Noise in models of neurological and psychiatric disorders

Computational Modeling of Genetic Contributions to Excitability and Neural Coding in Layer V Pyramidal Cells: Applications to Schizophrenia Pathology

Pyramidal cells in layer V of the neocortex are one of the most widely studied neuron types in the mammalian brain. Due to their role as integrators of feedforward and cortical feedback inputs, they are well-positioned to contribute to the symptoms and pathology in mental disorders-such as schizophrenia-that are characterized by a mismatch between the internal perception and external inputs. In this modeling study, we analyze the input/output properties of layer V pyramidal cells and their sensitivity to modeled genetic variants in schizophrenia-associated genes. We show that the excitability of layer V pyramidal cells and the way they integrate inputs in space and time are altered by many types of variants in ion-channel and Ca²⁺ transporter-encoding genes that have been identified as risk genes by recent genome-wide association studies. We also show that the variability in the output patterns of spiking and Ca²⁺ transients in layer V pyramidal cells is altered by these model variants. Importantly, we show that many of the predicted effects are robust to noise and qualitatively similar across different computational models of layer V pyramidal cells. Our modeling framework reveals several aspects of single-neuron excitability that can be linked to known schizophrenia-related phenotypes and existing hypotheses on disease mechanisms. In particular, our models predict that single-cell steady-state firing rate is positively correlated with the coding capacity of the neuron and negatively correlated with the amplitude of a prepulse-mediated adaptation and sensitivity to coincidence of stimuli in the apical dendrite and the perisomatic region of a layer V pyramidal cell. These results help to uncover the voltage-gated ion-channel and Ca²⁺ transporter-associated genetic underpinnings of schizophrenia phenotypes and biomarkers.

Implications of Information Theory for Computational Modeling of Schizophrenia

Information theory provides a formal framework within which information processing and its disorders can be described. However, information theory has rarely been applied to modeling aspects of the cognitive neuroscience of schizophrenia. The goal of this article is to highlight the benefits of an approach based on information theory, including its recent extensions, for understanding several disrupted neural goal functions as well as related cognitive and symptomatic phenomena in schizophrenia. We begin by demonstrating that foundational concepts from information theory—such as Shannon information, entropy, data compression, block coding, and strategies to increase the signal-to-noise ratio—can be used to provide novel understandings of cognitive impairments in schizophrenia and metrics to evaluate their integrity. We then describe more recent developments in information theory, including the concepts of infomax, coherent infomax, and coding with synergy, to demonstrate how these can be used to develop computational models of schizophrenia-related failures in the tuning of sensory neurons, gain control, perceptual organization, thought organization, selective attention, context processing, predictive coding, and cognitive control. Throughout, we demonstrate how disordered mechanisms may explain both perceptual/cognitive changes and symptom emergence in schizophrenia. Finally, we demonstrate that there is consistency between some information-theoretic concepts and recent discoveries in neurobiology, especially involving the existence of distinct sites for the accumulation of driving input and contextual information prior to their interaction. This convergence can be used to guide future theory, experiment, and treatment development.

Pathophysiology of involuntary emotional expression disorder

Extensive clinical experience and research suggest that a cortico-limbic-subcortico-thalamic-ponto-cerebellar network plays a significant role in the expression of human emotions. This network includes specific cerebral, cerebellar, and brainstem areas and their multiple projections/pathways, with activity modulated through serotonergic, dopaminergic, glutamatergic, and possibly sigma receptor neurotransmitter systems. Disruptions of regulatory and inhibitory mechanisms in the structure and function of this network likely constitute a pathophysiological basis for the crying and laughing episodes characteristic of involuntary emotional expression disorder. Pharmacologic interventions targeting the neurochemical modulators of the emotional expression systems may afford opportunities for symptom control among persons affected by this disorder.

Rating of textual associations in organized and nonorganized sentences for the assessment of semantic networks in schizophrenia

BACKGROUND

Collins and Quillian ( Acta Psychol 1970;33:304-314) proposed that semantic representations in the human brain could have a "networklike" theoretical construct. Thought disorders in schizophrenia have been described as disturbances in the spread of activation within semantic networks. Semantic networks are typically evaluated indirectly via reaction times of priming tasks. Medications may interfere with the reaction time of patients, thus, we sought to investigate semantic networks, independent of time, by having patients and controls rate textual associations in sentences organized to various degrees.

METHODS

Twenty-eight schizophrenic patients (17 non-thought-disordered and 11 thought-disordered) and 27 healthy controls performed a rating of textual associations task in which they were asked to rate the associative relationship between concepts in sentences on a scale from 1 (totally dissociated) to 10 (completely associated). The task contained 3 sets of sentences; organized meaningful sentences, vague sentences (intermediately disorganized), and completely disorganized sentences. To avoid order effects, sentences were randomly mixed at presentation.

RESULTS

Analysis of variance calculations indicated significant differences among the 3 groups (controls, thought-disordered, and non-thought-disordered). The differences were greater for the vague sentences. Compared with controls, schizophrenic patients demonstrated increased SDs in rating associative values between concepts in the sentences, which is higher in disorganized sentences. Inadequate ability to identify and rate associations in disorganized sentences is discussed in the context of disordered semantic networks of schizophrenic patients.

Cortical instability and the mechanism of mania: a neural network simulation and perceptual test

BACKGROUND

A previous neural network simulation suggested that manic states arise from excessive levels of noise that destabilize neural representations. The Necker cube stick figure provides a simple perceptual task that assesses stability of gestalt-type representations.

METHODS

A neural network was developed that included a simulation of the Necker cube task. Noise was added to induce maniclike jumps from one representation to another. A parallel study of Necker cube perception was conducted with 16 patients diagnosed with manic-spectrum disorder, 18 patients with schizophrenia, and 19 normal control subjects. Cognitive speed and rate of indiscriminate responses were assessed using an auditory continuous performance task.

RESULTS

During processing of the "Necker cube" stimulus, the reversal rate of the noise-destabilized "manic" network was increased by 30%. In the human subject study, the median score of Necker cube reversal rates for manic-spectrum patients was roughly twice that of normal control subjects and patients with schizophrenia. Accelerated reversal rates in the manic-spectrum group were not attributable to excessive cognitive speed or higher rates of indiscriminate responses.

CONCLUSIONS

The two studies, considered together, support the hypothesis that excessive cortical noise destabilizes neural representations in manic-spectrum patients.

Dopamine decreases the excitability of layer V pyramidal cells in the rat prefrontal cortex

In both primates and rodents, the prefrontal cortex (PFC) is highly innervated by dopaminergic fibers originating from the ventral tegmental area, and activation of this mesocortical dopaminergic system decreases spontaneous and evoked activity in the PFC in vivo. We have examined the effects of dopamine (DA), over a range of concentrations, on the passive and active membrane properties of layer V pyramidal cells from the rat medial PFC (mPFC). Whole-cell and perforated-patch recordings were made from neurons in rat mPFC. As a measure of cell excitability, trains of action potentials were evoked with 1-sec-long depolarizing current steps. Bath application of DA (0.05-30 microM) produced a reversible decrease in the number of action potentials evoked by a given current step. In addition, DA reversibly decreased the input resistance (RN) of these cells. In a subset of experiments, a transient increase in excitability was observed after the washout of DA. Control experiments suggest that these results are not attributable to changes in spontaneous synaptic activity, age-dependent processes, or strain-specific differences in dopaminergic innervation and physiology. Pharmacological analyses, using D1 agonists (SKF 38393 and SKF 81297), a D1 antagonist (SCH 23390), a D2 receptor agonist (quinpirole), and a D2 antagonist (sulpiride) suggest that decreases in spiking and RN are mediated by D2 receptor activation. Together, these results demonstrate that DA, over a range of concentrations, has an inhibitory effect on layer V pyramidal neurons in the rat mPFC, possibly through D2 receptor activation.