Decoding Heterogenous Single-cell Perturbation Responses.

Understanding diverse responses of individual cells to the same perturbation is central to many biological and biomedical problems. Current methods, however, do not precisely quantify the strength of perturbation responses and, more importantly, reveal new biological insights from heterogeneity in responses. Here we introduce the perturbation-response score (PS), based on constrained quadratic optimization, to quantify diverse perturbation responses at a single-cell level. Applied to single-cell transcriptomes of large-scale genetic perturbation datasets (e.g., Perturb-seq), PS outperforms existing methods for quantifying partial gene perturbation responses. In addition, PS presents two major advances. First, PS enables large-scale, single-cell-resolution dosage analysis of perturbation, without the need to titrate perturbation strength. By analyzing the dose-response patterns of over 2,000 essential genes in Perturb-seq, we identify two distinct patterns, depending on whether a moderate reduction in their expression induces strong downstream expression alterations. Second, PS identifies intrinsic and extrinsic biological determinants of perturbation responses. We demonstrate the application of PS in contexts such as T cell stimulation, latent HIV-1 expression, and pancreatic cell differentiation. Notably, PS unveiled a previously unrecognized, cell-type-specific role of coiled-coil domain containing 6 (CCDC6) in guiding liver and pancreatic lineage decisions, where CCDC6 knockouts drive the endoderm cell differentiation towards liver lineage, rather than pancreatic lineage. The PS approach provides an innovative method for dose-to-function analysis and will enable new biological discoveries from single-cell perturbation datasets.

Is there a continuum of agentive awareness across physical and mental actions? The case of quasi-movements.

While humans routinely distinguish between physical and mental actions, overt movements (OM) and kinesthetically imagined movements (IM) are often viewed as forming a continuum of activities. Here, we theoretically conceptualized this continuum hypothesis for agentive awareness related to OM and IM and tested it experimentally using quasi-movements (QM), a little studied type of covert actions, which is considered as an inner part of the OM-IM continuum. QM are performed when a movement attempt is minimized down to full extinction of overt movement and muscle activity. We asked participants to perform OM, IM and QM and collected their electromyography data. According to participants' reports, they experienced QM as OM in terms of intentions and expected sensory feedback, while the verbal descriptors were independent from muscle activation. These results do not fit the OM-QM-IM continuum and suggest qualitative distinction for agentive awareness between IM and QM/OM.

Quasi-Movements and "Quasi-Quasi-Movements": Does Residual Muscle Activation Matter?

Quasi-movements (QM) are observed when an individual minimizes a movement to an extent that no related muscle activation is detected. Likewise to imaginary movements (IM) and overt movements, QMs are accompanied by the event-related desynchronization (ERD) of EEG sensorimotor rhythms. Stronger ERD was observed under QMs compared to IMs in some studies. However, the difference could be caused by the remaining muscle activation in QMs that could escape detection. Here, we re-examined the relation between the electromyography (EMG) signal and ERD in QM using sensitive data analysis procedures. More trials with signs of muscle activation were observed in QMs compared with a visual task and IMs. However, the rate of such trials was not correlated with subjective estimates of actual movement. Contralateral ERD did not depend on the EMG but still was stronger in QMs compared with IMs. These results suggest that brain mechanisms are common for QMs in the strict sense and "quasi-quasi-movements" (attempts to perform the same task accompanied by detectable EMG elevation) but differ between them and IMs. QMs could be helpful in research aimed at better understanding motor action and at modeling the use of attempted movements in the brain-computer interfaces with healthy participants.

Does Real-Time Feedback Affect Sensorimotor EEG Patterns in Routine Motor Imagery Practice?

BACKGROUND: Motor imagery engages much of the same neural circuits as an overt movement. Therefore, the mental rehearsal of movements is often used to supplement physical training and might aid motor neurorehabilitation after stroke. One attempt to capture the brain's involvement in imagery involves the use, as a marker, of the depression or event-related desynchronization (ERD) of thalamocortical sensorimotor rhythms found in a human electroencephalogram (EEG). Using fast real-time processing, it is possible to make the subject aware of their own brain reactions or-even better-to turn them into actions through a technology called the brain-computer interface (BCI). However, it remains unclear whether BCI-enabled imagery facilitates a stronger or qualitatively different brain response compared to the open-loop training. METHODS: Seven healthy volunteers who were experienced in both closed and open-loop motor imagery took part in six experimental sessions over a period of 4.5 months, in which they performed kinesthetic imagery of a previously known set of finger and arm movements with simultaneous 30-channel EEG acquisition. The first and the last session mostly consisted of feedback trials in which the subjects were presented with the classification results of the EEG patterns in real time; during the other sessions, no feedback was provided. Spatiotemporal and amplitude features of the ERD patterns concomitant with imagery were compared across experimental days and between feedback conditions using linear mixed-effects modeling. RESULTS: The main spatial sources of ERD appeared to be highly stable across the six experimental days, remaining nearly identical in five of seven subjects (Pearson's ρ > 0.94). Only in one subject did the spatial pattern of activation statistically significantly differ (p = 0.009) between the feedback and no-feedback conditions. Real-time visual feedback delivered through the BCI did not significantly increase the ERD strength. CONCLUSION: The results imply that the potential benefits of MI could be yielded by well-habituated subjects with a simplified open-loop setup, e.g., through at-home self-practice.

MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer.

Gaze-based input is an efficient way of hand-free human-computer interaction. However, it suffers from the inability of gaze-based interfaces to discriminate voluntary and spontaneous gaze behaviors, which are overtly similar. Here, we demonstrate that voluntary eye fixations can be discriminated from spontaneous ones using short segments of magnetoencephalography (MEG) data measured immediately after the fixation onset. Recently proposed convolutional neural networks (CNNs), linear finite impulse response filters CNN (LF-CNN) and vector autoregressive CNN (VAR-CNN), were applied for binary classification of the MEG signals related to spontaneous and voluntary eye fixations collected in healthy participants (n = 25) who performed a game-like task by fixating on targets voluntarily for 500 ms or longer. Voluntary fixations were identified as those followed by a fixation in a special confirmatory area. Spontaneous vs. voluntary fixation-related single-trial 700 ms MEG segments were non-randomly classified in the majority of participants, with the group average cross-validated ROC AUC of 0.66 ± 0.07 for LF-CNN and 0.67 ± 0.07 for VAR-CNN (M ± SD). When the time interval, from which the MEG data were taken, was extended beyond the onset of the visual feedback, the group average classification performance increased up to 0.91. Analysis of spatial patterns contributing to classification did not reveal signs of significant eye movement impact on the classification results. We conclude that the classification of MEG signals has a certain potential to support gaze-based interfaces by avoiding false responses to spontaneous eye fixations on a single-trial basis. Current results for intention detection prior to gaze-based interface's feedback, however, are not sufficient for online single-trial eye fixation classification using MEG data alone, and further work is needed to find out if it could be used in practical applications.

A passive BCI for monitoring the intentionality of the gaze-based moving object selection.

Objective.The use of an electroencephalogram (EEG) anticipation-related component, the expectancy wave (E-wave), in brain-machine interaction was proposed more than 50 years ago. This possibility was not explored for decades, but recently it was shown that voluntary attempts to select items using eye fixations, but not spontaneous eye fixations, are accompanied by the E-wave. Thus, the use of the E-wave detection was proposed for the enhancement of gaze interaction technology, which has a strong need for a mean to decide if a gaze behavior is voluntary or not. Here, we attempted at estimating whether this approach can be used in the context of moving object selection through smooth pursuit eye movements.Approach.Eighteen participants selected, one by one, items which moved on a computer screen, by gazing at them. In separate runs, the participants performed tasks not related to voluntary selection but also provoking smooth pursuit. A low-cost consumer-grade eye tracker was used for item selection.Main results.A component resembling the E-wave was found in the averaged EEG segments time-locked to voluntary selection events of every participant. Linear discriminant analysis with shrinkage regularization classified the intentional and spontaneous smooth pursuit eye movements, using single-trial 300 ms long EEG segments, significantly above chance in eight participants. When the classifier output was averaged over ten subsequent data segments, median group ROC AUC of 0.75 was achieved.Significance.The results suggest the possible usefulness of the E-wave detection in the gaze-based selection of moving items, e.g. in video games. This technique might be more effective when trial data can be averaged, thus it could be considered for use in passive interfaces, for example, in estimating the degree of the user's involvement during gaze-based interaction.

Knomics-Biota - a system for exploratory analysis of human gut microbiota data.

BACKGROUND: Metagenomic surveys of human microbiota are becoming increasingly widespread in academic research as well as in food and pharmaceutical industries and clinical context. Intuitive tools for investigating experimental data are of high interest to researchers. RESULTS: Knomics-Biota is a web-based resource for exploratory analysis of human gut metagenomes. Users can generate and share analytical reports corresponding to common experimental schemes (like case-control study or paired comparison). Interactive visualizations and statistical analysis are provided in association with the external factors and in the context of thousands of publicly available datasets arranged into thematic collections. The web-service is available at https://biota.knomics.ru. CONCLUSIONS: Knomics-Biota web service is a comprehensive tool for interactive metagenomic data analysis.

Assessing motor imagery in brain-computer interface training: Psychological and neurophysiological correlates.

Motor imagery (MI) is considered to be a promising cognitive tool for improving motor skills as well as for rehabilitation therapy of movement disorders. It is believed that MI training efficiency could be improved by using the brain-computer interface (BCI) technology providing real-time feedback on person's mental attempts. While BCI is indeed a convenient and motivating tool for practicing MI, it is not clear whether it could be used for predicting or measuring potential positive impact of the training. In this study, we are trying to establish whether the proficiency in BCI control is associated with any of the neurophysiological or psychological correlates of motor imagery, as well as to determine possible interrelations among them. For that purpose, we studied motor imagery in a group of 19 healthy BCI-trained volunteers and performed a correlation analysis across various quantitative assessment metrics. We examined subjects' sensorimotor event-related EEG events, corticospinal excitability changes estimated with single-pulse transcranial magnetic stimulation (TMS), BCI accuracy and self-assessment reports obtained with specially designed questionnaires and interview routine. Our results showed, expectedly, that BCI performance is dependent on the subject's capability to suppress EEG sensorimotor rhythms, which in turn is correlated with the idle state amplitude of those oscillations. Neither BCI accuracy nor the EEG features associated with MI were found to correlate with the level of corticospinal excitability increase during motor imagery, and with assessed imagery vividness. Finally, a significant correlation was found between the level of corticospinal excitability increase and kinesthetic vividness of imagery (KVIQ-20 questionnaire). Our results suggest that two distinct neurophysiological mechanisms might mediate possible effects of motor imagery: the non-specific cortical sensorimotor disinhibition and the focal corticospinal excitability increase. Acquired data suggests that BCI-based approach is unreliable in assessing motor imagery due to its high dependence on subject's innate EEG features (e.g. resting mu-rhythm amplitude). Therefore, employment of additional assessment protocols, such as TMS and psychological testing, is required for more comprehensive evaluation of the subject's motor imagery training efficiency.