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    • Unlike quantitative features of the cortical sheet such

    2018-11-07

    Unlike quantitative features of the cortical sheet, such as thickness, surface area (Giedd and Rapoport, 2010) and curvature/gyrification (Armstrong et al., 1995; Li et al., 2014; Raznahan et al., 2011; White et al., 2010) – which can take decades to attain the levels observed in adulthood–the qualitative pattern formed by the characteristic set of primary, secondary and tertiary folds, or sulci, seen in human adulthood is already evident at birth (Chi et al., 1977; Mangin et al., 2010; Zilles et al., 2013). Inter-individual variation in this qualitative sulcal pattern–a classic example being the presence of a single or double parallel type of the ACC (Ono et al., 1990), determined between 10 and 15 weeks of fetal life (Chi et al., 1977) and present in 76 and 24% of adults respectively – has therefore been used as a marker for prenatal differences. Thus, ACC folding pattern has been linked to a host of cognitive domains including executive control (Cachia et al., 2014; Fornito et al., 2004), reality monitoring (Buda et al., 2011), temperament (Whittle et al., 2009) and social cognition (Fujiwara et al., 2007). The inference of these studies has been that observed qualitative differences reflect a constraint imposed by early neurodevelopmental processes on the subsequent abilities. However, a critical and as yet untested developmental assumption – upon which existing and future studies of sulcal pattern typology as an early developmental marker necessarily rest – is that the folding typologies are stable across postnatal AMD-070 manufacturer development. Here, we provide the first empirical test of this stability assumption using 263 magnetic resonance imaging (MRI) brain scans, taken from 75 healthy people longitudinally followed from 7 to 32 years. We focus on the ACC because it is a cortical region that presents two qualitatively distinct sulcal patterns (Ono et al., 1990) that can be easily and reliably classified with structural MRI from childhood (Cachia et al., 2014) to adulthood (Paus et al., 1996; Yucel et al., 2001). We evaluate the longitudinal stability of the ACC sulcal pattern from late childhood to adulthood, a developmental window characterized by significant structural remodeling of the brain as shown by the drastic ACC cortical thickness, surface area and curvature changes with time during this period (Hogstrom et al., 2013; Shaw et al., 2008). We complement our analysis of the longitudinal sulcal pattern stability by also investigating the longitudinal changes in cortical thickness.
    Material and methods
    Results
    Discussion Cortical folding is a hallmark of many, but not all, mammalian brains (Welker, 1988). The degree of folding increases with brain size across mammals, but at different scales between orders and families (Zilles et al., 2013). The mature sulcal pattern results from pre- and peri-natal processes that shape the cortex from a smooth lissencephalic structure to a highly convoluted surface (Chi et al., 1977; Haukvik et al., 2012). The precise mechanism underlying cortical folding is still unknown but several factors likely contribute to prenatal processes that influence the shape of the folded cerebral cortex, including cortical growth (Chenn and Walsh, 2002; Haydar et al., 1999; Kuida et al., 1996; Toro and Burnod, 2005), apoptosis or programmed cell death (Haydar et al., 1999), differential expansion of superior and inferior cortical layers (Kriegstein et al., 2006; Richmann et al., 1975), differential tangential expansion (Ronan et al., 2013) and/or structural connectivity through axonal tension forces (Dehay et al., 1996; Hilgetag and Barbas, 2006; Van Essen, 1997). Recent progress in characterizing basal progenitor cells and the genes that regulate their proliferation has contributed to our understanding of genetic basis of cortical folding (Sun and Hevner, 2014, for review). These different early processes lead to a compact layout that optimizes the transmission of neuronal signals between brain regions (Klyachko and Stevens, 2003) and thus the efficacy of brain network functioning.