How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging

High Resolution Imaging of Viscoelastic Properties of Intracranial Tumours by Multi-Frequency Magnetic Resonance Elastography

PURPOSE

In recent years Magnetic Resonance Elastography (MRE) emerged into a clinically applicable imaging technique. It has been shown that MRE is capable of measuring global changes of the viscoelastic properties of cerebral tissue. The purpose of our study was to evaluate a spatially resolved three-dimensional multi-frequent MRE (3DMMRE) for assessment of the viscoelastic properties of intracranial tumours.

METHODS

A total of 27 patients (63 ± 13 years) were included. All examinations were performed on a 3.0 T scanner, using a modified phase-contrast echo planar imaging sequence. We used 7 vibration frequencies in the low acoustic range with a temporal resolution of 8 dynamics per wave cycle. Post-processing included multi-frequency dual elasto-visco (MDEV) inversion to generate high-resolution maps of the magnitude |G*| and the phase angle φ of the complex valued shear modulus.

RESULTS

The tumour entities included in this study were: glioblastoma (n = 11), anaplastic astrocytoma (n = 3), meningioma (n = 7), cerebral metastasis (n = 5) and intracerebral abscess formation (n = 1). Primary brain tumours and cerebral metastases were not distinguishable in terms of |G*| and φ. Glioblastoma presented the largest range of |G*| values and a trend was delineable that glioblastoma were slightly softer than WHO grade III tumours. In terms of φ, meningiomas were clearly distinguishable from all other entities.

CONCLUSIONS

In this pilot study, while analysing the viscoelastic constants of various intracranial tumour entities with an improved spatial resolution, it was possible to characterize intracranial tumours by their mechanical properties. We were able to clearly delineate meningiomas from intraaxial tumours, while for the latter group an overlap remains in viscoelastic terms.

Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project

An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity—a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity.

Study protocol: The Whitehall II imaging sub-study

BACKGROUND

The Whitehall II (WHII) study of British civil servants provides a unique source of longitudinal data to investigate key factors hypothesized to affect brain health and cognitive ageing. This paper introduces the multi-modal magnetic resonance imaging (MRI) protocol and cognitive assessment designed to investigate brain health in a random sample of 800 members of the WHII study.

METHODS/DESIGN

A total of 6035 civil servants participated in the WHII Phase 11 clinical examination in 2012-2013. A random sample of these participants was included in a sub-study comprising an MRI brain scan, a detailed clinical and cognitive assessment, and collection of blood and buccal mucosal samples for the characterisation of immune function and associated measures. Data collection for this sub-study started in 2012 and will be completed by 2016. The participants, for whom social and health records have been collected since 1985, were between 60-85 years of age at the time the MRI study started. Here, we describe the pre-specified clinical and cognitive assessment protocols, the state-of-the-art MRI sequences and latest pipelines for analyses of this sub-study.

DISCUSSION

The integration of cutting-edge MRI techniques, clinical and cognitive tests in combination with retrospective data on social, behavioural and biological variables during the preceding 25 years from a well-established longitudinal epidemiological study (WHII cohort) will provide a unique opportunity to examine brain structure and function in relation to age-related diseases and the modifiable and non-modifiable factors affecting resilience against and vulnerability to adverse brain changes.

Heritability of structural brain network topology: a DTI study of 156 twins

Individual variation in structural brain network topology has been associated with heritable behavioral phenotypes such as intelligence and schizophrenia, making it a candidate endophenotype. However, little is known about the genetic influences on individual variation in structural brain network topology. Moreover, the extent to which structural brain network topology overlaps with heritability for integrity and volume of white matter remains unknown. In this study, structural network topology was examined using diffusion tensor imaging at 3T. Binary connections between 82 structurally defined brain regions per subject were traced, allowing for estimation of individual topological network properties. Heritability of normalized characteristic path length (λ), normalized clustering coefficient (γ), microstructural integrity (FA), and volume of the white matter were estimated using a twin design, including 156 adult twins from the newly acquired U-TWIN cohort. Both γ and λ were estimated to be under substantial genetic influence. The heritability of γ was estimated to be 68%, the heritability estimate for λ was estimated to be 57%. Genetic influences on network measures were found to be partly overlapping with volumetric and microstructural properties of white matter, but the largest component of genetic variance was unique to both network traits. Normalized clustering coefficient and normalized characteristic path length are substantially heritable, and influenced by independent genetic factors that are largely unique to network measures, but partly also implicated in white matter directionality and volume. Thus, network measures provide information about genetic influence on brain structure, independent of global white matter characteristics such as volume and microstructural directionality.

General overview on the merits of multimodal neuroimaging data fusion

Multimodal neuroimaging has become a mainstay of basic and cognitive neuroscience in humans and animals, despite challenges to consider when acquiring and combining non-redundant imaging data. Multimodal data integration can yield important insights into brain processes and structures in addition to spatiotemporal resolution complementarity, including: a comprehensive physiological view on brain processes and structures, quantification, generalization and normalization, and availability of biomarkers. In this review, we discuss data acquisition and fusion in multimodal neuroimaging in the context of each of these potential merits. However, limitations - due to differences in the neuronal and structural underpinnings of each method - have to be taken into account when modeling and interpreting multimodal data using generative models. We conclude that when these challenges are adequately met, multimodal data fusion can create substantial added value for neuroscience applications making it an indispensable approach for studying the brain.

Potential use of diffusion tensor imaging in level diagnosis of multilevel cervical spondylotic myelopathy

STUDY DESIGN

A prospective study on a series of consecutive patients.

OBJECTIVE

To investigate the use of diffusion tensor imaging (DTI) and orientation entropy in level localization in patients diagnosed with multilevel cervical spondylotic myelopathy (CSM).

SUMMARY OF BACKGROUND DATA

Multilevel CSM presents complex neurological signs that make level localization difficult. DTI is recently found to be able to assess the microstructural changes of the white matter caused by cord compression.

METHODS

Sixteen patients with CSM with multilevel compression were recruited. The level(s) responsible for the clinical symptoms were determined by detailed neurological examination, T2-weighted (T2W) magnetic resonance imaging (MRI), and DTI. On T2W MRI, anterior-posterior compression ratio and increased signal intensities were used to determine the affected level(s). The level diagnosis results from T2W MRI, increased signal intensities, DTI, and combination method were correlated to that of neurological examination on a level-to-level basis, respectively. The accuracy, sensitivity, and specificity were calculated.

RESULTS

When correlated with the clinical level determination, the weighted orientation entropy-based DTI analysis was found to have higher accuracy (82.76% vs. 75.86%) and sensitivity (84.62% vs. 76.92%) than those of the anterior-posterior compression ratio. The increased signal intensities have the highest specificity (100.00%) but the lowest accuracy (58.62%) and sensitivity (53.85%). When combined with the level diagnosis result of wOE with that of anterior-posterior compression ratio, it demonstrated the highest accuracy and sensitivity that were 93.10% and 96.15%, respectively, and equal specificity (66.67%) with using them individually.

CONCLUSION

DTI can be a useful tool to determine the pathological spinal cord levels in multilevel CSM. This information from orientation entropy-based DTI analysis, in addition to conventional MRI and clinical neurological assessment, should help spine surgeons in deciding the optimal surgical strategy.

Reconciling variable findings of white matter integrity in major depressive disorder

Diffusion tensor imaging (DTI) has been used to evaluate white matter (WM) integrity in major depressive disorder (MDD), with several studies reporting differences between depressed patients and controls. However, these findings are variable and taken from relatively small studies often using suboptimal analytic approaches. The presented DTI study examined WM integrity in large samples of medication-free MDD patients (n=134) and healthy controls (n=54) using voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) approaches, and rigorous statistical thresholds. Compared with health control subjects, MDD patients show no significant differences in fractional anisotropy, radial diffusivity, mean diffusivity, and axonal diffusivity with either the VBM or the TBSS approach. Our findings suggest that disrupted WM integrity does not have a major role in the neurobiology of MDD in this relatively large study using optimal imaging acquisition and analysis; however, this does not eliminate the possibility that certain patient subgroups show WM disruption associated with depression.

Diffusion-weighted imaging with dual-echo echo-planar imaging for better sensitivity to acute stroke

BACKGROUND AND PURPOSE

Parallel imaging facilitates the acquisition of echo-planar images with a reduced TE, enabling the incorporation of an additional image at a later TE. Here we investigated the use of a parallel imaging-enhanced dual-echo EPI sequence to improve lesion conspicuity in diffusion-weighted imaging.

MATERIALS AND METHODS

Parallel imaging-enhanced dual-echo DWI data were acquired in 50 consecutive patients suspected of stroke at 1.5T. The dual-echo acquisition included 2 EPI for 1 diffusion-preparation period (echo 1 [TE = 48 ms] and echo 2 [TE = 105 ms]). Three neuroradiologists independently reviewed the 2 echoes by using the routine DWI of our institution as a reference. Images were graded on lesion conspicuity, diagnostic confidence, and image quality. The apparent diffusion coefficient map from echo 1 was used to validate the presence of acute infarction. Relaxivity maps calculated from the 2 echoes were evaluated for potential complementary information.

RESULTS

Echo 1 and 2 DWIs were rated as better than the reference DWI. While echo 1 had better image quality overall, echo 2 was unanimously favored over both echo 1 and the reference DWI for its high sensitivity in detecting acute infarcts.

CONCLUSIONS

Parallel imaging-enhanced dual-echo diffusion-weighted EPI is a useful method for evaluating lesions with reduced diffusivity. The long TE of echo 2 produced DWIs that exhibited superior lesion conspicuity compared with images acquired at a shorter TE. Echo 1 provided higher SNR ADC maps for specificity to acute infarction. The relaxivity maps may serve to complement information regarding blood products and mineralization.

Isotropic non-white matter partial volume effects in constrained spherical deconvolution

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple non-parallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNRs), fiber configurations, and tissue fractions. Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35-50% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM-GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500-3000 s/mm(2), reasonable SNR (~30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.

Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS

We present a novel multi-shell position-orientation adaptive smoothing (msPOAS) method for diffusion weighted magnetic resonance data. Smoothing in voxel and diffusion gradient space is embedded in an iterative adaptive multiscale approach. The adaptive character avoids blurring of the inherent structures and preserves discontinuities. The simultaneous treatment of all q-shells improves the stability compared to single-shell approaches such as the original POAS method. The msPOAS implementation simplifies and speeds up calculations, compared to POAS, facilitating its practical application. Simulations and heuristics support the face validity of the technique and its rigorousness. The characteristics of msPOAS were evaluated on single and multi-shell diffusion data of the human brain. Significant reduction in noise while preserving the fine structure was demonstrated for diffusion weighted images, standard DTI analysis and advanced diffusion models such as NODDI. MsPOAS effectively improves the poor signal-to-noise ratio in highly diffusion weighted multi-shell diffusion data, which is required by recent advanced diffusion micro-structure models. We demonstrate the superiority of the new method compared to other advanced denoising methods.

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Method for structure preserving smoothing multi-shell dMRI data

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Does not rely on any dMRI diffusion model

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Outperforms naive single-shell POAS and other approaches

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Feasible for real data application

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Implemented within a freely available package dti for the R Language

In vivo evaluation of optic nerve aging in adult rhesus monkey by diffusion tensor imaging

Aging of the optic nerve can result in reduced visual sensitivity or vision loss. Normal optic nerve aging has been investigated previously in tissue specimens but poorly explored in vivo. In the present study, the normal aging of optic nerve was evaluated by diffusion tensor imaging (DTI) in non-human primates. Adult female rhesus monkeys at the ages of 9 to 13 years old (young group, n=8) and 21 to 27 years old (old group, n=7) were studied using parallel-imaging-based DTI on a clinical 3T scanner. Compared to young adults, the old monkeys showed 26% lower fractional anisotropy (P<0.01), and 44% greater radial diffusivity, although the latter difference was of marginal statistical significance (P=0.058). These MRI findings are largely consistent with published results of light and electron microscopic studies of optic nerve aging in macaque monkeys, which indicate a loss of fibers and degenerative changes in myelin sheaths.

Development of a high-resolution fat and CSF-suppressed optic nerve DTI protocol at 3T: application in multiple sclerosis

Clinical trials of neuroprotective interventions in multiple sclerosis require outcome measures that reflect the disease pathology. Measures of neuroaxonal integrity in the anterior visual pathways are of particular interest in this context, however imaging of the optic nerve is technically challenging. We therefore developed a 3T optic nerve diffusion tensor imaging protocol incorporating fat and cerebrospinal fluid suppression and without parallel imaging. The sequence used a scheme with six diffusion-weighted directions, b = 600 smm(-2) plus one b ≈ 0 (b(0)) and 40 repetitions, averaged offline, giving an overall scan time of 30 minutes. A coronal oblique orientation was used with voxel size 1.17 mm x 1.17 mm x 4 mm, We validated the sequence in 10 MS patients with a history of optic neuritis and 11 healthy controls: mean fractional anisotropy was reduced in the patients: 0.346(±0.159) versus 0.528(±0.123), p<0.001; radial diffusivity was increased: 0.940(±0.370)x10(-6) mm(2) s(-1) compared to 0.670(± 0.221)x10(-6) mm(2) s(-1) (p<0.01). No significant differences were seen for mean diffusivity or mean axial diffusivity.

PCLR: phase-constrained low-rank model for compressive diffusion-weighted MRI

PURPOSE

This work develops a compressive sensing approach for diffusion-weighted (DW) MRI.

THEORY AND METHODS

A phase-constrained low-rank (PCLR) approach was developed using the image coherence across the DW directions for efficient compressive DW MRI, while accounting for drastic phase changes across the DW directions, possibly as a result of eddy current, and rigid and nonrigid motions. In PCLR, a low-resolution phase estimation was used for removing phase inconsistency between DW directions. In our implementation, GRAPPA (generalized autocalibrating partial parallel acquisition) was incorporated for better phase estimation while allowing higher undersampling factor. An efficient and easy-to-implement image reconstruction algorithm, consisting mainly of partial Fourier update and singular value decomposition, was developed for solving PCLR.

RESULTS

The error measures based on diffusion-tensor-derived metrics and tractography indicated that PCLR, with its joint reconstruction of all DW images using the image coherence, outperformed the frame-independent reconstruction through zero-padding FFT. Furthermore, using GRAPPA for phase estimation, PCLR readily achieved a four-fold undersampling.

CONCLUSION

The PCLR is developed and demonstrated for compressive DW MRI. A four-fold reduction in k-space sampling could be readily achieved without substantial degradation of reconstructed images and diffusion tensor measures, making it possible to significantly reduce the data acquisition in DW MRI and/or improve spatial and angular resolutions.

Functional diffusion tensor imaging at 3 Tesla

In a previous study we reported on a non-invasive functional diffusion tensor imaging (fDTI) method to measure neuronal signals directly from subtle changes in fractional anisotropy along white matter tracts. We hypothesized that these fractional anisotropy changes relate to morphological changes of glial cells induced by axonal activity. In the present study we set out to replicate the results of the previous study with an improved fDTI scan acquisition scheme. A group of twelve healthy human participants were scanned on a 3 Tesla MRI scanner. Activation was revealed in the contralateral thalamo-cortical tract and optic radiations during tactile and visual stimulation, respectively. Mean percent signal change in FA was 3.47% for the tactile task and 3.79% for the visual task, while for the MD the mean percent signal change was only -0.10 and -0.09%. The results support the notion of different response functions for tactile and visual stimuli. With this study we successfully replicated our previous findings using the same types of stimuli but on a different group of healthy participants and at different field-strength. The successful replication of our first fDTI results suggests that the non-invasive fDTI method is robust enough to study the functional neural networks in the human brain within a practically feasible time period.

An anatomical substrate for integration among functional networks in human cortex

The human brain shows several characteristics of an efficient communication network architecture, including short communication paths and the existence of modules interlinked by a small set of highly connected regions. Studies of structural networks comprising macroscopic white matter projections have shown that these putative hubs are densely interconnected, giving rise to a spatially distributed and topologically central collective called the "rich club." In parallel, studies of intrinsic brain activity have consistently revealed distinct functional communities or resting-state networks (RSNs), indicative of specialized processing and segregation of neuronal information. However, the pattern of structural connectivity interconnecting these functional RSNs and how such inter-RSN structural connections might bring about functional integration between RSNs remain largely unknown. Combining high-resolution diffusion weighted imaging with resting-state fMRI, we present novel evidence suggesting that the rich club structure plays a central role in cross-linking macroscopic RSNs of the human brain. Rich club hub nodes were present in all functional networks, accounted for a large proportion of "connector nodes," and were found to coincide with regions in which multiple networks overlap. In addition, a large proportion of all inter-RSN connections were found to involve rich club nodes, and these connections participated in a disproportionate number of communication paths linking nodes in different RSNs. Our findings suggest that the brain's rich club serves as a macroscopic anatomical substrate to cross-link functional networks and thus plays an important role in the integration of information between segregated functional domains of the human cortex.

Improved B0 -distortion correction in diffusion MRI using interlaced q-space sampling and constrained reconstruction

PURPOSE

To enable high-quality correction of susceptibility-induced geometric distortion artifacts in diffusion magnetic resonance imaging (MRI) images without increasing scan time.

THEORY AND METHODS

A new method for distortion correction is proposed based on subsampling a generalized version of the state-of-the-art reversed-gradient distortion correction method. Rather than acquire each q-space sample multiple times with different distortions (as in the conventional reversed-gradient method), we sample each q-space point once with an interlaced sampling scheme that measures different distortions at different q-space locations. Distortion correction is achieved using a novel constrained reconstruction formulation that leverages the smoothness of diffusion data in q-space.

RESULTS

The effectiveness of the proposed method is demonstrated with simulated and in vivo diffusion MRI data. The proposed method is substantially faster than the reversed-gradient method, and can also provide smaller intensity errors in the corrected images and smaller errors in derived quantitative diffusion parameters.

CONCLUSION

The proposed method enables state-of-the-art distortion correction performance without increasing data acquisition time.

Brain aging in humans, chimpanzees (Pan troglodytes), and rhesus macaques (Macaca mulatta): magnetic resonance imaging studies of macro- and microstructural changes

Among primates, humans are uniquely vulnerable to many age-related neurodegenerative disorders. We used structural and diffusion magnetic resonance imaging (MRI) to examine the brains of chimpanzees and rhesus monkeys across each species' adult lifespan, and compared these results with published findings in humans. As in humans, gray matter volume decreased with age in chimpanzees and rhesus monkeys. Also like humans, chimpanzees showed a trend for decreased white matter volume with age, but this decrease occurred proportionally later in the chimpanzee lifespan than in humans. Diffusion MRI revealed widespread age-related decreases in fractional anisotropy and increases in radial diffusivity in chimpanzees and macaques. However, both the fractional anisotropy decline and the radial diffusivity increase started at a proportionally earlier age in humans than in chimpanzees. Thus, even though overall patterns of gray and white matter aging are similar in humans and chimpanzees, the longer lifespan of humans provides more time for white matter to deteriorate before death, with the result that some neurological effects of aging may be exacerbated in our species.

Preventing academic difficulties in preterm children: a randomised controlled trial of an adaptive working memory training intervention - IMPRINT study

Background

Very preterm children exhibit difficulties in working memory, a key cognitive ability vital to learning information and the development of academic skills. Previous research suggests that an adaptive working memory training intervention (Cogmed) may improve working memory and other cognitive and behavioural domains, although further randomised controlled trials employing long-term outcomes are needed, and with populations at risk for working memory deficits, such as children born preterm.

In a cohort of extremely preterm (<28 weeks’ gestation)/extremely low birthweight (<1000 g) 7-year-olds, we will assess the effectiveness of Cogmed in improving academic functioning 2 years’ post-intervention. Secondary objectives are to assess the effectiveness of Cogmed in improving working memory and attention 2 weeks’, 12 months’ and 24 months’ post-intervention, and to investigate training related neuroplasticity in working memory neural networks 2 weeks’ post-intervention.

Methods/Design

This double-blind, placebo-controlled, randomised controlled trial aims to recruit 126 extremely preterm/extremely low birthweight 7-year-old children. Children attending mainstream school without major intellectual, sensory or physical impairments will be eligible. Participating children will undergo an extensive baseline cognitive assessment before being randomised to either an adaptive or placebo (non-adaptive) version of Cogmed. Cogmed is a computerised working memory training program consisting of 25 sessions completed over a 5 to 7 week period. Each training session takes approximately 35 minutes and will be completed in the child’s home. Structural, diffusion and functional Magnetic Resonance Imaging, which is optional for participants, will be completed prior to and 2 weeks following the training period. Follow-up assessments focusing on academic skills (primary outcome), working memory and attention (secondary outcomes) will be conducted at 2 weeks’, 12 months’ and 24 months’ post-intervention.

Discussion

To our knowledge, this study will be the first randomised controlled trial to (a) assess the effectiveness of Cogmed in school-aged extremely preterm/extremely low birthweight children, while incorporating advanced imaging techniques to investigate neural changes associated with adaptive working memory training, and (b) employ long-term follow-up to assess the potential benefit of improved working memory on academic functioning. If effective, Cogmed would serve as a valuable, available intervention for improving developmental outcomes for this population.

Trial registration

Australian New Zealand Clinical Trials Registry ACTRN12612000124831.

Inter-individual differences in audio-motor learning of piano melodies and white matter fiber tract architecture

Humans vary substantially in their ability to learn new motor skills. Here, we examined inter-individual differences in learning to play the piano, with the goal of identifying relations to structural properties of white matter fiber tracts relevant to audio-motor learning. Non-musicians (n = 18) learned to perform three short melodies on a piano keyboard in a pure audio-motor training condition (vision of their own fingers was occluded). Initial learning times ranged from 17 to 120 min (mean ± SD: 62 ± 29 min). Diffusion-weighted magnetic resonance imaging was used to derive the fractional anisotropy (FA), an index of white matter microstructural arrangement. A correlation analysis revealed that higher FA values were associated with faster learning of piano melodies. These effects were observed in the bilateral corticospinal tracts, bundles of axons relevant for the execution of voluntary movements, and the right superior longitudinal fasciculus, a tract important for audio-motor transformations. These results suggest that the speed with which novel complex audio-motor skills can be acquired may be determined by variability in structural properties of white matter fiber tracts connecting brain areas functionally relevant for audio-motor learning.

Quantitative analysis of fiber tractography in cervical spondylotic myelopathy

BACKGROUND CONTEXT

Diffusion tensor fiber tractography is an emerging tool for the visualization of spinal cord microstructure. However, there are few quantitative analyses of the damage in the nerve fiber tracts of the myelopathic spinal cord.

PURPOSE

The aim of this study was to develop a quantitative approach for fiber tractography analysis in cervical spondylotic myelopathy (CSM).

STUDY DESIGN/SETTING

Prospective study on a series of patients.

MATERIALS AND METHODS

A total of 22 volunteers were recruited with informed consent, including 15 healthy subjects and 7 CSM patients. The clinical severity of CSM was evaluated using modified Japanese Orthopedic Association (JOA) score. The microstructure of myelopathic cervical cord was analyzed using diffusion tensor imaging. Diffusion tensor imaging was performed with a 3.0-T magnetic resonance imaging scanner using pulsed gradient, spin-echo, echo-planar imaging sequence. Fiber tractography was generated via TrackVis with fractional anisotropy threshold set at 0.2 and angle threshold at 40°. Region of interest (ROI) was defined to cover C4 level only or the whole-length cervical spinal cord from C1 to C7 for analysis. The length and density of tracked nerve bundles were measured for comparison between healthy subjects and CSM patients.

RESULTS

The length of tracked nerve bundles significantly shortened in CSM patients compared with healthy subjects (healthy: 6.85-77.90 mm, CSM: 0.68-62.53 mm). The density of the tracked nerve bundles was also lower in CSM patients (healthy: 086±0.03, CSM: 0.80±0.06, p<.05). Although the definition of ROI covering C4 only or whole cervical cord appeared not to affect the trend of the disparity between healthy and myelopathic cervical cords, the density of the tracked nerve bundle through whole myelopathic cords was in an association with the modified JOA score in CSM cases (r=0.949, p=.015), yet not found with ROI at C4 only (r=0.316, p=.684).

CONCLUSIONS

The quantitative analysis of fiber tractography is a reliable approach to detect cervical spondylotic myelopathic lesions compared with healthy spinal cords. It could be employed to delineate the severity of CSM.

Imaging human connectomes at the macroscale

At macroscopic scales, the human connectome comprises anatomically distinct brain areas, the structural pathways connecting them and their functional interactions. Annotation of phenotypic associations with variation in the connectome and cataloging of neurophenotypes promise to transform our understanding of the human brain. In this Review, we provide a survey of magnetic resonance imaging–based measurements of functional and structural connectivity. We highlight emerging areas of development and inquiry and emphasize the importance of integrating structural and functional perspectives on brain architecture.

RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI

The trade-off between signal-to-noise ratio (SNR) and spatial specificity governs the choice of spatial resolution in magnetic resonance imaging (MRI); diffusion-weighted (DW) MRI is no exception. Images of lower resolution have higher signal to noise ratio, but also more partial volume artifacts. We present a data-fusion approach for tackling this trade-off by combining DW MRI data acquired both at high and low spatial resolution. We combine all data into a single Bayesian model to estimate the underlying fiber patterns and diffusion parameters. The proposed model, therefore, combines the benefits of each acquisition. We show that fiber crossings at the highest spatial resolution can be inferred more robustly and accurately using such a model compared to a simpler model that operates only on high-resolution data, when both approaches are matched for acquisition time.

A functional model of cortical gyri and sulci

Diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) have been broadly used in the neuroimaging field to investigate the macro-scale fiber connection patterns in the cerebral cortex. Our recent analyses of DTI and HARDI data demonstrated that gyri are connected by denser, streamlined fibers than sulci are. Inspired by this finding and motivated by the fact that DTI-derived fibers provide the structural substrates for functional connectivity, we hypothesize that gyri are global functional connection centers and sulci are local functional units. To test this functional model of gyri and sulci, we examined the structural and functional connectivity among the landmarks on the selected gyral/sulcal areas in the frontal/parietal lobe and in the whole cerebral cortex via multimodal DTI and resting state fMRI (R-fMRI) datasets. Our results demonstrate that functional connectivity is strong among gyri, weak among sulci, and moderate between gyri and sulci. These results suggest that gyri are functional connection centers that exchange information among remote structurally connected gyri and neighboring sulci, while sulci communicate directly with their neighboring gyri and indirectly with other cortical regions through gyri. This functional model of gyri and sulci has been supported by a series of experiments, and provides novel perspectives on the functional architecture of the cerebral cortex.

Advances in diffusion MRI acquisition and processing in the Human Connectome Project

The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, whilst enabling scanning of a very large number of subjects. These advances result from 2 years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013.

Diffusion imaging quality control via entropy of principal direction distribution

Diffusion MR imaging has received increasing attention in the neuroimaging community, as it yields new insights into the microstructural organization of white matter that are not available with conventional MRI techniques. While the technology has enormous potential, diffusion MRI suffers from a unique and complex set of image quality problems, limiting the sensitivity of studies and reducing the accuracy of findings. Furthermore, the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts, reduced signal-to-noise ratio (SNR), and increased proneness to a wide variety of artifacts, including eddy-current and motion artifacts, "venetian blind" artifacts, as well as slice-wise and gradient-wise inconsistencies. Such artifacts mandate stringent Quality Control (QC) schemes in the processing of diffusion MRI data. Most existing QC procedures are conducted in the DWI domain and/or on a voxel level, but our own experiments show that these methods often do not fully detect and eliminate certain types of artifacts, often only visible when investigating groups of DWI's or a derived diffusion model, such as the most-employed diffusion tensor imaging (DTI). Here, we propose a novel regional QC measure in the DTI domain that employs the entropy of the regional distribution of the principal directions (PD). The PD entropy quantifies the scattering and spread of the principal diffusion directions and is invariant to the patient's position in the scanner. High entropy value indicates that the PDs are distributed relatively uniformly, while low entropy value indicates the presence of clusters in the PD distribution. The novel QC measure is intended to complement the existing set of QC procedures by detecting and correcting residual artifacts. Such residual artifacts cause directional bias in the measured PD and here called dominant direction artifacts. Experiments show that our automatic method can reliably detect and potentially correct such artifacts, especially the ones caused by the vibrations of the scanner table during the scan. The results further indicate the usefulness of this method for general quality assessment in DTI studies.

Using high angular resolution diffusion imaging data to discriminate cortical regions

Brodmann’s 100–year–old summary map has been widely used for cortical localization in neuroscience. There is a pressing need to update this map using non–invasive, high–resolution and reproducible data, in a way that captures individual variability. We demonstrate here that standard HARDI data has sufficiently diverse directional variation among grey matter regions to inform parcellation into distinct functional regions, and that this variation is reproducible across scans. This characterization of the signal variation as non–random and reproducible is the critical condition for successful cortical parcellation using HARDI data. This paper is a first step towards an individual cortex–wide map of grey matter microstructure, The gray/white matter and pial boundaries were identified on the high–resolution structural MRI images. Two HARDI data sets were collected from each individual and aligned with the corresponding structural image. At each vertex point on the surface tessellation, the diffusion–weighted signal was extracted from each image in the HARDI data set at a point, half way between gray/white matter and pial boundaries. We then derived several features of the HARDI profile with respect to the local cortical normal direction, as well as several fully orientationally invariant features. These features were taken as a fingerprint of the underlying grey matter tissue, and used to distinguish separate cortical areas. A support–vector machine classifier, trained on three distinct areas in repeat 1 achieved 80–82% correct classification of the same three areas in the unseen data from repeat 2 in three volunteers. Though gray matter anisotropy has been mostly overlooked hitherto, this approach may eventually form the foundation of a new cortical parcellation method in living humans. Our approach allows for further studies on the consistency of HARDI based parcellation across subjects and comparison with independent microstructural measures such as ex–vivo histology.

Parcellation of the human substantia nigra based on anatomical connectivity to the striatum

Substantia nigra/ventral tegmental area (SN/VTA) subregions, defined by dopaminergic projections to the striatum, are differentially affected by health (e.g. normal aging) and disease (e.g. Parkinson's disease). This may have an impact on reward processing which relies on dopaminergic regions and circuits. We acquired diffusion tensor imaging (DTI) with probabilistic tractography in 30 healthy older adults to determine whether subregions of the SN/VTA could be delineated based on anatomical connectivity to the striatum. We found that a dorsomedial region of the SN/VTA preferentially connected to the ventral striatum whereas a more ventrolateral region connected to the dorsal striatum. These SN/VTA subregions could be characterised by differences in quantitative structural imaging parameters, suggesting different underlying tissue properties. We also observed that these connectivity patterns differentially mapped onto reward dependence personality trait. We show that tractography can be used to parcellate the SN/VTA into anatomically plausible and behaviourally meaningful compartments, an approach that may help future studies to provide a more fine-grained synopsis of pathological changes in the dopaminergic midbrain and their functional impact.

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We use DTI to segment the substantia nigra/ventral tegmental area (SN/VTA).

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Dorsomedial and ventrolateral SN/VTA regions were defined by striatal connectivity.

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R2* and fractional anisotropy values differed between SN/VTA subregions.

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Connectivity patterns differentially mapped onto a reward personality trait.

The current state-of-the-art of spinal cord imaging: methods

A first-ever spinal cord imaging meeting was sponsored by the International Spinal Research Trust and the Wings for Life Foundation with the aim of identifying the current state-of-the-art of spinal cord imaging, the current greatest challenges, and greatest needs for future development. This meeting was attended by a small group of invited experts spanning all aspects of spinal cord imaging from basic research to clinical practice. The greatest current challenges for spinal cord imaging were identified as arising from the imaging environment itself; difficult imaging environment created by the bone surrounding the spinal canal, physiological motion of the cord and adjacent tissues, and small cross-sectional dimensions of the spinal cord, exacerbated by metallic implants often present in injured patients. Challenges were also identified as a result of a lack of "critical mass" of researchers taking on the development of spinal cord imaging, affecting both the rate of progress in the field, and the demand for equipment and software to manufacturers to produce the necessary tools. Here we define the current state-of-the-art of spinal cord imaging, discuss the underlying theory and challenges, and present the evidence for the current and potential power of these methods. In two review papers (part I and part II), we propose that the challenges can be overcome with advances in methods, improving availability and effectiveness of methods, and linking existing researchers to create the necessary scientific and clinical network to advance the rate of progress and impact of the research.

The minimal preprocessing pipelines for the Human Connectome Project

The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines.

Dopamine restores reward prediction errors in old age

Senescence affects the ability to utilize information about the likelihood of rewards for optimal decision-making. Using functional magnetic resonance imaging in humans, we found that healthy older adults had an abnormal signature of expected value, resulting in an incomplete reward prediction error (RPE) signal in the nucleus accumbens, a brain region that receives rich input projections from substantia nigra/ventral tegmental area (SN/VTA) dopaminergic neurons. Structural connectivity between SN/VTA and striatum, measured by diffusion tensor imaging, was tightly coupled to inter-individual differences in the expression of this expected reward value signal. The dopamine precursor levodopa (L-DOPA) increased the task-based learning rate and task performance in some older adults to the level of young adults. This drug effect was linked to restoration of a canonical neural RPE. Our results identify a neurochemical signature underlying abnormal reward processing in older adults and indicate that this can be modulated by L-DOPA.

Mapping putative hubs in human, chimpanzee and rhesus macaque connectomes via diffusion tractography

Mapping anatomical brain networks with graph-theoretic analysis of diffusion tractography has recently gained popularity, because of its presumed value in understanding brain function. However, this approach has seldom been used to compare brain connectomes across species, which may provide insights into brain evolution. Here, we employed a data-driven approach to compare interregional brain connections across three primate species: 1) the intensively studied rhesus macaque, 2) our closest living primate relative, the chimpanzee, and 3) humans. Specifically, we first used random parcellations and surface-based probabilistic diffusion tractography to derive the brain networks of the three species under various network densities and resolutions. We then compared the characteristics of the networks using graph-theoretic measures. In rhesus macaques, our tractography-defined hubs showed reasonable overlap with hubs previously identified using anterograde and retrograde tracer data. Across all three species, hubs were largely symmetric in the two hemispheres and were consistently identified in medial parietal, insular, retrosplenial cingulate and ventrolateral prefrontal cortices, suggesting a conserved structural architecture within these regions. However, species differences were observed in the inferior parietal cortex, polar and medial prefrontal cortices. The potential significance of these interspecies differences is discussed.

Human and monkey ventral prefrontal fibers use the same organizational principles to reach their targets: tracing versus tractography

This article is a comparative study of white matter projections from ventral prefrontal cortex (vPFC) between human and macaque brains. We test whether the organizational rules that vPFC connections follow in macaques are preserved in humans. These rules concern the trajectories of some of the white matter projections from vPFC and how the position of regions in the vPFC dictate the trajectories of their projections in the white matter. To address this question, we present a novel approach that combines direct tracer measurements of entire white matter trajectories in macaque monkeys with diffusion MRI tractography of both macaques and humans. The approach allows us to provide explicit validation of diffusion tractography and transfer tractography strategies across species to test the extent to which inferences from macaques can be applied to human neuroanatomy. Apart from one exception, we found a remarkable overlap between the two techniques in the macaque. Furthermore, the organizational principles followed by vPFC tracts in macaques are preserved in humans.

Structural brain network imaging shows expanding disconnection of the motor system in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease, which primarily targets the motor system. The structural integrity of the motor network and the way it is embedded in the overall brain network is essential for motor functioning. We studied the longitudinal effects of ALS on the brain network using diffusion tensor imaging and questioned whether over time an increasing number of connections become involved or whether there is progressive impairment of a limited number of connections. The brain network was reconstructed based on "whole brain" diffusion tensor imaging data. We examined: (1) network integrity in 24 patients with ALS at baseline (T = 1) and at a more advanced stage of the disease (T = 2; interval 5.5 months) compared with a group of healthy controls and (2) progressive brain network impairment comparing patients at two time-points in a paired-analysis. These analyses demonstrated an expanding subnetwork of affected brain connections over time with a central role for the primary motor regions (P-values T = 1 0.003; T = 2 0.001). Loss of structural connectivity mainly propagated to frontal and parietal brain regions at T = 2 compared with T = 1. No progressive impairment of the initially affected (motor) connections could be detected. The main finding of this study is an increasing loss of network structure in patients with ALS. In contrast to the theory of ALS solely affecting a fixed set of primary motor connections, our findings show that the network of impaired connectivity is expanding over time. These results are in support of disease spread along structural brain connections.

Prospective and retrospective high order eddy current mitigation for diffusion weighted echo planar imaging

PURPOSE

The proposed method is aimed at reducing eddy current (EC) induced distortion in diffusion weighted echo planar imaging, without the need to perform further image coregistration between diffusion weighted and T2 images. These ECs typically have significant high order spatial components that cannot be compensated by preemphasis.

THEORY AND METHODS

High order ECs are first calibrated at the system level in a protocol independent fashion. The resulting amplitudes and time constants of high order ECs can then be used to calculate imaging protocol specific corrections. A combined prospective and retrospective approach is proposed to apply correction during data acquisition and image reconstruction.

RESULTS

Various phantom, brain, body, and whole body diffusion weighted images with and without the proposed method are acquired. Significantly reduced image distortion and misregistration are consistently seen in images with the proposed method compared with images without.

CONCLUSION

The proposed method is a powerful (e.g., effective at 48 cm field of view and 30 cm slice coverage) and flexible (e.g., compatible with other image enhancements and arbitrary scan plane) technique to correct high order ECs induced distortion and misregistration for various diffusion weighted echo planar imaging applications, without the need for further image post processing, protocol dependent prescan, or sacrifice in signal-to-noise ratio.

Track-weighted functional connectivity (TW-FC): a tool for characterizing the structural-functional connections in the brain

MRI provides a powerful tool for studying the functional and structural connections in the brain non-invasively. The technique of functional connectivity (FC) exploits the intrinsic temporal correlations of slow spontaneous signal fluctuations to characterise brain functional networks. In addition, diffusion MRI fibre-tracking can be used to study the white matter structural connections. In recent years, there has been considerable interest in combining these two techniques to provide an overall structural-functional description of the brain. In this work we applied the recently proposed super-resolution track-weighted imaging (TWI) methodology to demonstrate how whole-brain fibre-tracking data can be combined with FC data to generate a track-weighted (TW) FC map of FC networks. The method was applied to data from 8 healthy volunteers, and illustrated with (i) FC networks obtained using a seeded connectivity-based analysis (seeding in the precuneus/posterior cingulate cortex, PCC, known to be part of the default mode network), and (ii) with FC networks generated using independent component analysis (in particular, the default mode, attention, visual, and sensory-motor networks). TW-FC maps showed high intensity in white matter structures connecting the nodes of the FC networks. For example, the cingulum bundles show the strongest TW-FC values in the PCC seeded-based analysis, due to their major role in the connection between medial frontal cortex and precuneus/posterior cingulate cortex; similarly the superior longitudinal fasciculus was well represented in the attention network, the optic radiations in the visual network, and the corticospinal tract and corpus callosum in the sensory-motor network. The TW-FC maps highlight the white matter connections associated with a given FC network, and their intensity in a given voxel reflects the functional connectivity of the part of the nodes of the network linked by the structural connections traversing that voxel. They therefore contain a different (and novel) image contrast from that of the images used to generate them. The results shown in this study illustrate the potential of the TW-FC approach for the fusion of structural and functional data into a single quantitative image. This technique could therefore have important applications in neuroscience and neurology, such as for voxel-based comparison studies.

Estimating false positives and negatives in brain networks

The human brain is a complex network of anatomically segregated regions interconnected by white matter pathways, known as the human connectome. Diffusion tensor imaging can be used to reconstruct this structural brain network in vivo and noninvasively. However, due to a wide variety of influences, both false positive and false negative connections may occur. By choosing a 'group threshold', brain networks of multiple subjects can be combined into a single reconstruction, affecting the occurrence of these false positives and negatives. In this case, only connections that are detected in a large enough percentage of the subjects, specified by the group threshold, are considered to be present. Although this group threshold has a substantial impact on the resulting reconstruction and subsequent analyses, it is often chosen intuitively. Here, we introduce a model to estimate how the choice of group threshold influences the presence of false positives and negatives. Based on our findings, group thresholds should preferably be chosen between 30% and 90%. Our results further suggest that a group threshold of circa 60% is a suitable setting, providing a good balance between the elimination of false positives and false negatives.

Connectivity-based functional analysis of dopamine release in the striatum using diffusion-weighted MRI and positron emission tomography

The striatum acts in conjunction with the cortex to control and execute functions that are impaired by abnormal dopamine neurotransmission in disorders such as Parkinson's and schizophrenia. To date, in vivo quantification of striatal dopamine has been restricted to structure-based striatal subdivisions. Here, we present a multimodal imaging approach that quantifies the endogenous dopamine release following the administration of d-amphetamine in the functional subdivisions of the striatum of healthy humans with [(11)C]PHNO and [(11)C]Raclopride positron emission tomography ligands. Using connectivity-based (CB) parcellation, we subdivided the striatum into functional subregions based on striato-cortical anatomical connectivity information derived from diffusion magnetic resonance imaging (MRI) and probabilistic tractography. Our parcellation showed that the functional organization of the striatum was spatially coherent across individuals, congruent with primate data and previous diffusion MRI studies, with distinctive and overlapping networks. d-amphetamine induced the highest dopamine release in the limbic followed by the sensory, motor, and executive areas. The data suggest that the relative regional proportions of D2-like receptors are unlikely to be responsible for this regional dopamine release pattern. Notably, the homogeneity of dopamine release was significantly higher within the CB functional subdivisions in comparison with the structural subdivisions. These results support an association between local levels of dopamine release and cortical connectivity fingerprints.

System for integrated neuroimaging analysis and processing of structure

Mapping brain structure in relation to neurological development, function, plasticity, and disease is widely considered to be one of the most essential challenges for opening new lines of neuro-scientific inquiry. Recent developments with MRI analysis of structural connectivity, anatomical brain segmentation, cortical surface parcellation, and functional imaging have yielded fantastic advances in our ability to probe the neurological structure-function relationship in vivo. To date, the image analysis efforts in each of these areas have typically focused on a single modality. Here, we extend the cortical reconstruction using implicit surface evolution (CRUISE) methodology to perform efficient, consistent, and topologically correct analyses in a natively multi-parametric manner. This effort combines and extends state-of-the-art techniques to simultaneously consider and analyze structural and diffusion information alongside quantitative and functional imaging data. Robust and consistent estimates of the cortical surface extraction, cortical labeling, diffusion-inferred contrasts, diffusion tractography, and subcortical parcellation are demonstrated in a scan-rescan paradigm. Accompanying this demonstration, we present a fully automated software system complete with validation data.

Degeneration of the injured cervical cord is associated with remote changes in corticospinal tract integrity and upper limb impairment

BACKGROUND

Traumatic spinal cord injury (SCI) leads to disruption of axons and macroscopic tissue loss. Using diffusion tensor imaging (DTI), we assessed degeneration of the corticospinal tract (CST) in the cervical cord above a traumatic lesion and explored its relationship with cervical atrophy, remote axonal changes within the cranial CST and upper limb function.

METHODS

Nine cervical injured volunteers with bilateral motor and sensory impairment and ten controls were studied. DTI of the cervical cord and brain provided measurements of fractional anisotropy (FA), while anatomical MRI assessed cross-sectional spinal cord area (i.e. cord atrophy). Spinal and central regions of interest (ROI) included the bilateral CST in the cervical cord and brain. Regression analysis identified correlations between spinal FA and cranial FA in the CST and disability.

RESULTS

In individuals with SCI, FA was significantly lower in both CSTs throughout the cervical cord and brain when compared with controls (p≤0.05). Reduced FA of the cervical cord in patients with SCI was associated with smaller cord area (p = 0.002) and a lower FA of the cranial CST at the internal capsule level (p = 0.001). Lower FA in the cervical CST also correlated with impaired upper limb function, independent of cord area (p = 0.03).

CONCLUSION

Axonal degeneration of the CST in the atrophic cervical cord, proximal to the site of injury, parallels cranial CST degeneration and is associated with disability. This DTI protocol can be used in longitudinal assessment of microstructural changes immediately following injury and may be utilised to predict progression and monitor interventions aimed at promoting spinal cord repair.

SIFT: Spherical-deconvolution informed filtering of tractograms

Diffusion MRI allows the structural connectivity of the whole brain (the 'tractogram') to be estimated in vivo non-invasively using streamline tractography. The biological accuracy of these data sets is however limited by the inherent biases associated with the reconstruction method. Here we propose a method to retrospectively improve the accuracy of these reconstructions, by selectively filtering out streamlines from the tractogram in a manner that improves the fit between the streamline reconstruction and the underlying diffusion images. This filtering is guided by the results of spherical deconvolution of the diffusion signal, hence the acronym SIFT: spherical-deconvolution informed filtering of tractograms. Data sets processed by this algorithm show a marked reduction in known reconstruction biases, and improved biological plausibility. Emerging methods in diffusion MRI, particularly those that aim to characterise and compare the structural connectivity of the brain, should benefit from the improved accuracy of the reconstruction.

HOMOR: higher order model outlier rejection for high b-value MR diffusion data

Diffusion MR images are prone to artefacts caused by head movement and cardiac pulsation. Previous techniques for the automated voxel-wise detection of signal intensity outliers have relied on the fit of the diffusion tensor to the data (RESTORE). However, the diffusion tensor cannot appropriately model more than a single fibre population, which may lead to inaccuracies when identifying outlier voxels in crossing fibre regions, particularly when high b-values are used to obtain increased angular contrast. HOMOR (higher order model outlier rejection) was developed to overcome this limitation and is introduced in this study. HOMOR is closely related to RESTORE, but employs a higher order model capable of resolving multiple fibre populations within a voxel. Using high b-value (b=3000 s/mm2) diffusion data from a population of 90 healthy participants, as well as simulations, HOMOR was found to identify a decreased number of outlier voxels compared to RESTORE primarily within areas of crossing, bending and fanning fibres. At lower b-values, however, RESTORE and HOMOR give similar results, which is demonstrated using diffusion data acquired at b=1000 s/mm2 in a mixed cohort. This study demonstrates that, although RESTORE is suitable for low b-value data, HOMOR is better suited for high b-value data.

Improving diffusion MRI using simultaneous multi-slice echo planar imaging

In diffusion MRI, simultaneous multi-slice single-shot EPI acquisitions have the potential to increase the number of diffusion directions obtained per unit time, allowing more diffusion encoding in high angular resolution diffusion imaging (HARDI) acquisitions. Nonetheless, unaliasing simultaneously acquired, closely spaced slices with parallel imaging methods can be difficult, leading to high g-factor penalties (i.e., lower SNR). The CAIPIRINHA technique was developed to reduce the g-factor in simultaneous multi-slice acquisitions by introducing inter-slice image shifts and thus increase the distance between aliased voxels. Because the CAIPIRINHA technique achieved this by controlling the phase of the RF excitations for each line of k-space, it is not directly applicable to single-shot EPI employed in conventional diffusion imaging. We adopt a recent gradient encoding method, which we termed "blipped-CAIPI", to create the image shifts needed to apply CAIPIRINHA to EPI. Here, we use pseudo-multiple replica SNR and bootstrapping metrics to assess the performance of the blipped-CAIPI method in 3× simultaneous multi-slice diffusion studies. Further, we introduce a novel image reconstruction method to reduce detrimental ghosting artifacts in these acquisitions. We show that data acquisition times for Q-ball and diffusion spectrum imaging (DSI) can be reduced 3-fold with a minor loss in SNR and with similar diffusion results compared to conventional acquisitions.

Delay discounting and frontostriatal fiber tracts: a combined DTI and MTR study on impulsive choices in healthy young adults

Delay discounting, a measure of impulsive choice, has been associated with decreased control of the prefrontal cortex over striatum responses. The anatomical connectivity between both brain regions in delaying gratification remains unknown. Here, we investigate whether the quality of frontostriatal (FS) white matter tracts can predict individual differences in delay-discounting behavior. We use tract-based diffusion tensor imaging and magnetization transfer imaging to measure the microstructural properties of FS fiber tracts in 40 healthy young adults (from 18 to 25 years). We additionally explored whether internal sex hormone levels affect the integrity of FS tracts, based on the hypothesis that sex hormones modulate axonal density within prefrontal dopaminergic circuits. We calculated fractional anisotropy (FA), mean diffusivity (MD), longitudinal diffusivity, radial diffusivity (RD), and magnetization transfer ratio (MTR), a putative measure of myelination, for the FS tract. Results showed that lower integrity within the FS tract (higher MD and RD and lower FA), predicts faster discounting in both sexes. MTR was unrelated to delay-discounting performance. In addition, testosterone levels in males were associated with a lower integrity (higher RD) within the FS tract. Our study provides support for the hypothesis that enhanced structural integrity of white matter fiber bundles between prefrontal and striatal brain areas is associated with better impulse control.

The influence of spatial registration on detection of cerebral asymmetries using voxel-based statistics of fractional anisotropy images and TBSS

The sensitivity of diffusion tensor imaging (DTI) for detecting microstructural white matter alterations has motivated the application of voxel-based statistics (VBS) to fractional anisotropy (FA) images (FA-VBS). However, detected group differences may depend on the spatial registration method used. The objective of this study was to investigate the influence of spatial registration on detecting cerebral asymmetries in FA-VBS analyses with reference to data obtained using Tract-Based Spatial Statistics (TBSS). In the first part of this study we performed FA-VBS analyses using three single-contrast and one multi-contrast registration: (i) whole-brain registration based on T2 contrast, (ii) whole-brain registration based on FA contrast, (iii) individual-hemisphere registration based on FA contrast, and (iv) a combination of (i) and (iii). We then compared the FA-VBS results with those obtained from TBSS. We found that the FA-VBS results depended strongly on the employed registration approach, with the best correspondence between FA-VBS and TBSS results when approach (iv), the “multi-contrast individual-hemisphere” method was employed. In the second part of the study, we investigated the spatial distribution of residual misregistration for each registration approach and the effect on FA-VBS results. For the FA-VBS analyses using the three single-contrast registration methods, we identified FA asymmetries that were (a) located in regions prone to misregistrations, (b) not detected by TBSS, and (c) specific to the applied registration approach. These asymmetries were considered candidates for apparent FA asymmetries due to systematic misregistrations associated with the FA-VBS approach. Finally, we demonstrated that the “multi-contrast individual-hemisphere” approach showed the least residual spatial misregistrations and thus might be most appropriate for cerebral FA-VBS analyses.

Axonal integrity predicts cortical reorganisation following cervical injury

BACKGROUND

Traumatic spinal cord injury (SCI) leads to disruption of axonal architecture and macroscopic tissue loss with impaired information flow between the brain and spinal cord-the presumed basis of ensuing clinical impairment.

OBJECTIVE

The authors used a clinically viable, multimodal MRI protocol to quantify the axonal integrity of the cranial corticospinal tract (CST) and to establish how microstructural white matter changes in the CST are related to cross-sectional spinal cord area and cortical reorganisation of the sensorimotor system in subjects with traumatic SCI.

METHODS

Nine volunteers with cervical injuries resulting in bilateral motor impairment and 14 control subjects were studied. The authors used diffusion tensor imaging to assess white matter integrity in the CST, T1-weighted imaging to measure cross-sectional spinal cord area and functional MRI to compare motor task-related brain activations. The relationships among microstructural, macrostructural and functional measures were assessed using regression analyses. Results Diffusion tensor imaging revealed significant differences in the CST of SCI subjects-compared with controls-in the pyramids, the internal capsule, the cerebral peduncle and the hand area. The microstructural white matter changes observed in the left pyramid predicted increased task-related responses in the left M1 leg area, while changes in the cerebral peduncle were predicted by reduced cord area.

CONCLUSION

The observed microstructural changes suggest trauma-related axonal degeneration and demyelination, which are related to cortical motor reorganisation and macrostructure. The extent of these changes may reflect the plasticity of motor pathways associated with cortical reorganisation. This clinically viable multimodal imaging approach is therefore appropriate for monitoring degeneration of central pathways and the evaluation of treatments targeting axonal repair in SCI.