Prior Knowledge Guided Ultra-high Dimensional Variable Screening with Application to Neuroimaging Data.

As discussed in the research of Mohamed et al. (2013), learning and studying via a different context, including the inter-cultural knowledge of a teacher, may present a challenge when trying to account for students’ prior knowledge. Enhancing their study, this paper addresses the question of whether or not an internal session comprising open discussion and brainstorming followed by asynchronous commenting and ending with self-reflection might help to assess students’ prior knowledge. Data was collected in the context of a series of academic teachings partly supported by the METIS project (The METIS Project is funded by the national German Federal Ministry of Science and Research, cp.: http://www.ihi.zittau.de/cms/de/621/Forschung.) in two separate phases. Graduate students on master level attended the phase reported here in order to study research methods as a joint activity of Technische Universitat Dresden, and Internationales Hochschulinstitut Zittau, both Germany. For the experimental group, the study found that there are no significant differences between the control and treatment group in terms of cognitive achievement and students’ attitudes. From the teacher’s perspective, however, this course is learning atmosphere challenged students’ reactions. Design is suggested to be enhanced to allow a closer look on different types of prior knowledge in the field of research training.

Investigating the Contributions of Different Types of Prior Knowledge on Chemistry Learning Outcomes (Poster 22)

Subspace-based parameter estimation of exponentially damped sinusoids using prior knowledge of frequency and phase

ABSTRACTBackground: Eye movement desensitization and reprocessing (EMDR) is an effective psychological treatment for posttraumatic stress disorder. Recalling a memory while simultaneously making eye movements (EM) decreases a memory’s vividness and/or emotionality. It has been argued that non-specific factors, such as treatment expectancy and experimental demand, may contribute to the EMDR’s effectiveness.Objective: The present study was designed to test whether expectations about the working mechanism of EMDR would alter the memory attenuating effects of EM. Two experiments were conducted. In Experiment 1, we examined the effects of pre-existing (non-manipulated) knowledge of EMDR in participants with and without prior knowledge. In Experiment 2, we experimentally manipulated prior knowledge by providing participants without prior knowledge with correct or incorrect information about EMDR’s working mechanism.Method: Participants in both experiments recalled two aversive, autobiographical memories during brief sets of EM (Recall+EM) or keeping eyes stationary (Recall Only). Before and after the intervention, participants scored their memories on vividness and emotionality. A Bayesian approach was used to compare two competing hypotheses on the effects of (existing/given) prior knowledge: (1) Prior (correct) knowledge increases the effects of Recall+EM vs. Recall Only, vs. (2) prior knowledge does not affect the effects of Recall+EM.Results: Recall+EM caused greater reductions in memory vividness and emotionality than Recall Only in all groups, including the incorrect information group. In Experiment 1, both hypotheses were supported by the data: prior knowledge boosted the effects of EM, but only modestly. In Experiment 2, the second hypothesis was clearly supported over the first: providing knowledge of the underlying mechanism of EMDR did not alter the effects of EM.Conclusions: Recall+EM appears to be quite robust against the effects of prior expectations. As Recall+EM is the core component of EMDR, expectancy effects probably contribute little to the effectiveness of EMDR treatment.

Facilitated by the powerful feature extraction ability of neural networks, deep clustering has achieved great success in analyzing high-dimensional and complex real-world data. The performance of deep clustering methods is affected by various factors such as network structures and learning objectives. However, as pointed out in this survey, the essence of deep clustering lies in the incorporation and utilization of prior knowledge, which is largely ignored by existing works. From pioneering deep clustering methods based on data structure assumptions to recent contrastive clustering methods based on data augmentation invariances, the development of deep clustering intrinsically corresponds to the evolution of prior knowledge. In this survey, we provide a comprehensive review of deep clustering methods by categorizing them into six types of prior knowledge. We find that in general the prior innovation follows two trends, namely, i) from mining to constructing, and ii) from internal to external. Besides, we provide a benchmark on five widely-used datasets and analyze the performance of methods with diverse priors. By providing a novel prior knowledge perspective, we hope this survey could provide some novel insights and inspire future research in the deep clustering community.

The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.

The analysis of neuroimaging data is frequently used to assist the diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) or Parkinson's disease (PD) and has become a routine procedure in the clinical practice. During the past decade, the pattern recognition community has proposed a number of machine learning-based systems that automatically analyse neuroimaging data in order to improve the diagnosis. However, the high dimensionality of the data is still a challenge and there is room for improvement. The development of novel classification frameworks as TensorFlow, recently released as open source by Google Inc., represents an opportunity to continue evolving these systems. In this work, we demonstrate several computer-aided diagnosis (CAD) systems based on Deep Neural Networks that improve the diagnosis for AD and PD and outperform those based on classical classifiers. In order to address the small sample size problem we evaluate two dimensionality reduction algorithms based on Principal Component Analysis and Non-Negative Matrix Factorization (NNMF), respectively. The performance of developed CAD systems is assessed using 4 datasets with neuroimaging data of different modalities.

When we have prior information of approximate and subjective nature other than those contained in the time series, that is knowledge not directly referable to exact linear constraints, we propose to use the penalty function method to tackle the problem of estimation of a linear statistical model alternatively to the mixed estimation proposed by Theil and Goldberger. This method bases itself on the minimization of a mathematical function known as penalty function where constraint expressed in a quadratic form is linearly combined with an objective function by some coefficients known as penalty parameters. The particular functional form utilized for the constraint can be seen as a way to catch all the uncertainty included in this type of prior knowledge. The algorithm that derives from the application of this method depends on the penalty parameters that can be interpreted in two different ways: a) as weights assigned to the constraint in the minimization procedure. In this case the result can be seen as a generalization of the ridge estimator. b) as variances of a stochastic constraint. By this way the algorithm is similar to the mixed estimator but, unlike this one, it has not unknown parameters in its formulation and so it can be seen as a feasible estimator. Whatever the interpretation of this parameters is, the penalty function method produces an algorithm that is a flexible instrument both from a theoretical viewpoint and a practical one.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social interaction, communication, and behavior. Traditional diagnostic methods rely heavily on behavioral assessments, such as the Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADI-R), which are often subjective and limited by the absence of definitive biomarkers. Recent advances in neuroimaging techniques, including structural MRI (sMRI), functional MRI (fMRI), and positron emission tomography (PET), have provided valuable insights into the structural and functional abnormalities associated with ASD. However, the analysis of neuroimaging data is complex, requiring sophisticated machine learning (ML) and artificial intelligence (AI) models. This paper reviews state-of-the-art machine learning approaches, such as Random Forests, Support Vector Machines (SVM), and deep learning models, aimed at improving the diagnostic accuracy of ASD. We particularly focus on multimodal deep learning models that integrate neuroimaging data with behavioral assessments. In this review, we critically evaluate the strengths and limitations of these approaches, and how emerging AI tools not only enhance classification accuracy but also hold potential for identifying biomarkers and tracking the disorder's progression. Our analysis underscores the need for advanced methods that exploit both spatial and temporal features of brain data to better understand ASD, paving the way for improved diagnostics and personalized treatments.

Neuroimaging data are an increasingly important part of etiological studies of neurological and psychiatric disorders. However, mitigating the influence of nuisance variables, including confounders, remains a challenge in image analysis. In studies of Alzheimer's disease, for example, an imbalance in disease rates by age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly accounted for, nuisance variables pose threats to the generalizability and interpretability of findings from these studies. Motivated by this critical issue, in this work, we examine the impact of nuisance variables on feature extraction methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between partially residualized imaging features and a variable of interest (e.g., Alzheimer's diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between partially residualized imaging features and those variables. Using features derived using PeDecURe's first direction of variation, we train a highly accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by challenges that arise in the analysis of neuroimaging data, it is broadly applicable to data sets with highly correlated features, where novel methods to handle nuisance variables are warranted.

Ultra-high dimensional variable selection has become increasingly important in analysis of neuroimaging data. For example, in the Autism Brain Imaging Data Exchange (ABIDE) study, neuroscientists are interested in identifying important biomarkers for early detection of the autism spectrum disorder (ASD) using high resolution brain images that include hundreds of thousands voxels. However, most existing methods are not feasible for solving this problem due to their extensive computational costs. In this work, we propose a novel multiresolution variable selection procedure under a Bayesian probit regression framework. It recursively uses posterior samples for coarser-scale variable selection to guide the posterior inference on finer-scale variable selection, leading to very efficient Markov chain Monte Carlo (MCMC) algorithms. The proposed algorithms are computationally feasible for ultra-high dimensional data. Also, our model incorporates two levels of structural information into variable selection using Ising priors: the spatial dependence between voxels and the functional connectivity between anatomical brain regions. Applied to the resting state functional magnetic resonance imaging (R-fMRI) data in the ABIDE study, our methods identify voxel-level imaging biomarkers highly predictive of the ASD, which are biologically meaningful and interpretable. Extensive simulations also show that our methods achieve better performance in variable selection compared to existing methods.

In positron emission tomographic (PET) or magnetic resonance imaging (MRI) neuroimaging studies, spatial smoothing technique is commonly applied to increase the signal to noise ratio and to condition neuroimaging data for subsequent statistical analysis, and to reduce errors associated with registration or spatial normalizations. Usually, the smoothing step is applied to the images without any masking. Thus, some artifacts adjacent but outside of the brain will enter the brain volume. Masking the brain volume before smoothing has been suggested as one way to eliminate the introduced artifact, but it will introduce zero-in (intensities within-mask voxels are reduced) and nonzero-out (intensities outside the mask become non-zero) artifacts. Here we proposed an adaptive smoothing method to reduce the influence of such artifacts. Unlike the conventional smoothing method, the adaptive strategy did not introduce artificial addition (due to nonzero-out artifact) and deletion (due to zero-in), suggesting that such adaptive smoothing methods may be helpful in reducing the influence of non-brain tissue in the analysis of neuroimaging data.

In this paper we focus on Bayesian blind and semi-blind adaptive signal processing based on a broadband MIMO FIR model (e.g., for blind source separation (BSS) and blind system identification (BSI)). Specifically, we study in this paper a framework allowing us to systematically incorporate various types of prior knowledge: (1) source signal statistics, (2) deterministic knowledge on the mixing system, and (3) stochastic knowledge on the mixing system. In order to exploit all possible types of source signal statistics (1), our considerations are based on TRINICON, a previously introduced generic framework for broadband blind (and semi-blind) adaptive MIMO signal processing. The motivation for this paper is threefold: (a) the extension of TRINICON to Bayesian point estimation to address (3) in addition to (1), and (b) more specifically to unify system-based blind adaptive MIMO signal processing with the tracking of time-varying scenarios, and finally (c) to show how the Bayesian TRINICON-based tracking can be formulated as a sequence estimation approach on arbitrary partly smooth manifolds. As we will see in this paper, the Bayesian approach to incorporate stochastic priors and the manifold learning approach to exploit deterministic system knowledge (2) complement one another very efficiently in the context of TRINICON.

Weakly supervised instance segmentation using multi-prior fusion

Suspense in the absence of uncertainty