1CHAPTER 1

Clinical Neurophysiology: An Overview

Edward C. Mader, Jr., Daniella Miller, and Piotr W. Olejniczak

CLINICAL NEUROPHYSIOLOGIC TESTING

Clinical neurophysiology (CNP) is a time-honored medical specialty that continues to make great strides, bolstered by rapid advances in neuroscience, biomedical engineering, and computer technology. It encompasses a wide range of methods and techniques for recording, presenting, and analyzing neurophysiologic signals in order to diagnose sensory, motor, autonomic, and central nervous system disorders. Testing performed in CNP or procedures used in current neurological practice include a variety of modality-specific and mixed-modality tests (Table 1.1).

Modality-specific CNP tests are performed to assess specific functional modalities using biomedical instruments that measure changes in neurophysiologic signals that occur spontaneously or with activation. Spontaneous fluctuations in electrical and magnetic fields during brain cortical activity are detected with EEG and magnetoencephalography (MEG) (1,2). Changes in electrical potentials with nerve and muscle activity are measured with electromyography (EMG) and nerve conduction studies (NCSs) (3). Subtle changes in the function of signaling pathways can be detected with evoked potentials (EPs), of which the most clinically useful are somatosensory (SSEPs), brainstem auditory (BAEPs), visual (VEPs), and motor (MEPs) evoked potentials (4). SSEPs are elicited by electrical stimulation of the median nerve (M-SSEP), the tibial nerve (T-SSEP), and rarely from other peripheral nerves. MEPs are activated by means of transcranial electrical stimulation (TES-MEP) or transcranial magnetic stimulation (TMS-MEP). The brain’s ability to anticipate or respond to cognitive events is examined using event-related potentials (ERPs), such as the Bereitschaftspotential (BSP), contingent negative variation (CNV), P300 (P3), and mismatch negativity (MN) (5). CNP autonomic function testing includes measurement of heart rate variability with deep breathing (HR-DB), recording of blood pressure (BP) and heart rate (HR), response to head-up tilt (HUT) and Valsalva (VAL) maneuver, and sudomotor function by means of the quantitative sudomotor axon reflex test (QSART) and the thermoregulatory sweat test (TST) (6).

Mixed–modality (aka multi-modality) CNP tests utilize two or more test modalities to assess complex states (e.g., sleep, coma), to track multiple physiologic parameters, or to obtain more accurate results. During sleep studies, scalp EEG, chin EMG, and EOG (electrooculogram) allow tracking of sleep–wake stages; additional physiologic processes, such as nasal airflow, ventilatory effort, snoring, blood oxygen saturation, ECG, and anterior tibialis EMG, are recorded during polysomnography (PSG) (7). As a rule, neurophysiologic intraoperative monitoring (NIOM) is unimodal with the test modality chosen based on the structures at risk during the procedure, but multimodal NIOM may be superior for selected surgical procedures, such as combining SSEP and MEP monitoring during surgery on the spine (8). In multimodal neuromonitoring (MNM), cerebral blood flow, brain tissue oxygenation, and brain tissue metabolism are monitored, in addition to intracranial pressure (ICP) and EEG, to improve outcome in acute brain injury (9). The multimodal approach, known as coregistration, involves superimposing the data of two or more test modalities to achieve a level of accuracy that is not attainable if each modality is analyzed separately (10). Examples of coregistration techniques are electric source imaging (ESI), magnetic source imaging (MSI), and combined electric-magnetic source imaging (EMSI).

Specific test methods or protocols are selected based on test objectives, patient characteristics, clinical setting, and other factors (11–14). While the routine approach is sufficient in most cases, some clinical problems and situations require modifications or refinements in the routine method. For example, the following EEG methods and protocols are aimed at different clinical scenarios: routine scalp EEG, extended EEG recording, ambulatory EEG, neonatal EEG, brain death study, ICU-EEG monitoring, IOM-EEG monitoring, long-term monitoring (LTM) of video and EEG in the epilepsy monitoring unit (EMU), electrocorticography (ECoG), stereo-EEG, and coregistration techniques, such as ESI and EMSI. It may be prudent to combine different test modalities to obtain more accurate results, such as simultaneous EEG and EMG recording to better define the seizure type and EEG recording during HUT to distinguish postsyncopal convulsions from epileptic seizures and psychogenic events.

2TABLE 1.1: Clinical Neurophysiologic Tests in Current neurological Practice

Note: The first seven categories comprise modality-specific tests, and the last three categories consist of mixed-modality tests (see text for explanation). Listed under each test category are test methods or protocols aimed at specific clinical settings and test objectives. The right column shows the signal of interest or physiologic parameter(s) recorded during neurophysiologic testing.

BAEP, brainstem auditory evoked potential; BP, blood pressure; BSP, Bereitschaftspotential; CMAP, compound motor action potential; CNV, contingent negative variation; EMG, electromyography; EOG, electrooculogram; EP, evoked potential; FVEP, flash visual evoked potential; HR, heart rate; LLR, long-latency reflex; MEG, magnetoencephalography; MEP, motor evoked potential; MN, mismatch negativity; M-NCS, motor nerve conduction study; M-SSEP, median nerve somatosensory evoked potentials; NCS, nerve conduction study; PSG, polysomnography; SSEP, somatosensory evoked potentials; T-SSEP, tibial nerve somatosensory evoked potentials; VEP, visual evoked potentials.

4CNP tests measure specific biosignals or physiological parameters (Table 1.1) allowing clinicians to probe and assess selected facets of the patient’s functional anatomy (Table 1.2). The biosignals or physiological parameters recorded reflect the function of specific anatomical structures (Table 1.3). Based on recorded signal characteristics (Figures 1.2 and 1.3), the clinical neurophysiologist can tell if anatomical structures, circuits, or pathways are functioning normally or are affected by pathological processes (Tables 1.4 and 1.5).

Brain function is reflected in the EEG, MEG, ERP, and EP cortical and subcortical components (1,2,6,8). Even when EEG and MEG signals arise from the same cellular processes in the cerebral cortex, there are differences in the way EEG and MEG data are recorded and interpreted (15). A reference is required to record EEG, but not MEG. Because the skull and scalp do not distort magnetic fields as much as electrical fields, the spatial resolution of MEG is superior to EEG (1,2). EEG can detect activity in “more” brain areas by recording radial dipoles, but MEG is more sensitive to tangential dipolar activity in the superficial cortical sulci, making it superior for source localization; for example, MEG is used for MSI and more precise localization of the epileptogenic focus (2). While EEG is widely used in clinical practice, MEG is available only in far fewer centers. The constant interaction between cortical and subcortical structures implies that diencephalic-brainstem disturbances will also affect the EEG/MEG. Assessment of modality-specific brain structures is performed with VEP, BAEP, SSEP, LLR, ERP, and other CNP tests (see later) (3–6). Brain function assessment is the goal of IOM-EEG, sleep studies, and MNM of cerebral perfusion, oxygenation, and metabolism (1,7–9).

Muscle, peripheral nerve, and lower motor neuron function is routinely tested with needle EMG and NCS (3,16). During needle EMG, motor unit action potential (MUAP) number and firing rate increase with the force of muscle contraction. Each MUAP represents activation of a motor unit (i.e., one motor neuron, its nerve fiber, and all muscle fibers innervated by branches of the same nerve fiber). NCS is performed to assess signal conduction along fast-conducting large-diameter nerve fibers: S-NCS for sensory and M-NCS for motor nerve fibers. Neuromuscular junction transmission is evaluated by repetitive nerve stimulation and single-fiber EMG (3,16). F-wave recording extends the utility of M-NCS for evaluating lower motor neurons, motor nerve roots, and proximal motor nerves. These motor structures, along with the sensory nerve roots and central components of reflex pathways, can be evaluated with the H reflex, long-latency reflex, or blink reflex (3,17).

Modality-specific neural pathway or circuit activity is reflected in the EP or ERP signals that are generated when components of the pathway or circuit are activated (4,5,8). The dorsal column pathway is evaluated with M-SSEP and T-SSEP (18), the visual pathway with pattern-shift or flash VEP (19), the auditory pathway with BAEP (20), and the central motor pathway with TES-MEP or TMS-MEP (21). IOM-SSEP and IOM-MEP can be combined if both dorsal and ventral territories of the spinal cord are surgically at risk (22). The neurophysiological substrates of ERP have not yet been identified with certainty. BSP and CNV correlate with movement preparation and motor processing in the frontal lobe, P300 with selective attention and stimulus discrimination in the parietal lobe, and MN with sound feature discrimination, sensory learning, and perceptual accuracy (5,23).

Autonomic regulation of somatic and visceral function by the parasympathetic and sympathetic nervous system is mediated via slow-conducting small-fiber autonomic nerves that innervate most organs and tissues (6,24). Some autonomic function tests are organ specific, such as pupillometry, lacrimation tests, tests of saliva production, gastric motility tests, and urodynamic studies. In CNP, autonomic testing is performed to evaluate parasympathetic cardiovagal reflexes (HR-DB and VAL), sympathetic vasomotor reflexes (VAL and HUT), and sympathetic sudomotor function (QSART and TST). QSART evaluates the sympathetic axon reflex and TST evaluates the integrity of the sympathetic thermoregulatory pathway from the hypothalamus to eccrine sweat glands (6,24).

The sensitivity of CNP tests is remarkable and most tests will show abnormal results before the onset of clinical signs and symptoms. Unfortunately, CNP tests are also sensitive to technical factors and to noise. Exceptional care should therefore be exercised always to avoid overinterpreting neurophysiologic data. The specificity of CNP tests is limited to the identification of physiological disturbances or disorders in functional anatomy. To reach an etiologic diagnosis, neurophysiological data should always be interpreted in the context of the clinical history, physical examination, and other laboratory test results.

5TABLE 1.2: Classification of Clinical Neurophysiologic Tests Based on Functional Anatomy or Neural Pathway Tested

Note: Modality-specific tests assess the function of specific structures or neural circuits and mixed-modality tests assess multiple functional modalities or complex states (see Table 1.1). Physiological function is assessed by measuring signals generated by specific anatomical structures during a brief time interval (routine method) or by tracking how signals change over time (continuous monitoring).

BAEP, brainstem auditory evoked potential; BSP, Bereitschaftspotential; BP-HUT, blood pressure head-up tilt; BP-VAL, blood pressure Valsalva; CMAP, compound motor action potential; CNV, contingent negative variation; ECoG, electrocorticography; EMG, electromyography; EMSI, Electric and magnetic source imaging; ESI, electric source imaging; HR-DB, heart rate variability with deep breathing; HR-VAL, heart rate Valsalva; IOM, intraoperative monitoring; MEP, motor evoked potential; M-NCS, motor nerve conduction studies; NCS, nerve conduction study; MEG, magnetoencephalography; MN, mismatch negativity; MNM, multimodal neuromonitoring; M-SSEP, median nerve somatosensory evoked potential; MSI, magnetic source imaging; QSART, quantitative sudomotor axon reflex test; S-NCS, sensory nerve conduction study; T-SSEP, tibial nerve somatosensory evoked potential; TES-MEP, transcranial electrical stimulation MEP; TMS-MEP, transcranial magnetic stimulation motor evoked potential; TST, thermoregulatory sweat test; VEP, visual evoked potential.

TABLE 1.3: Generators and Sources of Signal and Test Parameters Measured in Clinical Neurophysiology

Note: This table shows (a) CNP test or test modality (left column); (b) the macroscopic generator and signal or physiologic parameter measured by the test (middle column); and, whenever applicable, (c) the microscopic SGU and the microscopic signal or “building block” of the macroscopic signal (right column).

BAEP, brainstem auditory evoked potential; BP, blood pressure; CMAP, compound motor action potential; ECoG, electrocorticography; EMG, electromyography; EMSI, electric-magnetic source imaging; EOG, electrooculogram; EP, evoked potential; ERP, event-related potentials; ESI, electric source imaging; FVEP, flash visual evoked potentials; HR, heart rate; HR-DB, heart rate variability with deep breathing; HUT, head-up tilt; ICP, intracranial pressure; IOM-SSEP, intraoperative somatosensory evoked potentials monitoring; MEG, magnetoencephalography; MEP, motor evoked potential; MFAP, muscle fiber action potential; MNM, multimodal neuromonitoring; MSI, magnetic source imaging; M-SSEP, median nerve somatosensory evoked potentials; NCS, nerve conduction study; NIOM, neurophysiologic intraoperative monitoring; QSART, quantitative sudomotor axon reflex test; SGU, signal-generating unit; SSEP, somatosensory evoked potentials; TST, thermoregulatory sweat test; T-SSEP, tibial nerve somatosensory evoked potentials; VAL, Valsalva; VEP, visual evoked potentials.

^aNonsynaptic and nonneuronal mechanisms may also play a role in EEG/MEG generation.

^bLate responses are CMAPs that occur after the classic CMAP known as M wave.

^cGenerator(s) of FVEP waves I–VI, BAEP wave II, and SSEP subcortical components remain controversial.

^dPhysiologic parameters recorded during PSG in addition to EEG, EOG, and chin EMG (list is incomplete).

Translating bioelectric and biomagnetic signals into physiological information requires a priori knowledge of where and how signals are generated (discussed earlier) and how signals spread from the generator to the recording site. Once generated, bioelectric and biomagnetic fields propagate from the generator to other parts of the body. This mechanism of signal propagation, known as volume conduction, must be distinguished from another mechanism of signal propagation, known as neural transmission. These two mechanisms of signal propagation in biological tissue are quite distinct and result in waveforms with different spatiotemporal characteristics (Figure 1.4).

9TABLE 1.4: Spatiotemporal Characteristics of Bioelectric Signals Measured in Clinical Neurophysiology

^aNonsynaptic and non-neuronal mechanisms may play a role in the generation of EEG, MEG, ERPs, and cortical EPs

^bThe micro/macro stereotype is not applicable since the microscopic signal (MFAP) is also the signal that is recorded.

^cLate responses (F waves, H reflex, LL response, R1/R2 responses) are CMAPs that occur after the classic CMAP or M wave.

^dNo definite consensus on the generator(s) of BAEP wave II and SSEP subcortical components and of FVEP waves I-VI

^eECG is a traveling nearfield signal near its generator but behaves as a farfield stationary signal when recorded in CNP.

Note: For each CNP test or test modality (left column), the macroscopic signal recorded (middle column) and the spatiotemporal characteristics of the recorded signal (right column) are shown.

BAEP, brainstem auditory evoked potential; CMAP, compound motor action potential; EMG, electromyography; ERP, event-related potentials; MEG, magnetoencephalography; MFAP, muscle fiber action potential; M-SSEP, median nerve somatosensory evoked potentials; NCS, nerve conduction study; TES-MEP, transcranial electrical stimulation motor evoked potential; TMS-MEP, transcranial magnetic stimulation motor evoked potential; T-SSEP, tibial nerve somatosensory evoked potentials; VEP, visual evoked potentials.

10TABLE 1.5: Classification of Electrophysiologic Abnormalities^a

^aWhen the above framework is used to classify abnormalities in the “raw” MEG record, one must be aware of its limits; for example, magnetic signals are not sensitive to changes in volume conductor properties. The above framework is not suitable for signals that are the product of complex transduction events between the biological source and the signal-sensing probe.

Note: Graphical features, clinical context, and pathophysiology are all taken into consideration in this classification scheme. Macroscopic abnormalities appear in the record as aberrations in amplitude, latency, or configuration of otherwise physiological waves, as a change in activation, modulation, or repetition pattern of physiologically recurring waves, as distortion of the spatial characteristics of waves, or as normally appearing but out-of-context waves in terms of physiologic state or age. Each of these abnormalities occurs because of impairment in SGU or non-SGU circuit components or because of changes in the properties of the volume conductor.

BAEP, brainstem auditory evoked potential; CMAP, compound motor action potential; MEG, magnetoencephalography; MUAP, motor unit action potential; SGU, signal-generating unit; SNAP, sensory nerve action potential; SSEP, somatosensory evoked potentials.

12SIGNAL RECORDING AND GRAPHICAL DISPLAY

The signal that must be recorded and analyzed, or the “signal of interest,” is often simply called “signal.” Any other signal contaminating and potentially distorting the signal of interest is considered “noise.” Noise entering the recording system may appear on the record as artifact. Because signal and noise are physically similar (both are electromagnetic signals), neurophysiologic instruments are intricately engineered to maximize the signal-to-noise ratio during signal recording and processing (25). Moreover, CNP technologists and specialists are trained to distinguish signal from artifact by visual inspection and, if necessary, digital reformation of the record. Chapter 2 is dedicated to instrumentation and signal processing. In this chapter, it is sufficient to mention that neurophysiological recording involves four processes: analog signal acquisition, analog signal conditioning, analog-to-digital conversion, and digital signal processing (Figure 1.1).

Signal analysis and interpretation is performed by professionals trained in CNP. Neurophysiologic data are usually presented and analyzed in the time domain (Figure 1.2). Recorded signals are displayed as waveforms on a computer or oscilloscopic screen (paper recording is nearly obsolete and is omitted from our discussion). CNP records contain one or more lines of tracing, called channels. Each channel is a graph of signal amplitude versus time, with waves rising above and falling below the baseline, indicating fluctuations in signal amplitude with time. When differential amplification is employed in biopotential recording, the wave amplitude at any given time is proportional to the voltage difference between the amplifier inputs, G1 and G2, multiplied by the amplification factor or gain (see Chapter 2) (25).

Quantitative analysis of “raw” data can extract additional information that can complement or supplement basic time-domain data. The fast Fourier transform (FFT) converts data in the time domain to an alternative representation of the same data in the frequency domain (26). Decomposing EEG into its frequency components and using compressed spectral array (CSA) to track the changes in amplitude/power of EEG frequency bands is a useful quantitative EEG (qEEG) technique for detecting cerebral ischemia and seizures during ICU-EEG monitoring (11). EEG trending in neonates is commonly performed using amplitude-integrated EEG (aEEG), a qEEG technique that involves filtering, rectification, smoothing, and compression of time-domain data. Quantitative analysis of MUAP, compound motor action potential (CMAP), HR-DB, and other data can also be performed. Although waveform analysis in the time domain is still the bread and butter of the clinical neurophysiologist, rapidly advancing computer technology and machine learning, and the increasing availability of automated algorithms, will inevitably transform the future practice of CNP.

Spontaneously generated signals represent oscillating physiologic processes, appear graphically as sinusoidal waves, and are referred to as rhythmic activity or rhythms (Figure 1.2A) (27). In EEG/MEG, sustained rhythmic activity is considered “background.” Background activity, not individual waves, serves as the framework for time-domain analysis. Additional waves and rhythms may appear spontaneously or with activation and are mixed with, or replace, the background. Both background activity and emergent waves are signals of interest that are analyzed collectively. The most important graphical descriptor of rhythmic activity is frequency, the number of wave repeats per second, which is reported as Hertz (Hz) or cycles per second (cps or c/s). Frequency is described in precise terms (e.g., 60 Hz) or in terms of frequency bands, including EEG/MEG: less than 4 Hz is delta, 4 to less than 8 Hz is theta, 8 to 13 Hz is alpha, and greater than 13 Hz is beta (28). By interpreting a wave as a basic unit of a rhythm, an isolated waveform can be assigned a frequency that is equal to the reciprocal of its duration.

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