Towards a unified analysis of brain maturation and aging across the entire lifespan: A MRI analysis

Abstract

There is no consensus in literature about lifespan brain maturation and senescence, mainly because previous lifespan studies have been performed on restricted age periods and/or with a limited number of scans, making results instable and their comparison very difficult. Moreover, the use of nonharmonized tools and different volumetric measurements lead to a great discrepancy in reported results. Thanks to the new paradigm of BigData sharing in neuroimaging and the last advances in image processing enabling to process baby as well as elderly scans with the same tool, new insights on brain maturation and aging can be obtained. This study presents brain volume trajectory over the entire lifespan using the largest age range to date (from few months of life to elderly) and one of the largest number of subjects (N = 2,944). First, we found that white matter trajectory based on absolute and normalized volumes follows an inverted U-shape with a maturation peak around middle life. Second, we found that from 1 to 8-10 y there is an absolute gray matter (GM) increase related to body growth followed by a GM decrease. However, when normalized volumes were considered, GM continuously decreases all along the life. Finally, we found that this observation holds for almost all the considered subcortical structures except for amygdala which is rather stable and hippocampus which exhibits an inverted U-shape with a longer maturation period. By revealing the entire brain trajectory picture, a consensus can be drawn since most of the previously discussed discrepancies can be explained. Hum Brain Mapp 38:5501-5518, 2017. © 2017 Wiley Periodicals, Inc.

Structural covariance across the lifespan: Brain development and aging through the lens of inter‐network relationships

“…we ensured that each study contributes to a minimum of 3 age bins. These steps are comparable to other big data studies facing a similar problem (Coupé et al, 2017). Notably, previous studies have found that the impact of scanner manufacturer, magnet strength, and their interaction on gray matter volume measurements is limited in big data environments, with the majority of variance explained by TICV, age, and sex (Potvin, Dieumegarde, & Duchesne, 2017; Potvin, Mouiha, Dieumegarde, & Duchesne, 2016).…”

“…In order to take advantage of the big-data framework across the entire lifespan, specific site was not included as a regressor. Controlling for study-related variance is a key difficulty in big-data approaches (as discussed in Coupé, Catheline, Lanuza, & Manjón, 2017). Because study correlates with age, controlling for study would reduce the age effect.…”

Brain biomarkers and cognition across adulthood

“…A different study showed an association between amyloid burden and episodic memory, and white matter hyperintensity (WMH) burden to executive function (Hedden et al, ). Studies using magnetic resonance imaging (MRI) and positron emission tomography (PET) scans examining the link between brain measurements and aging across the lifespan revealed significant associations for functional and structural connectivity (Betzel et al, ), diffusion tensor imaging (DTI) white matter tract (Lebel et al, ; Storsve, Fjell, Yendiki, & Walhovd, ), and subcortical structures (Coupe et al, ) with aging. Across the lifespan, a recent examination of subcortical gray matter structures suggested that in a longitudinal aging cohort, the volume of all structures except the caudate and globus pallidus decreased linearly (Narvacan, Treit, Camicioli, Martin, & Beaulieu, ).…”

Mapping the neuroanatomical impact of very preterm birth across childhood

“…Progressive and regressive changes occur in cortical and deep gray matter during the period of development spanning earlier (3-8 years of age) and later (8-12 years of age) childhood (e.g., Coupé, Catheline, Lanuza, & Manjón, 2017;Sussman, Leung, Chakravarty, Lerch, & Taylor, 2016;Wierenga et al, 2014). From late infancy into earlier childhood, VPT children have been shown to have comparable brain development with their full term (FT) peers with regards to total brain volume (TBV), cortical surface area and cortical thickness (Phillips et al, 2011).…”