Multiple tools exist for studying the neural bases of saccade control, including human functional neuroimaging techniques. Functional neuroimaging is defined here to include techniques used for their excellent spatial resolution, such as positron emission tomography (PET) (see glossary) and functional magnetic resonance imaging (fMRI) (see glossary and Box 1), and techniques used for their excellent time resolution, such as multichannel electroencephalography (EEG) (see glossary and Box 2) and magnetoencephalography (MEG) (see glossary). The study of saccade control using functional neuroimaging began with EEG studies in the 1960s (e.g., Gaarder, Krauskopf, Graf, Kropfl, & Armington, 1964) and was followed by PET studies in the 1980s (e.g., Fox, Fox, Raichle, & Burde, 1985). Since then, the growth in published studies on human functional neuroimaging of saccades in the English language literature shows the citation list almost doubling every 4 years (see Fig. 1). This review will provide a brief summary of the contributions made by human functional imaging studies to the understanding of neural correlates of saccadic control.FMRI
Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique conducted using a MRI system similar to those found in hospital settings. This technique allows for the investigation of brain changes associated with perceptual, motor and cognitive processes. Typically such processes are supported by increased activity levels in task-related neural circuits, in which case increased neural activity is associated with a cascade of events that may include increased metabolic rate, vasodilation, and increased blood volume and blood flow. The resulting influx of oxygenated hemoglobin surpasses the local neuronal metabolic need so there is an increased ratio of oxygenated to deoxygenated hemoglobin. Because deoxygenated hemoglobin is paramagnetic (disrupts local magnetic signals), greater oxygenation results in a corresponding decrease in magnetic signal disruption (i.e., effectively a signal increase). Thus, fMRI is typically based on the “blood oxygen level dependent” (BOLD) signal, a measure of local hemodynamic changes that are indirectly associated with changes in neural activity.
BOLD data are often displayed on high-resolution structural brain images as a map of colored voxels indicating areas of signal change. Regions of signal change can be described in three dimensions using standard normalized brain space (e.g., Talairach Atlas or Montreal Neurologic Institute Atlas co-ordinates).
An advantage of fMRI when compared to other neuroimaging techniques is its excellent spatial resolution (i.e., detection of “where” in the brain changes occur). In whole brain fMRI studies the spatial resolution is typically on the order of a few millimeters. A disadvantage, however, is the relatively poor temporal resolution (i.e., detection of changes in the brain over time). Because of the hemodynamic basis of fMRI, the temporal resolution has a limit on the order of seconds, much slower than neuronal firing rates.
EEG
Electroencephalography (EEG) is a non-invasive neuroimaging technique for recording neural activity using sensors affixed to the scalp (numbering from only a few to as many as 256). The neural signals recorded by EEG sensors are thought to be primarily graded postsynaptic field potentials from the apical dendrites of thousands of synchronously-firing pyramidal cells in localized regions of superficial cerebral cortex. When EEG signals are extracted from the ongoing EEG in relation to specific stimuli (e.g., the onset of a visual target prior to saccade generation), cognitive events, or the onset of a particular motor response (e.g., generation of an anti-saccade), the resulting waveforms, typically averaged over many trials of the same type, are called event-related potentials (ERPs). ERPs are derived by averaging the ongoing EEG signals in relation to the onset of an event of interest (e.g., from 200 ms before to 800 ms after stimulus onset). After averaging, the voltage deviations specifically associated with processing the event(s) can be seen more clearly because the random neural activity not related to stimulus processing is “averaged out” (those signals are not time-locked to event processing).
ERP data historically have been displayed as single sensor voltage variations over time, showing peaks and valleys (positive and negative voltage variations) in the ERP signal in relation to event onset. It is also common to show topographic maps at an individual time point of interest (e.g., the P100, with ‘P’ indicating the voltage at the top of the head, historically at Cz, the most superior, or vertex, sensor position, and ‘100’ indicating that the neural activity occurred 100 ms after event onset). Topographic maps are 2D or 3D displays of isopotential contour lines (much like an elevation graph for mountainous terrain) that are useful for superficially inferring the brain regions that were most active at that time point. To more accurately infer the neural sources generating the signals recorded at the scalp, however, it is necessary to use sophisticated mathematical algorithms that transform the data from “sensor” to “source” space, with the source space solutions then being displayed on 3D reconstructions of the brain (which can end up looking much like fMRI data).
An advantage of EEG/ERPs when compared to fMRI is the excellent temporal resolution (detecting “when” changes in neural activity occurred in relation to an event of interest) on a millisecond time scale. A disadvantage of this technology, however, is uncertain spatial resolution (identifying where changes in neural activity occurred) because signals recorded at the scalp must be used to estimate from where in the brain the signals of interest originated.
Exploration of the visual environment relies on two types of saccadic control (a distinction that may be more qualitative than quantitative; e.g., see Hutton, this issue). On the one hand, visually guided saccades (also known as reflexive, refixation or pro-saccades) are generated to external cues and require simple and direct sensorimotor transformations (see glossary) for their successful implementation. In the following text, “pro-saccades” is used in the context of tasks requiring visually guided saccades to visual stimuli. On the other hand, volitional saccades are more cognitively complex responses that require higher-order control processes such as inhibition, spatial memory, and analysis of contextual cues. The basic neural circuitry supporting the sensorimotor transformation part of saccade generation is similar for simpler and more complex saccadic responses (Leigh & Zee, 1999). As the factors determining saccadic response requirements become more complex, however, additional neural regions are recruited to support the requisite higher level processes (e.g., Munoz and Everling, 2004, Pierrot-Deseilligny et al., 2005, Sweeney et al., 2007).
A number of characteristics make the saccadic system extremely useful for investigating models of cognitive control. First, the system is particularly well understood based on an extensive literature that ranges from single-unit recordings in primates (Johnston & Everling, this issue) to lesion studies in humans (Pierrot-Deseilligny, Milea, & Muri, 2004). Second, there is good convergence between that literature and the human functional neuroimaging studies. Third, saccades can be measured precisely and with a number of reliable parameters (Smyrnis, this issue). As such, the study of cognitive control via saccadic system manipulations has applications across a diverse range of topics, extending from studies of basic motor function to normal cognitive neuroscience studies of executive control to investigations of behavioral and brain activity correlates of psychiatric conditions.
The following report on the neuroanatomy of the saccadic system in humans begins with a review of information on the neural circuitries known to support simple saccadic responses, such as pro- and express saccades. Second, we describe information on additional brain regions involved in supporting saccadic responses in more cognitively complex situations, such as during anti-saccades (see Fig. 2), ocular motor delayed response tasks, predictive saccadic tracking and saccade sequencing tasks. Basic neurophysiology, lesion, and neuropathology studies will be incorporated if they help clarify the functional neuroimaging data. Information gleaned from fMRI (quantified as blood oxygenation level dependent signal; BOLD) and PET studies will be supplemented with EEG and MEG studies to the extent that the latter yield at least some information about the brain regions involved in generating the signals measured at the sensors (e.g., via source analysis).
