Compromised inhibitory control is a hallmark neuropsychological deficit underlying complex disorders and behaviors as drug addiction and aggression. Individuals with compromised mechanisms of control are difficult to identify unless they are subjected to challenging conditions directly. This is possibly due to the subtle ways in which poor inhibitory control is expressed in their baseline functioning, making classification and most importantly, prediction of future behavior, extremely challenging. We propose 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 (fMRl). These classification methods are applied on two distinct datasets: one of substance dependent individuals and the other of individuals with a particular genotype conferring vulnerability to poor inhibitory control. 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. Machine Learning techniques have been shown to be successful in discovering optimal features and patterns in complex high dimensional datasets. The diversity of the underlying questions when studying inhibitory control and the subtlety of the effects that can be used for classification, motivate us to 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. We hypothesize that: 1) A differential spatial brain pattern will indicate a diagnosis of drug addiction and a membership in one or another level ol MAOA genotype. Spatial information from static 3D contrast maps will be input to PCA-based and Voxel-based methods, Adaboosting and Learning with Side information 2) A temporally accounted intrasubject pattern of response to the inhibitory control challenge conditions in the fMRI paradigms will reveal group membership in both data sets. Temporal fMRl information wilt be used for Hidden Markov Models, Conditional Random Fields, etc. 3) A connectivity map corresponding to brain circuits functionally subserving inhibitory control wilt be revealed with indications of directionality of influence between brain regions by analyzing functional information with Dynamic Bayesian Networks, Dynamically Multi-Linked HMMs, etc.
| Han, Xufeng; Berg, Alexander C; Oh, Hwamee et al. (2013) Multi-voxel pattern analysis of selective representation of visual working memory in ventral temporal and occipital regions. Neuroimage 73:8-15 |
| Panagopoulos, Alexandros; Wang, Chaohui; Samaras, Dimitris et al. (2013) Simultaneous cast shadows, illumination and geometry inference using hypergraphs. IEEE Trans Pattern Anal Mach Intell 35:437-49 |
| Honorio, Jean; Tomasi, Dardo; Goldstein, Rita Z et al. (2012) Can a single brain region predict a disorder? IEEE Trans Med Imaging 31:2062-72 |
| Goldstein, Rita Z; Woicik, Patricia A; Maloney, Thomas et al. (2010) Oral methylphenidate normalizes cingulate activity in cocaine addiction during a salient cognitive task. Proc Natl Acad Sci U S A 107:16667-72 |
| Langs, Georg; Samaras, Dimitris; Paragios, Nikos et al. (2008) Task-specific functional brain geometry from model maps. Med Image Comput Comput Assist Interv 11:925-33 |
