Toward a more reliable characterization of fractal properties of the cerebral cortex of healthy subjects during the lifespan

• Published in: Scientific reports Vol. 10; no. 1; p. 16957
• Main Authors: Marzi, Chiara, Giannelli, Marco, Tessa, Carlo, Mascalchi, Mario, Diciotti, Stefano
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.10.2020
• Subjects: 631/114/1305; 631/1647/245/1628; 639/166/985; 639/705/1042; 692/53/2423; 692/698/1688/64; Adolescent; Adult; Aged; Aged, 80 and over; Cerebral Cortex - anatomy & histology; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - pathology; Child; Diffusion Magnetic Resonance Imaging; Female; Fractals; Healthy Volunteers; Humanities and Social Sciences; Humans; Longevity - physiology; Male; Middle Aged; multidisciplinary; Science; Science (multidisciplinary); Young Adult
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-73961-w

The cerebral cortex manifests an inherent structural complexity of folding. The fractal geometry describes the complexity of structures which show self-similarity in a proper interval of spatial scales. In this study, we aimed at evaluating in-vivo the effect of different criteria for selecting the interval of spatial scales in the estimation of the fractal dimension (FD) of the cerebral cortex in T 1 -weighted magnetic resonance imaging (MRI). We compared four different strategies, including two a priori selections of the interval of spatial scales, an automated selection of the spatial scales within which the cerebral cortex manifests the highest statistical self-similarity, and an improved approach, based on the search of the interval of spatial scales which presents the highest rounded R 2 adj coefficient and, in case of equal rounded R 2 adj coefficient, preferring the widest interval in the log–log plot. We employed two public and international datasets of in-vivo MRI scans for a total of 159 healthy subjects (age range 6–85 years). The improved approach showed strong associations of FD with age and yielded the most accurate machine learning models for individual age prediction in both datasets. Our results indicate that the selection of the interval of spatial scales of the cerebral cortex is thus critical in the estimation of FD.