Coherence of sequential movements and motor learning

The effect of inherent and incidental constraints on bimanual force control in simulated Martian gravity

The ability to coordinate actions between the limbs is important for many operationally relevant tasks associated with space exploration. A future milestone in space exploration is sending humans to Mars. Therefore, an experiment was designed to examine the influence of inherent and incidental constraints on the stability characteristics associated with the bimanual control of force in simulated Martian gravity. A head-up tilt (HUT)/head-down tilt (HDT) paradigm was used to simulate gravity on Mars (22.3° HUT). Right limb dominant participants (N = 11) were required to rhythmically coordinate patterns of isometric forces in 1:1 in-phase and 1:2 multifrequency patterns by exerting force with their right and left limbs. Lissajous displays were provided to guide task performance. Participants performed 14 twenty-second practice trials at 90° HUT (Earth). Following a 30-min rest period, participants performed 2 test trials for each coordination pattern in both Earth and Mars conditions. Performance during the test trials were compared. Results indicated very effective temporal performance of the goal coordination tasks in both gravity conditions. However, results indicated differences associated with the production of force between Earth and Mars. In general, participants produced less force in simulated Martian gravity than in the Earth condition. In addition, force production was more harmonic in Martian gravity than Earth gravity for both limbs, indicating that less force distortions (adjustments, hesitations, and/or perturbations) occurred in the Mars condition than in the Earth condition. The force coherence analysis indicated significantly higher coherence in the 1:1 task than in the 1:2 task for all force frequency bands, with the highest level of coherence in the 1-4 Hz frequency band for both gravity conditions. High coherence in the 1-4 Hz frequency band is associated with a common neural drive that activates the two arms simultaneously and is consistent with the requirements of the two tasks. The results also support the notion that neural crosstalk stabilizes the performance of the 1:1 in-phase task. In addition, significantly higher coherence in the 8-12 Hz frequency bands were observed for the Earth condition than the Mars condition. Force coherence in the 8-12 Hz bands is associated with the processing of sensorimotor information, suggesting that participants were better at integrating visual, proprioceptive, and/or tactile feedback in Earth than for the Mars condition. Overall, the results indicate less neural interference in Martian gravity; however, participants appear to be more effective at using the Lissajous displays to guide performance under Earth's gravity.

Post-Stroke Impairments of Manual Dexterity and Finger Proprioception: Their Contribution to Upper Limb Activity Capacity

BACKGROUND

Knowing how impaired manual dexterity and finger proprioception affect upper limb activity capacity is important for delineating targeted post-stroke interventions for upper limb recovery.

OBJECTIVES

To investigate whether impaired manual dexterity and finger proprioception explain variance in post-stroke activity capacity, and whether they explain more variance than conventional clinical assessments of upper limb sensorimotor impairments.

METHODS

Activity capacity and hand sensorimotor impairments were assessed using clinical measures in N = 42 late subacute/chronic hemiparetic stroke patients. Dexterity was evaluated using the Dextrain Manipulandum to quantify accuracy of visuomotor finger force-tracking (N = 36), timing of rhythmic tapping (N = 36), and finger individuation (N = 24), as well as proprioception (N = 27). Stepwise multivariate and hierarchical linear regression models were used to identify impairments best explaining activity capacity.

RESULTS

Dexterity and proprioceptive components significantly increased the variance explained in activity capacity: (i) Box and Block Test was best explained by baseline tonic force during force-tracking and tapping frequency (adjusted R2 = .51); (ii) Motor Activity Log was best explained by success rate in finger individuation (adjusted R2 = .46); (iii) Action Research Arm Test was best explained by release of finger force and proprioceptive measures (improved reaction time related to use of proprioception; adjusted R2 = .52); and (iv) Moberg Pick-Up test was best explained by proprioceptive function (adjusted R2 = .18). Models excluding dexterity and proprioception variables explained up to 19% less variance.

CONCLUSIONS

Manual dexterity and finger proprioception explain unique variance in activity capacity not captured by conventional impairment measures and should be assessed when considering the underlying causes of post-stroke activity capacity limitations.URL: https://www.clinicaltrials.gov. Unique identifier: NCT03934073.

Biological and environmental factors may affect children's executive function through motor and sensorimotor development: Preterm birth and cerebral palsy

Disruptive biological and environmental factors may undermine the development of children's motor and sensorimotor skills. Since the development of cognitive skills, including executive function, is grounded in early motor and sensorimotor experiences, early delays or impairments in motor and sensorimotor processing often trigger dynamic developmental cascades that lead to suboptimal executive function outcomes. The purpose of this perspective paper is to link early differences in motor/sensorimotor processing to the development of executive function in children born preterm or with cerebral palsy. Uncovering such links in clinical populations would improve our understanding of developmental pathways and key motor and sensorimotor skills that are antecedent and foundational for the development of executive function. This knowledge will allow the refinement of early interventions targeting motor and sensorimotor skills with the goal of proactively improving executive function outcomes in at-risk populations.

A novel tablet-based application for assessment of manual dexterity and its components: a reliability and validity study in healthy subjects

BACKGROUND

We developed five tablet-based tasks (applications) to measure multiple components of manual dexterity.

AIM

to test reliability and validity of tablet-based dexterity measures in healthy participants.

METHODS

Tasks included: (1) Finger recognition to assess mental rotation capacity. The subject taps with the finger indicated on a virtual hand in three orientations (reaction time, correct trials). (2) Rhythm tapping to evaluate timing of finger movements performed with, and subsequently without, an auditory cue (inter-stimulus interval). (3) Multi-finger tapping to assess independent finger movements (reaction time, correct trials, unwanted finger movements). (4) Sequence tapping to assess production and memorization of visually cued finger sequences (successful taps). (5) Line-tracking to assess movement speed and accuracy while tracking an unpredictably moving line on the screen with the fingertip (duration, error). To study inter-rater reliability, 34 healthy subjects (mean age 35 years) performed the tablet tasks twice with two raters. Relative reliability (Intra-class correlation, ICC) and absolute reliability (Standard error of measurement, SEM) were established. Task validity was evaluated in 54 healthy subjects (mean age 49 years, range: 20-78 years) by correlating tablet measures with age, clinical dexterity assessments (time taken to pick-up objects in Box and Block Test, BBT and Moberg Pick Up Test, MPUT) and with measures obtained using a finger force-sensor device.

RESULTS

Most timing measures showed excellent reliability. Poor to excellent reliability was found for correct trials across tasks, and reliability was poor for unwanted movements. Inter-session learning occurred in some measures. Age correlated with slower and more variable reaction times in finger recognition, less correct trials in multi-finger tapping, and slower line-tracking. Reaction times correlated with those obtained using a finger force-sensor device. No significant correlations between tablet measures and BBT or MPUT were found. Inter-task correlation among tablet-derived measures was weak.

CONCLUSIONS

Most tablet-based dexterity measures showed good-to-excellent reliability (ICC ≥ 0.60) except for unwanted movements during multi-finger tapping. Age-related decline in performance and association with finger force-sensor measures support validity of tablet measures. Tablet-based components of dexterity complement conventional clinical dexterity assessments. Future work is required to establish measurement properties in patients with neurological and psychiatric disorders.

From Hemispheric Asymmetry through Sensorimotor Experiences to Cognitive Outcomes in Children with Cerebral Palsy

Recent neuroimaging studies allowed us to explore abnormal brain structures and interhemispheric connectivity in children with cerebral palsy (CP). Behavioral researchers have long reported that children with CP exhibit suboptimal performance in different cognitive domains (e.g., receptive and expressive language skills, reading, mental imagery, spatial processing, subitizing, math, and executive functions). However, there has been very limited cross-domain research involving these two areas of scientific inquiry. To stimulate such research, this perspective paper proposes some possible neurological mechanisms involved in the cognitive delays and impairments in children with CP. Additionally, the paper examines the ways motor and sensorimotor experience during the development of these neural substrates could enable more optimal development for children with CP. Understanding these developmental mechanisms could guide more effective interventions to promote the development of both sensorimotor and cognitive skills in children with CP.

Using EEG to study sensorimotor adaptation

Sensorimotor adaptation, or the capacity to flexibly adapt movements to changes in the body or the environment, is crucial to our ability to move efficiently in a dynamic world. The field of sensorimotor adaptation is replete with rigorous behavioural and computational methods, which support strong conceptual frameworks. An increasing number of studies have combined these methods with electroencephalography (EEG) to unveil insights into the neural mechanisms of adaptation. We review these studies: discussing EEG markers of adaptation in the frequency and the temporal domain, EEG predictors for successful adaptation and how EEG can be used to unmask latent processes resulting from adaptation, such as the modulation of spatial attention. With its high temporal resolution, EEG can be further exploited to deepen our understanding of sensorimotor adaptation.

The functional characterization of callosal connections

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The functional characterization of callosal connections is informed by anatomical data.

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Callosal connections play a conditional driving role depending on the brain state and behavioral demands.

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Callosal connections play a modulatory function, in addition to a driving role.

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The corpus callosum participates in learning and interhemispheric transfer of sensorimotor habits.

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The corpus callosum contributes to language processing and cognitive functions.

The brain operates through the synaptic interaction of distant neurons within flexible, often heterogeneous, distributed systems. Histological studies have detailed the connections between distant neurons, but their functional characterization deserves further exploration. Studies performed on the corpus callosum in animals and humans are unique in that they capitalize on results obtained from several neuroscience disciplines. Such data inspire a new interpretation of the function of callosal connections and delineate a novel road map, thus paving the way toward a general theory of cortico-cortical connectivity. Here we suggest that callosal axons can drive their post-synaptic targets preferentially when coupled to other inputs endowing the cortical network with a high degree of conditionality. This might depend on several factors, such as their pattern of convergence-divergence, the excitatory and inhibitory operation mode, the range of conduction velocities, the variety of homotopic and heterotopic projections and, finally, the state-dependency of their firing. We propose that, in addition to direct stimulation of post-synaptic targets, callosal axons often play a conditional driving or modulatory role, which depends on task contingencies, as documented by several recent studies.

Long-Lasting Desynchronization of Plastic Neural Networks by Random Reset Stimulation

Excessive neuronal synchrony is a hallmark of neurological disorders such as epilepsy and Parkinson's disease. An established treatment for medically refractory Parkinson's disease is high-frequency (HF) deep brain stimulation (DBS). However, symptoms return shortly after cessation of HF-DBS. Recently developed decoupling stimulation approaches, such as Random Reset (RR) stimulation, specifically target pathological connections to achieve long-lasting desynchronization. During RR stimulation, a temporally and spatially randomized stimulus pattern is administered. However, spatial randomization, as presented so far, may be difficult to realize in a DBS-like setup due to insufficient spatial resolution. Motivated by recently developed segmented DBS electrodes with multiple stimulation sites, we present a RR stimulation protocol that copes with the limited spatial resolution of currently available depth electrodes for DBS. Specifically, spatial randomization is realized by delivering stimuli simultaneously to L randomly selected stimulation sites out of a total of M stimulation sites, which will be called L/M-RR stimulation. We study decoupling by L/M-RR stimulation in networks of excitatory integrate-and-fire neurons with spike-timing dependent plasticity by means of theoretical and computational analysis. We find that L/M-RR stimulation yields parameter-robust decoupling and long-lasting desynchronization. Furthermore, our theory reveals that strong high-frequency stimulation is not suitable for inducing long-lasting desynchronization effects. As a consequence, low and high frequency L/M-RR stimulation affect synaptic weights in qualitatively different ways. Our simulations confirm these predictions and show that qualitative differences between low and high frequency L/M-RR stimulation are present across a wide range of stimulation parameters, rendering stimulation with intermediate frequencies most efficient. Remarkably, we find that L/M-RR stimulation does not rely on a high spatial resolution, characterized by the density of stimulation sites in a target area, corresponding to a large M. In fact, L/M-RR stimulation with low resolution performs even better at low stimulation amplitudes. Our results provide computational evidence that L/M-RR stimulation may present a way to exploit modern segmented lead electrodes for long-lasting therapeutic effects.

A perspective on the development of hemispheric specialization, infant handedness, and cerebral palsy

Cerebral Palsy (CP), a common form of neurological pediatric disability, results from pre- or perinatal brain injury. Although there is growing evidence of the efficacy of motor learning-based therapies, several factors interact to produce variability in impairment and limit the effectiveness of these therapies. The variability of hand function present in children with CP indicates that a range of developmental pathways must contribute to the manifestation of individually unique characteristics of impairment. Despite two decades of progress using therapies derived from understanding the mechanisms controlling hand function, very little is known about the sensorimotor experiences occurring during development that likely shape later functional problems for children with CP. In this "perspective" paper, we propose that the study of the development of motor skills in typically developing infants may reveal experiential factors potentially important for creating remedial therapies for children with CP. Specifically, we use the development of infant handedness, a model of hemispheric specialization of function, as an example of how self-generated experiences and sensorimotor feedback can shape the development of limb control and hemispheric specialization. We illustrate how early sensorimotor asymmetries concatenate into pronounced differences in skill between the two hands. We suggest that this model of infant handedness provides a framework for studying the individual differences manifested in children with CP. These differences likely arise from aberrant sensorimotor experiences created by sensorimotor circuits disrupted by the early brain injury. We conclude that knowledge of the developmental events, including subtle motor behaviors, that shape sensorimotor pathways, can improve treatment options for children with CP.

Neurophysiological Correlates of Adaptation and Interference during Asymmetrical Bimanual Movements

In this study, we investigated brain dynamics during interference between hands during bimanual movements. Participants performed a bimanual center-out reaching task in which a visuomotor rotation was applied to the right hand while the left hand did not receive visual feedback of its movements. This manipulation resulted in interference from the adapting right hand to the kinesthetically guided left hand. Electroencephalography (EEG) recordings during the task showed that spectral power in the high and low beta frequency bands was elevated early in exposure, but decreased throughout learning. This may be representative of error-based updating of internal models of movement. Additionally, coherence, a measure of neural functional connectivity, was elevated both within and between hemispheres in the beta frequencies during the initial presentation of the visuomotor rotation, and then decreased throughout adaptation. This suggests that beta oscillatory neural activity may be marker for transmission of conflicting motor information between hemispheres, which manifests in interference between the hands during asymmetrical bimanual movements.

Abnormal Phase Coupling in Parkinson's Disease and Normalization Effects of Subthreshold Vestibular Stimulation

The human brain is a highly dynamic structure requiring dynamic coordination between different neural systems to perform numerous cognitive and behavioral tasks. Emerging perspectives on basal ganglia (BG) and thalamic functions have highlighted their role in facilitating and mediating information transmission among cortical regions. Thus, changes in BG and thalamic structures can induce aberrant modulation of cortico-cortical interactions. Recent work in deep brain stimulation (DBS) has demonstrated that externally applied electrical current to BG structures can have multiple downstream effects in large-scale brain networks. In this work, we identified EEG-based altered resting-state cortical functional connectivity in Parkinson's disease (PD) and examined effects of dopaminergic medication and electrical vestibular stimulation (EVS), a non-invasive brain stimulation (NIBS) technique capable of stimulating the BG and thalamus through vestibular pathways. Resting EEG was collected from 16 PD subjects and 18 age-matched, healthy controls (HC) in four conditions: sham (no stimulation), EVS1 (4-8 Hz multisine), EVS2 (50-100 Hz multisine) and EVS3 (100-150 Hz multisine). The mean, variability, and entropy were extracted from time-varying phase locking value (PLV), a non-linear measure of pairwise functional connectivity, to probe abnormal cortical couplings in the PD subjects. We found the mean PLV of Cz and C3 electrodes were important for discrimination between PD and HC subjects. In addition, the PD subjects exhibited lower variability and entropy of PLV (mostly in theta and alpha bands) compared to the controls, which were correlated with their clinical characteristics. While levodopa medication was effective in normalizing the mean PLV only, all EVS stimuli normalized the mean, variability and entropy of PLV in the PD subject, with the exact extent and duration of improvement a function of stimulus type. These findings provide evidence demonstrating both low- and high-frequency EVS exert widespread influences on cortico-cortical connectivity, likely via subcortical activation. The improvement observed in PD in a stimulus-dependent manner suggests that EVS with optimized parameters may provide a new non-invasive means for neuromodulation of functional brain networks.

Estimating Brain Connectivity With Varying-Length Time Lags Using a Recurrent Neural Network

OBJECTIVE

Computer-aided estimation of brain connectivity aims to reveal information propagation in brain automatically, which has great potential in clinical applications, e.g., epilepsy foci diagnosis. Granger causality is an effective tool for directional connection analysis in multivariate time series. However, most existing methods based on Granger causality assume fixed time lags in information transmission, while the propagation delay between brain signals is usually changing constantly.

METHODS

We propose a Granger causality estimator based on the recurrent neural network, called RNN-GC, to deal with the multivariate brain connectivity detection problem. Our model takes input of time-series signals with arbitrary length of transmission time lags and learns the information flow from the data using the gated RNN model, i.e., long short-term memory (LSTM). The LSTM model can sequentially update the gates in memory cells to determine how many preceding points should be considered for prediction. Therefore, the LSTM-based RNN-GC estimator works well on varying-length time lags and shows effectiveness even on very long transmission delays.

RESULTS

Experiments are carried out in comparison with other methods using both simulation data and epileptic electroencephalography signals. The RNN-GC estimator achieves superior performance in brain connectivity estimation and shows robustness in modeling multivariate connections with varying-length time lags.

CONCLUSION

The RNN-GC method is capable of modeling nonlinear and varying-length lagged information transmission and effective in directional brain connectivity estimation.

SIGNIFICANCE

The proposed method is promising to serve as a robust brain connection analysis tool in clinical applications.

Characterizing synchrony patterns across cognitive task stages of associative recognition memory

Numerous studies seek to understand the role of oscillatory synchronization in cognition. This problem is particularly challenging in the context of complex cognitive behavior, which consists of a sequence of processing steps with uncertain duration. In this study, we analyzed oscillatory connectivity measures in time windows that previous computational models had associated with a specific sequence of processing steps in an associative memory recognition task (visual encoding, familiarity, memory retrieval, decision making, and motor response). The timing of these processing steps was estimated on a single‐trial basis with a novel hidden semi‐Markov model multivariate pattern analysis (HSMM‐MVPA) method. We show that different processing stages are associated with specific patterns of oscillatory connectivity. Visual encoding is characterized by a dense network connecting frontal, posterior, and temporal areas as well as frontal and occipital phase locking in the 4–9 Hz theta band. Familiarity is associated with frontal phase locking in the 9–14 Hz alpha band. Decision making is associated with frontal and temporo‐central interhemispheric connections in the alpha band. During decision making, a second network in the theta band that connects left‐temporal, central, and occipital areas bears similarity to the neural signature for preparing a motor response. A similar theta band network is also present during the motor response, with additionally alpha band connectivity between right‐temporal and posterior areas. This demonstrates that the processing stages discovered with the HSMM‐MVPA method are indeed linked to distinct synchronization patterns, leading to a closer understanding of the functional role of oscillations in cognition.

Musical training increases functional connectivity, but does not enhance mu suppression

Musical training provides an ideal platform for investigating action representation for sound. Learning to play an instrument requires integration of sensory and motor perception-action processes. Functional neuroimaging studies have indicated that listening to trained music can result in the activity in premotor areas, even after a short period of training. These studies suggest that action representation systems are heavily dependent on specific sensorimotor experience. However, others suggest that because humans naturally move to music, sensorimotor training is not necessary and there is a more general action representation for music. We previously demonstrated that EEG mu suppression, commonly implemented to demonstrate mirror-neuron-like action representation while observing movements, can also index action representations for sounds in pianists. The current study extends these findings to a group of non-musicians who learned to play randomised sequences on a piano, in order to acquire specific sound-action mappings for the five fingers of their right hand. We investigated training-related changes in neural dynamics as indexed by mu suppression and task-related coherence measures. To test the specificity of training effects, we included sounds similar to those encountered in the training and additionally rhythm sequences. We found no effect of training on mu suppression between pre- and post-training EEG recordings. However, task-related coherence indexing functional connectivity between electrodes over audiomotor areas increased after training. These results suggest that long-term training in musicians and short-term training in novices may be associated with different stages of audiomotor integration that can be reflected in different EEG measures. Furthermore, the changes in functional connectivity were specifically found for piano tones, and were not apparent when participants listened to rhythms, indicating some degree of specificity related to training.

Pulsatile desynchronizing delayed feedback for closed-loop deep brain stimulation

High-frequency (HF) deep brain stimulation (DBS) is the gold standard for the treatment of medically refractory movement disorders like Parkinson’s disease, essential tremor, and dystonia, with a significant potential for application to other neurological diseases. The standard setup of HF DBS utilizes an open-loop stimulation protocol, where a permanent HF electrical pulse train is administered to the brain target areas irrespectively of the ongoing neuronal dynamics. Recent experimental and clinical studies demonstrate that a closed-loop, adaptive DBS might be superior to the open-loop setup. We here combine the notion of the adaptive high-frequency stimulation approach, that aims at delivering stimulation adapted to the extent of appropriately detected biomarkers, with specifically desynchronizing stimulation protocols. To this end, we extend the delayed feedback stimulation methods, which are intrinsically closed-loop techniques and specifically designed to desynchronize abnormal neuronal synchronization, to pulsatile electrical brain stimulation. We show that permanent pulsatile high-frequency stimulation subjected to an amplitude modulation by linear or nonlinear delayed feedback methods can effectively and robustly desynchronize a STN-GPe network of model neurons and suggest this approach for desynchronizing closed-loop DBS.

A novel method for the quantification of key components of manual dexterity after stroke

BACKGROUND

A high degree of manual dexterity is a central feature of the human upper limb. A rich interplay of sensory and motor components in the hand and fingers allows for independent control of fingers in terms of timing, kinematics and force. Stroke often leads to impaired hand function and decreased manual dexterity, limiting activities of daily living and impacting quality of life. Clinically, there is a lack of quantitative multi-dimensional measures of manual dexterity. We therefore developed the Finger Force Manipulandum (FFM), which allows quantification of key components of manual dexterity. The purpose of this study was (i) to test the feasibility of using the FFM to measure key components of manual dexterity in hemiparetic stroke patients, (ii) to compare differences in dexterity components between stroke patients and controls, and (iii) to describe individual profiles of dexterity components in stroke patients.

METHODS

10 stroke patients with mild-to-moderate hemiparesis and 10 healthy subjects were recruited. Clinical measures of hand function included the Action Research Arm Test and the Moberg Pick-Up Test. Four FFM tasks were used: (1) Finger Force Tracking to measure force control, (2) Sequential Finger Tapping to measure the ability to perform motor sequences, (3) Single Finger Tapping to measure timing effects, and (4) Multi-Finger Tapping to measure the ability to selectively move fingers in specified combinations (independence of finger movements).

RESULTS

Most stroke patients could perform the tracking task, as well as the single and multi-finger tapping tasks. However, only four patients performed the sequence task. Patients showed less accurate force control, reduced tapping rate, and reduced independence of finger movements compared to controls. Unwanted (erroneous) finger taps and overflow to non-tapping fingers were increased in patients. Dexterity components were not systematically related among each other, resulting in individually different profiles of deficient dexterity. Some of the FFM measures correlated with clinical scores.

CONCLUSIONS

Quantifying some of the key components of manual dexterity with the FFM is feasible in moderately affected hemiparetic patients. The FFM can detect group differences and individual profiles of deficient dexterity. The FFM is a promising tool for the measurement of key components of manual dexterity after stroke and could allow improved targeting of motor rehabilitation.

Neuroplasticity subserving the operation of brain-machine interfaces

Neuroplasticity is key to the operation of brain machine interfaces (BMIs)-a direct communication pathway between the brain and a man-made computing device. Whereas exogenous BMIs that associate volitional control of brain activity with neurofeedback have been shown to induce long lasting plasticity, endogenous BMIs that use prolonged activity-dependent stimulation--and thus may curtail the time scale that governs natural sensorimotor integration loops--have been shown to induce short lasting plasticity. Here we summarize recent findings from studies using both categories of BMIs, and discuss the fundamental principles that may underlie their operation and the longevity of the plasticity they induce. We draw comparison to plasticity mechanisms known to mediate natural sensorimotor skill learning and discuss principles of homeostatic regulation that may constrain endogenous BMI effects in the adult mammalian brain. We propose that BMIs could be designed to facilitate structural and functional plasticity for the purpose of re-organization of target brain regions and directed augmentation of sensorimotor maps, and suggest possible avenues for future work to maximize their efficacy and viability in clinical applications.

High-resolution EEG analysis of power spectral density maps and coherence networks in a proportional reasoning task

Proportional reasoning is very important logical skill required in mathematics and science problem solving as well as in everyday life decisions. However, there is a lack of studies on neurophysiological correlates of proportional reasoning. To explore the brain activity of healthy adults while performing a balance scale task, we used high-resolution EEG techniques and graph-theory based connectivity analysis. After unskilled subjects learned how to properly solve the task, their cortical power spectral density (PSD) maps revealed an increased parietal activity in the beta band. This indicated that subjects started to perform calculations. In addition, the number of inter-hemispheric connections decreased after learning, implying a rearrangement of the brain activity. Repeated performance of the task led to the PSD decrease in the beta and gamma bands among parietal and frontal regions along with a synchronization of lower frequencies. These findings suggest that repetition led to a more automatic task performance. Subjects were also divided in two groups according to their scores on the test of logical thinking (TOLT). Although no group differences in the accuracy and reaction times were found, EEG data showed higher activity in the beta and gamma bands for the group that scored better on TOLT. Learning and repetition induced changes in the pattern of functional connectivity were evident for all frequency bands. Overall, the results indicated that higher frequency oscillations in frontal and parietal regions are particularly important for proportional reasoning.

The principle of coherence in multi-level brain information processing

Synchronisation has become one of the major scientific tools to explain biological order at many levels of organisation. In systems neuroscience, synchronised subthreshold and suprathreshold oscillatory neuronal activity within and between distributed neuronal assemblies is acknowledged as a fundamental mode of neuronal information processing. Coherent neuronal oscillations correlate with all basic cognitive functions, mediate local and long-range neuronal communication and affect synaptic plasticity. However, it remains unclear how the very fast and complex changes of functional neuronal connectivity necessary for cognition, as mediated by dynamic patterns of neuronal synchrony, could be explained exclusively based on the well-established synaptic mechanisms. A growing body of research indicates that the intraneuronal matrix, composed of cytoskeletal elements and their binding proteins, structurally and functionally connects the synapses within a neuron, modulates neurotransmission and memory consolidation, and is hypothesised to be involved in signal integration via electric signalling due to its charged surface. Theoretical modelling, as well as emerging experimental evidence indicate that neuronal cytoskeleton supports highly cooperative energy transport and information processing based on molecular coherence. We suggest that long-range coherent dynamics within the intra- and extracellular filamentous matrices could establish dynamic ordered states, capable of rapid modulations of functional neuronal connectivity via their interactions with neuronal membranes and synapses. Coherence may thus represent a common denominator of neurophysiological and biophysical approaches to brain information processing, operating at multiple levels of neuronal organisation, from which cognition may emerge as its cardinal manifestation.

Decoding sequence learning from single-trial intracranial EEG in humans

We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

Large-scale cortical networks estimated from scalp EEG signals during performance of goal-directed motor tasks

The evaluation of the topological properties of brain networks is an emergent research topic, since the estimated cerebral connectivity patterns often have relatively large size and complex structure. Since a graph is a mathematical representation of a network, the use of a theoretical graph approach would describe concisely the topological features of the functional brain connectivity network estimated using neuroimaging techniques. In the present study, we analyze the changes in brain synchronization networks using high-resolution EEG signals obtained during performance of a complex goal-directed visuomotor task. Our results show that the cortical network is more stable when subjects reach the goal than when they fail by hitting an obstacle. These findings suggest the presence of a possible cerebral "marker" for motor actions that result in successful reaching of a target.

Defecting or not defecting: how to "read" human behavior during cooperative games by EEG measurements

Understanding the neural mechanisms responsible for human social interactions is difficult, since the brain activities of two or more individuals have to be examined simultaneously and correlated with the observed social patterns. We introduce the concept of hyper-brain network, a connectivity pattern representing at once the information flow among the cortical regions of a single brain as well as the relations among the areas of two distinct brains. Graph analysis of hyper-brain networks constructed from the EEG scanning of 26 couples of individuals playing the Iterated Prisoner's Dilemma reveals the possibility to predict non-cooperative interactions during the decision-making phase. The hyper-brain networks of two-defector couples have significantly less inter-brain links and overall higher modularity—i.e., the tendency to form two separate subgraphs—than couples playing cooperative or tit-for-tat strategies. The decision to defect can be “read” in advance by evaluating the changes of connectivity pattern in the hyper-brain network.

Brain-computer interface analysis of a dynamic visuo-motor task

BACKGROUND

The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data.

OBJECTIVE

In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could, therefore, be further used for the development of a closed-loop, non-invasive, brain-computer interface.

MATERIALS AND METHODS

For the case of this study two types of measurements were performed, i.e., the electroencephalographic (EEG) signals and the wrist movements were measured simultaneously, during the subject's performance of a dynamic visuo-motor task. Wrist-movement predictions were computed by using the EEG data-processing methodology of double brain-rhythm filtering, double phase demodulation and double principal component analyses (PCA), each with a separate set of parameters. For the movement-prediction model a fuzzy inference system was used.

RESULTS

The results have shown that the EEG signals measured during the dVM tasks carry enough information about the subjects' wrist movements for them to be successfully decoded using the presented methodology. Reasonably high values of the correlation coefficients suggest that the validation of the proposed approach is satisfactory. Moreover, since the causality of the rhythm filtering and the PCA transformation has been achieved, we have shown that these methods can also be used in a real-time, brain-computer interface. The study revealed that using non-causal, optimized methods yields better prediction results in comparison with the causal, non-optimized methodology; however, taking into account that the causality of these methods allows real-time processing, the minor decrease in prediction quality is acceptable.

CONCLUSION

The study suggests that the methodology that was proposed in our previous studies is also valid for identifying the EEG-coded content during dVM tasks, albeit with various modifications, which allow better prediction results and real-time data processing. The results have shown that wrist movements can be predicted in simulated or real time; however, the results of the non-causal, optimized methodology (simulated) are slightly better. Nevertheless, the study has revealed that these methods should be suitable for use in the development of a non-invasive, brain-computer interface.

Evaluation of the brain network organization from EEG signals: a preliminary evidence in stroke patient

Synchronous brain activity in motor cortex in perception or in complex cognitive processing has been the subject of several studies. The advanced analysis of cerebral electro-physiological activity during the course of planning (PRE) or execution of movement (EXE) in a high temporal resolution could reveal interesting information about the brain functional organization in patients following stroke damage. High-power (128 channels) electroencephalography registration was carried out on 8 healthy subjects and on a patient with stroke with capsular lacuna in the right hemisphere. For activation of motor cortex, the finger tapping paradigm was used. In this preliminary study, we tested a theoretical graph approach to characterize the task-related spectral coherence. All of the obtained brain functional networks were analyzed by the connectivity degree, the degree distribution, and efficiency parameters in the Theta, Alpha, Beta, and Gamma bands during the PRE and EXE intervals. All the brain networks were found to hold a regular and ordered topology. However, significant differences (P < 0.01) emerged between the patient with stroke and the control subjects, independently of the neural processes related to the PRE or EXE periods. In the Beta (13-29 Hz) and Gamma (30-40 Hz) bands, the significant (P < 0.01) decrease in global- and local-efficiency in the patient's networks, reflected a lower capacity to integrate communication between distant brain regions and a lower tendency to be modular. This weak organization is principally due to the significant (P < 0.01 Bonferroni corrected) increase in disconnected nodes together with the significant increase in the links in some other crucial vertices.

Gamma band oscillations under influence of bromazepam during a sensorimotor integration task: an EEG coherence study

The goal of the present study was to explore the dynamics of the gamma band using the coherence of the quantitative electroencephalography (qEEG) in a sensorimotor integration task and the influence of the neuromodulator bromazepam on the band behavior. Our hypothesis is that the needs of the typewriting task will demand the coupling of different brain areas, and that the gamma band will promote the binding of information. It is also expected that the neuromodulator will modify this coupling. The sample was composed of 39 healthy subjects. We used a randomized double-blind design and divided subjects into three groups: placebo (n=13), bromazepam 3mg (n=13) and bromazepam 6 mg (n=13). The two-way ANOVA analysis demonstrated a main effect for the factors condition (i.e., C4-CZ electrode pair) and moment (i.e., C3-CZ, C3-C4 and C4-CZ pairs of electrodes). We propose that the gamma band plays an important role in the binding among several brain areas in complex motor tasks and that each hemisphere is influenced in a different manner by the neuromodulator.

Spatial detection of multiple movement intentions from SAM-filtered single-trial MEG signals

OBJECTIVE

To test whether human intentions to sustain or cease movements in right and left hands can be decoded reliably from spatially filtered single-trial magnetoencephalographic (MEG) signals for motor execution and motor imagery.

METHODS

Seven healthy volunteers, naïve to BCI technology, participated in this study. Signals were recorded from 275-channel MEG, and synthetic aperture magnetometry (SAM) was employed as the spatial filter. The four-class classification was performed offline. Genetic algorithm based Mahalanobis linear distance (GA-MLD) and direct-decision tree classifier (DTC) techniques were adopted for the classification through 10-fold cross-validation.

RESULTS

Through SAM imaging, strong and distinct event-related desynchronization (ERD) associated with sustaining, and event-related synchronization (ERS) patterns associated with ceasing of right and left hand movements were observed in the beta band (15-30Hz) on the contralateral hemispheres for motor execution and motor imagery sessions. Virtual channels were selected from these areas of high activity for the corresponding events as per the paradigm of the study. Through a statistical comparison between SAM-filtered virtual channels from single-trial MEG signals and basic MEG sensors, it was found that SAM-filtered virtual channels significantly increased the classification accuracy for motor execution (GA-MLD: 96.51+/-2.43%) as well as motor imagery sessions (GA-MLD: 89.69+/-3.34%).

CONCLUSION

Multiple movement intentions can be reliably detected from SAM-based spatially filtered single-trial MEG signals.

SIGNIFICANCE

MEG signals associated with natural motor behavior may be utilized for a reliable high-performance brain-computer interface (BCI) and may reduce long-term training compared with conventional BCI methods using rhythm control.

Modality specific functional interaction in sensorimotor synchronization

Movement execution strongly relies on precise sensorimotor synchronization. In a finger-tapping task that requires subjects to synchronize their finger taps to regular pacing signal synchronization accuracy varies with respect to pacing signal's modality. This study aimed at elucidating functional brain dynamics associated with modality specific behavioral synchronization accuracy. To this end, 10 right-handed subjects performed a finger-tapping task with respect to regular auditory and visual pacing, respectively, whereas neuromagnetic activity was recorded using a 122-channel whole-head neuromagnetometer. Visual pacing was associated with significantly reduced tap-to-pacer asynchrony and increased intertap variability as compared to auditory pacing. The brain dynamics associated with task execution were analyzed using the frequency domain beamformer approach dynamic imaging of coherent sources (DICS). Both tasks were shown to be associated with comparable networks. However, during visual pacing involvement of the ventral premotor cortex (PMv) was shown, whereas during auditory pacing the dorsal premotor cortex (PMd) was concerned with task execution. Synchronization with respect to visual pacing was associated with significantly increased functional interaction between thalamus and PMv at beta frequency as compared to functional interplay between thalamus and PMd during auditory pacing. Auditory synchronization was associated with increased functional interaction between left superior temporal gyrus and PMd at alpha frequency. Furthermore, functional interaction between thalamus and premotor cortex at beta frequency was significantly correlated with synchronization accuracy. All in all the present data suggest that modality specific synchronization differences are associated with frequency and connectivity specific changes of functional interaction in distinct brain networks.

Estimation of time-varying connectivity patterns through the use of an adaptive directed transfer function

Frequency-derived identification of the propagation of information between brain regions has quickly become a popular area in the neurosciences. Of the various techniques used to study the propagation of activation within the central nervous system, the directed transfer function (DTF) has been well used to explore the functional connectivity during a variety of brain states and pathological conditions. However, the DTF method assumes the stationarity of the neural electrical signals and the time invariance of the connectivity among different channels over the investigated time window. Such assumptions may not be valid in the abnormal brain signals such as seizures and interictal spikes in epilepsy patients. In the present study, we have developed an adaptive DTF (ADTF) method through the use of a multivariate adaptive autoregressive model to study the time-variant propagation of seizures and interictal spikes in simulated electrocorticogram (ECoG) networks. The time-variant connectivity reconstruction is achieved by the Kalman filter algorithm, which can incorporate time-varying state equations. We study the performance of the proposed method through simulations with various propagation models using either sample seizures or interictal spikes as the source waveform. The present results suggest that the new ADTF method correctly captures the temporal dynamics of the propagation models, while the DTF method cannot, and even returns erroneous results in some cases. The present ADTF method was tested in real epileptiform ECoG data from an epilepsy patient, and the ADTF results are consistent with the clinical assessments performed by neurologists.

Perceptual influence on bimanual coordination: an fMRI study

In bimanual coordination subjects typically show a spontaneous preference for movement symmetry. While there is experimental evidence for the principle of muscle homology, recent evidence suggested that bimanual coordination may be mediated as perceptual goals (Mechsner et al., 2001). To explore this controversy we performed a fMRI study in 11 healthy, right-handed subjects using bimanual index finger abductions and adductions in a congruous condition, i.e. both palms down, and incongruous conditions with either the left or right palm up. Our fMRI data showed a widespread bihemispheric network mediating proprioceptive coordination of the two hands with significant differences mainly for a perceptual dissociation: in the incongruous conditions with the one palm up there was a BOLD signal increase in a bilateral frontoparietal network involving the motor and premotor cortical areas, particularly in the right palm-up condition. These results accord with the notion that perceptual cues play an important role in the control of bilateral hand movements.

Enhancement of long-range EEG coherence by synchronous bifocal transcranial magnetic stimulation

Interregional coupling of distant brain regions can be measured by electroencephalographic (EEG) coherence reflecting the spatial-temporal correlation between two oscillatory signals. It has been suggested that this coherence in activity is a signature of functional integration of multimodal neuronal networks. Repetitive transcranial magnetic stimulation (rTMS) is a well-established technique for non-invasive cortical stimulation. Its modulating effects outlast the train of stimulation and affect behavior. In the present study, we tested the hypothesis that cortico-cortical coherence between distant brain areas can be selectively enhanced by synchronous bifocal rTMS. Cortico-cortical coherence was assessed in 16 healthy human subjects before and after three trains of synchronous high-frequency (10 Hz) rTMS to the left primary motor cortex and the visual cortex at the occipital pole simultaneously. Stimulation of the left M1 alone served as the control condition. Coherence and spectral power were measured between these areas on the stimulated and the homologue contralateral side. Synchronous bifocal rTMS induced an increase of interregional coupling in the alpha and lower beta band on the stimulated side without effects on spectral power. These data indicate that synchronous bifocal rTMS is a feasible technique for selective modulation of interregional EEG coherence. Furthermore, they raise the hypothesis that interventional enhancement of long-range coherence may effectively modulate interregional integration with behavioral consequences.

Brain plasticity and tumors

Brain plasticity is the potential of the nervous system to reshape itself during ontogeny, learning or following injuries. The first part of this article reviews the pathophysiological mechanisms underlying plasticity at different functional levels. Such plastic potential means that the anatomo-functional organization of the brain in humans, both physiological and pathological, has flexibility. Patterns of reorganization may differ according to the time-course of cerebral damage, with better functional compensation in more slowly growing lesions. The second part of this review analyzes the interactions between tumor growth and brain reshaping, using non-invasive (neuroimaging) and invasive (electrophysiological) methods of functional mapping. Finally, the therapeutic implications provided by a greater understanding of these mechanisms of cerebral redistribution are explored from a surgical point of view. Enhanced preoperative prediction of an individual's potential for reorganization might be integrated into surgical planning and preserving quality of life through tailored rehabilitation programmes to optimize functional recovery following resection of a brain tumor.

Coherence and phase locking of intracerebral activation during visuo- and audio-motor learning of continuous tracking movements

The aim of the present study was to assess changes in EEG coherence and phase locking between fronto-parietal areas, including the frontal and parietal motor areas, during early audio- and visuo-motor learning of continuous tracking movements. Subjects learned to turn a steering-wheel according to a given trajectory in order to minimise the discrepancy between a changing foreground stimulus (controllable by the subjects) and a constant background stimulus (uncontrollable) for both the auditory and the visual modality. In the auditory condition, we uncovered a learning-related increase in inter-hemispheric phase locking between inferior parietal regions, suggesting that coupling between areas involved in audiomotor integration is augmented during early learning stages. Intra-hemispheric phase locking between motor and superior parietal areas increased in the left hemisphere as learning progressed, indicative of integrative processes of spatial information and movement execution. Further tests show a significant correlation of intra-hemispheric phase locking between the motor and the parietal area bilaterally and movement performance in the visual condition. These results suggest that the motor-parietal network is operative in the auditory and in the visual condition. This study confirms that a complex fronto-parietal network subserves learning of a new movement that requires sensorimotor transformation and demonstrates the importance of interregional coupling as a neural correlate for successful acquisition and implementation of externally guided behaviour.

Brain plasticity: From pathophysiological mechanisms to therapeutic applications

Cerebral plasticity, which is the dynamic potential of the brain to reorganize itself during ontogeny, learning, or following damage, has been widely studied in the last decade, in vitro, in animals, and also in humans since the development of functional neuroimaging. In the first part of this review, the main hypotheses about the pathophysiological mechanisms underlying plasticity are presented. At a microscopic level, modulations of synaptic efficacy, unmasking of latent connections, phenotypic modifications and neurogenesis have been identified. At a macroscopic level, diaschisis, functional redundancies, sensory substitution and morphological changes have been described. In the second part, the behavioral consequences of such cerebral phenomena in physiology, namely the "natural" plasticity, are analyzed in humans. The review concludes on the therapeutic implications provided by a better understanding of these mechanisms of brain reshaping. Indeed, this plastic potential might be 'guided' in neurological diseases, using rehabilitation, pharmacological drugs, transcranial magnetic stimulation, neurosurgical methods, and even new techniques of brain-computer interface - in order to improve the quality of life of patients with damaged nervous systems.

Decreased desychronisation during self-paced movements in frequency bands involving sensorimotor integration and motor functioning in Parkinson's disease

This study examined sensorimotor integration and motor functioning in seven patients with Parkinson's disease (PD) who had mild symptoms, and seven age-matched controls. Neuro-oscillations were recorded by high-density 128-channel electroencephalography (EEG). Participants were required to perform two tasks: simple tapping of the index finger and thumb and a complex Luria finger apposition task. Both tasks were performed unimanually and bimanually. There were no significant group differences in the task-related power (TRPow) within alpha 1 (mu1) or in beta 1 frequencies (beta1). In contrast, there were significant group differences in the alpha 2 (mu2) and beta 2 frequencies (beta2). Patients had less desychronisation than controls at the electrodes covering the central regions of the scalp. Alpha 2 and beta 2 frequencies have been associated with task-specific sensorimotor integration and motor function, respectively. This activity difference in patients with Parkinson's disease may be due to deficits in sensorimotor integration.

Extensive training of elementary finger tapping movements changes the pattern of motor cortex excitability

There is evidence of a strong capacity for functional and structural reorganization in the human motor system. However, past research has focused mainly on complex movement sequences over rather short training durations. In this study we investigated changes in corticospinal excitability associated with longer training of elementary, maximum-speed tapping movements. All participating subjects were consistent right-handers and were trained using either the right (experiment 1) or the left thumb (experiment 2). Transcranial magnetic stimulation was applied to obtain motor evoked potentials (MEPs) from the abductor pollicis brevis (APB) muscle of the right and the left hand before and after training. As a result of training, a significant increase was observed in tapping speed accompanied by increased MEPs, recorded from the trained APB muscle, following contralateral M1 stimulation. In the case of subdominant-hand training we additionally demonstrate increased MEP amplitudes evoked at the right APB (untrained hand) in the first training week. Enhanced corticospinal excitability associated with practice of elementary movements may constitute a necessary precursor for inducing plastic changes within the motor system. The involvement of the ipsilateral left M1 likely reflects the predominant role of the left M1 in the general control (modification) of simple motor parameters in right-handed subjects.