BrainPrint statistics

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Citations per year

Number of citations per year for the bioinformatics software tool BrainPrint
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Tool usage distribution map

This map represents all the scientific publications referring to BrainPrint per scientific context
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BrainPrint specifications

Information


Unique identifier OMICS_22430
Name BrainPrint
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
Computer skills Advanced
Stability Stable
Requirements
FreeSurfer, ShapeDNA-tria
Maintained Yes

Subtool


  • ShapeDNA

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Documentation


Publications for BrainPrint

BrainPrint citations

 (3)
library_books

Identification of individual subjects on the basis of their brain anatomical features

2018
Sci Rep
PMCID: 5884835
PMID: 29618790
DOI: 10.1038/s41598-018-23696-6

[…] nclude that human brain structures are unique to individuals and can be used for subject identification. Wachinger have used a new transformation for analyzing the shape of the brain, which is called ShapeDNA. By using this ShapeDNA they are in the position to calculate eigenvalues and eigenfunctions of the Laplace Beltrami operator using a higher-order finite elements method (FEM with Dirichlet o […]

library_books

Multidimensional heritability analysis of neuroanatomical shape

2016
Nat Commun
PMCID: 5116071
PMID: 27845344
DOI: 10.1038/ncomms13291

[…] he 2D Laplace–Beltrami operator on each of these representations, yielding the LBS-based shape descriptor. A python implementation of this pipeline is freely available (http://reuter.mit.edu/software/brainprint). […]

library_books

Machine Learning Approaches: From Theory to Application in Schizophrenia

2013
PMCID: 3893837
PMID: 24489603
DOI: 10.1155/2013/867924

[…] nomial (degree = 3), and radial basis function, and the learning by example approach was introduced by adopting leave-one-out cross-validation procedure. Finally, the descriptor was compared with the ShapeDNA descriptor [], which, however, does not deal with multiple scales and takes into account only global information. Both the GHKS and the ShapeDNA descriptor produced the best results when the […]


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BrainPrint institution(s)
Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA; Massachusetts General Hospital, Harvard Medical School, MA, USA; University of California, San Diego, CA, USA; VA San Diego, Center of Excellence for Stress and Mental Health, CA, USA
BrainPrint funding source(s)
Supported in part by the Humboldt foundation, the National Cancer Institute (1K25-CA181632-01), the Martinos Center for Biomedical Imaging (P41-RR014075, P41-EB015896), the National Alliance for Medical Image Computing (U54- EB005149), the Neurolmaging Analysis Center (P41-EB015902) the National Center for Research Resources (U24 RR021382), the National Institute for Biomedical Imaging and Bioengineering (5P41EB015896-15, R01EB006758), the National Institute on Aging (AG022381, 5R01AG008122-22, AG018344, AG018386), the National Center for Alternative Medicine (RC1 AT005728-01), the national Institute for Neurological Disorders and Stroke (R01 NS052585-01, 1R21NS072652-01, 1R01NS070963, R01NS083534), and was made possible by the resources provided by Shared Instrumentation Grants 1S10RR023401, 1S10RR019307, and 1S10RR023043; by The Autism & Dyslexia Project funded by the Ellison Medical Foundation, and by the NIH Blueprint for Neuroscience Research (5U01-MH093765), part of the multi-institutional Human Connectome Project.

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