Maria Stavrinou

• Mäki-Marttunen, Verónica; Hagen, Thomas; Aminihajibashi, Samira; Foldal, Maja Dyhre; Stavrinou, Maria; Halvorsen, Jens; Laeng, Bruno & Espeseth, Thomas (2018). Ocular signatures of proactive versus reactive cognitive control in young adults. Cognitive, Affective, & Behavioral Neuroscience.  ISSN 1530-7026.  s 1- 15 . doi: 10.3758/s13415-018-0621-5
• Aasebø, Ida E. J.; Lepperød, Mikkel Elle; Stavrinou, Maria; Nøkkevangen, Sandra; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2017). Temporal processing in the visual cortex of the awake and anesthetized rat. eNeuro.  ISSN 2373-2822.  4:e0059-17.2017(4), s 1- 26 . doi: 10.1523/ENEURO.0059-17.2017 Fulltekst i vitenarkiv. Vis sammendrag

  The activity pattern and temporal dynamics within and between neuron ensembles are essential features of information processing and believed to be profoundly affected by anesthesia. Much of our general understanding of sensory information processing, including computational models aimed at mathematically simulating sensory information processing, rely on parameters derived from recordings conducted on animals under anesthesia. Due to the high variety of neuronal subtypes in the brain, population-based estimates of the impact of anesthesia may conceal unit- or ensemble-specific effects of the transition between states. Using chronically implanted tetrodes into primary visual cortex (V1) of rats, we conducted extracellular recordings of single units and followed the same cell ensembles in the awake and anesthetized states. We found that the transition from wakefulness to anesthesia involves unpredictable changes in temporal response characteristics. The latency of single-unit responses to visual stimulation was delayed in anesthesia, with large individual variations between units. Pair-wise correlations between units increased under anesthesia, indicating more synchronized activity. Further, the units within an ensemble show reproducible temporal activity patterns in response to visual stimuli that is changed between states, suggesting state-dependent sequences of activity. The current dataset, with recordings from the same neural ensembles across states, is well suited for validating and testing computational network models. This can lead to testable predictions, bring a deeper understanding of the experimental findings and improve models of neural information processing. Here, we exemplify such a workflow using a Brunel network model.

• Hagen, Espen; Dahmen, David; Stavrinou, Maria; Linden, Henrik; Tetzlaff, Tom; Van Albada, Sacha; Gruen, Sonja; Diesmann, Markus & Einevoll, Gaute (2016). Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.. Cerebral Cortex.  ISSN 1047-3211.  26(12), s 4461- 4496 . doi: 10.1093/cercor/bhw237 Fulltekst i vitenarkiv. Vis sammendrag

  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.

• Stavrinou, Maria; Sakellaropoulos, G. C.; Trachani, E.; Sirrou, V; Polychronopoulos, P.; Nikiforidis, G. & Chroni, E. (2014). Methodological issues in the spectral analysis of the heart rate variability: Application in patients with epilepsy. Biomedical Signal Processing and Control.  ISSN 1746-8094.  13, s 1- 7 . doi: 10.1016/j.bspc.2014.03.002 Vis sammendrag

  Purpose: Spectral analysis of heart rate variability (HRV) constitutes a useful tool for the evaluation of autonomic function. However, it is difficult to compare the published data because different mathematical approaches for the calculation of the frequency bands are applied. Our aim was to compare the HRV frequency domain parameters obtained by application of 2 parametric and 2 non-parametric spectral methods in a group of patients with chronic epilepsy. Methods: Sixty-eight patients and 69 healthy controls underwent a 5-min recording of RR signal, which was analyzed off-line in time and in frequency domains. Results: The time domain parameters – variation RR ratio, standard deviation of normal-to-normal RR and coefficient of variation – were significantly lower in patients than in controls. In spectral analysis of the patient group deviation toward opposite directions of Low Frequency band (p = 0.034) and Total Power (p = 0.013) measures was detected depending on the method used. The results of Burg’s and Yule- Walker’s parametric methods fitted best to those of time domain estimates for both control and patient groups. Conclusions: Epilepsy-related abnormalities of HRV were disclosed by time as well as by frequency domain analysis. In the present setting, the parametric methods proved to be superior to the non-parametric ones in matching time domain parameters of patients and healthy subjects and at the same time in detecting abnormalities of the frequency domain measures of patients with epilepsy.

• Trachani, E; Constantoyiannis, C; Sakellaropoulos, GC; Stavrinou, Maria; Nikiforidis, G & Chroni, E (2012). Heart rate variability in Parkinson's disease unaffected by Deep-Brain Stimulation. Acta Neurologica Scandinavica.  ISSN 0001-6314.  126(1), s 56- 61 . doi: 10.1111/j.1600-0404.2011.1605.x Vis sammendrag

  Abstract 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.

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• Stavrinou, Maria; Asko, Olgerta; Hagen, Thomas; Foldal, Maja Dyhre & Espeseth, Thomas (2017). The long term effects of reward on spatial priority maps studied with Electroencephalography and Pupillometry.
• Hagen, Espen; Dahmen, David; Stavrinou, Maria; Linden, Henrik; Tetzlaff, Tom; Van albada, Sacha; Diesmann, Markus; Grün, Sonja & Einevoll, Gaute (2015). Hybrid scheme for modeling local field potentials from point-neuron networks.
• Hagen, Espen; Dahmen, David; Stavrinou, Maria; Linden, Henrik; Tetzlaff, Tom; Van albada, Sacha; Grün, Sonja; Diesmann, Markus & Einevoll, Gaute (2015). Hybrid scheme for modeling local field potentials from point-neuron networks.
• Tetzlaff, Tom; Dahmen, David; Hagen, Espen; Stavrinou, Maria; Linden, Henrik; Van albada, Sacha; Diesmann, Markus; Grün, Sonja & Einevoll, Gaute (2015). Hybrid scheme for modeling local field potentials from point neuron networks.
• Dahmen, David; Hagen, Espen; Stavrinou, Maria; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha; Diesmann, Markus; Gruen, Sonja & Einevoll, Gaute (2014). Computing local-field potentials based on a point-neuron network model of cat V1.
• Dahmen, David; Hagen, Espen; Stavrinou, Maria; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha; Diesmann, Markus; Gruen, Sonja & Einevoll, Gaute (2014). From spiking point-neuron networks to LFPs, a hybrid approach.
• Stavrinou, Maria; Hagen, E.; Dahmen, David; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha; Diesmann, Markus; Gruen, Sonja & Einevoll, Gaute (2014). Local field potentials and network dynamics in a model cortical column of cat V1.
• Stavrinou, Maria; Hagen, E; Dahmen, David; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha; Diesmann, Markus; Gruen, Sonja & Einevoll, Gaute (2014). Computing local field potentials based on spiking cortical networks.
• Dahmen, David; Hagen, Espen; Stavrinou, Maria; Linden, Henrik; Tetzlaff, Tom; Albada, Sacha van; Diesmann, Markus; Grün, Sonja & Einevoll, Gaute Tomas (2013). From spiking point-neuron networks to LFPs: a hybrid approach.
• Hagen, Espen; Stavrinou, Maria; Linden, Henrik; Dahmen, David; Tetzlaff, Tom; Albada, Sacha van; Grün, Sonja; Diesmann, Markus & Einevoll, Gaute Tomas (2013). Hybrid scheme for modeling LFPs from spiking cortical network models.
• Hagen, Espen; Stavrinou, Maria; Linden, Henrik; Tetzlaff, Tom; van Albada, Sacha; Dahmen, David; Diesmann, Markus; Grün, Sonja & Einevoll, Gaute Tomas (2013). Hybrid scheme for modeling LFPs from spiking cortical network models.
• Stavrinou, Maria; Larsson, Pål Gunnar & Kugiumtzis, Dimitris (2012). Brain connectivity in children with ADHD and CSWS.
• Stavrinou, Maria; Larsson, Pål Gunnar & Storm, Johan Frederik (2012). Increased temporo-parietal connectivity in children with ADHD and CSWS. Vis sammendrag

  Attention-Deficit hyperactivity disorder (ADHD) is one of the most common neuropsychiatric conditions in childhood. It is characterized by inappropriate expression of impulsivity, inattention and hyperactivity. A per- centage of children with ADHD appear to have continuous spikes and waves during their sleep a condition most often called Continuous spike and waves during slow sleep (CSWS). One of the hypotheses used to explain the affected cognitive functioning of those children concerns changes in resting-state connectivity. In this framework, we studied the functional and effective brain connectivity in children with ADHD and CSWS, focusing on the resting state network with Electroencephalographic recordings. Twenty four-hour recordings including wakefulness and sleep, of children admitted to the National Centre for Epilepsy at Oslo University Hospital were analyzed and compared with controls of the same age group without ADHD. Di- rected transfer function (DTF) was used to measure the effective connectivity between brain areas, in the theta and gamma frequency bands. The analysis have shown an increased temporo-parietal connectivity of the ADHD children, when compared to the control group (p<0.035), most prominently in the left hemi- sphere. Parietal to frontal connections were also significantly different between patients and controls for both frequency bands. The time dynamics of these most active connections have shown increased values of connectivity during the day compared to sleep. This may be related to arousal impairment in children with ADHD.

• Stavrinou, Maria; Larsson, Pål Gunnar & Kugiumtzis, Dimitris (2011). Brain connectivity in children with cognitive deficits and CSWS. Neuroscience Letters.  ISSN 0304-3940.  500, s e35- e35 . doi: 10.1016/j.neulet.2011.05.169

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