Cortical depth profiles of luminance contrast responses in human V1 and V2 using 7 T fMRI

Marquardt, Ingo; Schneider, Marian; Gulban, Omer Faruk; Ivanov, Dimo; Uludağ, Kâmil

doi:10.1002/hbm.24042

Cited by 65 publications

(87 citation statements)

References 116 publications

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“…The monotonic increase of attention modulation and stimulus driven response from deep to superficial cortical depth can be explained by the draining vein effect, which is the drainage of deoxygenated hemoglobin along ascending veins, causing stronger BOLD signals toward the cortical surface (Zhao, Wang et al 2004, Uludag andBlinder 2017). This effect was consistent with previous cortical depth-dependent BOLD fMRI studies using gradient echo sequences (Polimeni, Fischl et al 2010, Marquardt, Schneider et al 2018. To minimize the influence from draining veins which is a scaling effect ( Compared to the middle depth of V1, the attention modulation index was significantly larger in the superficial and deep cortical depths (superficial vs. middle, p=0.003, deep vs. middle, p=0.009, after Bonferroni correction, number of comparisons k=2).…”

Section: Ge Bold Showed Additive Effect Of Attention On Contrast Respsupporting

confidence: 85%

Layer-dependent multiplicative effects of spatial attention on contrast responses in human early visual cortex

Guo

Liu

Cui

et al. 2020

Preprint

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Attention mechanisms at different cortical layers of human visual cortex remain poorly understood. Using submillimeter-resolution fMRI at 7T, we investigated the effects of top-down spatial attention on the contrast responses across different cortical depths in human early visual cortex. Gradient echo (GE) T2* weighted BOLD signal showed an additive effect of attention on contrast responses across cortical depths. Compared to the middle cortical depth, attention modulation was stronger in the superficial and deep depths of V1, and also stronger in the superficial depth of V2 and V3. Using ultra-high resolution (0.3mm in-plane) balanced steadystate free precession (bSSFP) fMRI, a multiplicative scaling effect of attention was found in the superficial and deep layers, but not in the middle layer of V1. Attention modulation of low Highlights • Response or activity gain of spatial attention in superficial and deep layers • Contrast gain or baseline shift of attention in V1 middle layer • Nonlinearity of large blood vessel causes additive effect of attention on T2* BOLD

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Section: Ge Bold Showed Additive Effect Of Attention On Contrast Respsupporting

confidence: 85%

Layer-dependent multiplicative effects of spatial attention on contrast responses in human early visual cortex

Guo

Liu

Cui

et al. 2020

Preprint

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“…Therefore, there is a wide range of reported values on BOLD signal amplitude variation between lower depths (can be as low as ~0.5%) and upper depths (can be as high as ~12%) (Kashyap et al, 2017;Polimeni et al, 2010;Siero et al, 2011). Some studies report simple or superlinear increase of the amplitude towards the surface (De Martino et al, 2013;Kashyap et al, 2017), while others also show some nonmonotonic behavior (generally described as 'bumps') in the LBR (Chen et al, 2013;Huber et al, 2017;Koopmans et al, 2010;Marquardt et al, 2018).…”

Section: Steady-state Simulationsmentioning

confidence: 99%

“…mesoscopic) resolution (see review on MRI acquisition by Poser and Setsompop (2018)). As a consequence, it is now possible to measure fMRI activation as a function of cortical depth 2 and potentially study activity changes in histologically-defined cortical layers (De Martino et al, 2013;Fracasso et al, 2018;Kashyap et al, 2017;Kok et al, 2016;Koopmans et al, 2010;Marquardt et al, 2018;Muckli et al, 2015;Olman et al, 2012;Polimeni et al, 2010;Siero et al, 2011). The main motivation for these studies is to investigate the cortical microcircuit during cognitive processes (Douglas and Martin, 2004): Electrophysiological studies showed that feedforward-and feedback-related neuronal computations engage different cortical layers and exhibit clear differences in laminar distribution of neuronal activity (see review by Self et al (2018) and references therein).…”

Section: Introductionmentioning

confidence: 99%

A dynamical model of the laminar BOLD response

Havlíček

Uludağ

2019

Preprint

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High-resolution functional magnetic resonance imaging (fMRI) using blood oxygenation dependent level-dependent (BOLD) signal is an increasingly popular tool to non-invasively examine neuronal processes at the mesoscopic level. However, as the BOLD signal stems from hemodynamic changes, its temporal and spatial properties do not match those of the underlying neuronal activity. In particular, the laminar BOLD response (LBR), commonly measured with gradient-echo (GE) MRI sequence, is confounded by non-local changes in deoxygenated hemoglobin and cerebral blood volume propagated within intracortical ascending veins, leading to a unidirectional blurring of the neuronal activity distribution towards the cortical surface. Here, we present a new cortical depth-dependent model of the BOLD response based on the principle of mass conservation, which takes the effect of ascending (and pial) veins on the cortical BOLD responses explicitly into account. It can be used to dynamically model cortical depth profiles of the BOLD signal as a function of various baselineand activity-related physiological parameters for any spatiotemporal distribution of neuronal changes. We demonstrate that the commonly observed spatial increase of LBR is mainly due to baseline blood volume increase towards the surface. In contrast, an occasionally observed local maximum in the LBR (i.e. the so-called "bump") is mainly due to spatially inhomogeneous neuronal changes rather than locally higher baseline blood volume. In addition, we show that the GE-BOLD signal laminar point-spread functions, representing the signal leakage towards the surface, depend on several physiological parameters and on the level of neuronal activity. Furthermore, even in the case of simultaneous neuronal changes at each depth, inter-laminar delays of LBR transients are present due to the ascending vein. In summary, the model provides a conceptual framework for the biophysical interpretation of common experimental observations in high-resolution fMRI data. In the future, the model will allow for deconvolution of the spatiotemporal hemodynamic bias of the LBR and provide an estimate of the underlying laminar excitatory and inhibitory neuronal activity.

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“…Results were manually inspected and further adjusted where needed ( Figure 2). To validate the alignment of functional to anatomical data, we calculated the mean EPI image of each functional run for each region of interest (ROI) and estimated the spatial correlation between these images (e.g., Marquardt, Schneider, Gulban, Ivanov, & Uludağ, 2018). We performed manual adjustment of the alignment if the spatial correlation was below 0.85.…”

Section: Ge-epi Functional Data Analysismentioning

confidence: 99%

Fine-scale computations for adaptive processing in the human brain

Zamboni

Kemper

Goncalves

et al. 2020

Preprint

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Humans and animals are known to adapt to the statistics of the environment by reducing brain responses to repetitive sensory information. Despite the importance of this rapid form of brain plasticity for efficient information processing, the fine-scale circuits that support this adaptive processing in the human brain remain largely unknown. Here, we capitalize on the submillimetre resolution afforded by ultra-high field (UHF) imaging to examine BOLD-fMRI signals across cortical depth and discern competing hypotheses about the brain mechanisms (feedforward vs. feedback) that mediate visual adaptation. Combining UHF imaging with a visual adaptation paradigm comprising repeated presentation of gratings at the same orientation, we provide evidence for the fine-scale human brain circuits that mediate adaptive visual processing. We demonstrate that visual adaptation is implemented by suppressive local recurrent processing within visual cortex, as indicated by stronger BOLD decrease in superficial than middle and deeper layers. Further, functional connectivity analysis shows dissociable connectivity mechanisms for adaptive processing: feedforward connectivity within the visual cortex, while feedback connectivity from posterior parietal to visual cortex, reflecting top-down influences (i.e. expectation for repeated stimuli) on visual processing. Thus, our findings provide evidence for a circuit of local recurrent and feedback interactions that mediate rapid brain plasticity for adaptive information processing.

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Cortical depth profiles of luminance contrast responses in human V1 and V2 using 7 T fMRI

Cited by 65 publications

References 116 publications

Layer-dependent multiplicative effects of spatial attention on contrast responses in human early visual cortex

Layer-dependent multiplicative effects of spatial attention on contrast responses in human early visual cortex

A dynamical model of the laminar BOLD response

Fine-scale computations for adaptive processing in the human brain

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