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Featured tool/resource

Fast Lesion Extraction using Convolutional Neural Networks

Graphic for 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.

Featured tool/resource

UNC 4D Infant Cortical Surface Atlas

Graphic for UNC 4D Infant Cortical Surface Atlas

UNC 4D longitudinal infant surface atlases of cortical structures from neonates to 6 years of age contain 11 time points, including 1 month, 3 months, 6 months, 9 months, 12 months, 18 months, 24 months, 36 months, 48 months, 60 months and 72 months, thus densely covering and well characterizing the critical stages of the dynamic early brain development. 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), 22-36. DOI: https://doi.org/10.1016/j.media.2015.04.005

Featured tool/resource

Philadelphia Neurodevelopmental Cohort (PNC)

Graphic for 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.

Featured tool/resource

Neo

Graphic for 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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Latest News

  • Apr 24

    acpcdetect v2.0 released

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Calendar

  • Apr 8

    Australian Course in Advanced Neuroscience 2018

  • Apr 27

    NIF Webinar: Mouse Phenome Database: An Integrative Database And Analysis Suite For Curated Empirical Phenotype Data, Dr. Molly Bogue

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    EMBO Workshop: Molecular neurobiology

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    BIT's 9th Annual World Congress of Neurotalk 2018

  • May 18

    Announcing Vision Sciences Society Eighteenth Annual Meeting

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    Eigth Summer School of the Centre of Neural Dynamics (SSCND8)

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    Barcelona Computational, Cognitive and Systems Neuroscience (BARCCSYN) 2018

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    Australian Connectomics School

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    2018 Nengo Summer School

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    National Academies Workshop on Harnessing Mobile Technology to Predict, Diagnose, Monitor, and Develop Treatments for Nervous System Disorders

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    Dendrites 2018: Dendritic anatomy, molecules and function

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    AREADNE 2018 Research in Encoding And Decoding of Neural Ensembles

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Recently Updated Files

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    locallabel: Local label learning (LLL) for multi-atlas based image segmentation

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    individual morphological connectivity using wavelet transform

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National Institutes of Health Funded by the NIH Grant numbers: 2R44NS074540 and 1U24EB023398a