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index.xml
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<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
<channel>
<title>Wanze Xie</title>
<link>https://happytudouni.github.io/</link>
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<description>Wanze Xie</description>
<generator>Source Themes Academic (https://sourcethemes.com/academic/)</generator><language>en-us</language><lastBuildDate>Wed, 26 May 2021 00:00:00 +0000</lastBuildDate>
<image>
<url>https://happytudouni.github.io/img/static/img/LABID.jpg</url>
<title>Wanze Xie</title>
<link>https://happytudouni.github.io/</link>
</image>
<item>
<title>EEG Phase Amplitude Coupling Strength and Phase Preference: Association with Age over the First Three Years After Birth</title>
<link>https://happytudouni.github.io/publication/mariscal-2021-eneuro/</link>
<pubDate>Wed, 26 May 2021 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/mariscal-2021-eneuro/</guid>
<description></description>
</item>
<item>
<title>Infants’ neural responses to emotional faces are related to maternal anxiety</title>
<link>https://happytudouni.github.io/publication/bowman-2021-jcpp/</link>
<pubDate>Sun, 16 May 2021 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/bowman-2021-jcpp/</guid>
<description></description>
</item>
<item>
<title>Converging neural and behavioral evidence for a rapid, generalized response to threat-related facial expressions in 3-year-old children</title>
<link>https://happytudouni.github.io/publication/xie-2021-ni/</link>
<pubDate>Sat, 30 Jan 2021 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/xie-2021-ni/</guid>
<description></description>
</item>
<item>
<title>Evoked and induced power oscillations linked to audiovisual integration of affect</title>
<link>https://happytudouni.github.io/publication/gao-2020-biopsy/</link>
<pubDate>Thu, 31 Dec 2020 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/gao-2020-biopsy/</guid>
<description></description>
</item>
<item>
<title>Group penalized generalized estimating equation for correlated event-related potentials and biomarker selection</title>
<link>https://happytudouni.github.io/publication/lin-2020-bmc/</link>
<pubDate>Tue, 01 Dec 2020 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/lin-2020-bmc/</guid>
<description></description>
</item>
<item>
<title>Face-sensitive brain responses in the first year of life</title>
<link>https://happytudouni.github.io/publication/conte-2020-ni/</link>
<pubDate>Sat, 08 Feb 2020 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/conte-2020-ni/</guid>
<description></description>
</item>
<item>
<title>Relating anthropometric indicators to brain structure in 2-month-old Bangladeshi infants growing up in poverty: a pilot study</title>
<link>https://happytudouni.github.io/publication/turesky-2020-ni/</link>
<pubDate>Mon, 13 Jan 2020 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/turesky-2020-ni/</guid>
<description></description>
</item>
<item>
<title>Growth faltering is associated with altered brain functional connectivity and cognitive outcomes in urban Bangladeshi children exposed to early adversity</title>
<link>https://happytudouni.github.io/publication/xie-2019-stunting/</link>
<pubDate>Tue, 22 Oct 2019 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/xie-2019-stunting/</guid>
<description></description>
</item>
<item>
<title>Chronic inflammation is associated with neural responses to faces in Bangladeshi children</title>
<link>https://happytudouni.github.io/publication/xie-2019-inflammation/</link>
<pubDate>Mon, 02 Sep 2019 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/xie-2019-inflammation/</guid>
<description></description>
</item>
<item>
<title>Multimodal Network Analysis</title>
<link>https://happytudouni.github.io/project/multimodal-network-analysis-in-children/</link>
<pubDate>Wed, 21 Aug 2019 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/project/multimodal-network-analysis-in-children/</guid>
<description><p>My colleagues and I are estimating brain network activity in children using both neural and hemodynamic methods. These estimates will then be used to predict neurodevelopmental outcome later in life as quantified by Mullen and WPPSI tests. We expect that the combined brain network connectivity measures calculated using combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) will be more informative than metrics calculated from either modality alone. To accomplish this task, we are using an MRI-based localization scheme (inverse reconstruction) to project both modalities to a common brain space for each individual. The children in this project had EEG and fNIRS data collected with comparable baseline paradigms in two different sections.</p>
<p>We will compare the functional connectivity from both modalities within the same network(s) and between the same brain ROIs. The brain functional connectivity and network measures (e.g., graph theory) from both metrics will be associated with adverse experiences (e.g., SES and inflammation) and will be used to predict cognitive outcomes. The predictive power of each modality and class of metrics will be assessed separately and together.</p>
<p><strong>Analysis plan</strong> <br/>
<strong><em>Datasets</em></strong> <br/>
The Bangladesh Early Adversity Neuroimaging (BEAN) dataset collected at 6, 24 and 36 months will be analyzed. This dataset was collected in a cohort of children exposed to early adversity as a consequence of growing up in an urban low-income neighborhood in Dhaka, Bangladesh. Task-free fNIRS and EEG were collected in each of these participants, and they also have neurodevelopmental outcomes measured at later time points (e.g., 24, 36 and 48 months). The BEAN dataset will be combined with the <a href="https://jerlab.sc.edu/projects/neurodevelopmental-mri-database/" target="_blank">Neurodevelopmental MRI Database</a>, which provides age appropriate MRIs, in order to do analyses for both EEG and fNIRS in brain space.</p>
<p><strong><em>Connectivity profiles in EEG and fNIRS</em></strong> <br/>
Brain functional connectivity analysis will be conducted with EEG and fNIRS data for each participant. The fNIRS functional connectivity will be estimated between the virtual channels on the scalp with corresponding 10-10 or 10-20 locations, as well as between cortical ROIs “localized” with the scalp-level fNIRS signals for both HbO and HbR signals using our recently developed inverse reconstruction methods (for fNIRS data). EEG functional connectivity will be estimated in different frequency bands (e.g., theta, alpha, beta, and gamma) between the 10-10, 10-20 locations corresponding to the fNIRS virtual channels on the scalp, as well as the cortical regions of interest (ROIs) in the brain that are reconstructed with EEG source analysis.</p>
<p>The inverse cortical reconstruction procedure will result in cortical activation in the same brain voxels (i.e., common space) for the two modalities. Inverse source reconstruction applies a spatial filter to signals recorded from the scalp to provide an estimate of the signal in “neural space.” Thus, the frame of analysis is no longer the external scalp location where the recording was done, but the cortical generators of scalp signal. One advantage of this method for the current project is that brain connectivity can be estimated from separately recorded EEG and fNIRS and reconstructed in a commensurable recording space, allowing for direct comparison in brain network activity between modalities.</p>
<p>Seed-based connectivity analysis will be conducted for both modalities using the same 10-10 locations and ROIs. The cortical areas detected by the fNIRS channels will be used as the “seeds”. The brain voxels/volumes in these seed areas will be used as the target sources for EEG connectivity analysis. As a result, cortical functional connectivity can be estimated for the two modalities (EEG and fNIRS) between the same brain ROIs for each participant.</p>
<p>Age-appropriate structural MRIs will be used for the localization of the fNIRS and EEG signals. An anatomical MRI close in head size and age will be selected for each Bangladeshi infant from the Neurodevelopmental MRI Database (Richards, Sanchez, Phillips-Meek, &amp; Xie, 2016). Utilizing age-appropriate MRIs will improve the accuracy of the localization of EEG and fNIRS signals (Lloyd-Fox et al., 2014; Xie, Mallin, &amp; Richards, 2018; Xie &amp; Richards, 2017). This is especially important for infant participants, as changes in size, structure, and topology of the brain occur even within a narrowly defined age range.</p>
<p>Graph theory measures will finally be applied to capture the organization and overall architecture of the brain networks defined with EEG and fNIRS signals. Degree of nodes, betweeness centrality, and hub dependence will be measured to estimate the organization of the fNIRS and EEG brain networks, e.g., which brain areas are hubs in the brain functional networks during the task-free state. Path length, clustering coefficient, and small-worldness will be estimated to measure the local and global network efficiency in information transmission.</p>
</description>
</item>
<item>
<title>The neural sources of N170: Understanding timing of activation in face‐selective areas</title>
<link>https://happytudouni.github.io/publication/gao-2019-psychophysiology/</link>
<pubDate>Sat, 01 Jun 2019 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/gao-2019-psychophysiology/</guid>
<description></description>
</item>
<item>
<title>Neural correlates of facial emotion processing in infancy</title>
<link>https://happytudouni.github.io/publication/xie-2019-emotion/</link>
<pubDate>Wed, 01 May 2019 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/xie-2019-emotion/</guid>
<description></description>
</item>
<item>
<title>Neural correlates of early adversity among Bangladeshi infants</title>
<link>https://happytudouni.github.io/publication/jensen-2019-sr/</link>
<pubDate>Fri, 01 Mar 2019 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/jensen-2019-sr/</guid>
<description></description>
</item>
<item>
<title>Development of brain functional connectivity and its relation to infant sustained attention in the first year of life</title>
<link>https://happytudouni.github.io/publication/xie-2019-fc/</link>
<pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/xie-2019-fc/</guid>
<description></description>
</item>
<item>
<title>Development of infant sustained attention and its relation to EEG oscillations: An EEG and cortical source analysis study</title>
<link>https://happytudouni.github.io/publication/xie-2018-attnpow/</link>
<pubDate>Tue, 01 May 2018 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/xie-2018-attnpow/</guid>
<description></description>
</item>
<item>
<title>Emotion Perception Project</title>
<link>https://happytudouni.github.io/project/emotion/</link>
<pubDate>Fri, 27 Apr 2018 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/project/emotion/</guid>
<description><p><strong>Project Description</strong> <br/>
Paying attention to facial emotion is an essential building block of social attention that develops dramatically in early childhood and lays the foundation for later socioemotional and prosocial behavior. The purpose of the <a href="http://www.childrenshospital.org/research/labs/nelson-laboratory/ongoing-research/typical-development" target="_blank">Emotion Project</a> is to investigate the development of emotion processing in the first few years of life. Specifically, we are interested in how emotion processing changes from infancy to childhood and how it may be related to other cognitive domains, temperament, and physiology in children. In the lab, we measure brain activity and eye movements while young kids watch pictures of people displaying different emotions.</p>
<p><strong>Recent &amp; Ongoing Studies</strong> <br/>
One recent study examined the event-related potential (ERP) and cortical source responses of infants to happy, fearful, and angry faces at 5, 7, and 12 months of age <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/desc.12758" target="_blank">(Xie, McCormick, Westerlund, Bowman, &amp; Nelson, 2019)</a>. Reconstructed source activities in major cortical components (ROIs) of the face and emotion networks were estimated and compared between different emotion conditions. The findings of the study offer convergent evidence that 5 to 7 months is a critical period for development of the “fear-bias” in infants and provides novel insight into the development of the neural bases of infant social attention. We are currently extending these ERP and cortical source analyses with the infants&rsquo; data to their longitudinal data collected at 3 years of age.</p>
<p><strong><em>Could children’s (infants and 3 years old) brain responses to emotional expressions explain their looking behaviors in the eye-tracking task showing these emotions?</em></strong> This question is being tested in an ongoing study, in which the relation between children’s “ERP and behavioral fear-bias” is analyzed.</p>
<p><strong><em>Do children’s looking behaviors to emotional expressions also differ with their temperament traits?</em></strong> My colleague <a href="http://joebathelt.com/" target="_blank">(Joe Bathelt)</a> and I are investigating whether data-driven subgroups defined by community clustering of children&rsquo;s temperament traits show differences in their looking behaviours in the eye-tracking task. Please see <a href="https://doi.org/10.1016/j.jaac.2018.01.014" target="_blank">Bathelt et al. (2018, JAACAP)</a> as a reference for these machine learning and graph theory techniques. Preliminary results and programs (written in Python) for the community clustering analysis could be found <a href="https://github.com/happytudouni/CommunityDetection" target="_blank">here</a>.</p>
</description>
</item>
<item>
<title>BEAN Project</title>
<link>https://happytudouni.github.io/project/bean/</link>
<pubDate>Thu, 27 Apr 2017 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/project/bean/</guid>
<description><p><strong>Project Description</strong> <br/>
The <a href="https://www.lcn-bean.org/" target="_blank">Bangladesh Early Adversity Imaging (BEAN) Project</a> aims to investigate how does a child’s exposure to early adverse experiences could influence their brain and cognitive development. The project is funded by the <a href="https://www.gatesfoundation.org" target="_blank">Bill &amp; Melinda Gates Foundation</a>. Neuroimaging data (EEG, fNIRS and MRI) and behavioral assessments are collected in <a href="https://goo.gl/maps/pYNDhXZc8HNMdESL6" target="_blank">Dhaka, Bangladesh</a>.</p>
<p><strong>Recent &amp; Ongoing Studies</strong> <br/>
<strong><em>How deviations in early experience affect social attention and shape brain networks in children?</em></strong> <br/>
In a recent study, my colleagues and I <a href="https://www.sciencedirect.com/science/article/pii/S1053811919307013?via%3Dihub" target="_blank">(Xie et al., 2019)</a> found that chronic inflammation is associated with decreased neural differentiation between familiar and novel faces in Bangladeshi children. In specific, children who more frequently had elevated C-reactive protein (CRP) concentration, a biomarker of systemic inflammation, showed smaller difference between their brain responses to familiar and novel faces.</p>
<p>In another study, I worked with the team to explore the neural pathways by which stunting and poverty may affect future cognitive outcomes from a brain network perspective <a href="http://biorxiv.org/cgi/content/short/447722v1" target="_blank">(Xie et al., In press, BMC Medicine)</a>. The pipeline I developed in <a href="https://doi.org/10.1111/desc.12703" target="_blank">Xie, Mallin, &amp; Richards (2019)</a> to study EEG brain functional connectivity became a valuable tool for this line of research. Using structural equation modeling, we found that EEG brain functional connectivity at 36 months mediates the relation between growth faltering (measured between 24 and 36 months) and children’s future IQ (measured at 48 months).</p>
</description>
</item>
<item>
<title>The Relation between Infant Covert Orienting, Sustained Attention and Brain Activity</title>
<link>https://happytudouni.github.io/publication/xie-2017-bt/</link>
<pubDate>Wed, 01 Mar 2017 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/xie-2017-bt/</guid>
<description></description>
</item>
<item>
<title>Effects of interstimulus intervals on behavioral, heart rate, and event-related potential indices of infant engagement and sustained attention</title>
<link>https://happytudouni.github.io/publication/xie-2016-psyp/</link>
<pubDate>Mon, 01 Aug 2016 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/xie-2016-psyp/</guid>
<description></description>
</item>
<item>
<title>A database of age-appropriate average MRI templates</title>
<link>https://happytudouni.github.io/publication/richards-2016-ni/</link>
<pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/richards-2016-ni/</guid>
<description></description>
</item>
<item>
<title>The construction of MRI brain/head templates for Chinese children from 7 to 16 years of age</title>
<link>https://happytudouni.github.io/publication/xie-2015-dcn/</link>
<pubDate>Sat, 01 Aug 2015 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/xie-2015-dcn/</guid>
<description></description>
</item>
<item>
<title>Comparison of the brain development trajectory between Chinese and US children and adolescents</title>
<link>https://happytudouni.github.io/publication/xie-2015-frontiers/</link>
<pubDate>Mon, 02 Feb 2015 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/xie-2015-frontiers/</guid>
<description></description>
</item>
<item>
<title>Brains for all the ages: structural neurodevelopment in infants and children from a life-span perspective</title>
<link>https://happytudouni.github.io/publication/richardsxie-2016-bc/</link>
<pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate>
<guid>https://happytudouni.github.io/publication/richardsxie-2016-bc/</guid>
<description></description>
</item>
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