Relating neural oscillations to laminar fMRI connectivity in visual cortex.

Laminar functional magnetic resonance imaging (fMRI) holds the potential to study connectivity at the laminar level in humans. Here we analyze simultaneously recorded electroencephalography (EEG) and high-resolution fMRI data to investigate how EEG power modulations, induced by a task with an attentional component, relate to changes in fMRI laminar connectivity between and within brain regions in visual cortex. Our results indicate that our task-induced decrease in beta power relates to an increase in deep-to-deep layer coupling between regions and to an increase in deep/middle-to-superficial layer connectivity within brain regions. The attention-related alpha power decrease predominantly relates to reduced connectivity between deep and superficial layers within brain regions, since, unlike beta power, alpha power was found to be positively correlated to connectivity. We observed no strong relation between laminar connectivity and gamma band oscillations. These results indicate that especially beta band, and to a lesser extent, alpha band oscillations relate to laminar-specific fMRI connectivity. The differential effects for alpha and beta bands indicate that they relate to different feedback-related neural processes that are differentially expressed in intra-region laminar fMRI-based connectivity.

Inducing a mental context for associative memory formation with real-time fMRI neurofeedback.

Memory, one of the hallmarks of human cognition, can be modified when humans voluntarily modulate neural population activity using neurofeedback. However, it is currently unknown whether neurofeedback can influence the integration of memories, and whether memory is facilitated or impaired after such neural perturbation. In this study, participants memorized objects while we provided them with abstract neurofeedback based on their brain activity patterns in the ventral visual stream. This neurofeedback created an implicit face or house context in the brain while memorizing the objects. The results revealed that participants created associations between each memorized object and its implicit context solely due to the neurofeedback manipulation. Our findings shed light onto how memory formation can be influenced by synthetic memory tags with neurofeedback and advance our understanding of mnemonic processing.

Improved cortical boundary registration for locally distorted fMRI scans.

With continuing advances in MRI techniques and the emergence of higher static field strengths, submillimetre spatial resolution is now possible in human functional imaging experiments. This has opened up the way for more specific types of analysis, for example investigation of the cortical layers of the brain. With this increased specificity, it is important to correct for the geometrical distortions that are inherent to echo planar imaging (EPI). Inconveniently, higher field strength also increases these distortions. The resulting displacements can easily amount to several millimetres and as such pose a serious problem for laminar analysis. We here present a method, Recursive Boundary Registration (RBR), that corrects distortions between an anatomical and an EPI volume. By recursively applying Boundary Based Registration (BBR) on progressively smaller subregions of the brain we generate an accurate whole-brain registration, based on the grey-white matter contrast. Explicit care is taken that the deformation does not break the topology of the cortical surface, which is an important requirement for several of the most common subsequent steps in laminar analysis. We show that RBR obtains submillimetre accuracy with respect to a manually distorted gold standard, and apply it to a set of human in vivo scans to show a clear increase in spacial specificity. RBR further automates the process of non-linear distortion correction. This is an important step towards routine human laminar fMRI for large field of view acquisitions. We provide the code for the RBR algorithm, as well as a variety of functions to better investigate registration performance in a public GitHub repository, https://github.com/TimVanMourik/OpenFmriAnalysis, under the GPL 3.0 license.

Laminar specific fMRI reveals directed interactions in distributed networks during language processing.

Interactions between top-down and bottom-up information streams are integral to brain function but challenging to measure noninvasively. Laminar resolution, functional MRI (lfMRI) is sensitive to depth-dependent properties of the blood oxygen level-dependent (BOLD) response, which can be potentially related to top-down and bottom-up signal contributions. In this work, we used lfMRI to dissociate the top-down and bottom-up signal contributions to the left occipitotemporal sulcus (LOTS) during word reading. We further demonstrate that laminar resolution measurements could be used to identify condition-specific distributed networks on the basis of whole-brain connectivity patterns specific to the depth-dependent BOLD signal. The networks corresponded to top-down and bottom-up signal pathways targeting the LOTS during word reading. We show that reading increased the top-down BOLD signal observed in the deep layers of the LOTS and that this signal uniquely related to the BOLD response in other language-critical regions. These results demonstrate that lfMRI can reveal important patterns of activation that are obscured at standard resolution. In addition to differences in activation strength as a function of depth, we also show meaningful differences in the interaction between signals originating from different depths both within a region and with the rest of the brain. We thus show that lfMRI allows the noninvasive measurement of directed interaction between brain regions and is capable of resolving different connectivity patterns at submillimeter resolution, something previously considered to be exclusively in the domain of invasive recordings.

Laminar signal extraction over extended cortical areas by means of a spatial GLM.

There is converging evidence that distinct neuronal processes leave distinguishable footprints in the laminar BOLD response. However, even though the achievable spatial resolution in functional MRI has much improved over the years, it is still challenging to separate signals arising from different cortical layers. In this work, we propose a new method to extract laminar signals. We use a spatial General Linear Model in combination with the equivolume principle of cortical layers to unmix laminar signals instead of interpolating through and integrating over a cortical area: thus reducing partial volume effects. Not only do we provide a mathematical framework for extracting laminar signals with a spatial GLM, we also illustrate that the best case scenarios of existing methods can be seen as special cases within the same framework. By means of simulation, we show that this approach has a sharper point spread function, providing better signal localisation. We further assess the partial volume contamination in cortical profiles from high resolution human ex vivo and in vivo structural data, and provide a full account of the benefits and potential caveats. We eschew here any attempt to validate the spatial GLM on the basis of fMRI data as a generally accepted ground-truth pattern of laminar activation does not currently exist. This approach is flexible in terms of the number of layers and their respective thickness, and naturally integrates spatial regularisation along the cortex, while preserving laminar specificity. Care must be taken, however, as this procedure of unmixing is susceptible to sources of noise in the data or inaccuracies in the laminar segmentation.

Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches.

In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.

Laminar Organization of Working Memory Signals in Human Visual Cortex.

The human primary visual cortex (V1) is not only activated by incoming visual information but is also engaged by top-down cognitive processes, such as visual working memory, even in the absence of visual input [1-3]. This feedback may be critical to our ability to visualize specific visual features, as higher-order regions lack the selectivity to represent such information [4]. Clearly, such internally generated signals do not trigger genuine perception of the remembered stimulus, meaning they must be organized in a manner that is different to bottom-up-driven signals. Internally generated signals may be kept separate from incoming sensory data by virtue of the laminar organization of inter-area cortical connections. Namely, bottom-up driving connections target layer 4, located in the middle of the cortical column, and feedback connections target deep and superficial layers and avoid layer 4 [5-7]. Using lamina-resolved fMRI, we simultaneously measured the activity in three early visual cortical areas (V1-V3) that are recruited to represent stimulus information during visual working memory [8]. We observed item-specific working memory signals in early visual cortex. In V1, this item-specific activity was selectively present at deep and superficial cortical depths, avoiding the middle layers, and working-memory-related activity was present at all depths in V2 and V3. These results show for the first time the laminar organization of internally generated signals during visual working memory in the human visual system and provide new insights into how bottom-up and top-down signals in visual cortex are deployed.

Porcupine: A visual pipeline tool for neuroimaging analysis.

The field of neuroimaging is rapidly adopting a more reproducible approach to data acquisition and analysis. Data structures and formats are being standardised and data analyses are getting more automated. However, as data analysis becomes more complicated, researchers often have to write longer analysis scripts, spanning different tools across multiple programming languages. This makes it more difficult to share or recreate code, reducing the reproducibility of the analysis. We present a tool, Porcupine, that constructs one's analysis visually and automatically produces analysis code. The graphical representation improves understanding of the performed analysis, while retaining the flexibility of modifying the produced code manually to custom needs. Not only does Porcupine produce the analysis code, it also creates a shareable environment for running the code in the form of a Docker image. Together, this forms a reproducible way of constructing, visualising and sharing one's analysis. Currently, Porcupine links to Nipype functionalities, which in turn accesses most standard neuroimaging analysis tools. Our goal is to release researchers from the constraints of specific implementation details, thereby freeing them to think about novel and creative ways to solve a given problem. Porcupine improves the overview researchers have of their processing pipelines, and facilitates both the development and communication of their work. This will reduce the threshold at which less expert users can generate reusable pipelines. With Porcupine, we bridge the gap between a conceptual and an implementational level of analysis and make it easier for researchers to create reproducible and shareable science. We provide a wide range of examples and documentation, as well as installer files for all platforms on our website: https://timvanmourik.github.io/Porcupine. Porcupine is free, open source, and released under the GNU General Public License v3.0.

Multisite Phosphorylation of NuMA-Related LIN-5 Controls Mitotic Spindle Positioning in C. elegans.

During cell division, the mitotic spindle segregates replicated chromosomes to opposite poles of the cell, while the position of the spindle determines the plane of cleavage. Spindle positioning and chromosome segregation depend on pulling forces on microtubules extending from the centrosomes to the cell cortex. Critical in pulling force generation is the cortical anchoring of cytoplasmic dynein by a conserved ternary complex of Gα, GPR-1/2, and LIN-5 proteins in C. elegans (Gα-LGN-NuMA in mammals). Previously, we showed that the polarity kinase PKC-3 phosphorylates LIN-5 to control spindle positioning in early C. elegans embryos. Here, we investigate whether additional LIN-5 phosphorylations regulate cortical pulling forces, making use of targeted alteration of in vivo phosphorylated residues by CRISPR/Cas9-mediated genetic engineering. Four distinct in vivo phosphorylated LIN-5 residues were found to have critical functions in spindle positioning. Two of these residues form part of a 30 amino acid binding site for GPR-1, which we identified by reverse two-hybrid screening. We provide evidence for a dual-kinase mechanism, involving GSK3 phosphorylation of S659 followed by phosphorylation of S662 by casein kinase 1. These LIN-5 phosphorylations promote LIN-5-GPR-1/2 interaction and contribute to cortical pulling forces. The other two critical residues, T168 and T181, form part of a cyclin-dependent kinase consensus site and are phosphorylated by CDK1-cyclin B in vitro. We applied a novel strategy to characterize early embryonic defects in lethal T168,T181 knockin substitution mutants, and provide evidence for sequential LIN-5 N-terminal phosphorylation and dephosphorylation in dynein recruitment. Our data support that phosphorylation of multiple LIN-5 domains by different kinases contributes to a mechanism for spatiotemporal control of spindle positioning and chromosome segregation.

The relationship between oscillatory EEG activity and the laminar-specific BOLD signal.

Electrophysiological recordings in animals have indicated that visual cortex γ-band oscillatory activity is predominantly observed in superficial cortical layers, whereas α- and ß-band activity is stronger in deep layers. These rhythms, as well as the different cortical layers, have also been closely related to feedforward and feedback streams of information. Recently, it has become possible to measure laminar activity in humans with high-resolution functional MRI (fMRI). In this study, we investigated whether these different frequency bands show a differential relation with the laminar-resolved blood-oxygen level-dependent (BOLD) signal by combining data from simultaneously recorded EEG and fMRI from the early visual cortex. Our visual attention paradigm allowed us to investigate how variations in strength over trials and variations in the attention effect over subjects relate to each other in both modalities. We demonstrate that γ-band EEG power correlates positively with the superficial layers' BOLD signal and that ß-power is negatively correlated to deep layer BOLD and α-power to both deep and superficial layer BOLD. These results provide a neurophysiological basis for human laminar fMRI and link human EEG and high-resolution fMRI to systems-level neuroscience in animals.

Selective Activation of the Deep Layers of the Human Primary Visual Cortex by Top-Down Feedback.

In addition to bottom-up input, the visual cortex receives large amounts of feedback from other cortical areas [1-3]. One compelling example of feedback activation of early visual neurons in the absence of bottom-up input occurs during the famous Kanizsa illusion, where a triangular shape is perceived, even in regions of the image where there is no bottom-up visual evidence for it. This illusion increases the firing activity of neurons in the primary visual cortex with a receptive field on the illusory contour [4]. Feedback signals are largely segregated from feedforward signals within each cortical area, with feedforward signals arriving in the middle layer, while top-down feedback avoids the middle layers and predominantly targets deep and superficial layers [1, 2, 5, 6]. Therefore, the feedback-mediated activity increase in V1 during the perception of illusory shapes should lead to a specific laminar activity profile that is distinct from the activity elicited by bottom-up stimulation. Here, we used fMRI at high field (7 T) to empirically test this hypothesis, by probing the cortical response to illusory figures in human V1 at different cortical depths [7-14]. We found that, whereas bottom-up stimulation activated all cortical layers, feedback activity induced by illusory figures led to a selective activation of the deep layers of V1. These results demonstrate the potential for non-invasive recordings of neural activity with laminar specificity in humans and elucidate the role of top-down signals during perceptual processing.

Rb and FZR1/Cdh1 determine CDK4/6-cyclin D requirement in C. elegans and human cancer cells.

Cyclin-dependent kinases 4 and 6 (CDK4/6) in complex with D-type cyclins promote cell cycle entry. Most human cancers contain overactive CDK4/6-cyclin D, and CDK4/6-specific inhibitors are promising anti-cancer therapeutics. Here, we investigate the critical functions of CDK4/6-cyclin D kinases, starting from an unbiased screen in the nematode Caenorhabditis elegans. We found that simultaneous mutation of lin-35, a retinoblastoma (Rb)-related gene, and fzr-1, an orthologue to the APC/C co-activator Cdh1, completely eliminates the essential requirement of CDK4/6-cyclin D (CDK-4/CYD-1) in C. elegans. CDK-4/CYD-1 phosphorylates specific residues in the LIN-35 Rb spacer domain and FZR-1 amino terminus, resembling inactivating phosphorylations of the human proteins. In human breast cancer cells, simultaneous knockdown of Rb and FZR1 synergistically bypasses cell division arrest induced by the CDK4/6-specific inhibitor PD-0332991. Our data identify FZR1 as a candidate CDK4/6-cyclin D substrate and point to an APC/C(FZR1) activity as an important determinant in response to CDK4/6-inhibitors.

Diffusion tensor characteristics of gyrencephaly using high resolution diffusion MRI in vivo at 7T.

Gyrification of the human cerebral cortex allows for the surface expansion that accommodates many more cortical neurons in comparison to other mammals. For neuroimaging, however, it forms a feature that complicates analysis. For example, it has long been established that cortical layers do not occupy the same depth in gyri and sulci. Recently, in vivo diffusion imaging has provided insights into the fibre architecture of the cortex, usually showing radial tensor orientations. This makes it relevant to investigate whether cortical diffusion tensor metrics depend on the gyral pattern. High-resolution (1mm isotropic) diffusion weighted MRI of the medial wall of the hemispheres was performed at 7 T. Diffusion data were resampled to surfaces in the cortex and underlying white matter, where the cortical surfaces obeyed the equivolume principle for cortical laminae over the cortical curvature. Diffusion tensor metrics were averaged over bins of curvature to obtain maps of characteristic patterns in the gyrus. Diffusivity, anisotropy and radiality varied with curvature. Radiality was maximal in intermediate layers of the cortex next to the crown of the gyrus, not in white matter or on the crown. In the fundus, the deep cortical layers had tangential tensor orientations. In the white matter, tensor orientation changed from radial on the crown to tangential under the banks and fundus. White matter anisotropy gradually increased from the crown to the fundus. The characteristic pattern in the gyrus demonstrated here is in accordance with ex vivo diffusion MR microscopy and histological studies. The results indicate the necessity of taking into account the gyral pattern when cortical diffusion data is analysed. Additionally, the data suggest a confound for tractography approaches when reaching the gyrus, resulting in a possible bias towards the gyral crown. The implications for mechanisms that could drive cortical folding are discussed.