Oct
17
1:00 PM13:00

IIG: Introduction to GLM 2

Topics: GLM

Location: Ogura Lecture Hall (9th floor of the McMillan Building)

Helpful Readings

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Oct
10
1:00 PM13:00

IIG: Introduction to GLM

Topics: Introduction to GLM

Location: Ogura Lecture Hall (9th Floor of the McMillan Building) 


Helpful Readings:

Poline J-B, Brett M (2012) The general linear model and fMRI: Does love last forever? NeuroImage 62:871-880. https://doi.org/10.1016/j.neuroimage.2012.01.133

Worsley KJ, Friston KJ (1995) Analysis of fMRI time-series revisited—again. NeuroImage 2:173-181. https://doi.org/10.1006/nimg.1995.1023

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Sep
26
1:00 PM13:00

IIG: Spatial Registration & Normalization

Topics: Spatial registration & normalization

Location: Ogura Lecture Hall (9th floor of the McMillan Building)

Homework:

  • Use the Dartel to create a group template off of the dartel-imported gray matter (rc1*) images

  • Warp individual subjects’ gray matter (rc1*) to the template using flow fields with 0 smoothing.

Helpful Readings:

Ashburner J (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38:95-113. http://doi.org/10.1016/j.neuroimage.2007.07.007

Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, Chiang M-C, Christensen GE, Collins DL, Gee J, Hellier P, Song JH, Jenkinson M, Lepage C, Rueckert D, Thompson P, Vercauteren T, Woods RP, Mann JJ, Parsey RV (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46:786-802. http://dx.doi.org/10.1016/j.neuroimage.2008.12.037

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Sep
19
1:00 PM13:00

IIG: Tissue Class Segmentation

Topics: Tissue Class Segmentation

Location: Ogura Lecture Hall (9th floor of the McMillan Building)

Homework:

  • Segment all T1 images in the example dataset

  • Use Check Reg to verify that the resulting segmentations are sensible

  • Advanced: Pick a subject and segment their brain using two sets of options. Use ImCalc to subtract the images and view the difference. Are the segmentations identical? Which do you think is more accurate?

Helpful Readings:

Ashburner J, Friston KJ (2005) Unified segmentation. NeuroImage 26:839-851. http://doi.org/10.1016/j.neuroimage.2005.02.018

Ashburner J, Friston KJ (2009) Computing average shaped tissue probability templates. NeuroImage 45:333-341. http://doi.org/10.1016/j.neuroimage.2008.12.008

Avants BB, Tustison NJ, Wu J, Cook PA, Gee JC (2011) An open source multivariate framework for n-tissue segmentation with evaluation on public data Neuroinformatics 9:381-400. http://doi.org/10.1007/s12021-011-9109-y


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Sep
12
1:00 PM13:00

IIG: Introduction to SPM

Topics: Introduction to SPM

Location: Ogura Lecture Hall (9th floor of the McMillan Building)

Homework:

  • Make sure all data from ds000102 are downloaded and unzipped

  • Make sure you can start SPM and load in an image to look at

  • Practice basic command line operations (man, cd, pwd, ls, rm)

  • Visually inspect all T1 images in dataset

  • Advanced homework: read in a T1 image using Matlab and plot a histogram of the values

Helpful Readings:

Ashburner J (2012) SPM: A history. NeuroImage 62:791-800. https://doi.org/10.1016/j.neuroimage.2011.10.025

Mechelli A, Price CJ, Friston KJ, Ashburner J (2005) Voxel-based morphometry of the human brain: Methods and applications. Current Medical Imaging Reviews 1:105-113. http://dx.doi.org/10.2174/1573405054038726


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Sep
5
1:00 PM13:00

IIG: Overview

Topics: Introduction to Dataset & Data Sharing

 

Homework:

 

Helpful Readings:

Poldrack RA, Baker CI, et al. (2017) Scanning the horizon: towards transparent and reproducible neuroimaging research. Nature Reviews Neuroscience 18:115-126. https://doi.org/10.1038/nrn.2016.167

Poldrack RA, Gorgolewski KJ (2017) OpenfMRI: Open sharing of task fMRI data. NeuroImage 144:259-261. https://doi.org/10.1016/j.neuroimage.2015.05.073

Kelly AMC, Uddin LQ, Biswal BB, Castellanos FX, Milham MP (2008) Competition between functional brain networks mediates behavioral variability. NeuroImage 39:527-537. https://doi.org/10.1016/j.neuroimage.2007.08.008

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Nov
16
2:00 PM14:00

IIG: All about multiband fMRI (Jo Etzel)

Jo Etzel will talk about:

“Multi-band, simultaneous multi-slice, multi-echo, EPI ... oh my!”

The talk is an introduction to multiband imaging, both the general physics of how it works (is it the same as multi-echo fMRI?), and surprising artifacts and sensitivities Jo has encountered while starting to analyze multiband task fMRI datasets. Hopefully this will be a useful starting point for people working with or reading about multiband datasets (e.g., HCP, ABCD), or considering acquiring them in the future.

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Sep
28
2:00 PM14:00

Individual ROIs for language (with applications elsewhere) (Mike Jones)

Mike will talk us through:

Fedorenko E, Hsieh P-J, Nieto-Castañón A, Whitfield-Gabrieli S, Kansiwhser N (2010) New method for fMRI investigations of language: Defining ROIs functionally in individual subjects. J Neurophysiol 104:1177-1194. http://jn.physiology.org/content/104/2/1177

which suggests a method for group-constrained individual ROI creation—developed for language but useful for other domains!

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Aug
3
2:00 PM14:00

The impact of T1 versus EPI spatial normalization templates (Jonathan)

Jonathan will talk about this paper:

The impact of T1 versus EPI spatial normalization templates for fMRI data analyses

http://onlinelibrary.wiley.com/doi/10.1002/hbm.23737/full

 

Abstract

Spatial normalization of brains to a standardized space is a widely used approach for group studies in functional magnetic resonance imaging (fMRI) data. Commonly used template-based approaches are complicated by signal dropout and distortions in echo planar imaging (EPI) data. The most widely used software packages implement two common template-based strategies: (1) affine transformation of the EPI data to an EPI template followed by nonlinear registration to an EPI template (EPInorm) and (2) affine transformation of the EPI data to the anatomic image for a given subject, followed by nonlinear registration of the anatomic data to an anatomic template, which produces a transformation that is applied to the EPI data (T1norm). EPI distortion correction can be used to adjust for geometric distortion of EPI relative to the T1 images. However, in practice, this EPI distortion correction step is often skipped. We compare these template-based strategies empirically in four large datasets. We find that the EPInorm approach consistently shows reduced variability across subjects, especially in the case when distortion correction is not applied. EPInorm also shows lower estimates for coregistration distances among subjects (i.e., within-dataset similarity is higher). Finally, the EPInorm approach shows higher T values in a task-based dataset. Thus, the EPInorm approach appears to amplify the power of the sample compared to the T1norm approach when not using distortion correction (i.e., the EPInorm boosts the effective sample size by 12–25%). In sum, these results argue for the use of EPInorm over the T1norm when no distortion correction is used.

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Jun
15
2:00 PM14:00

Introductions to MVPA and SVM (Jo Etzel)

Jo will give us an intro to SVM, and also this talk:

A new MVPA-er’s guide to fMRI datasets
fMRI datasets have properties which make the application of machine learning (pattern recognition) techniques challenging – and exciting! This talk will introduce some of the properties most relevant for MVPA, particularly the strong temporal and spatial dependencies inherent in BOLD imaging. These dependencies mean that some fMRI experimental designs are more suitable for MVPA than others, due, for example, to how the tasks are distributed within scanner runs. I will also introduce some of the necessary analysis choices, such as how to summarize the response in time (e.g., convolving with an HRF), which brain areas to include, and feature selection techniques.

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May
6
3:00 PM15:00

Jo Etzel on permutation testing and MVPA (Danforth campus)

Psychology Building, Danforth campus Room 215.

Jo Etzel will talk about permutation testing as a general introduciton, and special considerations in designing tests for MVPA-type analyses that use cross-validation.

Here are two relevant conference papers:

http://dx.doi.org/10.1109/PRNI.2013.44

http://dx.doi.org/10.1109/PRNI.2015.29

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Apr
15
3:00 PM15:00

Representational similarity analysis (Dimitre Tomov)

Dimitre will provide an introduction to RSA based on the following paper:

Walther A, Ejaz N, Kriegeskorte N, Diedrichsen J. Representational fMRI analysis: An introductory tutorial. http://www.diedrichsenlab.org/pubs/representational_analysis.pdf

 

If you're interested, Jo Etzel has also blogged about RSA: http://mvpa.blogspot.com/search/label/RSA

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