
One of the main aims of neuroscience is to understand how the CNS deals with and adapts to external inputs and internal events. The altered molecular and cellular events form a basis for abnormal processing that underlies physiopathological events. Understanding these events can allow translation from bench science to the patient, and a key step in this process is using models of a clinical condition in animals and human volunteers. Pain is an example of an area where this process has reached a high level of sophistication and where the route from the bench to the patient is starting to be mapped out. The article by Iannetti et al. in this issue of PNAS (1) is a wonderful example of how an imaginative and well planned imaging study can build on findings from animals to help explain the complex mechanisms of pain and its modulation in humans. Pain is defined by the International Association for the Study of Pain as “an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage.” Key to this definition is the recognition that both the perception and experience of pain are multi-factorial. Detection of a noxious stimulus begins with nociceptors that are expressed on primary afferent fibers distributed throughout the body. These afferents, thinly myelinated Aδ fibers and small diameter unmyelinated C fibers, transduce stimulus energy into electrical signals (action potentials) that are conducted along neurones to the spinal cord and brain. There is no dependable relationship between the intensity of the stimulus and the pain that is eventually perceived; instead, the relationship is subject to individual variation and is influenced, at least in part, by the condition of the tissue and the environmental context in which the …
Central Nervous System, Diagnostic Imaging, Analgesics, Animals, Humans, Pain
Central Nervous System, Diagnostic Imaging, Analgesics, Animals, Humans, Pain
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 19 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
