Recent advances in brain imaging have made it possible to map brain activity in areas of tissue less than a millimeter in size. This resolution offers particular advantages for studying the brain’s outer surface, the cortex. The cortex is traditionally divided into several layers, each containing different types and arrangements of neurons. New high-resolution machines can now visualize the activity in individual layers of cortex, and this can reveal whether the layers also have different roles.

In humans, a large area in the cortex is devoted to vision. Our visual cortex receives sensory information that arrives from the eyes via the optic nerve. This is known as bottom-up processing. But what we see depends on more than just incoming sensory information: it also relies on where we focus our attention, and on our expectations about how things should look. Many optical illusions, for example, work because the brain attempts to decipher an ambiguous visual signal based on previous experiences. This use of existing knowledge to interpret sensory input is called top-down processing.

Using high-resolution brain scanning, Lawrence et al. show that bottom-up and top-down processing occur in different layers of visual cortex. Healthy volunteers viewed a series of images while lying inside a brain scanner. Lawrence et al. changed the contrast of the images to alter the volunteers’ bottom-up processing: this affected activity in the middle layer of visual cortex. To adjust their top-down processing, the volunteers were asked to attend to different features of the images on different trials: these changes in attention had more effect in the layers on either side of the middle layer. This suggests that bottom-up processing occurs in the middle layer of visual cortex, whereas top-down processing takes place in the layers above and below.

The findings by Lawrence et al. will help to better measure activity in cortical layers using modern brain imaging techniques. With further technological improvements, it may become possible to image each layer in the brain in more detail, in particular for other areas that support complex cognitive processes.

Using ‘ultra-high field’ MRI systems of 7T and above, it has become possible to non-invasively measure fMRI responses at lamina-resolved spatial resolutions in humans (Dumoulin et al., 2018; Koopmans et al., 2011; Polimeni et al., 2010). This has allowed researchers to ask new questions about the functional organization of the human brain, and examine communication between brain areas in more detail than previously possible (Kuehn and Sereno, 2018). One important promise of laminar fMRI is its potential ability to distinguish between bottom-up and top-down BOLD responses. While these are spatially amalgamated at standard imaging resolutions (Lawrence et al., 2017; Self et al., 2017), they are expected to be expressed at different cortical depths. Bottom-up connections between brain areas are known to target the granular layer 4, at middle cortical depths, while top-down connections target deeper and superficial layers but largely avoid layer 4 (Anderson and Martin, 2009; Felleman and Van Essen, 1991; Rockland and Pandya, 1979). It should therefore be possible to tease apart the bottom-up and top-down contributions to a stimulus-driven BOLD response by examining that response across cortical depth.

Previous laminar fMRI studies suggest that this is indeed the case. For example, stimulus-driven responses in visual cortex have been shown to be strongest at middle depths (Koopmans et al., 2010), while top-down signals embodying contextual inference, prediction, attention and working memory operate at deep and/or superficial, but not middle, cortical depths (Klein et al., 2018; Kok et al., 2016; Lawrence et al., 2018; Muckli et al., 2015; Scheeringa et al., 2016). Similar results for top-down influences have also been reported for auditory (De Martino et al., 2015) and motor cortex (Huber et al., 2017). Whilst these results are encouraging, these studies have typically measured top-down signals in the absence of a bottom-up response. The rationale for this choice is clear, as bottom-up drive could affect all cortical layers due to quick communication between layers (Self et al., 2013) and blurring from spatial hemodynamics (Uğurbil et al., 2003; Uludağ and Blinder, 2018; Yacoub et al., 2005) could obscure layer-specific top-down effects. However, being constrained to measuring top-down responses in isolation limits the potential power of laminar fMRI experiments for exploring brain function. If the overall BOLD response to a stimulus could be separated into its bottom-up, stimulus-driven component and a top-down, modulatory component, this would open the door for increasingly complex task design in laminar fMRI experiments.

Here we measured lamina-resolved fMRI responses from human participants as they viewed visual stimuli and were required to attend to a specific stimulus feature (orientation). Our stimulus paradigm was designed to elicit concurrent bottom-up and top-down modulations of the stimulus-driven response through orthogonal manipulations of stimulus contrast (bottom-up) and feature-based attention (top-down), both of which are known to influence early visual cortex responses (Boynton et al., 1999; Himmelberg and Wade, 2019; Kamitani and Tong, 2005; Martinez-Trujillo and Treue, 2004; Saenz et al., 2002; Treue and Martínez Trujillo, 1999).

We predicted that response modulations driven by attention would operate at different cortical depths to those driven by changes in stimulus contrast. Specifically, contrast modulations were expected to be largest at middle cortical depths, as increases in contrast should be associated with stronger bottom-up input to the granular layer (Hubel and Wiesel, 1972; Rockland and Pandya, 1979). Top-down influences are generally expected to be strongest in the deep and/or superficial cortical depths (agranular layers, Lawrence et al., 2017). However, previous research into laminar effects of attention have been varied. Studies by van Kerkoerle et al. (2017) and Klein et al. (2018) report largely agranular effects of attention in V1 while others report effects in all layers of V1 (Denfield et al., 2018; Hembrook-Short et al., 2017). Further studies have also reported attention effects in all layers of V4 (Nandy et al., 2017) and the superficial layers of primary auditory cortex (De Martino et al., 2015). Moreover, previous laminar attention studies have employed spatial or object-based attention, but the laminar circuits involved in feature-based attention have, to our knowledge, not yet been studied. It is therefore not clear whether we should expect feature-based attention to modulate responses in all layers or only agranular layers. Critically, both eventualities yield the prediction that modulations from feature-based attention should be more strongly expressed in agranular layers compared to those from stimulus contrast, which should only be strong in the granular layer.

To preview, we found that fMRI responses in the early visual regions (V1-V3) were strongly modulated by changes in stimulus contrast and feature-based attention, and that these effects were indeed expressed at different cortical depths. As predicted, attentional modulations were more strongly expressed in agranular layers, particularly the superficial layers, while stimulus contrast modulations were largest in the granular layer.

We report laminar-resolved fMRI responses from the early visual cortex (V1-V3) of 24 healthy human subjects while they viewed a series of plaid stimuli comprising two orthogonal sets of bars (one oriented 45°, hereafter referred to as ‘clockwise’, the other oriented 135°, hereafter referred to as ‘counter-clockwise’; see Figure 1). Plaids were presented in blocks of 8 stimuli, during which participants monitored changes in bar width of either the clockwise or counter-clockwise bars (Kamitani and Tong, 2005). Importantly, both sets of bars varied in width independently of each other, meaning attention had to be focused on the cued orientation to succeed at the task. Stimulus contrast was also manipulated: each block of stimuli comprised either high (80%) or low (30%) contrast plaid stimuli. As such, for each stimulus block participants attended to either clockwise or counter-clockwise bars within high or low contrast plaid stimuli, which provided our top-down and bottom-up task manipulations, respectively.

Figure 1

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Bottom-up and top-down modulations of the BOLD response

Subjects were able to focus their attention on one set of oriented bars within a plaid and accurately discriminate changes in bar width between stimuli. On average, subjects performed at 83.5% correct (SD = 2.3) for low and 84.5% correct (SD = 1.5) for high contrast stimuli. Task difficulty was controlled by separate staircases for high and low contrast stimuli to match task difficulty across contrast levels. Despite this, the numerically small difference in task performance was significant (t [22]=2.52, p=0.019). To assess the effects of attention and stimulus contrast on brain responses, we divided visually active voxels within V1, V2 and V3 into subpopulations with a strong preference for clockwise orientations over counter-clockwise or vice versa (Albers et al., 2018; see Materials and methods). It was expected that voxels would respond more strongly during blocks in which their preferred orientation was attended, and that all voxels would respond more strongly to higher contrast stimuli.

As expected, BOLD responses in early visual cortex were modulated by both subjects’ attention towards a specific orientation and changes in stimulus contrast (see Figure 2). Responses to high contrast stimuli were significantly higher than low contrast stimuli across V1-V3 (F [23, 1]=35.57, p=4.00e⁻⁶). The size of this effect varied across areas (F [30.2, 1.3]=46.53, p=1.77e⁻⁸), being larger in V1 than V2 and V3. Voxel responses were also higher when their preferred orientation was attended, compared to when the orthogonal orientation was attended (F [23, 1]=25.67, p=4.00e⁻⁵). This effect also varied across visual areas (F [46, 2]=4.91, p=0.012), being slightly smaller in V1 compared to V2 and V3. Overall, therefore, our paradigm was successful in inducing strong modulations of stimulus-driven BOLD responses using bottom-up (contrast) and top-down (feature-based attention) task manipulations.

Figure 2 with 1 supplement see all

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Dissociable laminar profiles of bottom-up and top-down response modulations

Next, we determined whether the effects of feature-based attention and stimulus contrast on BOLD responses varied across cortical depth, and whether they did so differently from each other. To this end we computed separate BOLD time courses specific to three equal volume gray matter depth bins defining deep, middle and superficial cortex (Lawrence et al., 2018; van Mourik et al., 2018a, see Materials and methods for more information). Depth-specific time courses were normalized to remove overall differences in signal intensity between layers (Figure 3—figure supplements 3 and 4). Note that this normalization was not critical to the results reported (Figure 3—figure supplement 5). Normalized depth-specific time courses were analyzed to compare the laminar profile of activity modulations resulting from top-down attention and bottom-up stimulus contrast. To get an overall picture of depth-specific modulations across the visual cortex, we first combined voxels from V1, V2 and V3 for this analysis.

Response modulations from both feature-based attention and stimulus contrast were clearly present in depth-specific time courses (Figure 3A–D). In order to fairly compare laminar profiles across conditions, we used data from the same time points (highlighted in Figure 3A & C), which comprised the peak of the BOLD response during a block of stimuli and during which both the effects of attention (F [23, 1]=19.95, p=1.76e⁻⁴) and contrast (F [23, 1]=35.98, p=4.00e⁻⁶) were significant. Within this time window, the effect of feature-based attention on neural responses was present at all cortical depths and was largest in the superficial layers (Figure 3b).

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There was a trend of activity differences between layers induced by the attentional manipulation (F [46, 2]=2.82, p=0.070). Unpacking this, the attentional modulation was significantly stronger in the superficial layers compared to the middle (t [23]=2.11, p=0.046) and deep layers (t [23]=2.15, p=0.042), while there was no significant difference in the strength of the attentional modulation between the deep and middle layers (t [23]=0.36, p=0.723). Modulations from changes in stimulus contrast were organized quite differently, peaking at middle depths (Figure 3d). Indeed, contrast modulations varied significantly across depth (F [46, 2]=8.43, p=0.001), being largest at middle compared to deep (t [23]=3.79, p=0.001) and superficial (t [23]=3.56, p=0.002) depths. Critically, the organization of contrast-related modulations across depth was significantly different to those caused by feature-based attention, as shown by a source (bottom-up, top-down) X layer (deep, middle, superficial) interaction (F [46, 2]=4.39, p=0.018). As such, the laminar profiles of responses modulations across the early visual cortex were dependent on whether those modulations were bottom-up or top-down in origin.

We predicted that top-down effects were more likely to be expressed in agranular layers compared to bottom-up effects. To explicitly test for this, we computed a score that described whether experimental effects were more agranular or granular. This was done by averaging the effect of feature-based attention (or contrast) from the superficial and deep depth bins (agranular) and subtracting that from the middle bin (granular). As such, a positive score indicates a largely agranular effect, while a negative score indicates a granular effect. As predicted, feature-based attention effects were more agranular compared to stimulus contrast (Figure 3E). This difference was significant (t [23]=3.11, p=0.005), and 20 of our 24 subjects showed an effect in this direction (Figure 3F). Therefore, it appears that top-down contributions to response modulations were stronger in the agranular layers compared to bottom-up contributions, which were strongest in the granular layer. As can be seen from Figure 3B, the agranular profile of attention was driven by the fact that the attentional modulation was strongest in the superficial layers.

Laminar profiles are similar across early visual areas

We next explored how modulations from feature-based attention and stimulus contrast varied across cortical depth within visual areas, and potential differences in organization between areas. We estimated depth-specific effects of attention and contrast for V1, V2 and V3 using the same methods applied to the three areas combined (Materials and methods). Similar to our original analysis, variation in the effect of attention across depth over V1-V3 (Figure 4A) did not reach significance (F [46, 2]=2.54, p=0.090), and attention depth profiles were similar across areas (F [69.67, 3.03]=0.44 p=0.778). The effect of contrast did vary across cortical depth (F [36.28, 1.58]=7.52, p=0.004) peaking at middle depths (Figure 4B), but this profile was not significantly different between the three areas (F [92, 4]=1.39, p=0.244. When directly contrasting these two modulatory factors, there was an overall, area independent, difference between feature-based attention and stimulus contrast that approached significance (F [46, 2]=2.74, p=0.075), but no significant differences between areas (F [80.38, 3.50]=1.00, p=0.407).

Figure 4

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We also computed scores describing how agranular or granular effects of attention and contrast were within V1, V2 and V3 (Figure 4C). In general, modulations from feature-based attention were more agranular compared to those from stimulus contrast (F [23, 1]=5.48, p=0.028), and this was consistent across visual areas (F [46, 2]=0.51, p=0.607). Overall, these results show highly similar behavior of the three early visual regions (V1, V2, V3) that we examined, in terms of both their bottom-up and top-down laminar activation profiles.

We measured laminar fMRI responses from the human visual cortex during a visual task designed to induce bottom-up and top-down response modulations via orthogonal manipulations of stimulus contrast and feature-based attention. BOLD responses were strongly modulated by both feature-based attention and stimulus contrast, and these effects were expressed at different cortical depths. Effects of stimulus contrast were considerably larger at middle cortical depths compared to deep and superficial depths, while effects of feature-based attention were more even across depth, peaking in superficial cortex. Moreover, by comparing the strength of attention and contrast modulation in agranular versus the granular layers, we found that attention effects were expressed more strongly in the agranular layers (specifically the superficial layers) compared to effects from stimulus contrast, which were more granular.

Our results show clear differences in how bottom-up and top-down aspects of perceptual processing affect brain responses across cortical depth and are consistent with the anatomical organization of feedforward and feedback connections between brain areas (Rockland and Pandya, 1979). To our knowledge, our study also provides the first report of how visual cortex responses are modulated by feature-based attention at the laminar level. Most importantly, we demonstrate that laminar fMRI methods can be used to examine both the bottom-up and top-down components of the overall BOLD response as they co-occur during the processing of a stimulus. Previous laminar fMRI studies have either measured depth-specific effects in the absence of a physical stimulus (Kok et al., 2016; Lawrence et al., 2018; Muckli et al., 2015), or in the presence of a stimulus that was held constant (De Martino et al., 2015; Klein et al., 2018). By orthogonally manipulating stimulus contrast and feature-based attention, we have shown that top-down effects can be separated from concurrent bottom-up modulations driven by the stimulus. This opens the door for future studies to further examine the dynamic interactions between bottom-up and top-down processing that occur in the context of stimulus processing.

Top-down modulations of the BOLD response were expressed at all cortical depths relatively evenly, slightly peaking in the superficial layers. This partly contrasts with our previous study, which observed top-down activation of V1 during visual working memory that was strong in the agranular layers, but much weaker in the middle layer (Lawrence et al., 2018). The most obvious difference between the two studies is the presence of a physical stimulus during top-down modulation in this study, while there was no stimulus during working memory in our previous study. Each stimulus is expected to trigger a large response in the middle layer of V1 driven by bottom-up connections from the LGN (Hubel and Wiesel, 1972). Interestingly, influences of feature-based attention have been reported in the LGN before (Ling et al., 2015; Schneider, 2011), suggesting that this bottom-up signal could carry attentional modulations, consistent with our data. Electrophysiological studies of laminar effects of attention report mixed results regarding the involvement of the granular layer of V1 in attention. van Kerkoerle et al. (2017) report increased spike rate and current sinks with attention that were largest in the agranular layers. In particular, Van Kerkoerle et al. report strong attentional modulations in the deep layers compared to the middle layer, which was not the case in our data. Other studies (Denfield et al., 2018; Hembrook-Short et al., 2017) report attentional effects on spike rate in all layers of V1. However, it should be noted that these studies utilized spatial attention as opposed to feature-based attention, making it unclear how comparable their results are to our study. More research is required to further elucidate the laminar circuits involved in different modes of attentional control.

The relatively similar strength of attentional modulations across cortical layers highlights an important aspect of our task design. Taken in isolation, the laminar profile of feature-based attention we report could be viewed as difficult to interpret, as there are no obvious differences between cortical depths. Crucially, however, the comparison of this profile to one derived from a manipulation of stimulus contrast revealed clear differences in how the visual cortex is modulated depending on the source of the modulation. We encourage future laminar studies exploring top-down responses to also include a bottom-up manipulation as a point of comparison, as the laminar organization of a BOLD activity difference between conditions on its own can be challenging to interpret (Self et al., 2017).

It is possible that we found top-down effects were similar across cortical depth due the blurring of BOLD responses across depth bins from spatial hemodynamics. Our task involved repeated presentation of a series of visual stimuli, which is expected to cause large swathes of stimulus-related activity in V1 that starts in layer four and quickly spreads to other layers (Self et al., 2013). This activity is in turn expected to be spatially blurred in the BOLD response by venous draining towards the pial surface, which smooths responses across cortical depth, causing stronger responses at superficial depths (Uğurbil et al., 2003; Uludağ and Blinder, 2018; Yacoub et al., 2005). It is therefore possible that repeated visual stimulation could have effectively washed out depth-specific responses, increasing the likelihood of experimental effects being uniform across depth. That said, any influence of spatial hemodynamics should be consistent across experimental conditions, and therefore accounted for in our calculation of bottom-up/top-down modulations via a subtraction of the responses to different contrast/attention conditions. Indeed, the strikingly distinct laminar profile of stimulus contrast effects that clearly peaked in the middle layers indicates that our analysis could account for the influence of hemodynamics. Nevertheless, accurate depth-estimates of BOLD responses continues to be the biggest challenge in laminar fMRI. Recent developments in modeling spatial aspects of the BOLD response for applying a spatial deconvolution to BOLD data (Markuerkiaga et al., 2016, ISMRM, abstract; Marquardt et al., 2018) and improved measurement protocols (Huber et al., 2017) could help to alleviate this issue.

We show that modulations of stimulus-driven responses were similar across areas within the early visual cortex. For stimulus contrast, this is consistent with a purely stimulus-driven effect that changes response amplitude at early, subcortical levels and is inherited through the visual system via bottom-up connections targeting layer 4 (Hubel and Wiesel, 1972; Rockland and Pandya, 1979). With regards to attention, there is little work addressing laminar differences between visual brain areas. Nandy et al. (2017) report attentional modulations in all layers of V4, consistent with our findings in extrastriate areas V2 and V3, as well as V1, but they do not provide a comparison to other brain areas. Buffalo et al. (2011) report attentional modulation of gamma and alpha oscillations in deep and superficial cortex that were similar in V1, V2 and V4. Though they did not measure from granular layer neurons, and thus cannot comment on whether attentional modulations occurred in all layers or only agranular layers, the similarity of results across visual brain areas appears consistent with our study. However, we again note that how these results compare to our own is unclear as these studies used spatial attention, not feature-based attention, as well as a variety of electrophysiological measurements with an unclear relation to the BOLD signal. For future studies, laminar fMRI is well suited to exploring laminar differences between brain areas as it affords simultaneous measurements over larger areas of cortex compared to electrophysiological methods.

In conclusion, we have shown that fMRI responses in visual cortex are strongly modulated by changes in stimulus contrast and feature-based attention, and that these effects operate at different cortical depths. Top-down modulations from attention were overall stronger in agranular layers (specifically the superficial layers) compared to those from stimulus contrast, which were strongest in the granular layer. We have shown that, in a task where bottom-up and top-down influences are manipulated independently, the overall BOLD response can be separated into top-down and bottom-up components by examining how these effects are organized across depth. Future studies can use similar strategies to further explore the dynamic interactions between bottom-up and top-down processing that occur in perception and cognition.

Data and code used for stimulus presentation and analysis are available online at the Donders Research Data Repository: https://data.donders.ru.nl/collections/di/dccn/DSC_3018028.04_752.

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Author details

1. Samuel JD Lawrence

   Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

2. David G Norris

   1. Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
   2. Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany

   Contribution

   Resources, Supervision, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

3. Floris P de Lange

   Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   floris.delange@donders.ru.nl

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-6730-1452

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