Localizing Age-Related Changes in Brain Structure between Childhood and Adolescence Using Statistical Parametric Mapping
ABSTRACT 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.
- SourceAvailable from: Katya Rubia
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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. "
ABSTRACT: Attention-deficit/hyperactivity disorder (ADHD) is currently diagnosed on the basis of subjective measures, despite evidence for multi-systemic structural and neurofunctional deficits. A consistently observed neurofunctional deficit is in fine-temporal discrimination (TD). The aim of this proof-of-concept study was to examine the feasibility of distinguishing patients with ADHD from controls using multivariate pattern recognition analyses of functional magnetic resonance imaging (fMRI) data of TD. A total of 20 medication-naive adolescent male patients with ADHD and 20 age-matched healthy controls underwent fMRI while performing a TD task. The fMRI data were analyzed with Gaussian process classifiers to predict individual ADHD diagnosis based on brain activation patterns. The pattern of brain activation correctly classified up to 80% of patients and 70% of controls, achieving an overall classification accuracy of 75%. The distributed activation networks with the highest delineation between patients and controls corresponded to a distributed network of brain regions involved in TD and typically compromised in ADHD, including inferior and dorsolateral prefrontal, insula, and parietal cortices, and the basal ganglia, anterior cingulate, and cerebellum. These regions overlapped with areas of reduced activation in patients with ADHD relative to controls in a univariate analysis, suggesting that these are dysfunctional regions. We show evidence that pattern recognition analyses combined with fMRI using a disorder-sensitive task such as timing have potential in providing objective diagnostic neuroimaging biomarkers of ADHD.Journal of the American Academy of Child and Adolescent Psychiatry 05/2014; 53(5):569-578.e1. DOI:10.1016/j.jaac.2013.12.024 · 6.35 Impact Factor
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- "Current neurodevelopmental models suggest that this profile depends on a hyperactive reward system readily activating consummatory behaviors (Galvan, 2010), although there is evidence that a hypoactive reward system may instead be promoting the pursuit of disproportionately large motivating stimuli (Bjork et al., 2004; Spear, 2000). Substantial structural changes in the adolescent reward system affect dopaminergic (Teicher, Andersen, & Hostetter, 1995) and frontostriatal mechanisms (Galvan et al., 2006; Giedd et al., 1999; Sowell et al., 1999). Accordingly , there is interest in adolescent neural reactivity to the anticipation or delivery of reward (Bjork, Smith, Chen, & Hommer, 2010; Van Leijenhorst et al., 2010; Eshel, Nelson, Blair, Pine, & Ernst, 2007; Bjork et al., 2004) as well as in the way rewards are integrated into adolescent behavior through prediction errors (PE), that is, signals of mismatch between expected and received outcomes (Cohen et al., 2010). "
ABSTRACT: We examined the maturation of decision-making from early adolescence to midadulthood using fMRI of a variant of the Iowa gambling task. We have previously shown that performance in this task relies on sensitivity to accumulating negative outcomes in ventromedial pFC and dorsolateral pFC. Here, we further formalize outcome evaluation (as driven by prediction errors [PE], using a reinforcement learning model) and examine its development. Task performance improved significantly during adolescence, stabilizing in adulthood. Performance relied on greater impact of negative compared with positive PEs, the relative impact of which matured from adolescence into adulthood. Adolescents also showed increased exploratory behavior, expressed as a propensity to shift responding between options independently of outcome quality, whereas adults showed no systematic shifting patterns. The correlation between PE representation and improved performance strengthened with age for activation in ventral and dorsal pFC, ventral striatum, and temporal and parietal cortices. There was a medial-lateral distinction in the prefrontal substrates of effective PE utilization between adults and adolescents: Increased utilization of negative PEs, a hallmark of successful performance in the task, was associated with increased activation in ventromedial pFC in adults, but decreased activation in ventrolateral pFC and striatum in adolescents. These results suggest that adults and adolescents engage qualitatively distinct neural and psychological processes during decision-making, the development of which is not exclusively dependent on reward-processing maturation.Journal of Cognitive Neuroscience 07/2013; 25(11). DOI:10.1162/jocn_a_00447 · 4.69 Impact Factor
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- "Given the role of the medial prefrontal cortex (mPFC) in executive function and regulation of motivated behaviors, it is not surprising that the prefrontal cortex undergoes remodeling during puberty and adolescence . However, it was startling when MRI studies revealed that the volume of the frontal cortex decreases during human adolescence (Giedd et al., 1999; Sowell et al., 1999), and that the period of cortical thinning begins earlier in girls than in boys (Giedd et al., 1999). Prior to these publications , there had been reports of a decline in synaptic density of the mPFC during adolescence in both monkeys (Anderson et al., 1995; Bourgeois et al., 1994) and humans (Huttenlocher, 1979; Huttenlocher and Dabholkar, 1997), but it was unexpected that these changes would be reflected in cortical volume. "
ABSTRACT: This article is part of a Special Issue "Puberty and Adolescence". Sexual differentiation is the process by which the nervous system becomes structurally and functionally dissimilar in females and males. In mammals, this process has been thought to occur during prenatal and early postnatal development, when a transient increase in testosterone secretion masculinizes and defeminizes the developing male nervous system. Decades of research have led to the views that structural sexual dimorphisms created during perinatal development are passively maintained throughout life, and that ovarian hormones do not play an active role in feminization of the nervous system. Furthermore, perinatal testosterone was thought to determine sex differences in neuron number by regulating cell death and cell survival, and not by regulating cell proliferation. As investigations of neural development during adolescence became more prominent in the late 20th century and revealed the extent of brain remodeling during this time, each of these tenets has been challenged and modified. Here we review evidence from the animal literature that 1) the brain is further sexually differentiated during puberty and adolescence; 2) ovarian hormones play an active role in the feminization of the brain during puberty; and 3) hormonally modulated, sex-specific addition of new neurons and glial cells, as well as loss of neurons, contribute to sexual differentiation of hypothalamic, limbic, and cortical regions during adolescence. This architectural remodeling during the adolescent phase of sexual differentiation of the brain may underlie the known sex differences in vulnerability to addiction and psychiatric disorders that emerge during this developmental period.Hormones and Behavior 07/2013; 64(2):203-10. DOI:10.1016/j.yhbeh.2013.05.010 · 4.51 Impact Factor