Coping with new Challenges in Clustering and Biomedical Imaging

The last years have seen a tremendous increase of data acquisition in different scientific fields such as molecular biology, bioinformatics or biomedicine. Therefore, novel methods are needed for automatic data processing and analysis of this large amount of data. Data mining is the process of applying methods like clustering or classification to large databases in order to uncover hidden patterns. Clustering is the task of partitioning points of a data set into distinct groups in order to minimize the intra cluster similarity and to maximize the inter cluster similarity. In contrast to unsupervised learning like clustering, the classification problem is known as supervised learning that aims at the prediction of group membership of data objects on the basis of rules learned from a training set where the group membership is known. Specialized methods have been proposed for hierarchical and partitioning clustering. However, these methods suffer from several drawbacks. In the first part of this work, new clustering methods are proposed that cope with problems from conventional clustering algorithms. ITCH (Information-Theoretic Cluster Hierarchies) is a hierarchical clustering method that is based on a hierarchical variant of the Minimum Description Length (MDL) principle which finds hierarchies of clusters without requiring input parameters. As ITCH may converge only to a local optimum we propose GACH (Genetic Algorithm for Finding Cluster Hierarchies) that combines the benefits from genetic algorithms with information-theory. In this way the search space is explored more effectively. Furthermore, we propose INTEGRATE a novel clustering method for data with mixed numerical and categorical attributes. Supported by the MDL principle our method integrates the information provided by heterogeneous numerical and categorical attributes and thus naturally balances the influence of both sources of information. A competitive evaluation illustrates that INTEGRATE is more effective than existing clustering methods for mixed type data. Besides clustering methods for single data objects we provide a solution for clustering different data sets that are represented by their skylines. The skyline operator is a well-established database primitive for finding database objects which minimize two or more attributes with an unknown weighting between these attributes. In this thesis, we define a similarity measure, called SkyDist, for comparing skylines of different data sets that can directly be integrated into different data mining tasks such as clustering or classification. The experiments show that SkyDist in combination with different clustering algorithms can give useful insights into many applications. In the second part, we focus on the analysis of high resolution magnetic resonance images (MRI) that are clinically relevant and may allow for an early detection and diagnosis of several diseases. In particular, we propose a framework for the classification of Alzheimer's disease in MR images combining the data mining steps of feature selection, clustering and classification. As a result, a set of highly selective features discriminating patients with Alzheimer and healthy people has been identified. However, the analysis of the high dimensional MR images is extremely time-consuming. Therefore we developed JGrid, a scalable distributed computing solution designed to allow for a large scale analysis of MRI and thus an optimized prediction of diagnosis. In another study we apply efficient algorithms for motif discovery to task-fMRI scans in order to identify patterns in the brain that are characteristic for patients with somatoform pain disorder. We find groups of brain compartments that occur frequently within the brain networks and discriminate well among healthy and diseased people

Efficient Knowledge Extraction from Structured Data

Knowledge extraction from structured data aims for identifying valid, novel, potentially useful, and ultimately understandable patterns in the data. The core step of this process is the application of a data mining algorithm in order to produce an enumeration of particular patterns and relationships in large databases. Clustering is one of the major data mining tasks and aims at grouping the data objects into meaningful classes (clusters) such that the similarity of objects within clusters is maximized, and the similarity of objects from different clusters is minimized. In this thesis, we advance the state-of-the-art data mining algorithms for analyzing structured data types. We describe the development of innovative solutions for hierarchical data mining. The EM-based hierarchical clustering method ITCH (Information-Theoretic Cluster Hierarchies) is designed to propose solid solutions for four different challenges. (1) to guide the hierarchical clustering algorithm to identify only meaningful and valid clusters. (2) to represent each cluster content in the hierarchy by an intuitive description with e.g. a probability density function. (3) to consistently handle outliers. (4) to avoid difficult parameter settings. ITCH is built on a hierarchical variant of the information-theoretic principle of Minimum Description Length (MDL). Interpreting the hierarchical cluster structure as a statistical model of the dataset, it can be used for effective data compression by Huffman coding. Thus, the achievable compression rate induces a natural objective function for clustering, which automatically satisfies all four above mentioned goals. The genetic-based hierarchical clustering algorithm GACH (Genetic Algorithm for finding Cluster Hierarchies) overcomes the problem of getting stuck in a local optimum by a beneficial combination of genetic algorithms, information theory and model-based clustering. Besides hierarchical data mining, we also made contributions to more complex data structures, namely objects that consist of mixed type attributes and skyline objects. The algorithm INTEGRATE performs integrative mining of heterogeneous data, which is one of the major challenges in the next decade, by a unified view on numerical and categorical information in clustering. Once more, supported by the MDL principle, INTEGRATE guarantees the usability on real world data. For skyline objects we developed SkyDist, a similarity measure for comparing different skyline objects, which is therefore a first step towards performing data mining on this kind of data structure. Applied in a recommender system, for example SkyDist can be used for pointing the user to alternative car types, exhibiting a similar price/mileage behavior like in his original query. For mining graph-structured data, we developed different approaches that have the ability to detect patterns in static as well as in dynamic networks. We confirmed the practical feasibility of our novel approaches on large real-world case studies ranging from medical brain data to biological yeast networks. In the second part of this thesis, we focused on boosting the knowledge extraction process. We achieved this objective by an intelligent adoption of Graphics Processing Units (GPUs). The GPUs have evolved from simple devices for the display signal preparation into powerful coprocessors that do not only support typical computer graphics tasks but can also be used for general numeric and symbolic computations. As major advantage, GPUs provide extreme parallelism combined with a high bandwidth in memory transfer at low cost. In this thesis, we propose algorithms for computationally expensive data mining tasks like similarity search and different clustering paradigms which are designed for the highly parallel environment of a GPU, called CUDA-DClust and CUDA-k-means. We define a multi-dimensional index structure which is particularly suited to support similarity queries under the restricted programming model of a GPU. We demonstrate the superiority of our algorithms running on GPU over their conventional counterparts on CPU in terms of efficiency

The Network Paradigm: New Niches for Psychosomatic Medicine

Psychosomatic medicine, as a philosophical frame and practical approach of the diagnostic and therapeutical agency, had been undergone several renewals and reframing in the past. We overview the history of psychosomatics and map its branches. Psychoanalytic and psychodynamic frameworks, the Engelian biopsychosocial concept, the paradigm of behavioral medicine, the clinical psychophysiological research background, the clinical fields of PNI, psychocardiology, biobehavioral oncology, the so-called mind-body medicine, and stress medicine frameworks reflect a converging pluralism. Psychoneuroimmunology offers a comprehensive framework to analyze key issues of psychosomatics in a social neuroscience framework and to demonstrate the significance of the network approach in bridging the gap between psychosomatics and biomedicine. Network medicine creates a shared denominator for analyzing socioeconomic, interpersonal, life event-based narrative factors together with psychophysiological features of the clinical and health psychological problems and promotes convergence of psychosomatics, biomedicine, and lifestyle medicine, too. On the other side, psychosomatic medicine as a particular professional medical specialization is not universal at all. In Europe, one can find such specialization only in Germany, while psychotherapy applied by somatic experts is practiced in wider circles. Finally, we explore the new niches for psychosomatic orientation offered by integrative frameworks like lifestyle medicine and network medicine

Cognitive Behavioral Therapy

Cognitive behavioral therapy (CBT) is a modern type of short-term psychotherapy that integrates cognitive and behavioral theories. The CBT approach is effective in the treatment of a wide range of mental issues and conditions, such as generalized anxiety disorders, general or post-traumatic stress, panic attacks, depression, eating and sleep dysfunctions, obsessive–compulsive disorders, and substance dependence. CBT is also effective as an intervention for psychotic, personality, and bipolar disorders or to approach fatigue and chronic pain conditions especially if associated with distress. This book explains both theoretical and practical aspects of CBT, along with case examples, and contains useful tools and specific interventions for different psychological situations

Evaluating Genetic Analysis and Neuroimaging Tools in Pain Research

This thesis employed volumetric and perfusion-based magnetic resonance imaging techniques in combination with gene expression data and genotyping of a candidate gene from peripheral whole blood samples in acute and chronic pain phenotypes

On psychosomatics and the maps in our hands: Modelling change over twelve months of counselling practice

Purpose The project is a multicase study of the researcher’s own clinical work with four clients who each presented with embodied expressions of distress. The researcher practised within a ‘narrative-dialogical’ framework and set out to build models of therapeutic change. However a parallel thesis emerged during the project: an autoethnographic account in which the researcher’s uses of supervision, personal therapy, dreams and life events, including the death of his father, intersect with formal stages of theory development. Design and Methods Sessions were audio recorded and coded for qualitative markers indicating the emergence of novel self-narratives. At the end of each client’s therapy they received a case report and were invited to provide their own commentaries. Across three ‘mini-studies’, methods from different approaches within the change process research tradition were applied to the data formalising the analytic approach and driving the evolving theoretical model. However a reflexive narrative running throughout the work highlights the superordinate role of reflexivity in theory development. Findings Therapeutic change was typified by an evolving internalised map of self and world, with corresponding change in embodied experience. The theoretical model was observed to develop through four chronological phases: 1) the migration of clients between I-positions, 2) longitudinal stages, 3) cognitive mapping, and 4) dialogism in the therapeutic relationship. In each phase the emergent template was layered on to the previous model, resulting in a new synthesis. Discussion As an analysis of one therapist’s practice and the experiences of their clients, the study generates hypotheses rather than formally establishing theory. The continuous evolution of change concepts reflects the theory building work of clinicians in their everyday practice. The study highlights the use of self as research instrument and offers a rich example of how practitioner research might be structured and delivered

In search for the etiology of the complex regional pain syndrome

The complex regional pain syndrome is poorly elucidated. In line with this its diagnosis and clinical management have remained suboptimal. The multifaceted nature makes it a fascinating study topic for scientists with varying interests, but unraveling the etiology has been proven a laborious mission. The first notification of what could have been (what is currently named) complex regional pain syndrome (CRPS) stems from 1634, when the surgeon Ambroise Pare described that King Charles IX suffered from persistent pain and contractures of his arm following a bloodletting procedure.1 The next remarks came from the military physician Scott Mitchell and date from the American Civil War: “…Long after the trace of the effect of a wound has gone neuralgic symptoms are apt to linger, and too many carry with them throughout long years this final reminder of the battle field...”.2 The first scientific publication on CRPS was issued in 1900 from a German surgeon named Paul Sudeck.3 His name became tied to the syndrome for long (Sudecks’ dystrophy)

The origins of pain in diverticular disease: peripheral or central?

This study was designed to identify the processes which underlie pain in symptomatic diverticular disease (SDD). Our hypothesis was that a spectrum of both peripheral and central pathologies were involved, with those that had a more peripheral problem having abdominal symptoms only while those with multiple symptoms throughout the body, having an altered central pain processing. The first study examining the brain response to cutaneous pain using functional magnetic resonance imaging (fMRI) has supported this hypothesis. Although a statistically significant difference in sensory pain threshold was not demonstrated between the groups, fMRI imaging has shown greater emotional processing during pain and reduced anticipatory inhibitory responses in the high somatising symptomatic diverticular disease (HSDD) groups. However this is not as clear cut as we had anticipated which may be due to subject selection and demonstrate a spectrum of mixed peripheral and central changes as well as those with only peripheral or central components. In the second part we performed a randomized placebo controlled study of mesalazine 3gm versus placebo. Mesalazine significantly reduced expression of many genes associated with inflammation in SDD patients. A reduction in the median number of hours of pain per week was seen. The study was not designed to allow intention to treat analysis but has shown promising results which will need to be consolidated with future large scale studies. Both these studies support a tailored approach to SDD patient treatment based on the underlying pain process which can be both central and peripheral. The Patient health questionnaire 12(PHQ12) may be one simple measure of doing this, but again needs to be confirmed with further larger studies

The origins of pain in diverticular disease: peripheral or central?

This study was designed to identify the processes which underlie pain in symptomatic diverticular disease (SDD). Our hypothesis was that a spectrum of both peripheral and central pathologies were involved, with those that had a more peripheral problem having abdominal symptoms only while those with multiple symptoms throughout the body, having an altered central pain processing. The first study examining the brain response to cutaneous pain using functional magnetic resonance imaging (fMRI) has supported this hypothesis. Although a statistically significant difference in sensory pain threshold was not demonstrated between the groups, fMRI imaging has shown greater emotional processing during pain and reduced anticipatory inhibitory responses in the high somatising symptomatic diverticular disease (HSDD) groups. However this is not as clear cut as we had anticipated which may be due to subject selection and demonstrate a spectrum of mixed peripheral and central changes as well as those with only peripheral or central components. In the second part we performed a randomized placebo controlled study of mesalazine 3gm versus placebo. Mesalazine significantly reduced expression of many genes associated with inflammation in SDD patients. A reduction in the median number of hours of pain per week was seen. The study was not designed to allow intention to treat analysis but has shown promising results which will need to be consolidated with future large scale studies. Both these studies support a tailored approach to SDD patient treatment based on the underlying pain process which can be both central and peripheral. The Patient health questionnaire 12(PHQ12) may be one simple measure of doing this, but again needs to be confirmed with further larger studies