Sowell ER, Thompson PM, Holmes CJ, Batth R, Jernigan TL, Toga AW. Localizing age-related changes in brain structure between childhood and adolescence using statistical parametric mapping. Neuroimage 9: 587-597

Laboratory of Neuroimaging, University of California at Los Angeles, Los Angeles, California 90095, USA.
NeuroImage (Impact Factor: 6.36). 06/1999; 9(6 Pt 1):587-97. DOI: 10.1006/nimg.1999.0436
Source: PubMed


Volumetric studies have consistently shown reductions in cerebral gray matter volume between childhood and adolescence, with the most dramatic changes occurring in the more dorsal cortices of the frontal and parietal lobes. The purpose of this study was to examine the spatial location of these changes employing methods typical of functional imaging studies. T1-weighted structural MRI data (1.2 mm) were analyzed for nine normally developing children and nine normal adolescents. Validity and reliability of the tissue segmentation protocol were assessed as part of several preprocessing analyses prior to statistical parametric mapping (SPM). Using SPM96, a simple contrast of average gray matter differences between the two age groups revealed 57 significant clusters (SPM[Z] height threshold, P<0.001, extent threshold 50, uncorrected). The pattern and distribution of differences were consistent with earlier findings from the volumetric assessment of the same subjects. Specifically, more differences were observed in dorsal frontal and parietal regions with relatively few differences observed in cortices of the temporal and occipital lobes. Permutation tests were conducted to assess the overall significance of the gray matter differences and validity of the parametric maps. Twenty SPMs were created with subjects randomly assigned to groups. None of the random SPMs approached the number of significant clusters observed in the age difference SPM (mean number of significant clusters = 5.8). The age effects observed appear to result from regions that consistently segment as gray matter in the younger group and consistently segment as white matter in the older group. The utility of these methods for localizing relatively subtle structural changes that occur between childhood and adolescence has not previously been examined.

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    • "Morphological and connectivity changes in a number of brain regions (Durston et al., 2001; Giedd, 2008; Sowell et al., 2002) make the adolescent brain more vulnerable to the effects of environmental stimuli (Schindler et al., 2014; Steinberg, 2005; Wheeler et al., 2013). Moreover, in adolescent rodents the HPA axis undergoes reorganization (Arnsten & Shansky, 2004; Meaney et al., 1985; Sowell et al., 1999), making this developmental period sensitive to the effects of stress. Due to the propensity of the developing adolescent brain to be influenced by external stimuli, we further hypothesize that adolescent mice will be more susceptible to the effects of PVN GR disruption. "
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    Stress (Amsterdam, Netherlands) 06/2015; 18(4):1-8. DOI:10.3109/10253890.2015.1046832 · 2.72 Impact Factor
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    • "GPC identified a distributed network predictive of controls in later developing lateral and medial fronto-striatal and parieto-temporal regions that are crucial for motor response inhibition [Aron and Poldrack , 2006; Cai et al., 2012; Chambers et al., 2006, 2009; Juan and Muggleton, 2012; Rubia et al., 2003, 2007b, 2013]. Structurally [Sowell et al., 1999, 2004] and functionally during inhibition tasks (Adleman et al., 2002; Bunge et al., 2002; Rubia et al., 2000; Rubia et al., 2007b; Rubia et al., 2006, Rubia et al., 2013) [for review see (Rubia, 2013)], these lateral prefrontal, striatal and parietal brain regions develop later than the ventromedial prefrontal, limbic (i.e., hippocampus, amygdala) and paralimbic areas (insula) that were predictive of ADHD patients. Our finding of high magnitude weights predictive of controls in later developing lateral fronto-striato-parietal regions and for ADHD in earlier developing ventromedial fronto-limbic regions hence suggest that the ADHD discrimination networks are reflective of more immature activation patterns, while the control discrimination patterns are reflective of a more mature activation pattern for Stop task performance. "
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    • "The cut-off point of 10–12 years of age was chosen because peak prevalence of ADHD is 10 years of age [25] and also because several MRI parameters greatly change until 8–10 years of age [26], whereas the rate of increase of neuronal growth and pruning reduces after 10 years of age. The cut off point of 23 years for matured brain in the adults is chosen in line with previous studies involving a comparison between matured versus immature brain [27]. "
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