Early development of sleep and brain functional connectivity in term-born and preterm infants

The proper development of sleep and sleep-wake rhythms during early neonatal life is crucial to lifelong neurological well-being. Recent data suggests that infants who have poor quality sleep demonstrate a risk for impaired neurocognitive outcomes. Sleep ontogenesis is a complex process, whereby alternations between rudimentary brain states-active vs. wake and active sleep vs. quiet sleep-mature during the last trimester of pregnancy. If the infant is born preterm, much of this process occurs in the neonatal intensive care unit, where environmental conditions might interfere with sleep. Functional brain connectivity (FC), which reflects the brain's ability to process and integrate information, may become impaired, with ensuing risks of compromised neurodevelopment. However, the specific mechanisms linking sleep ontogenesis to the emergence of FC are poorly understood and have received little investigation, mainly due to the challenges of studying causal links between developmental phenomena and assessing FC in newborn infants. Recent advancements in infant neuromonitoring and neuroimaging strategies will allow for the design of interventions to improve infant sleep quality and quantity. This review discusses how sleep and FC develop in early life, the dynamic relationship between sleep, preterm birth, and FC, and the challenges associated with understanding these processes. Impact Sleep in early life is essential for proper functional brain development, which is essential for the brain to integrate and process information. This process may be impaired in infants born preterm. The connection between preterm birth, early development of brain functional connectivity, and sleep is poorly understood. This review discusses how sleep and brain functional connectivity develop in early life, how these processes might become impaired, and the challenges associated with understanding these processes. Potential solutions to these challenges are presented to provide direction for future research.Peer reviewe

Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero

Fetal brain tissue annotation and segmentation challenge results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero

Early brain activity : Translations between bedside and laboratory

Neural activity is both a driver of brain development and a readout of developmental processes. Changes in neuronal activity are therefore both the cause and consequence of neurodevelopmental compromises. Here, we review the assessment of neuronal activities in both preclinical models and clinical situations. We focus on issues that require urgent translational research, the challenges and bottlenecks preventing translation of biomedical research into new clinical diagnostics or treatments, and possibilities to overcome these barriers. The key questions are (i) what can be measured in clinical settings versus animal experiments, (ii) how do measurements relate to particular stages of development, and (iii) how can we balance practical and ethical realities with methodological compromises in measurements and treatments.Peer reviewe

An ode to fetal, infant, and toddler neuroimaging: chronicling early clinical to research applications with MRI, and an introduction to an academic society connecting the field

Fetal, infant, and toddler neuroimaging is commonly thought of as a development of modern times (last two decades). Yet, this field mobilized shortly after the discovery and implementation of MRI technology. Here, we provide a review of the parallel advancements in the fields of fetal, infant, and toddler neuroimaging, noting the shifts from clinical to research use, and the ongoing challenges in this fast-growing field. We chronicle the pioneering science of fetal, infant, and toddler neuroimaging, highlighting the early studies that set the stage for modern advances in imaging during this developmental period, and the large-scale multi-site efforts which ultimately led to the explosion of interest in the field today. Lastly, we consider the growing pains of the community and the need for an academic society that bridges expertise in developmental neuroscience, clinical science, as well as computational and biomedical engineering, to ensure special consideration of the vulnerable mother-offspring dyad (especially during pregnancy), data quality, and image processing tools that are created, rather than adapted, for the young brain.UL1 TR001863 - NCATS NIH HHS; R01 MH117983 - NIMH NIH HHS; K24 MH127381 - NIMH NIH HHS; UL1 TR001873 - NCATS NIH HHS; TL1 TR001875 - NCATS NIH HHS; T32 MH018268 - NIMH NIH HHS; ZIA MH002782 - Intramural NIH HHS; UL1 TR003015 - NCATS NIH HHS; KL2 TR003016 - NCATS NIH HHS; R01 HD065762 - NICHD NIH HHS; R03 EB022754 - NIBIB NIH HHS; R21 HD095338 - NICHD NIH HHS; R01 HD093578 - NICHD NIH HHS; R01 HD099846 - NICHD NIH HHS; R01 HD100560 - NICHD NIH HHSPublished versio

Developing a portable, customizable, single-channel EEG device for homecare and validating it against a commercial EEG device

There are several commercial electroencephalography (EEG) devices on the market; however, affordable devices are not versatile for diverse research applications. The purpose of this project was to investigate how to develop a low-cost, portable, single-channel EEG system for a research institute that could be used for neurofeedback-related applications in homecare. A device comparison was intended to examine what system requirements such a system would need to achieve the secondary objective of developing a neurofeedback application that demonstrates the functionalities of the new device. A portable, single-channel EEG device prototype was realized that consisted of an amplifier module called EEG Click, a single-board microcontroller, an electrode cable, some disposable wet electrode pads, and a custom 3D-printed headband. Three pieces of software were developed: firmware for the prototype, two supporting computer applications for data recording, and visual neurofeedback. The neurofeedback application replayed a first-person view roller coaster video at a varying frame rate based on the theta band's mean power spectral density (PSD). The prototype was compared against a commercial device, InteraXon MUSE 2 (Muse). Technical measurements included determining the amplitude-frequency characteristics and signal quality, such as signal-to-noise ratio (SNR), spurious-free dynamic range (SFDR), and total harmonic distortion (THD). Furthermore, four physiological measurements were performed on six human test subjects, aged between 21-31 (mean: 26.0, std: 3.11), to compare the altered brain activity and induced artifacts between the two devices. The four tests were respiratory exercise, head movement exercise, eye movement exercise, and paced auditory serial addition test (PASAT), where each measurement included several epochs with various stimuli. After the recordings, PSD was calculated for each bandpass filtered epoch, then the spectra were split into theta (4-8 Hz), alpha (8-12 Hz), and beta bands (12-30 Hz). The PSD values were averaged within each frequency band, and then these baseline-corrected mean values were the input for the repeated measures ANOVA statistical analysis. Results revealed that the amplitude-frequency characteristic of the prototype was low-pass filter-like and had a smaller slope than Muse's. The prototype's SNR, including and excluding the first five harmonics, was 6 dB higher, while SFDR and THD for the first five harmonics were roughly the same as Muse's. The two devices were comparable in detecting changes in most physiological measurements. Some differences between the two devices were that Muse was able to detect changes in respiratory activity in the beta band (F(8,16) = 2.510, p = .056), while the prototype was more sensitive to eye movement, especially lateral and circular eye movement in theta (F(2,8) = 9.144, p = .009) and alpha (F(2,8) = 6.095, p = .025) bands. A low-cost, portable EEG prototype was successfully realized and validated. The prototype was capable of performing homecare neurofeedback in the theta band. The results indicated it is worth exploring further the capabilities of the prototype. Since the sample size was too small, more complex physiological measurements with more test subjects would be more conclusive. Nevertheless, the findings are promising; the prototype may become a product once

Analyse von Einflussfaktoren auf zerebrale Fluss- und Gewebevolumina im Lebenszeitverlauf

Obwohl die physiologische Gehirnentwicklung von großem wissenschaftlichem Interesse ist, sind die Zusammenhänge von zerebralen Flussvolumina und Gewebevolumina der grauen Substanz nicht breit erforscht und die Lebenszeitverläufe nicht abschließend geklärt. Die Haupthypothese unserer Studie war, dass sich die Lebenszeitverläufe der zerebralen Flussvolumina und der Gewebevolumina der grauen Substanz ähneln, sowie, dass die Gipfel zeitlich eng korreliert in der Präadoleszenz stattfinden. Wir analysierten die Rohdaten aus bereits durchgeführten Studien mittels farbduplexsonographischer Untersuchung der extrakraniellen Gefäße (Aa. carotides internae und Aa. vertebrales) zur Untersuchung des zerebralen Gesamtflussvolumens, sowie der Flussvolumina des vorderen und hinteren Stromgebietes. Eingeschlossen wurden 171 gesunde Probanden im Altersbereich von 3–87 Jahren (Datensatz 1). Zudem schlossen wir 1348 hochauflösende 3D MRT Datensätze (T1 gewichtete Sequenzen, 3T MRT) gesunder Probanden zwischen 2–86 Jahren zur Analyse der Gewebevolumina der grauen Substanz ein, welche im Rahmen der C-MIND, fCONN, und IXI Studien erhoben wurden (Datensatz 2). Die MRT Datensätze wurden mit SPM12 (University College London, UK) und der CAT12 Toolbox (Gaser und Dahnke, verfügbar unter http://www.neuro.uni-jena.de/cat/) innerhalb von Matlab (Mathworks, Natick, MA, USA) vorverarbeitet. Die Cerebromatic Toolbox wurde zur Erstellung eines gruppenspezifischen Templates genutzt. Es wurden (teilweise) veröffentlichte neonatale Startpunkte genutzt, um eine Ausbreitung des Altersbereichs über den gesamten Lebenszeitverlauf zu ermöglichen. Mittels eines Bootstrap Ansatzes und einer ‚sampling rate‘ von 0.8 wurden in einem iterativen Prozess 10.000 Subdatensätze generiert und an diese Polynomialfunktionen dritter Ordnung zur nichtlinearen Darstellung der Lebenszeitverläufe angepasst. Mithilfe dieser Methode konnte eine robuste Darstellung der Mediane und Interquartilsabstände erreicht werden. Die Berechnung des Fluss/Gewebevolumens erfolgte anhand der Division der Mediane des Blutflussvolumens durch die Mediane des Gewebevolumens in jedem Lebensmonat. Die statistische Auswertung erfolgte mittels Mann-Whitney-U-Test (Signifikanzniveau α = 0.05, Bonferroni korrigiert). Der Gipfel des zerebralen Gesamtflussvolumens (77 Monate/6.4 Jahre) wird deutlich vor dem Gipfel des Gewebevolumens der grauen Substanz (112 Monate/9.3 Jahre) erreicht. Sowohl Fluss- als auch Gewebevolumina sind in der Kindheit höher als im Erwachsenenalter. Während Jungen/Männer über den ganzen Lebenszeitverlauf höhere Gewebevolumina haben, zeigen Frauen ab einem Alter von 368 Monaten/30.7 Jahren höhere zerebrale Flussvolumina. Der Fluss/Gewebevolumen erreicht bereits in früher Kindheit sein Maximum (68 Monate/5.7 Jahre), wobei Mädchen einen späteren Gipfel erreichen, dann allerdings ab der Adoleszenz einen höheren Fluss/Gewebevolumen und einen kontinuierlichen Anstieg bis ins hohe Alter zeigen. Die Lebenszeitverläufe des zerebralen Flussvolumens und des Gewebevolumens der grauen Substanz ähneln sich somit, unterscheiden sich aber zeitlich. Da das Flussvolumen dem Gewebevolumen vorauseilt legt die These nahe, dass die Prozesse, welche zu höheren Gewebevolumina führen (Synaptogenese, Überproduktion an Neuronen) und mit einem hohen Glucosebedarf einhergehen, primär die hohen Flussvolumina bedingen. Auch die Geschlechterunterschiede der Fluss- und Gewebevolumina im Erwachsenenalter mit höheren Fluss- aber geringeren Gewebevolumina bei Frauen lassen darauf schließen, dass die Gewebevolumina der grauen Substanz nicht der treibende Faktor für höhere Flussvolumina sind. Das angewendete Bootstrap Verfahren erwies sich als gute Methode zur Auswertung des teilweise lückenhaften Sonographie Datensatzes, wobei der MRT Datensatz als stabile Referenz diente und sich viele Ergebnisse aus der Literatur bestätigen und neue Erkenntnisse dazu gewinnen ließen. Damit eignet sich der methodische Ansatz auch für zukünftige Auswertungen in kleineren Kohorten als stabile statistische Methode. Zudem ist der Kenntnisgewinn über die physiologischen Lebenszeitverläufe, Geschlechterunterschiede und Lateralisierungseffekte von enormer Wichtigkeit, um Abweichungen früher oder sensitiver zu erkennen

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Investigating the role of schizophrenia-associated gene expression in the developing human brain using Machine Learning

Schizophrenia is a debilitating condition that affects 1% of the population, causes significant hardship and though there are treatments available they are characterised by several limitations. It is a complex mental disorder where some individuals show mild subclinical cognitive symptoms before psychosis onset in adolescence. The treatments available only target a portion of the symptoms and although extensive research has been conducted, a comprehensive understanding of the nature of schizophrenia remains elusive. Unlike other neurodevelopmental disorders, schizophrenia symptoms do not typically present themselves until adolescence. This study aimed to discover gene co-expression networks at multiple developmental stages to identify candidate therapeutic targets to better treat and manage schizophrenia. Recent genome-wide association studies have identified 145 genetic loci associated with schizophrenia. Allen Brain Atlas’s BrainSpan resource provides brain development data from neurotypical brains. Using this resource, it was possible to study the gene expression of 316 schizophrenia-associated genes, identified previously in a large-scale GWAS, across each of the developmental stages available in the Allen Brain Atlas. K means Clustering and a systems biology approach (WGCNA) was applied to these schizophrenia-associated genes at each developmental stage where modules within networks were created by grouping co-expressed genes. To facilitate biological interpretation of these modules co-expressed genes were visualised using Cytoscape and gene ontology pathway enrichment analysis was applied. We identified 21 hub genes using WGCNA. Of the 316 schizophrenia-associated genes, 27 modules were identified and 3 hub genes GPR52, INA, SATB2 were common in multiple developmental stages. Our results suggest that GPR52, INA, SATB2 represent candidate genes for future evaluation of their potential as therapeutic targets of schizophrenia. Additional hub genes included TRANK1 and ALMS1, genes which were previously identified as expression quantitative trait loci. Taken together our results add further evidence that these genes could be good candidates for further research as they may regulate several schizophrenia-related genes in their respective modules. Finally, our enrichment analysis implicated a role for positive regulation of macrophage proliferation and cellular response to catecholamine stimulus, and cellular response to diacyl bacterial lipopeptide at each developmental stage. The immune system and catecholamines, including dopamine, have long been associated with schizophrenia and our results provide further support for these hypotheses

Investigating the role of Schizophrenia-associated gene expression in the developing human brain using Machine Learning

Schizophrenia is a debilitating condition that affects 1% of the population, causes significant hardship and though there are treatments available they are characterised by several limitations. It is a complex mental disorder where some individuals show mild subclinical cognitive symptoms before psychosis onset in adolescence. The treatments available only target a portion of the symptoms and although extensive research has been conducted, a comprehensive understanding of the nature of schizophrenia remains elusive. Unlike other neurodevelopmental disorders, schizophrenia symptoms do not typically present themselves until adolescence. This study aimed to discover gene co-expression networks at multiple developmental stages to identify candidate therapeutic targets to better treat and manage schizophrenia. Recent genome-wide association studies have identified 145 genetic loci associated with schizophrenia. Allen Brain Atlas’s BrainSpan resource provides brain development data from neurotypical brains. Using this resource it was possible to study the gene expression of 316 schizophrenia-associated genes, identified previously in a large-scale GWAS, across each of the developmental stages available in the Allen Brain Atlas. K means Clustering and a systems biology approach (WGCNA) was applied to these schizophrenia-associated genes at each developmental stage where modules within networks were created by grouping coexpressed genes. To facilitate biological interpretation of these modules co-expressed genes were visualised using Cytoscape and gene ontology pathway enrichment analysis was applied. We identified 21 hub genes using WGCNA. Of the 316 schizophrenia-associated genes, 27 modules were identified and 3 hub genes GPR52, INA, SATB2 were common in multiple developmental stages. Our results suggest that GPR52, INA, SATB2 represent candidate genes for future evaluation of their potential as therapeutic targets of schizophrenia. Additional hub genes included TRANK1 and ALMS1, genes which were previously identified as expression quantitative trait loci. Taken together our results add further evidence that these genes could be good candidates for further research as they may regulate several schizophrenia-related genes in their respective modules. Finally, our enrichment analysis implicated a role for positive regulation of macrophage proliferation and cellular response to catecholamine stimulus, and cellular response to diacyl bacterial lipopeptide at each developmental stage. The immune system and catecholamines, including dopamine, have long been associated with schizophrenia and our results provide further support for these hypotheses