Location: Danforth Campus Psychology Building room 412

Time: 1:00am to 12:00 pm

Location: Danforth Campus Psychology Building room 412

Time: 11:00am to 12:00 pm

Items to bring: Laptop with Matlab installed (not required but helpful if you want to participate in analysis)

Location: Danforth Campus Psychology Building room 412

Time: 2/18/2020 11:00am to 12:00 pm

Items to bring: Laptop with Matlab installed (not required but helpful if you want to participate in analysis)

Location: Danforth Campus Psychology Building room 412

Time: 2/11/2020 11:00am to 12:00 pm

Items to bring: Laptop with Matlab installed (not required but helpful if you want to participate in analysis)

To do:

Make sure all the things from last time are working (Matlab, SPM, data downloaded and unzipped)!

 The first Imaging Interest Group meeting will be held in the Psychology Building room 412. We may read some papers, but will focus on downloading and analyzing fMRI data. All are welcome - you'll need a laptop with Matlab in order to participate in data analysis (or you can look over someone's shoulder).

Location: Danforth Campus Psychology Building room 412

Time: 1/21/2020 11:00am to 12:00 pm

Items to bring: Laptop with Matlab installed (not required but helpful if you want to participate in analysis)

Downloading aa via the web interface or git; setting your Matlab path; overview of aa organization and example scripts.

Click through fMRI analysis of OpenNeuro dataset 107

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

Topics covered

• Permutation testing

• Exchangeability

• Voxel-level vs. cluster-level analysis

Helpful Readings

Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: A primer with examples. Hum Brain Mapp 15:1-25. http://dx.doi.org/10.1002/hbm.1058

Topics: Random Field Theory

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

Helpful Readings:

Bennett CM, Wolford GL, Miller MB (2009) The principled control of false positives in neuroimaging. Social Cognitive and Affective Neuroscience 4:417-422. http://dx.doi.org/10.1093/scan/nsp053

Bennett CM, Baird AA, Miller MB, Wolford GL (2009) Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: An argument for multiple comparisons correction. Journal of Serendipitous and Unexpected Results 1:1-5. http://jpeelle.net/reprints/Bennett-2011-Neural_correlates_of_interspecies_perspective_taking.pdf

Chumbley JR, Friston KJ (2009) False discovery rate revisited: FDR and topological inference using Gaussian random fields. NeuroImage 44:62-70. http://dx.doi.org/10.1016/j.neuroimage.2008.05.021

Nichols T, Hayasaka S (2003) Controlling the familywise error rate in functional neuroimaging: a comparative review. Statistical methods in medical research 12:419-446. http://dx.doi.org/10.1191/0962280203sm341ra

Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: A primer with examples. Hum Brain Mapp 15:1-25. http://dx.doi.org/10.1002/hbm.1058

Worsley KJ (1996) The geometry of random images. Chance 9:27-39. http://dx.doi.org/10.1080/09332480.1996.10542483

Topics: 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

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

Topics: Smoothing

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

Homework

• Smooth warped images at 6 mm and 12 mm

• Use ImCalc to look at differences between a couple of individual subjects

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

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

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

Topics: Introduction to Dataset & Data Sharing

Homework:

• Install Matlab

• Download and install SPM (http://www.fil.ion.ucl.ac.uk/spm)

• Download the practice dataset from openneuro (ds000102) (https://openneuro.org/datasets/ds000102/versions/58016286cce88d0009a335df)

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

Project Proposal: Exploring BOLD Variability in Aging and Alzheimer Disease

Pete Millar

Data discussion: Functional connectivity following prenatal SSRI exposure and across development in the mouse cortex

Rachel Rahn

No specific paper, but an informal discussion of preprocessing for resting state analyses. All welcome!

Mark McAvoy will do a project proposal on "The role of the global signal in language processing".

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.