Multifractal analysis of resting state fMRI series in default mode network: age and gender effects

Abstract

Age-related changes of resting state in default mode network (DMN) may provide new clues to the developing mechanism of normal brain as well as early diagnosis and therapy of some neuropsychiatric disorders. The application of multifractal theory to functional magnetic resonance imaging (fMRI) signals has recently raised increasing attention. We aim to explore the multifractal characteristics underlying the resting state functional magnetic resonance imaging (rs-fMRI) series extracted from DMN, and two issues are mainly discussed: (1) whether there exist multifractals in rs-fMRI series; (2) whether it is possible to distinguish between the different ages or genders by means of multifractal characteristics. Results demonstrated the existence of multifractals in rs-fMRI series in DMN. In addition, slight differences between young subjects and middle-aged or elderly subjects can be successfully detected by \( \Delta_{\text{as}} \alpha \), a modified measure we proposed. Furthermore, it is revealed that the rs-fMRI series from young subjects possess smaller averaged scale index and weaker long range correlation, while those from middle-aged or elderly people present increasing averaged scale index and stronger long range correlation. Whereas no significant statistical differences has been found between male and female group. Our results, therefore, highlight the potential usefulness of multifractal analysis in fMRI series of a certain brain region, and provide important insights into healthy aging in DMN.