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Next, all runs were individually despiked, scaled, and detrended. This step was implemented to remove spurious large fluctuations of signal amplitude between two time points. These spikes were replaced with the average of the closest two non-spike time points. The scaling step entailed converting the time-series of each run to percentage BOLD.

This was achieved by dividing the signal of each voxel by its temporal mean, multiplying that signal with , and subsequently subtracting to ensure that the temporal mean of that voxel was zero percent signal change. Detrending was performed to remove slow fluctuations of the fMRI signal. These functional runs were then temporally resampled to 1. Next, the warp field was applied to the average over the motion-corrected, despiked, scaled, detrended, temporally resampled runs, which was subsequently collapsed over all time points to calculate the mean EPI image.

This mean EPI image was then registered to the anatomy using a multi-step procedure. First, the anatomy was restricted to roughly the occipital lobe. Next, the mean EPI image and anatomy were brought into the same space by aligning the center of mass of the anatomy to the mean EPI image. These registration steps both consisted of affine transformations to further optimize the registration, using local Pearson correlation as cost function Saad et al.

The transformation matrices of the manual step and the two affine transformations were combined into a single affine matrix. As a control, this matrix was then applied to the original mean EPI image to check the registration quality of this one-step procedure and ensure the correctness of the combination of the aforementioned matrices. Next, we applied the combined affine matrix and the warp field to all temporally resampled motion-corrected, despiked, scaled, and detrended runs individually to align these volumes to the registered mean EPI image.

These registered, distortion-corrected volumes were then resampled to the anatomy, resulting in the registered time-series for each run. In total, the motion-corrected, despiked, detrended time-series were spatially resampled twice, resulting in the registered, topup-corrected time-series in anatomy space Fig. These level-sets were then projected on the distortion-corrected mean time-series for subsequent laminar analysis Fig.

For each subject we divided the distortion-corrected mean time-series into twelve blocks, each starting at stimulus onset. These blocks contained as many time points as the stimulus presentation duration 2. Blocks containing the same stimulus presentation duration were then averaged together. To assess the temporal additivity of BOLD responses for each visual field map at each depth bin, we generated predicted responses to longer stimulus presentations by temporally shifting the responses to shorter stimulus presentations, and subsequently adding this shifted response to the original response for the shorter stimulus see e.

For example, temporally shifting a 2. These predictions were generated using the measured responses to 4 out of 8 runs. Next, we estimated the goodness of fit of these predicted responses by calculating the Pearson correlation between the predictions and the original stimulus responses of the remaining 4 runs. This procedure was repeated for each possible combination of 2 sets of 4 runs 70 in total , and the median overall fit was used for further processing see below.

To estimate the theoretical best possible fit given the data, we calculated the Pearson correlations between each pair of original stimulus responses of the first and second set of 4 out of 8 runs. This is further called the noise ceiling. A linear fit was calculated and evaluated for these correlations across cortical depth, for each visual field map separately.

For temporal additivity across cortical depth to hold, this fit should not deviate significantly from horizontal. For each subject, the maximum BOLD amplitude increased both as a function of stimulus presentation duration and cortical depth for all tested visual field maps V1, V2, V3; see Fig. For the example subject, peak amplitudes in V1 at deep, middle, and superficial cortical depths for the short 2.

We observed similar patterns for the group averaged responses Fig. Group average peak amplitudes for V1 at deep, middle, and superficial cortical depth bins for the short 2. For the medium 5. Lastly, for the long For V2, the respective peak amplitudes were 0. For V3, peak amplitudes at deep, middle, and superficial cortical depth bins were 0.

The consistent increase towards the cortical surface and with visual hierarchy is in line with previous literature see e. Fracasso et al. Moreover, response amplitude increased with presentation durations, likewise in line with previous research e. BOLD response profiles for three stimulus presentation durations at three cortical depth bins deep, middle, and superficial.

Different colors represent different stimulus presentation durations. Average normalized depth: 0. To assess and visualize the temporal additivity assumption across cortical depth, we used responses elicited by shorter stimulus presentations to predict the responses to longer stimulus presentations.

Qualitatively, the predicted 5. Rise times matched well between the predicted and observed responses for all deep cortical depth bins, but were underestimated in the superficial ones. The post-stimulus undershoot was consistently overestimated.

Predicted and observed BOLD response profiles at three example cortical depths for visual field map V1. Overall, the predicted This included the peak amplitude, rise time and post-stimulus undershoot. The match between predicted and observed responses across cortical depth appeared to hold across visual field maps, though the match appeared qualitatively better in V1 compared to V2 or V3 Supplementary Fig.

Overall, group-average predictions for V1 were good fits to their respective observed responses Fig. However, the linear fit on these correlations did not differ significantly from horizontal Fig. Correlations were generally highest for the predictions made from the 2. Average correlations per subject can be found in Supplementary Fig. Correlations across cortical depth for visual field maps V1, V2, and V3.

Dashed black lines represent best linear fit. Dotted lines denote the noise ceiling maximum correlation given noise in the data. We assessed whether the temporal additivity assumption for a linear system holds in human visual cortex for laminar fMRI. We find that BOLD response amplitudes vary both as a function of cortical depth, and presentation duration, with higher BOLD amplitudes towards the cortical surface, and with increasing presentation duration.

In particular, we find that shorter presentations predict longer stimulus presentations across cortical depth according to the temporal additivity principles. In conclusion, we show that the temporal additivity assumption holds across cortical depth for sub-millimeter BOLD amplitude measurements. The majority of these studies have focused on the neurovascular coupling that underlies the fMRI signal in humans De Martino et al.

Even though relatively limited in number, the number of studies employing laminar fMRI for systems and cognitive neuroscience questions has been growing steadily in recent years Chen et al. However, most studies have interpreted BOLD amplitude measurements with caution, as these are susceptible to blood pooling effects across cortical depth since measures at different depths are inherently not independent.

Thus, these studies have largely focused on non-amplitude sensitive measures. In this study, we address the temporal additivity assumption for linear systems analysis that underlies many fMRI studies, paving the way for a wider application of BOLD-based laminar fMRI amplitude measurements for systems and cognitive neuroscience. Predictions for the medium 5. This held true for all visual field maps and most cortical depths.

This is expected, as a smaller number of time points generally results in a higher correlation. All correlations were consistently high and the trends across cortical depth were not significantly different from horizontal, indicating that the temporal additivity assumption for laminar fMRI holds across cortical depth for the tested range of stimulus parameters. While elegant, this formulation requires the hemodynamic transform to be defined both in space and time.

In practice, most analysis methods at regular resolutions only include a temporally defined transform, as a spatially varying one is difficult to estimate. When the spatial component of the hemodynamic transform is included, it is implemented as an isotropic component the point spread function, PSF. Due to the presence of draining veins across cortical depth, however, the spatial component of the hemodynamic transform at a laminar resolution is directional and non-isotropic.

While this component has been estimated for specific laminar fMRI applications see e. Havlicek and Uludag , it is difficult to widely implement in practice. Our results suggest that within a cortical compartment, draining contributions from deeper cortical layers are the same regardless of presentation duration. However, it has relatively low spatial sensitivity due to the blood draining effects Huber et al. Consequently, the observed signal is affected by blood pooling towards the cortical surface.

Thus, the hemodynamic consequences of neuronal signals elicited at deeper cortical depths propagate towards the cortical surface, affecting the observed signal at more superficial cortical depths. This then leads to less laminar specificity and the regularly observed increase of BOLD signal amplitude towards the cortical surface.

The increase in BOLD signal amplitude towards the cortical surface is also clearly present in the current study, with peak amplitudes for each individual presentation duration increasing in this direction. Undesired blood pooling effects across cortical depth can be dealt with in several ways. Firstly, these effects can be corrected for at the analysis level, for instance by means of spatial correction approaches see e. Markuerkiaga et al. In summary, despite blood pooling effects—which could be mitigated in several ways—temporal additivity holds for the early and intermediate visual cortex.

For the current results to be generalizable, even within the visual domain, linearity for stimuli varying in high- and other low-level domains such as spatial frequency content should also be evaluated in a similar way as presented here. Moreover, a wider range of stimulus presentation durations could be employed to better cover the extremes of this stimulus space.

However, the presentation durations used here are similar to the range used in a wide range of block-design fMRI studies. Additionally, because of pooling of large numbers of measurements in this region of interest-based approach, we cannot exclude that local pockets of non-linearity might be present within a visual field map. However, our results do provide support for the temporal additivity of BOLD amplitude measures across cortical depth as a function of stimulus duration, for region of interest-based approaches.

Together with our previous assessment of scaling across cortical depth van Dijk et al. We provide evidence that the temporal additivity assumption for linear systems theory is met for BOLD amplitude measures across cortical depth in V1, V2, and V3, with draining influences being constant across cortical depth. This is reflected by the similar capability across cortical depth for the shorter stimulus durations to predict the longer ones. Together with our previous work van Dijk et al. Neuroimage — Andersson JLR, Skare S, Ashburner J How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging.

Article PubMed Google Scholar. PLoS Comput Biol 8:e J Neurosci Methods — J Neurosci — Brainard DH The psychophysics toolbox. Spat Vis — J Psychol Neurol — Google Scholar. JA Barth, Leipzig. Neuron — Comput Biomed Res — Brain Res Bull — Magn Reson Imaging — Article Google Scholar. Fracasso A, Petridou N, Dumoulin SO Systematic variation of population receptive field properties across cortical depth in human visual cortex.

Data Br — Hum Brain Mapp — Gennari F De peculiari structura cerebri parma ex regio typographeo. Nat Neurosci — Neuron Jin T, Kim SG Cortical layer-dependent dynamic blood oxygenation, cerebral blood flow and cerebral blood volume responses during visual stimulation. Neuroimage —9. Front Neurosci — J Cereb Blood Flow Metab. Curr Biol — Logothetis NK The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. Magn Reson Med — Hum Brain Mapp.

Marques JP, et al. Investig Ophthalmol Vis Sci — Pelli DG The videotoolbox software for visual psychophysics: transforming numbers into movies. NMR Biomed — J Cereb Blood Flow Metab — Ratio — Smith FW, Muckli L Nonstimulated early visual areas carry information about surrounding context. Turner R How much cortex can a vein drain?

Downstream dilution of activation-related cerebral blood oxygenation changes. Sci Rep This website has mostly compressed JavaScripts. CSS files minification is very important to reduce a web page rendering time. The faster CSS files can load, the earlier a page can be rendered. According to our analytics all requests are already optimized. These are opportunities to improve keyboard navigation in your application. These are opportunities to improve the interpretation of your content by users in different locales.

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