Andreas Meinel

Measuring Local Viscosities near Plasma Membranes of Living Cells with Photonic Force Microscopy.

The molecular processes of particle binding and endocytosis are influenced by the locally changing mobility of the particle nearby the plasma membrane of a living cell. However, it is unclear how the particles hydrodynamic drag and momentum vary locally and how they are mechanically transferred to the cell. We have measured the thermal fluctuations of a 1 μm-sized polystyrene sphere, which was placed in defined distances to plasma membranes of various cell types by using an optical trap and fast three-dimensional (3D) interferometric particle tracking. From the particle position fluctuations on a 30 μs timescale, we determined the distance-dependent change of the viscous drag in directions perpendicular and parallel to the cell membrane. Measurements on macrophages, adenocarcinoma cells, and epithelial cells revealed a significantly longer hydrodynamic coupling length of the particle to the membrane than those measured at giant unilamellar vesicles (GUVs) or a plane glass interface. In contrast to GUVs, there is also a strong increase in friction and in mean first passage time normal to the cell membrane. This hydrodynamic coupling transfers a different amount of momentum to the interior of living cells and might serve as an ultra-soft stimulus triggering further reactions.

Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task.

We propose a framework for building electrophysiological predictors of single-trial motor performance variations, exemplified for SVIPT, a sequential isometric force control task suitable for hand motor rehabilitation after stroke. Electroencephalogram (EEG) data of 20 subjects with mean age of 53 years was recorded prior to and during 400 trials of SVIPT. They were executed within a single session with the non-dominant left hand, while receiving continuous visual feedback of the produced force trajectories. The behavioral data showed strong trial-by-trial performance variations for five clinically relevant metrics, which accounted for reaction time as well as for the smoothness and precision of the produced force trajectory. 18 out of 20 tested subjects remained after preprocessing and entered offline analysis. Source Power Comodulation (SPoC) was applied on EEG data of a short time interval prior to the start of each SVIPT trial. For 11 subjects, SPoC revealed robust oscillatory EEG subspace components, whose bandpower activity are predictive for the performance of the upcoming trial. Since SPoC may overfit to non-informative subspaces, we propose to apply three selection criteria accounting for the meaningfulness of the features. Across all subjects, the obtained components were spread along the frequency spectrum and showed a variety of spatial activity patterns. Those containing the highest level of predictive information resided in and close to the alpha band. Their spatial patterns resemble topologies reported for visual attention processes as well as those of imagined or executed hand motor tasks. In summary, we identified subject-specific single predictors that explain up to 36% of the performance fluctuations and may serve for enhancing neuroergonomics of motor rehabilitation scenarios.

EEG band power predicts single-trial reaction time in a hand motor task

The power of oscillatory brain sources can provide valuable information about trial-to-trial fluctuations considering the behavioural performance of subjects. Extracting such sources from electroencephalogram (EEG) recordings, however, proves to be difficult for most applications as the signal-to-noise ratio (SNR) in EEG typically is low. In an offline study with EEG data from three healthy subjects, we investigated the use of a recently introduced data-driven spatial filtering method called Source Power Comodulation (SPoC) [1]. Based on the trial-to-trial performance metric of a hand motor task, SPoC derives individually optimized linear spatial filters. They are optimized such that the resulting oscillatory signal component comodulates in band power with the performance metric at an increased SNR. Based on short intervals [-800; 0] ms prior to the go cue of ≈ 200 trials, we were able to identify individual oscillatory components. Their alpha band power comodulates with the reaction time (RT) during an isometric force control task of the hand. Using these components, it is possible to reach an average correlation of 0.19, with the best feature explaining up to 17% of the RT variation between single trials.

Probing meaningfulness of oscillatory EEG components with bootstrapping, label noise and reduced training sets.

As oscillatory components of the Electroencephalogram (EEG) and other electrophysiological signals may co-modulate in power with a target variable of interest (e.g. reaction time), data-driven supervised methods have been developed to automatically identify such components based on labeled example trials. Under conditions of challenging signal-to-noise ratio, high-dimensional data and small training sets, however, these methods may overfit to meaningless solutions. Examples are spatial filtering methods like Common Spatial Patterns (CSP) [1] and Source Power Comodulation (SPoC) [2]. It is difficult for the practitioner to tell apart meaningful from arbitrary, random components. We propose three approaches to probe the robustness of extracted oscillatory components and show their application to both, simulated and EEG data recorded during a visually cued hand motor reaction time task.

Commonalities of Motor Performance Metrics are Revealed by Predictive Oscillatory EEG Components

The power of oscillatory components of the electroencephalogram (EEG) can be predictive for the single-trial performance score of an upcoming task. State-of-the-art machine learning methods allow to extract such predictive subspace components even from noisy multichannel EEG recordings. In the context of an isometric hand motor rehabilitation task, we analyse EEG data of n=20 normally aged subjects. Predictive oscillatory EEG subspaces were derived with a spatial filtering method (source power comodulation, SPoC), and the transfer of these subspaces between five performance metrics but within data of single subjects was investigated. Findings suggest, that on the grand average of 20 subjects, informative SPoC subspace components were extracted, which could be shared between a set of three metrics describing the duration of subtasks and jerk characteristics of the force trajectories. Transfer to any other of the remaining four metrics was not possible above chance level for a metric describing the reaction time and a metric assessing the length of the force trajectory. Furthermore we show, that these transfer results are in line with the structure of cross-correlations between the performance metrics.

Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems: Guidelines Derived from Simulation and Real-World Data

We report on novel supervised algorithms for single-trial brain state decoding. Their reliability and robustness are essential to efficiently perform neurotechnological applications in closed-loop. When brain activity is assessed by multichannel recordings, spatial filters computed by the source power comodulation (SPoC) algorithm allow identifying oscillatory subspaces. They regress to a known continuous trial-wise variable reflecting, e.g. stimulus characteristics, cognitive processing or behavior. In small dataset scenarios, this supervised method tends to overfit to its training data as the involved recordings via electroencephalogram (EEG), magnetoencephalogram or local field potentials generally provide a low signal-to-noise ratio. To improve upon this, we propose and characterize three types of regularization techniques for SPoC: approaches using Tikhonov regularization (which requires model selection via cross-validation), combinations of Tikhonov regularization and covariance matrix normalization as well as strategies exploiting analytical covariance matrix shrinkage. All proposed techniques were evaluated both in a novel simulation framework and on real-world data. Based on the simulation findings, we saw our expectations fulfilled, that SPoC regularization generally reveals the largest benefit for small training sets and under severe label noise conditions. Relevant for practitioners, we derived operating ranges of regularization hyperparameters for cross-validation based approaches and offer open source code. Evaluating all methods additionally on real-world data, we observed an improved regression performance mainly for datasets from subjects with initially poor performance. With this proof-of-concept paper, we provided a generalizable regularization framework for SPoC which may serve as a starting point for implementing advanced techniques in the future.