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Whole brain fMRI activity at a high temporal resolution: A novel analytic framework

Niels Janssen1, 2* and Juan Hernández Cabrera1, 3
  • 1 Universidad de La Laguna, Spain
  • 2 Insitute for Biomedical Technologies, Spain
  • 3 Basque Center on Cognition, Brain and Language, Spain

We have developed a new framework for the analysis of functional Magnetic Resonance Imaging (fMRI) data. Whereas current analytic techniques primarily yield static, time-invariant maps of fMRI activity (1), our new technique yields dynamic, time-variant videos of whole-brain fMRI activity. The new framework relies on a fundamentally different method of fMRI BOLD signal extraction. Specifically, instead of the standard volume-based signal extraction, the new method extracts the fMRI BOLD signal based on the veridical MRI slice acquisition times. This yields an fMRI signal that is more temporally accurate (2). In addition, we improved the temporal resolution by presenting each slice to a different point in the progression of the BOLD signal (see also 3). The fMRI BOLD signal is then extracted using non-standard statistical modeling techniques. Specifically, the fMRI data is first broken up into epochs that are time-locked to the onset of a stimulus. Next, in line with techniques used in EEG (4), statistical models are run at each time-point in the epoch. As the baseline we used the fMRI signal intensity values available at time-point 0. For this particular choice of baseline, modeling involves extracting the fMRI BOLD signal across time points in the epoch. The number of available timepoints in the epoch (and therefore the temporal resolution) is scalable, up to a maximum that is determined by the rate at which MRI slices are acquired (typically on the order of tens of milliseconds). In order to account for the full complexity of the statistical model, we used Linear Mixed Effect modeling (5). Our method yields an fMRI signal for every voxel in the brain that is more temporally accurate and of a much higher temporal resolution that is available in current frameworks. The data manipulation in the new framework relies on functions written as part of the neuro-imaging data analysis package FSL (1) and various Python scripts of which the NiBabel package for reading neuro-imaging data forms an indispensable part (6). Statistical modeling of first order individual participant data relied on the data.table and lme4 packages available in the software R (7). Higher order modeling was performed with the randomise function of FSL (8). A key characteristic of the current approach is that it does not rely on data averaging but uses all data points from all epochs in an experiment to model the signal. Advantages of using this pipeline are that statistical modeling of first-order fMRI data is greatly simplified and handled by R. Disadvantages are the slow speed of R, and the large filesizes due to the long data table format requirements imposed by R. We will illustrate the new technique in the context of fMRI data collected during a visual object naming experiment. We will use these data to explore the spatio-temporal dynamics of the whole-brain fMRI BOLD signal at 390 milliseconds temporal resolution, focusing on task-based functional connectivity. Our new framework can be easily applied to data collected with other types of tasks, and provides a novel opportunity to gain insight into the spatio-temporal dynamics of fMRI activity during cognitive tasks.

Acknowledgements

This work was supported by The Spanish Ministry of Economy and Competitiveness (RYC2011-08433 and PSI2013-46334 to N.J.)

References

[1] Smith, S.M. (2004), 'Advances in functional and structural MR image analysis and implementation as FSL', Neuroimage, vol. 23, pp. S208-S219

[2] Sladky, R., Friston, K. J., Tröstl, J., Cunnington, R., Moser, E., & Windischberger, C. (2011). Slice-timing effects and their correction in functional mri. Neuroimage, 58 (2), 588–594.

[3] Price, C.J. (1999), 'The critical relationship between the timing of stimulus presentation and data acquisition in blocked designs with fMRI', Neuroimage, vol. 10, no. 1, pp. 36-44

[4] Janssen, N., Hernández-Cabrera, J. A., van der Meij, M., & Barber, H. A. (2014). Tracking the time course of competition during word production: Evidence for a post-retrieval mechanism of conflict resolution. Cerebral Cortex , bhu092.

[5] Pinheiro, J. C., & Bates, D. M. (2000). Mixed effects models in s and s-plus. Springer.

[6] http://nipy.org/nibabel/

[7] Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48.

[8] Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage, 2014;92:381-397.

Keywords: fMRI BOLD, fMRI methods, Statistical Modeling, temporal resolution, python language

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Investigator presentations

Topic: Neuroimaging

Citation: Janssen N and Hernández Cabrera J (2016). Whole brain fMRI activity at a high temporal resolution: A novel analytic framework. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00002

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Received: 25 Apr 2016; Published Online: 18 Jul 2016.

* Correspondence: Dr. Niels Janssen, Universidad de La Laguna, Santa Cruz de Tenerife, Spain, njanssen@ull.es