Biljana Petreska

Apraxia: a review

Praxic functions are frequently altered following brain lesion, giving rise to apraxia - a complex pattern of impairments that is difficult to assess or interpret. In this chapter, we review the current taxonomies of apraxia and related cognitive and neuropsychological models. We also address the questions of the neuroanatomical correlates of apraxia, the relation between apraxia and aphasia and the analysis of apraxic errors. We provide a possible explanation for the difficulties encountered in investigating apraxia and also several approaches to overcome them, such as systematic investigation and modeling studies. Finally, we argue for a multidisciplinary approach. For example, apraxia should be studied in consideration with and could contribute to other fields such as normal motor control, neuroimaging and neurophysiology.

Auditory motion affects visual biological motion processing

The processing of biological motion is a critical, everyday task performed with remarkable efficiency by human sensory systems. Interest in this ability has focused to a large extent on biological motion processing in the visual modality (see, for example, Cutting, J. E., Moore, C., & Morrison, R. (1988). Masking the motions of human gait. Perception and Psychophysics, 44(4), 339-347). In naturalistic settings, however, it is often the case that biological motion is defined by input to more than one sensory modality. For this reason, here in a series of experiments we investigate behavioural correlates of multisensory, in particular audiovisual, integration in the processing of biological motion cues. More specifically, using a new psychophysical paradigm we investigate the effect of suprathreshold auditory motion on perceptions of visually defined biological motion. Unlike data from previous studies investigating audiovisual integration in linear motion processing [Meyer, G. F. & Wuerger, S. M. (2001). Cross-modal integration of auditory and visual motion signals. Neuroreport, 12(11), 2557-2560; Wuerger, S. M., Hofbauer, M., & Meyer, G. F. (2003). The integration of auditory and motion signals at threshold. Perception and Psychophysics, 65(8), 1188-1196; Alais, D. & Burr, D. (2004). No direction-specific bimodal facilitation for audiovisual motion detection. Cognitive Brain Research, 19, 185-194], we report the existence of direction-selective effects: relative to control (stationary) auditory conditions, auditory motion in the same direction as the visually defined biological motion target increased its detectability, whereas auditory motion in the opposite direction had the inverse effect. Our data suggest these effects do not arise through general shifts in visuo-spatial attention, but instead are a consequence of motion-sensitive, direction-tuned integration mechanisms that are, if not unique to biological visual motion, at least not common to all types of visual motion. Based on these data and evidence from neurophysiological and neuroimaging studies we discuss the neural mechanisms likely to underlie this effect.

A reconfigurable hardware membrane system

P systems are massively parallel systems and software simulations do no usually allow to exploit this parallelism. We present a parallel hardware implementation of a special class of membrane systems. The implementation is based on a universal membrane hardware component that allows to efficiently run membrane systems on specialized hardware such as FPGAs. The implementation is presented in detail as well as performance results and an example.

Movement curvature planning through force field internal models

Human motion studies have focused primarily on modeling straight point-to-point reaching movements. However, many goal-directed reaching movements, such as movements directed towards oneself, are not straight but rather follow highly curved trajectories. These movements are particularly interesting to study since they are essential in our everyday life, appear early in development and are routinely used to assess movement deficits following brain lesions. We argue that curved and straight-line reaching movements are generated by a unique neural controller and that the observed curvature of the movement is the result of an active control strategy that follows the geometry of one’s body, for instance to avoid trajectories that would hit the body or yield postures close to the joint limits. We present a mathematical model that accounts for such an active control strategy and show that the model reproduces with high accuracy the kinematic features of human data during unconstrained reaching movements directed toward the head. The model consists of a nonlinear dynamical system with a single stable attractor at the target. Embodiment-related task constraints are expressed as a force field that acts on the dynamical system. Finally, we discuss the biological plausibility and neural correlates of the model’s parameters and suggest that embodiment should be considered as a main cause for movement trajectory curvature.

Revisiting a study of callosal apraxia: The right hemisphere can imitate the orientation but not the position of the hand

Callosal disconnection can reveal asymmetrical contributions of the two brain hemispheres to praxis. In this paper, we revisit a study of a patient with callosal disconnection (Goldenberg et al., 2001, Neuropsychologia, 39:1432-1443), who perfectly imitated meaningless gestures when imitation was controlled only by the left hemisphere, but was severely impaired when the right hemisphere was in charge of motor control. We decomposed the gestures into a set of geometric variables that were to be reproduced, such as the orientation of the hand and the position of contact between the hand and the face. Whereas orientation of the hand in extrinsic coordinates was replicated correctly by both hemispheres, only the left hemisphere reproduced correctly the position of contact between the hand and the face. This goal-dissociation as well as several partial perseveration errors speak against the hypothesis of a direct route from perception to motor replication of gestures, as interruption of a direct route would probably impair all the features of the gesture. We speculate that incorrect coordination between the reproductions of multiple goals may be the core deficit underlying callosal apraxia.

A neurocomputational model of an imitation deficit following brain lesion

This paper investigates the neural mechanisms of visuo-motor imitation in humans through convergent evidence from neuroscience. In particular, we consider a deficit in imitation following callosal brain lesion, based on the rational that looking at how imitation is impaired can unveil its underlying neural principles. We ground the functional architecture and information flow of our model in brain imaging studies and use findings from monkey brain neurophysiological studies to drive the choice of implementation of our processing modules. Our neural model of visuo-motor imitation is based on self-organizing maps with associated activities. Patterns of impairment of the model, realized by adding uncertainty in the transfer of information between the networks, account for the scores found in a clinical examination of imitation [1]. The model also allows several interesting predictions.

A neural model of demyelination of the mouse spinal cord

This paper presents a neural network model of demyelination of the mouse motor pathways, coupled to a central pattern generation (CPG) model for quadruped walking. Demyelination is the degradation of the myelin layer covering the axons which can be caused by several neurodegenerative autoimmune diseases such as multiple sclerosis. We use this model - to our knowledge first of its kind - to investigate the locomotion deficits that appear following demyelination of axons in the spinal cord. Our model meets several physiological and behavioral results and predicts that whereas locomotion can still occur at high percentages of demyelination damage, the distribution and location of the lesion are the most critical factors for the locomotor performance.

Focus on students

Reference EPFL-ARTICLE-160114doi:10.1109/MCI.2008.930982View record in Web of Science Record created on 2010-11-30, modified on 2017-05-10

Neurocomputational modeling of imitation deficits

We are interested in the question of human imitation and we address this through convergent evidence from neuroscience. The rationale of our work is that the nature of imitation deficits following brain lesion can unveil some of the neural and computational principles underlying normal imitation. In particular, we consider how imitation of meaningless gestures (i.e., hand postures relative to the head) is impaired in apraxia [1], i.e., an inability to perform voluntary movements that cannot be explained by elementary motor, sensory or cognitive deficits. We have first considered a clinical study of visuo-motor imitation of meaningless gestures in an apraxic patient with damage to the corpus callosum [2]. Interestingly the patient made different errors depending on the visual field of presentation of the stimulus and the hand used, with preserved imitation only in the right visual field – right hand condition. These observations brought us to consider modeling of the information pathway across the two hemispheres and the type of connectivity impairment which would account for them. We have thus developed a leaky integrator neurocomputational model of the neural representations of different sensory information (e.g., visual, tactile or proprioceptive) and reciprocal nonlinear transformations necessary to perform the imitation task [3]. The sensory representations in our model are self-organizing maps whose interconnections are trained with anti-hebbian learning. The information flow and implementation details of the model are consistent with evidence from brain imaging and neurophysiological studies [4,5]. To simulate callosal apraxia, we added uncertainty in the transfer of information between sensory representations localized in different hemispheres, successfully reproducing the results found in [3]. To summarize, our model makes hypotheses on the type of neural representations used and the computational mechanisms underlying human visuo-motor imitation and could possibly help to gain more understanding in the occurrence and nature of imitation errors in patients with brain lesions. In addition, to further test and validate the model against human motion experimental data, we conduct, in collaboration with the Geneva University Hospital (HUG) and Vaud University Hospital Center (CHUV), kinematic studies with brain damaged adults specifically disabled in gesture imitation.