Associating the Bulbar and Respiratory Dysfunctions in Patients
with Amyotrophic Lateral Sclerosis
by
Nicholas Alexander Wasylyk
A thesis submitted in conformity with the requirements
for the degree of Master of Science
Rehabilitation Science Institute
University of Toronto
© Copyright by Nicholas Alexander Wasylyk (2018)
ii
Associating the Bulbar and Respiratory Dysfunctions in Patients with
Amyotrophic Lateral Sclerosis
Nicholas Alexander Wasylyk
Master of Science
Rehabilitation Science Institute
University of Toronto
2018
Abstract
Motivated by the need to establish clinical measures of bulbar motor dysfunction, this study
investigated the associations between bulbar and respiratory measures in patients with
amyotrophic lateral sclerosis (ALS). A secondary analysis of 158 participants (female=59)
evaluated: (1) group differences in bulbar motor measures between patients with normal
respiratory function (ALSnr) and those with impaired respiratory function (ALSir); and (2)
correlations between the bulbar motor and respiratory measures. A significantly larger percent
pause time during passage reading was revealed in the ALSir (24.4±7.1) as compared to ALSnr
(19.6±6.5) group. A significant negative correlation between percent pause time and forced vital
capacity (%FVC) was identified; a partial correlation between percent pause time and speaking
rate remained significant after controlling for %FVC. The findings suggested that the respiratory
deficit minimally affected the selected bulbar motor measures, thus supporting their use in
clinical assessment of bulbar ALS.
iii
Acknowledgments
My learning throughout my graduate studies was influenced by many individuals. Although only
a few of them are mentioned below, I am grateful for everyone who contributed to my graduate
experience.
I express my sincerest gratitude towards Dr. Yana Yunusova, my primary supervisor. Her shared
knowledge, support and guidance have shaped me into the student I am today. I will forever be
thankful for the opportunity to join Dr. Yunusova’s lab and contribute to her research. My future
achievements will be attained in part because of what I learned through my experiences with Dr.
Yunusova.
Dr. Rosemary Martino, my co-supervisor, allowed me to partake in many learning opportunities
within her lab as well. I am thankful for all of Dr. Martino’s encouragement and contribution
towards the completion of my graduate studies.
Dr. Dina Brooks truly completed my supervisory committee. Her insight and expertise have
greatly assisted with the completion of this thesis.
Dr. Madhura Kulkarni, Dr. Elaine Kearney, Sanjana Shellikeri, Dr. Andrea Bandini, Elissa
Greco, Victoria Sherman, Dr. Ana Furkim and Dr. Gabriela Vanin have made my graduate
studies more enjoyable. They impacted me greatly, and I am thankful for all I have learned from
them and the bonds we developed.
Last, but definitely not least, my accomplishments would mean nothing without my family and
girlfriend—Len Wasylyk, Nina Alpejev, Natalie Wasylyk, Stephanie Wasylyk and Nadia
Larocca. They made the long days and stressful times all the more worthwhile. I cherish every
moment they shared with me and their never-ending love and support. They will always hold a
special place in my heart.
Table of Contents
Acknowledgments.......................................................................................................................... iii
List of Tables ................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
Introduction and Literature Review .........................................................................1
1.1 Introduction ..........................................................................................................................1
1.2 ALS as a Disease of the Motor System ...............................................................................1
1.3 ALS Epidemiology ..............................................................................................................2
1.4 Subtypes of ALS ..................................................................................................................3
1.5 Bulbar ALS ..........................................................................................................................4
1.6 Clinical Assessment of Bulbar Function..............................................................................4
1.7 The Subsystem Approach towards Measuring Bulbar Dysfunction in ALS .......................6
1.8 Physiological Measures of Bulbar Function by Subsystem ...............................................11
1.8.1 Articulatory Subsystem ..........................................................................................11
1.8.2 Respiratory Subsystem...........................................................................................13
1.8.3 Resonatory Subsystem ...........................................................................................14
1.8.4 Phonatory Subsystem .............................................................................................15
1.8.5 Summary of the Identified Subsystem-Based Bulbar Measures............................16
1.9 Clinical Measures of Respiratory Dysfunction in ALS .....................................................17
1.9.1 Forced Vital Capacity ............................................................................................17
1.9.2 Peak Expiratory Airflow ........................................................................................18
1.10 Research Objectives and Hypothesis ................................................................................18
Methods..................................................................................................................20
2.1 Study Design ......................................................................................................................20
2.2 Participants: Inclusion Criteria ..........................................................................................20
v
2.3 Data Collection Protocol: Instrumentation and Tasks .......................................................21
2.4 Statistical Analysis .............................................................................................................22
Results ....................................................................................................................24
3.1 Participant Characteristics: ALS Group Combined and ALS Subgroups .........................24
3.2 Physiological Subsystem Measures and ALS Subgroup Comparisons .............................27
3.3 Associations between Physiological Subsystem Measures and %FVC ............................29
3.4 Associations between Physiological Subsystem Measures and PEF .................................30
3.5 Partial Correlation between Percent Pause Time and Speaking Rate, Controlling for
%FVC ................................................................................................................................31
Discussion ..............................................................................................................32
4.1 Summary ............................................................................................................................32
4.2 Use and Interpretation of Physiological Subsystem Measures in the Assessment of
Bulbar ALS ........................................................................................................................32
4.3 Percent Pause Time During a Reading Task and Respiratory Dysfunction ......................35
4.4 PEF as a Measure of Respiratory Decline in ALS .............................................................36
4.5 Contribution to the Literature ............................................................................................37
4.6 Nature of Measures: Maximum versus Submaximal Performance ...................................37
4.7 Limitations of the Present Study ........................................................................................38
4.8 Conclusions ........................................................................................................................39
4.9 Future Directions ...............................................................................................................40
References ......................................................................................................................................41
vi
List of Tables
Table 1: Summary of descriptive statistics for physiological subsystem measures in healthy
adults. ............................................................................................................................................ 17
Table 2: Clinical respiratory measures and associated instruments and instructions. .................. 21
Table 3: Physiological subsystem measures and associated instruments and instructions. .......... 22
Table 4: Participant demographic and clinical characteristics.. .................................................... 26
Table 5: Participant demographic and clinical characteristics for ALSnr and ALSir subgroups.. 26
Table 6: Descriptive statistics of the subsystem measures by subgroup. ..................................... 28
Table 7: Test statistics for the group comparisons of the physiological subsystem measures
between the ALSnr and ALSir subgroups. ................................................................................... 28
Table 8: Correlations between physiological subsystem measures and %FVC. .......................... 30
Table 9: Correlations between physiological subsystem measures and PEF. .............................. 31
vii
List of Figures
Figure 1: The four speech subsystems. ........................................................................................... 7
Figure 2: Likelihood of maintaining function over time of speech intelligibility, speaking rate
and the four physiological subsystems. .......................................................................................... 9
Figure 3: A CONSORT diagram of study flow. .......................................................................... 25
Figure 4: Boxplot dipslaying percent pause time group comparison. .......................................... 29
Figure 5: Scatterplot displaying the correlation between percent pause time and %FVC. ........... 30
1
Introduction and Literature Review
1.1 Introduction
The aim of this thesis is to better understand the association between bulbar motor and
respiratory dysfunction in patients with amyotrophic lateral sclerosis (ALS). This research is
important because bulbar motor and respiratory functions are interlinked in the production of
speech and swallowing, and respiratory dysfunction may affect bulbar dysfunction
measurements. The current assessment of bulbar dysfunction in ALS is limited to symptom
reports and global measures of function such as speech intelligibility and speaking rate; there is a
substantial need for assessments capable of detecting early signs of bulbar dysfunction and
monitoring the bulbar disease progression over the course of disease progression (Ball, Willis,
Beukelman, & Pattee, 2001; Green et al., 2013; Yorkston, Strand, Miller, Hillel, & Smith, 1993).
Recent research into the instrumental physiological measures of speech shows that these
measures have the potential to improve clinical practice and the design of therapeutic drug trials
by identifying early changes of the bulbar musculature prior to significant loss of speech and/or
swallowing functions as well as provide a method for tracking disease progression (Allison et al.,
2017; Green et al., 2016; Rong, Yunusova, Wang, et al., 2016). However, the same measures
may be affected by the underlying respiratory deficit common in ALS, and their associations
with respiratory dysfunction need to be examined. This thesis aims to examine the association
between bulbar motor and respiratory dysfunction in a large group of participants with ALS. The
present chapter will provide an overview of ALS as a disease of the motor system as well as
summarize the literature regarding the current state of assessment of bulbar dysfunction. It will
also detail the rationale for the present study.
1.2 ALS as a Disease of the Motor System
ALS is a rapidly progressing and fatal neurodegenerative disease that has recently been termed a
multisystem disorder because of its effects on both motor and extra-motor (e.g., cognitive and
language) neural pathways (Phukan et al., 2012; Schreiber et al., 2005; Shellikeri et al., 2017).
Although cognitive and language dysfunction is often reported in ALS, the primary impairment
involves the motor system (Strong et al., 2017). The upper motor neurons (UMN) relay the
neural impulses from the brain to the cranial and spinal nerves along the corticobulbar and
2
corticospinal tracts. The lower motor neurons (LMN) then relay these neural impulses to the
muscles to instigate movement. In ALS, UMN and LMN degeneration results in functional
motor changes involving the limbs, trunk and head and neck musculature (Andersen et al., 2012).
UMN degeneration results in hyperreflexia and muscle spasticity, whereas LMN degeneration
results in muscle weakness, muscle atrophy and fasciculations (Brooks, Miller, Swash, &
Munsat, 2000).
Diagnosing ALS is a challenge for clinicians because a biomarker of the disease has yet to be
identified. A clinical diagnosis is provisional and provided based on the El Escorial Diagnostic
Criteria (Brooks et al., 2000). To receive a definite diagnosis of ALS, a patient must exhibit signs
of UMN and LMN degeneration in three regions (i.e., either 3 spinal regions or 2 spinal regions
and the bulbar region). A probable diagnosis is given to patients displaying signs of UMN and
LMN degeneration in two regions, or when one region displays signs of UMN degeneration and
two regions exhibit laboratory evidence of LMN degeneration. A possible diagnosis of ALS is
provided to patients exhibiting either UMN and LMN degeneration in one region, UMN
degeneration in two regions, or signs of LMN degeneration located rostrally to signs of UMN
degeneration (Brooks et al., 2000). Notably, UMN signs are difficult to detect as LMN signs
often mask them (Huynh et al., 2016; Swash, 2012). LMN signs in turn are challenging to detect
due to the limitations of muscle electromyography (EMG), the gold standard of LMN function
assessment (Dyck, 1990). These EMG limitations are particularly notable when assessing
muscles of the bulbar region (Finsterer, Fuglsang-Frederiksen, & Mamoli, 1998; Ludlow et al.,
1994). The lack of sensitive diagnostic measures contributes to the diagnostic delay, which is
approximately 12 months in bulbar onset ALS cases and over 2 years in spinal onset ALS cases
(Turner, Brockington, et al., 2010; Turner, Scaber, et al., 2010). To add to the diagnostic
challenge, several conditions (e.g., multifocal motor neuropathy with conduction block,
hereditary spastic paraparesis and spinobulbar muscular atrophy) resemble various signs and
symptoms of ALS requiring clinicians to distinguish ALS from these conditions (Turner &
Talbot, 2013).
1.3 ALS Epidemiology
ALS is a rare disease with an incidence rate of 1.90 (IQR = 1.37 – 2.40) for every 100,000
individuals across North America, South America, Europe and Asia (Chiò et al., 2013). For
3
every 100,000 individuals, the median prevalence of ALS is 4.48 (IQR = 3.03 – 6.70) (Chiò et
al., 2013). In Canada, the age-adjusted incidence rate of ALS ranges between 2.0-2.3 per
100,000 people (Wolfson, Kilborn, Oskoui, & Genge, 2009). The typical age of onset is between
50-65 years but can range from early to late adulthood (Kiernan et al., 2011; Nichols et al., 2013;
Talbot, 2009; Turner & Talbot, 2013). The incidence and prevalence of ALS occurs at a slightly
increased rate in men as compared to women; population-based studies reported that 1.11-2.32
men develop ALS relative to women (Cetin et al., 2015; McCombe & Henderson, 2010).
There is currently no known definitive cause of ALS (Blokhuis, Groen, Koppers, van den Berg,
& Pasterkamp, 2013; Talbott, Malek, & Lacomis, 2016). Genetic mutations (e.g., C9ORF72,
SOD1, TDP-43 and FUS) have been identified as leading to ALS in approximately 10% of
patients (Blokhuis et al., 2013; Kiernan et al., 2011; Talbott et al., 2016). The remaining
individuals are said to have a sporadic disease. In individuals with a sporadic form of ALS,
potential risk factors (e.g., exposure to various metals, pesticides and trauma, engagement in
professional sports and contribution to military service) have also been suspected (Talbott et al.,
2016).
Patients with ALS have an average life expectancy of 2-5 years from symptom onset with less
than 10% of patients surviving longer than 10 years (Berlowitz et al., 2015; Kasarskis,
Berryman, Vanderleest, Schneider, & McClain, 1996; Milonas, 1998; Weikamp, Schelhaas,
Hendriks, de Swart, & Geurts, 2012). Respiratory complications (e.g., respiratory failure,
aspiration and aspiration pneumonia) are the leading causes of death in ALS (Lechtzin,
Rothstein, Clawson, Diette, & Wiener, 2002). Respiratory distress, increased age, significant
weight loss and site of symptom onset – specifically bulbar onset ALS – are associated with
reduced survival (Kasarskis et al., 1996; Talbot, 2009).
1.4 Subtypes of ALS
ALS is an extremely heterogeneous disease and several methods of patient subgrouping have
been used in clinical practice and research (Swinnen & Robberecht, 2014; Turner & Talbot,
2013). Patients have been subgrouped by the genetic causes of the disease (e.g., SOD1,
C9ORF72), the rate of disease progression (e.g., fast versus slow progressors) or the relative
degree and type of motor (e.g., predominantly UMN or LMN presentation) or cognitive (e.g.,
ALS with cognitive deficit and ALS-Frontotemporal Dementia) dysfunction (Strong et al., 2017;
4
Talbot, 2009). The most common method for subgrouping participants, however, is based on the
region of symptom onset. Approximately, 70-80% of the ALS population initially display
symptoms involving the limb motor system (i.e., spinal onset), affecting mobility and arm/hand
use. The remaining 20-30% of patients with ALS present with oropharyngeal dysfunction (i.e.,
bulbar onset), affecting speech and swallowing functions. Approximately 2% or less of patients
with ALS present with respiratory dysfunction (i.e., respiratory onset), affecting the ability to
breathe independently and to clear the airway effectively (Shoesmith, Findlater, Rowe, & Strong,
2007).
1.5 Bulbar ALS
Although only 20-30% of patients develop bulbar deficits at the disease onset, more than 80% of
patients with spinal onset ALS will develop signs and symptoms of bulbar neurodegeneration as
the disease progresses (Sitver & Kraat, 1982; Talbot, 2009). Patients have indicated that the loss
of bulbar function is the most debilitating consequence of ALS (Hecht et al., 2002; Rosen, 1978).
The initially mild speaking and swallowing dysfunction progresses rapidly towards a state of
anarthria (i.e., complete loss of oral communication) and severe dysphagia (Brooks et al., 1990;
Clavelou, Blanquet, Peyrol, Ouchchane, & Gerbaud, 2013). The median time from onset to
anarthria and to gastrostomy is 18 months and 13 months, respectively, in patients with bulbar
onset ALS (Turner, Scaber, et al., 2010). Patients with ALS may require the use of alternative
communication devices to facilitate speech, exhibit rapid weight loss and be prone to aspirating
as well as developing aspiration pneumonia (Beukelman, Fager, & Nordness, 2011; Clavelou et
al., 2013; Kuhnlein et al., 2008; Mazzini et al., 1995; Turner, Scaber, et al., 2010). Bulbar signs
and symptoms are associated with a short life expectancy, which is approximately 24 months
after the development of bulbar signs (Gautier et al., 2010). Patients with bulbar symptoms
report feelings of helplessness, social isolation and psychological problems (Hecht et al., 2002).
The rapid progression and the consequences of bulbar neurodegeneration result in a reduced
quality of life (Bongioanni, 2012).
1.6 Clinical Assessment of Bulbar Function
The main goals of bulbar function assessment are to inform clinicians about the presence and
severity of bulbar deficit, to estimate the rate of the bulbar and overall disease progression and to
plan potential interventions needed to maintain function (e.g., alternative methods of
5
communication and nutrition supplements) (Andersen et al., 2012; Beukelman et al., 2011).
Clinically, the current standard assessment tools of bulbar function are the Amyotrophic Lateral
Sclerosis Functional Rating Scale-Revised (ALSFRS-R) and the Speech Intelligibility Test (SIT)
(Beukelman, Yorkston, Hakel, & Dorsey, 2007; Cedarbaum et al., 1999; Plowman, Tabor,
Wymer, & Pattee, 2017). Because dysarthria often emerges prior to dysphagia (Talbot, 2009),
the assessment of bulbar function often focuses on the changes in the speech production (Green
et al., 2013). The gold-standard swallowing assessment method – a videofluoroscopic assessment
of swallow (VFS) study – is not often used in the assessment of bulbar function in ALS because
it is perceived to not contribute to the medical management given the predictable rapid decline in
swallow ability (Plowman et al., 2017). As such, patients are instead often referred for a feeding
tube placement early in the course of their disease so that enteral nutritional support can be
graduated based on emerging patient reported dysphagia symptoms (e.g., choking) (Andersen et
al., 2012; Plowman et al., 2017).
The ALSFRS-R is a patient reported outcome measure that assesses patients’ symptoms and self-
perceived functioning ability during several activities of daily living, including speech and
swallowing (Cedarbaum et al., 1999). This scale includes questions related to gross and fine
limb, bulbar and respiratory functions. The ALSFRS-R total score ranges from 0 to 48, with a
score less than 48 indicating the presence of symptoms associated with the motor neuron
degeneration. The ALSFRS-R assesses bulbar dysfunction using three questions surveying
dysarthria, dysphagia and sialorrhea. The ALSFRS-R bulbar subscore ranges from 0 to 12 and an
ALSFRS-R bulbar subscore less than 12 indicates the presence of bulbar related symptoms
associated with at least 1 of these 3 impairment areas. The ALSFRS-R displays good internal
consistency, excellent inter- and intra-reliability as well as criterion-related validity (Cedarbaum
et al., 1999; Franchignoni, Mora, Giordano, Volanti, & Chiò, 2013; Gordon, Miller, & Moore,
2004; Kaufmann et al., 2007).
The SIT quantifies speech intelligibility and speaking rate based on an orthographic transcription
of 11 sentences read by the patient at their normal comfortable speaking rate and loudness and
transcribed by an unfamiliar listener (Beukelman et al., 2007). Speech intelligibility refers to the
percentage of words that are understood by the naive listener during this task, and speaking rate
refers to the number of words uttered per minute (Yorkston, Beukelman, & Tice, 1996). Speech
intelligibility is judged to be normal when it is between 97% and 100% (Green et al., 2013).
6
Using SIT, the normal speaking rate is considered at and above 190 words per minute (WPM)
(Beukelman et al., 2011; Yorkston, Hammen, Beukelman, & Traynor, 1990).
The ALSFRS-R and SIT possess great clinical utility. The ALSFRS-R is commonly used across
North America to monitor the disease progression and predict survival in patients with ALS both
in clinical trials and clinical practice (Kimura et al., 2006; Plowman et al., 2017; Ratti et al.,
2015). Clinical trials also use the ALSFRS-R to determine if symptoms related to bulbar,
respiratory and limb function improve or cease to worsen with the use of pharmaceutical
interventions (Smith et al., 2017). Speech intelligibility informs the ALS management team
about the severity of the dysarthria (Yorkston et al., 1993). Speaking rate declines prior to speech
intelligibility and has increased utility in tracking bulbar disease progression (Ball, Beukelman,
& Pattee, 2002; Rong, Yunusova, Wang, & Green, 2015). Moreover, speaking rate is the primary
measure used to inform the recommendation for prescription of the augmentative and alternative
communication systems to support daily communication (i.e., when speaking rate is 125 WPM
or less) (Beukelman et al., 2011).
Despite their advantages, the ALSFRS-R and the SIT measures fail to detect early functional
changes. As subjective assessment tools, these measures are limited in their usefulness in aiding
ALS diagnosis and tracking relatively small changes in performance with disease progression
(Allison et al., 2017). The ALSFRS-R does not effectively detect early functional changes since
symptoms of motor neuron degeneration emerge after the majority of motor neurons have
already been lost (Bouche, Le Forestier, Maisonobe, Fournier, & Willer, 1999). The SIT is a
perceptual test and is prone to many limitations of such assessments (see Kent, 1996). Both
speech intelligibility and speaking rate decline relatively late in the disease course and are
influenced by multiple components of speech production (e.g., articulator function,
velopharyngeal function, laryngeal function and respiratory function) (Rong, Yunusova, Wang,
et al., 2015). To improve the assessment of bulbar function in ALS, measures capable of
addressing these limitations are urgently needed (Green et al., 2013).
1.7 The Subsystem Approach towards Measuring Bulbar Dysfunction
in ALS
Recently, our group has been systematically testing physiological instrumental measures of
bulbar dysfunction conceptualized in the form of the four motor speech subsystems –
7
articulatory, resonatory, phonatory and respiratory – based on the framework proposed by
Ronald Netsell (see Figure 1) (Netsell, Lotz, & Barlow, 1989). The articulatory subsystem
consists of the lips, jaw and tongue musculature. The resonatory subsystem is comprised of the
velum and posterior pharyngeal wall. The phonatory subsystem includes the larynx. The
respiratory subsystem consists of the respiratory (e.g., expiratory and inspiratory) musculature.
Figure 1: The four speech subsystems.
Reprinted from “Bulbar and speech motor assessment in ALS: Challenges and future directions,”
Green et al, 2013, Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 14(7-8), p.
495. Copyright 2013 Informa Healthcare.
Across the four physiological subsystems, over one hundred measures assessing acoustic,
kinematic or aerodynamic features of speech have been identified as potential candidates in the
assessment of bulbar ALS (Yunusova, Green, Wang, Pattee, & Zinman, 2011). Subsequently, a
series of studies identified an association of a number of these physiological subsystem measures
with the clinical measures of bulbar dysfunction such as speaking rate and speech intelligibility
(Rong, Yunusova, Wang, et al., 2015; Rong, Yunusova, Wang, et al., 2016). These studies
revealed that among the articulatory subsystem measures, the upper and lower lip velocity during
sentence reading as well as the syllable rate and count achieved during an alternate motion rate
(AMR) task were the most promising bulbar disease indicators. Articulatory rate has also been
shown to correlate with bulbar function and statistically differ between patients with motor
8
impairments and healthy controls as well as patients with cognitive impairments (Nishio &
Niimi, 2006; Yunusova et al., 2016). Rong, Yunusova, Wang, et al (2015) found the most
promising of the resonatory subsystem measures included the median nasalance score achieved
during sentence reading and the peak nasal flow obtained during the production of syllables
containing a plosive consonant. The maximum fundamental frequency (F0 max) was identified
as sensitive to the phonatory subsystem impairment (Rong, Yunusova, Wang, et al., 2015; Rong,
Yunusova, Wang, et al., 2016). The respiratory speech subsystem is represented by the
percentage of time spent pausing (percent pause time), pause events and total pause duration
obtained from passage reading in addition to the maximum oral pressure obtained from serial
repetitions of syllables containing a plosive consonant (Rong, Yunusova, Wang, et al., 2015;
Rong, Yunusova, Wang, et al., 2016).
These studies revealed that these physiological subsystem measures are highly associated with a
decline in speaking rate and speech intelligibility, but also that they decline prior to both
speaking rate and speech intelligibility (see Figure 2), making the physiological subsystem
measures ideal for tracking bulbar dysfunction in ALS (Rong, Yunusova, Wang, et al., 2015;
Rong, Yunusova, Wang, et al., 2016). Furthermore, these physiological subsystem measures
displayed more accurate detection of bulbar changes compared to clinician perceptual ratings and
symptom report (Allison et al., 2017; Rong, Yunusova, Wang, et al., 2015; Rong, Yunusova,
Wang, et al., 2016). The use of these physiological subsystem measures in the assessment of
bulbar dysfunction is very promising, and research is ongoing to determine their diagnostic
sensitivity, specificity, responsiveness and clinical interpretability.
9
Figure 2: Likelihood of maintaining function over time of speech intelligibility, speaking rate
and the four physiological subsystems in patients diagnosed with ALS with an end-point of 120
words per minute speaking rate.
Reprinted from “Predicting early bulbar decline in amyotrophic lateral sclerosis: A speech
subsystem approach,” Rong et al, 2015, Behavioral Neurology, p. 7. Copyright 2015 Panying
Rong et al.
The long-term goal of the work by our research group is to improve bulbar motor assessment by
utilizing physiological subsystem measures. The problem, however, is that the physiological
subsystem measures may be highly dependent on the integrity of respiratory function, which is
the driving force behind speech and a critical component of swallowing (Hixon & Hoit, 2005;
Weismer, 2006). Motor speech disorders most often occur as a result of an impairment affecting
oropharyngeal musculature, but changes in speech may also occur due to the respiratory
10
dysfunction alone (Hixon, 1973; Hixon, Goldman, & Mead, 1973). For example, patients
diagnosed with cystic fibrosis demonstrate changes in voice quality – rough and breathy voice –
and reduced loudness when compared to age-matched controls (Lourenço, Costa, & da Silva
Filho, 2014). Patients with spinal cord injury show reduced loudness but also can present with
reduced utterance length (Brown, DiMarco, Hoit, & Garshick, 2006). Patients with chronic
obstructive pulmonary disease display short phrase durations (Binazzi, Lanini, Gigliotti, &
Scano, 2013). Likewise, shortness of breath and muscle fatigue may result in longer pauses to
increase the time available for gas exchange in patients with lung disease (Lee, Loudon,
Jacobson, & Stuebing, 1993).
In ALS, bulbar and respiratory dysfunction often co-occurs (Hardiman, Van Den Berg, &
Kiernan, 2011; Similowski et al., 2000; Yorkston, Strand, & Miller, 1996). Patients with bulbar
onset ALS are said to develop respiratory dysfunction soon after the onset of bulbar symptoms
and earlier in the disease progression than patients with spinal onset ALS (Brooks, 1996;
Clavelou et al., 2013; Pinto, Pinto, & de Carvalho, 2007; Pinto, Turkman, Pinto, Swash, & de
Carvalho, 2009; Poloni, Mento, Mascherpa, & Ceroni, 1983). Patients with bulbar onset ALS
exhibit a more significant and rapid decline in respiratory function during the initial 6 months
post-diagnosis when compared to patients with spinal onset ALS (Clavelou et al., 2013). As the
disease progresses, bulbar symptoms and respiratory dysfunction seem to decline at similar rates
in patients regardless of region of onset (Clavelou et al., 2013; Yorkston, Strand, et al., 1996).
Some researchers have hypothesized that the proximity of the brainstem motor neurons to the
respiratory brainstem centers as well as the neurodegeneration of shared neural pathways may be
responsible for these findings (Aydogdu, Tanriverdi, & Ertekin, 2011; Pinto et al., 2007; Richter
& Smith, 2014).
However, the bulbar and respiratory dysfunction might not be completely linked. Patients with
ALS can display either bulbar dysfunction with normal respiratory function or respiratory
dysfunction with normal bulbar function early in the disease and, occasionally, with disease
progression (De Carvalho et al., 1996; Lyall, Donaldson, Polkey, Leigh, & Moxham, 2001). A
number of studies have reported the absence of a connection between the region of onset and the
development of respiratory dysfunction in ALS (Chandrasoma et al., 2012; Easterling, Antinoja,
Cashin, & Barkhaus, 2013; Talakad, Pradhan, Nalini, Thennarasu, & Raju, 2009). For example,
both Chandrasoma et al. (2012) and Talakad et al. (2009) reported a lack of differences in
11
spirometric measures when comparing patients with ALS subgrouped by region of onset, bulbar
versus spinal. In another study, the longitudinal decline of respiratory function did not differ
between patients with ALS subgrouped by onset (Easterling et al., 2013). The development of
bulbar and respiratory dysfunction may simply relate to the natural course of the disease
progression (i.e., disease severity) rather than a potential interaction between the two
impairments, but this must be further investigated. Below, we review the physiological measures
by subsystem followed by measures of respiratory dysfunction.
1.8 Physiological Measures of Bulbar Function by Subsystem
Multiple physiological subsystem measures have been identified as important in assessing bulbar
function in ALS based on their ability to identify bulbar dysfunction earlier than the current
clinical standards and to predict bulbar motor and speech intelligibility decline (Allison et al.,
2017; Green et al., 2013; Rong, Yunusova, Wang, et al., 2015; Rong, Yunusova, Wang, et al.,
2016; Yunusova et al., 2016). The specific measures that may be influenced by respiratory
dysfunction were identified through literature review of normal and disordered speech
physiology and are presented, by subsystem, below.
1.8.1 Articulatory Subsystem
1.8.1.1 Syllable Count during an AMR task
The alternate motion rate (AMR) task is a maximum performance task executed on a single
breath. Patients are asked to repeat the syllables /pa/, /ta/ or /ka/, eliciting the rapid contraction of
the lips, anterior portion of the tongue and posterior portion of the tongue, respectively. The total
duration of the syllable train and syllable count and rate (syllables per second) are reported
during this task. The total syllable count assists with assessing dysarthria because the total
syllable count decreases as bulbar function declines (Darley, Aronson, & Brown, 1975, p. 93;
Kent et al., 1991; Mulligan et al., 1994; Nishio & Niimi, 2000; Yorkston et al., 1993). Males and
females typically produce a similar syllable count (number of cycles) ranging between
approximately 50 and 120 syllables when performing an AMR task involving the syllable /ta/
(Rong, Yunusova, Richburg, & Green, in press).
The syllable count has been identified as a sensitive measure of bulbar decline in ALS that is
capable of distinguishing patients with bulbar symptoms from patients without bulbar symptoms
12
(Allison et al., 2017; Rong, Yunusova, Wang, et al., 2016). A reduction in syllable count below
35 repetitions predicted a rapid decline in speech intelligibility and preceded speaking rate
decline (Rong et al., in press). However, respiratory decline, in addition to the reduction in rate
of articulatory muscle contractions, may affect the performance on this task, resulting in a
smaller number of syllables produced on one breath. The association between the syllable count
obtained during the AMR task and respiratory function has not been established in ALS.
1.8.1.2 Articulatory Rate during Passage Reading
Articulatory rate reflects the rate of syllable production during a passage reading task, when
pauses are excluded (Jacewicz, Fox, O'Neill, & Salmons, 2009). The contraction rate of the
articulatory musculature is primarily reflected in this measure (Mefferd, Pattee, & Green, 2014).
This measure aids in dysarthria assessment since articulatory rate is often altered in neurological
diseases that affect the integrity of oro-facial musculature (e.g., Parkinson’s disease,
Huntington’s disease and multiple sclerosis) (Kuo & Tjaden, 2016; Niimi & Nishio, 2001; Rusz
et al., 2014). Adult male and female speakers typically produce a similar articulatory rate ranging
between 220 and 350 syllables per minute when reading a passage (Jacewicz et al., 2009; Lee &
Doherty, 2017; Yunusova et al., 2016).
Articulatory rate is a promising measure in bulbar ALS assessment. In ALS, articulatory rate is
known to be sensitive to the early changes in bulbar function, and statistically significant
declines in articulatory rate occur as bulbar dysfunction progresses (Rong, Yunusova, & Green,
2015; Rong, Yunusova, Wang, et al., 2015; Yunusova et al., 2016). However, it is not well
understood whether and how articulatory rate may be influenced by respiratory dysfunction in
this disease. Normal speakers seem to adjust their articulatory rate depending on their pausing
behaviour preferences; individuals who tend to naturally pause more speak at slower articulatory
rates (Healey & Adams, 1981; Turner & Weismer, 1993). Similarly, patients with ALS displayed
similar articulatory rate adjustments in relation to pausing behaviour as exhibited by normal
speakers (Turner & Weismer, 1993). However, the association between articulatory rate and
respiratory deficit has not yet been investigated in ALS.
13
1.8.2 Respiratory Subsystem
1.8.2.1 Speech and Pause Measures in a Passage Reading Task
Pauses during a passage reading task serve to restore lung volume during connected speech
(Goldman-Eisler, 1972; Hixon & Hoit, 2005; Zellner, 1994). Passage reading requires speech
pauses to be produced according to the passage’s syntactical structure (Goldman-Eisler, 1972).
During passage reading, healthy adults, both males and females, generally spend between 10 and
20 percent of reading time pausing, with an approximate average phrase (i.e. breath group)
duration of 3.5 seconds and pause duration of 0.6 seconds (Solomon & Hixon, 1993; Wang,
Green, Nip, Kent, & Kent, 2010; Winkworth, Davis, Ellis, & Adams, 1994; Yunusova et al.,
2016). The speech and pause measures are often affected when dysarthria develops (Duffy,
2013; Green, Beukelman, & Ball, 2004; Lee et al., 1993; Yunusova et al., 2011). For example,
patients with dysarthria due to traumatic brain injury or Parkinson’s disease display increases in
the percentage of time spent pausing during speech (Campbell & Dollaghan, 1995; Hammen &
Yorkston, 1994).
Increased dependence on pausing frequency and duration has been reported in patients with ALS
as well (Nishio & Niimi, 2000; Turner & Weismer, 1993). Patients with ALS displayed
significant changes in pausing features of speech that were linked to reduced speech
intelligibility (Green et al., 2013; Rong, Yunusova, Wang, et al., 2015; Rong, Yunusova, Wang,
et al., 2016). Obtaining a percent pause time greater than 18% during passage reading greatly
increased the likelihood of participants having ALS (Allison et al., 2017). Additionally, the
measure of percent pause time displayed strong sensitivity and specificity when differentiating
between participants with ALS with bulbar symptoms and patients with ALS without bulbar
symptoms (Allison et al., 2017). In a study exploring the impact of motor and cognitive
dysfunction on pausing during continuous speech, the mean phrase duration and the percent
pause time were uniquely associated with primarily respiratory decline, whereas the total number
of pause events and the total pause duration were associated with the bulbar subscore of
ALSFRS-R (Yunusova et al., 2016). The initial findings from the Yunusova et al. (2016) study
were preliminary and need to be validated on a large sample of participants as well as target the
association between a variety of pause and speech measures and respiratory measures.
14
1.8.2.2 Maximum Oral Pressure
Oral pressure obtained during the production of voiceless plosive consonants is said to represent
subglottal pressure generated in the lungs for speech production (Hixon, Hawley, & Wilson,
1982). More specifically, when the lips and the velum are sealed and the vocal folds are
abducted, a closed space forms. In this moment, the oral pressure corresponds with the pressure
generated within the lungs (Baken & Orlikoff, 2000, p. 308; Ketelslagers, De Bodt, Wuyts, &
Van de Heyning, 2007), representing respiratory muscle function. Adult men and women
produce a similar maximum oral pressure ranging between 5 and 12 cm H2O when repeating
syllable /pi/ at a normal loudness (Andreassen, Smith, & Guyette, 1992; Baken & Orlikoff, 2000;
Zajac, 2000). A reduction in oral pressure can contribute to the development of dysarthric speech
as phoneme discrimination becomes impaired, which can reduce speech intelligibility (Barbosa,
Mangilli, Andrade, & Alonso, 2012; Ketelslagers et al., 2007; Solomon & Hixon, 1993).
Limited literature exists on oral pressure changes in ALS. Oral pressure has been found to
contribute to the decline of speech intelligibility and speaking rate in this clinical population
(Rong, Yunusova, Wang, et al., 2015; Rong, Yunusova, Wang, et al., 2016). As a measure
representing the respiratory motor speech subsystem (Yunusova et al., 2011), it is important to
assess the effect of a respiratory deficit on this measure in ALS.
1.8.3 Resonatory Subsystem
1.8.3.1 Peak Nasal Airflow and Nasalance
Peak nasal airflow is an aerodynamic measure used to assess the adequacy of the velopharyngeal
closure (Yunusova et al., 2011). Peak nasal airflow is obtained during the serial production of
syllables containing the plosive consonant /p/ followed by a vowel, either /a/ or /i/. Peak nasal
airflow is recorded when oral pressure is at its maximum for plosive consonants. Plosive
consonants are characterized by relatively high oral pressure and minimal nasal airflow (Ball et
al., 2001; Warren, Dalston, Morr, Hairfield, & Smith, 1989). A peak nasal flow below
approximately 10 mL/s when participants repeated the syllable /pi/ is considered normal for
males and females (Andreassen et al., 1992; Baken & Orlikoff, 2000; Zajac, 2000).
Nasalance, defined as a ratio between the oral to nasal acoustic energy, is obtained during the
reading of nasal (e.g., /Momma Made Lemon Jam/) and non-nasal (e.g., /Buy Bobby a Puppy/)
15
sentences using a device called the Nasometer (Bressmann et al., 2000). Normally, nasalance is
low when reading a non-nasal sentence compared to reading a nasal sentence (Hardin, Van
Demark, Morris, & Payne, 1992; Yunusova et al., 2011). Healthy males and females produce a
nasalance score below 25% when reading the sentence /Buy Bobby a Puppy/ (Gauster, 2009;
Gauster, Yunusova, & Zajac, 2010; Hutchinson, Robinson, & Nerbonne, 1978). Hypernasality, a
perceptual consequence of high nasalance, can result in a lack of clarity and vocal projection
during speech, thus affecting speech intelligibility (Calnan, 1959; Hoodin & Gilbert, 1989).
In ALS, an inadequate seal develops between the velum and posterior pharyngeal wall as
velopharyngeal muscles weaken (Delorey, Leeper, & Hudson, 1999), leading to notable
hypernasality and nasal emissions (Darley et al., 1975; Green et al., 2013; Kelhetter, 2013).
Aerodynamic measures of velopharyngeal function have been identified to aid in the early
assessment of speech loss in ALS (Allison et al., 2017; Green et al., 2013; Rong, Yunusova, &
Green, 2016; Rong, Yunusova, Wang, et al., 2016; Yunusova et al., 2011). Although indicators
of the velopharyngeal dysfunction, normal peak nasal airflow and median nasalance rely on
adequate airflow and pressure generation by the respiratory apparatus (Baken & Orlikoff, 2000,
p. 476) as the velopharyngeal and respiratory systems are tightly coupled (Sapienza, Brown,
Williams, Wharton, & Turner, 1996). Clinically, patients who exhibit velopharyngeal
dysfunction but normal respiratory performance often increase their respiratory effort during
speech by increasing frequency of breaths and decreasing utterance length (Kummer, 2018, p.
37). In ALS, a respiratory deficit may influence the measures of velopharyngeal function. The
potential influence of respiratory dysfunction on peak nasal airflow and nasalance has not been
investigated in ALS.
1.8.4 Phonatory Subsystem
1.8.4.1 Maximum Fundamental Frequency in a “High Pitch” Task
A high pitch phonation task requires participants to vocalize a vowel sound (e.g., /ah/), adjusting
one’s pitch from normal to the highest pitch possible for a short period of time. The F0 max is
recorded when pitch is at its highest (Baken & Orlikoff, 2000, p. 147). Healthy males and
females typically produce a F0 max between 390 and 600 Hz when performing a high pitch
phonation (Aithal, Bellur, John, Varghese, & Guddattu, 2012; Lin, Mautner, Ormond, &
Hornibrook, 2007). F0 max is often used in the assessment of dysarthric speech because
16
laryngeal spasticity and weakness often leads to a reduction in the F0 max (Goberman &
Blomgren, 2008; Kent et al., 2000; Kim, Kent, & Weismer, 2011).
The F0 max obtained during a high pitch phonation has been used to predict bulbar function
decline in ALS. A rapid decline in speech intelligibility occurred once the F0 max declined to
280 Hz or less in patients with ALS (Rong, Yunusova, Wang, et al., 2016). Although primarily
dependent on glottal tension, the vocal fold vibrations require adequate subglottal pressure and
thus adequate respiratory support (Lieberman, Knudson, & Mead, 1969; Marchal, 2009, p. 53;
Weismer, 2006, pp. 104-105). Increased respiratory effort can increase the F0 obtained (Lester &
Story, 2013; Sato, Watanabe, & Moriya, 2016). It is important to determine the extent to which
respiratory dysfunction may be associated with the F0 max obtained during a high pitch
phonation in patients with ALS.
1.8.5 Summary of the Identified Subsystem-Based Bulbar Measures
In summary, the following bulbar motor measures were identified by others as potentially
affected by respiratory dysfunction in patients with ALS: syllable count, articulatory rate, percent
pause time, total pause duration, pause events, maximum oral pressure, peak nasal flow, median
nasalance and F0 max. The expected ranges in healthy males and females for each of these
specific measures have been summarized in Table 1.
17
Table 1: Summary of means and standard deviations for physiological subsystem measures used
in this study obtained from healthy controls and available from published literature.
Subsystem Measure, Unit, Task Normative Data
Mean ± Standard Deviation
Articulatory Syllable Count, #, AMR 82.0 ± 48.6a
Articulatory Rate, syllables/min, Bamboo Passage 285.8 ± 29.9b
Respiratory
Percent Pause Time, %, Bamboo Passage 15.1 ± 3.2b
Total Pause Duration, s, Bamboo Passage 5.2 ± 1.5b
Pause Events, #, Bamboo Passage 8.4 ± 2.0b
Maximum Oral Pressure, cm H2O, /pi/ 6.6 ± 1.2c
Resonatory Peak Nasal Flow, mL/s, /pi/ 3.0 ± 3.4c
Median Nasalance, %, /Buy Bobby a Puppy/ 15.3 ± 6.8d
Phonatory F0 max, Hz, High Pitch Phonation 422.5 ± 82.0e
a: (Rong et al., in press)
b: (Yunusova et al., 2016)
c: (Andreassen et al., 1992)
d: (Gauster et al., 2010)
e: (Aithal et al., 2012)
1.9 Clinical Measures of Respiratory Dysfunction in ALS
Numerous methods are available for assessing respiratory dysfunction in ALS. Invasive
respiratory measures (e.g., needle EMG) are sensitive to early changes in respiratory muscle
function (Polkey et al., 2016); however, these measures are not ideal for serial measurements
(Lechtzin et al., 2002). Non-invasive measures (e.g., spirometry) are often utilized in clinical
settings since they are non-invasive, are implemented quickly and can monitor longitudinal
decline (Andersen et al., 2012; Polkey et al., 2016). Below, the clinical respiratory measures
incorporated into the present study are described and their justification for inclusion is provided.
1.9.1 Forced Vital Capacity
The percentage of the predicted forced vital capacity (%FVC), which varies by height, sex, age
and ethnicity (Miller et al., 2005; Ranu, Wilde, & Madden, 2011), indicates respiratory
dysfunction when it measures below 80% (Andersen et al., 2012; Wheaton et al., 2013).
Physiologically, this measure represents the adequacy of the inspiratory musculature (i.e., the
diaphragm, external intercostals and accessory muscles of respiration) as well as the expiratory
muscles (i.e., the abdominal muscles and the internal intercostal muscles) by determining the
proportion of volume of air forcefully expired following a maximal inhalation (Mitsumoto,
Przedborski, & Gordon, 2005, p. 738). It has been reported that an increasing residual volume, in
18
addition to a decreased total lung volume, could contribute to a reduction in %FVC in patients
with ALS (Chandrasoma et al., 2012; Fallat, Jewitt, Bass, Kamm, & Norris, 1979; Poloni et al.,
1983).
%FVC is frequently used to assess respiratory function in both clinical trials and clinical practice
due to its ability to track the progression of respiratory dysfunction over time and its value as a
surrogate marker of survival in patients with ALS (Andersen et al., 2012; Lyall et al., 2001;
Polkey et al., 2016; Rutkove, 2015; Schmidt et al., 2006). %FVC has been shown to predict
survival and disease progression, after adjusting for age, sex, site of onset, diagnostic delay,
riluzole use and respiratory support (Czaplinski, Yen, & Appel, 2006). As such, %FVC is the
current gold standard measure used to assess respiratory dysfunction in ALS (Andersen et al.,
2012). %FVC values are also critical in the clinical management of patients with ALS as they are
used to determine the need of ventilatory support and often direct the start of enteral nutrition
(Andersen et al., 2012; Berlowitz et al., 2015; Kleopa, Sherman, Neal, Romano, & Heiman-
Patterson, 1999).
1.9.2 Peak Expiratory Airflow
Peak expiratory airflow (PEF) is used primarily to assess the integrity of expiratory musculature
and the large airways (Sieck, Ferreira, Reid, & Mantilla, 2013; Yamada et al., 2016). In the
absence of an obstructed airway, PEF is said to represent the expiratory force generated
(Devadiga, Varghese, Bhat, Baliga, & Pahwa, 2015; Suárez et al., 2002). The clinical use of PEF
in ALS is limited, likely because PEF is more indicative of obstructive airway disease rather than
restrictive lung disease (e.g., ALS). However, patients with ALS have displayed significant
reductions in peak expiratory airflow in comparison to healthy adults (Suárez et al., 2002). PEF
has the potential to be important to evaluate speech of patients with ALS since speech typically
occurs during the expiratory phase of breathing (Hixon & Hoit, 2005; Weismer, 2006).
1.10 Research Objectives and Hypothesis
The overall goal of this study was to contribute to the understanding of the potential impact of
respiratory dysfunction on the clinical assessment of bulbar motor function using the
physiological subsystem-based measures. The specific objectives were to:
19
1. Compare selected physiological bulbar motor measures between patients with ALS
stratified by %FVC into groups with either normal or impaired respiratory function;
2. Examine the association between the physiological subsystem measures and %FVC;
3. Explore the association between the physiological subsystem measures and PEF; and
4. Explore the effect of respiratory function on the association between speaking rate and
the physiological subsystem measures.
We expected (1) group differences in physiological subsystem-based measures of bulbar
dysfunction between patients with normal respiratory function (ALSnr) and those with impaired
respiratory function (ALSir), (2) statistically significant correlations between these measures and
%FVC, (3) as well as statistically significant correlations between these measures and PEF.
Finally, (4) we expect that respiratory measures would contribute independently to the
association between the overall measure of bulbar dysfunction indexed by speaking rate and the
subsystem-based bulbar motor measures.
20
Methods
2.1 Study Design
Participants were recruited to participate in a multi-centre longitudinal study investigating the
progression of bulbar deterioration in patients with ALS. Data collected from two prospective
studies, with similar data collection protocols, were combined for the present retrospective study.
The first prospective study recruited participants from the Sunnybrook Health Sciences Centre
(Toronto, Ontario, Canada) and the University of Nebraska (Lincoln, Nebraska, USA) between
2009 and 2013. The second prospective study, ongoing, recruited participants from the
Sunnybrook Health Sciences Centre and the Massachusetts General Hospital Institute of Health
Professions (Boston, Massachusetts, USA) since 2014. For the present study, participants
recruited as part of the second prospective study were included up to June 2017. A total of 216
participants recruited between 2009 and June 2017 were potentially eligible for the present study.
The present study was a cross-sectional analysis using the last recorded session with a %FVC as
the time point of interest. Objective 1 was completed by subgrouping participants with ALS
according to their respiratory status; participants with a %FVC equal to or greater than 80%
formed the ALSnr (normal respiration) subgroup and participants with a %FVC less than 80%
entered the ALSir (impaired respiration) subgroup (Andersen et al., 2012). In contrast, for
objectives 2, 3 and 4, participants were analyzed as a single group.
2.2 Participants: Inclusion Criteria
The two prospective studies required participants with ALS to have: a definite or probable
diagnosis of ALS, as defined by the El Escorial Criteria (Brooks et al., 2000); be between 40-85
years of age; exhibit signs of bulbar motor neuron degeneration as determined by a neurologist;
and have no evidence of cognitive impairment, as determined by a score greater than 26 on the
Montreal Cognitive Assessment (Nasreddine et al., 2005). Further, all participants were native
English speakers that could read at a minimum of a grade 5 reading level and did not have any
major vision or hearing issues based on self-report. The sole exclusion criterion in the present
study was the lack of a documented %FVC during study participation.
21
2.3 Data Collection Protocol: Instrumentation and Tasks
All data were typically collected on the same day that participants visited the ALS/MND Clinic
for their regular appointments. When data could not be collected on the same day as a clinic
visit, participants completed the research protocol within 2 weeks of the respective clinic visit.
Often, patients with known bulbar onset ALS re-visited the clinic every 3 months, whereas
patients with spinal onset ALS returned to the clinic every 6 months. Patient demographics and
clinical characteristics were obtained during full medical histories and neurological exams. The
research protocol involved obtaining clinical respiratory measures, including %FVC and PEF, as
well as the physiological subsystem measures of bulbar dysfunction.
Instruments, tasks and instructions associated with obtaining clinical respiratory measures and
the physiological subsystem measures are presented in Table 2 and Table 3, respectively. The
clinical respiratory and physiological subsystem measures were obtained while participants were
in a seated position. Participants were instructed on how to perform the various tasks and were
allowed to practice a task until they were comfortable with the instructions. A forced vital
capacity maneuver was recorded by respiratory technicians with years of experience of operating
the device in a clinical setting; the speech measures were obtained by trained research assistants.
The staff identified any inconsistent measurements within subject attempts and incorrectly
performed procedures and determined if the recording should be repeated.
Table 2: Clinical respiratory measures and associated instruments and instructions.
Task Instrument Instruction
Measure
(Measurement
Unit)
Pulmonary
Function Test
Spirometer (Vmax Encore
22 PFT system,
CareFusion, Yorba Linda,
CA, USA)
Take a deep breath in and
exhale as fast and as hard as
you possibly can; best of 3
attempts recorded
%FVC, %
Phonatory Aerodynamic
System (PAS)
(Model 6600, PENTAX
Medical., Lincoln Park,
NJ, USA)
Take a deep breath in and
exhale fully and forcefully;
best of 2 attempts recorded
PEF, L/s
22
Table 3: Physiological subsystem measures and associated instruments and instructions.
Task Instrument Instruction Measure Subsystem
Alternate
Motion
Rate
Professional
microphone
(Countryman
B6P4FF05B, Menlo
Park, CA, USA); Adobe
Audition (Version 3.0,
Adobe Systems Inc.,
San Jose, CA, USA)
Repeat /ta/ as long
and as fast as you
can on one big
breath; 1 repetition
after practice
Syllable Count, #
Articulatory
Passage
Reading
Professional
microphone; Speech
Pause Analysis (SPA)
Software (Green et al.,
2004)
Read the Bamboo
Passage at your
normal speaking
rate and loudness;
1 repetition
Articulatory
Rate,
syllables/minute
Percent Pause
Time, %
Respiratory
Total Pause
Duration, s
Pause Events, #
Voicing
Efficiency
Protocol
Phonatory Aerodynamic
System (PAS)
Repeat syllable
/pi/; 7 repetitions; 5
middle repetitions
measured
Maximum Oral
Pressure, cm H2O
Peak Nasal Flow,
mL/s
Resonatory Sentence
Reading
Nasometer II (Model
6400, PENTAX
Medical, USA)
Repeat the
sentence /Buy
Bobby a Puppy/ at
your normal
speaking rate and
loudness; 3
repetitions
Median
Nasalance, %
High Pitch
Phonation
Professional
microphone; Multi-
Dimensional Voice
Profile software
(MDVP, Model 5105,
PENTAX Medical)
Start saying /a-a-a/
at a normal level
then increase your
pitch to its highest
level and hold for 5
seconds; 3
repetitions
F0 max, Hz Phonatory
2.4 Statistical Analysis
All statistical analyses were conducted using IBM SPSS Statistics version 24. The descriptive
statistics for the study measures are displayed as mean ± standard deviation or median (3rd
quartile – 1st quartile), depending on the type of their distributions. The differences between
demographic and clinical measures obtained for ALSnr and ALSir subgroups were determined
23
using independent samples t tests or Pearson’s chi-square tests (α was set at .05). In objective 1,
group comparisons between physiological subsystem measures obtained for the ALSnr and
ALSir subgroups were performed using independent samples t tests or Mann-Whitney U tests,
depending on the distribution of the dependent variables. For the independent samples t tests,
Levene’s test for equality of variances was used to determine whether equal variances for each
physiological subsystem measure could be assumed between the subgroups. For objectives 2 and
3, pairwise correlations were utilized to examine the association between each physiological
subsystem measure and %FVC and PEF. Spearman’s rank-order correlation was used when
assumptions for Pearson’s product-moment correlation were violated. For objective 4, any
physiological subsystem measure that achieved a statistically significant correlation with either
of the two clinical respiratory measures was subsequently correlated with speaking rate, while
controlling for the clinical respiratory measure. In all four objectives, the false discovery rate was
used to control for type I errors caused by multiple comparisons (Benjamini & Hochberg, 1995);
α was set at .05 prior to the adjustment.
24
Results
3.1 Participant Characteristics: ALS Group Combined and ALS
Subgroups
Figure 3 displays a CONSORT diagram of the study focusing on inclusion/exclusion and
missing data. Of the 216 potential participants with ALS from the two prospective studies, 158
were included in the present study based on the a priori exclusion criterion. Of these
participants, 135 were recorded in Toronto, Ontario, Canada, 19 were recorded in Lincoln,
Nebraska, USA and 4 were recorded in Boston, Massachusetts, USA. The participants displayed
a wide range of respiratory dysfunction as shown by the range of %FVC values and PEF values.
Missing data resulted from patient fatigue (i.e., the entire study protocol was not completed) and
equipment availability. The sample’s demographics and clinical characteristics, including those
necessary to determine respiratory status and used as independent variables in this study, are
summarized in Table 4.
25
Figure 3: A CONSORT diagram of study flow.
Potentially Eligible Participants n = 216
Study 1 n = 151
Study 2 n = 65
Missing Data
(Due to patient fatigue or equipment availability)
%FVC n = 0
PEF n = 78
Speaking Rate n = 12
Syllable Count n = 81
Articulatory Rate n = 43
Percent Pause Time n = 43
Total Pause Duration n = 43
Pause Events n = 43
Maximum Oral Pressure n = 47
Peak Nasal Flow n = 47
Median Nasalance n = 66
F0 max n = 47
Data Included in Analyses
%FVC n = 158
PEF n = 80
Speaking Rate n = 114
Syllable Count n = 77
Articulatory Rate n = 115
Percent Pause Time n = 115
Total Pause Duration n = 115
Pause Events n = 115
Maximum Oral Pressure n = 111
Peak Nasal Flow n = 111
Median Nasalance n = 92
F0 max n = 111
Participants Missing %FVC n = 58
Study 1 n = 23
Study 2 n = 35
Included Participants n = 158
Study 1 n = 128
Study 2 n = 30
26
Table 4: Participant demographic and clinical characteristics. Values are either counts
(percentage) or mean ± standard deviation (minimum - maximum).
Total N 158
Males, n (%) 99 (62.7%)
Age, years 59.7 ± 9.9 (40 – 88)
Bulbar Onset, n (%) 26 (16.5%)
Time Since Onset, months 40.7 ± 31.5 (2 – 231)
ALSFRS-R Total Score, /48 33.0 ± 7.0 (15 – 47)
ALSFRS-R Bulbar Subscore, /12 9.9 ± 2.1 (3 – 12)
ALSFRS-R Respiratory Subscore, /12 10.2 ± 2.2 (3 – 12)
%FVC, % 73.5 ± 23.6 (23 – 127)
PEF, L/s (n=80) 3.9 ± 2.1 (0.8 – 10.4)
Speaking Rate, words/minute 142.3 ± 44.5 (37.0 – 245.0)
The participant demographics and clinical characteristics by subgroup are displayed in Table 5.
The participants were stratified based on their clinical respiratory status into ALSnr and ALSir
subgroups. Based on independent samples t tests or Pearson’s chi-square tests, participant
demographics and clinical characteristics were similar for age, percentage of patients with
bulbar onset, time since onset, ALSFRS-R bulbar subscore and speaking rate; however the
percentage of males, the ALSFRS-R total score, the ALSFRS-R respiratory subscore, %FVC
and PEF significantly differed between the two subgroups.
Table 5: Participant demographic and clinical characteristics for ALSnr and ALSir subgroups.
Values are either counts (percentage) or mean ± standard deviation.
ALSnr ALSir p value
Total N 66 92
Males, n (%) 33 (50.0%) 66 (71.8%) .005*
Age, years 58.2 ± 10.1 60.72 ± 9.7 .113
Bulbar Onset, n (%) 15 (22.7%) 11 (12.0%) .072
Time Since Onset, months 41.1 ± 35.9 42.2 ± 28.4 .837
ALSFRS-R Total Score, /48 36.2 ± 6.2 30.7 ± 6.7 <.001*
ALSFRS-R Bulbar Subscore, /12 9.9 ± 2.3 9.9 ± 1.9 .999
ALSFRS-R Respiratory Subscore, /12 11.3 ± 1.0 9.4 ± 2.5 <.001*
%FVC, % 96.3 ± 12.2 57.2 ± 14.3 <.001*
PEF, L/s 4.6 ± 2.1 3.4 ± 2.1 .017*
Speaking Rate, words/minute 140.5 ± 48.5 143.6 ± 41.4 .676
* : Significant difference; α = .05
27
3.2 Physiological Subsystem Measures and ALS Subgroup
Comparisons
The summary descriptive statistics for the physiological subsystem measures for ALSnr and
ALSir subgroups are displayed in Table 6. For normally distributed data, means and standard
deviations are reported. For non-normally distributed data, medians and quartiles are reported.
The results of the ALSnr versus ALSir group comparisons are displayed in Table 7. After
adjusting for multiple comparisons, one significant difference was identified. The ALSir
subgroup showed a significantly greater percent pause time (24.4 ± 7.1) as compared to the
ALSnr subgroup (19.6 ± 6.5) [t(113) = -3.510, p < .001]. A bar chart of the significant group
difference is displayed in Figure 4. The data revealed that participants with ALS and respiratory
deficit display significant increases in the proportion of time spent pausing during a reading
task.
28
Table 6: Descriptive statistics of the physiological subsystem measures by subgroup.
ALSnr ALSir
Subsystem Measure N
Mean ±
Standard
Deviation
Median
(3rd
Quartile-
1st
Quartile)
N
Mean ±
Standard
Deviation
Median
(3rd
Quartile-
1st
Quartile)
Articulatory
Syllable
Count, # 37
42.0 (62.5-
22.0) 40
32.0 (46.3-
17.3)
Articulatory
Rate,
syllables/min
47 230.4 ±
60.2 68
253.2 ±
45.6
Respiratory
Percent Pause
Time, % 47 19.6 ± 6.5 68 24.4 ± 7.1
Total Pause
Duration, s 47
9.2 (12.4-
5.6) 68
9.9 (12.9-
7.6)
Pause Events,
# 47
13.0 (16.0-
9.0) 68
14.0 (16.8-
11.0)
Maximum
Oral Pressure,
cm H2O
46 7.3 (9.5-
6.1) 65
7.4 (8.7-
6.4)
Resonatory
Peak Nasal
Flow, mL/s 46
10.0 (30.0-
0.0) 65
20.0 (56.5-
10.0)
Median
Nasalance, % 35
12.0 (19.6-
7.3) 57
11.0 (18.1-
7.2)
Phonatory F0 max, Hz 51
207.5
(301.6-
147.4)
60
186.6
(258.1-
146.0)
Table 7: Test statistics for the group comparisons of the physiological subsystem measures
between the ALSnr and ALSir subgroups.
Subsystem Measure t U d.f. p value
Syllable Count 561.5 .069
Articulatory Rate -2.205 81 .030
Percent Pause Time -3.510 113 <.001**
Total Pause Duration 1348 .155
Pause Events 1256 .051
Maximum Oral Pressure 1459 .829
Peak Nasal Flow 1169.5 .048
Median Nasalance 948 .691
F0 max 1363 .323
** : Significant findings reported at p < FDR-adjusted α
29
Figure 4: Bar chart displaying the mean percent pause time, with 95% confidence internal error
bars, for the ALSnr and ALSir subgroups.
3.3 Associations between Physiological Subsystem Measures and
%FVC
The correlation results between the physiological subsystem measures and %FVC are displayed
in Table 8. After adjusting α according to the false discovery rate procedure, one significant
correlation was identified. A moderate negative correlation was identified between percent
pause time and %FVC [r(113) = -.411, p < .001]. A scatterplot of the statistically significant
correlation with %FVC is displayed in Figure 5. The data revealed that the decline in the
respiratory function was moderately associated with an increase in the percent pause time
obtained during passage reading.
t = -3.510
p < .001
30
Table 8: Correlations between physiological subsystem measures and %FVC.
Subsystem Measure r n p value
Syllable Count .220‡ 77 .055
Articulatory Rate -.216 115 .020
Percent Pause Time -.411 115 <.001**
Total Pause Duration -.181 115 .052
Pause Events -.213 115 .023
Maximum Oral Pressure -.016‡ 111 .871
Peak Nasal Flow -.164‡ 110 .086
Median Nasalance .067‡ 92 .523
F0 max .101‡ 111 .290
** : Significant findings reported at p < FDR-adjusted α
‡ : Spearman Rank-Order Correlation, rs
Figure 5: Correlation between percent pause time and %FVC.
3.4 Associations between Physiological Subsystem Measures and PEF
The correlation results between the physiological subsystem measures and PEF are displayed in
Table 9. One correlation between the physiological subsystem measures and PEF achieved
borderline significance, but none of the correlations was statistically significant after adjusting α
r = -.411
p < .001
31
according to the false discovery rate procedure. Notably, the same measure of percent pause
time had a tendency to be associated with this respiratory performance.
Table 9: Correlations between physiological subsystem measures and PEF.
Subsystem Measure r n p value
Syllable Count .211‡ 58 .111
Articulatory Rate .056 61 .670
Percent Pause Time -.319 61 .012
Total Pause Duration -.252 61 .050
Pause Events -.281 61 .028
Maximum Oral Pressure -.084‡ 71 .487
Peak Nasal Flow -.056‡ 71 .646
Median Nasalance .120‡ 60 .361
F0 max -.113‡ 59 .393
** : Significant findings reported at p < FDR-adjusted α
‡ : Spearman Rank-Order Correlation, rs
3.5 Partial Correlation between Percent Pause Time and Speaking
Rate, Controlling for %FVC
A negative correlation between percent pause time and speaking rate was statistically significant
[r(108) = -.246, p = .010]. A weak, negative partial correlation between percent pause time and
speaking rate, while controlling for %FVC, was statistically significant [r(107) = -.291, p =
.002] as well. The data suggested that controlling for %FVC slightly improved the correlation
between percent pause time and speaking rate.
32
Discussion
4.1 Summary
This study was conducted in order to better understand how respiratory deficit may affect
physiological subsystem measures of bulbar dysfunction in patients with ALS. The objectives of
the present study were to: (1) compare selected physiological subsystem measures between two
groups of patients with ALS stratified by into normal (ALSnr) versus impaired (ALSir)
respiratory function subgroups; (2) examine the associations between the physiological
subsystem measures and %FVC; (3) explore the associations between the same measures and
PEF; and (4) explore the effect of respiratory function on the association between speaking rate
and the bulbar subsystem measures. In our patient sample, group comparisons revealed that the
presence of respiratory dysfunction led to a significant increase in percent pause time during
passage reading, and a significant correlation revealed %FVC decline was associated with
increased percent pause time. Additionally, the correlation between percent pause time and
speaking rate did not change after controlling for %FVC. All other group comparisons and
correlations were non-significant after adjusting for multiple comparisons, suggesting limited
links between declining respiratory function and the selected physiological subsystem measures
of bulbar dysfunction in patients with ALS. These findings and their implications are discussed
in more detail below.
4.2 Use and Interpretation of Physiological Subsystem Measures in
the Assessment of Bulbar ALS
The currently used clinical measures for bulbar assessment such as speech intelligibility and
speaking rate have significant limitations (Green et al., 2013; Kent, 1996). Notably, they are
limited in their ability to detect early bulbar changes (Rong, Yunusova, Wang, et al., 2015),
which is vital for providing timely clinical management to patients with bulbar signs of ALS
(Andersen et al., 2012). Assessment of bulbar motor dysfunction in ALS has recently been
improved by research into the identification of novel physiological measures based on the
physiological subsystem approach (Allison et al., 2017; Green et al., 2016). The physiological
subsystem measures represent one of four physiological speech subsystems; articulatory,
phonatory, resonatory or respiratory (Netsell et al., 1989; Yunusova et al., 2011). The
physiological subsystem measures allow for earlier identification of bulbar motor dysfunction
33
and assist in evaluating the progression of the disease, improving upon the current clinical
bulbar assessment (Allison et al., 2017; Green et al., 2013; Rong, Yunusova, Wang, et al., 2015;
Rong, Yunusova, Wang, et al., 2016; Yunusova et al., 2011).
Although the physiological subsystem measures display promising use, additional research is
needed in order to better understand the potential factors influencing these measures (Green et
al., 2013). The present study investigated the degree to which the physiological subsystem
measures may be affected by a respiratory deficit. This study was needed for two primary
reasons: (1) the subsystem approach incorporates speech and speech-like tasks which are highly
dependent on adequate respiratory support (Hixon & Hoit, 2005) and (2) bulbar and respiratory
dysfunction are often reported simultaneously in patients with ALS (Hardiman et al., 2011;
Similowski et al., 2000; Yorkston, Strand, et al., 1996).
None of the articulatory, resonatory or phonatory subsystem measures differed between the
ALSnr and ALSir subgroups or correlated with either %FVC or PEF. The same was true for
pause events, total pause duration and maximum oral pressure, representing the respiratory
subsystem. We interpreted these results as indicating that respiratory deficit contributed
minimally, if at all, to syllable count, articulatory rate, peak nasal flow, median nasalance and
F0 max, pause events, total pause duration and maximum oral pressure, supporting the use of
these measures in the assessment of bulbar motor dysfunction in ALS.
The articulatory subsystem was represented by syllable count and articulatory rate. Syllable
count is measured as the total number of syllables produced on one maximal breath. Based on
the task characteristics, syllable count may have been influenced by a reduction in respiratory
support. Our findings did not reveal an association between syllable count and the clinical
respiratory measures. Likewise, articulatory rate – calculated by dividing the total number of
syllables by the total duration of speech in the passage with pausing removed – was not
associated with respiratory decline in ALS. It is possible that articulatory rate may have changed
in response to a decline in respiratory function in patients with ALS. In an earlier study by our
group, respiratory dysfunction was found to be associated with a decrease in the mean phrase
duration but not associated with increases in pause events and pause duration during passage
reading (Yunusova et al., 2016). These features combined (i.e., shorter breath groups, but
relatively unchanged pause events and duration) may translate to a faster articulatory rate as
patients attempt to compensate for declining respiratory function. However, the non-significant
34
findings between the ALSnr and ALSir subgroups in addition to the non-significant correlations
between the articulatory subsystem measures and both clinical respiratory measures likely
reflect that respiratory dysfunction exerts minimal influence on both syllable count and
articulatory rate.
The resonatory subsystem measures included the peak nasal flow and median nasalance. Peak
nasal flow is measured during repetitions of syllable /pi/ at the time point when oral pressure is
at its maximum. Normally, nasal flow is less than 10 mL/s (Andreassen et al., 1992; Baken &
Orlikoff, 2000; Zajac, 2000) and median nasalance is typically below 25% when reading the
sentence /Buy Bobby a Puppy/ (Gauster, 2009; Gauster et al., 2010; Hutchinson et al., 1978).
Both nasal flow and nasalance should increase in the presence of velopharyngeal incompetence
(Ball et al., 2001; Hardin et al., 1992; Warren et al., 1989). The two resonatory subsystem
measures were examined because they potentially depend on the generation of pressure by the
respiratory musculature (Hixon & Hoit, 2005). Since peak nasal flow is collected when oral
pressure is at a maximum (Baken & Orlikoff, 2000, p. 308), it is possible that peak nasal flow
will change when respiratory dysfunction is present. However, in our study, maximum oral
pressure neither differed between ALSnr and ALSir subgroups nor correlated with either clinical
respiratory measure. The acoustic measure of nasalance can also be potentially affected by
respiratory changes, but median nasalance was not associated with respiratory changes in our
data. Our findings reveal that there was no difference in median nasalance scores based on
respiratory status alone or associations between median nasalance and either clinical respiratory
measure. Thus, both peak nasal flow and median nasalance most likely reflect changes in
velopharyngeal function, rather than any changes in respiratory muscle function in ALS.
The phonatory subsystem measure included was F0 max. F0 max is determined based on the
frequency of the vocal fold vibrations, which require an adequate subglottal pressure to be
generated and as such a functional respiratory mechanism (Lieberman et al., 1969; Marchal,
2009, p. 53; Weismer, 2006, pp. 104-105). The data revealed the ALSnr and ALSir subgroups
did not statistically differ in regard to F0 max. Additionally, F0 max did not correlate with the
clinical respiratory measures in our patient sample. The findings seem to indicate that changes
in F0 max relate primarily to laryngeal spasticity and atrophy rather than respiratory
dysfunction.
35
This study supports that the respiratory subsystem measures of pause events, total pause
duration and maximum oral pressure link to the bulbar motor rather than respiratory decline in
ALS. The disassociation between pause measures and respiratory deficit supported previous
claims of their links to bulbar dysfunction (Yunusova et al., 2016). Specifically, Yunusova et al.
(2016) reported significant associations between pause events and total pause duration and the
ALSFRS-R bulbar subscore. The finding that maximum oral pressure was unrelated to the
respiratory dysfunction, on the other hand, was surprising because maximum oral pressure
obtained during the production of a plosive consonant relies on the adequate respiratory support
(Hixon et al., 1982). It is possible, however, that patients with ALS are able to compensate for
respiratory deficit during the production of plosive consonants and display oral pressure
reduction only when bulbar motor impairment becomes pronounced. The group comparisons did
not reveal any significant difference in maximum oral pressure between the ALSnr and ALSir
subgroups, suggesting patients with ALS can produce adequate oral pressure even as their
respiratory status declines. Oral pressure can be affected by bulbar dysfunction (e.g., labial
weakness and velopharyngeal dysfunction) in patients with ALS (Campbell & Enderby, 1984);
further investigation is required to understand the sensitivity of oral pressure to detect bulbar
motor deficit in patients with ALS, linking oral pressure measurements to measures of labial
weakness and velopharyngeal insufficiency.
4.3 Percent Pause Time During a Reading Task and Respiratory
Dysfunction
The present study revealed that one physiological subsystem measure in a set of 9 measures was
linked to the respiratory deficit in ALS. A significant difference in percent pause time was
reported between subgroups defined by clinical respiratory status (i.e., normal => 80% FVC or
impaired < 80% FVC) and a statistically significant correlation was identified between percent
pause time and %FVC. Percent pause time refers to the amount of time spent pausing relative to
the total duration of the passage reading. The group difference and correlation involving percent
pause time likely reflects the impact of reduced breath support in ALS. With a depleting source
of breath support, displayed by the reduction in %FVC, patients with ALS appeared to pause for
longer time periods relative to the total duration of speech in order to restore the required breath
support for continuous speech.
36
Percent pause time has been determined to be highly associated with bulbar measures of
speaking rate and speech intelligibility in previous research (Rong, Yunusova, Wang, et al.,
2015; Rong, Yunusova, Wang, et al., 2016). Percent pause time significantly increased in
patients with ALS and was shown to differentiate between patients with ALS with or without
bulbar symptoms (Allison et al., 2017). Percent pause time also responded to pharmacological
treatment with dextromethorphan/quinidine (i.e., Nuedextra) (Green et al., 2016). This current
study suggested that percent pause time was influenced by both respiratory and bulbar deficits,
but the influences were relatively independent from each other. Controlling for %FVC slightly
improved the correlation between percent pause time and speaking rate.
4.4 PEF as a Measure of Respiratory Decline in ALS
PEF, which represents the maximal airflow attained during spirometry (Sieck et al., 2013;
Yamada et al., 2016), was previously examined in conjunction with peak cough airflow to
identify expiratory muscle weakness and bulbar muscle weakness in patients with ALS (Suárez
et al., 2002). Suárez et al. (2002) indicated PEF represents expiratory muscle force and reported
significant reductions in PEF values obtained by patients with ALS in comparison to matched
controls. Since speech primarily occurs during the expiratory phase of breathing (Hixon & Hoit,
2005), PEF provided a unique contribution to the present study. In the present study, the
correlation between percent pause time and PEF approached but did not reach statistical
significance. The correlation with PEF was likely insignificant due to the limited number of
observations as compared to %FVC. PEF data was collected only on a sub-sample of the
participants due to limited equipment availability. Another possible reason for a lack of
significance could be related to the difference in measurement properties between %FVC and
PEF. %FVC is a volumetric measure whereas PEF is an airflow measure. Changes in a
volumetric measure may be more relevant to changes in speech breathing because reduced
breath support affects speech (Lee et al., 1993; Nishio & Niimi, 2000; Turner & Weismer,
1993), compared to only a minimal expiratory force being required to generate speech
(Lieberman et al., 1969; Marchal, 2009, p. 53; Weismer, 2006, pp. 104-105). Further
investigation into PEF is warranted based on the results of this study.
37
4.5 Contribution to the Literature
Previous studies have examined the relationship between respiratory and bulbar function in ALS
by subgrouping patients by region of disease onset (i.e., bulbar versus spinal) (Brooks, 1996;
Pinto et al., 2007; Pinto et al., 2009). Patients with bulbar onset ALS were reported to develop
respiratory dysfunction prior to patients with spinal onset ALS (see Pinto et al., 2007). However,
region of onset may not be sufficient for full understanding of bulbar disease progression in
ALS. In this study, we did not subgroup patients by the onset-site because previous literature
indicated that after the onset of bulbar symptoms in the spinal-onset cohort, the disease
progression is similar to patients with bulbar onset ALS (Turner, Brockington, et al., 2010). We
chose to examine participants based on their respiratory status in order to reveal a contribution
of respiratory dysfunction in the assessment of bulbar motor function.
Previous literature on bulbar motor dysfunction tends to focus on either symptom reports (e.g.,
ALSFRS-R) or global measures of bulbar dysfunction (e.g., speech intelligibility or speaking
rate). Understanding of the potential association between bulbar and respiratory dysfunction
using physiological subsystem measures is the next step in improving the clinical bulbar
assessment (Green et al., 2013). This is possible since physiological subsystem measures can
detect early functional bulbar changes (Rong, Yunusova, Wang, et al., 2015; Rong, Yunusova,
Wang, et al., 2016), with greater sensitivity and specificity than current clinical measures
(Allison et al., 2017). They also are capable of tracking the bulbar disease progression over time
(Green et al., 2013; Rong, Yunusova, & Green, 2015; Shellikeri, 2014; Yunusova et al., 2010;
Yunusova, Green, Lindstrom, Pattee, & Zinman, 2013). Since the physiological subsystem
measures represent four distinct motor speech subsystems innervated by specific bulbar motor
neurons (Netsell et al., 1989), the relative decline of the physiological subsystem measures
relative to the clinical respiratory measures may help reveal the origin of the neurodegeneration
and support patient subgroupings beyond the site of onset.
4.6 Nature of Measures: Maximum versus Submaximal Performance
The method of measurement collection is important to consider when discussing the findings of
this study. Both %FVC and PEF are maximal performance measures, whereas the majority of
physiological subsystem measures were collected using submaximal speech-based tasks. This
difference may have contributed to the weak and moderate correlations that were reported
38
(Rosenthal, 1996). Maximal performance measures often require peak effort; the maximal
performance measures are subsequently influenced by a participant’s willingness to exert
maximal effort (Lemstra, Olszynski, & Enright, 2004). The pulmonary function tests are often
more physically taxing when compared to the speech-based tasks (Godin & Hansen, 2015;
Lively, Pisoni, Van Summers, & Bernacki, 1993). Conversely, submaximal performance
measures assess habitual performance. Although submaximal measures do not account for
maximal effort, they may reveal various compensatory behaviours. For example, increased
lower lip and jaw movement speed seems to occur during the early stages of bulbar ALS
progression, possibly in response to the early velopharyngeal dysfunction (Rong, Yunusova,
Wang, et al., 2015). Utilizing submaximal measures may be more beneficial in ALS clinical
settings since subclinical changes in function can occur prior to clinically detectable changes
(Allison et al., 2017; Rong, Yunusova, Wang, et al., 2015).
4.7 Limitations of the Present Study
A number of limitations must be considered when interpreting the results. First, participants in
the present study were largely exhibiting a mild form of ALS, which is typical for these studies
(Chio et al., 2011). Patients with ALS are very difficult to recruit in the moderate or advanced
stages of the disease (Mehta, Antao, & Horton, 2015); ALS studies often have a high dropout
rate because patient fatigue and rapid disease progression result in missing data and censoring
(Aggarwal & Cudkowicz, 2008). In the present study, a total of 58 potential participants were
excluded because they did not obtain a %FVC during their participation in the study. Patient
fatigue during a typical data collection session lasting for approximately 1 to 1.5 hours also
resulted in missing data. Thus, it is important to note that the results of the study are likely more
generalizable to patients with ALS displaying a relatively mild disease severity. Patients with a
severe ALS status would likely display more extreme changes to the physiological subsystem
measures, which would potentially result in stronger correlations between bulbar motor and
respiratory measures.
Furthermore, the included clinical respiratory measures (i.e., %FVC and PEF) possess
limitations. First, %FVC and PEF were collected using different instruments. Although both
were obtained during a pulmonary function test, the use of two different instruments may have
impacted the results based on patient effort, equipment availability, different calibration
protocols and different environmental settings (i.e., clinic versus research). Second, performance
39
during the pulmonary function tests may have been impeded by muscle weakness and fatigue
(Lechtzin et al., 2002; Pinto et al., 2007). Also, although the most widely used clinical
respiratory measure (Plowman et al., 2017), %FVC is less sensitive to detecting respiratory
dysfunction compared to other spirometric measures (e.g., maximum inspiratory and expiratory
pressure and sniff nasal inspiratory pressure) (Polkey et al., 2016). Lastly, the inquiry regarding
comparing maximum to submaximal performance measures may be explored by utilizing a
plethysmograph (Sackner, Nixon, Davis, Atkins, & Sackner, 1980) or an accelerometer (Bates,
Ling, Geng, Turk, & Arvind, 2011).
Furthermore, the selection of measures evaluated in this study was based on our prior research
and knowledge and not all of the potential bulbar motor measures have been evaluated. The
development of novel measures is ongoing (Rong, Yunusova, Richburg, & Green, in press), and
the effect of the respiratory deficit on these measures would have to be evaluated as part of
measure validation. Additionally, more advanced statistical methods accounting for variance
due to sex, age and the site of data collection would have produced different results. More
sophisticated statistical modeling on a larger sample of data is planned for the future.
4.8 Conclusions
This study investigated group differences in physiological subsystem measures between
participants with ALS displaying either normal or impaired respiratory function and the
association between physiological subsystem measures and two clinical respiratory measures in
ALS. Findings generally support a notion that respiratory function decline does not affect the
selected physiological subsystem measures. The percent pause time measure was significantly
correlated with %FVC, yet the contribution of the respiratory impairment to this measure was
relatively independent of the association between percent pause time and speaking rate. As such,
all examined measures can be useful indicators of bulbar motor dysfunction in the assessment of
ALS. This study contributes to the literature examining the use of the subsystem approach to
monitor and track the decline of bulbar motor function in patients with ALS. Findings suggest
utilizing the selected articulatory, phonatory, resonatory and respiratory measures of speech in
the assessment of bulbar disease.
40
4.9 Future Directions
Future studies should continue developing the use of physiological subsystem measures in the
assessment of bulbar motor function in patients with ALS. These studies should examine the
longitudinal declines of the physiological subsystems and clinical respiratory measures.
Examining the relative longitudinal declines may reveal possible links and pathways between
the neurodegeneration of the bulbar and respiratory networks in ALS. The ongoing work in our
lab focuses on delineating diagnostic properties of the instrumental measures (e.g., sensitivity,
specificity, responsiveness, minimal detectable differences, etc.) in patients with ALS. With the
physiological subsystem measures being shown to effectively detect early bulbar changes and
track the disease progression, focus is currently placed on clinical implementation of these
measures, including technology development aspects such as the creation of automatic analysis
methods (e.g., Speech Pause Analysis algorithm) (Green et al., 2004). This work is expected to
improve clinical care and design of clinical trials for effective ALS management.
41
References
Aggarwal, S., & Cudkowicz, M. (2008). ALS drug development: Reflections from the past and a
way forward. Neurotherapeutics, 5(4), 516-527.
doi:https://doi.org/10.1016/j.nurt.2008.08.002
Aithal, V. U., Bellur, R., John, S., Varghese, C., & Guddattu, V. (2012). Acoustic analysis of
voice in normal and high pitch phonation: A comparative study. Folia Phoniatrica et
Logopaedica, 64(1), 48-53.
Allison, K. M., Yunusova, Y., Campbell, T. F., Wang, J., Berry, J. D., & Green, J. R. (2017).
The diagnostic utility of patient-report and speech-language pathologists’ ratings for
detecting the early onset of bulbar symptoms due to ALS. Amyotrophic Lateral Sclerosis
and Frontotemporal Degeneration, 1-9. doi:10.1080/21678421.2017.1303515
Andersen, P. M., Abrahams, S., Borasio, G. D., de Carvalho, M., Chio, A., Van Damme, P., . . .
Weber, M. (2012). EFNS guidelines on the clinical management of amyotrophic lateral
sclerosis (MALS) - revised report of an EFNS task force. European Journal of
Neurology, 19(3), 360-375. doi:10.1111/j.1468-1331.2011.03501.x
Andreassen, M. L., Smith, B. E., & Guyette, T. W. (1992). Pressure-flow measurements for
selected oral and nasal sound segments produced by normal adults. The Cleft Palate-
Craniofacial Journal, 29(1), 1-9.
Aydogdu, I., Tanriverdi, Z., & Ertekin, C. (2011). Dysfunction of bulbar central pattern
generator in ALS patients with dysphagia during sequential deglutition. Clinical
Neurophysiology, 122(6), 1219-1228. doi:10.1016/j.clinph.2010.11.002
Baken, R. J., & Orlikoff, R. F. (2000). Clinical measurement of speech and voice: Cengage
Learning.
Ball, L. J., Beukelman, D. R., & Pattee, G. L. (2002). Timing of speech deterioration in people
with amyotrophic lateral sclerosis. Journal of Medical Speech-Language Pathology,
10(4), 231-235.
42
Ball, L. J., Willis, A., Beukelman, D. R., & Pattee, G. L. (2001). A protocol for identification of
early bulbar signs in amyotrophic lateral sclerosis. Journal of Neurological Sciences, 191,
43-53.
Barbosa, D. A., Mangilli, L. D., Andrade, C. R. F. d., & Alonso, N. (2012). The presence of low
intraoral pressure in speech following surgical correction of cleft palate. Revista
Brasileira de Cirurgia Plástica, 27(4), 542-546.
Bates, A., Ling, M., Geng, C., Turk, A., & Arvind, D. K. (2011). Accelerometer-based
respiratory measurement during speech. Paper presented at the 2011 International
Conference on Body Sensor Networks (BSN).
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and
powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B
(Statistical Methodology), 289-300.
Berlowitz, D. J., Howard, M. E., Fiore, J. F., Jr., Vander Hoorn, S., O'Donoghue, F. J., Westlake,
J., . . . Talman, P. (2015). Identifying who will benefit from non-invasive ventilation in
amyotrophic lateral sclerosis/motor neurone disease in a clinical cohort. Journal of
Neurology, Neurosurgery & Psychiatry, 87(3), 280-286. doi:10.1136/jnnp-2014-310055
Beukelman, D., Fager, S., & Nordness, A. (2011). Communication support for people with ALS.
Neurology Research International, 2011.
Beukelman, D., Yorkston, K., Hakel, M., & Dorsey, M. (2007). Speech Intelligibility Test.
Computer software].(Madonna Rehabilitation Hospital, Lincoln, 2007).
Binazzi, B., Lanini, B., Gigliotti, F., & Scano, G. (2013). Breathing Pattern and Chest Wall
Kinematics during Phonation in Chronic Obstructive Pulmonary Disease Patients.
Respiration, 86(6), 462-471.
Blokhuis, A. M., Groen, E. J. N., Koppers, M., van den Berg, L. H., & Pasterkamp, R. J. (2013).
Protein aggregation in amyotrophic lateral sclerosis. Acta Neuropathologica, 125(6), 777-
794. doi:10.1007/s00401-013-1125-6
43
Bongioanni, P. (2012). Communication impairment in ALS patients assessment and treatment. In
M. Maurer (Ed.), Amyotrophic Lateral Sclerosis (pp. 665-682): InTech.
Bouche, P., Le Forestier, N., Maisonobe, T., Fournier, E., & Willer, J. C. (1999).
Electrophysiological diagnosis of motor neuron disease and pure motor neuropathy.
Journal of Neurology, 246(7), 520-525.
Bressmann, T., Sader, R., Whitehill, T. L., Awan, S. N., Zeilhofer, H.-F., & Horch, H.-H. (2000).
Nasalance distance and ratio: Two new measures. The Cleft Palate-Craniofacial Journal,
37(3), 248-256.
Brooks, B. R. (1996). Natural history of ALS Symptoms, strength, pulmonary function, and
disability. Neurology, 47(Suppl. 2), 71S-82S.
Brooks, B. R., Miller, R. G., Swash, M., & Munsat, T. L. (2000). El Escorial revisited: Revised
criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotrophic Lateral Sclerosis
and Other Motor Neuron Disorders, 1(5), 293-299. doi:10.1080/146608200300079536
Brooks, B. R., Sufit, R. L., DePaul, R., Tan, Y. D., Sanjak, M., & Robbins, J. (1990). Design of
clinical therapeutic trials in amyotrophic lateral sclerosis. Advances in Neurology, 56,
521-546.
Brown, R., DiMarco, A. F., Hoit, J. D., & Garshick, E. (2006). Respiratory dysfunction and
management in spinal cord injury. Respiratory Care, 51(8), 853-870.
Calnan, J. S. (1959). The surgical treatment of nasal speech disorders. Annals of the Royal
College of Surgeons of England, 25(2), 119.
Campbell, M. J., & Enderby, P. (1984). Management of motor neurone disease. Journal of the
Neurological Sciences, 64(1), 65-71.
Campbell, T. F., & Dollaghan, C. A. (1995). Speaking rate, articulatory speed, and linguistic
processing in children and adolescents with severe traumatic brain injury. Journal of
Speech, Language, and Hearing Research, 38(4), 864-875.
44
Cedarbaum, J. M., Stambler, N., Malta, E., Fuller, C., Hilt, D., Thurmond, B., . . . BDNF ALS
Study Group. (1999). The ALSFRS-R: A revised ALS functional rating scale that
incorporates assessments of respiratory function. Journal of the Neurological Sciences,
169, 13-21.
Cetin, H., Rath, J., Füzi, J., Reichardt, B., Fülöp, G., Koppi, S., . . . Zimprich, F. (2015).
Epidemiology of Amyotrophic Lateral Sclerosis and Effect of Riluzole on Disease
Course. Neuroepidemiology, 44(1), 6-15.
Chandrasoma, B., Balfe, D., Naik, T., Elsayegh, A., Lewis, M., & Mosenifar, Z. (2012).
Pulmonary function in patients with amyotrophic lateral sclerosis at disease onset.
Monaldi Archives for Chest Disease, 77(3-4).
Chio, A., Canosa, A., Gallo, S., Cammarosano, S., Moglia, C., Fuda, G., . . . Gabriele, M. (2011).
ALS clinical trials: Do enrolled patients accurately represent the ALS population?
Neurology, 77(15), 1432-1437.
Chiò, A., Logroscino, G., Traynor, B. J., Collins, J., Simeone, J. C., Goldstein, L. A., & White,
L. A. (2013). Global epidemiology of amyotrophic lateral sclerosis: A systematic review
of the published literature. Neuroepidemiology, 41(2), 118-130.
Clavelou, P., Blanquet, M., Peyrol, F., Ouchchane, L., & Gerbaud, L. (2013). Rates of
progression of weight and forced vital capacity as relevant measurement to adapt
amyotrophic lateral sclerosis management for patient Result of a French multicentre
cohort survey. Journal of the Neurological Sciences, 331(1-2), 126-131.
doi:10.1016/j.jns.2013.06.002
Czaplinski, A., Yen, A., & Appel, S. H. (2006). Forced vital capacity (FVC) as an indicator of
survival and disease progression in an ALS clinic population. Journal of Neurology,
Neurosurgery & Psychiatry, 77(3), 390-392.
Darley, F., Aronson, A., & Brown, J. (1975). Motor Speech Disorders. Philadelphia: Saunders.
45
De Carvalho, M., Matias, T., Coelho, F., Evangelista, T., Pinto, A., & Sales Luís, M. L. (1996).
Motor neuron disease presenting with respiratory failure. Journal of the Neurological
Sciences, 139, 117-122.
Delorey, R., Leeper, H., & Hudson, A. (1999). Measures of velopharyngeal functioning in
subgroups of individuals with amyotrophic lateral sclerosis. Journal of Medical Speech-
Language Pathology, 7(1), 19-31.
Devadiga, D., Varghese, A. L., Bhat, J., Baliga, P., & Pahwa, J. (2015). Peak flow measure: An
index of respiratory function? International Journal of Health Sciences and Research,
5(2), 240-245.
Duffy, J. R. (2013). Motor speech disorders: Substrates, differential diagnosis, and
management: Elsevier Health Sciences.
Dyck, P. J. (1990). Invited review: limitations in predicting pathologic abnormality of nerves
from the EMG examination. Muscle & Nerve, 13(5), 371-375.
Easterling, C., Antinoja, J., Cashin, S., & Barkhaus, P. E. (2013). Changes in tongue pressure,
pulmonary function, and salivary flow in patients with amyotrophic lateral sclerosis.
Dysphagia, 28(2), 217-225. doi:10.1007/s00455-012-9436-7
Fallat, R. J., Jewitt, B., Bass, M., Kamm, B., & Norris, F. H. (1979). Spirometry in amyotrophic
lateral sclerosis. Archives of Neurology, 36(2), 74-80.
Finsterer, J., Fuglsang-Frederiksen, A., & Mamoli, B. (1998). Needle electromyography of
bulbar muscles in patients with amyotrophic lateral sclerosis. Journal of Neurology,
Neurosurgery & Psychiatry, 63(2), 175-180.
Franchignoni, F., Mora, G., Giordano, A., Volanti, P., & Chiò, A. (2013). Evidence of
multidimensionality in the ALSFRS-R Scale: A critical appraisal on its measurement
properties using Rasch analysis. Journal of Neurology, Neurosurgery, and Psychiatry,
84(12). doi:10.1136/jnnp-2012-304701
Gauster, A. (2009). The effect of speaking rate on velopharyngeal function in healthy speakers
(Master of Science), University of Toronto.
46
Gauster, A., Yunusova, Y., & Zajac, D. (2010). The effect of speaking rate on velopharyngeal
function in healthy speakers. Clinical Linguistics & Phonetics, 24(7), 576-588.
Gautier, G., Verschueren, A., Monnier, A., Attarian, S., Salort-Campana, E., & Pouget, J. (2010).
ALS with respiratory onset: Clinical features and effects of non-invasive ventilation on
the prognosis. Amyotrophic Lateral Sclerosis, 11(4), 379-382.
doi:10.3109/17482960903426543
Goberman, A. M., & Blomgren, M. (2008). Fundamental frequency change during offset and
onset of voicing in individuals with Parkinson disease. Journal of Voice, 22(2), 178-191.
Godin, K. W., & Hansen, J. H. (2015). Physical task stress and speaker variability in voice
quality. EURASIP Journal on Audio, Speech, and Music Processing, 2015(1), 29-33.
Goldman-Eisler, F. (1972). Pauses, clauses, sentences. Language and speech, 15(2), 103-113.
Gordon, P. H., Miller, R. G., & Moore, D. H. (2004). ALSFRS‐R. Amyotrophic Lateral Sclerosis
and Other Motor Neuron Disorders, 5(sup1), 90-93.
Green, J. R., Allison, K., Cordella, C., Pioro, E., Pattee, G., & Smith, R. (2016). The effects of
nuedexta on speech pause time. Poster presented at the 27th International Symposium on
ALS/MND, Dublin, Ireland.
Green, J. R., Beukelman, D. R., & Ball, L. J. (2004). Algorithmic estimation of pauses in
extended speech samples of dysarthric and typical speech. Journal of Medical Speech-
Language Pathology, 12(4), 149.
Green, J. R., Yunusova, Y., Kuruvilla, M. S., Wang, J., Pattee, G. L., Synhorst, L., . . . Berry, J.
D. (2013). Bulbar and speech motor assessment in ALS: Challenges and future
directions. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 14(7-8),
494-500. doi:10.3109/21678421.2013.817585
Hammen, V. L., & Yorkston, K. M. (1994). Respiratory patterning and variability in dysarthric
speech. Journal of Medical Speech-Language Pathology, 2(4), 253-262.
47
Hardiman, O., Van Den Berg, L. H., & Kiernan, M. C. (2011). Clinical diagnosis and
management of amyotrophic lateral sclerosis. Nature Reviews Neurology, 7(11), 639-649.
Hardin, M. A., Van Demark, D., Morris, H. L., & Payne, M. M. (1992). Correspondence
between nasalance scores and listener judgments of hypernasality and hyponasality. The
Cleft Palate-Craniofacial Journal, 29(4), 346-351.
Healey, E. C., & Adams, M. R. (1981). Rate reduction strategies used by normally fluent and
stuttering children and adults. Journal of Fluency Disorders, 6(1), 1-14.
Hecht, M., Hillemacher, T., Gräsel, E., Tigges, S., Winterholler, M., Heuss, D., . . . Neundörfer,
B. (2002). Subjective experience and coping in ALS. Amyotrophic Lateral Sclerosis and
Other Motor Neuron Disorders, 3(4), 225-231.
Hirtz, D., Thurman, D., Gwinn-Hardy, K., Mohamed, M., Chaudhuri, A., & Zalutsky, R. (2007).
How common are the “common” neurologic disorders? Neurology, 68(5), 326-337.
Hixon, T. J. (1973). Respiratory function in speech. In F. D. Minifie, T. J. Hixon, & F. Williams
(Eds.), Normal aspects of speech, hearing, and language (pp. 73-125). Englewood Cliffs,
NJ, USA: Prentice-Hall.
Hixon, T. J., Goldman, M. D., & Mead, J. (1973). Kinematics of the chest wall during speech
production: Volume displacements of the rib cage, abdomen, and lung. Journal of
Speech, Language, and Hearing Research, 16(1), 78-115.
Hixon, T. J., Hawley, J. L., & Wilson, K. J. (1982). An around-the-house device for the clinical
determination of respiratory driving pressure: A note on making simple even simpler.
Journal of Speech and Hearing Disorders, 47(4), 413-415.
Hixon, T. J., & Hoit, J. D. (2005). Evaluation and management of speech breathing disorders:
Principles and methods. Tuscon, Arizona, USA: Redington Brown.
Hoodin, R. B., & Gilbert, H. R. (1989). Nasal airflows in parkinsonian speakers. Journal of
Communication Disorders, 22(3), 169-180.
48
Hutchinson, J. M., Robinson, K. L., & Nerbonne, M. A. (1978). Patterns of nasalance in a
sample of normal gerontologic subjects. Journal of Communication Disorders, 11(6),
469-481. doi:https://doi.org/10.1016/0021-9924(78)90021-7
Huynh, W., Simon, N. G., Grosskreutz, J., Turner, M. R., Vucic, S., & Kiernan, M. C. (2016).
Assessment of the upper motor neuron in amyotrophic lateral sclerosis. Clinical
Neurophysiology, 127(7), 2643-2660.
Jacewicz, E., Fox, R. A., O'Neill, C., & Salmons, J. (2009). Articulation rate across dialect, age,
and gender. Language Variation and Change, 21(2), 233-256.
Kasarskis, E. J., Berryman, S., Vanderleest, J. G., Schneider, A. R., & McClain, C. J. (1996).
Nutritional status of patients with amyotrophic lateral sclerosis: relation to the proximity
of death. The American Journal of Clinical Nutrition, 63, 130-137.
Kaufmann, P., Levy, G., Montes, J., Buchsbaum, R., Barsdorf, A. I., Battista, V., . . . Levin, B.
(2007). Excellent inter‐rater, intra‐rater, and telephone‐administered reliability of the
ALSFRS‐R in a multicenter clinical trial. Amyotrophic Lateral Sclerosis, 8(1), 42-46.
Kelhetter, K. M. (2013). Velopharyngeal function during speech production in amyotrophic
lateral sclerosis. (Bachelor of Science), University of Arizona.
Kent, R. D. (1996). Hearing and believing: Some limits to the auditory-perceptual assessment of
speech and voice disorders. American Journal of Speech-Language Pathology, 5(3), 7-
23.
Kent, R. D., Kent, J. F., Duffy, J. R., Thomas, J. E., Weismer, G., & Stuntebeck, S. (2000).
Ataxic dysarthria. Journal of Speech, Language, and Hearing Research, 43(5), 1275-
1289.
Kent, R. D., Sufit, R. L., Rosenbek, J. C., Kent, J. F., Weismer, G., Martin, R. E., & Brooks, B.
R. (1991). Speech deterioration in amyotrophic lateral sclerosis: A case study. Journal of
Speech, Language, and Hearing Research, 34(6), 1269-1275.
49
Ketelslagers, K., De Bodt, M., Wuyts, F., & Van de Heyning, P. (2007). Relevance of subglottic
pressure in normal and dysphonic subjects. European Archives of Oto-Rhino-
Laryngology, 264(5), 519-523.
Kiernan, M. C., Vucic, S., Cheah, B. C., Turner, M. R., Eisen, A., Hardiman, O., . . . Zoing, M.
C. (2011). Amyotrophic lateral sclerosis. The Lancet, 377(9769), 942-955.
doi:10.1016/s0140-6736(10)61156-7
Kim, Y., Kent, R. D., & Weismer, G. (2011). An acoustic study of the relationships among
neurologic disease, dysarthria type, and severity of dysarthria. Journal of Speech,
Language, and Hearing Research, 54(2), 417-429.
Kimura, F., Fujimura, C., Ishida, S., Nakajima, H., Furutama, D., Uehara, H., . . . Hanafusa, T.
(2006). Progression rate of ALSFRS-R at time of diagnosis predicts survival time in
ALS. Neurology, 66(2), 265-267.
Kleopa, K. A., Sherman, M., Neal, B., Romano, G. J., & Heiman-Patterson, T. (1999). Bipap
improves survival and rate of pulmonary function decline in patients with ALS. Journal
of the Neurological Sciences, 164(1), 82-88. doi:http://dx.doi.org/10.1016/S0022-
510X(99)00045-3
Kuhnlein, P., Gdynia, H. J., Sperfeld, A. D., Lindner-Pfleghar, B., Ludolph, A. C., Prosiegel, M.,
& Riecker, A. (2008). Diagnosis and treatment of bulbar symptoms in amyotrophic
lateral sclerosis. Nature Clinical Practice Neurology, 4(7), 366-374.
doi:10.1038/ncpneuro0853
Kummer, A. W. (2018). A pediatrician’s guide to communication disorders secondary to cleft
lip/palate. Pediatric Clinics of North America, 65(1), 31-46.
Kuo, C., & Tjaden, K. (2016). Acoustic variation during passage reading for speakers with
dysarthria and healthy controls. Journal of Communication Disorders, 62, 30-44.
doi:10.1016/j.jcomdis.2016.05.003
Lechtzin, N., Rothstein, J., Clawson, L., Diette, G. B., & Wiener, C. M. (2002). Amyotrophic
lateral sclerosis: Evaluation and treatment of respiratory impairment. Amyotrophic
50
Lateral Sclerosis and Other Motor Neuron Disorders, 3(1), 5-13.
doi:10.1080/146608202317576480
Lee, A., & Doherty, R. (2017). Speaking rate and articulation rate of native speakers of Irish
English. Speech, Language and Hearing, 20(4), 206-211.
doi:10.1080/2050571X.2017.1290337
Lee, L., Loudon, R. G., Jacobson, B. H., & Stuebing, R. (1993). Speech breathing in patients
with lung disease. American Review of Respiratory Disease, 147, 1199-1199.
Lemstra, M., Olszynski, W., & Enright, W. (2004). The sensitivity and specificity of functional
capacity evaluations in determining maximal effort: A randomized trial. Spine, 29(9),
953-959.
Lester, R. A., & Story, B. H. (2013). Acoustic characteristics of simulated respiratory-induced
vocal tremor. American Journal of Speech-Language Pathology, 22(2), 205-211.
Lieberman, P., Knudson, R., & Mead, J. (1969). Determination of the rate of change of
fundamental frequency with respect to subglottal air pressure during sustained phonation.
Journal of the Acoustical Society of America, 45(6), 1537-1543.
Lin, E., Mautner, H., Ormond, T., & Hornibrook, J. (2007). Task effect on voice measures in
voice patients and normals. Poster presented at the American Speech-Language and
Hearing Association's 2007 Annual Convention, Boston, Massachusetts, USA.
Lively, S. E., Pisoni, D. B., Van Summers, W., & Bernacki, R. H. (1993). Effects of cognitive
workload on speech production: Acoustic analyses and perceptual consequences. Journal
of the Acoustical Society of America, 93(5), 2962-2973.
Lourenço, B. M., Costa, K. M., & da Silva Filho, M. (2014). Voice disorder in cystic fibrosis
patients. PLoS One, 9(5), e96769. doi:10.1371/journal.pone.0096769
Ludlow, C. L., Van Pelt, F., Yeh, J., Rhew, K., Cohen, L. G., & Hallett, M. (1994). Limitations
of electromyography and magnetic stimulation for assessing laryngeal muscle control.
Annals of Otology, Rhinology & Laryngology, 103(1), 16-27.
51
Lyall, R. A., Donaldson, N., Polkey, M. I., Leigh, P. N., & Moxham, J. (2001). Respiratory
muscle strength and ventilatory failure in amyotrophic lateral sclerosis. Brain, 124(10),
2000-2013.
Marchal, A. (2009). From speech physiology to linguistic phonetics (Vol. 145): John Wiley &
Sons.
Mazzini, L., Corra, T., Zaccala, M., Mora, G., Del Piano, M., & Galante, M. (1995).
Percutaneous endoscopic gastrostomy and enteral nutrition in amyotrophic lateral
sclerosis. Journal of Neurology, 242, 695-698.
McCombe, P. A., & Henderson, R. D. (2010). Effects of gender in amyotrophic lateral sclerosis.
Gender Medicine, 7(6), 557-570. doi:https://doi.org/10.1016/j.genm.2010.11.010
Mefferd, A. S., Pattee, G. L., & Green, J. R. (2014). Speaking rate effects on articulatory pattern
consistency in talkers with mild ALS. Clinical Linguistics & Phonetics, 28(11), 799-811.
doi:10.3109/02699206.2014.908239
Mehta, P., Antao, V., & Horton, D. K. (2015). Recruiting Patients for Research, Clinical Trials,
and Epidemiological Studies Using the National Amyotrophic Lateral Sclerosis (ALS)
Registry (P4.150). Neurology, 84(Suppl. 14), P4.150.
Miller, M. R., Hankinson, J., Brusasco, V., Burgos, F., Casaburi, R., Coates, A., . . . Wanger, J.
(2005). Standardisation of spirometry. European Respiratory Journal, 26(2), 319-338.
doi:10.1183/09031936.05.00034805
Milonas, I. (1998). Amyotrophic lateral sclerosis: An introduction. Journal of Neurology,
245(Suppl. 2), S1-S3. doi:10.1007/s004150050640
Mitsumoto, H., Przedborski, S., & Gordon, P. H. (2006). Amyotrophic lateral sclerosis. New
York, NY, USA: Taylor & Francis.
Mulligan, M., Carpenter, J., Riddel, J., Delaney, M. K., Badger, G., Krusinski, P., & Tandan, R.
(1994). Intelligibility and the acoustic characteristics of speech in amyotrophic lateral
sclerosis (ALS). Journal of Speech, Language, and Hearing Research, 37(3), 496-503.
52
Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., . . .
Chertkow, H. (2005). The Montreal Cognitive Assessment, MoCA: A brief screening
tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53(4),
695-699.
Netsell, R., Lotz, W. K., & Barlow, S. (1989). A speech physiology examination for individuals
with dysarthria. In K. Yorkston & D. Beukelman (Eds.), Recent advances in dysarthria
(pp. 3-37). Boston, MA, USA: College-Hill Press.
Nichols, N. L., Van Dyke, J., Nashold, L., Satriotomo, I., Suzuki, M., & Mitchell, G. S. (2013).
Ventilatory control in ALS. Respiratory Physiology & Neurobiology, 189(2), 429-437.
doi:10.1016/j.resp.2013.05.016
Niimi, S., & Nishio, M. (2001). Speaking rate and its components in dysarthric speakers.
Clinical Linguistics & Phonetics, 15(4), 309-317. doi:10.1080/02699200010024456
Nishio, M., & Niimi, S. (2000). Changes over time in dysarthric patients with amyotrophic
lateral sclerosis (ALS): A study of changes in speaking rate and maximum repetition rate
(MRR). Clinical Linguistics & Phonetics, 14(7), 485-497.
Nishio, M., & Niimi, S. (2006). Comparison of speaking rate, articulation rate and alternating
motion rate in dysarthric speakers. Folia Phoniatrica et Logopaedica, 58(2), 114-131.
Phukan, J., Elamin, M., Bede, P., Jordan, N., Gallagher, L., Byrne, S., . . . Hardiman, O. (2012).
The syndrome of cognitive impairment in amyotrophic lateral sclerosis: A population-
based study. Journal of Neurology, Neurosurgery, and Psychiatry, 83(1), 102-108.
doi:10.1136/jnnp-2011-300188
Pinto, S., Pinto, A., & de Carvalho, M. (2007). Do bulbar onset amyotrophic lateral sclerosis
patients have an earlier respiratory involvement than spinal onset amyotrophic lateral
sclerosis patients? Europa Medicophyica, 43(4), 4505-4509.
Pinto, S., Turkman, A., Pinto, A., Swash, M., & de Carvalho, M. (2009). Predicting respiratory
insufficiency in amyotrophic lateral sclerosis: The role of phrenic nerve studies. Clinical
Neurophysiology, 120(5), 941-946. doi:10.1016/j.clinph.2009.02.170
53
Plowman, E. K., Tabor, L. C., Wymer, J., & Pattee, G. (2017). The evaluation of bulbar
dysfunction in amyotrophic lateral sclerosis: Survey of clinical practice patterns in the
United States. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 1-7.
Polkey, M. I., Lyall, R. A., Yang, K., Johnson, E., Leigh, P. N., & Moxham, J. (2016).
Respiratory muscle strength as a predictive biomarker for survival in amyotrophic lateral
sclerosis. American Journal of Respiratory and Critical Care Medicine.
doi:10.1164/rccm.201604-0848OC
Poloni, M., Mento, S., Mascherpa, C., & Ceroni, M. (1983). Value of spirometric investigations
in amyotrophic lateral sclerosis. The Italian Journal of Neurological Sciences, 4(1), 39-
46.
Ranu, H., Wilde, M., & Madden, B. (2011). Pulmonary function tests. The Ulster Medical
Journal, 80(2), 84-90.
Ratti, E., Berry, J., Vangel, M., Macklin, E., Schoenfeld, D., & Cudkowicz, M. (2015).
Progression to clinically meaningful changes in ALSFRS-R bulbar and fine motor
domains is faster in bulbar onset and in limb onset amyotrophic lateral sclerosis patients
respectively. Neurology, 84(14), P5.051.
Richter, D. W., & Smith, J. C. (2014). Respiratory rhythm generation in vivo. Physiology, 29(1),
58-71.
Rong, P., Yunusova, Y., & Green, J. R. (2015). Speech intelligibility decline in individuals with
fast and slow rates of ALS progression. Paper presented at the Sixteenth Annual
Conference of the International Speech Communication Association.
Rong, P., Yunusova, Y., & Green, J. R. (2016). Differential effects of velopharyngeal
dysfunction on speech intelligibility during early and late stages of amyotrophic lateral
sclerosis. Paper presented at the Interspeech.
Rong, P., Yunusova, Y., Richburg, B., & Green, J. R. (in press). A diagnostic tool for assessing
articulatory involvement in ALS: Automatic extraction of abnormal lip movement
54
features from the alternating motion rate (AMR) task. International Journal of Speech-
Language Pathology.
Rong, P., Yunusova, Y., Wang, J., & Green, J. R. (2015). Predicting early bulbar decline in
amyotrophic lateral sclerosis: A speech subsystem approach. Behavioral Neurology,
2015, 1-11. doi:10.1155/2015/183027
Rong, P., Yunusova, Y., Wang, J., Zinman, L., Pattee, G. L., Berry, J. D., . . . Green, J. R.
(2016). Predicting speech intelligibility decline in amyotrophic lateral sclerosis based on
the deterioration of individual speech subsystems. PLoS One, 11(5), e0154971.
Rosen, A. D. (1978). Amyotrophic lateral sclerosis: clinical features and prognosis. Archives of
Neurology, 35(10), 638-642.
Rosenthal, J. A. (1996). Qualitative descriptors of strength of association and effect size. Journal
of Social Service Research, 21(4), 37-59.
Rusz, J., Klempíř, J., Tykalová, T., Baborová, E., Čmejla, R., Růžička, E., & Roth, J. (2014).
Characteristics and occurrence of speech impairment in Huntington’s disease: Possible
influence of antipsychotic medication. Journal of Neural Transmission, 121(12), 1529-
1539. doi:10.1007/s00702-014-1229-8
Rutkove, S. B. (2015). Clinical measures of disease progression in amyotrophic lateral sclerosis.
Neurotherapeutics, 12(2), 384-393. doi:10.1007/s13311-014-0331-9
Sackner, J. D., Nixon, A. J., Davis, B., Atkins, N., & Sackner, M. A. (1980). Non-invasive
measurement of ventilation during exercise using a respiratory inductive plethysmograph.
American Review of Respiratory Disease, 122(6), 867-871.
Sapienza, C. M., Brown, W., Williams, W. N., Wharton, P. W., & Turner, G. E. (1996).
Respiratory and laryngeal function associated with experimental coupling of the oral and
nasal cavities. The Cleft Palate-Craniofacial Journal, 33(2), 118-126.
Sato, T. G., Watanabe, J., & Moriya, T. (2016). Presenting changes in acoustic features
synchronously to respiration alters the affective evaluation of sound. International
Journal of Psychophysiology, 110, 179-186.
55
Schmidt, E. P., Drachman, D. B., Wiener, C. M., Clawson, L., Kimball, R., & Lechtzin, N.
(2006). Pulmonary predictors of survival in amyotrophic lateral sclerosis: Use in clinical
trial design. Muscle & Nerve, 33(1), 127-132. doi:10.1002/mus.20450
Schreiber, H., Gaigalat, T., Wiedemuth-Catrinescu, U., Graf, M., Uttner, I., Muche, R., &
Ludolph, A. C. (2005). Cognitive function in bulbar- and spinal-onset amyotrophic lateral
sclerosis. A longitudinal study in 52 patients. Journal of Neurology, 252(7), 772-781.
doi:10.1007/s00415-005-0739-6
Shellikeri, S. (2014). Articulatory compensation in amyotrophic lateral sclerosis: Tongue and
jaw in speech. (Master of Science), University of Toronto.
Shellikeri, S., Karthikeyan, V., Martino, R., Black, S. E., Zinman, L., Keith, J., & Yunusova, Y.
(2017). The neuropathological signature of bulbar-onset ALS: A systematic review.
Neuroscience & Biobehavioral Reviews, 75, 378-392.
doi:http://dx.doi.org/10.1016/j.neubiorev.2017.01.045
Shoesmith, C. L., Findlater, K., Rowe, A., & Strong, M. J. (2007). Prognosis of amyotrophic
lateral sclerosis with respiratory onset. Journal of Neurology, Neurosurgery, and
Psychiatry, 78(6), 629-631.
Sieck, G. C., Ferreira, L. F., Reid, M. B., & Mantilla, C. B. (2013). Mechanical properties of
respiratory muscles. Comprehensive Physiology.
Similowski, T., Attali, V., Bensimon, G., Salachas, F., Mehiri, S., Arnulf, I., . . . Derenne, J. P.
(2000). Diaphragmatic dysfunction and dyspnoea in amyotrophic lateral sclerosis.
European Respiratory Journal, 15(2), 332-337.
Sitver, M., & Kraat, A. (1982). Augmentative communication for the person with amyotrophic
lateral sclerosis. American Speech-Language-Hearing Association, 24, 783.
Smith, R., Pioro, E., Myers, K., Sirdofsky, M., Goslin, K., Meekins, G., . . . Macklin, E. A.
(2017). Enhanced bulbar function in amyotrophic lateral sclerosis: The Nuedexta
treatment trial. Neurotherapeutics, 1-11.
56
Solomon, N. P., & Hixon, T. J. (1993). Speech breathing in Parkinson’s disease. Journal of
Speech, Language, and Hearing Research, 36(2), 294-310.
Strong, M. J., Abrahams, S., Goldstein, L. H., Woolley, S., McLaughlin, P., Snowden, J., . . .
Turner, M. R. (2017). Amyotrophic lateral sclerosis - frontotemporal spectrum disorder
(ALS-FTSD): Revised diagnostic criteria. Amyotrophic Lateral Sclerosis and
Frontotemporal Degeneration, 18(3-4), 153-174. doi:10.1080/21678421.2016.1267768
Suárez, A. A., Pessolano, F. A., Monteiro, S. G., Ferreyra, G., Capria, M. E., Mesa, L., . . . De
Vito, E. L. (2002). Peak flow and peak cough flow in the evaluation of expiratory muscle
weakness and bulbar impairment in patients with neuromuscular disease. American
Journal of Physical Medicine & Rehabilitation, 81(7), 506-511.
Swash, M. (2012). Why are upper motor neuron signs difficult to elicit in amyotrophic lateral
sclerosis? Journal of Neurology, Neurosurgery, and Psychiatry, 83(6), 659-662.
Swinnen, B., & Robberecht, W. (2014). The phenotypic variability of amyotrophic lateral
sclerosis. Nature Reviews Neurology, 10(11), 661-670. doi:10.1038/nrneurol.2014.184
Talakad, N. S., Pradhan, C., Nalini, A., Thennarasu, K., & Raju, T. R. (2009). Assessment of
pulmonary function in amyotrophic lateral sclerosis. The Indian Journal of Chest
Diseases & Allied Sciences, 51, 87-91.
Talbot, K. (2009). Motor neuron disease: the bare essentials. Practical Neurology, 9(5), 303-309.
doi:10.1136/jnnp.2009.188151
Talbott, E. O., Malek, A. M., & Lacomis, D. (2016). The epidemiology of amyotrophic lateral
sclerosis. In M. J. Aminoff, F. Boller, & D. F. Swaab (Eds.), Handbook of Clinical
Neurology (Vol. 138, pp. 225-238): Elsevier.
Turner, G. S., & Weismer, G. (1993). Characteristics of speaking rate in the dysarthria associated
with amyotrophic lateral sclerosis. Journal of Speech, Language, and Hearing Research,
36(6), 1134-1144.
57
Turner, M. R., Brockington, A., Scaber, J., Hollinger, H., Marsden, R., Shaw, P. J., & Talbot, K.
(2010). Pattern of spread and prognosis in lower limb-onset ALS. Amyotrophic Lateral
Sclerosis, 11(4), 369-373. doi:10.3109/17482960903420140
Turner, M. R., Scaber, J., Goodfellow, J. A., Lord, M. E., Marsden, R., & Talbot, K. (2010). The
diagnostic pathway and prognosis in bulbar-onset amyotrophic lateral sclerosis. Journal
of Neurological Sciences, 294(1-2), 81-85. doi:10.1016/j.jns.2010.03.028
Turner, M. R., & Talbot, K. (2013). Mimics and chameleons in motor neurone disease. Practical
Neurology, 13(3), 153-164. doi:10.1136/practneurol-2013-000557
Wang, Y. T., Green, J. R., Nip, I. S., Kent, R. D., & Kent, J. F. (2010). Breath group analysis for
reading and spontaneous speech in healthy adults. Folia Phoniatrica et Logopaedica,
62(6), 297-302. doi:10.1159/000316976
Warren, D. W., Dalston, R. M., Morr, K. E., Hairfield, W. M., & Smith, L. R. (1989). The
speech regulating system: Temporal and aerodynamic responses to velopharyngeal
inadequacy. Journal of Speech and Hearing Research, 32(3), 566-575.
Weikamp, J. G., Schelhaas, H. J., Hendriks, J. C. M., de Swart, B. J. M., & Geurts, A. C. H.
(2012). Prognostic value of decreased tongue strength on survival time in patients with
amyotrophic lateral sclerosis. Journal Neurology, 259(11), 2360-2365.
doi:10.1007/s00415-012-6503-9
Weismer, G. (2006). Motor Speech Disorders: Essays for Ray Kent. San Diego, CA, USA: Plural
Publishing.
Wheaton, A. G., Ford, E. S., Thompson, W. W., Greenlund, K. J., Presley-Cantrell, L. R., &
Croft, J. B. (2013). Pulmonary function, chronic respiratory symptoms, and health-related
quality of life among adults in the United States–National Health and Nutrition
Examination Survey 2007–2010. BMC Public Health, 13(1), 854.
Winkworth, A. L., Davis, P. J., Ellis, E., & Adams, R. D. (1994). Variability and consistency in
speech breathing during reading: Lung volumes, speech intensity, and linguistic factors.
Journal of Speech, Language, and Hearing Research, 37(3), 535-556.
58
Wolfson, C., Kilborn, S., Oskoui, M., & Genge, A. (2009). Incidence and prevalence of
amyotrophic lateral sclerosis in Canada: A systematic review of the literature.
Neuroepidemiology, 33(2), 79-88.
Yamada, S., Hashizume, A., Hijikata, Y., Inagaki, T., Suzuki, K., Kondo, N., . . . Banno, H.
(2016). Decreased peak expiratory flow associated with muscle fiber-type switching in
spinal and bulbar muscular atrophy. PLoS One, 11(12), e0168846.
Yorkston K, Beukelman D, Tice R. (1996). Sentence intelligibility test for windows. [computer
software]. Lincoln, NE, USA: Tice Technology Services.
Yorkston, K., Hammen, V. L., Beukelman, D. R., & Traynor, C. D. (1990). The effect of rate
control on the intelligibility and naturalness of dysarthric speech. Journal of Speech and
Hearing Disorders, 55(3), 550-560.
Yorkston, K., Strand, E., & Miller, R. (1996). Progression of respiratory symptoms in
amyotrophic lateral sclerosis: Implications for speech function. In D. A. Robin, K. M.
Yorkston, & D. R. Beukelman (Eds.), Disorders of Motor Speech: Assessment,
Treatment, and Clinical Characterization (pp. 193-202). Baltimore, MD, USA: Paul H
Brookes Publishing Company.
Yorkston, K., Strand, E., Miller, R., Hillel, A., & Smith, K. (1993). Speech deterioration in
amyotrophic lateral sclerosis: Implications for the timing of intervention. Journal of
Medical Speech-Language Pathology, 1(1), 35-46.
Yunusova, Y., Graham, N. L., Shellikeri, S., Phuong, K., Kulkarni, M., Rochon, E., . . . Green, J.
R. (2016). Profiling speech and pausing in amyotrophic lateral sclerosis (ALS) and
frontotemporal dementia (FTD). PLoS One, 11(1), e0147573.
doi:10.1371/journal.pone.0147573
Yunusova, Y., Green, J. R., Lindstrom, M. J., Ball, L. J., Pattee, G. L., & Zinman, L. (2010).
Kinematics of disease progression in bulbar ALS. Journal of communication disorders,
43(1), 6-20.
59
Yunusova, Y., Green, J. R., Lindstrom, M. J., Pattee, G. L., & Zinman, L. (2013). Speech in
ALS: Longitudinal changes in lips and jaw movements and vowel acoustics. Journal of
Medical Speech Language Pathology, 21(1), 1-13.
Yunusova, Y., Green, J. R., Wang, J., Pattee, G., & Zinman, L. (2011). A protocol for
comprehensive assessment of bulbar dysfunction in amyotrophic lateral sclerosis (ALS).
Journal of Visualized Experiments, (48), e2422-e2422.
Zajac, D. J. (2000). Pressure-flow characteristics of/m/and/p/production in speakers without cleft
palate: Developmental findings. The Cleft palate-craniofacial journal, 37(5), 468-477.
Zellner, B. (1994). Pauses and the temporal structure of speech. In E. Keller (Ed.), Fundamentals
of speech synthesis and speech recognition (pp. 41-62). Chichester, UK: John Wiley &
Sons.