Event-related potentials (ERPs) are changes in electrical currents in response to stimuli and cognitive tasks. By ERP it is possible to measure brain activity at a very high temporal scale, e.g. by doing registrations of brain activity thousand times a second. In contrast to measures of bloodstream, like fMRI and PET, ERPs are assumed to be direct reflections of neural activity, and is a very powerful tool in the study of human cognition. The three parameters of ERP are the strength of the response (amplitude), the speed of the response (latency), and the localization of the response (the scalp topography).

In our lab, we have used ERP to study cognitive function in participants from 7 to 94 years, especially in tasks related to attention and memory. A often-used task is the oddball task. A version of the oddball task, called a 3-stimuli oddball task is shown below.

In this task, two components called the P3a and the P3b is elicited. P3b latency is often regarded as a measure of the relative timing of the stimulus evaluation process (Coles & Rugg, 1995), and P3b amplitude is held to index resource allocation (Polich, 1996). In addition to the P3b, a P3a can be recorded to deviant non-target stimuli (a distractor). Agreement has not been reached on the exact nature of the neurocognitive processes underlying the P3a component, but it can be argued that P3a reflects involuntary, transient allocation of attention to salient stimuli changes and novel stimuli (Courchesne, et al., 1975). The cognitive interpretation of P3a and P3b also corresponds to the topography of the two components. P3a has a more fronto-central scalp distribution, while P3b, at least in young participants, has a parietal maximum.

Recently, we have focused a lot on the relationship between cognitive ERPs, as the P3 complex and different types of memory effects, and brain morphometry, that is, the thickness of the cerebral cortex and the volume of certain subcortical structures. Positive correlations between general cognitive abilities and gross measures of brain volume have been established (Deary and Caryl, 1997; Wicket et al., 2000). Such a positive relationship between cortical thickness and cognitive performance may be caused by a larger number of neurons or synaptic connections in thicker brains (Pakkenberg and Gundersen, 1997), which may benefit cognitive processing. The same reasoning can be applied to hypothesize a relationship between P300 and thickness: a thicker cortex may be able to process information in a faster and more efficient way due to a larger number of neurons and possibly synaptic connections, generating large and fast scalp-recorded potentials. This general view is further supported by moderate correlations between P300 and cognitive functions, even though some discrepant results have been reported (e.g. Houlihan et al., 1998). P300 are largely generated in the cerebral cortex, and may therefore have the potential to detect subtle changes in regional cortical thickness. Thus, it is an important question whether ERPs may be more sensitive to thickness differences than behavioral cognitive or psychometric tests. In a recently published paper in Human Brain Mapping (Fjell et al., in press), we found that P3a amplitude and P3b latency correlated with the thickness of the cerebral cortex in several regions, roughly corresponding to areas where P3 generators are assumed to be situated.

 The neurophysiological foundations of ERPs

Some knowledge of the neurophysiological foundation for the electrophysiological activity detected from the scull is necessary to better understand the relationship between ERP and cognition. The basis of ERP is the recording of EEG. Recordings of spontaneous EEG are themselves useful for several purposes. Electrical activity in the cerebral cortex reflects the firing patterns in the thalamocortical system. These firing patterns differentiate between wakefulness and sleep, and can be registered by EEG. According to Saper (2000), the specific rhythmic pattern of EEG reflects synchronized waves of excitatory synaptic potentials reaching the cerebral cortex from the thalamus. The thalamic relay neurons have two distinct states. When the neurons are hyperpolarized by inhibitory postsynaptic potentials, they respond to brief depolarizations with a burst of action potentials. Neurons in this state are in the burst mode, and produce an EEG slow-wave pattern of synchronized activity. As opposed to this, when the thalamic neurons are in a more depolarized state, incoming excitatory potentials produce single action potentials, and this state is called transmission mode. During wakefulness the thalamus is kept in the transmission mode by the action of cholinergic input from the rostral pons and basal forebrain. In this mode, incoming excitatory synaptic potentials can drive the neurons to fire in a pattern that reflects the sensory stimulus. Even though the thalamic neurons transmit sensory impulses to the cerebral cortex, the complex patterning of thalamic firing produces nearly constant, small-scale alterations in the dendrittic potentials of cortical neurons. The resulting EEG pattern of fast, low-voltage waves is termed desynchronized, reflecting ongoing sensory activity. To assess the activity caused by repeated stimuli, the variation in desynchronized EEG activity has to be cancelled out. This is the rationale for the averaging process in ERP assessments. Pyramidal neurons are the major projection neurons in the cortex, and post-synaptic, apical dendrittic activity in the pyramidal cells is the principal source of EEG activity (Westbrook, 2000). Presynaptic spikes have a high frequency and a short duration, and so it is more probable that the relatively slower postsynaptic activity is synchronized. The current flows into the dendrite at the site of the generation of an excitatory post-synaptic potential, creating a current sink. It then completes a loop by proceeding down the dendrite and back out across the membrane at other sites, creating a current source. Extracellular recordings at the scalp can then detect the synchronized activity of large number of cells. This type of activity has been termed field potentials. It is reasonable to assume that surface EEG predominantly reflects the activity of cortical neurons close to the particular electrode (ibid.). This does, however, depend both on differences in the depth and orientation of the neurons, as well as individual variability in craniocerebral topography (Steinmetz, Fürst, and Meyer, 1989).

The exact relationship between ERP measured at the scalp and the corresponding processes in the brain is not yet fully understood (Paller et al, 1992). Still, Rugg and Coles (1995) argue that some aspects of the relationship seem clear: ERP represent electrical fields associated with the activity from large samples of neurons. The individual neurons that constitute such a population have to be activated in a synchronized way, and have to possess certain geometrical properties, for this sample to be able to produce field potentials that can be recorded at the scalp. ERP can only be recorded when the neurons are organized in open fields, which means that all the dendrites have to be located on the same side, while the axons are located on the opposite side. Thus, the electrical fields generated from the activity of each neuron will be oriented in the same direction, such that their joint activity will be possible to record at a certain electrode site at the scalp. Open fields exist where neurons are organized in layers, or laminae (Rugg and Coles, 1995). Such an organization of the neurons exists in most of the cerebral cortex, parts of thalamus, cerebellum, as well as in some other brain structures (Brodal, 1995). Concentric or arbitrary organization of the neurons will constitute closed fields, and this makes it impossible to record their activity at the scalp. This will be the case e.g. with activity in the midbrain nuclei (Coles, Gratton, and Fabiani, 1990). Some fundamental aspects of human information processing partly depend on subcortical or closed field structures of the brain, and these processes will be inaccessible for extracerebral ERP recordings.

References

Brodal, P. (1995). Sentralnervesystemet. 2nd ed. Oslo: Tano.

Coles, M. G. H., Gratton, G., and Fabiani, M. (1990). Event-related potentials. In: J. T. Cacioppo and L. G. Tassinary (Eds.). Principles of Psychophysiology. Physical, Social, and Inferential Elements. Cambridge: Cambridge University Press.

Paller, K. A., McCarthy, G., Roessler, E., Allison, T., and Wood, C. C. (1992). Potentials evoked in human and monkey medial temporal lobe during auditory and visual oddball paradigms. Electroencephalography and Clinical Neurophysiology, 84, 269-279.

Rugg, M. D. and Coles, M. G. H. (1995). The ERP and Cognitive Psychology: Conceptual Issues. In: In: M. D. Rugg and M. G. H. Coles (Eds.). Electrophysiology of mind. Event-Related Brain Potentials and Cognition. New York: Oxford University Press.

Saper, C. B. (2000). Brain stem modulation of sensation, movement, and consciousness. In: E. R. Kandel, J. H. Schwartz, and T. M. Jessell (Eds.). Principles of Neural Science. 4th ed, 889-908. New York: McGraw-Hill.

Steinmetz, H., Fürst, G., and Meyer, B. U. (1989). Craniocerebral topography within the international 10-20 system. Electroencephalography and Clinical Neurophysiology, 72, 499-506.

Westbrook, G. L. (2000). Seizures and epilepsy. In: E. R. Kandel, J. H. Schwartz, and T. M. Jessell (Eds.). Principles of Neural Science. 4th ed, 910-934. New York: McGraw-Hill.