Abstract
Schizophrenia is associated with diverse cognitive deficits, including disorders of attention-related oculomotor behavior. At the structural level, schizophrenia is associated with abnormal inhibitory control in the circuit linking cortex and thalamus. We developed a spiking neural network model that demonstrates how dysfunctional inhibition can degrade attentive gaze control. Our model revealed that perturbations of two functionally distinct classes of cortical inhibitory neurons, or of the inhibitory thalamic reticular nucleus, disrupted processing vital for sustained attention to a stimulus, leading to distractibility. Because perturbation at each circuit node led to comparable but qualitatively distinct disruptions in attentive tracking or fixation, our findings support the search for new eye movement metrics that may index distinct underlying neural defects. Moreover, because the cortico-thalamic circuit is a common motif across sensory, association, and motor systems, the model and extensions can be broadly applied to study normal function and the neural bases of other cognitive deficits in schizophrenia.
Highlights
Schizophrenia affects large numbers of people worldwide and has been a focus of research for longer than a century
CRT Circuit The CRT circuit incorporates the main inhibitory systems in cortex and thalamus linked to schizophrenia and is the focus of our computational model (Figure 1A)
The CB-INs gate thalamo-cortical signaling by inhibiting the mid-distal dendrites of nearby middle-layer neurons (MLNs) (DeFelipe, 1997; Kawaguchi & Kubota, 1997)
Summary
Schizophrenia affects large numbers of people worldwide and has been a focus of research for longer than a century (reviewed in Jablensky, 2000). Despite this interest, an integrated causal conception of the disorder has proven elusive. Attentive stimulus tracking represents a cluster of such common symptoms. The ability to smoothly track a moving visual stimulus is degraded in schizophrenia patients. A recent study showed that a machine learning algorithm can use data from tests of oculomotor attention to distinguish schizophrenia patients from controls with a high degree of accuracy (Benson et al, 2012)
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