Connecting the dots: Relating the infant brain network to infant behavior

In this dissertation, we aimed to explore the relationship between the development of networks in the infant's brain and infant behaviour. Ultimately, asking the question: whether differences in characteristics of infant brain networks could explain differences in social competency and behavioural control. This dissertation specifically focused on social competence and self-regulation during infancy, since both types of behaviour develop considerably during the first year of life. Before we could look into this relationship, we needed to make sure that infant EEG networks were reliable. Therefore, in chapter 2, we looked into the reliability of graph theoretical characteristics of infant EEG networks. We found that global metrics, metrics that are averaged over the entire brain, are generally highly reliable in both the theta and the low alpha frequency bands. Local metrics were less reliable. In chapter 3, we looked into the external factors influencing infant EEG data quality. The factors influencing data attrition described in this study can be broadly divided into three groups: child-related factors, testing-related factors, and longitudinal (study-specific) factors. Three child-related factors were found to influence data loss: gender, age, and head shape. Four testing-related factors were found to influence data loss: time of testing, the season of testing, the research assistant present during the experiment, and task length all had considerable influence on data. Lastly, data attrition rates of the first session of testing were found to be related to the second session of testing, underlining possible longitudinal biases in terms of data loss. After confirming acceptable reliability and data quality of our infant EEG data, we looked into the relationship between infant brain networks and behaviour. In chapter 4, we describe the development of the infant connectome during the first year of life and find a reorganization of the theta network between 5 and 10 months old. After this reorganization, the theta network becomes more responsive towards social cues versus non-social cues. Lastly, in chapter 5, we study whether the infant brain network can predict behaviour and vice versa. We find that infant self-regulation at 5 months old predicts brain network optimization at 10 months old. Conversely, we find that total theta brain network strength at 5 months old predicts self-regulation at 10 months old. Underlining the bidirectional relationship between brain networks and behaviour during development. This dissertation shows the promise of studying infant brain networks to explain infant behaviour. Infant brain network characteristics are reasonably reliable and offer us a unique insight into the optimization of the brain in the first year of life

Gastric cancers of Western European and African patients show different patterns of genomic instability

<p>Abstract</p> <p>Background</p> <p>Infection with <it>H. pylori </it>is important in the etiology of gastric cancer. Gastric cancer is infrequent in Africa, despite high frequencies of <it>H. pylori </it>infection, referred to as the African enigma. Variation in environmental and host factors influencing gastric cancer risk between different populations have been reported but little is known about the biological differences between gastric cancers from different geographic locations. We aim to study genomic instability patterns of gastric cancers obtained from patients from United Kingdom (UK) and South Africa (SA), in an attempt to support the African enigma hypothesis at the biological level.</p> <p>Methods</p> <p>DNA was isolated from 67 gastric adenocarcinomas, 33 UK patients, 9 Caucasian SA patients and 25 native SA patients. Microsatellite instability and chromosomal instability were analyzed by PCR and microarray comparative genomic hybridization, respectively. Data was analyzed by supervised univariate and multivariate analyses as well as unsupervised hierarchical cluster analysis.</p> <p>Results</p> <p>Tumors from Caucasian and native SA patients showed significantly more microsatellite instable tumors (p < 0.05). For the microsatellite stable tumors, geographical origin of the patients correlated with cluster membership, derived from unsupervised hierarchical cluster analysis (p = 0.001). Several chromosomal alterations showed significantly different frequencies in tumors from UK patients and native SA patients, but not between UK and Caucasian SA patients and between native and Caucasian SA patients.</p> <p>Conclusions</p> <p>Gastric cancers from SA and UK patients show differences in genetic instability patterns, indicating possible different biological mechanisms in patients from different geographical origin. This is of future clinical relevance for stratification of gastric cancer therapy.</p

The emergence of a theta social brain network during infancy

Infants’ socio-cognitive ability develops dramatically during the first year of life. From the perspective of ontogeny, the early development of social behavior allows for parent-child attachment, which in turn enhances survival. Thus, it is theorized that the development of social behavior, driven by social brain networks, forms the core of developmental acquisitions during this period. Further, understanding the maturation within the neural networks during social development is crucial to obtain a better grasp of the development of social developmental disorders. Therefore, we performed a longitudinal study in 854 infants measured at around 5 and 10 months to map the development of functional networks in the brain when infants were processing social and non-social videos. Using EEG, we focused on the frequency bands most commonly connected to social behavior: theta and alpha. We found that alpha networks remained relatively stable over the first year of life and showed no selectivity for social versus non-social stimuli, theta networks, showed strong global reconfigurations. The development o

Using multiple short epochs optimises the stability of infant EEG connectivity parameters

Abstract: Atypicalities in connectivity between brain regions have been implicated in a range of neurocognitive disorders. We require metrics to assess stable individual differences in connectivity in the developing brain, while facing the challenge of limited data quality and quantity. Here, we examine how varying core processing parameters can optimise the test–retest reliability of EEG connectivity measures in infants. EEG was recorded twice with a 1-week interval between sessions in 10-month-olds. EEG alpha connectivity was measured across different epoch lengths and numbers, with the phase lag index (PLI) and debiased weighted PLI (dbWPLI), for both whole-head connectivity and graph theory metrics. We calculated intra-class correlations between sessions for infants with sufficient data for both sessions (N’s = 19–41, depending on the segmentation method). Reliability for the whole brain dbWPLI was higher across many short epochs, whereas reliability for the whole brain PLI was higher across fewer long epochs. However, the PLI is confounded by the number of available segments. Reliability was higher for whole brain connectivity than graph theory metrics. Thus, segmenting available data into a high number of short epochs and calculating the dbWPLI is most appropriate for characterising connectivity in populations with limited availability of EEG data

Late development of cue integration is linked to sensory fusion in cortex.

Adults optimize perceptual judgements by integrating different types of sensory information [1, 2]. This engages specialized neural circuits that fuse signals from the same [3-5] or different [6] modalities. Whereas young children can use sensory cues independently, adult-like precision gains from cue combination only emerge around ages 10 to 11 years [7-9]. Why does it take so long to make best use of sensory information? Existing data cannot distinguish whether this (1) reflects surprisingly late changes in sensory processing (sensory integration mechanisms in the brain are still developing) or (2) depends on post-perceptual changes (integration in sensory cortex is adult-like, but higher-level decision processes do not access the information) [10]. We tested visual depth cue integration in the developing brain to distinguish these possibilities. We presented children aged 6-12 years with displays depicting depth from binocular disparity and relative motion and made measurements using psychophysics, retinotopic mapping, and pattern classification fMRI. Older children (>10.5 years) showed clear evidence for sensory fusion in V3B, a visual area thought to integrate depth cues in the adult brain [3-5]. By contrast, in younger children (<10.5 years), there was no evidence for sensory fusion in any visual area. This significant age difference was paired with a shift in perceptual performance around ages 10 to 11 years and could not be explained by motion artifacts, visual attention, or signal quality differences. Thus, whereas many basic visual processes mature early in childhood [11, 12], the brain circuits that fuse cues take a very long time to develop.Supported by UK ESRC grant RES-061-25-0523, the Wellcome Trust (095183/ Z/10/Z), JSPS (KAKENHI 26870911), NIH grant R01-MH-081990, a Royal Society Wolfson Research Merit Award, the Swire Trust, and the NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.cub.2015.09.04

Causal Factors of Increased Smoking in ADHD: A Systematic Review

Background: ADHD is a highly prevalent disorder and poses a risk for a variety of mental disorders and functional impairments into adulthood. One of the most striking comorbidities of ADHD is nicotine dependence. Youth diagnosed with ADHD are 2-3 times more likely to smoke than their peers without ADHD, initiate smoking earlier in life and progress more quickly and more frequently to regular use and dependence. Possible explanations for these increased risks are: (a) self-medication of ADHD symptoms with the stimulant nicotine; (b) ADHD symptoms like inattention and hyperactivity/impulsivity predispose for smoking initiation and impede smoking cessation; (c) peer pressure; and/or (d) common genetic or environmental determinants for ADHD and smoking. Objective: Identify the most probable causes of the high prevalence of smoking and nicotine dependence in subjects with ADHD. Methods: A systematic literature review was performed and the causality of the observed relations was ranked using the Bradford Hill criteria. Findings: ADHD medication reduces early smoking initiation and alleviates smoking withdrawal. Nicotine patches, bupropion and (probably) varenicline ameliorate ADHD symptoms. Imitation of and interaction with peers and genetic and environmental determinants may contribute to the comorbidity, but seem to contribute less than self-medication. Conclusion: Smoking is probably best explained by a combination of imitation, peer pressure and typical traits of ADHD. In contrast, the positive relation between ADHD and nicotine dependence is currently best explained by the self-medication hypothesis. This hypothesis has a clear pharmacological rationale and is supported by ample evidence, but awaits confirmation from longitudinal naturalistic studie

Connecting the dots: Relating the infant brain network to infant behavior

In this dissertation, we aimed to explore the relationship between the development of networks in the infant's brain and infant behaviour. Ultimately, asking the question: whether differences in characteristics of infant brain networks could explain differences in social competency and behavioural control. This dissertation specifically focused on social competence and self-regulation during infancy, since both types of behaviour develop considerably during the first year of life. Before we could look into this relationship, we needed to make sure that infant EEG networks were reliable. Therefore, in chapter 2, we looked into the reliability of graph theoretical characteristics of infant EEG networks. We found that global metrics, metrics that are averaged over the entire brain, are generally highly reliable in both the theta and the low alpha frequency bands. Local metrics were less reliable. In chapter 3, we looked into the external factors influencing infant EEG data quality. The factors influencing data attrition described in this study can be broadly divided into three groups: child-related factors, testing-related factors, and longitudinal (study-specific) factors. Three child-related factors were found to influence data loss: gender, age, and head shape. Four testing-related factors were found to influence data loss: time of testing, the season of testing, the research assistant present during the experiment, and task length all had considerable influence on data. Lastly, data attrition rates of the first session of testing were found to be related to the second session of testing, underlining possible longitudinal biases in terms of data loss. After confirming acceptable reliability and data quality of our infant EEG data, we looked into the relationship between infant brain networks and behaviour. In chapter 4, we describe the development of the infant connectome during the first year of life and find a reorganization of the theta network between 5 and 10 months old. After this reorganization, the theta network becomes more responsive towards social cues versus non-social cues. Lastly, in chapter 5, we study whether the infant brain network can predict behaviour and vice versa. We find that infant self-regulation at 5 months old predicts brain network optimization at 10 months old. Conversely, we find that total theta brain network strength at 5 months old predicts self-regulation at 10 months old. Underlining the bidirectional relationship between brain networks and behaviour during development. This dissertation shows the promise of studying infant brain networks to explain infant behaviour. Infant brain network characteristics are reasonably reliable and offer us a unique insight into the optimization of the brain in the first year of life