Thomas Kirste

Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM

Alzheimers disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance imaging (MRI) modalities are most useful in a clinical context is as yet unresolved. We examined multimodal MRI data acquired from 28 subjects with clinically probable AD and 25 healthy controls. Specifically, we used fiber tract integrity as measured by diffusion tensor imaging (DTI), GM volume derived from structural MRI, and the graph‐theoretical measures ‘local clustering coefficient’ and ‘shortest path length’ derived from resting‐state functional MRI (rs‐fMRI) to evaluate the utility of the three imaging methods in automated multimodal image diagnostics, to assess their individual performance, and the level of concordance between them. We ran the support vector machine (SVM) algorithm and validated the results using leave‐one‐out cross‐validation. For the single imaging modalities, we obtained an area under the curve (AUC) of 80% for rs‐fMRI, 87% for DTI, and 86% for GM volume. When it came to the multimodal SVM, we obtained an AUC of 82% using all three modalities, and 89% using only DTI measures and GM volume. Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone. Hum Brain Mapp 36:2118–2131, 2015.

Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data.

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.

Interactive applications of personal situation-aware assistants

Abstract Ultraportable mobile computers provide electronic assistance for environments and usage situations, where computer support up to now has not been feasible. For the first time, a true physical and cognitive integration of computer support into the everyday business of the real-world becomes possible, as envisioned in Mark Weisers concept of “ubiquitous computing” (Weiser, Communication of the ICM 1993, 36 (12): 75–85). However, although Handheld-PC, etc. today; already support a good deal of personal information management and basic access to distributed multimedia information services such as the World-Wide Web, they are still surprisingly difficult to use in “full action”. Specifically, lengthy interaction sequences and the inability to find quickly that important piece of information which is embedded somewhere in the machine, makes using those devices sometimes a very disappointing experience. In this, paper, we outline a new approach to realizing an easy-to-use personal digital assistant systems, based on the concept of Situation Awareness. Using knowledge about task structures, situation dependencies, and task contexts, our concept allows a mobile assistant to proactively provide the right information at the right time and the right place, without intruding upon the users primary task: interacting with reality.

Computational state space models for activity and intention recognition. A feasibility study.

Background Computational state space models (CSSMs) enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algorithmic, representations allow the construction of increasingly complex human behaviour models. However, the symbolic models used in CSSMs potentially suffer from combinatorial explosion, rendering inference intractable outside of the limited experimental settings investigated in present research. The objective of this study was to obtain data on the feasibility of CSSM-based inference in domains of realistic complexity. Methods A typical instrumental activity of daily living was used as a trial scenario. As primary sensor modality, wearable inertial measurement units were employed. The results achievable by CSSM methods were evaluated by comparison with those obtained from established training-based methods (hidden Markov models, HMMs) using Wilcoxon signed rank tests. The influence of modeling factors on CSSM performance was analyzed via repeated measures analysis of variance. Results The symbolic domain model was found to have more than states, exceeding the complexity of models considered in previous research by at least three orders of magnitude. Nevertheless, if factors and procedures governing the inference process were suitably chosen, CSSMs outperformed HMMs. Specifically, inference methods used in previous studies (particle filters) were found to perform substantially inferior in comparison to a marginal filtering procedure. Conclusions Our results suggest that the combinatorial explosion caused by rich CSSM models does not inevitably lead to intractable inference or inferior performance. This means that the potential benefits of CSSM models (knowledge-based model construction, model reusability, reduced need for training data) are available without performance penalty. However, our results also show that research on CSSMs needs to consider sufficiently complex domains in order to understand the effects of design decisions such as choice of heuristics or inference procedure on performance.

Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion‐Tensor and Magnetic Resonance Imaging Data

Alzheimers disease (AD) patients show early changes in white matter (WM) structural integrity. We studied the use of diffusion tensor imaging (DTI) in assessing WM alterations in the predementia stage of mild cognitive impairment (MCI).

Ambient interaction by smart watches

The availability of new hardware allows new interaction and visualization metaphors for work environment and private life. Smart watches with integrated acceleration sensors and vibration feedback enable a new kind of interaction. In complement with a matrix display and connectivity to the Internet, smart watches provide assistance in ambient surroundings. This work presents challenges and opportunities of smart watches and introduces a concept of non-obtrusive interaction with smart watches by using physical activity recognition. Hereby the paper presents a prototype application of maintenance assistance and shows possible applications for ambient assistant living environments. In combination of information visualization, permanent sensing and notification, the smart watch becomes the next technology step in the row of smart devices for home-consumer and industrial users.

Information and communication technology solutions for outdoor navigation in dementia

Information and communication technology (ICT) is potentially mature enough to empower outdoor and social activities in dementia. However, actual ICT‐based devices have limited functionality and impact, mainly limited to safety. What is an ideal operational framework to enhance this field to support outdoor and social activities?

Towards an integrated approach for task modeling and human behavior recognition

Mobile and ubiquitous systems require task models for addressing the challenges of adaptivity and situation-aware assistance. Today, both challenges are seen as separate issues in system development, addressed by different modeling concepts. We propose an approach for a unified modeling concept that uses annotated hierarchical task trees for synthesizing models for both areas from a common basic description.

Finding Stops in Error-Prone Trajectories of Moving Objects with Time-Based Clustering

An important problem in the study of moving objects is the identification of stops. This problem becomes more difficult due to error-prone recording devices. We propose a method that discovers stops in a trajectory that contains artifacts, namely movements that did not actually take place but correspond to recording errors. Our method is an interactive density-based clustering algorithm, for which we define density on the basis of both the spatial and the temporal properties of a trajectory. The interactive setting allows the user to tune the algorithm and to study the stability of the anticipated stops.

Towards creating assistive software by employing human behavior models

Assistive software becomes more and more important part of our everyday life. As it is not straightforward to create such a system, the engineering of assistive systems is a topic of current research with different applications in healthcare, education and industry. In this paper we introduce three contributions to this field of research. Whereas most assistive systems use approaches for intention recognition based on training data applicable to specific environments and applications, we introduce a training-free approach. We do that by showing that it is possible to generate probabilistic inference systems from causal models for human behavior. Additionally, we collect a list of requirements for context aware assistive software and human behavior modeling for intention recognition and showed that our system satisfies them. We then introduce a software architecture for assistive systems that provides support for this kind of modeling. In addition to introducing the modeling approach and the architecture we show in an experimental way that our approach is suited for smart environments. The collected list of requirements could help a software engineer create a robust and easily adaptable to changes in the environment assistive software.