This technique is able to produce detailed images of the brain and spinal cord, the latter with the advantage of simultaneously investigating the upper and lower motor neurons. During several years, its application was related to the exclusion of other disorders, as tumors or hernias that can display certain of the ALS-mimic symptoms (51). The evolution and improvement of this multimodal tool has recently become essential for the diagnosis of ALS. MRI scans can show cerebral degeneration and gray/white matter atrophy [reviewed in (52)], and also detect abnormalities in ALS muscle, likely due to denervation atrophy process (53).

### Blood and Urine Tests

Testing hematological factors is helpful to exclude diseases that are capable of mimicking ALS symptoms. Recently, a populationbased study, proposed serum albumin, creatinine levels, and lymphocyte count as markers for ALS, indicating muscle waste and inflammation respectively (54). Other markers potentially related to a better ALS outcome have been proposed: LDL/HDL levels, which are elevated in ALS plasma and represent a general unexplained hypermetabolism (55, 56); serum uric acid levels, which are decreased among ALS patients, further demonstrating the possible role of oxidative stress in the induction and propagation of the disease (57); serum ferritin levels which are elevated in ALS patients and could reflect perturbation in iron metabolism (58); concentrations of certain amino acids, which are decreased in ALS (59); levels of serum proinflammatory cytokines, such as IL-6, which are increased in ALS (60). Finally, high level of circulating AChE and metalloproteinases (MMP) have been reported in ALS plasma (61, 62) and although the exact source of these two classes of enzymes remains uncertain, it could in part reflect a disruption of extracellularly bound AChE at the NMJ and early change in the nerve-muscle integrity.

### Spinal Tap (Lumbar Puncture)

Using this particular test, a small amount of cerebrospinal fluid (CSF) is taken from the lower back of the patient for laboratory

tests. Thanks to its proximity to the central nervous system, the CSF is considered one preferred tissue to search for ALS biomarkers [reviewed in (63)]. Several markers for ALS have been identified in CSF such as Tau, TDP43, Nefl, and MMP levels [reviewed in (64)]. In particular, MMPs with their ability to digest collagen, proteoglycan, and laminin (65), may reflect ongoing destruction of the matrix which wraps synapses (66) and pathological changes at the brain-blood barrier (62).

### Muscle Biopsy

With this technique, a small portion of muscle is removed by needle biopsy and sent to a laboratory for histopathological analysis. Rarely performed because of its painful and invasive nature, this tool is useful when ALS diagnosis is in doubt. Generally, ALS muscles present signs of active denervation/reinnervation and an increased number of atrophic fibers (67).

### Genetic Testing

People with familial ALS (fALS) background can get an efficient diagnosis through genetic testing (68, 69). This technique may help ALS patients to understand the basis of their condition, and improve the genotype-specific treatments (70). Unluckily, there is a lack of consensus among clinicians above the definition of fALS, since newly genes related to ALS are continuously found (71). Nowadays, genetic testing is not wildly used because of its high cost and the belief that ALS genetics is not well-enough understood to provide a better treatment plan, as reported in 2017 in a study which involved 167 clinicians from 21 different countries around the world (71).

# EXAMPLES OF NMJ PATHOLOGIES ALS-MIMIC

Here we report some examples of ALS-mimic pathologies. The Spinal muscular atrophy (SMA) is an inherited MND that prevalently affects children. Its incidence is 1 per 11,000 live births (72). All forms of SMA are caused by the loss of SMN1, a gene implicated in axonal mRNA transport and snRNP biogenesis (73). Studies involving mice and fly mutants demonstrated that the probable origin of this pathology resides in the early loss of sensory information from proprioceptive neurons (74), which in turn causes degeneration of α motor neurons. In consequence, progressive muscle weakness, and, in severe cases, respiratory failure appear (75). Despite being considered a child's illness, the SMA type 4, that has an adult onset, overlaps with ALS diagnosis (76). Furthermore, like in ALS, several studies reveal the early implication of NMJs in SMA, with synaptic pathology prior to the appearance of clinical symptoms (77–81). However, the first evidence of neuromuscular pathology occurred at different time points of the disease progression, with presynaptic pathology preceding morphological changes at the endplate in ALS, and simultaneous pre and post-synaptic pathologies in SMA, suggesting the possibility to study this particular zone in diagnosis (81). Histochemical skin biopsy comparison was suggested as a powerful diagnostic tool in differentiating ALS and SMA, since

the small collagen fibrils and the increased amount of amorphous material, which are characteristic of ALS, are not in SMA (82).

The Spinobulbar muscular atrophy [(SBMA) Kennedy's disease] is a X-linked hereditary lower motor neuron disease, where the expanded trinucleotide repeat (CAG > 37) in the androgen receptor gene (AR) causes its nuclear inclusions and impairment of its function (83, 84). The disease affects 1 per 200,000 males in Europe and Asia each year (85). In this pathology, degeneration of anterior horn cells of the spinal cord, where androgen receptors are widely expressed, is observed (86, 87). Although SBMA patients exhibit facial weakness as first sign of the disease, they progressively develop myopathic features, such as muscle atrophy and necrotic myofibers (88). Like ALS, SBMA disease reveals mixed pathological findings, with both myopathy and neurogenic atrophy features, which is the cause of misdiagnosis at the early stage of the disease.

Among the autoimmune syndromes, myasthenia gravis (MG) overlaps ALS syndrome. The annual incidence ranges from 3 to 30 per 1,000,000 people (89). In fact, the binding of autoantibodies to components of the NMJ in MG causes a characterized muscle weakness and fatigability (90). Even if acetylcholine receptor antibodies are considered to be highly specific for the diagnosis of MG, ALS patients can also present these autoantibodies at the blood test (91, 92). In these cases, it is very difficult to define the false positive cases and an experimental treatment with AChE inhibitors is necessary to differentiate MG from ALS (93).

The skeletal muscle disorders are represented with the term Myopathies. Myopathies hold a list of pathologies (**Table 1**) where muscle weakness can begin in the hands and feet (distal muscles) as well as in the muscles near the center of the body (proximal muscles) sometimes mimicking ALS features, confusing the diagnostic and the treatment decision. Among them, Inclusion Body Myositis (IBM) (94) is the most common ALS-mimic disease. It is the most common adult myopathy in 50 year-old persons and older, and its incidence is 3.5 per 100,000 (95). It is characterized by inflammatory cells surrounding and invading non-necrotic muscle fibers, rimmed vacuoles, congophilic inclusions, and protein aggregates in muscle (96, 97). In this case, the unique way to exclude ALS is the muscle

### REFERENCES

biopsy combined with quantitative electromyographic analysis, especially in those patients where disease progression is slow and atypical (98).

# CONCLUSION

Amyotrophic Lateral Sclerosis (ALS) and MNDs are not yet curable. However, accurate diagnosis is crucial to provide adequate counseling and information about the prognosis and disease course, and to avoid inappropriate therapy. Moreover, a good diagnosis could furnish a more equal stratification of cases and be important in the choice of additional medical support, as for example nutritional intervention strategies or physical therapy.

Currently, there is not a common consensus in the use of laboratory analysis for ALS diagnosis. Basically, clinicians decide for the application of certain techniques based on their experience, expertise and hospital practice. Progress in molecular genetics and identification of specific biomarkers is ongoing, which will translate to a refined diagnostic certitude. Therefore, there is the emerging need to establish a widely accepted protocol for laboratory tests to discriminate the majority of cases that present clinical features resembling ALS.

Increasing human and animal evidence proposed NMJ impairments as possible biomarkers for detection and discrimination of ALS and mimic diseases in an early, preclinical stage. However, further studies are needed to understand how these impairments could be monitored and specifically treated.

### AUTHOR CONTRIBUTIONS

M-LC and A-RB contributed to conception and drafting. EK provided useful comments and reviewed the manuscript.

# FUNDING

This study was funded by the ERC Consolidator Grant, ANR ToFU, E-rare ERA-NET program, ANR AFM, ARSLA, and the program Investissements d'avenir ANR-10-IAIHU-06 (EK).

synapsis and locomotive behavior. FEBS Lett. (2009) 583:1586–92. doi: 10.1016/j.febslet.2009.04.019

98. Dabby R, Lange DJ, Trojaborg W, Hays AP, Lovelace RE, Brannagan TH, et al. Inclusion body myositis mimicking motor neuron disease. Arch Neurol (2001) 58:1253–6. doi: 10.1001/archneur.58. 8.1253

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Campanari, Bourefis and Kabashi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Clinical Measures of Bulbar Dysfunction in ALS

Yana Yunusova1,2,3 \*, Emily K. Plowman4 , Jordan R. Green5,6, Carolina Barnett 7,8 and Peter Bede9

1 Department of Speech Language Pathology, University of Toronto, Toronto, ON, Canada, 2 Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada, 3 Volcal Tract Visualization Lab, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada, 4 Swallowing Systems Core, Department of Speech, Language, and Hearing Sciences, University of Florida, Gainesville, FL, United States, 5 Department of Communication Sciences and Disorders, MGH Institute of Health Professions, Boston, MA, United States, 6 Speech and Hearing Biosciences and Technology Program, Harvard University, Cambridge, MA, United States, 7 Division of Neurology, Department of Medicine, University of Toronto and University Health Network, Toronto, ON, Canada, 8 Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada, 9 Computational Neuroimaging Group, Academic Unit of Neurology, Trinity College Dublin, Dublin, Ireland

Bulbar impairment represents a hallmark feature of Amyotrophic Lateral Sclerosis (ALS) that significantly impacts survival and quality of life. Speech and swallowing dysfunction are key contributors to the clinical heterogeneity of ALS and require well-timed and carefully coordinated interventions. The accurate clinical, radiological and electrophysiological assessment of bulbar dysfunction in ALS is one of the most multidisciplinary aspects of ALS care, requiring expert input from speech-language pathologists (SLPs), neurologists, otolaryngologists, augmentative alternative communication (AAC) specialists, dieticians, and electrophysiologists—each with their own evaluation strategies and assessment tools. The need to systematically evaluate the comparative advantages and drawbacks of various bulbar assessment instruments and to develop integrated assessment protocols is increasingly recognized. In this review, we provide a comprehensive appraisal of the most commonly utilized clinical tools for assessing and monitoring bulbar dysfunction in ALS based on the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) evaluation framework. Despite a plethora of assessment tools, considerable geographical differences exist in bulbar assessment practices and individual instruments exhibit considerable limitations. The gaps identified in the literature offer unique opportunities for the optimization of existing and development of new tools both for clinical and research applications. The multicenter validation and standardization of these instruments will be essential for guideline development and best practice recommendations.

Keywords: amyotrophic lateral sclerosis, Bulbar ALS, outcome assessment (Health Care), dysphagia, dysarthria, COSMIN

# INTRODUCTION

ALS is a relentlessly progressive neurodegenerative disease with considerable clinical heterogeneity compared to other neurodegenerative conditions. Bulbar impairment (oro-motor, dysarthria and dysphagia) is a hallmark feature of the disease and has been associated with the condition since its earliest descriptions (1). While only approximately 30% of patients exhibit bulbar symptoms at onset, the majority of patients develop speech and swallowing difficulties with disease progression.

### Edited by:

Francesca Trojsi, Università degli Studi della Campania Luigi Vanvitelli Caserta, Italy

### Reviewed by:

Patrizia Longone, Fondazione Santa Lucia (IRCCS), Italy Rodolfo Gabriel Gatto, University of Illinois at Chicago, United States

\*Correspondence:

Yana Yunusova<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neurology

Received: 20 November 2018 Accepted: 28 January 2019 Published: 19 February 2019

### Citation:

Yunusova Y, Plowman EK, Green JR, Barnett C and Bede P (2019) Clinical Measures of Bulbar Dysfunction in ALS. Front. Neurol. 10:106. doi: 10.3389/fneur.2019.00106

**137**

Bulbar signs and symptoms play an important role in the diagnosis of ALS and pose unique management challenges. Bulbar presentation has been associated with shorter survival (2, 3), faster functional decline (4), reduced quality of life (5–7) and increased multidisciplinary support needs (8, 9). Dysarthria has been consistently associated with low mood, withdrawal from activities and social isolation (10, 11). Dysphagia in ALS may lead to weight loss, malnutrition, dehydration, aspiration pneumonia, hospitalization and reduced quality of life (12, 13). Despite these important sequelae, bulbar impairment in ALS is relatively understudied, and the research literature is sparse (14). Proxies of bulbar impairment are underrepresented among outcome measures in clinical trials (15). Validated diagnostic, monitoring and prognostic markers of bulbar dysfunction are lacking and clinical assessment practices vary considerably across various centers (16).

Assessment measures are broadly classified as "diagnostic" when their primary purpose is to confirm the diagnosis, exclude mimics, or classify individual patient according to disease-onset. Some measures have been optimized to characterize symptom severity, while other indices are primarily used to monitor longitudinal change. Depending on the primary purpose of a measure, it is subject to a specific set of requirements defined by the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines (17). These require that, in order to reliably integrate assessment tools into clinical practice, their measurement properties need to be firmly established relative to their primary purpose. All tests need to be assessed for validity and reliability (reproducibility). Diagnostic and screening tests should also be evaluated for their detection abilities (i.e., sensitivity/specificity). Discriminative measures need to be able to detect group differences and measures proposed to track longitudinal change need to be assessed for their ability to capture progressive changes.

The objective of this paper is to provide a review of established bulbar measures in ALS from a diagnostic, screening and disease monitoring perspective. This work is not intended as an exhaustive review of all available measures of bulbar impairment in ALS but as a summary of the current state of the field and its most pressing needs.

# TOOLS FOR DIAGNOSING AND SCREENING FOR BULBAR ALS

**Table 1** provides a summary of tools primarily used for the diagnosis of bulbar dysfunction in ALS highlighting their main advantages and limitations.

# Cranial Nerve Exam (CNE)

Clinical evidence of upper motor neuron (UMN) and lower motor neuron (LMN) degeneration is required for the diagnosis of ALS. With regards to bulbar impairment, clinical UMN signs include pathological reflexes (e.g., brisk jaw jerk, gag, and other facial reflexes) (18) and LMN signs encompass muscle weakness, atrophy and fasciculations in the jaw, face, tongue and palate (33). Although the clinical neurological examination remains "the best way to localize neurodegeneration in vivo and to follow the process in real time," (34) and the reliability of CNE has been evaluated in various neurologic populations (21, 35), the quantitative psychometric profile of CNE i.e., inter and intralater reliability, sensitivity, specificity, and responsiveness, have not been systematically evaluated in ALS to date. This represents a research priority for the standardization of assessments.

### Needle EMG

The role of electromyography (EMG) in ALS is the confirmation of acute and chronic denervation. The former may be evidenced by fibrillations, positive sharp waves and fasciculation potentials, which in the tongue are not readily detectable since complete relaxation is difficult to achieve (22). Polyphasic motor unit potentials (MUPs) with prolonged duration, increased amplitude and decreased recruitment are suggestive of chronic denervation. Quantitative motor unit action potential analysis in subclinical bulbar involvement is thought to be superior to peak ratio interference pattern analysis (36). Depending on local protocols, the genioglossus is the most commonly assessed muscle (37), but the evaluation of the sternocleidomastoid (38), masseter, temporalis, frontalis (39), mentalis (40), and trapezius (22) muscles have also been proposed to resolve diagnostic uncertainty. While Motor Unit Number Estimation (MUNE) techniques (41, 42), such as MUNIX (43) have been extensively utilized to quantify motor neuron loss in the limbs, they have only been relatively recently adopted to assess the denervation of the tongue (44) and further development is required for their acceptance to clinical practice.

### Clinical Neuroimaging

While brain imaging is not required to establish the diagnosis of ALS, MRI is commonly used as part of the diagnostic work-up to rule out alternative neurological conditions which may mimic ALS (45, 46). In bulbar onset patients the careful evaluation of the brain stem for structural, neoplastic, vascular, inflammatory and infiltrative processes is particularly important. Pathological processes superior to the brainstem; demyelination, neurovascular syndromes, neurosarcoidosis, leukodystrophies, malignancies, and neurodegenerative conditions may also manifest in bulbar symptoms if involving the corticobulbar tracts or the bulbar segments of the motor cortex. A number

**Abbreviations:** AAC, augmentative alternative communication; ALS, Amyotrophic Lateral Sclerosis; ALSFRS-R, Amyotrophic Lateral Sclerosis— Functional Rating Scale—Revised; ALSSS, ALS Severity Scale; CNS-BFS, Center for Neurologic Study-Bulbar Function Scale; COSMIN, COnsensus-based Standards for the selection of health Measurement Instruments; CNE, Cranial nerve exam; DDK, dysdiadochokinesis; EAT-10, Eating Assessment Tool-10; EIM, electrical impedance myography; EMG, electromyography; FLAIR, Fluid-attenuated inversion recovery; FDA, Frenchay Dysarthria Assessment; FEES, fiberoptic evaluation of swallowing; FSE, Videofluoroscopic swallowing evaluation; FT9, Fine'til 9; GRE/SWI, gradient recalled echo/susceptibility weighted imaging; IOPI, Iowa Oral Performance Instrument; KPa, kilopascal; LMN, lower motor neuron; MAIP, maximum anterior isometric pressure; MiToS, Milano-Torino staging; MR, magnetic resonance; MRI, magnetic resonance imaging; MUNE, Motor Unit Number Estimation; MTP, maximum tongue pressure; MUNIX, Motor unit number index; MUPs, motor unit potentials; NdSSS, Neuromuscular Disease Clinical Status Scale; OSS, Oral Secretion Scale; SCM, sternocleidomastoid; SLPs, speech-language pathologists; SSS, Sialorrhea Scoring Scale; SIT, Sentence Intelligibility Test; syl/sec, syllables per second; T1 W, T1 weighted; UMN, upper motor neuron; WPM, words-per-minute.

of extrapyramidal and cerebellar conditions may also present with localization-specific (ataxic, hypokinetic, hyperkinetic) dysarthria and imaging has a role to rule out gross striatal, nigral and cerebellar pathologies. The incidental identification of tongue tumors on MRI in patients with suspected ALS has also been reported (47). A number of radiological cues have been associated with ALS, such as high signal along the pyramidal tracts on T2 weighted or FLAIR imaging, low signal in the precentral gyrus on GRE/SWI, isolated motor cortex atrophy on T1W, but these qualitative visual cues are not specific to ALS and are not sensitive for diagnostic or monitoring purposes (48). Quantitative imaging studies of ALS on the other hand have successfully captured the cortical (UMN) components of bulbar dysfunction in a somatotopic distribution (49, 50) and characterized the pathological substrate of pseudobulbar affect (51, 52). With relentless methodological (53) and conceptual advances in neuroimaging (54), the establishment of multicenter data repositories (55) and the increasing availability of 7 Tesla systems (56), the anatomical underpinnings of bulbar dysfunction are likely to be characterized in further detail.

### Auditory-Perceptual Dysarthria Evaluation and Frenchay Dysarthria Assessment

"The Mayo Clinic" method of dysarthria categorization involves auditory-perceptual evaluation of specific voice and speech features during a passage reading, phonation of /a/, and oral dysdiadochokinesis (DDK) with /pa, /ta/, /ka/, and /pataka/ (57– 59). The identification of "harsh," "strained," or "strangled" voice quality, slow speaking rate and "excess and equal" stress pattern during passage reading and DDK are typically linked to UMN dysfunction and "spastic dysarthria." "Breathy" or weak voice, hypernasality, nasal emissions, and articulatory imprecision without changes in speaking rate are classically associated with LMN dysfunction and "flaccid dysarthria." ALS is typically characterized by mixed spastic-flaccid dysarthria presenting with articulatory imprecision, hypernasality, harshness, slow rate and prosodic abnormalities. Although the reliability of observational assessments have been repeatedly questioned (10), protocol standardization, assessor training, and reference samples are thought to improve assessment reliability (60). Despite these efforts, auditory-perceptual assessment remains surprisingly underutilized, requiring standardization of practices, psychometric evaluation and multi-center validation in ALS.

Tools like the Frenchay Dysarthria Assessment (FDA) (26) are particularly well-suited for diagnostic purposes as they can comprehensively assess both structure and function of the bulbar musculature through a combination of CNE items and the auditory-perceptual dysarthria assessment. However, FDA was not specifically developed for ALS, and the evaluation of its measurement properties in ALS is lacking. DDK, which is included in CNE, FDA and perceptual dysarthria assessments, is commonly used to track disease progression, and has shown high sensitivity but low specificity for detecting bulbar signs in the prodromal phase of bulbar ALS (61, 62). With further optimization, DDK may have a diagnostic potential, particularly if certain performance constrains are imposed or its complexity is increased (63, 64).

### Dysphagia Diagnosis and Screening

Videofluoroscopic swallowing evaluation (VFSE) remains the gold standard of dysphagia assessment in most neurological conditions allowing the direct visualization of swallowing safety and efficiency i.e., aspiration and the presence of residue, respectively. In ALS however, VFSE is underutilized (16) due to a number of factors such as the presumed lack of therapeutic relevance, lack of access to equipment or perceived patient burden etc. A number of screening tools have been recently evaluated for the early identification of those at risk for dysphagia in ALS. Currently, the Eating Assessment Tool-10 (EAT-10) demonstrated good sensitivity and adequate specificity for detecting aspiration in ALS (30), while instrumental measures of airflow during voluntary cough showed excellent sensitivity and specificity to detect aspiration (32). The bedside 3oz water swallow test is also extensively utilized, but its measurement properties in ALS are still unknown. There is a general consensus among SLPs that patients who fail dysphagia screening should be further evaluated by instrumental techniques to directly visualize the swallowing process using VFSE or fiberoptic evaluation of swallowing (FEES) techniques (65). This is an important consideration given the high incidence of "silent" aspiration in this patient population. Instrumental assessments, not only confirm the diagnosis of dysphagia, but inform on swallowing safety, help to identify the specific etiology of dysphagia, and guide therapeutic strategies that can be tested during the instrumental exam by directly visualizing their impact.

# TOOLS FOR DISEASE MONITORING—STAGING AND LONGITUDINAL TRACKING

Certain bulbar measures have been optimized to track the decline of bulbar function in individual patients and entire cohorts. **Table 2** summarizes proposed bulbar monitoring tools in ALS.

### Bulbar Monitoring (Overall)

A recent clinical practice survey of ALS care in the United States revealed that the Revised ALS—Functional Rating Scale (ALSFRS-R) bulbar sub-score, clinician or patient administered, represented the only measure routinely used to evaluate bulbar dysfunction in the clinical setting (16). It contains only 3 questions to address changes in speech, swallowing and salivation that are each merely rated on a four-point ordinal scale. While the total ALSFRS-R score is thought to have excellent reliability, the measurement properties of the individual sub-scores (e.g., bulbar) have not been specifically evaluated to date (66, 67, 91, 92). The Center for Neurologic Study-Bulbar Function Scale (CNS-BFS) is a 15-item questionnaire of bulbar involvement which has recently been validated against the ALSFRS-R and "timed" speech and swallowing tasks, and has already been successfully utilized in a clinical trial (71, 72). However, the CNS-BFS still needs to be validated against VFSE.

The Appel scale is one of the best characterized tools to track ALS-associated impairment and functional decline (73). Other clinician-administered instruments include the Norris (74), Tuffs (93), and Charing Cross (94) scales, but their original

(Continued)

Frontiers in Neurology | www.frontiersin.org

TABLE 2 | Tools to measure bulbar dysfunction

 severity and disease progression.

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TABLE

2

Continued

development, optimization and validation studies can be difficult to acquire and subsequently, their performance is relatively difficult to judge. The ability of these instruments to represent specific stages and their potential to track progressive bulbar impairment is largely unknown. A number of global ALS staging systems have been developed recently, such as the King's clinical staging system, the Milano-Torino (MiToS) functional staging, the Fine'til 9 (FT9) framework (95, 96), but bulbar impairment is just a small component of these instruments. Among the staging tools, the ALS Severity Scale (ALSSS) is particularly noteworthy, as it uses a 10-point scale for two bulbar functions, speech and swallowing. It was designed to guide rehabilitation efforts in ALS and, and pending formal psychometric evaluation, it may prove to be particularly useful (97).

## Functional Monitoring of Dysphagia and Oral Secretion Scales

The Neuromuscular Disease Clinical Status Scale (NdSSS), which focuses solely on dysphagia, underwent one of the most rigorous psychometric evaluations to date. This tool exhibited excellent inter- and intra- rater reliability and correlated well with the functional oral intake scale (77). It has not been validated against VFSE yet, and given the potential for considerable geographical differences in oral intake, it is unclear how this tool may be validated around the world. While there are several tools to assess sialorrhea in ALS, such as the Oral Secretion Scale (OSS) and Sialorrhea Scoring Scale (SSS) available (78), these also need comprehensive psychometric evaluation and validation.

### Functional Monitoring of Dysarthria

"Speech intelligibility" refers to the degree to which a speaker is understood by a listener, and "speaking rate" refers to speaking speed. Although both of these measures can be assessed on a 5 or 7-point Likert scale (49), the Sentence Intelligibility Test (SIT) is often preferred by SLPs, as it provides a more fine-grained estimate of speech intelligibility (i.e., percent of words transcribed correctly) and speaking rate (i.e., number of words produced per minute) (98). Speech intelligibility is considered abnormal when it falls below 97%, and speaking rate is considered abnormal below 160 words-per-minute (WPM) (99, 100). Speech intelligibility is a general indicator of the severity of dysarthria and it declines relatively late in the course of the disease (101). Speaking rate typically declines prior to significant changes in speech intelligibility, and it changes more linearly with symptom duration than speech intelligibility. Therefore, speaking rate is particularly useful in monitoring bulbar impairment longitudinally (102, 103). A speaking rate of 125 WPM or less is the recommended cut off for to trigger referral to the augmentative and alternative communication services (99).

Digital speech recordings and automated analyses can provide new opportunities for in-depth, observer-independent evaluations, especially during a passage reading and syllable repetition (DDK) tasks. In passage reading tasks, such as the Bamboo Passage, which has been specifically developed to support automatic analyses, certain phrases are semiautomatically identified, and speech duration and pause intervals can be accurately quantified (83). The measures derived from this analysis e.g., percentage pause time, mean phrase duration etc. have been identified to be sensitive to the prodromal stages of bulbar dysfunction (61) and also showed to detect response to pharmaceutical interventions such as dextromethorphan/quinidine (Nuedexta) therapy (85). A recent longitudinal study suggested that the main advantage of the DDK tasks may be in their ability to reliably distinguish slow- and fast-progressors (86).

## Physiological Monitoring

Muscle strength testing in ALS has been initially performed using force transducers (strain gauges) (104, 105) and later with pressure bulbs via the Iowa Oral Performance Instrument (IOPI) (IOPI Medical LLC) or TPM-01 (JMS, Hiroshima). Lingual pressure testing using the IOPI revealed adequate reliability of a maximum tongue pressure estimate (MTP, or maximum anterior isometric pressure, MAIP) but not for the measure of endurance (89). Only one study assessed longitudinal changes in MTP in ALS to date (90) and reported its decline in patients with bulbar onset within 3 months and for those with spinal onset within 6 months. Tongue strength has also been shown to be an independent predictor of survival (88); however, formal psychometric evaluation is awaited to determine the MTP's utility to measure progressive changes over time.

# DISCUSSION

In order to firmly establish the clinical utility of specific bulbar instruments and their potential as outcome measures in clinical trials, their measurement properties need to be comprehensively characterized. Among the diagnostic dysphagia instruments, screening tools, such as EAT-10 and voluntary cough (30, 32) have been well evaluated. Among speech measures, only DDK rate came close to demonstrating diagnostic utility (61). The remaining tools require extensive evaluation with regards to their diagnostic accuracy. While a large number of novel assessment tools have been proposed to track the progression of bulbar impairment, only the ALSFRS-R, the CNS-BFS and some bulbar staging systems (e.g., NdSSS, OSS, SSS) meet at least basic measurement requirements. Most existing disease monitoring tools lack the ability to capture subtle progressive changes, which is indispensable for disease tracking tools. Robust systematic psychometric evaluation is needed to improve the currently available clinical, academic and pharmacological-trial assessment tools.

Despite the gaps in the current literature and the limitations of current clinical trial designs, we are likely to witness considerable advances in standardized bulbar assessments and the emergence of purpose-designed, disease-specific, wellvalidated bulbar assessment tools. Emerging technologies such as quantitative neuroimaging, muscle ultrasound, electrical impedance myography (EIM), high-resolution manometry, videomanofluoroscopy, and speech acoustic monitoring are likely to soon complement our armamentarium of clinical tools. A number of promising imaging techniques have already been utilized to characterize the pathological substrate of bulbar impairment in ALS including diffusion tensor imaging (106, 107), cortical thickness measurements (50, 108, 109), morphometry-type analyses (49, 110), magnetization transfer Yunusova et al. Biomarkers of Bulbar ALS

ratio imaging (106), MR spectroscopy (111), MRI intensitometry (112), and task-based functional MRI (113, 114). Despite these advances, MRI-derived metrics remain underutilized in the clinical setting and as outcome measures in pharmacological trials. This is in sharp contrast with clinical trials in Multiple Sclerosis, where MRI plays an established role as a key outcome measure in phase III clinical trials (115). Muscle ultrasound may capture tongue fasciculations in the absence of fasciculation potentials on EMG and the combination of ultrasound and EMG may help the detection of early denervation (116). Likewise, EIM shows promise in detecting changes in the structural composition of the tongue in ALS and may evolve into an important tool to detect early bulbar involvement (117, 118). High-resolution manometry and videomanofluoroscopy may provide unique insights into the dynamics of bolus movement and swallowing pressures enabling early detection of bulbar dysfunction and thus, timely interventions (119, 120). Acoustic analysis of speech has been proposed as a means for the objective assessment of bulbar impairment for over two decades, but until recently extracting these measure has been extremely time consuming. Recent developments in automatic audio and video analysis methods and smart phone technologies make speech analysis technologically feasible, enabling observer-independent multiparametric analyses (121–123). These emerging methodologies will need careful development, optimization and evaluation according to established methodological guidelines (e.g., COSMIN framework).

### CONCLUSIONS

Recent advances in neuroimaging, development of staging systems, patient-reported outcome measures and the emergence of novel instrumental speech and swallowing assessment

### REFERENCES

techniques promise novel insights into bulbar dysfunction in ALS. However, in order for these methods to be integrated into routine clinical practice and pharmacological trials, they have to be rigorously evaluated with respect to their measurement properties, diagnostic performance and longitudinal tracking abilities. The establishment of large international collaborations and relentless biomarker research efforts give cause for optimism for the development of validated bulbar assessments, which in turn will contribute to best practice recommendations, enable well-timed clinical interventions and facilitate accurate patient stratification in clinical trials.

# AUTHOR CONTRIBUTIONS

YY and PB reviewed references, drafted the manuscript, and generated the tables. EP provided expert opinion on dysphagia assessments and edited the drafts of the manuscript. JG provided expert opinion on dysarthria assessments and edited the drafts of the manuscript. CB provided expert opinion on measurement development and edited the drafts of the manuscript.

### FUNDING

This work was supported by NIH R01DC01729 grant. Peter Bede is supported by the Health Research Board (HRB– Ireland; HRB EIA-2017-019), the Irish Institute of Clinical Neuroscience IICN–Novartis Ireland Research Grant, the Iris O'Brien Foundation, and the Research Motor Neuron (RMN-Ireland) Foundation.

### ACKNOWLEDGMENTS

We wish to thank Nicholas Wasylyk for his help with literature search and data extraction.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Yunusova, Plowman, Green, Barnett and Bede. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Diaphragmatic Neurophysiology and Respiratory Markers in ALS

Mamede de Carvalho1,2 \*, Michael Swash1,3 and Susana Pinto1

1 Instituto de Fisiologia-Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal, 2 Department of Neurosciences and Mental Health, Hospital de Santa Maria-CHLN, Lisbon, Portugal, 3 Barts and the London School of Medicine, Queen Mary University of London, London, United Kingdom

### Edited by:

Peter Bede, Trinity College Dublin, Ireland

### Reviewed by:

Aude-Marie Grapperon, Assistance Publique Hôpitaux de Marseille, France Timothée Lenglet, Assistance Publique Hopitaux de Paris, France Pierre-Francois Pradat, Hôpitaux Universitaires Pitié Salpêtrière, France

### \*Correspondence:

Mamede de Carvalho<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neurology

Received: 15 December 2018 Accepted: 04 February 2019 Published: 21 February 2019

### Citation:

de Carvalho M, Swash M and Pinto S (2019) Diaphragmatic Neurophysiology and Respiratory Markers in ALS. Front. Neurol. 10:143. doi: 10.3389/fneur.2019.00143 The main reason for short survival in amyotrophic lateral sclerosis (ALS) is involvement of respiratory muscles. Severe compromise of diaphragmatic function due to marked loss of motor units causes poor inspiratory strength leading to symptomatic respiratory fatigue, and hypercapnia and hypoxemia, often firstly detected while sleeping supine. Weakness of expiratory muscles leads to cough weakness and poor bronchial clearance, increasing the risk of respiratory infection. Respiratory tests should therefore encompass inspiratory and expiratory function, and include measurements of blood gases during sleep. Non-volitional tests, such as phrenic nerve stimulation, are particularly convenient for investigating respiratory function in patients unable to perform standard respiratory function tests due to poor cooperation or facial weakness. However, SNIP is a sensitive test when patients with bulbar involvement are able to perform the necessary maneuvers. It is likely that central respiratory regulation is disturbed in some ALS patients, but its evaluation is more complex and not regularly implemented. Practical tests should incorporate tolerability, sensitivity, easy application for regular monitoring, and prognostic value. Impending respiratory failure can cause increased circulating inflammatory markers, but molecular assessment of respiratory distress requires further study. In future, home-monitoring of patients with accessible devices should be developed.

Keywords: amyotrophic lateral sclerosis, diaphragm physiology, progression, respiratory function tests, survival

# INTRODUCTION

Respiratory insufficiency (RI) in ALS usually emerges in the late stage of disease progression, although it may sometimes be the presenting feature (1, 2). Respiratory complications, especially hypoventilation (2), reduced bronchial clearance and lung infection (3) account for the majority of deaths in ALS. Mild respiratory involvement causes fatigue in daily-life activities and disruption of sleep, with negative impact on quality of life (4, 5) and hypoxemia may impair cognitive function (6), especially important in a population with a lower cognitive reserve.

ALS affects both inspiratory and expiratory muscles, as well as upper airway muscles (7). Cough, an essential reflex for airway protection and clearance, depends on effective glottis closure and efficient expiratory muscle function. Bulbar muscle dysfunction impairs the former and, for this reason, cough effectiveness is not always correlated with expiratory muscle weakness (8). Weakness

**148**

of pharyngeal and laryngeal muscles increases the risk of aspiration and lung infection. The latter is more critical when associated with marked cough deficiency. For active inspiration the diaphragm is the most important muscle, although other muscles function as accessory muscles of inspiration, e.g., sternocleidomastoid, scalenus, trapezius, external intercostal, pectoralis, and paraspinal muscles. These are particularly important when the diaphragm is weak and during exercise. Severe diaphragm weakness leads to hypoxemia and carbon dioxide retention, since the work capacity of these accessory muscles is not sufficient to compensate. Furthermore, these muscles are themselves progressively involved in the disease process (2). In ALS, the major reason for frank respiratory failure is involvement of the diaphragm (2). The phrenic nerve motor nuclei in the cervical spinal cord are located in a region early affected in ALS, shown by early morphometric changes in these neurons (9). Dyspnea in ALS is closely correlated with diaphragmatic dysfunction (10). Indeed, diaphragm weakness as assessed by the evoked response to transcutaneous phrenic nerve stimulation is predictive of hypoventilation (11) and survival (12). It is therefore relevant to assess the physiology of the diaphragm in people with ALS.

# DIAPHRAGM PHYSIOLOGY

The diaphragm is the most important muscle of ventilation. It is a dome-shaped muscle that separates the thoracic and abdominal cavities. It has a musculo-fibrous structure, formed by a central non-contractile fibrous region, and contractile muscle fibers that radiate circumferentially from the central tendon to attach peripherally to the upper three lumbar vertebrae posteriorly (crural diaphragm) and onto the inner surface of the lower six ribs and costal cartilages antero-laterally (costal diaphragm). In humans, the diaphragm comprises approximately equal numbers of type I and type II fibers, but these muscle fibers are smaller than in the expiratory muscles. They have a rich capillary supply and are resistant to aging (13). Muscle spindles are present only in small numbers in the diaphragm (14), so muscle stretching does not much modulate phrenic neuronal excitability. Diaphragm is well adapted to the rhythmic continuous periodical inspiration of ventilation and to ocassional more forceful contractions, as in deep breaths and coughing. The mean diaphragm thickness at the point of functional residual capacity is 2.29 ± 0.4 mm, as measured by ultrasound (15) but is variable over its surface, and also dependent on body position. Diaphragmatic thickness can increase two-fold during full inspiration (16).

The motor innervation of the diaphragm is almost exclusively from the phrenic nerve (C3–5), which branches to innervate the entire muscle. Contraction of the diaphragm causes axial descent of the dome of the muscle, decreasing intrapleural pressure, and increassing intrabdominal pressure, thus exerting an expansive force on the lower thorax (17). This negative intrathoracic pressure causes an inflow of air to the lungs, promoting inspiration. The diaphragm is a very mobile muscle. With full inspiration it flattens, expanding the thorax down to the level of costal margin anteriorly, and during forced expiration it rises anteriorly to the level of the fourth or fifth intercostal space.

There is appreciable force reserve in the diaphragm. In humans the maximum transdiaphragmatic pressure is about 11 kPa, which more than 10 times the value measured during eupnea (18). Indeed, normal respiration activates fatigue-resistant slowunits (19). However, coughing and sneezing are demanding maneuvers requiring very strong diaphragmatic contraction, close to 50% of the maximum transdiaphragmatic pressure, which implies activation of fast-fatigable motor units (19).

During calm breathing at rest expiration, unlike inspiration, is a passive phenomenon resulting from the relaxation of the inspiratory muscles and reduction of lung compliance. However, active forced expiration relies on recruitment of expiratory muscles, namely the internal intercostals and the abdominal ventro-lateral muscles (20). Generation of an adequate expiratory flux is needed for coughing, sneezing or vomiting. This is only possible with strong inspiration, closure of the glottis, and a sudden increase of intra-abdominal and intra-thoracic pressures. Effective peak cough flow (PCF) in healthy subjects exceeds 360– 400 L/min (21). Peak flow values >160–200 L/min are needed for effective mucus expectoration (22) and values above 250–270 L/min are required to prevent aspiration pneumonia in patients with neuromuscular disorders (23).

The inspiratory pace-maker is located in the pre-Bötzinger Complex in the medulla (24). Its activity, both during inspiration and expiration, is modulated by inhibitory pre-motor neurons and by the Bötzinger Complex (18). Although expiration is a passive movement, active expiration involves a rostral generator, the retrotrapezoid nucleus (25). Synaptic drive to phrenic nerve nuclei is derived from pre-motor neurons located in the ipsilateral ventrolateral and dorsomedial medullary tracts, which respond to central chemoreceptors, sensitive to hypercapnia, and peripheral chemoreceptors, especially the carotid bodies, that are sensitive to hypoxemia. These premotor neurons are also sensitive to sleep-wake state modulation (18). Spinal interneurons can modulate phrenic motoneuronal activity, in particular via intercostal muscle afferents signaling strain of the chest wall (26). Voluntary control of breathing depends on fast, direct corticospinal inputs, which are also critical for respiratory control during speech (18). This pathway can be investigated by magnetic stimulation of cortical areas.

# ASSESSMENT OF RESPIRATORY DYSFUNCTION IN ALS

In ALS inspiratory and expiratory muscles, as well as upper airway muscles are progressively involved. Studies of a possible dysfunctional central respiratory drive are few, but it is likely this could be affected in some patients with ALS (27). As such, different tests are necessary to provide a global view of the respiratory function of diseased subjects. The American (28) and the European (29) guidelines agree that a first respiratory evaluation should be made at the baseline clinical assessment and then periodically thereafter. Nevertheless, this must be adjusted individually, according to the rate of progression of the disease and when there are intercurrent events, such as infection, that may affect respiratory function. A summary of the available tests, their utility and limitations is provided in **Table 1**.

# Global Respiratory Evaluation

Forced vital capacity (FVC) is a non-invasive respiratory test that has long been used in ALS. It assesses both the inspiratory and expiratory loops, requiring expiration done forcefully after a maximal inspiration, as opposed to slow vital capacity (SVC). This test is sensitive to change and predictive of hypoventilation and survival in ALS (30). The change of FVC is an adequate test to follow ALS patients (30, 31), since its decline rate tends to be linear (∼3.5/month), there is a high interpatient variability (32) but this rate is a strong predictor of survival (33). FVC can be an unreliable measure of ventilatory function in patients with bulbar involvement due to orofacial weakness, due to air leakage around the mouthpiece (2). FVC is more sensitive in detecting diaphragmatic weakness when performed in the supine position (34), but this position is often poorly tolerated due to secretions or to the extent of diaphragmatic weakness. In addition, it is not a very sensitive test to detect hypercapnia, since gas exchange is well maintained until FVC values are very low (35). SVC is easier to perform in patients with bulbar involvement, because the air is exhaled slowly, with less air-leakage around the mouthpiece. SVC has been preferred in a number of recent trials, as it is very strongly correlated with FVC (and with other respiratory tests such as Maximal Inspiratory Pressure and Maximal Expiratory Pressure), as well as with ALSFRS-R (36). It is a predictor of progression, the need for positive pressure ventilation, and survival in ALS (37, 38).

Maximal voluntary ventilation (MVV) assesses respiratory function on maintained efforts. The patient is asked to breathe in and out, through a mounthpiece, as deeply and quickly as possible during 12 s, for at least two trials (39). The value is extrapolated for 1 min. The test is demanding for ALS patients, due to their respiratory fatigue. It can be a sensitive measure of disease progression (30), but only in the early stages of the disease (39). This test is rarely performed in daily practice.

Nocturnal pulse oximetry (NPO) is a useful, non-invasive, inexpensive, and convenient method, which accesses respiratory function in a demanding state—when patients are lying and sleeping. It can be used individually or during polysonography, the latter allowing for clear characterization of possible central and/or peripheral apnea. NPO assesses percutaneous oxygen saturation (maximum, median, and minimum values), in relation with heart rate. Further, the pattern of the oxygen saturation curve overnight can be explored. NPO has been shown to be predictive of survival in ALS (40, 41). In addition, it can indicate central drive dysfunction in patients with normal respiratory muscles, a factor that is probably more common in spastic patients (42). NPO is a mandatory method to follow non-invasive ventilation adaptation in patients, which permits home-telemonitoring and distance alteration of ventilatory settings (43). Transcutaneous capnometry (PtcCO2) is a more modern approach to evaluate respiratory function in ALS and other neuromuscular disorders (44). PtcCO2 recordings show strong correlation with arterial measurements. A value higher than 49 mmHg during ≥10% of the total recording time indicates respiratory insufficiency (44). Transcutaneous capnography has been strongly recommended for detection of nocturnal hypoventilation in patients with ALS (45). In patients on non-invasive ventilation, PtcCO2 can be helpful to monitor a proper ventilation, in particular to differenciate between hypoventilation and hypoxemia related to other reasons like as ventilation/perfusion mismatch, as well as in detecting hyperventilation (46). Both techniques have some limitations, for example they cannot discriminate other causes of sleep disturbances, such as obstructive sleep apnea, drug-effect, or associated lung disorder. Nonetheless they are very convenient as a screening method.

Blood gas measurements provide information about CO2 retention and hypoxemia when respiratory failure is severe. Because respiratory assessment is desiged to evaluate early changes, this test is not extensively used in ALS; however, it can provide relevant information for respiratory management in some patients.

Sleep studies have been investigated for a long time in ALS. In this disorder, reduction of the rapid eye movement (REM) sleep stage is typically observed, in particular when the diaphragm is markedly affected and accessory respiratory muscles are weak (47). It has been speculated that disturbed REM sleep might protect patients from hypoventilation (48). However, in patients with preserved diaphragmatic function, signs of sleep hypoventilation are observed as frequently in REM and non-REM phases (27), probably due to reduced respiratory drive (42). Arnulf et al. (48) found that ALS patients with upper motor neuron involvement to respiratory muscles tended to have abnormal REM sleep and poor prognosis. There is a strong link between severity of respiratory function impairment, poor quality of sleep, and daytime somnolence, in ALS (49).

# Evaluation of Inspiration

Maximal inspiratory pressure (MIP) and nasal inspiratory pressure during a maximal sniff (SNIP) are inexpensive and noninvasive respiratory measures that access maximal inspiratory muscular strength, the first against a mouth occlusion and the second using a plug inserted in one nostril (50–52). In both, it is necessary to secure cooperation from patients to breath forcefully against a resistance. While 3 consistent measures are necessary to determine MIP (53), the number rises to 10 for SNIP, 5 in each nostril (53, 54), as the result improves with practice. Fatigue is a limiting factor for both techniques. MIP is more sensitive than FVC in detecting hypoventilation (55). However, its marked early decline (floor effect) limits its use in following patients and it is difficult to perform in patients with orofacial weakness (56) or with spasticity. SNIP is a sensitive tool especially suited for ALS patients with orofacial weakness. It is predictive of survival (57) and of the onset of significant hypoventilation in spinal-onset patients (5). There is some uncertainty about the best technical approach to test ALS patients in order to obtain reliable values (58). SNIP seems to depend more on diaphragm force and MIP

### TABLE 1 | A summary of the most relevant respiratory tests in ALS.

 \*Cost (greater number of plus symbol means higher cost) was estimated taking into account equipament price and the requirement of a technician.

\*\*Ease for monitoring was estimated considering patient confort and technical complexity.

more on the sternocleidomastoid muscle power, making these tests complementary (59).

Transdiaphragmatic pressure (Pdi) can be assessed by inserting balloon catheters in the stomach and mid-esophagus and measuring the differential pressure during active maximal inspiration (60) or following stimulation of the phrenic nerve (61). This is an uncomfortable test that is not suited to clinical application.

Diaphragmatic ultrasound (US) is a non-invasive technique that assesses diaphramatic dynamics, and measures the muscle thickness at tidal volume and on maximal inspiration, as well as the ratio between baseline and maximal inspiration, useful measures to detect diaphragm involvement (62, 63). Significant correlations have been found between these measurements and FVC, SNIP, and the amplitude of the motor response of the phrenic nerve (62–64). However, ultrasound studies are less sensitive than phrenic nerve motor responses in assessing early deterioration of the diaphragm in ALS (65).

Phrenic nerve stimulation by percutaneous electrical or magnetic stimulation in the neck to elicit diaphragm motor responses is an objective, non-volitional test (66, 67) that can

progression. Values were normalized to 100% of normal at first assessment. This figure is simply for representative purposes and not intended to present research findings.

be used to assess the number of functional motor units in the diaphragm (68). Abnormal amplitude (or area) of the motor response has good predictive value for hypoventilation in both bulbar- and spinal-onset patients, and is correlated to FVC (11). This technique is useful in patients with marked facial weakness or in those unable to cooperate, for example those with fronto-temporal dementia. The amplitude of the motor response declines significantly over 3–6 months, and correlates with FVC and SNIP change (69); it is predictive of survival in ALS (12). **Figure 1** represents the progressive and parallel decline of FVC and phrenic nerve compound muscle action potential in an ALS patient.

# Evaluation of Expiration

The efficiency of the expiratory muscles can be easily addressed by evaluating the peak expiratory flow (PEF) and the peak cough flow (PCF), and maximal expiratory pressure (MEP) evaluates the strength of these muscles. These three volitional tests are simple to perform, inexpensive, and non-invasive. Although they measure expiratory muscle function, they depend on central motor control as well as on the efficiency of the inspiratory muscles. Abnormally reduced values indicate inability to expel bronchial secretions, leading to a high risk of respiratory infections (39), leading to increased morbility and mortality (70). MEP values are measured by asking the patient to exhale forcefully against an ocluded mouthpiece. Abnormal values are common in ALS patients (8, 71), and correlate with inspiratory involvement. PEF and PCF use peak flow meters, coupled with a face mask for PCF testing, and assess the ability to exhale forcefully after a maximal inspiration (72, 73) and to cough after a submaximal inspiration (60). Coughing can also be assessed by the gastric pressure generated during a maximal cough, which is a sensitive method to assess expiratory muscle strength, but this is an invasive and poorly tolerated test (74).

# Evaluation of the Central Respiratory Drive

Both NPO and sleep studies can detect respiratory center dysfunction, in particular in patients with normal respiratory muscles in whom nocturnal hypoventilation is detected without obstructive apnea (27). Inspiratory mouth occlusion pressure at 100 ms during quiet breathing (P0.1) is considered an indicator of respiratory drive. Spastic patients with normal diaphragm function tend to show abnormal P0.1/FVC values, associated with a poor prognosis for survival (42). P01 values are similar in bulbar and spinal-onset patients at presentation (56), suggesting that impaired central drive does not depend on the region of onset. The observation of "respiratory apraxia" in ALS patients highlights the complexity and importance of the cortical control of respiration and its potential involvement in ALS (75).

# CONCLUSIONS

There are many tests available to evaluate different features of respiratory function in ALS. In general, most centers follow a conventional approach by evaluating SVC and FVC, which are are often applied in clinical trials. Patients may also be asked to undergo maximal pressure measurements, expiratory peak flows and nocturnal oximetry, sometimes associated with EEG recordings. Less commonly, diaphgram ultrasound or phenic nerve motor responses to percutaneous cervical electrical stimulation of the nerve are tested. Percutaneous capnography is emerging as a relevant technique. Disparity in patients'tolerability and technical limitations would recommend to apply more than one single test to assess respiratory function in ALS patients.

A future study combining most of these tests in a single set of ALS patients would provide more information about diagnostic accuracy, sensitivity, realibility, and convenience for monitoring disease progression. This would have major potential implications in clinical trials, since changing the rate of respiratory decline is critical for improving survival and functional capability.

The identification of a molecular marker of respiratory impairment in ALS would be a convenient and valuable test. Some research indicates that respiratory insufficiency can precipitate an inflammatory response (76, 77), and this is a new avenue yet to be fully explored. User-friendly devices for inhome respiratory evaluation is another future step. New tests to directly evaluate strength of respiratory muscles will require a better understanding of their physiology.

## REFERENCES

# AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

### FUNDING

This research was supported by UID/BIM/50005/2019, project funded by Fundação para a Ciência e a Tecnologia (FCT)/Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) through Fundos do Orçamento de Estado.

# ACKNOWLEDGMENTS

We are grateful to Mariana Pereira for preparing the figure.

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amyotrophic lateral sclerosis. Am J Resp Crit Care Med. (2000) 161:849–56. doi: 10.1164/ajrccm.161.3.9805008

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 de Carvalho, Swash and Pinto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions

Vincent Grollemund1,2 \*, Pierre-François Pradat 3,4,5, Giorgia Querin3,4, François Delbot 1,6 , Gaétan Le Chat 2 , Jean-François Pradat-Peyre1,6,7 and Peter Bede3,4,8

1 Laboratoire d'Informatique de Paris 6, Sorbonne University, Paris, France, 2 FRS Consulting, Paris, France, 3 Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Sorbonne Université, Paris, France, 4 APHP, Département de Neurologie, Hôpital Pitié-Salpêtrière, Centre Référent SLA, Paris, France, 5 Northern Ireland Center for Stratified Medecine, Biomedical Sciences Research Institute Ulster University, C-TRIC, Altnagelvin Hospital, Londonderry, United Kingdom, 6 Département de Mathématiques et Informatique, Paris Nanterre University, Nanterre, France, 7 Modal'X, Paris Nanterre University, Nanterre, France, 8 Computational Neuroimaging Group, Trinity College, Dublin, Ireland

### Edited by:

Laura Ferraiuolo, University of Sheffield, United Kingdom

### Reviewed by:

Marta Milo, University of Sheffield, United Kingdom Foteini Christidi, National and Kapodistrian University of Athens Medical School, Greece

\*Correspondence:

Vincent Grollemund<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neuroscience

Received: 22 November 2018 Accepted: 06 February 2019 Published: 28 February 2019

### Citation:

Grollemund V, Pradat P-F, Querin G, Delbot F, Le Chat G, Pradat-Peyre J-F and Bede P (2019) Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions. Front. Neurosci. 13:135. doi: 10.3389/fnins.2019.00135 Background: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic factors. Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers. Disease heterogeneity, late-stage recruitment into pharmaceutical trials, and inclusion of phenotypically admixed patient cohorts are some of the key barriers to successful clinical trials. Machine Learning (ML) models and large international data sets offer unprecedented opportunities to appraise candidate diagnostic, monitoring, and prognostic markers. Accurate patient stratification into well-defined prognostic categories is another aspiration of emerging classification and staging systems.

Methods: The objective of this paper is the comprehensive, systematic, and critical review of ML initiatives in ALS to date and their potential in research, clinical, and pharmacological applications. The focus of this review is to provide a dual, clinicalmathematical perspective on recent advances and future directions of the field. Another objective of the paper is the frank discussion of the pitfalls and drawbacks of specific models, highlighting the shortcomings of existing studies and to provide methodological recommendations for future study designs.

Results: Despite considerable sample size limitations, ML techniques have already been successfully applied to ALS data sets and a number of promising diagnosis models have been proposed. Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. These models also offer patient stratification opportunities for future clinical trials. Despite the enormous potential of ML in ALS research, statistical assumptions are often violated, the choice of specific statistical models is seldom justified, and the constraints of ML models are rarely enunciated. Conclusions: From a mathematical perspective, the main barrier to the development of validated diagnostic, prognostic, and monitoring indicators stem from limited sample sizes. The combination of multiple clinical, biofluid, and imaging biomarkers is likely to increase the accuracy of mathematical modeling and contribute to optimized clinical trial designs.

Keywords: amyotrophic lateral sclerosis, machine learning, diagnosis, prognosis, risk stratification, clustering, motor neuron disease

## 1. INTRODUCTION

Amyotrophic Lateral Sclerosis (ALS) is an adult-onset multisystem neurodegenerative condition with predominant motor system involvement. In Europe, its incidence varies between 2 or 3 cases per 100 000 individuals (Hardiman et al., 2017) and its prevalence is between 5 and 8 cases per 100 000 (Chiò et al., 2013b). An estimated 450 000 people are affected by ALS worldwide according to the ALS Therapy Development Institute. While no unifying pathogenesis has been described across the entire spectrum of ALS phenotypes, the incidence of the condition is projected to rise in the next couple of decades (Arthur et al., 2016) highlighting the urgency of drug development and translational research. Given the striking clinical and genetic heterogeneity of ALS, the considerable differences in disability profiles and progression rates, flexible individualized care strategies are required in multidisciplinary clinics (den Berg et al., 2005), and it is also possible that precision individualized pharmaceutical therapies will be required.

Depending on geographical locations, the terms "ALS" and "Motor Neuron Disease" (MND) are sometimes used interchangeably, but MND is the broader label, encompassing a spectrum of conditions, as illustrated by **Figure 1**. The diagnosis of ALS requires the demonstration of Upper (UMN) and Lower Motor Neuron (LMN) dysfunction. The diagnostic process is often protracted. The careful consideration of potential mimics and ruling out alternative neoplastic, structural, and infective etiologies, is an important priority (Hardiman et al., 2017). ALS often manifests with subtle limb or bulbar symptoms and misdiagnoses and unnecessary interventions in the early stage of the disease are not uncommon (Zoccolella et al., 2006; Cellura et al., 2012). Given the limited disability in early-stage ALS, many patients face a long diagnostic journey from symptom onset to definite diagnosis which may otherwise represent a valuable therapeutic window for neuroprotective intervention. Irrespective of specific healthcare systems the average time interval from symptoms onset to definite diagnosis is approximately 1 year (Traynor et al., 2000). ALS is now recognized as a multi-dimensional spectrum disorder. From a cognitive, neuropsychological perspective, an ALS-Frontotemporal Dementia (FTD) spectrum exists due to shared genetic and pathological underpinnings. Another important dimension of the clinical heterogeneity of ALS is the proportion of UMN / LMN involvement which contributes to the spectrum of Primary Lateral Sclerosis (PLS), UMN-predominant ALS, classical ALS, LMN-predominant ALS, and Progressive Muscular Atrophy (PMA), as presented in **Figure 1**.

The genetic profile of MND patients provides another layer of heterogeneity. Specific genotypes such as those carrying the C9orf72 hexanucleotide expansions or those with Super Oxide Dismutase 1 (SOD1) mutations have been associated with genotype-specific clinical profiles. These components of disease heterogeneity highlight the need for individualized management strategies and explain the considerable differences in prognostic profiles. Differences in survival due to demographic, phenotypic, and genotypic factors are particularly important in pharmaceutical trials so that the "treated" and "placebo-control" groups are matched in this regard.

With the ever increasing interest in Machine Learning (ML) models, a large number of research papers have been recently published using ML, classifiers, and predictive modeling in ALS (Bede, 2017). However, as these models are usually applied to small data sets by clinical teams, power calculations, statistical assumptions, and mathematical limitations are seldom discussed in sufficient detail. Accordingly our objective is the synthesis of recent advances, discussion of common shortcomings and outlining future directions. The overarching intention of this paper is to outline best practice recommendations for ML applications in ALS.

### 2. METHODS

Machine learning is a rapidly evolving field of applied mathematics focusing on the development and implementation of computer software that can learn autonomously. Learning is typically based on training data sets and a set of specific instructions. In medicine, it has promising diagnostic, prognostic, and risk stratification applications and it has been particularly successful in medical oncology (Kourou et al., 2015).

### 2.1. Main Approaches

Machine learning encompasses two main approaches; "supervised" and "unsupervised" learning. The specific method should be carefully chosen based on the characteristics of the available data and the overall study objective.

"Unsupervised learning" aims to learn the structure of the data in the absence of either a well-defined output or feedback (Sammut and Webb, 2017). Unsupervised learning models can help uncover novel arrangements in the data which

in turn can offer researchers new insights into the problem itself. Unsupervised learning can be particularly helpful in addressing patient stratification problems. Clustering methods can be superior to current clinical criteria, which are often based on a limited set of clinical observations, rigid thresholds, and conservative inclusion/exclusion criteria for class membership. The K-means algorithm is one of the most popular methods. It recursively repeats two steps until a stopping criterion is met. First, samples are assigned to the closest cluster, which are randomly initialized, then cluster centers are computed based on the centroid of samples belonging to each cluster. Unsupervised learning methods have been successfully used in other fields of medicine (Gomeni and Fava, 2013; Marin et al., 2015; Beaulieu-Jones and Greene, 2016; Ong et al., 2017; Westeneng et al., 2018). **Figure 2** represents an example of a patient stratification scheme using an unsupervised learning algorithm.

Supervised learning focuses on mapping inputs with outputs using training data sets (Sammut and Webb, 2017). Supervised learning problems can be divided into either classification or regression problems. Classification approaches allocate test samples into specific categories or sort them in a meaningful way (Sammut and Webb, 2017). The possible outcomes of the modeled function are limited to a set of predefined categories. For example, in the context of ALS, a possible classification task is to link demographic variables, clinical observations, radiological measures, etc. to diagnostic labels such as "ALS," "FTD," or "healthy." Schuster et al. (2016b), Bede et al. (2017), Ferraro et al. (2017), and Querin et al. (2018) have implemented diagnostic models to discriminate between patients with ALS and healthy subjects. Regression problems on the other hand, deal with inferring a realvalued function dependent on input variables, which can be dependent or independent of one another (Sammut and Webb, 2017). For instance, in the context of prognosis, a possible regression task could consist of designing a model which accurately predicts motor decline based on clinical observations (Hothorn and Jung, 2014; Taylor A. A. et al., 2016). When a regression task deals with time-related data sequences, often called "longitudinal data" in a medical context, it is referred to as "time series forecasting." The core characteristics of the data, which are most likely to define group-membership are referred to as "features."

### 2.2. Common Machine Learning Models

While a plethora of ML models have been developed and successfully implemented for economic, industrial, and biological applications (Hastie et al., 2009; Bishop, 2016; Goodfellow et al., 2017), this paper primarily focuses on ML

methods utilized in ALS research.  Generalized linear regression models are also commonly used (Gordon et al., 2009; Taylor A. A. et al., 2016; Huang et al., 2017; Li et al., 2018; Pfohl et al., 2018), but will not be presented here. Please refer to Bishop (2016) for additional information on linear modeling. Our review of ML model families does not intend to be comprehensive with regards to ML models utilized in other medical subspecialties. Additional models with successful implementation in neurological conditions include Latent Factor models (Geifman et al., 2018) and Hidden Markov Models (HMM) (Martinez-Murcia et al., 2016) which have been successfully implemented in Alzheimer disease cohorts.

cluster membership. (C) Stratify samples based on shared feature traits.

### 2.2.1. Random Forests

Tree-based methods partition the input space into sets that minimize an error function, impurity, or entropy (Hastie et al., 2009). A decision tree is a tree-based method that can be described as a series of bifurcations with yes/no questions. To compute the output of a data sample, one needs to start at the top of the tree, and iteratively decide where to go next based on the answer. **Figure 3** illustrates an example of a decision tree for diagnosis modeling in ALS.

"Random Forest" (RF) is a ensemble method based on decision trees. By relying on multiple learning algorithms to combine their results, ensemble methods obtain a more efficient prediction model. Each tree in the RF is built on a random subset of the training data and available features. This increases robustness to outliers and generalizability. The final estimation is the average or majority of the trees' estimation depending on whether the target is a regression or classification task (Louppe, 2014). Most RFs contain more than a hundred decision trees and decision tree length and width can also be sizable depending on the number of input features. In ML, the term "interpretability" refers to the degree to which the machine's decision is comprehensible to a human observer (Miller, 2017). While global model interpretability is de facto rather low, RFs evaluate feature importance with regards to its discriminatory power.

Callosum (CC) FA. For patient 0, these features are reduced CST FA, reduced MC thickness, reduced CC FA. The target is to classify subjects between healthy and ALS subjects. Establishing a diagnosis requires to run through the decision tree till there are no more questions to answer. At step 1, the closed question directs to the right node due to patient 0's CST pathology. At step 2, the closed question directs to the right node due to patient 0's MC pathology. At step 3, the closed question directs to the left node due to patient 0 CC involvement. Step 3 is the last step as there is no more steps below. The diagnosis for patient 0 is the arrival cell value which is ALS.

Feature relevance is appraised based on the error function upon which the decision trees were built. Extremely Randomized Trees (Extra Trees) have shown promising results for discriminating patients suffering from Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA) using speech analysis (Baudi et al., 2016). Please refer to Breiman (2001) for a more thorough description of decision trees and RFs and to Rokach (2016) and Shaik and Srinivasan (2018) for a general overview of forest models and their evolution. **Figure 4** illustrates a possible diagnostic application of RF in ALS.

### 2.2.2. Support Vector Machines

Support Vector Machines (SVM) map input data into high dimensional spaces, called feature spaces, using a non-linear mapping function (Vapnik, 2000). They define a hyperplane that best separates the data. While traditional linear modeling is performed in the input space, SVMs perform linear modeling after projecting the data into another space. The features which discriminate in the projected space, also known as "feature space," derive from input features but these are not readily interpretable. The feature space hyperplane is defined by a limited set of training points called support vectors, hence the name of the method. The chosen hyperplane maximizes the margins between the closest data samples on each side of the hyperplane, which is why SVMs are also referred to as "large margins classifier." These vectors are identified during the "learning phase" after solving a constrained optimization problem. SVMs work as a "black box" as the logic followed by the model cannot be directly interpreted. SVM were state-of-the-art models before being outperformed by NN architecture. That being said, SVM models can adjust well to imaging specific tasks such as anomaly detection using one class SVM. Medical applications of one class SVMs have addressed the issues of tumor detection (Zhang et al., 2004) or breast cancer detection (Zhang et al., 2014). Please refer to Bishop (2016) for more information on SVMs. **Figure 5** illustrates an example of a SVM used to predict prognosis in ALS.

### 2.2.3. Neural Networks

A "perceptron," also called "artificial neuron," is a simplified representation of a human neuron. It is defined by its afferents (inputs), the inputs' respective weights and a non-linear function. The perceptron's output is the linear combination of its inputs onto which the non-linear function is applied. The linear combination consists of the sum of the multiplications of each input and their respective weight. Perceptrons can be compiled, the output of one perceptron providing the input of the next perceptron. The resulting structure is called a "multi-layer perceptron" which is the most common Neural Network (NN) framework. The contribution of each input to the neuron is modulated by its respective weight which is commonly regarded as a "synapse." NN structures are chosen based on manual tuning and model weights are selected using iterative optimization methods. The stochastic gradient descent method is one of the most popular approaches. Specific model architectures are optimally-suited for specific data types such as "Recurrent NNs" (RNN) for time series or "Convolutional NNs" (CNN) for images. Deep learning models are NN models with significant depth or number of layers (hence the name deep learning) and extensive height or number of nodes per layer, which strongly limits their direct interpretability, similarly to SVMs. Deep learning models are currently state-of-the-art in multiple domains, specifically those which deal with imaging data. Substantial achievements were reached in the field of oncology with regards to melanoma (Esteva et al., 2017), breast cancer and prostate cancer detection (Litjens et al., 2016). Advanced neural network architecture such as the Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) have been tested in a medical imaging synthesis (Nie et al., 2017) or patient record generation (Choi et al., 2017) contexts. Please refer to Goodfellow et al. (2017) for additional material on NNs, Amato et al. (2013) for NN applications in medical diagnosis, Lisboa and Taktak (2006) for NN models in decision support in cancer and Suzuki (2017). **Figure 6** provides a schematic example of NNs to aid prognostic modeling in ALS using a two layer multi-layer perceptron.

### 2.2.4. Gaussian Mixture Models

Gaussian Mixture Models (GMM) are probabilistic models which can be used in supervised or unsupervised learning. The model hypothesis is that the data can be modeled as

FIGURE 4 | Random forest for diagnosis. The available data consist of basic biomarkers features which are MUNIX, CSF Neurofilament (NF) levels, Vital Capacity (VC), and BMI. The objective is to classify subjects between healthy and ALS patients. The RF contains 3 decisions trees which use different feature subsets to learn a diagnosis model. Tree A learns on all available features, Tree B learns on MUNIX and VC, Tree C learns on NF levels and BMI. Each tree proposes a diagnosis. RF diagnosis is computed based on the majority vote of each of the trees contained in the forest. Given that two out of three trees concluded that patient 0 had ALS, the final diagnosis suggested by the model is ALS.

a weighted-sum of finite Gaussian-component densities. Each density component is characterized by two parameters: a mean vector and a covariance matrix. Component parameters are estimated using the "Expectation Maximization" (EM) algorithm based on maximizing the log likelihood of the component densities. Inference is performed by drawing from the estimated mixture of Gaussian densities. GMM has achieved good results in medical applications, including medical imaging (de Luis-García et al., 2011) and diagnosing of PD (Khoury et al., 2019). Please refer to Rasmussen (2005) for additional material on GMMs,

Model output is computed after computing linear combination of CA and CB and applying a non-linear function (in this case a maximum function which can be seen as a thresholding function which accepts only positive values). The output is the predicted motor functions decline rate. For patient 0, the returned score is 26.

Moon (1996) for more information on the EM algorithm and Roweis and Ghahramani (1999) for a global overview of Gaussian mixture modeling.

### 2.2.5. k-nearest Neighbors

k-Nearest Neighbors (k-NN) is an instance-based model. Inference is performed according to the values of its nearest neighbors. The advantage of the model is that limited training is required: all of the training data is kept in memory and is used during the prediction phase. Based on a selected distance function, the K most similar neighbors to the new sample are identified. The new sample's label is the average of its nearest neighbors' label. An advanced version of the method is called Fuzzy k-NN (Fk-NN) which has been used to diagnose PD based on computational voice analyses (Chen et al., 2013). Please refer to Bishop (2016) for more information on k-NN models and Aha et al. (1991) for a review on instance-based ML models.

### 2.2.6. Boosting Methods

Boosting algorithms are ensemble methods: they rely on a combination of simple classifiers. In contrast to RF models, which are made up of decision trees and output a result based on the average or majority vote of the decision belonging to the RF mode, boosting algorithms are based on simple classifiers. The concept behind boosting is combining multiple "weak" (performance wise) learning models. This combination provides a more robust model than working with a simple base model. Model learning is based on finding the right weighting of the weak learners which make up the model to learn an efficient global model. Recent applications of boosting models include analysis of genetic information to inform on breast cancer prognosis (Lu et al., 2019) and cardiac autonomic neuropathy (Jelinek et al., 2014). Please refer to Bishop (2016) for more information on boosting methods and (Schapire, 2003) for a general overview of boosting methods.

As opposed to relying on a single ML model, models have been increasingly used in combination. For example, NN has been combined with a RF in Beaulieu-Jones and Greene (2016) where the NN output is fed into the RF model. Learning submodels on specific feature sets have been used to feed sub-model outputs to another ML model as in Fratello et al. (2017) which trained two RF models on different imaging data sets (functional and structural MRI features) and combined intermediate outputs as the final model output. Model combination and model integration can significantly enhance overall performance, but the complexity of both approaches is often underestimated. ML model constraints are even more stringent when used as part of combined or integrated models.

# 2.3. The Limitations of Machine Learning Approaches

ML models have considerable advantages over traditional statistical approaches for modeling complex datasets. Most ML models, including the six approaches presented above, do not require stringent assumptions on data characteristics. They offer novel insights by identifying statistically relevant correlations between features and, in the case of supervised learning, of a specific outcome. Despite the pragmatic advantages, the application of ML models requires a clear understanding of what determines model performance and the potential pitfalls of specific models. The most common shortcomings will be discussed in the following section. Concerns regarding data analyses will be examined first, which include data sparsity, data bias, and causality assumptions. Good practice recommendations for model design will then be presented, including the management of missing data, model overfitting, model validation, and performance reporting.

### 2.3.1. Data Sparsity

"Data sparsity" refers to working and interpreting limited data sets which is particularly common in medical applications. Medical data is often costly, difficult to acquire, frequently require invasive (biopsies, spinal fluid), uncomfortable (blood tests), or time consuming procedures (Magnetic Resonance Imaging). Other factors contributing to the sparsity of medical data include strict anonymization procedures, requirements for informed consent, institutional, and cross-border data management regulations, ethics approvals, and other governance issues. The processing, storage, and labeling of medical data is also costly and often requires specific funding to upkeep registries, DNA banks, brain banks, biofluid facilities, or magnetic resonance repositories (Turner et al., 2011; Bede et al., 2018b; NEALS Consortium, 2018; Neuroimaging Society in ALS, 2018). Multicenter protocols are particularly challenging and require additional logistics, harmonization of data acquisition, standardized operating procedures, and bio-sample processing, such as cooling, freezing, spinning, staining, etc.

Most ML models have originally been intended, developed, and optimized for huge quantities of data. Accordingly, the generalizability of most ML models depends heavily on the number of samples upon which it can effectively learn. Additionally, there is the "curse of dimensionality." The number of samples required for a specific level of accuracy grows exponentially with the number of features (i.e., dimensions) (Samet, 2006). If the number of samples is restrictively low, then the features lose their discriminating power, as all samples in the dataset seem very distinct from one another (Pestov, 2007). ML models learn the underlying relationship between data samples through feature correlations. This requires the ability to discriminate between similar and dissimilar samples in the dataset. Calculating the Sample to Feature Ratio (SFR), i.e., the number of samples available per feature, is a simple way to assess whether the sample size is satisfactory for a given model. An "SFR" of around 10–15 is often considered the bare minimum (Raudys, 2001), but this is based on historical statistical models and may be insufficient for working with complex ML models. Working with a low SFR can lead to both model "underfitting" or "overfitting." These concepts will be introduced below.

### 2.3.2. Data Bias

Discussing data bias is particularly pertinent when dealing with medical data. Most ML models assume that the training data used is truly representative of the entire population. The entire spectrum of data distribution should be represented in the training data, just as observed in the overall population, otherwise the model will not generalize properly. For example, if a model is presented with a phenotype which was not adequately represented in the training data set, the model will at best label it as an "outlier" or at worst associate it to the wrong category label. Medical data are particularly prone to suffer from a variety of data biases which affect recorded data at different analysis levels (Pannucci and Wilkins, 2010). The four most common types of bias include: study participation bias, study attrition bias , prognostic factor measurement bias, and outcome measurement bias (Hayden et al., 2013). In ALS, study participation bias, a.k.a. "clinical trial bias," is by far the most significant. It affects prognostic modeling in particular, as patients in clinical trials do not reflect the general ALS population: they are usually younger, tend to suffer from the spinal form of ALS and have longer survival (Chio et al., 2011). Unfortunately, very little can be done to correct for participation bias post-hoc, therefore its potential impact needs to be carefully considered when interpreting the results. Study attrition bias also influences ALS studies as data censoring is not always systematically recorded. "Censoring" is a common problem in medical research; it refers to partially missing data, typically to attrition in longitudinal studies. Prognostic factor measurements can be influenced by subjective and qualitative medical assessments and by "machine bias" in imaging data interpretation. The single most important principle to manage these factors, especially if limited data are available, is overtly discussing the type of bias affecting a particular study, and openly reporting them.

### 2.3.3. Causality Assumption

ML models identify strong (i.e., statistically significant) correlations between input features and the output in the case of supervised learning. Models can only capture observed correlations which are fully contained within the training data. Causality between features and the output cannot be solely established based on significant correlations in the dataset, especially when working with small and potentially unrepresentative population samples. Causality is sometimes inferred based on ML results which can be misleading.

# 2.4. Good Practice Recommendations 2.4.1. Feature Selection

Identifying the most appropriate features is a crucial step in model design. In "sparse data" situations, the number of features should be limited to achieve an acceptable SFR and to limit model complexity. Various feature selection and engineering approaches exist, which can be chosen and combined depending on primary study objectives. It can be performed manually based on a priori knowledge or using a RF model which ranks data features based on feature importance. This method is commonly used in medical contexts as it easily gives a broad overview of the feature set. Dimension reduction is another option, with linear methods such as Principal Component Analysis (PCA) or Independent Component Analysis (ICA) and nonlinear methods such as manifold learning methods. Automated feature selection methods, such as the "wrapper" or "filtering," undergo an iterative, sometimes time-consuming process where features are selected based on their impact on overall model performance. Finally, provided that sufficient data are available, NN Auto Encoders (AE) models can also reliably extract relevant features. To this day, feature selection and engineering cannot be fully automated and human insight is typically required for manual tuning of either the features or the algorithms performing feature selection. Please refer to Guyon et al. (2006) for further information on feature selection strategies, Fodor (2002) for an overview of dimension reduction techniques and (Lee and Verleysen, 2007) for additional material on non-linear dimension reduction.

### 2.4.2. Missing Data Management

While most ML models require complete data sets for adequate learning, medical data are seldom complete and missing features are also common. Missing data may originate from data censoring in longitudinal studies or differences in data acquisition. One common approach to missing data management is the discarding of incomplete samples. This has no effect on model design provided there is sufficient data left and that sample distribution is unaltered after discarding. This strategy usually requires large volumes of data with only a small and random subset of missing records. This condition however is rarely met in a clinical setting, where data is sparse, and missing data patterns are typically not random. Missing data can often be explained by censoring or specific testing procedures. Discarding data in these situations may increase data bias as it alters the sample distribution. The first step to missing data management is therefore to explore the mechanisms behind missing data features. Features can be "missing completely at random," without modifying the overall data distribution, "missing at random," when missing feature patterns are based on other features available in the dataset or "non-missing at random" for the remaining cases. Depending on the type of missing data, an appropriate imputation method should be selected. Basic data imputation methods, such as mean imputation, work well on "missing completely at random" cases but induce significant bias for "missing at random" scenarios. In this case, advanced imputation methods such as "Multiple Imputation using Chained Equations" (MICE) (van Buuren, 2007) or "Expectation Maximization" (EM) (Nelwamondo et al., 2007) algorithms operate well. Recently, missing data imputation has been managed using Denoising Auto-Encoders (DAE) models (Nelwamondo et al., 2007; Costa et al., 2018), which have a specific NN architecture. MICE and EM algorithms are statistical methods which substitute missing feature values with feature values from the most similar records in the training set. DAE models build a predictive model using the data available with no missing features to assess substitution values."Non-missing at random" patterns are usually dealt with missing at random imputation methods, but this induces bias in data which needs to be specifically acknowledged. Please refer to Little (2002) for general principles on missing data management and (Rubin, 1987) for missing data imputation for "non-random missing" patterns.

### 2.4.3. Model Overfitting

Each model design is invariably associated with a certain type of error. "Bias" refers to erroneous assumptions associated with a model, i.e., certain interactions between the input and the output may be overlooked by the model. 'Variance' refers to errors due to the model being too sensitive to training data variability. The learnt model may be excessively adjusted to the training data and poorly generalizable to the overall population if it has only captured the behavior of the training dataset. "Irreducible error" is inherent to model design and cannot be dealt with post-hoc. "Bias" and "variance" are interlinked, which is commonly referred to as the "biasvariance trade-off." A high level of bias will lead to model "underfitting," i.e., the model does not represent adequately the training data. A high level of variance will lead to model "overfitting," i.e., the model is too specific to the training data. Overfitting is critical, as it is easily overlooked when evaluating model performance and with the addition of supplementary data, the model will not be able to accurately categorize the new data. This severely limits the use of "overfitted" models. Complex models tend to "overfit" more than simpler models and they require finer tuning. Carefully balancing variance and bias is therefore a key requirement for ML model design. Please refer to Bishop (2016) for more information on overfitting.

### 2.4.4. Validation Schemes

Working with an optimal validation scheme is crucial in ML. Validation schemes usually split available data into "training" and "testing" datasets, so that performance can be assessed on novel data. Training and testing data should share the same distribution profile, which in turn should be representative of the entire population. Overfitting is a common shortcoming of model designs and carefully chosen validation schemes can help to avoid it. Several validation frameworks exist, "hold out validation" and "cross validation" being the two most popular. The former splits the initial dataset into two sets, one for training the other for testing. The latter performs the same splitting but multiple times. The model is learned and tested each time and the overall performance is averaged. Nevertheless, caution should be exercised in a sparse data context as validation schemes do not compensate well for poorly representative data. Please refer to Bishop (2016) for additional considerations regarding validation schemes.

### 2.4.5. Harmonization of Performance Evaluation and Reporting

Formal and transparent performance assessments are indispensable to compare and evaluate in ML frameworks. To achieve that, standardized model performance metrics are required. In classification methods, model evaluation should include sensitivity and specificity, especially in a diagnostic context. Sensitivity (or "recall") is the true positive rate, and specificity is the true negative rate. "Accuracy" and Area Under the "Receiver Operating Curve" (ROC) metrics can be added but should never be used alone to characterize model performance. Accuracy is the average of sensitivity and specificity. ROC is used to represent sensitivity and specificity trade-offs in a classifier model (Fawcett, 2004). The ROC space represents the relationship between the true positive rate (i.e., sensitivity) and the false positive rate (which is 1 - specificity). Given a threshold sensitivity rate, the prediction model will return a specificity rate, adding a data point to the ROC. Multiple thresholding enables the generation of the ROC curve. Perfect predictions lead to 100% sensitivity and 100% specificity (i.e., 0% false positives) which leads to an Area Under the ROC (AUC) of 1. Random predictions will return a 50% accuracy rate which is represented by a continuous straight line connecting the plot of 0% sensitivity with 100% specificity and the plot of 100% sensitivity with 0% specificity, which leads to an AUC of 0.5. Accuracy can hide a low specificity rate if there is a class imbalance and AUC can be misleading as it ignores the goodness of fit of the model and predicted probability values (Lobo et al., 2008). In regression approaches, Root Mean Squared Error (RMSE) (also referred to as Root Mean Square Deviation) and R 2 , the coefficient of determination, are good metrics. R 2 represents the ratio of explained variation over the total variation of the data (Draper and Smith, 1998). The closer this index is to one, the more the model explains all the variability of the response data around its mean. Hence the model fits the data well. It is advisable to report multiple performance index for model evaluation as each metric reflects on a different aspect of the model. Using confidence intervals when possible is another good practice, as it conveys the uncertainty relative to the achieved error rate. General reporting guidelines for model design and model evaluation are summarized in the Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis, or TRIPOD, statement (Moons et al., 2015).

Both "supervised" and "unsupervised" learning approaches have a role in clinical applications, the former for diagnosis and prognosis, the latter for patient stratification. There are a large number of ML models available, but recent work in medicine has primarily centered on three models: RF, SVM, and NN models. The advantages and drawbacks of the specific models are summarized in **Table 1** (Hastie, 2003). The following factors should be considered when implementing ML models for a specific medical project:

Data limitation considerations:

Model design considerations:

## 3. RESULTS

Diagnostic, prognostic, and risk stratification papers were systematically reviewed to outline the current state of the art in ML research efforts in ALS. Consensus diagnostic criteria, established monitoring methods, and validated prognostic indicators provide the gold standard to which emerging ML applications need to be compared to.

### 3.1. Current Practices in ALS

### 3.1.1. Current Practices in ALS for Diagnosis

The diagnosis of ALS is clinical, and the current role of neuroimaging, electrophysiology, and cerebrospinal fluid (CSF) analyses is to rule out alternative neurological conditions which may mimic the constellation of symptoms typically associated with ALS. Patients are formally diagnosed based on the revised El Escorial criteria (Brooks, 1994; Brooks et al., 2000; de Carvalho et al., 2008) which achieve low false negative rates (0.5%), but suffer from relatively high false positive rates (57%) (Goutman, 2017). As most clinical trials rely on the El Escorial criteria for patient recruitment, erroneous inclusions cannot be reassuringly ruled out (Agosta et al., 2014). Additionally, misdiagnoses are not uncommon in ALS (Traynor et al., 2000) and these, typically early-stage, ALS patients may be left out from pharmaceutical trials.

### 3.1.2. Established Prognostic Indicators

Providing accurate prognosis and survival estimates in the early-stage ALS is challenging, as these are influenced by a myriad of demographic, genetic and clinical factors. There is a growing consensus among ALS experts that the most important determinants of poor prognosis in ALS include, bulbar-onset, cognitive impairment, poor nutritional status, respiratory compromise, older age at symptom onset, and carrying the hexanucleotide repeat on C9orf72 (Chiò et al., 2009). Functional disability is monitored by the revised ALS Functional Rating Scale (ALSFRS-r) worldwide (Cedarbaum et al., 1999), which replaced the AALS scale (Appel ALS) (Appel et al., 1987). The ALSFRS-r is somewhat subjective as it is based on reported abilities in key domains of daily living, such as mobility, dexterity, respiratory and bulbar function. Despite its limitations, such as being disproportionately influenced by lower motor neuron dysfunction, the ALSFRS-r remains the gold standard instrument to monitor clinical trials outcomes. Prognostic modeling in ALS is typically approached in two ways; either focusing on survival or forecasting functional decline.

### 3.1.3. Current Practices in ALS for Patient Stratification

Current patient stratification goes little beyond key clinical features and core phenotypes. These typically include sporadic vs. familial, bulbar vs. spinal, ALS-FTD vs. ALS with no cognitive impairment (ALSnci) (Turner et al., 2013). A number of detailed patient classification schemes have been proposed based on the motor phenotype alone, as in Mora and Chiò (2015) and (Goutman, 2017): "classic," "bulbar," "flail arm," "flail TABLE 1 | Overview of model pros & cons, updated from Hastie (2003).

SVM, Support Vector Machine; RF, Random Forest; k-NN, k-Nearest Neighbors.

leg," "UMN-predominant," "LMN-predominant," "respiratoryonset," "PMA,""PLS," "Mills' syndrome," etc. Patients may also be classified into cognitive phenotypes such as ALS with cognitive impairment (ALSci), ALS with behavioral impairment (ALSbi), ALS-FTD, ALS with executive dysfunction (ALSexec) (Phukan et al., 2011), as presented in **Figure 1**. Diagnostic criteria for these phenotypes tend evolve, change and are often revisited once novel observations are made (Strong et al., 2017). Irrespective of the specific categorization criteria, these classification systems invariably rely on clinical evaluation, subjective observations, choice of screening tests, and are subsequently susceptible to classification error (Goutman, 2017). Adhering to phenotype definitions can be challenging, as performance cut-offs for some categories, such as cognitive subgroups (i.e., ALSbi/ ALSci) may be difficult to implement (Strong et al., 2009; Al-Chalabi et al., 2016). Al-Chalabi et al. (2016) used muscle bulk, tone, reflexes, age at onset, survival, diagnostic delay, ALSFRS-r decline, extra-motor involvement, symptom distribution, and family history as key features for patient stratification. ALS and FTD share common aetiological, clinical, genetic, radiological and pathological features and the existence of an ALS-FTD spectrum is now widely accepted. Up to 15% of patients develop frank dementia (Kiernan, 2018) and 60% show some form of cognitive or behavioral impairment (Phukan et al., 2011; Elamin et al., 2013; Kiernan, 2018). The presence of cognitive impairment is hugely relevant for machine-learning applications because neuropsychological deficits have been repeatedly linked to poorer survival outcomes (Elamin et al., 2011), increased caregiver burden (Burke et al., 2015), specific management challenges (Olney et al., 2005), and require different management strategies (Neary et al., 2000; Hu et al., 2009).

### **Clinical staging systems**

One aspect of patient stratification is to place individual patients along the natural history of the disease by allocating them to specific disease phases or "stages." The utility of staging in ALS is 2-fold; it guides the timing of medical interventions (noninvasive ventilation, gastrostomy, advance directives) and also allows the separation of patients early in their disease trajectory from "late-phase" patients in clinical trials. Three staging systems have been recently developed; Kings' (Roche et al., 2012), MiToS (Chiò et al., 2013a), and Fine Till 9 (FT9) (Thakore et al., 2018). While the MiToS stage can be directly calculated based on ALSFRS-r scores, the Kings' stage is a derived measure. It is noteworthy, that the stages and the ALSFRS-r score are highly correlated (Balendra et al., 2014a). Both staging systems have been cross-validated, compared and they are thought to reflect on different aspects of the disease (Hardiman et al., 2017). The MiToS system is more sensitive to the later phases of the disease, while Kings' system reflects more on the earlier phases of ALS. The FT9 system is not partial to earlier or later stages. The FT9 framework defines stages based on ALSFRS-r subscores, using 9 as a threshold after testing different values on the PRO-ACT dataset. One of the criticism of MiToS, is that stage reversion is possible and that it does not directly capture disease progression (Balendra et al., 2014b). Ferraro et al. (2016) compared MiToS and King clinical staging systems and Thakore et al. (2018) compared all three systems on PRO-ACT data.

Current diagnostic approaches in ALS are suboptimal and often lead to considerable diagnostic delay. Prognostic protocols are not widely validated and current patient stratification frameworks don't represent the inherent heterogeneity of ALS. Accordingly, machine-learning approaches have been explored to specifically address these three issues.

### 3.2. Results in Diagnosis

### 3.2.1. Advances in Biomarker Research

The majority of ML research projects focus on the development, optimization, and validation of diagnostic biomarkers. These typically include clinical, biofluid, and neuroimaging indicators. Diagnostic model performance depends on the feature's ability to describe how the disease affects the subjects. Optimal diagnostic biomarkers should not only discriminate between ALS patients and healthy controls but also between ALS patients and patients with mimic or alternative neurological conditions (Bede, 2017). Ideally, an optimal diagnostic model should have outstanding early-stage sensitivity and specificity so that patients can be recruited into clinical trials early in their disease.

### **Clinical biomarker research**

MUNIX (Fathi et al., 2016) is a non-invasive neurophysiological method which is extensively used in both clinical and research settings. It may also have the ability to capture pre-symptomatic motor neuron loss (Escorcio-Bezerra et al., 2018), therefore it has the potential to confirm early-stage disease in suspected cases. An earlier diagnosis would in turn enable the earlier initiation of neuroprotective therapy with established drugs and more importantly, earlier entry into clinical trials.

### **Biological biomarker research**

Cerebrospinal Fluid (CSF) Neurofilaments (NF) are regarded as one of the most promising group of "wet" biomarkers in ALS (Rossi et al., 2018; Turner, 2018). Typically, research studies assess both Neurofilament Light (NF-L) chain and phosphorylated Heavy (pNF-H) chain levels that are released due to axonal degeneration and can be detected in the CSF and serum. Studies have consistently shown increased CSF pNF-H levels in ALS and up to ten times higher levels than in patients with Alzheimer disease (Brettschneider et al., 2006) or other neurological conditions (Gresle et al., 2014; Steinacker et al., 2015). Even though ALS studies have consistently detected raised pNF-H concentrations, these values vary considerably in the different reports. CSF NF-L levels were linked to reduced pyramidal tract Fractional Anisotropy (FA) and increased Radial Diffusivity (RD) (Menke et al., 2015) and NF-L levels are also thought to correlate with progression rates (Tortelli et al., 2014). Other biological biomarkers include proxies of oxidative stress, such as CSF 4-hydroxy-2,3-nonenal (4-HNE) (Simpson et al., 2004) or 3-nitrotyrosine (3-NT) (Tohgi et al., 1999). Neuroinflammation is another important feature of ALS, and several studies have detected an increase in inflammationassociated molecules, such as interleukin-6 (IL-6) and TNF alpha (TNF − α) (Moreau et al., 2005) and galectin-3 (GAL-3) (Zhou et al., 2010). Increased levels of CSF Chitotriosidase-1 (CHIT1) is thought to indicate increased microglial activity (Varghese et al., 2013). Raised levels of CSF hydrogen sulfide (H2S) was also reported in ALS, which is released by astrocytes and migrolia and is known to be toxic for motor neurons (Davoli et al., 2015). These are all promising wet biomarkers, indicative of diseasespecific pathological processes and it is likely that a panel of several biomarkers may be best suited for diagnostic purposes.

### **Genetic biomarker research**

A shared pathological hallmark of neurodegenerative conditions is protein aggregation. The accumulation of the Transactive Response DNA Binding Protein 43 (TDP-43) is the most consistent pathological finding in approximately 95% of ALS cases (Neumann et al., 2006). Given the widespread aggregation and accumulation of TDP-43 in FTD-ALS spectrum, TDP-43 detection, measurement or imaging is one of the most promising biomarkers strategies. A recent meta-analysis evaluated the diagnostic utility of CSF TDP-43 levels in ALS (Majumder et al., 2018) and found that increased levels may be specific to ALS, as TDP-43 levels are significantly raised compared to FTD as well. Reports on SOD1 levels in the CSF of ALS patients have been inconsistent; some studies detected increased levels (Kokic et al., ´ 2005) whereas others have identified decreased levels (Ihara et al., 2005) or levels comparable to controls (Zetterström et al., 2011).

### **Proteomics biomarker research**

Beyond the interpretation of clinical and imaging data, ML models have an increasing role in genetics, RNA processing and proteomics (Bakkar et al., 2017). Using IBM Watson 5 new RNA-Binding Proteins (RBPs) were identified which were previously not linked to ALS; Heterogeneous nuclear ribonucleoprotein U (hnRNPU), Heterogeneous nuclear ribonucleoprotein Q (SYNCRIP), Putative RNA-binding protein 3 (RBMS3), ell Cycle Associated Protein 1 (Caprin-1) and Nucleoporin-like 2 (NUPL2). ML models play an important role in modern genetic analyses (Libbrecht and Noble, 2015) but considerable variations exist in their application between various medical subspecialties. One of the roles of ML in genomics is to identify the location of specific protein-encoding genes within a given DNA sequence (Mathé et al., 2002). In the field of proteomics, ML has been extensively utilized to predict 3-dimensional folding patterns of proteins. Approaches such as Deep Convolutional Neural Fields (DeepCNF) have been successful in predicting secondary structure configurations (Wang et al., 2016). In proteomics, ML models are also utilized for loop modeling, and protein side-chain prediction (Larranaga et al., 2006).

### **Imaging biomarker research**

Neuroimaging offers unique, non-invasive opportunities to characterize disease-associated structural and functional changes and imaging derived metrics have been repeatedly proposed as candidate biomarkers (Turner et al., 2011; Agosta et al., 2018a; Bede et al., 2018b). The primary role of MRI in current clinical practice is the exclusion of alternative structural, neoplastic and inflammatory pathology in the brain or spinal cord which could manifest in UMN or LMN dysfunction similar to ALS. Diffusion tensor imaging (DTI) has gained a lot of attention as DTIderived metrics, such as FA, Mean Diffusivity (MD), RD, or Axial Diffusivity (AD) have already been successfully used to identify ALS patients in ML models (RF) (Bede et al., 2017; Querin et al., 2018). The DTI signature of ALS is firmly established thanks to a myriad of imaging studies, and it includes the commissural fibers of the corpus callosum and the bilateral Corticospinal Tract (CST) (Turner et al., 2009; Bede et al., 2014). The latter has been associated to clinical UMN dysfunction, as well as rate of progression in specific sub-regions (Schuster et al., 2016a). White matter degeneration in frontal and temporal regions have been linked to cognitive and behavioral measures (Agosta et al., 2010; Christidi et al., 2017) and specific genotypes (Bede et al., 2013a). While callosal (Filippini et al., 2010; Bede et al., 2013a) and CST (Agosta et al., 2018b) degeneration seems to be a common ALS-associated signature, frontotemporal and cerebellar white matter degeneration seems to be more specific to certain phenotypes (Prell and Grosskreutz, 2013; Bede et al., 2014). From a gray matter perspective, motor cortex atrophy is a hallmark finding irrespective of specific genotypes and phenotypes (Bede Grollemund et al. ML in ALS: An Overview

et al., 2012) which is readily captured by cortical thickness or volumetric measures. Other gray matter regions, such as frontal (Lulé et al., 2007), basal ganglia (Bede et al., 2013c, 2018a; Machts et al., 2015), or cerebellar regions (Prell and Grosskreutz, 2013; Batyrbekova et al., 2018) may be more specific to certain patient cohorts. What is important to note, is that considerable white matter degeneration can already be detected around the time of diagnosis which progress relatively little, as opposed to the incremental gray matter findings in the post-symptomatic phase of the disease (Bede and Hardiman, 2017; Menke et al., 2018). The relevance of these observations is that white matter metrics may be particularly suitable for diagnostic models, whereas gray matter metrics in monitoring applications.

### 3.2.2. Overview of Research in Diagnosis

ML methods have already been extensively tested to aid the diagnosis of ALS (Gordon et al., 2009; Welsh et al., 2013; Sarica et al., 2016; Schuster et al., 2016b; Bede et al., 2017; Ferraro et al., 2017; Fratello et al., 2017; D'hulst et al., 2018; Li et al., 2018; Querin et al., 2018). Diagnostic models are typically developed within a classification framework with limited category labels, such as "healthy" vs. "ALS." Srivastava et al. (2012) implemented a model to discriminate patients within the Spinal Muscular Atrophy (SMA) spectrum. A similar attempt has not been made in ALS yet but could prove very valuable.

Performance was highest using combined imaging metrics (Bede et al., 2017) outperforming diagnostic models relying solely on clinical features (Li et al., 2018) which typically achieve up to 68% sensitivity and 87% specificity. Current models however are severely limited by small sample sizes and achieve lower true positive rates than the El Escorial's criteria but dramatically improve false negative rates. In general, diagnostic models based on imaging data achieve a sensitivity above 80% which is very encouraging especially given the emergence of larger data sets (Müller et al., 2016). It is crucial to evaluate model performance in comparison to the current gold standard criteria and report both sensitivity (true positive rate) and specificity (true negative rate). Additional metrics seem also necessary such as accuracy and AUC which provides a global indication of the model's performance.

### **Performance analysis**

Welsh et al. (2013),Schuster et al. (2016b),Bede et al. (2017),Ferraro et al. (2017),Fratello et al. (2017),D'hulst et al. (2018), and Querin et al. (2018) only used single-centre imaging data for their model design. Bede et al. (2017) used a canonical discriminant function and achieved an accuracy of 90% (for 90% sensitivity and 90% specificity). Sarica et al. (2016),Ferraro et al. (2017),Fratello et al. (2017), and Querin et al. (2018) used RFs achieving accuracy rates between 77.5 and 86.5%. Schuster et al. (2016b) used a binary logistic regression model and reached 78.4% (90.5% sensitivity and 62.5% specificity). Welsh et al. (2013) and D'hulst et al. (2018) used SVMs reaching an accuracy of 71 and 80%, respectively. A relatively low accuracy of 71% (Welsh et al., 2013) and low specificity of 12.5% (D'hulst et al., 2018) may stem from model overfitting. The complexity of SVM models, class imbalance (D'hulst et al., 2018), data sparsity (Welsh et al., 2013) are some of the factors which may contribute to their relatively poorer performance. Li et al. (2018) used a linear regression model based on clinical data and reached 77.5% accuracy, 68% sensitivity and 87% specificity. Half of the studies (Welsh et al., 2013; Sarica et al., 2016; Bede et al., 2017; D'hulst et al., 2018; Querin et al., 2018) focused on discriminating ALS patients from healthy controls. Four studies (Gordon et al., 2009; Ferraro et al., 2017; Fratello et al., 2017; Li et al., 2018) went further and attempted to identify ALS within a range of neurological diseases including patients with Parkinson's Disease (PD), Kennedy's Disease (KD), PLS, etc. Srivastava et al. (2012) focused on identifying specific SMA phenotypes. Please refer to **Table 2** for an overview of ML papers focusing on the diagnosis of ALS.

 Imaging analyses need to take the effect of age, gender, and education on MRI data into account, as these have a major impact on white and gray matter metrics. Studies control for these demographic factors differently; while age is generally adjusted for (Zhang et al., 2018), the effect of gender (Bede et al., 2013b) and education (Cox et al., 2016) are often overlooked which can affect model development. Judicious feature selection is paramount as model complexity is directly related to the number of features fed into the model. Limiting model complexity, especially in the context of sparse data is crucial to avoid model overfitting. Feature selection is often based, either on group comparisons or a priori imaging or pathological information. Features often include imaging measures of key, disease-associated anatomical regions, such as measures of the motor cortex or pyramidal tracts (Bede et al., 2016). Existing studies use very different validation schemes to test model performance.  While robust validation schemes are essential, they don't circumvent overfitting especially when limited data are available. "Cross validation" and "leave-one-out" approaches are generally more robust than holdout validation. Special caution should be exercised with regards to validation reports in sparse data situations, where validation schemes have a limited ability to assess model performance. Querin et al. (2018) and Li et al. (2018) both show SFR higher than ten (15 and 12 ,respectively) which comply with minimum SFR recommendations (Raudys, 2001).

### TABLE 2 | Research overview: diagnosis.

CV, Cross Validation; LOOV, Leave One Out Validation; HOV, Hold Out Validation; AUC, Area under the ROC Curve; Acc, Accuracy; Sens, Sensitivity; Spec, Specificity; PD, Parkinson's Disease; FS, Feature Selection; SP, Signal Processing.

### 3.3. Results in Prognosis

### 3.3.1. Advances in Biomarker Research

As the precise mechanisms of disease propagation in ALS are largely unknown (Ravits, 2014; Ayers et al., 2015), research has focused on the identification of candidate prognostic biomarkers including potential clinical, biological, imaging, and genetic indicators. Prognostic model performance depends on the feature's ability to capture the disease spread. Optimal prognostic biomarkers should not only discriminate between different ALS phenotypes but categorize individual patients to common disease progression rates (slow vs. fast progressors) (Schuster et al., 2015).

### **Clinical biomarker research**

Several recent studies examined the specific impact of psychosocial factors, cognitive impairment, nutritional status and respiratory compromise, on prognosis. Psychosocial adjustments in ALS may have an under-recognized impact on prognosis (Matuz et al., 2015). The potential effect of mood on disease progression has only been investigated on a relatively small number of samples to date (Johnston et al., 1999).

### **Biological biomarker research**

Recent research suggests that prognostic modeling that does not rely on a priori hypotheses could lead to more accurate prognostic models than does driven by pre-existing hypotheses. For instance, elevations in Creatine Kinase (CK) were linked to LMN involvement and faster disease progression (Rafiq et al., 2016; Goutman, 2017) using the PRO-ACT data (Ong et al., 2017).

### **Genetic biomarker research**