brain

sciences
Article
Effects of Lifelong Musicianship on White Matter Integrity and
Cognitive Brain Reserve
Edna Andrews 1,2,3, *, Cyrus Eierud 1 , David Banks 4 , Todd Harshbarger 5 , Andrew Michael 2
and Charlotte Rammell 6

1 Linguistics Program, Duke University, Durham, NC 27708, USA; cyrus.eierud@duke.edu

2 Duke Institute for Brain Sciences (DIBS), Duke University, Durham, NC 27710, USA;
andrew.michael@duke.edu
3 Center for Cognitive Neuroscience, Duke University, Durham, NC 27710, USA
4 Department of Statistical Science, Duke University, Durham, NC 27708, USA; david.banks@duke.edu
5 Brain Imaging and Analysis Center (BIAC), Duke University, Durham, NC 27710, USA;
todd.harshbarger@duke.edu
6 World Languages, Minot State University, Minot, ND 58707, USA; charlotte.rammell@minotstateu.edu
* Correspondence: eda@duke.edu

Abstract: There is a significant body of research that has identified specific, high-end cognitive
demand activities and lifestyles that may play a role in building cognitive brain reserve, including
volume changes in gray matter and white matter, increased structural connectivity, and enhanced
categorical perception. While normal aging produces trends of decreasing white matter (WM)
integrity, research on cognitive brain reserve suggests that complex sensory–motor activities across
the life span may slow down or reverse these trends. Previous research has focused on structural
and functional changes to the human brain caused by training and experience in both linguistic
(especially bilingualism) and musical domains. The current research uses diffusion tensor imaging to
examine the integrity of subcortical white matter fiber tracts in lifelong musicians. Our analysis, using



Tortoise and ICBM-81, reveals higher fractional anisotropy, an indicator of greater WM integrity, in


aging musicians in bilateral superior longitudinal fasciculi and bilateral uncinate fasciculi. Statistical
Citation: Andrews, E.; Eierud, C.; methods used include Fisher’s method and linear regression analysis. Another unique aspect of
Banks, D.; Harshbarger, T.; Michael, this study is the accompanying behavioral performance data for each participant. This is one of the
A.; Rammell, C. Effects of Lifelong first studies to look specifically at musicianship across the life span and its impact on bilateral WM
Musicianship on White Matter
integrity in aging.
Integrity and Cognitive Brain Reserve.
Brain Sci. 2021, 11, 67. https://
Keywords: cognitive reserve; musicianship; diffusion tensor imaging; fractional anisotropy; white
doi.org/10.3390/brainsci11010067
matter integrity
Received: 1 December 2020
Accepted: 1 January 2021
Published: 6 January 2021
1. Introduction
Publisher’s Note: MDPI stays neu- Since the appearance of the Bialystok, Craik and Freedman article, “Bilingualism
tral with regard to jurisdictional clai- as a protection against the onset of symptoms of dementia” (2007), which reported the
ms in published maps and institutio- protective effects of lifelong bilingualism in the appearance of dementia symptoms and the
nal affiliations. role of cognitive reserve, there have been a large number of articles devoted to this topic,
resulting in a robust set of research that has examined bilingualism and its role in building
cognitive brain reserve in healthy subjects and pathology [1]. Related to these studies is a
Copyright: © 2021 by the authors. Li-
series of analyses that point to the role played by cognitive reserve in musicians, which
censee MDPI, Basel, Switzerland.
parallels the work on bilingualism in significant ways. In order to clearly position the
This article is an open access article
research presented in the current paper, we will briefly review the definitions of different
distributed under the terms and con- types of brain reserve, as well as review the findings across studies that yield consensus
ditions of the Creative Commons At- and/or dissent. This will include work on cognitive reserve in musicians as an important
tribution (CC BY) license (https:// part of the literature. While normal aging produces trends of decreasing WM integrity,
creativecommons.org/licenses/by/ research on cognitive brain reserve suggests that complex sensory–motor activities across
4.0/). the life span may slow down or reverse these trends.

Brain Sci. 2021, 11, 67. https://doi.org/10.3390/brainsci11010067 https://www.mdpi.com/journal/brainsci

Brain Sci. 2021, 11, 67 2 of 11

One of the earlier sets of terms proposed in Valenzuela and Sachdev (2006) is “neuro-
logical brain reserve” and “behavioral brain reserve” [2]. Neurological (brain) reserve is
generally considered to be more biologically and genetically based, and it is articulated in
the following way by Stern (2012) and Guzmán-Vélez (2015) [3,4]:
The neurological brain reserve hypothesis proposes that individuals generally differ
in the numbers of neurons and synapses available to be lost before clinical symptoms
emerge (Stern, 2012). Following Guzmán-Vélez, “Brain reserve refers to ‘passive’ factors
(e.g., brain volume, synapse count) that confer a particular capacity to endure neuropatho-
logical processes until a critical threshold is reached, after which cognitive and functional
impairments are expressed” (Guzmán-Vélez, 2015 [4]).
By way of contrast, cognitive reserve, also called behavioral brain reserve, is acquired
through specific sensory–motor activities that span across the life cycle (including but not
restricted to musicianship and bilingualism). Bialystok et al. (2017) referred to this type
of cognitive brain reserve as “resilience to neural insult” and noted that the development
of strategies that strengthen alternative functional neural networks across the life cycle
can improve tolerance of atrophy (Bialystok, 2017) [1]. These changes may be observed
in gray and white matter. Gold et al. (2013) further suggested that (1) higher cognitive
reserve should require more structural decline in order for symptoms to manifest and (2)
cognitive reserve is an active form of reserve, described as the ability for plastic functional
brain reorganization of cognitive networks in response to injury, aging or disease [5]. For
more discussion on cognitive reserve, see Craik et al., 2010; Andrews et al., 2013; Andrews,
2014; and de Bot, 2009 [6–9]. Bialystok et al.’s definition for cognitive reserve serves as the
basis for the definition used in the current paper [1].
In the current work, we will examine white matter (WM) integrity and, in particular,
fractional anisotropy (FA) values in healthy subjects who are highly proficient musicians.
Fractional anisotropy (FA) is one of the measurements extracted from diffusion tensor
imaging (DTI) data and is based on the degree of movement of water molecules (between
0 and 1). Higher FA values indicate higher WM integrity.

1.1. Cognitive Reserve in Bilinguals

Pliatsikas et al. (2015) considered different groups of bilinguals based on their age
of acquisition, including (a) early bilinguals, (b) late bilinguals, (c) older bilinguals and
(d) young late bilinguals [10]. Findings showed that all of these different groups showed
increased FA values (compared to monolinguals) across several regions, including the genu,
body and anterior part of the splenium of the corpus callosum (CC), extending bilaterally
to the inferior frontal-occipital fasciculus (IFOF), the uncinate fasciculus (UF) and the
superior longitudinal fasciculus (SLF). Similar findings about the greater FA values in the
SLF and UF of bilinguals as opposed to monolinguals were given by Luk et al. (2011) [11].
Anderson et al. (2018) found greater WM integrity and higher axial diffusivity (AD) values
in bilinguals across several regions (including the bilateral superior posterior corona radiate,
the right external capsule, the midbody and splenium of the CC, the left STLF and the
anterior IFOF), while Coggins et al. (2004) and Felton et al. (2017) identified greater WM
volume specifically in the CC of bilinguals versus their monolingual peers [12–14]. The
ICBM-81 atlas used in the current analysis includes the AF within the SLF (Mori et al., 2008;
Oishi et al., 2008) [15,16].
The mechanisms for the different types of brain and cognitive reserve are poorly
understood but may include enhanced generation of neuronal, dendritic and synaptic
connections and functional reorganization of neural networks across multimodalities. For
example, Grundy et al. (2017) argued in favor of a shift to subcortical/posterior region-
based networks in bilinguals, as opposed to the more frontal regions in monolinguals [17].
Abutalebi et al. (2015) focused on increased gray matter (GM) in bilingualism as an example
of cognitive reserve and potential protective effects in aging [18,19].
Thus, while cognitive brain reserve may play an important role in delaying the
appearance of the symptoms of certain pathologies, it also may play a role in healthy
Brain Sci. 2021, 11, 67 3 of 11

subjects. That is the focus of our current study—lifelong musicians and the potential effects
of musicianship on subcortical WM matter fiber tracts. While we do not consider GM in
this paper, we do not exclude the importance of cognitive reserve in GM volume changes
(cf. Pliatsikas et al., 2015) [10].

1.2. Cognitive Reserve in Musicians

Elmer et al., (2018, 2019) have worked across the aisle on aspects of musicianship
and bilingualism, including studies focusing on bilingualism and simultaneous translators
(2019) and the relationship of music training with speech processing and lexical acquisition
(2018) [20,21]. Elmer and Jäncke (2018) pointed out that interest in professional musicians
is not new in 21st-century neuroscience but rather dates back to the mid-20th century,
and they included mention of studies using a variety of neuroimaging approaches that
analyzed the relationship of musicianship and neuroplasticity, neuroanatomical correlates
of musicianship, enhanced encoding of vowels and speech in professional musicians,
timbre-specific auditory cortical representations in musicians, shared networks for auditory
and motor processing in professional musicians, etc. (e.g., Jäncke, 2009; Munte et al., 2002;
Bermudez et al., 2009; Kuhnis et al., 2013; Pantev et al., 2001; Bangert et al., 2006; Weiss and
Bidelman, 2010) [20,22–28].
Two interesting DTI studies that provide a context for the current analysis were
conducted by Halwani et al. (2011) and Oechslin et al. (2010) [29,30]. In the work of Halwani
et al. two different groups of musicians (vocalists and instrumentalists) demonstrated
larger WM tract volume and higher FA values for bilateral arcuate fasciculus (AF) [29].
Additionally, differences were found between instrumentalists and vocalists by subdividing
the AF into a dorsal superior connection to the STG and IFG and the ventral inferior branch
connecting the MTG and IFG. The results confirmed that, in all cases, the FA values were
higher for both types of musicians compared to nonmusicians. As noted above, our analysis
uses the ICBM-81 atlas, where the AF is included in the SLF [15,16].
The Oechslin et al., study (2010) examined two groups of musicians (with absolute
pitch (AP) vs. relative pitch (RP)) and nonmusicians [30]. They analyzed the FA values
of the SLF, since the SLF is considered important in processing both speech and music.
Findings included specific differences in the SLF between AP and RP, where there was an
additional left hemisphere lateralization of FA values. Oechslin et al. also proposed the
Pioneer Axon Thesis, which states that “peripheral WM development (contrary to compact
WM core regions) is influenced considerably by environmental factors, in this case musical
training spanning a long period in postnatal life” (2010: 9) [30].
Other DTI research involving musicians and music-based treatments includes the UF.
Yuskaitis et al., (2015) and Wan et al. (2010) discussed the importance of this tract in pitch
perception in healthy subjects and its importance for music therapies in the treatment of
various disorders [31,32].

1.3. Research Question and Hypotheses

The background review of studies on cognitive reserve, the potential interactions
between bilingualism and musicianship and studies specifically focusing on musicianship
provided the foundation for our research question and hypotheses. It has been shown in
multiple studies that WM integrity decreases in normal aging (cf. Tang et al., 1997; Giorgio
et al., 2010; Westlye et al., 2010; Billiet et al., 2015; Rathee et al., 2016) [33–37].
Research interested in cognitive reserve has noted that the process of WM integrity loss
can be slowed down or changed by lifelong bilingualism and musicianship. Our hypothesis
was that there would be an increase in WM integrity in certain subcortical fiber tracts in
lifelong musicians, and that this increase would be reflected bilaterally in FA values for
the bilateral superior longitudinal fasciculus (SLF) and the uncinate fasciculus (UF)—two
specific WM tracts shown to be relevant in musicianship [29–32]. We also hypothesized
that the bilateral SS, which includes the IFOF (a tract important in bilingualism), may
Brain Sci. 2021, 11, 67 4 of 11

not show the same effects. This is one of the first studies to consider specifically lifelong
musicianship and include ages ranging from 20 to 67 years.

2. Materials and Methods

2.1. Participants
Eight musicians (five female; between the ages of 20 and 67 years, with a mean age
of 44.1 years) were scanned in 2019–2020 at Duke University Hospital at the Duke Brain
Imaging and Analysis Center. A ninth subject was unable to be included due to incomplete
behavioral data. All subjects began musical training between the ages of 3 and 12 years,
at an average of 6.4 years. The number of years the subjects had been musicians was a
correlate of chronological age since all were currently active musicians (mean 38 years).
All were currently active musicians and performed regularly. Subjects reported an
average of 3 h per day rehearsing/practicing and an average of 9 h per day in peak times
(i.e., during periods of performance). Additional credentials included college degrees in
music (including undergraduate minor, master’s and doctorate) and multiple affiliations
with orchestra, symphony or operatic theatre in the United States and abroad. Five of
the professional musicians had extensive experience in the United States and abroad. All
subjects played at least the piano or violin.

2.2. Data
2.2.1. Behavioral Data
All eight participants completed an extensive background questionnaire on their
musicianship and knowledge of languages. The musical information included detailed
information about their entire musical careers as learners, performers and teachers where
relevant, amount of practice and playing from inception, formal musical training and
education, musical juries and examinations, enjoyment and aesthetics, family ties to music
and musical memories. All participants were required to practice a specific piece by Bach
or Mendelssohn each day for 5 days prior to the scan date.

2.2.2. Image Acquisition

All subjects were scanned in the same scanner at Duke University Hospital, using
a GE 3T Discovery 750 MRI (General Electric, Milwaukee, WI, USA) with an 8-channel
head coil. Diffusion-weighted images (DWIs) were collected using an oblique single-
shot spin-echo echo-planar imaging (EPI) sequence with a repetition time (TR) = 9000 ms,
echo time (TE) = 93 ms, having a 256 × 256 image matrix over a 25.0 cm2 FOV, cover-
ing 69 interleaved axial slices, each 2 mm thick. Twenty-five diffusion directions were
uniformly distributed in 3D space with a b = 1000 s/mm2 , together with an acquisi-
tion with b = 0 s/mm2 . A T2 and proton-density-weighted sequence (FRFSE-XL) with a
TR = 3000 ms, TE = 90 ms, 256 × 256 matrix, 24.0 cm2 FOV and 5 mm slice thickness was
used to acquire 28 interleaved axial slices.

2.2.3. DTI Processing

All images were processed using Tortoise 3.1.4 (Pierpaoli et al., 2010; Irfanoglu et al.,
2017) and FMRIB Software Library (FSL; Woolrich et al., 2009; Smith et al., 2004; Jenkinson
et al., 2012) [38–42]. The raw DTI data were corrected for distortion, eddy currents and
motion using the DIFFPREP command in Tortoise and a subject-specific T2 weighted
image. A brain mask was then generated from the distortion corrected B0 using Extrac-
tImage and bet2. All Tortoise quality control outputs were inspected for signal dropouts,
excessive motion and B0 and T2 registration accuracy. Then, the Tortoise commands Esti-
mateTensorNLLSRESTORE, with the RESTORE option and ComputeFAMap, generated
the fractional anisotropy maps. Next, FSL was used to normalize the individual FA maps
to MNI space, using the FLIRT and FNIRT commands. Finally, the FA for each ROI in each
subject were averaged, using the commands fslmaths and fslstats. Using the ICBM-81 atlas
(Mori et al., 2008), FA values in the following regions of interests were computed [15]: the
Brain Sci. 2021, 11, 67 5 of 11

left and right superior longitudinal fasciculus (LSLF and RSLF), the left and right sagittal
striatum (LSS and RSS) and the left and right uncinate fasciculus (LUF and RUF).

2.2.4. Statistical Methods

In order to test our hypotheses concerning changes in WM integrity in aging musicians,
we used linear regression calculations of the six regions of interest (LSLF, RSLF, LUF, RUF,
LSS and RSS), according with Figure 1, followed by an application of Fisher’s method
for pooling p-values (a statistical method for combining results from multiple tests) [43].
Under the null hypothesis that a regression does not have a slope that is different from
0, the p-value on the slope parameter was uniformly distributed between 0 and 1. Fisher
(1925) showed that the test statistic

k
− 2 ∑ ln( pi )
i =1

has the chi-squared distribution with 2k degrees of freedom, where k is the number of
p-values being pooled [43]. The intuition is that if one has several independent experiments,
each of which modestly supports the alternative hypothesis, then by combining the weak
signals one can increase power for rejecting the null hypothesis.

Figure 1. The superior longitudinal fasciculi (SLF) and uncinate fasciculi (UF) are related to musical proficiency and age.
The sagittal striata (SS) are also related to age, but not musical proficiency. The SLF (red), UF (magenta) and SS (blue) are
depicted in accordance with the ICBM-81 atlas (Mori et al., 2008) [15]. The tracts are superimposed on a T1 weighted image
in the Montreal Neurological Institute 152 space. The figure uses the left-posterior-inferior convention. Legend: L, left.

3. Results
A simple linear regression of theFA values for bilateral SLF and UF in Table 1 yield the
results shown in Figure 2. There is greater WM integrity in aging musicians in the bilateral
SLF and bilateral UF.
Brain Sci. 2021, 11, 67 6 of 11

Table 1. Mean fractional anisotropy (FA) values with SD in parenthesis for each subject in specific
ICBM-81 brain regions *.

Subject LSLF RSLF LUF RUF LSS RSS

1 0.471 (0.14) 0.471 (0.13) 0.472 (0.12) 0.472 (0.10) 0.531 (0.12) 0.528 (0.10)
2 0.512 (0.14) 0.503 (0.13) 0.505 (0.15) 0.530 (0.14) 0.560 (0.10) 0.580 (0.09)
3 0.472 (0.14) 0.488 (0.13) 0.456 (0.13) 0.493 (0.11) 0.540 (0.11) 0.544 (0.09)
4 0.462 (0.14) 0.457 (0.13) 0.426 (0.13) 0.470 (0.12) 0.580 (0.12) 0.562 (0.10)
5 0.465 (0.12) 0.482 (0.11) 0.411 (0.12) 0.465 (0.10) 0.571 (0.10) 0.581 (0.10)
6 0.481 (0.12) 0.490 (0.12) 0.441 (0.15) 0.480 (0.14) 0.593 (0.11) 0.615 (0.10)
7 0.475 (0.13) 0.488 (0.13) 0.467 (0.13) 0.468 (0.12) 0.569 (0.09) 0.586 (0.09)
8 0.483 (0.12) 0.475 (0.12) 0.436 (0.11) 0.476 (0.12) 0.563 (0.10) 0.599 (0.10)
* LSLF—left superior longitudinal fasciculus, RSLF—right superior longitudinal fasciculus, LUF—left uncinate
fasciculus, RUF—right uncinate fasciculus, LSS—left sagittal striatum and RSS—right sagittal striatum. Regions
were defined according to the ICBM-81 diffusion tensor imaging (DTI) atlas.

Figure 2. Scatter plots and linear fits between fractional anisotropy (FA) and subjects’ ages that are
affected by musical proficiency (top two rows) and tracts that may be unrelated to musical proficiency
(bottom row). The vertical axes represent mean FA, and the horizontal axes represent subject age in
years. The superior longitudinal fasciculus (SLF) and uncinate fasciculus (UF) tracts (top two rows)
show a positive correlation between FA and age.

In this experiment, the research hypothesis predicted positive slopes for four of the
scatterplots (left and right SLF, left and right UF) and negative slopes for the left and right
sagittal striata. Since the research hypothesis was directional, we did not conduct two-sided
tests of whether the slopes were different from zero; instead, we predicted the signs of the
slopes. Thus, we could halve the p-values from the two-sided tests. The p-values for each
of the six regions of interest are as follows: LSLF, 0.3065; RSLF, 0.120; LUF, 0.1315; RUF,
0.197; LSS, 0.1435; RSS, 0.202. By applying the formula given above to all of the halved
p-values of the regressions shown and then comparing the test statistic to a chi-squared
distribution with 12 degrees of freedom (−2 Σ k i = 1 ln(pi ) = 20.994), the resulting pooled
p-value is 0.050458, which is close to significance.
Brain Sci. 2021, 11, 67 7 of 11

Based on previous research on cognitive brain reserve using DTI [33–37], we hypothe-
sized that we would find increased WM integrity in FA values of two important tracts, SLF
and UF, in lifelong musicians. We also hypothesized that the bilateral SS, a tract important
for bilingualism, would not be impacted by musicianship and show loss as seen in healthy
aging. Following our hypothesis, the superior longitudinal fasciculus (SLF) and uncinate
fasciculus (UF) tracts show a positive correlation between FA and age in subjects with high
musical proficiency, while FA decreases with age in the sagittal strata (SS) in the same
subjects. Thus, the SS may be unrelated to musical proficiency.
The results are in keeping with our hypothesis. In future analysis, we will explore the
WM integrity and FA values of bilateral SS and IFOF in bilinguals and multilinguals.

4. Discussion
The bilateral SLF and bilateral UF have been noted in the literature on cognitive
reserve and bilingualism (Friederici, 2009; Luk et al., 2011; Madhavan et al., 2014; Pliatsikis
et al., 2015) [10,11,44,45], while only the FA values of the bilateral SLF have been noted
in the literature on cognitive reserve in musicianship, specifically with regard to relative
and absolute pitch (Oechslin et al., 2010) [30]. Halwani et al. (2011) focused primarily on
increased FA values in bilateral AF in musicians, including instrumentalists and singers [29].
In terms of the function of these white matter tracts in processing language, the dorsal–
ventral pathway was often mentioned, but without further differentiation or explanation
(Pliatsikas et al., 2015) [10].
In studies of cognitive reserve and healthy aging in musicians, there are no studies
that focus specifically on the UF. The lesion–deficit DTI literature that included research
involving musicians and music-based treatments identified the UF as important in both
pitch perception in healthy subjects and music therapy [31,32]. There is a larger body of
lesion–deficit studies that consider the bilateral UF and FA values, including visual memory
delay (Riley et al., 2010; Diehl et al., 2008; McDonald et al., 2008) [46–48], naming impair-
ment (Lu et al., 2002; Hamberher and Drake, 2006; Grabowski et al., 2001) and psychopathy
(Craig et al., 2009) [49–52]. One possible point of interest emerges from the work of Duffau
et al. (2009) on the possible role of the IFOF and UF in language processing with patients
undergoing surgery (in the left anterior temporal lobe or orbitofrontal region) [53]. They
were not able to confirm or reject the idea that the UF is essential in language processing.
While our scans did not show any systematic scanner-related errors, there is some re-
search suggesting that all diffusion imaging may require additional correction (Krzyżak and
Olejniczak, 2015; Borkowski and Krzyżak, 2018) [54,55]. Duke University BIAC is part of
the Biomedical Informatics Research Network (BIRN) that examines potential system biases
across vendors and has taken measures to address any concerns about systematic errors in
DTI acquisition in Duke University Health System scanners (Helmer et al., 2016) [56].
The present study focuses on how musicianship may affect changes to WM integrity
across the life cycle and change the trend of reduction in FA structures that are relevant
in musicianship, specifically the SLF and UF. Previous research has shown that FA values
in the SLF and UF show a significant decrease in FA values with age (Tang et al., 1997;
Kochunov et al., 2007; Kochunov et al., 2011; Bennett et al., 2012; Giorgio et al., 2010;
Westlye et al., 2010; Billiet et al., 2015; Rathee et al., 2016) [31,34–37,57–59].
Westlye et al. (2010) examined age changes in WM integrity in 430 healthy subjects
between the ages of 8 and 85 years [35]. The SLF and UF were included bilaterally in
their analysis of seven major WM tracts. Their findings showed a significant (p < 0.0001)
decrease in FA values across whole-brain WM fiber tracts, and the maximum FA values
were found at 29.1 years of age (2010: 2058–60) [35]. The age of maxima for SLF and UF
were 28.8 and 28.6 years, respectively (2010: 2061). Rathee et al. (2016) also examined
FA values for 177 healthy subjects and divided the subjects into three age groups: 20–40,
41–60 and 61–85 years old [37]. Whole-brain values showed a consistent decrease in FA
values across each of the three age groups, and voxelwise FA values included a significant
decrease in 22 regions (middle to oldest group), 26 regions (youngest to oldest group) and
Brain Sci. 2021, 11, 67 8 of 11

4 regions (youngest to middle group). Furthermore, all FA values were lower in all regions
between the oldest and youngest groups, and explicit reference to the SLF and SS showed
significantly lower FA values in the oldest to youngest groups (2016: 14–15) [37].
As mentioned in the introductory section, the research focusing on cognitive reserve
has shown that the process of WM integrity loss in normal aging may be slowed or changed
by lifelong bilingualism and musicianship. Our study is unique in its requirement for
active musicianship across the life cycle.
Our hypotheses looked specifically at the bilateral SLF and the bilateral UF. While
previous studies on musicianship did not focus on the UF, given that it is one of the tracts
with a later period of maturation and connects temporal and frontal lobe structures, it was
included in our analysis. Our results support the research on cognitive reserve and show
that the FA values for bilateral SLF and bilateral UF were greater in older musicians. These
findings suggest that musicianship across the life cycle may change the expected decrease
in FA values in these two subcortical tracts. A third tract, the bilateral SS, which includes
the IFOF, a tract important in bi- and multilingualism, did not show the same effects.

Limitations and Future Directions

The COVID-19 crisis resulted in access to Duke Hospital and the scanners being
shut down, limiting the overall number of subjects and resulting in the lack of control
subject data. Future directions include a replication of the results across a larger data set,
nonmusician controls, investigation of the functional activations and connectivity through
fMRI and other direct comparisons of musicians and bi- or multilinguals.

5. Conclusions
The present study has focused on lifelong professional musicians who play at least
piano or violin. Our findings suggest that lifelong musicianship may contribute to WM
integrity in the bilateral SLF and the bilateral UF in aging. While earlier research identified
higher FA values in the right SLF (Li et al., 2014) [60], our findings show increases in bilateral
SLF. The identification of higher FA values in the bilateral UF is also interesting and expands
the examination of subcortical WM tracts that may be affected by lifelong musicianship.
This study is a preliminary contribution to the growing body of research on behavioral
cognitive reserve and suggests additional evidence that highly proficient musicianship may
produce similar effects of higher FA values in certain WM tracts, while differing in others.
Future studies that compare different types of musicianship (instrumentalists, vocalists,
conductors) and bi- or multilinguals will provide new points of comparison between these
groups and more specificity in defining the impact of cognitive reserve and changes to WM
integrity in healthy aging.

Author Contributions: Conceptualization: E.A., C.E., D.B., T.H., A.M., and C.R.; methodology: E.A.,
C.E., D.B., T.H., A.M., and C.R.; software: C.E., T.H., and A.M.; validation: E.A., C.E., D.B., and A.M.;
formal analysis: E.A., C.E., and A.M.; investigation: E.A., C.E., D.B., T.H., A.M., and C.R.; resources:
E.A., C.E., D.B., T.H., A.M., and C.R.; data curation: E.A., C.E., and C.R.; writing—original draft
preparation: E.A., C.E., D.B., T.H., A.M., and C.R.; writing—review and editing: E.A., C.E., D.B., and
A.M.; supervision: E.A., C.E., and A.M.; project administration: E.A. and C.E.; funding acquisition:
E.A. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by Duke University and the U.S. Department of Education, LRC
Grant CFDA 84.229A.
Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki, and approved by the Duke University Health System Institutional Review
Board (protocol code Pro00014272).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Conflicts of Interest: The authors declare no conflict of interest.
Brain Sci. 2021, 11, 67 9 of 11

References
1. Bialystok, E.; Craik, F.I.M.; Freedman, M. Bilingualism as a protection against the onset of symptoms of dementia. Neuropsychologia
2007, 45, 459–464. [CrossRef]
2. Valenzuela, M.J.; Sachdev, P. Brain reserve and dementia: A systematic review. Psychol. Med. 2006, 36, 441–454. [CrossRef]
3. Stern, Y. Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurol. 2012, 11, 1006–1012. [CrossRef]
4. Guzmán-Vélez, E.; Tranel, D. Does bilingualism contribute to cognitive reserve? Cognitive and neural perspectives. Neuropsychol-
ogy 2015, 29, 139–150. [CrossRef]
5. Gold, B.T.; Johnson, N.F.; Powell, D.K. Lifelong bilingualism contributes to cognitive reserve against white matter integrity
declines in aging. Neuropsychologia 2013, 51, 2841–2846. [CrossRef] [PubMed]
6. Craik, F.I.M.; Bialystok, E.; Freedman, M. Delaying the onset of Alzheimer disease Bilingualism as a form of cognitive reserve.
Neurology 2010, 75, 1726–1729. [CrossRef] [PubMed]
7. Andrews, E.; Frigau, L.; Voyvodic-Casabo, C.; Voyvodic, J.; Wright, J. Multilingualism and fMRI: Longitudinal Study of Second
Language Acquisition. Brain Sci. 2013, 3, 849–876. [CrossRef] [PubMed]
8. Andrews, E. Neuroscience and Multilingualism; Cambridge University Press: Cambridge, UK, 2014; p. 254.
9. De Bot, K. Multilingualism and aging. In The New Handbook of Second Language Acquisition; Bhatia, T.K., Ritchie, W.C., Eds.;
Emerald Group Publishing: Bingley, UK, 2009; pp. 425–442.
10. Pliatsikas, C.; Moschopoulou, E.; Saddy, J.D. The effects of bilingualism on the white matter structure of the brain. Proc. Natl.
Acad. Sci. USA 2015, 112, 1334–1337. [CrossRef] [PubMed]
11. Luk, G.; Bialystok, E.; Craik, F.I.M.; Grady, C.L. Lifelong Bilingualism Maintains White Matter Integrity in Older Adults. J.
Neurosci. 2011, 31, 16808–16813. [CrossRef]
12. Anderson, J.A.E.; Grundy, J.G.; De Frutos, J.; Barker, R.M.; Grady, C.; Bialystok, E. Effects of bilingualism on white matter integrity
in older adults. Neuroimage 2018, 167, 143–150. [CrossRef]
13. Coggins, P.E.; Kennedy, T.J.; Armstrong, T.A. Bilingual corpus callosum variability. Brain Lang. 2004, 89, 69–75. [CrossRef]
14. Felton, A.; Vazquez, D.; Ramos-Nunez, A.I.; Greene, M.R.; Macbeth, A.; Hernandez, A.E.; Chiarello, C. Bilingualism influences
structural indices of interhemispheric organization. J. Neurolinguist. 2017, 42, 1–11. [CrossRef] [PubMed]
15. Mori, S.; Oishi, K.; Jiang, H.; Jiang, L.; Li, X.; Akhter, K.; Hua, K.; Faria, A.V.; Mahmood, A.; Woods, R.; et al. Stereotaxic white
matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 2008, 40, 570–582. [CrossRef] [PubMed]
16. Oishi, K.; Zilles, K.; Amunts, K.; Faria, A.; Jiang, H.; Li, X.; Akhter, K.; Hua, K.; Woods, R.; Toga, A.W.; et al. Human brain
white matter atlas: Identification and assignment of common anatomical structures in superficial white matter. Neuroimage 2008,
43, 447–457. [CrossRef] [PubMed]
17. Grundy, J.G.; Anderson, J.A.; Bialystok, E. Neural correlates of cognitive processing in monolinguals and bilinguals. Ann. N. Y.
Acad. Sci. 2017, 1396, 183–201. [CrossRef]
18. Abutalebi, J.; Canini, M.; Della Rosa, P.A.; Green, D.W.; Weekes, B.S. The neuroprotective effects of bilingualism upon the inferior
parietal lobule: A Structural Neuroimaging Study in Aging Chinese Bilinguals. J. Neurolinguist. 2015, 33, 3–13. [CrossRef]
19. Abutalebi, J.; Guidi, L.; Borsa, V.; Canini, M.; Della Rosa, P.A.; Parris, B.A.; Weekes, B.S. Bilingualism provides a neural reserve for
aging populations. Neuropsychologia 2015, 69, 201–210. [CrossRef]
20. Elmer, S.; Jancke, L. Relationships between music training, speech processing, and word learning: A network perspective. Ann.
N. Y. Acad. Sci. 2018, 1423, 10–18. [CrossRef]
21. Elmer, S.; Hanggi, J.; Vaquero, L.; Cadena, G.O.; FranCois, C.; Rodriguez-Fornells, A. Tracking the microstructural properties
of the main white matter pathways underlying speech processing in simultaneous interpreters. Neuroimage 2019, 191, 518–528.
[CrossRef]
22. Jäncke, L. The plastic human brain. Restor. Neurol. Neurosci. 2009, 27, 521–538. [CrossRef]
23. Munte, T.F.; Altenmuller, E.; Jancke, L. The musician’s brain as a model of neuroplasticity. Nat. Rev. Neurosci. 2002, 3, 473–478.
[CrossRef] [PubMed]
24. Bermudez, P.; Lerch, J.P.; Evans, A.C.; Zatorre, R.J. Neuroanatomical Correlates of Musicianship as Revealed by Cortical Thickness
and Voxel-Based Morphometry. Cereb. Cortex 2009, 19, 1583–1596. [CrossRef] [PubMed]
25. Kuhnis, J.; Elmer, S.; Meyer, M.; Jancke, L. The encoding of vowels and temporal speech cues in the auditory cortex of professional
musicians: An EEG study. Neuropsychologia 2013, 51, 1608–1618. [CrossRef]
26. Pantev, C.; Roberts, L.E.; Schulz, M.; Engelien, A.; Ross, B. Timbre-specific enhancement of auditory cortical representations in
musicians. Neuroreport 2001, 12, 169–174. [CrossRef] [PubMed]
27. Bangert, M.; Peschel, T.; Schlaug, G.; Rotte, M.; Drescher, D.; Hinrichs, H.; Heinze, H.J.; Altenmuller, E. Shared networks
for auditory and motor processing in professional pianists: Evidence from fMRI conjunction. Neuroimage 2006, 30, 917–926.
[CrossRef] [PubMed]
28. Weiss, M.W.; Bidelman, G.M. Listening to the Brainstem: Musicianship Enhances Intelligibility of Subcortical Representations for
Speech. J. Neurosci. 2015, 35, 1687–1691. [CrossRef]
29. Halwani, G.F.; Loui, P.; Ruber, T.; Schlaug, G. Effects of practice and experience on the arcuate fasciculus: Comparing singers,
instrumentalists, and non-musicians. Front. Psychol. 2011, 2. [CrossRef]
30. Oechslin, M.S.; Imfeld, A.; Loenneker, T.; Meyer, M.; Jancke, L. The plasticity of the superior longitudinal fasciculus as a function
of musical expertise: A diffusion tensor imaging study. Front. Hum. Neurosci. 2010, 4. [CrossRef]
Brain Sci. 2021, 11, 67 10 of 11

31. Yuskaitis, C.J.; Parviz, M.; Loui, P.; Wan, C.Y.; Pearl, P.L. Neural Mechanisms Underlying Musical Pitch Perception and Clinical
Applications Including Developmental Dyslexia. Curr. Neurol. Neurosci. Rep. 2015, 15, 51. [CrossRef]
32. Wan, C.Y.; Schlaug, G. Neural pathways for language in autism: The potential for music-based treatments. Future Neurol. 2010,
5, 797–805. [CrossRef]
33. Tang, Y.; Nyengaard, J.R.; Pakkenberg, B.; Gundersen, H.J.G. Age-induced white matter changes in the human brain: A stereolog-
ical investigation. Neurobiol. Aging 1997, 18, 609–615. [CrossRef]
34. Giorgio, A.; Santelli, L.; Tomassini, V.; Bosnell, R.; Smith, S.; De Stefano, N.; Johansen-Berg, H. Age-related changes in grey and
white matter structure throughout adulthood. Neuroimage 2010, 51, 943–951. [CrossRef] [PubMed]
35. Westlye, L.T.; Walhovd, K.B.; Dale, A.M.; Bjornerud, A.; Due-Tonnessen, P.; Engvig, A.; Grydeland, H.; Tamnes, C.K.; Ostby, Y.;
Fjell, A.M. Life-Span Changes of the Human Brain White Matter: Diffusion Tensor Imaging (DTI) and Volumetry. Cereb. Cortex
2010, 20, 2055–2068. [CrossRef] [PubMed]
36. Billiet, T.; Vandenbulcke, M.; Madler, B.; Peeters, R.; Dhollander, T.; Zhang, H.; Deprez, S.; Van den Bergh, B.R.H.; Sunaert, S.;
Emsell, L. Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI. Neurobiol.
Aging 2015, 36, 2107–2121. [CrossRef] [PubMed]
37. Rathee, R.; Rallabandi, V.P.S.; Roy, P.K. Age-Related Differences in White Matter Integrity in Healthy Human Brain: Evidence
from Structural MRI and Diffusion Tensor Imaging. Magn. Reson. Insights 2016, 9, 9–20. [CrossRef] [PubMed]
38. Pierpaoli, C.; Walker, L.; Irfanoglu, M.O.; Barnett, A.; Basser, P.; Chang, L.-C.; Koay, C.; Pajevic, S.; Rohde, G.; Sarlls, J.; et al.
TORTOISE: An integrated software package for processing of diffusion MRI data. In Proceedings of the ISMRM 18th Annual
Meeting, Stockholm, Sweden, 5 May 2010. abstract #1597.
39. Irfanoglu, M.O.; Nayak, A.; Jenkins, J.; Pierpaoli, P. TORTOISE v3: Improvements and New Features of the NIH Diffusion MRI
Processing Pipeline. In Proceedings of the ISMRM 25th Annual Meeting, Honolulu, HI, USA, 25 April 2017. abstract #3540.
40. Woolrich, M.W.; Jbabdi, S.; Patenaude, B.; Chappell, M.; Makni, S.; Behrens, T.; Beckmann, C.; Jenkinson, M.; Smith, S.M. Bayesian
analysis of neuroimaging data in FSL. Neuroimage 2009, 45, S173–S186. [CrossRef]
41. Smith, S.M.; Jenkinson, M.; Woolrich, M.W.; Beckmann, C.F.; Behrens, T.E.; Johansen-Berg, H.; Bannister, P.R.; De Luca, M.;
Drobnjak, I.; Flitney, D.E.; et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage
2004, 23 (Suppl. 1), S208–S219. [CrossRef]
42. Jenkinson, M.; Beckmann, C.F.; Behrens, T.E.; Woolrich, M.W.; Smith, S.M. Fsl. Neuroimage 2012, 62, 782–790. [CrossRef]
43. Fisher, R.A. Statistical Methods for Research Workers; Oliver and Boyd: Edinburgh, UK, 1925.
44. Friederici, A.D. Pathways to language: Fiber tracts in the human brain. Trends Cogn. Sci 2009, 13, 175–181. [CrossRef]
45. Madhavan, K.M.; McQueeny, T.; Howe, S.R.; Shear, P.; Szaflarski, J. Superior longitudinal fasciculus and language functioning in
healthy aging. Brain Res. 2014, 1562, 11–22. [CrossRef]
46. Riley, J.D.; Franklin, D.L.; Choi, V.; Kim, R.C.; Binder, D.K.; Cramer, S.C.; Lin, J.J. Altered white matter integrity in temporal lobe
epilepsy: Association with cognitive and clinical profiles. Epilepsia 2010, 51, 536–545. [CrossRef] [PubMed]
47. Diehl, B.; Busch, R.M.; Duncan, J.S.; Piao, Z.; Tkach, J.; Luders, H.O. Abnormalities in diffusion tensor imaging of the uncinate
fasciculus relate to reduced memory in temporal lobe epilepsy. Epilepsia 2008, 49, 1409–1418. [CrossRef] [PubMed]
48. McDonald, C.R.; Ahmadi, M.E.; Hagler, D.J.; Tecoma, E.S.; Iragui, V.J.; Gharapetian, L.; Dale, A.M.; Halgren, E. Diffusion tensor
imaging correlates of memory and language impairments in temporal lobe epilepsy. Neurology 2008, 71, 1869–1876. [CrossRef]
[PubMed]
49. Lu, L.H.; Crosson, B.; Nadeau, S.E.; Heilman, K.M.; Gonzalez-Rothi, L.J.; Raymer, A.; Gilmore, R.L.; Bauer, R.M.; Roper, S.N.
Category-specific naming deficits for objects and actions: Semantic attribute and grammatical role hypotheses. Neuropsychologia
2002, 40, 1608–1621. [CrossRef]
50. Hamberger, M.J.; Drake, E.B. Cognitive functioning following epilepsy surgery. Curr. Neurol. Neurosci. Rep. 2006, 6, 319–326.
[CrossRef]
51. Grabowski, T.J.; Damasio, H.; Tranel, D.; Ponto, L.L.; Hichwa, R.D.; Damasio, A.R. A role for left temporal pole in the retrieval of
words for unique entities. Hum. Brain Mapp. 2001, 13, 199–212. [CrossRef]
52. Craig, M.C.; Catani, M.; Deeley, Q.; Latham, R.; Daly, E.; Kanaan, R.; Picchioni, M.; McGuire, P.K.; Fahy, T.; Murphy, D.G. Altered
connections on the road to psychopathy. Mol. Psychiatry 2009, 14, 907, 946–953. [CrossRef]
53. Duffau, H.; Gatignol, P.; Moritz-Gasser, S.; Mandonnet, E. Is the left uncinate fasciculus essential for language? A cerebral
stimulation study. J. Neurol. 2009, 256, 382–389. [CrossRef]
54. Krzyżak, A.T.; Olejniczak, Z. Improving the accuracy of PGSE DTI experiments using the spatial distribution of b matrix. Magn.
Reson. Imaging 2015, 33, 286–295. [CrossRef]
55. Borkowski, K.; Krzyżak, A.T. Analysis and correction of errors in DTI-based tractography due to diffusion gradient inhomogeneity.
J. Magn. Reson. 2018, 296, 5–11. [CrossRef]
56. Helmer, K.G.; Chou, M.C.; Preciado, R.I.; Gimi, B.; Rollins, N.K.; Song, A.; Turner, J.; Mori., S. Multi-site study of diffusion metric
variability: Characterizing the effects of site, vendor, field strength, and echo time using the histogram distance. In Proceedings
of the SPIE, San Diego, CA, USA, 29 March 2016; p. 9788. [CrossRef]
57. Kochunov, P.; Thompson, P.M.; Lancaster, J.L.; Bartzokis, G.; Smith, S.; Coyle, T.l.; Royall, D.R.; Laird, A.; Fox, P.T. Relationship
between white matter fractional anisotropy and other indices of cerebral health in normal aging: Tract-based spatial statistics
study of aging. NeuroImage 2007, 35, 478–487. [CrossRef] [PubMed]
Brain Sci. 2021, 11, 67 11 of 11

58. Kochunov, P.; Glahn, D.C.; Lancaster, J.; Thompson, P.M.; Kochunov, V.; Rogers, B.; Fox, P.; Blangero, J.; Williamson, D.E.
Fractional anisotropy of cerebral white matter and thickness of cortical gray matter across the lifespan. Neuroimage 2011, 58, 41–49.
[CrossRef] [PubMed]
59. Bennett, I.J.; Motes, M.A.; Rao, N.K.; Rypma, B. White matter tract integrity predicts visual search performance in young and
older adults. Neurobiol. Aging 2012, 33, 433–e421. [CrossRef]
60. Li, J.F.; Luo, C.; Peng, Y.H.; Xie, Q.K.; Gong, J.N.; Dong, L.; Lai, Y.X.; Li, H.; Yao, D.Z. Probabilistic Diffusion Tractography Reveals
Improvement of Structural Network in Musicians. PLoS ONE 2014, 9. [CrossRef] [PubMed]