Testing quantitative magnetization transfer models with membrane lipids.

PURPOSE: Quantitative magnetization transfer (qMT) models aim to quantify the contributions of lipids and macromolecules to the MRI signal. Hence, a model system that relates qMT parameters and their molecular sources may improve the interpretation of the qMT parameters. Here we used membrane lipid phantoms as a meaningful tool to study qMT models. By controlling the fraction and type of membrane lipids, we could test the accuracy, reliability, and interpretability of different qMT models. METHODS: We formulated liposomes with various lipid types and water-to-lipids fractions and measured their signals with spoiled gradient-echo MT. We fitted three known qMT models and estimated six parameters for every model. We tested the accuracy and reproducibility of the models and compared the dependency among the qMT parameters. We compared the samples' qMT parameters with their water-to-lipid fractions and with a simple MTnorm (= MTon/MToff) calculation. RESULTS: We found that the three qMT models fit the membrane lipids signals well. We also found that the estimated qMT parameters are highly interdependent. Interestingly, the estimated qMT parameters are a function of the membrane lipid type and also highly related to the water-to-lipid fraction. Finally, we find that most of the lipid sample's information can be captured using the common and easy to estimate MTnorm analysis. CONCLUSION: qMT parameters are sensitive to both the water-to-lipid fraction and to the lipid type. Estimating the water-to-lipid fraction can improve the characterization of membrane lipids' contributions to qMT parameters. Similar characterizations can be obtained using the MTnorm analysis.

A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla.

Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres' values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism.

Non-invasive assessment of normal and impaired iron homeostasis in the brain.

Strict iron regulation is essential for normal brain function. The iron homeostasis, determined by the milieu of available iron compounds, is impaired in aging, neurodegenerative diseases and cancer. However, non-invasive assessment of different molecular iron environments implicating brain tissue's iron homeostasis remains a challenge. We present a magnetic resonance imaging (MRI) technology sensitive to the iron homeostasis of the living brain (the r1-r2* relaxivity). In vitro, our MRI approach reveals the distinct paramagnetic properties of ferritin, transferrin and ferrous iron ions. In the in vivo human brain, we validate our approach against ex vivo iron compounds quantification and gene expression. Our approach varies with the iron mobilization capacity across brain regions and in aging. It reveals brain tumors' iron homeostasis, and enhances the distinction between tumor tissue and non-pathological tissue without contrast agents. Therefore, our approach may allow for non-invasive research and diagnosis of iron homeostasis in living human brains.

Spatial profiles provide sensitive MRI measures of the midbrain micro- and macrostructure.

The midbrain is the rostral-most part of the brainstem. It contains numerous nuclei and white matter tracts, which are involved in motor, auditory and visual processing, and changes in their structure and function have been associated with aging, as well as neurodegenerative disorders. Current tools for estimating midbrain subregions and their structure with MRI require high resolution and multi-parametric quantitative MRI measures. We propose an approach that relies on morphology to calculate profiles along the midbrain and show these profiles are sensitive to the underlying macrostructure of the midbrain. First, we show that the midbrain structure can be sampled, within subject space, along three main axes of the left and right midbrain, producing profiles that are similar across subjects. We use two data sets with different field strengths, that contain R1, R2* and QSM maps and show that the profiles are highly correlated both across subjects and between datasets. Next, we compare profiles of the midbrain that sample ROIs, and show that the profiles along the first two axes sample the midbrain in a way that reliably separates the main structures, i.e., the substantia nigra, the red nucleus, and periaqueductal gray. We further show that age differences which are localized to specific nuclei, are reflected in the profiles. Finally, we generalize the same approach to calculate midbrain profiles on a third clinically relevant dataset using HCP subjects, with metrics such as the diffusion tensor and semi-quantitative data such as T1w/T2w maps. Our results suggest that midbrain profiles, both of quantitative and semi-quantitative estimates are sensitive to the underlying macrostructure of the midbrain. The midbrain profiles are calculated in native space, and rely on simple measurements. We show that it is robust and can be easily expanded to different datasets, and as such we hope that it will be of great use to the community and to the study of the midbrain in particular.

Mapping microstructural gradients of the human striatum in normal aging and Parkinson's disease.

Mapping structural spatial change (i.e., gradients) in the striatum is essential for understanding the function of the basal ganglia in both health and disease. We developed a method to identify and quantify gradients of microstructure in the single human brain in vivo. We found spatial gradients in the putamen and caudate nucleus of the striatum that were robust across individuals, clinical conditions, and datasets. By exploiting multiparametric quantitative MRI, we found distinct, spatially dependent, aging-related alterations in water content and iron concentration. Furthermore, we found cortico-striatal microstructural covariation, showing relations between striatal structural gradients and cortical hierarchy. In Parkinson's disease (PD) patients, we found abnormal gradients in the putamen, revealing changes in the posterior putamen that explain patients' dopaminergic loss and motor dysfunction. Our work provides a noninvasive approach for studying the spatially varying, structure-function relationship in the striatum in vivo, in normal aging and PD.

White matter properties underlying reading abilities differ in 8-year-old children born full term and preterm: A multi-modal approach.

Many diffusion magnetic resonance imaging (dMRI) studies document associations between reading skills and fractional anisotropy (FA) within brain white matter, suggesting that efficient transfer of information across the brain contributes to individual differences in reading. Use of complementary imaging methods can determine if these associations relate to myelin content of white matter tracts. Compared to children born at term (FT), children born preterm (PT) are at risk for reading deficits. We used two MRI methods to calculate associations of reading and white matter properties in FT and PT children. Participants (N=79: 36 FT and 43 PT) were administered the Gray's Oral Reading Test at age 8. We segmented three dorsal (left arcuate and bilateral superior longitudinal fasciculus) and four ventral (bilateral inferior longitudinal fasciculus and bilateral uncinate) tracts and quantified (1) FA from dMRI and (2) R1 from quantitative T1 relaxometry. We examined correlations between reading scores and these metrics along the trajectories of the tracts. Reading positively correlated with FA in segments of left arcuate and bilateral superior longitudinal fasciculi in FT children; no FA associations were found in PT children. Reading positively correlated with R1 in segments of the left superior longitudinal, right uncinate, and left inferior longitudinal fasciculi in PT children; no R1 associations were found in FT children. Birth group significantly moderated the associations of reading and white matter metrics. Myelin content of white matter may contribute to individual differences in PT but not FT children.

Infants' cortex undergoes microstructural growth coupled with myelination during development.

Development of cortical tissue during infancy is critical for the emergence of typical brain functions in cortex. However, how cortical microstructure develops during infancy remains unknown. We measured the longitudinal development of cortex from birth  to six months of age  using multimodal quantitative imaging of cortical microstructure. Here we show that infants' cortex undergoes profound microstructural tissue growth during the first six months of human life. Comparison of postnatal to prenatal transcriptomic gene expression data demonstrates that myelination and synaptic processes are dominant contributors to this postnatal microstructural tissue growth. Using visual cortex as a model system, we find hierarchical microstructural growth: higher-level visual areas have less mature tissue at birth than earlier visual areas but grow at faster rates. This overturns the prominent view that visual areas that are most mature at birth develop fastest. Together, in vivo, longitudinal, and quantitative measurements, which we validated with ex vivo transcriptomic data, shed light on the rate, sequence, and biological mechanisms of developing cortical systems during early infancy. Importantly, our findings propose a hypothesis that cortical myelination is a key factor in cortical development during early infancy, which has important implications for diagnosis of neurodevelopmental disorders and delays in infants.

The glial framework reveals white matter fiber architecture in human and primate brains.

Uncovering the architecture of white matter axons is fundamental to the study of brain networks. We developed a method for quantifying axonal orientations at a resolution of ~15 micrometers. This method is based on the common Nissl staining technique for postmortem histological slices. Nissl staining reveals the spatial organization of glial cells along axons. Using structure tensor analysis, we leveraged this patterned organization to uncover local axonal orientation. We used Nissl-based structure tensor analysis to extract fine details of axonal architecture and demonstrated its applicability in multiple datasets of humans and nonhuman primates. Nissl-based structure tensor analysis can be used to compare fine-grained features of axonal architecture across species and is widely applicable to existing datasets.

Neurobiological underpinnings of rapid white matter plasticity during intensive reading instruction.

Diffusion MRI is a powerful tool for imaging brain structure, but it is challenging to discern the biological underpinnings of plasticity inferred from these and other non-invasive MR measurements. Biophysical modeling of the diffusion signal aims to render a more biologically rich image of tissue microstructure, but the application of these models comes with important caveats. A separate approach for gaining biological specificity has been to seek converging evidence from multi-modal datasets. Here we use metrics derived from diffusion kurtosis imaging (DKI) and the white matter tract integrity (WMTI) model along with quantitative MRI measurements of T1 relaxation to characterize changes throughout the white matter during an 8-week, intensive reading intervention (160 total hours of instruction). Behavioral measures, multi-shell diffusion MRI data, and quantitative T1 data were collected at regular intervals during the intervention in a group of 33 children with reading difficulties (7-12 years old), and over the same period in an age-matched non-intervention control group. Throughout the white matter, mean 'extra-axonal' diffusivity was inversely related to intervention time. In contrast, model estimated axonal water fraction (AWF), overall diffusion kurtosis, and T1 relaxation time showed no significant change over the intervention period. Both diffusion and quantitative T1 based metrics were correlated with pre-intervention reading performance, albeit with distinct anatomical distributions. These results are consistent with the view that rapid changes in diffusion properties reflect phenomena other than widespread changes in myelin density. We discuss this result in light of recent work highlighting non-axonal factors in experience-dependent plasticity and learning.

Conduction delays in the visual pathways of progressive multiple sclerosis patients covary with brain structure.

In developed countries, multiple sclerosis (MS) is the leading cause of non-traumatic neurological disability in young adults. MS is a chronic demyelinating disease of the central nervous system, in which myelin is attacked, changing white matter structure and leaving lesions. The demyelination has a direct effect on white matter conductivity. This effect can be examined in the visual system, where damage is highly prevalent in MS, leading to substantial delays in conduction, commonly measured with visual evoked potentials (VEPs). The structural damage to the visual system in MS is often estimated with MRI measurements in the white matter. Recent developments in quantitative MRI (qMRI) provide improved sensitivity to myelin content and new structural methods allow better modeling of the axonal structure, leading researchers to link white matter microstructure to conduction properties of action potentials along fiber tracts. This study attempts to explain the variance in conduction latencies down the visual pathway using structural measurements of both the retina and the optic radiation (OR). Forty-eight progressive MS patients, participants in a longitudinal stem-cell therapy clinical trial, were included in this study, three and six months post final treatment. Twenty-seven patients had no history of optic neuritis, and were the main focus of this study. All participants underwent conventional MRI scans, as well as diffusion MRI and qMRI sequences to account for white matter microstructure. Optical coherence tomography scans were also obtained, and peripapillary retinal nerve fiber layer (pRNFL) thickness and macular volume measurements were extracted. Finally, latencies of recorded VEPs were estimated. Our results show that in non-optic neuritis progressive MS patients there is a relationship between the VEP latency and both retinal damage and OR lesion load. In addition, we find that qMRI values, sampled along the OR, are also correlated with VEP latency. Finally, we show that combining these parameters using PCA we can explain more than 40% of the inter-subject variance in VEP latency. In conclusion, this study contributes to understanding the relationship between the structural properties and conduction in the visual system in disease. We focus on the visual system, where the conduction latencies can be estimated, but the conclusions could be generalized to other brain systems where the white matter structure can be measured. It also highlights the importance of having multiple parameters when assessing the clinical stages of MS patients, which could have major implications for future studies of other white matter diseases.

Associations of Reading Efficiency with White Matter Properties of the Cerebellar Peduncles in Children.

Reading in children has been associated with microstructural properties of the cerebellar peduncles, the white matter pathways connecting the cerebellum to the cerebrum. In this study, we used two independent neuroimaging modalities to assess which features of the cerebellar peduncles would be associated with reading. Twenty-three 8-year-old children were evaluated on word reading efficiency and imaged using diffusion MRI (dMRI) and quantitative T1 relaxometry (qT1). We segmented the superior (SCP), middle, and inferior cerebellar peduncles and extracted two metrics: fractional anisotropy (FA) from dMRI and R1 from qT1. Tract-FA was significantly correlated with tract-R1 in left and right SCPs (left: rP(21) = .63, right: rP(21) = .76, p ≤ .001) suggesting that FA of these peduncles, at least in part, indexed myelin content. Tract-FA and tract R1 were not correlated in the other cerebellar peduncles. Reading efficiency negatively correlated with tract-FA of the left (rP(21) = - .43, p = .040) and right SCP (rP(21) = - .37, p = .079). Reading efficiency did not correlate with tract-R1 in the SCPs. The negative association of reading efficiency with tract-FA and the lack of association of reading efficiency with tract-R1 implicate properties other than myelin content as relevant to the information flow between the cerebellum and the cerebrum for individual differences in reading skills in children.

A phantom system for assessing the effects of membrane lipids on water proton relaxation.

Quantitative MRI (qMRI) is a method for the non-invasive study of brain-structure-associated changes expressed with measurable units. The qMRI-derived parameters have been shown to reflect brain tissue composition such as myelin content. Nevertheless, it remains a major challenge to identify and quantify the contributions of specific molecular components to the MRI signal. Here, we describe a phantom system that can be used to evaluate the contribution of membrane lipids to qMRI-derived parameters. We used a hydration-dehydration dry film technique to formulate liposomes that can be used as a model of the bilayer lipid membrane. The liposomes were comprised of the most abundant types of lipid found in the human brain. We then applied clinically available qMRI techniques with adjusted bias corrections in order to test the ability of the phantom system to estimate multiple qMRI parameters such as proton density (PD), T1 , T2 , T2 * and magnetization transfer. In addition, we accurately measured the phantom sample water fraction (normalized PD). A similar protocol was also applied to the human brain in vivo. The phantom system allows for a reliable estimation of qMRI parameters for phantoms composed of various lipid types using a clinical MRI scanner. We also found a comparable reproducibility between the phantom and in vivo human brain qMRI estimations. To conclude, we have successfully created a biologically relevant liposome phantom system whose lipid composition can be fully controlled. Our lipid system and analysis can be used to measure the contributions to qMRI parameters of membrane lipids found in the human brain under scanning conditions that are relevant to in vivo human brain scans. Such a model system can be used to test the contributions of lipidomic changes in normal and pathological brain states.

Automatic Segmentation of the Dorsal Claustrum in Humans Using in vivo High-Resolution MRI.

The claustrum is a thin sheet of neurons enclosed by white matter and situated between the insula and the putamen. It is highly interconnected with sensory, frontal, and subcortical regions. The deep location of the claustrum, with its fine structure, has limited the degree to which it could be studied in vivo. Particularly in humans, identifying the claustrum using magnetic resonance imaging (MRI) is extremely challenging, even manually. Therefore, automatic segmentation of the claustrum is an invaluable step toward enabling extensive and reproducible research of the anatomy and function of the human claustrum. In this study, we developed an automatic algorithm for segmenting the human dorsal claustrum in vivo using high-resolution MRI. Using this algorithm, we segmented the dorsal claustrum bilaterally in 1068 subjects of the Human Connectome Project Young Adult dataset, a publicly available high-resolution MRI dataset. We found good agreement between the automatic and manual segmentations performed by 2 observers in 10 subjects. We demonstrate the use of the segmentation in analyzing the covariation of the dorsal claustrum with other brain regions, in terms of macro- and microstructure. We identified several covariance networks associated with the dorsal claustrum. We provide an online repository of 1068 bilateral dorsal claustrum segmentations.

Subdividing the superior longitudinal fasciculus using local quantitative MRI.

The association fibers of the superior longitudinal fasciculus (SLF) connect parietal and frontal cortical regions in the human brain. The SLF comprises of three distinct sub-bundles, each presenting a different anatomical trajectory, and specific functional roles. Nevertheless, in vivo studies of the SLF often consider the entire SLF complex as a single entity. In this work, we suggest a data-driven approach that relies on microstructure measurements for separating SLF-III from the rest of the SLF. We apply the SLF-III separation procedure in three independent datasets using parameters of diffusion MRI (fractional anisotropy), as well as relaxometry-based parameters (T1, T2, T2* and T2-weighted/T1-weighted). We show that the proposed procedure is reproducible across datasets and tractography algorithms. Finally, we suggest that differential crossing with different white-matter tracts is the source of the distinct MRI signatures of SLF-II and SLF-III.

The robust and independent nature of structural STS asymmetries.

The superior temporal sulcus (STS) is an important region for speech comprehension. The greater language network is known to exhibit asymmetries in both structure and function, and consistent with that theory are reports of STS structural asymmetry in MRI-based, morphological measures such as mean thickness and sulcal depth. However, it is not known how these individual STS structural asymmetries relate to each other, or how they interact with the broader language asymmetry that manifests in other brain regions. In this study, we assess the interrelations of STS asymmetries in the human brain in vivo, using four independent datasets to validate our findings. For morphological measurements, we identify STS laterality effects consistent between our datasets and with the literature: leftward for surface area, and rightward for sulcal depth and mean thickness. We then add two more measurements of STS asymmetry: in T1, a quantitative index of the tissue's underlying biophysical properties; and in the projections to the STS from the arcuate fasciculus, a left-lateralized white-matter bundle that connects temporal regions (including STS) with frontal regions (including Broca's area). For these two new measurements, we identify no effect for T1 and a leftward effect for arcuate projections. We then test for correlations between these STS asymmetries, and find associations mainly between measurements of the same type (e.g., two morphological measurements). Finally, we ask if STS asymmetry is preferentially related to Broca asymmetry, as these are both important language regions and connected via the arcuate fasciculus. Using a linear model with cross-validation, we find that random regions are as successful as Broca's area in predicting STS, and no indication of a hypothesized leftward asymmetry. We conclude that although these different STS asymmetries are robust across datasets, they are not trivially related to each other, suggesting different biological or imaging sources for different aspects of STS lateralities.

Tractography delineation of the vertical occipital fasciculus using quantitative T1 mapping.

The vertical occipital fasciculus (VOF) is a white-matter tract that connects the ventral and dorsal visual streams. The precise borders of the VOF have been a matter of dispute since its discovery in the 19th century. The presence of an adjacent vertical pathway, the posterior arcuate fasciculus, makes it especially hard to determine the anterior extent of the VOF. By integrating diffusion MRI tractography with quantitative T1 mapping we found that the vertical streamlines originating in the ventral occipito-temporal cortex show a pattern of lower T1 in more posterior streamlines. We used this pattern to develop an automatic procedure for VOF identification based on a sharp increase in the streamline T1 signature along the posterior-anterior axis. We studied the cortical endpoints of the VOF and their relation to known cytoarchitectonic and functional divisions of the cortex. These results show that multi-modal MRI information, which characterizes local tissue microstructure such as myelination, can be used to delineate white-matter tracts in vivo.

Disentangling molecular alterations from water-content changes in the aging human brain using quantitative MRI.

It is an open question whether aging-related changes throughout the brain are driven by a common factor or result from several distinct molecular mechanisms. Quantitative magnetic resonance imaging (qMRI) provides biophysical parametric measurements allowing for non-invasive mapping of the aging human brain. However, qMRI measurements change in response to both molecular composition and water content. Here, we present a tissue relaxivity approach that disentangles these two tissue components and decodes molecular information from the MRI signal. Our approach enables us to reveal the molecular composition of lipid samples and predict lipidomics measurements of the brain. It produces unique molecular signatures across the brain, which are correlated with specific gene-expression profiles. We uncover region-specific molecular changes associated with brain aging. These changes are independent from other MRI aging markers. Our approach opens the door to a quantitative characterization of the biological sources for aging, that until now was possible only post-mortem.

Evaluating arcuate fasciculus laterality measurements across dataset and tractography pipelines.

The arcuate fasciculi are white-matter pathways that connect frontal and temporal lobes in each hemisphere. The arcuate plays a key role in the language network and is believed to be left-lateralized, in line with left hemisphere dominance for language. Measuring the arcuate in vivo requires diffusion magnetic resonance imaging-based tractography, but asymmetry of the in vivo arcuate is not always reliably detected in previous studies. It is unknown how the choice of tractography algorithm, with each method's freedoms, constraints, and vulnerabilities to false-positive and -negative errors, impacts findings of arcuate asymmetry. Here, we identify the arcuate in two independent datasets using a number of tractography strategies and methodological constraints, and assess their impact on estimates of arcuate laterality. We test three tractography methods: a deterministic, a probabilistic, and a tractography-evaluation (LiFE) algorithm. We extract the arcuate from the whole-brain tractogram, and compare it to an arcuate bundle constrained even further by selecting only those streamlines that connect to anatomically relevant cortical regions. We test arcuate macrostructure laterality, and also evaluate microstructure profiles for properties such as fractional anisotropy and quantitative R1. We find that both tractography choice and implementing the cortical constraints substantially impact estimates of all indices of arcuate laterality. Together, these results emphasize the effect of the tractography pipeline on estimates of arcuate laterality in both macrostructure and microstructure.

Diffusivity and quantitative T1 profile of human visual white matter tracts after retinal ganglion cell damage.

In patients with retinal ganglion cell diseases, recent diffusion tensor imaging (DTI) studies have revealed structural abnormalities in visual white matter tracts such as the optic tract, and optic radiation. However, the microstructural origin of these diffusivity changes is unknown as DTI metrics involve multiple biological factors and do not correlate directly with specific microstructural properties. In contrast, recent quantitative T1 (qT1) mapping methods provide tissue property measurements relatively specific to myelin volume fractions in white matter. This study aims to improve our understanding of microstructural changes in visual white matter tracts following retinal ganglion cell damage in Leber's hereditary optic neuropathy (LHON) patients by combining DTI and qT1 measurements. We collected these measurements from seven LHON patients and twenty age-matched control subjects. For all individuals, we identified the optic tract and the optic radiation using probabilistic tractography, and evaluated diffusivity and qT1 profiles along them. Both diffusivity and qT1 measurements in the optic tract differed significantly between LHON patients and controls. In the optic radiation, these changes were observed in diffusivity but were not evident in qT1 measurements. This suggests that myelin loss may not explain trans-synaptic diffusivity changes in the optic radiation as a consequence of retinal ganglion cell disease.

More than myelin: Probing white matter differences in prematurity with quantitative T1 and diffusion MRI.

OBJECTIVE: We combined diffusion MRI (dMRI) with quantitative T1 (qT1) relaxometry in a sample of school-aged children born preterm and full term to determine whether reduced fractional anisotropy (FA) within the corpus callosum of the preterm group could be explained by a reduction in myelin content, as indexed by R1 (1/T1) from qT1 scans. METHODS: 8-year-old children born preterm (n = 29; GA 22-32 weeks) and full term (n = 24) underwent dMRI and qT1 scans. Four subdivisions of the corpus callosum were segmented in individual native space according to cortical projection zones (occipital, temporal, motor and anterior-frontal). Fractional anisotropy (FA) and R1 were quantified along the tract trajectory of each subdivision and compared across two birth groups. RESULTS: Compared to controls, preterm children demonstrated significantly decreased FA in 3 of 4 analyzed corpus callosum subdivisions (temporal, motor, and anterior frontal segments) and decreased R1 in only 2 of 4 corpus callosum subdivisions (temporal and motor segments). FA and RD were significantly associated with R1 within temporal but not anterior frontal subdivisions of the corpus callosum in the term group; RD correlated with R1 in the anterior subdivision in the preterm group only. CONCLUSIONS: Myelin content, as indexed by R1, drives some but not all of the differences in white matter between preterm and term born children. Other factors, such as axonal diameter and directional coherence, likely contributed to FA differences in the anterior frontal segment of the corpus callosum that were not well explained by R1.