Sparse coding reveals greater functional connectivity in female brains during naturalistic emotional experience

• Published in: PloS one Vol. 12; no. 12; p. e0190097
• Main Authors: Ren, Yudan, Lv, Jinglei, Guo, Lei, Fang, Jun, Guo, Christine Cong
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22.12.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Autism; Automation; Biology and Life Sciences; Brain; Brain - physiology; Brain mapping; Brain research; Coding; Computer and Information Sciences; Connectivity; Data processing; Distance learning; Emotions; Female; Females; Functional magnetic resonance imaging; Gender aspects; Gender differences; Humans; Independent component analysis; Machine learning; Magnetic resonance; Magnetic Resonance Imaging; Male; Mathematical models; Medical imaging; Medical research; Medicine and Health Sciences; Mental disorders; Modelling; Network reliability; Neural coding; Neural networks; Neuroimaging; Neurology; Neurosciences; NMR; Nuclear magnetic resonance; Physiological aspects; Psychological aspects; Representations; Reproducibility of Results; Research and Analysis Methods; Sex differences; Sex differences (Biology); Signal processing; Social Sciences; Statistical methods; Stimuli; Studies; Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0190097

Functional neuroimaging is widely used to examine changes in brain function associated with age, gender or neuropsychiatric conditions. FMRI (functional magnetic resonance imaging) studies employ either laboratory-designed tasks that engage the brain with abstracted and repeated stimuli, or resting state paradigms with little behavioral constraint. Recently, novel neuroimaging paradigms using naturalistic stimuli are gaining increasing attraction, as they offer an ecologically-valid condition to approximate brain function in real life. Wider application of naturalistic paradigms in exploring individual differences in brain function, however, awaits further advances in statistical methods for modeling dynamic and complex dataset. Here, we developed a novel data-driven strategy that employs group sparse representation to assess gender differences in brain responses during naturalistic emotional experience. Comparing to independent component analysis (ICA), sparse coding algorithm considers the intrinsic sparsity of neural coding and thus could be more suitable in modeling dynamic whole-brain fMRI signals. An online dictionary learning and sparse coding algorithm was applied to the aggregated fMRI signals from both groups, which was subsequently factorized into a common time series signal dictionary matrix and the associated weight coefficient matrix. Our results demonstrate that group sparse representation can effectively identify gender differences in functional brain network during natural viewing, with improved sensitivity and reliability over ICA-based method. Group sparse representation hence offers a superior data-driven strategy for examining brain function during naturalistic conditions, with great potential for clinical application in neuropsychiatric disorders.