to recognition of different disorders, requires an understanding of the wide variation of normal fontanel size. The anterior fontanel is the largest, prominent and most important for clinical evaluation. The aim of this study was to establish and define normal range of fontanel size from birth to 24 months of age in healthy Iranian children that might be generalized to other populations.Materials & MethodsTotally, 550 subjects enrolled randomly in this cross sectional study. They were apparently normal healthy children, from birth to 24 months of age, including 208-term newborn and 342 infant from birth to 2 yr old. Fontanel size was measured and recorded as the mean of the length (anterior- posterior dimension) and width (transverse dimension). Mean anterior fontanel sizes in our samples were classified for periods of 3 months. Nomograms and statistical analyses were performed and depicted by Excel Microsoft Office 2007 and two-tailed t-test respectively.ResultsThe mean ±2SD of anterior fontanel size was 2.55±1.92 cm in newborns, 3.37±2.48 (largest size) in 3 months of age. It was closed in all cases in 15-18 months of age. The mean posterior fontanel size was 0.8 cm in newborns and closed in all infants in 2 months of age. There was no significant difference in anterior fontanel size between two genders except in newborn and 6-9 months old (P>0.05).ConclusionAbnormal fontanel can indicate a serious medical condition. Therefore, it is important to understand normal variations, to utilize standardized techniques for measurement and appropriate standards of normal range in different age groups and populations. This study provides a normal range of mean fontanel size in Iranian infants as a local reference. It might be generalized to other populations.

Skull defects in finite element head models for source reconstruction from magnetoencephalography signals

Magnetoencephalography (MEG) signals are influenced by skull defects. However, there is a lack of evidence of this influence during source reconstruction. Our objectives are to characterize errors in source reconstruction from MEG signals due to ignoring skull defects and to assess the ability of an exact finite element head model to eliminate such errors. A detailed finite element model of the head of a rabbit used in a physical experiment was constructed from magnetic resonance and co-registered computer tomography imaging that differentiated nine tissue types. Sources of the MEG measurements above intact skull and above skull defects respectively were reconstructed using a finite element model with the intact skull and one incorporating the skull defects. The forward simulation of the MEG signals reproduced the experimentally observed characteristic magnitude and topography changes due to skull defects. Sources reconstructed from measured MEG signals above intact skull matched the known physical locations and orientations. Ignoring skull defects in the head model during reconstruction displaced sources under a skull defect away from that defect. Sources next to a defect were reoriented. When skull defects, with their physical conductivity, were incorporated in the head model, the location and orientation errors were mostly eliminated. The conductivity of the skull defect material non-uniformly modulated the influence on MEG signals. We propose concrete guidelines for taking into account conducting skull defects during MEG coil placement and modeling. Exact finite element head models can improve localization of brain function, specifically after surgery

Impact of skull sutures, spongiform bone distribution, and aging skull conductivities on the EEG forward and inverse problems

Source imaging is a principal objective for electroencephalography (EEG), the solutions of which require forward problem (FP) computations characterising the electric potential distribution on the scalp due to known sources. Additionally, the EEG-FP is dependent upon realistic, anatomically correct volume conductors and accurate tissue conductivities, where the skull is particularly important. Skull conductivity, however, deviates according to bone composition and the presence of adult sutures. The presented study therefore analyses the effect the presence of adult sutures and differing bone composition have on the EEG-FP and inverse problem (IP) solutions. Utilising a well-established head atlas, detailed head models were generated including compact and spongiform bone and adult sutures. The true skull conductivity was considered as inhomogeneous according to spongiform bone proportion and sutures. The EEG-FP and EEG-IP were solved and compared to results employing homogeneous skull models, with varying conductivities and omitting sutures, as well as using a hypothesised aging skull conductivity model. Significant localised FP errors, with relative error up to 85%, were revealed, particularly evident along suture lines and directly related to the proportion of spongiform bone. This remained evident at various ages. Similar EEG-IP inaccuracies were found, with the largest (maximum 4.14 cm) across suture lines. It is concluded that modelling the skull as an inhomogeneous layer that varies according to spongiform bone proportion and includes differing suture conductivity is imperative for accurate EEG-FP and source localisation calculations. Their omission can result in significant errors, relevant for EEG research and clinical diagnosis

Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity

Electroencephalography (EEG) source imaging is an ill-posed inverse problem that requires accurate conductivity modelling of the head tissues, especially the skull. Unfortunately, the conductivity values are difficult to determine in vivo. In this paper, we show that the exact knowledge of the skull conductivity is not always necessary when the Bayesian approximation error (BAE) approach is exploited. In BAE, we first postulate a probability

Studying connectivity in the neonatal EEG

In humans the few months surrounding birth comprise a developmentally critical period characterised by the growth of major neuronal networks as well as their initial tuning towards more functionally mature large-scale constellations. Proper wiring in the neonatal brain, especially during the last trimester of pregnancy and the first weeks of postnatal life, relies on the brain’s endogenous activity and remains critical throughout one’s life. Structural or functional abnormalities at the stage of early network formation may result in a neurological disorder later during maturation. Functional connectivity measures based on an infant electroencephalographic (EEG) time series may be used to monitor these processes. A neonatal EEG is temporally discrete and consists of events (e.g., spontaneous activity transients (SATs)) and the intervals between them (inter-SATs). During early maturation, communication between areas of the brain may be transmitted through two distinct mechanisms: synchronisation between neuronal oscillations and event co-occurrences. In this study, we proposed a novel algorithm capable of assessing the coupling on both of these levels. Our analysis of real data from preterm neonates using the proposed algorithm demonstrated its ability to effectively detect functional connectivity disruptions caused by brain lesions. Our results also suggest that SAT synchronisation represents the dominant means through which inter-areal cooperation occurs in an immature brain. Structural disturbances of the neuronal pathways in the brain carry a frequency selective effect on the functional connectivity decreasing at the event level. Next, we used mathematical models and computational simulations combined with real EEG data to analyse the propagation of electrical neuronal activity within the neonatal head. Our results show that the conductivity of the neonatal skull is much higher than that found in adults. This leads to greater focal spread of cortical signals towards the scalp and requires high-density electrode meshes for quality monitoring of neonatal brain activity. Additionally, we show that the specific structure of the neonatal skull fontanel does not represent a special pathway for the spread of electrical activity because of the overall high conductivity of the skull. Finally, we demonstrated that the choice of EEG recording montage may strongly affect the fidelity of non-redundant neuronal information registration as well as the output of functional connectivity analysis. Our simulations suggest that high-density EEG electrode arrays combined with mathematical transformations, such as the global average or current source density (CSD), provide more spatially accurate details about the underlying cortical activity and may yield results more robust against volume conduction effects. Furthermore, we provide clear instruction regarding how to optimise recording montages for different numbers of sensors.Lähikuukaudet ennen ja jälkeen syntymää ovat ihmisillä hermoston kehityksen kannalta kriittisiä vaiheita, joita luonnehtii mittavien hermostollisten verkostojen kasvu sekä niiden alustava virittäytyminen toiminnallisesti kypsiksi suuren mittakaavan yhteenliittymiksi. Vastasyntyneen aivojen koko loppuelämään vaikuttavien hermoverkostojen muodostuminen määräytyy ensimmäisten syntymän jälkeisten viikkojen mutta erityisesti raskauden viimeisen kolmanneksen aikaisen aivojen sisäsyntyisen aktiivisuuden mukaan. Rakenteelliset tai toiminnalliset epämuodostumat näiden varhaisten hermoverkostojen muodostumisvaiheessa voi johtaa neurologisiin häiriöihin myöhemmässä kypsymisessä. Varhaisen kehityksen vaiheita voidaan valvoa vastasyntyneillä mittaamalla hermoyhteyksien toiminnallista liittyvyyttä aivosähkökäyrien (EEG) aikasarjojen avuilla. Vastasyntyneen aivosähkökäyrä on ajallisesti epäjatkuva ja koostuu lyhytkestoisista spontaanin aktiivisuuden tapahtumista, SATeista (Spontaneous Activity Transients) sekä niiden välisistä ajanhetkistä, inter-SATeista. Varhaisessa hermostollisessa kypsymisessä aivoalueiden välinen yhteydenpito voi tapahtua kahdella eri mekanismilla: hermostollisten oskillaatioiden välisellä synkronisaatioilla ja tapahtumien samanaikaisuudella. Tässä tutkimuksessa me kehitimme uuden matemaattisen mallin, algoritmin, jolla voi arvioida näiden kahden mekanismin välistä kytkeytymistä. Vastasyntyneiden keskosten mittausdataan pohjautuva analyysimme osoitti, että kehittämämme algoritmi on toimiva väline aivovaurioiden aiheuttamien toiminnallisten liittyvyyskatkoksien havaitsemisessa. Tuloksemme osoittavat myös, että SAT-synkronisaatio on aivoalueiden pääasiallisin yhteydenpitokeino kypsymättömissä vastasyntyneen aivoissa. Hermostollisten yhteyksien rakenteelliset epämuodostumat heikentävät toiminnallista liittyvyyttä taajuuskohtaisesti tapahtumatasolla. Seuraavaksi me käytimme matemaattisia malleja ja tietokonesimulaatioita yhdistettynä varsinaiseen EEG-mittausdataan analysoidaksemme sähköisen hermostollisen aktiivisuuden leviämistä vastasyntyneen päässä. Tulostemme mukaan vastasyntyneen kallon sähkönjohtavuus on paljon korkeampi kuin aikuisilla ihmisillä. Tämä aiheuttaa aivokuoren signaalien suurempaa paikallista leviämistä päänahkaa kohti, minkä takia vastasyntyneen aivoaktiivisuuden luotettava rekisteröinti vaatii enemmän ja tiheämmin mittauselektrodeja kuin aikuisilla. Lisäksi todistimme, että vastasyntyneen kallon aukileet eivät muodosta erityistä reittiä sähköisen aktiivisuuden leviämiselle, kallon suuren johtavuuden takia. Lopuksi osoitimme, että EEG-mittausasetelman valinta voi vahvasti vaikuttaa ei-päällekkäisen hermostollisen datan mittaustarkkuuteen ja sitä seuraaviin liittyvyysanalyyseihin. Simulaatiomme mukaan suuritiheyksinen EEG-mittauselektrodiasetelma yhdistettynä matemaattisiin muunnoksiin, kuten virtalähdetiheyden (Current Source Density) globaalikeskiarvoon, tarjoavat spatiaalisesti tarkkoja yksityiskohtia alla sijaitsevasta aivokuoren aktiivisuudesta ja voi erottaa selkeästi sekundääristen virtatihentymien osuuden. Lisäksi laadimme selkeät ohjeet kuinka optimoida mittausasetelma eri elektrodimäärille

Influence of the head model on EEG and MEG source connectivity analysis

The results of brain connectivity analysis using reconstructed source time courses derived from EEG and MEG data depend on a number of algorithmic choices. While previous studies have investigated the influence of the choice of source estimation method or connectivity measure, the effects of the head modeling errors or simplifications have not been studied sufficiently. In the present simulation study, we investigated the influence of particular properties of the head model on the reconstructed source time courses as well as on source connectivity analysis in EEG and MEG. Therefore, we constructed a realistic head model and applied the finite element method to solve the EEG and MEG forward problem. We considered the distinction between white and gray matter, the distinction between compact and spongy bone, the inclusion of a cerebrospinal fluid (CSF) compartment, and the reduction to a simple 3-layer model comprising only skin, skull, and brain. Source time courses were reconstructed using a beamforming approach and the source connectivity was estimated by the imaginary coherence (ICoh) and the generalized partial directed coherence (GPDC). Our results show that in both EEG and MEG, neglecting the white and gray matter distinction or the CSF causes considerable errors in reconstructed source time courses and connectivity analysis, while the distinction between spongy and compact bone is just of minor relevance, provided that an adequate skull conductivity value is used. Large inverse and connectivity errors are found in the same regions that show large topography errors in the forward solution. Moreover, we demonstrate that the very conservative ICoh is relatively safe from the crosstalk effects caused by imperfect head models, as opposed to the GPDC

Applied Visualization in the Neurosciences and the Enhancement of Visualization through Computer Graphics

The complexity and size of measured and simulated data in many fields of science is increasing constantly. The technical evolution allows for capturing smaller features and more complex structures in the data. To make this data accessible by the scientists, efficient and specialized visualization techniques are required. Maximum efficiency and value for the user can only be achieved by adapting visualization to the specific application area and the specific requirements of the scientific field. Part I: In the first part of my work, I address the visualization in the neurosciences. The neuroscience tries to understand the human brain; beginning at its smallest parts, up to its global infrastructure. To achieve this ambitious goal, the neuroscience uses a combination of three-dimensional data from a myriad of sources, like MRI, CT, or functional MRI. To handle this diversity of different data types and sources, the neuroscience need specialized and well evaluated visualization techniques. As a start, I will introduce an extensive software called \"OpenWalnut\". It forms the common base for developing and using visualization techniques with our neuroscientific collaborators. Using OpenWalnut, standard and novel visualization approaches are available to the neuroscientific researchers too. Afterwards, I am introducing a very specialized method to illustrate the causal relation of brain areas, which was, prior to that, only representable via abstract graph models. I will finalize the first part of my work with an evaluation of several standard visualization techniques in the context of simulated electrical fields in the brain. The goal of this evaluation was clarify the advantages and disadvantages of the used visualization techniques to the neuroscientific community. We exemplified these, using clinically relevant scenarios. Part II: Besides the data preprocessing, which plays a tremendous role in visualization, the final graphical representation of the data is essential to understand structure and features in the data. The graphical representation of data can be seen as the interface between the data and the human mind. The second part of my work is focused on the improvement of structural and spatial perception of visualization -- the improvement of the interface. Unfortunately, visual improvements using computer graphics methods of the computer game industry is often seen sceptically. In the second part, I will show that such methods can be applied to existing visualization techniques to improve spatiality and to emphasize structural details in the data. I will use a computer graphics paradigm called \"screen space rendering\". Its advantage, amongst others, is its seamless applicability to nearly every visualization technique. I will start with two methods that improve the perception of mesh-like structures on arbitrary surfaces. Those mesh structures represent second-order tensors and are generated by a method named \"TensorMesh\". Afterwards I show a novel approach to optimally shade line and point data renderings. With this technique it is possible for the first time to emphasize local details and global, spatial relations in dense line and point data.In vielen Bereichen der Wissenschaft nimmt die Größe und Komplexität von gemessenen und simulierten Daten zu. Die technische Entwicklung erlaubt das Erfassen immer kleinerer Strukturen und komplexerer Sachverhalte. Um solche Daten dem Menschen zugänglich zu machen, benötigt man effiziente und spezialisierte Visualisierungswerkzeuge. Nur die Anpassung der Visualisierung auf ein Anwendungsgebiet und dessen Anforderungen erlaubt maximale Effizienz und Nutzen für den Anwender. Teil I: Im ersten Teil meiner Arbeit befasse ich mich mit der Visualisierung im Bereich der Neurowissenschaften. Ihr Ziel ist es, das menschliche Gehirn zu begreifen; von seinen kleinsten Teilen bis hin zu seiner Gesamtstruktur. Um dieses ehrgeizige Ziel zu erreichen nutzt die Neurowissenschaft vor allem kombinierte, dreidimensionale Daten aus vielzähligen Quellen, wie MRT, CT oder funktionalem MRT. Um mit dieser Vielfalt umgehen zu können, benötigt man in der Neurowissenschaft vor allem spezialisierte und evaluierte Visualisierungsmethoden. Zunächst stelle ich ein umfangreiches Softwareprojekt namens \"OpenWalnut\" vor. Es bildet die gemeinsame Basis für die Entwicklung und Nutzung von Visualisierungstechniken mit unseren neurowissenschaftlichen Kollaborationspartnern. Auf dieser Basis sind klassische und neu entwickelte Visualisierungen auch für Neurowissenschaftler zugänglich. Anschließend stelle ich ein spezialisiertes Visualisierungsverfahren vor, welches es ermöglicht, den kausalen Zusammenhang zwischen Gehirnarealen zu illustrieren. Das war vorher nur durch abstrakte Graphenmodelle möglich. Den ersten Teil der Arbeit schließe ich mit einer Evaluation verschiedener Standardmethoden unter dem Blickwinkel simulierter elektrischer Felder im Gehirn ab. Das Ziel dieser Evaluation war es, der neurowissenschaftlichen Gemeinde die Vor- und Nachteile bestimmter Techniken zu verdeutlichen und anhand klinisch relevanter Fälle zu erläutern. Teil II: Neben der eigentlichen Datenvorverarbeitung, welche in der Visualisierung eine enorme Rolle spielt, ist die grafische Darstellung essenziell für das Verständnis der Strukturen und Bestandteile in den Daten. Die grafische Repräsentation von Daten bildet die Schnittstelle zum Gehirn des Menschen. Der zweite Teile meiner Arbeit befasst sich mit der Verbesserung der strukturellen und räumlichen Wahrnehmung in Visualisierungsverfahren -- mit der Verbesserung der Schnittstelle. Leider werden viele visuelle Verbesserungen durch Computergrafikmethoden der Spieleindustrie mit Argwohn beäugt. Im zweiten Teil meiner Arbeit werde ich zeigen, dass solche Methoden in der Visualisierung angewendet werden können um den räumlichen Eindruck zu verbessern und Strukturen in den Daten hervorzuheben. Dazu nutze ich ein in der Computergrafik bekanntes Paradigma: das \"Screen Space Rendering\". Dieses Paradigma hat den Vorteil, dass es auf nahezu jede existierende Visualiserungsmethode als Nachbearbeitunsgschritt angewendet werden kann. Zunächst führe ich zwei Methoden ein, die die Wahrnehmung von gitterartigen Strukturen auf beliebigen Oberflächen verbessern. Diese Gitter repräsentieren die Struktur von Tensoren zweiter Ordnung und wurden durch eine Methode namens \"TensorMesh\" erzeugt. Anschließend zeige ich eine neuartige Technik für die optimale Schattierung von Linien und Punktdaten. Mit dieser Technik ist es erstmals möglich sowohl lokale Details als auch globale räumliche Zusammenhänge in dichten Linien- und Punktdaten zu erfassen