Interhemispheric effective and functional connectivity of spina bifida subjects is consistent with anatomical connectivity | Risk Analysis in Stock Trading via Feedback-Based Strategies

Sheida’s talk:
In this research we analyzed effective and functional connectivity in a population of five subjects with spina bifida hydrocephalus (SBH) and five healthy control subjects using resting state magnetoencephalography (MEG) recordings. Three types of connectivity are used to describe the brain. Anatomical connectivity refers to physical connections between different regions. Functional connectivity describes the statistical dependencies between the regions, for example, correlation between measurements of neural activity. Effective connectivity refers to the influence of one neural system on another and thus seeks to represent cause and effect, that is, directed interactions between cortical regions. A distinguishing feature of SBH is reduced anatomical connectivity in posterior portions of the corpus callosum, so we studied the interhemispheric effective and functional connectivity using a four-region network model. A measurement equation that describes the blurring of cortical signals measured by scalp MEG is combined with a causal network model for the interaction between underlying cortical regions of interest. An expectation maximization algorithm is used to estimate the unknown parameters of the network model. Conditional Granger causality is used as a metric of effective connectivity while coherence is used to represent functional connectivity. The results show the posterior functional and effective interhemispheric connectivity in SBH are reduced, consistent with the anatomical dysgnesis, while anterior functional and effective effective connectivity is elevated in SBH. In contrast, intrahemispheric connectivity does not show a marked contrast between the populations
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Shirzad’s talk:
In classical finance, when a stochastic investment outcome is characterized in terms of its mean and variance, it is implicitly understood that the probability distribution being considered is not heavily skewed. For example, in the “perfect” case when outcomes are normally distributed, mean-variance considerations tell the entire story.

The main point of this presentation is that mean-variance based measures of performance may be entirely inappropriate when a feedback control law is used to modulate one’s stock position as a function of time. More specifically, a highly skewed distribution of gains and losses can result and lead to a large “drawdown” in wealth. Control of drawdown is one of the greatest concerns to both stock traders and portfolio managers. That is, one typically monitors “drops in wealth” over time from highs to subsequent lows and investors often shy away from funds with a past history of large drawdowns. With this motivation in mind, after introducing the basics of trading via feedback-based strategies, I will go over the analysis of drawdown in this context. To this end, the so-called SLS stock-trading algorithm will be used to demonstrate the ideas above in an idealized market with prices governed by Geometric Brownian Motion.