Computational models of schizophrenia and dopamine modulation in the prefrontal cortex

NMDARs mediate the role of monoamine oxidase A in pathological aggression

Converging evidence shows that monoamine oxidase A (MAO A), the key enzyme catalyzing serotonin (5-hydroxytryptamine; 5-HT) and norepinephrine (NE) degradation, is a primary factor in the pathophysiology of antisocial and aggressive behavior. Accordingly, male MAO A-deficient humans and mice exhibit an extreme predisposition to aggressive outbursts in response to stress. As NMDARs regulate the emotional reactivity to social and environmental stimuli, we hypothesized their involvement in the modulation of aggression mediated by MAO A. In comparison with WT male mice, MAO A KO counterparts exhibited increases in 5-HT and NE levels across all brain regions, but no difference in glutamate concentrations and NMDAR binding. Notably, the prefrontal cortex (PFC) of MAO A KO mice exhibited higher expression of NR2A and NR2B, as well as lower levels of glycosylated NR1 subunits. In line with these changes, the current amplitude and decay time of NMDARs in PFC was significantly reduced. Furthermore, the currents of these receptors were hypersensitive to the action of the antagonists of the NMDAR complex (dizocilpine), as well as NR2A (PEAQX) and NR2B (Ro 25-6981) subunits. Notably, systemic administration of these agents selectively countered the enhanced aggression in MAO A KO mice, at doses that did not inherently affect motor activity. Our findings suggest that the role of MAO A in pathological aggression may be mediated by changes in NMDAR subunit composition in the PFC, and point to a critical function of this receptor in the molecular bases of antisocial personality.

Opposite effects of ketamine and deep brain stimulation on rat thalamocortical information processing

Sensory and cognitive deficits are common in schizophrenia. They are associated with abnormal brain rhythms, including disturbances in γ frequency (30-80 Hz) oscillations (GFO) in cortex-related networks. However, the underlying anatomofunctional mechanisms remain elusive. Clinical and experimental evidence suggests that these deficits result from a hyporegulation of glutamate N-methyl-D-aspartate receptors. Here we modeled these deficits in rats with ketamine, a non-competitive N-methyl-D-aspartate receptor antagonist and a translational psychotomimetic substance at subanesthetic doses. We tested the hypothesis that ketamine-induced sensory deficits involve an impairment of the ability of the thalamocortical (TC) system to discriminate the relevant information from the baseline activity. Furthermore, we wanted to assess whether ketamine disrupts synaptic plasticity in TC systems. We conducted multisite network recordings in the rat somatosensory TC system, natural stimulation of the vibrissae and high-frequency electrical stimulation (HFS) of the thalamus. A single systemic injection of ketamine increased the amount of baseline GFO, reduced the amplitude of the sensory-evoked TC response and decreased the power of the sensory-evoked GFO. Furthermore, cortical application of ketamine elicited local and distant increases in baseline GFO. The ketamine effects were transient. Unexpectedly, HFS of the TC pathway had opposite actions. In conclusion, ketamine and thalamic HFS have opposite effects on the ability of the somatosensory TC system to discriminate the sensory-evoked response from the baseline GFO during information processing. Investigating the link between the state and function of the TC system may conceptually be a key strategy to design innovative therapies against neuropsychiatric disorders.

Prefrontal cortical mechanisms underlying individual differences in cognitive flexibility and stability

The pFC is critical for cognitive flexibility (i.e., our ability to flexibly adjust behavior to changing environmental demands), but also for cognitive stability (i.e., our ability to follow behavioral plans in the face of distraction). Behavioral research suggests that individuals differ in their cognitive flexibility and stability, and neurocomputational theories of working memory relate this variability to the concept of attractor stability in recurrently connected neural networks. We introduce a novel task paradigm to simultaneously assess flexible switching between task rules (cognitive flexibility) and task performance in the presence of irrelevant distractors (cognitive stability) and to furthermore assess the individual "spontaneous switching rate" in response to ambiguous stimuli to quantify the individual dispositional cognitive flexibility in a theoretically motivated way (i.e., as a proxy for attractor stability). Using fMRI in healthy human participants, a common network consisting of parietal and frontal areas was found for task switching and distractor inhibition. More flexible persons showed reduced activation and reduced functional coupling in frontal areas, including the inferior frontal junction, during task switching. Most importantly, the individual spontaneous switching rate antagonistically affected the functional coupling between inferior frontal junction and the superior frontal gyrus during task switching and distractor inhibition, respectively, indicating that individual differences in cognitive flexibility and stability are indeed related to a common prefrontal neural mechanism. We suggest that the concept of attractor stability of prefrontal working memory networks is a meaningful model for individual differences in cognitive stability versus flexibility.

Excessive attractor instability accounts for semantic priming in schizophrenia

One of the most pervasive findings in studies of schizophrenics with thought disorders is their peculiar pattern of semantic priming, which presumably reflects abnormal associative processes in the semantic system of these patients. Semantic priming is manifested by faster and more accurate recognition of a word-target when preceded by a semantically related prime, relative to an unrelated prime condition. Compared to control, semantic priming in schizophrenics is characterized by reduced priming effects at long prime-target Stimulus Onset Asynchrony (SOA) and, sometimes, augmented priming at short SOA. In addition, unlike controls, schizophrenics consistently show indirect (mediated) priming (such as from the prime ‘wedding’ to the target ‘finger’, mediated by ‘ring’). In a previous study, we developed a novel attractor neural network model with synaptic adaptation mechanisms that could account for semantic priming patterns in healthy individuals. Here, we examine the consequences of introducing attractor instability to this network, which is hypothesized to arise from dysfunctional synaptic transmission known to occur in schizophrenia. In two simulated experiments, we demonstrate how such instability speeds up the network’s dynamics and, consequently, produces the full spectrum of priming effects previously reported in patients. The model also explains the inconsistency of augmented priming results at short SOAs using directly related pairs relative to the consistency of indirect priming. Further, we discuss how the same mechanism could account for other symptoms of the disease, such as derailment (‘loose associations’) or the commonly seen difficulty of patients in utilizing context. Finally, we show how the model can statistically implement the overly-broad wave of spreading activation previously presumed to characterize thought-disorders in schizophrenia.

Toward the neural mechanisms of reduced working memory capacity in schizophrenia

People with schizophrenia (PSZ) demonstrate reliable reductions in working memory (WM) capacity (i.e., the number of objects that can be held in memory). The present study asked whether WM impairments in PSZ can be explained by the same neural mechanisms that underlie individual differences in WM capacity among healthy individuals. Specifically, we examined event-related potentials in PSZ and healthy matched controls during a change detection task that required the storage of multiple objects in WM. The amplitude of contralateral delay activity (CDA), which correlates with WM capacity in healthy individuals, was larger in controls than in PSZ for memory loads of 3 and 5 objects, but larger in PSZ than in controls for a memory load of 1. This same pattern was found in the subgroups of PSZ and controls with an equivalent WM capacity. Moreover, the increase in CDA amplitude was correlated with individual differences in capacity in controls, but not in PSZ. These results demonstrate that WM impairment in PSZ is not associated with the same patterns of neural activity that characterize low WM capacity in healthy individuals. We propose that WM impairment in PSZ instead reflects a specific impairment in the ability to distribute attention broadly.

Attenuation of long-range temporal correlations in the amplitude dynamics of alpha and beta neuronal oscillations in patients with schizophrenia

Although schizophrenia was previously associated with affected spatial neuronal synchronization, surprisingly little is known about the temporal dynamics of neuronal oscillations in this disease. However, given that the coordination of neuronal processes in time represents an essential aspect of practically all cognitive operations, it might be strongly affected in patients with schizophrenia. In the present study we aimed at quantifying long-range temporal correlations (LRTC) in patients (18 with schizophrenia; 3 with schizoaffective disorder) and 28 healthy control subjects matched for age and gender. Ongoing neuronal oscillations were recorded with multi-channel EEG at rest condition. LRTC in the range 5-50s were analyzed with Detrended Fluctuation Analysis. The amplitude of neuronal oscillations in alpha and beta frequency ranges did not differ between patients and control subjects. However, LRTC were strongly attenuated in patients with schizophrenia in both alpha and beta frequency ranges. Moreover, the cross-frequency correlation between LRTC belonging to alpha and beta oscillations was stronger for patients than healthy controls, indicating that similar neurophysiological processes affect neuronal dynamics in both frequency ranges. We believe that the attenuation of LRTC is most likely due to the increased variability in neuronal activity, which was previously hypothesized to underlie an excessive switching between the neuronal states in patients with schizophrenia. Attenuated LRTC might allow for more random associations between neuronal activations, which in turn might relate to the occurrence of thought disorders in schizophrenia.

Dopamine, affordance and active inference

The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level.

Spatiotemporal and frequency domain analysis of auditory paired stimuli processing in schizophrenia and bipolar disorder with psychosis

Individuals with schizophrenia (SZ) or bipolar disorder with psychosis (BPP) may share neurophysiological abnormalities as measured in auditory paired-stimuli paradigms with electroencephalography (EEG). Such investigations have been limited, however, by quantifying only event-related potential peaks and/or broad frequency bands at limited scalp locations without considering possible mediating factors (e.g., baseline differences). Results from 64-sensor EEG collected in 180 age- and gender-matched participants reveal (i) accentuated prestimulus gamma oscillations and (ii) reduced P2 amplitudes and theta/alpha oscillations to S1 among participants with both SZ and BPP. Conversely, (iii) N1s in those with SZ to S1 were reduced compared to healthy volunteers and those with BPP, whereas (iv) beta range oscillations 200-300 ms following S2 were accentuated in those with BPP but not those with SZ. Results reveal a pattern of both unique and shared neurophysiological phenotypes occurring within major psychotic diagnoses.

A neurocomputational approach to obsessive-compulsive disorder

A recent article shows that a change in a single parameter in a neural-network model of brain dynamics leads to repetitive behaviors that resist termination and towards which the network tends. These findings may have implications for obsessive-compulsive disorder and are consistent with evidence of glutamatergic hyperactivity in this disorder.

Sustained versus transient brain responses in schizophrenia: the role of intrinsic neural activity

Schizophrenia patients (SZ) show early visual processing deficits in many, but not all, tasks. These deficits may be associated with dysregulation of intrinsic oscillatory activity that compromises signal-to-noise in the SZ brain. This question was studied using visual steady-state stimulation and post-steady-state presentation of transient visual stimuli. SZ had higher intrinsic oscillatory activity at the steady-state stimulation frequency (12.5 Hz) and at the 6.25 Hz subharmonic, showed a significant decrease in visual steady-state magnitude over 2s of stimulation, and were unable to promptly terminate the steady-state response following stimulation offset. If adjustment for levels of intrinsic brain activity were made, however, it would have appeared that SZ had activity of similar magnitude as healthy subjects following steady-state stimulus termination, indicating that such adjustments could substantially alter theoretical interpretations. Visual evoked potential abnormalities (N1/P2 amplitudes) present among SZ at the initiation of steady-state stimulation were less apparent in the 750 ms immediately following steady-state stimulation offset. Higher intrinsic oscillatory brain activity may be a fundamental characteristic of SZ that merits further evaluation for understanding this disorder's neuropathological correlates and associated symptomatology.

Representational switching by dynamical reorganization of attractor structure in a network model of the prefrontal cortex

The prefrontal cortex (PFC) plays a crucial role in flexible cognitive behavior by representing task relevant information with its working memory. The working memory with sustained neural activity is described as a neural dynamical system composed of multiple attractors, each attractor of which corresponds to an active state of a cell assembly, representing a fragment of information. Recent studies have revealed that the PFC not only represents multiple sets of information but also switches multiple representations and transforms a set of information to another set depending on a given task context. This representational switching between different sets of information is possibly generated endogenously by flexible network dynamics but details of underlying mechanisms are unclear. Here we propose a dynamically reorganizable attractor network model based on certain internal changes in synaptic connectivity, or short-term plasticity. We construct a network model based on a spiking neuron model with dynamical synapses, which can qualitatively reproduce experimentally demonstrated representational switching in the PFC when a monkey was performing a goal-oriented action-planning task. The model holds multiple sets of information that are required for action planning before and after representational switching by reconfiguration of functional cell assemblies. Furthermore, we analyzed population dynamics of this model with a mean field model and show that the changes in cell assemblies' configuration correspond to those in attractor structure that can be viewed as a bifurcation process of the dynamical system. This dynamical reorganization of a neural network could be a key to uncovering the mechanism of flexible information processing in the PFC.

The prefrontal cortex plays a highly flexible role in various cognitive tasks e.g., decision making and action planning. Neurons in the prefrontal cortex exhibit flexible representation or selectivity for task relevant information and are involved in working memory with sustained activity, which can be modeled as attractor dynamics. Moreover, recent experiments revealed that prefrontal neurons not only represent parametric or discrete sets of information but also switch the representation and transform a set of information to another set in order to match the context of the required task. However, underlying mechanisms of this flexible representational switching are unknown. Here we propose a dynamically reorganizable attractor network model in which short-term modulation of the synaptic connections reconfigures the structure of neural attractors by assembly and disassembly of a network of cells to produce flexible attractor dynamics. On the basis of computer simulation as well as theoretical analysis, we showed that this model reproduced experimentally demonstrated representational switching, and that switching on certain characteristic axes defining neural dynamics well describes the essence of the representational switching. This model has the potential to provide unique insights about the flexible information representations and processing in the cortical network.

The contribution of NMDA receptor signaling in the corticobasal ganglia reward network to appetitive Pavlovian learning

NMDA receptors (NMDARs) contribute to phasic transmission and synaptic plasticity and are thought to be important for learning. To better understand where NMDAR signaling is necessary for learning, we combined viral genetic strategies with genetic mouse models to investigate the contribution of NMDARs in the dopamine system to appetitive Pavlovian conditioning. NMDAR signaling in dopamine neurons was not required for Pavlovian conditioning; however, NMDARs in D(1) dopamine receptor (D(1)R)-expressing medium spiny neurons (MSNs), which receive input from dopamine neurons, were critical for this type of learning. NMDAR signaling was also required in brain regions that project to dopamine neurons, because removing NMDARs from afferent neurons to the ventral tegmental area (VTA) also prevented learning. This effect was likely attributable to loss of NMDAR signaling in the neurons of the prefrontal cortex (PFC), because learning could be restored in these animals by rescuing NMDAR expression in the PFC. Moreover, removing NMDARs exclusively from the PFC also prevented learning. Our findings suggest that NMDARs in neurons that project to and receive projections from the VTA are necessary for Pavlovian conditioning and specifically implicate the PFC and D(1)R-expressing MSNs in associative learning.

Noise in attractor networks in the brain produced by graded firing rate representations

Representations in the cortex are often distributed with graded firing rates in the neuronal populations. The firing rate probability distribution of each neuron to a set of stimuli is often exponential or gamma. In processes in the brain, such as decision-making, that are influenced by the noise produced by the close to random spike timings of each neuron for a given mean rate, the noise with this graded type of representation may be larger than with the binary firing rate distribution that is usually investigated. In integrate-and-fire simulations of an attractor decision-making network, we show that the noise is indeed greater for a given sparseness of the representation for graded, exponential, than for binary firing rate distributions. The greater noise was measured by faster escaping times from the spontaneous firing rate state when the decision cues are applied, and this corresponds to faster decision or reaction times. The greater noise was also evident as less stability of the spontaneous firing state before the decision cues are applied. The implication is that spiking-related noise will continue to be a factor that influences processes such as decision-making, signal detection, short-term memory, and memory recall even with the quite large networks found in the cerebral cortex. In these networks there are several thousand recurrent collateral synapses onto each neuron. The greater noise with graded firing rate distributions has the advantage that it can increase the speed of operation of cortical circuitry.

Impaired long-term depression in schizophrenia: a cathodal tDCS pilot study

BACKGROUND

Neural plasticity involves the reorganization of synaptic connections and represents the ability of the brain to adjust its function in response to challenge. Disturbed cortical plasticity has been linked to the pathophysiology of schizophrenia, with indirect evidence for disturbed plasticity in the disease state having been provided by postmortem studies and various animal models. However, glutamate-dependent long-term depression (LTD)-like cortical plasticity has not yet been investigated.

OBJECTIVE

To investigate LTD-like cortical plasticity after transcranial direct current stimulation (tDCS) in schizophrenia patients.

METHODS

Using excitability-diminishing cathodal tDCS, we performed the first in vivo assessment of glutamate-dependent LTD-like cortical plasticity in 21 schizophrenia patients and 21 matched healthy control subjects. To reveal the physiologic basis of the hypothesized plasticity deficits, we tested different inhibitory and excitatory neuronal circuits with transcranial magnetic stimulation (TMS).

RESULTS

Cathodal tDCS failed to reduce motor-evoked potential amplitudes in schizophrenia patients, indicating abolished LTD-like plasticity. Furthermore, schizophrenia patients had a prolonged GABA(B)-dependent cortical silent period (CSP) at baseline and tDCS failed to modulate the duration of CSP in the patient group. Finally, schizophrenia patients presented an elevated resting-motor threshold at baseline in comparison to healthy controls.

CONCLUSIONS

The pattern of our results provides evidence for a specific plasticity deficit in schizophrenia patients, which might be associated with a hyperglutamatergic state. These findings may reflect a reduced signal-to-noise ratio and a disturbed filter function in schizophrenia patients. An increase of GABA(B)-activity may be a compensatory mechanism to dysfunctional LTD-like plasticity in schizophrenia.

Effects of chronic oral treatment with aripiprazole on the expression of NMDA receptor subunits and binding sites in rat brain

RATIONALE

The glutamatergic theory of schizophrenia proposes a dysfunction of ionotropic N-methyl-D: -aspartate receptors (NMDA-R). Several therapeutic strategies address NMDA-R function and the effects of antipsychotic agents on NMDA-R expression have been described. Within the second-generation antipsychotics, the partial dopaminergic and serotonergic agonist aripiprazole (APZ) was able to counteract the behavioral effects of NMDA-R antagonists.

OBJECTIVES

This study aims to investigate the effects of APZ on NMDA-R subunit expression and binding.

METHODS

We treated Sprague-Dawley rats for 4 weeks or 4 months with APZ in daily oral doses of 10 and 40 mg per kilogram of body weight. Gene expression of the NMDA-R subunits NR1, NR2A, NR2B, NR2C, and NR2D, respectively, was assessed by semiquantitative radioactive in situ hybridization and in parallel receptor binding using (3)H-MK-801 receptor autoradiography.

RESULTS

Increased expression levels of NR1 (4 weeks), NR2A (4 weeks), NR2C (4 weeks and 4 months), and NR2D (4 months) were observed in several hippocampal and cortical brain regions. The parallel reduced expression of NR2B mRNAs (4 months) resulted in a relative increase of the NR2A/NR2B ratio. Marked differences between specific brain regions, the doses of APZ, and the time points of assessment became obvious. On the receptor level, increased MK-801-binding was found after 4 weeks in the 40-mg group and after 4 months in the 10-mg group.

CONCLUSIONS

The effects of APZ converge in enhanced NMDA receptor expression and a shift of subunit composition towards adult-type receptors. Our results confirm the regulatory connections between dopaminergic, serotonergic, and glutamatergic neurotransmissions with relevance for cognitive and negative symptoms of schizophrenia.

Temporal variability and spatial diffusion of the N2 event-related potential in high-functioning patients with schizophrenia

Recent theories of schizophrenia have proposed a fundamental instability of information processing on a neurophysiological level, which can be measured as an increase in latency variability of event-related potentials (ERPs). If this reflects a fundamental deficit of the schizophrenic illness, it should also occur in high-functioning patients. These patients have also been observed to show a more diffuse activation pattern in neuroimaging studies, which is thought to reflect compensatory processes to maintain task performance. In the present study we investigated temporal variability and spatial diffusion of the visual N2 component in a group of high-functioning patients with preserved cognitive performance. 28 patients with schizophrenia and 28 control participants matched for gender, age and education participated in the study. Subjects performed a visual Go/Nogo task, while event-related potentials were obtained. Trial-to-trial latency variability was calculated with a Wavelet-based method. Patients with schizophrenia showed a robust increase in N2 latency variability at electrodes Fz and Cz in all task conditions. Regarding spatial distribution healthy participants showed a focused fronto-central N2 peak. In contrast, patients with schizophrenia showed a more diffuse pattern and additional negative peaks over lateral electrodes in the Nogo condition. These results clearly show that even in high-functioning patients with schizophrenia a higher temporal variability of ERPs can be observed. This provides support for temporal instability of information processing as a fundamental deficit associated with schizophrenia. The more diffuse scalp distribution might reflect processes that compensate for this instability when cognitive control is required.

The role of tonic and phasic dopamine for long-term synaptic plasticity in the prefrontal cortex: a computational model

This work presents a computational model of dopamine (DA) influence on long-term potentiation (LTP) and long-term depression (LTD) in the prefrontal cortex. Distinct properties of the model are a DA-concentration-dependent switch from depression to potentiation during induction of plasticity, and an inverted-U-shaped dependence of protein synthesis on the level of background DA. Protein synthesis is responsible for the maintenance of LTP/LTD in the model. Our simulations suggest that in vitro experimental data on prefrontal plasticity, induced by high-frequency stimulation, may be accounted for by a single synaptic mechanism that is slowly (on the timescale of minutes) activated in the presence of DA in a concentration-dependent manner. The activation value determines the direction of plasticity during induction, while it also modulates the magnitude of plasticity during maintenance. More generally, our results support the hypothesis that phasic release of endogenous DA is necessary for the maintenance of long-term changes in synaptic efficacy, while the concentration of tonic DA determines the direction and magnitude of these changes in the PFC.

Categorization and timbre perception of environmental sounds in schizophrenia

Perception of environmental sounds from impacted materials (Wood, Metal and Glass) was examined by conducting a categorization experiment. Stimuli consisted of sound continua evoking progressive transitions between material categories. Results highlighted shallower response curves in subjects with schizophrenia than healthy participants, and are discussed in the framework of Signal Detection Theory and in terms of impaired perception of specific timbre features in schizophrenia.

Cortical attractor network dynamics with diluted connectivity

The connectivity of the cerebral cortex is diluted, with the probability of excitatory connections between even nearby pyramidal cells rarely more than 0.1, and in the hippocampus 0.04. To investigate the extent to which this diluted connectivity affects the dynamics of attractor networks in the cerebral cortex, we simulated an integrate-and-fire attractor network taking decisions between competing inputs with diluted connectivity of 0.25 or 0.1, and with the same number of synaptic connections per neuron for the recurrent collateral synapses within an attractor population as for full connectivity. The results indicated that there was less spiking-related noise with the diluted connectivity in that the stability of the network when in the spontaneous state of firing increased, and the accuracy of the correct decisions increased. The decision times were a little slower with diluted than with complete connectivity. Given that the capacity of the network is set by the number of recurrent collateral synaptic connections per neuron, on which there is a biological limit, the findings indicate that the stability of cortical networks, and the accuracy of their correct decisions or memory recall operations, can be increased by utilizing diluted connectivity and correspondingly increasing the number of neurons in the network, with little impact on the speed of processing of the cortex. Thus diluted connectivity can decrease cortical spiking-related noise. In addition, we show that the Fano factor for the trial-to-trial variability of the neuronal firing decreases from the spontaneous firing state value when the attractor network makes a decision. This article is part of a Special Issue entitled "Neural Coding".

Cholinergic modulation of working memory activity in primate prefrontal cortex

The prefrontal cortex, a cortical area essential for working memory and higher cognitive functions, is modulated by a number of neurotransmitter systems, including acetylcholine; however, the impact of cholinergic transmission on prefrontal activity is not well understood. We relied on systemic administration of a muscarinic receptor antagonist, scopolamine, to investigate the role of acetylcholine on primate prefrontal neuronal activity during execution of working memory tasks and recorded neuronal activity with chronic electrode arrays and single electrodes. Our results indicated a dose-dependent decrease in behavioral performance after scopolamine administration in all the working memory tasks we tested. The effect could not be accounted for by deficits in visual processing, eye movement responses, or attention, because the animals performed a visually guided saccade task virtually error free, and errors to distracting stimuli were not increased. Performance degradation under scopolamine was accompanied by decreased firing rate of the same cortical sites during the delay period of the task and decreased selectivity for the spatial location of the stimuli. These results demonstrate that muscarinic blockade impairs performance in working memory tasks and prefrontal activity mediating working memory.

Variable global dysconnectivity and individual differences in schizophrenia

BACKGROUND

A fundamental challenge for understanding neuropsychiatric disease is identifying sources of individual differences in psychopathology, especially when there is substantial heterogeneity of symptom expression, such as is found in schizophrenia (SCZ). We hypothesized that such heterogeneity might arise in part from consistently widespread yet variably patterned alterations in the connectivity of focal brain regions.

METHODS

We used resting state functional connectivity magnetic resonance imaging to identify variable global dysconnectivity in 23 patients with DSM-IV SCZ relative to 22 age-, gender-, and parental socioeconomic status-matched control subjects with a novel global brain connectivity method that is robust to high variability across individuals. We examined cognitive functioning with a modified Sternberg task and subtests from the Wechsler Adult Intelligence Scale-Third Edition. We measured symptom severity with the Scale for Assessment of Positive and Negative Symptoms.

RESULTS

We identified a dorsolateral prefrontal cortex (PFC) region with global and highly variable dysconnectivity involving within-PFC underconnectivity and non-PFC overconnectivity in patients. Variability in this "under/over" pattern of dysconnectivity strongly predicted the severity of cognitive deficits (matrix reasoning IQ, verbal IQ, and working memory performance) as well as individual differences in every cardinal symptom domain of SCZ (poverty, reality distortion, and disorganization).

CONCLUSIONS

These results suggest that global dysconnectivity underlies dorsolateral PFC involvement in the neuropathology of SCZ. Furthermore, these results demonstrate the possibility that specific patterns of dysconnectivity with a given network hub region might explain individual differences in symptom presentation in SCZ. Critically, such findings might extend to other neuropathologies with diverse presentation.

P50 sensory gating and smoking in the general population

P50 gating is a major functional biomarker in research on schizophrenia and other psychiatric conditions with high smoking prevalence. It is used as endophenotype for studying nicotinic systems genetics and as surrogate endpoint measure for drug development of nicotinic agonists. Surprisingly, little is known about P50 gating in the general population and the relationship to smoking-related characteristics. In this multicenter study at six academic institutions throughout Germany, n=907 never-smokers (NS<20 cigarettes/lifetime), n=463 light smokers (LS) with Fagerström Test for Nicotine Dependence (FTND)≥4 and n=353 heavy smokers (HS, FTND<4) were randomly selected from the general population. As part of a standardized protocol for investigating the genetics of nicotine dependence (ND), an auditory P50 paradigm was applied. The main outcome measure was P50-amplitude difference followed by time-frequency analyses and functional imaging (sLORETA). Reduced P50 gating was found in HS compared to NS with LS taking an intermediate position-correlating with the degree of ND. sLORETA and time-frequency analyses indicate that high-frequency oscillations in frontal brain regions are particularly affected. With growing age, P50 gating increased in (heavy) smokers. This is the first large-scale study (normative sample data) on P50 sensory gating and smoking in the general population. Diminished gating of P50 and associated high-frequency oscillations in the frontal brain region are indications of a deficient inhibitory cortical function in nicotine-dependent smokers. The suitability and application of sensory P50 gating as functional biomarker with regard to genetic and pharmacological studies is discussed.

Ketamine effects on brain function--simultaneous fMRI/EEG during a visual oddball task

BACKGROUND

Behavioral and electrophysiological human ketamine models of schizophrenia are used for testing compounds that target the glutamatergic system. However, corresponding functional neuroimaging models are difficult to reconcile with functional imaging and electrophysiological findings in schizophrenia. Resolving the discrepancies between different observational levels is critical to understand the complex pharmacological ketamine action and its usefulness for modeling schizophrenia pathophysiology.

METHODS

We conducted a within-subject, randomized, placebo-controlled pharmacoimaging study in twenty-four male volunteers. Subjects were given low-dose S-ketamine (bolus prior to functional imaging: 0.1mg/kg during 5min, thereafter continuous infusion: 0.015625mg/kg/min reduced by 10% every ten minutes) or placebo while performing a visual oddball task during simultaneous functional magnetic resonance imaging (fMRI) with continuous recording of event-related potentials (P300) and electrodermal activity (EDA). Before and after intervention, psychopathological status was assessed using the Positive and Negative Syndrome Scale (PANSS) and the Altered State of Consciousness (5D-ASC) Rating Scale.

RESULTS

P300 amplitude and corresponding BOLD responses were diminished in the ketamine condition in cortical regions being involved in sensory processing/selective attention. In both measurement modalities separation of drug conditions was achieved with area under the curve (AUC) values of up to 0.8-0.9. Ketamine effects were also observed in the clinical, behavioral and peripheral physiological domains (Positive and Negative Syndrome Scale, reaction hit and false alarm rate, electrodermal activity and heart rate) which were in part related to the P300/fMRI measures.

CONCLUSION

The findings from our ketamine experiment are consistent across modalities and directly related to observations in schizophrenia supporting the validity of the model. Our investigation provides the first prototypic example of a pharmacoimaging study using simultaneously acquired fMRI/EEG.

Glutamate, obsessive-compulsive disorder, schizophrenia, and the stability of cortical attractor neuronal networks

A computational neuroscience approach to the symptoms of obsessive-compulsive disorder based on a stochastic neurodynamical framework is described. An increased depth in the basins of attraction of attractor neuronal network states in the brain makes each state too stable, so that it tends to remain locked in that state, and cannot easily be moved on to another state. It is suggested that the different symptoms that may be present in obsessive--compulsive disorder could be related to changes of this type in different brain regions. In integrate-and-fire network simulations, an increase in the NMDA and/or AMPA receptor conductances, which increases the depth of the attractor basins, increases the stability of attractor networks, and makes them less easily moved on to another state by a new stimulus. Increasing GABA-receptor activated currents can partly reverse this overstability. There is now some evidence for overactivity in glutamate transmitter systems in obsessive-compulsive disorder, and the hypothesis presented here shows how some of the symptoms of obsessive-compulsive disorder could be produced by the increase in the stability of attractor networks that is produced by increased glutamatergic activity. In schizophrenia, a reduction of the firing rates of cortical neurons caused for example by reduced NMDA receptor function, present in schizophrenia, can lead to instability of the high firing rate attractor states that normally implement short-term memory and attention, contributing to the cognitive and negative symptoms of schizophrenia. Reduced cortical inhibition caused by a reduction of GABA neurotransmission, present in schizophrenia, can lead to instability of the spontaneous firing states of cortical networks, leading to a noise-induced jump to a high firing rate attractor state even in the absence of external inputs, contributing to the positive symptoms of schizophrenia.

Dysfunctional long-term potentiation-like plasticity in schizophrenia revealed by transcranial direct current stimulation

Neural and cortical plasticity represent the ability of the brain to reorganize its function in response to a challenge. Plasticity involves changing synaptic activity and connectivity. Long-term-potentiation is one important mechanism underlying these synaptic changes. Disturbed neuronal plasticity is considered to be part of the pathophysiology of schizophrenia and has been linked to the different clinical features of this severe illness. The aim of the present study was to investigate nonfocal cortical plasticity and cortical excitability in recent-onset and multi-episode schizophrenia compared with healthy subjects. Nonfocal cortical plasticity can be induced in the motor cortex of healthy subjects with anodal transcranial direct current stimulation. Animal and human research indicates that this long-term-potentiation-like plasticity is glutamate-dependent and that these plasticity shifts can last for several hours. Transcranial direct current stimulation-induced plasticity was monitored by transcranial magnetic stimulation-generated motor evoked potentials. Well-characterized transcranial magnetic stimulation protocols were applied to determine the physiological basis of plasticity changes. Multi-episode schizophrenia patients showed significantly reduced long-term-potentiation-like plasticity compared to recent-onset schizophrenia patients and healthy controls. All schizophrenia patients demonstrated reduced cortical inhibition. Our results indicate that the long-term-potentiation-like plasticity deficit in schizophrenia patients is related to the disease course. Disturbances of N-methyl-d-aspartate, gamma-aminobutyric acid and dopamine receptors may account for this plasticity deficit. LTP-like plasticity deficits might be related to disturbed information processing in schizophrenia patients.

From reinforcement learning models to psychiatric and neurological disorders

Over the last decade and a half, reinforcement learning models have fostered an increasingly sophisticated understanding of the functions of dopamine and cortico-basal ganglia-thalamo-cortical (CBGTC) circuits. More recently, these models, and the insights that they afford, have started to be used to understand important aspects of several psychiatric and neurological disorders that involve disturbances of the dopaminergic system and CBGTC circuits. We review this approach and its existing and potential applications to Parkinson's disease, Tourette's syndrome, attention-deficit/hyperactivity disorder, addiction, schizophrenia and preclinical animal models used to screen new antipsychotic drugs. The approach's proven explanatory and predictive power bodes well for the continued growth of computational psychiatry and computational neurology.

Deficits in positive reinforcement learning and uncertainty-driven exploration are associated with distinct aspects of negative symptoms in schizophrenia

BACKGROUND

Negative symptoms are core features of schizophrenia (SZ); however, the cognitive and neural basis for individual negative symptom domains remains unclear. Converging evidence suggests a role for striatal and prefrontal dopamine in reward learning and the exploration of actions that might produce outcomes that are better than the status quo. The current study examines whether deficits in reinforcement learning and uncertainty-driven exploration predict specific negative symptom domains.

METHODS

We administered a temporal decision-making task, which required trial-by-trial adjustment of reaction time to maximize reward receipt, to 51 patients with SZ and 39 age-matched healthy control subjects. Task conditions were designed such that expected value (probability × magnitude) increased, decreased, or remained constant with increasing response times. Computational analyses were applied to estimate the degree to which trial-by-trial responses are influenced by reinforcement history.

RESULTS

Individuals with SZ showed impaired Go learning but intact NoGo learning relative to control subjects. These effects were most pronounced in patients with higher levels of negative symptoms. Uncertainty-based exploration was substantially reduced in individuals with SZ and selectively correlated with clinical ratings of anhedonia.

CONCLUSIONS

Schizophrenia patients, particularly those with high negative symptoms, failed to speed reaction times to increase positive outcomes and showed reduced tendency to explore when alternative actions could lead to better outcomes than the status quo. Results are interpreted in the context of current computational, genetic, and pharmacological data supporting the roles of striatal and prefrontal dopamine in these processes.

Computational cognitive models of prefrontal-striatal-hippocampal interactions in Parkinson's disease and schizophrenia

Disruption to different components of the prefrontal cortex, basal ganglia, and hippocampal circuits leads to various psychiatric and neurological disorders including Parkinson's disease (PD) and schizophrenia. Medications used to treat these disorders (such as levodopa, dopamine agonists, antipsychotics, among others) affect the prefrontal-striatal-hippocampal circuits in a complex fashion. We have built models of prefrontal-striatal and striatal-hippocampal interactions which simulate cognitive dysfunction in PD and schizophrenia. In these models, we argue that the basal ganglia is key for stimulus-response learning, the hippocampus for stimulus-stimulus representational learning, and the prefrontal cortex for stimulus selection during learning about multidimensional stimuli. In our models, PD is associated with reduced dopamine levels in the basal ganglia and prefrontal cortex. In contrast, the cognitive deficits in schizophrenia are associated primarily with hippocampal dysfunction, while the occurrence of negative symptoms is associated with frontostriatal deficits in a subset of patients. In this paper, we review our past models and provide new simulation results for both PD and schizophrenia. We also describe an extended model that includes simulation of the different functional role of D1 and D2 dopamine receptors in the basal ganglia and prefrontal cortex, a dissociation we argue is essential for understanding the non-uniform effects of levodopa, dopamine agonists, and antipsychotics on cognition. Motivated by clinical and physiological data, we discuss model limitations and challenges to be addressed in future models of these brain disorders.

Value, pleasure and choice in the ventral prefrontal cortex

Rapid advances have recently been made in understanding how value-based decision-making processes are implemented in the brain. We integrate neuroeconomic and computational approaches with evidence on the neural correlates of value and experienced pleasure to describe how systems for valuation and decision-making are organized in the prefrontal cortex of humans and other primates. We show that the orbitofrontal and ventromedial prefrontal (VMPFC) cortices compute expected value, reward outcome and experienced pleasure for different stimuli on a common value scale. Attractor networks in VMPFC area 10 then implement categorical decision processes that transform value signals into a choice between the values, thereby guiding action. This synthesis of findings across fields provides a unifying perspective for the study of decision-making processes in the brain.

Neurogenetics and pharmacology of learning, motivation, and cognition

Many of the individual differences in cognition, motivation, and learning-and the disruption of these processes in neurological conditions-are influenced by genetic factors. We provide an integrative synthesis across human and animal studies, focusing on a recent spate of evidence implicating a role for genes controlling dopaminergic function in frontostriatal circuitry, including COMT, DARPP-32, DAT1, DRD2, and DRD4. These genetic effects are interpreted within theoretical frameworks developed in the context of the broader cognitive and computational neuroscience literature, constrained by data from pharmacological, neuroimaging, electrophysiological, and patient studies. In this framework, genes modulate the efficacy of particular neural computations, and effects of genetic variation are revealed by assays designed to be maximally sensitive to these computations. We discuss the merits and caveats of this approach and outline a number of novel candidate genes of interest for future study.

DRD2/AKT1 interaction on D2 c-AMP independent signaling, attentional processing, and response to olanzapine treatment in schizophrenia

The D2/AKT1/GSK-3β signaling pathway has been involved in the downstream intracellular effects of dopamine, in the pathophysiology of cognitive deficits and related brain activity in schizophrenia, as well as in response to treatment with antipsychotics. Polymorphisms in the D2 (DRD2 rs1076560) and AKT1 (AKT1 rs1130233) genes have been associated with their respective protein expression and with higher-order cognition and brain function, including attention. Given the strong potential for their relationship, we investigated the interaction of these polymorphisms on multiple molecular and in vivo phenotypes associated with this signaling pathway. We measured AKT1 and GSK-3β proteins and phosphorylation in human peripheral blood mononuclear cells, functional MRI cingulate response during attentional control, behavioral accuracy during sustained attention, and response to 8 wk of treatment with olanzapine in a total of 190 healthy subjects and 66 patients with schizophrenia. In healthy subjects, we found that the interaction between the T allele of DRD2 rs1076560 and the A allele of AKT1 rs1130233 was associated with reduced AKT1 protein levels and reduced phosphorylation of GSK-3β, as well as with altered cingulate response and reduced behavioral accuracy during attentional processing. On the other hand, interaction of these two alleles was associated with greater improvement of Positive and Negative Syndrome Scale scores in patients with schizophrenia after treatment with olanzapine. The present results indicate that these functional polymorphisms are epistatically associated with multiple phenotypes of relevance to schizophrenia. Our results also lend support to further investigation of this downstream molecular pathway in the etiology and treatment of this disorder.

The missing developmental dimension in the network perspective

We welcome network theory as a tool for modelling the multi-directional interactions that characterise disease. However, we feel that Cramer et al. have neglected one important aspect: how diseases change over developmental time. We discuss principles such as fan in, fan out, bottlenecks, and common pathways, and argue that modelling these developmental aspects can be vital, particularly in deriving properly targeted treatments.

A computational neuroscience approach to schizophrenia and its onset

Computational neuroscience integrate-and-fire attractor network models can be used to understand the factors that alter the stability of cortical networks in the face of noise caused for example by neuronal spiking times. A reduction of the firing rates of cortical neurons caused for example by reduced NMDA receptor function (present in schizophrenia) can lead to instability of the high firing rate attractor states that normally implement short-term memory and attention, contributing to the cognitive and negative symptoms of schizophrenia. Reduced cortical inhibition caused by a reduction of GABA neurotransmission (present in schizophrenia) can lead to instability of the spontaneous firing states of cortical networks, leading to a noise-induced jump to a high firing rate attractor state even in the absence of external inputs, contributing to the positive symptoms of schizophrenia. We consider how effects occurring at the time of late adolescence including synaptic pruning, decreases in grey matter volume, and changes in GABA-mediated inhibition and dopamine may contribute to the onset in some individuals of schizophrenia at this time.

Decision-making, errors, and confidence in the brain

To provide a fundamental basis for understanding decision-making and decision confidence, we analyze a neuronal spiking attractor-based model of decision-making. The model predicts probabilistic decision-making with larger neuronal responses and larger functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) responses on correct than on error trials because the spiking noise-influenced decision attractor state of the network is consistent with the external evidence. Moreover, the model predicts that the neuronal activity and the BOLD response will become larger on correct trials as the discriminability ΔI increases and confidence increases and will become smaller as confidence decreases on error trials as ΔI increases. Confidence is thus an emergent property of the model. In an fMRI study of an olfactory decision-making task, we confirm these predictions for cortical areas including medial prefrontal cortex and the cingulate cortex implicated in choice decision-making, showing a linear increase in the BOLD signal with ΔI on correct trials, and a linear decrease on error trials. These effects were not found in a control area, the orbitofrontal cortex, where reward value useful for the choice is represented on a continuous scale but that is not implicated in the choice itself. This provides a unifying approach to decision-making and decision confidence and to how spiking-related noise affects choice, confidence, synaptic and neuronal activity, and fMRI signals.

A neural model of hippocampal-striatal interactions in associative learning and transfer generalization in various neurological and psychiatric patients

Building on our previous neurocomputational models of basal ganglia and hippocampal region function (and their modulation by dopamine and acetylcholine, respectively), we show here how an integration of these models can inform our understanding of the interaction between the basal ganglia and hippocampal region in associative learning and transfer generalization across various patient populations. As a common test bed for exploring interactions between these brain regions and neuromodulators, we focus on the acquired equivalence task, an associative learning paradigm in which stimuli that have been associated with the same outcome acquire a functional similarity such that subsequent generalization between these stimuli increases. This task has been used to test cognitive dysfunction in various patient populations with damages to the hippocampal region and basal ganglia, including studies of patients with Parkinson's disease (PD), schizophrenia, basal forebrain amnesia, and hippocampal atrophy. Simulation results show that damage to the hippocampal region-as in patients with hippocampal atrophy (HA), hypoxia, mild Alzheimer's (AD), or schizophrenia-leads to intact associative learning but impaired transfer generalization performance. Moreover, the model demonstrates how PD and anterior communicating artery (ACoA) aneurysm-two very different brain disorders that affect different neural mechanisms-can have similar effects on acquired equivalence performance. In particular, the model shows that simulating a loss of dopamine function in the basal ganglia module (as in PD) leads to slow acquisition learning but intact transfer generalization. Similarly, the model shows that simulating the loss of acetylcholine in the hippocampal region (as in ACoA aneurysm) also results in slower acquisition learning. We argue from this that changes in associative learning of stimulus-action pathways (in the basal ganglia) or changes in the learning of stimulus representations (in the hippocampal region) can have similar functional effects.

Genetic modulation of GABA levels in the anterior cingulate cortex by GAD1 and COMT

Gamma-aminobutyric acid (GABA)-ergic transmission is critical for normal cortical function and is likely abnormal in a variety of neuropsychiatric disorders. We tested the in vivo effects of variations in two genes implicated in GABA function on GABA concentrations in prefrontal cortex of living subjects: glutamic acid decarboxylase 1 (GAD1), which encodes GAD67, and catechol-o-methyltransferase (COMT), which regulates synaptic dopamine in the cortex. We studied six single nucleotide polymorphisms (SNPs) in GAD1 previously associated with risk for schizophrenia or cognitive dysfunction and the val158met polymorphism in COMT in 116 healthy volunteers using proton magnetic resonance spectroscopy. Two of the GAD1 SNPs (rs1978340 (p=0.005) and rs769390 (p=0.004)) showed effects on GABA levels as did COMT val158met (p=0.04). We then tested three SNPs in GAD1 (rs1978340, rs11542313, and rs769390) for interaction with COMT val158met based on previous clinical results. In this model, rs11542313 and COMT val158met showed significant main effects (p=0.001 and 0.003, respectively) and a trend toward a significant interaction (p=0.05). Interestingly, GAD1 risk alleles for schizophrenia were associated with higher GABA/Cre, and Val-Val homozygotes had high GABA/Cre levels when on a GAD1 risk genotype background (N=6). These results support the importance of genetic variation in GAD1 and COMT in regulating prefrontal cortical GABA function. The directionality of the effects, however, is inconsistent with earlier evidence of decreased GABA activity in schizophrenia.

Intra-individual variability in high-functioning patients with schizophrenia

Intra-individual variability of reaction times (IIV) can be employed as a measure of the stability of information processing, which has been proposed to be fundamentally disturbed in schizophrenia. However, the theoretical and clinical significance of IIV is not clear, in part because it has previously been investigated in subject groups with generalized cognitive impairment. Therefore, the purpose of the study was to assess IIV in high-functioning patients with schizophrenia and relatively preserved cognitive performance. 28 high-functioning patients with schizophrenia and 28 controls performed a Go/Nogo task and a Continuous Performance Test. In contrast to average measures of task performance, IIV differentiated consistently and with large effect size between groups. Modelling with an Ex-Gaussian distribution revealed that patients have a higher proportion of slow responses reflected by an increased tau parameter. The tau parameter was correlated with work capability in the sample with schizophrenia. In conclusion, IIV is an easily obtained measure, which is highly sensitive to fundamental cognitive deficits not directly visible in a high-functioning patient group. The response pattern with more exceedingly slow reactions could reflect a core deficit in the stability of information processing. The relationship with work capability suggests investigation of IIV as a clinical measure.

Reduced capacity but spared precision and maintenance of working memory representations in schizophrenia

CONTEXT

Working memory deficits are considered a core feature of schizophrenia. Several recent integrative articles have offered mechanistic computational and neurobiological models of the origins of this cognitive deficit.

OBJECTIVE

To test predictions of these models using a new experimental paradigm from the basic science literature that makes it possible to determine whether patients with schizophrenia show (1) deficits in working memory storage capacity, (2) deficits in the precision of working memory representations, and (3) an amplification of these deficits as the retention interval increases.

DESIGN

Case-control design. All subjects performed a color working memory test in which they were asked to recall 3 or 4 items after a 1- or 4-second delay. All subjects also received a standard measure of intelligence and the Matrics Consensus Cognitive Battery.

SETTING

A tertiary care research outpatient clinic. Patients A total of 31 clinically stable patients with a DSM-IV diagnosis of schizophrenia or schizoaffective disorder and 26 healthy volunteers participated. The 2 groups were similar in age, sex, and ethnicity distribution.

MAIN OUTCOME MEASURES

(1) The number of items stored in working memory and (2) the precision of the working memory representations.

RESULTS

Patients showed a clear reduction in the number of items stored in working memory. Patients did not differ from controls in the precision of their working memory representations. There was no evidence of delay-related amplification of impairment in either capacity or precision.

CONCLUSIONS

Patients do not show the type of imprecision or delay-dependent amplification of impairment that is predicted on the basis of current models of the neurobiology of schizophrenia. The models need to be revised to account for a pure reduction in the number of items that patients are able to store in working memory.

Treatment with olanzapine is associated with modulation of the default mode network in patients with Schizophrenia

Earlier studies have shown widespread alterations of functional connectivity of various brain networks in schizophrenia, including the default mode network (DMN). The DMN has also an important role in the performance of cognitive tasks. Furthermore, treatment with second-generation antipsychotic drugs may ameliorate to some degree working memory (WM) deficits and related brain activity. The aim of this study was to evaluate the effects of treatment with olanzapine monotherapy on functional connectivity among brain regions of the DMN during WM. Seventeen patients underwent an 8-week prospective study and completed two functional magnetic resonance imaging (fMRI) scans at 4 and 8 weeks of treatment during the performance of the N-back WM task. To control for potential repetition effects, 19 healthy controls also underwent two fMRI scans at a similar time interval. We used spatial group-independent component analysis (ICA) to analyze fMRI data. Relative to controls, patients with schizophrenia had reduced connectivity strength within the DMN in posterior cingulate, whereas it was greater in precuneus and inferior parietal lobule. Treatment with olanzapine was associated with increases in DMN connectivity with ventromedial prefrontal cortex, but not in posterior regions of DMN. These results suggest that treatment with olanzapine is associated with the modulation of DMN connectivity in schizophrenia. In addition, our findings suggest critical functional differences in the regions of DMN.

Why do patients with schizophrenia smoke?

PURPOSE OF REVIEW

Among the mentally ill, smoking prevalence is highest in patients with schizophrenia ( approximately 70-80%). This can impose a significant financial burden on patients, not to speak of increased smoking-related morbidity and mortality. Therefore, it is critical for clinicians to understand why patients with schizophrenia smoke in order to adapt treatment schemes. Understanding the reasons may also help to develop new drugs that target the nicotinic system in the brain as well as smoking cessation programs that are specifically designed for this particular patient population.

RECENT FINDINGS

So far, several reasons have been identified which are believed to explain tobacco consumption in patients with schizophrenia. Originally, it was widely believed that patients with schizophrenia smoke to increase hepatic clearance and to restore the dopamine blockade of certain antipsychotic drugs to diminish their side effects. However, more recently it became obvious that cigarette smoking may also be reinforcing for patients because it improves psychiatric symptoms, most notably negative and cognitive symptoms. The underlying molecular mechanisms of these nicotine effects are currently under intensive investigation.

SUMMARY

Heavy smoking in schizophrenia cannot simply be viewed as a 'bad habit'. Rather, self-medication of clinical symptoms and side effects of antipsychotic drugs appear to play a major role.

Drosophila brain development: closing the gap between a macroarchitectural and microarchitectural approach

Neurobiologists address neural structure, development, and function at the level of "macrocircuits" (how different brain compartments are interconnected; what overall pattern of activity they produce) and at the level of "microcircuits" (how connectivity and physiology of individual neurons and their processes within a compartment determine the functional output of this compartment). Work in our lab aims at reconstructing the developing Drosophila brain at both levels. Macrocircuits can be approached conveniently by reconstructing the pattern of brain lineages, which form groups of neurons whose projections form cohesive fascicles interconnecting the compartments of the larval and adult brain. The reconstruction of microcircuits requires serial section electron microscopy, due to the small size of terminal neuronal processes and their synaptic contacts. Because of the amount of labor that traditionally comes with this approach, very little is known about microcircuitry in brains across the animal kingdom. Many of the problems of serial electron microscopy reconstruction are now solvable with digital image recording and specialized software for both image acquisition and postprocessing. In this chapter, we introduce our efforts to reconstruct the small Drosophila larval brain and discuss our results in light of the published data on neuropile ultrastructure in other animal taxa.

Attractor networks

An attractor network is a network of neurons with excitatory interconnections that can settle into a stable pattern of firing. This article shows how attractor networks in the cerebral cortex are important for long-term memory, short-term memory, attention, and decision making. The article then shows how the random firing of neurons can influence the stability of these networks by introducing stochastic noise, and how these effects are involved in probabilistic decision making, and implicated in some disorders of cortical function such as poor short-term memory and attention, schizophrenia, and obsessive-compulsive disorder. Copyright © 2009 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.

Glutamatergic deficits and parvalbumin-containing inhibitory neurons in the prefrontal cortex in schizophrenia

BACKGROUND

We have previously reported that the expression of the messenger ribonucleic acid (mRNA) for the NR2A subunit of the N-methyl-D-aspartate (NMDA) class of glutamate receptor was decreased in a subset of inhibitory interneurons in the cerebral cortex in schizophrenia. In this study, we sought to determine whether a deficit in the expression of NR2A mRNA was present in the subset of interneurons that contain the calcium buffer parvalbumin (PV) and whether this deficit was associated with a reduction in glutamatergic inputs in the prefrontal cortex (PFC) in schizophrenia.

METHODS

We examined the expression of NR2A mRNA, labeled with a 35S-tagged riboprobe, in neurons that expressed PV mRNA, visualized with a digoxigenin-labeled riboprobe via an immunoperoxidase reaction, in twenty schizophrenia and twenty matched normal control subjects. We also immunohistochemically labeled the glutamatergic axon terminals with an antibody against vGluT1.

RESULTS

The density of the PV neurons that expressed NR2A mRNA was significantly decreased by 48-50% in layers 3 and 4 in the subjects with schizophrenia, but the cellular expression of NR2A mRNA in the PV neurons that exhibited a detectable level of this transcript was unchanged. In addition, the density of vGluT1-immunoreactive boutons was significantly decreased by 79% in layer 3, but was unchanged in layer 5 of the PFC in schizophrenia.

CONCLUSION

These findings suggest that glutamatergic neurotransmission via NR2A-containing NMDA receptors on PV neurons in the PFC may be deficient in schizophrenia. This may disinhibit the postsynaptic excitatory circuits, contributing to neuronal injury, aberrant information flow and PFC functional deficits in schizophrenia.

Mental training enhances attentional stability: neural and behavioral evidence

The capacity to stabilize the content of attention over time varies among individuals, and its impairment is a hallmark of several mental illnesses. Impairments in sustained attention in patients with attention disorders have been associated with increased trial-to-trial variability in reaction time and event-related potential deficits during attention tasks. At present, it is unclear whether the ability to sustain attention and its underlying brain circuitry are transformable through training. Here, we show, with dichotic listening task performance and electroencephalography, that training attention, as cultivated by meditation, can improve the ability to sustain attention. Three months of intensive meditation training reduced variability in attentional processing of target tones, as indicated by both enhanced theta-band phase consistency of oscillatory neural responses over anterior brain areas and reduced reaction time variability. Furthermore, those individuals who showed the greatest increase in neural response consistency showed the largest decrease in behavioral response variability. Notably, we also observed reduced variability in neural processing, in particular in low-frequency bands, regardless of whether the deviant tone was attended or unattended. Focused attention meditation may thus affect both distracter and target processing, perhaps by enhancing entrainment of neuronal oscillations to sensory input rhythms, a mechanism important for controlling the content of attention. These novel findings highlight the mechanisms underlying focused attention meditation and support the notion that mental training can significantly affect attention and brain function.

Introduction: genes, cognition and neuropsychiatry

Numerous genes modulate dopamine and consequently prefrontal cortical function. Some of these genes - notably catechol-O-methyltransferase - have been shown to impact a variety of core cognitive processes that are dependent upon the prefrontal cortex. This demonstration that a single functional polymorphism can contribute so dramatically to individual differences heralds a new era for neuropsychiatry. Although enormous detail remains to be discovered about these various genes that regulate neurotransmitters, how they interact and how they affect brain systems, there is much excitement and promise regarding new neuropsychopharmacological possibilities. However, this new research program is magnitudes more complex than any enterprise embarked on hitherto and requires the development, validation and deployment of novel behavioural and neurophysiological phenotypes in order to unravel the pathologies within neural functional systems. This research foundation is essential if these genetic breakthroughs are to be translated to successful clinical agents.

Prefrontal cognitive systems in schizophrenia: towards human genetic brain mechanisms

Schizophrenia has complex genetic heritability. It is also genetically heterogeneous. To the extent that genes are associated with symptom constellations in schizophrenia, they do so by affecting the development and function of neural systems that mediate the expression of such diverse behavioral, cognitive and perceptual phenomena. The genetic mechanisms of human brain dysfunction remain to be well understood. "Imaging genetics" is an emerging field that attempts to integrate the basic biology of putative disease mechanisms with physiological correlates from the live human brain. Here, we review recent imaging genetics work on prefrontal brain systems associated with working memory and executive function - heritable traits relevant to schizophrenia. Starting with genetic variation in dopaminergic systems (e.g., COMT), we examined the modulation of prefrontal brain networks during active cognitive processing; there is also evidence that variation in the expression of dopamine-related downstream intra-cellular signaling molecules (e.g., AKT1) are implicated. Moreover, these genetic variants evidence epistasis on neuroimaging measures, lending further support to the conceptualization that non-additive combinations of multiple genes modulate active human cognitive brain mechanisms. The imaging genetics platform therefore could extend understanding of genetic mechanisms of human cognitive brain processes relevant to neuropsychiatric disease.