Modulation of Mediotemporal and Ventrostriatal Function in Humans by Delta 9-Tetrahydrocannabinol A Neural Basis for the Effects of Cannabis sativa on Learning and Psychosis

Context: Cannabis sativa use can impair verbal learning, provoke acute psychosis, and increase the risk of schizophrenia. It is unclear where C sativa acts in the human brain to modulate verbal learning and to induce psychotic symptoms. Objectives: To investigate the effects of 2 main psychoactive constituents of C sativa, Delta 9-tetrahydrocannabinol (Delta 9-THC) and cannabidiol, on regional brain function during verbal paired associate learning. Design: Subjects were studied on 3 separate occasions using a block design functional magnetic resonance imaging paradigm while performing a verbal paired associate learning task. Each imaging session was preceded by the ingestion of Delta 9-THC (10 mg), cannabidiol (600 mg), or placebo in a double-blind, randomized, placebo-controlled, repeated-measures, within-subject design. Setting: University research center. Participants: Fifteen healthy, native English-speaking, right-handed men of white race/ethnicity who had used C sativa 15 times or less and had minimal exposure to other illicit drugs in their lifetime. Main Outcome Measures: Regional brain activation ( blood oxygen level-dependent response), performance in a verbal learning task, and objective and subjective ratings of psychotic symptoms, anxiety, intoxication, and sedation. Results: Delta 9-Tetrahydrocannabinol increased psychotic symptoms and levels of anxiety, intoxication, and sedation, whereas no significant effect was noted on these parameters following administration of cannabidiol. Performance in the verbal learning task was not significantly modulated by either drug. Administration of Delta 9-THC augmented activation in the parahippocampal gyrus during blocks 2 and 3 such that the normal linear decrement in activation across repeated encoding blocks was no longer evident. Delta 9-Tetrahydrocannabinol also attenuated the normal time-dependent change in ventrostriatal activation during retrieval of word pairs, which was directly correlated with concurrently induced psychotic symptoms. In contrast, administration of cannabidiol had no such effect. Conclusion: The modulation of mediotemporal and ventrostriatal function by Delta 9-THC may underlie the effects of C sativa on verbal learning and psychotic symptoms, respectively.

Distinct Effects of Delta 9-Tetrahydrocannabinol and Cannabidiol on Neural Activation During Emotional Processing

Context: Cannabis use can both increase and reduce anxiety in humans. The neurophysiological substrates of these effects are unknown. Objective: To investigate the effects of 2 main psycho-active constituents of Cannabis sativa (Delta 9-tetrahydrocannabinol [Delta 9-THC] and cannabidiol [CBD]) on regional brain function during emotional processing. Design: Subjects were studied on 3 separate occasions using an event-related functional magnetic resonance imaging paradigm while viewing faces that implicitly elicited different levels of anxiety. Each scanning session was preceded by the ingestion of either 10 mg of Delta 9-THC, 600 mg of CBD, or a placebo in a double-blind, randomized, placebo-controlled design. Participants: Fifteen healthy, English-native, right-handed men who had used cannabis 15 times or less in their life. Main Outcome Measures: Regional brain activation (blood oxygenation level-dependent response), electrodermal activity (skin conductance response [SCR]), and objective and subjective ratings of anxiety. Results: Delta 9-Tetrahydrocannabinol increased anxiety, as well as levels of intoxication, sedation, and psychotic symptoms, whereas there was a trend for a reduction in anxiety following administration of CBD. The number of SCR fluctuations during the processing of intensely fearful faces increased following administration of Delta 9-THC but decreased following administration of CBD. Cannabidiol attenuated the blood oxygenation level dependent signal in the amygdala and the anterior and posterior cingulate cortex while subjects were processing intensely fearful faces, and its suppression of the amygdalar and anterior cingulate responses was correlated with the concurrent reduction in SCR fluctuations. Delta 9-Tetrahydrocannabinol mainly modulated activation in frontal and parietal areas. Conclusions: Delta 9-Tetrahydrocannabinol and CBD had clearly distinct effects on the neural, electrodermal, and symptomatic response to fearful faces. The effects of CBD on activation in limbic and paralimbic regions may contribute to its ability to reduce autonomic arousal and subjective anxiety, whereas the anxiogenic effects of Delta 9-THC may be related to effects in other brain regions.

Neural Basis of Delta-9-Tetrahydrocannabinol and Cannabidiol: Effects During Response Inhibition

Background: This study examined the effect of Delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) on brain activation during a motor inhibition task. Methods: Functional magnetic resonance imaging and behavioural measures were recorded while 15 healthy volunteers performed a Go/No-Go task following administration of either THC or CBD or placebo in a double-blind, pseudo-randomized, placebo-controlled repeated measures within-subject design. Results: Relative to placebo, THC attenuated activation in the right inferior frontal and the anterior cingulate gyrus. In contrast, CBD deactivated the left temporal cortex and insula. These effects were not related to changes in anxiety, intoxication, sedation, and psychotic symptoms. Conclusions: These data suggest that THC attenuates the engagement of brain regions that mediate response inhibition. CBD modulated function in regions not usually implicated in response inhibition.

Glutamate and the neural basis of the subjective effects of ketamine

Context: Ketamine evokes psychosislike symptoms, and its primary action is to impair N-methyl-D-aspartate glutamate receptor neurotransmission, but it also induces secondary increases in glutamate release. Objectives: To identify the sites of action of ketamine in inducing symptoms and to determine the role of increased glutamate release using the glutamate release inhibitor lamotrigine. Design: Two experiments with different participants were performed using a double-blind, placebo-controlled, randomized, crossover, counterbalanced-order design. In the first experiment, the effect of intravenous ketamine hydrochloride on regional blood oxygenation level dependent (BOLD) signal and correlated symptoms was compared with intravenous saline placebo. In the second experiment, pretreatment with lamotrigine was compared with placebo to identify which effects of ketamine are mediated by increased glutamate release. Setting: Wellcome Trust Clinical Research Facility, Manchester, England. Participants: Thirty-three healthy, right-handed men were recruited by advertisements. Interventions: In experiment 1, participants were given intravenous ketamine (1-minute bolus of 0.26 mg/ kg, followed by a maintenance infusion of 0.25 mg/ kg/ h for the remainder of the session) or placebo (0.9% saline solution). In experiment 2, participants were pretreated with 300 mg of lamotrigine or placebo and then were given the same doses of ketamine as in experiment 1. Main Outcome Measures: Regional BOLD signal changes during ketamine or placebo infusion and Brief Psychiatric Rating Scale and Clinician- Administered Dissociative States Scale scores. Results: Ketamine induced a rapid, focal, and unexpected decrease in ventromedial frontal cortex, including orbitofrontal cortex and subgenual cingulate, which strongly predicted its dissociative effects and increased activity in mid- posterior cingulate, thalamus, and temporal cortical regions (r= 0.90). Activations correlated with Brief Psychiatric Rating Scale psychosis scores. Lamotrigine pretreatment prevented many of the BOLD signal changes and the symptoms. Conclusions: These 2 changes may underpin 2 fundamental processes of psychosis: abnormal perceptual experiences and impaired cognitive- emotional evaluation of their significance. The results are compatible with the theory that the neural and subjective effects of ketamine involve increased glutamate release.

The neural histogenetic origin of the oral granular cell tumor: An immunohistochemical evidence

Aims: Granular cell tumor (GCT) is a rare neoplasm that can appear in any site of the body, but most are located intraorally. Its histogenetic origin remains unclear. This report analyzes the immunoprofile of 15 cases of granular cell tumors, occurring in 13 women and 2 men and the lesions were located on the tongue or upper lip. Patient age ranged from 7 to 52. Methods: The patients demographic data and the cytological and architectural features of the lesions were analyzed in oral GCTs (n = 15). The lesions were also submitted to a panel of immunohistochemical stains with antibodies against S-100, p75, NSE, CD-68, Ki-67, Synaptofisin, HHF-35, SMA, EMA, Chromogranin, Progesterone, Androgen and Estrogen. Results: Among the fifteen cases analyzed, the most common location was the tongue (84.6%). Histologically, the tumors exhibited cellular proliferation composed mainly by polygonal cells presenting an abundant granular eosinophilic cytoplasm. The nuclei were central, and the cell membranes were moderately clear. No mitotic figures were observed. The immunohistochemical analysis showed positivity in all cases for S-100, p75, NSE and CD-68, and no immunoreactivity for Ki-67, Synaptofisin, HHF-35, SMA, EMA, Chromogranin, Progesterone, Androgen and Estrogen. Conclusion: The immunoprofile of granular cell tumors showed nerve sheath differentiation - lending support to their neural origin - and helping to establish a differential diagnosis between this lesion and other oral granular cell tumors, whether benign or malignant.

Peripheral nervous system and grounds for the neural insult in leprosy

The mechanism of interaction between Mycobacterium leprae and neural cells has not been elucidated so far. No satisfactory interpretation exists as to the bacterium tropism to the peripheral nervous system in particular. The present study is a review of the micro-physiology of the extracellular apparatus attached to Schwann cells, as well as on the description of morphological units probably involved in the process of the binding to the bacterial wall.

Maintenance and differentiation of neural stem cells

The adult mammalian brain contains self-renewable, multipotent neural stem cells (NSCs) that are responsible for neurogenesis and plasticity in specific regions of the adult brain. Extracellular matrix, vasculature, glial cells, and other neurons are components of the niche where NSCs are located. This surrounding environment is the source of extrinsic signals that instruct NSCs to either self-renew or differentiate. Additionally, factors such as the intracellular epigenetics state and retrotransposition events can influence the decision of NSC`s fate into neurons or glia. Extrinsic and intrinsic factors form an intricate signaling network, which is not completely understood. These factors altogether reflect a few of the key players characterized so far in the new field of NSC research and are covered in this review. (C) 2010 John Wiley & Sons, Inc. WIREs Syst Biol Med 2011 3 107-114 DOI:10.1002/wsbm:100

Samba, a Xenopus hnRNP Expressed in Neural and Neural Crest Tissues

RNA binding proteins regulate gene expression at the posttranscriptional level and play important roles in embryonic development. Here, we report the cloning and expression of Samba, a Xenopus hnRNP that is maternally expressed and persists at least until tail bud stages. During gastrula stages, Samba is enriched in the dorsal regions. Subsequently, its expression is elevated only in neural and neural crest tissues. In the latter, Samba expression overlaps with that of Slug in migratory neural crest cells. Thereafter, Samba is maintained in the neural crest derivatives, as well as other neural tissues, including the anterior and posterior neural tube and the eyes. Overexpression of Samba in the animal pole leads to defects in neural crest migration and cranial cartilage development. Thus, Samba encodes a Xenopus hnRNP that is expressed early in neural and neural crest derivatives and may regulate crest cells migratory behavior. Developmental Dynamics 238:204-209, 2009. (C) 2008 Wiley-Liss, Inc.

Enhanced Neural Progenitor/Stem Cells Self-Renewal via the Interaction of Stress-Inducible Protein 1 with the Prion Protein

Prion protein (PrPC), when associated with the secreted form of the stress-inducible protein 1 (STI1), plays an important role in neural survival, neuritogenesis, and memory formation. However, the role of the PrP(C)-STI1 complex in the physiology of neural progenitor/stem cells is unknown. In this article, we observed that neurospheres cultured from fetal forebrain of wild-type (Prnp(+/+)) and PrP(C)-null (Prnp(0/0)) mice were maintained for several passages without the loss of self-renewal or multipotentiality, as assessed by their continued capacity to generate neurons, astrocytes, and oligodendrocytes. The homogeneous expression and colocalization of STI1 and PrP(C) suggest that they may associate and function as a complex in neurosphere-derived stem cells. The formation of neurospheres from Prnp(0/0) mice was reduced significantly when compared with their wild-type counterparts. In addition, blockade of secreted STI1, and its cell surface ligand, PrP(C), with specific antibodies, impaired Prnp(+/+) neurosphere formation without further impairing the formation of Prnp(0/0) neurospheres. Alternatively, neurosphere formation was enhanced by recombinant STI1 application in cells expressing PrP(C) but not in cells from Prnp(0/0) mice. The STI1-PrP(C) interaction was able to stimulate cell proliferation in the neurosphere-forming assay, while no effect on cell survival or the expression of neural markers was observed. These data suggest that the STI1-PrP(C) complex may play a critical role in neural progenitor/stem cells self-renewal via the modulation of cell proliferation, leading to the control of the stemness capacity of these cells during nervous system development. STEM CELLS 2011;29:1126-1136

Administration of Neural Precursor Cells Ameliorates Renal Ischemia-Reperfusion Injury

In this study we evaluated whether administration of stem cells of neural origin (neural precursor cells, NPCs) could be protective against renal ischemia-reperfusion injury (IRI). We hypothesized that stem cell outcomes are not tissue-specific and that NPCs can improve tissue damage through paracrine mechanisms, especially due to immunomodulation. To this end, Wistar rats (200-250 g) were submitted to 1-hour ischemia and treated with NPCs (4 x 10(6) cells/animal) at 4 h of reperfusion. To serve as controls, ischemic animals were treated with cerebellum homogenate harvested from adult rat brain. All groups were sacrificed at 24 h of reperfusion. NPCs were isolated from rat fetus telencephalon and cultured until neurosphere formation (7 days). Before administration, NPCs were labeled with carboxyfluorescein diacetate succinimydylester (CFSE). Kidneys were harvested for analysis of cytokine profile and macrophage infiltration. At 24 h, NPC treatment resulted in a significant reduction in serum creatinine (IRI + NPC 1.21 + 0.18 vs. IRI 3.33 + 0.14 and IRI + cerebellum 2.95 + 0.78mg/dl, p < 0.05) and acute tubular necrosis (IRI + NPC 46.0 + 2.4% vs. IRI 79.7 + 14.2%, p < 0.05). NPC-CFSE and glial fibrillary acidic protein (GFAP)-positive cells (astrocyte marker) were found exclusively in renal parenchyma, which also presented GFAP and SOX-2 (an embryonic neural stem cell marker) mRNA expression. NPC treatment resulted in lower renal proinflammatory IL1-beta and TNF-alpha expression and higher anti-inflammatory IL-4 and IL-10 transcription. NPC-treated animals also had less macrophage infiltration and decreased serum proinflammatory cytokines (IL-1 beta, TNF-alpha and INF-gamma). Our data suggested that NPC therapy improved renal function by influencing immunological responses. Copyright (C) 2009 S. Karger AG, Basel

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

Building binary-tree-based multiclass classifiers using separability measures

Various popular machine learning techniques, like support vector machines, are originally conceived for the solution of two-class (binary) classification problems. However, a large number of real problems present more than two classes. A common approach to generalize binary learning techniques to solve problems with more than two classes, also known as multiclass classification problems, consists of hierarchically decomposing the multiclass problem into multiple binary sub-problems, whose outputs are combined to define the predicted class. This strategy results in a tree of binary classifiers, where each internal node corresponds to a binary classifier distinguishing two groups of classes and the leaf nodes correspond to the problem classes. This paper investigates how measures of the separability between classes can be employed in the construction of binary-tree-based multiclass classifiers, adapting the decompositions performed to each particular multiclass problem. (C) 2010 Elsevier B.V. All rights reserved.

A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.

Image indexing and retrieval using an ART-2A neural network architecture

Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.

HIERARCHICAL DECOMPOSITION OF MULTICLASS PROBLEMS

Several popular Machine Learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems using binary classifiers is to decompose the multiclass problem into multiple binary sub-problems disposed in a binary tree. This approach requires a binary partition of the classes for each node of the tree, which defines the tree structure. This paper presents two algorithms to determine the tree structure taking into account information collected from the used dataset. This approach allows the tree structure to be determined automatically for any multiclass dataset.