Jorik Blaas

Diffusion tensor imaging in the early phase of schizophrenia: What have we learned?

The dysconnectivity model suggests that disturbed integration of neural communication is central to schizophrenia. The integrity of macro-structural brain circuits can be examined with diffusion tensor imaging (DTI), an MRI application sensitive to microstructural abnormalities of brain white matter. DTI studies in first-episode schizophrenia patients and individuals at high-risk of psychosis can provide insight into the role of structural dysconnectivity in the liability, onset and early course of psychosis. This review discusses (i) views on the role of white matter abnormalities in schizophrenia, (ii) DTI and its application in schizophrenia, (iii) DTI findings in first-episode patients and subjects at high-risk of psychosis; their timing, anatomical location and early course, (iv) the hypothesized underlying pathological substrate and possible causes of DTI white matter alterations, including effects of adolescent cannabis use, and (v) some methodological issues and future recommendations. In summary, there is evidence that DTI abnormalities convey a liability for psychosis and additional abnormalities occur around onset of psychosis. However, findings in first-episode patients are less robust than in chronic patients, and progression of disturbances may occur in the early course of poor-outcome patients. In addition, acceleration of the normal aging process may occur. Adolescent cannabis use has specific effects on DTI measures. An unresolved issue is the underlying pathology of DTI abnormalities, and combining DTI with other MRI indices can provide more insight. More research is needed on which genetic and environmental factors play a role in the variability of current results.

White matter fibertracking in first-episode schizophrenia, schizoaffective patients and subjects at ultra-high risk of psychosis

There is increasing evidence of white matter pathology in schizophrenia. The aim of this study was to examine whether white matter abnormalities found with diffusion tensor imaging (DTI) in previous schizophrenia studies are present in the early phase of the illness. DTI was performed at 3 T on 10 male patients with a first (n = 8) or second (n = 2) psychotic episode of schizophrenia or schizoaffective disorder, 10 male patients at ultra-high risk of psychosis with (pre)psychotic symptoms and 10 healthy controls. Fibertracts found to be abnormal in other DTI studies (uncinate and arcuate fasciculus, anterior and dorsal cingulum, subdivisions of the corpus callosum) were calculated and visualized; tract-specific measurements (fractional anisotropy and trace) were performed. No differences were found between the healthy subjects and the 2 patient groups. These preliminary findings suggest that there is no white matter pathology of these association tracts detectable with DTI in the early stages of schizophrenic illness in males. Our findings are in contrast with DTI abnormalities found in some other first-episode studies. This discrepancy in findings may be related to differences in subject characteristics and DTI methodology. Possible effects of age, gender, level of education and illicit substance use on DTI findings in schizophrenia are discussed.

A mobile instrument for in situ scanning macro-XRF investigation of historical paintings

Scanning macro-X-ray fluorescence analysis (MA-XRF) is rapidly being established as a technique for the investigation of historical paintings. The elemental distribution images acquired by this method allow for the visualization of hidden paint layers and thus provide insight into the artists creative process and the paintings conservation history. Due to the lack of a dedicated, commercially available instrument the application of the technique was limited to a few groups that constructed their own instruments. We present the first commercially available XRF scanner for paintings, consisting of an X-ray tube mounted with a Silicon-Drift (SD) detector on a motorized stage to be moved in front of a painting. The scanner is capable of imaging the distribution of the main constituents of surface and sub-surface paint layers in an area of 80 by 60 square centimeters with dwell times below 10 ms and a lateral resolution below 100 μm. The scanner features for a broad range of elements between Ti (Z = 22) and Mo (Z = 42) a count rate of more than 1000 counts per second (cps)/mass percent and detection limits of 100 ppm for measurements of 1 s duration. Next to a presentation of spectrometric figures of merit, the value of the technique is illustrated through a case study of a painting by Rembrandts student Govert Flinck (1615–1660).

Fast and reproducible fiber bundle selection in DTI visualization

Diffusion tensor imaging (DTI) is an MRI-based technique for quantifying water diffusion in living tissue. In the white matter of the brain, water diffuses more rapidly along the neuronal axons than in the perpendicular direction. By exploiting this phenomenon, DTI can be used to determine trajectories of fiber bundles, or neuronal connections between regions, in the brain. The resulting bundles can be visualized. However, the resulting visualizations can be complex and difficult to interpret. An effective approach is to pre-determine trajectories from a large number of positions throughout the white matter (full brain fiber tracking) and to offer facilities to aid the user in selecting fiber bundles of interest. Two factors are crucial for the use and acceptance of this technique in clinical studies: firstly, the selection of the bundles by brain experts should be interactive, supported by real-time visualization of the trajectories registered with anatomical MRI scans. Secondly, the fiber selections should be reproducible, so that different experts will achieve the same results. In this paper we present a practical technique for the interactive selection of fiber-bundles using multiple convex objects that is an order of magnitude faster than similar techniques published earlier. We also present the results of a clinical study with ten subjects that show that our selection approach is highly reproducible for fractional anisotropy (FA) calculated over the selected fiber bundles.

White matter connectivity and psychosis in ultra-high-risk subjects: A diffusion tensor fiber tracking study

This study assessed with diffusion tensor imaging (DTI) whether ultra-high-risk subjects who later develop a psychotic disorder (UHR-P) show abnormalities in association white matter fiber tracts as compared to UHR subjects who do not convert to psychosis (UHR-NP) and healthy controls. Participants comprised 17 male UHR subjects and 10 male healthy controls, who received baseline DTI scans before clinical follow-up. The uncinate and arcuate fasciculi, anterior and dorsal cingulate, and subdivisions of the corpus callosum were calculated and visualized, and tract-specific measurements were performed. At 24-month follow-up seven UHR subjects had developed a first psychotic episode. Fractional anisotropy in baseline DTI scans, including left-right asymmetry measures, did not differ between the groups. Thus, DTI measures of these association white matter tracts were not biological markers of psychosis in our UHR sample. Abnormalities of these fiber tracts may develop around or after onset of psychosis. However, further DTI studies in UHR subjects are needed in larger samples.

Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration

We propose a visual analytics approach for the exploration and analysis of dynamic networks. We consider snapshots of the network as points in high-dimensional space and project these to two dimensions for visualization and interaction using two juxtaposed views: one for showing a snapshot and one for showing the evolution of the network. With this approach users are enabled to detect stable states, recurring states, outlier topologies, and gain knowledge about the transitions between states and the network evolution in general. The components of our approach are discretization, vectorization and normalization, dimensionality reduction, and visualization and interaction, which are discussed in detail. The effectiveness of the approach is shown by applying it to artificial and real-world dynamic networks.

Smooth Graphs for Visual Exploration of Higher-Order State Transitions

In this paper, we present a new visual way of exploring state sequences in large observational time-series. A key advantage of our method is that it can directly visualize higher-order state transitions. A standard first order state transition is a sequence of two states that are linked by a transition. A higher-order state transition is a sequence of three or more states where the sequence of participating states are linked together by consecutive first order state transitions. Our method extends the current state-graph exploration methods by employing a two dimensional graph, in which higher-order state transitions are visualized as curved lines. All transitions are bundled into thick splines, so that the thickness of an edge represents the frequency of instances. The bundling between two states takes into account the state transitions before and after the transition. This is done in such a way that it forms a continuous representation in which any subsequence of the timeseries is represented by a continuous smooth line. The edge bundles in these graphs can be explored interactively through our incremental selection algorithm. We demonstrate our method with an application in exploring labeled time-series data from a biological survey, where a clustering has assigned a single label to the data at each time-point. In these sequences, a large number of cyclic patterns occur, which in turn are linked to specific activities. We demonstrate how our method helps to find these cycles, and how the interactive selection process helps to find and investigate activities.

Interactive visualization of multi-field medical data using linked physical and feature-space views

Multi-field datasets contain multiple parameters defined over the same spatio-temporal domain. In medicine, such multi-field data is being used more often every day, and there is an urgent need for exploratory visualization approaches that are able to deal effectively with the data-analysis. In this paper, we present a highly interactive, coordinated view-based visualization approach that has been developed especially for dealing with multi-field medical data. It can show any number of views of the physical domain and also of the abstract high-dimensional feature space. The approach has been optimized for interactive use with very large datasets. It is based on intuitive interaction techniques, and integrates analysis techniques from pattern classification to guide the exploration process. We will give some details about the implementation, and we demonstrate the utility of our approach with two real medical use cases.

Dynamic Network Visualization withExtended Massive Sequence Views

Networks are present in many fields such as finance, sociology, and transportation. Often these networks are dynamic: they have a structural as well as a temporal aspect. In addition to relations occurring over time, node information is frequently present such as hierarchical structure or time-series data. We present a technique that extends the Massive Sequence View ( msv) for the analysis of temporal and structural aspects of dynamic networks. Using features in the data as well as Gestalt principles in the visualization such as closure, proximity, and similarity, we developed node reordering strategies for the msv to make these features stand out that optionally take the hierarchical node structure into account. This enables users to find temporal properties such as trends, counter trends, periodicity, temporal shifts, and anomalies in the network as well as structural properties such as communities and stars. We introduce the circular msv that further reduces visual clutter. In addition, the (circular) msv is extended to also convey time-series data associated with the nodes. This enables users to analyze complex correlations between edge occurrence and node attribute changes. We show the effectiveness of the reordering methods on both synthetic and a rich real-world dynamic network data set.

Reordering Massive Sequence Views: Enabling temporal and structural analysis of dynamic networks

Networks are present in many fields such as finance, sociology, and transportation. Often these networks are dynamic: they have a structural as well as a temporal aspect. We present a technique that extends the Massive Sequence View (MSV) for the analysis of the temporal and structural aspects of dynamic networks. Using features in the data as well as in the visualization based on the Gestalt principles closure, proximity, and similarity, we developed node reordering strategies for the MSV to make these features stand out. This enables users to find temporal properties such as trends, counter trends, periodicity, temporal shifts, and anomalies in the network as well as structural properties such as communities and stars. We show the effectiveness of the reordering methods on both synthetic and real-world transaction data sets.