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Amsterdam-Zuidoost, Netherlands

Nikulin V.V.,Franklin University | Nikulin V.V.,Bernstein Center for Computational Neuroscience | Linkenkaer-Hansen K.,Center for Neurogenomics and Cognitive Research | Nolte G.,Fraunhofer FIRST | And 2 more authors.
Clinical Neurophysiology | Year: 2010

Objective: The aim of the present study was to show analytically and with simulations that it is the non-zero mean of neuronal oscillations, and not an amplitude asymmetry of peaks and troughs, that is a prerequisite for the generation of evoked responses through a mechanism of amplitude modulation of oscillations. Secondly, we detail the rationale and implementation of the "baseline-shift index" (BSI) for deducing whether empirical oscillations have non-zero mean. Finally, we illustrate with empirical data why the "amplitude fluctuation asymmetry" (AFA) index should be used with caution in research aimed at explaining variability in evoked responses through a mechanism of amplitude modulation of ongoing oscillations. Methods: An analytical approach, simulations and empirical MEG data were used to compare the specificity of BSI and AFA index to differentiate between a non-zero mean and a non-sinusoidal shape of neuronal oscillations. Results: Both the BSI and the AFA index were sensitive to the presence of non-zero mean in neuronal oscillations. The AFA index, however, was also sensitive to the shape of oscillations even when they had a zero mean. Conclusions: Our findings indicate that it is the non-zero mean of neuronal oscillations, and not an amplitude asymmetry of peaks and troughs, that is a prerequisite for the generation of evoked responses through a mechanism of amplitude modulation of oscillations. Significance: A clear distinction should be made between the shape and non-zero mean properties of neuronal oscillations. This is because only the latter contributes to evoked responses, whereas the former does not. © 2009 International Federation of Clinical Neurophysiology. Source


Jain S.,University Medical Center | van Kesteren R.E.,Center for Neurogenomics and Cognitive Research | Heutink P.,University Medical Center
Journal of Visualized Experiments | Year: 2012

The functional annotation of genomes, construction of molecular networks and novel drug target identification, are important challenges that need to be addressed as a matter of great urgency. Multiple complementary 'omics' approaches have provided clues as to the genetic risk factors and pathogenic mechanisms underlying numerous neurodegenerative diseases, but most findings still require functional validation. For example, a recent genome wide association study for Parkinson's Disease (PD), identified many new loci as risk factors for the disease, but the underlying causative variant(s) or pathogenic mechanism is not known. As each associated region can contain several genes, the functional evaluation of each of the genes on phenotypes associated with the disease, using traditional cell biology techniques would take too long. There is also a need to understand the molecular networks that link genetic mutations to the phenotypes they cause. It is expected that disease phenotypes are the result of multiple interactions that have been disrupted. Reconstruction of these networks using traditional molecular methods would be time consuming. Moreover, network predictions from independent studies of individual components, the reductionism approach, will probably underestimate the network complexity8. This underestimation could, in part, explain the low success rate of drug approval due to undesirable or toxic side effects. Gaining a network perspective of disease related pathways using HT/HC cellular screening approaches, and identifying key nodes within these pathways, could lead to the identification of targets that are more suited for therapeutic intervention. High-throughput screening (HTS) is an ideal methodology to address these issues. but traditional methods were one dimensional whole-well cell assays, that used simplistic readouts for complex biological processes. They were unable to simultaneously quantify the many phenotypes observed in neurodegenerative diseases such as axonal transport deficits or alterations in morphology properties. This approach could not be used to investigate the dynamic nature of cellular processes or pathogenic events that occur in a subset of cells. To quantify such features one has to move to multi-dimensional phenotypes termed high-content screening (HCS. HCS is the cell-based quantification of several processes simultaneously, which provides a more detailed representation of the cellular response to various perturbations compared to HTS. HCS has many advantages over HTS, but conducting a high-throughput (HT)-high-content (HC) screen in neuronal models is problematic due to high cost, environmental variation and human error. In order to detect cellular responses on a 'phenomics' scale using HC imaging one has to reduce variation and error, while increasing sensitivity and reproducibility. Herein we describe a method to accurately and reliably conduct shRNA screens using automated cell culturing and HC imaging in neuronal cellular models. We describe how we have used this methodology to identify modulators for one particular protein, DJ1, which when mutated causes autosomal recessive parkinsonism. Combining the versatility of HC imaging with HT methods, it is possible to accurately quantify a plethora of phenotypes. This could subsequently be utilized to advance our understanding of the genome, the pathways involved in disease pathogenesis as well as identify potential therapeutic targets. © 2012 Creative Commons Attribution License. Source


van der Wal C.N.,VU University Amsterdam | Irrmischer M.,Center for Neurogenomics and Cognitive Research
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Wireless brain computer interfaces (BCI’s) are promising for new intelligent applications in which emotions are detected by measuring brain activity. Applications, such as serious games and video game therapy, are measuring and using the user’s emotional state in order to determine the intensity level of the game. This experimental study was designed to validate the measurement of emotion regulation with a single dry electrode wireless BCI during an emotion interference computer task by comparing it with the behavioural performance of this task. The behavioural measures showed significant main and interaction effects indicating that emotion regulatory mechanisms are present in the participants. The EEG measure Attention detected by the Myndplay Brainband XL showed a significant interaction indicating that type of training (meditation or laughter) increases or decreases attention during the emotion interference task. Overall, these results point in the direction of single electrode BCI’s being able to detect emotion interference. © Springer International Publishing Switzerland 2015. Source


van der Wal C.N.,VU University Amsterdam | Irrmischer M.,Center for Neurogenomics and Cognitive Research
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Future applications for the detection of attention can be helped by the development and validation of single electrode brain computer interfaces that are small and user-friendly. The two objectives of this study were: to (1) understand the correlates of attention regulation as detected with the Myndplay Brainband XL and (2) compare these to existing neuroscientific literature. The Myndplay Brainband did succeed in highlighting the EEG frequency band Alpha as the main biomarker for sustained attention as measured with behavioral correlates. These results give an optimistic outlook to future applications that can detect mind wandering with single electrode brain computer interfaces. © Springer International Publishing Switzerland 2015. Source


Te Lindert B.H.W.,Institute of the Royal Netherlands Academy of Arts and science | Van Someren E.J.W.,Institute of the Royal Netherlands Academy of Arts and science | Van Someren E.J.W.,Center for Neurogenomics and Cognitive Research
Sleep | Year: 2013

Study Objectives: Although currently more affordable than polysomnography, actigraphic sleep estimates have disadvantages. Brand-specific differences in data reduction impede pooling of data in large-scale cohorts and may not fully exploit movement information. Sleep estimate reliability might improve by advanced analyses of three-axial, linear accelerometry data sampled at a high rate, which is now feasible using microelectrome-chanical systems (MEMS). However, it might take some time before these analyses become available. To provide ongoing studies with backward compatibility while already switching from actigraphy to MEMS accelerometry, we designed and validated a method to transform accelerometry data into the traditional actigraphic movement counts, thus allowing for the use of validated algorithms to estimate sleep parameters. Design: Simultaneous actigraphy and MEMS-accelerometry recording. Setting: Home, unrestrained. Participants: Fifteen healthy adults (23-36 y, 10 males, 5 females). Interventions: None. Measurements: Actigraphic movement counts/15-sec and 50-Hz digitized MEMS-accelerometry. Analyses: Passing-Bablok regression optimized transformation of MEMS-accelerometry signals to movement counts. Kappa statistics calculated agreement between individual epochs scored as wake or sleep. Bland-Altman plots evaluated reliability of common sleep variables both between and within actigraphs and MEMS-accelerometers. Results: Agreement between epochs was almost perfect at the low, medium, and high threshold (kappa = 0.87 ± 0.05, 0.85 ± 0.06, and 0.83 ± 0.07). Sleep parameter agreement was better between two MEMS-accelerometers or a MEMS-accelerometer and an actigraph than between two actigraphs. Conclusions: The algorithm allows for continuity of outcome parameters in ongoing actigraphy studies that consider switching to MEMS-accel-erometers. Its implementation makes backward compatibility feasible, while collecting raw data that, in time, could provide better sleep estimates and promote cross-study data pooling. Source

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