Schizophrenic deficits: neuroleptics and the prefrontal cortex

Noradrenergic terminals are the primary source of α2-adrenoceptor mediated dopamine release in the medial prefrontal cortex

In various psychiatric disorders, deficits in dopaminergic activity in the prefrontal cortex (PFC) are implicated. Treatments involving selective augmentation of dopaminergic activity in the PFC primarily depend on the inhibition of α₂-adrenoreceptors singly or in combination with the inhibition of the norepinephrine transporter (NET). We aimed to clarify the relative contribution of dopamine (DA) release from noradrenergic and dopaminergic terminals to DA output induced by blockade of α₂-adrenoreceptors and NET. To this end, we assessed whether central noradrenergic denervation modified catecholamine output in the medial PFC (mPFC) of rats elicited by atipamezole (an α₂-adrenoreceptor antagonist), nisoxetine (an NET inhibitor), or their combination. Intraventricular administration of anti-dopamine-beta-hydroxylase-saporin (aDBH) caused a loss of DBH-positive fibers in the mPFC and almost total depletion of tissue and extracellular NE level; however, it did not reduce tissue DA level but increased extracellular DA level by 70% in the mPFC. Because noradrenergic denervation should have caused a loss of NET and reduced NE level at α₂-adrenoceptors, the actual effect of an aDBH-induced lesion on DA output elicited by blockade of α₂-adrenoceptors and NET was evaluated by comparing denervated and control rats following blockade of α₂-adrenoceptors and NET with atipamezole and nisoxetine, respectively. In the control rats, extracellular NE and DA levels increased by approximately 150% each with 3 mg/kg atipamezole; 450% and 230%, respectively, with 3 mg/kg nisoxetine; and 2100% and 600%, respectively, with combined atipamezole and nisoxetine. In the denervated rats, consistent with the loss of NET, nisoxetine failed to modify extracellular DA level, whereas atipamezole, despite the lack of NE-induced stimulation of α₂-adrenoceptors, increased extracellular DA level by approximately 30%. Overall, these results suggest that atipamezole-induced DA release mainly originated from noradrenergic terminals, possibly through the inhibition of α₂-autoreceptors. Furthermore, while systemic and local administration of the α₂-adrenoceptor agonist clonidine into the mPFC of the controls rats reduced extracellular NE level by 80% and 60%, respectively, and extracellular DA level by 50% and 60%, respectively, it failed to reduce DA output in the denervated rats, consistent with the loss of α₂-autoreceptors. To eliminate the possibility that denervation reduced DA release potential via the effects at dopaminergic terminals in the mPFC, the effect of systemic administration of the D₂-DA antagonist raclopride (0.5 mg/kg IP) on DA output was analyzed. In the control rats, raclopride was found to be ineffective when administered alone, but it increased extracellular DA level by 380% following NET inhibition with nisoxetine. In the denervated rats, as expected due to the loss of NET, raclopride-alone or with nisoxetine-increased DA release to approximately the same level as that observed in the control rats after NET inhibition. Overall, these results suggest that noradrenergic terminals in the mPFC are the primary source of DA released by blockade of α₂-adrenoreceptors and NET and that α₂-autoreceptors, and not α₂-heteroreceptors, mediate DA output induced by α₂-adrenoceptor blockade.

Comprehensive review: Computational modelling of schizophrenia

Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behavior (e.g. Bayesian models - top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist and neural networks - bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioral vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence. Here we present a review that first synthesizes the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the dopamine, glutamate, GABA, dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated.

Neuropoiesis: proposal for a connectionistic neurobiology

Given current assumptions about the biology of neural organization, some connectionists believe that it may not be possible to accurately model the brain's neural architecture. We have identified five restrictive neurobiological dogmas that we believe have limited the exploration of more fundamental correlations between computational and biological neural networks. We postulate that: 1) the dendritic tree serves as a synapse storage device rather than a simple summation device; 2) connection strength between neurons depends on the number and location of synapses of similar weight, not on synapses of variable weights; 3) axonal sprouting occurs regularly in adult organisms; 4) the postsynaptic genome directly controls the presynaptic cell via mRNA, rather than indirectly by the expression of NCAMs, reverse neurotransmitters, etc.; 5) dendritic spines serve a trophic function by controlling development of new sprouts via a process we term retroduction. We entertain an alternative formulation of a computational neural element that is fully consistent with modern neuroscience research. We then show how our model neuron can learn under Hebbian conditions, and extend the model to explain non-Hebbian, one-trial learning. This work is significant because by stretching the theoretical boundaries of modern neuroscience, we show how connectionists can potentially create new, more biologically-based neural elements which, when, interconnected into networks, exhibit not only properties of existing backpropagation networks, but other physiological properties as well.