Cortical depth dependent resting state fMRI with motion correction
Ying-Hua Chu1,2, Pu-Yeh Wu2, Maxim Zaitsev1, Yi-Cheng Hsu1,2, and Fa-Hsuan Lin2,3

1Dept. of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, Freiburg, Germany, 2Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan, 3Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland


We used high resolution EPI (1 mm isotropic), cortical depth dependent analysis, and prospective motion correction (MOCO) to delineate the default mode network. Most sensitive within-subject characterization of the default-mode network was obtained from data at intermediate/superficial cortical depths with MOCO.


Resting-state fMRI 1 explores brain areas showing synchronized activity. One set of such orchestrated brain regions include the posterior cingulate cortex (PCC), medial pre-frontal cortex (mPFC), hippocampus, and lateral parietal (LP) lobes 2. This resting-state network has been named as the “default mode” network (DMN) 3-5. As fMRI monitors neuronal activity indirectly, any measurement and physiological confounds, such as motion and vascular reactivity, can affect the characterization of DMN One way to mitigate this challenge is the characterization of hemodynamic responses across cortical depths in the human brain6. Specifically, examining BOLD signal with a small voxel in the range of 1 µL can improve functional specificity by alleviating the vascular bias caused by draining veins coursing along the pial surface 6,7, reducing partial volume effects 8,9, and suppressing physiological noise 10. The other approach is to use prospective motion correction 11 to dynamically change the excitation and reception of BOLD signal when involuntary motion occurs.

Here we characterized the DMN across cortical depths with the state-of-the-art motion tracking. We hypothesize that the DMN characterized by data from intermediate and superficial cortical depths because of the coursing venal vasculature. Prospective motion correction can further improve the detection sensitivity during the measurements of several minutes.


Six 10-minute functional scans were collected from a 3T scanner (Prisma, Siemens). Three runs were acquired with and without prospective correction (MOCO), respectively12. The EPI sequence with 3x GRAPPA (TE=38 ms, TR=3410 ms, flip angle=90°, resolution=1 mm isotropic, and 40 slices) was integrated with an in-bore optical tracking system. The tracking marker was fixed to the subject using a mouthpiece to record the involuntary subject motion of the head. T1-weighted structural images (MPRAGE: TR= 2300 ms, TE= 2.32 ms, resolution=0.9x0.9x0.9mm3) were acquired to generate triangulated mesh surfaces of gray-white matter boundary, gray-pial surface boundary, and nine cortical depths with equally spaced cortical thickness using FreeSurfer 6,13,14. A gray-white matter boundary-based registration method implemented in FreeSurfer was used to form the rigid transformation between the functional and the anatomical data. Using this registration file, individual’s functional time-series volumes were projected to nine intermediate cortical surfaces by the nearest-neighbor interpolation method after correction motion using SPM12 (http://www.fil.ion.ucl.ac.uk/spm/).

DMN was identified by the seed-based correlation method 15. We separately calculated correlation maps with seeds at the PCC (Brodmann Area 10) 16,17 using data with and without MOCO. Correlation coefficients maps were transformed to Z-statistic maps.


Reduced image translation and rotations were observed with MOCO. Specifically, the x, y, and z translation estimated by motion correction were 0.05±33.4 μm, -235±749 μm, and 17.2±57.7 μm for sessions with MOCO and 90.3±66.2 μm, -169.9±58.7 μm, -24±132.4 μm for sessions without MOCO, respectively. The pitch, roll, and yaw rotation were 0.017°±0.04°, -0.013°±0.02°, and -0.006°±0.016° for sessions with MOCO, and 0.017°±0.088°, -0.13°±0.11°, and -0.07°±0.05° for sessions without MOCO session, respectively.

Figure 1 shows maps of the statistical significance of correlation in Z-scores with the seed ROI at left and right PCC at 0.1 (closer to the white matter), 0.5, and 0.9 (closer to the pial surface) normalized depth (n.d.). Minimal parietal lobe areas of the DMN were identified at 0.1 n.d., while more correlated BOLD signals were found at intermediate and superficial depths. Comparing to results without MOCO, DMN derived from data with MOCO had involved broader areas and with more significant correlation. More significantly correlated BOLD signals at intermediate and superficial layers (n.d. >= 0.6) at LP and mPFC were observed with seed ROI at left and right PCC (Figure 2). At intermediate depths (n.d. = 0.6 and 0.7), the Z-score was about 3 to 5 times higher than that at deep depth (n.d.= 0.1) and about 20% higher than that at superficial depth (n.d.=0.9). Data with MOCO consistently suggested more significant correction to the seed ROI (Figure 2)


While cortical depth depending fMRI has been studied in the human visual 18-25 and auditory cortex 7,26,27, to the best of our knowledge, it has not been used to study resting state fMRI. Our results suggested that more sensitive detection of DMN can be achieved at intermediate and superficial cortical depths. Prospective motion correction is also useful to improve the sensitivity. However, in the group analysis, we expect that the DMN identified from data at intermediate cortical depths can be more reliable, because the superficial vasculature varies across subjects. Further study on different subject’s positions, multiple subjects, and the contrast between resting and task conditions, is expected to demonstrate the advantage of improved sensitivity and specificity of characterizing the resting state network using cortical depth dependent analysis and prospective motion correction.


This work was partially supported by Ministry of Science and Technology, Taiwan (103-2628-B-002-002-MY3, 105-2221-E-002-104, 106-2911-I-002-502), and the Academy of Finland (No. 298131).


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Figure 1. Maps of the correlations in Z-scores to the time series at left (top) and right (bottom) posterior cingulate cortex using data at deep (normalized depth 0.1), intermediate (normalized depth 0.5), and superficial (normalized depth 0.9) cortical depths. Results using data with (right columns) and without (left columns) prospective motion correction (MOCO) were shown.

Figure 2. The average and standard deviation of the correlation (in Z-score) to the seed ROI at left and right posterior cingulate cortex (PCC) across cortical depths at left lateral parietal lobe (A), right lateral parietal lobe (B), left medial prefrontal cortex (C), and right medial prefrontal cortex (D).

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)