Biomedical subjects

Antonia F de C Hamilton

Publications and source records attributed to Antonia F de C Hamilton.

7 recordsLinked to original sources

PubMed2006

Building a motor simulation de novo: observation of dance by dancers.

Research on action simulation identifies brain areas that are active while imagining or performing simple overlearned actions. Are areas engaged during imagined movement sensitive to the amount of actual physical practice? In the present study, participants were expert dancers who learned and rehearsed novel, complex whole-body dance sequences 5 h a week across 5 weeks. Brain activity was recorded weekly by fMRI as dancers observed and imagined performing different movement sequences. Half these sequences were rehearsed and half were unpracticed control movements. After each trial, participants rated how well they could perform the movement. We hypothesized that activity in premotor areas would increase as participants observed and simulated movements that they had learnt outside the scanner. Dancers' ratings of their ability to perform rehearsed sequences, but not the control sequences, increased with training. When dancers observed and simulated another dancer's movements, brain regions classically associated with both action simulation and action observation were active, including inferior parietal lobule, cingulate and supplementary motor areas, ventral premotor cortex, superior temporal sulcus and primary motor cortex. Critically, inferior parietal lobule and ventral premotor activity was modulated as a function of dancers' ratings of their own ability to perform the observed movements and their motor experience. These data demonstrate that a complex motor resonance can be built de novo over 5 weeks of rehearsal. Furthermore, activity in premotor and parietal areas during action simulation is enhanced by the ability to execute a learned action irrespective of stimulus familiarity or semantic label.

Adolescent↗

PubMed2006

Action understanding requires the left inferior frontal cortex.

Numerous studies have established that inferior frontal cortex is active when hand actions are planned, imagined, remembered, imitated, and even observed. Furthermore, it has been proposed that these activations reflect a process of simulating the observed action to allow it to be understood and thus fully perceived. However, direct evidence for a perceptual role for left inferior frontal cortex is rare, and linguistic or motor contributions to the reported activations have not been ruled out. We used repetitive transcranial magnetic stimulation (rTMS) over inferior frontal gyrus during a perceptual weight-judgement task to test the hypothesis that this region contributes to action understanding. rTMS at this site impaired judgments of the weight of a box lifted by a person, but not judgements of the weight of a bouncing ball or of stimulus duration, and rTMS at control sites had no impact. This demonstrates that the integrity of left inferior frontal gyrus is necessary to make accurate perceptual judgments about other people's actions.

Brain Mapping↗

PubMed2006

Goal representation in human anterior intraparietal sulcus.

When a child reaches toward a cookie, the watching parent knows immediately what the child wants. The neural basis of this ability to interpret other people's actions in terms of their goals has been the subject of much speculation. Research with infants has shown that 6 month olds respond when they see an adult reach to a novel goal but habituate when an adult reaches to the same goal repeatedly. We used a similar approach in an event-related functional magnetic resonance imaging experiment. Adult participants observed a series of movies depicting goal-directed actions, with the sequence controlled so that some goals were novel and others repeated relative to the previous movie. Repeated presentation of the same goal caused a suppression of the blood oxygen level-dependent response in two regions of the left intraparietal sulcus. These regions were not sensitive to the trajectory taken by the actor's hand. This result demonstrates that the anterior intraparietal sulcus represents the goal of an observed action.

Adult↗

PubMed2005

Where does your own action influence your perception of another person's action in the brain?

Activation of premotor cortex during the observation and imitation of human actions is now increasingly accepted, but it remains unclear how the CNS is able to resolve potential conflicts between the observation of another person's action and the ongoing control of one's own action. Recent data suggest that this overlap leads to a systematic bias, where lifting a box influences participant's perceptual judgments of the weight of a box lifted by another person. We now investigate the neural basis of this bias effect using fMRI. Seventeen participants performed a perceptual weight judgment task or two control conditions while lifting a light box, a heavy box or no box during scanning. Brain regions related to perceptual bias were localized by correlating individual differences in bias with BOLD signal. Five regions were found to show correlations with psychophysical bias: left inferior frontal gyrus, left central sulcus, left extrastriate body area, left lingual gyrus and right intraparietal sulcus. The cluster in primary motor cortex was also activated by box lifting, and the cluster in extrastriate body area by the observation of hand actions and the weight judgment task. We suggest that these brain areas are part of a network where motor processing modulates perceptual judgment of observed human actions, and thus visual and motor processes cannot be thought of as two distinct systems, but instead interact at many levels.

Adolescent↗

PubMed2004

The scaling of motor noise with muscle strength and motor unit number in humans.

Understanding the origin of noise, or variability, in the motor system is an important step towards understanding how accurate movements are performed. Variability of joint torque during voluntary activation is affected by many factors such as the precision of the descending motor commands, the number of muscles that cross the joint, their size and the number of motor units in each. To investigate the relationship between the peripheral factors and motor noise, the maximum voluntary torque produced at a joint and the coefficient of variation of joint torque were recorded from six adult human subjects for four muscle/joint groups in the arm. It was found that the coefficient of variation of torque decreases systematically as the maximum voluntary torque increases. This decreasing coefficient of variation means that a given torque or force can be more accurately generated by a stronger muscle than a weaker muscle. Simulations demonstrated that muscles with different strengths and different numbers of motor units could account for the experimental data. In the simulations, the magnitude of the coefficient of variation of muscle force depended primarily on the number of motor units innervating the muscle, which relates positively to muscle strength. This result can be generalised to the situation where more than one muscle is available to perform a task, and a muscle activation pattern must be selected. The optimal muscle activation pattern required to generate a target torque using a group of muscles, while minimizing the consequences of signal dependent noise, is derived.

Action Potentials↗

PubMed2002

Neurophysiology: cerebral carbon copies.

Predicting the consequences of our actions is essential for sensorimotor control. A candidate neural pathway underlying the prediction of eye position during saccades has been reported.

Animals↗

PubMed2002

Controlling the statistics of action: obstacle avoidance.

Task optimization in the presence of signal-dependent noise (TOPS) has been proposed as a general framework for planning goal-directed movements. Within this framework, the motor command is assumed to be corrupted by signal-dependent noise, which leads to a distribution of possible movements. A task can then be equated with optimizing some function of the statistics of this distribution. We found the optimal trajectory for obstacle avoidance by minimizing the mean-squared error at the end of the movement while keeping the probability of collision with the obstacle below a fixed limit. The optimal paths accurately predicted the empirical trajectories. This demonstrates that controlling the statistics of movements in the presence of signal-dependent noise may be a fundamental and unifying principle of goal-directed movements.

Avoidance Learning↗