Lecturas importantes

• Why Most Published Research Findings Are False: http://robotics.cs.tamu.edu/RSS2015NegativeResults/pmed.0020124.pdf
• 1,500 scientists lift the lid on reproducibility: https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970
• Power failure: why small sample size undermines the reliability of neuroscience: https://www.nature.com/articles/nrn3475
• A manifiesto for reproducible science: https://www.nature.com/articles/s41562-016-0021.pdf
• Scanning the Horizon: Towards transparent and reproducible neuroimaging research: https://www.biorxiv.org/content/biorxiv/early/2016/10/25/059188.full.pdf
• Best Practices in Data Analysis and Sharing in Neuroimaging using MRI: https://www.biorxiv.org/content/biorxiv/early/2016/05/20/054262.full.pdf

Sitios de interés

• Center for reproducible neuroscience: http://reproducibility.stanford.edu
• Human Connectome Project: https://www.humanconnectome.org

Herramientas

• p-hacker: http://shinyapps.org/apps/p-hacker/
• NeuroPowerTools: http://neuropowertools.org
• BIDS: http://bids.neuroimaging.io
• Neurosynth: http://neurosynth.org
• Nipype: http://nipype.readthedocs.io/en/latest/
• Jupyter: http://jupyter.org
• Docker: https://www.docker.com
• Github: https://github.com/topics/neuroscience
• Open Science Framework: https://osf.io
• ClinicalTrials: https://clinicaltrials.gov

Bases de datos libres

• OASIS: http://www.oasis-brains.org
• Child Mind Institute: http://fcon_1000.projects.nitrc.org
• NKI: http://fcon_1000.projects.nitrc.org/indi/pro/nki.html
• ABIDE: http://fcon_1000.projects.nitrc.org/indi/abide/
• Neurovault: https://neurovault.org
• OpenfMRI: https://openfmri.org
• Un ejemplo de HCP: https://www.humanconnectome.org/study/hcp-young-adult/data-releases
• ADNI: http://adni.loni.usc.edu

• FSL:https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation
• SPM: http://www.fil.ion.ucl.ac.uk/spm/
• CONN: https://www.nitrc.org/projects/conn
• AFNI: https://afni.nimh.nih.gov

Programación

• Bash:

  • https://www.datacamp.com/courses/introduction-to-shell-for-data-science
  • https://www.codecademy.com/learn/learn-the-command-line

• MATLAB:

  • https://www.coursera.org/learn/matlab

• Python:

  • https://www.datacamp.com/courses/intro-to-python-for-data-science

• R:

  • https://www.datacamp.com/courses/free-introduction-to-r

• Python y Neuroimagen: http://nipy.org
• R y Neuroimagen: https://neuroconductor.org
• Python y paradigmas experimentales: http://www.psychopy.org
• Máquina virtual con todo lo esencial para fMRI: http://neuro.debian.net

¿Cómo puedo seguir aprendiendo acerca de neuroimagen?

• IMAIOS: https://www.imaios.com/en/e-Courses/e-MRI

• Neuroimaging Tutorials: https://talks.stanford.edu/tutorials/neuroimaging-tutorials/

• Nipy: http://nipy.org/nipy/users/tutorial.html

• Neuroimaging Analysis within R: http://johnmuschelli.com/imaging_in_r/

• Introduction to Neurohacking In R: https://www.coursera.org/learn/neurohacking

• Dos excelentes cursos en línea (gratuitos), enfocado en las bases y los avances recientes en el campo de fMRI (principalmente conceptual):

  • Principles of fMRI 1: https://www.coursera.org/learn/functional-mri
  • Principles of fMRI 2: https://www.coursera.org/learn/functional-mri-2

• Blog con tutoriales de fMRI: http://andysbrainblog.blogspot.mx; http://www.fmri4newbies.com/tutorials/

• Resources for data driven neuroscientists: https://pbeukema.github.io/LabHacks/

Libros

• Functional Magnetic Resonance Imaging: http://sites.sinauer.com/fmri3e/
• Handbook of Functional MRI Data Analysis: http://www.fmri-data-analysis.org
• Handbook of Neuroimaging Data Analysis: https://www.crcpress.com/Handbook-of-Neuroimaging-Data-Analysis/Ombao-Lindquist-Thompson-Aston/p/book/9781482220971

Artículos

• The Statistical Analysis of fMRI Data. http://www.stat.columbia.edu/~martin/Papers/STS282.pdf
• Nature focus on human brain mapping: http://www.nature.com/neuro/focus/humanbrainmapping/index.html

Neuroimagen y psiquiatría

• The Challenges and Promise of Neuroimaging in Psychiatry http://www.cell.com/neuron/pdf/S0896-6273(11)01095-6.pdf

• Computational psychiatry as a bridge from neuroscience to clinical applications. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5443409/pdf/nihms869778.pdf

• Neuroimaging in psychiatry: bringing neuroscience into clinical practice. http://bjp.rcpsych.org/content/bjprcpsych/201/1/1.full.pdf

• Neuroimaging in psychiatry: from bench to bedside. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2807751/pdf/fnhum-03-049.pdf

Contacto:

Dr. Eduardo Garza-Villarreal: [email protected]; Josué Mendoza: [email protected]