A neural network model of cortical information processing in schizophrenia. II--Role of hippocampal-cortical interaction: a review and a model

The 3-hit animal models of schizophrenia: Improving strategy to decipher and treat the disease?

Schizophrenia is a complex disease related to combination and interactions between genetic and environmental factors, with an epigenetic influence. After the development of the first mono-factorial animal models of schizophrenia (1-hit), that reproduced patterns of either positive, negative and/or cognitive symptoms, more complex models combining two factors (2-hit) have been developed to better fit with the multifactorial etiology of the disease. In the two past decades, a new way to design animal models of schizophrenia have emerged by adding a third hit (3-hit). This review aims to discuss the relevance of the risk factors chosen for the tuning of the 3-hit animal models, as well as the validities measurements and their contribution to schizophrenia understanding. We intended to establish a comprehensive overview to help in the choice of factors for the design of multiple-hit animal models of schizophrenia.

Spurious Autobiographical Memory of Psychosis: A Mechanistic Hypothesis for the Resolution, Persistence, and Recurrence of Positive Symptoms in Psychotic Disorders

Psychotic disorders are complex disorders with multiple etiologies. While increased dopamine synthesis capacity has been proposed to underlie psychotic episodes, dopamine-independent processes are also involved (less responsive to dopamine receptor-blocking medications). The underlying mechanism(s) of the reduction in antipsychotic responsiveness over time, especially after repeated relapses, remain unclear. Despite the consistent evidence of dopamine overactivity and hippocampal volume loss in schizophrenia, few accounts have been provided based on the interactive effect of dopamine on hippocampal synapse plasticity mediating autobiographical memory processes. The present hypothesis builds upon previous works showing the potential effects of dopamine overactivity on hippocampal-mediated neuroplasticity underlying autobiographical memory, alongside known patterns of autobiographical memory dysfunction in psychosis. We propose that spurious autobiographical memory of psychosis (SAMP) produced during active psychosis may be a key mechanism mediating relapses and treatment non-responsiveness. In a hyperdopaminergic state, SAMP is expected to be generated at an increased rate during active psychosis. Similar to other memories, it will undergo assimilation, accommodation, and extinction processes. However, if SAMP fails to integrate with existing memory, a discontinuity in autobiographical memory may result. Inadequate exposure to normalizing experiences and hyposalience due to overmedication or negative symptoms may also impede the resolution of SAMP. Residual SAMP is hypothesized to increase the propensity for relapse and treatment non-responsiveness. Based on recent findings on the role of dopamine in facilitating hippocampal synapse plasticity and autobiographical memory formation, the SAMP hypothesis is consistent with clinical observations of DUP effects, including the repetition of contents in psychotic relapses as well as the emergence of treatment non-responsiveness after repeated relapses. Clinical implications of the hypothesis highlight the importance of minimizing active psychosis, integrating psychosis memory, avoiding over-medication, and fostering normalizing experiences.

Reflections on measuring disordered thoughts as expressed via language

Thought disorder, as inferred from disorganized and incoherent speech, is an important part of the clinical presentation in schizophrenia. Traditional measurement approaches essentially count occurrences of certain speech events which may have restricted their usefulness. Applying speech technologies in assessment can help automate traditional clinical rating tasks and thereby complement the process. Adopting these computational approaches affords clinical translational opportunities to enhance the traditional assessment by applying such methods remotely and scoring various parts of the assessment automatically. Further, digital measures of language may help detect subtle clinically significant signs and thus potentially disrupt the usual manner by which things are conducted. If proven beneficial to patient care, methods where patients' voice are the primary data source could become core components of future clinical decision support systems that improve risk assessment. However, even if it is possible to measure thought disorder in a sensitive, reliable and efficient manner, there remain many challenges to then translate into a clinically implementable tool that can contribute towards providing better care. Indeed, embracing technology - notably artificial intelligence - requires vigorous standards for reporting underlying assumptions so as to ensure a trustworthy and ethical clinical science.

DC Motor Control Technology Based on Multisensor Information Fusion

To solve these uncertain problems by studying the motor fault diagnosis technology, so as to ensure the normal operation of the motor equipment is the primary problem to be solved in the field of motor fault diagnosis. The traditional DC motor is one of the most widely used motors at present. It has excellent speed regulation performance and is easy to control. It is widely used in applications that require high motor startup and speed regulation characteristics. This research mainly discusses DC motor control technology. Evidence theory can combine various fault information at different levels to enhance mutual support between pieces of evidence, thereby improving the accuracy of motor fault detection. Based on the steps of signal processing, feature extraction, feature dimensionality reduction, and state recognition, the research on the state recognition method of belt conveyor drive motor based on multisource information fusion is carried out. By studying the multisource information fusion, this paper proposes a two-stage belt conveyor drive motor information fusion model based on the optimal D-S evidence theory. The correct identification rate of broken rotor bars during fault monitoring is 99.8%. This method divides the specific motor fault feature set into multiple fault subspaces and uses different diagnostic neural networks and different fault feature parameters for local diagnosis, respectively. The scheme designed in this study significantly improves the recognition accuracy of the motor in the same working condition and under variable working conditions. The drive motor state recognition and intelligent decision-making system designed by combining the results of multisource information fusion can effectively describe the fault type and has strong operability.

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.

On the Complexity of Brain Disorders: A Symptom-Based Approach

Mounting evidence shows that brain disorders involve multiple and different neural dysfunctions, including regional brain damage, change to cell structure, chemical imbalance, and/or connectivity loss among different brain regions. Understanding the complexity of brain disorders can help us map these neural dysfunctions to different symptom clusters as well as understand subcategories of different brain disorders. Here, we discuss data on the mapping of symptom clusters to different neural dysfunctions using examples from brain disorders such as major depressive disorder (MDD), Parkinson’s disease (PD), schizophrenia, posttraumatic stress disorder (PTSD) and Alzheimer’s disease (AD). In addition, we discuss data on the similarities of symptoms in different disorders. Importantly, computational modeling work may be able to shed light on plausible links between various symptoms and neural damage in brain disorders.

Cognitive correlates of psychosis in patients with Parkinson's disease

INTRODUCTION

Psychosis and hallucinations occur in 20-30% of patients with Parkinson's disease (PD). In the current study, we investigate cognitive functions in relation to the occurrence of psychosis in PD patients.

METHODS

We tested three groups of subjects - PD with psychosis, PD without psychosis and healthy controls - on working memory, learning and transitive inference tasks, which are known to assess prefrontal, basal ganglia and hippocampal functions.

RESULTS

In the working memory task, results show that patients with and without psychosis were more impaired than the healthy control group. In the transitive inference task, we did not find any difference among the groups in the learning phase performance. Importantly, PD patients with psychosis were more impaired than both PD patients without psychosis and controls at transitive inference. We also found that the severity of psychotic symptoms in PD patients [as measured by the Unified Parkinson Disease Rating Scale Thought Disorder (UPDRS TD) item] is directly associated with the severity of cognitive impairment [as measured by the mini-mental status exam (MMSE)], sleep disturbance [as measured by the Scales for Outcome in Parkinson Disease (SCOPA) sleep scale] and transitive inference (although the latter did not reach significance).

CONCLUSIONS

Although hypothetical, our data may suggest that the hippocampus is a neural substrate underlying the occurrence of psychosis, sleep disturbance and cognitive impairment in PD patients.

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.

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.

Phenotyping psychosis: room for neurocomputational and content-dependent cognitive endophenotypes?

INTRODUCTION

The endophenotype research strategy aims at reducing complex clinical phenomena to reveal a more tractable mapping to underlying genes. Cognitive dysfunctions have been widely pursued as target endophenotype in schizophrenia. We critically discuss the promise and limitations of this approach.

METHODS

Relevant theoretical and empirical issues on genes and behaviour, neurocognitive structure and psychopathology were selectively reviewed and discussed.

RESULTS

Some important inherent limitations of the current cognitive endophenotype approach were identified. These include reliance on (1) classic neuropsychology; (2) deficit measures; and (3) a general information processing approach with the use of content-independent, neutral stimuli. As a result, many current cognitive endophenotypes are likely to overlap and converge with general cognitive impairments, which may be shared with other disorders.

CONCLUSIONS

We propose three novel directions for further psychosis endophenotype research: (1) in addition to such content-independent computational processes, which operate in a similar way regardless of the stimuli, it is important to consider the potential roles of "content-dependent endophenotypes", which operate on different stimuli in consistently different manners. Advances in cognitive studies suggest there may be evolutionarily important aspects of cognition which are content-dependent. We propose that both content-independent and content-dependent processes should be addressed in psychosis research. (2) In line with the emphasis on content, close attention should be paid to the study of "psychopathological endophenotypes" in addition to cognitive endophenotypes. (3) "Neurocomputational endophenotypes" may be defined by parsing cognitive processes into "subsystems" with specific computational processing algorithms and considering key computational parameters suggested from these models. These potential "neurocomputational endophenotypes" (such as neuronal noise, synaptic learning algorithms) are potentially intermediate variables located between the levels of cognition and neurobiology.

Jumping to conclusions: a network model predicts schizophrenic patients' performance on a probabilistic reasoning task

This article extends computational models of schizophrenia that focus on the negative aspects of this syndrome to behavioral biases that are associated with a positive symptom of schizophrenia, namely delusions. The phenomenon studied is the "jump-to-conclusions" style of reasoning that is characterized by delusional patients--in comparison with controls--whereby they make less-informed decisions when an option to collect more decision-specific information is available. Simulations show that these differences can be mimicked by modulating the gain parameter-associated with variations in dopamine level-in a simple network model.

Reduction of the parahippocampal gyrus and the hippocampus in patients with chronic schizophrenia

BACKGROUND

There have been many studies reporting reduced volume of the hippocampus or other limbic structures in patients with schizophrenia, but the literature is inconsistent.

AIMS

To compare patients with either first-episode or chronic schizophrenia with controls using high-resolution volumetric magnetic resonance imaging (MRI) scans.

METHOD

Thirteen patients with first-episode schizophrenia, 27 with chronic schizophrenia and 31 controls had 1.5 mm coronal slices taken through the whole brain using a spoiled-grass MRI acquisition protocol.

RESULTS

The parahippocampal gyrus was reduced significantly on the left side in patients with chronic schizophrenia compared with controls for both male and female patients, whereas the hippocampus was reduced significantly on both sides only in female patients. There were no significant reductions in any structure between patients with first-episode schizophrenia and controls.

CONCLUSIONS

Volumetric reduction seen in patients with chronic schizophrenia may be due to an active degenerative process occurring after the onset of illness.

Neural networks as models of psychopathology

Neural network modeling is situated between neurobiology, cognitive science, and neuropsychology. The structural and functional resemblance with biological computation has made artificial neural networks (ANN) useful for exploring the relationship between neurobiology and computational performance, i.e., cognition and behavior. This review provides an introduction to the theory of ANN and how they have linked theories from neurobiology and psychopathology in schizophrenia, affective disorders, and dementia.

Neonatal insult to the hippocampal region and schizophrenia: a review and a putative animal model

OBJECTIVE

To review the mounting evidence implicating early hippocampal dysfunction in the pathogenesis and the pathophysiology of schizophrenia. An account is made of recent neurodevelopmental hypotheses indicating how an early dysfunction of the hippocampal region disrupts maturational events in brain systems connected to that structure, thus inducing dysfunctional connectional development. Finally, an animal model is presented.

METHOD

Socioemotional behaviour of monkeys (Macaca mulatta) with selective neonatal hippocampal lesions was assessed by analyzing their interactions with their age-matched controls at 2 months, 6 months, and 5 to 8 years of age and by comparing the social interactions at each age with those of normal controls paired together.

RESULTS

At 2 months of age, monkeys with neonatal hippocampal lesions presented minor disturbances in initiation of social interactions. These subtle changes of behaviour were less evident at 6 months, although by that age, the operated monkeys displayed more withdrawals in response to an increase in aggressive responses from their unoperated peers. In adulthood, the amount of time spent by the hippocampectomized monkeys in social contacts with their normal peers decreased markedly. In addition, operated monkeys exhibited more locomotor stereotypies than normal controls.

CONCLUSION

These experimental findings indicate that the time-course and nature of the behavioural disturbances resulting from early trauma to the hippocampal region have some similarities with the clinical symptoms of schizophrenic patients and the typical time-course of the disease.