Biomedical subjects

Stefan Thurner

Publications and source records attributed to Stefan Thurner.

10 recordsLinked to original sources

PubMed2006

Self-awareness and the subconscious effect of personal pronouns on word encoding: a magnetoencephalography (MEG) study.

The effect of personal pronouns such as "ein" (German for "a"), "mein" (German for "my") and "sein" (German for "his") on the processing of associated nouns was investigated using MEG. Three different encoding strategies were provided in order to vary the level of consciousness involved in verbal information processing. A shallow (alphabetic), a deep (semantic) and a very deep (contextual) encoding instruction related to visual word presentation were given to all study participants. After the encoding of pronoun-noun pairs, recognition performances of nouns only were tested. The number of correctly recognized nouns previously associated with "sein" was significantly lower than the number of correctly recognized nouns previously associated with "ein" in the shallow encoding condition. The same trend was found for "mein" associated nouns which were also less accurately recognized compared to "ein" associated nouns. Magnetic field distributions recorded during the encoding phases revealed two significant effects, one between about 200 and 400ms after stimulus onset and the other between about 500 and 800ms. The earlier effect was found over occipito-parietal sensors, whereas the later effect occurred over left frontal sensors. Within both time ranges, brain activation varied significantly as a function of associated pronoun independent of depth of word processing. In the respective areas of both time ranges, conditions including personal pronouns ("mein" and "sein") showed higher magnetic field components compared to the control condition of no personal pronouns ("ein"). Evidence is shown that early stage processing is able to distinguish between no personal and personal information, whereas later stage processing is able to distinguish between information related to oneself and to another person (self and non-self). Along with other previous reports our MEG findings support the notion that particular human brain functions involved in processing neurophysiological correlates of self and non-self can be identified.

Adult↗

PubMed2006

Statistically consistent identification of differentially expressed genes in DNA chip data over the whole expression range: relative variance method.

BACKGROUND: It is a well known problem that standard techniques for analysing DNA chip data misspecify genes. In particular, genes that are confirmed to be active, often do not show up as potential candidates. This is possibly due to non-homogeneous distributions of expression levels over the whole expression range. METHODS: We introduce a method that allows the detection of genes based on a self-adaptive threshold. The threshold is determined for equally-populated expression bands by assuming a normal distribution of logarithms of expression level ratios. By specifying a significance level, the threshold is set according to 'local' expression statistics within a band. We call this method the relative variance method (RVM). We derive a test statistic for the RVM and compare it with other methods. On this statistical basis, we show that RVM is a complementary approach to the t-test, significance analysis of microarrays (SAM) or empirical Bayes analysis of microarrays (EBAM). The RVM should be particularly useful for experiments with small sample size. RESULTS: Using a clinical dataset, we demonstrate that the RVM can correctly identify known marker genes, which are not found by the t-test, SAM or EBAM. CONCLUSION: In situations with limited sample material and small number of replicates, as is often the case in clinical datasets, use of the proposed RVM provides a higher reliability of potential candidate genes.

Algorithms↗

PubMed2005

Phase transition in random catalytic networks.

The notion of (auto)catalytic networks has become a cornerstone in understanding the possibility of a sudden dramatic increase of diversity in biological evolution as well as in the evolution of social and economical systems. Here we study catalytic random networks with respect to the final outcome diversity of products. We show that an analytical treatment of this long-standing problem is possible by mapping the problem onto a set of nonlinear recurrence equations. The solution of these equations shows a crucial dependence of the final number of products on the initial number of products and the density of catalytic production rules. For a fixed density of rules we can demonstrate the existence of a phase transition from a practically unpopulated regime to a fully populated and diverse one. The order parameter is the number of final products. We are able to fully understand the origin of this phase transition as a crossover from one set of solutions from a quadratic equation to the other. We observe a remarkable similarity of the solution of the system and the PVT diagrams in standard thermodynamics.

Algorithms↗

PubMed2005

Complex networks emerging from fluctuating random graphs: analytic formula for the hidden variable distribution.

In analogy to superstatistics, which connects Boltzmann-Gibbs statistical mechanics to its generalizations through temperature fluctuations, complex networks are constructed from fluctuating Erdös-Rényi random graphs. Using a quantum-mechanical method, the exact analytic formula for the hidden variable distribution is presented which describes the nature of the fluctuations and generates a generic degree distribution through the Poisson transformation. As an example, a static scale-free network is discussed and the corresponding hidden variable distribution is found to decay as a power law and to diverge at the origin.

Algorithms↗

PubMed2004

Traffic on complex networks: Towards understanding global statistical properties from microscopic density fluctuations.

We study the microscopic time fluctuations of traffic load and the global statistical properties of a dense traffic of particles on scale-free cyclic graphs. For a wide range of driving rates R the traffic is stationary and the load time series exhibits antipersistence due to the regulatory role of the superstructure associated with two hub nodes in the network. We discuss how the superstructure affects the functioning of the network at high traffic density and at the jamming threshold. The degree of correlations systematically decreases with increasing traffic density and eventually disappears when approaching a jamming density R(c). Already before jamming we observe qualitative changes in the global network-load distributions and the particle queuing times. These changes are related to the occurrence of temporary crises in which the network-load increases dramatically, and then slowly falls back to a value characterizing free flow.

Journal Article↗

PubMed2004

Wavelet-based multifractal analysis of fMRI time series.

Functional magnetic resonance imaging (fMRI) time series are investigated with a multifractal method based on the Wavelet Modulus Maxima (WTMM) method to extract local singularity ("fractal") exponents. The spectrum of singularity exponents of each fMRI time series is quantified by spectral characteristics including its maximum and the corresponding dimension. We found that the range of Hölder exponents in voxels with activation is close to 1, whereas exponents are close to 0.5 in white matter voxels without activation. The maximum dimension decreases going from white matter to gray matter, and is lower still for activated time series. The full-width-at-half-maximum of the spectra is higher in activated areas. The proposed method becomes particularly effective when combining these spectral characteristics into a single parameter. Using these multifractal parameters, it is possible to identify activated areas in the human brain in both hybrid and in vivo fMRI data sets without knowledge of the stimulation paradigm applied.

Brain↗

PubMed2004

The lack of focused anticipation of verbal information in stutterers: a magnetoencephalographic study.

The motivation of this work was to investigate stuttering--a disorder of speech motor control--in the light of preparatory neural activity of voluntary movements related to speech. To this end, brain activity was recorded with a whole cortex magnetoencephalograph (MEG) in developmental stutterers and nonstutterers while three different tasks of single-word reading were performed. Visually presented words had to be silently read immediately after word presentation (condition 1), spoken aloud immediately after word presentation (condition 2), or spoken aloud after a delay of 1.3 s as indicated by a second visual stimulus (condition 3). Condition 2 clearly showed marked neurophysiological differences between stutterers and nonstutterers. Only nonstutterers showed clear neural activity before speech onset, which is interpreted as being linked to visual word presentation and to reflect focused verbal anticipation. This prespeech activity might reflect the "Bereitschaftsfeld2" (BF2) that is the later component of the "Bereitschaftsfeld", a well-known preparatory activity described for many other voluntary movements. Our results strongly link the lack of such preparatory brain activity at the single-word level to the disability of fluent speech in stutterers. The present results strongly support the notion that stuttering is related to impaired focused attention or anticipation.

Adolescent↗

PubMed2003

Quantitative measurement of cell migration using time-lapse videomicroscopy and non-linear system analysis.

Epithelial cells of the mammary gland possess the inherent capacity to form epithelial monolayers in vitro. This requires coordination of cell migration, cell-cell contact formation, and cell proliferation. Using time-lapse phase contrast videomicroscopy we have observed mammary gland epithelial cells over different time scales. We show the generation of a complete polarized epithelial monolayer in real-time, starting from a few cells. We subsequently concentrated on the early stages of this process by tracking epithelial cells during phases of polarized migration. We performed migration analysis using fractal measures. With this technology the structure of seemingly random processes not accessible to the usual methods of linear analysis can be measured. As a control and proof of principle approach we applied infection of cells with an adenoviral vector, which is used as a gene targeting vector for many applications. Infection markedly influenced the patterns of migratory behavior. We, therefore, believe that time-lapse videomicroscopy in combination with fractal analysis can contribute to differential characterization of distinct cellular migration patterns. This will be useful in situations of long-term alterations in cell culture systems.

Adenoviridae↗

PubMed2002

Short-term rhythmic proliferation of human breast cancer cell lines: surface effects and fractal growth patterns.

Kinetic studies of cell proliferation rates shed light on the growth dynamics of cancer. Most such studies are based on measurements of cell numbers that were evaluated in time intervals of about 12 h. Studies of the initial tumour growth with short measuring intervals are rare. This study was therefore designed with 1 h measuring intervals over a 24 h period. Human breast cancer cell lines (ZR-75-1, SK-BR-3, MCF-7) and a benign cell line (HBL-100) were used to study the hourly thymidine uptake as a measure of cells in synthesis. In parallel experiments, the same cell lines were also exposed to tumour necrosis factor alpha (TNF-alpha) to explore the effect of an apoptosis-inducing substance on initial tumour growth kinetics. In time-evolution plots, there was an oscillation of the labelling index of thymidine uptake for all investigated cell lines, with and without TNF-alpha. Based on the results obtained, a mathematical model was developed mimicking the real experiment. To describe the system dynamically a cellular automaton model was studied. The growth kinetics revealed by the simulation were in accordance with our experimental data. Two- and three-dimensional growth simulations of this computer model yielded objects morphologically similar to real images of human breast cancer. Almost identical fractal dimensions of the virtual and real tumours further supported this visual similarity. The cellular automata models could, therefore, be seen as a bridge towards realistic in vivo scenarios. From a clinical point of view, the results obtained may be applicable not only to primary tumours, but even to tumour cell microfoci and small metastases, which are a major concern in early metastasizing tumours such as breast cancer.

Breast Neoplasms↗

PubMedDate not supplied

Change of complexity patterns in human posture during aging.

Human posture is a prototypical example of a complex control system. The joint output of several physiological - most likely nonlinearly interacting - processes leads to correctional movements which enable us to stand upright. These correctional body movements reflect some features of the underlying control mechanisms. We analyze the movements of quietly standing persons by means of various types of fractal measures, which are designed to capture 'degrees of complexity'. We observe changes of these fractal measures as a function of age and show that aging goes hand in hand with a decrease of complexity in movement patterns towards more regular movements. We try to explain these results in a stochastic resonance framework. We conjecture that the reduction of posture complexity is linked to deteriorated balance performance and argue that clinical treatment of age-related balance problems should focus on regaining this complexity therapeutically. We line out two possible starting points for actual therapy.

Adult↗