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Machine Learning Techniques to Analyze Dynamic Functional Neuroimaging Patterns Underlying Inhibitory Control Mechanisms

We investigate novel computational techniques to analyze brain-behavior relationships underlying mechanisms of inhibitory control, focusing on performing classification of hard-to-categorize groups of subjects based on brain activation response patterns to behavioral challenges of inhibitory control using functional magnetic resonance imaging (fMRI). We hypothesize that unique patterns of variability in brain function can assist in identification of brain mechanisms rooted in compromised inhibitory control. Such patterns will increase our understanding of brain connectivity and circuitry as we move iteratively between a-priori and exploratory means of describing circuits of inhibitory control. We propose an integrated machine learning framework for the joint exploration of spatial, temporal and functional information for the analysis of fMRI signals, thus allowing the testing of hypotheses and development of applications that are not supported by traditional analysis methods.

Modeling Neuronal Interactivity using Dynamic Bayesian Networks

[publications]

Functional Magnetic Resonance Imaging (fMRI) has enabled scientists to look into the active brain by providing sequences of 3D brain function images. However, interactivity between functional brain regions, is still little studied. In this paper, we contribute a novel framework for modeling the interactions between multiple active brain regions, using Dynamic Bayesian Networks (DBNs) as generative models for brain activation patterns. This framework is applied to modeling of neuronal circuits associated with reward. The novelty of our framework from a Machine Learning perspective lies in the use of DBNs to reveal the brain connectivity and interactivity. Such interactivity models which are derived from fMRI data are then validated through a classification task: separating drug addicted subjects from healthy non-drug-using controls. We employ and compare four different types of DBNs: Parallel Hidden Markov Models (PaHMM), Coupled Hidden Markov Models (CHMM), Fully-linked Hidden Markov Models (FHMM) and Dynamically Multi-Linked HMMs (DML-HMM). Moreover, we propose and compare two schemes of learning DML-HMMs. Experimental results show that by using DBNs, group classification can be performed even if the DBNs are constructed from as few as 5 brain regions. We also demonstrate that, by using the proposed learning algorithms, different DBN structures characterize drug addicted subjects vs. control subjects. This finding provides an independent test for the effect of psychopathology on brain function. In general, we demonstrate that incorporation of computer science principles into functional neuroimaging clinical studies provides a novel approach for probing human brain function.

Publications

Machine Learning for Clinical Diagnosis from Functional Magnetic Resonance Imaging

[publications]

Functional Magnetic Resonance Imaging (fMRI) has enabled scientists to look into the active human brain. fMRI provides a sequence of 3D brain images with intensities representing brain activations. Standard techniques for fMRI analysis traditionally focused on finding the area of most significant brain activation for different sensations or activities. In this paper, we explore a new application of machine learning methods to a more challenging problem: classifying subjects into groups based on the observed 3D brain images when the subjects are performing the same task. Here we address the separation of drug-addicted subjects from healthy non-drug-using controls. In this paper, we explore a number of classification approaches. We introduce a novel algorithm that integrates side information into the use of boosting. Our algorithm clearly outperformed well established classifiers as documented in extensive experimental results. This is the first time that machine learning techniques based on 3D brain images are applied to a clinical diagnosis that currently is only performed through patient self-report. Our tools can therefore provide information not addressed by traditional analysis methods and substantially improve diagnosis.

Publications

Exploiting Temporal Information in Functional Magnetic Resonance Imaging Brain Data

[publications]

Functional Magnetic Resonance Imaging(fMRI) has enabled scientists to look into the active human brain, leading to a flood of new data, thus encouraging the development of new data analysis methods. In this paper, we contribute a comprehensive framework for spatial and temporal exploration of fMRI data, and apply it to a challenging case study: separating drug addicted subjects from healthy non-drug-using controls. To our knowledge, this is the first time that learning on fMRI data is performed explicitly on temporal information for classification. Experimental results demonstrate that, by selecting discriminative features, group classification can be successfully performed on our case study although training data are exceptionally high dimensional, sparse and noisy fMRI sequences. The classification performance can be significantly improved by incorporating temporal information into machine learning. Both statistical and neuroscientific validation of the method's generalization ability are provided. We demonstrate that incorporation of computer science principles into functional neuroimaging clinical studies, facilitates deduction about the behavioral probes from the brain activation data, thus providing a valid tool that incorporates objective brain imaging data into clinical classification of psychopathologies and identification of genetic vulnerabilities.

Publications