Spatial frequency selectivity across the visual pathway in HC animals at t = 0.

(A) Anatomical images with the ROIs highlighted. (B) Optimal SF estimated per voxel for HC, averaged across animals. (C) Maximum PSC during the activation period as a function of the SF of the stimulus, calculated for HC. The error bar represents the 10% confidence interval across animals. The continuous lines represent the Gaussian model fitted to the data. The goodness of fit is shown in Table D in S1 Text. The orange band denotes the range of optimal SF values reported in the literature measured using electrophysiology. A compilation of 22 studies reporting on the optimal SF of the rat and mouse visual pathway can be found in Table C in S1 Text. The data underlying this figure can be found here: doi:10.18112/openneuro.ds004509.v1.0.0. HC, healthy control; PSC, percentage signal change; ROI, region of interest; SF, spatial frequency.</p

Visual experience following VDM promotes the specialization of spatial frequency tuning curves.

(A) Optimal SF estimated per voxel obtained for 4 different slices of HC and VD, at t = 0, t = 7d, t = 17d, and t = 27d, respectively. (B) Maximum PSC during the activation period as a function of the SF of the stimulus, calculated for each ROI (LGN, SC, and VC) of VD (orange) and HC (green) at the 4 measured time points. The error bar represents the std. The continuous lines represent the Gaussian model fitted to the data. (C, D) Estimated optimal SF (Gaussian center, panel C) and broadness of the SF tuning curve (Gaussian width, panel D) for the LGN, SC, and VC at t = 0, t = 7d, t = 17d, and t = 27d for HC and VD. The error bar corresponds to std. The *** represents a p-value p-value p-value 10.18112/openneuro.ds004509.v1.0.0. HC, healthy control; LGN, lateral geniculate nucleus; PSC, percentage signal change; ROI, region of interest; SC, superior colliculus; SF, spatial frequency; VC, visual cortex; VD, visual deprivation; VDM, visual deprivation model.</p

Refinement of RF position across time for VD and HC.

(A) Average phase maps obtained for 4 different slices of VD and HC at the 4 measured time points (t = 0, t = 7d, t = 17d, and t = 27d). (B) Variation of the pRF estimated phase as function of the cortical distance across gradient direction, shown in Fig F in S1 Text, measured for the SC of HC (green) and VD (orange) across multiple time points (t = 0, t = 7d, t = 17d, and t = 27d). The green and orange lines correspond to the linear fit for HC and VD, respectively. (C) Violin plot of the slope of the correlation between the pRF phase variation and cortical distance measured for HC and VD, for each ROI (LGN, SC, and VC) for multiple time points (t = 0, t = 7d, t = 17d, and t = 27d). Each dot corresponds to a different animal. Only the animals that performed the 4 scanning sessions were included in the analysis. The *** represents a p-value p-value p-value 10.18112/openneuro.ds004509.v1.0.0. HC, healthy control; LGN, lateral geniculate nucleus; pRF, population receptive field; RF, receptive field; ROI, region of interest; SC, superior colliculus; VC, visual cortex; VD, visual deprivation.</p

Size Distribution Imaging by Non-Uniform Oscillating-Gradient Spin Echo (NOGSE) MRI

<div><p>Objects making up complex porous systems in Nature usually span a range of sizes. These size distributions play fundamental roles in defining the physicochemical, biophysical and physiological properties of a wide variety of systems – ranging from advanced catalytic materials to Central Nervous System diseases. Accurate and noninvasive measurements of size distributions in opaque, three-dimensional objects, have thus remained long-standing and important challenges. Herein we describe how a recently introduced diffusion-based magnetic resonance methodology, Non-Uniform-Oscillating-Gradient-Spin-Echo (NOGSE), can determine such distributions noninvasively. The method relies on its ability to probe confining lengths with a (<i>length</i>)⁶ parametric sensitivity, in a constant-time, constant-number-of-gradients fashion; combined, these attributes provide sufficient sensitivity for characterizing the underlying distributions in μm-scaled cellular systems. Theoretical derivations and simulations are presented to verify NOGSE’s ability to faithfully reconstruct size distributions through suitable modeling of their distribution parameters. Experiments in yeast cell suspensions – where the ground truth can be determined from ancillary microscopy – corroborate these trends experimentally. Finally, by appending to the NOGSE protocol an imaging acquisition, novel MRI maps of cellular size distributions were collected from a mouse brain. The ensuing micro-architectural contrasts successfully delineated distinctive hallmark anatomical sub-structures, in both white matter and gray matter tissues, in a non-invasive manner. Such findings highlight NOGSE’s potential for characterizing aberrations in cellular size distributions upon disease, or during normal processes such as development.</p></div

PRF estimates of HC animals across ROIs and cortical layers at t = 0.

(A) Phase maps averaged across animals obtained for VC, LGN and SC, respectively. The color bar shows the preferred angle estimated per each voxel. (B) Visual representation of 2 pRF profiles located in the left and right hemispheres, respectively. The color bar shows the VE of each individual probe. (C) Average visual field reconstruction maps across animals for VC (obtained by summing the RF maps across some voxels of VC) and an image of the visual setup depicting the portion of the field of view covered by the animal bed. (D) Profile of the pRF size across cortical depth averaged across subjects, obtained from 2 slices of the VC. The green area corresponds to the 10% confidence interval. (E) Visual representation of 8 pRF profiles located across layers of the VC. (F) pRF size maps averaged across animals in 4 different slices of the VC. The color bar corresponds to the degree of visual angle. The data underlying this figure can be found here: doi:10.18112/openneuro.ds004509.v1.0.0. HC, healthy control; LGN, lateral geniculate nucleus; pRF, population receptive field; RF, receptive field; ROI, region of interest; SC, superior colliculus; VC, visual cortex; VE, variance explained.</p

Characterizing size distributions from NOGSE data.

<p><b>(A)</b> NOGSE MRI sequence used, encompassing an initial block probing the confinements over a time T_NOGSE, and a single-shot spin-echo Echo-Planar-Imaging readout (NOGSE gradients are shown along the RO direction, but can be applied in arbitrary orientations). <b>(B)</b><i>x</i> time-dependence of the NOGSE signal attenuation <i>E(T</i>_<i>NOGSE</i><i>)</i> for different size distributions. Note that as the lognormal distribution width increases the <i>E(T</i>_<i>NOGSE</i><i>)</i> changes both in curvature and in overall amplitude; the inset highlights this by normalizing the curves to their first point (Min(<i>x</i>)). <b>(C)</b> Probability distributions <i>P(l)</i> extracted from fitting the simulations in (B) for a given restricting length <i>l</i> in a noise-less reconstruction. The extracted distributions overlap perfectly with the simulated ones. <b>(D)</b> Effects of adding noise to the NOGSE signal for the widest distribution considered in (C): notice that even when fluctuations reach 10% of the signal, the fits remain robust and the distributions are well reconstructed (inset). Throughout this Fig symbols represent the synthetic data whereas solid curves represent fits to these data. For all distributions <i>l</i>_<i>c</i> = 2 μm, <i>G</i> = 40 G/cm, <i>N</i> = 8, <i>T</i>_<i>NOGSE</i> = 30ms.</p

The complex visual stimuli setup for preclinical MRI scanners, stimulation paradigms, and scheme of the dark rearing timeline.

(A) Visual stimulus display setup. (B) Picture of the visual stimulus displayed inside the scanner. (C) The retinotopy stimulation paradigm: checkerboard bar moving in 8 different directions (2 directions per stimulation block during 36 s followed by a 45 s rest period, repeated 4 times). (D) The SF tuning paradigm: 15 s stimulation period followed by a 45 s baseline. Ten different SFs were randomly presented at each stimulation block ranging between 0.003 and 0.5 cpd. (E) Timeline of the visual deprivation experiment for HCs and visually deprived (VD) animals. HC, healthy control; MRI, magnetic resonance imaging; SF, spatial frequency; VD, visual deprivation.</p

Refinement of RF size across time for VD and HC.

(A) Average pRF size measured for HC and VD at multiple time points for VC, LGN, and SC. The error bar corresponds to std. The ** represents a p-value p-value p-values are detailed in Table B in S1 Text. (B) Variation of the pRF size averaged across animals as a function of the cortical depth for 2 slices of the VC for VD and HC at multiple time points. The data underlying this figure can be found here: doi:10.18112/openneuro.ds004509.v1.0.0. HC, healthy control; LGN, lateral geniculate nucleus; pRF, population receptive field; RF, receptive field; SC, superior colliculus; VC, visual cortex; VD, visual deprivation.</p

S1 Text -

Fig A in S1 Text. Photographs of the visual setup mounted to an MRI animal cradle. Animal life-support equipment: temperature and respiration sensors, subcutaneous catheter, and homeothermic water blanket were connected to the cradle. The surface coil was placed on the head of the rats and the preamplifier was placed behind the animal. The visual stimulus was projected to a mirror that reflected the image to the screen placed in front of the animal eyes. Fig B in S1 Text. Retinotopic and SF tuning visual stimuli result in robust BOLD signals confined to the visual pathway in HC at t = 0. A, B, and E: Percentage of BOLD signal change (PSC) of the ROIs defined in D averaged across animals, runs (B), and cycles (A, E), upon retinotopic (A, B) and SF tuning (E) visual stimulation. The colored areas correspond to the 95% confidence interval and the gray area to the stimulation period. C and F: GLM functional maps obtained after retinotopic (C) and SF tuning (F) visual stimulation. The maps are FDR corrected using a p-value of 0.001 and minimum cluster size of 20 voxels. The ROIs defined based on the SIGMA atlas are overlaid on the functional maps. D: Anatomical images with the delineation of the ROIs. The data underlying this figure can be found here: doi:10.18112/openneuro.ds004509.v1.0.0. Fig C in S1 Text. Differential responses between VD animals and HC driven by the SF tuning stimulus. A: Raw fMRI images with the ROIs (LGN, SC, and VC) overlaid. B, E, H, K: fMRI activation patterns of t-contrast maps obtained for HC and VD animals at t = 0, t = 7d, t = 17d, and t = 27d, respectively. The GLM maps are FDR corrected using a p-value of 0.001 and minimum cluster size of 20 voxels. C, F, I, L: PSC of the LGN, SC and VC for the HC and VD animals at t = 0, t = 7d, t = 17d, and t = 27d, respectively. The gray area represents the stimulation period. D,G, J, M: Violin plot of the amplitude of the BOLD response of VD and HC during the total duration of the activation period obtained with the SF tuning stimulus (right) at t = 0, t = 7d, t = 17d, and t = 27d, respectively. The white dot represents the mean, and the gray bar represents the 25% and 75% percentiles. The blue, yellow, and red colors represent the VC, LGN, and SC, respectively. The *** represents a p-value p-value p-value 10.18112/openneuro.ds004509.v1.0.0. Fig D in S1 Text. Differential responses between VD animals and HC driven by the retinotopic stimulus. A: Raw fMRI images with the ROIs (LGN, SC, and VC) overlaid. B, E, H, K: fMRI activation patterns of t-contrast maps obtained for HC and VD animals at t = 0, t = 7d, t = 17d, and t = 27d, respectively. The GLM maps are FDR corrected using a p-value of 0.001 and minimum cluster size of 20 voxels. C, F, I, L: PSC of the LGN, SC, and VC for the HC and VD animals at t = 0, t = 7d, t = 17d, and t = 27d, respectively. The gray area represents the stimulation period. D, G, J, M: Violin plot of the amplitude of the BOLD response of VD and HC during the total duration of the activation period obtained with the SF tuning stimulus (right) at t = 0, t = 7d, t = 17d, and t = 27d, respectively. The white dot represents the mean, and the gray bar represents the 25% and 75% percentiles. The blue, yellow, and red colors represent the VC, LGN, and SC, respectively. The *** represents a p-value p-value p-value 10.18112/openneuro.ds004509.v1.0.0. Fig E. Spatial frequency selectivity across the visual pathway in HC animals at t = 0. A: Anatomical images with the ROIs highlighted. B: Optimal spatial frequency estimated per voxel for HC, averaged across animals. C: Maximum PSC during the activation period as a function of the spatial frequency of the stimulus, calculated for HC. The error bar represents the 10% confidence interval across animals. The continuous lines represent the Gaussian model fitted to the data. The goodness of fit is shown in Table D. The orange and red bands denote the range of optimal spatial frequency values reported in the literature measured using electrophysiology for rats and mice, respectively. A compilation of 22 studies reporting on the optimal spatial frequency of the rat and mouse visual pathway can be found in Table C. The data underlying this figure can be found here: doi:10.18112/openneuro.ds004509.v1.0.0. Fig F in S1 Text. Gradient direction for VC, LGN, and SC. Fig G in S1 Text. Quantification of the topographical organization of the visual pathway. A: pRF profiles of 2 adjacent pRFs. B: Distance matrix of all the SC voxels. C: PRF similarity analysis of all SC voxels averaged for all the animals. The data underlying this figure can be found here: doi:10.18112/openneuro.ds004509.v1.0.0. Fig H in S1 Text. PRF estimates in the AC, MC, and BG. A and B: Violin plots of the variance explained calculated for visual areas (VC, LGN, and SC) and for areas not visually responsive (AC, MC, and BG) for HC and VD, respectively. C: Anatomical image with the ROIs AC, MC, and BG overlapped. D: PRF profiles obtained for HC and VD animals (left and right, respectively) for AC, MC, and BG. The data underlying this figure can be found here: doi:10.18112/openneuro.ds004509.v1.0.0. Fig I in S1 Text. Hypercapnia experiment testing the dynamics of vascular responses. A: Hypercapnia paradigm consisted of a manual switch, after 1.5 min of medical air, to a hypercapnic state with 6.5% CO2 for 1.5 min. This was followed by a manual switch again to medical air for 1.5 min. Each run consisted in only 1 repetition of this block. B, C, and D: PSC response profile (mean ± std) obtained for VD (red) and HC (blue) for different ROIs: VC, LGN, and SC, respectively. The shaded gray area indicates the hypercapnic period. E, F and G: Normalized PSC response profile (mean ± std) obtained for VD (red) and HC (blue) for different ROIs: VC, LGN, and SC, respectively. The data underlying this figure can be found here: doi:10.18112/openneuro.ds004509.v1.0.0. Table A in S1 Text. Statistical analysis HC vs. VD BOLD changes. P-values associated with the ANOVA Bonferroni corrected for multiple comparisons (brain areas and sessions) statistical analysis of the BOLD amplitude changes between HC and VD in response to the retinotopic stimulus (Fig 4). The data underlying this table can be found here: doi:10.18112/openneuro.ds004509.v1.0.0. Table B in S1 Text. P-values obtained for the pRF size changes between HC and VD, calculated using ANOVA Bonferroni corrected for multiple comparisons (brain areas and sessions). The data underlying this table can be found here: doi:10.18112/openneuro.ds004509.v1.0.0. Table C in S1 Text. Summary of the optimal spatial frequency measured for rat and mice across multiple studies. The rat studies used to define the reference interval of Fig 3 are highlighted in yellow. Table D in S1 Text. Pearson’s coefficient between the maximum BOLD response to each SF and the Gaussian fit. The data underlying this table can be found here: doi:10.18112/openneuro.ds004509.v1.0.0. Table E in S1 Text. Fraction of voxels excluded by the variance explained threshold. The data underlying this table can be found here: doi:10.18112/openneuro.ds004509.v1.0.0. (DOCX)</p

NOGSE’s size-resolving potential in human- and materials-oriented setting.

<p><b>(A-B)</b> Simulations predicting NOGSE’s ability to extract cellular size distributions in clinically-relevant settings, involving <i>G =</i> 6 G/cm, <i>N =</i> 64, and <i>T</i>_<i>NOGSE</i><i>=</i> 120 ms, <i>D</i>_<i>0</i> = 3.0E-5 (cm)²/sec. Notice that even when assuming the relatively weak gradients available in whole body MRIs, cell-sized distributions can be resolved and characterized. The inset in panel B analyzes the effect of 0.1 and 3% noise added to the third distribution, showing that with some noise levels, distribution can still be reconstructed. All definitions are akin to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133201#pone.0133201.g001" target="_blank">Fig 1B and 1C</a>. <b>(C-D)</b> Simulations demonstrating NOGSE’s ability to extract pore distributions in mesoporous materials (10–1000 nm range), using the stronger diffusion gradients available in NMR scanners (<i>G =</i> 200 G/cm, <i>N =</i> 160, and <i>T</i>_<i>NOGSE</i><i>=</i> 150 ms, <i>D</i>_<i>0</i> = 0.41E-5 (cm)²/sec). Notice the strong differences in signals arising when pores are distributed around <i>l</i>_<i>c</i> = 300nm.</p