A Single Mechanism for Global and Selective Response Inhibition under the Influence of Motor Preparation.

In our everyday behavior, we frequently cancel one movement while continuing others. Two competing models have been suggested for the cancellation of such specific actions: (1) the abrupt engagement of a unitary global inhibitory mechanism followed by reinitiation of the continuing actions; or (2) a balance between distinct global and selective inhibitory mechanisms. To evaluate these models, we examined behavioral and physiological markers of proactive control, motor preparation, and response inhibition using a combination of behavioral task performance measures, electromyography, electroencephalography, and motor evoked potentials elicited with transcranial magnetic stimulation. Healthy human participants of either sex performed two versions of a stop signal task with cues incorporating proactive control: a unimanual task involving the initiation and inhibition of a single response, and a bimanual task involving the selective stopping of one of two prepared responses. Stopping latencies, motor evoked potentials, and frontal ß power (13-20 Hz) did not differ between the unimanual and bimanual tasks. However, evidence for selective proactive control before stopping was manifest in the bimanual condition as changes in corticomotor excitability, µ (9-14 Hz), and ß (15-25 Hz) oscillations over sensorimotor cortex. Together, our results favor the recruitment of a single inhibitory stopping mechanism with the net behavioral output depending on the levels of action-specific motor preparation.SIGNIFICANCE STATEMENT Response inhibition is a core function of cognitive flexibility and movement control. Previous research has suggested separate mechanisms for selective and global inhibition, yet the evidence is inconclusive. Another line of research has examined the influence of preparation for action stopping, or what is called proactive control, on stopping performance, yet the neural mechanisms underlying this interaction are unknown. We combined transcranial magnetic stimulation, electroencephalography, electromyography, and behavioral measures to compare selective and global inhibition models and to investigate markers of proactive control. The results favor a single inhibitory mechanism over separate selective and global mechanisms but indicate a vital role for preceding motor activity in determining whether and which actions will be stopped.

Frontal-midline theta reflects different mechanisms associated with proactive and reactive control of inhibition.

Reactive control of response inhibition is associated with a right-lateralised cortical network, as well as frontal-midline theta (FM-theta) activity measured at the scalp. However, response inhibition is also governed by proactive control processes, and how such proactive control is reflected in FM-theta activity and associated neural source activity remains unclear. To investigate this, simultaneous recordings of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data was performed while participants performed a cued stop-signal task. The cues (0%, 25% or 66%) indicated the likelihood of an upcoming stop-signal in the following trial. Results indicated that participants adjusted their behaviour proactively, with increasing go-trial reaction times following increasing stop-signal probability, as well as modulations of both go-trial and stop-trial accuracies. Target-locked theta activity was higher in stop-trials than go-trials and modulated by probability. At the single-trial level, cue-locked theta was associated with shorter reaction-times, while target-locked theta was associated with both faster reaction times and higher probability of an unsuccessful stop-trial. This dissociation was also evident at the neural source level, where a joint ICA revealed independent components related to going, stopping and proactive preparation. Overall, the results indicate that FM-theta activity can be dissociated into several mechanisms associated with proactive control, response initiation and response inhibition processes. We propose that FM-theta activity reflects both heightened preparation of the motor control network, as well as stopping-related processes associated with a right lateralized cortical network.

Differences in unity: The go/no-go and stop signal tasks rely on different mechanisms.

Response inhibition refers to the suppression of prepared or initiated actions. Typically, the go/no-go task (GNGT) or the stop signal task (SST) are used interchangeably to capture individual differences in response inhibition. On the one hand, factor analytic and conjunction neuroimaging studies support the association of both tasks with a single inhibition construct. On the other hand, studies that directly compare the two tasks indicate distinct mechanisms, corresponding to action restraint and cancellation in the GNGT and SST, respectively. We addressed these contradictory findings with the aim to identify the core differences in the temporal dynamics of the functional networks that are recruited in both tasks. We extracted the time-courses of sensory, motor, attentional, and cognitive control networks by group independent component (G-ICA) analysis of electroencephalography (EEG) data from both tasks. Additionally, electromyography (EMG) from the responding effector muscles was recorded to detect the timing of response inhibition. The results indicated that inhibitory performance in the GNGT may be comparable to response selection mechanisms, reaching peripheral muscles at around 316 â€‹ms. In contrast, inhibitory performance in the SST is achieved via biasing of the sensorimotor system in preparation for stopping, followed by fast sensory, motor and frontal integration during outright stopping. Inhibition can be detected at the peripheral level at 140 â€‹ms after stop stimulus presentation. The GNGT and the SST therefore seem to recruit widely different neural dynamics, implying that the interchangeable use of superficially similar inhibition tasks in both basic and clinical research is unwarranted.

A Tutorial Review on Multi-subject Decomposition of EEG.

Over the last years we saw a steady increase in the relevance of big neuroscience data sets, and with it grew the need for analysis tools capable of handling such large data sets while simultaneously extracting properties of brain activity that generalize across subjects. For functional magnetic resonance imaging, multi-subject or group-level independent component analysis provided a data-driven approach to extract intrinsic functional networks, such as the default mode network. Meanwhile, this methodological framework has been adapted for the analysis of electroencephalography (EEG) data. Here, we provide an overview of the currently available approaches for multi-subject data decomposition as applied to EEG, and highlight the characteristics of EEG that warrant special consideration. We further illustrate the importance of matching one's choice of method to the data characteristics at hand by guiding the reader through a set of simulations. In sum, algorithms for group-level decomposition of EEG provide an innovative and powerful tool to study the richness of functional brain networks in multi-subject EEG data sets.

Filling the void-enriching the feature space of successful stopping.

The ability to inhibit behavior is crucial for adaptation in a fast changing environment and is commonly studied with the stop signal task. Current EEG research mainly focuses on the N200 and P300 ERPs and corresponding activity in the theta and delta frequency range, thereby leaving us with a limited understanding of the mechanisms of response inhibition. Here, 15 functional networks were estimated from time-frequency transformed EEG recorded during processing of a visual stop signal task. Cortical sources underlying these functional networks were reconstructed, and a total of 45 features, each representing spectrally and temporally coherent activity, were extracted to train a classifier to differentiate between go and stop trials. A classification accuracy of 85.55% for go and 83.85% for stop trials was achieved. Features capturing fronto-central delta- and theta activity, parieto-occipital alpha, fronto-central as well as right frontal beta activity were highly discriminating between trial-types. However, only a single network, comprising a feature defined by oscillatory activity below 12 Hz, was associated with a generator in the opercular region of the right inferior frontal cortex and showed the expected associations with behavioral inhibition performance. This study pioneers by providing a detailed ranking of neural features regarding their information content for stop and go differentiation at the single-trial level, and may further be the first to identify a scalp EEG marker of the inhibitory control network. This analysis allows for the characterization of the temporal dynamics of response inhibition by matching electrophysiological phenomena to cortical generators and behavioral inhibition performance. Hum Brain Mapp 38:1333-1346, 2017. © 2016 Wiley Periodicals, Inc.

The Temporal Dynamics of Response Inhibition and their Modulation by Cognitive Control.

Behavioral adjustments require interactions between distinct modes of cognitive control and response inhibition. Hypothetically, fast and global inhibition is exerted in the reactive control mode, whereas proactive control enables the preparation of inhibitory pathways in advance while relying on the slower selective inhibitory system. We compared the temporal progression of inhibition in the reactive and proactive control modes using simultaneous electroencephalography (EEG) and electromyography (EMG) recordings. A selective stop signal task was used where go stimuli required bimanual responses, but only one hand's response had to be suppressed in stop trials. Reactive and proactive conditions were incorporated by non-informative and informative cues, respectively. In 47% of successful stop trials, subthreshold EMG activity was detected that was interrupted as early as 150 ms after stop stimulus presentation, indicating that inhibition occurs much earlier than previously thought. Inhibition latencies were similar across the reactive and proactive control modes. The EMG of the responding hand in successful selective stop trials indicated a global suppression of ongoing motor actions in the reactive condition, and less inhibitory interference on the ongoing actions in the proactive condition. Group-level second order blind separation (SOBI) was applied to the EEG to dissociate temporally overlapping event-related potentials. The components capturing the N1 and N2 were larger in the reactive than the proactive condition. P3 activity was distributed across four components, three of which were augmented in the proactive condition. Thus, although EEG indices were modulated by the control mode, the inhibition latency remained unaffected.

BOLD signal effects of transcranial alternating current stimulation (tACS) in the alpha range: A concurrent tACS-fMRI study.

Many studies have proven transcranial alternating current stimulation (tACS) to manipulate brain activity. Until now it is not known, however, how these manipulations in brain activity are represented in brain metabolism or how spatially specific these changes are. Alpha-tACS has been shown to enhance the amplitude of the individual alpha frequency (IAF) and a negative correlation between alpha amplitude and occipital BOLD signal was reported in numerous EEG/fMRI experiments. Thus, alpha-tACS was chosen to test the effects of tACS on the BOLD signal. A reduction thereof was expected during alpha-tACS which shows the spatial extent of tACS effects beyond modeling studies. Three groups of subjects were measured in an MRI scanner, receiving tACS at either their IAF (N=11), 1Hz (control; N=12) or sham (i.e., no stimulation - a second control; N=11) while responding to a visual vigilance task. Stimulation was administered in an interleaved pattern of tACS-on runs and tACS-free baseline periods. The BOLD signal was analyzed in response to tACS-onset during resting state and in response to seldom target stimuli. Alpha-tACS at 1.0mA reduced the task-related BOLD response to visual targets in the occipital cortex as compared to tACS-free baseline periods. The deactivation was strongest in an area where the BOLD signal was shown to correlate negatively with alpha amplitude. A direct effect of tACS on resting state BOLD signal levels could not be shown. Our findings suggest that tACS-related changes in BOLD activity occur only as a modulation of an existing BOLD response.

Mapping the spatiotemporal dynamics of processing task-relevant and task-irrelevant sound feature changes using concurrent EEG-fMRI.

The cortical processing of changes in auditory input involves auditory sensory regions as well as different frontoparietal brain networks. The spatiotemporal dynamics of the activation spread across these networks has, however, not been investigated in detail so far. We here approached this issue using concurrent functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), providing us with simultaneous information on both the spatial and temporal patterns of change-related activity. We applied an auditory stimulus categorization task with switching categorization rules, allowing to analyze change-related responses as a function of the changing sound feature (pitch or duration) and the task relevance of the change. Our data show the successive progression of change-related activity from regions involved in early change detection to the ventral and dorsal attention networks, and finally the central executive network. While early change detection was found to recruit feature-specific networks involving auditory sensory but also frontal and parietal brain regions, the later spread of activity across the frontoparietal attention and executive networks was largely independent of the changing sound feature, suggesting the existence of a general feature-independent processing pathway of change-related information. Task relevance did not modulate early auditory sensory processing, but was mainly found to affect processing in frontal brain regions. Hum Brain Mapp 37:3400-3416, 2016. © 2016 Wiley Periodicals, Inc.

Functional and effective connectivity of stopping.

Behavioral inhibition often is studied by comparing the electroencephalographic responses to stop and to go signals. Most studies simply assess amplitude differences of the N200 and P300 event-related potentials, which seem to best correspond to increased activity in the theta and delta frequency bands, respectively. However, neither have reliable indicators for successful behavioral inhibition been identified nor have the causal dependencies of stop-related neurocognitive processes been addressed yet. By studying functional and effective connectivity underlying stopping behavior, this study opens new directions for the investigation of behavioral inhibition. Group independent component analysis was used to infer functionally coherent networks from electroencephalographic data, which were recorded from healthy human participants during processing of a stop signal task. Then, the temporal dynamics of causal dependencies between independent components were identified by means of Bayesian network estimations. The mean clustering coefficient and the characteristic path length measure indicated time windows between 130 and 180 ms and between 420 and 500 ms to express significantly different connectivity profiles between conditions. Three components showed significant correlations between 120 and 260 ms with stop signal reaction times and the number of failed stops. Two of these components acted as sources of causal flow, one capturing P300/delta characteristics while the other was characterized by alpha power depletion putatively representing the evaluation or processing of stimulus features. Although results suggest that the P300 and associated delta activity seem to be statistically dependent on earlier processes associated with behavioral inhibition, the time window critical for inhibition coincides with early changes in causal patterns and largely precedes peak amplitude differences between go and stop trials. Altogether, utilizing the analysis of stopping-related connectivity, previously undetected patterns emerged that warrant further investigation.

High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia.

Identifying the complex activity relationships present in rich, modern neuroimaging data sets remains a key challenge for neuroscience. The problem is hard because (a) the underlying spatial and temporal networks may be nonlinear and multivariate and (b) the observed data may be driven by numerous latent factors. Further, modern experiments often produce data sets containing multiple stimulus contexts or tasks processed by the same subjects. Fusing such multi-session data sets may reveal additional structure, but raises further statistical challenges. We present a novel analysis method for extracting complex activity networks from such multifaceted imaging data sets. Compared to previous methods, we choose a new point in the trade-off space, sacrificing detailed generative probability models and explicit latent variable inference in order to achieve robust estimation of multivariate, nonlinear group factors ("network clusters"). We apply our method to identify relationships of task-specific intrinsic networks in schizophrenia patients and control subjects from a large fMRI study. After identifying network-clusters characterized by within- and between-task interactions, we find significant differences between patient and control groups in interaction strength among networks. Our results are consistent with known findings of brain regions exhibiting deviations in schizophrenic patients. However, we also find high-order, nonlinear interactions that discriminate groups but that are not detected by linear, pairwise methods. We additionally identify high-order relationships that provide new insights into schizophrenia but that have not been found by traditional univariate or second-order methods. Overall, our approach can identify key relationships that are missed by existing analysis methods, without losing the ability to find relationships that are known to be important.

Corticospinal excitability reductions during action preparation and action stopping in humans: Different sides of the same inhibitory coin?

Motor functions and cognitive processes are closely associated with each other. In humans, this linkage is reflected in motor system state changes both when an action must be prepared and stopped. Single-pulse transcranial magnetic stimulation showed that both action preparation and action stopping are accompanied by a reduction of corticospinal excitability, referred to as preparatory and response inhibition, respectively. While previous efforts have been made to describe both phenomena extensively, an updated and comprehensive comparison of the two phenomena is lacking. To ameliorate such deficit, this review focuses on the role and interpretation of single-coil (single-pulse and paired-pulse) and dual-coil TMS outcome measures during action preparation and action stopping in humans. To that effect, it aims to identify commonalities and differences, detailing how TMS-based outcome measures are affected by states, traits, and psychopathologies in both processes. Eventually, findings will be compared, and open questions will be addressed to aid future research.

On the (un)reliability of common behavioral and electrophysiological measures from the stop signal task: Measures of inhibition lack stability over time.

Response inhibition, the intentional stopping of planned or initiated actions, is often considered a key facet of control, impulsivity, and self-regulation. The stop signal task is argued to be the purest inhibition task we have, and it is thus central to much work investigating the role of inhibition in areas like development and psychopathology. Most of this work quantifies stopping behavior by calculating the stop signal reaction time as a measure of individual stopping latency. Individual difference studies aiming to investigate why and how stopping latencies differ between people often do this under the assumption that the stop signal reaction time indexes a stable, dispositional trait. However, empirical support for this assumption is lacking, as common measures of inhibition and control tend to show low test-retest reliability and thus appear unstable over time. The reasons for this could be methodological, where low stability is driven by measurement noise, or substantive, where low stability is driven by a larger influence of state-like and situational factors. To investigate this, we characterized the split-half and test-retest reliability of a range of common behavioral and electrophysiological measures derived from the stop signal task. Across three independent studies, different measurement modalities, and a systematic review of the literature, we found a pattern of low temporal stability for inhibition measures and higher stability for measures of manifest behavior and non-inhibitory processing. This pattern could not be explained by measurement noise and low internal consistency. Consequently, response inhibition appears to have mostly state-like and situational determinants, and there is little support for the validity of conceptualizing common inhibition measures as reflecting stable traits.

Assessing the Longitudinal Associations Between Decision-Making Processes and Attention Problems in Early Adolescence.

Cognitive functions and psychopathology develop in parallel in childhood and adolescence, but the temporal dynamics of their associations are poorly understood. The present study sought to elucidate the intertwined development of decision-making processes and attention problems using longitudinal data from late childhood (9-10 years) to mid-adolescence (11-13 years) from the Adolescent Brain Cognitive Development (ABCD) Study (n = 8918). We utilised hierarchical drift-diffusion modelling of behavioural data from the stop-signal task, parent-reported attention problems from the Child Behavior Checklist (CBCL), and multigroup univariate and bivariate latent change score models. The results showed faster drift rate was associated with lower levels of inattention at baseline, as well as a greater reduction of inattention over time. Moreover, baseline drift rate negatively predicted change in attention problems in females, and baseline attention problems negatively predicted change in drift rate. Neither response caution (decision threshold) nor encoding- and responding processes (non-decision time) were significantly associated with attention problems. There were no significant sex differences in the associations between decision-making processes and attention problems. The study supports previous findings of reduced evidence accumulation in attention problems and additionally shows that development of this aspect of decision-making plays a role in developmental changes in attention problems in youth.

Methods for simultaneous EEG-fMRI: an introductory review.

The simultaneous recording and analysis of electroencephalography (EEG) and fMRI data in human systems, cognitive and clinical neurosciences is rapidly evolving and has received substantial attention. The significance of multimodal brain imaging is documented by a steadily increasing number of laboratories now using simultaneous EEG-fMRI aiming to achieve both high temporal and spatial resolution of human brain function. Due to recent developments in technical and algorithmic instrumentation, the rate-limiting step in multimodal studies has shifted from data acquisition to analytic aspects. Here, we introduce and compare different methods for data integration and identify the benefits that come with each approach, guiding the reader toward an understanding and informed selection of the integration approach most suitable for addressing a particular research question.

Functional parcellation of the inferior frontal and midcingulate cortices in a flanker-stop-change paradigm.

Conflict monitoring and motor inhibition are engaged in the performance of complex tasks. The midcingulate cortex (MCC) has been suggested to detect conflicts, whereas the right inferior frontal cortex (IFC) seems to be of relevance for the inhibition process. The current experiment investigates the neural underpinnings of their interplay via a modified flanker paradigm. Conflict was manipulated by the congruency of flanking stimuli relative to a target (congruent vs. incongruent) and motor inhibition by a within-trial response change of the initiated response (keep response vs. stop-change). We used event-related functional magnetic resonance imaging, decomposition with high model order ICA, and single trial analysis to derive a functional parcellation of the whole-brain data. Results demonstrate the segmentation of the MCC into anterior and posterior subregions, and of the IFC into the pars opercularis, pars triangularis, and pars orbitalis. The pars opercularis and pars triangularis of the right IFC constituted the foundation of inhibition-related networks. With high conflict on incongruent trials, activity in the posterior MCC network, as well as in one right IFC network was observed. Stop-change trials modulated both the MCC as well as networks covering extended parts of the IFC. Whereas conflict processing and inhibition most often are studied separately, this study provides a synopsis of functionally coupled brain regions acting in concert to enable an optimal performance in situations involving interference and inhibition.

Long-term improvements in executive functions after frontal-midline theta neurofeedback in a (sub)clinical group.

Impairments in executive functions (EFs) are common across disorders and can greatly affect daily functioning. Frontal-midline (FM) theta neurofeedback (NF) has been shown effective in enhancing EFs in healthy adults, prompting interest in exploring its potential as an alternative treatment for EFs in (sub)clinical samples. This study aims to determine the effects of FM theta NF on EFs in a sample of 58 adults (aged 20-60 years) with pronounced subjective EF complaints in daily life. Using a pre/post/follow-up design with a sham NF group, the present study assessed upregulation of FM theta in an eight-session individualized FM theta NF training and its immediate and long-term transfer effects on objective and subjective measures of EFs. These included behavioral performance on EF tasks assessing working memory updating (N-back task), set-shifting (Switching task), conflict monitoring (Stroop task), and response inhibition (Stop-signal task), as well as FM theta power during these tasks, and subjective EFs in daily life (BRIEF-A). The results indicate that there are only differences in FM theta self-upregulation between the NF group and sham group when non-responders are excluded from the analysis. Regarding behavioral transfer effects, NF-specific improvements are found in working memory updating reaction time (RT) and conflict monitoring RT variability at 6-month follow-up, but not immediately after the NF training. The effects on FM theta power during the EF tasks and subjective changes in EFs in daily life were not specific to the NF training. As a next step, research should identify the best predictors to stratify NF training, as well as explore ways to improve NF responsiveness, for instance by increasing neuroplasticity.

Reaction Time Variability in Children Is Specifically Associated With Attention Problems and Regional White Matter Microstructure.

BACKGROUND: Increased intraindividual variability (IIV) in reaction times (RTs) has been suggested as a key cognitive and behavioral marker of attention problems, but findings for other dimensions of psychopathology are less consistent. Moreover, while studies have linked IIV to brain white matter microstructure, large studies testing the robustness of these associations are needed. METHODS: We used data from the Adolescent Brain Cognitive Development (ABCD) Study baseline assessment to test the associations between IIV and psychopathology (n = 8622, age = 8.9-11.1 years) and IIV and white matter microstructure (n = 7958, age = 8.9-11.1 years). IIV was investigated using an ex-Gaussian distribution analysis of RTs in correct response go trials in the stop signal task. Psychopathology was measured by the Child Behavior Checklist and a bifactor structural equation model was performed to extract a general p factor and specific factors reflecting internalizing, externalizing, and attention problems. To investigate white matter microstructure, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity were examined in 23 atlas-based tracts. RESULTS: Increased IIV in both short and long RTs was positively associated with the specific attention problems factor (Cohen's d = 0.13 and d = 0.15, respectively). Increased IIV in long RTs was also positively associated with radial diffusivity in the left and right corticospinal tract (both tracts, d = 0.12). CONCLUSIONS: Using a large sample and a data-driven dimensional approach to psychopathology, the results provide novel evidence for a small but specific association between IIV and attention problems in children and support previous findings on the relevance of white matter microstructure for IIV.

Auditory event-related response in visual cortex modulates subsequent visual responses in humans.

Growing evidence from electrophysiological data in animal and human studies suggests that multisensory interaction is not exclusively a higher-order process, but also takes place in primary sensory cortices. Such early multisensory interaction is thought to be mediated by means of phase resetting. The presentation of a stimulus to one sensory modality resets the phase of ongoing oscillations in another modality such that processing in the latter modality is modulated. In humans, evidence for such a mechanism is still sparse. In the current study, the influence of an auditory stimulus on visual processing was investigated by measuring the electroencephalogram (EEG) and behavioral responses of humans to visual, auditory, and audiovisual stimulation with varying stimulus-onset asynchrony (SOA). We observed three distinct oscillatory EEG responses in our data. An initial gamma-band response around 50 Hz was followed by a beta-band response around 25 Hz, and a theta response around 6 Hz. The latter was enhanced in response to cross-modal stimuli as compared to either unimodal stimuli. Interestingly, the beta response to unimodal auditory stimuli was dominant in electrodes over visual areas. The SOA between auditory and visual stimuli--albeit not consciously perceived--had a modulatory impact on the multisensory evoked beta-band responses; i.e., the amplitude depended on SOA in a sinusoidal fashion, suggesting a phase reset. These findings further support the notion that parameters of brain oscillations such as amplitude and phase are essential predictors of subsequent brain responses and might be one of the mechanisms underlying multisensory integration.

Partial response electromyography as a marker of action stopping.

Response inhibition is among the core constructs of cognitive control. It is notoriously difficult to quantify from overt behavior, since the outcome of successful inhibition is the lack of a behavioral response. Currently, the most common measure of action stopping, and by proxy response inhibition, is the model-based stop signal reaction time (SSRT) derived from the stop signal task. Recently, partial response electromyography (prEMG) has been introduced as a complementary physiological measure to capture individual stopping latencies. PrEMG refers to muscle activity initiated by the go signal that plummets after the stop signal before its accumulation to a full response. Whereas neither the SSRT nor the prEMG is an unambiguous marker for neural processes underlying response inhibition, our analysis indicates that the prEMG peak latency is better suited to investigate brain mechanisms of action stopping. This study is a methodological resource with a comprehensive overview of the psychometric properties of the prEMG in a stop signal task, and further provides practical tips for data collection and analysis.