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Ivor Cribben

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5 papers

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NeurIPS Conference 2025 Conference Paper

A Latent Multilayer Graphical Model For Complex, Interdependent Systems

Martin Ondrus
Ivor Cribben
Yang Feng

Networks have been extensively used and have provided novel insights across a wide variety of research areas. However, many real-world systems are, in fact, a ``network of networks'', or a multilayer network, which interact as components of a larger multimodal system. A major difficulty in this multilayer framework is the estimation of interlayer edges or connections. In this work, we propose a new estimation method, called multilayer sparse + low-rank inverse covariance estimation (multiSLICE), which estimates the interlayer edges. multiSLICE bridges latent variable Gaussian graphical methods with multilayer networks, offering a flexible framework for modeling processes with irregular sampling and heterogeneous graph structures. We develop an effective algorithm to compute the estimator. We also establish theoretical conditions for the recoverability of the joint space, analyze how inter-layer interactions influence joint parameter estimation, and provide theoretical bounds on their relationships. Finally, we rigorously evaluate our method on both simulated and multimodal neuroimaging data, demonstrating improvements over state-of-the-art approaches. Finally, all the relevant R code implementing the method in the article is available on GitHub.

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YNIMG Journal 2020 Journal Article

Diffusion tensor imaging of the corpus callosum in healthy aging: Investigating higher order polynomial regression modelling

Wojciech Pietrasik
Ivor Cribben
Fraser Olsen
Yushan Huang
Nikolai V. Malykhin

Previous diffusion tensor imaging (DTI) studies confirmed the vulnerability of corpus callosum (CC) fibers to aging. However, most studies employed lower order regressions to study the relationship between age and white matter microstructure. The present study investigated whether higher order polynomial regression modelling can better describe the relationship between age and CC DTI metrics compared to lower order models in 140 healthy participants (ages 18–85). The CC was found to be non-uniformly affected by aging, with accelerated and earlier degradation occurring in anterior portion; callosal volume, fiber count, fiber length, mean fibers per voxel, and FA decreased with age while mean, axial, and radial diffusivities increased. Half of the parameters studied also displayed significant age-sex interaction or intracranial volume effects. Higher order models were chosen as the best fit, based on Bayesian Information Criterion minimization, in 16 out of 23 significant cases when describing the relationship between DTI measurements and age. Higher order model fits provided different estimations of aging trajectory peaks and decline onsets than lower order models; however, a likelihood ratio test found that higher order regressions generally did not fit the data significantly better than lower order polynomial or linear models. The results contrast the modelling approaches and highlight the importance of using higher order polynomial regression modelling when investigating associations between age and CC white matter microstructure.

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YNIMG Journal 2018 Journal Article

A longitudinal model for functional connectivity networks using resting-state fMRI

Brian Hart
Ivor Cribben
Mark Fiecas

Many neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current fMRI literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC model using a variance components approach. First, for all subjects' visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the baseline FC strength, and 3) the FC's longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI time series data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in the baseline FC network and change in FC over longitudinal time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Overall, we found no difference in the global FC network between Alzheimer's disease patients and healthy controls, but did find differing local aging patterns in the FC between the left hippocampus and the posterior cingulate cortex.

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YNIMG Journal 2017 Journal Article

A variance components model for statistical inference on functional connectivity networks

Mark Fiecas
Ivor Cribben
Reyhaneh Bahktiari
Jacqueline Cummine

We propose a variance components linear modeling framework to conduct statistical inference on functional connectivity networks that directly accounts for the temporal autocorrelation inherent in functional magnetic resonance imaging (fMRI) time series data and for the heterogeneity across subjects in the study. The novel method estimates the autocorrelation structure in a nonparametric and subject-specific manner, and estimates the variance due to the heterogeneity using iterative least squares. We apply the new model to a resting-state fMRI study to compare the functional connectivity networks in both typical and reading impaired young adults in order to characterize the resting state networks that are related to reading processes. We also compare the performance of our model to other methods of statistical inference on functional connectivity networks that do not account for the temporal autocorrelation or heterogeneity across the subjects using simulated data, and show that by accounting for these sources of variation and covariation results in more powerful tests for statistical inference.

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YNIMG Journal 2012 Journal Article

Dynamic connectivity regression: Determining state-related changes in brain connectivity

Ivor Cribben
Ragnheidur Haraldsdottir
Lauren Y. Atlas
Tor D. Wager
Martin A. Lindquist

Most statistical analyses of fMRI data assume that the nature, timing and duration of the psychological processes being studied are known. However, often it is hard to specify this information a priori. In this work we introduce a data-driven technique for partitioning the experimental time course into distinct temporal intervals with different multivariate functional connectivity patterns between a set of regions of interest (ROIs). The technique, called Dynamic Connectivity Regression (DCR), detects temporal change points in functional connectivity and estimates a graph, or set of relationships between ROIs, for data in the temporal partition that falls between pairs of change points. Hence, DCR allows for estimation of both the time of change in connectivity and the connectivity graph for each partition, without requiring prior knowledge of the nature of the experimental design. Permutation and bootstrapping methods are used to perform inference on the change points. The method is applied to various simulated data sets as well as to an fMRI data set from a study (N =26) of a state anxiety induction using a socially evaluative threat challenge. The results illustrate the method's ability to observe how the networks between different brain regions changed with subjects' emotional state.

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