Lesion network mapping of Autism-related Symptoms (2018 - present)

• This project seeks to understand whether there are particular networks of regions impacted by lesions that are associated with particular symptoms that are also seen in Autism Spectrum Disorders. We have begun several projects to help answer this question, starting with the symptom of face processing difficulties:

  1. Studying Face Processing in children with Tuberous Sclerosis Complex:
     • This study investigates whether the pattern of cortical tubers in children with Tuberous Sclerosis Complex predicts their face processing ability. If this is true, it would indicate which networks are causally involved in face processing difficulties.

  2. Studying Face Processing in adolescents with Autism Spectrum Disorder:
     • This study investigates whether face processing difficulties in adolescents with ASD is correlated with specific brain network differences, a hypothesis generated from patients with acquired prosopagnosia, i.e., face blindness after brain injury. If this is true, it would mean that the same brain regions are causally involved in face processing difficulties in patients without brain injury.

  3. Studying the correlation between Face Processing and Social Affect:
     • This study leverages large-scale existing datasets to quantify the relationship between face recognition ability and social affect.

Moving forward, we have already started to investigate additional symptoms and are happy to collaborate with researchers interested in applying lesion network mapping to their data.

Generating Developmental Atlases of Brain Connectivity (2018 - present)

• This project utilizes publicly available brain connectivity data, currently from more than 20,000 children and adolescents, into single consistently processed and quality-controlled datasets that can be used by medical researchers as a ‘gold-standard’ reference of typical development.

  1. The GSP1000 Processed Connectome is derived from data acquired by the Brain Genomics Superstruct Project (GSP), which contained 1570 subjects in total (ages 18-36). From this dataset, 1000 subjects (1:1 M/F) were chosen and processed using publicly available tools to generate a normative functional connectivity dataset. Click this link to download:‘https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ILXIKS'

Quick tutorials for scientific computing

Learn to use Markdown to make ‘modern’ text files (10 minutes):

• https://commonmark.org/help/tutorial/

The ‘command line’ and writing shell scripts (~1 hour each):

• http://linuxcommand.org/lc3_learning_the_shell.php
• http://linuxcommand.org/lc3_writing_shell_scripts.php
• Both of the above as a book/pdf: http://linuxcommand.org/tlcl.php

An introduction to pandas (an alternative to excel):

• https://bitbucket.org/hrojas/learn-pandas
• Many Python books: https://github.com/EbookFoundation/free-programming-books/blob/master/free-programming-books.md#python

An introduction to R (an alternative to SPSS/STATA):

• https://psyteachr.github.io/msc-data-skills/
• https://www.edx.org/course/introduction-to-r-for-data-science-2

Jupyter Notebooks, a great way to organize your python, matlab, and R code:

• https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook

Git, a way to keep track of versions of your code and share files:

• https://guides.github.com/

Good articles to read:

• https://software-carpentry.org/lessons/index.html
• http://swcarpentry.github.io/good-enough-practices-in-scientific-computing/
• (after Best Practices article): https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745
• https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004947
• http://swcarpentry.github.io/git-novice/
• https://guides.github.com/introduction/flow/
• https://hbctraining.github.io/Training-modules/
• https://realpython.com/documenting-python-code/
• http://statsthinking21.org/

Introduction to MRI-fMRI

Useful Courses (free):

• https://www.coursera.org/learn/neuroscience-neuroimaging
• https://www.coursera.org/learn/functional-mri
• https://www.coursera.org/learn/functional-mri-2