Reduced GABA/glutamate in the thalamus of individuals at clinical high risk for psychosis.

Youth at clinical high risk (CHR) are a unique population enriched for precursors of major psychiatric disorders, especially schizophrenia (SCZ). Recent neuroimaging findings point to abnormalities in the thalamus of patients with SCZ, including chronic and early course patients, as well as in CHR individuals relative to healthy comparison groups, thus suggesting that thalamic dysfunctions are present even before illness onset. Furthermore, modeling data indicate that alteration between excitatory and inhibitory control, as reflected by alteration in GABAergic and glutamatergic balance (i.

Sleep spindle and slow wave abnormalities in schizophrenia and other psychotic disorders: Recent findings and future directions.

Sleep spindles and slow waves are the two main oscillatory activities occurring during NREM sleep. Slow waves are ∼1 Hz, high amplitude, negative-positive deflections that are primarily generated and coordinated within the cortex, whereas sleep spindles are 12-16 Hz, waxing and waning oscillations that are initiated within the thalamus and regulated by thalamo-cortical circuits. In healthy subjects, these oscillations are thought to be responsible for the restorative aspects of sleep and have been increasingly shown to be involved in learning, memory and plasticity.

The role of low-level image features in the affective categorization of rapidly presented scenes.

It remains unclear how the visual system is able to extract affective content from complex scenes even with extremely brief (< 100 millisecond) exposures. One possibility, suggested by findings in machine vision, is that low-level features such as unlocalized, two-dimensional (2-D) Fourier spectra can be diagnostic of scene content. To determine whether Fourier image amplitude carries any information about the affective quality of scenes, we first validated the existence of image category differences through a support vector machine (SVM) model that was able to discriminate our intact aversive and neutral images with ~ 70% accuracy using amplitude-only features as inputs.