Brain structure & function
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Cortico-ventral basal ganglia circuitry is associated with a variety of mental health disorders including obsessive-compulsive disorder and drug addiction, disorders that emerge during childhood through young adulthood, a period in which the cortex and striatum continue to development. Moreover, cell proliferation, which is associated with development and plasticity, also continues in the cortex and striatum through adulthood. Given the implication of cortico-basal ganglia circuitry in diseases emerging during postnatal development, we studied cell proliferation at different ages in striatal regions associated with specific frontal cortical areas. ⋯ Finally, throughout the juvenile years, the ventral striatal areas receiving input from the ventral, medial prefrontal cortex and orbital prefrontal cortex have significantly more new cells compared to other striatal regions. Integrity of the ventral striatum is critical for the development of goal-directed behaviors. The high number of new cells in the ventral striatum during postnatal development may be particularly important for the refinement of the cortico-striatal network, and in the formation of neural ensembles fundamental to learning during behavioral development.
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One of the major challenges of functional magnetic resonance imaging (fMRI) data analysis is to develop simple and reliable methods to correlate brain regions with functionality. In this paper, we employ a detrending-based fractal method, called detrended fluctuation analysis (DFA), to identify brain activity from fMRI data. We perform three tasks: (a) Estimating noise level from experimental fMRI data; (b) Assessing a signal model recently introduced by Birn et al.; and (c) Evaluating the effectiveness of DFA for discriminating brain activations from artifacts. ⋯ This suggests that the proposed algorithm for estimating noise level is very effective and that Birn's model fits our experimental data very well. The brain activation maps for experimental data derived by DFA are similar to maps derived by deconvolution using a widely used software, AFNI. Considering that deconvolution explicitly uses the information about the experimental paradigm to extract the activation patterns whereas DFA does not, it remains to be seen whether one can effectively integrate the two methods to improve accuracy for detecting brain areas related to functional activity.