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Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

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Nitric oxide (NO) is a gaseous neurotransmitter thought to play important roles in several behavioral domains. On a neurobiological level, NO acts as the second messenger of the Nmethyl-D-aspartate receptor and interacts with both the dopaminergic as well as the serotonergic system. Thus, NO is a promising candidate molecule in the pathogenesis of endogenous psychoses and a potential target in their treatment. Furthermore, the chromosomal locus of the gene for the NO-producing enzyme NOS-I, 12q24.2, represents a major linkage hot spot for schizophrenic and bipolar disorder. To investigate whether the gene encoding NOS-I (NOS1) conveys to the genetic risk for those diseases, five NOS1 polymorphisms as well as a NOS1 mini-haplotype, consisting of two functional polymorphisms located in the transcriptional control region of NOS1, were examined in 195 chronic schizophrenic, 72 bipolar-I patients and 286 controls. Single-marker association analysis showed that the exon 1c promoter polymorphism was linked to schizophrenia (SCZ), whereas synonymous coding region polymorphisms were not associated with disease. Long promoter alleles of the repeat polymorphism were associated with less severe psychopathology. Analysis of the mini-haplotype also revealed a significant association with SCZ. Mutational screening did not detect novel exonic polymorphisms in patients, suggesting that regulatory rather than coding variants convey the genetic risk on psychosis. Finally, promoter polymorphisms impacted on prefrontal functioning as assessed by neuropsychological testing and electrophysiological parameters elicited by a Go-Nogo paradigm in 48 patients (continuous performance test). Collectively these findings suggest that regulatory polymorphisms of NOS1 contribute to the genetic risk for SCZ, and modulate prefrontal brain functioning.

Functional near-infrared spectroscopy (fNIRS) is an established optical neuroimaging method for measuring functional hemodynamic responses to infer neural activation. However, the impact of individual anatomy on the sensitivity of fNIRS measuring hemodynamics within cortical gray matter is still unknown. By means of Monte Carlo simulations and structural MRI of 23 healthy subjects (mean age: years), we characterized the individual distribution of tissue-specific NIR-light absorption underneath 24 prefrontal fNIRS channels. We, thereby, investigated the impact of scalp-cortex distance (SCD), frontal sinus volume as well as sulcal morphology on gray matter volumes () traversed by NIR-light, i.e. anatomy-dependent fNIRS sensitivity. The NIR-light absorption between optodes was distributed describing a rotational ellipsoid with a mean penetration depth of considering the deepest of light. Of the detected photon packages scalp and bone absorbed and absorbed of the energy. The mean volume was negatively correlated () with the SCD and frontal sinus volume () and was reduced by in subjects with relatively large compared to small frontal sinus. Head circumference was significantly positively correlated with the mean SCD () and the traversed frontal sinus volume (). Sulcal morphology had no significant impact on . Our findings suggest to consider individual SCD and frontal sinus volume as anatomical factors impacting fNIRS sensitivity. Head circumference may represent a practical measure to partly control for these sources of error variance.

Transforming the barrage of sensory signals into a coherent multisensory percept relies on solving the binding problem – deciding whether signals come from a common cause and should be integrated or, instead, segregated. Human observers typically arbitrate between integration and segregation consistent with Bayesian Causal Inference, but the neural mechanisms remain poorly understood. Here, we presented people with audiovisual sequences that varied in the number of flashes and beeps, then combined Bayesian modelling and EEG representational similarity analyses. Our data suggest that the brain initially represents the number of flashes and beeps independently. Later, it computes their numbers by averaging the forced-fusion and segregation estimates weighted by the probabilities of common and independent cause models (i.e. model averaging). Crucially, prestimulus oscillatory alpha power and phase correlate with observers’ prior beliefs about the world’s causal structure that guide their arbitration between sensory integration and segregation.