Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Time is a fundamental dimension of life. It is crucial for decisions about quantity, speed of movement and rate of return, as well as for motor control in walking, speech, playing or appreciating music, and participating in sports. Traditionally, the way in which time is perceived, represented and estimated has been explained using a pacemaker-accumulator model that is not only straightforward, but also surprisingly powerful in explaining behavioural and biological data. However, recent advances have challenged this traditional view. It is now proposed that the brain represents time in a distributed manner and tells the time by detecting the coincidental activation of different neural populations.

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What makes us tick? Functional and neural mechanisms of interval timing

Theories of instrumental learning are centred on understanding how success and failure are used to improve future decisions. These theories highlight a central role for reward prediction errors in updating the values associated with available actions. In animals, substantial evidence indicates that the neurotransmitter dopamine might have a key function in this type of learning, through its ability to modulate cortico-striatal synaptic efficacy. However, no direct evidence links dopamine, striatal activity and behavioural choice in humans. Here we show that, during instrumental learning, the magnitude of reward prediction error expressed in the striatum is modulated by the administration of drugs enhancing (3,4-dihydroxy-L-phenylalanine; L-DOPA) or reducing (haloperidol) dopaminergic function. Accordingly, subjects treated with L-DOPA have a greater propensity to choose the most rewarding action relative to subjects treated with haloperidol. Furthermore, incorporating the magnitude of the prediction errors into a standard action-value learning algorithm accurately reproduced subjects' behavioural choices under the different drug conditions. We conclude that dopamine-dependent modulation of striatal activity can account for how the human brain uses reward prediction errors to improve future decisions.

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Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans

Parallel neural networks for learning sequential procedures

Background— The precise mechanisms of and biological basis for motor recovery after stroke in adults are still largely unknown. Reorganization of the motor system after stroke as assessed by functional neuroimaging is an intriguing but challenging new field of research. Provocative but equivocal findings have been reported to date. Summary of Review— We present an overview of functional neuroimaging studies (positron emission tomography or functional MRI) of motor tasks in patients recovered or still recovering from motor deficit after stroke. After a brief account of the connectivity of motor systems and the imaging findings in normal subjects, the literature concerning stroke patients is reviewed and discussed, and a general model is proposed. Conclusions— Both cross-sectional and longitudinal studies have demonstrated that the damaged adult brain is able to reorganize to compensate for motor deficits. Rather than a complete substitution of function, the main mechanism underlying recovery of motor abili...

/pdf/functional-neuroimaging-studies-of-motor-recovery-after-1qedeac67w.pdf

Functional neuroimaging studies of motor recovery after stroke in adults: a review.

The role of the basal ganglia and cerebellum in the control of movements is unclear. We summarize results from three groups of PET studies of regional CBF. The results show a double dissociation between (i) selection of movements, which induces differential effects in the basal ganglia but not the cerebellum, and (ii) sensory information processing, which involves the cerebellum but not the basal ganglia. The first set of studies concerned motor learning of a sequence of finger movements; there was a shift of activation in the anterior-posterior direction of the basal ganglia which paralleled changes in the motor areas of the frontal cortex. During new learning, the dorsolateral prefrontal cortex and striatum (caudate nucleus and anterior putamen) were activated. When subjects had to select movements, the premotor cortex and mid-putamen were activated. With automatic (overlearned) movements, the sensorimotor cortex and posterior putamen were activated. When subjects paid attention to overlearned actions, activation shifted back to the dorsolateral prefrontal cortex and striatum. The cerebellum was not activated when subjects made new decisions, attended to their actions or selected movements. These results demonstrate components of basal ganglia-(thalamo)-cortical loops in humans. According to earlier studies in animals we propose that the basal ganglia may be concerned with selecting movements or the selection of appropriate muscles to perform a movement selected by cortical areas (e.g. premotor cortex). Secondly, a visuomotor co-ordination task was examined. In the absence of visual control over arm movements, subjects were required to use a computer mouse to either generate new lines or to re-trace lines on a computer screen. The neocerebellum (hemispheres of the posterior lobe, cerebellar nuclei and cerebellar vermis), not the basal ganglia, was more engaged when lines were re-traced (compared with new line generation). Animal experiments have shown that error detection (deviation from given lines) and correction occurs during line re-tracing but not line generation. Our data suggest that the neocerebellum (not the basal ganglia) is involved in monitoring and optimizing movements using sensory (proprioceptive) feedback. Thirdly, the relative contribution of sensory information processing to the signal during active/passive execution of a motor task (flexion and extension of the elbow) was examined; it was found that 80-90% of the neocerebellar signal could be attributed to sensory information processing. The basal ganglia were not involved in sensory information processing. They may be concerned with movement/ muscle selection (efferent motor component); the neocerebellum may be concerned with monitoring the outcome (afferent sensory component) and optimizing movements using sensory (feedback) information.

/pdf/a-review-of-differences-between-basal-ganglia-and-cerebellar-22blmfvp1z.pdf

C Weiller

Papers

A review of differences between basal ganglia and cerebellar control of movements as revealed by functional imaging studies.