LayNii: A software suite for layer-fMRI

Huber, Laurentius; Poser, Benedikt A.; Bandettini, Peter A.; Arora, Kabir; Wagstyl, Konrad; Cho, Shinho; Goense, Jozien; Nothnagel, Nils; Morgan, Andrew; Hurk, Job van den; Müller, Anna K; Reynolds, Richard C.; Glen, Daniel R.; Goebel, Rainer; Gulban, Omer Faruk

doi:10.1016/j.neuroimage.2021.118091

Cited by 89 publications

(82 citation statements)

References 100 publications

(166 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 𝑇𝑇 1 − 𝐸𝐸𝐸𝐸𝐼𝐼 images of each subject were used for WM/GM and GM/CSF boundary delineation, and a region of interest (ROI) was manually defined such that the activated regions in the calcarine sulcus from both contrasts were included (see Figure 4). Then, this ROI was used to create ten equi-volume layers (Waehnert et al, 2014) using the open-source LAYNII package (Huber et al, 2021) and extract depthdependent BOLD and VASO responses. The average of the percentage signal change extracted from each layer forms the layer profile in both VASO and BOLD contrast.…”

Section: Image Analysismentioning

confidence: 99%

“…A non-invasive method for CBV imaging is vascular-space-occupancy (VASO) (Lu et al, 2003), which takes advantage of the difference in blood and tissue 𝑇𝑇 1 to image the tissue signal while the blood signal is nulled (Huber et al, 2014b;Lu et al, 2003). Since the development of this contrast and its translation to 7 Tesla (T), several studies in animals and humans have been conducted in the areas of method development (Beckett et al, 2019;Chai et al, 2019;Huber et al, 2015;Huber et al, 2016;Yu et al, 2014), analysis strategies (Huber et al, 2021;Polimeni et al, 2018), and applications to cognitive neuroscience (Finn et al, 2019;Huber et al, 2014a;Huber et al, 2017a;Kashyap et al, 2018;Oliveira et al, 2021b;Van Kerkoerle et al, 2017). However, to interpret the experimental results and account for both neural and vascular contributions to the fMRI signal, detailed models are required (Buxton et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling the depth-dependent VASO and BOLD responses in human primary visual cortex

Akbari

Bollmann

et al. 2021

Preprint

View full text Add to dashboard Cite

Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) contrast is a common method for studying human brain function non-invasively. Gradient-echo (GRE) BOLD is highly sensitive to the blood oxygenation change in blood vessels; however, the signal specificity can be degraded due to signal leakage from the activated lower layers to the superficial layers in depth-dependent (also called laminar or layer-specific) fMRI. Alternatively, physiological variables such as cerebral blood volume using VAscular-Space-Occupancy (VASO) measurements have shown higher spatial specificity compared to BOLD. To better understand the physiological mechanisms (e.g., blood volume and oxygenation change) and to interpret the measured depth-dependent responses we need models that reflect vascular properties at this scale. For this purpose, we adapted a cortical vascular model previously developed to predict the layer-specific BOLD signal change in human primary visual cortex to also predict layer-specific VASO response. To evaluate the model, we compared the predictions with experimental results of simultaneous VASO and BOLD measurements in a group of healthy participants. Fitting the model to our experimental findings provided an estimate of CBV change in different vascular compartments upon neural activity. We found that stimulus-evoked CBV changes mainly occur in intracortical arteries as well as small arterioles and capillaries and that the contribution from venules is small for a long stimulus (~30 sec). Our results confirm the notion that VASO contrast is less susceptible to large vessel effects compared to BOLD.

show abstract

Section: Image Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling the depth-dependent VASO and BOLD responses in human primary visual cortex

Akbari

Bollmann

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…With the near-whole-brain MT-weighted EPI images, we are able to run a cortical surface reconstruction using FreeSurfer ( Fischl, 2012 ) and then a cortical layer computation using LAYNII ( Huber et al, 2021 ) automatically. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…With the cortical surface automatically generated by FreeSurfer, we calculated cortical depths based on the equi-volume approach ( Waehnert et al, 2014 ) using the LAYNII software suite ( Huber et al, 2021 ) and divided the cortex into 20 equi-volume layers. Since at the acquired spatial resolution (0.8 mm) a voxel can lie across several cortical depths, MT-weighted anatomical images were upsampled by a factor of 4 for the cortical layer reconstruction.…”

Section: Methodsmentioning

confidence: 99%

Magnetization transfer weighted EPI facilitates cortical depth determination in native fMRI space

Chai¹,

Wang

et al. 2021

NeuroImage

Self Cite

View full text Add to dashboard Cite

The increased availability of ultra-high field scanners provides an opportunity to perform fMRI at sub-millimeter spatial scales and enables in vivo probing of laminar function in the human brain. In most previous studies, the definition of cortical layers, or depths, is based on an anatomical reference image that is collected by a different acquisition sequence and exhibits different geometric distortion compared to the functional images. Here, we propose to generate the anatomical image with the fMRI acquisition technique by incorporating magnetization transfer (MT) weighted imaging. Small flip angle binomial pulse trains are used as MT preparation, with a flexible duration (several to tens of milliseconds), which can be applied before each EPI segment without constraining the acquisition length (segment or slice number). The method’s feasibility was demonstrated at 7T for coverage of either a small slab or the near-whole brain at 0.8 mm isotropic resolution. Tissue contrast was found to be similar to that obtained with a state-of-art anatomical reference based on MP2RAGE. This MT-weighted EPI image allows an automatic reconstruction of the cortical surface to support laminar analysis in native fMRI space, obviating the need for distortion correction and registration.

show abstract

“…To enable effective and accurate visualizations of the convoluted cortical surface, we designed a cortex flattening procedure that is optimized for partial brain coverage. We implemented this procedure as two separate programs: LN2_MULTILATERATE and LN2_PATCH_FLATTEN within LayNii v2.2.0 (Huber et al, 2021). First, we use the LN2_MULTILATERATE program to inject a flat coordinate system within our segmented regions (01_multilaterate.py).…”

Section: Patch Flatteningmentioning

confidence: 99%

Mesoscopic in vivo human T₂* dataset acquired using quantitative MRI at 7 Tesla

Gulban

Bollmann

Huber

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Mesoscopic (0.1-0.5 mm) interrogation of the living human brain is critical for a comprehensive understanding of brain structure and function. However, in vivo techniques for mesoscopic imaging have been hampered by the sensitivity challenges of acquiring data at very high resolutions and the lack of analysis tools that can retain fine-scale detail while also accurately positioning measurements relative to the complex folded structure of the cerebral cortex. Here, we present an experimental dataset in which we image the anatomical structure of the visual and auditory cortices of five participants at 0.35 × 0.35 × 0.35 mm3 resolution. To analyze this challenging dataset, we design and implement two sets of novel methodology: a method for mitigating imaging artifacts related to blood motion and a suite of software tools for accurate quantification and visualization of the mesoscopic structure of the cortical surface. Applying these methods, we demonstrate the ability to clearly identify structures that are visible only at the mesoscopic scale, including cortical layers and intracortical blood vessels. We freely share our dataset and tools with the research community, thereby enabling investigations of fine-scale neurobiological structures in both the current and future datasets. Overall, our results demonstrate the viability of mesoscopic imaging as a quantitative tool for studying the living human brain.

show abstract

LayNii: A software suite for layer-fMRI

Cited by 89 publications

References 100 publications

Modelling the depth-dependent VASO and BOLD responses in human primary visual cortex

Modelling the depth-dependent VASO and BOLD responses in human primary visual cortex

Magnetization transfer weighted EPI facilitates cortical depth determination in native fMRI space

Mesoscopic in vivo human T₂* dataset acquired using quantitative MRI at 7 Tesla

Contact Info

Product

Resources

About

LayNii: A software suite for layer-fMRI

Cited by 89 publications

References 100 publications

Modelling the depth-dependent VASO and BOLD responses in human primary visual cortex

Modelling the depth-dependent VASO and BOLD responses in human primary visual cortex

Magnetization transfer weighted EPI facilitates cortical depth determination in native fMRI space

Mesoscopic in vivo human T2* dataset acquired using quantitative MRI at 7 Tesla

Contact Info

Product

Resources

About

Mesoscopic in vivo human T₂* dataset acquired using quantitative MRI at 7 Tesla