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RESEARCH ARTICLE
The role of the arcuate and middle longitudinal fasciculi
in speech perception in noise in adulthood
Pascale Tremblay
1,2
| Maxime Perron
1
| Isabelle Deschamps
1,2
| Dan Kennedy-Higgins
1,3
|
Jean-Christophe Houde
4
| Anthony Steven Dick
5
| Maxime Descoteaux
4
1
CERVO Brain Research Center, Quebec City,
Canada
2
Département de Réadaptation, Faculté de
Médecine, Université Laval, Quebec City,
Canada
3
Department of Speech, Hearing and Phonetic
Sciences, University College London, United
Kingdom
4
Département d'informatique, Faculté des
Sciences, Sherbrooke Connectivity Imaging
Lab, Université de Sherbrooke, Sherbrooke,
Canada
5
Department of Psychology, Florida
International University, Miami, Florida
Correspondence
Pascale Tremblay, Département de
réadaptation, Université Laval, 1050 avenue
de la Médecine, Québec (QC), Office 4109.
Canada G1V 0A6.
Email: pascale.tremblay@fmed.ulaval.ca
Funding information
Fonds de la Recherche en Santé du Québec,
Grant/Award Number: 27170, 35016; Natural
Sciences and Engineering Research Council of
Canada, Grant/Award Number: Discovery
Grant (#195812603); Québec Bio-imaging
network, Grant/Award Number: Pilot project
grant #5886; Brain Canada Foundation, Grant/
Award Number: Platform support grant
(#3456)
Abstract
In this article, we used High Angular Resolution Diffusion Imaging (HARDI) with advanced
anatomically constrained particle filtering tractography to investigate the role of the arcuate
fasciculus (AF) and the middle longitudinal fasciculus (MdLF) in speech perception in noise in
younger and older adults. Fourteen young and 15 elderly adults completed a syllable discrimi-
nation task in the presence of broadband masking noise. Mediation analyses revealed few
effects of age on white matter (WM) in these fascicles but broad effects of WM on speech
perception, independently of age, especially in terms of sensitivity and criterion (response
bias), after controlling for individual differences in hearing sensitivity and head size. Indirect
(mediated) effects of age on speech perception through WM microstructure were also found,
after controlling for individual differences in hearing sensitivity and head size, with AF micro-
structure related to sensitivity, response bias and phonological priming, and MdLF microstruc-
ture more strongly related to response bias. These findings suggest that pathways of the
perisylvian region contribute to speech processing abilities, with relatively distinct contribu-
tions for the AF (sensitivity) and MdLF (response bias), indicative of a complex contribution of
both phonological and cognitive processes to age-related speech perception decline. These
results provide new and important insights into the roles of these pathways as well as the fac-
tors that may contribute to elderly speech perception deficits. They also highlight the need
for a greater focus to be placed on studying the role of WM microstructure to understand
cognitive aging.
KEYWORDS
cognitive aging, diffusion MRI, HARDI, hearing, language, normal aging, speech discrimination,
white matter
1|INTRODUCTION
One of the most common complaints of elderly adults is a difficulty in
perceiving speech in the presence of background noise. In healthy
young and older adults, the perception of consonants in noise is more
affected than the perception of vowels (Gelfand, Piper, & Silman,
1985, 1986; Meyer, Dentel, & Meunier, 2013). Whether different
types of consonants are more difficult to perceive than others remains
uncertain. Meyer et al. showed that the identification of specific frica-
tive consonants presented in noise, such as /f/, were among those
most often confused (Meyer et al., 2013). Others found that the
ability to identify temporal cues in fricative sounds does decline with
age (Gordon-Salant, Yeni-Komshian, Fitzgibbons, & Barrett, 2006).
Traditionally, age-related speech perception deficits have been
ascribed to presbycusis, the biological aging of the peripheral hearing
system (Gates & Mills, 2004; Mazelová, Popelar, & Syka, 2003) which
manifests itself as a loss of sensitivity to all sound, but, most notably,
to high-frequency sounds. Presbycusis is estimated to affect more
than 60% of individuals aged over 70 years (Feder, Michaud, Ramage-
Morin, McNamee, & Beauregard, 2015; Lin, Thorpe, Gordon-Salant, &
Ferrucci, 2011). Yet, deficits in elderly speech perception are present
even when elderly participants have normal hearing (Gordon-Salant &
Received: 31 January 2018 Revised: 7 August 2018 Accepted: 8 August 2018
DOI: 10.1002/hbm.24367
Hum Brain Mapp. 2018;1–16. wileyonlinelibrary.com/journal/hbm © 2018 Wiley Periodicals, Inc. 1
Fitzgibbons, 1993; Tun, 1998; Tun & Wingfield, 1999; Wong et al.,
2009); when they are compared to younger participants with equally
poor hearing (Frisina & Frisina, 1997; Jerger, 1992); or when statistical
(Bilodeau-Mercure, Lortie, Sato, Guitton, & Tremblay, 2015) or experi-
mental (Moore, Peters, & Stone, 1999) adjustments are made to con-
trol for the increased auditory thresholds of the older group. This
suggests that factors in addition to the decreasing sensitivity of the
peripheral auditory system are contributing to the speech in noise dif-
ficulties such as a central processing deficit related to brain aging.
In addition to the peripheral hearing system, the human brain also
undergoes numerous morphological changes with age, including a
reduction in total gray matter volume, increased cerebrospinal fluid
volume and, of most interest to the current study, significant white
matter (WM) changes including malformations of the myelin sheath
and a notable loss of myelinated fibers (Gunning-Dixon, Brickman,
Cheng, & Alexopoulos, 2009; Guttmann et al., 1998; Marner, Nyen-
gaard, Tang, & Pakkenberg, 2003; Pakkenberg & Gundersen, 1997).
Degeneration of this kind is known to impair conduction of neural sig-
nals throughout the brain resulting in slower or incomplete transmis-
sion of neural impulses (Bartzokis, 2004). Such changes in WM
properties in old age have repeatedly been linked with declines in cog-
nitive abilities (Bennett & Madden, 2014; Madden et al., 2012) espe-
cially on tasks requiring speeded responses (Andrews-Hanna et al.,
2007; Charlton et al., 2006; O'Sullivan et al., 2001). Diffusion-
weighted magnetic resonance imaging (dMRI) provides a means to
study, noninvasively, the WM fascicles of the human brain, and to
investigate the types of changes that occur in these structures
throughout the lifespan as well as associated behavioral consequences
(Descoteaux, 2015; Lebel et al., 2012).
Although different approaches to dMRI exist (Daducci et al.,
2014), one of the most common approaches is to compute the diffu-
sion tensor, from which measures such as Fractional Anisotropy (FA),
Axial Diffusivity (AD), Radial Diffusivity (RD), and Mean Diffusivity
(MD) can be extracted to quantify microstructural properties of WM
(Le Bihan et al., 2001; Soares Jé, Marques, Alves, & Sousa, 2013).
Supporting Information 1 provides a brief description of the main dif-
fusion tensor imaging (DTI) metrics (FA, AD, MD, RD). However, DTI
metrics present some limitations. The most important of these limits is
the inability of the tensor model to resolve complex fiber configura-
tions, such as crossing (X), kissing (>> <<) and highly curved (>>) fibers
within a voxel (e.g., Alexander, Barker, & Arridge, 2002; Alexander,
Hasan, Lazar, Tsuruda, & Parker, 2001; Frank, 2002). With a recent
study estimating that between 60 and 90% of the WM voxels in the
human brain contains complex fiber configurations (Jeurissen,
Leemans, Tournier, Jones, & Sijbers, 2013), and with additional
research showing that FA, AD, RD, and MD are affected by this het-
erogeneity (Alexander et al., 2001; Tournier, Mori, & Leemans, 2011;
Wheeler-Kingshott & Cercignani, 2009), an alternative analysis model
is needed. High angular resolution diffusion imaging (HARDI) tracto-
graphy combined to non-DTI reconstruction methods provide alterna-
tive strategies that are more robust to complex fiber architectures
(Raffelt et al., 2012).
One such non-DTI model focuses on estimating Fiber Orientation
Distribution functions (fODF) instead of diffusion tensors. FODF—the
continuous distribution of fiber orientations within the voxel—are
obtained using spherical deconvolution (Tournier, Calamante, &
Connelly, 2007; Tournier, Calamante, Gadian, & Connelly, 2004) and
can be used for tractography. Apparent Fiber Density (AFD) is a mea-
sure based on fODF amplitude that provides information about the
fraction of space occupied by a fiber bundle (Raffelt et al., 2012), a
measure that is age sensitive (Mito et al., 2018). Another measure
extracted from fODF is the Number of Fiber Orientations (NuFO)
within each voxel. This measure provides information about WM
complexity (Dell'Acqua, Simmons, Williams, & Catani, 2013). Support-
ing Information 1 provides a description of these two non-DTI
measures.
The overall goal of this study was to examine whether WM
microstructure contributes to speech perception abilities, focusing on
two long association fascicles associated of the perisylvian area: the
Arcuate Fasciculus (AF) and the Middle Longitudinal Fascicu-
lus (MdLF).
The AF is well-known for its contribution to speech and language
functions. First described in 1809 by Reil and colleagues (Reil, 1809),
the AF connects the posterior superior temporal cortex (pSTC) to the
inferior frontal gyrus (IFG)/ventral premotor cortex (PMv) forming a
“dorsal language stream,”which is believed to be involved in language
processing, sensorimotor mapping and phonological processing
(Brauer, Anwander, Perani, & Friederici, 2013; Saur et al., 2008).
Despite its central role for the neurobiology of language, the course,
origins, terminations, and number of subcomponents of the AF remain
contentious and several models of AF have been proposed (for a
review see Dick, Bernal, & Tremblay, 2014; Dick & Tremblay, 2012).
One of the dominant AF models proposes that the AF consists of two
segments: direct and indirect (Catani, Jones, & ffytche, 2005). The
direct segment arches around the lateral fissure and connects the
pSTC to the IFG, middle frontal gyrus (MFG) and PMv. The indirect
segment is divided into two components, a posterior AF segment that
connects STC to inferior parietal regions, and an anterior segment that
connects inferior parietal regions to IFG/MFG and PMv (Catani,
Jones, & ffytche, 2005; Catani & Thiebaut de Schotten, 2012;
Thiebaut et al., 2011; Weiner, Yeatman, & Wandell, 2016). Other
models of the AF connectivity have also been proposed. For example,
a dual-pathway architecture in the dorsal language system was
recently proposed (Berwick, Friederici, Chomsky, & Bolhuis, 2013;
Brauer et al., 2013). According to this model, the connections from
pSTC to IFG and PMv would mature at distinct rates and support dis-
tinct functions, with the pSTC-IFG involved in processing higher-order
aspects of language (e.g.,syntax) and the pSTC-PMv involved in
sensory-motor mapping and phonological processes for speech. While
the specific number of components in AF and their functions remains
uncertain, clearly, the AF is a major pathway for language, although
there remain important questions about its structure and functions.
Although limited, there is some evidence to suggest that the AF
undergoes changes in FA in normal aging (Voineskos et al., 2010;
Voineskos et al., 2012). Given the importance of the AF for speech/
language, it is expected that any change in its microstructure will
affect speech and language processing.
The MdLF is, like the AF, a long perisylvian association pathway.
However, it has received much less attention than the AF and its func-
tions are less well understood. The MdLF runs through the entire
2TREMBLAY ET AL.
STC. It was first discovered in the macaque using autoradiographic
techniques (Seltzer & Pandya, 1984), and several recent studies have
confirmed its presence in the human brain using diffusion MRI
(e.g., Makris et al., 2009; Makris et al., 2013; Makris & Pandya, 2009;
Menjot de Champfleur et al., 2013) and fiber dissection (Maldonado
et al., 2013). It was recently proposed that the MdLF is divided into
two components: a ventral segment linking the inferior parietal lobule
(IPL) to the temporal pole and a more caudal segment linking the supe-
rior parietal lobule (SPL) to the temporal pole (Makris et al., 2013;
Makris, Preti, Asami, et al., 2013). Though sometimes seen as forming
part of the ventral language streams (Saur et al., 2008), involved in
mapping auditory speech sounds to meaning, the specific role of the
two MdLF segments remains unclear. Indeed, it has been reported
that electrostimulation of this fascicle does not elicit semantic para-
phasias, and that electrostimulation and resection of the anterior part
of MdLF have no impact on picture naming (De Witt Hamer, Moritz-
Gasser, Gatignol, & Duffau, 2010). It has been proposed that based on
its connectivity, the MdLF could play a role in audiovisual integration
(Wang et al., 2013) or attention (Steffens, Wang, Manning, & Pearlson,
2017). For example, a correlation was found between poor attention
and the microstructure of MdLF in patients with schizophrenia, sug-
gesting a role for the MdLF in attention (Steffens et al., 2017). No
study has investigated the changes that occur in the MdLF in normal
aging, yet this could contribute to understanding its roles. Clearly,
additional data is needed to understand the functions of this pathway.
As such, the overall goal of this study was to advance current knowl-
edge of the neurobiological foundation of speech perception in noise in
adulthood. The main objective of the study was to determine whether
the macrostructure and the microstructure of the AF and MdLF affects
speech perception performance for consonants with different manners
of articulation (fricative and stop consonants), and whether age-related
changes in these tracts contribute to age-related decline in speech per-
ception in noise. A secondary aim was to confirm the presence of the
MdLF, using dMRI with advanced anatomically constrained particle
filtering tractography (PFT) algorithms robust to crossing fibers and par-
tial volume effects. Our main hypotheses were that (a) speech perception
performance would decline with age (b) because speech perception relies
on phonological processing, the structure of the AF would be associated
with speech perception performance, especially in terms of sensibility
(d0), and (c) because of its potential role in cognition/attention and lan-
guage processing, we hypothesized that the structure of the MdLF would
also affect speech perception, particularly in terms of response bias.
2|METHODS
2.1 |Participants
A total of 32 participants were initially recruited for this study; three
subjects were removed from the final statistical analyses due to tech-
nical faults that caused a loss of data. The 29 remaining participants
were divided into two subgroups; a younger age group contained
14 participants (M = 29.5 10.49; range: 19–46 years; 5 females)
and an older age group contained 15 participants (M = 71 5.85;
range: 65–84 years; 3 females). Both groups were matched on distri-
bution of gender (χ
2(1)
= 0.895, p= .43, Cramer's V= 0.18). All partici-
pants were native speakers of Canadian French, were right-handed as
assessed by the Edinburgh Handedness Inventory (Oldfield, 1971),
had normal or corrected to normal vision and were highly educated
(Younger: M = 16.92 2.34 years; Older: M = 16.4 4.6 years). No
participants reported a history of language, speech, neurological or
psychiatric disorders. Neither group showed any sign of depression as
assessed by the Geriatric Depression Scale (Yesavage et al., 1983), or
mild cognitive impairment using the Montreal Cognitive Assessment
(Nasreddine et al., 2005). A summary of the participant data is out-
lined in Table 1. The study was approved by the Comité d'Éthique de
la Recherche, Institut Universitaire en Santé Mentale de Québec (#
360–2014).
TABLE 1 Participants' characteristics
Age group
Younger (N= 14; 5F) Older (N= 15; 3F)
MSDRange MSDRange
Age 29.43 10.49 19–46 71.93 5.85 65–84
Handedness 83.78 18.27 50–100 96.24 8.87 68.42–100
Education (years) 16.92 2.34 13–21 16.40 4.60 10–30
GDS (/30) 3.79 3.68 0–12 2.53 3.07 0–8
MOCA (/30) 28.57 1.09 27–30 27.47 1.68 25–30
Right ear PTA 4.95 9.58 1–37 13.76 8.21 3–32
Left ear PTA 3.00 8.24 4–29 15.64 9.74 2–43
Right ear 6 KHz 8.79 7.81 4–20 44.67 23.72 11–90
SRT 27.04 8.56 15–52 41.50 7.74 30–55
Health (/7) 4.78 1.069 2–6 5.4 1.00 3–7
Note. M = Mean; SD = standard deviation of the mean; N= number of participants per group; F= number of female participants; MoCA = Montreal Cog-
nitive Assessment scale. The MOCA is a short cognitive test that is scored on a 30-point scale. Higher scores indicate better cognitive functions. GDS =
Geriatric Depression Screening Scale. The GDS includes 30 questions. Each “negative”answer is worth one point; thus, a higher score indicates a more
depressed state. For example, question one asks whether the person is globally satisfied with his/her life. A “no”answer is worth one point, whereas a
“yes”answer is worth no points. Participants with scores between 0 and 9 are considered normal, while scores between 10 and 19 indicate a depression,
and 20–30 indicate a severe depression. PTA = pure tone average, measured in dB. Normal hearing should range between 0 and −10 dB. All participants
had a hearing that ranged between normal and mild hearing loss, which is normal in elderly populations. Right ear 6 KHz = hearing threshold at 6 KHz in
the right ear. SRT = Speech Reception Threshold in dB. SRT represents the lowest sound intensity level at which participants are able to correctly identify
50% of monosyllabic words. Health = self-reported general health status on a scale of 0 to 7, with 0 being lowest health level and 7 the maximal one.
TREMBLAY ET AL.3
2.2 |Procedure
Participants underwent audiological and cognitive testing and they
completed a speech perception task. All procedures took place in a
double-walled soundproof room. The session had duration of approxi-
mately 2 to 3 hr and included several breaks. MRI images were
acquired on a separate day.
2.2.1 |Audiological assessment
Audiometric evaluations were performed in a double-walled sound-
proof room. It consisted of three parts: (a) Pure tone audiometry using
a clinical audiometer (AC40, Interacoustic) with each ear tested sepa-
rately, at the following frequencies: .25, .5, 1, 2, 3, 4, 6, 8, 10 kHz. For
each participant, a standard pure tone average (PTA: average of
thresholds at .5, 1 and 2 kHz) was computed for the left and right ear
(Stach, 2010). (b) A speech recognition threshold (SRT) test was used
to assess the lowest sound intensity level at which participants were
able to correctly identify 50% of monosyllabic words. (c) Distortion
product otoacoustic emissions recording (DPOAEs) provides an objec-
tive measure of cochlear outer hair cell function through the measure-
ment of cochlear acoustic emissions.
2.3 |Speech perception task
An auditory discrimination task was used to evaluate speech percep-
tion skills. Three sixty unique pairs of syllables were presented, one at
a time, at an individually adjusted intensity. Participants were asked to
determine if the syllables were identical or different. The syllables
were presented 200 ms apart to minimize working memory demand.
The presentation of the second syllable was followed by a question
mark cueing participant to respond. Participants were asked to answer
as quickly as possible using a response box (RB-840 model, Cedrus,
San Pedro, California). All stimuli were presented using Presentation
Software (Neurobehavioral System, CA) through high quality head-
phones (DT 770 Pro, Beyerdynamic Inc.), while participants were com-
fortably seated in a soundproof room. The pairing of the responses
and button on the response box was counterbalanced across partici-
pants. Unlike speech audiometry, which determines the minimal
sound intensity necessary for a person to hear a word correctly,
speech discrimination evaluates sensitivity to the phonetic details of
native speech sounds. Unlike identification tasks, discrimination does
not require explicit categorical judgment.
One eighty pairs (50%) were identical (/fe/ vs. /fe/), 60 pairs had
different fricative consonants (/fe/ vs. /se/), 60 had different stop
consonants (/pe/ vs. /ge/), and 60 had different vowels (/f/ vs. /fe/).
The syllables that were used to create the pairs were 48 different syl-
lables recorded by three native adult male French speakers (See Sup-
porting Information 2 for more details). Participants were asked to
ignore the speaker and focus on the consonant and vowel that were
presented. Syllables were used instead of words to avoid lexical/
semantic effects, which may mask perceptual difficulties. All syllables
were peak amplitude normalized across all speakers using Pratt to a
mean intensity of 70 dB HL. The syllables had an average duration
(M SD) of 350 0.05 ms. Intelligibility was manipulated by adding
pink noise to the syllables to reach a dB signal-noise ratio (SNR) of
either 15 (mid) or −5 (low intelligibility) according to the following for-
mula: dB SNR = 10log
10
(Pressure
signal
/Pressure
noise
), as initially
described by (Wong, Uppunda, Parrish, & Dhar, 2008). In the high
intelligibility condition, syllables were presented in the absence of
noise.
Response accuracy was analyzed within the framework of signal
detection theory. Specifically, d-prime (d0) and criterion (c) were calcu-
lated. D-prime is defined as the ability to accurately discriminate
between target (identical syllable pairs) and nontarget trials whereas
criterion (c) is defined as the tendency to choose one response over
the other, that is, response bias (Macmillan & Creelman, 1990). If
c= 0, then participants do not have a tendency to choose one
response over the other (i.e., no bias). A negative value of cindicates a
bias toward responding “yes”whereas a positive value of crepresents
a bias towards responding “no.”In addition, a measure of facilitation
operationalized as repetition priming (measured in milliseconds) was
also computed. Repetition priming is the facilitation in cognitive pro-
cessing that occurs as a consequence of repeated exposure to a stim-
ulus (here identical syllable pairs); it manifests itself behaviorally by a
decrease in reaction times (Logan, 1990; Schacter & Buckner, 1998).
This behavioral measure was used to evaluate speed of processing in
the speech domain.
2.4 |MR image acquisition
The data were acquired on a whole-body Philips 3.0 Tesla Achieva TX
at the Clinic IRM Québec-Mailloux in Québec City. Structural MR
images were acquired with 3D T1-weighted MPRAGE sequence
(TR = 8.2 ms, TE = 3.7 ms, FoV = 250 mm, flip angle = 8, 256 ×256
matrix, 180 slices/volume, no gap, 1 mm
3
). HARDI were collected to
allow for analysis of age differences in WM (TR = 8.5 ms; TE = 76.7
ms; b= 1,500 s/m
2
, 60 directions, 128 slices/volume, no gap,
1.8 mm
3
). Single shot EPI BOLD functional images and Susceptibility
Weighted images were also collected for each participant as part of a
separate experiment. Throughout the procedure, each participant's
head was immobilized using a set of cushions and pads. The complete
image acquisition protocol (all four sequences) lasted no more than
45 min per participant.
2.5 |Image processing
All diffusion weighted images were visually inspected by two of the
authors (DKH, ID) for the presence of artifacts with particular focus
on ‘striping’due to its frequent occurrence. If such artifacts were pre-
sent, the corresponding directions were removed (13 participants had
at least one direction removed; M=4,SD = 3.24) before continuing
with preprocessing (Sharman et al., 2011). Next, susceptibility artifacts
(caused by magnetic field fluctuations that occur at the boundary
between substances with different magnetic sensitivities and result in
image distortions) were corrected using the FSL TOPUP procedure
(http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TOPUP; (Andersson, Skare, &
Ashburner, 2003; Smith et al., 2004). Data were also preprocessed for
eddy currents and subject motion using the FSL eddy tool
(Andersson & Sotiropoulos, 2016). Finally, images were denoised to
improve the signal to noise ratio and thus aid subsequent processing
4TREMBLAY ET AL.
using a Non-Local Means filter robust to Rician noise implemented
using DIPY (Descoteaux, Wiest-Daesslé, Prima, Barillot, & Deriche,
2008; Garyfallidis et al., 2014). All processing was performed in each
participants' native anatomical space. Using FreeSurfer (v5.3.0), the
T1 images were segmented into gray and WM and parceled. The
resulting WM cortical parcellation images were registered to individ-
ual diffusion scans to preserve the correct orientation of diffusion-
weighted images. Eigenvalues reflecting perpendicular and parallel
diffusion, also called axial and (AD-RD) respectively, MD and FA were
calculated from the diffusion tensor (Basser, Mattiello, & Lebihan,
1994) using a nonlinear least squares model, as implemented in DIPY
(Garyfallidis et al., 2014).
Fiber tractography was performed on the field of fiber orientation
distribution function (fODF) (Descoteaux, Deriche, Knosche, &
Anwander, 2009; Tournier et al., 2007), of spherical harmonics order
eight, as implemented in DIPY. The T1 anatomy was registered with
the symmetric image normalization registration algorithm available in
ANTS (Avants, Tustison, & Song, 2011). Then, the white/gray matter
interface was extracted using the FSL fast command and probabilistic
PFT (with anatomical priors) was run with 3 seeds per voxel of the
WM/gray matter interface leading to full brain tractograms with
approximately 1 million streamlines per participant (Girard,
Whittingstall, Deriche, & Descoteaux, 2014a). Default parameters
were used as optimized in Girard, Whittingstall, Deriche, and Desco-
teaux (2014a). This streamline-based PFT seeded from the WM/gray
matter interface has been shown to reduce tractography length and
size biases and limitations (Girard et al., 2014a) in making sure that
streamlines connect cortical or subcortical areas, or exit the brainstem,
and do not stop in anatomically invalid areas such as WM or cortico-
spinal fluid (ventricles). Hence, the streamline count is less biased by
the shape, length, and volume of bundles.
The White Matter Query Language (WMQL) (Wassermann et al.,
2013, 2016) was used in conjunction with the FreeSurfer Desikan
atlas (Desikan et al., 2006) to define the three segments of the AF and
the two segments of the MdLF in both hemispheres. In addition, a
control fasciculus, the bilateral uncinate fasciculus (UF), was also
added to test for the specificity of potential relationships between
age, speech perception and the fasciculi of interest (AF, MdLF). The
WMQL adopts a text-based, human-readable approach to defining
WM fascicles. Use of this language reduces the amount of variance
introduced into the data when drawing regions of interest directly
onto anatomical or diffusion scans. Additionally, this language allows
for direct comparison between datasets through simple application of
the same written definition (for the definitions see Supporting Infor-
mation 3). Finally, once extracted, these WM bundles were processed
through an additional pipeline as described in (Cousineau et al., 2017),
available as part of the Sherbrooke Connectivity Imaging Lab (SCIL)
python toolbox (SCILPY), with elimination of spurious fascicles using a
pruning and outlier-rejection step (Cote et al., 2013) after which the
means and standard deviations of each metric were computed. Each
fascicle of each participant was observed with MI-Brain software
(https://www.imeka.ca/mi-brain) to determine a weight interval to
remove the spurious fascicles (Rheault, Houde, Goyette, Morency, &
Descoteaux, 2016). A mean of intervals was calculated and the final
fascicles were extracted.
2.6 |Statistical analyzes
Linear mixed model (LMM) analyses were conducted in SPSS Version
25 for Mac (IBM), separately for each dependent variable (d0, C, repe-
tition priming), with Consonant type (fricative, stop) as within-subject
(repeated) fixed factors, Age group as a between-subject categorical
fixed factor, and hearing, operationalized as hearing thresholds at 6 K
Hz in the right ear, was used as continuous between-subject covari-
ates to control for individual differences in hearing. Participants were
included as a random factor in the model.
Next, to address the main objective of the study, which was to
determine whether the macrostructure (volume) and the microstruc-
ture of the AF and MdLF affects speech perception performance in
adulthood, and whether age-related changes in these tracts contribute
to age-related decline in speech perception in noise, a series of simple
mediation analyses were conducted using ordinary least squares path
analysis. Prior to running the mediation analyses, the data were visu-
ally inspected using histograms and boxplots in SPSS. Outliers were
removed. The Shapiro–Wilk test of normality was used to verify nor-
mality of the distribution for each diffusion metric and each group
(p> .05). The Levene's test for equality of variances was used to ver-
ify the homogeneity of variances across groups, the (p> .05). If one of
these assumptions was not met, the data were transformed. For AF,
Nufo (cubic), AFD max (cubic), AFD total (squared) and Volume
(square root) were transformed. For MdLF, AFD max (cubic), AFD
total (squared) and the volume (square root) was transformed and the
normality and homogeneity were verified again. Finally, for UF only
NuFO (squared) and AFD total (squared) were transformed.
The mediation analyses were conducted using PROCESS macro
version 2.16 (model #4) for SPSS (Hayes, 2013; Preacher & Hayes,
2004, 2008). In this model, Age was used as the continuous predictor
variable (X), speech perception scores were used as dependent vari-
ables (Y) and diffusion metrics were used as continuous mediators (M).
In all analyses, hearing was used as continuous between-subject cov-
ariates to control for individual differences in hearing. To control for
individual differences in head size, the proportion approach was
adopted (O'Brien et al., 2011), whereby each diffusion metric value
for a tract was divided by the diffusion metric of total brain (adjusted
value = [tract value/total brain value]). A bootstrapping approach was
used to test for the significance of the indirect effects (ab) (p< .05,
using bias-corrected bootstrapping with 20,000 samples). Bootstrap-
ping involves the repeated extraction of samples, with replacement,
from a data set and the estimation of the indirect effect in each
resampled data set.
To correct for multiple comparisons, we applied a false discovery
rate (FDR) procedure (Benjamini & Hochberg, 1995) to the tests of
direct effects (c0)andb-paths. For the purpose of FDR correction, a
family, that is, the smallest set of items of inference in an analysis,
was defined as all the tests conducted in one segment (e.g., NuFO in
the Left Anterior AF) (N= 6, one for each behavioral measure). The
a-paths, which test for the effect of age on WM, were only tested
once per tract and were therefore left uncorrected. The indirect
effects (ab) are bootstrap-corrected; no additional correction was
applied.
TREMBLAY ET AL.5
3|RESULTS
3.1 |Speech perception
The LMM analyses revealed a significant interaction between Age
group and Consonant type for sensitivity (F
[1, 24,433]
= 5,929,
p= .023), with a significant effect of Age group only for the fricatives.
Older adults exhibited a lower sensitivity (M= 1.08, SE = 0.19) than
younger adults (M= 1,638, SE = 0.35). Although a similar pattern was
found for Priming, it did not reach statistical significance (F
[1,
24,433]
= .130, p= .722). Older adults exhibited a lower priming
(M= .288, SE = 0.079) than younger adults (M= .129, SE = 0.046) for
the fricatives. There was no Age group by Consonant interaction for
criterion (F
[1, 24,389]
= 2,402, p= .134). The behavioral data are illus-
trated in Figure 1, and descriptive statistics are provided in Table 2.
As part of the mediation analysis framework described in
section 3.2, we tested the effects of Age as a continuous factor on
Speech perception, holding WM and hearing constant. The results
show that the direct effect of Age on Priming for the fricatives was
significant in a large number of models. In all these analyses, the effect
of Age on speech perception was negative, indicative of lower prim-
ing. These effects are reported in Supporting Information 4.
3.2 |Relationship between WM, speech perception
and age
Figures 2 and 3 illustrate the average AF and MdLF tracts, respec-
tively. The individual pathways are illustrated in Supporting Informa-
tion 5 and descriptive statistics are provided in Supporting
Information 6. In all but two participants (one young and one older),
all fascicles were found. In the two participants with missing data, only
the direct segment of the AF was missing (in both hemispheres for
one and only in the left hemisphere for the other). The missing data
was replaced by the age group average of each metric for the media-
tion analyses. The mediation analyses are detailed in the next para-
graph, pathway by pathway (a,b,ab).
3.2.1 |Aging of AF and MdLF (a pathways)
Holding hearing constant, the mediation analysis revealed limited age
effects on the AF and MdLF (significant a-paths), most of which in AF,
and mainly positive. These effects are detailed in Supporting Informa-
tion 7. Negative effects of age (decline) were found in the volume of
the left posterior AF (a= 0.0007), MD of the left direct AF
(a= 0.0042) (that is, an increase in MD) and FA of the right MdLF-IPL
(a=−0.0019). Positive age effects were more widespread and
included an age-related increase in the volume of the right direct AF
(a= 0.0014), AFD max of the right direct AF (a= 0.0091), NuFO of
the left direct AF (a= 0.0040), NuFO of the right MdLF-IPL
(a= 0.0012), and AFD total of the right MdLF-SPL (a= 0.0019).
3.2.2 |Age-independent impact of WM on speech
perception (b pathways)
There were several instances of an age independent effect of WM on
speech perception, holding hearing constant. These effects are
detailed in Supporting Information 8A and 8B. The majority (62%) of
these effects were positive, meaning that, two people of the same age
that differed in one unit of WM also differed on speech perception in
noise, with higher WM value associated with better speech percep-
tion in noise, holding hearing constant. Better sensitivity to speech
sounds, in particular, was associated with higher AD in all tracts. More
positively biased responses (i.e., higher criterion) were associated
mainly with higher ODF metric values (AFD total, AFD max, NuFO) in
the MdLF and, to a lesser extent, with lower values in the left poste-
rior AF and right MdLF-SPL, meaning that a negative bias (tendency
to say “same”) was associated with higher value in these segments.
Finally, greater priming was associated mainly with higher ODF metric
values (AFD total, NuFO) in the MdLF and, to a lesser extent, also in
the AF.
Sensitivity
1.00
.80
.60
.40
.20
.00
A. Sensitivity (d’)
B. Criterion (C)
A. Priming (sec)
Fricative Stops
CriterionPriming (sec)
Young adults
Older adults
40
.30
.20
.10
.00
-.10
2.00
1.50
1.00
.50
.00
*
FIGURE 1 Results of the linear mixed model analyses shown
separately for the younger and older adults, at each level of the
consonant variable. (a) Sensitivity (d0). (b) Criterion (C), (c) priming in
seconds. The error bars represent the confidence interval of the
group mean. The asterisk indicates statistical significance (p≤.05)
6TREMBLAY ET AL.
3.2.3 |Indirect effect of age on speech perception
through WM (ab)
We found indirect effects of Age on speech perception through WM
in the bilateral AF (all three segments) and right MdLF (IPL and SPL
segments). These effects are detailed in Figure 4 (AF) and 5 (MdLF).
There was no indirect effect of Age on Speech perception through the
structure of the UF. Most indirect effects were positive (70%), mean-
ing that aging was likely to lead to better speech perception (mainly in
terms of sensitivity and criterion) as a result of the positive effect of
Age on WM value, which, in turn, affected speech perception perfor-
mance positively. In the right MdLF-IPL (Figure 5b), the indirect effect,
albeit positive, has a different meaning. There, aging was likely to lead
to better speech perception (sensitivity for the stops) as a result of
the negative effect of Age on FA, which, in turn, affected speech per-
ception performance negatively (higher FA associated with worse sen-
sitivity). In two other cases, in the AF (Figure 4c,f) the indirect effects,
albeit positive, also have a different meaning because they involve
MD. A higher value of MD indicates a decline in the microstructure of
the WM. For these two cases, aging was likely to lead to better
speech perception (sensitivity for the fricatives) as a result of the neg-
ative effect of Age on MD, which, in turn, affected speech perception
performance positively (higher MD [i.e., decline] associated with bet-
ter sensitivity).
4|DISCUSSION
In this study, we aimed to advance current knowledge about the rela-
tionship between speech perceptions in noise in healthy adults focus-
ing on fiber pathways of the perisylvian region. Our main hypotheses
were that (a) speech perception performance would decline with age,
(b) because speech perception relies on phonological processing, the
microstructure of the AF would be associated with speech perception
performance, especially in terms of sensibility (d0) and phonological
repetition priming, and (c) because of its potential role in cognition/
executive processes, we also hypothesized that the microstructure of
the MdLF would also affect speech perception, especially in terms of
response bias (C), the most cognitive component of speech
discrimination.
To gain a broader understanding of the mechanisms that contrib-
ute to age differences in speech perception in noise, we measured
speech perception using three different behavioral measures that pro-
vide information about the sensitivity to differences in speech sounds
(d0), but also response bias (also referred to as the internal decision cri-
teria and phonological processing (phonological repetition priming).
Based on available evidence, we expected that sensitivity and phono-
logical processing would be most affected by aging. Our behavioral
analyses indicated that sensitivity was the most affected of all
Average AF (all segments)
(a) (b)
(c) (d)
Right Left
Average Direct AF Segment
Average Anterior AF Segment Average Posterior AF Segment
FIGURE 2 Average AF segments displayed on the linear ICBM average brain (ICBM152) stereotaxic registration model. (a) the three segments
are shown on the left and right hemisphere; (b) the direct segment (red); (c) the indirect anterior segment (green) and (d) the indirect posterior
segment (cyan) [Color figure can be viewed at wileyonlinelibrary.com]
TABLE 2 Descriptive statistics for the behavioral measures computed from the discrimination task
Behavioral metrics
Young N= 14 Older N= 15 Overall N=29
MSD95% CI [LCI, UCI] MSD95% CI [LCI, UCI] MSD95% CI [LCI, UCI]
Priming fricatives 0.06 0.10 [.002, .12] −0.01 0.11 [−.07, .05] 0.03 0.11 [−.13, .29]
Priming stops 0.28 0.22 [.16, 41] 0.14 0.17 [.05, 24] 0.21 0.21 [−.02, .07]
C fricatives 0.34 0.35 [0.14, 0.55] 0.25 0.25 [0.11, 0.38] 0.29 0.30 [0.18, 0.41]
C stops 0.61 0.34 [0.41, 0.80] 0.50 0.48 [0.24, 0.77] 0.55 0.42 [0.40, 0.71]
d’fricatives 1.30 0.39 [1.07, 1.52] 0.86 0.65 [0.50, 1.22] 1.07 0.58 [0.85, 1.29]
d’stops 0.43 0.65 [0.06, 0.81] 0.51 0.82 [0.05, 0.96] 0.47 0.73 [0.19, 0.75]
Note. M = Mean; SD = standard deviation of the mean; N= number of participants per group or total; 95%CI: confidence interval at 95%. LCI: lower bound
of the CI, UCI: upper bound of CI.
TREMBLAY ET AL.7
measures, followed by phonological priming. There were also indirect
effects of age on response bias through WM integrity in the right
MdLF and right AF. Indirect age effects on Sensitivity and Priming
through WM were also found. These results are discussed in the fol-
lowing paragraphs.
4.1 |Aging of speech perception
The results of the LMM analyses show that after controlling for hear-
ing acuity, older adults are less sensitive to phonetic details in low
intelligibility situations, operationalized as differences in sensitivity
(d0), for fricative consonants. The finding of an age difference in sensi-
tivity is consistent with prior studies that have shown an age-related
decline in the sensitivity to phonetic details even in the absence of
noise (Harkrider, Plyler, & Hedrick, 2005; Nilsson, Soli, & Sullivan,
1994; Plomp & Mimpen, 1979; Strouse, Ashmead, Ohde, & Grantham,
1998; Tremblay, Piskosz, & Souza, 2002, 2003). Whether this diffi-
culty reflects a central auditory processing deficit or a speech-specific
deficit that could be related to a decline in phonological processing
remains to be determined. The finding of indirect effects of age on
sensitivity through the left direct and right posterior segments of the
AF suggests that this decline may be related to speech-related
mechanisms that are supported by the AF, which is involved in map-
ping sound to actions (see Section 4.2).
Interestingly, when using more complex statistical models that
also included individual differences in WM, additional direct and indi-
rect effects of age emerged. Indeed, when WM was entered in the
mediation models, indirect effects of age on response bias (C) were
found. While sensitivity is related to the sensory processes and is
untainted by response bias, criterion is a measure of the decision pro-
cess that reflects potential bias towards responding “same”or “differ-
ent”that is uncoloured by sensitivity. A value of 0 indicates the
absence of a response bias, while a positive criterion indicates a liberal
approach (i.e., a tendency to declare the stimuli to be the same) and a
negative value indicates a conservative approach (i.e., a tendency to
declare the stimuli to be different). Our results show that older partici-
pants in our study had a more liberal approach to the task, especially
for the fricative consonants, a finding that is consistent with a prior
study that showed less strict decision criterion in older adults in a
speech perception task (Gordon-Salant, 1986). The finding of age dif-
ferences in criterion is also consistent with studies that have shown
that the reliance on acoustic and temporal cues changes with age
(e.g., Strouse et al., 1998; Toscano & Lansing, 2017; Tremblay et al.,
2002, 2003), with a decreased ability to interpret certain acoustic cues
in older adults. Age differences in decision criterion may thus reflect
the reliance on different cues or a different weighting of the impor-
tance of making different kinds of mistakes (saying the stimuli are
identical when they are not). It may also reflect a more global change
in cognitive strategy (less cautious decision-making process) or even
personality.
Lastly, the examination of the direct effect of Age on Speech per-
ception controlling for WM and hearing revealed age-related decline
in phonological repetition priming. Repetition priming is the facilita-
tion in cognitive processing that occurs as a consequence of repeated
exposure to a stimulus, in this case auditory syllables (Schacter &
Buckner, 1998). It is also referred to as implicit memory. Though it has
been suggested that implicit memory processes are age-invariant (for
a review see Fleischman, 2007), phonological processes are known to
decline with age, which is shown by increased word retrieval failures,
such as the tip-of-the-tongue phenomenon (Brown & Nix, 1996;
Burke, MacKay, Worthley, & Wade, 1991; Heine, Ober, & Shenaut,
1999; Rastle & Burke, 1996). The current finding of an age-related
reduction in phonological repetition priming is, therefore, consistent
with the literature on the aging of phonological processes and suggest
that deterioration of phonological processing mechanisms may be a
contributing factor to age-related speech perception difficulties.
In sum, we found that the discrimination of fricative consonants,
in terms of sensitivity, criterion and priming, was more affected by
aging than the discrimination of stop consonants. These findings add
to the literature on age differences in the perception of consonants.
They are consistent with the results of Gordon-Salant et al., who
found that the ability to identify temporal cues in fricative sounds
declines with age, even in the absence of noise (Gordon-Salant et al.,
2006). Our results are also consistent with a study by Meyer et al., in
which identification of certain fricative consonants presented in noise,
such as /f/, were amongst those most often confused (Meyer et al.,
2013). That study also reported that voiceless consonants, in general,
Average MdLF (all segments)
(a)
(b)
(c)
Right Left
Average MdLF-IPL Segment
Average MdLF-SPL Segment
FIGURE 3 Average MdLF segments displayed on the linear ICBM
average brain (ICBM152) stereotaxic registration model. (a) the two
segments are shown on the left and right hemisphere; (b) the MdLF-
IPL (yellow) and (c) the MdLF-SPL (blue). AF = arcuate fasciculus.
MdLF = middle longitudinal fasciculus. UF = uncinate fasciculus
[Color figure can be viewed at wileyonlinelibrary.com]
8TREMBLAY ET AL.
were more often confused than voiced consonants. In the present
study, we did not distinguish between voiced and voiceless fricative
consonants. Our results nevertheless show that, overall, manner of
articulation appears to be an important factor affecting speech per-
ception in aging, with fricative consonants being more vulnerable to
aging than stop consonants.
Taken together, our findings suggest that age differences in
speech processing in noise are complex, involving differences at the
level of sensory/phonological processes and decision process, and
strongly affected by individual differences in AF and MdLF. The rela-
tionship between speech perception and WM in these pathways is
described in Sections 4.2 and 4.3.
4.2 |Speech discrimination and the AF
Consistent with our hypothesis, the structural properties of all three
segments of the AF have an impact on speech perception in noise. To
the best of our knowledge, this is the first study to investigate the role
of the AF and its subcomponents in speech perception in noise in a
healthy adult population. We found that the structure of all three seg-
ments of the AF was relevant to the processing of speech sounds in
noise, especially in terms of sensitivity to phonetic details, operationa-
lized as sensitivity (d0).
In contemporary literature, the AF is usually regarded as consist-
ing of at least two segments. According to one dominant model, AF is
composed of a direct and an indirect segment, with the indirect seg-
ment composed of two segments: anterior and posterior (Catani et al.,
2005; Catani & Mesulam, 2008). Catani and all have proposed that
the direct segment is involved with phonological processing, while the
anterior segment is involved in articulation and the posterior segment
in auditory comprehension (Catani et al., 2005). Others have proposed
that AF contains two distinct fiber pathways, one connecting pSTC to
IFG and the other connecting pSTC to PMv (Berwick et al., 2013),
with the pSTC-PMv involved in sensory-motor mapping and phono-
logical processing for speech.
Using the 3-component model of AF, here we found that the
structure of all three segments of AF was relevant to the processing
of speech sounds in noise, in particular in terms of sensitivity to pho-
netic details. The AF appears to be instrumental in mapping degraded
auditory speech input to speech representations at all ages. According
to the DIVA model of speech production (Guenther, 1994, 1995;
Guenther, Ghosh, & Tourville, 2006), speech sounds are represented
Age
M
XY
Criterion
fricatives
NuFO
eM
eY
Hearing
Right Anterior AF Segment and Criterion
ab = .0035
95 % CI [.000, .0096]
b = 1.9031,
p = .0421
Cov
Y
Right Anterior AF Segment and Priming
Age
M
X
Priming
fricatives
NuFO
a = .0019,
p = .090
eM
eY
Hearing
Cov
ab = .0015
95 % CI [.000, .0046]
b = .8283,
p = .0177
Left Direct AF Segment and Sensitivity
Age
M
X
Sensitivity
fricatives
MD
a = .0042,
p = .0475
eM
eY
Hearing
Cov
ab = .0070
95 % CI [.0021, .0171]
b = 1.6808,
p = .0235
Left Direct AF Segment and Sensitivity
Age
M
X
Sensitivity
fricatives
AD
a = .0039,
p = .1364
eM
eY
Hearing
Cov
ab = .0086
95 % CI [.0005, .0236
b = 2.2147,
p = .0123
Right Posterior AF Segment and Sensitivity
Age
M
XY
Criterion
fricatives
Volume
a = .0014,
p = .0367
eM
eY
Hearing
Right Direct AF Segment and Criterion
ab = .0060
95 % CI [.0001, .0165]
b = .4.2634,
p = .0194
Cov
a = .0019,
p = .090
Age
M
X
Sensitivity
fricatives
MD
a = .0029,
p = .2121
eM
eY
Hearing
Cov
ab = .0076
95 % CI [.000, .0206
]
b = 2.6506,
p = .0017
c’ = -.0066,
p
= .1476
c’ = -.0053, p = .0038
c’ = -.0036, p = .6357 c’ = -.0035, p = .6942
c’ = -.0087,
p
= .1216 c’ = -.0052, p = .4809
(a) (b)
(c) (d)
(e) (f)
FIGURE 4 Structural path diagrams of the indirect effects of age on speech perception through white matter in AF. Each of the six indirect
effects found in AF is illustrated separately. Unstandardized coefficients are provided with probability value (for the a-path, b-path, c-path, and c0
path). The bootstrapped 95% confidence interval is provided for the indirect effects [Color figure can be viewed at wileyonlinelibrary.com]
TREMBLAY ET AL.9
in the PMv, one of the termination sites of AF. This supports the
notion of phonological processing within AF. A recent fMRI study
using multi-voxel pattern analysis (MVPA) found sensitivity to place of
articulation during passive listening of syllables in several brain regions
including the posterior IFG and PMv (Correia, Jansma, & Bonte, 2015).
Transcranial magnetic stimulation (TMS) to PMv has a strong impact
on speech perception when intelligibility is low (Meister, Wilson,
Deblieck, Wu, & Iacoboni, 2007), but not when it is high (Sato, Trem-
blay, & Gracco, 2009), suggesting that this region is sensitive to the
quality of the auditory speech signal and that it could play a causal
role in speech perception. TMS has also revealed that the PMv can
influence speech sound categorization (Grabski, Tremblay, Gracco,
Girin, & Sato, 2013). Moreover, an fMRI study has shown that correct
identification of phonemes presented in noise is associated with
increased activation in PMv relative to trials in which phonemes are
incorrectly identified (Callan, Callan, Gamez, Sato, & Kawato, 2010).
Increased PMv activation was also seen for time-compressed speech
(Adank & Devlin, 2010). Stimulation of the adjacent posterior IFG was
also found to disrupt performance during phonological tasks, suggest-
ing a role for this region in phonological processes (e.g., Gough,
Nobre, & Devlin, 2005; Hartwigsen et al., 2010). Taken together,
these results support a role for the PMv/posterior IFG in speech pro-
cessing, particularly in difficult listening conditions (distorted speech
or presence of background noise) or when a challenging phonological
task is performed.
A role for the AF in phonological processes is thus consistent with
prior evidence. For example, direct stimulation of AF in awake neuro-
surgical patients produces phonological paraphasias (Maldonado,
Moritz-Gasser, & Duffau, 2011; Mandonnet, Nouet, Gatignol,
Capelle, & Duffau, 2007). The microstructural properties of AF are
also linked with phonological awareness (Yeatman et al., 2011),
reading ability in children (Deutsch et al., 2005; Niogi & McCandliss,
2006) and pseudoword language learning in adults (López-Barroso
et al., 2013).
The AF could also be involved more specifically in phonological
working memory during speech perception, consistent with a study
that showed that phonological awareness is related to FA of the direct
AF segment in adults with and without dyslexia (Vandermosten et al.,
2012). Interestingly, the segment that was most related to our mea-
sure of phonological priming was the anterior AF, which connects the
IPL to IFG/PMv, which have also been implicated in articulatory
rehearsal mechanisms (e.g., Fegen, Buchsbaum, & D'Esposito, 2015;
Romero, Walsh, & Papagno, 2006). Articulatory rehearsal serves to
refresh memory traces by way of subvocal speech allowing the traces
to remain in working memory for longer periods of time.
In addition to numerous age-independent effects, we also found
indirect effects of Age on Speech perception through the microstruc-
ture of AF. Here the effects were less specific and affected sensitivity,
priming and criterion. This suggests that age-related changes to AF
may have a global impact on speech perception in noise. It also sug-
gests that phonological processing and phonological working memory
may be part of the etiology of speech difficulties in aging. We hypoth-
esized that indirect effects would be generalized and negative; instead
we found that age effects on AF were limited, and slightly positive,
meaning that aging was likely to lead to better speech perception
(mainly in terms of sensitivity), at least in our sample of healthy highly
educated adults, as a result of the positive effect of Age on AF, which,
in turn, affected speech perception performance positively. The neu-
rophysiology of aging and WM is discussed in Section 4.4.
Taken together, our results are suggestive of a relationship
between the microstructure of AF and speech perception in noise,
predominantly in terms of sensitivity. While our results may suggest a
Age
M
XY
Priming
fricatives
NuFO
a = .0012,
p = .0407
eM
eY
Hearing
Right IPL and Phonological Priming
(a)
(c)
(b)
(d)
ab = .0014
95 % CI [.0000, .0042]
b = 1.1513,
p = .0484
Cov
Right SPL and Criterion
Age
M
XY
Criterion
fricatives
AFD total
a = .067,
p = .035
eM
eY
Hearing
Cov
ab = .0056
95 % CI [.005, .0164
]
b = 2.9641,
p = .0304
Y
Right SPL and Criterion
Age
M
X
Criterion
Stops
AFD total
a = .0019,
p = .0304
eM
eY
Hearing
Cov
ab = .0074
95 % CI [.003, .0194]
b = 3.913
p = .0310
c’ = -.0020,
p
= .7658 c’ = -.0058,
p
= .2423
c’ = -.0035,
p
= .0417
Age
M
XY
FA
a = -.0019,
p = .0312
eM
eY
Hearing
Right IPL and Phonological Priming
ab = .007
95 % CI [.002, .0223
b = -3.6228,
p = .1266
Cov
c’ = -.0105,
p
= .3428 Sensitivity
stops
FIGURE 5 Structural path diagrams of the indirect effects of age on speech perception through white matter in MdLF. Each of the four indirect
effects found in MdLF is illustrated separately. Unstandardized coefficients are provided with probability value (for the a-path, b-path, c-path, and
c0path). The bootstrapped 95% confidence interval is provided for the indirect effects [Color figure can be viewed at wileyonlinelibrary.com]
10 TREMBLAY ET AL.
slightly more important contribution of the anterior tract to phonolog-
ical working memory, additional studies comparing different types of
speech tasks, such a perceptual and motor tasks, are needed to refine
current understanding of the role of each segment on speech func-
tions in adulthood and in aging. Studies are also needed to compare
the ability for different AF models (e.g., Berwick et al., 2013; Catani
et al., 2005) to account for age-related changes in speech and lan-
guage functions across the lifespan, which is beyond the scope of the
current investigation.
4.3 |Speech processing and the MdLF
A novel and important finding of this study is that the structural integ-
rity of the two components of the MdLF impacts speech perception in
noise, especially in terms of sensitivity and response bias (C), in an
age-independent manner. In addition, the structure of the right MdLF-
SPL is related to response bias in an age dependent manner.
The MdLF is a long association pathway that runs through the
superior temporal cortex. It was first discovered in the macaque mon-
key using autoradiographic techniques (Seltzer & Pandya, 1984), but
several recent studies have confirmed its presence in the human brain
(Burks et al., 2017; Maldonado et al., 2013). Because of its overall tra-
jectory, the left MdLF, as a whole, is sometimes regarded as forming
part of the ventral language streams (Saur et al., 2008) which is
involved in mapping auditory speech sounds to meaning. However,
like AF, the anatomy of MdLF has undergone some revisions since the
introduction of diffusion MRI techniques. From its original description
as a single track, the MdLF has been proposed to be divided into two
components, with a first segment connecting the anterior temporal
lobe to the IPL through a ventrolateral trajectory, and another seg-
ment, more caudal and running steeper, connecting the anterior tem-
poral lobe to the SPL through a dorsomedial trajectory (Makris, Preti,
Asami, et al., 2013; Makris, Preti, Wassermann, et al., 2013).
Evidence for a role for MdLF in speech and language is limited,
mainly because the tract has not been investigated extensively yet.
Using direct electrical stimulation of left MdLF on eight awake
patients, De Witt and colleagues were unable to interfere with a
picture-naming task. Moreover, following resection of MdLF, no per-
manent language deficits were found (De Witt Hamer et al., 2010).
Yet, given its connectivity, it is likely that MdLF has a role in cognition
even if not as a principle pathway, as suggested by Burks et al. (Burks
et al., 2017). Because of its connectivity with the IPL, which is part of
the ventral attention network (Corbetta, Patel, & Shulman, 2008;
Vallesi, McIntosh, & Stuss, 2011), a right-hemisphere lateralised net-
work involved in attention, in terms of stimulus-driven attentional
(e.g., reorienting) responses (Corbetta et al., 2008; Katsuki &
Constantinidis, 2014; Vossel, Geng, & Fink, 2014), the MdLF-IPL could
be supporting attentional functions. In support of this notion, a recent
study reported a correlation between poor attention and the micro-
structure of MdLF in patients with schizophrenia, suggesting a role for
the MdLF in attention (Steffens et al., 2017). The MdLF-SPL, in con-
trast, is part of the dorsal attention network (DAN), which is involved
in top-down bias of attentional resources (Corbetta et al., 2008).
The right SPL was found to be modulated by speech intelligibility in
previous studies (Bilodeau-Mercure et al., 2015; Bishop & Miller,
2009). In a study from our group, we found that this modulation was
age dependent (Bilodeau-Mercure et al., 2015), suggestive of a trans-
formation in the role of DAN to the processing of speech in noise
throughout adulthood, which could contribute to compensating for
age-related decline occurring within the auditory and language net-
works. The current finding of a positive indirect effect of age on
speech processing through the integrity of the MdLF-SPL tract, com-
bined with the direct relationship between the tract's integrity and
speech processing, especially in terms of response bias, suggests a role
for this tract in supporting speech processing, possibly through
changes in attentional biases.
4.4 |Aging of Perisylvian white matter pathways
One of the main findings of the present study is that the micro-
structure of AF and MdLF did not massively decline with age, at
least in our sample of well-educated individuals. Indeed, partici-
pants in our study had an average of 16.6 years of education (SD =
3.62 years) and 76% had 15 or more years of education, making
this sample relatively well educated compared to their peers. For
comparison, only 30% of the Canadian population attained at least
15 years of education (i.e.,university level) in 2011 (https://www12.
statcan.gc.ca). Further, in our sample, education level did not vary
as a function of age (r
2
= .006, β=−.012, p= .692). Moreover, par-
ticipants in our study self-evaluated their general health level as
high (5,103 1.06, on a scale of 1 to 7), and this evaluation did not
vary as a function of age (r
2
= .106, β= .05, p= .084). Finally, their
attitude towards life, as measured by the GDS, was very positive
(mean of 3.14 on a possibility of 30, with scores between 0 and
9 being considered normal). GDS scores also did not vary with age
(r
2
= .03, β=−.025, p= .369).
While aging of WM is a well-established phenomenon, with the
literature on the normal aging of WM in humans and animals showing
several types of ultrastructural alterations in myelin and axons (for a
review, see Liu et al., 2017), to the best of our knowledge, only a few
studies have examined aging of AF in a healthy population (Voineskos
et al., 2010; Voineskos et al., 2012) and none has examined MdLF.
In the present study, negative age-related microstructural
changes took the form of lower FA in the right MdLF-IPL, lower vol-
ume in the left posterior AF segment and higher MD in the left direct
AF segment. Age-related decline in FA is a well-established phenome-
non (e.g., Gunning-Dixon et al., 2009). Though the relationship
between specific microstructural and ultrastructural properties of
WM fibers and diffusion MRI metrics have not yet been fully eluci-
dated, changes in diffusivity in the WM is consistent with evidence of
a decline in the ultrastructure of both myelin and axons. A study com-
bining Diffusion MRI and light microscopy techniques in intact rat
brains, showed that FA is sensitive to myelination in WM regions with
coherent fiber orientations (low fiber crossing) and low fiber disper-
sion (Chang et al., 2017). A relationship was also found between low
FA in young adult and elderly adults and increased pulsativity index, a
marker of cerebrovascular small vessel disease (Fleysher et al., 2017;
Jolly et al., 2013) that signals a decrease in arterial compliance which
may be related to arteriosclerosis.
TREMBLAY ET AL.11
In addition to lower FA, higher MD was also found in older adults
in the left direct AF segment. MD indexes the average rate of diffu-
sion in WM fibers, which depends on the density of physical barriers
such as cellular membranes and the distribution of water molecules
between cellular compartments. Increased MD has been reported in
patients with reduced membrane density (Sen & Basser, 2005) such as
tissue degeneration following injury (Concha, Gross, Wheatley, &
Beaulieu, 2006). Although more research combining microscopy tech-
niques and diffusion MRI are needed, it is possible that an increase in
MD in aging is a marker of deteriorated cellular membrane in WM
fibers of the AF and MdLF.
In addition to demonstrating a limited decline in AF and MdLF,
our results suggest relative gain in WM integrity with age in these
tracts, particularly in the bilateral AF. Though speculative, it is possi-
ble that these results demonstrate a form of age-dependent plastic-
ity. There is growing empirical evidence that experience can alter
WM structure throughout the entire lifespan. Learning, physical exer-
cise, sleep and even social experience have been shown to impact
myelination in adulthood (for a review, see Sampaio-Baptista &
Johansen-Berg, 2017). In our sample, education, general health status
and mood were not related to age, and therefore cannot explain the
WM gains that were found. However, we did not measure lifestyle
factors, which could contribute to explaining these effects. Future
studies on normal aging such as this one should strive to characterize
their sample as thoroughly as possible to tease apart age effects from
those related to experience-dependent plasticity in brain structure,
and more precisely WM structure, such as the amount and quality of
social interactions, general quality of life, amount of cognitive activ-
ity, but also strive to collect data from more diverse social-economic
backgrounds.
5|LIMITATIONS
This study presents a number of limitations worth discussing. First,
cross-sectional designs cannot be used to determine if age differences
are the result of natural changes that occur over the course of a life-
time, or if they result from secular and historical differences between
cohorts, such as changes in nutritional habits between generations
(Carlson & Morrison, 2009; Robinson, Schmidt, & Teti, 2008). How-
ever, it has been suggested that such cohort effects are likely to have
less of an impact when the dependent variable is of a basic biological
nature (Miller, 1998) as is the case here. Another limitation to this
work is the small sample consisting mainly of highly educated individ-
uals. Though this limitation does constraints the external validity of
our results, we believe our findings provide important preliminary evi-
dence for a role for the AF and MdLF in speech processing in noise.
The validity of these preliminary results is strengthened by a thorough
procedure for the analysis of diffusion data and tractography, our
demonstration of our ability to track each participant's fasciculi, a very
strict verification of the normality and homogeneity of the distribution
of each variable, the illustration of individual data (Supporting Infor-
mation 8), and the use of bias-corrected bootstrap confidence inter-
vals for the indirect effects.
6|CONCLUSIONS
By examining the macrostructural and microstructural properties of
two important fiber pathways of the perisylvian regions, using robust
tractography methods, our approach allows for an integrative and ana-
tomically informed investigation of WM fascicles involved in speech
perception in noise. Our findings reveal that pathways of the perisyl-
vian region contribute to speech processing abilities in an age-
independent and in an age-dependent fashion, with relatively distinct
contributions for AF (sensitivity) and MdLF (response bias), suggestive
of a complex contribution of WM in terms of phonological and cogni-
tive processes to speech perception. Importantly, our findings suggest
that individual differences in tract microstructure have a stronger
impact on speech perception than age alone. It will be important to
replicate these preliminary findings and continue investigate the spe-
cific roles of these two pathways in speech processing using a larger
and more socioeconomically diverse sample, and more complex statis-
tical models taking into consideration additional factors such as edu-
cation and cognitive abilities.
ACKNOWLEDGMENTS
This study was supported by research funds from the Québec Bio-
imaging network to PT, MD, and ASD, and from the Fonds de la
Recherche en Santé du Québec (FRQ-S, #27170) to PT, who also
holds a Career Awards from the “Fonds de Recherche du Québec –
Santé”(FRQS). PT and MD are supported by NSERC Discovery Grants
and MD is supported by institutional research Chair in NeuroInfor-
matics. DK-H was supported by a UCL Bogue Fellowship. Technical
support for protocol development and data acquisition was provided
by the “Consortium d'imagerie en neuroscience et santé mentale de
Québec”(CINQ) via a platform support grant (#3456) from the Brain
Canada Foundation to PT. Thanks to all of the individuals who partici-
pated in this study, and to A.-M. Audet, and C. Ouellet for their help
with participant recruitment and testing.
ORCID
Pascale Tremblay http://orcid.org/0000-0001-7161-9255
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How to cite this article: Tremblay P, Perron M, Deschamps I,
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