Contrary to the intense debate about brain oxygen dynamics and its uncoupling in mammals, very little is known in birds. In zebra finches, picosecond optical tomography (POT) with a white laser and a streak camera can measure in vivo oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) concentration changes following physiological stimulation (familiar calls and songs). POT demonstrated sufficient sub-micromolar sensitivity to resolve the fast changes in hippocampus and auditory forebrain areas with 250 \mu m resolution. The time-course is composed of (i) an early 2s-long event with a significant decrease in Hb and HbO2, respectively -0.7 \mu Moles/L and -0.9 \mu Moles/L (ii) a subsequent increase in blood oxygen availability with a plateau of HbO2 (+0.3 \mu Moles/L) and (iii) pronounced vasodilatation events immediately following the end of the stimulus. One of the findings of our work is the direct link between the blood oxygen level-dependent (BOLD) signals previously published in birds and our results. Furthermore, the early vasoconstriction event and post-stimulus ringing seem to be more pronounced in birds than in mammals. These results in bird, a tachymetabolic vertebrate with a long lifespan, can potentially yield new insights for example in brain aging.
Electrocorticogram (ECoG)-based brain computer interfaces (BCI) can potentially control upper extremity prostheses to restore independent function to paralyzed individuals. However, current research is mostly restricted to the offline decoding of finger or 2D arm movement trajectories, and these results are modest. This study seeks to improve the fundamental understanding of the ECoG signal features underlying upper extremity movements to guide better BCI design. Subjects undergoing ECoG electrode implantation performed a series of elementary upper extremity movements in an intermittent flexion and extension manner. It was found that movement velocity, θ˙, had a high positive (negative) correlation with the instantaneous power of the ECoG high-γ band (80-160 Hz) during flexion (extension). Also, the correlation was low during idling epochs. Visual inspection of the ECoG high-γ band revealed power bursts during flexion/extension events that have a waveform that strongly resembles the corresponding flexion/extension event as seen on θ˙. These high-γ bursts were present in all elementary movements, and were spatially distributed in a somatotopic fashion. Thus, it can be concluded that the high-γ power of ECoG strongly encodes for movement trajectories, and can be used as an input feature in future BCIs.
In this paper, we present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and “let-go” mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional mutual information from mixed embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain neural networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and “let-go” mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found musicians responding differently to listeners when playing music in different conditions.
Several efforts are currently underway to decipher the connectome or parts thereof in a variety of organisms. Ascertaining the detailed physiological properties of all the neurons in these connectomes, however, is out of the scope of such projects. It is therefore unclear to what extent knowledge of the connectome alone will advance a mechanistic understanding of computation occurring in these neural circuits, especially when the high-level function of the said circuit is unknown. We consider, here, the question of how the wiring diagram of neurons imposes constraints on what neural circuits can compute, when we cannot assume detailed information on the physiological response properties of the neurons. We call such constraints — that arise by virtue of the connectome — connectomic constraints on computation. For feedforward networks equipped with neurons that obey a deterministic spiking neuron model which satisfies a small number of properties, we ask if just by knowing the architecture of a network, we can rule out computations that it could be doing, no matter what response properties each of its neurons may have. We show results of this form, for certain classes of network architectures. On the other hand, we also prove that with the limited set of properties assumed for our model neurons, there are fundamental limits to the constraints imposed by network structure. Thus, our theory suggests that while connectomic constraints might restrict the computational ability of certain classes of network architectures, we may require more elaborate information on the properties of neurons in the network, before we can discern such results for other classes of networks.