Although our findings suggest that the association between suicidal ideation and later suicide is stronger in schizophrenia spectrum psychosis than in mood disorders this result should be interpreted cautiously due to the high degree of between-study heterogeneity and because studies that used stronger methods of reporting had a weaker association between suicidal ideation and suicide.

No abstract

The interaction of large populations of neurons gives rise to electrical events in the brain, which can be observed at several spatial scales. We show that mutually consistent explanations and simulation of experimental data can be achieved for cortical gamma activity, synchronous oscillation, and the main features of the EEG power spectrum including the cerebral rhythms, and evoked potentials. These simulations include consideration of dendritic and synaptic dynamics, AMPA, NMDA and GABA receptors, and intracortical and cortical/subcortical interactions. The dynamic properties exhibited in the simulations, Hebbian synaptic modification regulated by a limited set of innate "reward" mechanisms, and infomax principles, can be combined to yield an explanation of elementary adaptive learning.

Zero-lag synchronisation arises between points on the cerebral cortex receiving concurrent independent inputs; an observation generally ascribed to nonlinear mechanisms. Using simulations of cerebral cortex and Principal Component Analysis (PCA) we show patterns of zero-lag synchronisation (associated with empirically realistic spectral content) can arise from both linear and nonlinear mechanisms. For low levels of activation, we show the synchronous field is described by the eigenmodes of the resultant damped wave activity. The first and second spatial eigenmodes (which capture most of the signal variance) arise from the even and odd components of the independent input signals. The pattern of zero-lag synchronisation can be accounted for by the relative dominance of the first mode over the second, in the near-field of the inputs. The simulated cortical surface can act as a few millisecond response coincidence detector for concurrent, but uncorrelated, inputs. As cortical activation levels are increased, local damped oscillations in the gamma band undergo a transition to highly nonlinear undamped activity with 40 Hz dominant frequency. This is associated with "locking" between active sites and spatially segregated phase patterns. The damped wave synchronisation and the locked nonlinear oscillations may combine to permit fast representation of multiple patterns of activity within the same field of neurons.

A lumped continuum model for electrocortical activity was used to simulate several established experimental findings of synchronous oscillation which have not all been previously embodied in a single explanatory model. Moving-bar visual stimuli of different extension, stimuli moving in different directions, the impact of non-specific cortical activation upon synchronous oscillation, and the frequency content of EEG associated with synchrony were considered. The magnitude of zero lag synchrony was primarily accounted for by the properties of the eigenmodes of the travelling local field potential superposition waves generated by inputs to the cortex, largely independent of the oscillation properties and associated spectral content. Approximation of the differences in cross-correlation observed with differently moving bar stimuli, and of the impact of cortical activation, required added assumptions on (a) spatial coherence of afferent volleys arising from parts of a single stimulus object and (b) the presence of low-amplitude diffuse field noise, with enhancement of cortical signal/noise ratio with respect to the spatially coherent inputs, at higher levels of cortical activation. Synchrony appears to be a ubiquitous property of cortex-like delay networks. Precision in the modelling of synchronous oscillation findings will require detailed description of input pathways, cortical connectivity, cortical stability, and aspects of cortical/subcortical interactions.