VCBM 18: Eurographics Workshop on Visual Computing for Biology and Medicine

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Annotated Dendrograms for Neurons From the Larval Fruit Fly Brain
(The Eurographics Association, 2018) Strauch, Martin; Hartenstein, Volker; Andrade, Ingrid V.; Cardona, Albert; Merhof, Dorit; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Recent advances in neuroscience have made it possible to reconstruct all neurons of an entire neural circuit in the larval fruit fly brain from serial electron microscopy image stacks. The reconstructed neurons are morphologically complex 3D graphs whose nodes are annotated with labels representing different types of synapses. Here, we propose a method to draw simplified, yet realistic 2D neuron sketches of insect neurons in order to help biologists formulate hypotheses on neural function at the microcircuit level. The sketches are dendrograms that capture a neuron's branching structure and that preserve branch lengths, providing realistic estimates for distances and signal travel times between synapses. To improve readability of the often densely clustered synapse annotations, synapses are automatically summarized in local clusters of synapses of the same type and arranged to minimize label overlap. We show that two major neuron classes of an olfactory circuit in the larval fruit fly brain can be discriminated visually based on the dendrograms. Unsupervised and supervised data analysis reveals that class discrimination can be performed using morphological features derived from the dendrograms.
Automatic Generation of Web-Based User Studies to Evaluate Depth Perception in Vascular Surface Visualizations
(The Eurographics Association, 2018) Meuschke, Monique; Smit, Noeska N.; Lichtenberg, Nils; Preim, Bernhard; Lawonn, Kai; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
User studies are often required in biomedical visualization application papers in order to provide evidence for the utility of the presented approach. An important aspect is how well depth information can be perceived, as depth encoding is important to enable an understandable representation of complex data. Unfortunately, in practice there is often little time available to perform such studies, and setting up and conducting user studies may be labor-intensive. In addition, it can be challenging to reach enough participants to support the contribution claims of the paper. In this paper, we propose a system that allows biomedical visualization researchers to quickly generate perceptual task-based user studies for novel surface visualizations, and to perform the resulting experiment via a web interface. This approach helps to reduce effort in the setup of user studies themselves, and at the same time leverages a web-based approach that can help researchers attract more participants to their study. We demonstrate our system using the specific application of depth judgment tasks to evaluate vascular surface visualizations, since there is a lot of recent interest in this area. However, the system is also generally applicable for conducting other task-based user studies in biomedical visualization.
A Critical Analysis of the Evaluation Practice in Medical Visualization
(The Eurographics Association, 2018) Preim, Bernhard; Ropinski, Timo; Isenberg, Petra; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Medical visualization aims at directly supporting physicians in diagnosis and treatment planning, students and residents in medical education, and medical physicists as well as other medical researchers in answering specific research questions. For assessing whether single medical visualization techniques or entire medical visualization systems are useful in this respect, empirical evaluations involving participants from the target user group are indispensable. The human computer interaction field developed a wide range of evaluation instruments, and the information visualization community more recently adapted and refined these instruments for evaluating (information) visualization systems. However, often medical visualization lacks behind and should pay more attention to evaluation, in particular to evaluations in realistic settings that may assess how visualization techniques contribute to cognitive activities, such as deciding about a surgical strategy or other complex treatment decisions. In this vein, evaluations that are performed over a longer period are promising to study, in order to investigate how techniques are adapted. In this paper, we discuss the evaluation practice in medical visualization based on selected examples and contrast these evaluations with the broad range of existing empirical evaluation techniques. We would like to emphasize that this paper does not serve as a general call for evaluation in medical visualization, but argues that the individual situation must be assessed and that evaluations when they are carried out should be done more carefully.
Estimation of Muscle Activity in One-Leg Stance from 3D Surface Deformation
(The Eurographics Association, 2018) Metzler, Johannes; Neumann, Thomas; Gassel, Stefanie; Friedrich, Jens; Wacker, Markus; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Muscular activity during human motion is usually quantified by measuring the electrical potential during muscle activation using electromyography (EMG). However, apart from producing electrical activity, muscular contraction of many skeletal muscles also induces subtle deformation of the skin surface. In this paper, we present a method to estimate muscular activation from such 3D skin deformation. To this end, we introduce a capture system that reconstructs the 3D motion of the skin from multi-view video data and simultaneously measures true muscle activity with EMG sensors. Our data reveals strong correlations between the skin deformation and muscular activity during one-leg stances. We propose a pose normalization procedure and a novel model based on Supervised Principal Component Regression that automatically segments individual muscles and estimates their activation from 3D surface deformation. Our evaluation shows that the model generalizes to varying body shapes and that the estimated activation closely fits the measured EMG data.
A Framework for Visual Comparison of 4D PC-MRI Aortic Blood Flow Data
(The Eurographics Association, 2018) Behrendt, Benjamin; Ebel, Sebastian; Gutberlet, Matthias; Preim, Bernhard; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) allows for the non-invasive acquisition of in-vivo blood flow, producing a patient-specific blood flow model in selected vascular structures, e.g. the aorta. In the past, many specialized techniques for the visualization and exploration of such datasets have been developed, yet a tool for the visual comparison of multiple datasets is missing. Due to the complexity of the underlying data, a simple side-by-side comparison of two datasets using traditional visualization techniques can only yield coarse results. In this paper, we present a toolkit that allows for an efficient and robust registration of different 4D PC-MRI datasets and offers a variety of both qualitative and quantitative comparison techniques. Differences in the segmentation and time frame can be amended semi-automatically using landmarks on the vessel centerline and flow curve of the datasets. A set of measures quantifying the difference between the datasets, such as the flow jet displacement or flow angle and velocity difference, is automatically computed. To support the orientation in the spatio-temporal domain of the flow dataset, we provide bulls-eye plots that highlight potentially interesting regions. In an evaluation with three experienced radiologists, we confirmed the usefulness of our technique. With our application, they were able to discover previously unnoticed artifacts occurring in a dataset acquired with an experimental MRI sequence.
Frontmatter: Eurographics Workshop on Visual Computing for Biology and Medicine 2018
(The Eurographics Association, 2018) Puig Puig, Anna; Schultz, Thomas; Vilanova, Anna; Hotz, Ingrid; Kozlikova, Barbora; Vázquez, Pere-Pau; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Global and Local Mesh Morphing for Complex Biological Objects from µCT Data
(The Eurographics Association, 2018) Knötel, David; Becker, Carola; Scholtz, Gerhard; Baum, Daniel; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
We show how biologically coherent mesh models of animals can be created from µCT data to generate artificial yet naturally looking intermediate objects. The whole pipeline of processing algorithms is presented, starting from generating topologically equivalent surface meshes, followed by solving the correspondence problem, and, finally, creating a surface morphing. In this pipeline, we address all the challenges that are due to dealing with complex biological, non-isometric objects. For biological objects it is often particularly important to obtain deformations that look as realistic as possible. In addition, spatially non-uniform shape morphings that only change one part of the surface and keep the rest as stable as possible are of interest for evolutionary studies, since functional modules often change independently from one another. We use Poisson interpolation for this purpose and show that it is well suited to generate both global and local shape deformations.
ICG based Augmented-Reality-System for Sentinel Lymph Node Biopsy
(The Eurographics Association, 2018) Noll, Matthias; Noa-Rudolph, Werner; Wesarg, Stefan; Kraly, Michael; Stoffels, Ingo; Klode, Joachim; Spass, Cédric; Spass, Gerrit; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
In this paper we introduce a novel augmented-reality (AR) system for the sentinel lymph node (SLN) biopsy. The AR system consists of a cubic recording device with integrated stereo near-infrared (NIR) and stereo color cameras, an head mounted display (HMD) for visualizing the SLN information directly into the physicians view and a controlling software application. The labeling of the SLN is achieved using the fluorescent dye indocyanine green (ICG). The dye accumulates in the SLN where it is excited to fluorescence by applying infrared light. The fluorescence is recorded from two directions by the NIR stereo cameras using appropriate filters. Applying the known rigid camera geometry, an ICG depth map can be generated from the camera images, thus creating a live 3D representation of the SLN. The representation is then superimposed to the physicians field of view, by applying a series of coordinate system transformations, that are determined in four separate system calibration steps. To compensate for the head motion, the recording systems is continuously tracked by a single camera on the HMD using fiducial markers. Because the system does not require additional monitors, the physicians attention is kept solely on the operation site. This can potentially decrease the intervention time and render the procedure safer for the patient.
Improving Perception of Molecular Surface Visualizations by Incorporating Translucency Effects
(The Eurographics Association, 2018) Hermosilla, Pedro; Maisch, Sebastian; Vázquez, Pere-Pau; Ropinski, Timo; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Molecular surfaces are a commonly used representation in the analysis of molecular structures as they provide a compact description of the space occupied by a molecule and its accessibility. However, due to the high abstraction of the atomic data, fine grain features are hard to identify. Moreover, these representations involve a high degree of occlusions, which prevents the identification of internal features and potentially impacts shape perception. In this paper, we present a set of techniques which are inspired by the properties of translucent materials, that have been developed to improve the perception of molecular surfaces: First, we introduce an interactive algorithm to simulate subsurface scattering for molecular surfaces, in order to improve the thickness perception of the molecule. Second, we present a technique to visualize structures just beneath the surface, by still conveying relevant depth information. And lastly, we introduce reflections and refractions into our visualization that improve the shape perception of molecular surfaces. We evaluate the benefits of these methods through crowd-sourced user studies as well as the feedback from several domain experts.
Introducing CNN-Based Mouse Grim Scale Analysis for Fully Automated Image-Based Assessment of Distress in Laboratory Mice
(The Eurographics Association, 2018) Kopaczka, Marcin; Ernst, Lisa; Schock, Justus; Schneuing, Arne; Guth, Alexander; Tolba, Rene; Merhof, Dorit; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
International standards require close monitoring of distress of animals undergoing laboratory experiments in order to minimize the stress level and allow choosing minimally stressful procedures for the experiments. Currently, one of the the best established severity assessment procedures is the mouse grimace scale (MGS), a protocol in which images of the animals are taken and scored by assessing five key visual features that have been shown to be highly correlated with distress and pain. While proven to be highly reliable, MGS assessment is currently a time-consuming task requiring manual video processing for key frame extraction and subsequent expert grading. Additionally, due to the the high per-picture expert time required, MGS scoring is performed on a small number of selected frames from a video. To address these shortcomings, we introduce a method for fully automated real-time MGS scoring of orbital eye tightening, one of the five sub-scores. We define and evaluate the method which is centered around a set of convolutional neural networks (CNNs) and allows live continuous MGS assessment of a mouse in real time. We additionally describe a multithreaded client-server architecture with a graphical user interface that allows convenient use of the developed method for simultaneous real-time MGS scoring of several animals.
Iterative Exploration of Big Brain Network Data
(The Eurographics Association, 2018) Ganglberger, Florian; Swoboda, Nicolas; Frauenstein, Lisa; Kaczanowska, Joanna; Haubensak, Wulf; Bühler, Katja; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
A current quest in neurscience is the understanding of how genes, structure and behavior relate to one another. In recent years, big brain-initiatives and consortia have created vast resources of publicly available brain data that can be used by neuroscientists for their own research experiments. This includes microscale connectivity data - brain-network graphs with billions of edges - whose analysis for higher order relations in structural or functional neuroanatomy together with genetic data may reveal novel insights into brain functionality. This creates a need for joint exploration of spatial data, such as gene expression patterns, whole brain gene co-expression correlation, structural and functional connectivities together with neuroanatomical parcellations. Current experimental workflows involve time-consuming manual aggregation and extensive graph theoretical analysis of data from different sources, which rarely provide spatial context to operate continuously on different scales. We propose a web-based framework to explore heterogeneous neurobiological data in an integrated visual analytics workflow. On-demand queries on spatial gene expression and connectivity data enable an interactive dissection of dense network graphs - with of billion-edges on voxel-resolution - in real-time based on their spatial context. In order to take higher order connections between brain regions into account, queries can be executed in a cascading way. Relating data to the hierarchical structure of common anatomical atlases allows experts to quantitatively compare multimodal networks on different scales. Additionally, 3D visualizations have been optimized to accommodate for the domain experts’' need for publishable network figures. We demonstrate the relevance of our approach for neuroscience by exploring social-behavior and memory/learning functional neuroanatomy in mice.
A Multilinear Model for Bidirectional Craniofacial Reconstruction
(The Eurographics Association, 2018) Achenbach, Jascha; Brylka, Robert; Gietzen, Thomas; Hebel, Katja zum; Schömer, Elmar; Schulze, Ralf; Botsch, Mario; Schwanecke, Ulrich; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
We present a bidirectional facial reconstruction method for estimating the skull given a scan of the skin surface and vice versa estimating the skin surface given the skull. Our approach is based on a multilinear model that describes the correlation between the skull and the facial soft tissue thickness (FSTT) on the one hand and the head/face surface geometry on the other hand. Training this model requires to densely sample the Cartesian product space of skull shape times FSTT variation, which cannot be obtained by measurements alone. We generate this data by enriching measured data-volumetric computed tomography scans and 3D surface scans of the head-by simulating statistically plausible FSTT variations. We demonstrate the versatility of our novel multilinear model by estimating faces from given skulls as well as skulls from given faces within just a couple of seconds. To foster further research in this direction, we will make our multilinear model publicly available.
A New Vessel Enhancement Transform on Retinal Blood Vessels Segmentation
(The Eurographics Association, 2018) Soares, Ivo; Castelo-Branco, Miguel; Pinheiro, António M. G.; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
The automatic detection of retinal blood vessels is a very important task in computer aided-diagnosis of retinal diseases. In this work a new method is proposed for the automated detection of the retinal vessels. Three new and important contributions are made. First, a new method capable of vessel enhancement is presented. Second, a new criterium to remove some false vessels caused by the proximity to bright regions is presented, avoiding the false vessels created by the presence of exudates or bright artifacts. Third, a new method that discards the false vessel regions that usually tends to appear in the border of the optic disc. This is achieved using the derivatives of the gradient magnitude local maxima over different scales. The performance evaluation is made on two publicly available databases, namely, STARE, and HRF with state-of-the-art results. Particularly, the described method reveals to be very reliable on retinal images with large pathological signs.
Parameterization and Feature Extraction for the Visualization of Tree-like Structures
(The Eurographics Association, 2018) Lichtenberg, Nils; Lawonn, Kai; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
The study and visualization of vascular structures, using 3D models obtained from medical data, is an active field of research. Illustrative visualizations have been applied to this domain in multiple ways. Researchers have tried to make the geometric properties of vasculature more comprehensive and to augment the surface with representations of multivariate clinical data. Techniques that head beyond the application of color-maps or simple shading approaches require a sort of surface parameterization, i.e., texture coordinates, in order to overcome locality. When extracting 3D models, the computation of texture coordinates on the mesh is not always part of the data processing pipeline. We combine existing techniques to a simple, yet effective, parameterization approach that is suitable for tree-like structures. The parameterization is done w.r.t. to a pre-defined source vertex. For this, we present an automatic algorithm, that detects the root of a tree-structure. The parameterization is partly done in screen-space and recomputed per frame. However, the screen-space computation comes with positive features that are not present in object-space approaches. We show how the resulting texture coordinates can be used for varying hatching, contour parameterization, the display of decals, as an additional depth cue and feature extraction.
Progressive and Efficient Multi-Resolution Representations for Brain Tractograms
(The Eurographics Association, 2018) Mercier, Corentin; Gori, Pietro; Rohmer, Damien; Cani, Marie-Paule; Boubekeur, Tamy; Thiery, Jean-Marc; Bloch, Isabelle; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Current tractography methods generate tractograms composed of millions of 3D polylines, called fibers, making visualization and interpretation extremely challenging, thus complexifying the use of this technique in a clinical environment. We propose to progressively simplify tractograms by grouping similar fibers into generalized cylinders. This produces a fine-grained multiresolution model that provides a progressive and real-time navigation through different levels of detail. This model preserves the overall structure of the tractogram and can be adapted to different measures of similarity. We also provide an efficient implementation of the method based on a Delaunay tetrahedralization. We illustrate our method using the Human Connectome Project dataset.
A Prototype Holographic Augmented Reality Interface for Image-Guided Prostate Cancer Interventions
(The Eurographics Association, 2018) Morales Mojica, Cristina M.; Velazco Garcia, Jose D.; Navkar, Nikhil V.; Balakrishnan, Shidin; Abinahed, Julien; El Ansari, Walid; Al-Rumaihi, Khalid; Darweesh, Adham; Al-Ansari, Abdulla; Gharib, Mohamed; Karkoub, Mansour; Leiss, Ernst L.; Seimenis, Ioannis; Tsekos, Nikolaos V.; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Motivated by the potential of holographic augmented reality (AR) to offer an immersive 3D appreciation of morphology and anatomy, the purpose of this work is to develop and assess an interface for image-based planning of prostate interventions with a head-mounted display (HMD). The computational system is a data and command pipeline that links a magnetic resonance imaging (MRI) scanner/data and the operator, that includes modules dedicated to image processing and segmentation, structure rendering, trajectory planning and spatial co-registration. The interface was developed with the Unity3D Engine (C#) and deployed and tested on a HoloLens HMD. For ergonomics in the surgical suite, the system was endowed with hands-free interactive manipulation of images and the holographic scene via hand gestures and voice commands. The system was tested in silico using MRI and ultrasound datasets of prostate phantoms. The holographic AR scene rendered by the HoloLens HMD was subjectively found superior to desktop-based volume or 3D rendering with regard to structure detection and appreciation of spatial relationships, planning access paths and manual co-registration of MRI and Ultrasound. By inspecting the virtual trajectory superimposed to rendered structures and MR images, the operator observes collisions of the needle path with vital structures (e.g. urethra) and adjusts accordingly. Holographic AR interfacing with wireless HMD endowed with hands-free gesture and voice control is a promising technology. Studies need to systematically assess the clinical merit of such systems and needed functionalities.
Semantic Screen-Space Occlusion for Multiscale Molecular Visualization
(The Eurographics Association, 2018) Koch, Thomas Bernhard; Kouril, David; Klein, Tobias; Mindek, Peter; Viola, Ivan; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Visual clutter is a major problem in large biological data visualization. It is often addressed through the means of level of detail schemes coupled with an appropriate coloring of the visualized structures. Ambient occlusion and shadows are often used to improve the depth perception. However, when used excessively, these techniques are sources of visual clutter themselves. In this paper we present a new approach to screen-space illumination algorithms suitable for use in illustrative visualization. The illumination effect can be controlled so that desired levels of semantic scene organization cast shadows while other remain flat. This way the illumination design can be parameterized to keep visual clutter, originating from illumination, to a minimum, while also guiding the user in a multiscale model exploration. We achieve this by selectively applying occlusion shading based on the inherent semantics of the visualized hierarchically-organized data. The technique is in principle generally applicable to any hierarchically organized 3D scene and has been demonstrated on an exemplary scene from integrative structural biology.
Ultrasound Decompression for Large Field-of-View Reconstructions
(The Eurographics Association, 2018) Schulte zu Berge, Christian; Salehi, Mehrdad; Bender, Frederik; Wein, Wolfgang; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Tracked medical ultrasound allows for cost-effective and radiation-free imaging of anatomy featuring a very high spatial resolution. To overcome the limitations of the small field-of-view, sonographers can acquire multiple adjacent sweeps and compound them into a single volumetric representation. However, due to the inherent local and non-uniform compression of the underlying anatomy (caused by the ultrasound probe) the adjacent sweeps often exhibit poor alignment and discontinuities. We propose a novel decompression model to compensate for probe pressure related artifacts. It incorporates domain knowledge of the global acquisition pattern for regularization and allows for seamless stitching of multiple overlapping 3D freehand sweeps into one volume. The resulting extended field-of-view visualization provides the clinician with spatial context so that the relationship between individual features are easier to understand. Our experiments show that the resulting extended field-of-view reconstructions have a superior image quality in terms of alignment and continuity of the visible anatomy compared to the original acquisitions. Comparison to ground truth MRI data demonstrates the plausibility of our non-rigid decompression model.
Uncertainty-Guided Semi-Automated Editing of CNN-based Retinal Layer Segmentations in Optical Coherence Tomography
(The Eurographics Association, 2018) Zadeh, Shekoufeh Gorgi; Wintergerst, Maximilian W. M.; Schultz, Thomas; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Convolutional neural networks (CNNs) have enabled dramatic improvements in the accuracy of automated medical image segmentation. Despite this, in many cases, results are still not reliable enough to be trusted ''blindly''. Consequently, a human rater is responsible to check correctness of the final result and needs to be able to correct any segmentation errors that he or she might notice. For a particular use case, segmentation of the retinal pigment epithelium and Bruch's membrane from Optical Coherence Tomography, we develop a system that makes this process more efficient by guiding the rater to segmentations that are most likely to require attention from a human expert, and by developing semi-automated tools for segmentation correction that exploit intermediate representations from the CNN.We demonstrate that our automated ranking of segmentation uncertainty correlates well with a manual assessment of segmentation quality, and with distance to a ground truth segmentation. We also show that, when used together, uncertainty guidance and our semi-automated editing tools decrease the time required for segmentation correction by more than a factor of three.
Visual Analysis of Evolution of EEG Coherence Networks employing Temporal Multidimensional Scaling
(The Eurographics Association, 2018) Ji, Chengtao; Maurits, Natasha M.; Roerdink, Jos B. T. M.; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
The community structure of networks plays an important role in their analysis. It represents a high-level organization of objects within a network. However, in many application domains, the relationship between objects in a network changes over time, resulting in the change of community structure (the partition of a network), their attributes (the composition of a community and the values of relationships between communities), or both. Previous animation or timeline-based representations either visualize the change of attributes of networks or the community structure. There is no single method that can optimally show graphs that change in both structure and attributes. In this paper we propose a method for the case of dynamic EEG coherence networks to assist users in exploring the dynamic changes in both their community structure and their attributes. The method uses an initial timeline representation which was designed to provide an overview of changes in community structure. In addition, we order communities and assign colors to them based on their relationships by adapting the existing Temporal Multidimensional Scaling (TMDS) method. Users can identify evolution patterns of dynamic networks from this visualization.

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