Biology subjects

Cribben, I.

Publications and source records attributed to Cribben, I..

7 recordsLinked to original sources

bioRxiv2025

Change point based dynamic functional connectivity estimation outperforms sliding window and static estimation for classification of early mild cognitive impairment in resting-state fMRI

The most widely used inputs in classification models of brain disorders such as early mild cognitive impairment (eMCI) or Alzheimers disease are estimates of static-based functional connectivity (SFC) and sliding window dynamic functional connectivity (swDFC). Although these methods are convenient for estimation and computational purposes, as it keeps the estimation tractable, they present a simplified version of a highly integrated and dynamic phenomenon. Change point dynamic functional connectivity (cpDFC) methods, which are far less commonly used, offer an alternative to swDFC approaches. In this study, we consider a classification task between controls and patients with eMCI using resting-state functional magnetic resonance imaging (fMRI) data from the Alzheimers Disease Neuroimaging Initiative (ADNI) studies, ADNI2 and ADNIGO. Our results indicate that the DFC methods are generally superior to the SFC methods when used as inputs into the classification model. Most importantly, we find that the cpDFC methods are generally superior to the widely used swDFC methods. We discuss how cpDFC methods offer many distinct advantages over swDFC methods, namely, the parsimony of network features and ease of interpretability. We validate the robustness and consistency of our results by testing the methods on an additional resting-state fMRI dataset of mild cognitive impairment patients. These findings call into question the validity of numerous fMRI studies that have utilized inferior approaches, such as SFC and swDFC, as inputs to classification models to predict various brain disorders. Finally, we present an ensemble model of the best models, which achieves an accuracy of 91.17% from leave-one-out cross-validation of subjects with eMCI. Our results suggest that the underlying functional networks are dynamic, multiscale, and that different FC methods capture distinct information for classification efficacy.

bioinformatics↗

bioRxiv2021

Beyond linear dynamic functional connectivity: a vine copula change point model

To estimate dynamic functional connectivity for functional magnetic resonance imaging (fMRI) data, two approaches have dominated: sliding window and change point methods. While computationally feasible, the sliding window approach has several limitations. In addition, the existing change point methods assume a Gaussian distribution for and linear dependencies between the fMRI time series. In this work, we introduce a new methodology called Vine Copula Change Point (VCCP) to estimate change points in the functional connectivity network structure between brain regions. It uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between brain regions including tail, symmetric and asymmetric dependence, which has not been explored before in the dynamic analysis of neuroimaging data. We apply VCCP to various simulation data sets and to two fMRI data sets: a reading task and an anxiety inducing experiment. In particular, for the former data set, we illustrate the complexity of textual changes during the reading of Chapter 9 in Harry Potter and the Sorcerers Stone and find that change points across subjects are related to changes in more than one type of textual attributes. Further, the graphs created by the vine copulas indicate the importance of working beyond Gaussianity and linear dependence. Finally, the R package vccp implementing the methodology from the paper is available from CRAN.

neuroscience↗

bioRxiv2020

Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity

Evidence of the non stationary behavior of functional connectivity (FC) networks has been observed in task based functional magnetic resonance imaging (fMRI) experiments and even prominently in resting state fMRI data. This has led to the development of several new statistical methods for estimating this time-varying connectivity, with the majority of the methods utilizing a sliding window approach. While computationally feasible, the sliding window approach has several limitations. In this paper, we circumvent the sliding window, by introducing a statistical method that finds change-points in FC networks where the number and location of change-points are unknown a priori. The new method, called cross-covariance isolate detect (CCID), detects multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. CCID allows for change-point detection in the presence of frequent changes of possibly small magnitudes, can assign change-points to one or multiple brain regions, and is computationally fast. In addition, CCID is particularly suited to task based data, where the subject alternates between task and rest, as it firstly attempts isolation of each of the change-points within subintervals, and secondly their detection therein. Furthermore, we also propose a new information criterion for CCID to identify the change-points. We apply CCID to several simulated data sets and to task based and resting state fMRI data and compare it to recent change-point methods. CCID may also be applicable to electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. Similar to other biological networks, understanding the complex network organization and functional dynamics of the brain can lead to profound clinical implications. Finally, the R package ccid implementing the method from the paper is available from CRAN.

neuroscience↗

bioRxiv2020

Investigating the effects of healthy cognitive aging on brain functional connectivity using 4.7 T resting-state functional Magnetic Resonance Imaging.

IntroductionFunctional changes in the aging human brain have been previously reported using functional magnetic resonance imaging (fMRI). Earlier resting-state fMRI studies revealed an age-associated weakening of intra-system functional connectivity (FC) and age-associated strengthening of inter-system FC. However, the majority of such FC studies did not investigate the relationship between age and network amplitude, without which correlation-based measures of FC can be challenging to interpret. Consequently, the main aim of this study was to investigate how three primary measures of resting-state fMRI signal - network amplitude, network topography, and inter-network FC - are affected by healthy cognitive aging. MethodsWe acquired resting-state fMRI data on a 4.7 T scanner for 105 healthy participants representing the entire adult lifespan (18-85 years of age). To study age differences in network structure, we combined ICA-based network decomposition with sparse graphical models. ResultsOlder adults displayed lower blood-oxygen-level-dependent (BOLD) signal amplitude in all functional systems with sensorimotor networks showing the largest age differences. Our age comparisons of network topography and inter-network FC demonstrated a substantial amount of age-invariance in the brains functional architecture. Despite architecture similarities, old adults displayed a loss of communication efficiency in our inter-network FC comparisons, driven primarily by FC reduction in frontal and parietal association cortices. Together, our results provide a comprehensive overview of age effects on fMRI-based FC.

neuroscience↗

bioRxiv2019

Nonparametric Anomaly Detection on Time Series of Graphs

Identifying change points and/or anomalies in dynamic network structures has become increasingly popular across various domains, from neuroscience to telecommunication to finance. One of the particular objectives of the anomaly detection task from the neuroscience perspective is the reconstruction of the dynamic manner of brain region interactions. However, most statistical methods for detecting anomalies have the following unrealistic limitation for brain studies and beyond: that is, network snapshots at different time points are assumed to be independent. To circumvent this limitation, we propose a distribution-free framework for anomaly detection in dynamic networks. First, we present each network snapshot of the data as a linear object and find its respective univariate characterization via local and global network topological summaries. Second, we adopt a change point detection method for (weakly) dependent time series based on efficient scores, and enhance the finite sample properties of change point method by approximating the asymptotic distribution of the test statistic using the sieve bootstrap. We apply our method to simulated and to real data, particularly, two functional magnetic resonance imaging (fMRI) data sets and the Enron communication graph. We find that our new method delivers impressively accurate and realistic results in terms of identifying locations of true change points compared to the results reported by competing approaches. The new method promises to offer a deeper insight into the large-scale characterizations and functional dynamics of the brain and, more generally, into intrinsic structure of complex dynamic networks.

neuroscience↗

bioRxiv2017

A Longitudinal Model for Functional Connectivity Using Resting-State fMRI

Many neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current network modeling 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 network 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 FC network, and 3) the FC networks 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 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 baseline FC and change in FC over 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 Alzheimers Disease Neuroimaging Initiative database.

neuroscience↗

bioRxiv2017

Graphical models for functional connectivity networks: best methods and the autocorrelation issue

Sparse graphical models are frequently used to explore both static and dynamic functional brain networks from neuroimaging data. However, the practical performance of the models has not been studied in detail for brain networks. In this work, we have two objectives. First, we compare several sparse graphical model estimation procedures and several selection criteria under various experimental settings, such as different dimensions, sample sizes, types of data, and sparsity levels of the true model structures. We discuss in detail the superiority and deficiency of each combination. Second, in the same simulation study, we show the impact of autocorrelation and whitening on the estimation of functional brain networks. We apply the methods to a resting-state functional magnetic resonance imaging (fMRI) data set. Our results show that the best sparse graphical model, in terms of detection of true connections and having few false-positive connections, is the smoothly clipped absolute deviation (SCAD) estimating method in combination with the Bayesian information criterion (BIC) and cross-validation (CV) selection method. In addition, the presence of autocorrelation in the data adversely affects the estimation of networks but can be helped by using the CV selection method. These results question the validity of a number of fMRI studies where inferior graphical model techniques have been used to estimate brain networks.

neuroscience↗