Mark McAvoy will do a project proposal on "The role of the global signal in language processing".
Chad will present:
Caceres A, Hall DL, Zelaya FO, Williams SC, Mehta MA (2009) Measuring fMRI reliability with the intra-class correlation coefficient. Neuroimage 45:758-768. http://www.sciencedirect.com/science/article/pii/S105381190801327X
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!
Jonathan will talk about this paper:
The impact of T1 versus EPI spatial normalization templates for fMRI data analyses
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.
Ashley Nielsen will be talking about:
"Machine Learning and Resting-State Functional Connectivity: The power of prediction and the limits of interpretation"
(which I suspect could also be titled "The good, the bad, and the ugly".)
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.
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:
Shelly will present this paper:
Geerligs L, Rubinov M, Cam-CAN, Henson RN (2015) State and trait components of functional connectivity: Individual differences vary with mental state. J Neurosci 35:13949-13961. http://www.jneurosci.org/content/35/41/13949.short
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
Timely and important topic! Ben will discuss the following paper as a lead-in:
Pernet CR, Poline J-B (2015) Improving functional magnetic resonance imaging reproducibility. GigaScience 4:15. doi:10.1186/s13742-015-0055-8
Psychology Building, Danforth campus Room 412.
Psychology Building, Danforth campus Room 412
I (Jonathan) will be discussing:
Mumford JA, Poline J-B, Poldrack RA (2015) Orthogonalization of regressors in fMRI models. PLoS One 10:e0126255. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0126255
If you'd like to go over to the psych building together, I will be leaving my office promptly at 2:30 and we can go together. (My office is McMillan 804C.) We'll take the metro to the Skinker stop and walk 1/2 mile to the psychology building.
Andrew will lead a discussion of this paper:
Boubela, RN et al. (2015) fMRI measurements of amygdala activation are confounded by stimulus correlated signal fluctuation in nearby veins draining distant brain regions. Scientific Reports 5:10499.
Ashley will talk to us about scanning children, and about one of her specific research projects:
Using task demand to further illuminate group differences in the neural strategy to accomplish a task: do children and adults accomplish single word comprehension in different ways?
Church, Jessica A., Steven E. Petersen, and Bradley L. Schlaggar. "The “Task B problem” and other considerations in developmental functional neuroimaging." Human brain mapping 31.6 (2010): 852-862. http://onlinelibrary.wiley.com/doi/10.1002/hbm.21036/full
Krishnan, Saloni, et al. "Convergent and divergent fMRI responses in children and adults to increasing language production demands." Cerebral Cortex(2014): bhu120.
Relevant papers, though not required reading:
Power JD, et al. (2014). Methods to detect, characterize, adn remove motion artifact in resting state fMRI. NeuroImage 84:320-341.
Siegel JS, et al. (2014). Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points. Human Brain Mapping 35:1981-1996.
Discussion of hypercapnia (breath holding) and other ways to potentially adjust the BOLD signal:
Bandettini PA, Wong EC (1997) A hypercapnia-based normalization method for improved spatial localization of human brain activation with fMRI. NMR Biomed 10:197-203.
Thomason ME, Foland LC, Glover GH (2007) Calibration of BOLD fMRI using breath holding reduces group variance during a cognitive task. Hum Brain Mapp 28:59-68.