The point of no return in vetoing self-initiated movements.

In humans, spontaneous movements are often preceded by early brain signals. One such signal is the readiness potential (RP) that gradually arises within the last second preceding a movement. An important question is whether people are able to cancel movements after the elicitation of such RPs, and if so until which point in time. Here, subjects played a game where they tried to press a button to earn points in a challenge with a brain-computer interface (BCI) that had been trained to detect their RPs in real time and to emit stop signals. Our data suggest that subjects can still veto a movement even after the onset of the RP. Cancellation of movements was possible if stop signals occurred earlier than 200 ms before movement onset, thus constituting a point of no return.

Switch-Independent Task Representations in Frontal and Parietal Cortex.

Alternating between two tasks is effortful and impairs performance. Previous fMRI studies have found increased activity in frontoparietal cortex when task switching is required. One possibility is that the additional control demands for switch trials are met by strengthening task representations in the human brain. Alternatively, on switch trials, the residual representation of the previous task might impede the buildup of a neural task representation. This would predict weaker task representations on switch trials, thus also explaining the performance costs. To test this, male and female participants were cued to perform one of two similar tasks, with the task being repeated or switched between successive trials. Multivoxel pattern analysis was used to test which regions encode the tasks and whether this encoding differs between switch and repeat trials. As expected, we found information about task representations in frontal and parietal cortex, but there was no difference in the decoding accuracy of task-related information between switch and repeat trials. Using cross-classification, we found that the frontoparietal cortex encodes tasks using a generalizable spatial pattern in switch and repeat trials. Therefore, task representations in frontal and parietal cortex are largely switch independent. We found no evidence that neural information about task representations in these regions can explain behavioral costs usually associated with task switching.SIGNIFICANCE STATEMENT Alternating between two tasks is effortful and slows down performance. One possible explanation is that the representations in the human brain need time to build up and are thus weaker on switch trials, explaining performance costs. Alternatively, task representations might even be enhanced to overcome the previous task. Here, we used a combination of fMRI and a brain classifier to test whether the additional control demands under switching conditions lead to an increased or decreased strength of task representations in frontoparietal brain regions. We found that task representations are not modulated significantly by switching processes and generalize across switching conditions. Therefore, task representations in the human brain cannot account for the performance costs associated with alternating between tasks.

Encoding of sequence boundaries in the subthalamic nucleus of patients with Parkinson's disease.

Sequential behaviour is widespread not only in humans but also in animals, ranging in different degrees of complexity from locomotion to birdsong or music performance. The capacity to learn new motor sequences relies on the integrity of basal ganglia-cortical loops. In Parkinson's disease the execution of habitual action sequences as well as the acquisition of novel sequences is impaired partly due to a deficiency in being able to generate internal cues to trigger movement sequences. In addition, patients suffering from Parkinson's disease have difficulty initiating or terminating a self-paced sequence of actions. Direct recordings from the basal ganglia in these patients show an increased level of beta (14-30 Hz) band oscillatory activity associated with impairment in movement initiation. In this framework, the current study aims to evaluate in patients with Parkinson's disease the neuronal activity in the subthalamic nucleus related to the encoding of sequence boundaries during the explicit learning of sensorimotor sequences. We recorded local field potential activity from the subthalamic nucleus of 12 patients who underwent deep brain stimulation for the treatment of advanced Parkinson's disease, while the patients in their usual medicated state practiced sequences of finger movements on a digital piano with corresponding auditory feedback. Our results demonstrate that variability in performance during an early phase of sequence acquisition correlates across patients with changes in the pattern of subthalamic beta-band oscillations; specifically, an anticipatory suppression of beta-band activity at sequence boundaries is linked to better performance. By contrast, a more compromised performance is related to attenuation of beta-band activity before within-sequence elements. Moreover, multivariate pattern classification analysis reveals that differential information about boundaries and within-sequence elements can be decoded at least 100 ms before the keystroke from the amplitude of oscillations of subthalamic nucleus activity across different frequency bands, not just from the beta-band. Additional analysis was performed to assess the strength of how much the putative signal encoding class of ordinal position (boundaries, within-sequence elements) is reflected in each frequency band. This analysis demonstrates that suppression of power in the beta-band contains the most class-related information, whereas enhancement of gamma band (31-100 Hz) activity is the second main contributor to the encoding. Our findings support the hypothesis that subthalamic nucleus-mediated gating of salient boundary elements during sequence encoding may be a prerequisite for the adequate acquisition of action sequences and the transition to habitual behaviour.

Predict or classify: The deceptive role of time-locking in brain signal classification.

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

Bayesian selection of Markov models for symbol sequences: application to microsaccadic eye movements.

Complex biological dynamics often generate sequences of discrete events which can be described as a Markov process. The order of the underlying Markovian stochastic process is fundamental for characterizing statistical dependencies within sequences. As an example for this class of biological systems, we investigate the Markov order of sequences of microsaccadic eye movements from human observers. We calculate the integrated likelihood of a given sequence for various orders of the Markov process and use this in a Bayesian framework for statistical inference on the Markov order. Our analysis shows that data from most participants are best explained by a first-order Markov process. This is compatible with recent findings of a statistical coupling of subsequent microsaccade orientations. Our method might prove to be useful for a broad class of biological systems.

Effect of stochastic resonance on bone loss in osteopenic conditions.

We investigate the effect of noise on the remodelling process of the inner spongy part of the trabecular bone. Recently, a new noise-induced phenomenon in bone formation has been reported experimentally. We propose the first conceptual model for this finding, explained by the stochastic resonance effect, and provide a theoretical basis for the development of new countermeasures for bone degeneration in long space flights, which currently has dramatic consequences on return to standard gravity. These results may also be applicable on Earth for patients under osteopenic conditions.