Christoforos Anagnostopoulos

Estimating time-varying brain connectivity networks from functional MRI time series

At the forefront of neuroimaging is the understanding of the functional architecture of the human brain. In most applications functional networks are assumed to be stationary, resulting in a single network estimated for the entire time course. However recent results suggest that the connectivity between brain regions is highly non-stationary even at rest. As a result, there is a need for new brain imaging methodologies that comprehensively account for the dynamic nature of functional networks. In this work we propose the Smooth Incremental Graphical Lasso Estimation (SINGLE) algorithm which estimates dynamic brain networks from fMRI data. We apply the proposed algorithm to functional MRI data from 24 healthy patients performing a Choice Reaction Task to demonstrate the dynamic changes in network structure that accompany a simple but attentionally demanding cognitive task. Using graph theoretic measures we show that the properties of the Right Inferior Frontal Gyrus and the Right Inferior Parietal lobe dynamically change with the task. These regions are frequently reported as playing an important role in cognitive control. Our results suggest that both these regions play a key role in the attention and executive function during cognitively demanding tasks and may be fundamental in regulating the balance between other brain regions.

When is the area under the receiver operating characteristic curve an appropriate measure of classifier performance

The area under the receiver operating characteristic curve is a widely used measure of the performance of classification rules. This paper shows that when classifications are based solely on data describing individual objects to be classified, the area under the receiver operating characteristic curve is an incoherent measure of performance, in the sense that the measure itself depends on the classifier being measured. It significantly extends earlier work by showing that this incoherence is not a consequence of a cost-based interpretation of misclassifications, but is a fundamental property of the area under the curve itself. The paper also shows that if additional information, such as the class assignments of other objects, is taken into account when making a classification, then the area under the curve is a coherent measure, although in those circumstances it makes an assumption which is seldom if ever appropriate.

Online linear and quadratic discriminant analysis with adaptive forgetting for streaming classification

Advances in data technology have enabled streaming acquisition of real-time information in a wide range of settings, including consumer credit, electricity consumption, and internet user behavior. Streaming data consist of transiently observed, temporally evolving data sequences, and poses novel challenges to statistical analysis. Foremost among these challenges are the need for online processing, and temporal adaptivity in the face of unforeseen changes, both smooth and abrupt, in the underlying data generation mechanism. In this paper, we develop streaming versions of two widely used parametric classifiers, namely quadratic and linear discriminant analysis. We rely on computationally efficient, recursive formulations of these classifiers. We additionally equip them with exponential forgetting factors that enable temporal adaptivity via smoothly down-weighting the contribution of older data. Drawing on ideas from adaptive filtering, we develop an online method for self-tuning forgetting factors on the basis of an approximate gradient scheme. We provide extensive simulation and real data analysis that demonstrate the effectiveness of the proposed method in handling diverse types of change, while simultaneously offering monitoring capabilities via interpretable behavior of the adaptive forgetting factors.

The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI

Functional neuroimaging typically explores how a particular task activates a set of brain regions. Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system. Here, we propose and validate an alternative framework, the Automatic Neuroscientist, which turns the standard fMRI approach on its head. We use real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state. In this work, we present two proof-of-principle studies involving perceptual stimuli. In both studies optimization algorithms of varying complexity were employed; the first involved a stochastic approximation method while the second incorporated a more sophisticated Bayesian optimization technique. In the first study, we achieved convergence for the hypothesized optimum in 11 out of 14 runs in less than 10 min. Results of the second study showed how our closed-loop framework accurately and with high efficiency estimated the underlying relationship between stimuli and neural responses for each subject in one to two runs: with each run lasting 6.3 min. Moreover, we demonstrate that using only the first run produced a reliable solution at a group-level. Supporting simulation analyses provided evidence on the robustness of the Bayesian optimization approach for scenarios with low contrast-to-noise ratio. This framework is generalizable to numerous applications, ranging from optimizing stimuli in neuroimaging pilot studies to tailoring clinical rehabilitation therapy to patients and can be used with multiple imaging modalities in humans and animals.

Temporally-Adaptive Linear Classification for Handling Population Drift in Credit Scoring

Classification methods have proven effective for predicting the creditworthiness of credit applications. However, the tendency of the underlying populations to change over time, population drift, is a fundamental problem for such classifiers. The problem manifests as decreasing performance as the classifier ages and is typically handled by periodic classifier reconstruction. To maintain performance between rebuilds, we propose an adaptive and incremental linear classification rule that is updated on the arrival of new labeled data. We consider adapting this method to suit credit application classification and demonstrate, with real loan data, that the method outperforms static and periodically rebuilt linear classifiers.

Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme’s effectiveness in both real and simulated streaming environments.

Deciding what to observe next: adaptive variable selection for regression in multivariate data streams

Variable selection can be valuable in the analysis of streaming data with costly measurements, as in intensive care monitoring or battery-powered sensor networks. In the presence of drift, selections must be constantly revised, calling for adaptive variable selection schemes. An important and novel problem arises from the fact that non-selected variables become missing variables, which induces bias upon subsequent decisions. Here, we consider adaptive variable selection in the context of linear regression, using only a fraction of the available regressors per timepoint. We suggest a scheme that fits a multivariate Gaussian over a sliding window using the EM algorithm and selects which variables to observe next using the Lasso algorithm. We experiment with simulated and real data to demonstrate that very high prediction accuracy may be retained using as little as 10% of the data.

Real-time estimation of dynamic functional connectivity networks

Two novel and exciting avenues of neuroscientific research involve the study of task‐driven dynamic reconfigurations of functional connectivity networks and the study of functional connectivity in real‐time. While the former is a well‐established field within neuroscience and has received considerable attention in recent years, the latter remains in its infancy. To date, the vast majority of real‐time fMRI studies have focused on a single brain region at a time. This is due in part to the many challenges faced when estimating dynamic functional connectivity networks in real‐time. In this work, we propose a novel methodology with which to accurately track changes in time‐varying functional connectivity networks in real‐time. The proposed method is shown to perform competitively when compared to state‐of‐the‐art offline algorithms using both synthetic as well as real‐time fMRI data. The proposed method is applied to motor task data from the Human Connectome Project as well as to data obtained from a visuospatial attention task. We demonstrate that the algorithm is able to accurately estimate task‐related changes in network structure in real‐time. Hum Brain Mapp 38:202–220, 2017.

Graph Embeddings of Dynamic Functional Connectivity Reveal Discriminative Patterns of Task Engagement in HCP Data

There is increasing evidence to suggest functional connectivity networks are non-stationary. This has lead to the development of novel methodologies with which to accurately estimate time-varying functional connectivity networks. Many of these methods provide unprecedented temporal granularity by estimating a functional connectivity network at each point in time, resulting in high-dimensional output which can be studied in a variety of ways. One possible method is to employ graph embedding algorithms. Such algorithms effectively map estimated networks from high-dimensional spaces down to a low dimensional vector space, thus facilitating visualization, interpretation and classification. In this work, the dynamic properties of functional connectivity are studied using working memory task data from the Human Connectome Project. A recently proposed method is employed to estimate dynamic functional connectivity networks. The results are subsequently analyzed using two graph embedding methods based on linear projections. These methods are shown to provide informative embeddings that can be directly interpreted as functional connectivity networks.

Learning population and subject-specific brain connectivity networks via mixed neighborhood selection

In neuroimaging data analysis, Gaussian graphical models are often used to model statistical dependencies across spatially remote brain regions known as functional connectivity. Typically, data is collected across a cohort of subjects and the scientific objectives consist of estimating population and subject-specific graphical models. A third objective that is often overlooked involves quantifying inter-subject variability and thus identifying regions or sub-networks that demonstrate heterogeneity across subjects. Such information is fundamental in order to thoroughly understand the human connectome. We propose Mixed Neighborhood Selection in order to simultaneously address the three aforementioned objectives. By recasting covariance selection as a neighborhood selection problem we are able to efficiently learn the topology of each node. We introduce an additional mixed effect component to neighborhood selection in order to simultaneously estimate a graphical model for the population of subjects as well as for each individual subject. The proposed method is validated empirically through a series of simulations and applied to resting state data for healthy subjects taken from the ABIDE consortium.