Exploratory fMRI analysis without spatial normalization

Author Manuscript received 2010 March 11. 21st International Conference, IPMI 2009, Williamsburg, VA, USA, July 5-10, 2009. ProceedingsWe present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI response. We employ a Variational Bayes approximation to fit the model to the data. The resulting algorithm finds a functional parcellation of the individual brains along with a set of population-level clusters, establishing correspondence between these two levels. The model eliminates the need for spatial normalization while still enabling us to fuse data from several subjects. We demonstrate the application of our method on a visual fMRI study.McGovern Institute for Brain Research at MIT. Neurotechnology ProgramNational Science Foundation (U.S.) (CAREER Grant 0642971)National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41-RR13218

Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus

A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 × 10-20), ER-negative BC (P=1.1 × 10-13), BRCA1-associated BC (P=7.7 × 10-16) and triple negative BC (P-diff=2 × 10-5). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 × 10-3) and ABHD8 (P<2 × 10-3). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3′-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk

Comparing Dynamic Causal Models using AIC, BIC and Free Energy

In neuroimaging it is now becoming standard practise to fit multiple models to data and compare them using a model selection criterion. This is especially prevalent in the analysis of brain connectivity. This paper describes a simulation study which compares the relative merits of three model selection criteria (i) Akaike's Information Criterion (AIC), (ii) the Bayesian Information Criterion (BIC) and (iii) the variational Free Energy. Differences in performance are examined in the context of General Linear Models (GLMs) and Dynamic Causal Models (DCMs). We find that the Free Energy has the best model selection ability and recommend it be used for comparison of DCMs

The Bayesian paradigm: second generation neural computing

When reasoning in the presence of uncertainty there is a unique and self-consistent set of rules for induction and model selection – Bayesian inference. Recent advances in neural networks have been fuelled by the adoption of this Bayesian framework, either implicitly, for example through the use of committees, or explicitly through Bayesian evidence and sampling frameworks. In this chapter, we show how this ‘second generation’ of neural network techniques can be applied to biomedical data and focus on the networks’ ability to provide assessments of the confidence associated with its predictions. This is an essential requirement for any automatic biomedical pattern recognition system. It allows low confidence decisions to be highlighted and deferred, possibly to a human expert, and falls naturally out of the Bayesian framework

Covariance-based weighting for optimal combination of network predictions

This paper introduces a method for calcu- lating the covariance between different neu- ral network solutions. It is based on a generalisation of the delta method for cal- culating the network Hessian and gener- ates what we call the ‘cross-covariance’ ma- trix (its inverse is the ‘cross-Hessian’). Us- ing this matrix we are able to estimate the covariance between network predictions at each point in input space, using train- ing data alone. Whilst this is a signifi- cant result in itself we have also applied the method to the problem of finding optimal linear combinations of models. This results in a ‘covariance-based’ weighted committee, where the weights are input-dependent. If the individual networks are unbiased then the covariance-based weighted committee is optimal in the sense of minimum expected prediction error

An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers

This article gives a concise overview of Bayesian sampling for neural networks, and then presents an extensive evaluation on a set of various benchmark classification problems. The main objective is to study the sensitivity of this scheme to changes in the prior distribution of the parameters and hyperparameters, and to evaluate the efficiency of the so-called automatic relevance determination (ARD) method. The article concludes with a comparison of the achieved classification results with those obtained with (i) the evidence scheme and (ii) with non-Bayesian methods

Empirical evaluation of Bayesian sampling for neural classifiers

Adopting a Bayesian approach and sampling the network parameters from their posterior distribution is a rather novel and promising method for improving the generalisation performance of neural network predictors. The present empirical study applies this scheme to a set of different synthetic and real-world classification problems. The paper focuses on the dependence of the prediction results on the prior distribution of the network parameters and hyperparameters, and provides a critical evaluation of the automatic relevance determination (ARD) scheme for detecting irrelevant inputs