277 research outputs found
Bayesian inference for group-level cortical surface image-on-scalar-regression with Gaussian process priors
In regression-based analyses of group-level neuroimage data researchers
typically fit a series of marginal general linear models to image outcomes at
each spatially-referenced pixel. Spatial regularization of effects of interest
is usually induced indirectly by applying spatial smoothing to the data during
preprocessing. While this procedure often works well, resulting inference can
be poorly calibrated. Spatial modeling of effects of interest leads to more
powerful analyses, however the number of locations in a typical neuroimage can
preclude standard computation with explicitly spatial models. Here we
contribute a Bayesian spatial regression model for group-level neuroimaging
analyses. We induce regularization of spatially varying regression coefficient
functions through Gaussian process priors. When combined with a simple
nonstationary model for the error process, our prior hierarchy can lead to more
data-adaptive smoothing than standard methods. We achieve computational
tractability through Vecchia approximation of our prior which, critically, can
be constructed for a wide class of spatial correlation functions and results in
prior models that retain full spatial rank. We outline several ways to work
with our model in practice and compare performance against standard vertex-wise
analyses. Finally we illustrate our method in an analysis of cortical surface
fMRI task contrast data from a large cohort of children enrolled in the
Adolescent Brain Cognitive Development study
Modern Views of Machine Learning for Precision Psychiatry
In light of the NIMH's Research Domain Criteria (RDoC), the advent of
functional neuroimaging, novel technologies and methods provide new
opportunities to develop precise and personalized prognosis and diagnosis of
mental disorders. Machine learning (ML) and artificial intelligence (AI)
technologies are playing an increasingly critical role in the new era of
precision psychiatry. Combining ML/AI with neuromodulation technologies can
potentially provide explainable solutions in clinical practice and effective
therapeutic treatment. Advanced wearable and mobile technologies also call for
the new role of ML/AI for digital phenotyping in mobile mental health. In this
review, we provide a comprehensive review of the ML methodologies and
applications by combining neuroimaging, neuromodulation, and advanced mobile
technologies in psychiatry practice. Additionally, we review the role of ML in
molecular phenotyping and cross-species biomarker identification in precision
psychiatry. We further discuss explainable AI (XAI) and causality testing in a
closed-human-in-the-loop manner, and highlight the ML potential in multimedia
information extraction and multimodal data fusion. Finally, we discuss
conceptual and practical challenges in precision psychiatry and highlight ML
opportunities in future research
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
By promising more accurate diagnostics and individual treatment
recommendations, deep neural networks and in particular convolutional neural
networks have advanced to a powerful tool in medical imaging. Here, we first
give an introduction into methodological key concepts and resulting
methodological promises including representation and transfer learning, as well
as modelling domain-specific priors. After reviewing recent applications within
neuroimaging-based psychiatric research, such as the diagnosis of psychiatric
diseases, delineation of disease subtypes, normative modeling, and the
development of neuroimaging biomarkers, we discuss current challenges. This
includes for example the difficulty of training models on small, heterogeneous
and biased data sets, the lack of validity of clinical labels, algorithmic
bias, and the influence of confounding variables
Tensor Regression
Regression analysis is a key area of interest in the field of data analysis
and machine learning which is devoted to exploring the dependencies between
variables, often using vectors. The emergence of high dimensional data in
technologies such as neuroimaging, computer vision, climatology and social
networks, has brought challenges to traditional data representation methods.
Tensors, as high dimensional extensions of vectors, are considered as natural
representations of high dimensional data. In this book, the authors provide a
systematic study and analysis of tensor-based regression models and their
applications in recent years. It groups and illustrates the existing
tensor-based regression methods and covers the basics, core ideas, and
theoretical characteristics of most tensor-based regression methods. In
addition, readers can learn how to use existing tensor-based regression methods
to solve specific regression tasks with multiway data, what datasets can be
selected, and what software packages are available to start related work as
soon as possible. Tensor Regression is the first thorough overview of the
fundamentals, motivations, popular algorithms, strategies for efficient
implementation, related applications, available datasets, and software
resources for tensor-based regression analysis. It is essential reading for all
students, researchers and practitioners of working on high dimensional data.Comment: 187 pages, 32 figures, 10 table
MULTIVARIATE MODELING OF COGNITIVE PERFORMANCE AND CATEGORICAL PERCEPTION FROM NEUROIMAGING DATA
State-of-the-art cognitive-neuroscience mainly uses hypothesis-driven statistical testing to characterize and model neural disorders and diseases. While such techniques have proven to be powerful in understanding diseases and disorders, they are inadequate in explaining causal relationships as well as individuality and variations. In this study, we proposed multivariate data-driven approaches for predictive modeling of cognitive events and disorders. We developed network descriptions of both structural and functional connectivities that are critical in multivariate modeling of cognitive performance (i.e., fluency, attention, and working memory) and categorical perceptions (i.e., emotion, speech perception). We also performed dynamic network analysis on brain connectivity measures to determine the role of different functional areas in relation to categorical perceptions and cognitive events. Our empirical studies of structural connectivity were performed using Diffusion Tensor Imaging (DTI). The main objective was to discover the role of structural connectivity in selecting clinically interpretable features that are consistent over a large range of model parameters in classifying cognitive performances in relation to Acute Lymphoblastic Leukemia (ALL). The proposed approach substantially improved accuracy (13% - 26%) over existing models and also selected a relevant, small subset of features that were verified by domain experts. In summary, the proposed approach produced interpretable models with better generalization.Functional connectivity is related to similar patterns of activation in different brain regions regardless of the apparent physical connectedness of the regions. The proposed data-driven approach to the source localized electroencephalogram (EEG) data includes an array of tools such as graph mining, feature selection, and multivariate analysis to determine the functional connectivity in categorical perceptions. We used the network description to correctly classify listeners behavioral responses with an accuracy over 92% on 35 participants. State-of-the-art network description of human brain assumes static connectivities. However, brain networks in relation to perception and cognition are complex and dynamic. Analysis of transient functional networks with spatiotemporal variations to understand cognitive functions remains challenging. One of the critical missing links is the lack of sophisticated methodologies in understanding dynamics neural activity patterns. We proposed a clustering-based complex dynamic network analysis on source localized EEG data to understand the commonality and differences in gender-specific emotion processing. Besides, we also adopted Bayesian nonparametric framework for segmentation neural activity with a finite number of microstates. This approach enabled us to find the default network and transient pattern of the underlying neural mechanism in relation to categorical perception. In summary, multivariate and dynamic network analysis methods developed in this dissertation to analyze structural and functional connectivities will have a far-reaching impact on computational neuroscience to identify meaningful changes in spatiotemporal brain activities
Graph-constrained Analysis for Multivariate Functional Data
Functional Gaussian graphical models (GGM) used for analyzing multivariate
functional data customarily estimate an unknown graphical model representing
the conditional relationships between the functional variables. However, in
many applications of multivariate functional data, the graph is known and
existing functional GGM methods cannot preserve a given graphical constraint.
In this manuscript, we demonstrate how to conduct multivariate functional
analysis that exactly conforms to a given inter-variable graph. We first show
the equivalence between partially separable functional GGM and graphical
Gaussian processes (GP), proposed originally for constructing optimal
covariance functions for multivariate spatial data that retain the conditional
independence relations in a given graphical model. The theoretical connection
help design a new algorithm that leverages Dempster's covariance selection to
calculate the maximum likelihood estimate of the covariance function for
multivariate functional data under graphical constraints. We also show that the
finite term truncation of functional GGM basis expansion used in practice is
equivalent to a low-rank graphical GP, which is known to oversmooth marginal
distributions. To remedy this, we extend our algorithm to better preserve
marginal distributions while still respecting the graph and retaining
computational scalability. The insights obtained from the new results presented
in this manuscript will help practitioners better understand the relationship
between these graphical models and in deciding on the appropriate method for
their specific multivariate data analysis task. The benefits of the proposed
algorithms are illustrated using empirical experiments and an application to
functional modeling of neuroimaging data using the connectivity graph among
regions of the brain.Comment: 23 pages, 6 figure
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