Brain structure & function
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The circuit-related consequences of activating the ventral pallidum (VP) are not well known, and lacking in particular is how these effects are altered in various neuropathological states. To help to address these paucities, this study investigated the brain regions affected by VP activation by quantifying neurons that stain for Fos-like immunoreactivity (ir). Fos-ir was assessed after intra-pallidal injections of the excitatory amino acid agonist, NMDA, or the GABA(A) antagonist, bicuculline in normal rats and in those rendered Parkinsonian-like by lesioning dopaminergic neurons with the neurotoxin, 6-OHDA. ⋯ Comparisons of responses to intra-VP NMDA between the hemispheres ipsilateral and contralateral to the lesion revealed that Fos-ir cells in the pedunculopontine nucleus was reduced by 62%, whereas Fos-ir for the basolateral amygdala and STN was reduced by 32 and 42%, respectively. These findings support the concept that the VP can influence both the basal ganglia and the limbic system, and that that the nature of this influence is modified in an animal model of PD. As the VP regulates motivation and cognition, adaptations in this system may contribute to the mood and mnemonic disorders that can accompany PD.
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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.