With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm² patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.

The primary somatosensory cortex (SI) exhibits a detailed topographic organization of the hand and fingers, which has been found to undergo plastic changes following modifications of the sensory input. Although the spatial properties of these changes have been extensively investigated, little is known about their temporal dynamics. In this study, we adapted the paradigm of finger webbing, in which 4 fingers are temporarily webbed together, hence modifying their sensory feedback. We used magnetoencephalography, to measure changes in the hand representation in SI, before, during, and after finger webbing for about 5 h. Our results showed a decrease in the Euclidean distance (ED) between cortical sources activated by electrical stimuli to the index and small finger 30 min after webbing, followed by an increase lasting for about 2 h after webbing, which was followed by a return toward baseline values. These results provide a unique frame in which the different representational changes occur, merging previous findings that were only apparently controversial, in which either increases or decreases in ED were reported after sensory manipulation for relatively long or short duration, respectively. Moreover, these observations further confirm that the mechanisms that underlie cortical reorganization are extremely rapid in their expression and, for the first time, show how brain reorganization occurs over time.

With the advent of Information Technology in the medical world, various radiological modalities produce a variety of digital medical files most often datasets and images. These files as any digital asset should be protected from unwanted modification of their contents, especially as they contain vital medical information. Thus their protection and authentication seems of great importance and this need will rise along with the future standardization of exchange of data between hospitals or between patients and doctors. Watermarking, a technique first introduced for multimedia files, provides a method for authentication and protection and has been recently applied to medical images. In this paper, we propose a novel watermarking technique where the region of non-interest (RONI) of medical Magnetic Resonance Imaging (MRI) images, is used to embed the region of interest (ROI). In this way, any tampering attempt, not only will be detected, but also the image could be self-restored, back to its previous, ldquooriginalrdquo form by extracting the ROI from the RONI.

Trachani E, Constantoyannis C, Sakellaropoulos GC, Stavrinou ML, Nikiforidis G, Chroni E. Heart rate variability in Parkinson’s disease unaffected by deep brain stimulation.Acta Neurol Scand: 2012: 126: 56–61.© 2011 John Wiley & Sons A/S. Objectives – Our aim was to investigate the impact of subthalamic nucleus deep brain stimulation (STN-DBS) on the cardiovagal control of patients with advanced Parkinson’s disease. Materials and methods – Twenty-four patients (mean age: 62.1 ± 9.4 years) were examined 3 days before and 6 months after DBS by a questionnaire, blood pressure monitoring and a battery of neurophysiological tests: time domain analysis of RR interval variation during normal and deep breathing (DB), Valsalva manoeuvre, and tilt test. By off-line, performed frequency domain analysis of heart rate variation, total power (TP), low frequency band (LF) band, high-frequency (HF) band, and their normalized units were estimated. The neurophysiological measurements were compared to those of 24 healthy controls. Results – The values of time domain variables were pre- and postoperatively lower in patients than in controls. A significant reduction was found in LF band after the implantation. Orthostatic hypotension was present in 45.8% of the patients preoperatively and 12.5% postoperatively. There was no correlation between DBS-related changes of motor function and corresponding neurophysiological measurements, but patients with more than 60% motor improvement had higher time domain parameters’ values than the others. Conclusions – STN-DBS offered no considerable impact on autonomic cardiovascular control.

While recordings of extracellular potentials in neural tissue are commonly used for monitoring neural activity, interpretation of the low frequency part, the Local Field Potential (LFP), in terms of the underlying network activity, remains ambiguous. Several studies have shown that the LFP depends on the electrode position, extracellular volume conductor, neuronal morphology, synapse distributions and synaptic input correlations [1,2]. In order to relate spiking network dynamics to the LFP, we have developed a hybrid scheme that uses the spiking activity of a network of single-compartment leaky integrate-and-fire model neurons (implemented in NEST [3], Fig. 1a,b). This network provides synaptic input to populations of detailed multi-compartmental neuron model which in turn generate the LFP (implemented with LFPy [4] using NEURON [5], Fig. 1c,d). We here apply the methodology to a novel network model describing 1 mm2 of cat primary visual cortex with a total of 78000 cells spread across layer 2/3 to layer 6 with one excitatory and one inhibitory population per layer [6]. For the LFP model, sixteen cell types with passive membrane properties are used. The cell-type and layer-specific connectivity is specified on the basis of the connectivity of the spiking network model and further anatomical data [7]. Our results show that both spontaneous and stimulus-evoked LFPs depend critically on the underlying network state, i.e., whether the ongoing network state is synchronous or asynchronous. Moreover, we show that full-scale simulations, i.e., simulations including all cells in the network, are required to address the effect of network correlations on the LFP. Further, the hybrid scheme can be used to develop simplified proxies for LFP generation from simplified network models, and provides insight on the link between experimental measurements and the underlying network activity. Figure 1: Schematic illustration of the hybrid scheme. Spiking activity generated in a layered spiking-neuron network simulation (panel a modified from [6], b where E and I denote excitatory and inhibitory populations) are in turn used as synapse activation times onto equivalent, but morphologically detailed populations of multi-compartment neuron models (c). The resulting trans-membrane currents of single cells allows e.g., superimposing the generated LFP contribution from each population (d).