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Keywords:

  • corpus callosum;
  • fractionator;
  • human anatomy;
  • quantification;
  • stereology

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Using the fractionator principle, the total number, density and diameter size of myelinated callosal fibers were estimated in the corpus callosum (CC) of 10 Danish males between 39 and 60 years of age. All sampled brains had been used in previous quantitative studies, for example, studies of neocortical neuron number, and were selected to determine whether the variability in the neocortical neuron number correlated with the total number of myelinated callosal fibers. Middle-aged males had an average of 138 × 106 (coefficient of variance; CV = 0.19) myelinated fibers, but did not show any correlation with the neocortical neuron number (= 0.25; = 0.49). The mean area of the CC was estimated to be 7.2 cm2 (CV = 0.17), and showed a significant correlation with the number of callosal fibers (= 0.69, = 0.041). Additionally, an overall density decline from the anterior to the posterior region of the CC was observed, with an inverse relationship between the distribution of large and small fibers along the callosal axis. This study suggests that many mechanisms are involved in the development and determination of axonal projections across the CC that cannot simply be explained by the neocortical neuron number. Further, a positive correlation between callosal fibers and the CC area verifies that callosal fibers are the factor responsible for CC size. Finally, the number of callosal fibers and their diameters are distributed along the CC in a specific pattern that reflects interactions with different brain regions.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The corpus callosum (CC) has been a focus of extensive research for several decades in the understanding of its anatomical organization and function. Research in the field showed major progress, with the pioneering work performed by scientists such as Sperry and Myers (Myers & Sperry, 1958; Sperry, 1961) that introduced the world to the modern era of split-brain research in the late 1950s (Liss, 1984; Gazzaniga, 2005). These studies and the later appearance of neuronal pathway tracing techniques have contributed vigorously to our present conception of the hemispheric specialization, brain connectivity and regional specificity of the CC.

In general, the CC constitutes the major white matter tract that connects the two brain hemispheres through callosal fibers crossing the midline in the depths of the great longitudinal fissure. These inter-hemispheric connections pass into various parts of the cerebral cortex along the antero-posterior axis of the CC and establish a relative topographical representation of the different cortical areas. Thus, each of the different callosal subregions are functionally specialized, although exceptions may occur, through specific pathways, which ensures coordinated and integrated brain function by relaying sensory, motor and cognitive information between the two hemispheres (Aboitiz et al., 1992a; Aboitiz & Montiel, 2003; Ota et al., 2006; Chao et al., 2009).

Investigations of subregions have unfortunately been complicated by missing macroscopic anatomical landmarks that delimit different areas of the CC. Therefore, several midsagittal geometric sampling schemes have been applied to segregate the CC into recognizable segments, typically consisting of three anatomical parts: the genu, the body and the splenium. More comprehensive division has been specified by vertical lines spaced into fractions as seen in either Witelson’s classification: rostrum, genu, rostral body, anterior midbody, posterior midbody, isthmus and splenium (Witelson, 1989); or De Lacoste’s five equal portion principle (de Lacoste et al., 1985). However, in recent years, these methods have been questioned as Witelson’s classification originates in non-human primates (Hofer & Frahm, 2006; Hofer et al., 2008) and partitioning methods generally avoid taking neuronal fiber composition or CC connections into account. Nevertheless, the explicit heterogeneous callosal fiber distribution along the CC supports the rationale for studies of the subdivided CC (Aboitiz et al., 1992a,b). Despite decades of interest in the understanding of the callosal structure little attention has been given to quantitative in vitro investigations of the callosal fiber number, and studies addressing this issue have shown considerable variations (Tomasch, 1954; Highley et al., 1999). Additionally, these studies have either been performed using semi-quantitative methods or only included information from callosal subregions, which have made comparisons difficult. In recent years, in vivo studies using different variations of magnetic resonance imaging, such as diffusion tensor imaging and fiber tractography, have gained ground in the investigation of CC morphology as they provide insight to the orientation and integrity of callosal microstructures by mean diffusivity and fractional anisotropy (Aboitiz et al., 1992a; Biegon et al., 1994; Ota et al., 2006). However, the image resolution has not been able to distinguish individual fibers, and the statistical model-based approach, though improving, is sensitive to a variety of effects. Quantitative callosal imaging results are therefore difficult to compare with histological studies (Alexander et al., 2010), and the need for a stereological approach emerges to both clarify differences in in vitro studies and to create a reference for future imaging studies.

In the present in vitro study, a stereological method based on the fractionator principle (Gundersen & Jensen, 1985) was applied to quantitatively estimate the total number of myelinated callosal fibers, the distribution of the fibers and average fiber diameter along the length of the CC.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Subjects

Ten post-mortem Danish brains (from male subjects between 39 and 60 years of age) were selected from the brain bank at Bispebjerg Hospital, Denmark, and included in the study. The subjects had no neurological or psychiatric disorder. All autopsies were carried out within 20–72 h post-mortem, and the brains were subsequently fixed in 0.1 m sodium phosphate-buffered (pH 7.2) 4% formaldehyde for at least 5 months. Although there was a substantial difference in length of fixation in formalin and some differences in the post-mortem interval, we encountered no correlation between the quality of the tissue/counting conditions and the two selected parameters.

All specimens selected for the study were used in previous quantitative investigations, such as studies of neocortical neuron number, estimation of total neocortical glial cell number and estimation of total white matter volume (Pakkenberg & Gundersen, 1997; Marner et al., 2003; Pelvig et al., 2008).

When selecting the subjects to be included we deliberately chose brains of males with very different neocortical neuron number. Our brain bank contains approximately 25 brains aged from 40 to 60 years, from which 10 male subjects were selected with low (e.g. 17.0 × 109), intermediate and high neocortical neuron numbers (e.g. 30.2 × 109). Our sample is not representative of the normal population of males, but was selected to compare the total number of neocortical neurons with the total number of myelinated fibers in the CC. The neocortical neuron number can therefore not be directly compared with previous counts in humans. We expected the number of myelinated fibers to be higher in brains with a high number of neocortical neurons, although evidently not all neocortical neurons project through the callosum.

The stereological design

The fractionator is a systematic uniform random sampling (SURS) method, primarily used for the estimation of total particle number within a population. Its uniquely tidy sampling scheme makes it one of the most efficient stereological estimators (Gundersen & Jensen, 1985; Gundersen, 1986, 2002; West, 1993). The principle of the fractionator involves sampling of particles in a systematic, random and uniform manner with known and predetermined precision. Only a known fraction of the object is sampled, and an estimate of the total population in the object can be found as the inverse of the sampling fractions multiplied by ΣQ, the number of counted fibers. Originally, all sampling units could be placed in an arbitrary sequence order, but this was modified in the current study so fractions along the antero-posterior axis of the specimen, starting at the anterior region, created a smooth gradient of the investigated structure. The determined gradient provided a simple and direct estimation of fiber distribution along the axis of the CC. The number of fibers, N, is equal to:

  • image(1)

where f1 is the fraction of strips sampled (ssf) in the total specimen and f2 is the area-sampling fraction (asf). No height-sampling fraction (hsf) is needed in the modified fractionator as callosal fibers traverse through the whole width of the CC.

Sampling strategies

Establishment of unbiased principles is the optimal solution for the estimation of true quantitative values. The true value of a given parameter is approached as the amount of sampling increases; for example, the precision of an estimate is determined by the amount of sampling. The coefficient of variance (CV) is an estimate of the biological variance between groups of individuals. Differences between groups are therefore related to the difference in means and the variance of the means, standard deviation (SD).

  • image(2)

If all particles were counted in all individuals, the variance would exclusively be due to the real inherent biological difference (ICV). However, performing such a task would be both time-consuming and inefficient, due to the rather high biological variation existing between individuals. Stereological quantification is therefore based on an estimate of the true value with an observed CV (OCV). OCV includes a contribution from both the ICV and the variance from the estimation in each individual. The latter contributer, the estimated precision made in an individual, is denoted by the coefficient of error (CE). It is in three-dimensional calculations dominated by sampling errors related to counting noise and SURS.

However, as the modified fractionator does not contain any hsf, the CE becomes a two-dimensional estimate with an unknown SURS contribution from blocks, fields and frames. Sampling errors concerning SURS will thus be excluded in the present study. CE will then exclusively be explained by counting noise, although this might be an underestimate. In stereological terms, noise can be defined as the variance of the counted fibers, representing the interval in which the estimate could change if the counting grid had been placed differently. It is equal to ΣQ and contributes to CE as stated below.

  • image(3)

The observed coefficient of variance is summarized as an estimate of the true CV determined by ICV and CE (West & Gundersen, 1990; West, 1993; Slomianka & West, 2005)

  • image(4)

The real ICV can be derived from the above stated equation:

  • image(5)

Counts of 100–200 particles, counted in approximately 75 counting frames on approximately seven–eight systematically random samples, are often adequate for producing CE values sufficient for most biological studies (West, 1993). However, this is only a guideline; for example, a cluster distribution or very inhomogeneous distributions can require counts of additional counting frames. As a rule, the CE should be approximately half or less of the OCV to obtain an acceptable precision. If this is fulfilled, the variance will be dominated by the true inherent variance among brains (ICV; Gundersen, 1986).

The modified fractionator

Brains were divided medially in the sagittal plan of the CC, and the maximal linear length of the CC in the antero-posterior dimension was determined. For biological reasons, three vertical lines divided the maximal length into three arbitrary regions that were marked with tissue-marking dye (Cancer Diagnostics). Because the area of the frontal region was larger than the other two subdivisions, the frontal region was divided into two roughly equal-sized pieces orthogonally to the axis of the CC. The subdivision now included four parts, two by two having roughly the same size. These four parts were further divided into two equal pieces, which gave eight strata. Two additional divisions of each stratum were subsequently performed, producing 32 strips with an orthogonal orientation to the axis of the CC (Fig. 1). A fixed sampling period, k, of four was used as a standard in the study, starting with a random number between 1 and 4, selected from a random number table. The sampling fraction obtained was = 1/k. The number of sampled pieces therefore reached eight strips (¼ of the CC).

image

Figure 1.  Illustration of the stereological fractionator principle. (a) The brain is divided medially in the sagittal plane of the CC, and the maximal linear length of the CC in the antero-posterior dimension is determined. The CC is divided into a total of 32 strips positioned orthogonally to the axis of the CC (detailed description in section "The modified fractionator" above). (b) A fixed sampling period of four is used as a standard (random start between 1 and 4), giving a total of eight strips (f1/ssf). (c) Each of the eight strips is embedded in Epon with the medial surface orientated so a 1-μm transverse section can be cut from each of the eight Epon-embedded strips. (d) Fiber counting is performed on each of the eight 1-μm-thick transverse sections by unbiased counting frames and an xy step length, generating approximately seven–eight frames per section (f2/asf).

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Embedding and microtomy

Each of the eight strips was cut from the specimen, with an approximate thickness of 2 mm, and embedded in 2% agar. The agar helped to maintain the orientation of the tissue strips. The tissue was rinsed in 0.1 m cacodylate buffer for 4 × 15 min and fixed for 2 h in 2% OsO4 to preserve the myelin sheaths. Subsequently, the strips were rinsed in 0.1 m cacodylate buffer for 2 × 30 min before dehydration with increasing ethanol concentrations (30–100%). To complete dehydration, the tissue was submerged in propyleneoxid for 3 × 10 min. Infiltration of the tissue with Epon (poly/bed®812 embedding media; Polyscience) was performed gradually with Epon-propyleneoxid compounds of increasing Epon concentration. Pure infiltration of Epon was executed for 48 h before polymerization in a heating chamber for 24 h at 60 ºC. Sections of 1 μm were cut from the block faces of 2–4 mm side length using an ultra microtome (UCT Ultra cut; Leica) equipped with an 8-mm diamond knife (Histoknife wet; Drukker). All sections were transferred onto glass slides and allowed to dry at 50 ºC before a specific staining of the myelin sheets of the callosal axons was performed with p-phenylenediamine for 4 min (Fig. 2). The non-myelinated fibers are prone to disintegrate by fixation techniques and long post-mortem intervals such as ours, therefore they were not included in the counts.

image

Figure 2.  One-micron transverse sections of the CC as seen in the p-phenylenediamine stain. (a) Section from genu. (b) Section from the midbody. (c) Section from splenium.

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The fiber counting was performed in a fractionator sampling design on each sampled tissue strip using unbiased counting frames of 14.9 μm2 (corrected for magnification) and an xy step length that generated approximately seven–eight frames per strip. The counting was performed at a final magnification of 3100× using CAST software (Visopharm, Hørsholm, Denmark). Further, the axon diameter of the myelinated fibers was estimated by measuring the profile diameter perpendicular to their longest axis of the fiber profile. Our ruler had an unmagnified length of 26 mm and was divided into eight classes, class zero to class seven, not including the interval between the start of the ruler to the start of class zero. The classes of the ruler are of equidistant width on a logarithmic scale of length as the lower limit of the kth class is calculated as 10k/7. The lower limit of class eight is 13.89. The length of the ruler when measuring on the micrographs was 11.7 μm when corrected for magnification. Both fiber counts and diameter measurements were performed directly on the photographed areas with randomly overlaid counting frames of the same area but not necessarily on the same position (for further details, see Ratner et al., 2010).

Statistical analysis

The data were analysed using SigmaPlot version 11.0 (Systat Software). Relationships between myelinated fibers, neocortical neurons, CC area and age were tested with Pearson’s product moment correlation. In addition, variations causing differences in fiber distribution and density along the CC axis were tested by one-way analysis of variance (one-way anova), while a non-parametric Kruskal–Wallis anova ranking test was used when variances were unequal. An alpha level of 0.05 was used for all statistical tests.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The mean number of myelinated callosal fibers in 10 male subjects was estimated to be 138 × 106 (CV = 0.19; CE = 0.059) and spanned 104 × 106 to 194.5 × 106 (Table 1). Neocortical neurons had been estimated in a previous study (Pakkenberg & Gundersen, 1997) and corresponded to a mean value of 21.7 × 109, including values from 17.0 × 109 to 30.2 × 109 (CV = 0.17). The two variables were correlated for any interrelation, but reached a non-significant value (= −0.25; = 0.49). Thus, the number of neocortical neurons had no influence on the total number of myelinated fibers in CC (Fig. 3a).

Table 1.   Subject details and general results.
Age (years)OccupationCC area (cm2)Callosal fibers (102)Body height (cm)Body weight (kg)Brain weight (g)CoDNeocortical neurons (109)
  1. AMI, anterior myocardial infarction; CC, corpus callosum; CoD, cause of death; o.p., obs pro.

  2. *Data could not be obtained due to technical problems in one specimen; therefore, graphs and statistical analysis involving the callosal area only include values from nine out of the 10 subjects.

39Fire-inspector7.07150183801550AMI o.p.21.0
40Workman*124179851620AMI22.6
40Caretaker8.03148167751710AMI23.0
48Cook8.841951861101290AMI17.6
52Farmer6.81153171771340AMI o.p.21.0
52Semi-skilled worker5.43126175731435AMI30.2
52Workman7.54133169741650AMI o.p.22.8
59Shop-owner6.43104178871475AMI21.8
59Barber6.91106185981400AMI17.0
60Janitor7.85139171781375AMI20.2
Mean
50.1 7.21138176841484.5 21.7
image

Figure 3.  (a) Myelinated callosal fibers (CF) shown as a function of total number of neocortical neurons. (b) Callosal area shown as a function of callosal fibers. Regression line indicated. (c) The distribution of fiber densities along eight callosal segments spaced by equal fractions (f = 4/32). The SEM is indicated. CC, corpus callosum.

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The area of CC had a mean value of 7.2 cm2 (CV = 0.17; CE = 0.066), which showed a strong significant correlation to the number of myelinated fibers (= 0.69; = 0.041). Therefore, the total number of fibers is presumably deciding the area of the CC (Fig. 3b).

Fiber densities were calculated based on the counted fibers per frame along the CC and compared for density differences among the eight investigated callosal segments by Kruskal–Wallis one-way anova. These findings showed a significant variation from the anterior to posterior part [H(7) = 45; < 0.001], with a density decline towards the posterior regions. However, an increase in density was observed in the most posterior region (Fig. 3c). The average fiber density was estimated to be 216 000 fibers mm−2.

Differences in fiber diameter were tested for significant variation in their distribution along the CC axis. For statistical convenience, the seven fiber classes applied in the current study (Table 2) were simplified into three broad classes (< 1.1 μm; 1.1 < < 2.2 μm; > 2.2 μm; Fig. 4). Fibers smaller than 1.1 μm showed significant variation along the callosal axis, with a decrease in number towards the posterior regions (< 0.001). A comparable significant variation was established for fibers between 1.1 and 2.2 μm (< 0.001), but with an inverse increase in numbers towards the posterior regions. As the normality test failed for fibers larger than 2.2 μm, a non-parametric anova (Kruskal–Wallis one-way anova on Ranks) was performed. This led to significant increases in the diameter from the anterior to posterior parts of CC (H = 26.441; < 0.001). No significant difference in fiber composition among the 10 subjects was observed (fibers < 1.1 μm, = 0.48; 1.1 μm < fibers < 2.2 μm, =  0.18; fibers > 2.2 μm, = 0.33).

Table 2.   Diameter of myelinated fibers classified by a mathematical size ruler.
Fibers (size interval)Region 1 (%)Region 2 (%)Region 3 (%)Region 4 (%)Region 5 (%)Region 6 (%)Region 7 (%)Region 8 (%)
0.0 < < 0.8 μm9.277.139.747.877.715.159.093.98
0.8 < < 1.1 μm60.657.856.656.654.549.448.048.5
1.1 < < 1.6 μm20.726.124.025.727.128.928.330.3
1.6 < < 2.2 μm8.607.947.708.578.6812.210.912.7
2.2 < < 3.1 μm0.901.021.801.151.843.402.673.29
3.1 < < 4.3 μm0.100.000.190.100.100.980.911.18
4.3 < < 5.9 μm0.000.000.000.000.120.000.130.00
image

Figure 4.  Fiber diameter along different callosal segments, grouped in three classes: (a) < 1.1 μm; (b) 1.1 μm < < 2.2 μm; (c) > 2.2 μm; displayed as the percentage of the total number of fibers. The SEM is indicated. CF, callosal fibers.

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Fiber sizes and fiber densities described above were not corrected for shrinkage, as tissue shrinkage had a diminutive value (< 1%) negligible for the results.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

In the current study, we investigated the relationship between the area forming the CC (mean 7.2 cm2) and the total number of myelinated fibers (mean 138 × 106). The callosal fibers clearly showed a strong correlation with the callosal area, with an increase in the latter as the number of fibers increased. Literature concerning this topic, based on the number of fibers, is very brief and, to our knowledge, only one study has previously established a positive correlation between the midsagittal CC area and the number of fibers (Aboitiz et al., 1992a).

A mean value of 138 × 106 myelinated fibers (Table 1) was estimated in the current study, which is high compared with earlier results (Tomasch, 1954; Highley et al., 1999) and low compared with others (Sargon et al., 2007). The discrepancy between the present and previous results could presumably be due to several important factors. First, a sampling scheme based on a non-stereological approach could ultimately lead to biased estimates of the true value, as seen in earlier in vitro studies. Secondly, age differences between studies could lead to variations, as imaging investigations have reported a steep increase in CC size until adulthood, after which it steadily declines (Plessen et al., 2004; Hasan et al., 2008).

As the majority of axons crossing the CC arise from neurons within the neocortex (Jacobson & Trojanowski, 1974; Ivy & Killackey, 1981; Ribeiro-Carvalho et al., 2006), we expected a close relationship between the quantity of neocortical neurons and the number of myelinated callosal fibers; a lack of correlation has not previously been described. Nevertheless, we were unable to retrieve a correlation in the present study. These results suggest that many undefined processes are involved in the development and establishment of neuronal projections in the CC. Previously, animal models and human fetal brain studies have revealed that neocortical axons in the developing brain use a number of different molecular components to find their correct growth path. In particular, guidance factors and midline glial structures have shown important roles in the targeting of fibers across the CC, as secreted and cell-bound factors shape the direction of axon growth (Richards et al., 2004; Ren et al., 2006). Presumably, axons from the cingulated cortex could play an important role as they enter the contralateral hemisphere prior to the neocortical axons during CC development and pioneer a tract for callosal axons into which neocortical neurons can grow (Koester & O’Leary, 1994; Rash & Richards, 2001; Ren et al., 2006; Piper et al., 2009). Thus, cingulate axons, guidance factors and midline glial structures may be central to the overall connectivity between contralateral neocortical neurons during development. Further, it has been proposed that environmental factors may affect some of these underlying molecular mechanisms. For example, rats raised in a complex environment have shown an increase in the number of myelinated fibers (Juraska & Kopcik, 1988), which could reflect a rescue of axons destined for cell death during postnatal overlapping myelination and axon elimination (LaMantia & Rakic, 1990; Gilmore et al., 2007; Markham et al., 2009). Human in vivo studies confirm these findings as non-invasive imaging studies have implied an increased anisotropy of the CC in musicians compared with non-musicians (Schmithorst & Wilke, 2002; Schlaug et al., 2009) and a 17% decrease in the callosal area of individuals with childhood neglect (Teicher et al., 2004). These results support the assumption of an association between the environment and the number of myelinated fibers. Thus, environmental factors appear to possess the ability to alter and shape the developing CC, which could recruit or rescue callosal axons in periods of fiber tract maturation. The connections between neocortical neurons and callosal fibers are therefore presumably more complex than our correlation study can address at the present time.

The fiber density studies were undertaken to determine the distribution of fibers along the CC. We found an average density of 260 000 fibers mm−2 with a steep decline in density from the anterior to the posterior regions, although a small increase in the most posterior region was observed. This decline is more excessive than reported by Highley et al. (1999), but the overall patterns show similarities.

Additionally, the distribution pattern of small and large fibers was investigated in the callosal structure. This was accomplished by the measurement of fiber diameters, classified on a logarithmic scale. Most prevalent were fibers in the interval 0.8–1.1 μm, which constituted approximately 54% of the myelinated fibers and closely resembled the findings of Tang et al. (1997)– average subcortical white matter neuronal axon size of 1.14 μm. Fibers of smaller diameters (< 1.1 μm) were found in high proportions continuously along the callosal axis, but had an observable decline from the genu towards the splenium. In contrast, intermediate fibers (1.1 < < 2.2 μm) and large fibers (> 2.2 μm) increased in proportion towards the posterior CC with a notable increase from the posterior midbody, especially for larger fibers. These regional variations may reflect the different tasks transmitted between topographically distinct areas of the hemispheres (Aboitiz & Montiel, 2003). Inter-hemispheric connections located in the posterior midbody to the most posterior regions have previously been shown to transfer motor (Wahl et al., 2007), somatosensory (Fabri et al., 2001), auditory (Pollmann et al., 2002) and visual information (Westerhausen et al., 2009), all of which need fast conduction and processing. Hence, as the large myelinated fibers were preferentially located in the posterior region, these fibers may be important for the speed of conduction (Aboitiz et al., 1992a,b). Likewise, the larger proportion of small, thin fibers in the anterior parts of the CC could be an indication of a negligible need for fast inter-hemispheric conduction, due to long and complex processing time in the frontal cortex (Jancke & Steinmetz, 1994; Aboitiz & Montiel, 2003). Aboitiz et al. previously reported a distinct distribution of small and large fibers, with small fibers (< 1.0 μm) preferentially located in the genu and the splenium, and large fibers (> 3 μm) predominately located in the posterior midbody. We were unable to retrieve this pattern in the present study as the percentage of large fibers tended to increase towards the splenium rather than peak at the posterior midbody. The increase in fiber size from anterior to posterior follows the decline in fiber density towards the posterior parts of the CC and presumably determines fiber density.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

We sincerely thank Prof. Hans Jørgen G. Gundersen, Stereology and Electron Microscopy Research Laboratory, Aarhus University for stereological advice and technical help. Further, we thank Susanne Sørensen for technical assistance, Aagaard Design for graphical support, and Højesteretssagfører Davids Legat for slægt og venner and Sofus Carl Emil Friis og Hustru Olga Doris Friis Legat for financial support. Ethical approval: this project is approved by the Danish Regional Scientific Ethical Committee. Protocol number H-C-2009-027.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References