Lab Materials

Each day of the course will end with one or two brief lab sessions during which we will demonstrate software and techniques for working with connectomics data. In particular, these will provide tutorials that will assist the students of 0407-03: Instrumentação Biomédica e Imagens Médicas complete their research project. Each lab session will last roughly 30 minutes. In addition to the information listed below, lab materials will be available on the course's github repository.

Lab #1: Working with neuroimaging data using Python and Nipy

Monday, July 28
Instructor: Caroline Froehlich
Objective: Python is quickly becoming the predominate programming language for neuroimaging research. This is due in part to the overall popularity of python, but also reflects the maturity Python resources, such as Nipy, for working with neuroimaging data. This demonstration will provide a brief overview Python and Nipy, that is aimed at course attendees who are familiar with programming. During this demonstration, attendees will learn to write Python scripts to read, write, and visualize neuroimaging data, as well as perform basic manipulations on the data such as ROI time series extraction, and calculating basic functional connectivity measures.
Resources: Python web page, Nipy web page, Python Notebook

Lab #2: Open Science resources for connectomes research

Monday, July 28
Instructor: Cameron Craddock
Objective: Grass-roots and centralized initiatives such as the International Neuroimaging Data-Sharing Initiative, The Human Connectome Project, The Preprocessed Connectomes Project, and OpenFMRI are amassing and sharing raw and preprocessed neuroimaging data. These Open Science initiatives combined with the plethora of free software tools for performing analysis, has made tools and data necessary to perform neuroimaging analysis accessible to everyone. This 30 minute lecture will provide a survey of these various resources.
Resources: list of open science data repositories and tools, slides

Lab #3: Preprocessing fMRI data using CPAC

Tuesday, July 29
Instructor: Cameron Craddock
Objective: The Configurable Pipeline for the Analysis of Connectomes (CPAC) is an open-source toolkit for preforming connectomes preprocessing and analyses on fMRI data. CPAC is written in Python using the Nipype pipelining library, and integrates tools from several different neuroimaging software packages (AFNI, ANTs, and FSL). The object of CPAC is to enable neuroimaging researchers who lack programming skills to perform state-of-the-art preprocessing and analyses on vary large datasets using high-performance computer resources in an infrastructure. This 30-minute tutorial will provide a overview of preprocessing data with CPAC, and evaluating the quality of the results.
Resources: CPAC website

Lab #4: Mapping functional connectome from fMRI

Tuesday, July 29
Instructor: Caroline Froehlich
Objective: This 30-minute tutorial will illustrate the construction of connectome graphs from preprocessed fMRI datasets using Python, and Nipy, and visualize the results as both graphs and connectivity matrices.
Resources: Nipy webpage, Python Notebook

Lab #5: Mapping structural connectomes from dMRI

Wednesday, July 30
Instructor: Guilherme Peixoto
Objective: This hour long tutorial will demonstrate the generation of structural connectomes from preprocessing the raw data and estimating orientation distribution functions, through to fiber tracking and generating connectivity graphs.
Resources: Tortoise website, TrackVis website, UCLA Multimodal Connectivity Package

Lab #6: Calculating graph statistics

Thrusday, July 31
Instructor: Nathassia Aurich
Objective: This 30-minute long tutorial will demonstrate the calculation of graph invariants from structural and functional graphs using the Brain Connectivity Toolbox website and Matlab.
Resources: Brain Connectivity Toolbox website