Biology subjects

Gramann, K.

Publications and source records attributed to Gramann, K..

27 records · Page 2Linked to original sources

bioRxiv2020

Alteration of brain dynamics during natural dual-task walking

While walking in our natural environment, we continuously solve additional cognitive tasks. This increases the demand of resources needed for both the cognitive and motor systems, resulting in Cognitive-Motor Interference (CMI). While it is well known that a performance decrease in one or both tasks can be observed, little is known about human brain dynamics underlying CMI during dual-task walking. Moreover, a large portion of previous investigations on CMI took place in static settings, emphasizing the experimental rigor but overshadowing the ecological validity. To address these problems, we developed a dual-task walking scenario in virtual reality (VR) combined with Mobile Brain/Body Imaging (MoBI). We aimed at investigating how brain dynamics are modulated during natural overground walking while simultaneously performing a visual discrimination task in an ecologically valid scenario. Even though the visual task did not affect performance while walking, a P3 amplitude reduction along with changes in power spectral densities (PSDs) during dual-task walking were observed. Replicating previous results, this reflects the impact of walking on the parallel processing of visual stimuli, even when the cognitive task is particularly easy. This standardized and easy to modify VR-paradigm helps to systematically study CMI, allowing researchers to control the complexity of different tasks and sensory modalities. Future investigations implementing an improved virtual design with more challenging cognitive and motor tasks will have to investigate the roles of both cognition and motion, allowing for a better understanding of the functional architecture of attention reallocation between cognitive and motor systems during active behavior.

neuroscience↗

bioRxiv2019

Overcoming Spatial Deskilling Using Landmark-Based Navigation Assistance Systems

The repeated use of navigation assistance systems leads to decreased processing of the environment. Previous studies demonstrated that auditory augmentation of landmarks in navigation instructions can improve incidental spatial knowledge acquisition when driving a single route through an unfamiliar virtual environment. Based on these results, three experiments were conducted to investigate the generalizability and ecological validity of incidental spatial knowledge acquisition by landmark augmentation in navigation instructions. In the first experiment, spatial knowledge acquisition was tested after watching an interactive video showing the navigation of a real-world urban route. A second experiment investigated incidental spatial knowledge acquisition during assisted navigation when participants walked through the same real-world, urban environment. The third experiment tested the acquired spatial knowledge two weeks after an assisted pedestrian navigation phase through the real-world. All experiments demonstrated better performance in a cued-recall task for participants navigating with landmark-based navigation instructions as compared to standard instructions. Different levels of information provided with landmark-based instructions impacted landmark recognition dependent on the delay between navigation and test. The results replicated an improved landmark and route knowledge when using landmark-based navigation instructions emphasizing that auditory landmark augmentation enhances incidental spatial knowledge acquisition, and that this enhancement can be generalized to real-life settings. This research is paving the way for navigation assistants that, instead of impairing spatial knowledge acquisition, incidentally foster the acquisition of landmark and route knowledge during every-day navigation.

neuroscience↗

bioRxiv2019

A Bayesian framework for unifying data cleaning, source separation and imaging of electroencephalographic signals

Electroencephalographic (EEG) source imaging depends upon sophisticated signal processing algorithms for data cleaning, source separation, and localization. Typically, these problems are addressed separately using a variety of heuristics, making it difficult to systematize a methodology for extracting robust EEG source estimates on a wide range of experimental paradigms. In this paper, we propose a unifying Bayesian framework in which these apparently dissimilar problems can be understood and solved in a principled manner using a single algorithm. We explicitly model the effect of non-brain sources by augmenting the lead field matrix with a dictionary of stereotypical artifact scalp projections. We propose to populate the artifact dictionary with non-brain scalp projections obtained by running Independent Component Analysis (ICA) on an EEG database. Within a parametric empirical Bayes (PEB) framework, we use an anatomical brain atlas to parameterize a source prior distribution that encourages sparsity in the number of cortical regions. We show that, in our inversion algorithm, PEB+ (PEB with the addition of artifact modeling), the sparsity prior has the property of inducing the segregation of the cortical activity into a few maximally independent components with known anatomical support. Artifacts produced by electrooculographic and electromyographic activity as well as single-channel spikes are also segregated into their respective components. Of theoretical relevance, we use our framework to point out the connections between Infomax ICA and distributed source imaging. We use real data to demonstrate that PEB+ outperforms Infomax for source separation on short segments of data and, unlike the popular Artifact Subspace Removal algorithm, it can reduce artifacts without significantly distorting clean epochs. Finally, we analyze mobile brain/body imaging data to characterize the brain dynamics supporting heading computation during full-body rotations. In this example, we run PEB+ followed by the spectral analysis of the activity in the retrosplenial cortex, largely replicating the findings of previous experimental literature.

neuroscience↗

bioRxiv2019

Sensory-motor brain dynamics reflect architectural affordances

Anticipating meaningful actions in the environment is an essential function of the brain. Such predictive mechanisms originate from the motor system and allow for inferring actions from environmental affordances, the potential to act within a specific environment. Using architecture, we provide a unique perspective to the abiding debate in cognitive neuroscience and philosophy on whether cognition depends on movement or is decoupled from our physical structure. To investigate cognitive processes associated with architectural affordances, we used a Mobile Brain/Body Imaging approach recording brain activity synchronized to head-mounted virtual reality. Participants perceived and acted upon virtual transitions ranging from non-passable to easily passable. We demonstrate that early sensory brain activity, upon revealing the environment and before actual movement, differed as a function of affordances. Additionally, movement through transitions was preceded by a motor-related negative component also depended on affordances. Our results suggest that potential actions afforded by an environment influence perception.\n\nSIGNIFICANCE STATEMENTBy using electroencephalography and virtual reality, our research provide a unique perspective to the centurial open-ended debate in cognitive neuroscience and philosophy on the relation between cognition, movement and environment. Our results indicate that cortical potentials vary as a function of bodily affordances reflected by the physical environment. Firstly, the results of this study implies that cognition is inherently related to potential movement of the body, thus we advance that action is interrelated with perception, actively influencing the perceivable environment. Secondly, as cortical potentials are influenced by the potential to move, which in turn is the task of architectural design, architects holds largely a privilege of human health, and thus potentially capable of provoking and preventing physiological conditions.

neuroscience↗

bioRxiv2018

Heading computation in the human retrosplenial complex during full-body rotation

The retrosplenial complex (RSC) plays a crucial role in spatial orientation by computing heading direction and translating between distinct spatial reference frames based on multi-sensory information. While invasive studies allow investigating heading computation in moving animals, established non-invasive analyses of human brain dynamics are restricted to stationary setups. To investigate the role of the RSC in heading computation of actively moving humans, we used a Mobile Brain/Body Imaging approach synchronizing electroencephalography with motion capture and virtual reality. Data from physically rotating participants were contrasted with rotations based only on visual flow. During physical rotation, varying rotation velocities were accompanied by pronounced wide frequency band synchronization in RSC, the parietal and occipital cortices. In contrast, the visual flow rotation condition was associated with pronounced alpha band desynchronization, replicating previous findings in desktop navigation studies, and notably absent during physical rotation. These results suggest an involvement of the human RSC in heading computation based on visual, vestibular, and proprioceptive input and implicate revisiting traditional findings of alpha desynchronization in areas of the navigation network during spatial orientation in movement-restricted participants.

neuroscience↗

bioRxiv2018

SEREEGA: Simulating Event-Related EEG Activity

Electroencephalography (EEG) is a popular method to monitor brain activity, but it can be difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings, ensuring that it is known beforehand which e ects are present in the data. As such, simulated data can be used, among other things, to assess or compare signal processing and machine learn-ing algorithms, to model EEG variabilities, and to design source reconstruction methods. In this paper, we present SEREEGA, short for Simulating Event-Related EEG Activity. SEREEGA is a MATLAB-based open-source toolbox dedicated to the generation of sim-ulated epochs of EEG data. It is modular and extensible, at initial release supporting ve different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general work ow of this toolbox, as well as a simulated data set demonstrating some of its functions.\n\nHighlightsO_LISimulated EEG data has a known ground truth, which can be used to validate methods.\nC_LIO_LIWe present a general-purpose open-source toolbox to simulate EEG data.\nC_LIO_LIIt provides a single framework to simulate many different types of EEG recordings.\nC_LIO_LIIt is modular, extensible, and already includes a number of head models and signals.\nC_LIO_LIIt supports noise, oscillations, event-related potentials, connectivity, and more.\nC_LI

neuroscience↗

bioRxiv2018

Electrocortical Evidence for Long-Term Incidental Spatial Learning Through Modified Navigation Instructions

The use of Navigation Assistance Systems for spatial orienting has become increasingly popular. Such automated navigation support, however, comes with a reduced processing of the surrounding environment and often with a decline of spatial orienting ability. To prevent such deskilling and to support spatial learning, the present study investigated incidental spatial learning by comparing standard navigation instructions with two modified navigation instruction conditions. The first modified instruction condition highlighted landmarks and provided additional redundant information regarding the landmark (contrast condition), while the second highlighted landmarks and included personal relevant information regarding the landmark (personal-reference condition). Participants spatial knowledge of the previously unknown virtual city was tested three weeks later. Behavioral and electroencephalographic (EEG) data demonstrated enhanced memory performance for participants in the modified navigation instruction conditions without further differentiating between modified instructions. Recognition performance of landmarks was better and the late positive complex of the event-related potential (ERP) revealed amplitude differences reflecting an increased amount of recollected information for modified navigation instructions. The results indicate a significant long-term spatial learning effect when landmarks are highlighted during navigation instructions.

neuroscience↗

bioRxiv2018

Investigating established EEG parameter during real-world driving

In real life, behavior is influenced by dynamically changing contextual factors and is rarely limited to simple tasks and binary choices. For a meaningful interpretation of brain dynamics underlying more natural cognitive processing in active humans, ecologically valid test scenarios are essential. To understand whether brain dynamics in restricted artificial lab settings reflect the neural activity in complex natural environments, we systematically tested the eventrelated P300 in both settings. We developed an integrative approach comprising an initial P300-study in a highly controlled laboratory set-up and a subsequent validation within a realistic driving scenario. Using a simulated dialog with a speech-based input system, increased P300 amplitudes reflected processing of infrequent and incorrect auditory feedback events in both the laboratory setting and the real world setup. Environmental noise and movement-related activity in the car driving scenario led to higher data rejection rates but revealed no effect on signal-to-noise ratio in theta and alpha frequency band or the amplitudes of the event-related P300. Our results demonstrate the possibility to investigate cognitive functions like context updating in highly adverse driving scenarios and encourage the consideration of more realistic task settings in prospective brain imaging approaches.

neuroscience↗

bioRxiv2018

The Invisible Maze Task (IMT): Interactive Exploration of Sparse Virtual Environments to Investigate Action-Driven Formation of Spatial Representations

The neuroscientific study of human navigation has been con-strained by the prerequisite of traditional brain imaging studies that re-quire participants to remain stationary. Such imaging approaches neglect a central component that characterizes navigation -the multisensory ex-perience of self-movement. Navigation by active movement through space combines multisensory perception with internally generated self-motion cues. We investigated the spatial micro genesis during free ambulatory exploration of interactive sparse virtual environments using motion cap-ture synchronized to high resolution electroencephalographic (EEG) data as well psychometric and self-report measures. In such environments, map-like allocentric representations must be constructed out of transient, egocentric first-person perspective 3-D spatial information. Considering individual differences of spatial learning ability, we studied if changes in exploration behavior coincide with spatial learning of an environment. To this end, we analyzed the quality of sketch maps (a description of spatial learning) that were produced after repeated learning trials for differently complex maze environments. We observed significant changes in active exploration behavior from the first to the last exploration of a maze: a decrease in time spent in the maze predicted an increase in subsequent sketch map quality. Furthermore, individual differences in spatial abilities as well as differences in the level of experienced immersion revealed an impact on the quality of spatial learning. Our results demonstrate the feasibility to observe behavioral changes associated with spatial learning, opening the way to the study of cortical dynamics of navigation.

neuroscience↗