Biology subjects

Goebel, R. W.

Publications and source records attributed to Goebel, R. W..

15 recordsLinked to original sources

bioRxiv2026

Early Vision Shapes Recurrent Processing in the Human Visual Cortex

Recurrent processing involves feedforward, feedback and lateral connections and is thought to allow efficient visual processing. Anatomical and behavioral studies in humans have suggested that feedback connections mature later in development than feedforward connections and thus were proposed to depend to a larger degree on experience. In order to isolate feedforward from feedback activity and to investigate the role of early visual experience, we assessed seven individuals with reversed congenital cataracts and nine sighted controls using an "occlusion paradigm" with 7T magnetic resonance imaging (Smith & Muckli, 2010): Grayscale images of scenes were presented with the lower right quadrant covered by a white rectangle. We examined whether information about category (beaches, buildings, highways) and individual scenes could be extracted from early visual region vertices (V1 - V3) associated with the occluded quadrant of the visual field, in the absence of bottom-up visual input. This was achieved by decoding individual category or scene context utilizing a linear support vector machine. In addition, bidirectional information flow was assessed using connective field modeling. While both groups showed successful decoding of scene and category from vertices receiving bottom-up visual input, the accuracy was higher in normally sighted individuals than in individuals with reversed congenital cataracts. When bottom-up input was removed, decoding of categories remained successful in both groups, but decoding of individual scenes was only possible in normally sighted control individuals. Connective field modeling results indicated a less precise alignment of feedforward and feedback processing during visual stimulation in individuals with reversed congenital cataracts. These findings suggest that early visual experience is crucial for the refinement of feedback activity which in turn is crucial for well-tuned feedforward processing.

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bioRxiv2025

In the brain of the beholder: whole brain dynamics shape the perception during ambiguous motion

Visual perception is typically based on a one-to-one mapping between stimuli and conscious experiences. However, under bistable conditions, identical sensory inputs can elicit alternating perceptions, requiring the brain to resolve ambiguity. The mechanisms underlying transitions between distinct perceptual states and their sustained maintenance remain poorly understood. In this ultra-high-field (7T) fMRI study, we investigated the neural dynamics of perception using a bistable motion stimulus (ambiguous motion quartet) that evoked endogenous alternations between horizontal and vertical motion, compared to a control condition (physical motion quartet) with unambiguous sensory input. Consistent with previous findings, the human motion complex (hMT+) played a central role in processing both physical and ambiguous motion conditions. By dissociating neural activity during perceptual transitions from sustained perceptions, we found that hMT+ mostly interacts dynamically with area 46 in the frontal cortex and PF/PFm within the inferior parietal lobe during transitions and with subregions of the superior parietal lobe during sustained perceptions. Beyond local activity, computational modeling revealed an increase in hierarchical organization across cortical networks during the ambiguous condition. In particular, the same frontal and parietal regions exhibited ascension within the functional hierarchy, likely reflecting their specific role in coordinating computations for resolving ambiguity.

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bioRxiv2024

Multimodal laminar characterization of visual areas along the cortical hierarchy

Understanding the relationship between brain structure and function is a central goal in neuroscience. While post-mortem studies using microscopic techniques have provided detailed insights into the brains cytoarchitectonic and myeloarchitectonic patterns, linking these structural findings to functional outcomes remains challenging. Magnetic resonance imaging (MRI) has emerged as a powerful non-invasive tool for studying both structure and function, but discrepancies in spatial resolution between structural and functional imaging, especially in layer-fMRI, complicate the interpretation of functional results. In this study, we explore how visual cortical hierarchy relates to microscopic and mesoscopic laminar features. Focusing on visual areas that span progressive hierarchical levels, V1, V2, V3, and hMT+, we apply a multimodal approach combining post-mortem histology, post-mortem and in-vivo quantitative MRI (qMRI), and resting-state layer-fMRI. Using the open-access post-mortem AHEAD dataset, which integrates histological and qMRI contrasts from the same brain samples, we bridge microscopic observations with qMRI data. In parallel, we incorporate high-resolution [Formula] MRI and resting-state layer-fMRI from the same participant, allowing for a comparative analysis of laminar profiles across cortical depth. For computing laminar profiles, we developed an analysis pipeline that bridges histology images, mesoscopic qMRI, and layer-fMRI. Our findings highlight parvalbumin laminar profiles (reflecting interneuron parvalbumin density) as the most discriminative feature for differentiating brain areas. Additionally, we report laminar quantitative [Formula] profiles from post-mortem and in-vivo data, together with [Formula]-weighted resting-state layer-fMRI, all of which exhibit a similar overall shape across modalities. Using our methodological framework, a similar laminar characterization can be extended to study other brain regions. Generative models for layer fMRI will benefit from incorporating these new empirical microstructural (parvalbumin) and physical quantitative [Formula] data, leading to more area-specific and accurate models. HighlightsO_LIWe present a multimodal analysis of the laminar organization of four visual regions (V1, V2, V3, hMT+), characterizing progressive visual hierarchy levels in humans. This analysis spans from post-mortem microscopy and quantitative MRI (qMRI) to in-vivo qMRI and laminar fMRI during resting state. C_LIO_LIAmong the three microscopy contrasts, parvalbumin, a marker of interneuron density, emerges as the most distinctive regional feature. Notably, the parvalbumin laminar profiles vary across hierarchy levels, with hMT+ showing the greatest divergence compared to V1, V2, and V3. C_LIO_LIQuantitative [Formula] measurements, from both post-mortem and in-vivo data, reveal a clear increase towards the superficial cortical layers. These depth-dependent patterns closely mirror the laminar profiles observed in both task and resting-state fMRI. C_LIO_LISurprisingly, no substantial difference was observed in laminar [Formula] profiles between post-mortem and in-vivo data across the visual areas. C_LI

neuroscience↗

bioRxiv2024

Riemannian Geometry for the Classification of Brain States with fNIRS

BackgroundFunctional near-infrared spectroscopy (fNIRS) has recently gained momentum as a reliable and accurate tool for assessing brain states. This increase in popularity is due to its robustness to movement, non-invasive nature, portability, and user-friendly application. However, compared to functional magnetic resonance imaging (fMRI), fNIRS is less sensitive to deeper brain activity and offers less coverage. Additionally, due to fewer advancements in method development, the performance of fNIRS-based brain-state classification still lags behind more prevalent methods like fMRI. MethodsWe introduce a novel classification approach grounded in Riemannian geometry for the classification of kernel matrices, leveraging the temporal and spatial channel relationships and inherent duality of fNIRS signals--more specifically, oxygenated and deoxygenated hemoglobin. For the Riemannian geometry-based models, we compared different kernel matrix estimators and two classifiers: Riemannian Support Vector Classifier and Tangent Space Logistic Regression. These were benchmarked against four models employing traditional feature extraction methods. Our approach was tested in two brain-state classification scenarios based on the same fNIRS dataset: an 8-choice classification, which includes seven established plus an individually selected imagery task, and a 2-choice classification of all possible 28 2-task combinations. ResultsThe novel approach achieved a mean 8-choice classification accuracy of 65%, significantly surpassing the mean accuracy of 42% obtained with traditional methods. Additionally, the best-performing model achieved an average accuracy of 96% for 2-choice classification across all possible 28 task combinations, compared to 78% with traditional models. ConclusionTo our knowledge, we are the first to demonstrate that the proposed Riemannian geometry-based classification approach is both powerful and viable for fNIRS data, considerably increasing the accuracy in binary and multi-class classification of brain activation patterns.

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bioRxiv2024

Laminar CBV and BOLD response-characteristics over time and space in the human primary somatosensory cortex at 7T

Uncovering the cortical representation of the body has been at the core of human brain mapping for decades, with special attention given to the digits. In the last decade, advances in functional magnetic resonance imaging (fMRI) technologies have opened the possibility of noninvasively unraveling the 3rd dimension of digit representations in humans along cortical layers. In laminar fMRI it is common to combine the use of the highly sensitive blood oxygen level dependent (BOLD) contrast with cerebral blood volume sensitive measurements, like vascular space occupancy (VASO), that are more specific to the underlying neuronal populations. However, the spatial and temporal VASO response characteristics across cortical depth to passive stimulation of the digits are still unknown. Therefore, we characterized haemodynamic responses to vibrotactile stimulation of individual digit-tips across cortical depth at 0.75 mm in-plane spatial resolution using BOLD and VASO fMRI at 7T. We could identify digit-specific regions of interest (ROIs) in putative Brodmann area 3b, following the known anatomical organization. In the ROIs, the BOLD response increased towards the cortical surface due to the draining vein effect, while the VASO response was more shifted towards middle cortical layers, likely reflecting bottom-up input from the thalamus, as expected. Interestingly, we also found slightly negative BOLD and VASO responses for non-preferred digits in the ROIs, potentially indicating neuronal surround inhibition. Finally, we explored the temporal signal dynamics for BOLD and VASO as a function of distance from activation peaks resulting from stimulation of contralateral digits. With this analysis, we showed a triphasic response consisting of an initial peak and a subsequent negative deflection during stimulation, followed by a positive post-stimulus response in BOLD and to some extent in VASO. While similar responses were reported with invasive methods in animal models, here we demonstrate a potential neuronal excitation-inhibition mechanism in a center-surround architecture across layers in the human somatosensory cortex. Given that, unlike in animals, human experiments do not rely on anesthesia and can readily implement extensive behavioral testing, obtaining this effect in humans is an important step towards further uncovering the functional significance of the different aspects of the triphasic response.

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bioRxiv2024

Characterisation of laminar and vascular spatiotemporal dynamics of CBV and BOLD signals using VASO and ME-GRE at 7T in humans

Interpretation of cortical laminar functional magnetic resonance imaging (fMRI) activity requires detailed knowledge of the spatiotemporal haemodynamic response across vascular compartments due to the well-known vascular biases (e.g. the draining veins). Further complications arise from the spatiotemporal hemodynamic response that differs depending on the duration of stimulation. This information is crucial for future studies using depth-dependent cerebral blood volume (CBV) measurements, which promise higher specificity for the cortical microvasculature than the blood oxygenation level dependent (BOLD) contrast. To date, direct information about CBV dynamics with respect to stimulus duration, cortical depth and vasculature is missing in humans. Therefore, we characterized the cortical depth-dependent CBV-haemodynamic responses across a wide set of stimulus durations with 0.9 mm isotropic spatial and 0.785 seconds effective temporal resolution in humans using slice-selective slabinversion vascular space occupancy (SS-SI VASO). Additionally, we investigated signal contributions from macrovascular compartments using fine-scale vascular information from multiecho gradient-echo (ME-GRE) data at 0.35 mm isotropic resolution. In total, this resulted in >7.5h of scanning per participant (n=5). We have three major findings: (I) While we could demonstrate that 1 second stimulation is viable using VASO, more than 12 seconds stimulation provides better CBV responses in terms of specificity to microvasculature, but durations beyond 24 seconds of stimulation may be wasteful for certain applications. (II) We observe that CBV responses show dilation patterns across the cortex. (III) While we found increasingly strong BOLD signal responses in vessel-dominated voxels with longer stimulation durations, we found increasingly strong CBV signal responses in vessel-dominated voxels only until 4 second stimulation durations. After 4 seconds, only the signal from non-vessel dominated voxels kept increasing. This might explain why CBV responses are more specific to the underlying neuronal activity for long stimulus durations.

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bioRxiv2023

AngoraPy: A Python Toolkit For Modelling Anthropomorphic Goal-Driven Sensorimotor Systems

AO_SCPLOWBSTRACTC_SCPLOWGoal-driven deep learning is increasingly used to supplement classical modeling approaches in computational neuroscience. The strength of deep neural networks lies in their ability to autonomously learn the connectivity required to solve complex and ecologically valid tasks, obviating the need for hand-engineered or hypothesis-driven connectivity patterns. Consequently, goal-driven models can generate hypotheses about the neurocomputations underlying cortical processing. Whereas goal-driven modeling is becoming increasingly common in perception neuroscience, its application to sensorimotor control is currently hampered by the complexity of the methods required to train models comprising the closed sensation-action loop. To mitigate this hurdle, we introduce AngoraPy, a modeling library that provides researchers with the tools to train complex recurrent convolutional neural networks that model sensorimotor systems.

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bioRxiv2023

Learning Equivariant Object Recognition and its Reverse Application to Imagery

To investigate the relationship of perception and imagery, we model the visual ventral stream with an encoder and decoder part with the help of capsule networks. The proposed network consists of V1 and V2 from CorNet-Z, as well as the Capsule Network architecture with the routing by agreement algorithm for V4 and IT. The decoder reverses this architecture to model the feedback activation patterns of the visual ventral stream. The model was trained using EMNIST (letters H, S, C, T). Resulting classification performance was high with good generalization performance to different sizes, positions, and rotations. Contextual information was used for occluded stimuli in the feedback path for reconstructions resulting in high classification performance. Additionally, a pre-trained network was used to reconstruct remapped fMRI activation patterns from higher visual areas. Reconstructions of single-trial imagery data showed significant correlations to physical letter stimuli. The fMRI activation patterns of V1 and V2 and their reconstructions with population receptive field mapping and an autoencoder were related to activation patterns of the network to test biological plausibility. Representational Similarity Analysis and spatial correlations indicated an overlap of information content between the capsule network and the fMRI activations. Due to the capsule networks high generalization performance and the implemented feedback connections, the proposed network is a promising approach to improve current modelling efforts of perception and imagery. Further research is needed to compare the presented network to established networks that model the visual ventral stream.

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bioRxiv2023

Fast layer-fMRI VASO with short stimuli and event-related designs at 7T

Layers and columns are the dominant processing units in the human (neo)cortex at the meso-scopic scale. While the blood oxygenation dependent (BOLD) signal has a high detection sensitivity, it is biased towards unwanted signals from large draining veins at the cortical sur-face. The additional fMRI contrast of vascular space occupancy (VASO) has the potential to augment the neuroscientific interpretability of layer-fMRI results by means of capturing com-plementary information of locally specific changes in cerebral blood volume (CBV). Specifically, VASO is not subject to unwanted sensitivity amplifications of large draining veins. Because of constrained sampling efficiency, it has been mainly applied in combination with efficient block task designs and long trial durations. However, to study cognitive processes in neuroscientific contexts, or probe vascular reactivity, short stimulation periods are often necessary. Here, we developed a VASO acquisition procedure with a short acquisition period (895 ms volume acqui-sition) and sub-millimetre resolution. During visual event-related stimulation, we show reliable responses in visual cortices within a reasonable number of trials ([~]20). Furthermore, the short TR and high spatial specificity of our VASO implementation enabled us to show differences in laminar reactivity and onset times. Finally, we explore the generalizability to a different stimu-lus modality (somatosensation). With this, we showed that CBV-sensitive VASO provides the means to capture layer-specific haemodynamic responses with high spatio-temporal resolution and is able to be used with event-related paradigms.

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bioRxiv2021

Subjective understanding of actions and emotions involves the interplay of the semantic and action observation networks in the brain

Previous studies have investigated the neural basis of action and emotion perception conveyed by whole body postures and movements. However, subjective understanding of other peoples bodily actions and emotions, raises different issues that are currently not well understood and have so far seldom been investigated. In this 7T fMRI study, we proceeded beyond conventional univariate/multivariate analyses with predefined categories, and examined the representational geometry of subjective action- and emotion-understanding by mapping individual subjective reports with word embeddings. Dimensionality reduction revealed that the representations for perceived action and emotion were high dimensional, each correlated to but were not reducible to the predefined action and emotion categories. Searchlight representational similarity analysis showed that the representations in the left middle superior temporal sulcus and left dorsal premotor cortex corresponded to the subjective action and emotion understanding of the participants. Furthermore, using task-residual functional connectivity and hierarchical clustering, we found that areas in the action observation network and the semantic/default-mode network were functionally connected to these two seed regions, and showed similar representations. Our study provides direct evidence that both networks were concurrently involved in subjective action and emotion understanding. Our approach shows how subjective understanding of action and emotion stimuli can be reliably studied and complement insights based on from predefined stimulus categories.

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bioRxiv2020

Towards Semantic fMRI Neurofeedback: Navigating among Mental States using Real-time Representational Similarity Analysis

ObjectiveReal-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a non-invasive MRI procedure allowing examined participants to learn to self-regulate brain activity by performing mental tasks. A novel two-step rt-fMRI-NF procedure is proposed whereby the feedback display is updated in real-time based on high level (semantic) representations of experimental stimuli via real-time representational similarity analysis of multi-voxel patterns of brain activity. ApproachIn a localizer session, the stimuli become associated with anchored points on a two-dimensional representational space where distances approximate between-pattern (dis)similarities. In the NF session, participants modulate their brain response, displayed as a movable point, to engage in a specific neural representation. The developed method pipeline is verified in a proof-of-concept rt-fMRI-NF study at 7 Tesla using imagery of concrete objects. The dependence on noise is more systematically assessed on artificial fMRI data with similar (simulated) spatio-temporal structure and variable (injected) signal and noise. A series of brain activity patterns from the ventral visual cortex is evaluated via on-line and off-line analyses and the performances of the method are reported under different noise conditions. Main resultsThe participant in the proof-of-concept study exhibited robust activation patterns in the localizer session and managed to control the neural representation of a stimulus towards the selected target, in the NF session. The offline analyses validated the rt-fMRI-NF results, showing that the rapid convergence to the target representation is noise-dependent. SignificanceOur proof-of-concept study demonstrates the potential of semantic NF designs where the participant navigates among different mental states. Compared to traditional NF designs (e.g. using a thermometer display to set the level of the neural signal), the proposed approach provides content-specific feedback to the participant and extra degrees of freedom to the experimenter enabling real-time control of the neural activity towards a target brain state without suggesting a specific mental strategy to the subject.

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bioRxiv2020

Validating layer-specific VASO across species

Cerebral blood volume (CBV) has been shown to be a robust and important physiological parameter for quantitative interpretation of functional (f)MRI, capable of delivering highly localized mapping of neural activity. Indeed, with recent advances in ultra-high-field (>=7T) MRI hardware and associated sequence libraries, it has become possible to capture non-invasive CBV weighted fMRI signals across cortical layers. One of the most widely used approaches to achieve this (in humans) is through vascular-space-occupancy (VASO) fMRI. Unfortunately, the exact contrast mechanisms of layer-dependent VASO fMRI have not been validated and thus interpretation of such data is confounded. Here we cross-validate layer-dependent VASO fMRI contrast in a preclinical rat model using well established (but invasive) imaging methods in response to neuronal activation (somatosensory cortex) and respiratory challenge (hypercapnia). In particular VASO derived CBV measures are directly compared to concurrent measures of total haemoglobin changes from high resolution intrinsic optical imaging spectroscopy (OIS). Through direct comparison of response magnitude, across time, negligible changes in hematocrit ratio during activation (neuronal or vascular) are inferred. Quantified cortical layer profiling is demonstrated and in agreement between both VASO and contrast enhanced fMRI (using monocrystalline iron oxide nanoparticles, MION). Responses show high spatial localisation to layers of cortical excitatory and inhibitory processing independent of confounding large draining veins which hamper BOLD fMRI studies. While we find increased VASO based CBV reactivity (3.1 {+/-} 1.2 fold increase) in humans compared to rats it is demonstrated that this reflects differences in stimulus design rather than confounds of the VASO signal source. Together, our findings confirm that the VASO contrast is indeed a reliable estimate of layer-specific CBV changes. This validation study increases the neuronal interpretability of human layer-dependent fMRI results and should supersede BOLD fMRI as the method of choice in neuroscience application studies. HighlightsO_LIOur goal is to validate layer-specific VASO fMRI with gold standard methods C_LIO_LILayer-specific VASO sequences are implemented for 7T imaging in humans and rats C_LIO_LIComparisons of VASO, optical imaging, and MION confirm the expected contrast origin C_LIO_LISomatosensory stimulation in humans and rats reveal the same layer-fMRI signatures C_LIO_LIWe confirm that VASO is a valid measure to estimate layer-specific neural activity C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/219378v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@4f9733org.highwire.dtl.DTLVardef@14fccd2org.highwire.dtl.DTLVardef@101edaorg.highwire.dtl.DTLVardef@598bae_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2020

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 open-source 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. HighlightsO_LIA new software toolbox is introduced for layer-specific functional MRI: LayNii. C_LIO_LILayNii is a suite of command-line executable C++ programs for Linux, Windows, and macOS. C_LIO_LILayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints. C_LIO_LILayNii performs layerification in the native voxel space of functional data. C_LIO_LILayNii performs layer-smoothing, GE-BOLD deveining, QA, and VASO analysis. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/148080v3_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@e12e9org.highwire.dtl.DTLVardef@1fbb564org.highwire.dtl.DTLVardef@41da13org.highwire.dtl.DTLVardef@1545106_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

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bioRxiv2020

A probabilistic functional parcellation of human occipito-temporal cortex

Human visual cortex contains many retinotopic and category-specific regions. These brain regions have been the focus of a large body of functional MRI research, significantly expanding our understanding of visual processing. As studying these regions requires accurate localization of their cortical location, researchers perform functional localizer scans to identify these regions in each individual. However, it not always possible to conduct these localizer scans. Here, we developed and validated a functional region of interest atlas of early visual and category-selective regions in human ventral and lateral occipito-temporal cortex. Results show that for the majority of fROIs, cortex-based alignment results in lower between-subject variability compared to nonlinear volumetric alignment. Furthermore, we demonstrate that (1) the atlas accurately predicts the location of an independent dataset of ventral temporal cortex ROIs and other atlases of place-selectivity, motion-selectivity, and retinotopy. Next, (2) we show that the majority of voxel within our atlas are responding mostly to the labelled category in a left-out subject cross-validation, demonstrating the utility of this atlas. The functional atlas is publicly available (download.brainvoyager.com/data/visfAtlas.zip) and can help identify the location of these regions in healthy subjects as well as populations (e.g. blind people, infants) in which functional localizers cannot be run.

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bioRxiv2019

Motion displaces population receptive fields in the direction opposite to motion

Motion signals can bias the perceived position of visual stimuli. While the apparent position of a stimulus is biased in the direction of motion, electro-physiological studies have shown that the receptive field (RF) of neurons is shifted in the direction opposite to motion, at least in cats and macaque monkeys. In humans, it remains unclear how motion signals affect population RF (pRF) estimates. We addressed this question using psychophysical measurements and functional magnetic resonance imaging (fMRI) at 7 Tesla. We systematically varied two factors: the motion direction of the carrier pattern (inward, outward and flicker motion) and the contrast of the mapping stimulus (low and high stimulus contrast). We observed that while physical positions were identical across all conditions, presence of low-contrast motion, but not high-contrast motion, shifted perceived stimulus position in the direction of motion. Correspondingly, we found that pRF estimates in early visual cortex were shifted against the direction of motion for low-contrast stimuli but not for high stimulus contrast. We offer an explanation in form of a model for why apertures are perceptually shifted in the direction of motion even though pRFs shift in the opposite direction.

neuroscience↗