[Methods for assessing aberrant and adaptive salience]. / Metody otsenki aberrantnoi i adaptivnoi salientnosti.

The term «salience¼ is most often used to describe «aberrant salience¼, which means assigning false significance to insignificant facts and details, that is inherent to patients with schizophrenia. Most often it is used in combination with «aberrant salience¼, which is understood as the assignment of false significance to insignificant facts and details. The term «adaptive salience¼ is less commonly used and means the «correct¼ assignment of the significance to important biological information. It is believed that in schizophrenia there is a decrease of adaptive salience in combination with an increase of aberrant salience. The concepts of aberrant and adaptive salience are a kind of link between the dopamine imbalance underlying the pathogenesis of schizophrenia and the diverse clinic of the disease. This article provides a review of the literature on methods for assessing, including quantitatively assessment, salience in schizophrenia. The comparison of these methods and their possible clinical and scientific application are provided.

Motor Decision-Making as a Common Denominator in Motor Pathology and a Possible Rehabilitation Target.

Despite the substantial progress in motor rehabilitation, patient involvement and motivation remain major challenges. They are typically addressed with communicational and environmental strategies, as well as with improved goal-setting procedures. Here we suggest a new research direction and framework involving Neuroeconomics principles to investigate the role of Motor Decision-Making (MDM) parameters in motivational component and motor performance in rehabilitation. We argue that investigating NE principles could bring new approaches aimed at increasing active patient engagement in the rehabilitation process by introducing more movement choice, and adapting existing goal-setting procedures. We discuss possible MDM implementation strategies and illustrate possible research directions using examples of stroke and psychiatric disorders.

Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings.

Instantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.

Cortical response variability is driven by local excitability changes with somatotopic organization.

Identical sensory stimuli can lead to different neural responses depending on the instantaneous brain state. Specifically, neural excitability in sensory areas may shape the brain´s response already from earliest cortical processing onwards. However, whether these dynamics affect a given sensory domain as a whole or occur on a spatially local level is largely unknown. We studied this in the somatosensory domain of 38 human participants with EEG, presenting stimuli to the median and tibial nerves alternatingly, and testing the co-variation of initial cortical responses in hand and foot areas, as well as their relation to pre-stimulus oscillatory states. We found that amplitude fluctuations of initial cortical responses to hand and foot stimulation - the N20 and P40 components of the somatosensory evoked potential (SEP), respectively - were not related, indicating local excitability changes in primary sensory regions. In addition, effects of pre-stimulus alpha (8-13 Hz) and beta (18-23 Hz) band amplitude on hand-related responses showed a robust somatotopic organization, thus further strengthening the notion of local excitability fluctuations. However, for foot-related responses, the spatial specificity of pre-stimulus effects was less consistent across frequency bands, with beta appearing to be more foot-specific than alpha. Connectivity analyses in source space suggested this to be due to a somatosensory alpha rhythm that is primarily driven by activity in hand regions while beta frequencies may operate in a more hand-region-independent manner. Altogether, our findings suggest spatially distinct excitability dynamics within the primary somatosensory cortex, yet with the caveat that frequency-specific processes in one sub-region may not readily generalize to other sub-regions.

Modulation of neural activity in frontopolar cortex drives reward-based motor learning.

The frontopolar cortex (FPC) contributes to tracking the reward of alternative choices during decision making, as well as their reliability. Whether this FPC function extends to reward gradients associated with continuous movements during motor learning remains unknown. We used anodal transcranial direct current stimulation (tDCS) over the right FPC to investigate its role in reward-based motor learning. Nineteen healthy human participants practiced novel sequences of finger movements on a digital piano with corresponding auditory feedback. Their aim was to use trialwise reward feedback to discover a hidden performance goal along a continuous dimension: timing. We additionally modulated the contralateral motor cortex (left M1) activity, and included a control sham stimulation. Right FPC-tDCS led to faster learning compared to lM1-tDCS and sham through regulation of motor variability. Bayesian computational modelling revealed that in all stimulation protocols, an increase in the trialwise expectation of reward was followed by greater exploitation, as shown previously. Yet, this association was weaker in lM1-tDCS suggesting a less efficient learning strategy. The effects of frontopolar stimulation were dissociated from those induced by lM1-tDCS and sham, as motor exploration was more sensitive to inferred changes in the reward tendency (volatility). The findings suggest that rFPC-tDCS increases the sensitivity of motor exploration to updates in reward volatility, accelerating reward-based motor learning.

Improving motor imagery classification during induced motor perturbations.

Objective.Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements.Approach.We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop.Main results.When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances.Significance.We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems.

Voluntary Inhibition of Physiological Mirror Activity: An EEG-EMG Study.

Physiological mirror activity (pMA), observed in healthy human adults, describes the involuntary co-activation of contralateral homologous muscles during unilateral limb movements. Here we provide novel evidence, using neuromuscular measurements (electromyography; EMG), that the amplitude of pMA can be voluntarily inhibited during unilateral isometric contractions of intrinsic hand muscles after informing human participants (10 male, 10 female) about its presence and establishing a basic understanding of pMA mechanisms through a standardized protocol. Importantly, significant suppression of pMA was observed immediately after participants were asked to inhibit it, despite the absence of any online feedback during task execution and without special training. Moreover, we observed that the decrease of pMA was specifically accompanied by an increase in relative frontal δ power recorded with electroencephalography (EEG). Correlation analysis further revealed an inverse association between the individual amplitude of pMA and frontal δ power that reached significance once participants started to inhibit. Taken together, these results suggest that δ power in frontal regions might reflect executive processes exerting inhibitory control over unintentional motor output, in this case pMA. Our results provide an initial reference point for the development of therapeutic applications related to the neurorehabilitation of involuntary movements which could be realized through the suppression of pMA observed in the elderly before it would fully manifest in undesirable overt movement patterns.

BOLD and EEG signal variability at rest differently relate to aging in the human brain.

Variability of neural activity is regarded as a crucial feature of healthy brain function, and several neuroimaging approaches have been employed to assess it noninvasively. Studies on the variability of both evoked brain response and spontaneous brain signals have shown remarkable changes with aging but it is unclear if the different measures of brain signal variability - identified with either hemodynamic or electrophysiological methods - reflect the same underlying physiology. In this study, we aimed to explore age differences of spontaneous brain signal variability with two different imaging modalities (EEG, fMRI) in healthy younger (25 â€‹± â€‹3 years, N â€‹= â€‹135) and older (67 â€‹± â€‹4 years, N â€‹= â€‹54) adults. Consistent with the previous studies, we found lower blood oxygenation level dependent (BOLD) variability in the older subjects as well as less signal variability in the amplitude of low-frequency oscillations (1-12 â€‹Hz), measured in source space. These age-related reductions were mostly observed in the areas that overlap with the default mode network. Moreover, age-related increases of variability in the amplitude of beta-band frequency EEG oscillations (15-25 â€‹Hz) were seen predominantly in temporal brain regions. There were significant sex differences in EEG signal variability in various brain regions while no significant sex differences were observed in BOLD signal variability. Bivariate and multivariate correlation analyses revealed no significant associations between EEG- and fMRI-based variability measures. In summary, we show that both BOLD and EEG signal variability reflect aging-related processes but are likely to be dominated by different physiological origins, which relate differentially to age and sex.

Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets.

Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.

Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation.

An important goal in Brain-Computer Interfacing (BCI) is to find and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) sensory threshold neuromuscular electrical stimulation during performance of motor imagery (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MI or BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition, we observed that the performance in MI condition could be predicted on the basis of a more pronounced connectivity within sensorimotor areas in the frequency bands providing the best performance in BOTH. These finding might offer a new avenue for training SMR-based BCI systems particularly for users having difficulties to achieve efficient BCI control. It might also be an alternative strategy for users who cannot perform real movements but still have remaining afferent pathways (e.g., ALS and stroke patients).

On optimal spatial filtering for the detection of phase coupling in multivariate neural recordings.

INTRODUCTION: Neuronal oscillations synchronize processing in the brain over large spatiotemporal scales and thereby facilitate integration of individual functional modules. Up to now, the relation between the phases of neuronal oscillations and behavior or perception has mainly been analyzed in sensor space of multivariate EEG/MEG recordings. However, sensor-space analysis distorts the topographies of the underlying neuronal sources and suffers from low signal-to-noise ratio. Instead, we propose an optimized source reconstruction approach (Phase Coupling Optimization, PCO). METHODS: PCO maximizes the 'mean vector length', calculated from the phases of recovered neuronal sources and a target variable of interest (e.g., experimental performance). As pre-processing, the signal-to-noise ratio in the search-space is maximized by spatio-spectral decomposition. PCO was benchmarked against several competing algorithms and sensor-space analysis using realistic forward model simulations. As a practical example, thirteen 96-channel EEG measurements during a simple reaction time task were analyzed. After time-frequency decomposition, PCO was applied to the EEG to examine the relation between the phases of pre-stimulus EEG activity and reaction times. RESULTS: In simulations, PCO outperformed other spatial optimization approaches and sensor-space analysis. Scalp topographies of the underlying source patterns and the relation between the phases of the source activity and the target variable could be reconstructed accurately even for very low SNRs (-10dB). In a simple reaction time experiment, the phases of pre-stimulus delta waves (<0.1Hz) with widely distributed fronto-parietal source topographies were found predictive of the reaction times. DISCUSSION AND CONCLUSIONS: From multivariate recordings, PCO can reconstruct neuronal sources that are phase-coupled to a target variable using a data-driven optimization approach. Its superiority has been shown in simulations and in the analysis of a simple reaction time experiment. From this data, we hypothesize that the phase entrainment of slow delta waves (<1Hz) facilitates sensorimotor integration in the brain and that this mechanism underlies the faster processing of anticipated stimuli. We further propose that the examined slow delta waves, observed to be phase-coupled to reaction times, correspond to the compound potentials typically observed in paradigms of stimulus anticipation and motor preparation.

Neural correlates of lexical decisions in Parkinson's disease revealed with multivariate extraction of cortico-subthalamic interactions.

OBJECTIVE: Neural interactions between cortex and basal ganglia are pivotal for sensorimotor processing. Specifically, coherency between cortex and subthalamic structures is a frequently studied phenomenon in patients with Parkinson's disease. However, it is unknown whether cortico-subthalamic coherency might also relate to cognitive aspects of task performance, e.g., language processing. Furthermore, standard coherency studies are challenged by how to efficiently handle multi-channel recordings. METHODS: In eight patients with Parkinson's disease treated with deep brain stimulation, simultaneous recordings of surface electroencephalography and deep local field potentials were obtained from bilateral subthalamic nuclei, during performing a lexical decision task. A recent multivariate coherency measure (maximized imaginary part of coherency, MIC) was applied, simultaneously accounting for multi-channel recordings. RESULTS: Cortico-subthalamic synchronization (MIC) in 14-35Hz oscillations positively correlated with accuracy in lexical decisions across patients, but not in 7-13Hz oscillations. In contrast to multivariate MIC, no significant correlation was obtained when extracting cortico-subthalamic synchronization by "standard" bivariate coherency. CONCLUSIONS: Cortico-subthalamic synchronization may relate to non-motor aspects of task performance, here reflected in lexical accuracy. SIGNIFICANCE: The results tentatively suggest the relevance of cortico-subthalamic interactions for lexical decisions. Multivariate coherency might be effective to extract neural synchronization from multi-channel recordings.

Correlation between cortical and subcortical neural dynamics on multiple time scales in Parkinson's disease.

Complex amplitude dynamics of dominant alpha oscillations (8-13 Hz) in the cortex can be captured with long-range temporal correlations (LRTC) in healthy subjects and in various diseases. In patients with Parkinson's disease (PD), intra-nuclear coherence was demonstrated in dominant beta rhythms (10-30 Hz) in the basal ganglia. However, so far the relation between cortical LRTC (across tens of seconds) and subcortical coherence (millisecond scale) is unknown. We addressed these "multiscale interactions" by simultaneous recordings of surface electroencephalography (EEG) and deep local field potentials (LFP) from the bilateral subthalamic nucleus (STN) in eight patients with severe PD eligible for deep brain stimulation, who performed a lexical decision task on medication. In the continuous data set LRTC up to 20s were calculated in the amplitude envelope of 8-13-Hz EEG oscillations (across whole scalp), and subcortical coherence was assessed with measures being insensitive to volume conduction artifacts (imaginary part of coherency; iCOH) in 10-20 and 21-30-Hz oscillations in STN-LFP. We showed a significant positive correlation across patients between cortical LRTC (8-13Hz) and subcortical iCOH selectively in 10-20-Hz oscillations in the left STN. Our results suggest a relation between neural dynamics in the most dominant rhythms in the cortex and basal ganglia in PD, extending across multiple time scales (milliseconds vs. tens of seconds). Furthermore, the investigation of multiscale interactions might contribute to our understanding of cortical-subcortical neural coupling in PD.

Voluntary control of corticomuscular coherence through neurofeedback: a proof-of-principle study in healthy subjects.

Corticomuscular coherence (CMC) relates to synchronization between activity in the motor cortex and the muscle activity. The strength of CMC can be affected by motor behavior. In a proof-of-principle study, we examined whether independent of motor output parameters, healthy subjects are able to voluntarily modulate CMC in a neurofeedback paradigm. Subjects received visual online feedback of their instantaneous CMC strength, which was calculated between an optimized spatial projection of multichannel electroencephalography (EEG) and electromyography (EMG) in an individually defined target frequency range. The neurofeedback training consisted of either increasing or decreasing CMC strength using a self-chosen mental strategy while performing a simple motor task. Evaluation of instantaneous coherence showed that CMC strength was significantly larger when subjects had to increase than when to decrease CMC; this difference between the two task conditions did not depend on motor performance. The exclusion of confounding factors such as motor performance, attention and task complexity in study design provides evidence that subjects were able to voluntarily modify CMC independent of motor output parameters. Additional analysis further strengthened the assumption that the subjects' response was specifically shaped by the neurofeedback. In perspective, we suggest that CMC-based neurofeedback could provide a therapeutic approach in clinical conditions, such as motor stroke, where CMC is altered.

Interhemispheric functional interactions between the subthalamic nuclei of patients with Parkinson's disease.

Parkinson's disease (PD) is characterized by widespread neural interactions in cortico-basal-ganglia networks primarily in beta oscillations (approx. 10-30 Hz), as suggested by previous findings of levodopa-modulated interhemispheric coherence between the bilateral subthalamic nuclei (STN) in local field potential recordings (LFPs). However, due to confounding effects of volume conduction the existence of 'genuine' interhemispheric subcortical coherence remains an open question. To address this issue we utilized the imaginary part of coherency (iCOH) which, in contrast to the standard coherence, is not susceptible to volume conduction. LFPs were recorded from eight patients with PD during wakeful rest before and after levodopa administration. We demonstrated genuine coherence between the bilateral STN in both 10-20 and 21-30 Hz oscillations, as revealed by a non-zero iCOH. Crucially, increased iCOH in 10-20 Hz oscillations positively correlated with the worsening of motor symptoms in the OFF medication condition across patients, which was not the case for standard coherence. Furthermore, across patients iCOH was increased after levodopa administration in 21-30 Hz oscillations. These results suggest a functional distinction between low and high beta oscillations in STN-LFP in line with previous studies. Furthermore, the observed functional coupling between the bilateral STN might contribute to the understanding of bilateral effects of unilateral deep brain stimulation. In conclusion, the present results imply a significant contribution of time-delayed neural interactions to interhemispheric coherence, and the clinical relevance of long-distance neural interactions between bilateral STN for motor symptoms in PD.

Distinction between added-energy and phase-resetting mechanisms in non-invasively detected somatosensory evoked responses.

Non-invasively recorded averaged event-related potentials (ERP) represent a convenient opportunity to investigate human brain perceptive and cognitive processes. Nevertheless, generative ERP mechanisms are still debated. Two previous approaches have been contested in the past: the added-energy model in which the response raises independently from the ongoing background activity, and the phase-reset model, based on stimulus-driven synchronization of oscillatory ongoing activity. Many criteria for the distinction of these two models have been proposed, but there is no definitive methodology to disentangle them, owing also to the limited information at the single trial level. Here, we propose a new approach combining low-noise EEG technology and multivariate decomposition techniques. We present theoretical analyses based on simulated data and identify in high-frequency somatosensory evoked responses an optimal target for the distinction between the two mechanisms.

Functional and effective connectivity in subthalamic local field potential recordings of patients with Parkinson's disease.

In Parkinson's disease (PD) levodopa-associated changes in the power and long-range temporal correlations of beta oscillations have been demonstrated, yet the presence and modulation of genuine connectivity in local field potentials (LFP) recorded from the subthalamic nucleus (STN) remains an open question. The present study investigated LFP recorded bilaterally from the STN at wakeful rest in ten patients with PD after overnight withdrawal of levodopa (OFF) and after a single dose levodopa administration (ON). We utilized connectivity measures being insensitive to volume conduction (functional connectivity: non-zero imaginary part of coherency; effective connectivity: phase-slope index). We demonstrated the presence of neuronal interactions in the frequency range of 10-30 Hz in STN-LFP without a preferential directionality of interactions between different contacts along the electrode tracks. While the direction of neuronal interactions per se was preserved after levodopa administration, functional connectivity and the ventral-dorsal information flow were modulated by medication. The OFF-ON differences in functional connectivity were correlated with the levodopa-induced improvement in clinical Unified Parkinson's Disease Rating Scale scores. We hypothesize that regional neuronal interactions, as reflected in STN-LFP connectivity, might represent a basis for the intra-nuclear spatial specificity of deep brain stimulation. Moreover, our results suggest the potential use of volume conduction-insensitive measures of connectivity in STN-LFP as a marker of clinical motor symptoms in PD.

Long-range temporal correlations in the subthalamic nucleus of patients with Parkinson's disease.

Neuronal activity in the subthalamic nucleus (STN) of patients with Parkinson's disease (PD) is characterised by excessive neuronal synchronization, particularly in the beta frequency range. However, less is known about the temporal dynamics of neuronal oscillations in PD. In this respect long-range temporal correlations (LRTC) are of special interest as they quantify the neuronal dynamics on different timescales and have been shown to be relevant for optimal information processing in the brain. While the presence of LRTC has been demonstrated in cortical data, their existence in deep brain structures remains an open question. We investigated (i) whether LRTC are present in local field potentials (LFP) recorded bilaterally from the STN at wakeful rest in ten patients with PD after overnight withdrawal of levodopa (OFF) and (ii) whether LRTC can be modulated by levodopa treatment (ON). Detrended fluctuation analysis was utilised in order to quantify the temporal dynamics in the amplitude fluctuations of LFP oscillations. We demonstrated for the first time the presence of LRTC (extending up to 50 s) in the STN. Importantly, the ON state was characterised by significantly stronger LRTC than the OFF state, both in beta (13-35 Hz) and high-frequency (> 200 Hz) oscillations. The existence of LRTC in subcortical structures such as STN provides further evidence for their ubiquitous nature in the brain. The weaker LRTC in the OFF state might indicate limited information processing in the dopamine-depleted basal ganglia. The present results implicate LRTC as a potential biomarker of pathological neuronal processes in PD.

Anticipatory activity in the human thalamus is predictive of reaction times.

Responding to environmental stimuli in a fast manner is a fundamental behavioral capacity. The pace at which one responds is known to be predetermined by cortical areas, but it remains to be shown if subcortical structures also take part in defining motor swiftness. As the thalamus has previously been implicated in behavioral control, we tested if neuronal activity at this level could also predict the reaction time of upcoming movements. To this end we simultaneously recorded electrical brain activity from the scalp and the ventral intermediate nucleus (VIM) of the thalamus in patients undergoing thalamic deep brain stimulation. Based on trial-to-trial analysis of a Go/NoGo task, we demonstrate that both cortical and thalamic neuronal activity prior to the delivery of upcoming Go stimulus correlates with the reaction time. This result goes beyond the demonstration of thalamic activity being associated with but potentially staying invariant to motor performance. In contrast, it indicates that the latencies at which we respond to environmental stimuli are not exclusively related to cortical pre-movement states but are also correlated with anticipatory thalamic activity.