8 research outputs found
brainlife.io: a decentralized and open-source cloud platform to support neuroscience research
Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants
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Improving Structural Brain Connectomes through Statistical Evaluation via Model Optimization
Accurate mapping of the structural brain connectomes is fundamental to understanding the role of white matter in health and disease. Diffusion-weighted magnetic resonance imaging (dMRI) and fiber tractography provide the only way to map brain connectomes in living human brains. Several studies have shown technical gaps in robustly mapping brain connectomes. The lack of connectome evaluation methods is evident from the recent findings. The present work focuses on developing methods for the statistical evaluation of brain connectomes. We present a new method that builds on LiFE and COMMIT2 methods to reduce a candidate tractography to an optimized one by identifying the brain connections that best model the dMRI signal. We used sparse group regularization, which requires finding a parameter (λ) for the trade-off between better fitting the signal with individual streamlines while maintaining the bundle's cohesion. Previous methods using regularizations to evaluate connectomes set fixed λs, refitting the model for several values of λ. We propose an efficient approach to selecting the optimal λ value. We performed experiments to test the complexity and efficacy of the approach using two datasets: simulated and real datasets. The simulated data were generated using Phantomas, with simple bundles and tissue factors. In addition, we used diffusion data from the Human Connectome Project (HCP). Results show that our approach can identify the optimal λ in a reliable amount of time. The full λ optimization process for 100 different λ took 17 min on a standard desktop computer, while it takes 4x more time than COMMIT 2 to select the optimal λ. In addition, the model's mean squared error is 0.0036 for the HCP dataset and 3.89e-5 for the simulated dataset. This is 14.78x less than COMMIT 2 (0.0544). The reduction in error is due precisely to the optimized selection of λ.Texas Advanced Computing Center (TACC
lsa-pucrs/acerta-abide: Code companion to Neuroimage: Clinical submission
Code companion to the paper "Identification of Autism Spectrum Disorder using Deep Learning and the ABIDE Dataset" submitted to Neuroimage: Clinica
Identification of autism spectrum disorder using deep learning and the ABIDE dataset
The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model. Keywords: Autism, fMRI, ABIDE, Resting state, Deep learnin