[diss] Perustieteiden korkeakoulu / SCI

Permanent URI for this collection

https://aaltodoc.aalto.fi/handle/123456789/52

Browse

Now showing 1 - 20 of 63

Accurate modelling of tissue properties in diffuse optical imaging of the human brain
(Teknillinen korkeakoulu, 2009) Heiskala, Juha; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos
Diffuse optical imaging (DOI) is an emerging imaging modality for non-invasive functional medical imaging, using near infrared (NIR) or visible red light. The innovation is to derive functional information about living tissue from measurements of light that has passed through it. Optical imaging can be applied to imaging of tissues as diverse as the central nervous system, female breast, muscle, and joints of fingers. This thesis addresses the application of DOI to studying the human brain. In this thesis, the problems of modelling light propagation in the adult and infant human head, and reconstructing three-dimensional images of functional changes in the brain using optical measurements, are addressed. Difference imaging, where changes from baseline optical parameters rather than absolute parameter values are reconstructed, is considered. The goal was to develop methods for accurate modelling of light propagation and to clarify how specific aspects of the computational modelling affect the reconstruction of functional images from optical measurements of the human brain. Specifically, the significance of anisotropic light propagation in the white matter, and a priori knowledge of the anatomy and the optical properties of the head and brain are studied. Moreover, a generic probabilistic atlas model of the infant head to enhance image reconstruction is developed. Significance of anisotropic light propagation was found to be small in optical imaging of the adult brain. Although anisotropic light propagation may have a larger impact on the measured signal when infants are imaged, results suggest that image reconstruction can be performed without taking anisotropy into consideration. The use of a priori anatomical knowledge was found to significantly improve the accuracy and robustness of image reconstruction in difference imaging. The results suggest that for optimal reconstructions, individual MR imaging based anatomical data should be used when possible. For cases where individual anatomical data is not available, atlas models should be developed. An important consideration is how to obtain the baseline optical parameters of tissue classes in the anatomical model. Literature-derived parameters can be used as a starting point. For optimal results however, methods should be developed for estimating the baseline parameters from measured data.
Advances in Approximate Bayesian Inference for Gaussian Process Models
(Aalto University, 2013) Riihimäki, Jaakko; Vehtari, Aki, Dr., Aalto University, Finland; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Perustieteiden korkeakoulu; School of Science; Lampinen, Jouko, Prof., Aalto University, Finland
Gaussian processes (GPs) provide a flexible approach to construct probabilistic models for Bayesian data analysis. In the Bayesian approach, GPs are used to specify prior assumptions on the latent function values that describe the underlying relationships between the explanatory variables and the associated target variables. These prior assumptions are combined with information from the observations using Bayes' rule. The obtained result is the posterior distribution that represents the uncertainty about the latent function values of interest, conditioned on the observations and the model assumptions. A challenge with the Bayesian approach is that exact inference is analytically intractable to calculate for most GP models used in practice. Therefore, approximate methods are needed in order to evaluate the posterior distribution and to make predictions for new observations. This thesis develops methods for approximate Bayesian inference in various modelling problems involving GP models. The focus is on efficient ways to form Gaussian posterior approximations based on Laplace's method or expectation propagation (EP). The inference for the studied GP models is challenging in two aspects. Firstly, observation models are generalized in the way that the probability distribution for each observation can depend on multiple values of the latent function instead of only one value, or on the derivative values of the latent function. Secondly, instead of one prior process, the models can have multiple uncorrelated prior processes that are coupled through the observation model. This thesis presents improvements to approximate Bayesian inference for GP models in density estimation, survival analysis, and multiclass classification. We describe Laplace's method for a logistic GP model and for a Cox-type survival model constructed from GP priors to speed up the inference. We develop a novel nested EP algorithm for multinomial probit GP classification that does not require numerical quadratures and scales linearly in the number of classes. In addition, we extend the existing methodology proposed for regression and binary classification by introducing monotonicity information into a GP model with EP. We demonstrate the practical accuracy of the described methods with several experiments and apply them to real-life modelling problems.
Approximate Bayesian Inference Methods for Regression and Classification with Gaussian Processes and Neural Networks
(Aalto University, 2013) Jylänki, Pasi; Vehtari, Aki, Dr., Aalto University, Department of Biomedical Engineering and Computational Science, Finland; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Bayesian Methodology; Perustieteiden korkeakoulu; School of Science; Lampinen, Jouko, Prof., Aalto University, Department of Biomedical Engineering and Computational Science, Finland
During the recent decades much research has been done on a very general approximate Bayesian inference framework known as expectation propagation (EP), which has been found to be a fast and very accurate method in many experimental comparisons. A challenge with the practical application of EP is that a numerically robust and computationally efficient implementation is not straightforward with many model specifications, and that there is no guarantee for the convergence of the standard EP algorithm. This thesis considers robust and efficient application of EP using Gaussian approximating families in three challenging inference problems. In addition, various experimental results are presented to compare the accuracy of EP with several alternative methods for approximate Bayesian inference. The first inference problem considers Gaussian process (GP) regression with the Student-t observation model, where standard EP may run into convergence problems, because the posterior distribution may contain multiple modes. This thesis illustrates the situations where standard EP fails to converge, reviews different modifications and alternative algorithms for improving the convergence, and presents a robust EP implementation that relies primarily on parallel EP updates and uses a provably convergent double-loop algorithm with adaptively selected step size in difficult cases. The second inference problem considers multi-class GP classification with the multinomial probit model, where a straightforward EP implementation requires either multi-dimensional numerical integrations or a factored posterior approximation for the latent values related to the different classes. This thesis describes a novel nested EP approach that does not require numerical integrations and approximates accurately all between-class posterior dependencies of the latent values, but still scales linearly in the number of classes. The third inference problem considers nonlinear regression using two-layer neural networks (NNs) with sparsity-promoting hierarchical priors on the inputs, where the challenge is to construct sufficiently accurate and computationally efficient approximations for the likelihood terms that depend in a non-linear manner on the network weights. This thesis describes a novel computationally efficient EP approach for simultaneous approximate integration over the posterior distribution of the weights, the hierarchical scale parameters of the priors, and the residual scale. The approach enables flexible definition of weight priors with different sparseness properties, and it can be extended beyond standard activation functions and NN model structures to form flexible nonlinear predictors from multiple sparse linear models.
Bayesian spatial and temporal epidemiology of non-communicable diseases and mortality
(Aalto University, 2011) Havulinna, Aki S; Vehtari, Aki, Dr.; Salomaa, Veikko, Prof., National Institute for Health and Welfare, Finland; Karvonen, Marjatta, Dr., National Public Health Institute, Finland; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Perustieteiden korkeakoulu; Lampinen, Jouko, Prof.
Spatial epidemiology combines spatial statistical modelling and disease epidemiology for studying geographic variation in mortality and morbidity. The effects of putative risk factors may be examined using ecological regression models. On the other hand, age-period-cohort models can be used to study the variation of mortality and morbidity through time. Bayesian hierarchical statistical models offer a flexible framework for these studies and enable the estimation of uncertainties in the results. The models are usually estimated using computer-intensive Markov chain Monte Carlo simulations. In this dissertation the first four publications present practical epidemiological studies on geographic variation in non-communicable diseases in Finland. In the last publication we study the long-time variation in all-cause mortality in several European countries. New statistical models are developed for these studies. This work provides new epidemiological information on the geographic variation of acute myocardial infarctions (AMI), ischaemic stroke and parkinsonism in Finland. An extended model for studying shared and disease specific geographic variation is presented using data on AMI and ischaemic stroke incidence. Existing results on the inverse association of water hardness and AMI are refined. New models for interpolation of geochemical data with non-detected values are presented with case studies using real data. Finally, the Bayesian age-period-cohort model is extended with versatile interactions and better prediction ability. The model is then used to study long-term variation in mortality in Europe.
Bioinformatics approaches for the analysis of lipidomics data
(VTT, 2010) Yetukuri, Laxmana Rao; Oresic, Matej, Research Prof., VTT; Department of Biomedical Engineering and Computational Science; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Aalto-yliopiston teknillinen korkeakoulu; Informaatio- ja luonnontieteiden tiedekunta; Kaski, Kimmo, Prof.
The potential impact of lipid research has been increasingly realised both in disease treatment and prevention. Recent advances in soft ionization mass spectrometry (MS) such as electrospray ionization (ESI) have permitted parallel monitoring of several hundreds of lipids in a single experiment and thus facilitated lipidomics level studies. These advances, however, pose a greater challenge for bioinformaticians to handle massive amounts of information-rich MS data from modern analytical instruments in order to understand complex functions of lipids. The main aims of this thesis were to 1) develop bioinformatics approaches for lipid identification based on ultra performance liquid chromatography coupled to mass spectrometry (UPLC/MS) data, 2) predict the functional annotations for unidentified lipids, 3) understand the omics data in the context of pathways and 4) apply existing chemometric methods for exploratory data analysis as well as biomarker discovery. A bioinformatics strategy for the construction of lipid database for major classes of lipids is presented using simplified molecular input line entry system (SMILES) approach. The database was annotated with relevant information such as lipid names including short names, SMILES information, scores, molecular weight, monoisotopic mass, and isotope distribution. The database was tailored for UPLC/MS experiments by incorporating the information such as retention time range, adduct information and main fragments to screen for the potential lipids. This database information facilitated building experimental tandem mass spectrometry libraries for different biological tissues. Non-targeted metabolomics screening is often get plagued by the presence of unknown peaks and thus present an additional challenge for data interpretation. Multiple supervised classification methods were employed and compared for the functional prediction of class labels for unidentified lipids to facilitate exploratory analysis further as well as ease the identification process. As lipidomics goes beyond complete characterization of lipids, new strategies were developed to understand lipids in the context of pathways and thereby providing insights for the phenotype characterization. Chemometric methods such as principal component analysis (PCA) and partial least squares and discriminant analysis (PLS/DA) were utilised for exploratory analysis as well as biomarker discovery in the context of different disease phenotypes.
Boundary element method in spatial characterization of the electrocardiogram
(Teknillinen korkeakoulu, 2008) Stenroos, Matti; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos
The electrochemical activity of the heart gives rise to an electric field. In electrocardiography, cardiac electrical activity is assessed by analyzing the potential distribution of this field on the body surface. The potential distribution, or the set of measured surface-voltage signals, is called the electrocardiogram (ECG). Spatial properties of the ECG can be captured with body surface potential mapping (BSPM), in which the electrocardiogram is measured using dozens of electrodes. In this Thesis, methods for solving the forward and inverse problems of electrocardiography are developed and applied to characterization of acute myocardial ischemia. The methodology is based on numerical computation of quasi-static electric fields in a volume conductor model. An open-source Matlab toolbox for solving volume conductor problems with the boundary element method (BEM) is presented. The Galerkin BEM and analytical operator-integrals are, for the first time, applied to the epicardial potential problem; the formulation for a piece-wise homogeneous volume conductor is presented in detail, enabling straightforward inclusion of the lungs or other inhomogeneities in the thorax model. The results show that errors due to discretization and forward-computation are smaller with the linear Galerkin (LG) method than with the conventional methods. These benefits do, however, not reflect to the Tikhonov-regularized inverse solution. If the lungs are omitted, as commonly is done, the choice of the computational method is not significant. In a set of 22 patients measured with BSPM during coronary angioplasty (PTCA), the application of a BEM thorax model with dipolar equivalent sources enabled accurate discrimination between occluded coronary arteries: the correct classification was obtained in 21 patients using the BSPM and in 20 patients using a 5-electrode set suggested elsewhere. The ischemic regions could also be localized anatomically correctly with simplified epicardial potential imaging, even though patient-specific thorax models were not used. In another set, comprising 79 acute ischemic patients and 84 controls, dipole-markers performed well in detection and quantification of acute ischemia. These results show that the modeling-approach can provide valuable information also without patient-specific models and complicated protocols.
Brain Mechanisms Underlying Perception of Naturalistic Social Events
(Aalto University, 2014) Lahnakoski, Juha; Nummenmaa, Lauri, Assistant Prof., Aalto University, Department of Biomedical Engineering and Computational Science, Finland; Jääskeläinen, Iiro, Adjunct Prof., Aalto University, Department of Biomedical Engineering and Computational Science, Finland; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Brain and Mind Laboratory; Perustieteiden korkeakoulu; School of Science; Sams, Mikko, Prof., Aalto University, Department of Biomedical Engineering and Computational Science, Finland
Understanding the brain basis of the wide variety of skills needed to seamlessly interact with other people in the social world is one of the most important goals of social cognitive neuroscience. However, it has remained unclear how the elementary processes of social interaction that have been studied so far generalize to complex naturalistic settings where multiple social cues have to be dynamically tracked at the same time. The studies presented here employ movies to depict real-life-like social interactions and map the brain systems that participate in the perception of different aspects of the stimuli, particularly focusing on their social signals. Brain activity during viewing of the movies was recorded with functional magnetic resonance imaging. Additionally, the eye gaze of the subjects was recorded and behavioral measures were acquired in a subset of the experiments. Subjects' viewing patterns and interpretation of the movie were manipulated in one of the studies by asking the subjects to adopt two different perspectives. The brain activity was analyzed by multiple methods: parametric models of stimulus contents, inter-subject correlations of brain activity, independent component analysis, and network analysis. First study compared methods for analyzing both stimulus-related activity and covariation of networks of brain regions during naturalistic conditions to find networks responding to speech, sound, motion categories and low-level visual information. Second study elucidated the organization of the brain regions participating in processing several types of social contents. The results highlight the role of the posterior superior temporal sulcus as a key structure potentially integrating multiple types of social information. Finally, the third study provides first direct experimental evidence for the hypothesis that shared brain activity across individuals reflects shared understanding of the external world. Consistency of the findings demonstrate the feasibility of studying brain responses to simple stimulus features, social movie content as well as high-level perspective taking tasks during very rich naturalistic audiovisual stimulus conditions.
Combined ultra-low-field MRI and MEG: instrumentation and applications
(Aalto University, 2013) Vesanen, Panu; Ilmoniemi, Risto, Academy Prof., Aalto University, Finland; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Perustieteiden korkeakoulu; School of Science; Ilmoniemi, Risto, Academy Prof., Aalto University, Finland
Magnetic resonance imaging (MRI) is a noninvasive method that allows the study of the interior structure of matter. Today, MRI is widely used in medical diagnosis and research, thanks to its versatile contrast and the lack of ionizing radiation. Conventionally, the signal-to-noise ratio of an MRI measurement scales with the strength of the applied magnetic field. This has driven the development of MRI scanners towards fields of 3 T and above. Ultra-low-field (ULF) MRI is an emerging technology that uses microtesla-range magnetic fields for image formation. The low signal-to-noise ratio is partly compensated for by prepolarizing the sample in a field of 1 – 200 mT and using superconducting quantum interference devices (SQUIDs) for signal detection. Advantages of ULF MRI include unique low-field contrast mechanisms, flexibility in the sequence design, and the possibility to construct a silent scanner with an open geometry. ULF MRI is also compatible with magnetoencephalography (MEG), which uses SQUIDs to record the magnetic field produced by neuronal activity. With a hybrid scanner combining MEG and MRI, both the structure and function of the human brain can be studied with a single device. In this Thesis, a hybrid MEG-MRI device was designed, constructed, and tested. The system is based on a commercial whole-head MEG device that was modified to accommodate ULF-MRI functionality. In particular, the effects of the various magnetic fields applied inside a magnetically shielded room were studied. To prevent the harmful effects of the eddy currents caused by changing magnetic fields, a self-shielded polarizing coil was designed and constructed. Moreover, the conventional SQUID design was modified in order to develop sensor modules that tolerate the relatively strong polarizing field. Finally, the device was used to measure MEG data and ULF-MR images of the human brain. In addition to the instrumentation development, several applications of ULF MRI were investigated. A method for imaging electric current density was presented. The technique takes advantage of the flexibility of ULF MRI by encoding the signal in zero magnetic field. Furthermore, the temperature dependence of the MRI relaxation times was studied. Drastic variations were found as a function of the field strength. The results were used to reconstruct temperature maps using ULF MRI. The results presented in this Thesis demonstrate that upgrading MRI functionality into an existing commercial MEG device is a feasible concept. Such a device has the potential to enable new methods and paradigms for neuroscientific research. The possibility of taking advantage of the unique low-field contrast is an interesting subject for further research.
Community structures in complex networks : detection and modeling
(Teknillinen korkeakoulu, 2008) Kumpula, Jussi; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos
Complex systems are composed of a large number of interacting elements such that the system as a whole exhibits emergent properties not obvious from the properties of its individual parts. In the network approach, complex systems are represented as networks whose vertices and edges correspond to the elements and their interactions, respectively. Many networks, such as networks of protein interactions or social relationships, contain sets of densely interconnected nodes, communities, which play a concrete functional role in the original system, such as the group of proteins related to cancer metastasis. Detecting such communities in large networks has rapidly become one of the focal topics in the science of complex networks. The challenge in community detection is to define what constitutes a community in such a way that this definition not only yields meaningful communities but also allows for sufficiently fast algorithmic implementation to find them. This thesis contributes to our understanding of community detection in complex networks in three ways. 1) The limitations of global optimization based community detection methods are analyzed. Here, the focus is on the dependence of the lower size limit of detectable communities on the tuning parameters of the methods. 2) This thesis significantly improves two community detection methods by extending their applicability domain: the Potts method is extended such that it can be applied to dense weighted networks, and a new algorithmic implementation for the k-clique percolation method is presented. The main advantage of the first method is that it allows analysis of dense weighted networks without discarding any of the link weights, whereas the advantage of the second method is its speed especially in the community analysis of weighted networks. 3) This thesis attempts to shed light on the formation of communities in networks. This is done by introducing a weighted model for social networks, whose mechanisms are based on empirical observations of social tie formation as well as observations on the topological role of tie strengths. In this model, communities emerge only if nodes sufficiently favor their strong connections in the process of establishing new ones. The model is also utilized in studies of the effects of correlations of link weights and community structure on dynamics taking place on networks. Simulations of an opinion formation model show that the dynamics is significantly slowed down due to trapping of opinions in homogenized regions corresponding to communities.
Complex networks and spectral methods : an econophysics approach to equity markets
(Teknillinen korkeakoulu, 2009) Heimo, Tapio; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos
In analysing the top-level structure of financial markets rough simplifications are often needed due to their high degree of complexity. The framework of complex networks provides a modern way to do this effectively. In this approach, a market is seen as a network, such that the nodes of the network correspond to market participants and the links reflect the dynamic interactions between them. This thesis contributes to the analysis of equity returns from the network point of view. By using empirical data on stock exchanges, we show how the general problem of extracting information from correlated time series can be formulated as a network problem. Then, we apply and develop network methods in order to analyse this problem. The purpose of the research is two folded. On the one hand, the goal is to extract new information on financial markets. On the other hand financial markets are seen as a data-rich test-bed for developing new methods and theories of more general interest. In addition to network methods, correlations between equity returns are analysed on the basis of the spectral properties of the correlation matrix. We show how these two approaches can be combined to study the top-level structure of equity markets, and believe that the results are of very general interest as well. Most emphasis is given to the analysis of the information content of correlation matrices of equity returns.
Computational analysis of large and time-dependent social networks
(Aalto University, 2013) Kovanen, Lauri; Saramäki, Jari, Associate Prof., Aalto University, Finland; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Perustieteiden korkeakoulu; School of Science; Saramäki, Jari, Associate Prof., Aalto University, Finland
Complex systems consist of a large number of elements that interact in a non-trivial way; for example the human brain, society, Internet, and biological organisms can all be modelled as complex systems. Complex systems can be naturally represented as networks, mathematical objects that consist of nodes and edges connecting these nodes, and the study of large networks based on empirical data has become known as complex networks. Since the first articles on complex networks appeared in the end of the 1990's, various technological, biological, and social networks have been analyzed. In recent years introductory text books on the subject have also been published. The study of social networks of course has a longer history. Small social networks have been studied for decades in sociology, social psychology and anthropology, and the influence that social networks have on both performance and well being of individuals has been well documented. The availability of electronic communication records—mobile phone calls, emails, online social networking sites and even multiplayer computer games—have changed the scale and detail at which social networks can be analyzed. The largest data set studied so far includes over 700 million individuals, and the mobile phone call records studied in this Thesis contain information of over 6 million people. The combination of powerful computers and large data sets have enabled the emergence of computational social science. Several aspects of large social networks are studied in this Thesis. Models of social networks are commonly used as a way to gain insight about the structure of these networks. The first article studies a number of models suggested for social networks and discusses their advantages and shortcomings. The community structure of various networks has also been a subject of great interest. It is widely accepted that nearly all networks have modular structure, evidenced by local densifications of connectivity. However, identifying communities in empirical data has turned out to be difficult both theoretically and in practice. We apply three state-of-art community detections methods to a large social network and evaluate the quality of the identified communities. One important aspect of human interactions is omitted when analyzing networks: time. Temporal networks have become a common framework for studying data sets where the relations between nodes vary with time, and this framework can be readily applied to study mobile phone calls. The last part of this Thesis introduces the concept of temporal motifs—recurring patterns of events in temporal networks—that can be used to analyze the meso-scale structure of temporal networks.
Computational analysis of the metabolic phenotypes in type 1 diabetes and their associations with mortality and diabetic complications
(Aalto-yliopiston teknillinen korkeakoulu, 2010) Mäkinen, Ville-Petteri; Groop, Per-Henrik, Prof.; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Aalto-yliopiston teknillinen korkeakoulu; Kaski, Kimmo, Prof.
Type 1 diabetes is an autoimmune disease that destroys the secretion of insulin (in the pancreas); insulin is a vital hormone for maintaining normal glucose metabolism. Insulin replacement therapy can prevent the acute symptoms, but is not able to fully match the natural regulation, which puts a metabolic stress on tissues. For some patients, the stress manifests as gradual damage to blood vessels and the nervous system over the next few decades after diabetes diagnosis. The aim of the thesis was to describe the metabolic profiles and to investigate their connections with the spectrum of clinical symptoms. Simultaneously, new techniques were applied to measure the profiles (1H NMR spectroscopy) and to visualize the multivariate statistical associations (the self-organizing map). A total of 4,197 patients with type 1 diabetes were recruited for the thesis by the Finnish Diabetic Nephropathy Study. A quarter of the patients exhibited an obesity-related phenotype (high triglycerides, cholesterol, apolipoprotein B-100, low high-density lipoprotein cholesterol, high C-reactive protein). A third of the individuals had a diabetic kidney disease phenotype (high urinary albumin and serum creatinine). The combination of the two was associated with a 10-fold population-adjusted mortality. Nevertheless, there was no discernible metabolic threshold between the phenotype models, nor were there any single variable that could predict the outcomes accurately. These results suggest a need for multifactorial and multidisciplinary paradigms for the research, treatment and prevention of diabetic complications.
Computational models and methods for lipoprotein research
(Aalto University, 2011) Kumpula, Linda; Ala-Korpela, Mika, Prof., Universtity of Oulu; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Perustieteiden korkeakoulu; Kaski, Kimmo, Prof.
Lipoproteins are self-assembled nanoparticles for water-insoluble lipid transportation in the circulation. Lipoprotein particles form a key metabolic system in a variety of normal physiological processes but also play an essential role in many pathological conditions. In particular, certain lipoprotein abnormalities are associated with the development of atherosclerosis, a disease state of arteries, common in cardiovascular disease. Computational modelling is a potential but so far rarely used method to study lipoprotein particles. This thesis contributes to lipoprotein research by various computational approaches where experimentally isolated and biochemically characterised lipoprotein particles serve as a starting point. This thesis deals with estimating the number of lipid molecules within lipoprotein particles, i.e., composition information, and approximating the molecular structure of lipoprotein particles in each subclass. It also proceed the ultracentrifugal particle isolation by a kind of in silico sub-classification resulting from utilisation of the self-organising map (SOM) method. This, when applied to experimental data, with lipoprotein lipid concentration and composition information combined, shows that there is variability in the compositional/metabolic relations between individuals, i.e., distinct lipoprotein phenotypes. Furthermore, this thesis introduces a method to estimate lipoprotein particle concentrations in each subclass, which also provides a reference particle library for NMR-based lipoprotein particle concentration estimation. Applications of the models to experimental data show that triglyceride and cholesterol ester molecules, which are conventionally held as core lipids, may also locate in significant amounts in the surface. The lipoprotein phenotype analysis shows that per particle compositions, which appear as a fundamental issue in metabolic and clinical corollaries, can not be deduced solely from the regularly measured plasma lipid concentrations nor from the particle concentration estimates.
Control of Protein Oligomerization and De-oligomerization on Lipid Membranes
(Aalto University, 2013) Mahalka, Ajay; Kinnunen, Paavo K. J., Prof., Aalto University, Department of Biomedical Engineering and Computational Science, Finland; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Helsinki Biophysics and Biomembrane Group (HBBG); Perustieteiden korkeakoulu; School of Science; Kinnunen, Paavo K. J., Prof., Aalto University, Department of Biomedical Engineering and Computational Science, Finland
Oligomerization of protein into amyloid fibrils is central to the pathogenesis of several neurodegenerative disorders. Amyloid fibrillation and the cytotoxic actions of amyloids are membrane-associated processes. The interactions of amyloid-forming proteins with lipids at the membrane surface accelerate fibrillation and induce membrane permeabilization. Oligomerization also plays a functional role in antimicrobial defense and controls the catalytic activity of phospholipase A2 (PLA2). The protein oligomerization and amyloid formation can be modulated by heat shock protein 70 (Hsp70). Thus, the aim of the present work was to study membrane-associated protein oligomerization and its modulation by Hsp70 on the phospholipid model membrane system. Sequence analyses revealed that antimicrobial peptides (AMPs) contained sequence motifs that showed propensities for self-assembly, aggregation, and oligomerization into amyloid fibrils. The presence of such oligomerization-mediating sequences was characteristic of amyloidogenic cytotoxic proteins, including gelsolin involved in familial Finnish type amyloidosis (FAF). 1-Palmitoyl-2-(9'-oxo-nonanoyl)-sn-glycero-3-phosphocholine (PoxnoPC), an oxidized phospholipid, accelerated fibrillation of the core amyloidogenic segment of gelsolin.The PoxnoPC-mediated fibrillation of gelsolin was dependent on both the concentration and the aggregation state of PoxnoPC. Fibril growth followed simple nucleation-dependent kinetics with the formation of transient prefibrillar oligomers in the lag phase. Subsequently, in order to understand the functional role of membrane-associated Hsp70, we studied lipid-Hsp70 interactions. The association of Hsp70 with phospholipid membranes was highly dependent on their lipid compositions. Hsp70 associated with phosphatidylcholine bilayers and penetrated into the hydrocarbon region. In contrast to the above data, in the presence of negatively charged phospholipids, Hsp70 bound peripherally to membrane surfacesby extended phospholipid anchorage. A specific pH-dependent association of Hsp70 with bis(monoacylglycero)phosphate, an acidic phospholipid enriched in the inner lysosomal membrane, activated lysosomal acid sphingomyelinase and promoted cell survival. We also showed that the Hsp70 sustained the hydrolytic activity of PLA2 by modulating the oligomerization and transformation of PLA2 into amyloid fibers. Hsp70 attenuated the lysophosphatidylcholine-induced inhibition and amyloid formation of PLA2 in an ATP-dependent manner. Finally, an oligomerization-mediating sequence in PLA2 was identified. Synthetic peptides corresponding to amyloidogenic, aggregation-promoting regions inhibited the hydrolytic activity of PLA2.
Data-analysis perspectives on naturalistic stimulation in functional magnetic resonance imaging
(Aalto-yliopiston teknillinen korkeakoulu, 2010) Malinen, Sanna; Hari, Riitta, Academy Prof.; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Aalto-yliopiston teknillinen korkeakoulu; Ilmoniemi, Risto, Prof.
Modern brain imaging allows to study human brain function during naturalistic stimulus conditions, which entail specific challenges for the analysis of the brain signals. The conventional analysis of data obtained by functional magnetic resonance imaging (fMRI) is based on user-specified models of the temporal behavior of the signals (general linear model, GLM). Alongside these approaches, data-based methods can be applied to model the signal behavior either on the basis of the measured data, as in seed-point correlations or inter-subject correlations (ISC), or alternatively the temporal behavior is not modeled, but spatial signal sources and related time courses are estimated directly from the measured data (independent component analysis, ICA). In this Thesis, fMRI data-analysis methods were studied and compared in experiments that gradually proceeded towards more naturalistic and complex stimuli. ICA showed superior performance compared with GLM-based method in the analysis of naturalistic situations. The particular strengths of the ICA were its capability to reveal activations when signal behavior deviated from an expected model, and to show similarities between signals of different brain areas and of different individuals. The practical difficulty of ICA in naturalistic conditions is that the user may not be able to determine, purely on the basis of the components' spatial distribution or temporal behavior, the brain networks that are related to the given stimuli. In this Thesis, a new solution to sort the components was proposed that ordered the components according to the ISC map, and thereby facilitated the selection of stimulus-related components. The method prioritized brain areas closely related to sensory processing, but it also revealed circuitries of intrinsic processing if they were affected similarly across individuals by external stimulation. Analysis issues related to the impact of physiological noise in fMRI signals were also considered. Cardiac-triggered fMRI improved detection of touch-related activation both in the thalamus and in the secondary somatosensory cortex. The most common way to eliminate noise is to filter the data. In this Thesis, however, aberrations in temporal behavior, as well as in functional connectivities in chronic pain patients were observed, which likely could not have been revealed with conventional temporal filtering.
Developing neurophysiological metrics for the assessment of mental workload and the functional state of the brain
(Aalto-yliopiston teknillinen korkeakoulu, 2010) Holm, Anu; Muller, Kiti, Dr.; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Aalto-yliopiston teknillinen korkeakoulu; Ilmoniemi, Risto, Prof.
Modern working environments are often information intensive and work performance requires acting on multiple tasks simultaneously, i.e., multitasking. Also, irregular and prolonged work schedules, shift work and night work are typical in many work sectors. This causes both acute and chronic sleep loss, which results in performance impairment, such as increased reaction times, memory difficulties, cognitive slowing, and lapses of attention. Long lasting sleep loss and sustained overloading increase the risk of human errors and may cause work related stress and even occupational burn-out. According to the Finnish Occupational Safety and Health Act (738/2002, Työturvallisuuslaki), Section 25 Avoiding and reducing workloads, an employer should assess the workload the employee is exposed to. Despite the fact that this important issue is enacted in the law, the objective measures to assess the workload are lacking. This Thesis reviews neurophysiologic methods for assessment of cognitive workload and sleep loss. Then it describes experimental studies where the feasibility of conventional event related potential (ERP) and electroencephalography (EEG) methods were tested both in assessment of internal state of participants during challenging task performance after sleep debt and in diagnostic of work-related central nervous system disorder. After that, methodological improvements both on ERPs and EEG metrics are shown: ERPs were analysed with a single-trial method, and EEG methodology was developed for estimation of both internal (caused by sleep loss) and external (caused by task demands) load. The methods were tested in healthy controls. The most promising metric to study overall brain load, including both cognitive workload as well as sleep loss, is suggested to be theta Fz / alpha Pz -ratio. It increases both with growing cognitive workload level and time spent awake, being sensitive also to sleep loss. This metric is possible to measure both in laboratory and in the field conditions. Measurements may be carried out even during real work tasks, at least in professions where most work is done in office-like environments. As the ratio increases with cognitive brain load similar to the heartbeat with increasing physical load, the ratio was named "brainbeat".
Developmental biology of sex determination : establishing a basis for systems approach
(Helsinki University of Technology, 2008-03-14) Segerståhl, Margareta; Department of Biomedical Engineering and Computational Science; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Faculty of Information and Natural Sciences; Informaatio- ja luonnontieteiden tiedekunta
The existence of males and females is a fundamental aspect of animal biology but the developmental mechanisms of sex determination and sexual differentiation exhibit surprising evolutionary diversity. Depending on the species, sex determination mechanisms range from genetic sex chromosome effects to environmental temperature effects and even to social cues that derive from local population structure. Many genes and molecules that play a role in sex determination biology have been identified, yet it has often remained unclear how these factors work together. In this thesis, developmental biology of sex determination is approached as a complex dynamic system. The scope is widened from animal biology to an examination of a general biological phenomenon, complexity of which is addressed directly. The method of investigation is a theoretical one relying mostly on heuristic problem solving methods and system identification procedures. Posing of novel questions enables formulation of alternative hypothesis about some fundamental aspects of sex determination biology. For instance, according to current view, two distinctly different developmental programs form ovaries and testes. Here, an integrating system description is provided where both testicular and ovarian development are derivatives of a common developmental scheme. Also, in this thesis sexual germ line development receives a much more prominent role than what is usually the case in sex determination studies. A premis is formed that the foundation of system properties of sex determination biology can be found in the developmental life cycle progression of the germ cell line. This enables formulation of testable prediction about the nature of the relationship between germ cell biology and developmental mechanisms of complex multicellularity. This work shows that a dynamical systems view can be applied to developmental biology and sex determination studies. Conceptual tools are provided for the attainment of a better understanding of genetic and molecular aspects of sex determination biology and sexually dimorphic development.
Dynamic correlations in ongoing neuronal oscillations in humans - perspectives on brain function and its disorders
(Aalto-yliopiston teknillinen korkeakoulu, 2010) Monto, Simo; Voipio, Juha, Prof., University of Helsinki; Palva, J. Matias, Dr., University of Helsinki; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Aalto-yliopiston teknillinen korkeakoulu; Ilmoniemi, Risto, Prof.
This Thesis is involved with neuronal oscillations in the human brain and their coordination across time, space and frequency. The aim of the Thesis was to quantify correlations in neuronal oscillations over these dimensions, and to elucidate their significance in cognitive processing and brain disorders. Magnetoencephalographic (MEG) recordings of major depression patients revealed that long-range temporal correlations (LRTC) were decreased, compared to control subjects, in the 5 Hz oscillations in a manner that was dependent on the degree of the disorder. While studying epileptic patients, on the other hand, it was found that the LRTC in neuronal oscillations recorded intracranially with electroencephalography (EEG) were strengthened in the seizure initiation region. A novel approach to map spatial correlations between cortical regions was developed. The method is based on parcellating the cortex to patches and estimating phase synchrony between all patches. Mapping synchrony from inverse-modelled MEG / EEG data revealed wide-spread phase synchronization during a visual working memory task. Furthermore, the network architectures of task-related synchrony were found to be segregated over frequency. Cross-frequency interactions were investigated with analyses of nested brain activity in data recorded with full-bandwidth EEG during a somatosensory detection task. According to these data, the phase of ongoing infra-slow fluctuations (ISF), which were discovered in the frequency band of 0.01-0.1 Hz, was correlated with the amplitude of faster > 1 Hz neuronal oscillations. Strikingly, the behavioral detection performance displayed similar dependency on the ISFs as the > 1 Hz neuronal oscillations. The studies composing this Thesis showed that correlations in neuronal oscillations are functionally related to brain disorders and cognitive processing. Such correlations are suggested to reveal the coordination of neuronal oscillations across time, space and frequency. The results contribute to system-level understanding of brain function.
Dynamic tactile feedback in human computer interaction
(Aalto University, 2012) Ahmaniemi, Teemu; Kangas, Jari; Kildal, Johan; Lantz, Vuokko; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Perustieteiden korkeakoulu; School of Science; Sams, Mikko
Vibrotactile stimulation is an efficient way to provide feedback when a user is interacting with a mobile device. Because the device is held in hand, the user's input, such as touch and gesturing, can be easily measured with force- and motion sensors. In addition, the feedback can be provided into the same device with a vibrotactile actuator. This thesis introduces a set of methods to provide the vibrotactile feedback dynamically i.e. proportionally to the applied input in real-time during the interaction. The effects of dynamic tactile feedback in gesture and touch interaction are investigated in five studies reported in the thesis. The first two studies introduce the method for providing the dynamic feedback based on wavetable synthesis and assess the perceptual dimensions of the feedback when coupled with gesture movements. The studies indicated that the method is a powerful tool for creating an illusion of real textures with a simple vibrotactile actuator and motion sensor. Modifications in texture ridge length and spatial density lead into large variation in subjective evaluations of texture roughness, bumpiness, stickiness and pleasantness. In study III, mobile device was used to search for targets in the environment. Tactile feedback turned out to be an efficient way to indicate target directions. The finding hints that vibration could be efficiently used as a private and transparent signal to indicate directions and target locations in augmented reality interaction. In study IV, dynamic tactile feedback was provided when playing a gesture controlled virtual musical instrument. The feedback reflecting the distance to a virtual control surface assisted the temporal accuracy of the playing. In study V, vibrotactile feedback was provided proportionally to the applied force on a rigid surface. In a force repetition task, all the dynamic feedback models assisted the accuracy of the force control. On contrast, in a force hold task, the feedback did not assist the performance. Dynamic tactile feedback appeared to be a powerful tool to create illusions of real textures and it improved the motor accuracy in repetitive gesture- and force based interaction. The findings make a contribution to research focusing on touch and gesture based user interfaces as well as psychophysics research dealing with augmented feedback. In order to apply the findings in future user interfaces of mobile devices, more studies are needed. In further work, system latency should be minimized and tactile feedback should be design in parallel with visual and auditory feedback.
Dynamics of single biopolymer translocation and sedimentation
(Aalto-yliopiston teknillinen korkeakoulu, 2010) Lehtola, Ville; Linna, Riku, Dr.; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Aalto-yliopiston teknillinen korkeakoulu; Kaski, Kimmo, Prof.
In this Thesis the dynamics of translocation and sedimentation of a single biopolymer (typically DNA, RNA or a protein) is studied. A coarse-graining paradigm is invoked to justify the various computational models by use of which the results are obtained. The transport of biopolymers through a nano-scale pore in a membrane is a ubiquitous process in biology. Experimental interest in translocation process focuses on its potential applicability in ultra-fast sequencing of DNA and RNA molecules. Polymer translocation has been under intense study for over a decade. Inspite of the vast theoretical research, the experimental results on the driven case have not been explained. We claim that the reason for this is that the translocation process must be treated as (at least) two dynamically distinct cases, where the dynamics takes place either close to or out of equilibrium. Here, we find that the latter case corresponds to the experiments. We make a comprehensive investigation on how the process can be discriminated based on its dynamics, and define and use some indicators to this end. In addition, we study the role of hydrodynamics, and find it to govern the dynamics when the process takes place out of equilibrium. Sedimentation is a natural process induced by gravity that can be applied experimentally in a quickened form by the use of ultracentrifuges, and which is similar to electrophoresis. Our study on behavior of single polymers under non-equilibrium conditions falls within the rapidly developing field of nano- and microfluidics that has important applications in "lab-on-a-chip" based technologies. In polymer sedimentation, we study the settling of the polymer in a steady-state in the limit of zero Péclet number, i.e. where no thermal fluctuations exist. The hydrodynamic coupling of the polymer beads leads to chaotic time-dependent behavior of the chain conformations that in turn are coupled with the velocity fluctuations of the polymer's center of mass.

Browse

Browsing [diss] Perustieteiden korkeakoulu / SCI by Department "Lääketieteellisen tekniikan ja laskennallisen tieteen laitos"

Results Per Page

Sort Options