![]() (10:45am-11:30am) Viewing the Results (Practical) Second-level covariates and between-subject factorsĬorrection mechanisms: FWE, FDR, and cluster-forming thresholds (10:00am-10:45am) Group-level analysis (Lecture & Practical)Īn overview of how to set up group-level analyses, as well as caveats to be aware of. After reviewing some common examples of quality failures, you will examine your own data.Ĭhecking registration between T1 and T2 modalitiesĪlignment of the outline of the brain vs. (9:00am-10:00am) Quality Assurance (Practical)Īs with all neuroimaging data, quality assurance checks are very important. These meetings will be set up ahead of the workshop. One session runs from 3:30pm-4:15pm, and the second one runs from 4:15pm-5:00pm. (3:30pm-5:00pm) Individual Consulting SessionsĪndy will help individuals (or small groups of individuals) with their data. We will take a group photo at the end of the first day. Realignment, slice-timing, and outlier detection This practical will show how to preprocess the data for a single subject in CONN. (2:45-3:30pm) Preprocessing the Individual Subject (Lecture & Practical) During this session we will double-check our installation of CONN and related toolboxes (e.g., ART) and take a tour through the graphical user interface. ![]() ![]() (2:00pm-2:45pm) Introduction to the CONN Toolbox (Lecture & Practical)Īfter reviewing some of the most common errors in both task-based and resting-state analysis, we will begin working with the CONN toolbox: a software package for connectivity analysis. Motion artifact removal since 2011: What are the best approaches? (1:00pm-2:00pm) Pitfalls of Functional Connectivity (Lecture)Ī brief overview of functional connectivity, and some of the common pitfalls encountered in both task-based and resting-state connectivity analysesīasics of functional connectivity: Scrubbing, ROIs, correlations between regions QC checks to guard against spurious resultsĬhecking timing files through reverse inferenceĬorrelated regressors and what to do about them Orthogonalization of task-based regressors and parametric modulators (The sample dataset can be found here.) This practical will guide you through some of the most common scenarios, including:Ĭreating your own biased analysis: circular analyses using your own data, and data peeking (using this script) One of the best ways to spot questionable research practices is to be aware of when you do them yourself. Overview of openneuro, BIDS, and other online repositories Reproducibility: Tools for making your data easier to analyze by other groups. Voodoo correlations: How to spot biased region of interest (ROI) analyses and how to avoid doing them yourselfĬluster failure: How to accurately calculate your cluster threshold The structure of fMRI data: Time-series, signal intensity, and how artifacts are introducedĭead salmon and multiple comparisons: How to correct for multiple tests and why How have fMRI analysis problems changed over time? This lecture looks at the history of controversial issues that have come up. (9:30am-11:00am) Overview of Issues in fMRI Analysis (Lecture)
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