perpendicular to the direction of flow of the magnetic field, current is induced in the wire coil when it is placed over an area of active cortex (Reite et al. 1999). The wire detector is itself inductively coupled to the SQUID and its electronics, which together comprise a sensitive magnetic field measuring device. Because the magnetic fields produced by cortical activity are closer to the magnetic field detector than are most environ-mental sources, this device is reasonably sensitive to the fluctuating gradients produced by cortical activity and less affected by the more stable field gradients of distant environmental magnetic sources (Rojas et al. 1999). A variety of MEG detection coils are available, each dif-fering in their signal sensitivity and capacity for noise reduction. Modern magnetoencephalographic systems may have as many as 300 individual magnetic detectors (which are analogous to electroencephalographic elec-trodes). Pairing magnetic field detectors creates chan-nels for signal recording; these chanchan-nels can be arranged to create recording montages. Arrays of mul-tiple magnetoencephalographic channels may also be used for these purposes or arranged in a variety of ways to create magnetoencephalographic counterparts to electroencephalographic montages. Smaller arrays offer more limited and/or focused areas of signal detec-tion, as might be used in magnetoencephalographic evoked field or MSI recordings.
Magnetic field strength is not significantly attenuated by the tissue interposed between the source of the signal and the magnetometer positioned to detect it (Cuffin
1993). As such, MEG may be better able to detect both very high-frequency (up to 400–700 Hz) and ultra-low frequency (<1 Hz) signals that are not amenable to elec-troencephalographic recording (Lewine et al. 1999; Reite et al. 1999). However, there remain substantial technical challenges to recording cortically generated magnetic fields that offset this theoretical advantage (see Rojas et al.
1999 for a review). Although many of these technological challenges are manageable by presently available record-ing devices, the equipment, the magnetically shielded en-vironment in which it must be operated, and the routine operation of such recording systems are cost, expertise, and labor intensive. These challenges may be reasons for the limited availability and application of MEG in TBI research to date.
Electrophysiological
physiological disturbances in these periods when such are available.
Electroencephalography
EEG was the first clinical diagnostic tool to provide evi-dence of transient abnormal brain function due to TBI (Glaser and Sjaardema 1940; Jasper et al. 1940). Williams and Denny-Brown (1941) experimentally demonstrated similar electroencephalographic abnormalities after TBI, including electroencephalographic attenuation and slow-ing in the acute injury period followed by resolution of these abnormalities over time. Consistent with these observations, there is general agreement among electro-encephalographers that in the acute injury period the EEG often demonstrates a variety of abnormalities con-sistent with the severity of injury, the type and location of injury, and the patient’s age (Table 7–2).
Immediately after mild TBI, the EEG is typically nor-mal or only mildly abnornor-mal, but may demonstrate slowing of the background rhythm into the theta range, attenuation of alpha, and increase in delta activity. More severe TBIs, particularly those affecting cortical, subcortical, and mes-encephalic areas, may result in more severe electroenceph-alographic abnormalities such as prominent and diffuse delta with minimal or no alpha and theta activity, lack of re-activity, a burst suppression pattern, or frank electrocere-bral silence (Gütling et al. 1995; Theilen et al. 2000; Tip-pin and Yamada 1996). In general, there is a relatively robust correlation between depth of coma and the degree of electroencephalographic abnormality, and clinically ap-parent focal neurological deficits tend to be associated with electroencephalographic abnormalities referable to the cortical injuries responsible for such deficits (Rumpl et al.
1979). Electroencephalographic abnormalities of this sort may include focal and asymmetrical slowing, generalized T A B L E 7 – 2 . Normal and trauma-related electroencephalographic findings
Condition Typical electroencephalographic findings
Healthy adult Low-voltage beta frequencies predominate with eyes open, posterior dominant (alpha) rhythm emerges with eyes closed; central alpha may be present, but is of lower amplitude than posterior alpha; theta and delta are not prominent, although a small amount of bihemispheric theta may be detectable with digital frequency (spectral) analysis Normal aging Diminished amplitude of beta activity; decreased amplitude of the posterior dominant
rhythm, possible shift of the posterior dominant rhythm to the low alpha range; possible increase in temporal theta; possible diffuse increase in delta and theta in advanced aging Focal cortical contusion, hemorrhage,
infarction, or abscess
Focal slowing at the borders of infarction and decrease in beta activity over the area of contusion or infarction; focal slowing may be superimposed on a relatively normal-appearing background if there is only a small, discrete contusion or infarction; rhythms overlying such lesions consist of intermittent or continuous polymorphic delta and superimposed theta; sharp waves or spikes
White matter injury (relatively severe) Continuous polymorphic delta activity that is not reactive to stimuli; deeper lesions causing a disconnection of subcortical nuclei and cortex may also produce FIRDA
Anterior brainstem/diencephalic injury Bilateral FIRDA that is reactive to stimuli and not apparent during sleep; bifrontal theta may be seen with slow-growing deep midline tumors
Encephalopathy (delirium) Diffuse slowing with irregular high-voltage delta activity Acute agitated delirium Low-voltage fast activity
Acute confusional state Diffuse intermixed slowing
Seizure disorders Focal or generalized spikes, sharp waves, and spike-and-wave complexes Complex partial seizures Focal spike-and-wave or sharp-wave discharges
Skull defect Markedly asymmetrical, high-amplitude, focal beta activity recorded from the scalp overlying the defect (breach rhythm)
Subdural hematoma Asymmetrical suppression of normal rhythms recorded from the scalp overlying the subdural hematoma; slower rhythms may eventually develop
Medications Increased beta activity (sedative-hypnotics, anticonvulsants); diffuse intermixed slowing Note. FIRDA = frontal intermittent rhythmic delta activity.
slowing of the background rhythm, focal spikes or spike-and-wave discharges, focal loss or asymmetry of reactivity, or some combination of these (Gütling et al. 1995; Rumpl et al. 1979; Tippin and Yamada 1996). In the acute injury period, and particularly in children, electroencephalo-graphic abnormalities may be present even in the absence of frank neuroimaging (computed tomography) abnormal-ities (Liguori et al. 1989); when present, such abnormalabnormal-ities should raise clinical concern for the possibility of a trau-matically induced structural abnormality.
Several studies suggest that EEG may be a useful tool for monitoring cerebral function after TBI (Jordan 1993), including the identification of focal ischemia, diffuse hy-poxia, nonconvulsive seizures, the efficacy of pentobar-bital treatment of increased intracerebral pressure (Win-ter et al. 1991), and the effect of hyperventilation on cerebral function (Bricolo et al. 1972). Prognosis after TBI may also be predicted using EEG, other comple-mentary electrophysiological techniques, or combina-tions of these (Evans and Bartlett 1995; Gütling et al.
1995; Rae-Grant et al. 1991).
For example, Rae-Grant et al. (1996) studied EEG, somatosensory and brainstem auditory EPs (SSEPs and BAEPs, respectively), ocular plethysmography, transcra-nial Doppler sonography, and computed tomographic as-sessments in 69 acutely injured patients for the purpose of determining the techniques’ ability to predict long-term outcome after TBI. Among these several assessments, only EEG (based on ratings of background activity, sym-metry, reactivity, variability, and additional abnormal pat-terns) independently predicted the Glasgow Outcome Scale score at 6 months. However, electroencephalo-graphic assessment in the acute injury period offered no advantage in outcome prediction over the Glasgow Coma Scale (GCS) score determined at day 7 postinjury.
Synek (1990a, 1990b) suggests that the pattern of EEGs obtained during acute posttraumatic coma may yet be of prognostic value. He reports that benign patterns (e.g., alpha or theta background, reactivity) predict sur-vival and relatively good outcome, whereas malignant patterns (e.g., burst suppression, low-output or isoelectric EEG, nonreactive alpha or theta coma patterns) are highly associated with death. Hutchinson et al. (1991) demonstrated similar but less striking findings, including the association of either isoelectric EEG and lack of elec-troencephalographic reactivity with poor outcome and benign patterns with relatively good outcome after TBI.
This study also demonstrated that modestly abnormal electroencephalographic patterns did not consistently predict outcome after TBI.
Among patients with mild TBI, the value of EEG in the acute setting is less clear. Although generalized
slow-ing may occur in the first several hours after injury (Geets and De Zegher 1985), these and other abnormalities are seen in less than 20% of mildly injured individuals and tend to abate with time after injury (Tippin and Yamada 1996). Voller et al. (1999) compared MRI, EEG, and neu-ropsychological testing results of 12 patients with very mild TBI (no or only brief loss of consciousness [LOC], posttraumatic amnesia of less than 1 hour, GCS = 15, no disorientation, and normal neurological examination) within 24 hours of injury and at 6 weeks to those of com-parably aged and educated control subjects. Significant differences in neuropsychological performance between these groups were demonstrated. MRI abnormalities were observed in 25% of the subjects with TBI. However, none of the subjects with very mild TBI had electroen-cephalographic abnormalities of any kind, including those with mild structural abnormalities, suggesting that routine EEG is not sensitive to subtle electroencephalo-graphic abnormalities even in patients with mild TBI with structural abnormalities on MRI.
Early studies suggested that as many as 44%–50% of patients with persistent postconcussive symptoms have electroencephalographic abnormalities in the late postin-jury period, including generalized or focal slowing and occasional epileptiform discharges (Denker and Perry 1954; Torres and Shapiro 1961). More recent studies us-ing rigidly defined conventional electroencephalographic rating criteria do not support these earlier observations (Haglund and Persson 1990; Jacome and Risko 1984), leaving uncertain the relationship between postconcus-sive symptoms and conventional electroencephalographic findings.
It is possible for patients to have electroencephalo-graphic abnormalities on a post-TBI recording that are unrelated to their symptoms or that may have antedated their injuries. Conversely, patients may have postconcus-sive symptoms, including posttraumatic epilepsy, without readily apparent abnormalities on conventional EEG.
Nonetheless, abnormal electroencephalographic findings whose location, type, and severity correlate well with clin-ical problems occurring after TBI should be regarded as strongly suggestive of injury-induced electrophysiologi-cal abnormalities. It is important to note that epileptiform electroencephalographic abnormalities are relatively un-common findings in the immediate postinjury period, and, even when present, they do not robustly predict the development of posttraumatic epilepsy (Tippin and Ya-mada 1996). Nonetheless, persistence of epileptiform ab-normalities in a patient with paroxysmal clinical events consistent with seizures after TBI strongly suggests post-traumatic epilepsy. Additionally, a markedly abnormal background rhythm, mildly abnormal rhythms not better
accounted for by medications or concurrent medical con-ditions, focal slowing, or focal epileptiform discharges in the late postinjury period should raise concern for the possibility of underlying structural abnormalities.
In summary, conventional EEG may contribute to the evaluation of severely brain-injured patients in the days to weeks after injury. Severe electroencephalographic ab-normalities, as well as combinations of less severe but still abnormal findings, may be of value when making prog-noses about survival and functional outcome after severe TBI. Less severe electroencephalographic abnormalities tend to improve significantly or resolve over time in pa-tients who survive their TBI. However, persistent electro-encephalographic abnormalities whose type and location are clinically correlated with certain neurological or neu-ropsychiatric disturbances in the late period after TBI in-dicate the presence of functionally important physiologi-cal and, possibly structural, brain abnormalities.
Conventional electroencephalographic evaluations may be particularly useful in the evaluation of patients with events suggestive of posttraumatic epilepsy in either the acute or late postinjury periods. However, the absence of epileptiform abnormalities on EEG does not necessarily suggest that such events are of a nonepileptic nature (e.g., psychogenic or cardiogenic). Put another way, an absence of evidence of electrophysiological abnormalities on con-ventional EEG does not constitute evidence of absence of such. Because routine EEG is relatively insensitive to many of the subtleties of cerebral electrophysiology and to deeper sources of electrophysiological activity, it should be regarded as having only limited utility in the neuropsychiatric evaluation of patients with TBIs.
Quantitative Electroencephalography
Quantification of the EEG provides methods of data analysis that may be more sensitive to electrophysiologi-cal subtleties than conventional visual inspection of the electroencephalographic record (Hughes and John 1999).
Although there has been considerable debate about the validity, reliability, sensitivity, and specificity of quantita-tive electroencephalographic findings associated with TBI (Hughes and John 1999; Nuwer 1997; Thatcher et al. 1999), these methods of electroencephalographic interpretation and analysis continue to hold promise for the investigation of neuropsychiatric disorders in general and the neuropsychiatric consequences of TBI in partic-ular (Gevins et al. 1992).
Several early studies of acutely brain-injured patients suggested that spectral analysis of frequency data demon-strated abnormalities that predicted outcome (Bricolo et al. 1979; Steudel and Kruger 1979; Strnad and Strnadova
1987). In these studies, slower monotonous rhythms and limited or poor reactivity after TBI were associated with death in as many as 86% of subjects, whereas relatively greater amounts of alpha and theta activity portended better survival rates. More recently, Theilen et al. (2000) applied spectral analysis to frontally acquired electroen-cephalographic data in acutely severely injured patients to determine the predictive value of the electroencephalo-gram silence ratio (ESR). The ESR was defined as inter-vals of suppression of electroencephalographic activity lasting more than 240 milliseconds in which the electro-encephalographic amplitude did not exceed 5 µV (also known as the burst-suppression ratio). This measure was in-versely correlated with outcome at 6 months as assessed using Glasgow Outcome Scale scores and Rappaport Dis-ability Rating Scale scores. In other words, increased electrical silence in the EEG in the acute injury period was highly correlated with poor functional outcome and/
or death at 6 months. Although this finding echoes early reports of poor outcome in association with electrocere-bral silence assessed by visual inspection of conventional electroencephalographic recordings (Hockaday et al.
1965), the ESR offers an easily measured and quantified variable for inclusion in postinjury prognostications.
When used in the fashion described by Theilen et al.
(2000), the ESR predicted outcome with an accuracy of 90%, exceeding that offered by somatosensory evoked potentials (84%), GCS at 6 hours postinjury (75%), or age (68%).
Kane et al. (1998) demonstrated the potential value of topographic analysis of relative electroencephalographic power in the prediction of 6-month and 1-year outcome after severe TBI. In particular, they demonstrated signif-icant correlations between left frontocentral beta and al-pha; left centrotemporal beta, alpha, theta, and delta;
right frontocentral beta; and right centrotemporal beta and alpha power and outcome from posttraumatic coma.
In particular, loss of left frontocentral beta and cen-trotemporal beta and alpha power was associated with poor outcome after TBI.
Thatcher et al. (1991) applied a topographic analysis of electroencephalographic power, coherence, phase, and symmetry to outcome predictions in a group of 162 pa-tients with TBI at various levels of severity. They demon-strated highly significant correlations between Rappaport Disability Rating Scale scores and measures of electroen-cephalographic coherence and phase between multiple frontal and frontocentral electrodes. In this study, the combined GCS scores obtained at the time of electroen-cephalographic recording (on average, 7.5 days after TBI) and the measures of electroencephalographic coherence and phase provided 95.8% discriminant accuracy
be-tween good outcome and death. Unlike the more recent study by Kane et al. (1998), Thatcher and colleagues did not find electroencephalographic power values of similar significance in prognostic predictions. It is possible that the inclusion of a relatively more mildly injured group of subjects may have reduced the likelihood of significant power reductions, as mild injuries are less likely to pro-duce the types and severities of cortical, diencephalic, and brainstem injuries likely to produce coma (as in the Kane et al. study) and related reductions in beta and alpha power. Instead, the inclusion of relatively more mildly in-jured patients may have increased the likelihood of find-ing significant changes in more subtle measures of brain network function (i.e., coherence and phase) in these sub-jects. Despite their methodological differences, both studies demonstrate that topographic quantitative elec-troencephalographic analyses offer information not avail-able with conventional EEG that may be useful in pre-dicting outcome after TBI.
QEEG may also be useful for the evaluation of pa-tients in the postacute and late periods after TBI. Mont-gomery et al. (1991) evaluated bilateral temporoparietal electroencephalographic spectra in 26 patients with mild TBI and postconcussive symptoms acutely and at 6 weeks after TBI and demonstrated a relative excess of theta power bilaterally immediately after TBI that significantly improved by the time of subsequent assessment. This study did not report correlations between relative nor-malization of theta power and resolution of postconcus-sive symptoms, leaving unanswered the strength of this relationship, if any. Additionally, more comprehensive as-sessment of other measures (coherence, phase, and sym-metry) were not undertaken by Montgomery and col-leagues. Nonetheless, this study suggests that QEEG may be useful for tracking the recovery of electrophysiological function after TBI.
Other neuropsychiatric consequences of TBI, includ-ing hostility (Demaree and Harrison 1996), postconcus-sive syndrome (Fenton 1996), and treatment-resistant de-pression (Mas et al. 1993), have been studied using QEEG. In these conditions, the principal application of QEEG has been to define electrophysiological abnormal-ities (typical changes in power in one or more frequency bands) that might improve understanding of the neurobi-ology of these sequelae of TBI.
Comparatively greater efforts have been put toward the development of QEEG-based discriminant functions (a statistically derived set of measures that permit pattern recognition in complex data sets) capable of accurately identifying electrophysiological changes that discrimi-nate robustly those individuals with TBI from those with-out TBI (Thatcher et al. 1989, 2001b). QEEG-based
dis-criminant functions that index injury severity might improve predictions of clinical outcome and assist in the development of rehabilitation strategies for patients with known TBI. Additionally, such discriminant functions might improve diagnostic accuracy if capable of robustly distinguishing between individuals with and without TBI.
Such functions might also be of benefit in the medicolegal evaluation of patients with mild TBI whose clinical symp-toms and neuropsychological impairments are not cor-roborated by abnormalities on conventional EEG or structural neuroimaging.
In an early study of the potential usefulness of dis-criminant functions comprised of multiple quantitative electroencephalographic variables, Randolph and Miller (1988) studied 10 patients with neuropsychologically sig-nificant TBI in the late (2-to 4-year) postinjury period and 10 matched controls. Spectral analysis demonstrated increased amplitudes in the beta, theta, and delta ranges;
increased amplitude variance; and reduced correlation coefficients between homologous electrode sites. Among these findings, increased amplitude variance in temporal areas correlated with poorer neuropsychological perfor-mance. The authors note that these findings suggest the persistence of clinical significant electrophysiological dysfunction after TBI that is not amenable to detection with conventional electroencephalographic analysis, and that several quantitative electroencephalographic vari-ables appear to offer some discriminant validity for the detection of symptomatic TBI survivors.
In an effort to develop a QEEG-based discriminant function capable of accurately distinguishing between in-dividuals with and without mild TBI, Thatcher et al.
(1989) studied 608 individuals with documented uncom-plicated mild TBI (GCS = 13–15) producing either no LOC or LOC less than 20 minutes and 108 noninjured comparison subjects. The initial phases of the study in-cluded the assessment of 243 patients with mild TBI and 83 noninjured comparison subjects, the results of which were used to build sets of variables to be entered into the discriminant function. After defining the relevant electro-encephalographic variables, their use in the proposed dis-criminant function was independently cross-validated in three additional series of patients. Data from one of these series demonstrated that the discriminant function of-fered a high level of test-retest reliability. From these studies, three classes of neurophysiological variables pro-vided the basis for the discriminant function: increased coherence and decreased phase in frontal and frontotem-poral regions, decreased power differences between ante-rior and posteante-rior cortical regions, and reduced alpha power in posterior cortical regions. Using these variables, the discriminant function affords 96.6% sensitivity and
89.2% specificity for mild TBI versus no injury, and also offers a positive predictive value of 93.6% and a negative predictive value of 97.4% (Thatcher et al. 1999).
Increased coherence and decreased phase in frontal and frontotemporal regions may suggest a loss of func-tional differentiation between frontal and frontotemporal areas that would not be expected in a noninjured brain (Thatcher et al. 1989). A similar interpretation of reduced anteroposterior power differences was also offered. Re-duced posterior alpha was taken to suggest reRe-duced corti-cal excitability, consistent with previous observations of postinjury alpha reductions described in the conventional EEG literature. Thus, each of three classes of neurophys-iological variables comprising the discriminant function were understood as modifications of brain function at-tributable to the effects of mechanical brain injury.
Thatcher and colleagues subsequently demonstrated correlations between electroencephalographic coherence (1998b), amplitude (1998a), and power (2001a) and in-creases in T2 relaxation times in cortical gray matter and white matter in patients with TBI. These findings suggest that subtle alterations in the composition of these tissues are associated with abnormalities of electrophysiological function and provide support for the hypothesis that the variables in the TBI discriminant function reflect reduced functional differentiation of the brain areas whose func-tion they index.
Thornton (1999) reported a similar study of a mild TBI discriminant function predicated on the work of Thatcher et al. (1989) but extending the frequency spec-trum of interest to include higher ranges (32–64 Hz) than those included previously. Quantitative electroencepha-lographic variables were collected from 91 adult and ado-lescent subjects, including 32 TBI subjects with LOC less than 20 minutes (“mild TBI”), seven TBI subjects with LOC greater than 20 minutes, and 52 noninjured com-parison subjects. Thornton reported that the mild TBI discriminant function correctly identified 79% of sub-jects, even 43 years postinjury. His additional high-fre-quency discriminant correctly identified 87% of the mild TBI subjects across all time periods after injury and 100%
of subjects within 1 year of accident. The combination of the original mild TBI discriminant function and the addi-tional high-frequency discriminant variables correctly classified 100% of the TBI subjects.
In the most recent study of this sort, Thatcher et al.
(2001b) extended the discriminant function to patients with moderate and severe TBI and noted similar alter-ations in coherence, phase, and amplitude to those de-scribed in the mild TBI discriminant function. Addition-ally, more severe QEEG discriminant function scores were correlated with more severe neuropsychological
im-pairments, even when such assessments were performed months to years after TBI. Taken together, these studies suggest that quantitative electroencephalographic vari-ables may usefully index the presence, severity, and neu-ropsychological effects of TBI at all levels of severity.
Although the quantitative electroencephalographic discriminant functions described by Thatcher and col-leagues (1989, 2001b) appear to distinguish robustly be-tween patients with TBI at various levels of initial injury severity and also between TBI and noninjured compari-son subjects, they are not intended to provide a method for distinguishing patients with TBI and those presenting with similar cognitive impairments due to other causes such as depression, attention deficit hyperactivity disor-der, substance abuse, and so forth. Although these other neuropsychiatric conditions have been characterized us-ing QEEG (see Evans and Abarbanel 1999 for a review), direct comparisons of the discriminant validity of these patterns when compared not against controls subjects but against other clinical conditions are not available at present. Therefore, it is not appropriate to compare an individual patient’s quantitative electroencephalographic data with one or another of these databases in the hope of identifying the “correct diagnosis.” It is entirely likely that the set of quantitative electroencephalographic vari-ables that discriminate between patients with mild TBI and controls will not be the same as those that discrimi-nate between mild TBI and other neuropsychiatric con-ditions. With this in mind, Thatcher et al. (1999) and Duffy et al. (1994) stated quite clearly that clinical diag-noses should not be made solely by virtue of fitting elec-troencephalographic data with one or another quantita-tive electroencephalographic discriminant score. Until studies designed to ascertain the accuracy with which the TBI discriminant function distinguishes TBI from these other conditions are completed, the routine clinical use of discriminant function databases claiming to offer diag-noses across a range of neuropsychiatric conditions is not advisable.
It is also important for clinicians working with trau-matically brain-injured patients in either clinical or med-icolegal contexts to be aware that the use of QEEG and the mild TBI discriminant function are subjects of sub-stantial, and at times acrimonious, debate. Shortly after the mild TBI discriminant function was described (Thatcher et al. 1989), a position paper offered by the American Academy of Neurology (AAN) (1989) charac-terized QEEG as experimental and therefore without clear indication for use in routine clinical practice. Almost a decade later, Nuwer (1997), writing on behalf of the AAN and American Clinical Neurophysiology Society (ACNS), offered a review of the evidence supporting the
usefulness of QEEG and, in particular, the mild TBI dis-criminant function described by Thatcher et al. (1989).
He concluded that “evidence of clinical usefulness or con-sistency or results are not considered sufficient for us to support its [QEEG] use in diagnosis of patients with post-concussion syndrome, or minor or moderate head injury.”
Additionally, this position paper rejected the use of QEEG in medicolegal contexts. This paper was followed by two rebuttals by Thatcher et al. (1999) and Hoffman et al. (1999). These rebuttal papers described problems in the AAN and AAN/ACNS reports, including factual mis-representations, omissions, and biases, and their authors suggested that these problems are of a severity sufficient to merit reconsideration and/or frank dismissal of the of-ficial AAN/ACNS position on QEEG in TBI. It is not our intention here to offer an opinion with respect to the merits of the AAN/ACNS position paper or the rebuttal papers it prompted. Instead, we strongly suggest that cli-nicians involved in the care and medicolegal evaluation of individuals with mild TBI review these papers indepen-dently before forming either a clinical or a medicolegal opinion about these issues.
Evoked Potentials and Event-Related Potentials
EPs reflect neurophysiological processing along the path-ways from sensation to primary sensory cortex (Misulis and Fakhoury 2001). EPs develop 1–150 milliseconds after pre-sentation of the stimulus used to evoke them, with the exact timing (latency) of the EP after stimulus delivery depen-dent on the location of its neural generators along the pro-cessing pathway in which it is evoked. In general, EPs reflect automatic sensory information processes occurring before conscious recognition and intentional processing of the stimulus. ERPs reflect the neurophysiological pro-cesses associated with cognitive, sensory, or motor events (Pfefferbaum et al. 1995). ERPs develop 70–500 millisec-onds after the event that evokes them. The speed with which these neurophysiological processes occur makes them relatively inaccessible to study using self-report, neu-ropsychological assessment, behavioral assessments, or functional neuroimaging methods (Pfefferbaum et al.
1995; Reeve 1996). The exquisite temporal resolution of EPs and ERPs offers a method of investigating the earliest components of sensory and cognitive function and dys-function that would otherwise be difficult, if not impossi-ble, to study in living human subjects.
EPs and ERPs are generally named according to their polarity and latency; the names of EPs are often also qual-ified by indicating the sensory modality in which they are evoked. The polarity of an EP or ERP is defined by the
positive or negative deflection of its waveform in the elec-troencephalographic tracing. The latency of an EP refers to the time after stimulus delivery at which the EP or ERP develops. For example, the positive waveforms evoked approximately 30 and 50 milliseconds after the delivery of an auditory stimulus are referred to as the P30 and P50, respectively; the largest auditory evoked nega-tive waveform between 70–100 milliseconds is designated the N100 (Figure 7–7).
The amplitude of EPs and ERPs is quite small (0.1–10 µV) compared with that of the background EEG (10–100 µV). Consequently, computer-assisted signal averaging of many stimulus-evoked response sets is used to improve de-tection of these small signals. The signal-averaging process assumes that the amplitude of EP or ERP is stable (signal) and that the waveforms in the background EEG are random (noise). Averaging the results of many stimulus-EP trials re-sults in reduction of the amplitude of the background elec-troencephalographic waveforms because the mathematical average of random noise approximates zero. This process improves the signal-to-noise ratio within EP and ERP data sets, enhances signal detection, and facilitates recognition of subtle differences in the effects of stimuli or events on the waveforms they evoke (Cudmore and Segalowitz 2000).
Short-Latency Evoked Potentials
A number of studies have used short-latency somatosen-sory, auditory, or visual EPs to characterize brain function in deeply comatose, sedated, or pharmacologically para-F I G U R E 7 – 7 . P30 and P50 evoked potentials (EPs).
P30 and P50 EPs to a short-duration, moderate intensity, broad-frequency binaural stimulus in a 34-year-old male control sub-ject. The actual latencies of these EPs vary from their stated la-tency by approximately 10 milliseconds (ms); this degree of variability is normal and is expected in most recordings. The low-amplitude N100 in this tracing is “split,” meaning that two definable but partially overlapping waveforms contribute to the EP observed in this tracing.