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Impaired associative learning in schizophrenia: behavioral and computational studies.

ABSTRACT: Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto-hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits. We present a preliminary computational model designed to capture learning dynamics in healthy control and schizophrenia subjects. Experimental data was collected on a spatial-object paired-associate learning task. The task evinces classic patterns of negatively accelerated learning in both healthy control subjects and patients, with patients demonstrating lower rates of learning than controls. Our rudimentary computational model of the task was based on biologically plausible assumptions, including the separation of dorsal/spatial and ventral/object visual streams, implementation of rules of learning, the explicit parameterization of learning rates (a plausible surrogate for synaptic plasticity), and learning capacity (a plausible surrogate for memory capacity). Reductions in learning dynamics in schizophrenia were well-modeled by reductions in learning rate and learning capacity. The synergy between experimental research and a detailed computational model of performance provides a framework within which to infer plausible biological bases of impaired learning dynamics in schizophrenia.

SUBMITTER: Diwadkar VA

PROVIDER: S-EPMC2518754 | biostudies-other | 2008 Sep

REPOSITORIES: biostudies-other

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Project description:The dysconnection hypothesis of schizophrenia (SZ) proposes that psychosis is best understood in terms of aberrant connectivity. Specifically, it suggests that dysconnectivity arises through aberrant synaptic modulation associated with deficits in GABAergic inhibition, excitation-inhibition balance and disturbances of high-frequency oscillations. Using a computational model combined with a graded-difficulty visual orientation discrimination paradigm, we demonstrate that, in SZ, perceptual performance is determined by the balance of excitation-inhibition in superficial cortical layers. Twenty-eight individuals with a DSM-IV diagnosis of SZ, and 30 age- and gender-matched healthy controls participated in a psychophysics orientation discrimination task, a visual grating magnetoencephalography (MEG) recording, and a magnetic resonance spectroscopy (MRS) scan for GABA. Using a neurophysiologically informed model, we quantified group differences in GABA, gamma measures, and the predictive validity of model parameters for orientation discrimination in the SZ group. MEG visual gamma frequency was reduced in SZ, with lower peak frequency associated with more severe negative symptoms. Orientation discrimination performance was impaired in SZ. Dynamic causal modeling of the MEG data showed that local synaptic connections were reduced in SZ and local inhibition correlated negatively with the severity of negative symptoms. The effective connectivity between inhibitory interneurons and superficial pyramidal cells predicted orientation discrimination performance within the SZ group; consistent with graded, behaviorally relevant, disease-related changes in local GABAergic connections. Occipital GABA levels were significantly reduced in SZ but did not predict behavioral performance or oscillatory measures. These findings endorse the importance, and behavioral relevance, of GABAergic synaptic disconnection in schizophrenia that underwrites excitation-inhibition balance.

Project description:With computational biology striving to provide more accurate theoretical accounts of biological systems, use of increasingly complex computational models seems inevitable. However, this trend engenders a challenge of optimal experimental design: due to the flexibility of complex models, it is difficult to intuitively design experiments that will efficiently expose differences between candidate models or allow accurate estimation of their parameters. This challenge is well exemplified in associative learning research. Associative learning theory has a rich tradition of computational modeling, resulting in a growing space of increasingly complex models, which in turn renders manual design of informative experiments difficult. Here we propose a novel method for computational optimization of associative learning experiments. We first formalize associative learning experiments using a low number of tunable design variables, to make optimization tractable. Next, we combine simulation-based Bayesian experimental design with Bayesian optimization to arrive at a flexible method of tuning design variables. Finally, we validate the proposed method through extensive simulations covering both the objectives of accurate parameter estimation and model selection. The validation results show that computationally optimized experimental designs have the potential to substantially improve upon manual designs drawn from the literature, even when prior information guiding the optimization is scarce. Computational optimization of experiments may help address recent concerns over reproducibility by increasing the expected utility of studies, and it may even incentivize practices such as study pre-registration, since optimization requires a pre-specified analysis plan. Moreover, design optimization has the potential not only to improve basic research in domains such as associative learning, but also to play an important role in translational research. For example, design of behavioral and physiological diagnostic tests in the nascent field of computational psychiatry could benefit from an optimization-based approach, similar to the one presented here.

Project description:Schizophrenia (Sz) is associated with deficits in fluent reading ability that compromise functional outcomes. Here, we utilize a combined eye-tracking, neurophysiological, and computational modeling approach to analyze underlying visual and oculomotor processes. Subjects included 26 Sz patients (SzP) and 26 healthy controls. Eye-tracking and electroencephalography data were acquired continuously during the reading of passages from the Gray Oral Reading Tests reading battery, permitting between-group evaluation of both oculomotor activity and fixation-related potentials (FRP). Schizophrenia patients showed a marked increase in time required per word (d = 1.3, P < .0001), reflecting both a moderate increase in fixation duration (d = .7, P = .026) and a large increase in the total saccade number (d = 1.6, P < .0001). Simulation models that incorporated alterations in both lower-level visual and oculomotor function as well as higher-level lexical processing performed better than models that assumed either deficit-type alone. In neurophysiological analyses, amplitude of the fixation-related P1 potential (P1f) was significantly reduced in SzP (d = .66, P = .013), reflecting reduced phase reset of ongoing theta-alpha band activity (d = .74, P = .019). In turn, P1f deficits significantly predicted increased saccade number both across groups (P = .017) and within SzP alone (P = .042). Computational and neurophysiological methods provide increasingly important approaches for investigating sensory contributions to impaired cognition during naturalistic processing in Sz. Here, we demonstrate deficits in reading rate that reflect both sensory/oculomotor- and semantic-level impairments and that manifest, respectively, as alterations in saccade number and fixation duration. Impaired P1f generation reflects impaired fixation-related reset of ongoing brain rhythms and suggests inefficient information processing within the early visual system as a basis for oculomotor dyscontrol during fluent reading in Sz.

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Impaired associative learning in schizophrenia: behavioral and computational studies.

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