White Matter Microstructure and Cognition in Female Anabolic-Androgenic Steroid Users: A Cross-Sectional Diffusion Tensor Imaging Study

White Matter Microstructure and Cognition in Female Anabolic-Androgenic Steroid Users:

A Cross-Sectional Diffusion Tensor Imaging Study Anne Ravndal

Submitted as a master thesis at

Spring, 2021

First of all, I would like to express my most profound appreciation and thankfulness to Astrid Kristine Bjørnebekk for guiding and encouraging me, not only throughout the writing of this master thesis, but ever since supervising my bachelor thesis in 2019. You have included me and made me feel like a part of your team, and over the past two years, you have taught me so much. Further, I would like to thank my internal supervisor Lars Tjelta Westlye for invaluable advice and help. Many thanks also to Sandra Klonteig for all support, motivation, and

feedback.

Without you this would never have been possible.

White Matter Microstructure and cognition in Female Anabolic-Androgenic Steroid Users: A Cross-Sectional DTI Study

By: Anne Ravndal

Supervised by Prof. Lars Tjelta Westlye1 and Researcher Astrid Kristine Bjørnebekk 2 Department of Psychology, University of Oslo, Norway1

The Anabolic-Androgenic Steroid Research Group, Oslo University Hospital, Norway2

Abstract

Evidence suggesting harmful effects of supraphysiological doses of anabolic-androgenic steroids (AAS) on the brain and cognition is growing. However, as most research is conducted in AAS-exposed males, less is known about the potential effects on brain and cognition in females. There is also a significant gap in knowledge on the association between AAS and white matter (WM), with only one prior study investigating this in AAS-exposed males. The present study is designed as a cross-sectional study with the aim to investigate WM microstructure and cognition in AAS-exposed females. For this purpose, 14 AAS- exposed females and 14 female weightlifting controls underwent diffusion tensor imaging (DTI) and neuropsychological testing. Functional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) was calculated in 15 tracts of interest (TOI) and cognitive domains scores were created based on performance on one or several

neuropsychological tests. Linear models were used to compare groups, and correlation

analysis was carried out to test for associations between DTI measures and cognition. Relative to non-exposed females, AAS-females had higher FA and lower MD, AD, and RD in several TOI. No statistical differences were found between AAS-exposed females compared to non- exposed females on cognitive performance. In addition, a relationship was also found between DTI measures and cognitive domain scores in several tracts. Based on these findings, we argue that AAS use might cause a masculinization of WM fiber tracts in females. Our results support the role of WM in cognition. However, since no differences were found between the groups on cognitive performance, contrary to previous findings in AAS exposed males and female to male transexuals on cross-sex hormone therapy, the study lends no support that cognition is affected by the observed differences in WM tracts.

Table of contents

1 Introduction ... 1

1.1 Background ... 1

1.2 Literature review ... 9

1.3 Rational ... 12

1.4 Aims ... 12

1.5 Hypotheses ... 13

2 Methods ... 14

2.1 Design ... 14

2.2 Ethics ... 14

2.3 Participants ... 14

2.4 Setup and materials ... 16

2.5 Statistical analysis ... 20

3 Results ... 21

3.1 Demographics ... 21

3.2 DTI findings ... 22

3.3 Behavioral findings ... 26

3.4 Brain-behavior correlations ... 26

4 Discussion ... 29

3.5 DTI findings ... 29

3.6 Behavioral findings ... 34

3.7 Brain-behavior correlations ... 36

5 Methodological considerations and future directions ... 39

6 Conclusion ... 41

7 Contribution ... 42

8 Literature. ... 43

List of tables

Table 1 ... 21

Sample characteristics ... 21

Table 2 ... 21

AAS use pattern in AAS-exposed females ... 21

Table 3 ... 22

Group differences in FA ... 22

Table 4 ... 23

Group differences in MD ... 23

Table 5 ... 24

Group differences in AD ... 24

Table 6 ... 25

Group differences in RD ... 25

Table 7 ... 26

Test performance on cognitive domains of WLC and AAS exposed females ... 26

Table 8 ... 26

Correlation between mean FA in TOIs and Cognitive components ... 26

Table 9 ... 27

Correlation between mean MD in TOIs and Cognitive components ... 27

Table 10 ... 28

Correlation between mean AD in TOIs and Cognitive components ... 28

Table 11 ... 28

Correlation between mean RD in TOIs and Cognitive components ... 28

List of figures

Figure 1 ... 3

The pathways by which testosterone mediate its effects ... 3

Figure 2 ... 4

The influence of myelin on synaptic plasticity ... 4

Figure 3 ... 5

Diffusion in an isotropic and anisotropic environment ... 5

Figure 4 ... 6

Spin-echo single-shot echo-planar imaging ... 6

Figure 5 ... 7

Diffusion ellipsoid ... 7

Figure 6 ... 15

Flowchart of participant inclusion and exclusion. ... 15

List of abbreviations

AAS: Anabolic-androgenic steroids AD: Axial diffusivity

AR: Androgen receptor

ATR: Anterior thalamic radiation CG: Cingulum of the cingulate gyrus CST: Corticospinal tract

CVLT: California Verbal Learning Test CWIT: Color-Word Interference Test DHT: Dihydrotestosterone

DTI: Diffusion tensor imaging EPI: Echo-planar imaging FA: Functional anisotropy

FDT: FMRIBs diffusion toolbox’s FMAJ: Forceps major

FMIN: Forceps minor FtoM: Female to male GLM: General linear model

HIV: Human immunodeficiency virus IFOF: Inferior front-occipital fasciculus ILF: Inferior longitudinal fasciculus JHU: Johns Hopkins University LMT: Letter Memory Task MD: Mean diffusivity

MRI: Magnetic resonance imaging RD: Radial diffusivity

REC: Regional Committees for Medical and Health Research Ethics RF: Radio frequency

RT: Repetition time

SHBG: Sex hormone-binding globulin SLF: Superior longitudinal fasciculus

SPSS: Statistical Package for the Social Sciences TBSS: Tracked based spatial statistic

TE: Time to echo

TMT: Trail Making Task TOI: Tracts of interest UF: Uncinate fasciculus

WADA: World Anti-Doping Agency

WASI: Wechsler Abbreviated Scale of Intelligence WLC: Weightlifting control

WM: White matter

1 1 Introduction

This thesis will investigate the association between supraphysiological doses of anabolic- androgenic steroids (AAS) and white matter (WM) microstructure and cognition in females.

The cognitive functions that will be studied include learning and memory, executive functions, processing speed, and visuospatial memory. We also will test for an association between WM microstructure and cognition. The introduction provides a review of the current research on the field, justifying the rationale, aim, and hypothesis. Firstly, the introduction builds a theoretical foundation for the main concepts, specifically on the topics of AAS, WM microstructure, and cognition. We also present diffusion tensor imaging (DTI) as an imaging technique to investigate WM microstructure. Since no prior studies have investigated how supraphysiological doses of AAS influence WM microstructure and cognition in females, we will review how physiological doses of androgens influence WM microstructure and

cognition in males and females. Then, studies of how supraphysiological doses of AAS have influenced WM microstructure and cognition in males will be presented. Finally, we will present research on the role of WM in cognition.

1.1 Background

1.1.1 Anabolic-androgenic steroids – history, use-patterns and prevalence AAS are steroidal androgens, including natural testosterone and its synthetic

derivatives. It has anabolic properties that promote muscle growth and androgenic properties promoting male sexual organs and characteristics (Fragkaki et al., 2009). Testosterone was isolated and synthesized in the 1930s and has since been used therapeutically (Nieschlag &

Nieschlag, 2017). Today it is used in the treatment of late puberty and hypogonadism, which is a condition characterized by low androgen levels, in addition to several conditions causing muscle weakening and weight loss, such as human immunodeficiency virus (HIV) and cancer (Basaria et al., 2001)

In the 1950s, weightlifters discovered that AAS could increase muscle mass and strength beyond what could be achieved naturally. This finding did not go unnoticed, and it did not take long before AAS was used by athletes within a significant number of strength sports (Wade, 1972). In the 1980s, the use of AAS reached the general population. This can be explained in part by the growing interest in bodybuilding and a new body ideal-promoting masculinity and muscularity (Pope et al., 2001).

2 When AAS are taken to increase muscle mass, supraphysiological doses that exceed the body's natural testosterone production are used. In males, these doses are 5-100 times higher than what a man naturally produces (Bjørnebekk et al., 2017; Handelsman et al., 2018;

Kanayama et al., 2008). To prevent hypogonadism, in this case as a consequence of suppressing the body's natural hormone production, the doses are often administered in cycles. The cycles are lasting from weeks to months, where AAS are administrated, followed by a drug-free period. It is also common to use several types of AAS simultaneously; a technique referred to as stacking (Kanayama et al., 2008)

Due to the masculinizing effects of AAS, the majority of steroid users are men (Sagoe et al., 2014). However, a recent change in the female body ideal from skinny to more

muscular appearance has led to increased use among females in fitness disciplines and the gym culture (Andreasson & Johansson, 2019; Tiggemann & Zaccardo, 2018). In Nordic countries, it is estimated that during their lifetime, 2.9% will try AAS, with a significantly higher prevalence for males (2.9%), compared to females (0.2%) (Sagoe et al., 2015). Having a male partner, friend, acquaintance, or coach who uses AAS seems to be important for the introduction of AAS use in females. These males, which are trusted by the females, also advise on the type of drug to use, dose, and administration pattern (Havnes et al., 2020).

While some studies report that females take lower doses than males, others demonstrate that females take doses equivalent or even higher than males. Similar to males, most females take AAS in cycles. However, they take fewer types of drugs and choose AAS with presumed lower androgenic properties (Franke & Berendonk, 1997; Ip et al., 2010).

1.1.2 Testosterone – biosynthesis, metabolism, and action in brief

The primary biosynthesis of testosterone occurs in the testicles in males and the ovaries in females. However, small amounts are also produced in the adrenal glands in both males and females (Burger, 2002; McQuaid & Tanrikut, 2014). It is synthesized from cholesterol, and the amount is regulated by hormones in the hypothalamic-pituitary-gonadal axis (McQuaid & Tanrikut, 2014). In healthy individuals between 18-40 years, the reference range for serum testosterone is between 7.7 nmol/L to 29.4 nmol/L in males and between 0 to 1.7 nmol/L in females (Handelsman et al., 2018). After testosterone is synthesized, it enters the bloodstream, where 44% binds with sex hormone-binding globulin (SHBG), 54% binds with albumin, whereas only 2% circulates freely (McQuaid & Tanrikut, 2014).

Testosterone, and its synthetic derivates, mediates its effects by interacting with androgen receptors (AR). AR receptors are located in the cell's cytoplasm, located between

3 the cell membrane and the cell core (Keller et al., 1996). Therefore, only unbound

testosterone can pass through the cell membrane to activate the receptors. Since albumin dissociates readily from testosterone, both free circulating testosterone and albumin-bound testosterone make up the level of what has been referred to as bioavailable testosterone in the blood (Mendel, 1992).

While most testosterone activates the androgen receptors directly, some testosterone is converted to the more potent 5α-dihydrotestosterone (DHT) (Figure 1). Additionally,

testosterone can be aromatized to estradiol by aromatase, which acts upon the estrogen

receptors (Figure 1). This hormone is known as the primary female sex hormone (McQuaid &

Tanrikut, 2014).

Figure 1

The pathways by which testosterone mediate its effects

1.1.3 White matter

In the human brain, we have two types of tissue, white and gray matter. Gray matter is mainly composed of neural cell bodies, dendrites, and unmyelinated axons, and white matter

Testosterone

Aromatase 5-alpha-

reductase

Dihydrotestosterone Estradiol

Androgen receptor Estrogen

receptor Androgen

receptor

4 is composed of axons. What makes white matter white is that axons are coated in myelin, a lipid-rich substance produced by the glial cell oligodendrocyte (Chorghay et al., 2018).

Myelin provides metabolic support for axons and is further essential for sufficient and fast transmissions of electrical impulses. In an unmyelinated axon, the action potential travels along the entire length of the axon. However, in a myelinated axon, the action potential only transmits in ranvier's nodes, the unmyelinated area between the myelin sheaths, causing the transmission to be faster (Chorghay et al., 2018).

Figure 2

The influence of myelin on synaptic plasticity

Note. Reprinted from Chorghay et al. (2018). In the upper picture, we see three unmyelinated nerve cells. They all fire simultaneously, but due to different lengths, their action potential does not reach the postsynaptic neuron at the same time.

Consequently, the threshold potential is not reached. In the second picture, the longest nerve cells are myelinated, and as a result of this, their action potentials reach the postsynaptic neuron simultaneously. In this scenario, the threshold potential is reached, and action potentials are generated in the postsynaptic cell.

The myelin characteristics, such as the number of myelin sheaths on an axon, the length of the sheaths, the thickness of the sheaths, and the location of the sheaths, vary across the brain. This might be a mechanism for regulating different circuits' communication speed so that signals from axons of different lengths arrive at the same target within a narrow time window (Figure 2). This is often needed for sufficient depolarization and firing of an action

5 potential. In addition, timing affects synaptic strengthening and weakening. More specifically, when a presynaptic cell stimulates the postsynaptic cell in a narrow time window before it fires, the connection strengthens, and when a spike from a presynaptic cell arrives after the postsynaptic cell has fired, the connection between them weakens (Dan & Poo, 2004).

On a macro level, white matter is organized in tracts, a bundle of myelinated nerve fibers connecting cortical and subcortical gray matter regions. How these tracts are studied will be discussed in the chapter below.

1.1.4 DTI

DTI is a magnetic resonance imaging (MRI) technique used to study the WM

architecture of the brain, and it has become one of the most frequently used MRI techniques in brain research (Assaf & Pasternak, 2008). The method captures the movement of water molecules in three dimensions, and it provides information about the WM microstructure based upon the knowledge that water diffusion is faster along the WM tract than

perpendicular to them, as described below.

Figure 3

Diffusion in an isotropic and anisotropic environment

Note. Diffusion illustrated in an (A) isotropic and (B) anisotropic environment. The blue dots represent water molecules, and the blue lines show how they diffuse. The yellow lines and triangles represent hinders for the water molecules. The yellow lines in specific illustrate a WM tract.

In gray matter, diffusion is relatively isotropic, meaning that water diffusion is equal in all directions. In white matter, however, diffusion is anisotropic, meaning that diffusion varies depending on direction. More specifically, diffusion is relatively restricted to the directions perpendicular to the fibers (Figure 3) (Beaulieu, 2002).

X X

Y Z

Y Z

A) B)

A) B)

6 The most common approach for DTI is the pulsed-gradient spin-echo pulse sequence with single-shot, echo-planar imaging (EPI). EPI begins just like the spin-echo sequence with a 90 pulse followed by a 180-degree pulse (Poustchi-Amin et al., 2001). However, after the 180-degree pulse, the frequency gradient pulse oscillates from a positive to a negative amplitude several times, generating a gradient echoes train. For each osculates, there is a different phase encoding gradient. This allows the k-space to be filled out faster, limiting motion artifacts, which is essential for successful diffusion imaging. If the k-space is filled after only one radio frequency pulse, it is a single-shot EPI (Figure 4) (Poustchi-Amin et al., 2001).

Figure 4

Spin-echo single-shot echo-planar imaging

Note. Adapted from Alexander et al. (2007). The b-value indicates the degree of diffusion weighting, a parameter that reflects the amplitude (G), length (δ), and distance between (Δ) the diffusion gradients (in blue). The radio frequency pulses are illustrated in dark blue, and the yellow squares represent the imaging gradients.

The pulsed gradients are diffusion gradients with an opposite polarity applied at different time points after the radio frequency (RF) pulse (Figure 4). The first gradient is called the dephasing gradient. When this gradient is applied, molecules located at various sites along the magnetic gradient start to precess at different frequencies, causing them to dephase. When this gradient is turned off, the molecules will begin to precess at the same frequency; however, the phase remains dephased. To rephrase the molecules, a second

gradient is applied with the exact opposite polarity. If the protons have moved, the signal will be weaker due to water diffusion (Alexander et al., 2007; de Figueiredo et al., 2011).

7 Rapid changes in strong magnetic fields induce currents in the surrounding conducting materials, referred to as eddy currents (Reese et al., 2003). These eddy currents generate magnetic fields, which act as spatial gradients and affect the image's distortion. To counteract this effect, protons can be refocused twice, which cancels out the eddy currents (Reese et al., 2003).

The diffusion gradients can be applied in the x, y, and z plane and in a combination of them. The direction they measure diffusion is referred to as the diffusion sensitizing direction (Alexander et al., 2007; de Figueiredo et al., 2011). The b-value characterizes the degree of diffusion weighting, which is how sensitive the image is to diffusion. The parameter is dependent on the strength, length, and time between the diffusion gradients, where a higher b- value will make the image more sensitive to slow motion and short distances (Figure 4) (de Figueiredo et al., 2011).

Figure 5

Diffusion ellipsoid

Note. Diffusion illustrated as an (A) sphere in an isotropic environment and as a (B) ellipsoid in an anisotropic environment.

Based on diffusion measures in at least six different directions, a 3 x 3 diffusion tensor matrix is created for each voxel. This tensor has three eigenvectors (e1, e2, e3) that reflect the direction of the diffusion and three eigenvalues (λ1, λ2, λ3) that reflect the magnitude of diffusion along the eigenvectors. Together, the eigenvectors and eigenvalues make up an ellipsoid, where the eigenvectors determine the orientation in space, while the eigenvalues determine the shape and size (Figure 5) (Alexander et al., 2007; de Figueiredo et al., 2011).

Based on the tensor values, different measures can be calculated.

The λ1 value represents longitudinal diffusivity (AD), while the mean of λ two and λ three represents the radial diffusivity (RD). The mean of the eigenvalues is called the mean

e1, λ1 e1, λ1

A) B)

A) B) e3, λ3

e2, λ2 e3, λ3

e2, λ2

8 diffusivity (MD). The degree to which λ1 dominates λ2 and λ3 is reflected by the Fractional anisotropy (FA), a given value between 0 and 1. This value represents the amount of

preferential diffusion within a voxel (Alexander et al., 2007; de Figueiredo et al., 2011).

DTI is used to investigate white matter microstructure in the brain associated with development, aging, and neuropathology (Beaulieu, 2002; Moseley et al., 1990). Since the diffusion of water depends on many factors, such as fiber diameter, fiber density, membrane permeability, myelination, and intra-voxel coherence, the exact biological mechanisms behind the diffusion measures can't be determined (Beaulieu, 2002). However, previous studies have found evidence suggesting that RD is sensitive to myelination, while AD is sensitive to fiber coherence within the voxel, intrinsic axial properties, and extra-axial space. More specifically, increased RD has been associated with a reduction in myelin, while decreased AD has been related to the growth of neurofibrils, microfilaments, and glial cells, in addition to damage and loss of axons (Aung et al., 2013; Kinoshita et al., 1999; Kumar et al., 2012; Takahashi et al., 2000).

1.1.5 Cognitive functions

The field of neuropsychology aims to investigate the relationship between brain and behavior, and neuropsychological testing is a commonly applied method to assess cognitive functioning in both clinical- and research settings (Harvey, 2012). This thesis will apply neuropsychological tests to assess performance on learning and memory, visuospatial

abilities, executive functions, and processing speed. Whereas the specific tests and procedure of administration will be described in the methods chapter, a brief introduction to the

cognitive functions will be presented below.

While learning can be defined as a lasting change in behavior brought about due to a new experience, memory can be defined as a process with three stages: Encoding, which refers to the acquisition of information; storage, which involves the maintenance of

information over time; and retrieval, the stage at which the information is brought back to the conscious mind (R. E. Smith et al., 2015). Memory storage can further be divided into short- term memory, working memory, and long-term memory. In brief, these constructs refer to the maintenance of information over a short period of time, the maintenance plus manipulation of information over a short period of time, and the maintenance of information over an extensive period of time, respectively (Cowan, 2008).

Based upon a meta-analysis of spatial tests conducted by Linn and Petersen (1985), visuospatial abilities can be divided into three related but separable abilities: Spatial

9 perception, the ability to comprehend spatial relationships with respect to one’s orientation;

mental rotation, the ability to manipulate mental representations of two- dimensional and three- dimensional objects; and spatial visualization, the ability to manipulate complex spatial information when several steps are needed two complete a task (Linn & Petersen, 1985).

Executive functions referrers to the cognitive functions that control and coordinate other cognitive functions necessary to complete goal-directed behavior (Diamond, 2013).

Research suggests three core executive functions: Inhibition of dominant, automatic, and prepotent responses, shifting between tasks, mental sets and operations, and updating and monitoring representations in the working memory (Miyake et al., 2000). It is further suggested that these core functions build higher-order executive functions, like planning, problem-solving, and reasoning (Diamond, 2013).

Processing speed can be defined as the time it takes to process information, and it is argued that it plays a substantial role in higher-order cognitive functioning (Kail & Salthouse, 1994; Rommelse et al., 2020). Evidence supporting this includes studies showing that

processing speed can explain much of the age-related variance in cognition throughout life (Kail & Salthouse, 1994). Furthermore, processing speed is associated with several clinical and functional correlates in neurological, psychiatric, and neurodevelopmental disorders (Rommelse et al., 2020). In addition, variabilities in processing speed in healthy young participants are associated with IQ and creativity (Rindermann & Neubauer, 2004).

1.2 Literature review

1.2.1 The effects of testosterone on WM microstructure

When describing the effects of physiological doses of sex hormones on the brain, the organizational/activational dichotomy is often used. Organizational effects refer to permanent structural and functional changes in the brain that happens in periods of elevated hormonal levels during development. Schulz and Sisk (2016) proposed a two-way model of

organizational effects of gonadal hormones. The first stage of this model is prenatally when male fetuses are exposed to high testosterone levels resulting in what has been called a masculinization, or de-feminization, of the brain. The second stage is in adolescence, when pubertal hormones, both male and female, refine the neural circuits organized prenatally. The immediate (temporary) effects of circulating hormones, called activational effects, occur throughout life and are thought to mediate sex-typical behaviors and differences in cognition (Schulz & Sisk, 2016).

10 DTI studies have found sex differences in WM microstructure in adults and

adolescence. This suggests a role for sex hormones in WM development, which is further supported by studies demonstrating that sex differences in WM development are associated with measures of pubertal maturation, including hormonal essays, physical exams, and self- report (Herting et al., 2017; Herting et al., 2012; Ho et al., 2020; Menzies et al., 2015).

Whereas the sex differences found in studies of adolescents are conflicting (Bava et al., 2011;

Herting et al., 2012; Schmithorst et al., 2008; Seunarine et al., 2015), a trend can be

recognized in the studies of adults. More specifically, adult males generally have higher FA and AD and lower MD and RD than adult females (Hsu et al., 2008; Inano et al., 2011;

Menzler et al., 2011). Interestingly, female’s receiving cross-sex female to male (FtoM) hormonal treatment show a similar pattern, with higher FA and lower MD compared to

female controls, suggesting that androgens have a masculinizing effect on WM microstructure in females as well (Burke et al., 2018; Hahn et al., 2016; Kranz et al., 2017; Rametti et al., 2012). Furthermore, in the only study to our knowledge that has looked at AD and RD in FtoM, testosterone-related decreases in MD were associated with both AD and RD decreases (Hahn et al., 2016). Thus, whereas a decrease in RD is masculinization-related, a decrease in AD is not. While the specific biological mechanisms underlying these differences cannot be determined by the DTI findings, previous research conducted on both human and animals, propose a role for sex hormones in the initiation of myelinization and axon caliber growth (Abi Ghanem et al., 2017; Bielecki et al., 2016; Hussain et al., 2013; Perrin et al., 2009;

Pesaresi et al., 2015).

Only a handful of studies have investigated associations between high dose AAS use and brain structure or function, and these studies have selectively looked at adult male AAS users. These studies have found an association between long-term high dose AAS use and neurochemical (Kaufman et al., 2015), functional (Kaufman et al., 2015; Westlye et al., 2017), and structural abnormalities (Bjørnebekk et al., 2017; Bjørnebekk et al., 2019;

Kaufman et al., 2015), including smaller grey matter, cortical and putamen volume, and thinner cortex in AAS-exposed males (Bjørnebekk et al., 2017). Moreover, cell-culture studies have found evidence for neurotoxic effects of common AAS (Basile et al., 2013;

Caraci et al., 2011; Orlando et al., 2007). Collectively, these findings suggest that AAS is associated with increased brain aging, which is further supported by a study showing that long-term AAS use in males is associated with increased apparent gray matter brain aging (Bjørnebekk et al., 2020).

11 To our knowledge, only one study has examined the association between

supraphysiological doses of AAS on WM microstructure. This study found that AAS - exposed males had higher FA in the inferior fronto-occipital fasciculus (IFOF) than weightlifter controls. However, whereas this finding is consistent with the findings of

organizational effects of testosterone on the brain, the study is based upon a small sample size of only nine male AAS-users and eight male weightlifting controls. Further, only FA in one WM region was investigated (Seitz et al., 2017).

1.2.2 The effects of testosterone on cognitive behavior

In line with the organizational/activational effects of sex hormones on the brain, sex differences are apparent in cognitive abilities, where the most well documented are the verbal advantage in females and the visuospatial and mathematical advantage in males (Weiss et al., 2003). Whereas meta-analysis conducted on the topic challenges the mathematical advantage in males and the verbal advantage in females (Hedges & Nowell, 1995; Hyde & Linn, 1988;

Lindberg et al., 2010), they lend support to a visuospatial advantage in males (Linn &

Petersen, 1985; Voyer et al., 1995). Conversely, a recent meta-analysis of studies conducted on FtoM transexuals demonstrated a statistically significant moderate increase in visuospatial abilities in FtoM transexuals, explained by cross-sex hormonal treatment, suggesting that masculinization of the brain consequently results in a masculinization of behavior (Karalexi et al., 2020).

Long-term high dose AAS use, however, has been associated with lower performance on several cognitive domains, including learning and memory, processing speed, problem- solving (Bjørnebekk et al., 2019), working memory (Bjørnebekk et al., 2019; Hauger et al., 2020), executive functions (Bjørnebekk et al., 2019; Hauger et al., 2020; Heffernan et al., 2015), and visuospatial memory (Kanayama et al., 2013; Kaufman et al., 2015) in AAS exposed males. This is consistent with the abovementioned studies linking AAS use to brain abnormalities (Bjørnebekk et al., 2020; Bjørnebekk et al., 2017; Bjørnebekk et al., 2019;

Kaufman et al., 2015; Westlye et al., 2017).

1.2.3 WM and cognition

As previously described, WM is essential for the coordination of neural signals and the connection of cortical and subcortical regions (Chorghay et al., 2018; Dan & Poo, 2004).

Consequently, it is suggested that WM plays an integral part in cognition. Whereas WM maturation has been associated with increased FA and decreased MD, AD, and RD (Bava et

12 al., 2010; Krogsrud et al., 2016; Lebel et al., 2008), WM changes and abnormalities related to aging (Coelho et al., 2021; Fjell et al., 2017; Lebel et al., 2012; Sexton et al., 2014; Westlye et al., 2010), Alzheimer’s disease (Acosta-Cabronero et al., 2010; Genc et al., 2016; Mayo et al., 2019; O'Dwyer et al., 2011) and neurodevelopmental and psychiatric disorders (Aoki et al., 2017; Koshiyama et al., 2020) has been associated with the opposite pattern. Conversely, a positive correlation with FA and cognition and a negative correlation with MD, AD, RD, and cognition has been found across populations (Bava et al., 2010; Coelho et al., 2021; Krogsrud et al., 2016; Mayo et al., 2019; Nir et al., 2013; Peters et al., 2014; Peters et al., 2012; Østby et al., 2011). However, inconsistent results do exist across tracts, tasks, and populations,

emphasizing that the brain-behavioral relationship is far from understood (see Bennett and Madden (2014), and Schmithorst and Yuan (2010), Podwalski et al. (2021), and Alves et al.

(2015) for reviews on the literature of aging, developing, psychiatric and Alzheimer’s disease populations, respectably).

1.3 Rational

Only a few studies have investigated associations between AAS use and brain and cognition, and these are all conducted in adult male AAS users. Of these, only one small study has looked at WM microstructure. However, this study was limited to looking at one DTI measure in one TOI. Nevertheless, due to the observed effects of androgen hormonal treatment in FtoM transexuals, AAS may masculinize the female brain and cognition. In this case, one can anticipate, at least to some degree, differences between AAS-exposed females and female WLC in WM microstructure reflecting the previously observed sex differences in the brain. Furthermore, it is possible that cognitive abilities associated with a male advantage, such as visuospatial abilities, are better in AAS-exposed compared to non-exposed females.

However, since studies on male AAS-users seem to associate AAS with poorer cognition, including visuospatial abilities, the contrary might be the case, emphasizing the need to investigate AAS use in females.

1.4 Aims

This study aims to explore the influence of AAS exposure on a broad range of TOI and cognitive functions among female weightlifting athletes. By analyzing DTI data, we will first evaluate whether AAS use among females is associated with WM microstructure

abnormalities. Lastly, to improve our understanding of whether AAS use affects cognitive performance in females, we will test for differences in cognitive performance on selected

13 cognitive tasks representing key cognitive domains and for associations between DTI

measures and cognitive functioning.

1.5 Hypotheses

This study aims to test the following hypotheses:

1. AAS-exposed females will show higher FA and lower MD and RD compared to female WLC.

a. Based on studies of FtoM transexuals, we hypothesize that AAS-exposed females will show WM microstructure deviations in some tracts of interest (TOI),

potentially reflecting a masculinization of WM microstructure.

2. AAS-exposed females will show lower AD compared to female WLC in some TOI.

a. Due to the observed decrease in AD associated with cross-sex hormonal treatment in FtoM transsexuals, we hypothesize that AAS -exposed females will show lower AD in some TOI, potentially reflecting AAS- promoted changes to WM

microstructure.

3. There will be differences between AAS-exposed females and female WLC in some cognitive domains.

a. Based on previous literature of exogenous administration of androgens, we hypothesize that AAS-exposed females deviate from female WLC in some cognitive domains. However, since the findings of the studies on FtoM transsexuals and AAS-exposed males are contradictory, the hypothesis is non- directional and non-specific.

4. Higher FA, and lower MD, AD, and RD will be associated with better cognitive performance in some TOIs.

a. Based on findings demonstrating that DTI indices of WM microstructure have been associated with cognitive performance, we hypothesize that deviations in WM microstructure will be associated with cognitive performance to some degree.

14 2 Methods

2.1 Design

The research design is guided by the research problem and questions and can be defined as the strategic framework, or strategies of inquiry, that provides directions for the methods and procedures of the research (Blanche et al., 2006; Creswell, 2014). Guided by the aim and hypotheses in this study, which is qualitative in nature, the research design can be categorized as quantitative, as opposed to qualitative.

Quantitative research designs are typically classified as either observational or

experimental depending on whether the researcher observes or manipulates the exposure(s) in a study, respectively (Rezigalla, 2020). Since the exposure, in this case the exposure of AAS, is not manipulated, it is an observational study. Observational study designs can further be classified as either descriptive or analytic based on the inclusion of a control group. If a control group is not included, it is a descriptive study, whereas if a control group is included, it is an analytic study (Das & Ghosh, 2017). This study included both an AAS-exposed group and a WLC group, making the present study an analytical study.

Further, the participants were selected based on selected inclusion and exclusion criteria and not based on outcome status, as in case-control studies or exposure status, as in cohort studies. Finally, since data on both AAS (exposure) and cognition and WM microstructure (outcome) was collected at a single point in time and not over time, the study is cross- sectional, as opposed to longitudinal (Setia, 2016).

2.2 Ethics

Before enrollment, the participants received an informational brochure containing a detailed description of the study and its purpose. Written informed consent was obtained before any testing commenced, and the participants were informed that they could withdraw at any time. The Regional Committees for Medical and Health Research Ethics (REC) South East Norway (2013/601) approved the study. A gift certificate equivalent to 500 NOK was granted to the participants as compensation for their time.

2.3 Participants

For this thesis, an already existing data set collected by the Anabolic-Androgenic Steroid Research Group at the Oslo university hospital in Norway was used. The dataset

15 comprises AAS-exposed males and females and weightlifter controls (WLC), where data on AAS-exposed females and female WLC were included in this thesis.

To ensure the inclusion of females, which makes up only 2% of the AAS population, broader inclusion criteria and less restricting exclusion criteria was used during recruitment of females compared to males. Females had to report being either a) a female weightlifter reporting a current or previous exposure to AAS use lasting for at least one cycle, or b) female weightlifter with no previous exposure to AAS. In addition, prospective female subjects younger than 18 years old, with a neurological disorder, history of head trauma, or IQ below 80 was excluded.

Figure 6

Flowchart of participant inclusion and exclusion.

A total of 30 females was initially enrolled in the study. Two WLC were excluded based upon the inclusion/exclusion criteria, where one had used performance-enhancing substances listed on the World Anti-Doping Agency (WADA) list of prohibited substances and methods, and one due to epilepsy. In addition, an AAS user had a cerebral hemorrhage after the test section and was therefore excluded from the MRI section. Thus, a total of 28 females (14 WLC and 14 AAS-exposed) were included in the analysis (Figure 6). All

participants were recruited through targeted ads in social media, in online forums addressing heavy resistance training or AAS use, through posters and flyers distributed in selected gyms in Oslo, and snowball sampling.

Included in the analysis Total (n= 28) Female WLC (n= 14)

Female AAS (n=14) Did not meet

Exclusion/Inclusion criteria

n= 2

Enroled in the study n= 30

Excluded due to missing data

n=1

16 2.4 Setup and materials

The data used in the following analysis were extracted from a larger dataset, including numerous data on brain scanning, personality, and mental health data, cognitive and social- cognitive, cardiac, vascular, and hormonal data. It was collected from 2013 to 2020 by members of The Anabolic Androgenic Steroid Research Group.

2.4.1 DTI

MRI data were acquired with a Siemens 3T Skyra scanner (MAGNETOM Skyra, Siemens AG, Erlangen, Germany) at Oslo University Hospital (Rikshospitalet), using a 20- channel Siemens head coil. A single-shot, twice refocused, spin-echo echo-planar imaging pulse sequence was used. The following parameters was applied: repetition time (RT) = 9300 ms, echo time (TE) = 87 ms, voxel size = 2.0 x 2.0 x 2.0 mm, number of slices = 70, slice spacing = 2.6 mm, FOV = 256, matrix size = 128×130×70, b-value = 1000 s/mm2, diffusion directions = 64, number of non-diffusion-weighted (b= 0) images=1. In addition, one b = 0 volume with reversed phase-encoding direction was acquired for correction of susceptibility distortions, which will be described further below. Acquisition time was 10 min and 34. sec.

The data was transferred offline to a Linux workstation, where it was preprocessed using the Functional Magnetic resonance Imaging of the Brain (FMRIB) software library (FSL). First, all data were processed using topup (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup) to estimate the susceptibility-induced off-resonance field, which is field inhomogeneities caused by head movements, using the b=0 volumes with opposite phase encodings

(Andersson et al., 2003; S. M. Smith et al., 2004). Then, further processing was carried out using eddy (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy), which offers an integrated framework for correction of susceptibility-induced distortions, eddy currents, and motion, including the detection and replacement outliers, which is slices affected by signal loss due to bulk motion coinciding with the diffusion encoding (Andersson & Sotiropoulos, 2016).

Image quality was manually inspected, combined with estimation of temporal signal- to-noise ratio (tSNR) across the diffusion-weighted volumes. Although lower tSNR was seen in the AAS group compared to WLC, inspection suggested adequate quality, and no scans were discarded.

Next, the brain was extracted with the brain extracting tool (BET) (S. M. Smith, 2002), and the diffusion tensor was fitted within each voxel using FSL diffusion toolbox (FDT). Tract-based spatial statistics (TBSS), which also is a part of FSL, was used for further

17 analysis (S. M. Smith et al., 2006; S. M. Smith et al., 2004). TBSS aligns all the participants' images to the FMRIB FA template (MNI space) using FMRIB's Nonlinear Registration Tool (FNIRT) (Andersson et al., 2007a, 2007b). Further, the mean FA map was created and thinned to create a mean FA skeleton, representing the center of all tracts common across subjects in the analysis. We thresholded and binarized the mean FA skeleton at FA>0.2. A threshold value is set to exclude eras of low FA and areas of high between-subject variability.

To account for misalignment between subjects after the nonlinear registration, each subject's aligned FA images were projected onto the skeleton. The highest maximum value in the subject's FA image was found and projected onto the skeleton by searching perpendicular to the local skeleton structure. The procedure was repeated for MD, AD, and RD.

For each subject, we calculated the mean skeleton value for each metric. In addition, a binary mask based on two probabilistic WM atlases [CBM-DTI-81 WM labels atlas and the Johns Hopkins University WM tractography atlas] was created (Hua et al., 2008; Mori(Mori et al., 2005) et al., 2005; Wakana et al., 2007). To account for individual differences in WM microstructure, the probability threshold was set at 5%. The tracts of interest (TOI) selected for further analyses include the anterior thalamic radiation (ATR), the cingulate gyrus (CG), corticospinal tract (CST), inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF), uncinate fasciculus (UF), forceps minor (FMIN), forceps major (FMAJ), the hippocampal part of the cingulum (CINGH), and the temporal part of the superior longitudinal fasciculus (SLFT). Mean DTI values within the TOI's are based on voxels intersecting the TBSS skeleton and the atlas-based TOIs.

2.4.2 Neuropsychological tests

In this thesis, we used the following neuropsychological tests to measure cognitive functions: The Wechsler Abbreviated Scale of Intelligence (WASI), the California Verbal Learning Test (CVLT), the Letter Memory Task (LMT) and the Color-Word Interference Test (CWIT), and the Trail Making Task (TMT) from the Delis-Kaplan Executive Function

System (D-KEFS)^.

The WASI consists of four subtests: vocabulary, similarities, matrix reasoning, and block design. In this study, the similarities subtest got excluded. In the vocabulary subtest, subjects are presented orally with 42 words that they have to define. In the matrix reasoning test, the subjects are presented visually with 35 uncompleted patterns. For each uncompleted pattern, they have to choose between five possible choices in order to complete them. In the block design subtest, the subjects have to reproduce red and white designs with blocks within

18 a specified time limit. The blocks have one white side, one red side, and two sides that are half white and half red (Wechsler, 1992, 2007).

In the CVLT, a list of 16 words (that can be divided into four logical categories) is read to the participants. This is done five times, and following each time, the subject has to list the words they recall. After the fifth time, a new list is read from which the subject has to list the words they recall. After five minutes of delay, the subjects are asked to list as many words as they recall from the first list without cues before they are asked to do it again after cues are given. After 30 minutes of delay, the subjects are asked to list as many words as they remember from the first list again. Finally, they are presented with 44 words (16 from the first list, eight from the second list, and 20 random words). During this task, they have to answer

"yes" or "no" to whether the word was included in the first list (Delis, 2000).

In the LMT, a list of letters of varying lengths gets presented on a computer screen.

Each letter is presented for 2000 ms, and the task is to remember the four last letters that got presented. The participants have to complete 12 trials, with lists randomly varying in lengths of 5, 7, 9, 12 letters (Miyake et al., 2000).

The CWIT consists of four trials. In the first and second trials, participants are asked to name the colors of 50 boxes and read a list of 50 color words (e.g., "BLUE"). In the third trial, the inhibition trial, they are presented with a list of 50 color words, printed in incongruent colors (e.g., "GREEN" in blue ink) and asked to say the color of the ink. In the last trial, the inhibition/shifting trial, they are presented with a list with color words printed in incongruent colors have to switch between saying the color of incongruent color words (as in trial three).

However, some of the words are inside boxes. When the word is inside a box, they have to read the word; however, when it is not, they have to name the color of the ink (Delis et al., 2001, 2005).

TMT originally consist of five trials; however, the motor speed trial got excluded. In the first trial, the visual scanning trial, the participants have to slash through the number three in a pool of numbers printed on a large paper. In the second trial, the number sequencing task, the participants have to draw a line between the numbers in ascending order. In the third trial, the letter sequencing task, they have to draw a line between the letters in alphabetic order.

Finally, in the last trial, they have to draw a line between the letters and the numbers and do so in the correct order (e.g., 1-A-2-B-3-C) (Delis et al., 2001, 2005).

Based on the neuropsychological test, we created the following cognitive domains:

19 Learning and memory: This domain is based upon sub-scores of the CVLT. In specific, the sum of the recalled words following the first five times, the recalled words after a five min delay, the recalled words after a 30 min delay, the recognized words in the recognition test, and the false positives in the recognition test. The false positives scores were inversed, and all the scores were z-transformed (Howell, 2012). The mean of the z-transformed sub-scores compromises the domain score. Cronbach's alfa was used to test for reliability, and the domain obtained an alpha value of 0.86, which is cited as acceptable (Tavakol & Dennick, 2011). Higher scores indicate better performance.

Visuospatial abilities: The score on the WASI block design subtest compromises the visuospatial abilities domain. Higher scores indicate better performance.

Working memory: The score of the LMT, which is the sum of correct letters in all tasks, compromises the working memory domain. Higher scores indicate better performance.

Inhibition: The reaction time in the CWIT inhibition trial compromises the inhibition domain.

Lower scores indicate better performance.

Shifting: The reaction time in the last trial of the TMT compromises the shifting domain.

Lower scores indicate better performance.

Processing speed: The mean of the z-transformed baseline scores of the CWIT (word reading and color naming) and TMT (visual scanning, number sequencing, and letter sequencing) compromises the processing speed domain. The Cronbach's alfa of the component reached 0.71. Lower scores indicate better performance.

In addition, IQ was calculated based on the vocabulary and matrix reasoning subtest of the WAIS (Wechsler, 1992, 2007).

2.4.3 Interview and questionnaire.

Background information on each participant was obtained using a semi-structured

interview. Information on participants' age, education, cigarette, and alcohol use was included in this analysis. In addition, AAS- exposed females, was further asked about patterns of AAS use, including when they started taking AAS, how long they have been using AAS, how much they use in a week.

20 2.5 Statistical analysis

Descriptive and inferential statistics were conducted on demographic, DTI, and cognitive data using Statistical Package for the Social Sciences (SPSS) version 26.

Descriptive statistics were used to find the means and standard deviations, while multivariate general linear models (GLMs) were used to test for differences between female WLC and AAS-exposed weightlifters. Because there is a relationship between age and white matter microstructure (Krogsrud et al., 2016; Tamnes et al., 2010; Westlye et al., 2010) and age and cognition (Walhovd et al., 2016), age was included as a covariate in all the models.

Furthermore, education was included as a covariate in the cognition models (Lövdén et al., 2020). Effect sizes were reported in models for DTI measures and cognitive domains. Lastly, to investigate the relationship between neuropsychological test performance and WM

microstructure in TOI, partial correlations were run while adjusting for age and education. To limit the amount of testing in a small sample, we only tested for correlations between DTI indices that deviated between the groups and cognition domain scores. Moreover, testing was done across all subjects and not within the subgroups. A two-tailed significance level of 0.05 was set in all analyses (Howell, 2012).

21 3 Results

3.1 Demographics

Sample characteristics are summarized in table 1. The two groups did not statistically differ in age, IQ, or years of education. There was also no significant difference between the groups in the amount of alcohol consumed or cigarettes smoked during a week.

Table 1

Sample characteristics

Characteristics Female WLC (SD) AAS-exposed

females (SD) F-value P-value

Age 28.3 (4.8) 29.3 (6.5) .056 .800

IQ 107.2 (7.6) 102.4 (16.2) 2.583 .121

Education (Years) 15.8 (2.1) 14.4 (1.8) 4.076 .054

Smokers (%) - - - -

Alcohol (Units pr.

week) .6 (0.9) 1.2 (2.8) 2.594 .120

Note. Mean and standard deviations (SD) per group. Bold P-vales represent a significant (P<0.05) difference between the groups.

Characteristics of the pattern of AAS use in the AAS-exposed group are summarized in table 2. The mean age of onset was 24 years old (range 19-32 years), the mean of the total years used was 2.14 years (range 0.5-5 years), and the mean of calculated dose estimate was 328.88 mg (range 150-1000 mg).

Table 2

AAS use pattern in AAS-exposed females

Characteristics AAS-exposed females (SD)

Debut age 24 (3.95)

Total years used 2.14 (1.64)

Weekly dose estimate 328.88 (281.35) Note. Mean and standard deviations (SD).

22 3.2 DTI findings

Results from TOI based analysis found statistical differences in DTI metrics between female WLC and female AAS users. In AAS-exposed females a higher mean FA was found in the left ATR (F = 5.550, p = 0.031), right ATR (F = 5.455, p = 0.028) and FMIN (F = 6.066, p = 0.021) (Table 3).

Table 3

Group differences in FA

Note. Left (L) and right (R). Mean and standard deviation (SD) per group. Bold P-vales represent a significant (P<0.05) difference between the groups adjusting for age.

In addition, AAS- exposed females had lower mean MD in the left CST (F = 4.240, p

= 0.050), left CING (F = 8.423, p = 0.008), right CING (F = 6.656, p = 0.016), FMIN (F = 8.070, p = 0.009), left IFOF (F = 4.970, p = 0.035), left SLFT (F = 4.985, p = 0.035) and the right SLFT (F = 4.688, p = 0.040) compared to female WLC (Table 4).

Tract Female WLC

(SD)

AAS-exposed

females (SD) F-value P-value Eta-squared

ATR (L) .505(.009) .515(.013) 5.250 .031* .174

ATR (R) .509(.011) .518(.009) 5.455 .028* .179

CST (L) .596 (.013) .605 (.013) 3.722 .065 .130

CST (R) .588 (.017) .595 (.016) 1.341 .258 .051

CG (L) .514 (.015) .521 (.016) 1.407 .247 .053

CG (R) .487 (.018) .493 (.014) .775 .387 .030

CINGH (L) .445 (.017) .448 (.025) .126 .726 .005

CINGH (R) .453 (.022) .460 (.018) .899 .352 .035

FMAJ .576 (.015) .580 (.013) .501 .486 .020

FMIN .545 (.017) .558 (.016) 6.066 .021* .195

IFOF (L) .497 (.014) .500 (.014) .435 .515 .017

IFOF (R) .504 (.016) .506 (.012) .277 .603 .011

ILF (L) .479 (.014) .479 (.014) .000 .989 .000

ILF (R) .452 (.016) .455 (.009) .416 .525 .016

SLF (L) .472 (.008) .477 (.016) .883 .356 .034

SLF (R) .474 (.012) .478 (.013) .794 .381 .031

UF (L) .464 (.010) .470 (.017) 1.272 .270 .048

UF (R) .452 (.016) .460 (.012) 2.202 .150 .081

SLFT (L) .486 (.016) .488 (.027) .134 .717 .005

SLTF (R) .510 (.015) .515 (.017) .783 .385 .030

23 Table 4

Group differences in MD

Tract Female WLC

(SD)

AAS-exposed

females (SD) F-value P-value Eta-squared

ATR (L) .767 (.022) .750 (.024) 3.556 .071 .125

ATR (R) .772 (.026) .755 (.017) 3.827 .062 .133

CST (L) .707 (.013) .693 (.018) 4.240 .050* .145

CST (R) .705 (.019) .699 (.020) .450 .508 .018

CG (L) .757 (.024) .740 (.023) 3.314 .081 .117

CG (R) .762 (.021) .748 (.021) 2.634 .117 .095

CINGH (L) .826 (.023) .796 (.031) 8.423 .008* .252

CINGH (R) .822 (.029) .798 (.015) 6.656 .016* .210

FMAJ .762 (.020) .747 (.015) 4.037 .055 .139

FMIN .754 (.024) .728 (.020) 8.070 .009* .244

IFOF (L) .766 (.018) .749 (.019) 4.970 .035* .166

IFOF (R) .774 (.022) .761 (.017) 2.821 .106 .101

ILF (L) .784 (.022) .772 (.026) 1.491 .234 .056

ILF (R) .787 (.025) .774 (.012) 2.674 .115 .097

SLF (L) .748 (.020) .735 (.022) 2.198 .151 .081

SLF (R) .743 (.023) .732 (.000) 1.726 .201 .065

UF (L) .777 (.021) .759 (.025) 3.889 .060 .135

UF (R) .810 (.023) .795 (.017) 3.279 .082 .116

SLFT (L) .767 (.017) .749 (.024) 4.985 .035* .166

SLTF (R) .779 (.024) .760 (.020) 4.688 .040* .158

Note. Left (L) and right (R). Mean and standard deviation (SD) per group. Bold P-vales represent a significant (P<0.05) difference between the groups adjusting for age. NOTE: MD × 10–3 mm2/s.

Lower L1 was found in the left CINGH (F = 11.846, p = 0.002), FMAJ (F = 6.768, p = 0.015), FMIN (F = 5.385, p = 0.029), left IFOF (F = 6.239, p = 0.019), and left SLFT (F = 6.561, p = 0.017) (Table 5).

24 Table 5

Group differences in AD

Tract Female WLC

(SD)

AAS-exposed

females (SD) F-value P-value Eta-squared

ATR (L) 1.233 (.026) 1.219 (.026) 1.822 .189 .068

ATR (R) 1.245 (.031) 1.229 (.021) 2.063 .163 .076

CST (L) 1.255 (.027) 1.244 (.026) .931 .344 .036

CST (R) 1.239 (.030) 1.239 (.029) .142 .710 .006

CG (L) 1.250 (.032) 1.229 (.026) 3.127 .089 .111

CG (R) 1.220 (.031) 1.203 (.023) 2.243 .147 .082

CINGH (L) 1.255 (.032) 1.207 (.038) 11.846 .002* .321

CINGH (R) 1.251 (.033) 1.225 (.029) 4.079 .054 .140

FMAJ 1.356 (.027) 1.335 (.017) 6.768 .015* .213

FMIN 1.281 (.030) 1.254 (.032) 5.385 .029* .177

IFOF (L) 1.231 (.026) 1.207 (.024) 6.239 .019* .200

IFOF (R) 1.252 (.028) 1.233 (.021) 3.340 .080 .118

ILF (L) 1.241 (.031) 1.219 (.033) 2.975 .097 .106

ILF (R) 1.204 (.033) 1.187 (.017) 2.840 .104 .102

SLF (L) 1.162 (.027) 1.146 (.027) 1.990 .171 .074

SLF (R) 1.157 (.028) 1.143 (.022) 1.886 .182 .070

UF (L) 1.207 (.029) 1.184 (.035) 3.051 .093 .109

UF (R) 1.248 (.039) 1.235 (.027) .794 .382 .031

SLFT (L) 1.213 (.025) 1.188 (.025) 6.561 .017* .208

SLTF (R) 1.261 (.035) 1.237 (.028) 4.105 .054 .141

Note. Left (L) and right (R). Mean and standard deviation (SD) per group. Bold P-vales represent a significant (P<0.05) difference between the groups adjusting for age. NOTE: AD × 10–3 mm2/s.

Lower RD was found in the left ATR (F = 4.475, p = 0.045), right ATR (F = 4.632, p

= 0.041), left CST (F = 4.916, p = 0.036), right CING (F = 5.400, p = 0.029), FMIN (F = 7.870, p = 0.010) and right UF (F = 4.570, p = 0.042) (Table 6).

25 Table 6

Group differences in RD

Tract Female WLC

(SD)

AAS-exposed

females (SD) F-value P-value Eta-squared

ATR (L) .534 (.022) .516 (.023) 4.475 .045* .152

ATR (R) .536 (.026) .518 (.017) 4.632 .041* .156

CST (L) .433 (.015) .418 (.018) 4.916 .036* .164

CST (R) .438 (.021) .428 (.020) 1.253 .274 .048

CG (L) .511 (.023) .496 (.024) 2.675 .114 .097

CG (R) .533 (.023) .521 (.021) 1.929 .177 .072

CINGH (L) .612 (.026) .590 (.034) 3.841 .061 .133

CINGH (R) .607 (.031) .584 (.018) 5.400 .029* .178

FMAJ .464 (.023) .453 (.018) 1.792 .193 .067

FMIN .490 (.026) .466 (.021) 7.870 .010* .239

IFOF (L) .533 (.019) .520 (.020) 2.876 .102 .103

IFOF (R) .535 (.023) .524 (.017) 1.828 .188 .068

ILF (L) .556 (.021) .549 (.024) .556 .463 .022

ILF (R) .579 (.025) .568 (.011) 1.945 .175 .072

SLF (L) .541 (.018) .530 (.023) 1.985 .171 .074

SLF (R) .536 (.022) .526 (.020) 1.373 .252 .052

UF (L) .563 (.019) .547 (.024) 3.439 .076 .121

UF (R) .591 (.020) .576 (.016) 4.570 .042* .155

SLFT (L) .545 (.020) .530 (.031) 2.158 .154 .079

SLTF (R) .539 (.024) .522 (.022) 3.418 .076 ,120

Note. Left (L) and right (R). Mean and standard deviation (SD) per group. Bold P-vales represent a significant (P<0.05) difference between the groups adjusting for age. NOTE: RD × 10–3 mm2/s.