Many research efforts spanning multiple neuroimaging modalities have sought to yield insights into the neural bases of reading ability and disability (Vandermosten et al., 2012; Landi et al., 2013; Richlan et al., 2013). Among these studies are those that employ diffusion-weighted imaging (DWI) to study the properties of anatomical connections in the brain. The most commonly reported measure of white matter microstructure is fractional anisotropy (FA). FA is a metric derived from the diffusion tensor imaging (DTI) model (Basser et al., 1994) that quantifies the degree to which water diffusion is directionally restricted in each voxel (Hagmann et al., 2006; Basser and Pierpaoli, 2011). FA is high in white matter compared with gray matter and cerebrospinal fluid (CSF) due to preferential water movement along the axis of axons. Studies of white matter microstructural properties’ relationships to reading skill have primarily used FA (for overviews, see Ben et al., 2007; Vandermosten et al., 2012; Moreau et al., 2018; Meisler and Gabrieli, 2022). However, several factors confound the ability to draw meaningful interpretations from FA results (Farquharson et al., 2013; Riffert et al., 2014). As a metric defined on the voxel-level, FA is prone to partial volume effects, manifesting as reduced FA in regions where white matter borders gray matter or CSF (Vos et al., 2011). Due to the limited degrees of freedom in the tensor model, FA is artificially lower in regions of crossing fibers, affecting up to 90% of white matter voxels (Behrens et al., 2007; Jeurissen et al., 2013). In addition to sensitivity to myelination, FA also tends to covary with other elements such as axonal diameter, density, permeability, and coherence (Beaulieu, 2009; Johansen-Berg and Behrens, 2013; Shemesh, 2018; Friedrich et al., 2020; Lazari and Lipp, 2021), and information from DTI alone is not sufficient to gauge the individual contributions of these features. Thus, FA has often been reduced to a nonspecific (and arguably inappropriate; see Jones et al., 2013) term, 'white matter integrity’.

Early cross-sectional studies of FA and reading skills seemed to converge towards a consensus of greater FA relating to better reading ability, particularly in left temporoparietal white matter tracts that connect neocortical regions known to be important for language, such as the arcuate fasciculus (AF) and superior longitudinal fasciculus (SLF) (Klingberg et al., 2000; Ben et al., 2007; Vandermosten et al., 2012). As tract segmentation algorithms became more robust and widely used, subsequent studies, empowered to address tract-specific hypotheses, began describing a range of results. These included significant FA-reading relationships in different areas, such as commissural (Frye et al., 2008; Lebel et al., 2013), cerebellar (Travis et al., 2015; Bruckert et al., 2020), and right-lateralized bundles (Horowitz-Kraus et al., 2015), as well as regions where higher FA was associated with worse reading skills (Carter et al., 2009; Frye et al., 2011; Christodoulou et al., 2017). The inconsistency in past results is potentially driven by a variety of factors such as publication bias (Begg, 1994), small participant cohorts, inhomogeneous acquisition parameters, different covariates and reading measures, variation in age groups, and different processing techniques (Moreau et al., 2018; Ramus et al., 2018; Schilling et al., 2021a; Schilling et al., 2021b). Few studies have sought to resolve these inconclusive results. A meta-analysis of whole-brain voxel-based studies found no regions where FA either varied with reading ability or was reduced in dyslexic compared with typically reading, individuals (Moreau et al., 2018). Geeraert et al., 2020 used principal component analysis to draw out white matter structural indices from several scalar maps, including metrics from DTI (such as FA) and neurite orientation dispersion and density imaging (NODDI; Zhang et al., 2012), and found that variance in these measures was driven by age-related development, but not reading. Three large-scale cross-sectional studies using publicly available datasets found largely null associations between FA and reading skills in several tracts (Koirala et al., 2021; Meisler and Gabrieli, 2022; Roy et al., 2022).

Despite the mixed empirical findings relating FA to reading skill, it is reasonable to hypothesize that there ought to be such a brain structure–behavior correlate of reading ability. Reading involves the functioning of a widely distributed brain network (Cattinelli et al., 2013; Wandell and Yeatman, 2013; Murphy et al., 2019), and white matter tracts are conduits for information sent within this network (Ben et al., 2007). Lesion-mapping analyses (Wang et al., 2020; Li et al., 2021) and clinical case studies (Epelbaum et al., 2008; Rauschecker et al., 2009) have demonstrated that white matter connections, primarily in the left hemisphere, are necessary for reading. Since white matter exhibits learning-driven plasticity and can also modulate neuronal firing patterns (Fields, 2015; Xin and Chan, 2020), one may expect that functional variation, such as differences in reading ability, may be reflected by some white matter structural property (Ramus et al., 2018; Protopapas and Parrila, 2018; Protopapas and Parrila, 2019). The largely null findings in higher-powered meta-analyses (Moreau et al., 2018) and large-scale studies (Koirala et al., 2021; Meisler and Gabrieli, 2022; Roy et al., 2022) suggest that FA is not a specific enough metric to effectively capture this relationship in cross-sectional designs (however, see Van Der Auwera et al., 2021 and Roy et al., 2022 for evidence that FA tracks individual longitudinal trajectories in reading achievement).

More advanced diffusion models have yielded metrics that better reflect variance in reading skills. Sihvonen et al., 2021 found that connectometry from quantitative anisotropy modeling (Yeh et al., 2013) in multiple pathways covaried with better reading skill independently from phonological abilities. Quantitative anisotropy is less prone to artifacts from partial volume effects and crossing fibers than FA (Yeh et al., 2016). Zhao et al., 2016 found that more right-sided laterality of hinderance-modulated orientation anisotropy (HMOA; Dell’Acqua et al., 2013) in the SLF and inferior frontal-occipital fasciculus was related to worse reading skills. Koirala et al., 2021 reconstructed multiple diffusion models in children and concluded that lower orientation dispersion and neurite density indices from NODDI modeling related to better reading abilities in several bilateral tracts, while FA was not associated with reading. Although not a DWI sequence, myelin water imaging (MWI) studies have suggested both positive (Beaulieu et al., 2020) and negative (Economou et al., 2022) associations of myelination with reading skill in children. Economou et al., 2022 also replicated null associations between FA and reading ability in their experimental cohort. These results collectively suggest that studies of reading (and perhaps other cognitive domains; see Lazari et al., 2021) should begin to move beyond traditional DTI modeling. However, NODDI metrics, being a voxel-level metric, cannot ascribe properties to particular fiber populations if multiple exist in a voxel. MWI acquisitions, while showing higher specificity to variation in myelin, tend to have relatively long scan times (Alonso-Ortiz et al., 2015) one would also still need to collect a DWI scan if one wanted to associate MWI metrics with fiber bundles and properly account for MWI variation due to fiber orientations (Birkl et al., 2021). Collecting all of these data in children and clinical populations is challenging and not always practical.

Subsequently, a DWI analytical paradigm was introduced that performs statistical inferences on 'fixels,' or individual fiber populations within voxels, using a set of three fixel-derived metrics: fiber density (FD), fiber cross-section (FC), and their product (FDC) (Raffelt et al., 2015). This framework is enabled by constrained spherical deconvolution (CSD) (Tournier et al., 2007), a data-driven approach for resolving fiber orientation distributions (FODs) even in the presence of crossing fibers. Unlike other fiber-specific metrics, such as quantitative anisotropy, fixel-based analyses (FBA) can yield distinct micro- and macrostructural components, and these can be studied on a fixel-by-fixel basis, affording increased spatial specificity. FD is a microstructural measure that reflects the intra-axonal volume fraction (Raffelt et al., 2012b; Genc et al., 2020), while FC is a macrostructural measure related to the cross-sectional area of fiber bundles (Raffelt et al., 2012b). The product of FD and FC, or FDC, is therefore related to the total estimated intra-axonal volume and is sensitive to both white matter micro- and macrostructure. Increased intra-axonal volume may reflect either an increased number of axons in a given area or the presence of wider axons (or some combination thereof), although conventional DWI alone may not be able to resolve the respective contributions of these two possibilities. Wider axons conduct action potentials more quickly and can fire more often at their terminals (Perge et al., 2012). Thus, FDC is thought to more closely relate to the conductive capacity of white matter (Raffelt et al., 2017b).

In addition to enabling investigations of these more specific fixel-derived metrics, FBA present several additional advantages compared to traditional FA whole-brain approaches (Dhollander et al., 2021a). Since FBAs operate on the level of fixels, and fixels are generated from FODs in white matter, FBAs are by nature restricted to white matter, thus mitigating the effects of multiple comparison correction from redundant regions in other neural compartments. Spatial smoothing in FBAs is performed within local neighborhoods of white matter bundles informed by fixel connectivity (Raffelt et al., 2015). Thus, the signal in a given fixel is not influenced by different tissue classes or other fiber populations, in contrast to traditional voxel-based spatial smoothing, which operates more indiscriminately.

FBAs have been quickly adopted and used to investigate several clinical and developmental populations (reviewed in Dhollander et al., 2021a). However, they have not yet been used to examine reading abilities. With the increased specificity of FBAs, this approach might reveal fiber-specific biomarkers that are more sensitive to variation in reading abilities than FA or other tensor-derived metrics, providing valuable insights into the neural basis of literacy. In this study (Figure 1), we examined the relationship between single-word reading skill and FDC (primary analysis), FD, and FC (secondary analyses) in a pediatric dataset of 983 children and adolescents ages 6–18 from the Healthy Brain Network (HBN) biobank (Alexander et al., 2017). We additionally looked for differences in fixel metrics between participants with (n = 102) and without reading disabilities (n = 570) using the criteria based on diagnostic and standardized reading assessments. In a set of exploratory analyses, we tested whether DWI metrics from other models – DTI, diffusion kurtosis imaging (DKI; Jensen et al., 2005), and NODDI – were related to reading abilities. In all analyses, we employed generalized additive modeling (GAM) (Hastie and Tibshirani, 1990) to more flexibly model age-related variance given the wide age range of participants (Zhao et al., 2022; Bethlehem et al., 2022). We hypothesized that we would see positive associations between FDC and reading abilities, as well as lower FDC among dyslexic readers, in several tracts spanning both hemispheres, but especially the left arcuate fasciculus, left inferior fronto-occipital fasciculus, and cerebellar peduncles, as these tracts yielded significant relationships in multiple studies of advanced diffusion models and reading (Beaulieu et al., 2020; Koirala et al., 2021; Sihvonen et al., 2021; Economou et al., 2022). However, since this was the first FBA involving reading skill, and one with considerably high statistical power, we took a more conservative approach and ran a whole-brain FBA. Using tract segmentation, we ascribed locations of significant results to bundles to guide future research efforts.

Figure 1

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In this study, we employed a method to study fiber-specific properties as they relate to single-word reading abilities and disabilities among children and adolescents. We hypothesized that FDC would covary with reading abilities and be lower in dyslexic readers, especially in the left arcuate fasciculus, left inferior fronto-occipital fasciculus, and cerebellum. Unlike our secondary analyses and recent cross-sectional studies that yielded few-to-no regions exhibiting significant FA-reading relationships or group differences in FA (Moreau et al., 2018; Koirala et al., 2021; Economou et al., 2022; Meisler and Gabrieli, 2022; Roy et al., 2022), we found that higher FDC related to better single-word reading skills throughout the brain. This relationship was stable across ages. However, FDC did not differ between those with and without reading disabilities. Although significant correlations were observed bilaterally, the strongest effect sizes were in the left hemisphere, and especially in temporoparietal and cerebellar white matter. The tracts most likely associated with the regions of strongest correlations were the left-hemisphere AF, SLF, MLF, and SCP.

It is encouraging that the fixel-based results highlighted left-hemisphere dorsal temporoparietal white matter as its importance to reading and language has been well-established. The AF and SLF connect inferior frontal and temporoparietal gray matter regions that are essential for language and reading processing (Catani et al., 2005). Lesion symptom mapping studies have demonstrated that the AF and SLF are vital connections in the reading network (Baldo et al., 2018; Li et al., 2021). These tracts, particularly in the left hemisphere, are associated with phonological processing skills (Yeatman et al., 2011), which are critical to reading (Vellutino and Scanlon, 1987) and impaired in dyslexia (Swan and Goswami, 1997). However, the strongest effects in our study were not found in reading-related tracts projecting from the occipital lobe, such as the inferior fronto-occipital fasciculus (IFOF) and inferior longitudinal fasciculus (ILF). Longitudinal studies have suggested that these ventral tracts are more associated with visual orthographic, as opposed to phonological, processing (Yeatman et al., 2012; Vanderauwera et al., 2018). Our results suggest that phonological skills, as opposed to lower-level visual and orthographic processing, may provide more of a bottleneck to single-word reading abilities in children. The present results are supported by a large-scale longitudinal study finding that FA of the left AF, but not ILF, covaries with single-word reading skill trajectories in children (Roy et al., 2022). This notion is also consistent with a behavioral study demonstrating that orthographic skills are more related with the ability to read longer passages as opposed to single words (Barker et al., 1992). Thus, FBAs of skills relating to reading longer texts, as opposed to single words, might instead highlight ventral tracts. We also note that the MLF intersected with the significant fixel clusters. This tract has received less attention due to a lack of clear characterization of its structure and function. However, some clinical cases suggest that the left MLF may be associated with verbal-auditory learning and comprehension (Latini et al., 2021). We reiterate that the tract masks largely overlapped and should not be used to make definitive associations between fixel-location and bundles, especially because tracts were defined in template, as opposed to native, space.

The present findings suggest that higher FDC in the SCP is associated with better reading skills. Although the cerebellum is not commonly perceived as a core hub in the reading network, theories of reading suggest the cerebellum has a role in fluent word recognition (Alvarez and Fiez, 2018; D’Mello et al., 2020; Li et al., 2022), and cerebellar deficits have been hypothesized as central impairments in dyslexia (Nicolson et al., 2001). In particular, the SCP contains efferent fibers that connect deep cerebellar nuclei to contralateral thalamic cortical regions. Co-activation of language-dominant hemispheric inferior frontal regions and contralateral cerebellar regions during verbal tasks (Jansen et al., 2005) suggests that the SCP may be a putative tract for cortico-cerebellar interactions in verbal processing. Previous studies have reported that FA of bilateral SCP inversely relates to reading skills (Travis et al., 2015; Bruckert et al., 2020). We did not find an inverse relationship between FDC and reading abilities, although one should not a priori expect FA and FDC to covary. Despite the lack of a clear consensus of cerebellar contributions to reading abilities, our findings suggest that the cerebellum should remain a focus in studies of reading skills, especially since it is often cropped out of MRI acquisitions.

While the present results suggest a left-sided laterality in FDC-TOWRE correlation effect sizes, it is noteworthy that statistically significant fixels were distributed across the brain. The left-hemispheric laterality is consistent with the frequent focus on predominantly left-sided networks used in reading (Houdé et al., 2010; Paulesu et al., 2014). However, since some theories of dyslexia etiology, such as the anchoring hypothesis (Ahissar, 2007) and cerebellar hypothesis (Alvarez and Fiez, 2018; D’Mello et al., 2020; Li et al., 2022), imply that deficits in reading could also arise from domain-general deficits, it is plausible that neural signatures outside of the putative reading network may be informative for predicting reading abilities and disabilities, and that these neural bases are not restrained to properties of white matter. Poor reading abilities have been associated with more global neural differences, most consistently manifested as reductions in intracranial volume (Ramus et al., 2018), which we replicate here (Table 1, Figure 2—figure supplement 1). A functional MRI study found that whole-brain patterns of reading-driven activity conferred advantages to predicting reading outcomes among dyslexic children compared with targeted region of interest analyses (Hoeft et al., 2011). A machine-learning approach to classifying dyslexic from neurotypical children found that white matter features outside of the putative reading network meaningfully improved discriminability (Cui et al., 2016). The same group conducted a similar study finding that morphometry of bilateral gray matter regions contributed to predicting continuous reading comprehension scores (Cui et al., 2018). Our study adds to these by suggesting that diffuse white matter variation, as indexed by FDC, relates to individual differences in reading abilities independent of ICV (since it was regressed out), although not to a categorical distinction between typical reading ability and reading disability. Future studies should investigate whether multivariate whole-brain patterns of brain morphometry, microstructure, and activity can improve prediction of reading skills, and whether these patterns share biological bases. Such diffuse and multimodal models for predicting reading abilities would likely achieve higher effect sizes than our fixel-specific measures. The changes in $R_{adj}^2$ attributed to the reading measures in predicting fixel metrics were modest, peaking at around 0.030 for the primary analysis, although they are in a similar range of $\mathrm{\Delta }{R}_{adj}^2$ values reported in other brain–behavior correlation studies (e.g., Pines et al., 2022).

Although there were significant correlations between single-word reading ability and FDC, there was not an analogous group difference between those with typical reading ability and those with reading disability. This could be in part due to fewer participants being included in the group analyses (total n = 672) compared to the continuous analyses (n = 983). It is also important to consider that collapsing participants into reading proficiency groups loses information about individual differences in reading ability. This could lead to reductions in statistical power if variation in neural metrics truly lies along a spectrum of reading skill. Although it is a worthwhile pursuit to investigate neurodevelopmental bases of dyslexia, which may be addressed by group comparisons, these questions may be better asked in pre-readers based on future reading outcomes (i.e., comparing children who later do and do not develop typical reading skills). Studying pre-readers would help rule out concerns that findings are due to the consequences of developing typical or poor reading skills, as opposed to the etiology, which is a concern for studies of late-stage readers (Protopapas and Parrila, 2018; Protopapas and Parrila, 2019). There has not yet been a fixel-based analysis focusing on pre-reading skills, but other studies have found white matter microstructural alterations, largely in the left arcuate fasciculus, among pre-readers who have either a familial risk for dyslexia, lower pre-reading skills associated with risk for dyslexia, or future diagnoses of dyslexia (Saygin et al., 2013; Vanderauwera et al., 2015; Vandermosten et al., 2015; Langer et al., 2017; Vanderauwera et al., 2017; Wang et al., 2017; Yu et al., 2020).

In our secondary analyses, we replicated recent studies suggesting that FA does not relate to reading skills (Moreau et al., 2018; Koirala et al., 2021; Meisler and Gabrieli, 2022; Roy et al., 2022). These null results may arise from the many biological factors that influence FA (Beaulieu, 2009; Johansen-Berg and Behrens, 2013; Shemesh, 2018; Friedrich et al., 2020; Lazari and Lipp, 2021) and lack of specificity from being defined on the voxel-level (in which crossing fibers may be present), as opposed to fixel-level. Different manifestations of white matter plasticity from learning to read, such as axonal pruning and increased myelination, can have opposing effects on FA (Yeatman et al., 2012), confounding FA analyses and their interpretations. The present secondary results, to a limited extent, also replicated Koirala et al., 2021, which found negative associations between NODDI metrics and reading skills. The authors attributed this relationship to a more efficient neural architecture. Interestingly, in the present results, areas where ODI negatively related with reading skills approximately overlapped with where FD positively with reading abilities (Figure 4, Figure 3—figure supplement 3). One may have a priori expected significant FD regions to overlap with those from neurite density (NDI), given both metrics are neural density measures. We can only speculate as to what underlies the observed overlap, and future work should further investigate the relationship between fixel metrics and measures from other DWI models.

Our study contributes to, but still leaves open, the discussion of what properties of brain structure change when developing reading skills. There is a frequent focus on myelin plasticity in learning-driven brain development (Xin and Chan, 2020). However, DWI signal is largely insensitive to myelination (Beaulieu, 2002). Since this study is cross-sectional, an important unanswered question is whether axonal differences that drive higher FDC are induced by learning how to read, or alternatively whether the presence of higher FDC in putative reading white matter is static and predisposes one to better reading outcomes. Longitudinal studies of white matter and reading skills have provided some related insights. Roy et al., 2022 suggests that variance in reading skill over multiple years precedes changes in FA. However, Van Der Auwera et al., 2021 found that lower FA in the left AF among future-dyslexic children existed prior to formal reading instruction and predicted future reading scores. The biological basis of FA and its longitudinal change are unclear, and these studies seem to differ regarding the temporal dependence between white matter microstructure and reading skills. At the very least, these studies jointly suggest that white matter is not static in its relation to reading skills. Multimodal studies probing rapid intervention-driven changes have suggested that properties of myelin do not change throughout reading intervention (Huber et al., 2021), with mixed evidence for whether MD (which relates to extra-axonal properties) tracks intervention responses (Huber et al., 2018; Partanen et al., 2021). Future work will need to be done to distinguish age-related from learning-related development in different time scales and to ascribe these changes to biophysical phenomena, which are nontrivial tasks (Jelescu et al., 2020). Liang et al., 2021 demonstrated that fixel metrics can undergo even rapid plasticity. Thus, we hope future studies of reading will use longitudinal FBA (Genc et al., 2018) to investigate long-term and rapid reading-driven plasticity. FDC from the fixel-based analysis is more specific than FA, conveying information about intra-axonal volume on the fixel-level (Dhollander et al., 2021a). However, one should still interpret FDC findings cautiously. DWI alone cannot discern whether intra-axonal volume is driven by the number of axons or width of axons, and fixel-based metrics have not yet been validated against histological standards.

Our findings contribute to a growing list of cross-sectional studies suggesting that models more nuanced than the diffusion tensor better capture variance in reading skills (Zhao et al., 2016; Koirala et al., 2021; Sihvonen et al., 2021; Economou et al., 2022). Unlike many prior studies, we ran a whole-brain analysis instead of running statistics on metrics averaged within tracts. This has important implications for interpreting results. Our whole-brain findings suggest a relationship between reading skills and FDC in fixel-specific regions shared across participants. However, this does not preclude the possibility of tract-averaged diffusion metrics relating to reading skills, even among areas that yielded few significant fixels. A disruption in white matter leading to a deficit in reading might happen at any location along a tract, and variance in such locations across participants could lead to null findings on a fixel-by-fixel level. Whole-brain analyses are also prone to stricter correction for multiple tests. On the other hand, the spatial specificity achieved by whole-brain FBAs could be informative for speculating about the outcomes of white matter disruptions. White matter bundles do not only deliver signals from one end to the other; they branch off and synapse at multiple locations along its course. Thus, spatially specific disruptions of signal could have different downstream effects, warranting a more nuanced approach. Considering that tracts such as the AF and SLF have distinct cores that subserve reading and math processing (Grotheer et al., 2019), averaging over an entire tract may introduce noise by considering parts of the bundle that are not relevant to the behavior being studied. However, one can functionally localize white matter tracts by finding the streamlines that connect participant-specific reading functional regions (Grotheer et al., 2022). One can also extract tract-wise measures similar to FDC that relate to the intra-axonal volume of the bundles (Smith et al., 2022). This approach may lead to appreciable insights into properties of long-range connections that underlie reading skills with higher and more interpretable effect sizes.

In our previous work (Meisler and Gabrieli, 2022), we correlated diffusion metrics with each TOWRE subtest score individually. However, in this study, we used the composite TOWRE measure as the primary phenotypic variable of interest. Our rationale in doing so is the same as in Sihvonen et al., 2021: a composite score is more stable as it is more robust to variance due to temporary attention lapses, which may only affect performance on one test. In addition, running fewer models mitigates the problem of multiple hypothesis testing. We acknowledge, however, that real word and pseudoword reading may rely on different skills. Pseudoword reading ability, for example, is considered a more pure gauge of phonological processing skills because the novelty of these nonwords precludes one from relying on memorized representations. We share the model results for relating FDC to SWE and PDE scores in the supplementary materials (Figure 3—figure supplement 4). The two maps were qualitatively similar to the model results for the composite TOWRE measure. This is not entirely surprising given the high degree of correlation between the subscores (Figure 2).

Our model outputs also allowed us to visualize the impact that intracranial volume and image quality had on DWI-derived metrics. Recent studies relating ICV (Eikenes et al., 2022) and image quality (Koirala et al., 2022) to DTI measures have important implications for model specification that should be extended to fiber-specific metrics in future work. For each metric across fixel-based, DTI, DKI, and NODDI models, we found diffuse significant correlations with both ICV and neighbor correlation (with the exception of FD, since ICV was not part of the model). The nature of these associations varied between the different DWI metrics and covariates, and a full characterization of these relationships falls outside the scope of this report. However, we encourage interested readers to visualize these associations using the model outputs we shared (see 'Data and code availability') and to consider including these metrics in their own fixel-based analysis models. It should be emphasized that our models include multiple predictors that may covary with ICV and image quality, or otherwise not be of interest if one wanted to rigorously characterize the effects of ICV or image quality. However, future work should comprehensively characterize and explain the impacts of brain volume and image quality on diffusion-weighted signal.

The present findings should be interpreted in the context of several other limitations. First, it was not made available what specific criteria were used to diagnose reading disabilities. This is why we used stringent criteria based on clinical and reading assessments to define the RD group. Secondly, most participants in the HBN present with at least one psychological, learning, or neurodevelopmental disorder (Alexander et al., 2017). The diversity of the cohort, while perhaps more representative of a population, presents multiple phenotypic factors that could confound results. To maintain high statistical power and a diverse sample, we did not exclude participants based on the presence of other neurodevelopmental or learning disorders such as ADHD or specific language impairments. Such co-occurring difficulties occur at high rates in reading disorders; for example, approximately 50% of children with reading disorders also qualify for a diagnosis of ADHD (Willcutt et al., 2010; DuPaul et al., 2013; Al Dahhan et al., 2022). Exclusion of such co-occurring difficulties would yield a nonrepresentative sample of those with reading disability.

Further, since white matter bundles can have different shapes across participants (Yeatman et al., 2011; Wassermann et al., 2011) and analyses are performed in a single template space, an effect in a region of fixels could be partially driven by global geometric variations across participants. Similarly, the fixel-to-tract attributions should be cautiously interpreted since our tracts were delineated on the FOD template of 38 participants, and tract segmentations tend to overlap (Schilling et al., 2022). The b-value of 2000 s/mm², while higher than the b-value of typical DTI acquisitions, is not exceptionally large compared to the spectrum of values typically employed in FBA. Thus, our measures of FD, and therefore FDC as well, may have been partially undermined by contamination from extra-axonal signal (Genc et al., 2020). Finally, we reemphasize that our study is cross-sectional and correlational. Thus, it cannot be used to make causal conclusions of white matter’s contributions to reading skills. We hope our work will inform future fixel-based investigations using longitudinal, mediation, modeling, or prediction approaches that can warrant stronger claims.

Raw and preprocessed neuroimaging data from the Healthy Brain Network (Alexander et al., 2017) are publicly available without restriction, and can be downloaded from Amazon Simple Storage Service (S3) using Amazon Web Services tools following directions from the HBN-POD2 manuscript (Richie-Halford et al., 2022). Raw neuroimaging data may also be downloaded directly from the Healthy Brain Network data portal (http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/sharing_neuro.html#Direct%20Down). Access to full Healthy Brain Network phenotypic and behavioral data, which are stored at https://data.healthybrainnetwork.org/main.php, is restricted. For this reason, we cannot make our full study outputs publicly available. These data can be collected by any entity for non-commercial purposes following directions on the Healthy Brain Network data portal (http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/Pheno_Access.html) after signing a data use agreement. Study-specific code and instructions for processing data and running the statistical models can be found at https://github.com/smeisler/Meisler_Reading_FBA (copy archived at swh:1:rev:aefac140776bd0f04ac4abae38e6458a7cf7ec27). We share the population FOD template, tract segmentations, and model outputs (which only report data in the aggregate) at https://osf.io/3ady4/. These can all be viewed using MRview from MRtrix3.

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Author details

1. Steven Lee Meisler

   Program in Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, United States

   Present address

   Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   smeisler@g.harvard.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8888-1572

2. John DE Gabrieli

   McGovern Institute for Brain Research, Cambridge, United States

   Present address

   Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Supervision, Funding acquisition, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

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