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Reduced natural oscillatory frequency of frontal thalamocortical circuits in schizophrenia.

ABSTRACT: Converging evidence from electrophysiological studies suggests that in individuals with schizophrenia, electroencephalographic frontal fast oscillations are reduced. It is still unclear whether this reduction reflects an intrinsic deficit of underlying cortical/thalamocortical circuits and whether this deficit is specific for frontal regions. Recent electrophysiological studies in healthy individuals have established that, when perturbed, different brain regions oscillate at a specific, intrinsically generated dominant frequency, the natural frequency.To assess the natural frequency of the posterior parietal, motor, premotor, and prefrontal cortices in patients with schizophrenia and healthy control subjects.High-density electroencephalographic recordings during transcranial magnetic stimulation of 4 cortical areas were performed. Several transcranial magnetic stimulation–evoked electroencephalographic oscillation parameters, including synchronization, amplitude, and natural frequency, were compared across the schizophrenia and healthy control groups.Wisconsin Psychiatric Institute and Clinic, University of Wisconsin–Madison.Twenty patients with schizophrenia and 20 age-matched healthy control subjects.High-density electroencephalographic measurements of transcranial magnetic stimulation–evoked activity in 4 cortical areas, scores on the Positive and Negative Syndrome Scale, and performance scores (reaction time, accuracy) on 2 computerized tasks (word memory [Penn Word Recognition Test] and facial memory [Penn Facial Memory Test]).Patients with schizophrenia showed a slowing in the natural frequency of the frontal/prefrontal regions compared with healthy control subjects (from an average of a 2-Hz decrease for the motor area to an almost 10-Hz decrease for the prefrontal cortex). The prefrontal natural frequency of individuals with schizophrenia was slower than in any healthy comparison subject and correlated with both positive Positive and Negative Syndrome Scale scores and reaction time on the Penn Word Recognition Test.These findings suggest that patients with schizophrenia have an intrinsic slowing in the natural frequency of frontal cortical/thalamocortical circuits, that this slowing is not present in parietal areas, and that the prefrontal natural frequency can predict some of the symptoms as well as the cognitive dysfunctions of schizophrenia.

SUBMITTER: Ferrarelli F

PROVIDER: S-EPMC3394893 | biostudies-other | 2012 Aug

REPOSITORIES: biostudies-other

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Reduced natural oscillatory frequency of frontal thalamocortical circuits in schizophrenia.

Ferrarelli Fabio F Sarasso Simone S Guller Yelena Y Riedner Brady A BA Peterson Michael J MJ Bellesi Michele M Massimini Marcello M Postle Bradley R BR Tononi Giulio G

Archives of general psychiatry 20120801 8

<h4>Context</h4>Converging evidence from electrophysiological studies suggests that in individuals with schizophrenia, electroencephalographic frontal fast oscillations are reduced. It is still unclear whether this reduction reflects an intrinsic deficit of underlying cortical/thalamocortical circuits and whether this deficit is specific for frontal regions. Recent electrophysiological studies in healthy individuals have established that, when perturbed, different brain regions oscillate at a sp ...[more]

PMID: 22474071

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Project description:BACKGROUND: Constructing predictive dynamic models of interacting signalling networks remains one of the great challenges facing systems biology. While detailed dynamical data exists about individual pathways, the task of combining such data without further lengthy experimentation is highly nontrivial. The communicating links between pathways, implicitly assumed to be unimportant and thus excluded, are precisely what become important in the larger system and must be reinstated. To maintain the delicate phase relationships between signals, signalling networks demand accurate dynamical parameters, but parameters optimised in isolation and under varying conditions are unlikely to remain optimal when combined. The computational burden of estimating parameters increases exponentially with increasing system size, so it is crucial to find precise and efficient ways of measuring the behaviour of systems, in order to re-use existing work. RESULTS: Motivated by the above, we present a new frequency domain-based systematic analysis technique that attempts to address the challenge of network assembly by defining a rigorous means to quantify the behaviour of stochastic systems. As our focus we construct a novel coupled oscillatory model of p53, NF-kB and the mammalian cell cycle, based on recent experimentally verified mathematical models. Informed by online databases of protein networks and interactions, we distilled their key elements into simplified models containing the most significant parts. Having coupled these systems, we constructed stochastic models for use in our frequency domain analysis. We used our new technique to investigate the crosstalk between the components of our model and measure the efficacy of certain network-based heuristic measures. CONCLUSIONS: We find that the interactions between the networks we study are highly complex and not intuitive: (i) points of maximum perturbation do not necessarily correspond to points of maximum proximity to influence; (ii) increased coupling strength does not necessarily increase perturbation; (iii) different perturbations do not necessarily sum and (iv) overall, susceptibility to perturbation is amplitude and frequency dependent and cannot easily be predicted by heuristic measures.Our methodology is particularly relevant for oscillatory systems, though not limited to these, and is most revealing when applied to the results of stochastic simulation. The technique is able to characterise precisely the distance in behaviour between different models, different systems and different parts within the same system. It can also measure the difference between different simulation algorithms used on the same system and can be used to inform the choice of dynamic parameters. By measuring crosstalk between subsystems it can also indicate mechanisms by which such systems may be controlled in experiments and therapeutics. We have thus found our technique of frequency domain analysis to be a valuable benchmark systems-biological tool.

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Reduced natural oscillatory frequency of frontal thalamocortical circuits in schizophrenia.

Publications

Reduced natural oscillatory frequency of frontal thalamocortical circuits in schizophrenia.

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