Detectability of ocular dominance and orientation preference in V1 using fMRI

Poster No:

W381 

Submission Type:

Abstract Submission 

Authors:

Marilia Menezes de Oliveira1,2, James Pang3,1,2, Peter Robinson1,2, Mark Schira4

Institutions:

1University of Sydney, Sydney, Australia, 2ARC research Council Center for Integrative Brain Function (CIBF), Sydney, Australia, 3QIMR Berghofer Medical Research Institute, Brisbane, Australia, 4School of Psychology, University of Wollongong, Wollongong, Australia

E-Poster

Introduction:

The primary visual cortex (V1) contains columns of cells that respond preferentially to left- or right-eye stimuli, and to edges of particular orientation. These ocular dominance (OD) and orientation preference (OP) columns are further grouped into hypercolumns of 1 – 2 mm in size that contain all possible feature combinations and which each correspond to one visual field (VF) in the overall field of view. OP-OD columns have mainly been mapped through optical imaging; however, this technique is too invasive to be performed on human subjects. It would thus be highly desirable to map OD-OP feature preferences via fMRI, but so far the spatial resolution has not been sufficient. However, 7T fMRI machines have now resolved submillimeter cortical and subcortical structures, which places them on the verge of the necessary spatial resolution. Simultaneously, advances in quantitative modeling of the link between neural activity and the blood oxygen level dependent (BOLD) response have enabled improved fMRI resolution to be obtained by numerically deconvolving hemodynamic spreading from BOLD measurements to sharpen images of underlying neural activity. The present work brings these aspects together to (i) investigate the detectability of OD and OP in V1 using fMRI, both directly and with deconvolution, (ii) determine the optimal conditions and necessary resolution for detection, and (iii) explore the effects of OP and noise on detectability.

Methods:

The neural activity directly evoked by a spatially-short oriented-bar stimulus to one eye, lasting 7 s, is calculated via the retinotopic map, via which it excites neurons of OP similar to the bar orientation. This neural activity then propagates to similar OP cortical cells in neighboring VFs (i.e., neighboring hypercolumns) via patchy connections. The overall neural activity then drives hemodynamic processes that result in the measured BOLD signal. These processes are modeled using differential equations that embody spatiotemporal conservation of mass, momentum, and oxygen, and which have been verified in recent experiments. In the reverse direction, a recently developed Wiener deconvolution method is used to estimate the underlying neural activity from the BOLD response in the presence of noise [1-7].

Results:

The hemodynamic model predicts a BOLD response with OP-induced spatial modulations on scales of 1-2 mm of ~6% for OP 0º and 90º relative to local OD column boundaries, and ~10% for OP 45º and 135º. These modulations enable the regions of corresponding OP to be detected and mapped. The dependence of observable BOLD on fMRI resolution is then analyzed by coarse-graining the results to simulate resolutions of 0.25-1 mm. At 0.25 mm resolution, the modulation is fully resolved; at 0.5 mm, the model is marginally detectable; and for coarser resolutions, there is no BOLD modulation. Next, the Wiener deconvolution method is applied to the BOLD data in the presence of added noise to estimate the underlying neural activity. For a noise amplitude of under 1% of the peak BOLD signal, the modulation is ~5% and ~9% for OP 0º and 45º, respectively. However, if the noise level is higher than about 5%, the modulation is severely degraded, preventing OP detection. A sensitivity analysis shows that tissues with higher than average damping of hemodynamic waves, and lower than average wave speed give rise to less spreading of the hemodynamic response and thus the clearest OP detectability.

Conclusions:

It is concluded that OD and OP features can be detected directly in the BOLD response provided a resolution of 0.5 mm or better is attained. Wiener deconvolution relaxes the required resolution to 0.75 mm, provided the noise level is less than about 5% of the peak BOLD signal. OPs that correspond to directions at 45º and 135º to the local OD column boundaries are the most easily detected.

Imaging Methods:

BOLD fMRI 1

Physiology, Metabolism and Neurotransmission :

Cerebral Metabolism and Hemodynamics 2

Keywords:

FUNCTIONAL MRI
Other - Ocular dominance; orientation preference; visual cortex; BOLD response

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.

Other

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

Healthy subjects

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.

Not applicable

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

Provide references using author date format

1. Robinson, P. A., Drysdale, P. M., Van der Merwe, H., Kyriakou, E., Rigozzi, M. K., Germanoska, B., and Rennie, C. J. (2006). Bold responses to stimuli: dependence on frequency, stimulus form, amplitude, and repetition rate. NeuroImage, 31(2):585–599.

2. Aquino, K. M., Schira, M. M., Robinson, P. A., Drysdale, P. M., and Breakspear, M. (2012). Hemodynamic traveling waves in human visual cortex. PLoS Comp. Biol., 8(3):e1002435.

3. Aquino, K. M., Robinson, P. A., and Drysdale, P. M. (2014a). Spatiotemporal hemodynamic response functions derived from physiology. J. Theor. Biol., 347:118–136.

4. Aquino, K. M., Robinson, P. A., Schira, M. M., and Breakspear, M. (2014b). Deconvolution of neural dynamics from fMRI data using a spatiotemporal hemodynamic response function. NeuroImage, 94:203–215.

5. Pang, J. C., Aquino, K. M., Robinson, P. A., Lacy, T. C., and Schira, M. M. (2018). Biophysically based method to deconvolve spatiotemporal neurovascular signals from fMRI data. J. Neurosci. Meth., 308:6–20.

6. Liu, X., Sanz-Leon, P., and Robinson, P. A. (2019). Gamma-band correlations in primary visual cortex. Phys. Rev. E, submitted.

7. Oliveira, M. M., Pang, J. C., Robinson, P. A., Schira, M. M., Liu, X. (2018). Feasibility of functional magnetic resonance imaging of ocular dominance and orientation preference in primary visual cortex. Human Brain Mapping, submitted.