Creating Layered Surfaces to Visualize with AFNI + SUMA, with applications to laminar fMRI

Poster No:

1537 

Submission Type:

Abstract Submission 

Authors:

Salvatore Torrisi1, Peter Lauren2, Paul Taylor2, Suhyung Park3, David Feinberg1, Daniel Glen4

Institutions:

1UC Berkeley, Berkeley, CA, 2National Institute of Mental Health, Bethesda, MD, 3Chonnam National University, Gwangju, N/A, 4NIMH, Bethesda, MD

First Author:

Salvatore Torrisi, PhD  
UC Berkeley
Berkeley, CA

Co-Author(s):

Peter Lauren, PhD  
National Institute of Mental Health
Bethesda, MD
Paul Taylor, PhD  
National Institute of Mental Health
Bethesda, MD
Suhyung Park, PhD  
Chonnam National University
Gwangju, N/A
David Feinberg, MD, PhD  
UC Berkeley
Berkeley, CA
Daniel Glen  
NIMH
Bethesda, MD

Introduction:

The field of depth-dependent ("laminar") fMRI strives to investigate the in vivo input and output circuitry in the human brain. It has rapidly grown due to improvements in all facets of MRI, from hardware to data acquisition to analysis and visualization methods. Somewhat mirroring the evolution of fMRI itself, much work in this domain has initially focused on unimodal cortex with known underlying circuitry, but gradually the scope is widening to "higher" cognitive processes1. A critical part of such advancements will be to have flexibility in exploring and visualizing neuroanatomy and function which is less understood at the meso-scale. Here we introduce two new tools, SurfLayers and quickspecSL, which extend existing AFNI2 and SUMA3 functionality and allow for unique, fine-grained exploration, and visualization of structure and function within layers of the cortical ribbon.

Methods:

For an example dataset, one subject passively viewed a retinotopic meridian mapping block design task presented with PsychoPy4. Data were collected with a Siemens 7T MAGNETOM scanner with a 32 channel Nova Medical head coil. A zoomed, accelerated 3D GRASE with reduced PSF blurring was collected in a functional protocol at 0.8mm isotropic with 24 slices and TR=3 seconds5. A 0.7mm isotropic MP2RAGE structural scan6 was also collected and standard surface reconstruction was performed using FreeSurfer7 ver. 7.1.1 with mesh up-sampling using @SUMA_Make_Spec_FS. Data were analyzed with an afni_proc.py pipeline tailored to small FOV epi-anatomy co-registration. Temporal autocorrelation-corrected activation T-statistics were also spatially cluster-corrected8.
SurfLayers generates a given number of intermediate surfaces between two existing 'bookend' surfaces, usually white matter and pial, for either whole hemispheres or patches thereof. Optionally, quickspecSL can create specification files from these surfaces, enabling single- or two-hemisphere visualizations along with the ability to 'page through' each surface. The functional data can be mapped to these surfaces with 3dVol2Surf on the command line or interactively with the AFNI GUI and its Vol2surf plugin.
The SUMA GUI has been modified to show these layers better. Clipping planes allow for ready interaction by sliding and tilting up to six planes through displayed surfaces or surface patches. At the same time, the SUMA interface "talks" with the volumetric interface of AFNI via TCP/IP interprocess communication.

Results:

Figure 1 demonstrates SUMA's new object clipping plane behavior with 3 interpolated surface meshes created by SurfLayers. Figure 2 demonstrates functional retinotopy results (general linear contrast "vertical - horizontal") in tethered volumetric+surface views. Multiple SUMA functions and visualization features can be quickly assessed with keystrokes controlling, e.g., surface, triangle or point viewing, ROI drawing, transparency or hiding of individual layers, opacity and lighting and custom color tables, etc.
Supporting Image: figure1.png
   ·Screenshot of clipping planes in interactive SUMA display: 5 of 6 possible clipping planes were orthogonally defined to isolate a portion of superior frontal cortex.
Supporting Image: figure2.png
   ·AFNI and SUMA views linked (“talking”). 6 surface patches were loaded simultaneously but the first 2 (pial + one interpolation towards white matter) are interactively hidden from view.
 

Conclusions:

We have created new extensions to AFNI+SUMA enabling easy creation of intermediate cortical surfaces for laminar fMRI/MRI data exploration, visualization and potential preprocessing steps (e.g., surface smoothing at different depths). Intermediate surfaces from SurfLayers integrate into the AFNI+SUMA ecosystem for interactive assessments of layers of surfaces. Such utilities complement other popular voxel-level, volumetric approaches to laminar fMRI analysis and visualization9. SurfLayers processes both native and standardized surfaces in GIFTI format. Future directions will incorporate equi-volume cortical divisions10, allow for interpolation of non-FreeSurfer surfaces with nodal correspondence, more easily integrate cortical hand-drawn (Figure 2) or atlas-based patches of cortex and to allow the saving and driving of clipping plane definitions.

Modeling and Analysis Methods:

Other Methods

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Cortical Anatomy and Brain Mapping 1

Novel Imaging Acquisition Methods:

Anatomical MRI
BOLD fMRI
Imaging Methods Other 2

Keywords:

Acquisition
Cortex
Cortical Layers
Data analysis
FUNCTIONAL MRI
HIGH FIELD MR

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.

Yes

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. Finn ES, Huber L, Bandettini PA. Higher and deeper: Bringing layer fMRI to association cortex. Prog Neurobiol. 2020;(xxxx):101930. doi:10.1016/j.pneurobio.2020.101930
2. Cox RW. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996;29(3):162-173. doi:10.1006/cbmr.1996.0014
3. Saad ZS, Reynolds RC. Suma. Neuroimage. 2012;62(2):768-773. doi:10.1016/j.neuroimage.2011.09.016
4. Peirce J, Gray JR, Simpson S, et al. PsychoPy2: Experiments in behavior made easy. Behav Res Methods. 2019;51(1):195-203. doi:10.3758/s13428-018-01193-y
5. Park S, Torrisi S, Beckett A, Townsend JD, Feinberg DA. Highly accelerated submillimeter resolution 3D GRASE with controlled T 2 blurring in T 2 -weighted functional MRI at 7 Tesla : A feasibility study. Magn Reson Med. 2020;(March):1-17. doi:10.1002/mrm.28589
6. Marques JP, Kober T, Krueger G, van der Zwaag W, Van de Moortele PF, Gruetter R. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage. 2010;49(2):1271-1281. doi:10.1016/j.neuroimage.2009.10.002
7. Fischl B. FreeSurfer. Neuroimage. 2012;62(2):774-781. doi:10.1016/j.neuroimage.2012.01.021
8. Cox RW, Chen G, Glen DR, Reynolds RC, Taylor PA. FMRI clustering and false-positive rates. Proc Natl Acad Sci U S A. 2017;114(17):E3370-E3371. doi:10.1073/pnas.1614961114
9. Huber L (Renzo), Poser BA, Bandettini PA, et al. LAYNII: A software suite for layer-fMRI. bioRxiv. Published online 2020:1-20. doi:10.1101/2020.06.12.148080
10. Waehnert MD, Dinse J, Weiss M, et al. Anatomically motivated modeling of cortical laminae. Neuroimage. 2014;93:210-220. doi:10.1016/j.neuroimage.2013.03.078