Abstract Feature Space Representation for Volumetric Transfer Function Exploration

The application of n-dimensional transfer functions for feature segmentation has become increasingly popular in volume rendering. Recent work has focused on the utilization of higher order dimensional transfer functions incorporating spatial dimensions (x,y, and z) along with traditional feature space dimensions (value and value gradient). However, as the dimensionality increases, it becomes exceedingly difficult to abstract the transfer function into an intuitive and interactive workspace. In this work we focus on populating the traditional two-dimensional histogram with a set of derived metrics from the spatial (x, y and z) and feature space (value, value gradient, etc.) domain to create a set of abstract feature space transfer function domains. Current two-dimensional transfer function widgets typically consist of a two-dimensional histogram where each entry in the histogram represents the number of voxels that maps to that entry. In the case of an abstract transfer function design, the amount of spatial variance at that histogram coordinate is mapped instead, thereby relating additional information about the data abstraction in the projected space. Finally, a non-parametric kernel density estimation approach for feature space clustering is applied in the abstracted space, and the resultant transfer functions are discussed with respect to the space abstraction

Leveraging Self-Supervised Vision Transformers for Neural Transfer Function Design

In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity. They are commonly defined as 1D or 2D functions that map simple features to these optical properties. As the process of designing a transfer function is typically tedious and unintuitive, several approaches have been proposed for their interactive specification. In this paper, we present a novel method to define transfer functions for volume rendering by leveraging the feature extraction capabilities of self-supervised pre-trained vision transformers. To design a transfer function, users simply select the structures of interest in a slice viewer, and our method automatically selects similar structures based on the high-level features extracted by the neural network. Contrary to previous learning-based transfer function approaches, our method does not require training of models and allows for quick inference, enabling an interactive exploration of the volume data. Our approach reduces the amount of necessary annotations by interactively informing the user about the current classification, so they can focus on annotating the structures of interest that still require annotation. In practice, this allows users to design transfer functions within seconds, instead of minutes. We compare our method to existing learning-based approaches in terms of annotation and compute time, as well as with respect to segmentation accuracy. Our accompanying video showcases the interactivity and effectiveness of our method

Relationship between electroencephalographic data and comfort perception captured in a Virtual Reality design environment of an aircraft cabin

Successful aircraft cabin design depends on how the different stakeholders are involved since the first phases of product development. To predict passenger satisfaction prior to the manufacturing phase, human response was investigated in a Virtual Reality (VR) environment simulating a cabin aircraft. Subjective assessments of virtual designs have been collected via questionnaires, while the underlying neural mechanisms have been captured through electroencephalographic (EEG) data. In particular, we focused on the modulation of EEG alpha rhythm as a valuable marker of the brain's internal state and investigated which changes in alpha power and connectivity can be related to a different visual comfort perception by comparing groups with higher and lower comfort rates. Results show that alpha-band power decreased in occipital regions during subjects' immersion in the virtual cabin compared with the relaxation state, reflecting attention to the environment. Moreover, alpha-band power was modulated by comfort perception: lower comfort was associated with a lower alpha power compared to higher comfort. Further, alpha-band Granger connectivity shows top-down mechanisms in higher comfort participants, modulating attention and restoring partial relaxation. Present results contribute to understanding the role of alpha rhythm in visual comfort perception and demonstrate that VR and EEG represent promising tools to quantify human-environment interactions

The laminar profile of spatial attention in macaque V1 and V4

PhD ThesisSpatial attention allows processing to be prioritised for one or more locations in the visual field, even in the presence of other distracting or irrelevant stimuli. Previous work has shown that attention modulates the activity of the brain at the level of spiking activity, local field potentials and coherence between and within neuronal groups. However currently little is known about how these attentional modulations differ between groups of neurons in different cortical layers and areas. We trained two adult male rhesus macaques to perform a covert visuospatial attention task whilst we recorded simultaneously from V1 and V4. Recordings were taken with multichannel laminar electrodes allowing recording from supragranular, granular and infragranular cells within the same cortical microcolumns. We used current source density analysis to align our recording contacts to the cortical laminar profile (layers). The receptive fields of the V1 and V4 cells we recorded from were overlapping which meant they could be driven by the same stimulus in the task. To measure the attentional modulation of information flow between different groups of neurons we calculated field coherence, Granger causality and spike-rate correlations. Attention increased firing rates for all of the cell types, layers and areas in our study. We also show that variability as measured by gain variance and noise correlations is reduced by attention. Although we find differences between the two monkeys regarding LFP power changes and regarding coherence measures within and between the areas investigated, we find that attention consistently increased the Granger causality in the gamma frequency band between V1 and V4. We demonstrate that the flow of information in the alpha/beta and gamma bands follows expected interareal feedback and feedforward patterns between V1 and V4. We also provide evidence that feedforward gamma oscillations are generated, contrary to expectations, in the infragranular layers of V1

Untangling cross-frequency coupling in neuroscience

Cross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pathological brain function, the standard CFC analysis and physiological interpretation come with fundamental problems. For example, apparent CFC can appear because of spectral correlations due to common non-stationarities that may arise in the total absence of interactions between neural frequency components. To provide a road map towards an improved mechanistic understanding of CFC, we organize the available and potential novel statistical/modeling approaches according to their biophysical interpretability. While we do not provide solutions for all the problems described, we provide a list of practical recommendations to avoid common errors and to enhance the interpretability of CFC analysis.Comment: 47 pages, 12 figures, including supplementary materia

Automated Quantification of Human Alpha Rhythm

This thesis seeks to quantify human alpha rhythm in order to obtain better measures to test theoretical models of neurodynamics, with potential clinical applications for the method of identification. An automated algorithm is developed in Chapter 2 to facilitate collection of objective data from an expanded alpha band (4–14 Hz) in a large number of subjects. This method avoids subjective bias inherent to traditional visual identification of the alpha activity, produced multiple peak information (if multiple peaks exist) that is absent in qEEG measures, and uses information from multiple electrode sites to eliminate spurious peaks. This method is tested and validated on 100 subjects. In addition to traditional measures of alpha activities such as the frequency and amplitude, further measures were devised to better quantify the alpha rhythm and its spatial characteristics. Background spectra in the alpha range are also characterized. In Chapter 3 the algorithm is applied to a large (1498 subjects) database of normal healthy subjects of approximately equal number in each sex, as well as a large span in age (6–86 years), in order to establish typical parameter ranges. Analysis is done comparing the age and the topological trends that are known variants in the alpha rhythm. Investigations are also performed to test for potential sex differences and/or lateralities. Key results are that double alpha peaks are resolved in a large proportion of the subjects ( 50%), while single alpha peak cases are likely to be double-peak cases in which the two peaks are not resolved. Age trends in measures of alpha activity show increase of alpha frequency from childhood to adolescence, a plateau in adulthood, and a slight decline in old age; a decrease in alpha amplitude in old age is also observed. These findings are consistent with previous literature and provide better statistics. Topological distribution of the alpha activity on the head is also explored, with little lateral asymmetry observed. No statistically significant differences between the sexes are found. The C++ code that was developed and utilized in this thesis is included in Appendix B. It is provided on disk and is available online. A study carried out on the same group of subjects to determine age-related trends of EEG parameters produced by model fitting is included in Appendixes C, to provide a comparison between the methods and highlights corresponding results

Neural synchrony in cortical networks : history, concept and current status

Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies

Neural synchrony in cortical networks : history, concept and current status

Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies