(Medical campus)

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.

http://www.nature.com/articles/srep10499

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.

Chad will lead a discussion of:

Pernet CR, Poline J-B (2015) Improving functional magnetic resonance imaging reproducibility. GigaScience 4:15.

http://www.gigasciencejournal.com/content/4/1/15

Ashley will lead a discussion of:

Introduction to RSA from Kriegeskorte et al., assuming no background with MVPA. First half whiteboard, second half this paper:

Mur M, Meys M, Bodurka J, Goebel R, Bandettini PA, Kriegeskorte N (2013) Human object-similarity judgments reflect and transcent the primate-IT object representation. Frontiers in Psychol 4:128.

http://journal.frontiersin.org/article/10.3389/fpsyg.2013.00128/abstract

Most task-based functional MRI analyses assume something approaching a "standard" or "canonical" hemodynamic response function (HRF). But how consistent is the hemodynamic response really? Does it change in aging? We'll have an informal discussion of two early and influential articles on the topic:

Aguirre GK, Zarahn E, D'Esposito M (1998) The variability of human, BOLD hemodynamic responses. NeuroImage 8:360-369. http://cl.ly/aMmI

D'Esposito M, Zarahn E, Aguirre GK, Rypma B (1999) The effect of normal aging on the coupling of neural activity to the bold hemodynamic response. NeuroImage 10:6-14. http://cl.ly/aLpx

In a well-known investigation of session-to-session variability, McGonigle et al. published a data set in which a single subject participated in 99 imaging sessions (33 repeats of simple finger tapping, checkerboard viewing, or counting). These two papers present different views on how the results might be interpreted.

McGonigle DJ, Howseman AM, Athwal BS, Friston KJ, Frackowiak RSJ, Holmes AP (2000) Variability in fMRI: An examination of intersession differences. NeuroImage 11:708-734.

Smith SM, Beckmann CF, Ramnani N, Woolrich MW, Bannister PR, Jenkinson M, Matthews PM, McGonigle DJ (2005) Variability in fMRI: A re-examination of inter-session differences. Hum Brain Mapp 24:248-257.

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.

Continuing our series on intersubject correlation analyses, Chad will be leading a discussion of:

Lerner Y, Honey CJ, Silbert LJ, Hasson U (2011) Topographic mapping of a hierarchy of temporal receptive windows using a narrated story. J Neurosci 31:2906-2915. http://dx.doi.org/10.1523/JNEUROSCI.3684-10.2011

Discussion of Hasson et al (2004) intersubject correlation during movie watching.

Hasson U, Nir Y, Levy I, Fuhrmann G, Malach R (2004) Intersubject synchronization of cortical activity during natural vision. Science 303:1634-1640.

Head motion is problematic in task and resting state fMRI studies. Head motion during scans causes image intensity to reflect not only blood oxygenation but also frank motion-related artifact. It is a common practice to align the data throughout a scan by estimating the position of the head in space at each volume. Realignment is an essential part of data processing, but it cannot correct the signal alterations or image distortions that occur as a result of movement. Investigators have demonstrated the utility of a variety of methods for dealing with motion in general linear model (GLM) estimation. We will discuss motion-related artifact and some of the approaches developed to correct it in the context of this paper:

Siegel et al. (2014) Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points. Human Brain Mapping 35:1981-1996.

http://www.sciencedirect.com/science/article/pii/S1053811914007769

Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in the study of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5.3.0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R2 = 0.940, 95% Confidence Interval (0.924, 0.953)), with a small mean difference (SPM12 40.4 ± 35.4 ml lower than manual, amounting to 2.8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p < 0.001) than for either SPM8 (R2 = 0.577 CI (0.500, 0.644)) or FreeSurfer (R2 = 0.801 CI (0.744, 0.843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV.