EEG activities in the orbitofrontal cortex and dorsolateral prefrontal cortex during the development of morphine dependence, tolerance and withdrawal in rhesus monkeys

Investigating the activities of the prefrontal cortex (PFC) in the process of addiction is valuable for understanding the neural mechanism underlying the impairments of the PFC after drug abuse. However, limited data are obtained from primate animals and few studies analyze Electroencephalogram (EEG) in the gamma band, which plays an important role in cognitive functions. In addition, it is yet unclear whether drug abuse affects the orbitofrontal cortex (OFC) and dorsolateral PFC (DLPFC) - the two most important subregions of the PFC - in similar ways or not. The aim of this study is to address these issues. We recorded EEG in the OFC and DLPFC in three rhesus monkeys. All animals received a course of saline (NaCl 0.9%, 2 ml) injection (5 days) followed by 10 days of morphine injection (every 12 h), and then a further series of saline injection (7 days). A main finding in the present study was that morphine decreased EEG power in all frequency bands in a short period after injection in both the OFC and DLPFC in monkeys. And gamma power decreased not just in short period after morphine injection but lasted to 12 h after injection. Moreover, we found that although the changes in EEG activities in the OFC and DLPFC at 30-35 min after injection were similar, the DLPFC was more sensitive to the effect of morphine than the OFC. (c) 2005 Elsevier B.V. All rights reserved.

Neurochemical changes in brain induced by chronic morphine treatment: NMR studies in thalamus and somatosensory cortex of rats

To investigate the effects of chronic morphine treatment and its cessation on thalamus and the somatosensory cortex, an ex vivo high resolution (500 MHz) H-1 nuclear magnetic resonance spectroscopy (NMRS), in the present study, was applied to detect multi

Metabolic changes in rat prefrontal cortex and hippocampus induced by chronic morphine treatment studied ex vivo by high resolution H-1 NMR spectroscopy

Ex vivo H-1 NMR spectroscopy was used to measure changes in the concentrations of cerebral metabolites in the prefrontal cortex (PFC) and hippocampus of rats subjected to repeated morphine treatment known to cause tolerance/dependence. The results show th

Distracters Impair and Create Working Memory-Related Neuronal Activity in the Prefrontal Cortex

The prefrontal cortex (PFC) has a central role in working memory (WM). Resistance to distraction is considered a fundamental feature of WM and PFC neuronal activity. However, although unexpected stimuli often disrupt our work, little is known about the un

Schizophrenia patients and their healthy siblings share disruption of white matter integrity in the left prefrontal cortex and the hippocampus but not the anterior cingulate cortex

Healthy siblings of schizophrenia patients have an almost 9-fold higher risk for developing the illness than the general population. Disruption of white matter (WM) integrity as indicated by reduced fractional anisotropy (FA) derived from diffusion tensor

Frontal and cingulate gray matter volume reduction in heroin dependence: Optimized voxel-based morphometry

Aims: Repeated exposure to heroin, a typical opiate, causes neuronal adaptation and may result in anatomical changes in specific brain regions, particularly the frontal and limbic cortices. The volume changes of gray matter (GM) of these brain regions, ho

Imaging the femoral cortex: thickness, density and mass from clinical CT.

There is growing evidence that focal thinning of cortical bone in the proximal femur may predispose a hip to fracture. Detecting such defects in clinical CT is challenging, since cortices may be significantly thinner than the imaging system's point spread function. We recently proposed a model-fitting technique to measure sub-millimetre cortices, an ill-posed problem which was regularized by assuming a specific, fixed value for the cortical density. In this paper, we develop the work further by proposing and evaluating a more rigorous method for estimating the constant cortical density, and extend the paradigm to encompass the mapping of cortical mass (mineral mg/cm(2)) in addition to thickness. Density, thickness and mass estimates are evaluated on sixteen cadaveric femurs, with high resolution measurements from a micro-CT scanner providing the gold standard. The results demonstrate robust, accurate measurement of peak cortical density and cortical mass. Cortical thickness errors are confined to regions of thin cortex and are bounded by the extent to which the local density deviates from the peak, averaging 20% for 0.5mm cortex.

Imaging the femoral cortex: Thickness, density and mass from clinical CT

There is growing evidence that focal thinning of cortical bone in the proximal femur may predispose a hip to fracture. Detecting such defects in clinical CT is challenging, since cortices may be significantly thinner than the imaging system's point spread function. We recently proposed a model-fitting technique to measure sub-millimetre cortices, an ill-posed problem which was regularized by assuming a specific, fixed value for the cortical density. In this paper, we develop the work further by proposing and evaluating a more rigorous method for estimating the constant cortical density, and extend the paradigm to encompass the mapping of cortical mass (mineral mg/cm 2) in addition to thickness. Density, thickness and mass estimates are evaluated on sixteen cadaveric femurs, with high resolution measurements from a micro-CT scanner providing the gold standard. The results demonstrate robust, accurate measurement of peak cortical density and cortical mass. Cortical thickness errors are confined to regions of thin cortex and are bounded by the extent to which the local density deviates from the peak, averaging 20% for 0.5mm cortex. © 2012 Elsevier B.V.

Learning based automatic face annotation for arbitrary poses and expressions from frontal images only

Statistical approaches for building non-rigid deformable models, such as the Active Appearance Model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases. © 2009 IEEE.

The role of human orbitofrontal cortex in value comparison for incommensurable objects.

The human orbitofrontal cortex is strongly implicated in appetitive valuation. Whether its role extends to support comparative valuation necessary to explain probabilistic choice patterns for incommensurable goods is unknown. Using a binary choice paradigm, we derived the subjective values of different bundles of goods, under conditions of both gain and loss. We demonstrate that orbitofrontal activation reflects the difference in subjective value between available options, an effect evident across valuation for both gains and losses. In contrast, activation in dorsal striatum and supplementary motor areas reflects subjects' choice probabilities. These findings indicate that orbitofrontal cortex plays a pivotal role in valuation for incommensurable goods, a critical component process in human decision making.

Neural activity associated with the passive prediction of ambiguity and risk for aversive events.

In economic decision making, outcomes are described in terms of risk (uncertain outcomes with certain probabilities) and ambiguity (uncertain outcomes with uncertain probabilities). Humans are more averse to ambiguity than to risk, with a distinct neural system suggested as mediating this effect. However, there has been no clear disambiguation of activity related to decisions themselves from perceptual processing of ambiguity. In a functional magnetic resonance imaging (fMRI) experiment, we contrasted ambiguity, defined as a lack of information about outcome probabilities, to risk, where outcome probabilities are known, or ignorance, where outcomes are completely unknown and unknowable. We modified previously learned pavlovian CS+ stimuli such that they became an ambiguous cue and contrasted evoked brain activity both with an unmodified predictive CS+ (risky cue), and a cue that conveyed no information about outcome probabilities (ignorance cue). Compared with risk, ambiguous cues elicited activity in posterior inferior frontal gyrus and posterior parietal cortex during outcome anticipation. Furthermore, a similar set of regions was activated when ambiguous cues were compared with ignorance cues. Thus, regions previously shown to be engaged by decisions about ambiguous rewarding outcomes are also engaged by ambiguous outcome prediction in the context of aversive outcomes. Moreover, activation in these regions was seen even when no actual decision is made. Our findings suggest that these regions subserve a general function of contextual analysis when search for hidden information during outcome anticipation is both necessary and meaningful.