Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory

Background  

Although a large body of knowledge about both brain structure and function has been gathered over the last decades, we still have a poor understanding of their exact relationship. Graph theory provides a method to study the relation between network structure and function, and its application to neuroscientific data is an emerging research field. We investigated topological changes in large-scale functional brain networks in patients with Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) by means of graph theoretical analysis of resting-state EEG recordings. EEGs of 20 patients with mild to moderate AD, 15 FTLD patients, and 23 non-demented individuals were recorded in an eyes-closed resting-state. The synchronization likelihood (SL), a measure of functional connectivity, was calculated for each sensor pair in 0.5–4 Hz, 4–8 Hz, 8–10 Hz, 10–13 Hz, 13–30 Hz and 30–45 Hz frequency bands. The resulting connectivity matrices were converted to unweighted graphs, whose structure was characterized with several measures: mean clustering coefficient (local connectivity), characteristic path length (global connectivity) and degree correlation (network 'assortativity'). All results were normalized for network size and compared with random control networks.     

Asymmetric Fabry-Perot modulators with an InGaAs/AlGaAs active region

Strained layer superlattices have been used as the active region in asymmetric Fabry-Perot cavity optical modulators. The active layer of the Fabry-Perot modulator consisted of a 50 period In_0.15 Ga_0.85As/Al_0.30Ga_0.70As (10nm/10nm) superlattice. These quantum wells operate at typical wavelength of around 960 nm. By varying the length of the Fabry-Perot cavity in the modulator by including AlGaAs space layers of different thicknesses in the cavity, it is shown experimentally that both normally on and normally off devices can be obtained using the same stack of quantum wells. For the first type of device operation, a maximum contrast ratio of 8.3 dB could be measured for a reverse voltage of 7 V at 969 nm, while for the second type, a maximum of 8.9 dB at 957 nm was obtained for a 20 V reverse voltage. Using the same structure with an extra Bragg reflector on top of the quantum well layers to increase the surface reflection, a device with a higher finesse of the cavity was obtained. A maximum contrast ratio of 11.5 dB was measured for a reverse bias voltage of 30 V at 978 nm, with an insertion loss of –4.2dB.