Magnetic Resonance Imaging

As discussed in Chapter 3, magnetic resonance imaging, especially when combined with advanced techniques or other diagnostics, can show anatomical images of the brain or spinal cord, measure blood flow, or reveal deposits of minerals such as iron. According to the National Institute of Neurological Disorders and Stroke, “MRI is used to diagnose stroke, traumatic brain injury, brain and spinal cord tumors, inflammation, infection, vascular irregularities, brain damage associated with epilepsy, abnormally developed brain regions, and some neurodegenerative disorders. MRI is also used to diagnose and monitor disorders such as multiple sclerosis” (NINDS, 2022). Advances in MRI techniques and the combination of MRI technology with other diagnostic techniques represent a large percentage of the new diagnostic techniques developed over the past 30 years.

Because so many different MRI-based diagnostic tests exist—a large percentage of which have been developed over the past three decades—the committee chose to not attempt to uncover and list all of them. Instead, the committee has assembled representative examples of how various forms of MRI are being used in diagnostics today, including functional MRI, diffusion-weighted MRI (dwMRI), and others. Table 5-1 offers a (necessarily incomplete) list of the ways that magnetic resonance imaging of various types is currently being used in diagnosing neurological disorders.

TABLE 5-1

Uses of Magnetic Resonance Imaging in Neurological Diagnoses.

Genetic Testing for Hereditary Disorders

Chapter 3 provides an overview molecular genetic testing for hereditary disorders. Currently, some form of genetic sequencing is used in the diagnosis of a wide array of neurological disorders, so the responses to the questions (a)–(j) will be for the genetic diagnosis of any neurological disorder rather than for a specific one. There are obviously differences in the specifics of the use and effectiveness of genetic testing for different neurological disorders, but delving into those differences is beyond the scope of this report. So what follows are general answers to the questions which relate to neurological genetic testing in general:

(a)

The accepted use of genetic testing in neurology is to diagnose a wide variety of neurological disorders, particularly in cases where clinical examination and other diagnostic techniques cannot identify a disorder with certainty. These disorders include Duchenne muscular dystrophy, Charcot-Marie-Tooth disease, Huntington’s disease, spinocerebellar ataxias, Kennedy’s disease, Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and dopa-responsive dystonia, among many others (Fischbeck, 2014). Genetic testing is also used to guide treatment in various disorders, such as autism.

(b)

The technique represents an advancement in diagnostic ability by identifying the specific gene(s) responsibility for a patient’s disability, which can provide a definitive diagnosis and also help to guide treatment. This improved ability is seen with equally strong effect among different racial, ethnic, or gender sub-populations in the United States.

(c)

Genetic testing identifies the causes of specific impairments rather than the impairments themselves, although genetic testing can help predict the likely trajectory of specific impairments before they fully appear.

(d)

As a practical clinical tool, genetic sequencing has been available since the mid 2000s, when next-generation sequencing was developed and tools for it become commercially available.

(e)

There are disparities related to access due to cost. The technique remains somewhat expensive as a diagnostic tool and is not always paid for by insurance, so people who cannot afford to pay out of pocket will have limited access in some cases.

(f)

The major drawback or limitation of the previously used techniques is that they did not identify the specific gene responsible for a condition and that they could sometimes not conclusively identify the presence of a particular disorder.

(g)

The outcome of a genetic test is the identification of particular genetic variants in a patient’s genome that are related to or responsible for a particular condition. By identifying the particular variant responsible, the test points to the specific physiological pathway(s) involved in the disorder as well as potential treatments.

(h)

A clinical examination must precede and inform the ordering of genetic testing. In addition to a physician familiar with genetic disorders, a team should include a geneticist, pathologist, and genetic counselor who works with the patient to help the patient understand the diagnosis and make informed decisions in response to it. Testing should be done in an accredited lab. The validity of the test results can be seriously affected if the appropriate personnel do not perform the genetic analysis.

(i)

The major impediments to the increasingly widespread use of the technique as a medical best practice are the costs of genetic testing, which are coming down, and the availability of personnel trained in administering and interpreting the tests.

(j)

Genetic testing does not provide information about the severity of physical deficits in functioning.

Positron Emission Tomography

Positron emission tomography (PET) is a particularly powerful way to peer into the brain and observe what is happening in real time. It is used to examine brain metabolism, alterations in regional blood flow, and receptor binding of various neurotransmitters. It can be used to diagnose such neurological disorders as multiple sclerosis, Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and various dementias. The major reason it is not used in clinical diagnoses more widely is that at this point it is very expensive because it requires a cyclotron to create the beam of photons and radiochemicals for injection into the bloodstream systems (Politis and Piccini, 2012).

Single Photon Emission Computed Tomography

As discussed in Chapter 3, single photon emission computed tomography (SPECT) is an advanced nuclear imaging technique that can be used to evaluate certain brain functions as part of diagnosing neurologic and psychiatric conditions. In particular, SPECT makes it possible to directly measure various elements of neurochemical transmission in the body, allowing clinicians to assess and monitor the pathophysiology of complex brain disorders (Hussain et al., 2022; Yandrapalli and Puckett, 2022). For example, SPECT scans can be used to diagnose Alzheimer’s Disease or other neurodegenerative diseases, stroke, seizure disorders such as epilepsy, or evaluating memory loss (Cedar Sinai, 2023). The detailed three-dimensional map of blood flow in the brain created from the procedure can also allow for detection of altered blood flow and clogged blood vessels, helping to diagnose or evaluate vascular brain disorders (Mayo Clinic, 2023).

Among the various uses of SPECT is assisting in the diagnosis of Parkinson’s disease, described below.

Digitalized Tools for the Diagnosis of Migraines

The diagnosis of migraines is a complicated process involving medical history, clinical examination, and, in some cases, diagnostic tests to rule out such potential causes as a tumor or abcess (NYU Langone, 2022). The diagnostic tests used may include computerized tomography (CT), MRI, sinus X-ray, lumbar puncture, or electroencephalogram (WebMD, 2020). Currently there are no definitive diagnostic tests for migraine, and modern diagnostic technologies such as CT or MRI are used only as aids in helping a clinician reach a diagnosis based on a holistic assessment of multiple factors.

There is, however, one technology that may have the potential to revolutionize migraine diagnosis—a new generation of computerized diagnostic tools based on various algorithms and machine learning approaches that offer an objective, accessible means of determining the presence or absence of migraine. According to a recent review of computerized migraine diagnostic tools (Woldeamanuel and Cowan, 2022), several dozen such tools now exist, most of them developed since 2006. A dozen of them are focused solely on the diagnosis of migraines—i.e., produce a yes/no answer to the question of whether a patient has a migraine—while the remainder diagnose migraines and other types of headaches as well.

Many of the tools are based on the International Classification of Headache Disorders, Third Edition (ICHD-3), and they generally rely on a patient’s answers to a number of questions to make a diagnosis. The digital tests are sometimes administered by clinicians, but, importantly, a large number of them are intended to be self-administered. According to published research, some of these tools performed with high accuracy in diagnosing migraines compared to interview-based diagnosis (Woldeamanuel and Cowan, 2022). The tools do not—at least at present—outperform a clinician experienced in headaches and migraines, but given the large number of headache sufferers relative to the number of specialists familiar with migraines, these computerized diagnostic tools could play a number of important roles. For instance, because migraine is a chronic condition whose attacks can be triggered by various stimuli (loss of sleep, depression or anxiety, even skipped meals), the tools could help migraine sufferers monitor their condition and adjust their behavior accordingly. More generally, “computerized migraine diagnostic tools have the potential to provide efficient, patient-centered, and improved headache care by delivering early diagnosis and management, enhancing diagnostic accuracy, saving time, boosting accessibility, enabling remote care with reduced costs, and decreasing travel to hospital/clinic care setting thereby reducing the exposure to communicable diseases in healthcare settings” (Woldeamanuel and Cowan, 2022).

The requested details related to the use of digitalized diagnostic tools for migraine are as follows:

(a)

The accepted uses are for the diagnosis of migraine and, depending on the tool, other types of headaches as well.

(b)

The technique represents an advance in that it makes diagnosis more easily available to patients, without direct interaction with a clinician. Improved access is important given the scarcity in the number of headache specialists. Cohen (2022) estimates only 1 headache specialist per 60,000 people suffering from migraines, which is a barrier for people hoping to get evaluated (Cowan et al., 2022).

(c)

Migraines are not assessed more accurately with digital tools than by a clinician using state-of-the-art diagnostic approaches, but the digital tools are more consistent and may provide more accurate diagnoses than clinicians who are unfamiliar with headaches and migraines. A systematic review found a high level of agreement between a self-administered computer based diagnostic engine and a semi-structured interview conducted by headache specialists, with high specificity and sensitivity of the diagnostic engine (Cowan et al., 2022). The use of tracking tools has also been shown to improve communication between patients and their providers and can aid in understanding of the burden and optimized treatment (Bensink et al., 2021).

(d)

Digital tools for diagnosing migraines have been available for several decades, but over the past decade and a half, a large number of new tools have been developed that rely on machine learning and other state-of-the-art digital tools.

(e)

There are no known disparities in current access to the technique among various sub-populations in the United States along racial, ethnic, socioeconomic, or geographic lines other than the general disparities that exist in access to health care.

(f)

In addition to the limited supply of headache specialists for a large patient population, other drawbacks to previous in person interviews and assessments include the higher costs and longer wait times. Digital tools can reduce these costs, improve patientcenteredness in the health care interaction, and restructure the clinical encounter and speed up decision-making and diagnosis (Cowan et al., 2022). Additionally, some, some clinicians who are available are not trained in diagnosing migraine and patients may be hesitant to approach clinicians about their migraines for a variety of reasons.

(g)

The range of outcomes generally seen with digital migraine diagnostic tools are the same as with standard clinical diagnostics—that is, the presence or absence of migraine or other type of headache.

(h)

Digital migraine diagnostic tools are designed to be either self-administered or administered by clinicians with no specialized training other than familiarity with headaches and migraine. The validity of the tools should not depend on any specialized abilities.

(i)

There are no known impediments to increasingly widespread use of the technique as a medical best-practice except the general impediments to digital health technologies, such as attitudes of patients, clinicians, and health care administrators. Many have been tested in specialized clinical settings, and more research can guide the implementation and use in community settings and primary care (Cowan et al., 2022).

(j)

There are small limitations with the use of digital tools, as they are unable to identify or interpret nonverbal behavior or conversational cues to allow for more sharing of sensitive information by the patient with the provider administering a test (Cowan et al., 2022). However, the technique’s diagnostic and evaluative efficacy are still high, and, with machine learning and artificial intelligence advancements in the future, it is conceivable that these limitations will be addressed.

Surface Electromyography

Electromyography (EMG) records the electrical activity in skeletal muscles. Because muscles develop abnormal electrical signals when there is nerve or muscle damage, EMG can be is used to diagnose nerve and muscle disorders, spinal nerve root compression, and motor neuron disorders such as amyotrophic lateral sclerosis. Surface EMG (sEMG) is a variant of EMG in which multiple electrodes are used on a person’s skin to get multiple readings of muscle activity just under the skin. While it cannot detect signals from muscles at the same depth that is possible with typical EMG—which uses needles inserted into muscles—it has the advantage that it can gather information on muscle activity over a much greater area than EMG.

While sEMG is not a new technique—it was originally developed in the 1940s to study muscle physiology—the power of the technology has grown remarkably over time, in large part due to advances in the analytical tools used to analyze sEMG signals (Felici and Del Vecchio, 2020), and today the technique is widely used in neurophysiological research (Campanini et al., 2020) and rehabilitation (Kotov-Smolenskiy et al., 2021). It has a number of potential clinical applications as well, and although its clinical use has been limited to date, a number of neurophysiologists have argued that it should be more widely adopted in the clinic (Campanini et al., 2020; Feldner et al., 2019; Felici and Del Vecchio, 2020; Hogrel, 2005). Among the current clinical applications of sEMG are the assessment of muscle coordination, particularly in clinical gait analysis; the functional diagnosis of and monitoring of therapeutic outcomes for neurological impairments such as cerebral palsy and stroke; and the study of orthopedic issues such as degenerative joint disease and back pain research (Campanini et al., 2020). However, because standards and protocols for sEMG lack consensus, they cannot yet be recommended to assess muscle co-contraction during gait in people with neurological impairment (Rosa et al., 2014).

The requested details related to the use of sEMG in diagnosis are as follows:

(a)

The accepted clinical uses of sEMG include the assessment of muscle coordination, particularly in clinical gait analysis; the functional diagnosis of and monitoring of therapeutic outcomes for neurological impairments such as cerebral palsy and stroke; and the study of orthopedic issues such as degenerative joint disease and back pain research.

(b)

The technique represents an advancement in its ability to provide a detailed examination of muscle firing and gain insights into the level and type of disability resulting from muscle firing issues.

(c)

Specific impairments that are more accurately assessed with the technique include muscle injuries and impairments caused by cerebral palsy and stroke as well as orthopedic issues related to degenerative joint disease.

(d)

The technique was developed in the 1940s and has grown in power and applicability over time, becoming particularly valuable for clinical applications in the past couple of decades, but there is no specific date at which it became generally available.

(e)

There are no known disparities in current access to the technique among various sub-populations in the United States along racial, ethnic, socioeconomic, or geographic lines.

(f)

The technique is not replacing a previously used method.

(g)

Surface EMG provides information about patterns of muscle firing, including details about irregularities and weakness of the firing, which in turn offers information about muscle impairments caused by an injury or disease.

(h)

Surface EMG equipment, which typically costs between $10,000 and $40,000, is relatively straightforward to operate and can be used in almost any clinical setting, but its results can be difficult to interpret. Thus, the main barrier to its clinical use is the lack of specialists who have been trained in the analysis and interpretation of its output (Campanini et al., 2020).

(i)

The main impediments to the increasingly widespread use of sEMG are the lack of clinicians trained in its use and, more broadly, a lack of recognition of its clinical value (Campanini et al., 2020).

(j)

At this point, because of the limited amount of clinical experience with the technique, there is little known about the limitations of sEMG’s diagnostic or evaluative efficacy.

Neuromuscular Ultrasound

While ultrasonography using sound waves to create images has been around for decades, its use in the diagnosis of neuromuscular disorders is growing and becoming a standard element in the evaluation of peripheral nerve and muscle disease. Neuromuscular ultrasound is noninvasive, low risk, painless for the patient, easily available, and generally inexpensive, and can refine diagnosis and improve patient care. Recently, the Centers for Medicare & Medicaid Services updated their Relative Value file to include updated reimbursement for neuromuscular ultrasound (ACR, 2023), enabling providers to bill separately for the professional component and the technical component; and thus, demonstrating the general acceptance and usability of this technique into the mainstream of neurological diagnosis procedures.

The requested details related to the use of neuromuscular ultrasound are as follows:

(a)

The most common use of ultrasound is to aid in localization of entrapment neuropathies (Gonzalez and Hobson-Webb, 2019), and it is also used in evaluation of neuropathies, myopathies, and motor neuron diseases. These conditions can include carpal tunnel syndrome, ulnar nerve entrapment, nerve trauma, or peripheral nerve tumors. It is increasingly being used for non-traumatic brachial plexus lesions and can also act as a potential marker of disease progression in Duchenne muscular dystrophy (Gonzalez and Hobson-Webb, 2019).

(b)

Ultrasound offers a valuable adjunct to electrodiagnostics helping to characterize nerve entrapments, neuropathies, and myopathies (Hommel et al., 2017). This technique is more sensitive than MRI in detecting peripheral nerve pathologies, and can be used as a biomarker for conditions such as muscular dystrophy and spinal muscular dystrophy (Mah and Van Alfen, 2018).

(c)

For the detection of fasciculation in patients with amyotrophic lateral sclerosis, ultrasound provided superior rates than those achieved with the standard of electromyography (Duarte et al., 2020).

(d)

Neuromuscular ultrasound has developed rapidly over the past 20 years, and is now used worldwide at the point of care to augment diagnostic capabilities (Gonzalez and Hobson-Webb, 2019).

(e)

Ultrasound is becoming more widespread, but there may still be geographic areas where there are access issues such as the lack needed expertise or equipment. For example, one study on general ultrasound access found more than 38 percent of rural counties had access to point of care ultrasound, compared to more than 88 percent of metropolitan counties (Peterman et al., 2022).

(f)

Previously used electrodiagnostic studies would often give inconclusive or unreliable results in the first few weeks following nerve injury; therefore, ultrasound is an important tool during this acute period (Gonzalez and Hobson-Webb, 2019). In comparison to the gold standard EMG, ultrasound is better tolerated, as EMG results can be less than optimal due to pain intolerance (Mah and Van Alfen, 2018). Additionally, MRI would often be used for neuromuscular imaging, but ultrasound is much more portable and readily available—more easily used for children and adults at the point of care.

(g)

Overall, the ultrasound detects neuromuscular changes examining echogenicity and cross-sectional area of nerves (Bulinski et al., 2022). In carpal tunnel syndrome, increased cross sectional area of the median nerve seen on the ultrasound at a certain point on the bone is an accurate marker for diagnosis (Shook, 2015). Diagnosis of complete nerve transection or stump neuromas can be performed after suspected nerve trauma.

(h)

Neuromuscular ultrasound is within the scope of practice for specialists in neurology and physical medicine and rehabilitation who have demonstrated certain prerequisites for performance (AANEM, 2023). This typically includes many physiatrists and neurologists, as well as physicians certified by the American Board of Electrodiagnostic Medicine.

(i)

One barrier to this technique becoming more widespread is the concern by clinical neurophysiologists over how to identify and manage non-neuromuscular findings (Walker et al., 2021). Other factors preventing more widespread uptake include the significant costs of equipment, available teaching and training experience, and the gaps in knowledge and applicability (Carrol and Simon, 2020).

(j)

Potential limitations of this technique include the inability to image deeper structures within the body, and reduced sensitivity in detecting certain neuromuscular diseases in young children and those with mitochondrial myopathies (Mah and Van Alfen, 2018). It also has not yet been proven to detect critical illness neuromyopathy.

Electroencephalogram

Electroencephalogram (EEG) is a procedure that measures electrical activity in the brain using small electrodes attached to the scalp. The patterns it records in different regions of the brain will show normal or abnormal activity, such as slower waves or sharper spikes, that can indicate brain disorders. Routine EEG has been around since the 1920s and used extensively as one of the main diagnostic tests for epilepsy as well as diagnosing other brain disorders such as, tumors, stroke, sleep disorders, or brain damage from a head injury (Mayo Clinic, 2023b). But as digital technology has advanced in more recent decades, advances in EEG have emerged, including prolonged EEG either with or without video, which can provide more assured diagnoses, and identify regions of the brain where the abnormalities are occurring to inform surgical evaluation.

The requested details related to the use of prolonged EEG for diagnosis are as follows:

(a)

EEGs can be used to evaluate various brain disorders including epilepsy, brain tumors, stroke, narcolepsy, Alzheimer’s disease, or certain psychoses (Johns Hopkins Medicine, 2023).

(b)

The combination of prolonged EEG and video can provide answers to questions of whether the events are related to epilepsy or not, and where the likely focus is. The ability of this technique to be used in ambulatory or home settings more recently also makes it much more accessible to a greater proportion of patients and reduces the costs associated with hospital visits.

(c)

A video-EEG recording of events in question is the most definitive test to clarify a seizure type (Benbadis et al., 2020). Especially when seizures do not respond to medications, video-EEG monitoring is the best available diagnostic tool and a key technique for epilepsy centers (Benbadis et al., 2004).

(d)

Over the past 20 years, prolonged EEG monitoring with video has become increasingly used and relevant for assessing types of seizures.

(e)

Given the variability in insurance coverage for patients based on a number of factors, as well as general healthcare disparities, some have more difficulty accessing techniques such as prolonged EEG with video. One study found uninsured individuals and those using Medicare and Medicaid had significant gaps in access to video-EEG monitoring (Schiltz et al., 2013). Additionally, this technique was least commonly used by Black patients, male patients, and those with multiple comorbid conditions. Another study noted the influence of structural racism, and found that despite Black patients being admitted at high rates, they tended to seek more emergency care for seizures, with less access to video-EEG monitoring and specialized epilepsy services (Kamitaki et al., 2022).

(f)

A prolonged EEG may be more likely than to catch a seizure while it is happening, compared to a routine EEG that cannot diagnose epilepsy if a seizure does not happen during the 20-minute test itself; the results of a routine EEG may appear normal in between seizures (Rossetti et al., 2020).

(g)

Prolonged EEG measure include the presence or absence of epileptiform discharges, and presence or absence of “events” of interest, and presence of patterns associated with coma (Benbadis et al., 2020).

(h)

A technician measures the patient’s head and attaches electrodes to the scalp, connecting them with wires to the machine. Prolonged EEGs, either ambulatory or inpatient, may last from several hours to a few days. After the test, physicians or neurologists trained to interpret the readout, typically someone board certified in neurophysiology. More recently, ambulatory EEG with video has been possible given improvements in computers and remote access, making this possible in outpatient or home settings for the first time (Benbadis et al., 2020).

(i)

While prolonged EEG monitoring can now be done inpatient or in ambulatory settings, there are advantages and disadvantages to both. A reason the ambulatory version has not yet surpassed the inpatient video-EEG is the frequency at which patients are not on camera during events in question, resulting in inconclusive study, thereby requiring inpatient EEG (Benbadis et al., 2020). The ability to safely reduce medications during the procedure is also challenging in this scenario.

(j)

Some factors can influence the reading or accuracy of an EEG test, such as low blood sugar in the patient, body movement, certain medications, and caffeine intake (Johns Hopkins Medicine, 2023). There can also be overreliance on EEG as a diagnostic test, leading to misinterpretation of the results, which is a well-known problem in epilepsy management. Without recording EEG patterns during an active seizure, it is only useful as a supportive tool for epilepsy diagnosis (Tatum, 2013).

Spinal Cord Independence Measure

The Spinal Cord Independence Measure (SCIM) is a disability scale developed to specifically address the ability of SCI patients to perform basic activities of daily living and mobility with or without assistance from others and/or assistive devices. Previous versions, SCIM I and SCIM II, were valid and reliable instruments, but they did not take into account intercultural differences in the patient population. Researchers developed the SCIM III in 2002 as an international version of the prior forms (Catz et al., 2007). There are versions that can be performed by a therapists and self report. SCIM III contains 16 items covering three specific areas of function in patients with SCI: self-care (feeding, grooming, bathing, and dressing), respiration and sphincter management, and mobility abilities (bed and transfers and indoors/outdoors).

The Spinal Cord Injury Functional Ambulation Inventory

The Spinal Cord Injury Functional Ambulation Inventory (SCI-FAI) was designed to assess walking ability among patients with spinal cord injuries, with a particular focus on abnormalities in gait. It has three parts, each of which is scored separately: an assessment of gait (weight shift, step width, step rhythm, step height, foot contact with floor, and step length), with each leg scored individually; an assessment of the use of assistive devices (cane, crutches, walker, parallel bars, and various types of braces); and temporal and distance measures of walking (how much a person walks in a day, how far a person can walk in 2 minutes) (Field-Fote et al., 2001; SCIRE, 2022). The inventory is designed for patients who can walk independently, either with or without assistance, and the assessment can be carried out during a regular check-up or during a home visit by a clinician (SCIRE, 2022). The technique represents an advance over previously available instruments because of its flexibility (it can be used in at-home assessments, in the clinic, or via video recordings) and its accuracy and inter-rater consistency.

It is worth noting that the SCI-FAI is just one of multiple measures that have been developed to assess gait and ambulation in those with spinal cord injuries. Others include the Walking Index for Spinal Cord Injury II, the 50-Foot Walk Test, the 10-Meter Walk Test, Functional Independence Measure–Locomotor (Jackson et al., 2008), and the Spinal Cord Injury Functional Ambulation Profile (Musselman et al., 2011). SCI-FAI is recommended for assessment of gait and ambulation among those with spinal cord injuries by the Academy of Neurologic Physical Therapy (ANPT, n.d.).

Magnetic Resonance Imaging-based Advanced Techniques

As noted above, MRI has been used in a wide variety of ways to explore the structure and function of the brain, and the many different varieties of MRI, such as functional MRI and diffusion-weighted MRI, offer different types of information (Levin, 2022). In particular, the various different types of MRI are widely used by clinical researchers looking to get insights into the etiology and treatment of different neurological diseases and injuries (Zhan and Yu, 2015). Many of these research uses of MRI can be applied in the clinic for diagnostic or prognostic purposes, and it would be practically impossible to list every technique in use today that might be put to work in the future as a neurological diagnostic tool. fMRI is particularly salient because it is being widely used in research into various neurodegenerative diseases and movement disorders, and it could make its way into the clinical diagnosis of many of these diseases in the near future (Zhan and Yu, 2015). Resting-state functional MRI has also shown promise in the diagnosis of Parkinsonian syndromes (Filippi et al., 2019).

Electrical Impedance Myography

In electrical impedance myography (EIM) “a weak, high-frequency electrical current is applied to a muscle or muscle group of interest and the resulting voltages measured” (Sanchez and Rutkove, 2017, p. 107). The technique, which can be used noninvasively, is useful for assessing various neuromuscular disorders and has been suggested as a possible primary diagnostic tool (Cebrián-Ponce et al., 2021; Sanchez and Rutkove, 2017; Spieker et al., 2013; Tarulli et al., 2005).

To date, although EIM has the potential to aid in an initial diagnosis, the main focus of clinicians and researchers using the technique has been on evaluating the severity of a disease as well as its progression over time and response to therapy. Specifically, EIM has been used in clinical trials involving a number of neuromuscular disorders, including amyotrophic lateral sclerosis (ALS), Duchenne muscular dystrophy, spinal muscular atrophy, facioscapulohumeral muscular dystrophy, and sarcopenia (Sanchez and Rutkove, 2017; Spieker et al., 2013). The technique allows clinicians to evaluate muscles more easily or with greater sensitivity than existing techniques. A recent review of the technique concludes:

As evidenced by the number of recent or ongoing studies employing EIM, which are focused either on technological development or the procedure’s direct application in the clinical arena, it is clear that the technique may have wide value in the assessment of neuromuscular disorders and muscle health more broadly. As the medical community learns the underlying principles of bioimpedance theory and familiarizes itself with the outcomes and specific applications of this technology, we believe that EIM will gradually be adopted into the standard repertoire of neuromuscular assessment tools. (Sanchez and Rutkove, 2017, p. 115)

In addition, other emerging techniques include advances in remote patient monitoring devices with applications in the detection of various neurological disorders. See this discussion in Chapter 3 under digital diagnostic technologies.