Fast event-related mapping of fingertip population receptive fields in human somatosensory and motor cortex

The majority of fMRI studies investigating somatotopic body representations in the human cortex have used either block or phase-encoding stimulation designs. Event-related (ER) designs allow for more natural and flexible stimulation sequences, while enabling the independent estimation of responses to different body parts in the same cortical location. Here we compared an efficiency-optimized fast ER design (2s inter stimulus interval, ISI) to a slow ER design (8s ISI) for mapping fingertip voxelwise tuning properties in the sensorimotor cortex of 6 participants at 7 Tesla. The fast ER design resulted in similar, but more robust, estimates compared to the slow ER design. Concatenating the fast and slow ER data, we demonstrate in each individual brain the existence of two separate somatotopically-organized representations of the fingertips, one in S1 on the post-central gyrus and the other at the border of the motor and pre-motor cortices on the pre-central gyrus. In both post-central and pre-central representations, fingertip tuning width increases progressively, from narrowly-tuned Brodmann areas 3b and 4a respectively, towards parietal and frontal regions responding equally to all fingertips.

Reproducibility of knee extensor and flexor contraction velocity in healthy men and women assessed using tensiomyography: A study protocol

Tensiomyography measures the radial displacement of a muscle during an electrically evoked twitch contraction. The rate of muscle displacement is increasingly reported to assess contractile properties. Several formulas currently exist to calculate the rate of displacement during the contraction phase of the maximal twitch response. However, information on the reproducibility of these formulas is scarce. Further, different rest intervals ranging from 10 s to 30 s are applied between consecutive stimuli during progressive electrical stimulation until the maximum twitch response. The effect of different rest intervals on the rate of displacement has not been investigated so far. The first aim of this study is to investigate the within and between-day reliability of the most frequently used formulas to calculate the rate of displacement. The second aim is to investigate the effect of changing the inter-stimulus interval on the rate of displacement. We will determine the rectus femoris and biceps femoris rate of displacement of twenty-four healthy subjects’ dominant leg on two consecutive days. The maximum displacement curve will be determined two times within three minutes on the first day and a third time 24 h later. On day two, we will also apply three blocks of ten consecutive stimuli at a constant intensity of 50 mA. Inter-stimuli intervals will be 10 s, 20 s or 30 s in each block, respectively, and three minutes between blocks. The order of inter-stimulus intervals will be randomized. This study will allow a direct comparison between the five most frequently used formulas to calculate the rate of displacement in terms of their reproducibility. Our data will also inform on the effect of different inter-stimulus intervals on the rate of displacement. These results will provide helpful information on methodical considerations to determine the rate of displacement and may thus contribute to a standardized approach.

Testing the state-dependent model of time perception against experimental evidence

Coordinated movements, speech and other actions are impossible without precise timing. Realistic computational models of interval timing in the mammalian brain are expected to provide key insights into the underlying mechanisms of timing. Existing computational models of time perception have only been partially replicating experimental observations, such as the linear increase of time, the dopaminergic modulation of this increase, and the scalar property, i.e., the linear increase of the standard deviation of temporal estimates. In this work, we incorporate the state-dependent computational model, which encodes time in the dynamic evolution of network states without the need for a specific network structure into a biologically plausible prefrontal cortex (PFC) model based on in vivo and in vitro recordings of rodents. Specifically, we stimulated 1000 neurons in the beginning and in the end of a range of different time intervals, extracted states of neurons and trained the readout layer based on these states using least squares to predict the respective inter stimulus interval. We show that the naturally occurring heterogeneity in cellular and synaptic parameters in the PFC is sufficient to encode time over several hundreds of milliseconds. The readout faithfully represents the duration between two stimuli applied to the superficial layers of the network, thus fulfilling the requirement of a linear encoding of time. A simulated activation of the D2 dopamine receptor leads to an overestimation and an inactivation to an underestimation of time, in line with experimental results. Furthermore, we show that the scalar property holds true for intervals of several hundred milliseconds, and provide a mechanistic explanation for the origin of the scalar property as well as its deviations. We conclude that this model can represent durations up to 750 ms in a biophysically plausible setting, compatible with experimental findings in this regime.

On the reliability of behavioral measures of cognitive control: retest reliability of task-inhibition effect, task-preparation effect, Stroop-like interference, and conflict adaptation effect

This study examined the reliability (retest and split-half) of four common behavioral measures of cognitive control. In Experiment 1 (N = 96), we examined N – 2 task repetition costs as a marker of task-level inhibition, and the cue-stimulus interval (CSI) effect as a marker of time-based task preparation. In Experiment 2 (N = 48), we examined a Stroop-like face-name interference effect as a measure of distractor interference control, and the sequential congruency effect (“conflict adaptation effect”) as a measure of conflict-triggered adaptation of cognitive control. In both experiments, the measures were assessed in two sessions on the same day, separated by a 10 min-long unrelated filler task. We observed substantial experimental effects with medium to large effect sizes. At the same time, split-half reliabilities were moderate, and retest reliabilities were poor, for most measures, except for the CSI effect. Retest reliability of the Stroop-like effect was improved when considering only trials preceded by congruent trials. Together, the data suggest that these cognitive control measures are well suited for assessing group-level effects of cognitive control. Yet, except for the CSI effect, these measures do not seem suitable for reliably assessing interindividual differences in the strength of cognitive control, and therefore are not suited for correlational approaches. We discuss possible reasons for the discrepancy between robustness at the group level and reliability at the level of interindividual differences.

Handling EEG artifacts and searching individually optimal experimental parameter in real time: a system development and demonstration

Objective. Neuroadaptive paradigms that systematically assess ERP features across many different experimental parameters have the potential to improve the generalizability of ERP findings and may help to accelerate ERP-based biomarker discovery by identifying the exact experimental conditions for which ERPs differ most for a certain clinical population. Obtaining robust and reliable ERPs online is a prerequisite for ERP-based neuroadaptive research. One of the key steps involved is to correctly isolate EEG artifacts in real time because they contribute a large amount of variance that, if not removed, will greatly distort the ERP obtained. Another key factor of concern is the computational cost of the online artifact handling method. This work aims to develop and validate a cost-efficient system to support ERP-based neuroadaptive research. Approach. We developed a simple online artifact handling method, single trial PCA-based artifact removal (SPA), based on variance distribution dichotomies to distinguish between artifacts and neural activity. We then applied this method in an ERP-based neuroadaptive paradigm in which Bayesian optimization was used to search individually optimal inter-stimulus-interval (ISI) that generates ERP with the highest signal-to-noise ratio. Main results. SPA was compared to other offline and online algorithms. The results showed that SPA exhibited good performance in both computational efficiency and preservation of ERP pattern. Based on SPA, the Bayesian optimization procedure was able to quickly find individually optimal ISI. Significance. The current work presents a simple yet highly cost-efficient method that has been validated in its ability to extract ERP, preserve ERP effects, and better support ERP-based neuroadaptive paradigm.

Unbiased post-error slowing in interference tasks: A confound and a simple solution

We typically slow down after committing an error, an effect termed post-error slowing (PES). Traditionally, PES has been calculated by subtracting post-correct from post-error RTs. Dutilh et al. (Journal of Mathematical Psychology, 56(3), 208-216, 2012), however, showed PES values calculated in this way are potentially biased. Therefore, they proposed to compute robust PES scores by subtracting pre-error RTs from post-error RTs. Based on data from a large-scale study using the flanker task, we show that both traditional and robust PES estimates can be biased. The source of the bias are differential imbalances in the percentage of congruent vs. incongruent post-correct, pre-error, and post-error trials. Specifically, we found that post-correct, pre-error, and post-error trials were more likely to be congruent than incongruent, with the size of the imbalance depending on the trial type as well as the length of the response-stimulus interval (RSI). In our study, for trials preceded by a 700-ms RSI, the percentages of congruent trials were 62% for post-correct trials, 66% for pre-error trials, and 56% for post-error trials. Relative to unbiased estimates, these imbalances inflated traditional PES estimates by 37% (9 ms) and robust PES estimates by 42% (16 ms) when individual-participant means were calculated. When individual-participant medians were calculated, the biases were even more pronounced (40% and 50% inflation, respectively). To obtain unbiased PES scores for interference tasks, we propose to compute unweighted individual-participant means by initially calculating mean RTs for congruent and incongruent trials separately, before averaging congruent and incongruent mean RTs to calculate means for post-correct, pre-error and post-error trials.

Head-Restrained Lever Go/NoGo Visual Discrimination Task with Visiomode v2

This protocol is an adaptation of Michelle's lever Go/NoGo auditory discrimination task, which uses visual instead of auditory stimuli. Water-restricted, headplated mice learn to discriminate between a target and a distractor stimulus presented serially in pseudo-random order, pushing a lever to indicate when the target stimulus appears. The protocol is designed for across-learning recordings, and as such the inter-trial interval and stimulus interval remain constant throughout. Correction trials are also enabled throughout the duration of the protocol. The task in implemented in the Visiomode platform, using the lever apparatus for response input instead of the touchscreen. This protocol uses the Citric Acid Water Restriction protocol with sucrose rewards, instead of the traditional water deprivation protocol.

Using adaptive psychophysics to identify the neural network reset time in subsecond interval timing

State-dependent network models of sub-second interval timing propose that duration is encoded in states of neuronal populations that need to reset prior to a novel timing operation to maintain optimal timing performance. Previous research has shown that the approximate boundary of this reset interval can be inferred by varying the inter-stimulus interval between two to-be-timed intervals. However, the estimated boundary of this reset interval is broad (250–500 ms) and remains under-specified with implications for the characteristics of state-dependent network dynamics sub-serving interval timing. Here, we probed the interval specificity of this reset boundary by manipulating the inter-stimulus interval between standard and comparison intervals in two sub-second auditory duration discrimination tasks (100 and 200 ms) and a control (pitch) discrimination task using adaptive psychophysics. We found that discrimination thresholds improved with the introduction of a 333 ms inter-stimulus interval relative to a 250 ms inter-stimulus interval in both duration discrimination tasks, but not in the control task. This effect corroborates previous findings of a breakpoint in the discrimination performance for sub-second stimulus interval pairs as a function of an incremental inter-stimulus delay but more precisely localizes the minimal inter-stimulus delay range. These results suggest that state-dependent networks sub-serving sub-second timing require approximately 250–333 ms for the network to reset to maintain optimal interval timing.