Assessing the effects of cocaine dependence and pathological gambling using group-wise sparse representation of natural stimulus FMRI data

• Published in: Brain imaging and behavior Vol. 11; no. 4; pp. 1179 - 1191
• Main Authors: Ren, Yudan, Fang, Jun, Lv, Jinglei, Hu, Xintao, Guo, Cong Christine, Guo, Lei, Xu, Jiansong, Potenza, Marc N., Liu, Tianming
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2017; Springer Nature B.V
• Subjects: Activation analysis; Addictions; Biomedical and Life Sciences; Biomedicine; Brain; Brain mapping; Cocaine; Coding; Data management; Disorders; Distance learning; Drug addiction; Functional magnetic resonance imaging; Gambling; Mental disorders; Neural coding; Neuropsychology; Neuroradiology; Neurosciences; Original Research; Psychiatry; Stimuli; Cocaine dependence; Pathological gambling; Group-wise sparse representation; Functional brain networks; Naturalistic stimuli
• ISSN: 1931-7557; 1931-7565; 1931-7565
• DOI: 10.1007/s11682-016-9596-4

Assessing functional brain activation patterns in neuropsychiatric disorders such as cocaine dependence (CD) or pathological gambling (PG) under naturalistic stimuli has received rising interest in recent years. In this paper, we propose and apply a novel group-wise sparse representation framework to assess differences in neural responses to naturalistic stimuli across multiple groups of participants (healthy control, cocaine dependence, pathological gambling). Specifically, natural stimulus fMRI (N-fMRI) signals from all three groups of subjects are aggregated into a big data matrix, which is then decomposed into a common signal basis dictionary and associated weight coefficient matrices via an effective online dictionary learning and sparse coding method. The coefficient matrices associated with each common dictionary atom are statistically assessed for each group separately. With the inter-group comparisons based on the group-wise correspondence established by the common dictionary, our experimental results demonstrated that the group-wise sparse coding and representation strategy can effectively and specifically detect brain networks/regions affected by different pathological conditions of the brain under naturalistic stimuli.