Biology subjects

Treder, M.

Publications and source records attributed to Treder, M..

4 recordsLinked to original sources

bioRxiv2021

Ongoing neural oscillations predict the post-stimulus outcome of closed loop auditory stimulation during slow-wave sleep.

The large slow oscillation (SO, 0.5-2Hz) that characterises slow-wave sleep is crucial to memory consolidation and other physiological functions. Manipulating slow oscillations can enhance sleep and memory, as well as benefitting the immune system. Closed-loop auditory stimulation (CLAS) has been demonstrated to increase the SO amplitude and to boost fast sleep spindle activity (11-16Hz). Nevertheless, not all such stimuli are effective in evoking SOs, even if they are precisely phase-locked. Here, we studied whether it is possible to use ongoing activity patterns to determine which oscillations to stimulate in order to effectively enhance SOs or SO-locked spindle activity. To this end, we trained classifiers using the morphological characteristics of the ongoing SO, as measured by electroencephalography (EEG), to predict whether stimulation would lead to a benefit in terms of the resulting SO and spindle amplitude. Separate classifiers were trained using trials from spontaneous control and stimulated datasets, and we evaluated their performance by applying them to held-out data both within and across conditions. We were able to predict both when large SOs will occur spontaneously, and whether a phase-locked auditory click will effectively enlarge them with an accuracy of ~70%. We were also able to predict when stimulation would elicit spindle activity with an accuracy of ~60%. Finally, we evaluate the importance of the various SO features used to make these predictions. Our results offer new insight into SO and spindle dynamics and provide a new method for online optimisation of stimulation. HIGHLIGHTS- Random forest classifiers can predict spontaneous and stimulated SOs and spindle amplitudes. - Morphological wave features predicted the response of SOs and spindles to CLAS. - SO amplitude during the click is the main predictor for post-stimulus SO amplitude. - Prediction of spindle activity did not differ in accuracy for stimulated vs spontaneous data.

neuroscience↗

bioRxiv2020

The hippocampus as the switchboard between perception and memory

Adaptive memory recall requires a rapid and flexible switch from external perceptual reminders to internal mnemonic representations. However, owing to the limited temporal or spatial resolution of brain imaging modalities used in isolation, the hippocampal-cortical dynamics supporting this process remain unknown. We thus employed an object-scene cued recall paradigm across two studies, including intracranial Electroencephalography (iEEG) and high-density scalp EEG. First, a sustained increase in hippocampal high gamma power (60-110 Hz) emerged 500 ms after cue onset and distinguished successful vs. unsuccessful recall. This increase in gamma power for successful recall was followed by a decrease in hippocampal alpha power (8-12 Hz). Intriguingly, the hippocampal gamma power increase marked the moment at which extrahippocampal activation patterns shifted from perceptual cue towards mnemonic target representations. In parallel, source-localised EEG alpha power revealed that the recall signal progresses from hippocampus to posterior parietal cortex and then to medial prefrontal cortex. Together, these results identify the hippocampus as the switchboard between perception and memory and elucidate the ensuing hippocampal-cortical dynamics supporting the recall process. SignificanceHow do we adaptively switch from perceiving the external world to retrieving goal-relevant internal memories? To tackle this question, we used - in a cued-recall paradigm - direct intracranial recordings from the human hippocampus complemented by high-density scalp Electroencephalography (EEG). We found that a hippocampal signal ~500 ms after a perceptual cue marks the conversion from external (perceptual) to internal (mnemonic) representations. This sets in motion a recall cascade involving posterior parietal and medial prefrontal cortex, revealed via source-localised and time-resolved EEG alpha power. Together, these results unveil the hippocampal-cortical dynamics supporting rapid and flexible memory recall.

neuroscience↗

bioRxiv2019

Towards an Objective Evaluation of EEG/MEG Source Estimation Methods: The Linear Tool Kit

The question \"What is the spatial resolution of EEG/MEG?\" can only be answered with many ifs and buts, as the answer depends on a large number of parameters. Here, we describe a framework for resolution analysis of EEG/MEG source estimation, focusing on linear methods. The spatial resolution of linear methods can be evaluated using the resolution matrix, which contains the point-spread and cross-talk functions (PSFs and CTFs), respectively. Both of them have to be taken into account for a full characterization of spatial resolution. They can be used to compute a range of quantitative resolution metrics, which should cover at the last three aspects of those functions: localization accuracy, spatial extent, and relative amplitude. Here, we first provide a tutorial-style introduction to resolution analysis of linear source estimation methods. We then apply these concepts to evaluate the benefit of combining EEG and MEG measurements, and to compare weighted and normalized L2-minimum-norm estimation and spatial filters. We confirm and extend previous results, showing that adding EEG to MEG improves spatial resolution, and that different methods offer different compromises among different resolution criteria. We hope that our approach will help EEG/MEG researchers in the interpretation of source estimation results, the design of new experiments, and the development of new MEG systems.

neuroscience↗

bioRxiv2018

Source reconstruction of broadband EEG/MEG data using the frequency-adaptive broadband (FAB) beamformer

A beamformer enhances the signal from a voxel of interest by minimising interference from all other locations represented in the sensor covariance matrix. However, the presence of narrowband oscillations in EEG/MEG implies that the spatial structure of the covariance matrix, and hence also the optimal beamformer, depends on the frequency. The frequency-adaptive broadband (FAB) beamformer introduced here exploits this fact in the Fourier domain by partitioning the covariance matrix into cross-spectra corresponding to different frequencies. For each frequency bin, an individual spatial filter is constructed. This assures optimal noise suppression across the frequency spectrum. After applying the spatial filters in the frequency domain, the broadband source signal is recovered using the inverse Fourier transform. MEG simulations using artificial data and real resting-state measurements were used to compare the FAB beamformer to the LCMV beamformer and MNE. The FAB beamformer significantly outperforms both methods in terms of the quality of the reconstructed time series. To our knowledge, the FAB beamformer is the first beamforming approach tailored for the analysis of broadband neuroimaging data. Due to its frequency-adaptive noise suppression, the reconstructed source time series is suited for further time-frequency or connectivity analysis in source space.

neuroscience↗