| 1. |
- Andersen, Oluf, 1941, et al.
(författare)
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Diffusion tensor imaging in multiple sclerosis at different final outcomes
- 2018
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Ingår i: Acta Neurologica Scandinavica. - : Hindawi Limited. - 1600-0404 .- 0001-6314. ; 137:2, s. 165-173
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVES:Methods to evaluate the relative contributions of demyelination vs axonal degeneration over the long-term course of MS are urgently needed. We used magnetic resonance diffusion tensor imaging (DTI) to estimate degrees of demyelination and axonal degeneration in the corpus callosum (CC) in cases of MS with different final outcomes.MATERIALS AND METHODS:We determined DTI measures mean diffusivity (MD), fractional anisotropy (FA), and axial (AD) and radial (RD) diffusivities in the CC of 31 MS patients, of whom 13 presented a secondary progressive course, 11 a non-progressive course, and seven a monophasic course. The study participants were survivors from an incidence cohort of 254 attack-onset MS patients with 50 years of longitudinal follow-up. As reference, we included five healthy individuals without significant morbidity.RESULTS:In patients with secondary progression, compared to all other groups, the corpus callosum showed increased RD and reduced FA, but no change in AD. None of the parameters exhibited differences among non-progressive and monophasic course groups and controls.CONCLUSION:Increased RD was observed in secondary progressive MS, indicating significant myelin loss. Normal RD values observed in the clinically isolated syndrome and non-progressive groups confirm their benign nature. AD was not a characterizing parameter for long-term outcome. Demyelination revealed by increased RD is a distinguishing trait for secondary progression.
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| 2. |
- Eliasdottir, Olöf, et al.
(författare)
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A nationwide survey of the influence of month of birth on the risk of developing multiple sclerosis in Sweden and Iceland
- 2018
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Ingår i: Journal of Neurology. - : Springer Science and Business Media LLC. - 0340-5354 .- 1432-1459. ; 265:1, s. 108-114
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Tidskriftsartikel (refereegranskat)abstract
- Previous studies have shown that the risk of multiple sclerosis (MS) is associated with season of birth with a higher proportion of MS patients being born in spring. However, this relationship has recently been questioned and may be due to confounding factors. Our aim was to assess the influence from season or month of birth on the risk of developing MS in Sweden and Iceland. Information about month of birth, gender, and phenotype of MS for patients born 1940-1996 was retrieved from the Swedish MS registry (SMSR), and their place of birth was retrieved from the Swedish Total Population Registry (TPR). The corresponding information was retrieved from medical journals of Icelandic MS patients born 1981-1996. The control groups consisted of every person born in Sweden 1940-1996, their gender and county of birth (TPR), and in Iceland all persons born between 1981 and 1996 and their gender (Statistics Iceland). We calculated the expected number of MS patients born during each season and in every month and compared it with the observed number. Adjustments were made for gender, birth year, and county of birth. We included 12,020 Swedish and 108 Icelandic MS patients in the analyses. There was no significant difference between expected and observed MS births related to season or month of birth in Sweden or Iceland. This was even the results before adjustments were made for birth year and birth place. No significant differences were found in subgroup analyses including data of latitude of birth, gender, clinical phenotype, and MS onset of 30 years or less. Our results do not support the previously reported association between season or month of birth and MS risk. Analysis of birth place and birth year as possible confounding factors showed no major influence of them on the seasonal MS risk in Sweden and Iceland.
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| 3. |
- Longfils, Marco, 1990
(författare)
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Quantitative methods for diffusion measurements in fluorescence microscopy
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this work, statistical methods are developed for mapping mass transport locally based on images collected using a confocal laser scanning microscope. Besides presenting raster image correlation spectroscopy as an established method in fluorescence microscopy, we introduce a single particle tracking method which takes advantage of the raster scanning of the image in a confocal microscope. In single particle tracking, particles are identified and followed in consecutive frames of a video to measure their diffusive mobility. Both a maximum likelihood and a centroid-based method have been developed to locate the particles and hence to estimate the diffusion coefficient. The method is generalized to analyse mixtures of particles having different diffusion coefficients. The proposed method allows us to study the entire distribution of diffusion coefficients, enabling the characterization of heterogeneous systems. Motivated by experiments with particle mixtures, we investigate the use of cross-validation to perform model selection, i.e. to select the number of mixture components, and compare it to some existing model selection criteria. In the specific case of normal mixtures, we prove a bound on the error between the cross-validated conditional risk and an oracle benchmark conditional risk, which assumes the knowledge of the true density generating the data. Furthermore, a detailed statistical analysis of the raster image correlation spectroscopy method is presented, uncovering the relationship between molecular and experimental parameters and the estimated diffusion coefficient. We propose a statistical method to compare different experimental conditions and apply it to find the optimal parameters to perform an experiment. The methods and models investigated and developed in this thesis are of general interest. In particular, the quantitative methods considered to study confocal images can be used in a wide range of applications, while the use of crossvalidation to perform model selection of mixture models is a valuable contribution to the statistical literature.
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| 4. |
- Longfils, Marco, 1990
(författare)
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Raster Image Analysis of Diffusion via Single Particle Methods
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Soft biomaterials are widely used in many application areas, spanning from packaging materials to pharmaceuticals. To enhance their functionalities, understanding the interplay between microstructure and mass transport properties in these materials is fundamental. Consequently, there is a growing need to introduce new and improve existing methods for estimating mass transport heterogeneity in materials with high spatial resolution. In this work, statistical methods are developed for mapping mass transport locally based on raster images collected using a confocal laser scanning microscope.The methods introduced resemble single particle tracking methods, where molecules are identified using image analysis techniques and followed in successive frames of a video to measure their diffusive mobility. Both a maximum likelihood and a centroid-based method have been applied to locate particles and hence to estimate the diffusion coefficient. The method has been generalized to analyse mixtures of particles having different diffusion coefficients. The single particle approach allows to reveal and study the entire distribution of diffusion coefficients, enabling to examine heterogeneous systems. Further, for the case of particle mixtures, a simple criterion for model selection, i.e. the number of components, is proposed.
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| 5. |
- Longfils, Marco, 1990, et al.
(författare)
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Raster Image Correlation Spectroscopy Performance Evaluation
- 2019
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Ingår i: Biophysical Journal. - : Elsevier BV. - 0006-3495 .- 1542-0086. ; 117:10, s. 1900-1914
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Tidskriftsartikel (refereegranskat)abstract
- Raster image correlation spectroscopy (RICS) is a fluorescence image analysis method for extracting the mobility, concentration, and stoichiometry of diffusing fluorescent molecules from confocal image stacks. The method works by calculating a spatial correlation function for each image and analyzing the average of those by model fitting. Rules of thumb exist for RICS image acquisitioning, yet a rigorous theoretical approach to predict the accuracy and precision of the recovered parameters has been lacking. We outline explicit expressions to reveal the dependence of RICS results on experimental parameters. In terms of imaging settings, we observed that a twofold decrease of the pixel size, e.g., from 100 to 50 nm, decreases the error on the translational diffusion constant (D) between three- and fivefold. For D = 1 mu m(2) s(-1), a typical value for intracellular measurements, similar to 25-fold lower mean-squared relative error was obtained when the optimal scan speed was used, although more drastic improvements were observed for other values of D. We proposed a slightly modified RICS calculation that allows correcting for the significant bias of the autocorrelation function at small (<<50 x 50 pixels) sizes of the region of interest. In terms of sample properties, at molecular brightness E = 100 kHz and higher, RICS data quality was sufficient using as little as 20 images, whereas the optimal number of frames for lower E scaled pro rata. RICS data quality was constant over the nM-mM concentration range. We developed a bootstrap-based confidence interval of D that outperformed the classical leastsquares approach in terms of coverage probability of the true value of D. We validated the theory via in vitro experiments of enhanced green fluorescent protein at different buffer viscosities. Finally, we outline robust practical guidelines and provide free software to simulate the parameter effects on recovery of the diffusion coefficient.
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| 6. |
- Longfils, Marco, 1990, et al.
(författare)
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Single particle raster image analysis of diffusion
- 2017
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Ingår i: Journal of Microscopy. - : Wiley. - 0022-2720 .- 1365-2818. ; 266:1, s. 3-14
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Tidskriftsartikel (refereegranskat)abstract
- As a complement to the standard RICS method of analysing Raster Image Correlation Spectroscopy images with estimation of the image correlation function, we introduce the method SPRIA, Single Particle Raster Image Analysis. Here, we start by identifying individual particles and estimate the diffusion coefficient for each particle by a maximum likelihood method. Averaging over the particles gives a diffusion coefficient estimate for the whole image. In examples both with simulated and experimental data, we show that the new method gives accurate estimates. It also gives directly standard error estimates. The method should be possible to extend to study heterogeneous materials and systems of particles with varying diffusion coefficient, as demonstrated in a simple simulation example. A requirement for applying the SPRIA method is that the particle concentration is low enough so that we can identify the individual particles. We also describe a bootstrap method for estimating the standard error of standard RICS.
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| 7. |
- Longfils, Marco, 1990, et al.
(författare)
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Single particle raster image analysis of diffusion for particle mixtures
- 2018
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Ingår i: Journal of Microscopy. - : Wiley. - 0022-2720 .- 1365-2818. ; 269:3, s. 269-281
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Tidskriftsartikel (refereegranskat)abstract
- Recently we complemented the raster image correlation spectroscopy (RICS) method of analysing raster images via estimation of the image correlation function with the method single particle raster image analysis (SPRIA). In SPRIA, individual particles are identified and the diffusion coefficient of each particle is estimated by a maximum likelihood method. In this paper, we extend the SPRIA method to analyse mixtures of particles with a finite set of diffusion coefficients in a homogeneous medium. In examples with simulated and experimental data with two and three different diffusion coefficients, we show that SPRIA gives accurate estimates of the diffusion coefficients and their proportions. A simple technique for finding the number of different diffusion coefficients is also suggested. Further, we study the use of RICS for mixtures with two different diffusion coefficents and investigate, by plotting level curves of the correlation function, how large the quotient between diffusion coefficients needs to be in order to allow discrimination between models with one and two diffusion coefficients. We also describe a minor correction (compared to published papers) of the RICS autocorrelation function. Lay description Diffusion is a key mass transport mechanism for small particles. Efficient methods for estimating diffusion coefficients are crucial for analysis of microstructures, for example in soft biomaterials. The sample of interest may consist of a mixture of particles with different diffusion coefficients. Here, we extend a method called Single Particle Raster Image Analysis (SPRIA) to account for particle mixtures and estimation of the diffusion coefficients of the mixture components. SPRIA combines elements of classical single particle tracking methods with utilizing the raster scan with which images obtained by using a confocal laser scanning microscope. In particular, single particles are identified and their motion estimated by following their center of mass. Thus, an estimate of the diffusion coefficient will be obtained for each particle. Then, we analyse the distribution of the estimated diffusion coefficients of the population of particles, which allows us to extract information about the diffusion coefficients of the underlying components in the mixture. On both simulated and experimental data with mixtures consisting of two and three components with different diffusion coefficients, SPRIA provides accurate estimates and, with a simple criterion, the correct number of mixture components is selected in most cases.
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| 8. |
- Skoog, Bengt, et al.
(författare)
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Short-term prediction of secondary progression in a sliding window: A test of a predicting algorithm in a validation cohort
- 2019
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Ingår i: Multiple Sclerosis Journal - Experimental, Translational and Clinical. - : SAGE Publications. - 2055-2173. ; 5:3
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: The Multiple Sclerosis Prediction Score (MSPS, www.msprediction.com) estimates, for any month during the course of relapsing–remitting multiple sclerosis (MS), the individual risk of transition to secondary progression (SP) during the following year. Objective: Internal verification of the MSPS algorithm in a derivation cohort, the Gothenburg Incidence Cohort (GIC, n = 144) and external verification in the Uppsala MS cohort (UMS, n = 145). Methods: Starting from their second relapse, patients were included and followed for 25 years. A matrix of MSPS values was created. From this matrix, a goodness-of-fit test and suitable diagnostic plots were derived to compare MSPS-calculated and observed outcomes (i.e. transition to SP). Results: The median time to SP was slightly longer in the UMS than in the GIC, 15 vs. 11.5 years (p = 0.19). The MSPS was calibrated with multiplicative factors: 0.599 for the UMS and 0.829 for the GIC; the calibrated MSPS provided a good fit between expected and observed outcomes (chi-square p = 0.61 for the UMS), which indicated the model was not rejected. Conclusion: The results suggest that the MSPS has clinically relevant generalizability in new cohorts, provided that the MSPS was calibrated to the actual overall SP incidence in the cohort.
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