国际群体神经科学及队列研究研讨会

Welcome and update on Centre for Population Neuroscience and Stratified Medicine (PONS)

群体神经科学和分层医学中心欢迎致辞

Gunter Schumann

Leveraging multi-omics data towards a molecular and neurobiological understanding of mental illness

Sylvane Desrivieres

Abstracts：Mental illnesses represent a huge and growing burden for Europe, both at individual and societal level. A deeper molecular and neurobiological understanding of the interplay between genetic, epigenetic and environmental risk and resilience factors, including neural circuit alterations, is critical for the development of objective biomarkers and evidence-based interventions that will significantly improve mental health outcomes. I will present our strategies for fulfilling this aim using our deeply phenotyped neuroimaging clinical and population cohorts IMAGEN, STRATIFY and ESTRA.

Big data neuroimaging Standardisation of preprocessing and analysis using HALFpipe

Henrik Walter

Abstracts：In neuroimaging, sample size is critical. In order to achieve large sample sizes, in particular in clinical neuroimaging, it is necessary to analyze multisite data. This requires harmonized preprocessing, quality control and analysis, ideally using pipelines that are tuned for distributed (local) analyses and metaanalyses because of restrictions for raw data exchange. For structural MRI and resting state data there are existing protocols.

However, distributed analyses of task-based fMRI have been neglected. Here, I will present HALFpipe a containerized pipeline for interactive, reproducible, and efficient analyses for resting state and task-based fMRI data (1,2) that has been developed in our lab for the ENIGMA working group task-based fMRI. It is already used in several ENIGMA working groups (MDD, PTSD, TBI). We also use it in IMAGEN and the pipeline might be interesting for intercontinental cooperations. After a short presentation there will be time for specific questions regarding use and implementation.

(1) Waller et al. (2021) bioRxiv doi.org/10.1101/2021.05.07.442790

(2) https://github.com/HALFpipe

Analysis strategies for large scale imaging-genetic data

大规模影像遗传数据的分析策略

Tristram Lett

Abstracts：Analysis of multimodal neuroimaing and genetic data poses a number of difficult challenges. We introduce TFCE_mediation (github.com/trislett/TFCE_mediation), software that specializes at detecting putative causal effects for neuroimaging analyzes, and its application in IMAGEN in which brain structure mediates the genetic association with g-factor. Last, we discuss novel statistical analyses including sparse partial least squares.

Break

Beijing Cohort Study of School Functions and Child Brain:

Progresses and Its Possible Contributions to the National Cohort Study of Child Brain and Mind Development

北京学校功能与儿童大脑队列研究：

全国儿童脑和心智发展队列研究的进展及其可能的贡献

Sha Tao（陶沙）

Abstracts：How do children adjust to schooling and make progresses in their brain structural and functional development? And how do children’s brain and their school functions connect during the development? Exploration in this area would deepen our understanding of child brain and mind development, and further help transform developmental assessments and predictive models for developmental risks. Despite of huge progresses, mainly in the western countries, our understanding is significantly constrained by relatively small samples, cross-sectional designs, limited measures that may be observed in many existing studies. It is particularly the case for non-western countries, like China. Thus, prospective cohort studies of larger samples in more countries beyond the western countries are in great needs.

Since 2016, we have been conducting a cohort study on brain development and school function in school children in Beijing. We have 800 children completed the baseline assessments, and have been completing follow-up assessments yearly. For each year, we collected structural, resting and functional MRI imaging data, major cognitive skills, academic achievements, emotional and behavioral characteristics, etc. I will share the recent progresses of the Beijing Cohort Study, and also discuss the possible contributions that the Beijing Cohort Study may make to the coming Chinese national cohort study of school-age children brain and mind development.

An Update on the Current Progress of the Zhangjiang International Brain Biobank

Tianye Jia（贾天野）

Abstracts：Despite the serious disruption due to the pandemic, the Zhangjiang International Brain Biobank (ZIB) has been grown fast in the last two years. We have acquired data for more than 3500 individuals since April 2019, across the five brain disease cohorts (i.e. first-episode depression, first-episode schizophrenia, neurodegeneration, autism and stroke) and the populational college cohort. Also, the ZIB database has been officially launched, and its website could provide secured and convenient access to the data collected.

Break

Federated analysis of Neuroimaging Data with COINSTAC

Vince Calhoun

Abstracts：Data sharing efforts have propelled neuroimaging science forward at a more rapid pace. However, a large amount of data is not able to be shared due to regulatory or privacy issues. These data can still be combined within a larger analysis using decentralized/federated analysis in a way that does not allow access to individual level data, but does allow easy integration and analysis. Here we present the latest updates to COINSTAC, and open source platform for federated neuroimaging analysis. As we show, the COINSTAC approach both opens up sharing of much more data, and also democratizes sharing since it does not require a single site to obtain or have access to the computational resources or time required for a centralized analysis of multiple neuroimaging studies.

Updates from the ABCD study

Hugh Garavan

Abstracts：The ABCD study, a longitudinal single-cohort study of almost 12,000 children, is now in its 5th year. Recruited at ages 9 and 10 the 21-site study has a 98.4% retention rate. It’s fourth annual data release will be available through the NIMH Data Archive in the coming two months. Despite disruptions to in-person data collection arising from the COVID-19 pandemic, remote (on-line) data collection has continued. In-person data elements (e.g., MRI and biosamples) have been most affected. Over 60 separate grants to analyze ABCD data have been awarded and there are now approximately 200 publications utilizing ABCD data (https://abcdstudy.org/publications/ ). I will provide an overview of the study design and some early notable results.