A perspective on cortical layering and layer-spanning neuronal elements

This review article addresses the function of the layers of the cerebral cortex. We develop the perspective that cortical layering needs to be understood in terms of its functional anatomy, i.e., the terminations of synaptic inputs on distinct cellular compartments and their effect on cortical activity. The cortex is a hierarchical structure in which feed forward and feedback pathways have a layer-specific termination pattern. We take the view that the influence of synaptic inputs arriving at different cortical layers can only be understood in terms of their complex interaction with cellular biophysics and the subsequent computation that occurs at the cellular level. We use high-resolution fMRI, which can resolve activity across layers, as a case study for implementing this approach by describing how cognitive events arising from the laminar distribution of inputs can be interpreted by taking into account the properties of neurons that span different layers. This perspective is based on recent advances in measuring subcellular activity in distinct feed-forward and feedback axons and in dendrites as they span across layers

A protocol for ultra-high field laminar fMRI in the human brain.

Ultra-high field (UHF) neuroimaging affords the sub-millimeter resolution that allows researchers to interrogate brain computations at a finer scale than that afforded by standard fMRI techniques. Here, we present a step-by-step protocol for using UHF imaging (Siemens Terra 7T scanner) to measure activity in the human brain. We outline how to preprocess the data using a pipeline that combines tools from SPM, FreeSurfer, ITK-SNAP, and BrainVoyager and correct for vasculature-related confounders to improve the spatial accuracy of the fMRI signal. For complete details on the use and execution of this protocol, please refer to Jia et al. (2020) and Zamboni et al. (2020).This work was supported by grants to Z.K. from the Biotechnology and Biological Sciences Research Council (H012508 and BB/P021255/1), the Wellcome Trust (205067/Z/16/Z) and European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement No 840271

Point-spread function of the BOLD response across columns and cortical depth in human extra-striate cortex

Columns and layers are fundamental organizational units of the brain. Well known examples of cortical columns are the ocular dominance columns (ODCs) in primary visual cortex and the column-like stripe-based arrangement in the second visual area V2. The spatial scale of columns and layers is beyond the reach of conventional neuroimaging, but the advent of high field magnetic resonance imaging (MRI) scanners (UHF, 7 Tesla and above) has opened the possibility to acquire data at this spatial scale, in-vivo and non-invasively in humans. The most prominent non-invasive technique to measure brain function is blood oxygen level dependent (BOLD) fMRI, measuring brain activity indirectly, via changes in hemodynamics. A key determinant of the ability of high-resolution BOLD fMRI to accurately resolve columns and layers is the point-spread function (PSF) of the BOLD response in relation to the spatial extent of neuronal activity. In this study we take advantage of the stripe-based arrangement present in visual area V2, coupled with sub-millimetre anatomical and gradient-echo BOLD (GE BOLD) acquisition at 7 T to obtain PSF estimates and along cortical depth in human participants. Results show that the BOLD PSF is maximal in the superficial part of the cortex (1.78 mm), and it decreases with increasing cortical depth (0.83 mm close to white matter)

Linear systems analysis for laminar fMRI: evaluating BOLD amplitude scaling for luminance contrast manipulations

A fundamental assumption of nearly all functional magnetic resonance imaging (fMRI) analyses is that the relationship between local neuronal activity and the blood oxygenation level dependent (BOLD) signal can be described as following linear systems theory. With the advent of ultra-high field (7T and higher) MRI scanners, it has become possible to perform sub-millimeter resolution fMRI in humans. A novel and promising application of sub-millimeter fMRI is measuring responses across cortical depth, i.e. laminar imaging. However, the cortical vasculature and associated directional blood pooling towards the pial surface strongly influence the cortical depth-dependent BOLD signal, particularly for gradient-echo BOLD. This directional pooling may potentially affect BOLD linearity across cortical depth. Here we assess whether the amplitude scaling assumption for linear systems theory holds across cortical depth. For this, we use stimuli with different luminance contrasts to elicit different BOLD response amplitudes. We find that BOLD amplitude across cortical depth scales with luminance contrast, and that this scaling is identical across cortical depth. Although nonlinearities may be present for different stimulus configurations and acquisition protocols, our results suggest that the amplitude scaling assumption for linear systems theory across cortical depth holds for luminance contrast manipulations in sub-millimeter laminar BOLD fMRI

The mirage of big-data phrenology

The goal of mapping psychological functions to brain structures has a venerable history. With the advent of neuroimaging techniques, this elusive goal regained vigor and became the main purpose of cognitive neuroscience. Unfortunately, as the field continues to develop, the ideal of finding one-to-one mappings from psychological functions to brain areas looks increasingly unrealistic. In the past few years, however, many cognitive neuroscientists have advocated for mining large sets of neuroimaging data in order to find the elusive one-to-one mapping. One recent strategy, proposed by Genon and colleagues (2018), constitutes one of the most concrete proposals for discovering the mappings from brain regions to cognitive functions by using big-data repositories of neuroimaging results. In this paper we offer several challenges for their proposal and argue that big-data approaches to finding one-to-one mappings between brain regions and cognitive functions suffer from significant difficulties of their own

LayNii: a software suite for layer-fMRI

High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed opensource and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain ‘layerification’ and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data

Neural Representations of Visual Motion Processing in the Human Brain Using Laminar Imaging at 9.4 Tesla

During natural behavior, much of the motion signal falling into our eyes is due to our own movements. Therefore, in order to correctly perceive motion in our environment, it is important to parse visual motion signals into those caused by self-motion such as eye- or head-movements and those caused by external motion. Neural mechanisms underlying this task, which are also required to allow for a stable perception of the world during pursuit eye movements, are not fully understood. Both, perceptual stability as well as perception of real-world (i.e. objective) motion are the product of integration between motion signals on the retina and efference copies of eye movements. The central aim of this thesis is to examine whether different levels of cortical depth or distinct columnar structures of visual motion regions are differentially involved in disentangling signals related to self-motion, objective, or object motion. Based on previous studies reporting segregated populations of voxels in high level visual areas such as V3A, V6, and MST responding predominantly to either retinal or extra- retinal (‘real’) motion, we speculated such voxels to reside within laminar or columnar functional units. We used ultra-high field (9.4T) fMRI along with an experimental paradigm that independently manipulated retinal and extra-retinal motion signals (smooth pursuit) while controlling for effects of eye-movements, to investigate whether processing of real world motion in human V5/MT, putative MST (pMST), and V1 is associated to differential laminar signal intensities. We also examined motion integration across cortical depths in human motion areas V3A and V6 that have strong objective motion responses. We found a unique, condition specific laminar profile in human area V6, showing reduced mid-layer responses for retinal motion only, suggestive of an inhibitory retinal contribution to motion integration in mid layers or alternatively an excitatory contribution in deep and superficial layers. We also found evidence indicating that in V5/MT and pMST, processing related to retinal, objective, and pursuit motion are either integrated or colocalized at the scale of our resolution. In contrast, in V1, independent functional processes seem to be driving the response to retinal and objective motion on the one hand, and to pursuit signals on the other. The lack of differential signals across depth in these regions suggests either that a columnar rather than laminar segregation governs these functions in these areas, or that the methods used were unable to detect differential neural laminar processing. Furthermore, the thesis provides a thorough analysis of the relevant technical modalities used for data acquisition and data analysis at ultra-high field in the context of laminar fMRI. Relying on our technical implementations we were able to conduct two high-resolution fMRI experiments that helped us to further investigate the laminar organization of self-induced and externally induced motion cues in human high-level visual areas and to form speculations about the site and the mechanisms of their integration

Investigating visual and auditory scene feedback to early visual foveal and peripheral cortex using fMRI

No abstract available

Improving Data Quality for High Resolution Functional MRI in Cognitive Neuroscience Applications

Since the first successful Magnetic Resonance Imaging (MRI) image was produced by Paul Lauterbur in 1973, the field of MRI has been improving by leaps and bounds. The number of MRI and functional MRI (fMRI) papers have sky rocketed over the last decade, alongside with advancements in MRI field strength and techniques. In this thesis, I explore various methods for improving data quality for high resolution fMRI in 3T and 7T MRI scanners. Firstly, I studied the effect of Prospective Motion Correction (PMC) on 3T data using a simple visual paradigm. In contrast to most conventional techniques that use retrospective motion correction (RMC), PMC collects real-time motion data and uses it to update the acquisition field of view prior to each radiofrequency (RF) pulse. This allows for the correction of spin-history effects and intra-volume distortions. In this study, I utilized a secondary optical camera in the bore of the scanner to track a Moiré phase marker attached to the participant via a custom-moulded dental mouthpiece. I demonstrated that the camera is capable of accurately tracking the participant’s head motion. While simple metrics such as temporal signal-to-noise ratio (tSNR) and functional contrast-to-noise ratio (fCNR) showed no difference between the two methods, more complex analysis such as the Linear Discriminant Contrast (LDC) showed that the PMC data was indeed cleaner than the RMC data for higher resolution data. Next, I compared the sensitivity of two multi-voxel pattern analysis (MVPA) methods, Support Vector Machines (SVM) and Linear Discriminant Contrast (LDC). MVPA attempts to capture the relationship between the spatial fMRI activity and the experimental manipulations by treating it as a supervised learning problem. This is a promising technique that can capture spatial activation patterns that are lost in univariate analysis. I demonstrated through both actual fMRI data and computer simulations that LDC is a better MVPA metric than SVM. This agrees with our theory that SVM has more inherent variability and less sensitivity due to its limitations, discretization of results, rigid decision boundaries and ceiling effects. Subsequently, I analysed the quality of fMRI data acquired in a 3T Prisma scanner vs a 7T Terra scanner using a visual attention paradigm. While 7T scanners are becoming increasingly commonplace with over 70 of them worldwide now, the higher field strength also comes with its own host of problems. Field inhomogeneities and artefacts are a larger problem at 7T, and the smaller voxel sizes also cause data to be more susceptible to motion. As such, it is important to establish if there is a real benefit to using a 7T scanner. I observed that both 3T and 7T data showed similar trends with comparable z-scores and concluded that both scanners yielded comparable results. However, the 7T data was acquired at a much higher resolution (64x smaller volume per voxel) and thus, these results indicate a benefit of 7T as comparable results were achieved in spite of the smaller voxel volume. I hypothesized that acquiring data in a 7T scanner would be informative if studies sought to probe further into laminar or columnal structures which require submillimetre resolution, while a 3T scanner should suffice for studies looking at coarse regional activations. I did not explore the benefits of using 7T MRI at coarser resolutions. I also assessed the utility of boundary-based registration (BBR) realignment to improve on conventional RMC techniques to realign fMRI time series. Some motion artefacts affect the image in non-rigid ways and thus, voxel-based registration (VBR), generally utilized in conventional RMC, might be insufficient to properly realign fMRI time series. I demonstrated that BBR realignment outperforms VBR realignment across multiple metrics at submillimetre resolution, but no difference was observed at lower resolutions. Lastly, I examined the process of cleaning up 7T fMRI data for laminar analysis. Gradient echo (GE) sequences have been widely used for fMRI studies due to the high signal-to-noise ratio (SNR) and low specific absorption rate (SAR) relative to other sequences. However, GE sequences have been shown to exhibit superficial bias due to the presence of draining veins. I employed two methods- excluding venous voxels and utilizing a regression analysis, to remove superficial bias in an attempt to unmask any laminar effects for a visual attention task. In summary, I have explored various methods of optimizing fMRI data, ranging from initial setup decisions, such as which field strength scanner to use, to final MVPA analysis methods. I also analysed methods to remove motion artefacts, through both PMC and RMC, as well as post-processing methods to remove superficial bias in laminar data.A*STAR PhD Scholarshi