Quantitative gait analysis value as a predictor of shunt surgery effectiveness in normal pressure hydrocephalus: A technical note.

INTRODUCTION: Shunt surgery (SS) remains the most effective treatment for idiopathic Normal pressure hydrocephalus (iNPH), but the selection of the patients with the greatest potential benefit remains elusive. OBJECTIVE: Identify gait features predictive of best response to SS in iNPH. METHODS: Eight patients with iNPH were assessed at baseline, after Cerebrospinal fluid tap-test (CSF-TT) and SS, with clinical scales (Clinical/Patient Global Clinical Impression, EuroQol-5D, Clinical Dementia Rating Scale(CDR), MoCA test, Hoehn-Yahr Scale) and gait analysis with inertial sensors. RESULTS: The 8 included iNPH patients had a mean age of 73 years(59-81), moderate cognitive (CDR-1.5 (0.5-2); MoCA-9.5 (3-21)) and motor impairment (Hoehn-Yahr-2.75(2-3)). After SS, patients had a significant improvement in cognition (MoCA, p = 0.001) and quality of life. At baseline, patients with lower improvement (no change/ minimally improved) (n = 2), in comparison to patient with higher improvement (much/very much improved) (n = 6), already had higher cognitive impairment (MoCa-3(3-3) vs. 11(7-21)). Patients with lower improvement had a lower % of change in gait performance at LP (mean 10.2 %) and were absent of additional benefit after SS(mean -0.8 %). In contrast, gait performance in patients with higher improvement consistently got better from baseline to LP (mean 23.1 %) and from baseline to SS (mean 82.9 %). A significant negative correlation was observed between CDR score and several gait variables: speed (rpb=-0.92,p = 0.009); stride length (rpb=-0.92,p = 0.009); lift-off angle (rpb=-0.96,p = 0.003); and maximum heel (rpb=-0.81,p = 0.049). CONCLUSION: The magnitude of gait improvement after CSF-TT, quantified by gait analysis, can be used as an integral variable in the multimodal clinical approach to the prediction of improvement after SS.

A dynamic neural field model of continuous input integration.

The ability of neural systems to turn transient inputs into persistent changes in activity is thought to be a fundamental requirement for higher cognitive functions. In continuous attractor networks frequently used to model working memory or decision making tasks, the persistent activity settles to a stable pattern with the stereotyped shape of a "bump" independent of integration time or input strength. Here, we investigate a new bump attractor model in which the bump width and amplitude not only reflect qualitative and quantitative characteristics of a preceding input but also the continuous integration of evidence over longer timescales. The model is formalized by two coupled dynamic field equations of Amari-type which combine recurrent interactions mediated by a Mexican-hat connectivity with local feedback mechanisms that balance excitation and inhibition. We analyze the existence, stability and bifurcation structure of single and multi-bump solutions and discuss the relevance of their input dependence to modeling cognitive functions. We then systematically compare the pattern formation process of the two-field model with the classical Amari model. The results reveal that the balanced local feedback mechanisms facilitate the encoding and maintenance of multi-item memories. The existence of stable subthreshold bumps suggests that different to the Amari model, the suppression effect of neighboring bumps in the range of lateral competition may not lead to a complete loss of information. Moreover, bumps with larger amplitude are less vulnerable to noise-induced drifts and distance-dependent interaction effects resulting in more faithful memory representations over time.

Discrimination of idiopathic Parkinson's disease and vascular parkinsonism based on gait time series and the levodopa effect.

Idiopathic Parkinson's disease (IPD) and vascular parkinsonism (VaP) present highly overlapping phenotypes, making it challenging to distinguish between these two parkinsonian syndromes. Recent evidence suggests that gait assessment and response to levodopa medication may assist in the objective evaluation of clinical differences. In this paper, we propose a new approach for gait pattern differentiation that uses convolutional neural networks (CNNs) based on gait time series with and without the influence of levodopa medication. Wearable sensors positioned on both feet were used to acquire gait data from 14 VaP patients, 15 IPD patients, and 34 healthy subjects. An individual's gait features are affected by physical characteristics, including age, height, weight, sex, and walking speed or stride length. Therefore, to reduce bias due to intersubject variations, a multiple regression normalization approach was used to obtain gait data. Recursive feature elimination using the linear support vector machine, lasso, and random forest were applied to infer the optimal feature subset that led to the best results. CNNs were implemented by means of various hyperparameters and feature subsets. The best CNN classifiers achieved accuracies of 79.33%±6.46, 82.33%±10.62, and 86.00%±7.12 without (off state), with (on state), and with the simultaneous consideration of the effect of levodopa medication (off/on state), respectively. The response to levodopa medication improved classification performance. Based on gait time series and response to medication, the proposed approach differentiates between IPD and VaP gait patterns and reveals a high accuracy rate, which might prove useful when distinguishing other diseases related to movement disorders.

Differential width discrimination task for active and passive tactile discrimination in humans.

The neurophysiological basis of width discrimination has been extensively studied in rodents and has shown that active and passive tactile discrimination engage fundamentally different neural networks. Although previous studies have analyzed active and passive tactile processing in humans, little is known about the neurophysiological basis of width discrimination in humans. Here we present a width discrimination task for humans that reproduces the main features of the width discrimination task previously developed for rodents. The task required subjects to actively or passively sample two movable bars forming a "narrow" or "wide" aperture. Subjects were then required to press one of two buttons to indicate if the bar width was "narrow" or "wide". Behavioral testing showed that subjects were capable of discriminating between wide or narrow apertures up to distances of 0.1 cm. Electroencephalography (EEG) recordings further suggested distinct topographic maps for active and passive versions of the task during the period associated with the aperture discrimination. These results indicate that the Human Differential Width Discrimination Task is a valuable tool to describe the behavioral characteristics and neurophysiological basis of tactile processing.•Active and passive width discrimination has been extensively studied in rodents but not in humans.•Human subjects were capable of discriminating aperture widths of 0.1 cm.•Electroencephalography recordings showed that active and passive versions of the task were associated with different topographic maps.

Parkinson's Disease and Fabry Disease: Clinical, Biochemical and Neuroimaging Analysis of Three Pedigrees.

BACKGROUND: Sporadic Parkinson's disease (PD) patients have lower α-galactosidase A (α-GAL A) enzymatic activity and Fabry disease (FD) patients potentially carry an increased risk of PD. OBJECTIVE: Determination of PD prevalence in FD and clinical, biochemical and vascular neuroimaging description of FD pedigrees with concomitant PD. METHODS: Clinical screening for PD in 229 FD patients belonging to 31 families, harbouring GLA gene mutation p.F113L, and subsequent pedigree analysis. Gender-stratified comparison of FD+/PD+ patients with their family members with FD but without PD (FD+/PD-) regarding Mainz scores, plasma & leukocytes α-GAL A enzymatic activity, urinary Gb3 and plasma Lyso-Gb3, vascular brain neuroimaging. RESULTS: Prevalence of PD in FD was 1.3% (3/229) (3% in patients aged ≥50 years). Three FD patients, one female (73 years old) (P1) and two males (60 and 65 years old) (P2 and P3), three different pedigrees, presented akinetic-rigid PD, with weak response to levodopa (16% - 36%), and dopaminergic deficiency on 18F-DOPA PET. No pathogenic mutations were found in a PD gene panel. FD+/PD+ patients had worse clinical severity of FD (above upper 75% IQR in Mainz scores), and cortico-subcortical white matter/small vessel lesions. P3 patient was under enzyme therapy, started 1 year before PD diagnosis. P2-P3 patients had higher leucocyte α-GAL A activity (2,2-3 vs.1,0 (median)(nmol/h/mg)). CONCLUSION: We have shown a high prevalence of PD in a late-onset phenotype of FD, presenting high cerebrovascular burden and weak response to levodopa. Further studies will untangle how much of this PD phenotype is due to Gb3 deposition versus cerebrovascular lesions in the nigro-striatal network.

Gait stride-to-stride variability and foot clearance pattern analysis in Idiopathic Parkinson's Disease and Vascular Parkinsonism.

The literature on gait analysis in Vascular Parkinsonism (VaP), addressing issues such as variability, foot clearance patterns, and the effect of levodopa, is scarce. This study investigates whether spatiotemporal, foot clearance and stride-to-stride variability analysis can discriminate VaP, and responsiveness to levodopa. Fifteen healthy subjects, 15 Idiopathic Parkinson's Disease (IPD) patients and 15 VaP patients, were assessed in two phases: before (Off-state), and one hour after (On-state) the acute administration of a suprathreshold (1.5 times the usual) levodopa dose. Participants were asked to walk a 30-meter continuous course at a self-selected walking speed while wearing foot-worn inertial sensors. For each gait variable, mean, coefficient of variation (CV), and standard deviations SD1 and SD2 obtained by Poincaré analysis were calculated. General linear models (GLMs) were used to identify group differences. Patients were subject to neuropsychological evaluation (MoCA test) and Brain MRI. VaP patients presented lower mean stride velocity, stride length, lift-off and strike angle, and height of maximum toe (later swing) (p < .05), and higher %gait cycle in double support, with only the latter unresponsive to levodopa. VaP patients also presented higher CV, significantly reduced after levodopa. Yet, all VaP versus IPD differences lost significance when accounting for mean stride length as a covariate. In conclusion, VaP patients presented a unique gait with reduced degrees of foot clearance, probably correlated to vascular lesioning in dopaminergic/non-dopaminergic cortical and subcortical non-dopaminergic networks, still amenable to benefit from levodopa. The dependency of gait and foot clearance and variability deficits from stride length deserves future clarification.

Different protocols for analyzing behavior and adaptability in obstacle crossing in Parkinson's disease.

Imbalance and tripping over obstacles as a result of altered gait in older adults, especially in patients with Parkinson's disease (PD), are one of the most common causes of falls. During obstacle crossing, patients with PD modify their behavior in order to decrease the mechanical demands and enhance dynamic stability. Various descriptions of dynamic traits of gait that have been collected over longer periods, probably better synthesize the underlying structure and pattern of fluctuations in gait and can be more sensitive markers of aging or early neurological dysfunction and increased risk of falls. This confirmation challenges the clinimetric of different protocols and paradigms used for gait analysis up till now, in particular when analyzing obstacle crossing. The authors here present a critical review of current knowledge concerning the interplay between the cognition and gait in aging and PD, emphasizing the differences in gait behavior and adaptability while walking over different and challenging obstacle paradigms, and the implications of obstacle negotiation as a predictor of falls. Some evidence concerning the effectiveness of future rehabilitation protocols on reviving obstacle crossing behavior by trial and error relearning, taking advantage of dual-task paradigms, physical exercise, and virtual reality have been put forward in this article.

Compensatory Postural Adjustments in an Oculus Virtual Reality Environment and the Risk of Falling in Alzheimer's Disease.

BACKGROUND/AIMS: Alzheimer's disease (AD) patients have an impaired ability to quickly reweight central sensory dependence in response to unexpected body perturbations. Herein, we aim to study provoked compensatory postural adjustments (CPAs) in a conflicting sensory paradigm with unpredictable visual displacements using virtual reality goggles. METHODS: We used kinematic time-frequency analyses of two frequency bands: a low-frequency band (LB; 0.3-1.5 Hz; mechanical strategy) and a high-frequency band (HB; 1.5-3.5 Hz; cognitive strategy). We enrolled 19 healthy subjects (controls) and 21 AD patients, divided according to their previous history of falls. RESULTS: The AD faller group presented higher-power LB CPAs, reflecting their worse inherent postural stability. The AD patients had a time lag in their HB CPA reaction. CONCLUSION: The slower reaction by CPA in AD may be a reflection of different cognitive resources including body schema self-perception, visual motion, depth perception, or a different state of fear and/or anxiety.

Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer's Disease.

The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer's disease (AD). In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support Vector Machines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%). Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.

Compensatory postural adjustments in Parkinson's disease assessed via a virtual reality environment.

Postural control is a complex dynamic mechanism, which integrates information from visual, vestibular and somatosensory systems. Idiopathic Parkinson's disease (IPD) patients are unable to produce appropriate reflexive responses to changing environmental conditions. Still, it is controversial what is due to voluntary or involuntary postural control, even less what is the effect of levodopa. We aimed to evaluate compensatory postural adjustments (CPA), with kinematic and time-frequency analyzes, and further understand the role of dopaminergic medication on these processes. 19 healthy subjects (Controls) and 15 idiopathic Parkinson's disease (IPD) patients in the OFF and ON medication states, wearing IMUs, were submitted to a virtual reality scenario with visual downward displacements on a staircase. We also hypothesized if CPA would involve mechanisms occurring in distinct time scales. We subsequently analyzed postural adjustments on two frequency bands: low components between 0.3 and 1.5 Hz (LB), and high components between 1.5 and 3.5 Hz (HB). Vertical acceleration demonstrated a greater power for discriminating IPD patients from healthy subjects. Visual perturbation significantly increased the power of the HB in all groups, being particularly more evident in the OFF state. Levodopa significantly increased their basal power taking place on the LB. However, controls and IPD patients in the ON state revealed a similar trend of the control mechanism. Results indicate an improvement in muscular stiffness provided by levodopa. They also suggest the role of different compensatory postural adjustment patterns, with LB being related to inertial properties of the oscillating mass and HB representing reactions to the ongoing visual input-changing scenario.

Off-line simulation inspires insight: A neurodynamics approach to efficient robot task learning.

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

Review of Robotic Technology for Stereotactic Neurosurgery.

The research of stereotactic apparatus to guide surgical devices began in 1908, yet a major part of today's stereotactic neurosurgeries still rely on stereotactic frames developed almost half a century ago. Robots excel at handling spatial information, and are, thus, obvious candidates in the guidance of instrumentation along precisely planned trajectories. In this review, we introduce the concept of stereotaxy and describe a standard stereotactic neurosurgery. Neurosurgeons' expectations and demands regarding the role of robots as assistive tools are also addressed. We list the most successful robotic systems developed specifically for or capable of executing stereotactic neurosurgery. A critical review is presented for each robotic system, emphasizing the differences between them and detailing positive features and drawbacks. An analysis of the listed robotic system features is also undertaken, in the context of robotic application in stereotactic neurosurgery. Finally, we discuss the current perspective, and future directions of a robotic technology in this field. All robotic systems follow a very similar and structured workflow despite the technical differences that set them apart. No system unequivocally stands out as an absolute best. The trend of technological progress is pointing toward the development of miniaturized cost-effective solutions with more intuitive interfaces.

Role of the Visual and Auditory Systems in Postural Stability in Alzheimer's Disease.

BACKGROUND: Postural stability requires the integration of multisensory input information and translation into appropriate motor responses. Surprisingly, few previous studies have addressed the role of auditory input on postural stability in healthy subjects, and none has investigated this in Alzheimer's disease (AD). OBJECTIVE: To assess the influence of the visual and auditory systems on postural stability in patients with AD and healthy subjects. METHODS: Twenty-four patients with AD and healthy age-matched subjects were examined by kinematic postural analysis (inertia measurement units placed at the center of mass of the body) under four different conditions: stance with eyes open and eyes closed, with and without suppression of background noise (using ear defenders). The effects of visual and auditory influences were analyzed independently and in conjunction. RESULTS: In both groups, visual suppression had a negative impact on postural stability, while suppression of background noise, non-specifically and without spatial cues, significantly benefited postural stability. We also observed that in both groups, the positive effect of background noise suppression was insufficient to compensate for the negative effect of visual suppression, to which the patients were significantly more vulnerable. CONCLUSIONS: Audition, albeit less significant than vision, also plays a role in the multi-sensorial dynamic control of postural stability by the central nervous system. In everyday life, audition is likely to be a relevant factor in postural stability. This is especially relevant in AD in which, even when the peripheral sensory system is intact, the central processing is impaired and sensory dependence is re-weighted.

The effect of levodopa on postural stability evaluated by wearable inertial measurement units for idiopathic and vascular Parkinson's disease.

BACKGROUND: Postural stability analysis has shown that postural control is impaired in untreated idiopathic Parkinson's disease (IPD), even in the early stages of the disease. Vascular Parkinson's disease (VPD) lacks consensus clinical criteria or diagnostic tests. Moreover, the levodopa effect on postural balance remains undefined for IPD and even less so for VPD. OBJECTIVE: To characterize postural stability, using kinematic analysis with wearable inertial measurement units, in IPD and VPD patients without clinical PI, and to subsequently analyze the response to levodopa. METHODS: Ten patients with akinetic-rigid IPD and five patients with VPD were included. Clinical and postural stability kinematic analysis was performed before and after levodopa challenge, on different standing tasks: normal stance (NS), Romberg eyes open (REO) and Romberg eyes closed. RESULTS: In the "off state", VPD patients had higher mean distances and higher maximal distance of p ostural sway on NS and REO tasks, respectively. VPD patients maintained a higher range of anterior-posterior (AP) postural sway after levodopa. In the absence of PI and non-significant differences in UPDRS-III, a higher mPIGD score in the VPD patients was mainly due to gait disturbance. Gait disturbance, and not UPDRS-III, influenced the degree of postural sway response to levodopa for VPD patients. CONCLUSION: Quantitative postural sway evaluation is useful in the investigation of Parkinsonian syndromes. VPD patients have higher AP postural sway that is correlated with their gait disturbance burden and also not responsive to levodopa. These observations corroborate the interconnection of postural control and locomotor networks.

Validation of a stereo camera system to quantify brain deformation due to breathing and pulsatility.

PURPOSE: A new stereo vision system is presented to quantify brain shift and pulsatility in open-skull neurosurgeries. METHODS: The system is endowed with hardware and software synchronous image acquisition with timestamp embedding in the captured images, a brain surface oriented feature detection, and a tracking subroutine robust to occlusions and outliers. A validation experiment for the stereo vision system was conducted against a gold-standard optical tracking system, Optotrak CERTUS. A static and dynamic analysis of the stereo camera tracking error was performed tracking a customized object in different positions, orientations, linear, and angular speeds. RESULTS: The system is able to detect an immobile object position and orientation with a maximum error of 0.5 mm and 1.6° in all depth of field, and tracking a moving object until 3 mm/s with a median error of 0.5 mm. Three stereo video acquisitions were recorded from a patient, immediately after the craniotomy. The cortical pulsatile motion was captured and is represented in the time and frequency domain. The amplitude of motion of the cloud of features' center of mass was inferior to 0.8 mm. Three distinct peaks are identified in the fast Fourier transform analysis related to the sympathovagal balance, breathing, and blood pressure with 0.03-0.05, 0.2, and 1 Hz, respectively. CONCLUSIONS: The stereo vision system presented is a precise and robust system to measure brain shift and pulsatility with an accuracy superior to other reported systems.

Postural stability analysis with inertial measurement units in Alzheimer's disease.

BACKGROUND: The cause of frequent falls in patients with Alzheimer's disease (AD) is still not well understood. Nevertheless, balance control and sensory organization are known to be critical for moving safely and adapting to the environment. METHODS: We evaluated postural stability in 20 AD patients (11 fallers and 9 nonfallers) and 16 healthy controls with an inertial measurement unit (triaxial accelerometers and gyroscopes) attached to the center of mass (COM) in different balance conditions (Romberg on flat surface and frontward/backward-inclined surface, with or without visual suppression) in a motor lab. RESULTS: In AD patients, the group of fallers showed a different kinetic pattern of postural stability characterized by higher vulnerability to visual suppression, higher total/maximal displacement and a mediolateral/anteroposterior range of sway, and a consequent need for more corrections of COM pitch and roll angles. CONCLUSION: Further studies are needed to consolidate the normative values of the discriminatory kinetic variables with the potential of inclusion in a multifactorial analysis of the risk of falls. Nevertheless, these results highlight signs of impairment of central postural control in AD, which may require early therapeutic intervention.

Neuro-cognitive mechanisms of decision making in joint action: a human-robot interaction study.

In this paper we present a model for action preparation and decision making in cooperative tasks that is inspired by recent experimental findings about the neuro-cognitive mechanisms supporting joint action in humans. It implements the coordination of actions and goals among the partners as a dynamic process that integrates contextual cues, shared task knowledge and predicted outcome of others' motor behavior. The control architecture is formalized by a system of coupled dynamic neural fields representing a distributed network of local but connected neural populations. Different pools of neurons encode task-relevant information about action means, task goals and context in the form of self-sustained activation patterns. These patterns are triggered by input from connected populations and evolve continuously in time under the influence of recurrent interactions. The dynamic model of joint action is evaluated in a task in which a robot and a human jointly construct a toy object. We show that the highly context sensitive mapping from action observation onto appropriate complementary actions allows coping with dynamically changing joint action situations.

Integrating verbal and nonverbal communication in a dynamic neural field architecture for human-robot interaction.

How do humans coordinate their intentions, goals and motor behaviors when performing joint action tasks? Recent experimental evidence suggests that resonance processes in the observer's motor system are crucially involved in our ability to understand actions of others', to infer their goals and even to comprehend their action-related language. In this paper, we present a control architecture for human-robot collaboration that exploits this close perception-action linkage as a means to achieve more natural and efficient communication grounded in sensorimotor experiences. The architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of neural populations that encode in their activation patterns goals, actions and shared task knowledge. We validate the verbal and nonverbal communication skills of the robot in a joint assembly task in which the human-robot team has to construct toy objects from their components. The experiments focus on the robot's capacity to anticipate the user's needs and to detect and communicate unexpected events that may occur during joint task execution.

The dynamic neural field approach to cognitive robotics.

This tutorial presents an architecture for autonomous robots to generate behavior in joint action tasks. To efficiently interact with another agent in solving a mutual task, a robot should be endowed with cognitive skills such as memory, decision making, action understanding and prediction. The proposed architecture is strongly inspired by our current understanding of the processing principles and the neuronal circuitry underlying these functionalities in the primate brain. As a mathematical framework, we use a coupled system of dynamic neural fields, each representing the basic functionality of neuronal populations in different brain areas. It implements goal-directed behavior in joint action as a continuous process that builds on the interpretation of observed movements in terms of the partner's action goal. We validate the architecture in two experimental paradigms: (1) a joint search task; (2) a reproduction of an observed or inferred end state of a grasping-placing sequence. We also review some of the mathematical results about dynamic neural fields that are important for the implementation work.

A dynamic model for action understanding and goal-directed imitation.

The understanding of other individuals' actions is a fundamental cognitive skill for all species living in social groups. Recent neurophysiological evidence suggests that an observer may achieve the understanding by mapping visual information onto his own motor repertoire to reproduce the action effect. However, due to differences in embodiment, environmental constraints or motor skills, this mapping very often cannot be direct. In this paper, we present a dynamic network model which represents in its layers the functionality of neurons in different interconnected brain areas known to be involved in action observation/execution tasks. The model aims at substantiating the idea that action understanding is a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and action execution. The model is tested in variations of an imitation task in which an observer with dissimilar embodiment tries to reproduce the perceived or inferred end-state of a grasping-placing sequence. We also propose and test a biologically plausible learning scheme which allows establishing during practice a goal-directed organization of the distributed network. The modeling results are discussed with respect to recent experimental findings in action observation/execution studies.