Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach

In standard clinical within-subject analyses of event-related fMRI data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic response. Because these two steps are inherently linked, we adopt the so-called region-based Joint Detection-Estimation (JDE) framework that addresses this joint issue using a multivariate inference for detection and estimation. JDE is built by making use of a regional bilinear generative model of the BOLD response and constraining the parameter estimation by physiological priors using temporal and spatial information in a Markovian modeling. In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques to approximate the resulting intractable posterior distribution, we recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. A variational approximation is used to approximate the Markovian model in the unsupervised spatially adaptive JDE inference, which allows fine automatic tuning of spatial regularisation parameters. It follows a new algorithm that exhibits interesting properties compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model mis-specification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery

PARALLEL INDEPENDENT COMPONENT ANALYSIS WITH REFERENCE FOR IMAGING GENETICS: A SEMI-BLIND MULTIVARIATE APPROACH

Imaging genetics is an emerging field dedicated to the study of genetic underpinnings of brain structure and function. Over the last decade, brain imaging techniques such as magnetic resonance imaging (MRI) have been increasingly applied to measure morphometry, task-based function and connectivity in living brains. Meanwhile, high-throughput genotyping employing genome-wide techniques has made it feasible to sample the entire genome of a substantial number of individuals. While there is growing interest in image-wide and genome-wide approaches which allow unbiased searches over a large range of variants, one of the most challenging problems is the correction for the huge number of statistical tests used in univariate models. In contrast, a reference-guided multivariate approach shows specific advantage for simultaneously assessing many variables for aggregate effects while leveraging prior information. It can improve the robustness of the results compared to a fully blind approach. In this dissertation we present a semi-blind multivariate approach, parallel independent component analysis with reference (pICA-R), to better reveal relationships between hidden factors of particular attributes. First, a consistency-based order estimation approach is introduced to advance the application of ICA to genotype data. The pICA-R approach is then presented, where independent components are extracted from two modalities in parallel and inter-modality associations are subsequently optimized for pairs of components. In particular, prior information is incorporated to elicit components of particular interests, which helps identify factors carrying small amounts of variance in large complex datasets. The pICA-R approach is further extended to accommodate multiple references whose interrelationships are unknown, allowing the investigation of functional influence on neurobiological traits of potentially related genetic variants implicated in biology. Applied to a schizophrenia study, pICA-R reveals that a complex genetic factor involving multiple pathways underlies schizophrenia-related gray matter deficits in prefrontal and temporal regions. The extended multi-reference approach, when employed to study alcohol dependence, delineates a complex genetic architecture, where the CREB-BDNF pathway plays a key role in the genetic factor underlying a proportion of variation in cue-elicited brain activations, which plays a role in phenotypic symptoms of alcohol dependence. In summary, our work makes several important contributions to advance the application of ICA to imaging genetics studies, which holds the promise to improve our understating of genetics underlying brain structure and function in healthy and disease

Elucidation Of Histone Modifications And Nucleosomal Structure Using Novel Mass Spectrometry Approaches

The fundamental repeating unit of chromatin is the nucleosome, composed of 147 base pairs of DNA wrapped around a histone protein octamer containing two copies of H2A, H2B, H3, and H4. Histone proteins are involved in many critical nuclear processes including transcription and maintenance of chromatin structure. Histone function is mediated by a dynamic and extensive array of post-translational modifications (PTMs). Mass spectrometry (MS) has emerged as a leading tool to study these complex histone PTM profiles. Generally, MS experiments utilize data dependent acquisition (DDA) methods on high-resolution MS instruments because they can more readily distinguish PTMs with small mass differences. I demonstrate here that low-resolution instruments are capable of this analysis with data dependent acquisition (DDA) and data independent acquisition (DIA) methods, thereby expanding the repertoire of instruments that can be used. However, DIA methods improve quantification of isobaric peptides compared to DIA and also allows for re-mining of data post-experiment. This dissertation also highlights work I have done to develop MS methods to identify and quantify ADP-ribosylation PTMs, which are critical for DNA damage repair pathways. We identified 30 ADP-ribosylation marks on histones, 20 of which are novel. We quantified 10 of these sites throughout a DNA damage and found that all of these sites increase in abundance over time, indicating that it is unlikely that specific sites are required for repair, but rather that ADP-ribosylation of the nucleosome surface in general is needed. Histone function is also mediated through its structure and dynamic properties. Hydrogen-deuterium exchange (HDX) coupled to MS is a powerful technique to monitor these properties in solution. However, traditional HDX-MS studies on histone proteins were unable to monitor histone N-terminal tail domains, where a majority of PTM sites are located. Here, we demonstrate that by incorporating electron transfer dissociation (ETD) MS/MS methodology with middle-down and top-down MS, we are able to measure deuterium content of tail domains with near site-specific resolution for the first time. We find that all tails undergo decreased structural rigidity upon incorporation into the nucleosome, lending the first detailed experimentally-obtained insight into histone tail structure in solution

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Aspects of multi-resolutional foveal images for robot vision

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