Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50-100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Can we observe collective neuronal activity from macroscopic aggregate signals?

The fundamental problem faced by noninvasive neuroimaging techniques such as EEG/MEG1 is to elucidate functionally important aspects of the microscopic neuronal network dynamics from macroscopic aggregate measurements. Due to the mixing of the activities of large neuronal populations in the observed macroscopic aggregate, recovering the underlying network that generates the signal in the absence of any additional information represents a considerable challenge. Recent MEG studies have shown that macroscopic measurements contain sufficient information to allow the differentiation between patterns of activity, which are likely to represent different stimulus-specific collective modes in the underlying network (Hadjipapas, A., Adjamian, P., Swettenham, J.B., Holliday, I.E., Barnes, G.R., 2007. Stimuli of varying spatial scale induce gamma activity with distinct temporal characteristics in human visual cortex. NeuroImage 35, 518–530). The next question arising in this context is whether aspects of collective network activity can be recovered from a macroscopic aggregate signal. We propose that this issue is most appropriately addressed if MEG/EEG signals are to be viewed as macroscopic aggregates arising from networks of coupled systems as opposed to aggregates across a mass of largely independent neural systems. We show that collective modes arising in a network of simulated coupled systems can be indeed recovered from the macroscopic aggregate. Moreover, we show that nonlinear state space methods yield a good approximation of the number of effective degrees of freedom in the network. Importantly, information about hidden variables, which do not directly contribute to the aggregate signal, can also be recovered. Finally, this theoretical framework can be applied to experimental MEG/EEG data in the future, enabling the inference of state dependent changes in the degree of local synchrony in the underlying network.

The impact of the post-1993 economic reforms on Chinese state-owned enterprises:evidence from the electronics industry

Since 1979, China has embarked on a series of economic reform programmes, leading its socialist economy away from a Soviet planning model towards a much greater reliance on the market. In the course of the last twenty years, the Chinese economy has enjoyed a phenomenally high economic growth rate. However, earlier research suggests that Chinese state-owned enterprises remain a financial 'black hole' for the Chinese economy, in spite of various enterprise reform measures. This thesis tries to assess the impact of the reforms after 1993, especially the so-called Modern Enterprise System, on the behaviour and management practices of state firms. The central research question is whether the new rounds of economic reform have changed state firms into commercial entities operating according to market signals, as intended. In order to explore this question, an institutional approach is employed. More specifically, the thesis examines how the behaviour and management practices of state enterprises have changed with changes in the institutional environmental resulting from the introduction of new reform measures and especially the MES. The main evidence used in this research comes from the Chinese electronics industry (CEI). Non-state firms, namely collectives and joint ventures, are involved in the study to provide a benchmark against which changes in the behaviour of state firms in the mid and late 1990s are compared. A comparative statistical analysis shows that state-owned firms, both traditional and corporatised ones, still lag behind collectives and joint ventures in terms of both labour and total factor productivity. The further empirical work of this research consists of a questionnaire survey and case studies that are based on interviews with senior managers of 17 firms in the CEI. The findings of these analyses suggest that there has been little fundamental change in the behaviour pattern of state firms in the 1990s, despite the introduction of the Modern Enterprise System, and that the economic reforms after 1993 so far seem to have failed to transform the state firms into commercial entities operating according to market signals.