Layer-dependent BOLD/VAPER fMRI signal fluctuations show distinct cortical depth profiles

Poster No:

2014 

Submission Type:

Abstract Submission 

Authors:

Arman Khojandi1, Yuhui Chai2, Daniel Handwerker1, Linqing Li3, Laurentius Huber4, Peter Bandettini1

Institutions:

1National Institute of Mental Health, Bethesda, MD, 2National Institute of Mental Health, BETHESDA, MD, 3National Institute of Mental Health, Bethesda, VA, 4Maastricht University, Maastricht, Limburg

First Author:

Arman Khojandi, B.S.  
National Institute of Mental Health
Bethesda, MD

Co-Author(s):

Yuhui Chai  
National Institute of Mental Health
BETHESDA, MD
Daniel Handwerker, PhD  
National Institute of Mental Health
Bethesda, MD
Linqing Li  
National Institute of Mental Health
Bethesda, VA
Laurentius Huber, Ph.D.  
Maastricht University
Maastricht, Limburg
Peter Bandettini, PhD  
National Institute of Mental Health
Bethesda, MD

Introduction:

Cerebral-blood-flow (CBF) and cerebral-blood-volume (CBV) based fMRI capture layer-dependent activity with greater spatial specificity than blood-oxygenation-level-dependent (BOLD).1 Recently, an integrated vasodilation and perfusion (VAPER) technique has been designed to incorporate both CBF and CBV contrasts, and has been shown to reliably resolve laminar activity at different cortical depths at 7T.2
Gradient-echo BOLD is susceptible to signal variation in draining veins, and thus BOLD response3 and signal fluctuation4 are strongly biased toward the cortical surface. The VAPER signal comes primarily from small arteries.5 However, we are uncertain of the laminar features of VAPER signal fluctuation, which includes 1) physiological and motion noise, and 2) meaningful neuronal signal. We examine laminar VAPER signal profiles and the degree to which they are influenced by physiological and motion noise.

Methods:

Data were collected on a Siemens MAGNETOM 7T scanner with a Nova single-channel transmit/32-channel receive head coil. A 3D-EPI sequence6 was modified to acquire fMRI images alternating between blood-signal-suppressed (DANTE-prepared 3D-EPI) and blood-signal-augmented conditions (CTRL 3D-EPI). Imaging parameters: 0.8x0.8x0.9mm3 resolution, PF 7/8, and GRAPPA 3. VAPER contrast was computed via dynamic subtraction of the signal in DANTE and CTRL volumes, to be sensitive to CBV and CBF changes while suppressing BOLD weighting.
Four healthy volunteers were scanned for a total of nine sessions. Slice position was covered the auditory cortex, premotor cortex, superior temporal sulcus (STS), intraparietal sulcus (IPS), and middle temporal visual area (MT) of the right hemisphere (Fig. 1A & 2A).
To determine physiological and head motion noise contributions to BOLD and VAPER signals, we constructed a regression model containing drift, task, motion, and physiological components. Physiological time courses (respiration, cardiac) were produced by AFNI's RetroTS.py program using the RETROICOR method.7 The sum of squared error for the full model is SSE (Full), while ΔSSEMO and ΔSSEPS exclude physiological noise or motion respectively to estimate their relative noise contributions.
All laminar analyses were conducted in the original EPI space. Cerebral spinal fluid (CSF)- gray matter (GM) borders and GM- white matter (WM) borders in all five regions were manually drawn on anatomical EPI images.8 Signal was interpolated from 20 layers across GM using the LAYNII software.9

Results:

Figure 1B shows vascular influence in residual and RETROICOR-explained signal fluctuations: auditory cortex has both the strongest signal fluctuation and strongest blood inflow. With gray and white matter masks (Fig 1C), we conduct a group-level analysis of these fluctuations (Fig 1D). The RETROICOR model can explain more physiological noise in BOLD than in VAPER for all tissue types. Head motion explains more variance in BOLD than in VAPER, suggesting the subtraction used to compute VAPER contrast mitigates motion-induced noise. Unmodeled residual signal in VAPER is ~27% larger than in BOLD. However, VAPER's decreased sensitivity is outweighed by its improved specificity with respect to other non-BOLD alternatives.10
Residual layer-dependent signal fluctuation decreases with cortical depth in both contrasts for the five ROIs, but notably, this depth-wise decrease is less pronounced in VAPER (Fig 2B). Regional physiological noise modeled by RETROICOR in both contrasts decreases with cortical depth, with less explained physiological noise in VAPER than in BOLD (Fig 2C). Note signal fluctuation in auditory cortex is much higher than other regions and is more strongly weighted toward the cortical surface in both contrasts.
Supporting Image: ohbm2020_fig1_1219.png
Supporting Image: ohbm2020_fig2_1219.png
 

Conclusions:

Signal fluctuation in both BOLD and VAPER contrasts is weighted toward the cortical surface, but VAPER contrast provides a more stable layer profile less influenced by this weighting. As such, it could be a more precise contrast for layer-specific analyses.

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Cortical Anatomy and Brain Mapping 2

Novel Imaging Acquisition Methods:

Non-BOLD fMRI 1

Keywords:

Acquisition
Cerebral Blood Flow
Cortical Layers
fMRI CONTRAST MECHANISMS
HIGH FIELD MR
MRI PHYSICS

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.

No

Please indicate below if your study was a "resting state" or "task-activation” study.

Task-activation

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

Healthy subjects

Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.

Yes

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.

Yes

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
Structural MRI

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

7T

Which processing packages did you use for your study?

AFNI
Free Surfer

Provide references using author date format

[1] Jin, T., & Kim, S.-G. (2008). Cortical layer-dependent dynamic blood oxygenation, cerebral blood flow and cerebral blood volume responses during visual stimulation. NeuroImage, 43(1), 1–9. doi: 10.1016/j.neuroimage.2008.06.029
[2] Chai, Y., Li, L., Huber, L., Poser, B. A., & Bandettini, P. A. (2019). Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. NeuroImage, In press, 116358. doi: 10.1016/j.neuroimage.2019.116358
[3] Huber, L., Handwerker, D. A., Jangraw, D. C., Chen, G., Hall, A., Stüber, C., … Bandettini, P. A. (2017). High-Resolution CBV-fMRI Allows Mapping of Laminar Activity and Connectivity of Cortical Input and Output in Human M1. Neuron, 96(6), 1253–1263. doi: 10.1016/j.neuron.2017.11.005
[4] Polimeni, J., Fischl, B., Greve, D., & Wald, L. (2009). Laminar analysis of high isotropic resolution BOLD activation with a resolution pattern stimulus in human V1 at 7T. NeuroImage, 47(4), 1334–1346. doi: 10.1016/s1053-8119(09)72220-7
[5] Chai et al. (2019)
[6] Poser, B., Koopmans, P., Witzel, T., Wald, L., & Barth, M. (2010). Three dimensional echo-planar imaging at 7 Tesla. NeuroImage, 51(1), 261–266. doi: 10.1016/j.neuroimage.2010.01.108
[7] Glover, G. H., Li, T.-Q., & Ress, D. (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine, 44(1), 162–167. doi: 10.1002/1522-2594(200007)44:1<162::aid-mrm23>3.0.co;2-e
[8] Y. Chai, L. Li, Y. Wang L. Huber, B. Poser, J. Duyn, P. Bandettini. A magnetization transfer weighted anatomical reference allows laminar fMRI analysis in native functional image space. Submitted to ISMRM 2020.
[9] Huber at al. (2017)
[10] Chai et al. (2019)