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Fast Lesion Extraction using Convolutional Neural Networks
FLEXCONN (Fast Lesion Extraction using Convolutional Neural Networks) is a toolbox for segmenting white matter lesions from multi-contrast MR images. Using T1-w and FLAIR images, a fully convolutional neural network (CNN) is trained using manually labeled training data. The trained CNN model can be applied to pre-processed pair of T1 and FLAIR images to generate a lesion membership as well as a hard segmentation. The algorithm is described in the following paper: https://arxiv.org/abs/1803.09172 Python codes for training and prediction are provided. Trained models using manually labeled atlases from ISBI-2015 challenge (https://www.nitrc.org/projects/longitudinal_ms) are also provided. The CNN is implemented in Tensorflow and Keras (https://github.com/fchollet/keras). This work was developed with funding support from the National Multiple Sclerosis Society (RG-1507-05243) and from the Center for Neuroscience and Regenerative Medicine in the Department of Defense.
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UNC Neonate and Infant Cortical Surface Atlases
UNC infant surface atlases of cortical structures include 2 atlases, 1) The UNC 4D infant cortical surface atlas, which includes surface atlases at 1, 3, 6, 9, 12, 18, 24, 36, 48, 60 and 72 months, thus densely covering and well characterizing the critical stages of the dynamic infant brain development; 2) The spatiotemporal neonatal cortical surface atlas, which mainly focuses on the neonatal cortical surface, includes surface atlases at 39, 40, 41, 42, 43, and 44 gestational weeks. Please refer to the following article for the atlases: Li, G., Wang, L., Shi, F., Gilmore, J. H., Lin, W., & Shen, D. (2015). Construction of 4D high-definition cortical surface atlases of infants: Methods and applications. Medical image analysis, 25(1). Wu Z., Li, G., Wang L., Lin, W., Gilmore, J.H., & Shen, D. (2018). Construction of spatiotemporal neonatal cortical surface atlas using a large-scale dataset. IEEE intern. Symp. on Biomed. Imaging (ISBI).
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Philadelphia Neurodevelopmental Cohort (PNC)
The Philadelphia Neurodevelopmental Cohort (PNC) initiative focuses on characterizing brain and behavior interaction with genetics. This is a collaborative research effort between the Brain Behavior Laboratory at the University of Pennsylvania and the Center for Applied Genomics at the Children’s Hospital of Philadelphia. The PNC includes a population-based sample of over 9500 individuals from the greater Philadelphia area, ages 8-21 years who received medical care at the CHOP network. The participants presented for diverse medical conditions, ranging from a well child visit and minor problems to chronic condition management to potentially life threatening health problems. Participants were genotyped during the time of their clinical visit and provided written permission to be recontacted for studies of complex pediatric disorders.
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Neo
Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these formats plus non-proprietary formats including HDF5. The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data (such as OpenElectrophy, Elephant, G-node, Helmholtz, PyNN) by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization.
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CONN : functional connectivity toolbox
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1000 Functional Connectomes Project
Can't Download ABIDE from nitrc
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