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doi: 10.1093/cercor/bhz221 Original Article Downloaded from https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhz221/5652191 by guest on 10 February 2020

ORIGINAL ARTICLE The Relationship Between Density, Myelination, and Fractional Anisotropy in the Human Patrick Friedrich 1,2,*, Christoph Fraenz 1, Caroline Schlüter 1, Sebastian Ocklenburg1, Burkhard Mädler 3, Onur Güntürkün1 and Erhan Genç1

1Department of Psychology, Institute of Cognitive Neuroscience, Biopsychology, Ruhr University Bochum, 44801 Bochum, Germany 2Brain Connectivity and Behaviour Laboratory (BCBLab), Sorbonne Universities, 75013 Paris, France 3Health Systems Department, Philips GmBH, 22335 Hamburg, Germany.

Address correspondence to Patrick Friedrich, Fakultät für Psychologie, Abteilung Biopsychologie, Institut für Kognitive Neurowissenschaft, Ruhr-Universität Bochum, Universitätsstraße 150, 44780 Bochum, Germany. Email: [email protected]

Abstract The corpus callosum serves the functional integration and interaction between the two hemispheres. Many studies investigate callosal microstructure via diffusion tensor imaging (DTI) fractional anisotropy (FA) in geometrically parcellated segments. However, FA is influenced by several different microstructural properties such as myelination and axon density, hindering a neurobiological interpretation. This study explores the relationship between FA and more specific measures of microstructure within the corpus callosum in a sample of 271 healthy participants. DTI tractography was used to assess 11 callosal segments and gain estimates of FA. We quantified axon density and myelination via neurite orientation dispersion and density imaging (NODDI) to assess intra-neurite volume fraction and a multiecho gradient spin-echo sequence estimating water fraction. The results indicate three common factors in the distribution of FA, myelin content and axon density, indicating potentially shared rules of topographical distribution. Moreover, the relationship between measures varied across the corpus callosum, suggesting that FA should not be interpreted uniformly. More specific magnetic resonance imaging-based quantification techniques, such as NODDI and multiecho myelin water imaging, may thus play a key role in future studies of clinical trials and individual differences.

Key words: corpus callosum, diffusion MRI, microstructure, myelin water imaging, neurite density

Introduction 2017). Due to its topological organization, the corpus callosum With about 200 million , the corpus callosum is the largest serves different cognitive functions (Hofer and Frahm 2006), commissural fiber bundle in mammals (Aboitiz et al. 1992)and ranging from perceptual information transfer (Genc et al. 2011a, thus a central factor for interhemispheric transmission (van 2011b; Horowitz et al. 2015) and motor inhibition (Wahl et al. der Knaap and van der Ham 2011) and functional hemispheric 2007) to complex domains such as working memory (Siffredi asymmetries (Ocklenburg et al. 2016; Ocklenburg and Güntürkün et al. 2017) and language (Friedrich et al. 2017; Hinkley et al. 2016).

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Patient studies underline the importance of the corpus cal- controversy about FA’s major neurobiological contributor, with losum for interhemispheric processes. For instance, studies on the two candidates being myelination and fiber geometry. acallosal patients reveal reductions in interhemispheric infor- On the one hand, some studies suggest a strong link between mation transfer (Fischer et al. 1992), motor suppression (Genc FA and myelin (Bengtsson et al. 2005; Boorman et al. 2007; et al. 2015), and reduced language asymmetries (Ocklenburg Fleming et al. 2010; Johansen-Berg et al. 2007; Kanai et al. 2010; et al. 2015) when callosal connections are absent. Given that Rudebeck et al. 2009; Scholz et al. 2009; Tomassini et al. 2011; the callosal connections show a topological organization (Hofer Wahl et al. 2007). For instance, in a study of Sampaio-Baptista and Frahm 2006), studies on patients with partial sectioning et al. (2013), rats were trained in a motoric task, which induces Downloaded from https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhz221/5652191 by guest on 10 February 2020 of specific callosal regions gave direct evidence for the func- plastic changes in the sensorimotor cortex contralateral to the tional heterogeneity of its constituting parts. Moreover, callosal trained limb. Comparing the training group with a control group regions do not only vary regarding their cortical termination via diffusion MRI revealed significantly higher FA values in white regions but also in their fiber architecture. In a histological matter subjacent to the sensorimotor cortex. Importantly, a postmortem study, Aboitiz et al. (1992) investigated the regional subsequent immunohistological analysis revealed an increase differentiation of the density of large and thin diameter fibers in myelin staining in the WM underneath the motor cortex. of the human corpus callosum. Fibers with a diameter below Moreover, both FA and myelin staining density were signifi- 1 μm densely populated the anterior (genu) and posterior (sple- cantly correlated with the learning rate in the motor task and nium) parts of the corpus callosum with less density in the thus suggesting an association between FA and myelin. Com- callosal midbody. Complementarily, the density of thick fibers plimentarily, a study with patients suffering from poisoning- (diameter above 3 μm) was low in the genu and splenium but induced demyelination showed a significant negative corre- showed a peak in the midbody. Microstructural factors such lation between FA values in the centrum semiovale and the as axonal diameter and myelination strongly determine the concentration of myelin basic protein in the cerebrospinal fluid speed of information transmission by influencing the conduc- (CSF) (Beppu et al. 2012). This evidence suggests that myelination tion velocity of a given axon (Caminiti et al. 2013; Knaap et al. can modulate the degree of anisotropy. However, other stud- 2005). Importantly, the topological differences in microstruc- ies indicate that myelination is not necessary for significant tural factors are functionally relevant. The shortest callosal anisotropy (Beaulieu 2002; Genc et al. 2011b; Takahashi et al. conduction delays in both human and macaque are for motor, 2002). somatosensory, and premotor areas, whereas longer conduc- On the other hand, some studies suggest other factors to be tion delays are evident in parietal, temporal, and visual areas important for FA rather than myelination (Dougherty et al. 2007; (Caminiti et al. 2013). Elmer et al. 2011; Genc et al. 2011a; Hanggi et al. 2010; Imfeld Although the architecture of axons cannot be investigated et al. 2009; Jancke et al. 2009; Tuch et al. 2005; Westerhausen in living humans directly, magnetic resonance imaging (MRI) et al. 2006). Using a choice reaction time task, Tuch et al. (2005) techniques allow the quantification of microstructural integrity showed a positive correlation between reaction time and FA based on diffusion. The most commonly used method is values. This positive correlation, however, is in conflict with diffusion tensor imaging (DTI), in which water diffusivity the myelin hypothesis: Increased myelin thickness should cause is modeled into isotropic, freely moving, and anisotropic increased FA, which in turn should result in faster conduction diffusivity (Basser et al. 1994; Le Bihan 2003; Zarei et al. 2006). The velocity and thus dictating a negative relationship between FA functionality of DTI is 2-fold. First, fiber bundles can be virtually and reaction time. Therefore, the authors suggested that prop- reconstructed based on the measurement of diffusivity along erties of fiber geometry, such as axon diameter or axon density, multiple directions. Second, diffusion properties of single voxels may be reflected by FA (Tuch et al. 2005). Concordantly, a recent can be quantified in terms of different metrics. In turn, these study that investigated in schizophrenia showed metrics represent estimates of the microstructural integrity a positive association between FA and fiber density in white within a given voxel. The most widely used metric is fractional matter regions that differ between patients and healthy controls anisotropy (FA), which represents the degree of directed water (Grazioplene et al. 2018). Besides the link between FA values diffusion. and neural microstructure, FA may also vary as a function of FA is a widely used measure in cognitive and clinical neu- macrostructural fiber complexity, since FA is based on the diffu- roscience. For instance, clinical studies demonstrate decreased sion tensor’s geometry and thus decreased in areas containing FA values in patients suffering from neurological diseases, such high number of fiber crossings (Basser 1995; Behrens et al. 2007; as Alzheimer’s disease (Teipel et al. 2014), Parkinson’s disease Riffert et al. 2014). (Lee et al. 2011), and amyotrophic lateral sclerosis (Budrewicz FA is a widely used measure in neuroscience; thus, it et al. 2016), as well as in stroke patients (Puig et al. 2013; Yang is important to understand its physiological foundation. et al. 2015). Furthermore, an age-dependent decline of FA in Therefore, a comparison with more direct measures of myelin frontal, temporal, and parietal lobes is associated with poorer content or axon density would help to disambiguate between performance in executive function tests (Grieve et al. 2007). possible interpretations of FA values. Since crossing fibers On the other hand, studies on plasticity evince that training in white matter increase the difficulty of interpreting FA induces increase of FA values, for example, in the arcuate values, the corpus callosum represents an ideal test bed to fasciculus after music-cued motor training (Moore et al. 2017), elucidate FAs physiological foundation, because callosal fibers in sensorimotor cortex following training of the contralateral are strongly oriented in the left–right direction (Dougherty limb in rats (Sampaio-Baptista et al. 2013), and within the et al. 2007; Genc et al. 2011a; Madler et al. 2008). However, genu of the corpus callosum after extensive cognitive training the lack of practical methods to examine myelination or axon (Lövdén et al. 2010). density in healthy human samples has led to a shortage Although FA undoubtedly relates to white matter microstruc- of studies investigating the influence of both myelin and ture, the metric is unspecific and influenced by various axon density on FA values in different callosal segments physiological factors (Beaulieu 2009). There is a long-lasting (Billiet et al. 2015). The Relationship Between Axon Density, Myelination, and Fractional Anisotropy Friedrich et al. 3

Recent advances in neuroimaging present promising tech- Methods niques to surmount this issue. Myelin content, for instance, can Participants be estimated by the myelin water fraction (MWF), based on the T2 transversal relaxation time which is measured using 3D ME- Two hundred and seventy one healthy adult participants (150 GRASE imaging protocols (Prasloski et al. 2012; Uddin et al. 2018). males, mean age: 23.3 ± 2.9 years, range: 18–30 years) took In short, the water signal in the is constituted of three part in this study. All participants matched the standard MR visible components at 3T: a long T2 component (>2s)that inclusion criteria for MRI examinations. Handedness of all is caused by CSF, an intermediate T2 component (70–100 ms) participants was assessed using the Edinburgh Handedness Downloaded from https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhz221/5652191 by guest on 10 February 2020 affiliated with the intracellular and extracellular water (IE), and Inventory (Oldfield 1971), with positive values indicating right- a short T2 component (10–40 ms) rising from water confined and negative values indicating left-handedness. In our sample, between the myelin bilayers. Based on these values, the MWF 223 participants were right-handed (mean laterality quotient: can be determined for each voxel and can then be visualized 87.45 ± 15.69) and 48 participants were left-handed (mean in a MWF brain map. Histopathological studies with multiple laterality quotient: −73.59 ± 26.05). The study protocol was sclerosis patients (Laule et al. 2006)aswellasarecentaging approved by the local ethics committee of the Faculty of study (Billiet et al. 2015) validated the specificity of MWF for Psychology at Ruhr University Bochum. All participants gave myelin density. written informed consent and were treated in accordance with Furthermore, a promising technique for quantifying the axon the declaration of Helsinki. density in white matter is the so-called neurite orientation dispersion and density imaging (NODDI) (Zhang et al. 2012). This Image Acquisitions and Preprocessing method is based on the unique diffusion properties of the space within neurites contrary to outside of neurites (Le Bihan 1995). All scans took place within one session at the Bergmannsheil NODDI features a three-compartment model distinguishing Hospital in Bochum, Germany, using a 3T Phillips Archieva scan- intra-neurite, extra-neurite, and CSF environments based ner (Best, the Netherlands) with a 32-channel head coil. on their respective diffusion characteristics. This allows to estimate NODDI markers as a proxy of different properties Anatomical Images of neurite morphology, such as the orientation dispersion First, we acquired a T1-weighted high-resolution anatomical index (ODI) representing the degree of neurite dispersion or image with a 3D-Inversion prepared Turbo Field echo—3D-IR- the intra-neurite volume fraction (INVF), which represents turbo field echo (TFE) (MPRAGE; time repetition [TR] = 8.2 ms, ◦ neurite density. Importantly, histological studies validate time echo (TE) = 3.7 ms, flip angle = 8 , time to inversion delay the NODDI model. A recent study using postmortem spinal [min, TI delay] = 617.7 ms; TFE shot duration = 1487 ms, 220 cord specimens showed that both ODI and INVF match slices, matrix size = 240 × 240, voxel size = 1 × 1 × 1 mm) for their respective physiological basis. ODI was associated with image registration and automatic parcellation of seed regions. orientation dispersion quantified by path-wise circular variance We used FreeSurfer (http://surfer.nmr.mgh.harvard.edu, version of neurite orientation. Similarly, INVF was associated with 5.3.0) for cortical surface reconstruction on each individual T1 neurite density and myelin density obtained from image. The automatic reconstruction steps included skull strip- (Grussu et al. 2017). ping, gray and white matter segmentation, and reconstruction. Both MWF and NODDI have been utilized in clinical and After preprocessing, results of the segmentation procedure were cognitive studies to more specifically investigate the neural controlled slice by slice. Inaccuracies were corrected by manual structure of different phenomena. For instance, differences in editing if necessary. For each individual image, we applied atlas- MWF have been shown in autism (Deoni et al. 2015), prenatal based automatic parcellation within the individual T1 space on alcohol exposure (McLachlan et al. 2019), and traumatic brain basis of the Desikan–Killiany atlas (Desikan et al. 2006). This injury (Wright et al. 2016) compared to healthy controls. More- algorithm is implemented in FreeSurfer and can be used after over, MWF values are sensitive to age-dependent changes in tissue segmentation. We selected a set of 11 homotopic regions myelination (Faizy et al. 2018). Similarly, NODDI has been used of interest (ROIs) per hemisphere (Fig. 1A), which serve as target to investigate the neural basis of interindividual differences in regions for probabilistic fiber tractography in a later step. Going intelligence (Genc et al. 2018) and speech-related phonological from posterior to anterior, we included the following homotopic processing (Ocklenburg et al. 2018b), and it shows a unique pairs of regions in the left and right hemisphere for tractog- pattern of asymmetry across the two brain halves (Schmitz et al. raphy: pericalcarine, cuneus, lateral occipital, inferior parietal, 2019). Importantly, all of these aspects have been investigated bankSTS, postcentral gyrus, precentral gyrus, pars opercularis, by other studies using FA values to quantify microstructural pars triangularis, frontal pole, and the medial orbitofrontal integrity. Therefore, it is important to understand the relation cortex. We transformed these 11 homotopic ROI pairs lin- between FA values and more specific measures of neural archi- early into the native diffusion-weighted space using FSL’s tecture to develop a more precise understanding of clinical and FLIRT. cognitive phenomena. Our study aims at understanding the possible physiological Diffusion-Weighted Imaging interpretation of FA values in terms of myelin content and axon Three consecutive diffusion-weighted single-shot spin-echo density. To this extent, our study investigates the relationship echo-planar images were acquired (echo-planar imaging [EPI]; ◦ between FA values, as an unspecific measurement of white TR = 8200 ms, TE = 99 ms, flip angle = 90 , 60 slices, matrix matter microstructure, and MWF or NODDI metrics, represent- size = 96 × 96, resolution = 2 × 2 × 2 mm, parallel imaging ing more sensitive measures of myelin and axon geometry. All SENSE factor = 2, direction of acquisition = AP), with b-value analyses concern tractography informed segments within the of 1000 s/mm2 along 60 isotropically distributed directions. In corpus callosum, which neglects potential influence of multiple addition, 10 data sets were acquired without diffusion weighting fiber directions. for motion correction and computation of diffusion coefficients. 4 Cerebral Cortex, 2020, Vol. 00, No. 00 Downloaded from https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhz221/5652191 by guest on 10 February 2020

Figure 1. Procedure for microstructural quantification of the callosal segments. Colors of the callosal projections correspond to the colors of the cortical areas. (A)ROI definition via atlas-based automatic parcellation. Eleven homotopic pairs of ROIs were chosen as target regions for probabilistic diffusion tractography. This results in the identification of callosal segments for each homotopic ROI pair. (B) Averaged callosal segments are visualized on a common template space. (C) For each individual participant, we created image maps for FA, INVF, and MWF. The individual callosal segments were transformed into each one of these maps to gain an estimateofFA, INVF, and MWF values.

The acquisition of these diffusion-weighted images took place factor = 2, direction of acquisition = AP), along 120 isotropically sequentially in the same MRI session, without the subject distributed direction (20 directions with b = 1000 s/mm2;40direc- leaving the scanner in between. The total acquisition time was tions with b = 1800 s/mm2; 60 directions with b = 2500 s/mm2). 26 min. Prior to data preprocessing, a rigid-body registration All diffusion directions within and between shells were gen- across images was performed and the three images were erated noncolinear to each other using the MASSIVE toolbox averaged to increase signal-to-noise ratio. The FDT (FMRIB’s (Froeling et al. 2017). In addition, eight volumes with no diffusion Diffusion Toolbox) implemented in FSL (www.fmrib.ox.ac. weighting (b = 0 s/mm2) were acquired for motion correction and uk/fsl) was utilized for preprocessing of the diffusion data. computation of NODDI coefficients. Total acquisition time of the Preprocessing steps included correction for eddy currents and diffusion-weighted images was 18 min. motion as well as volume-wise correction of the gradient Preprocessing of multishell images included a correction for direction using the rotation parameters from head motion. eddy currents and head motion as well as a volume-wise cor- rection of the gradient direction using the rotation parameters Multishell Diffusion-Weighted Imaging from head motion. NODDI coefficients were computed using the A diffusion-weighted three-shell image was acquired using EPI AMICO toolbox (Daducci et al. 2015), which features a three- ◦ (TR = 7652 ms, TE = 87 ms, flip angle = 90 , 60 slices, matrix size compartment model that distinguishes between intra-neurite, 112 × 112, resolution = 2 × 2 × 2 mm, parallel imaging SENSE extra neurite, and CSF environments, similar to the NODDI The Relationship Between Axon Density, Myelination, and Fractional Anisotropy Friedrich et al. 5

model of Zhang et al. (2012). Using a two-level approach, AMICO studies (Genc et al. 2011b; Westerhausen et al. 2009), we used first determines the proportion of free moving water within a two-step approach for tractography of callosal tracts: first, each voxel based on the diffusion signal (Billiet et al. 2015; a mask of the corpus callosum was created in the individual Jespersen et al. 2012). The ratio gained from this procedure is diffusion space of each participant and was used as seed termed isotropic volume fraction (ISO) and reflects the amount region for tractography. For each pair of homotopic regions, of isotropic diffusion with Gaussian properties. Consecutively, tractography was performed from corpus callosum mask to the remaining portion of the diffusion signal is divided into two the left ROI and to the right ROI, respectively, resulting in complementary fractions: INVF representing stick-like or cylin- two connectivity estimates per ROI pair. Fiber tracking was Downloaded from https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhz221/5652191 by guest on 10 February 2020 drical diffusion and the hindered diffusion within the extra- done using 5000 tract-following samples at each voxel, with neurite environment. Importantly, INVF has been shown to be step length set to 0.5 mm and curvature threshold of 0.2. The a sensitive measure of axon proportion in white matter by resulting connectivity distribution maps were thresholded to comparison with light microscopy and electron microscopy of ensure specificity of voxels that were interpreted as part of histological samples (Jespersen et al. 2010). Although NODDI can the white matter connection. For pericalcarine, cuneus, lateral be applied to access the microstructural architecture in both occipital, postcentral, precentral, medial orbital frontal, and gray and white matter, the degree of intrinsic free diffusivity frontal pole ROIs, only voxels were included that had at least differs considerably across different types of tissues (Grussu 2500 samples passing through. For regions that are more difficult et al. 2017; Guerrero et al. 2016; Kaden et al. 2016). As we were to tract bilaterally, we chose a more liberal threshold of at least interested in measuring neurite density in white matter only, we 500 samples. This threshold was applied to bankSTS, inferior optimized the intrinsic free diffusivity parameter of the AMICO parietal, pars opercularis, and pars triangularis. A subsequent − toolbox to 1.5 × 10 3 mm2/s (Grussu et al. 2017). step aimed at finding the overlap of the two resulting tract estimates within the corpus callosum. This was achieved by Myelin Water Imaging multiplying the connectivity distribution map of the left and We acquired a previously published 32 multiecho (ME) 3D- right ROI with the binarized corpus callosum mask. This results GRASE sequence with refocusing angle sweep (Billiet et al. 2015; in two maps containing only voxels in the corpus callosum, Prasloski et al. 2012) using the following parameters: TR = 800 ms, which are connected to the left or right ROI of the given ROI TE = 32 echoes at 10 ms echo spacing (ranging from 10 to 320 ms), pair. For each homotopic ROI pair, these maps were added with 60 slices, partial Fourier acquisition in both phase encoding one another to determine voxels in the corpus callosum that directions, matrix size = 112 × 112, with 2 × 2 × 2 mm resolution, are connected to the left and right ROI. Hence, this procedure parallel imaging SENSE = 2, direction of acquisition = RL. The resulted in 11 callosal segments (Fig. 1B). total acquisition time for this sequence was 8 min. The top of the figure shows the average callosal segments For evaluating the myelin content of the callosal segments, that interconnect left and right homotopic ROI pairs. Callosal we constructed parameter maps representing the MWF using segments connecting ROIs in the pericalcarine gyrus (red), an in-house algorithm (Ocklenburg et al. 2018a) written in MAT- cuneus (light blue), lateral occipital (yellow) regions, and the LAB (Version R2015a, The MathWorks Inc., Natick, MA, USA). inferior parietal (orange) lobe show strong overlap and are This algorithm analyzed the ME decay curves on a voxel-wise located in the splenium of the corpus callosum. Callosal fibers basis by using the multicomponent T2 analysis with concurrent that interconnect the left and right bankSTS (pink) form a correction for stimulated echo contamination of decay curves segment at the frontal end of the splenium and cover posterior that resulted from B1 inhomogeneities and imperfect refocusing parts of the isthmus. The mid of the corpus callosum inhabits pulses. Details are outlined in Prasloski et al. (2012). Voxel- two segments that contain fibers to the postcentral (dark wise ME decay curves were obtained from the 3D ME-GRASE green) and precentral (marine blue) gyri. Anterior part of the image of each participant and transformed into a continuous T2- corpus callosum presents segments containing tracts to the distribution by using a regularized non-negative least squares pars opercularis (light green), pars triangularis (purple), with the approach (Whittall et al. 1997). We used an extended phase rostrum showing callosal segments trajecting to the frontal pole graph algorithm to account for possible stimulated echoes due (yellow), and medial orbital frontal gyrus (dark blue). to nonideal refocusing pulse flip angles (Hennig et al. 2003; For each callosal segment, the FA value was extracted as Prasloski et al. 2012). To increase robustness of the ill-posed the average of all voxels within the segment. To quantify INVF fitting problem and assurance of smooth T2 amplitude dis- and MWF values, all callosal segments were linearly trans- tributions, a regularization factor of 1.02 was applied during formed from the participants’ native diffusion space to the the fitting procedure. T2 distributions were created via a 101 native NODDI space or MWF space. Similar to FA values, esti- logarithmically spaced exponential decay base function for echo mates for INVF and MWF were extracted as the average of all decay with T2-values ranging from 0.01 to 2 s. T2 distributions voxels within the given callosal segment. The described pro- were used to determine MWF for each voxel as the signal integral cedure resulted in one estimate for each microstructural index fraction between 10 and 40 ms relative to the total T2 distribu- (FA, INVF, and MWF) per homotopic ROI pair for each participant tion integral. This resulted in whole-brain MWF maps for each (Fig. 1C). participant. Statistical Analyses Fiber Tractography and Quantification of All statistical analyses were performed using SPSS (version 20, Microstructural Indices SPSS Inc.). For all analyses, we used linear parametric methods Probabilistic tractography was performed using FDT imple- with an α-level of 0.05. We used principal component analysis mented in FSL, utilizing the probtrackx algorithm to generate (PCA) with varimax rotation to investigate the overall dominant a connectivity distribution between seed region and target patterns of correlation in the three microstructural indices. To region (Behrens et al. 2007; Behrens et al. 2003). Similar to other investigate the association between FA and MWF or INVF in 6 Cerebral Cortex, 2020, Vol. 00, No. 00

each callosal segment, we utilized partial correlations control- showed potential associations between FA and MWF, which ling for the other microstructural integrity value. For example, were not significant after Bonferroni correction (postcentral: when assessing the relationship between FA value and MWF r(258) = 0.172, P = 0.005; frontal pole: r(263) = 0.153, P = 0.013). All in a callosal segment, we controlled for the INVF value of this other correlation analysis did not reach conventional or Bonfer- segment. Concordantly, the relationship between FA values and roni adjusted significance (all r < 0.100, all P > 0.102). INVF was assessed by controlling for the MWF value. We chose Results of the two alternative analyses are reported in sup- this method to identify the unique association between FA plementary tables S1 and S2, with Supplementary Table 1 show- and either one of the two other measures. Furthermore, we ing simple Pearson’s correlations without correcting for the Downloaded from https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhz221/5652191 by guest on 10 February 2020 computed two alternative analyses. First, we calculated sim- other measures and Supplementary Table 2 displaying partial ple Pearson’s correlations without correcting other measures. correlation analysis that was controlled for age, sex, and ROI size Second, we carried out partial correlation analyses in which in addition to the other microstructural integrity value. Simple we controlled for the other microstructural integrity value with Pearson correlations (S1) displayed no changes in the FA and additional corrections for age, sex, and ROI size. Due to multiple INVF result pattern. The result pattern of the FA and MWF rela- comparisons of callosal segments and microstructural metrics tionship, however, showed significant positive relations in the

(22 partial correlations), we utilized Bonferroni correction in postcentral (r(260) = 0.187; P < 0.002) and frontal pole (r(265) = 0.244; correlation analyses, leading to an adjusted P value of 0.0023. P < 0.001) callosal segments, which did not reach significance when controlling for INVF.Comparing the reported results above (controlling only for the other microstructural integrity value) Results with the results from the supplementary analyses S2 (control- Topography of Callosal Microstructure ling for the other microstructural integrity value, age, sex, and ROI size) shows no differences in the pattern of significant First, we estimated the topographic distributions of FA, MWF, correlations. and INVF values across the 11 callosal segments (Fig. 2A). The mean FA values of these segments are illustrated in Figure 2B, the INVF in Figure 2C,andtheMWFinFigure 2D. Discussion DTI is a standard technique in cognitive neuroscience that has Factor Analyses been used in a large number published studies. Many of these To find potential common factors in FA, INVF,or MWF across the studies use FA as their main dependent variable, making it callosal segments, we computed a PCA for each microstructural crucial to understand the relation of the value to the two con- index. All PCAs showed good sampling adequacy indicated stituents of white matter, namely axons and myelin. Therefore, by Kaiser–Meyer–Olkin measures (FA: KMO = 0.783; INVF: this study investigated for the first time the relation between FA KMO = 0.798; MWF: KMO = 0.788) and all Bartlett’s tests of and other microstructural indices that represent myelin content sphericity were significant (all P < 0.001) indicating sufficiently (MWF) or the axonal density (INVF) within distinct segments of large correlations between microstructure of the callosal callosal projections. segments for each index. Furthermore, Kaiser’s criteria yielded The spatial distribution of FA values (Fig. 2) was in line with justification for three factors with eigenvalue > 1 in each PCA. previous reports (Hofer and Frahm 2006; Stikov et al. 2011; For the PCA of FA values, the three factors accounted for 64.178% Zarei et al. 2006). Going from posterior to anterior, FA values of the total variance. For the PCA of INVF values, the three increase from early visual regions to later visual regions (lateral retained factors accounted for 73.277% of the total variance. occipital gyrus), then decrease toward middle segments (post For the PCA of MWF values, 79.768% of the total variance central and precentral segments) before rising again toward was accounted for by the three factors. All factor loadings are the frontal pole segment. Due to the ROI-based approach used reported in Table 1. here, our study extends previous reports by showing differ- ences in microstructural integrity between segments of callosal projects that reside within the same anatomically defined cal- Correlation Between the Structural Metrics losal region. For instance, the distribution of axonal density A detailed overview of all partial correlation analyses is (INVF) shows continuously high values in callosal segments of depicted in Table 2. Nine out of 11 segments showed significant the visual cortex (pericalcarine, cuneus, lateral occipital). Similar partial correlations between FA and INVF. Bonferroni adjusted to FA values, axon density decreases toward the middle seg- P values reached significance in callosal segments that ments but shows a peak low in the pars opercularis callosal seg- contain homotopic fibers to the lateral occipital (r(260) = 0.204; ment. Going further in the anterior direction shows an increase P = 0.001), inferior parietal (r(259) = 0.299; P < 0.001), bankSTS in axon density with a peak in the frontal pole. The distribution (r(236) = 0.440; P < 0.001), postcentral (r(258) = 0.283; P < 0.001), of myelin content (MWF) represents a continuous decrease from precentral (r(262) = 0.272; P < 0.001), pars opercularis (r(261) = 0.430; posterior to anterior callosal segments with one upper and P < 0.001), pars triangularis (r(262) = 0.500; P < 0.001), frontal poles one lower plateau. The highest values of myelin content are (r(263) = 0.379; P < 0.001), and medial orbitofrontal (r(263) = 0.290; shown in the visual callosal segments (pericalcarine, cuneus, P < 0.001) ROIs. However, no relationship between FA and INVF and lateral occipital). Callosal segment that links temporal and became evident in callosal segments that contain homotopic parietal brain regions exhibits decreasing myelin content, with fibers to pericalcarine (r(261) = 0.093; P = 0.131) or to the left and an upward trend in the precentral segment. The myelin content right cuneus (r(262) = 0.141; P = 0.022). in the anterior third segments of the corpus callosum (pars For the relationship between FA and MWF, we found a pos- opercularis, pars triangularis, frontal pole, and medial orbital itive partial correlation between FA and MWF in the precentral frontal gyrus) represents a relatively stable plateau of lower callosal segment (r(262) = 0.235; P < 0.001). Moreover, partial cor- values. The distribution pattern of axon density and myelination relation for the postcentral and frontal pole callosal segment is consistent with previous studies. For instance, Sepehrband The Relationship Between Axon Density, Myelination, and Fractional Anisotropy Friedrich et al. 7 Downloaded from https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhz221/5652191 by guest on 10 February 2020

Figure 2. (A) averaged callosal segments presented on the midsagital slice. (B) fractional anisotropy. (C) intraneurite volume fraction as a ,measure of neurite density. (D) Myelin water fraction as a measure of myelination. Topography of FA, INVF, and MWF across the callosal segments. Colors of the boxplots indicate belongingness to callosal segments shown in the sagittal section of the corpus callosum in the top of the figure. 8 Cerebral Cortex, 2020, Vol. 00, No. 00

Table 1 PCA results for FA, INVF, and MWF over all callosal segments

Principal Component Analysis (PCA) FA INVF MWF Segments factor 1 factor 2 factor 3 factor 1 factor 2 factor 3 factor 1 factor 2 factor 3 Downloaded from https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhz221/5652191 by guest on 10 February 2020

0.847 0.067 0.063 0.933 0.143 0.047 0.929 0.102 0.014 Pericalcarine

0.792 0.261 0.109 0.928 0.169 0.089 0.920 0.133 0.049 Cuneus

0.854 0.124 0.101 0.873 0.310 0.064 0.940 0.134 0.065 Lateral occipital

0.458 0.466 0.007 0.244 0.709 0.073 0.694 0.489 0.055 Inferior parietal

0.154 0.747 0.333 0.159 0.769 0.133 0.414 0.716 0.048 BankSTS

0.153 0.689 0.362 0.124 0.660 0.360 0.150 0.853 0.155 Post central

0.123 0.706 0.362 0.228 0.690 0.351 0.077 0.815 0.275 Pre central

-0.095 0.266 0.634 0.046 0.440 0.589 0.023 0.419 0.668 Pars opercularis

0.100 0.138 0.795 -0.128 0.362 0.743 0.068 0.227 0.870 Pars triangularis

0.137 0.113 0.843 0.138 0.97 0.916 0.031 0.046 0.941 Frontal pole

0.223 0.117 0.806 0.146 0.135 0.903 0.045 0.043 0.934 Med. orb. frontal

Under each microstructural index (FA, INVF, MWF), each column represents one factor with eigenvalue > 1. Each cell in a column represents the factor loading of the respective microstructural index per callosal segment. For each callosal segment, the relatively highest factor loadings (when compared to neighboring cells) are colored in green indicating potential belongingness to the respective factor (column). et al. (2015) estimated the tissue density by multishell diffusion segments, whereas in our study, axon density was highest in data as the summation of myelin and axon density based on posterior callosal segments. This divergence may be due to an alternative diffusion model. In line with histological studies difference in the measurement of axon density. In LaMantia (Aboitiz et al. 1992; LaMantia and Rakic 1990), they reported and Rakic (1990), axon density was calculated as the number of higher tissue density (myelin density plus axon density) in axons divided by the area enclosed within the counting frame. the splenium compared to the midbody or genu of the corpus This procedure, however, does not account for varying axon callosum, which is the same for our study. Moreover, the steady diameter. Hence, counting frames that possess a high number posterior–anterior regression of myelin content is consistent to of large diameter axons will display less axon density under this estimates of myelin g-ratio in previous studies (Stikov et al. operationalization. In contrast, NODDI models both the intra- 2011). Concordantly, the distribution of axonal density shown in cellular and extracellular compartment of the diffusion signal our study is to a large part comparable to histological studies (Zhang et al. 2012) and therefore implicitly accounts for varying that show lowest density of thin fibers in the posterior midbody diameter. and isthmus (Aboitiz and Montiel 2003; Aboitiz et al. 1992). A Despite the differences between callosal segments in each histological study in rhesus monkey corpus callosum (LaMantia microstructural index, our factor analyses revealed three com- and Rakic 1990) shows highest axon density in anterior callosal mon factors for each microstructural index. Importantly, poste- The Relationship Between Axon Density, Myelination, and Fractional Anisotropy Friedrich et al. 9

Table 2 Partial correlation analyses

Partial correlation analyses INVF and FA MWF and FA Segments

rpdfrpdfDownloaded from https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhz221/5652191 by guest on 10 February 2020

0.093 0.131 261 0.048 0.441 261 Pericalcarine

0.141 0.022 262 0.091 0.141 262 Cuneus

0.204 0.001260 0.083 0.183 260 Lateral occipital

0.299 <0.001259 0.074 0.236 259 Inferior parietal

0.440 <0.001236 0.074 0.254 236 BankSTS

0.283 <0.001258 0.172 0.005 258 Post central

0.272 <0.001 262 0.235 <0.001 262 Pre central

0.430 <0.001 261 0.043 0.490 261 Pars opercularis

0.500 <0.001 262 0.032 0.610 262 Pars triangularis

0.379 <0.001 263 0.153 0.013 263 Frontal pole

0.290 <0.001 263 0.100 0.104 263 Med. orb. frontal

All partial correlation analyses are controlled for the other microstructural index of no interest to ensure specificity of the analyses. For example, partial correlations between INVF and FA were controlled for the MWF value of the respective callosal segment. Correlation coefficients that are significant and corrected for multiple comparisons (Bonferroni correction) are indicated by colored backgrounds. rior segments, middle segments, and frontal segments strongly on factor 2, which can be interpreted as a temporal-motoric loaded onto the same factor in each microstructural index. The factor, and the remaining frontal segments loaded onto factor posterior segments are grouped in factor 1, which can be inter- 3 as a frontal factor. Importantly, this result shows on a multi- preted as a visual-parietal factor. The middle segments loaded modal level that the microstructural architecture of the corpus 10 Cerebral Cortex, 2020, Vol. 00, No. 00

callosum supports the use of geometric parcellation schemes This is at odds with studies supporting the relationship between (Witelson 1989) as means of partializing the corpus callosum, FA and myelination. For instance, FA values are decreased in albeit their lack of spatial detail. Our results indicate that the (Hasan et al. 2005) and genetic studies show architecture of neighboring callosal segments is potentially that abnormal expression of myelin genes is accompanied with influenced by similar genetic or developing factors. For instance, FA alterations (Fatemi et al. 2009). However, FA was not asso- a previous study investigated the link between polymorphisms ciated to myelin content in another study that examined the in the myelin-related gene PLP1 or the axon guidance-related different projection and association fibers, as well as the genu gene CNTN1 and microstructure of difference segments of and splenium of the corpus callosum (Arshad et al. 2016), thus Downloaded from https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhz221/5652191 by guest on 10 February 2020 the corpus callosum (Anderson et al. 2018). In line with this supporting our results. Furthermore, a recent study by Uddin study, Anderson et al. (2018) found a double dissociation et al. (2019) investigated the degree to which myelin water between genes and microstructural indices. The results indicate imaging, T1w/T2w ratio mapping, and diffusion tensor-based that the PLP1 genotype is linked to differences in FA values metrics relate to each other. The relationships were determined between anatomically defined callosal segments, whereas the across 25 brain areas in white and gray matter, including three CNTN1 genotype was associated with differences in INVF (axon sections of the corpus callosum (genu, body, and splenium). density) of these callosal segments. This observation implies While their results indicated that FA and MWF were related an interaction between gene–microstructure relationship and when all areas were combined, neither Pearson nor Spearman the topographic location within the corpus callosum. From a correlations between FA and MWF reached significance in any developmental perspective, the total number of callosal fibers single area. Their results are thus in accordance with our study. is relatively fixed around birth, but the corpus callosum is Our findings suggest that myelin may not contribute to FA still subject to developmental changes. Previous studies show values to a significant extend under certain circumstances such alternating phases of growth and shrinkage over the whole as uniform fiber orientation and controlling for axon density. In corpus callosum with region-specific rates during adolescence line with this interpretation, animal models demonstrate that (Luders et al. 2010). These changes in callosal thickness may be the loss of myelin results in increased radial diffusivity but not caused in part by increases in myelination, axonal redirection, axial diffusivity (Song et al. 2002, 2003, 2005). Therefore, changes and pruning processes (Luo and O’Leary 2005). For instance, in myelination may increase FA values by decreasing the extra- activity-dependent myelination has been suggested as a axonal space if other properties such as fiber sensitivity and potential mechanism of developmental changes in white matter axon diameter are kept equal. Concordantly, we found a signif- microstructure (Fields 2015). Future studies could benefit from icant association between FA and myelin content only in the MRI-based imaging techniques, such as myelin water imaging callosal segment connecting the precentral gyri. This segment and NODDI, to uncover the neural basis of developmental lies in the posterior part of the callosal midbody. Histological changes in the corpus callosum. studies demonstrate that this part contains a high number of The second aim of this study was to evaluate the relationship thick diameter fibers (Aboitiz et al. 1992), which are typically between different measures of microstructural architecture. We highly myelinated (Tomasch 1954). Hence our finding suggests correlated FA values with MWF values representing myelination that myelin content in this callosal segment influences extra- and INVF values representing axon density, respectively. The axonal space on par with axon density, which is reflected by results of the correlation analyses showed that the association its positive correlation with FA. It is worth noting the absence to either MWF or INVF depends on the specific callosal segment. of significant correlations between FA values and either axon In 9 out of 11 segments, INVF was significantly associated with density or myelin content in the two most posterior callosal FA values. The strongest association is shown in the callosal segments (connecting the pericalcarine and cuneus areas). As segments connecting the pars triangularis (r = 0.495) or bankSTS shown in histological studies, the splenium contains an inho- (r = 0.446), whereas the weakest significant correlation is shown mogeneous distribution of fiber diameters compared to other in the segment connecting the lateral occipital (r = 0.247) and callosal areas. The genu contains a low density of thick fibers precentral areas (r = 0.247). In contrast, MWF was only signifi- (>3 μm) and a high density of thin fibers (<3 μm). Middle cantly associated with FA in the precentral segment (r = 0.196). FA callosal segments contain a high density of thick fibers and a values of no other segment were partially correlated with MWF comparably low number of thin fibers. However, the density of when controlled for INVF. This pattern of result suggests that thin fibers in the splenium is on par with the genu, while the FA in the corpus callosum may be more strongly associated to splenium also contains more thick fibers than the genu. More- axon density than myelination. In accordance with our finding, over, DTI tractography suggests that the splenium may contain Roberts et al. (2005) investigated white matter fiber attenuation asymmetric and widely distributed projections, thus supporting in patients with glioblastoma multiforme. They quantified FA the argument of a potentially high inhomogeneity of callosal values and fiber density in locations that differ in their prox- architecture in the splenium. This inhomogeneity might impact imity to the tumor. Both FA and fiber density were decreased the relation between the microstructural measures, leading to when adjacent to the glioblastoma. Importantly, FA and fiber the nonsignificant results. density were highly correlated across the investigated white There are some limitations to our study. First, we decided to matter regions, which is in line with our results. Confirmatively, use probabilistic tractography, which is, compared to determin- models of diffusion show that FA is most sensible to the amount istic tractography, a more inclusive approach that propagates of extra-axonal space, which in turn is linked to fiber packing better through regions with crossed fibers but therefore also density (Beaulieu 2002; Stikov et al. 2011), thus supporting our displays more false-positive voxels (Zalesky et al. 2016). For results. some regions, however, our tractography approach did not dis- The presented correlation analyses between myelin content play voxels in the callosal segments in some participants. This and FA values found no association for most callosal segments. leads to differences in the degrees of freedom across callosal The Relationship Between Axon Density, Myelination, and Fractional Anisotropy Friedrich et al. 11

segments, which we reported in Results section. Regions that Notes were especially problematic to tract include the inferior parietal The authors thank the student assistants for her support during ROI, bankSTS, pars opercularis, and pars triangularis. To ensure the measurements. Furthermore, the authors thank PHILIPS similar numbers of included participants for these ROIs, we Germany for the scientific support with the MRI measurements used a more liberal threshold of 10% (500 out of 5000 samples) and Tobias Otto for technical assistance. Conflict of Interest: None instead of the more conservative 50% threshold (2500 out of 5000 declared. samples). Differences in number of participants between the two thresholds are reported in Supplementary Table 3. 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