Physiological Gaussian Process Priors for the Hemodynamics in fMRI Analysis

Background: Inference from fMRI data faces the challenge that the hemodynamic system that relates neural activity to the observed BOLD fMRI signal is unknown. New Method: We propose a new Bayesian model for task fMRI data with the following features: (i) joint estimation of brain activity and the underlying hemodynamics, (ii) the hemodynamics is modeled nonparametrically with a Gaussian process (GP) prior guided by physiological information and (iii) the predicted BOLD is not necessarily generated by a linear time-invariant (LTI) system. We place a GP prior directly on the predicted BOLD response, rather than on the hemodynamic response function as in previous literature. This allows us to incorporate physiological information via the GP prior mean in a flexible way, and simultaneously gives us the nonparametric flexibility of the GP. Results: Results on simulated data show that the proposed model is able to discriminate between active and non-active voxels also when the GP prior deviates from the true hemodynamics. Our model finds time varying dynamics when applied to real fMRI data. Comparison with Existing Method(s): The proposed model is better at detecting activity in simulated data than standard models, without inflating the false positive rate. When applied to real fMRI data, our GP model in several cases finds brain activity where previously proposed LTI models does not. Conclusions: We have proposed a new non-linear model for the hemodynamics in task fMRI, that is able to detect active voxels, and gives the opportunity to ask new kinds of questions related to hemodynamics.Comment: 18 pages, 14 figure

The risk of colorectal cancer in relation to dietary patterns, physical activity and BMI in southeastern Sweden

Bakgrund: Tidigare studier har identifierat flera riskfaktorer, såsom kost, fysisk aktivitet och BMI, gällande kolorektal cancer. Att analysera kost utifrån kostmönster istället för enskilda livsmedel har visat sig vara effektivt för att undersöka risker för kolorektal cancer. Datamaterial samlades in med hjälp av en fall-kontroll studie med 257 fall och 805 kontroller. Syfte: Identifiera faktorer som ger en höjd eller sänkt risk för kolorektal cancer utifrån områdena kost, fysisk aktivitet och BMI. Metod: Faktoranalys användes för att upptäcka kostmönster. Logistisk regression användes för att skatta oddskvoter och 95 % konfidensintervall. Resultat: Tio stycken kostmönster erhölls från faktoranalysen. Kostmönstren ”Läsk, juice och mjölkprodukter” (OR=1,288; ORQ4=2,159), ”Te, men inte kaffe”(OR=1,228; ORQ3=1,891; ORQ4=1,668) och ”Fågel, rött kött och fisk”( ORQ4=1,724) gav alla en ökad risk. Däremot visade kostmönstret ”Mat från säd och ost”( ORQ2=0,546; ORQ4=0,592) en minskad risk. BMI för tio år sedan (OR=1,079; ORÖvervikt=1,491; ORFetma=2,260) identifierades som en riskfaktor. Att arbeta inom stillasittande (OR=0,975; OR>15 år=0,517) och mellanaktiva (OR=0,977; OR6-10 år=0,497;OR>15 år=0,565) yrken visade på en minskad risk. Slutsats: Flera kostmönster visade sig vara riskfaktorer, detta gäller även BMI för tio år sedan. Kostmönstret ”Mat från säd och ost” och att arbeta i fysiskt lätta till medeltunga yrken visade sig vara skyddande faktorer.Background: Previous studies have shown several risk factors for developing colorectal cancer such as diet, physical activity and BMI. The method of analyzing diets based on dietary patterns, rather than individual food items, have been shown to be effective when investigating the colorectal cancer risk. The data was collected using a case-control study of 257 cases and 805 controls. Aim: Identify factors that cause increased or decreased risk in developing colorectal cancer based on diet, physical activity and BMI. Methods: Factor analysis was conducted to identify dietary patterns. Logistic regression was used to estimate odds ratio and 95 % confidence interval. Results: Factor analysis conducted ten dietary patterns, three of these patterns showed an increased risk “Soft drinks, juice and milk products” (OR=1,288; ORQ4=2,159), “Tea, but not coffee” (OR=1,228; ORQ3=1,891; ORQ4=1,668) and “Poultry, red meats and fish” (ORQ4=1,724).The dietary pattern “Food based on grain and cheese” (ORQ2=0,546; ORQ4=0,592) showed a decreased risk. BMI ten years ago (OR=1,079; OROverweight=1,491; ORObese=2,260) identified as a risk factor. To work in sedentary (OR=0,975; OR>15 years=0,517) or physically medium heavy (OR=0,977; OR6-10 years=0,497; OR>15 years=0,565) occupations indicated a decreased risk. Conclusions: Several dietary patterns has been identified as risk factors, this also includes BMI ten years ago. The dietary pattern “Food based on grain and cheese” and to work in sedentary or physically medium heavy occupations proved to be protective factors