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. 2022 May 1:251:118978.
doi: 10.1016/j.neuroimage.2022.118978. Epub 2022 Feb 7.

Cortical layer-specific differences in stimulus selectivity revealed with high-field fMRI and single-vessel resolution optical imaging of the primary visual cortex

Shinho Cho  1 , Arani Roy  2 , Chao J Liu  2 , Djaudat Idiyatullin  1 , Wei Zhu  1 , Yi Zhang  1 , Xiao-Hong Zhu  1 , Phillip O'Herron  3 , Austin Leikvoll  2 , Wei Chen  1 , Prakash Kara  4 , Kâmil Uğurbil  5
Affiliations

Cortical layer-specific differences in stimulus selectivity revealed with high-field fMRI and single-vessel resolution optical imaging of the primary visual cortex

Shinho Cho et al. Neuroimage. .

Abstract

The mammalian neocortex exhibits a stereotypical laminar organization, with feedforward inputs arriving primarily into layer 4, local computations shaping response selectivity in layers 2/3, and outputs to other brain areas emanating via layers 2/3, 5 and 6. It cannot be assumed a priori that these signatures of laminar differences in neuronal circuitry are reflected in hemodynamic signals that form the basis of functional magnetic resonance imaging (fMRI). Indeed, optical imaging of single-vessel functional responses has highlighted the potential limits of using vascular signals as surrogates for mapping the selectivity of neural responses. Therefore, before fMRI can be employed as an effective tool for studying critical aspects of laminar processing, validation with single-vessel resolution is needed. The primary visual cortex (V1) in cats, with its precise neuronal functional micro-architecture, offers an ideal model system to examine laminar differences in stimulus selectivity across imaging modalities. Here we used cerebral blood volume weighted (wCBV) fMRI to examine if layer-specific orientation-selective responses could be detected in cat V1. We found orientation preference maps organized tangential to the cortical surface that typically extended across depth in a columnar fashion. We then examined arterial dilation and blood velocity responses to identical visual stimuli by using two- and three- photon optical imaging at single-vessel resolution-which provides a measure of the hemodynamic signals with the highest spatial resolution. Both fMRI and optical imaging revealed a consistent laminar response pattern in which orientation selectivity in cortical layer 4 was significantly lower compared to layer 2/3. This systematic change in selectivity across cortical layers has a clear underpinning in neural circuitry, particularly when comparing layer 4 to other cortical layers.

Keywords: Cerebral blood volume response; Functional magnetic resonance imaging; Layer-specific orientation selectivity; Multi-photon optical imaging; Primary visual cortex; Vessel dilation.

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Conflict of interest statement

Declaration of Competing Interest The authors declare no competing interests.

Figures

Fig. 1.
Fig. 1.. CBV-weighted (wCBV) fMRI signal changes across the cortex evoked by visual stimuli.
Blue-purple colors represent the F-statistics of general linear model (GLM) fitting to cross-trial averaged responses across all 8 stimulus orientations (F-stats > 4.0, P < 0.001, cluster-extent based false discovery rate corrected at q = 0.5). (A) Area 18 of cat V1 approximately delineated by the white rectangle on a 3D rendered cat brain model, with the stimulus-evoked signal changes observed on the cortical surface. (B–D) The stimulus-evoked wCBV changes observed in the axial (B), sagittal (C), and coronal (D) planes (acquired separately using axial, sagittal and coronal imaging slices) are shown overlaid on corresponding GRE anatomic images. The white-dotted line shows the experimentally determined upper and lower boundary of cortical layer 4 (see Fig. 2C). (E) Activation maps for six contiguous 250 μm thickness axial slices. (F) This shows the procedure of cortical flattening for laminar analysis. The cortical boundary between CSF and GM, GM and WM was defined, then ‘layerification’ and ‘columnizing’ was carried on the LAYNII software, (Huber et al., 2021). The depth and column labeled voxels were reconstructed into multiple equal-depth 2D planes (‘flattening’) by the mesh parameterization (Peyre, 2021). The in-plane orientation preference was estimated, and the planes were stacked up along with cortical depth (0 – 1500 μm) (right most panel in Fig. 1F) (see the Materials and Methods for details) Abbreviations in panel A: aLS, anterior lateral sulcus; CSF, cerebrospinal fluid; GM, gray matter; MG, marginal gyrus; pLS, posterior lateral sulcus; WM, white matter.
Fig. 2.
Fig. 2.. Depth-dependent wCBV fMRI responses across anatomically-validated cortical layers.
(A) The stimulus-induced wCBV signal changes across cortical depth from different animals; n = 7 cats, 4 studied with axial slices (Cat 1–4) and 3 with sagittal slices (Cat 5–7). The measurements on the data acquired with axial slices were derived from the flattened, layerified cortex (Fig. 1F). (B) A coronal section from cat area 18, immuno-stained for VGLUT2, which labels the end terminals of thalamic afferents in cortical layer 4. A brightness profile was measured over straight lines orthogonal to the cortical surface of the brain. Scale bar 500 μm. (C) Black line represents the group averaged wCBV fMRI response from the individual cases (Cat 1–7) shown in A (error bars denote standard error of the mean, SEM). Red line is the depth-dependent VGLUT2 signal along the cortical depth, averaged across all tissue sections and animals. The pink error band represents the SEM of the normalized fluorescence. Dotted vertical lines represent the upper (650 μm) and lower (1150 μm) boundaries of layer 4 as determined from our histological data.
Fig. 3.
Fig. 3.. Columnar structure of iso-orientation domains using wCBV fMRI.
The flattened volume data derived from the images obtained with axial slices were employed to extract planes perpendicular to the cortical surface running in the rostral-caudal direction. Data from an exemplar cat is illustrated. (A) An anatomical image of a coronal slice acquired before the exogenous contrast agent injection, showing dark lines running within the cortex and perpendicular to the cortical surface arising from penetrating (radial) intracortical veins; the yellow lines on the right hemisphere indicate the locations of planes-of-interest as they intersect this anatomical coronal image perpendicularly to the plane of this image. The orientation of these yellow lines are also perpendicular to the cortical surface. (B) Orientation preference maps observed on the seven planes corresponding to the yellow lines shown in (A) (400 µm equally distanced in the flattened volume); each color map depicts the orientation preference along the cortical depth (vertical dimension) vs. the horizontal axis representing the distance along the rostro-caudal direction. Scale bars indicate 1 mm; (C) Orientation preference map as in panel B, but after excluding pixels (black) that had a goodness-of-fit statistics that was lower than R2 < 0.2 (F < 13.01 and P > 1.0 ×10−7 ).
Fig. 4.
Fig. 4.. Depth-dependent orientation selectivity index (OSI) estimated from wCBV fMRI.
(A) Depth-dependent OSI calculated from stimulus-induced wCBV signal changes. Data from individual cats shown as gray lines, mean from 7 cats shown as black line and error bars are ±1 SEM. The spatial location of layer 4 is indicated by the dashed vertical lines. (B) Average OSI values (binned in 250 µm steps); the differences between the binned OSI values were statistically significant except for the depth between 750 and 1000 µm versus the 1000–1250 µm bins. (ANOVA P < 0.05 for each pair of consecutive 250 µm bins). All analyzed voxels had a R2 > 0.2 for orientation tuning curve fits. (C) Depth-dependent OSIs of wCBV fMRI where bars represent the individual cats’ OSI calculated from the equal-distanced depth aligned voxels binned into 250 µm thick slabs.
Fig. 5.
Fig. 5.. Orientation selectivity of single-vessel dilation and blood velocity in cortical layers 2/3 versus layer 4 using optical imaging.
(A–D) Anatomy of the vasculature from visual cortical layers 1 through 4 using either two-photon or three-photon imaging. (A) Side view perspective of a 3D volume imaged using two-photon microscopy where the blood vessels were labeled with Alexa 680 dextran. The maximum imaging depth shown was 850 µm below the brain surface. The gray slice corresponds to the single z plane shown in panel B. (B) Dorsal view of a single z plane at 850 µm depth, imaged at a higher zoom compared to the data shown in panel A. (C) Side view perspective of a 3D volume from another cat using three-photon microscopy and where blood vessels were labeled with fluorescein dextran. The maximum imaging depth with three-photon imaging was 1200 µm. The gray slice corresponds to the single z plane image shown in panel D. (D) Dorsal view of a single z plane at 830 µm depth. Magenta dashed lines in panels A and C indicate the upper limit of layer 4 (650 µm). All scale bars shown in A-D are 100 µm. (E) Time courses of dilation responses of two individual arterioles to visual stimulation (top panel shown in magenta: layer 2/3, depth 234 µm, 6 trials); bottom panel shown in green: layer 4, depth 900 µm, 3 trials). Solid lines and error bands represent the mean and SEM dilation responses; gray vertical bars represent the periods of visual stimulation. (F) Time courses of changes in blood velocity upon visual stimulation in two individual blood vessels (top panel shown in magenta: layer 2/3, 418 µm depth, 3 trials; bottom panel shown in green: layer 4, depth 700 µm, 4 trials). The OSI calculated from responses in each of the 4 example vessels are shown above the time course plots. (G) All OSI values measured for dilation (diamonds) and velocity (circles) from individual layer 2/3 (magenta) and layer 4 (green) vessels across cortical depth. Solid lines represent the mean and dashed lines represent the SEM. The vertical dashed line in black delineates the superficial boundary of layer 4 at 650 µm.
Fig. 6.
Fig. 6.. Orientation selectivity index (OSI) obtained from wCBV fMRI and optical imaging in layers 2/3 and 4.
The box-violin plots indicate the OSIs in layer 2/3 (magenta) and layer 4 (green). In each box, bold horizontal lines represent the median, boxes the 25% and 75% quartiles and whiskers the 95% confidence intervals. The shaded violin represents the overall distribution of the data where the width indicates the frequency and the height represents the minimum and maximum values. The cortical depth sampled in optical imaging was 160–410 µm for layer 2/3 and 650–910 µm for layer 4. wCBV fMRI data was interpolated to approximately match the same depth. Asterisks indicate significant OSI differences at P < 0.05 (Welch’s t-test).
Fig. 7.
Fig. 7.. Comparison of cortical layer 2/3 tuning curves between wCBV fMRI (panel A) and artery dilation from optical imaging (B).
The CBV data are for all voxels taken from cat 1. Refer to Fig. 4C for the remarkable consistency of OSI measurements across all seven cats where fMRI was performed. In order to compare all tuning curves, for each voxel or blood vessel, the peak response was re-centered to the zero-degree orientation. The tuning curves between fMRI and optical imaging are very similar, as is confirmed by the OSI measurements (CBV layer 2/3 OSI = 0.19 ± 0.09 mean ± SEM, n = 300 voxels vs. artery dilation OSI 0.21 ± 0.01 mean ± SEM, n = 80 vessels). Also note the similarity of the tails of the tuning curves Y axis ∼ 0.4 across both imaging modalities.
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