Depth dependent attentional modulations observed with high temporal resolution BOLD fMRI at 7T

Poster No:


Submission Type:

Abstract Submission 


Luca Vizioli1, Alexander Bratch1, Kamil Ugurbil1, Essa Yacoub1


1CMRR, University of Minnesota, Minneapolis, United States


At ultra-high field (UHF), functional voxels can span the sub-millimeter range (e.g. 1), allowing recording BOLD (2) responses at the spatial scale of the most fundamental units of neural computation: cortical layers and columns. 
Because of the hemodynamic sluggishness, however, the temporal information in the BOLD signal has been largely ignored. More recently a number of groups have demonstrated that (specifically at UHF), not only do sub-second BOLD measurements carry substantial neuro-temporal information, but they do so with a much higher precision than previously thought (e.g. 3; 4). 
Thus far, due to SNR limitations, spatial and temporal dynamics of the BOLD signal have been studied independently, optimizing one at the detriment of the other. Spatially fine sub-millimeter measurements have been recorded with a temporal resolution well above the second range (e.g. >2 seconds - 1). Conversely, temporally fine measurements (e.g. =<600 ms TR) have been acquired with coarse spatial resolutions (e.g. => 2 mm isotropic voxels; 4).
Here, we exploit the advantages of UHF and SNR efficient parallel accelerations, to record BOLD images at 7T with unprecedented spatio-temporal resolution (i.e. .85 isotropic voxels with a 625 ms TR). These data allow investigating a number of neuroscience questions that have thus far remained elusive, including the neuro-temporal dynamics of cortical layers.


GE-EPI (isotropic 0.85 mm3, TE = 24ms, flip angle =41°, 26 slices, TR= 625 ms, IPAT = 3, partial Fourier = 6/8,  MB = 2) images were recorded on a 7 Tesla scanner with a 4 Tx x 32 Rx. custom built coil (figure 1). B1 and B0 Optimizations (for visual cortex) were done via localized B1 and B0 shimming; shorter echo spacings were achieved via improved gradient performance with coronal acquisitions. 
Stimuli and task: We used face stimuli and modulated the phase-coherence of the images to create 5 visual conditions (with a total of 96 trials each), ranging from 0% to 40% in steps of 10%. Stimuli were presented for 2 seconds followed by a 2 second fixation period, with 10 % blank trials. A fixation cross changing color every 250 ms was held constant in the middle of the screen. Subjects performed either a face detection (i.e. stimulus-relevant) or fixation color (i.e. stimulus irrelevant) task. Tasks were blocked by runs and stimuli were identical across tasks. 
Analysis: After manually segmenting the cortex, we parcellated the cortical sheet into 6 depths, ranging from 5% to 95% distance from the Pial surface (e.g. figure 2a). We identified OFA and V1 respectively through standard face localizer and retinotopic mapping, and confined all analyses within this ROI. Independently per cortical depth, we implemented standard univariate FIR analysis and temporal MVPA (tMVPA – measuring the synchrony of multi-voxel patterns across all time points - 5) to test latency and amplitude differences across conditions and tasks.
Supporting Image: Fig1_OHBM19.png


Attentional demands significantly (p<.05 FDR corrected) impacted BOLD amplitude, leading to larger responses during the stimulus-relevant compared to the stimulus-irrelevant task in all ROIs. In V1, task-induced BOLD differences began as early as ~1.2 s after stimulus onset, and varied in extent across depths, being generally most prominent in the inner-depths (figure 2b).
Supporting Image: Fig2_OHBM2019.png


The data presented here could have profound implications for fMRI as we demonstrated the feasibility of recording BOLD images with concurrent sub-second temporal sampling and sub-millimeter spatial resolution, while retaining sufficient signal and contrast to noise ratios.  The concomitant acquisition of highly precise spatial and temporal BOLD recordings has the potential to disentangle neuronal and venous BOLD contributions. Ultimately, the data could permit tapping into neuro-cognitive processes that have thus far been elusive to fMRI, while also bridging the gap between invasive animal electrophysiology and human neuroscience.

Imaging Methods:


Perception and Attention:

Attention: Visual 2


Cortical Layers

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My abstract is being submitted as a Software Demonstration.


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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

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Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.


Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI

For human MRI, what field strength scanner do you use?


Which processing packages did you use for your study?

Brain Voyager
Other, Please list  -   Matlab

Provide references using author date format

1. Muckli, L., De Martino F., Vizioli, L., Petro. L., S., Smith, F. W., Ugurbil, K., Goebel, R., Yacoub, E. (2015) Contextual feed-back to superficial layers of V1. Current Biology
2. Ogawa, S., Menon, R. S., Tank, D. W., Kim, S. G., Merkle, H., Ellermann, J. M., Ugurbil, K. (1993). Functional brain mapping by blood oxygenation level- dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. Biophysical Journal.
3. Lewis, L.D., Setsompop, K., Rosen, B.R., Polimeni, J.R. (2016) Fast fMRI can detect oscillatory neural activity in humans. PNAS.
4. Vizioli, L. and Yacoub, E. (2018) Probing temporal information in fast-TR fMRI data during attention modulations. Proc. ISMRM
5. Vizioli, L., Bratch, A., Lao, J., Ugurbil, K., Muckli, L., Yacoub, E. (2018) Temporal Multivariate Pattern Analysis (tMVPA): a single trial approach exploring the temporal dynamics of the BOLD signal. Journal of Neuroscience Methods