Functional MRI (fMRI) techniques have provided the addiction field with a unique and profoundly valuable ability to explore brain mechanisms in human subjects. Despite the resultant advances in our understanding of neural mechanisms, the diagnostic utility of neuroimaging methods has not been realized. In addition, our ability to assess the reactivity of neural networks over time remains limited. To explore new approaches to further our understanding and diagnosis of substance use disorders, this proposal will utilize novel and powerful analytic methods to assess an existing functional magnetic resonance imaging (fMRI) data set in cocaine-addicted subjects and healthy controls. The PI, new to the substance abuse field, will bring analytic approaches developed in other areas of investigation to the field of addiction. The broad goal of this project is to examine differentiations of brain functions in cocaine-addicted subjects using fMRI data and to reveal functional connectivity between regions involving disinhibition and decision-making tasks. This study will exploit data from an ongoing NIDA-funded study, """"""""Impulsivity, Neural Deficits and Cocaine Addiction."""""""" This study assesses BOLD response during a motor disinhibition task and a decision- making task of response reversal as well as basal measures of interconnectivity measures during rest. A data driven entropy-based algorithm, independent component analysis (ICA), will be used to extract spatially independent regions that show differences in addicted and control subjects (Aim 1). We hypothesize that the proposed method will identify certain groups of voxels (not necessarily spatially connected) serving separation of two groups who are under disinhibition and decision-making tasks. To uncover how the functional connectivity changes over time between those differentiated regions, we will perform dynamic (changes over short time) dynamic functional network connectivity (DFNC) analysis using various window sizes on time course (Aim 2). These findings are expected to reveal a temporal distinction between ICA-derived network changes in cocaine-addicted and healthy control populations. Since the first aim will find significantly discriminative clouds of voxels, we will use a machine-learning algorithm, the support vector machines (SVM), to automatically map a subject into one of the groups, cocaine-addicted or control, by using subject's fMRI data (Aim 3). We hypothesize that SVM based subject classification tool will provide high sensitivity in selection between the two groups. The framework potentially offers a useful clinical diagnostic instrument as well as demonstrating key brain neural differences between the groups. The last aim is designed to reveal dynamic connectivity between pairs of given anatomic regions of relevance, such as the mesial prefrontal cortex (PFC), inferior PFC, insular cortex, and anterior cingulate (for the disinhibition task) and the orbitofrontal cortex, dorsolateral prefrontal cortex, and anterior cingulate (for the decision-making task). We hypothesize that a clear boundary will be observed in interaction patterns of two groups regarding two tasks. The contrast between brain regions in fMRI data of cocaine-addicted and control subjects will be investigated for the first time using very powerful data analysis tool (ICA) and will be used in classification framework of SVM. The proposal will bring together two extremely innovative techniques (DFNC and SVM) and a promising junior investigator into the field of addiction. The utilization of a pre-existing database offers a highly cost-effective approach to a clinically important, perplexing and persistent problem in the field.
The abuse and dependence of stimulants is a major public health problem with significant health, legal, social and occupational associated costs. While biologically-oriented studies, particularly those using imaging techniques, have produced significant advances in our understanding of brain activity relevant to the development and persistence of addiction, this knowledge has done little to assist in the diagnosis of these disorders. This study will bring statistical procedures used in other fields of research to imaging data obtained from cocaine-addicted subjects to assess changes in brain networks and determine whether the cocaine-addicted patients can be distinguished from non-cocaine using subjects.
| Mete, Mutlu; Sakoglu, Unal; Spence, Jeffrey S et al. (2016) Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach. BMC Bioinformatics 17:357 |
| Akgün, Devrim; Sako?lu, Ünal; Esquivel, Johnny et al. (2015) GPU accelerated dynamic functional connectivity analysis for functional MRI data. Comput Med Imaging Graph 43:53-63 |
