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Adolescent Brain Cognitive Development (ABCD) Study

The Adolescent Brain Cognitive Development (ABCD) Study is the largest long-term study of brain development and child health in the United States. The ABCD Research Consortium consists of a Coordinating Center, a Data Analysis and Informatics Center, and 21 research sites across the country, which will invite approximately 10,000 children ages 9-10 to join the study. Researchers will track their biological and behavioral development through adolescence into young adulthood. This study will determine how childhood experiences (such as sports, videogames, social media, unhealthy sleep patterns, and smoking) interact with each other and with a child’s changing biology to affect brain development and social, behavioral, academic, health, and other outcomes. The results of the ABCD Study will provide families; school superintendents, principals, and teachers; health professionals; and policymakers with practical information to promote the health, well-being, and success of children.

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TAPAS - Translational Algorithms for Psychiatry-Advancing Science

TAPAS is a collection of algorithms and software tools developed by the Translational Neuromodeling Unit (TNU, Zurich) and collaborators. The goal of these tools is to support clinical neuromodeling, particularly computational psychiatry, computational neurology, and computational psychosomatics. Currently, TAPAS includes the following packages: HGF: Hierarchical Gaussian Filter; Bayesian inference on computational processes from observed behavior HUGE: Variational Bayesian inversion for hierarchical unsupervised generative embedding MICP: Bayesian Mixed-effects Inference for Classification Studies MPDCM: Massively Parallel DCM; Efficient numerical integration PhysIO: Physiological Noise Correction for fMRI rDCM: Efficient inference on effective brain connectivity for fMRI. SEM: SERIA Model for (anti-)Saccadic Eye Movements and Reaction Times VBLM: Variational Bayesian Linear Regression TAPAS is written in MATLAB and distributed as open source code under GNU General Public License 3.0.

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NetPET: Covariance statistics and network analysis of brain PET imaging studies

The analysis of brain structural and functional imaging data using graph theory has increasingly become a popular approach since brain topological representations have proven to be helpful for visualising and understanding anatomical and functional relationships between different cerebral areas. NetPET is a tool for graph-based analysis of brain PET studies which exploits population-based covariance matrices. NetPET aims to explore topological tracer kinetics differences in cross-sectional investigations. Simulations, test-retest studies and applications to cross-sectional datasets from three different tracers ([18F]FDG, [18F]FDOPA and [11C]SB217045) and more than 400 PET scans have been used to test the applicability of NetPET in healthy controls and patients. Results showed good reproducibility and general applicability of the methods within the range of experimental settings typical of PET neuroimaging studies. NetPET is a Matlab-based software package.