Background
Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with classification-awareness.
Results
The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis.
Conclusion
We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself.
Neuromodulators alter network output and have the potential to destabilize a circuit. The mechanisms maintaining stability in the face of neuromodulation are not well described. Using the pyloric network in the crustacean stomatogastric nervous system, we show that dopamine (DA) does not simply alter circuit output, but activates a closed loop in which DA-induced alterations in circuit output consequently drive a change in an ionic conductance to preserve a conductance ratio and its activity correlate. DA acted at low affinity type 1 receptors (D1Rs) to induce an immediate modulatory decrease in the transient potassium current (IA) of a pyloric neuron. This, in turn, advanced the activity phase of that component neuron, which disrupted its network function and thereby destabilized the circuit. DA simultaneously acted at high affinity D1Rs on the same neuron to confer activity-dependence upon the hyperpolarization activated current (Ih) such that the DA-induced changes in activity subsequently reduced Ih. This DA-enabled, activity-dependent, intrinsic plasticity exactly compensated for the modulatory decrease in IA to restore the IA:Ih ratio and neuronal activity phase, thereby closing an open loop created by the modulator. Activation of closed loops to preserve conductance ratios may represent a fundamental operating principle neuromodulatory systems use to ensure stability in their target networks.
Expression of appropriate ion channels is essential to allow developing neurons to form functional networks. Our previous studies have identified LIM-homeodomain (HD) transcription factors (TFs), expressed by developing neurons, that are specifically able to regulate ion channel gene expression. In this study, we use the technique of DNA adenine methyltransferase identification (DamID) to identify putative gene targets of four such TFs that are differentially expressed in Drosophila motoneurons. Analysis of targets for Islet (Isl), Lim3, Hb9, and Even-skipped (Eve) identifies both ion channel genes and genes predicted to regulate aspects of dendritic and axonal morphology. Significantly, some ion channel genes are bound by more than one TF, consistent with the possibility of combinatorial regulation. One such gene is Shaker (Sh), which encodes a voltage-dependent fast K+ channel (Kv1.1). DamID reveals that Sh is bound by both Isl and Lim3. We used body wall muscle as a test tissue because in conditions of low Ca2+, the fast K+ current is carried solely by Sh channels (unlike neurons in which a second fast K+ current, Shal, also contributes). Ectopic expression of isl, but not Lim3, is sufficient to reduce both Sh transcript and Sh current level. By contrast, coexpression of both TFs is additive, resulting in a significantly greater reduction in both Sh transcript and current compared with isl expression alone. These observations provide evidence for combinatorial activity of Isl and Lim3 in regulating ion channel gene expression.
Activity of voltage-gated Na channels (Nav) is modified by alternative splicing. However, whether altered splicing of human Nav’s contributes to epilepsy remains to be conclusively shown. We show here that altered splicing of the Drosophila Nav (paralytic, DmNav) contributes to seizure-like behaviour in identified seizure-mutants. We focus attention on a pair of mutually-exclusive alternate exons (termed K and L), which form part of the voltage sensor (S4) in domain III of the expressed channel. The presence of exon L results in a large, non-inactivating, persistent INap. Many forms of human epilepsy are associated with an increase in this current. In wildtype (WT) Drosophila larvae ~70-80% of DmNav transcripts contain exon L, the remainder contain exon K. Splicing of DmNav to include exon L is increased to ~100% in both the slamdance and easily-shocked seizure-mutants. This change to splicing is prevented by reducing synaptic activity levels through exposure to the antiepileptic phenytoin or the inhibitory transmitter GABA. Conversely, enhancing synaptic activity in WT, by feeding of picrotoxin, is sufficient to increase INap and promote seizure through increased inclusion of exon L to 100%. We also show that the underlying activity-dependent mechanism requires the presence of Pasilla, an RNA-binding protein. Finally, we use computational modelling to show that increasing INap is sufficient to potentiate membrane excitability consistent with a seizure phenotype. Thus, increased synaptic excitation favors inclusion of exon L which, in turn, further increases neuronal excitability. Thus, at least in Drosophila, this self-reinforcing cycle may promote the incidence of seizure.