Neuronal odor coding in the larval sensory cone of Anopheles coluzzii: Complex responses from a simple system.

Anopheles mosquitoes are the sole vectors of malaria. Although adult females are directly responsible for disease transmission and accordingly have been extensively studied, the survival of pre-adult larval stages is vital. Mosquito larvae utilize a spectrum of chemosensory and other cues to navigate their aquatic habitats to avoid predators and search for food. Here we examine larval olfactory responses, in which the peripheral components are associated with the antennal sensory cone. Larval behavior and sensory cone responses to volatile stimuli in Anopheles coluzzii demonstrate the sensory cone is particularly tuned to alcohols, thiazoles, and heterocyclics, and these responses can be assigned to discrete groups of sensory cone neurons with distinctive profiles. These studies reveal that the anopheline larvae actively sample volatile odors above their aquatic habitats via a highly sophisticated olfactory system that is sensitive to a broad range of compounds with significant behavioral relevance.

Spectrally resolved fast transient brain states in electrophysiological data.

The brain is capable of producing coordinated fast changing neural dynamics across multiple brain regions in order to adapt to rapidly changing environments. However, it is non-trivial to identify multiregion dynamics at fast sub-second time-scales in electrophysiological data. We propose a method that, with no knowledge of any task timings, can simultaneously identify and describe fast transient multiregion dynamics in terms of their temporal, spectral and spatial properties. The approach models brain activity using a discrete set of sequential states, with each state distinguished by its own multiregion spectral properties. This can identify potentially very short-lived visits to a brain state, at the same time as inferring the state's properties, by pooling over many repeated visits to that state. We show how this can be used to compute state-specific measures such as power spectra and coherence. We demonstrate that this can be used to identify short-lived transient brain states with distinct power and functional connectivity (e.g., coherence) properties in an MEG data set collected during a volitional motor task.

Fast transient networks in spontaneous human brain activity.

To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100-200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states. DOI: http://dx.doi.org/10.7554/eLife.01867.001.