Biology subjects

Goebel, R.

Publications and source records attributed to Goebel, R..

34 records · Page 2Linked to original sources

bioRxiv2021

Online self-evaluation of fMRI-based neurofeedback performance

This study explores the subjective evaluation of supplementary motor area (SMA) regulation performance in a real-time functional magnetic resonance imaging neurofeedback (fMRI-NF) task. In fMRI-NF, people learn how to self-regulate their brain activity by performing mental actions to achieve a certain target level of blood-oxygen-level-dependent (BOLD) activation. This setup offers the possibility to study performance monitoring in the absence of somatosensory feedback. Here, we studied two types of self-evaluation expressed before receiving neurofeedback: performance predictions and perceived confidence in the prediction judgement. We hypothesized that throughout learning, participants would (1) improve the precision of their performance predictions about the actual changes in their BOLD response, and (2) that reported confidence would progressively increase with improved metacognitive precision. Participants completed three sessions of SMA regulation in a 7T fMRI scanner, performing a drawing motor imagery task. During each trial, they modulated their mental drawing strategy to achieve one of two different levels of target fMRI signal change. They then reported a performance prediction and their confidence in the prediction before receiving delayed BOLD-activation feedback. Results show that participants performance predictions improved with learning throughout the three sessions, and that these improvements were not driven exclusively by their knowledge of previous performance. Confidence reports on the other hand showed no change throughout training and did not differentiate between the better and worse predictions. In addition to shedding light on mechanisms of internal monitoring during neurofeedback training, these results also point to a dissociation between self-evaluation of performance and corresponding reported confidence in the presence of feedback.

neuroscience↗

bioRxiv2021

Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience. HighlightsO_LIAcute stress affects the allocation of neural resources between large-scale brain networks C_LIO_LIWe provide a first successful demonstration of neurofeedback training based on stress-related large-scale brain networks C_LIO_LINovel approach has the potential to train control over central response to stressors in real-life C_LIO_LICould build foundation for future clinical interventions to increase resilience C_LI

neuroscience↗

bioRxiv2021

Extremely Fast pRF Mapping for Real-Time Applications

Population receptive field (pRF) mapping is a popular tool in computational neuroimaging that allows for the investigation of receptive field properties, their topography and interrelations in health and disease. Furthermore, the possibility to invert population receptive fields provides a decoding model for constructing stimuli from observed cortical activation patterns. This has been suggested to pave the road towards pRF-based brain-computer interface (BCI) communication systems, which would be able to directly decode internally visualized letters from topographically organized brain activity. A major stumbling block for such an application is, however, that the pRF mapping procedure is computationally heavy and time consuming. To address this, we propose a novel and fast pRF mapping procedure that is suitable for real-time applications. The method is build upon hashed-Gaussian encoding of the stimulus, which significantly reduces computational resources. After the stimulus is encoded, mapping can be performed using either ridge regression for fast offline analyses or gradient descent for real-time applications. We validate our model-agnostic approach in silico, as well as on empirical fMRI data obtained from 3T and 7T MRI scanners. Our approach is capable of estimating receptive fields and their parameters for millions of voxels in mere seconds. This method thus facilitates real-time applications of population receptive field mapping.

neuroscience↗

bioRxiv2021

Improved correspondence of fMRI visual field localizer data after macroanatomical alignment

Studying the visual system with fMRI often requires using localizer paradigms to define regions of interest (ROIs). However, the considerable interindividual variability of the cerebral cortex represents a crucial confound for group-level analyses. Cortex-based alignment (CBA) techniques reliably reduce interindividual macroanatomical variability. Yet, their utility has not been assessed for visual field localizer paradigms, which map specific parts of the visual field within retinotopically organized visual areas. We evaluated CBA for an attention-enhanced visual field localizer mapping homologous parts of each visual quadrant in 50 participants. We compared CBA with volume-based alignment and a surface-based analysis, which did not include macroanatomical alignment. CBA led to the strongest increase in the probability of activation overlap (up to 40 percent). On the group level, CBA led to the most consistent increase in ROI size while preserving vertical ROI symmetry. Overall, our results indicate, that in addition to the increased signal-to-noise ratio of a surface-based analysis macroanatomical alignment considerably improves statistical power. These findings confirm and extend the utility of CBA for the study of the visual system in the context of group analyses. CBA should be particularly relevant when studying neuropsychiatric disorders with abnormally increased interindividual macroanatomical variability.

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bioRxiv2020

Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data: Effects on signal quality and sensitivity

Designing optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain-computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Available resources often dictate the approach researchers use for optode-layout design. Here we compared four approaches that incrementally incorporated subject-specific magnetic resonance imaging (MRI) information while participants performed mental-calculation, mental-rotation and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB), employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional and vascular information of the same subject (fVASC). The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI and fVASC approaches resulted in a similar outcome. We conclude that additional individual MRI data leads to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.

neuroscience↗

bioRxiv2020

Neurofeedback fMRI in the motor system elicits bi-directional changes in activity and white-matter structure in the healthy adult human brain

Neurofeedback can be used to alter brain activity and is therefore an attractive tool for neuromodulation in clinical contexts. Different contexts might call for different patterns of activity modulation. For example, following stroke, alternative therapeutic strategies could involve up or down-regulation of activity in the ipsilateral motor cortex. However, effects of such strategies on activity and brain structure are unknown. In a proof of concept study in healthy individuals, we showed that fMRI neurofeedback can be used to drive activity up or down in ipsilateral motor cortex during hand movement. Given evidence for activity-dependent white matter plasticity, we also tested effects of activity modulation on white matter microstructure using diffusion tensor imaging (DTI). We show rapid opposing changes in corpus callosum microstructure that depend on the direction of activity modulation. Bidirectional modulation of ipsilateral motor cortex activity is therefore possible, and results not only in online changes in activity patterns, but also in changes in microstructure detectable 24 hours later.

neuroscience↗

bioRxiv2020

Recurrent plasticity mechanisms for perceptual decisions in the human brain

Learning and experience are critical for translating ambiguous sensory information from our environments to perceptual decisions. Yet, evidence on how training molds the adult human brain remains controversial, as fMRI at standard resolution does not allow us to discern the finer-scale mechanisms that underlie sensory plasticity. Here, we combine ultra-high field (7T) functional imaging at sub-millimetre resolution with orientation discrimination training to interrogate experience-dependent plasticity across cortical depths. Our results provide evidence for recurrent plasticity, by contrast to sensory encoding vs. feedback mechanisms. We demonstrate that learning alters orientation-specific representations in superficial rather than middle V1 layers, suggesting changes in read-out rather than input signals. Further, learning increases feedforward rather than feedback layer-to-layer connectivity in occipito-parietal regions, suggesting that sensory plasticity gates perceptual decisions. Our findings propose finer-scale plasticity mechanisms that re-weight sensory signals to inform improved decisions, bridging the gap between micro- and macro-circuits of experience-dependent plasticity.

neuroscience↗

bioRxiv2020

Improving a probabilistic cytoarchitectonic atlas of auditory cortex using a novel method for inter-individual alignment

The human superior temporal plane, the site of the auditory cortex, displays a high inter-individual macro-anatomical variation. This questions the validity of curvature based alignment (CBA) methods for in vivo imaging data. Here, we have addressed this issue by developing CBA+, which is a cortical surface registration method that uses prior macro-anatomical knowledge. We validate this method by using cyto-architectonic areas on ten individual brains (which we make publicly available). Compared to volumetric and standard surface registration, CBA+ results in a more accurate cyto-architectonic auditory atlas. The improved correspondence of micro-anatomy following the improved alignment of macro-anatomy validates the superiority of CBA+ compared to CBA. In addition, we use CBA+ to align in vivo and post mortem data. This allows projection of functional and anatomical information collected in vivo onto the cyto-architectonic areas, which has potential to contribute to ongoing debate on the parcellation of the human auditory cortex.

neuroscience↗

bioRxiv2020

Suppressive recurrent and feedback computations for adaptive processing in the human brain

Adapting to the environment statistics by reducing brain responses to repetitive sensory information is key for efficient information processing. Yet, the fine-scale computations that support this adaptive processing in the human brain remain largely unknown. Here, we capitalize on the sub-millimetre resolution afforded by ultra-high field imaging to examine BOLD-fMRI signals across cortical depth and discern competing hypotheses about the brain mechanisms (feedforward vs. feedback) that mediate adaptive visual processing. We demonstrate suppressive recurrent processing within visual cortex, as indicated by stronger BOLD decrease in superficial than middle and deeper layers for gratings that were repeatedly presented at the same orientation. Further, we show dissociable connectivity mechanisms for adaptive processing: enhanced feedforward connectivity within visual cortex, while feedback occipito-parietal connectivity, reflecting top-down influences on visual processing. Our findings provide evidence for a circuit of local recurrent and feedback interactions that mediate rapid brain plasticity for adaptive information processing.

neuroscience↗

bioRxiv2019

Active head motion reduction in Magnetic Resonance Imaging using tactile feedback

Head motion is a common problem in clinical as well as empirical (functional) Magnetic Resonance Imaging applications, as it can lead to severe artefacts that reduce image quality. The scanned individuals themselves, however, are often not aware of their head motion. The current study explored whether providing subjects with this information using tactile feedback would reduce their head motion and consequently improve image quality. In a single session that included six runs, 24 participants performed three different cognitive tasks: (1) passive viewing, (2) mental imagery, and (3) speeded responses. These tasks occurred in two different conditions: (a) with a strip of medical tape applied from one side of the MR head-coil, via the participants forehead, to the other side, and (b) without the medical tape being applied. Results revealed that application of medical tape to the forehead of subjects to provide tactile feedback significantly reduced both translational as well as rotational head motion. While this effect did not differ between the three cognitive tasks, there was a negative quadratic relationship between head motion with and without feedback. That is, the more head motion a subject produced without feedback, the stronger the motion reduction given the feedback. In conclusion, the here tested method provides a simple and cost-efficient way to reduce subjects head motion, and might be especially beneficial when extensive head motion is expected a priori.

neuroscience↗

bioRxiv2019

Transfer learning of deep neural network representations for fMRI decoding

BackgroundDeep neural networks have revolutionised machine learning, with unparalleled performance in object classification. However, in brain imaging (e.g. fMRI), the direct application of Convolutional Neural Networks (CNN) to decoding subject states or perception from imaging data seems impractical given the scarcity of available data. New methodIn this work we propose a robust method to transfer information from deep learning (DL) features to brain fMRI data with the goal of decoding. By adopting Reduced Rank Regression with Ridge Regularisation we establish a multivariate link between imaging data and the fully connected layer (fc7) of a CNN. We exploit the reconstructed fc7 features by performing an object image classification task on two datasets: one of the largest fMRI databases, taken from different scanners from more than two hundred subjects watching different movie clips, and another with fMRI data taken while watching static images, ResultsThe fc7 features could be significantly reconstructed from the imaging data, and led to significant decoding performance. Comparison with existing methodsThe decoding based on reconstructed fc7 outperformed the decoding based on imaging data alone. ConclusionIn this work we show how to improve fMRI-based decoding benefiting from the mapping between functional data and CNN features. The potential advantage of the proposed method is twofold: the extraction of stimuli representations by means of an automatic procedure (unsupervised) and the embedding of high-dimensional neuroimaging data onto a space designed for visual object discrimination, leading to a more manageable space from dimensionality point of view.

neuroscience↗

bioRxiv2018

Configural properties underlie the perceived faceness of a stimulus

The global geometrical arrangement of face parts (eyes over nose over mouth), commonly referred to as first order configural (FOC) properties, is believed to constitute a fundamental aspect of face detection. Indeed, several brain regions in the face processing network have been shown to display increased activity in response to abstract stimuli for face-like as compared to random spatial arrangements. However, the absence of a mathematical formalization of FOC properties makes it difficult to systematically study their contribution to face perception. To fill this gap, we used analytical geometry to define a set of 19 FOC features. In two psychophysical studies, a 2-alternative forced choice task assessing difference in perceived faceness between two stimuli and a Likert test assessing measuring perceived faceness of individual stimuli, we evaluated the contribution of each feature to the perceived faceness of abstract stimuli consisting of four black rectangular shapes reflecting the two eyes, the nose and the mouth. From the two experiments two subsets of features relevant for face detection were selected, including 10 and 11 features, respectively. Interestingly, 7 features are shared between the two sets. The difference between the subsets reflects processing of individual FOC features in the first study and holistic processing in the second study. The partial superimposition, on the other hand, is reflective of a shared basic mechanism in the perception of first order configural properties.

neuroscience↗

bioRxiv2018

Real-time functional connectivity-based neurofeedback of amygdala-frontal pathways reduces anxiety

Deficient emotion regulation and exaggerated anxiety represent a major transdiagnostic psychopathological marker. On the neural level these deficits have been closely linked to impaired, yet treatment-sensitive, prefrontal regulatory control over the amygdala. Gaining direct control over these pathways could therefore provide an innovative and promising strategy to regulate exaggerated anxiety. To this end the current proof-of-concept study evaluated the feasibility, functional relevance and maintenance of a novel connectivity-informed real-time fMRI neurofeedback training. In a randomized within-subject sham-controlled design high anxious subjects (n = 26) underwent real-time fMRI-guided training to enhance connectivity between the ventrolateral prefrontal cortex (vlPFC) and the amygdala (target pathway) during threat exposure. Maintenance of regulatory control was assessed after three days and in the absence of feedback. Training-induced changes in functional connectivity of the target pathway and anxiety ratings served as primary outcomes. Training of the target, yet not the sham-control, pathway significantly increased amygdala-vlPFC connectivity and decreased subjective anxiety levels. On the individual level stronger connectivity increases were significantly associated with anxiety reduction. At follow-up, volitional control over the target pathway and decreased anxiety level were maintained in the absence of feedback. The present results demonstrate for the first time that successful self-regulation of amygdala-prefrontal top-down regulatory circuits may represent a novel strategy to control anxiety. As such, the present findings underscore both the critical contribution of amygdala-prefrontal circuits to emotion regulation and the therapeutic potential of connectivity-informed real-time neurofeedback.

clinical trials↗

bioRxiv2018

Somatosensory imagery induces topographically specific activation patterns instrumental to fMRI-based Brain Computer Interfacing

Real-time functional magnetic resonance imaging (fMRI) is a promising non-invasive method for brain computer interfaces (BCIs). BCIs translate brain activity into signals that allow communication with the outside world. Visual and motor imagery are often used as information-encoding strategies, but can be challenging if not grounded in recent experience in these modalities, e.g. in patients with locked-in-syndrome (LIS). In contrast, somatosensory imagery might constitute a more suitable information-encoding strategy as somatosensory function is often very robust. Somatosensory imagery has been shown to activate somatotopic cortex, but it has been unclear so far whether it can be reliably detected on a single-trial level and successfully classified according to specific somatosensory imagery content.\n\nUsing ultra-high field 7-T fMRI, we show reliable and high-accuracy single-trial decoding of left-foot vs. right-hand somatosensory imagery. Correspondingly, higher decoding accuracies were associated with greater spatial separation of hand and foot decoding-weight patterns in primary somatosensory cortex (S1). Exploiting these novel neuroscientific insights, we developed - and provide a proof of concept for - basic BCI communication by showing that binary (yes/no) answers encoded by somatosensory imagery can be decoded with high accuracy not only offline but also in real-time.\n\nThis study demonstrates that body part-specific somatosensory imagery differentially activates somatosensory cortex in a topographically specific manner; evidence which was surprisingly still lacking in the literature. It is also offers a promising novel somatosensory imagery based fMRI-BCI control strategy, with particularly high potential for visually and motor-impaired patients. The strategy could also be transferred to lower MRI field strengths and to mobile functional near-infrared spectroscopy. Finally, given that communication BCIs provide the BCI user with a form of feedback based on their brain signals and can thus be considered as a specific form of neurofeedback, and that repeated use of a BCI has been shown to enhance underlying representations, we expect that the current BCI could also offer an interesting new approach for somatosensory rehabilitation training in the context of stroke and phantom limb pain.

neuroscience↗

bioRxiv2018

Reconstructing and decoding imagined letters from early visual cortex using ultra-high field fMRI

Visual mental imagery is the quasi-perceptual experience of \"seeing in the minds eye\". While a tight correspondence between imagery and perception in terms of subjective experience is well established, their correspondence in terms of neural representations remains insufficiently understood. In the present study, we exploit the high spatial resolution of functional magnetic resonance imaging (fMRI) at 7T, the retinotopic organization of early visual cortex, and machine learning techniques to investigate whether visual imagery of letter shapes preserves the topographic organization of perceived shapes. Sub-millimeter resolution fMRI images were obtained from early visual cortex in six subjects performing visual imagery of four different letter shapes. Predictions of imagery voxel activation patterns based on a population receptive field encoding model and physical letter stimuli provided first evidence in favor of detailed topographic organization. Subsequent visual field reconstructions of imagery data based on the inversion of the encoding model further showed that visual imagery preserves the geometric profile of letter shapes. These results open new avenues for decoding as we show that a denoising autoencoder can be used to pretrain a classifier purely based on perceptual data before fine-tuning it on imagery data. Finally, we show that the autoencoder can project imagery-related voxel activations onto their perceptual counterpart allowing for visually recognizable reconstructions even at the single-trial level. The latter may eventually be utilized for the development of content-based BCI letter-speller systems.

neuroscience↗

bioRxiv2017

Task-related effective connectivity reveals that the cortical rich club gates cortex-wide communication

Higher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs - referred to as the rich club - has been hypothesized to support task-specific functional integration. In the present paper, we use a whole-cortex model to estimate directed interactions between 68 cortical regions from fMRI activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state-dependent input and output effective connectivity of the structural rich club and relate these to whole-cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing effective connectivity during task performance as compared to rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task-specific. Furthermore, we observe that whole-cortex dynamics are faster during task as compared to rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing effective connectivity. Together our findings speak to a gating mechanism of the rich club that supports fast-paced information exchange among relevant peripheral regions in a task-specific and goal-directed fashion, while constantly listening to the whole network.

neuroscience↗