Individual differences in nerve fiber myelination: A methodological study

Although there are many studies that investigate overall gender difference in brain structure, maturation, and development, much fewer studies focus on gender-related difference in white matter across distributed brain areas(Gur et al., 1999; Kanaan et al., 2012; Schmithorst et al., 2008; Szeszko et al., 2003). Moreover, no MPF data on similarities and differences between male and female brains are available. Therefore, the investigation of gender-associated variations in nerve fiber myelination and fiber density may contribute to a better understanding of what underlies behavioral differences and differences in cognitive performance between males and females.

3.3 Myelination and Cognitive Abilities

The integrity of white matter pathways, which connect distributed brain areas, plays a fundamental role in the maintenance of higher-level cognitive functions. The majority of the studies focus on the role of abnormal myelination process in cognitive dysfunction (e.g., Roberts et al., 2016;Cardoso et al., 2015for reviews). Studies also investigate the association among white matter characteristics and cognitive performance in normally developing children (e.g., Matejko & Ansari, 2015). Moreover, most of the studies are performed using DTI and fMRI metrics(Mori & Zhang, 2006; Moseley et al., 2002; Poldrack, 2012; Takeuchi et al., 2010; Westerhausen et al., 2010), and there are no studies which used the MPF method to investigate cognitive functions.

The integrity of myelin sheath within the largest white matter tracts is pivotal for cognitive and behavioral performance. A DTI study by Johansen-Berg and colleagues(2007)found an association between bimanual coordination and characteristics of white matter microstructure in the corpus callosum, which connects the supplementary motor area and the caudal cingulate motor area. Compared to an object recognition task(Baird et al., 2005), high FA values in the splenium and genu of the corpus callosum correlated with shorter and longer reaction times, respectively. The task on object recognition was presented from unusual viewpoints, which required transduction of the information from the right parietal cortex to the left inferior cortex. Since myelin sheath increases signal transduction and its formation continues during adulthood, brain myelination plays an important role in inhibitory control and executive functions in children and adolescents, thus underlying healthy maturation(Bartzokis & Altshuler, 2005).

Recent findings(Chung et al., 2018)suggest that greater axonal volume and myelination in particular brain regions, including the parietal cortical areas, left superior and posterior corona radiata, and left body of the corpus callosum, contribute to better performance on auditory working memory tasks, such as letter-number sequencing, by sustaining a higher speed of information processing. Tamnes and colleagues(2013)suggest that myelination and maturation of synaptic connections in fronto-parietal brain networks during adolescence is implicated in greater specialization and processing efficiency, which, in turn, mediate the development of executive functions in children and adolescents. As shown in a DTI study by Tuch and colleagues(2005), the correlation between microstructural measurements of the white matter in the right projection and association pathways, such as the right optic radiation, right posterior thalamus, and right medial precuneus, with visual self-paced choice task performance, reflects a fundamental role of myelination in these regions in the maintenance of visuospatial attention. Notably, microstructural characteristics of the white matter pathways, including the fractional anisotropy and neurite density, show a positive correlation with mathematical performance in 13-years old children(Collins et al., 2019). What is more, indices of myelination in bilateral superior longitudinal fasciculus predict math performance in normally developing children (mean age ~12 years;Denyer et al., 2019), and in younger children (7-9 years) FA in the left superior corona radiata correlates with numerical operations and mathematical reasoning, whereas white matter microstructure characteristics in the left inferior longitudinal fasciculus show associations with numerical operations specifically(van Eimeren et al., 2008).

It is proposed that myelination can be activity-dependent, i.e. it depends on electrical activity of the axon and various molecular mediators synthesized and released in response to electrical events, and activity-independent, which is regulated by oligodendrocytes and does not depend on the axonal electrical activity (for review de Faria et al., 2019). An intriguing work of Bechler and colleagues(2018)discusses a “smart wiring” model of myelination, which includes two phases - intrinsic and adaptive, and suggests possible mechanisms by which active axons may become more myelinated and how brain circuits may be modified in response to learning and new experience. Bechler discusses adaptive myelination which is provided by several properties of oligodendrocytes. Oligodendrocytes in the central nervous system are generated from special progenitor cells which actively divide throughout the whole life(Hughes et al., 2013; Rivers et al., 2008). Moreover, proliferation and differentiation of oligodendrocyte precursor cells was demonstrated to improve motor learning and learning new motor skills, which in turn, changes the structure of the white matter containing many late-born oligodendrocytes(McKenzie et al., 2014). Overall, brain myelination appears to be adaptive, and it may change in response to learning and new experience.

White matter pathways, which connect close and distant brain regions are critical components of cognitive processing. Understanding of how and when myelination changes may help to understand when cognitive performance changes throughout development. Although progress in neuroimaging enables researchers to visualize and provide quantitative characteristics of the majority of the fiber tracts, there are still disagreements regarding the degree to which structure and function of a given tract can vary in healthy brain. The following section will briefly review current knowledge about the main fiber tracts associated with cognitive functions in healthy individuals.

myelination assessment іmaging proton

3.3.1 Corpus Callosum (CC)

The largest fiber tract in the brain is the CC. It consists of more than 200 million axons and plays a key role in integration and interhemispheric transfer of information(Fitsiori et al., 2011). Malformation of the CC results in deficits in higher-level cognitive functions and social communication(Paul et al., 2014), as well as in a slower rate of cognitive processing, impaired language function(Banich & Brown, 2000; Paul et al., 2003), abstract reasoning, and concept formation(Brown & Paul, 2000). Myelination of the CC in children is incomplete (Brown et al., 2005), and with age it continues to mature and it becomes one of the critical factors which sustains interhemispheric interaction(Banich & Brown, 2000). Incomplete myelination of the CC in 6-7-year old children is associated with behavioral abnormalities and results in impaired transfer of complex visuomotor skills learned by the dominant hand from one hemisphere to another(Chicoine et al., 2000). The observation that myelination of the anterior portion of the CC may be induced by working memory training suggests plasticity of white matter microstructure in this region. Reduced integrity of the CC, in turn, may lead to deficits in working memory function(Treble et al., 2013). It is important to highlight that different subdivisions of the corpus callosum demonstrate high degree of variability in size and rates of formation(Giedd et al., 1996). Posterior and mid regions of the CC show greater dependence on age compared to anterior portion of the fiber tract, including the rostrum and genu of the CC and in young children they reach size observed in adult individuals, thus exhibiting anterior to posterior gradient of myelination (Giedd et al., 1996). Findings on gender influence on the structure and development of the corpus callosum are inconsistent. Sullivan and colleagues(2010) show a significant gender-related difference in the development of the CC with age and their association with lower performance on cognitive and motor tasks observed in older adults. Although many studies investigated anatomical and microstructural properties of the CC, there is still no consensus regarding its maturation pattern and individual variability. In the current study fiber tract integrity in the anterior (i.e., genu), mid (i.e., body), and posterior (i.e., splenium) sections of the CC will be evaluated.

3.3.2 Superior Longitudinal Fasciculus (SLF) and Arcuate Fasciculus (AF)

The major association white matter tract that links the temporoparietal junction and parietal areas with the frontal lobe is the SLF. DTI studies show that the right SLF is associated with various complex cognitive functions, including attention(Frye et al., 2010)and visuospatial abilities(Hoeft et al., 2007), whereas left SLF plays a fundamental role in language function(Dick & Tremblay, 2012) and reading skills (Frye et al., 2010). In adolescents, bilateral SLF is involved in verbal working memory and verbal fluency(Peters et al., 2012). It is proposed that the SLF consists of five branches(Kamali et al., 2014).Three SLF branches connect the ipsilateral frontal and opercular areas to the superior parietal lobe (dorsal subdivision, SLF I), to the angular gyrus (middle branch, SLF II), and to the supramarginal gyrus (ventral subdivision, SLF III) and comprise the frontoparietal network of the human brain with the SLF I being symmetric between the two hemispheres, the SLF II demonstrating rightward lateralization, and the SLF III being right lateralized(Thiebaut de Schotten et al., 2011). The AF is the fourth subdivision of the SLF which links the posterior and middle superior temporal gyrus with the ventrolateral prefrontal cortex and the posterior region of Broca's area (pars opercularis, Brodmann's area 44;Kamali et al., 2014; Webb, 2017). However, some authors suggest the AF is distinct from the SLF linking the caudal part of the superior temporal area with the dorsal prefrontal region of the cerebral cortex(Schmahmann & Pandya, 2006). The branch of the SLF, which connects the temporal cortex to the inferior parietal cortex (temporoparietal subdivision, SLF TP) is considered as the fifth subcomponent of the SLF(Catani et al., 2005; Makris et al., 2005; Webb, 2017; Zhang et al., 2010).

The SLF I was proposed to be involved in the regulation of motor behavior and the initiation of motor activity. In addition, it was suggested to transfer somatosensory and kinesthetic information about location of body parts, including trunk and limbs, to the frontal regions of the brain (Schmahmann& Pandya, 2006). The SLF II sustains visual perception and it is a part of the circuit involved in visual awareness and the maintenance of attention. The SLF II fibers connecting the prefrontal cortex with the posterior parietal are implicated in regulation of the focusing of attention(Petrides & Pandya, 2002). The SLF III was found to convey higher-level somatosensory information and to be involved in gestural communication (Petrides& Pandya, 2002). The dorsal pathway through the lateral SLF supports articulatory function, phonological working memory(Duffau, 2019), and perception and production of speech(Hickok & Poeppel, 2007; Indefrey & Levelt, 2004). The AF is implicated in intelligence, reasoning abilities(Catherine Lebel & Beaulieu, 2009), and language processing (Geschwind, 1970), playing a critical role in auditory to motor connection(Webb, 2017) and in semantic and phonological processes associated with visual information (Zemmoura et al., 2015). The indicators of white matter microstructure in the left SLF demonstrate a negative association with inhibitory control, whereas in the left AF they correlate with intelligence tests and attention(Urger et al., 2015). Therefore, findings from the neuroimaging studies demonstrate a critical role of the microstructural integrity of the SLF and AF in neurophysiological abilities and cognitive function.

3.3.3 Inferior Longitudinal Fasciculus (ILF)

The ILF is a ventral association white matter tract, which connects the anterior temporal lobe, including the superior, middle, inferior temporal, and fusiform gyri, to the lingual, cuneate, lateral-occipital and occipito-poral regions of the occipital cortex(Panesar et al., 2018; Shinoura et al., 2007). Connectivity pattern of the ILF is characterized by a significant leftward-dominance(Panesar et al., 2018)and ventral position. The ILF (Shinoura et al., 2007) together with the middle longitudinal fasciculus(Saur et al., 2008)and the uncinate fasciculus (Papagno et al., 2011)comprise the ventral stream of language processing and were found to sustain speech comprehension and general semantic processes(Rizio & Diaz, 2016). A proposed role of the ILF in semantic functionality(Mandonnet et al., 2007)is supported by neuroimaging data obtained in the study by Shin and colleagues (2019), which demonstrates the role of the ILF in language comprehension. The study has also shown that ILF connectivity pattern was leftward-lateralized and greater fiber density and myelination in this region were associated with better semantic processing. Moreover, the ILF was also found to be implicated in object(Ortibus et al., 2012)and face (Taddei et al., 2012)recognition. Notably, age has a great impact on lateral tracts in dorsal areas of the brain, including the SLF, while the inferior regions are less affected by aging(Sullivan et al., 2010). Therefore, the ILF plays a critical role in complex cognitive functions, including semantic processing which is vital for healthy cognitive development. The investigation of how myelination in the ILF changes with age will help to better understand white matter correlates of language function.In the current study fiber tract integrity in the right and left ILF will be evaluated.

3.3.4 Uncinate Fasciculus (UF)

One of the long-range association bidirectional monosynaptic white matter pathway is the uncinate fasciculus, which connects the anterior temporal lobes and amygdala to the lateral orbitofrontal cortex and the anterior portion of the prefrontal cortex(Von Der Heide et al., 2013). The maturation of the UF continues into the third decade of life and its anatomy and connectivity pattern is suggestive of its role in limbic function, including emotion, episodic memory, and personality traits(Olson et al., 2015). A tractography study by Sato and colleagues(2012)demonstrates the association between DTI measures in the UF and performance on a visual memory task. Studies of mild cognitive impairment and psychopathology support a critical role of the microstructural integrity of the UF in higher-order cognitive functions, including attention, spatial memory, and emotion recognition(Fujie et al., 2008; Hiyoshi-Taniguchi et al., 2015; Olson et al., 2015; Singh et al., 2016). The involvement of the UF in visual associative learning was found in the DTI study by Thomas and colleagues(2012)who demonstrated a strong positive association between learning rate and microstructural properties of the left UF, suggesting a critical role of the UF in information retrieval. Other DTI studies demonstrated strong relations among microstructural characteristics of this white matter tract in the left(McDonald et al., 2008; Niogi et al., 2008)and right(Fink et al., 2010)hemispheres and auditory-verbal processes, but not visual memory in adults (McDonald et al., 2008; Niogi et al., 2008), children, and adolescents(Mabbott et al., 2009). The left and right UF may sustain different neurophysiological processes. As such, Thomas and colleagues(Thomas et al., 2015) suggest that the left UF contributes to rapid learning of conditional visual-visual associations, while the right UF may mediate immediate retrieval of these associations. Although ample studies have shown the involvement of the UF in cognitive dysfunction associated with various psychiatric and other pathological conditions(Kitis et al., 2012; Mahoney et al., 2012; Park et al., 2019; Serra et al., 2012), there is still a limited number of researches investigating the role of the uncinate fasciculus in cognitive performance in healthy population, including children, and examining age-related changes in the microstructural characteristics of the UF(Sato et al., 2012; Thomas et al., 2012; Thomas et al., 2015; Von Der Heide et al., 2013).

3.3.5 Cingulum (C)

The cingulum is an association white matter tract which extends sagittally and connects the orbital frontal regions with the pole, passing along the dorsal surface of the corpus callosum down the temporal lobe(Bubb et al., 2018). This bundle is viewed as a part of the limbic system and considered one of the central components of Papez circuit(Papez, 1995) which constitutes bilateral white matter pathway between the anterior thalamic nuclei and cingulate cortex, as well as the parahippocampal region and the cingulate cortex. A substantial portion of cingular association fibers that run in sagittal plane form intracortical connections, linking the medial parts of the frontal, temporal, and parietal lobes(Schmahmann & Pandya, 2006; Yakovlev & Locke, 1961). Diffusion MRI tractography reveals three components of the cingulum: subgenual, retrosplenial or supracallosal, and ventral parahippocampal subdivisions(Jones et al., 2013), which are characterized by different FA values. Based on current anatomical evidence(Heilbronner & Haber, 2014; Vogt et al., 2005), the cingulum can be divided into five subdivisions, including the subgenual, rostral dorsal (anterior cingulate), caudal dorsal (retrosplenial), and temporal (parahippocampal) subcomponents and midcingulate cortical area.

A number of DTI studies show that the cingulum, especially its dorsal portion, is implicated in cognitive control and various executive functions, including shifting and inhibition(Bettcher et al., 2016; K. Kantarci et al., 2011), updating and working memory(Bettcher et al., 2016; Charlton et al., 2010; Kantarci et al., 2011), processing speed(Bettcher et al., 2016; Takahashi et al., 2010), and sustained attention (Takahashi et al., 2010). In particular, FA values in the right cingulum correlate with task performance in Continuous Performance Test which measures sustained attention (Takahashi et al., 2010). Microstructural characteristics in posterior cingulum also demonstrate a strong association with language (category fluency and naming to confrontation) and visual-spatial functions, whereas FA in both posterior and anterior portions of the cingulum are implicated in attention, executive functions, and memory assessed by free recall retention task performance and auditory verbal learning test (Kantarci et al., 2011). The strongest evidence about the contribution of the cingulum to episodic and verbal memory functions are demonstrated in clinical samples, including Alzheimer's disease(Kantarci et al., 2017; Lin et al., 2014), amnestic mild cognitive impairment(Lin et al., 2014; Yu et al., 2017), cerebral small vessel disease(van der Holst et al., 2013), and acute mild traumatic brain injury(Wu et al., 2010).

An intriguing work by Bathelt and colleagues(2019) who used a data-driven community-clustering algorithm to analyze differences in white matter microstructure in children and adolescents (mean age ~11) identified two subgroups characterized by a prominent difference in FA in bilateral cingulum. Critically, these two brain types differed in cognitive abilities with the higher FA group exhibiting better performance in tasks on working memory, long-term memory, fluid intelligence, and vocabulary.

3.3.6 Inferior Fronto-Occipital Fasciculus (IFOF)

The inferior fronto-occipital fasciculus is one of the major association fiber systems which is recognized as a part of the dorsal visual stream (Schmahmann& Pandya, 2006). It links the occipital cortex, temporo-basal areas, and superior parietal lobe with the frontal regions, passing through the temporal lobe and insula(Martino et al., 2010) and crossing the superior longitudinal fasciculus, arcuate fasciculus, inferior longitudinal fasciculus, and middle longitudinal fasciculus (Wu et al., 2016). High-angular-resolution diffusion imaging (HARDI) shows that the IFOF fibers vary in the sites of origin with most of the fibers originating from the lateral and medial orbital frontal cortices, frontal polar cortex, including the fronto-marginal gyrus and transverse frontopolar gyrus, superior frontal gyrus and inferior frontal gyri, including the pars opercularis, pars triangularis, and pars orbitalis, and from the middle frontal gyrus (Wu et al., 2016). Fibers of the posterior portion of the IFOF terminate in the pericalcarine (cuneus and lingual gyrus) and fusiform gyri and occipital region, including the inferior, middle, and superior occipital lobes, with some fibers terminating in the superior parietal lobe, angular and postcentral gyri (Wu et al., 2016). Similar to other major white matter fiber tracts, including the genu and body of the corpus callosum, inferior longitudinal fasciculus, posterior thalamic radiations, superior longitudinal fasciculus, internal capsule, and subdivisions of the cingulum, FA in the inferior fronto-occipital fasciculus decreases with age (Bendlin et al., 2010).

Tractography studies suggest the role of the IFOF in semantic and visual processingand attention(Catani & Thiebaut de Schotten, 2008; Leng et al., 2016). In particular, bilateral IFOF (especially in the left frontal and right occipital parts) is implicated in the maintenance of executive functions, whereas the parietal and insular parts of the left IFOF are involved in alerting (Leng et al., 2016). Higher FA values in the IFOF predict better reading scores(Lebel et al., 2013), and FA in the left IFOF were found to be related to better orthographic processing(Vandermosten et al., 2012). In addition, the IFOF plays a crucial role in multimodal semantic processes, including naming and non-verbal semantic associations(Gil-Robles et al., 2013; Han et al., 2013; Moritz-Gasser et al., 2013; Rollans et al., 2017), constituting a bilateral network which sustains non-verbal semantic system(Herbet et al., 2017). Moreover, microstructural properties of the IFOF predict the object working memory task performance and therefore underlie nonspatial working memory processing(Walsh et al., 2011).

Anatomical features of the IFOF are still a matter of discussion and there is still no consensus achieved regarding the question to what degree the anatomical and microstructural properties of this bundle can vary in healthy brains. Moreover, there is a limited number of studieswhich provide normative values for IFOF and characterize the maturation pattern of this fiber tract over development (Conner et al., 2018; Wu et al., 2016). Therefore, the investigation of IFOF myelination and microstructure may make a great contribution to what is known about one of the major association white matter pathways in the brain. In the present study, microstructural properties of the right and left IFOF will be evaluated.

Biological development is associated with maturation of white matter connecting distant and nearby regions of the brain. The mechanisms which underlie myelination and are associated with alterations in microstructural characteristics of the white matter are poorly understood. DTI is a widely-used neuroimaging technique which not only provides a measure of the degree of myelination, but it also allows for a non-invasive assessment of the fiber orientation, diameter, packing, and density, as well as fiber integrity and myelin thickness, while MPF is a new method for white matter evaluation. The present study will be evaluated the maturation pattern, as well as differences related to gender and hemisphere in three fiber tracts: the corpus callosum with its subdivisions, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus, in children, adolescents, and adults. Since the changes in DTI metrics may indicate not only the alterations in myelination which occur over development, but may also reflect changes in other characteristics of white matter, which makes it difficult to determine what stands behind a given change, and, therefore, highlights the importance of conducting studies which provide normative structural values for each fiber tract across development.

Hypothesis

The goal of the present study was to examine effects among white matter microstructure, development, hemispheric lateralization, and gender inthree fibers tracts associated with complex cognitive functions: the corpus callosum (CC) with its subdivisions, inferior longitudinal fasciculus (ILF), and inferior fronto-occipital fasciculus (IFOF), in children (8-11years), teenagers (12-15 years), and adults (20-30 years), using DTI and fast MPF mapping.Specifically,based on the aforementioned literature it is hypothesized: A) Age effects:Myelination in all fiber tracts increases with age, which is accompanied by an increase in fractional anisotropy and a decrease in mean and radial diffusivities; B) Gender effects: Males and females differ in myelination indices in the CC, but not in the IFOF and ILF; C) Hemispheric asymmetry: Myelination indices in the ILF exhibit left>right asymmetry; D) Imaging method effects: Differences and effects observed in DTI will be replicated in MPF.



Chapter 4. Materials and Methods

4.1 Participants

A total of 49 Russian healthy volunteers aged 8 to 30 years (8-11 years, n =19, 11 females, mean age 10±0.79; 12-15 years, n = 18, 9 females, mean age 14±0.87; 20-30 years, n = 12, 7 females, 20-30 years, mean age 26±2.7) participated in the study. All the participants had no contraindications to MRI scanning, family history of psychiatric or neurological disorders, head trauma, and drug or alcohol abuse. The study was approved by the local Ethics Committee of the National Research University Higher School of Economics. All adult participants and parents of children signed written informed consent. After the scanning, participants received a monetary reward (3000 RUB ~ $40)and structural 3D T1 images of their brain.Statistical power considerations are discussed in the literature, and there are no specific recommendations yet, but regular approach is to involve at least 7 to 12 participants (Hayasaka et al., 2007).

4.2 MRI Data Acquisition

The study was carried out in the facility of the Research Institute of Emergency Children's Surgery and Traumatology. MRI acquisition was performed on a 3T magnetic resonance scanner (Philips AchievadStream 3.0T; the Netherlands) and included an anatomical T1-weighted image, DTI, and MPF scans. The high-resolution T1-weighted images were collected with a 3D fast gradient inversion recovery sequence MRI (MPRage) sequence (TR = 8.2 ms, TE = 3.7 ms, 1 mm isotropic voxel, flip angle = 8°; 179 slices). Diffusion tensor imaging data were collected using a DW-SE-EPI echo planar imaging sequence (TR = 7 000 ms, TE = 72 ms, matrix size = 128Ч128, field of view = 224Ч224 mm2, 2 mm isotropic voxel, flip angle = 90°, b0 = 0 (1 acquisition), b = 700 s/mm2 , 32 orientations of diffusion gradient, slice thickness = 2 mm, 60 slices, parallel imaging SENSE factor 2, partial Fourier sampling factor 6/8). Fast macromolecular proton fraction acquisition was carried out using cross-relaxation imaging sequence (TR = 50 ms, TE = 2.3 ms, matrix size = 240Ч240Ч240, field of view = 240Ч240Ч180 mm3, 1 mm isotropic voxel, flip angle = 15°, slice thickness: 2 mm (acquisition) and 1 mm (reconstruction), 180 slices, imaging SENSE factor 1.3 (AP and RL), NSA = 1, pulse angle = 450°, pulse duration = 24 ms, offset 4000, Gaussian pulse sg = 300_100_0) (Yarnykh, 2002). Scanning these sequences took about35 minutes during whichparticipants were offered to watch a video.

4.3 Data Preprocessing

The preprocessing of the diffusion-weighted data was carried out using ExploreDTI software (Leemans et al., 2009) and included the following steps: (1) affine registration of the diffusion-weighted images to the anatomical (T1) non-diffusion-weighted images using SPM 12 software; (2) correction for participant motion and distortions and EC/EPI distortions with an appropriate re-orientation of the phase encoding vectors (Leemans& Jones, 2009); (3) whole-brain tractography and automatic reconstruction of the whole-brain maps for FA, RD, and MDwith a turning angle of 45° and an amplitude threshold of 0.2. The preprocessing and processing of the MPF data were performed using an in-house software developed by Yarnykh (2016) according to a standard pipeline (Yarnykh, 2012).

4.4 Data Processing

Tractography was performed using TrackVis 0.6.1 software. The regions of interest (ROI) were drawn on the FA map reconstructed during preprocessing. The bundles considered in this thesis are the CC with its subdivisions (genu, body, and splenium) and the bilateral IFOF and ILF. The tracts on the right and left sides were analyzed independently.The processing of the MPF data included coregistration of the MPF map to FA map (mipav 10.0.0) using constrained automatic registration algorithmand further extraction of MPF values for each fiber tract reconstructed during DTI processing step.

4.5 Statistical Analysis

Statistical processing was carried out using GraphPad Prism 8 software. The distribution of the data was assessed using Shapiro-Wilk test. Age-related differences in FA, RD, MD, and MPF between the subjects of three age groups were assessed using one-way analysis of variance (ANOVA) for normal distribution or Kruskal-Wallis test if the data distribution was not normal. The interaction between gender and age, as well as the effect of each parameter were assessed using two-way ANOVA. The IFOF and ILF were analyzed by each hemisphere separately. Gender-related differences in myelination between males and females within one age group were assessed using Student's t-test or Mann-Whitney U test. The correlation between myelination indices and age was assessed using Pearson's correlation.The differences were considered statistically significant at p<0.5.The following effect size conventions were used for Pearson's r(Akoglu, 2018):weak - r?0.3, moderate - 0.3 ? r?0.6, and strong - r?0.6. The effect size was evaluated using Cohen's d for t test and Cohen's f2 for one-way ANOVA. The following effect size conventions were usedfor Cohen's d(Chen et al., 2010): small -d?0.2, medium - d?0.5, and large - d?0.8; for Cohen's f2(Cohen, 1988): small - f2??0.1, medium -f2??0.25, and large -f2??0.40.



Chapter 5. Results

Results are reported by method (DTI and MPF), considering age, gender, and hemispheric laterality for fiber tracts: CC (genu, body, and splenium), IFOF, and ILF

5.1 Diffusion tensor imaging

5.1.1 Corpus callosum: fractional anisotropy

An example of the CC reconstructed in an adult participant is illustrated in Figure 5.

Figure 5 - In vivo fiber tractography of the CC:sagittal view

Descriptive statistics on fractional anisotropy in the CC is tabulated in Table 1. Table 1 also marks the differences in FA between three regions of the CC for each age group.

Table 1 - Fractional anisotropy in the CC of children, adolescents, and adults

FA in CC		Children
	Males M ± SD (N = 8)	Females M ± SD (N = 11)	All M ± SD (N = 19)
Genu	0.52±0.031	0.53±0.029	0.52±0.029****
Body	0.52±0.029	0.52±0.029	0.52±0.029****
Splenium	0.55±0.019	0.57±0.020	0.56±0.021
		Adolescents
	Males M ± SD (N = 9)	Females M ± SD (N = 9)	All M ± SD (N = 18)
Genu	0.54±0.026	0.53±0.016	0.53±0.022****
Body	0.52±0.022	0.53±0.019	0.52±0.021****
Splenium	0.56±0.025	0.58±0.017	0.57±0.023
		Adults
	Males M ± SD (N = 5)	Females M ± SD (N = 7)	All M ± SD (N = 12)
Genu	0.54±0.037	0.54±0.029	0.54±0.031*
Body	0.53±0.023	0.54±0.023	0.53±0.022
Splenium	0.56±0.031	0.57±0.026	0.57±0.027

Notes. *p<0.05, ****p<0.0001 compared to the splenium

One-way analysis of variance indicated higher FA values in the splenium of the corpus callosum compared to the genu and body in children (F=19, ****p<0.0001, f2=0.66) and adolescents (F=19, ****p<0.0001, f2=0.65), and higher FA in the splenium than in the genu of the CC in adults (F=3.8, *p=0.048, f2=0.50).

Table 2 shows correlations between three DTI metrics in the genu, body, and splenium of the CC and age.

Table 2 - Correlation between age and FA, RD, and MD in three subdivisions of the CC across all participants (n=49)

Age by DTI index	Genu	Body	Splenium
FA	r = 0.32*	r = 0.41**	r = 0.23
RD	r = -0.38**	r = -0.55****	r = -0.05
MD	r = -0.31*	r = -0.51***	r = -0.05

Notes. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001



Microstructural characteristics of the genu and body of the CC demonstrate weak to moderate correlation with age, whereas the posterior regions of the tract show no significant associations.

One-way ANOVA revealed a pronounced age-related age incline (F=7, **p=0.002) in FA in the body of the CC during the transition from childhood to adolescence (*p=0.04, f2=0.33), and from childhood to adult age (**p=0.0015, f2=0.33). Two-way ANOVA demonstrated that this difference was influenced by age (F=3.3, *p=0.045), but not gender(Figure6). No interaction between age and gender was found.

Figure 6 - Fractional anisotropy (Mean ± SD) in three subdivisions of the CC in children, adolescents, and adults, and the difference in FA in the body of the CC in adolescents and adults compared to children. Ordinary one-way ANOVA, Holm-Sidak multiple comparison test, *p<0.5, **p<0.01

Notably, gender-related differences were found only in the splenium of the CC in the group of younger children where FA in females was higher than in males (t=2.1,*p=0.046, d=0.18).

5.1.2 Corpus callosum: radial diffusivity

Normative values for radial diffusivity in three subdivisions of the CC in children, adolescents, and adults are shown in Table 3.

Table 3 - Radial diffusivity in the CC of children, adolescents, and adults

RD in CC		Children
	Males M ± SD (N = 8)	Females M ± SD (N = 11)	All M ± SD (N = 19)
Genu	0.00061±8.6e-005	0.00059±9.3e-005	0.00060±8.8e-005
Body	0.00064±0.00012	0.00061±7.4e-005	0.00061±6.7e-005
Splenium	0.00060±3.0e-005	0.00055±3.2e-005	0.00057±3.7e-005
		Adolescents
	Males M ± SD (N = 9)	Females M ± SD (N = 9)	All M ± SD (N = 18)
Genu	0.00054±3.8e-005	0.00056±2.3e-005	0.00055±3.2e-005
Body	0.00057±3.0e-005	0.00055±1.7e-005	0.00056±2.5e-005
Splenium	0.00054±3.3e-005	0.00057±3.3e-005	0.00055±3.5e-005
		Adults
	Males M ± SD (N = 5)	Females M ± SD (N = 7)	All M ± SD (N = 12)
Genu	0.00053±5.1e-005	0.00053±4.5e-005	0.00053±4.5e-005
Body	0.00053±3.4e-005	0.00053±4.0e-005	0.00053±3.6e-005
Splenium	0.00056±6.5e-005	0.00054±4.8e-005	0.00055±5.4e-005

RD values in the genu (Kruskal-Wallis statistic=9.1, *p=0.01) and body (Kruskal-Wallis statistic=17,***p=0.0002) of the corpus callosum exhibited a decreasing pattern with age, which was reflected in a decrease in RD values in the genu (**p=0.01, f2=0.43) and body (***p=0.0001, f2=0.56) of the CC in adults compared to children (Fig. 7). In adolescents and adults, the mean RD values were within the same range. Two-way analysis of variance indicated a pronounced effect of age both in the genu (F = 5.6, **p=0.007) and body (F=9.2, ***p=0.0005) of the CC (Figure7).

Figure 7 - Radial diffusivity (Mean ± SD) in three subdivisions of the corpus callosum in children, adolescents, and adults. Kruskal-Wallis test, Dunn's multiple comparison test, **p<0.01, ***p<0.001.

In children, RD in the splenium of the corpus callosum was significantly lower in female participants (t=2, *p=0.013, d=0.2) than in male, indicating differences in myelination between two genders during the first decade of life.

5.1.3 Corpus callosum: mean diffusivity

Normative values for mean diffusivity in three subdivisions of the CC in children, adolescents, and adults are shown in Table 4.

Table 4 - Mean diffusivity in the CC of children, adolescents, and adults

MD in CC		Children
	Males M ± SD (N = 8)	Females M ± SD (N = 11)	All M ± SD (N = 19)
Genu	0.00085±5.0e-005	0.00085±3.0e-005	0.00085±3.9e-005
Body	0.00088±8.6e-005	0.00086±3.7e-005	0.00087±6.1e-005
Splenium	0.00093±0.00014	0.00087±4.8e-005	0.00089±0.00010
		Adolescents
	Males M ± SD (N = 9)	Females M ± SD (N = 9)	All M ± SD (N = 18)
Genu	0.00081±3.2e-005	0.00084±2.2e-005	0.00082±3.0e-005
Body	0.00084±3.5e-005	0.00084±1.5e-005	0.00084±2.7e-005
Splenium	0.00089±3.8e-005	0.00087±4.1e-005	0.00088±3.9e-005
		Adults
	Males M ± SD (N = 5)	Females M ± SD (N = 7)	All M ± SD (N = 12)
Genu	0.00081±3.7e-005	0.00081±4.2e-005	0.00081±3.8e-005с
Body	0.00080±3.1e-005	0.00081±4.1e-005	0.00080±3.6e-005
Splenium	0.00088±7.6e-005	0.00087±4.9e-005	0.00087±5.9e-005

Notes. *p<0.05, ****p<0.0001 compared to splenium

One-way ANOVA demonstrated a trend of decrease in MD was observed in the genu of the CC (F=4.7, *p=0.01) in adults compared to children (*p=0.025, f2=0.43, effect of age: F=4, *p=0.025), and in the body of the CC (F=9.3, ***p=0.0004) in adults compared to children (***p=0.0003, f2=0.5) and adolescents(*p=0.024, f2=0.5) with a significant effect of age (F=7.7, **p=0.0014), indicating a gradual increase in myelination in these fiber tracts during the transition from childhood to adolescence (Figure 8).

Figure 8 - Mean diffusivity (Mean ± SD) in three subdivisions of the corpus callosum in children, adolescents, and adults. Ordinary one-way ANOVA, Holm-Sidak multiple comparison test, *p<0.05, ***p<0.001



5.2.1 Inferior fronto-occipital fasciculus: fractional anisotropy

An example of the IFOF reconstructed in an adult participant is illustrated in Figure 9.

Figure 9 - In vivo fiber tractography of the right IFOF: sagittal view. Red color indicates right/left direction of the diffusion gradient; green color indicates anterior/posterior diffusion direction; blue color indicates the predominant superior/inferior direction

Normative values for fractional anisotropy in the right and left IFOF in children, adolescents, and adults are shown in Table 5.

Table5 - Fractional anisotropy in the IFOF of children, adolescents, and adults

FA in IFOF		Children
	Males M ± SD (N = 8)	Females M ± SD (N = 11)	All M ± SD (N = 19)
Right	0.47±0.026	0.48±0.016	0.48±0.02
Left	0.47±0.023	0.47±0.020	0.47±0.02
		Adolescents
	Males M ± SD (N = 9)	Females M ± SD (N = 9)	All M ± SD (N = 18)
Right	0.48±0.019	0.50±0.018	0.49±0.019
Left	0.48±0.025	0.49±0.017	0.49±0.021
		Adults
	Males M ± SD (N = 5)	Females M ± SD (N = 7)	All M ± SD (N = 12)
Right	0.49±0.029	0.50±0.028	0.50±0.027
Left	0.49±0.026	0.49±0.033	0.49±0.029

The correlation between three DTI metrics in the right and left IFOF and age are shown in Table 6.

Table 6 - Correlation between age and FA, RD, and MD in the right and left IFOF in all participants (n=49)

Age by DTI index	Right	Left
FA	r = 0.33*	r = 0.46***
RD	r = -0.55****	r = -0.35*
MD	r = -0.43*	r = -0.21

Notes. * p<0.05, *** p<0.001, **** p<0.0001

Both right and left IFOF demonstrate significant association with age across all indices, except for mean diffusivity in the left IFOF.

One-way analysis of variance indicated an increasein FA values in the right IFOF (F=4.2, *p=0.02) in adolescents (*p=0.04, f2=0.34) and adults (*p=0.04, f2=0.34) compared to children with a significant effect of age (F=4.4, *p=0.02), but not gender. In the left hemisphere, FA in the IFOF exhibited an increasing pattern (F=5.4, **p=0.008) with age (adolescents>children, *p=0.02; adults>children, *p=0.016, f2=0.4; effect of age: F=4.3, *p=0.02) (Figure10).



Figure 10 - Fractional anisotropy (Mean ± SD) in the inferior fronto-occipital fasciculus in children, adolescents, and adults. Ordinary one-way ANOVA, Holm-Sidak multiple comparison test, *p<0.05.

No overall gender differences for FA in the left and right IFOF were found.

5.2.2 Inferior fronto-occipital fasciculus: radial diffusivity

Normative values for radial diffusivity in the right and left IFOF in children, adolescents, and adults are shown in Table 7.

Table 7 - Radial diffusivity in the IFOF of children, adolescents, and adults

RD in IFOF		Children
	Males M ± SD (N = 8)	Females M ± SD (N = 11)	All M ± SD (N = 19)
Right	0.00067±0.00013	0.00060±3.2e-005	0.00061±4.1e-005
Left	0.00065±9.1e-005	0.00060±3.1e-005	0.00061±4.2e-005
		Adolescents
	Males M ± SD (N = 9)	Females M ± SD (N = 9)	All M ± SD (N = 18)
Right	0.00058±1.7e-005	0.00057±2.7e-005	0.00058±2.3e-005
Left	0.00057±3.5e-005	0.00058±2.0e-005	0.00057±2.8e-005
		Adults
	Males M ± SD (N = 5)	Females M ± SD (N = 7)	All M ± SD (N = 12)
Right	0.00055±4.2e-005	0.00055±4.1e-005	0.00055±4.0e-005
Left	0.00058±4.1e-005	0.00058±4.7e-005	0.00058±4.3e-005

Radial diffusivity in the right (adolescents<children, *p=0.01; adults<children, ****p<0.0001, f2=0.6) and left (adolescents<children, *p=0.015, f2=0.44)IFOF decreased(F=11, ***p=0.0001) as a function of age, whose effect is much more pronounced in the right hemisphere (F=12, ****p<0.0001), than in the left (F=4.9, *p=0.013). Notably, during the transition from adolescence to adulthood,IFOF in the left hemisphere increases again and approaches the values observed in children, which may indicate cyclic pattern of myelination in this region (Figure11).

Figure 11 - Radial diffusivity (Mean ± SD) in the inferior fronto-occipital fasciculus in children, adolescents, and adults. Ordinary one-way ANOVA, Holm-Sidak multiple comparison test, *p<0.05, ****p<0.0001



5.2.3 Inferior fronto-occipital fasciculus: mean diffusivity

Normative values for mean diffusivity in the right and left IFOF in children, adolescents, and adults are shown in Table 8.

Table 8 - Mean diffusivity in the IFOF of children, adolescents, and adults

MD in IFOF		Children
	Males M ± SD (N = 8)	Females M ± SD (N = 11)	All M ± SD (N = 19)
Right	0.00089±0.00011	0.00085±3.7e-005	0.00086±7.8e-005
Left	0.00085±5.8e-005	0.00084±3.4e-005	0.00084±4.5e-005
		Adolescents
	Males M ± SD (N = 9)	Females M ± SD (N = 9)	All M ± SD (N = 18)
Right	0.00083±3.3e-005	0.00083±4.1e-005	0.00083±3.6e-005
Left	0.00080±3.3e-005	0.00082±2.3e-005	0.00081±2.9e-005
		Adults
	Males M ± SD (N = 5)	Females M ± SD (N = 7)	All M ± SD (N = 12)
Right	0.00079±3.2e-005	0.00080±4.8e-005	0.00080±4.1e-005
Left	0.00083±4.1e-005	0.00083±4.1e-005	0.00083±3.9e-005

Similar to RD, MD in the right IFOF (F=6.4, **p=0.003)shows decreasing pattern (adults<children, **p=0.0025, f2=0.44) with age (effect of age: F=6.4, **p=0.0036), whereas no differences between age groups are found in the left hemisphere. MD in the IFOF was the only index which indicated a trend (t=2.1, *p=0.05, d=0.07) of difference in myelination between two hemispheres in adult participants (Figure 12).



Figure 12 - Mean diffusivity (Mean ± SD) in the inferior fronto-occipital fasciculus in children, adolescents, and adults. Ordinary one-way ANOVA, Holm-Sidak multiple comparison test, **p<0.01

5.3.1 Inferior longitudinal fasciculus: fractional anisotropy

An example of the ILF reconstructed in an adult participant is illustrated in Figure 13.

Figure 13 - In vivo fiber tractography of the right ILF: sagittal view. Red color indicates right/left direction of the diffusion gradient; green color indicates anterior/posterior diffusion direction; blue color indicates the predominant superior/inferior direction

Normative values for fractional anisotropy in the right and left ILF in children, adolescents, and adults are shown in Table 9. Although weak age correlations were observed, between age-group differences (e.g., children vs adults) in FA were not found in none of the hemisphere.

Table 9 - Fractional anisotropy in the ILF of children, adolescents, and adults

FA in ILF		Children
	Males M ± SD (N = 8)	Females M ± SD (N = 11)	All M ± SD (N = 19)
Right	0.47±0.020	0.48±0.023	0.47±0.023
Left	0.46±0.015	0.47±0.019	0.47±0.017
		Adolescents
	Males M ± SD (N = 9)	Females M ± SD (N = 9)	All M ± SD (N = 18)
Right	0.48±0.021	0.50±0.013	0.49±0.018
Left	0.48±0.026	0.49±0.0078	0.48±0.020
		Adults
	Males M ± SD (N = 5)	Females M ± SD (N = 7)	All M ± SD (N = 12)
Right	0.48±0.029	0.48±0.021	0.48±0.023
Left	0.48±0.025	0.48±0.036	0.48±0.031

The correlation between three DTI metrics in the right and left inferior longitudinal fasciculus and age is shown in Table 10.



Table 10 - Correlation between age and FA, RD, and MD in the right and left IFOF in all participants (n=49)

Age by DTI index	Right	Left
FA	r = 0.35*	r = 0.32*
RD	r = -0.49***	r = -0.29*
MD	r = -0.47***	r = -0.25

Notes. * p<0.05, *** p<0.001, **** p<0.0001

5.3.2 Inferior longitudinal fasciculus: radial diffusivity

The values for radial diffusivity in the right and left ILF in children, adolescents, and adults are shown in Table 11.

Table 11 - Radial diffusivity in the ILF of children, adolescents, and adults

RD in ILF		Children
	Males M ± SD (N = 8)	Females M ± SD (N = 11)	All M ± SD (N = 19)
Right	0.00065±6.0e-005	<...

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