Evaluating the Reliability of Human Brain White Matter Tractometry

Tractometry of Human Visual White Matter Pathways in Health and Disease

Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.

Gradient of microstructural damage along the dentato-thalamo-cortical tract in Friedreich ataxia

OBJECTIVE

The dentato-thalamo-cortical tract (DTT) is the main cerebellar efferent pathway. Degeneration of the DTT is a core feature of Friedreich ataxia (FRDA). However, it remains unclear whether DTT disruption is spatially specific, with some segments being more impacted than others. This study aimed to investigate microstructural integrity along the DTT in FRDA using a profilometry diffusion MRI (dMRI) approach.

METHODS

MRI data from 45 individuals with FRDA (mean age: 33.2 ± 13.2, Male/Female: 26/19) and 37 healthy controls (mean age: 36.5 ± 12.7, Male/Female:18/19) were included in this cross-sectional multicenter study. A profilometry analysis was performed on dMRI data by first using tractography to define the DTT as the white matter pathway connecting the dentate nucleus to the contralateral motor cortex. The tract was then divided into 100 segments, and dMRI metrics of microstructural integrity (fractional anisotropy, mean diffusivity and radial diffusivity) at each segment were compared between groups. The process was replicated on the arcuate fasciculus for comparison.

RESULTS

Across all diffusion metrics, the region of the DTT connecting the dentate nucleus and thalamus was more impacted in FRDA than downstream cerebral sections from the thalamus to the cortex. The arcuate fasciculus was minimally impacted.

INTERPRETATION

Our study further expands the current knowledge about brain involvement in FRDA, showing that microstructural abnormalities within the DTT are weighted to early segments of the tract (i.e., the superior cerebellar peduncle). These findings are consistent with the hypothesis of DTT undergoing anterograde degeneration arising from the dentate nuclei and progressing to the primary motor cortex.

Tractometry of the Human Connectome Project: resources and insights

Introduction

The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data.

Methods

We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines.

Results

We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry-"Tractoscope" (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

Exploring the Structural Plasticity Mechanism of Corticospinal Tract during Stroke Rehabilitation Based Automated Fiber Quantification Tractography

BACKGROUND

Corticospinal tract (CST) is the principal motor pathway; we aim to explore the structural plasticity mechanism in CST during stroke rehabilitation.

METHODS

A total of 25 patients underwent diffusion tensor imaging before rehabilitation (T1), 1-month post-rehabilitation (T2), 2 months post-rehabilitation (T3), and 1-year post-discharge (T4). The CST was segmented, and fractional anisotropy (FA), axial diffusion (AD), mean diffusivity (MD), and radial diffusivity (RD) were determined using automated fiber quantification tractography. Baseline level of laterality index (LI) and motor function for correlation analysis.

RESULTS

The FA values of all segments in the ipsilesional CST (IL-CST) were lower compared with normal CST. Repeated measures analysis of variance showed time-related effects on FA, AD, and MD of the IL-CST, and there were similar dynamic trends in these 3 parameters. At T1, FA, AD, and MD values of the mid-upper segments of IL-CST (around the core lesions) were the lowest; at T2 and T3, values for the mid-lower segments were lower than those at T1, while the values for the mid-upper segments gradually increased; at T4, the values for almost entire IL-CST were higher than before. The highest LI was observed at T2, with a predominance in contralesional CST. The LIs for the FA and AD at T1 were positively correlated with the change rate of motor function.

CONCLUSIONS

IL-CST showed aggravation followed by improvement from around the lesion to the distal end. Balance of interhemispheric CST may be closely related to motor function, and LIs for FA and AD may have predictive value for mild-to-moderate stroke rehabilitation. Clinical Trial Registration. URL: http://www.chictr.org.cn; Unique Identifier: ChiCTR1800019474.

An in vivo Dissection, and Analysis of Socio-Affective Symptoms related to Cerebellum-Midbrain Reward Circuitry in Humans

Emerging research in non-human animals implicates cerebellar projections to the ventral tegmental area (VTA) in appetitive behaviors, but these circuits have not been characterized in humans. Here, we mapped cerebello-VTA white-matter connectivity in humans using probabilistic tractography on diffusion imaging data from the Human Connectome Project. We uncovered the topographical organization of these connections by separately tracking from parcels of cerebellar lobule VI, crus I/II, vermis, paravermis, and cerebrocerebellum. Results revealed that connections from the cerebellum to the VTA predominantly originate in the right hemisphere, interposed nucleus, and paravermal cortex, and terminate mostly ipsilaterally. Paravermal crus I sends the most connections to the VTA compared to other lobules. We discovered a medial-to-lateral gradient of connectivity, such that the medial cerebellum has the highest connectivity with the VTA. Individual differences in microstructure were associated with measures of negative affect and social functioning. By splitting the tracts into quarters, we found that the socio-affective effects were driven by the third quarter of the tract, corresponding to the point at which the fibers leave the deep nuclei. Taken together, we produced detailed maps of cerebello-VTA structural connectivity for the first time in humans and established their relevance for trait differences in socio-affective regulation.

Convolutional neural network-based classification of glaucoma using optic radiation tissue properties

BACKGROUND

Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections.

METHODS

We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models.

RESULTS

We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration.

CONCLUSIONS

Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.

Development of the Alpha Rhythm Is Linked to Visual White Matter Pathways and Visual Detection Performance

Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and adulthood. Here, we tested the hypothesis that these changes in alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large diffusion MRI (dMRI)-EEG dataset (dMRI n = 2,747, EEG n = 2,561) of children and adolescents of either sex (age range, 5-21 years old) and showed that maturation of the optic radiation specifically accounts for developmental changes of alpha frequency. Behavioral analyses also confirmed that variations of alpha frequency are related to maturational changes in visual perception. The present findings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior.

White matter and literacy: A dynamic system in flux

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

Alterations of interhemispheric functional homotopic connectivity and corpus callosum microstructure in bulimia nervosa

Background

Accumulating evidence indicates maladaptive neural information interactions between different brain regions underlie bulimia nervosa (BN). However, little is known about the alterations in interhemispheric communication of BN, which is facilitated by the corpus callosum (CC), the major commissural fiber connecting the two hemispheres. To shed light on the interhemispheric communications in BN, the present study aims to explore alterations of interhemispheric homotopic functional connectivity and the CC microstructure in BN.

Methods

Based on magnetic resonance imaging (MRI) data collected from 42 BN patients and 38 healthy controls (HCs), the group differences of voxel-mirrored homotopic connectivity (VMHC) index and CC white matter microstructure were compared. Then brain regions with significant group differences in VMHC were selected as seeds for subsequent functional connectivity (FC) analysis. Seed-based fiber tracking and correlation analysis were used to analyze the relationship between VMHC and CC changes. And correlation analysis was used to reveal the correlation between abnormal imaging variables and the clinical features of BN.

Results

Compared with HCs, the BN group showed decreased fractional anisotropy (FA) in middle part of CC (CCMid) and increased VMHC in bilateral orbitofrontal cortex (OFC) and middle temporal gyrus (MTG) [false discovery rate (FDR) correction with a corrected threshold of P<0.05]. Subsequent FC analyses indicated increased FC between left OFC and right OFC, bilateral MTG, left middle occipital gyrus and right precuneus (PCUN); between right OFC and left cerebellum crus II and right PCUN; and between left MTG and right inferior temporal gyrus, right cerebellum lobule VI and right medial superior frontal gyrus (FDR correction with a corrected threshold of P<0.05). The VMHC values of OFC and MTG showed no correlations with FA values of the CCMid and the white fibers between the bilateral OFC and MTG were not through the CCMid. In addition, several regions with abnormal FC had a potential correlation trend with abnormal eating behaviors in BN patients (P<0.05, uncorrected).

Conclusions

Aberrant interhemispheric homotopic functional connectivity and CC microstructure were observed in BN, and they may be independent of each other. Regions with aberrant interhemispheric homotopic functional connectivity showed hyperconnectivity with regions related to reward processing, body shape perception, and self-reference.

Human white matter myelinates faster in utero than ex utero

The formation of myelin, the fatty sheath that insulates nerve fibers, is critical for healthy brain function. A fundamental open question is what impact being born has on myelin growth. To address this, we evaluated a large (n = 300) cross-sectional sample of newborns from the Developing Human Connectome Project (dHCP). First, we developed software for the automated identification of 20 white matter bundles in individual newborns that is well suited for large samples. Next, we fit linear models that quantify how T1w/T2w (a myelin-sensitive imaging contrast) changes over time at each point along the bundles. We found faster growth of T1w/T2w along the lengths of all bundles before birth than right after birth. Further, in a separate longitudinal sample of preterm infants (N = 34), we found lower T1w/T2w than in full-term peers measured at the same age. By applying the linear models fit on the cross-section sample to the longitudinal sample of preterm infants, we find that their delay in T1w/T2w growth is well explained by the amount of time they spent developing in utero and ex utero. These results suggest that white matter myelinates faster in utero than ex utero. The reduced rate of myelin growth after birth, in turn, explains lower myelin content in individuals born preterm and could account for long-term cognitive, neurological, and developmental consequences of preterm birth. We hypothesize that closely matching the environment of infants born preterm to what they would have experienced in the womb may reduce delays in myelin growth and hence improve developmental outcomes.

Characteristics of white matter alterations along fibres in patients with bulimia nervosa: A combined voxelwise and tractography study

Accumulating evidence supports the hypothesis that white matter (WM) abnormalities are involved in the pathophysiology of bulimia nervosa (BN); however, findings from in vivo neuroimaging studies have been inconsistent. We aimed to investigate the possible brain WM alterations, including WM volume and microstructure, in patients with BN. We recruited 43 BN patients and 31 healthy controls (HCs). All participants underwent structural and diffusion tensor imaging. Differences in WM volume and microstructure were evaluated using voxel-based morphometry, tract-based spatial statistics, and automated fibre quantification analysis. Compared with HCs, BN patients showed significantly decreased fractional anisotropy in the middle part of the corpus callosum (nodes 31-32) and increased mean diffusivity in the right cranial nerve V (CN V) (nodes 27-33 and nodes 55-88) and vertical occipital fasciculus (VOF) (nodes 58-85). Moreover, we found decreased axial diffusivity in the right inferior fronto-occipital fasciculus (node 67) and increased radial diffusivity in the CN V (nodes 22-34 and nodes 52-89) and left VOF (nodes 60-66 and nodes 81-85). Meanwhile, WM microstructural changes were correlated with patients' clinical manifestations. We did not find any significant differences in WM volume and the main WM fibre bundle properties between BN patients and HCs. Taken together, these findings provide that BN shows significant brain WM reorganization, but primarily in microstructure (part of WM fibre bundle), which is not sufficient to cause changes in WM volume. The automated fibre quantification analysis could be more sensitive to detect the subtle pathological changes in a point or segment of the WM fibre bundle.

Optic radiations representing different eccentricities age differently

The neural pathways that carry information from the foveal, macular, and peripheral visual fields have distinct biological properties. The optic radiations (OR) carry foveal and peripheral information from the thalamus to the primary visual cortex (V1) through adjacent but separate pathways in the white matter. Here, we perform white matter tractometry using pyAFQ on a large sample of diffusion MRI (dMRI) data from subjects with healthy vision in the U.K. Biobank dataset (UKBB; N = 5382; age 45-81). We use pyAFQ to characterize white matter tissue properties in parts of the OR that transmit information about the foveal, macular, and peripheral visual fields, and to characterize the changes in these tissue properties with age. We find that (1) independent of age there is higher fractional anisotropy, lower mean diffusivity, and higher mean kurtosis in the foveal and macular OR than in peripheral OR, consistent with denser, more organized nerve fiber populations in foveal/parafoveal pathways, and (2) age is associated with increased diffusivity and decreased anisotropy and kurtosis, consistent with decreased density and tissue organization with aging. However, anisotropy in foveal OR decreases faster with age than in peripheral OR, while diffusivity increases faster in peripheral OR, suggesting foveal/peri-foveal OR and peripheral OR differ in how they age.

Functional and Structural Properties of Interhemispheric Interaction between Bilateral Precentral Hand Motor Regions in a Top Wheelchair Racing Paralympian

Long-term motor training can cause functional and structural changes in the human brain. Assessing how the training of specific movements affects specific parts of the neural circuitry is essential to understand better the underlying mechanisms of motor training-induced plasticity in the human brain. We report a single-case neuroimaging study that investigated functional and structural properties in a professional athlete of wheelchair racing. As wheelchair racing requires bilateral synchronization of upper limb movements, we hypothesized that functional and structural properties of interhemispheric interactions in the central motor system might differ between the professional athlete and controls. Functional and diffusion magnetic resonance imaging (fMRI and dMRI) data were obtained from a top Paralympian (P1) in wheelchair racing. With 23 years of wheelchair racing training starting at age eight, she holds an exceptional competitive record. Furthermore, fMRI and dMRI data were collected from three other paraplegic participants (P2-P4) with long-term wheelchair sports training other than wheelchair racing and 37 able-bodied control volunteers. Based on the fMRI data analyses, P1 showed activation in the bilateral precentral hand sections and greater functional connectivity between these sections during a right-hand unimanual task. In contrast, other paraplegic participants and controls showed activation in the contralateral hemisphere and deactivation in the ipsilateral hemisphere. Moreover, dMRI data analysis revealed that P1 exhibited significantly lower mean diffusivity along the transcallosal pathway connecting the bilateral precentral motor regions than control participants, which was not observed in the other paraplegic participants. These results suggest that long-term training with bilaterally synchronized upper-limb movements may promote bilateral recruitment of the precentral hand sections. Such recruitment may affect the structural circuitry involved in the interhemispheric interaction between the bilateral precentral regions. This study provides valuable evidence of the extreme adaptability of the human brain.

Reproducible Tract Profiles 2 (RTP2) suite, from diffusion MRI acquisition to clinical practice and research

Diffusion MRI is a complex technique, where new discoveries and implementations occur at a fast pace. The expertise needed for data analyses and accurate and reproducible results is increasingly demanding and requires multidisciplinary collaborations. In the present work we introduce Reproducible Tract Profiles 2 (RTP2), a set of flexible and automated methods to analyze anatomical MRI and diffusion weighted imaging (DWI) data for reproducible tractography. RTP2 reads structural MRI data and processes them through a succession of serialized containerized analyses. We describe the DWI algorithms used to identify white-matter tracts and their summary metrics, the flexible architecture of the platform, and the tools to programmatically access and control the computations. The combination of these three components provides an easy-to-use automatized tool developed and tested over 20 years, to obtain usable and reliable state-of-the-art diffusion metrics at the individual and group levels for basic research and clinical practice.

A comparison of diffusion tensor imaging tractography and constrained spherical deconvolution with automatic segmentation in traumatic brain injury

Detection of microstructural white matter injury in traumatic brain injury (TBI) requires specialised imaging methods, of which diffusion tensor imaging (DTI) has been extensively studied. Newer fibre alignment estimation methods, such as constrained spherical deconvolution (CSD), are better than DTI in resolving crossing fibres that are ubiquitous in the brain and may improve the ability to detect microstructural injuries. Furthermore, automatic tract segmentation has the potential to improve tractography reliability and accelerate workflow compared to the manual segmentation commonly used. In this study, we compared the results of deterministic DTI based tractography and manual tract segmentation with CSD based probabilistic tractography and automatic tract segmentation using TractSeg. 37 participants with a history of TBI (with Glasgow Coma Scale 13-15) and persistent symptoms, and 41 healthy controls underwent deterministic DTI-based tractography with manual tract segmentation and probabilistic CSD-based tractography with TractSeg automatic segmentation.Fractional anisotropy (FA) and mean diffusivity of corpus callosum and three bilateral association tracts were measured. FA and MD values derived from both tractography methods were generally moderately to strongly correlated. CSD with TractSeg differentiated the groups based on FA, while DTI did not. CSD and TractSeg-based tractography may be more sensitive in detecting microstructural changes associated with TBI than deterministic DTI tractography. Additionally, CSD with TractSeg was found to be applicable at lower b-value and number of diffusion-encoding gradients data than previously reported.

Scalable transcriptomics analysis with Dask: applications in data science and machine learning

BACKGROUND

Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary.

METHODS

In this paper we review the main steps and bottlenecks in machine learning pipelines, as well as the main concepts behind scalable data science including those of concurrent and parallel programming. We discuss the benefits of the Dask framework and how it can be integrated with the Python scientific environment to perform data analysis in computational biology and bioinformatics.

RESULTS

This review illustrates the role of Dask for boosting data science applications in different case studies. Detailed documentation and code on these procedures is made available at https://github.com/martaccmoreno/gexp-ml-dask .

CONCLUSION

By showing when and how Dask can be used in transcriptomics analysis, this review will serve as an entry point to help genomic data scientists develop more scalable data analysis procedures.

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

White Matter Characteristics of Damage Along Fiber Tracts in Patients with Type 2 Diabetes Mellitus

Purpose

The white matter (WM) of the brain of type 2 diabetes mellitus (T2DM) patients is susceptible to neurodegenerative processes, but the specific types and positions of microstructural lesions along the fiber tracts remain unclear.

Methods

In this study 61 T2DM patients and 61 healthy controls were recruited and underwent diffusion spectrum imaging (DSI). The results were reconstructed with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). WM microstructural abnormalities were identified using tract-based spatial statistics (TBSS). Pointwise WM tract differences were detected through automatic fiber quantification (AFQ). The relationships between WM tract abnormalities and clinical characteristics were explored with partial correlation analysis.

Results

TBSS revealed widespread WM lesions in T2DM patients with decreased fractional anisotropy and axial diffusivity and an increased orientation dispersion index (ODI). The AFQ results showed microstructural abnormalities in T2DM patients in specific portions of the right superior longitudinal fasciculus (SLF), right arcuate fasciculus (ARC), left anterior thalamic radiation (ATR), and forceps major (FMA). In the right ARC of T2DM patients, an aberrant ODI was positively correlated with fasting insulin and insulin resistance, and an abnormal intracellular volume fraction was negatively correlated with fasting blood glucose. Additionally, negative associations were found between blood pressure and microstructural abnormalities in the right ARC, left ATR, and FMA in T2DM patients.

Conclusion

Using AFQ, together with DTI and NODDI, various kinds of microstructural alterations in the right SLF, right ARC, left ATR, and FMA can be accurately identified and may be associated with insulin and glucose status and blood pressure in T2DM patients.

Structural Covariance and Heritability of the Optic Tract and Primary Visual Cortex in Living Human Brains

Individual differences among human brains exist at many scales, spanning gene expression, white matter tissue properties, and the size and shape of cortical areas. One notable example is an approximately 3-fold range in the size of human primary visual cortex (V1), a much larger range than is found in overall brain size. A previous study (Andrews et al., 1997) reported a correlation between optic tract (OT) cross-section area and V1 size in postmortem human brains, suggesting that there may be a common developmental mechanism for multiple components of the visual pathways. We evaluated the relationship between properties of the OT and V1 in a much larger sample of living human brains by analyzing the Human Connectome Project (HCP) 7 Tesla Retinotopy Dataset (including 107 females and 71 males). This dataset includes retinotopic maps measured with functional MRI (fMRI) and fiber tract data measured with diffusion MRI (dMRI). We found a negative correlation between OT fractional anisotropy (FA) and V1 surface area (r = -0.19). This correlation, although small, was consistent across multiple dMRI datasets differing in acquisition parameters. Further, we found that both V1 surface area and OT properties were correlated among twins, with higher correlations for monozygotic (MZ) than dizygotic (DZ) twins, indicating a high degree of heritability for both properties. Together, these results demonstrate covariation across individuals in properties of the retina (OT) and cortex (V1) and show that each is influenced by genetic factors.SIGNIFICANCE STATEMENT The size of human primary visual cortex (V1) has large interindividual differences. These differences do not scale with overall brain size. A previous postmortem study reported a correlation between the size of the human optic tract (OT) and V1. In this study, we evaluated the relationship between the OT and V1 in living humans by analyzing a neuroimaging dataset that included functional MRI (fMRI) and diffusion MRI (dMRI) data. We found a small, but robust correlation between OT tissue properties and V1 size, supporting the existence of structural covariance between the OT and V1 in living humans. The results suggest that characteristics of retinal ganglion cells (RGCs), reflected in OT measurements, are correlated with individual differences in human V1.

Reproducible protocol to obtain and measure first-order relay human thalamic white-matter tracts

The "primary" or "first-order relay" nuclei of the thalamus feed the cerebral cortex with information about ongoing activity in the environment or the subcortical motor systems. Because of the small size of these nuclei and the high specificity of their input and output pathways, new imaging protocols are required to investigate thalamocortical interactions in human perception, cognition and language. The goal of the present study was twofold: I) to develop a reconstruction protocol based on in vivo diffusion MRI to extract and measure the axonal fiber tracts that originate or terminate specifically in individual first-order relay nuclei; and, II) to test the reliability of this reconstruction protocol. In left and right hemispheres, we investigated the thalamocortical/corticothalamic axon bundles linking each of the first-order relay nuclei and their main cortical target areas, namely, the lateral geniculate nucleus (optic radiation), the medial geniculate nucleus (acoustic radiation), the ventral posterior nucleus (somatosensory radiation) and the ventral lateral nucleus (motor radiation). In addition, we examined the main subcortical input pathway to the ventral lateral posterior nucleus, which originates in the dentate nucleus of the cerebellum. Our protocol comprised three components: defining regions-of-interest; preprocessing diffusion data; and modeling white-matter tracts and tractometry. We then used computation and test-retest methods to check whether our protocol could reliably reconstruct these tracts of interest and their profiles. Our results demonstrated that the protocol had nearly perfect computational reproducibility and good-to-excellent test-retest reproducibility. This new protocol may be of interest for both basic human brain neuroscience and clinical studies and has been made publicly available to the scientific community.

Brain tract structure predicts relapse to stimulant drug use

Diffusion tractography allows identification and measurement of structural tracts in the human brain previously associated with motivated behavior in animal models. Recent findings indicate that the structural properties of a tract connecting the midbrain to nucleus accumbens (NAcc) are associated with a diagnosis of stimulant use disorder (SUD), but not relapse. In this preregistered study, we used diffusion tractography in a sample of patients treated for SUD (n = 60) to determine whether qualities of tracts projecting from medial prefrontal, anterior insular, and amygdalar cortices to NAcc might instead foreshadow relapse. As predicted, reduced diffusion metrics of a tract projecting from the right anterior insula to the NAcc were associated with subsequent relapse to stimulant use, but not with previous diagnosis. These findings highlight a structural target for predicting relapse to stimulant use and further suggest that distinct connections to the NAcc may confer risk for relapse versus diagnosis.

White matter myelination during early infancy is linked to spatial gradients and myelin content at birth

Development of myelin, a fatty sheath that insulates nerve fibers, is critical for brain function. Myelination during infancy has been studied with histology, but postmortem data cannot evaluate the longitudinal trajectory of white matter development. Here, we obtained longitudinal diffusion MRI and quantitative MRI measures of longitudinal relaxation rate (R1) of white matter in 0, 3 and 6 months-old human infants, and developed an automated method to identify white matter bundles and quantify their properties in each infant's brain. We find that R1 increases from newborns to 6-months-olds in all bundles. R1 development is nonuniform: there is faster development in white matter that is less mature in newborns, and development rate increases along inferior-to-superior as well as anterior-to-posterior spatial gradients. As R1 is linearly related to myelin fraction in white matter bundles, these findings open new avenues to elucidate typical and atypical white matter myelination in early infancy.

General additive models address statistical issues in diffusion MRI: An example with clinically anxious adolescents

Statistical models employed to test for group differences in quantized diffusion-weighted MRI white matter tracts often fail to account for the large number of data points per tract in addition to the distribution, type, and interdependence of the data. To address these issues, we propose the use of Generalized Additive Models (GAMs) and supply code and examples to aid in their implementation. Specifically, using diffusion data from 73 periadolescent clinically anxious and no-psychiatric-diagnosis control participants, we tested for group tract differences and show that a GAM allows for the identification of differences within a tract while accounting for the nature of the data as well as covariates and group factors. Further, we then used these tract differences to investigate their association with performance on a memory test. When comparing our high versus low anxiety groups, we observed a positive association between the left uncinate fasciculus and memory overgeneralization for negatively valenced stimuli. This same association was not evident in the right uncinate or anterior forceps. These findings illustrate that GAMs are well-suited for modeling diffusion data while accounting for various aspects of the data, and suggest that the adoption of GAMs will be a powerful investigatory tool for diffusion-weighted analyses.