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Dar, A. H.

Publications and source records attributed to Dar, A. H..

2 recordsLinked to original sources

Hemispheric difference of adaptation lifetime in human auditory cortex measured with MEG

Adaptation is the attenuation of a neuronal response when a stimulus is repeatedly presented. The phenomenon has been linked to sensory memory, but its exact neuronal mechanisms are under debate. One defining feature of adaptation is its lifetime, that is, the timespan over which the attenuating effect of previous stimulation persists. This can be revealed by varying the stimulus-onset interval (SOI) of the repeated stimulus. As SOI is increased, the peak amplitude of the response grows before saturating at large SOIs. The rate of this growth can be quantified and used as an estimate of adaptation lifetime. Here, we studied whether adaptation lifetime varies across the left and the right auditory cortex of the human brain. Event-related fields of whole-head magnetoencephalograms (MEG) were measured in 14 subjects during binaural presentation of pure tone stimuli. To make statistical inferences on the single-subject level, additional event-related fields were generated by resampling the original single-trial data via bootstrapping. For each hemisphere and SOI, the peak amplitude of the N1m response was then derived from both original and bootstrap-based data sets. Finally, the N1m peak amplitudes we used for deriving subject-and hemisphere-specific estimates of adaptation lifetime. Comparing subject-specific adaptation lifetime across hemispheres, we found a significant difference, with longer adaptation lifetimes in the left than in the right auditory cortex (p = 0.004). This difference might have a functional relevance in the context of temporal binding of auditory stimuli, leading to larger integration time windows in the left than in the right hemisphere.

neuroscience↗

REMoDNaV: Robust Eye Movement Detection for Natural Viewing

Tracking of eye movements is an established measurement for many types of experimental paradigms. More complex and more prolonged visual stimuli have made algorithmic approaches to eye movement event classification the most pragmatic option. A recent analysis revealed that many current algorithms are lackluster when it comes to data from viewing dynamic stimuli such as video sequences. Here we present an event classification algorithm--built on an existing velocity-based approach--that is suitable for both static and dynamic stimulation, and is capable of classifying saccades, post-saccadic oscillations, fixations, and smooth pursuit events. We validated classification performance and robustness on three public datasets: 1) manually annotated, trial-based gaze trajectories for viewing static images, moving dots, and short video sequences, 2) lab-quality gaze recordings for a feature length movie, and 3) gaze recordings acquired under suboptimal lighting conditions inside the bore of a magnetic resonance imaging (MRI) scanner for the same full-length movie. We found that the proposed algorithm performs on par or better compared to state-of-the-art alternatives for static stimulation. Moreover, it yields eye movement events with biologically plausible characteristics on prolonged dynamic recordings. Lastly, algorithm performance is robust on data acquired under suboptimal conditions that exhibit a temporally varying noise level. These results indicate that the proposed algorithm is a robust tool with improved classification accuracy across a range of use cases. The algorithm is cross-platform compatible, implemented using the Python programming language, and readily available as free and open source software from public sources.

bioinformatics↗