Assessing and minimizing the effects of noise and motion in clinical DTI at 3 T

The effect of elimination of gibbs ringing, noise and systematic errors on the DTI metrics and tractography in a rat brain

Diffusion tensor imaging (DTI) metrics and tractography can be biased due to low signal-to-noise ratio (SNR) and systematic errors resulting from image artifacts and imperfections in magnetic field gradients. The imperfections include non-uniformity and nonlinearity, effects caused by eddy currents, and the influence of background and imaging gradients. We investigated the impact of systematic errors on DTI metrics of an isotropic phantom and DTI metrics and tractography of a rat brain measured at high resolution. We tested denoising and Gibbs ringing removal methods combined with the B matrix spatial distribution (BSD) method for magnetic field gradient calibration. The results showed that the performance of the BSD method depends on whether Gibbs ringing is removed and the effectiveness of stochastic error removal. Region of interest (ROI)-based analysis of the DTI metrics showed that, depending on the size of the ROI and its location in space, correction methods can remove systematic bias to varying degrees. The preprocessing pipeline proposed and dedicated to this type of data together with the BSD method resulted in an even - 90% decrease in fractional anisotropy (FA) (globally and locally) in the isotropic phantom and - 45% in the rat brain. The largest global changes in the rat brain tractogram compared to the standard method without preprocessing (sDTI) were noticed after denoising. The direction of the first eigenvector obtained from DTI after denoising, Gibbs ringing removal and BSD differed by an average of 56 and 10 degrees in the ROI from sDTI and from sDTI after denoising and Gibbs ringing removal, respectively. The latter can be identified with the amount of improvement in tractography due to the elimination of systematic errors related to imperfect magnetic field gradients. Based on the results, the systematic bias for high resolution data mainly depended on SNR, but the influence of non-uniform gradients could also be seen. After denoising, the BSD method was able to further correct both the metrics and tractography of the diffusion tensor in the rat brain by taking into account the actual distribution of magnetic field gradients independent of the examined object and uniquely dependent on the scanner and sequence. This means that in vivo studies are also subject to this type of errors, which should be taken into account when processing such data.

Deep Learning-Based Denoising of CEST MR Data: A Feasibility Study on Applying Synthetic Phantoms in Medical Imaging

Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) provides a novel method for analyzing biomolecule concentrations in tissues without exogenous contrast agents. Despite its potential, achieving a high signal-to-noise ratio (SNR) is imperative for detecting small CEST effects. Traditional metrics such as Magnetization Transfer Ratio Asymmetry (MTRasym) and Lorentzian analyses are vulnerable to image noise, hampering their precision in quantitative concentration estimations. Recent noise-reduction algorithms like principal component analysis (PCA), nonlocal mean filtering (NLM), and block matching combined with 3D filtering (BM3D) have shown promise, as there is a burgeoning interest in the utilization of neural networks (NNs), particularly autoencoders, for imaging denoising. This study uses the Bloch-McConnell equations, which allow for the synthetic generation of CEST images and explores NNs efficacy in denoising these images. Using synthetically generated phantoms, autoencoders were created, and their performance was compared with traditional denoising methods using various datasets. The results underscored the superior performance of NNs, notably the ResUNet architectures, in noise identification and abatement compared to analytical approaches across a wide noise gamut. This superiority was particularly pronounced at elevated noise intensities in the in vitro data. Notably, the neural architectures significantly improved the PSNR values, achieving up to 35.0, while some traditional methods struggled, especially in low-noise reduction scenarios. However, the application to the in vivo data presented challenges due to varying noise profiles. This study accentuates the potential of NNs as robust denoising tools, but their translation to clinical settings warrants further investigation.

Association between attention-deficit/hyperactivity disorder symptom severity and white matter integrity moderated by in-scanner head motion

Attention-deficit/hyperactivity disorder (ADHD) is a common and debilitating neurodevelopmental disorder associated with various negative life impacts. The manifestation of ADHD is very heterogeneous, and previous investigations on neuroanatomical alterations in ADHD have yielded inconsistent results. We investigated the mediating effect of in-scanner head motion and ADHD hyperactivity severity on motion-corrected fractional anisotropy (FA) using diffusion tensor imaging in the currently largest sample (n = 739) of medication-naïve children and adolescents (age range 5-22 years). We used automated tractography to examine whole-brain and mean FA of the tracts most frequently reported in ADHD; corpus callosum forceps major and forceps minor, left and right superior-longitudinal fasciculus, and left and right corticospinal tract (CST). Associations between FA and hyperactivity severity appeared when in-scanner head motion was not accounted for as mediator. However, causal mediation analysis revealed that these effects are fully mediated through in-scanner head motion for whole-brain FA, the corpus callosum forceps minor, and left superior-longitudinal fasciculus. Direct effect of hyperactivity severity on FA was only found for the left CST. This study illustrates the crucial role of in-scanner head motion in the identification of white matter integrity alterations in ADHD and shows how neglecting irremediable motion artifacts causes spurious findings. When the mediating effect of in-scanner head motion on FA is accounted for, an association between hyperactivity severity and FA is only present for the left CST; this may play a crucial role in the manifestation of hyperactivity and impulsivity symptoms in ADHD.

Effect of number of diffusion encoding directions in Diffusion Metrics of 5-year-olds using Tract-Based Spatial Statistical analysis

Methodological aspects and effects of different imaging parameters on DTI (diffusion tensor imaging) results and their reproducibility have been recently studied comprehensively in adult populations. Although MR imaging of children's brains has become common, less interest has been focussed on researching whether adult‐based optimised parameters and pre‐processing protocols can be reliably applied to paediatric populations. Furthermore, DTI scalar values of preschool aged children are rarely reported. We gathered a DTI dataset from 5‐year‐old children (N = 49) to study the effect of the number of diffusion‐encoding directions on the reliability of resultant scalar values with TBSS (tract‐based spatial statistics) method. Additionally, the potential effect of within‐scan head motion on DTI scalars was evaluated. Reducing the number of diffusion‐encoding directions deteriorated both the accuracy and the precision of all DTI scalar values. To obtain reliable scalar values, a minimum of 18 directions for TBSS was required. For TBSS fractional anisotropy values, the intraclass correlation coefficient with two‐way random‐effects model (ICC[2,1]) for the subsets of 6 to 66 directions ranged between 0.136 [95%CI 0.0767;0.227] and 0.639 [0.542;0.740], whereas the corresponding values for subsets of 18 to 66 directions were 0.868 [0.815;0.913] and 0.995 [0.993;0.997]. Following the exclusion of motion‐corrupted volumes, minor residual motion did not associate with the scalar values. A minimum of 18 diffusion directions is recommended to result in reliable DTI scalar results with TBSS. We suggest gathering extra directions in paediatric DTI to enable exclusion of volumes with motion artefacts and simultaneously preserve the overall data quality.

Translational Magnetic Resonance Imaging in Autism Spectrum Disorder From the Mouse Model to Human

Autism spectrum disorder (ASD) is a heterogeneous syndrome characterized by behavioral features such as impaired social communication, repetitive behavior patterns, and a lack of interest in novel objects. A multimodal neuroimaging using magnetic resonance imaging (MRI) in patients with ASD shows highly heterogeneous abnormalities in function and structure in the brain associated with specific behavioral features. To elucidate the mechanism of ASD, several ASD mouse models have been generated, by focusing on some of the ASD risk genes. A specific behavioral feature of an ASD mouse model is caused by an altered gene expression or a modification of a gene product. Using these mouse models, a high field preclinical MRI enables us to non-invasively investigate the neuronal mechanism of the altered brain function associated with the behavior and ASD risk genes. Thus, MRI is a promising translational approach to bridge the gap between mice and humans. This review presents the evidence for multimodal MRI, including functional MRI (fMRI), diffusion tensor imaging (DTI), and volumetric analysis, in ASD mouse models and in patients with ASD and discusses the future directions for the translational study of ASD.

Participant factors that contribute to magnetic resonance imaging motion artifacts in children with mild traumatic brain injury or orthopedic injury

Motion can compromise image quality and confound results, especially in pediatric research. This study evaluated qualitative and quantitative approaches to motion artifacts detection and correction, and whether motion artifacts relate to injury history, age, or sex in children with mild traumatic brain injury or orthopedic injury relative to typically developing children. The concordance between qualitative and quantitative motion ratings was also examined. Children aged 8-16 years with mild traumatic brain injury (n = 141) or orthopedic injury (n = 73) were recruited from the emergency department and completed an MRI scan roughly 2 weeks post-injury. Typically developing children (n = 41) completed a single MRI scan. T1- and diffusion-weighted images were visually inspected and rated for motion artifacts by trained examiners. Quantitative estimates of motion artifacts were derived from FreeSurfer and FSL. Age (younger > older) and sex (boys > girls) were significantly associated with motion artifacts on both T1- and diffusion-weighted images. Children with mild traumatic brain or orthopedic injury had significantly more motion-corrupted diffusion-weighted volumes than typically developing children, but mild traumatic brain injury and orthopedic injury groups did not differ from each other. The exclusion of motion-corrupted volumes did not significantly change diffusion tensor imaging metrics. Results indicate that automated quantitative estimates of motion artifacts, which are less labour-intensive than manual methods, are appropriate. Results have implications for the reliability of structural MRI research and highlight the importance of considering motion artifacts in studies of pediatric mild traumatic brain injury.

Ventralis intermedius nucleus anatomical variability assessment by MRI structural connectivity

The ventralis intermedius nucleus (Vim) is centrally placed in the dentato-thalamo-cortical pathway (DTCp) and is a key surgical target in the treatment of severe medically refractory tremor. It is not visible on conventional MRI sequences; consequently, stereotactic targeting currently relies on atlas-based coordinates. This fails to capture individual anatomical variability, which may lead to poor long-term clinical efficacy. Probabilistic tractography, combined with known anatomical connectivity, enables localisation of thalamic nuclei at an individual subject level. There are, however, a number of confounds associated with this technique that may influence results. Here we focused on an established method, using probabilistic tractography to reconstruct the DTCp, to identify the connectivity-defined Vim (cd-Vim) in vivo. Using 100 healthy individuals from the Human Connectome Project, our aim was to quantify cd-Vim variability across this population, measure the discrepancy with atlas-defined Vim (ad-Vim), and assess the influence of potential methodological confounds. We found no significant effect of any of the confounds. The mean cd-Vim coordinate was located within 1.88 mm (left) and 2.12 mm (right) of the average midpoint and 3.98 mm (left) and 5.41 mm (right) from the ad-Vim coordinates. cd-Vim location was more variable on the right, which reflects hemispheric asymmetries in the probabilistic DTC reconstructed. The method was reproducible, with no significant cd-Vim location differences in a separate test-retest cohort. The superior cerebellar peduncle was identified as a potential source of artificial variance. This work demonstrates significant individual anatomical variability of the cd-Vim that atlas-based coordinate targeting fails to capture. This variability was not related to any methodological confound tested. Lateralisation of cerebellar functions, such as speech, may contribute to the observed asymmetry. Tractography-based methods seem sensitive to individual anatomical variability that is missed by conventional neurosurgical targeting; these findings may form the basis for translational tools to improve efficacy and reduce side-effects of thalamic surgery for tremor.

Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders

Structural magnetic resonance imaging (sMRI) offers immense potential for increasing our understanding of how anatomical brain development relates to clinical symptoms and functioning in neurodevelopmental disorders. Clinical developmental sMRI may help identify neurobiological risk factors or markers that may ultimately assist in diagnosis and treatment. However, researchers and clinicians aiming to conduct sMRI studies of neurodevelopmental disorders face several methodological challenges. This review offers hands-on guidelines for clinical developmental sMRI. First, we present brain morphometry metrics and review evidence on typical developmental trajectories throughout adolescence, together with atypical trajectories in selected neurodevelopmental disorders. Next, we discuss challenges and good scientific practices in study design, image acquisition and analysis, and recent options to implement quality control. Finally, we discuss choices related to statistical analysis and interpretation of results. We call for greater completeness and transparency in the reporting of methods to advance understanding of structural brain alterations in neurodevelopmental disorders.

Standard radiotherapy for patients with nasopharyngeal carcinoma results in progressive tract-specific brain white matter alterations: A one-year follow-up via diffusion tensor imaging

BACKGROUND AND PURPOSE

Radiation therapy (RT)-induced neurocognitive disability may be mediated by brain tissue damage. The aim of the present study was to investigate the effects of standard RT on normal brain tissue via in vivo neuroimaging in patients with nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

A total of 146 newly diagnosed NPC patients who were treated with standard RT were longitudinally followed up at multiple time points during the first year post-RT, with 19 comparable healthy controls followed up in parallel serving as normal age-related benchmarks. Magnetic resonance diffusion tensor imaging was used to evaluate longitudinal brain white matter tract changes in NPC patients. The relationships between RT-related white matter changes, hippocampal atrophy, and cognitive impairment were also assessed.

RESULTS

Bilateral cingulate angular bundle (CAB) fibers had progressive diffusion reduction [radial diffusivity (RD) and mean diffusivity] over time (P < 0.05, corrected for multiple comparisons) in NPC patients during the first year after RT. RT-associated progressive RD reduction in the left CAB correlated with longitudinal atrophy of the ipsilateral hippocampus (P = 0.033). Additionally, RT-associated progressive RD reduction in the left CAB correlated with progressive cognitive impairment in NPC patients post-RT (P = 0.048).

CONCLUSION

We present evidence of progressive RT-associated changes in the bilateral CAB in NPC patients, which may underlie RT-related cognitive impairment. These findings illustrate that the use of white matter tract alterations as potential biomarkers to detect RT-related brain injury in NPC patients may be useful for better understanding the pathogenesis of RT-induced cognitive decline.

Prospective motion correction for diffusion weighted EPI of the brain using an optical markerless tracker

PURPOSE

To enable motion-robust diffusion weighted imaging of the brain using well-established imaging techniques.

METHODS

An optical markerless tracking system was used to estimate and correct for rigid body motion of the head in real time during scanning. The imaging coordinate system was updated before each excitation pulse in a single-shot EPI sequence accelerated by GRAPPA with motion-robust calibration. Full Fourier imaging was used to reduce effects of motion during diffusion encoding. Subjects were imaged while performing prescribed motion patterns, each repeated with prospective motion correction on and off.

RESULTS

Prospective motion correction with dynamic ghost correction enabled high quality DWI in the presence of fast and continuous motion within a 10° range. Images acquired without motion were not degraded by the prospective correction. Calculated diffusion tensors tolerated the motion well, but ADC values were slightly increased.

CONCLUSIONS

Prospective correction by markerless optical tracking minimizes patient interaction and appears to be well suited for EPI-based DWI of patient groups unable to remain still including those who are not compliant with markers.

The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

Head motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 294), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction.

Comparison of PGSE and STEAM DTI acquisitions with varying diffusion times for probing anisotropic structures in human kidneys

PURPOSE

To evaluate the sensitivity of stimulated-echo acquisition mode (STEAM) and pulsed-gradient spin-echo (PGSE) diffusion tensor imaging (DTI) acquisitions with different diffusion times for measuring renal tissue anisotropy.

METHODS

Twelve healthy volunteers underwent an MRI examination at a 3T scanner including STEAM and PGSE DTI with variable diffusion times Δ (20.3, 37 and 125 ms). Three volunteers were scanned twice to test the reproducibility for repeated examinations. Diffusion parameters fractional anisotropy (FA) and apparent diffusion coefficient (ADC) in the automatically segmented cortical and medullary regions of interests in both kidneys were calculated and averaged over all subjects for further analysis. Moreover, 5-grade qualitative evaluation of the FA and ADC maps from each sequence was conducted by two experienced radiologists in a consensus.

RESULTS

The cortex-medulla difference in the STEAM sequence was significantly higher than that in PGSE with short ∆ = 20.3 ms (P < 0.001) and in PGSE with intermediate ∆ = 37 ms (P < 0.05) diffusion times. Reproducibility of the FA/ADC measurements was very good and comparable for all acquisition modes investigated. For the FA maps, the PGSE sequence with intermediate diffusion time scored highest in the subjective visual assessment of radiologists.

CONCLUSION

The delineation of anisotropy in renal tissue is depending on the used diffusion time of the DTI sequence. A PGSE acquisition at a diffusion time of about 37 ms provides reproducible results with optimal corticomedullary contrast in FA and ADC maps and good image quality.

Feasibility of diffusion and probabilistic white matter analysis in patients implanted with a deep brain stimulator

Deep brain stimulation (DBS) for Parkinson's disease (PD) is an established advanced therapy that produces therapeutic effects through high frequency stimulation. Although this therapeutic option leads to improved clinical outcomes, the mechanisms of the underlying efficacy of this treatment are not well understood. Therefore, investigation of DBS and its postoperative effects on brain architecture is of great interest. Diffusion weighted imaging (DWI) is an advanced imaging technique, which has the ability to estimate the structure of white matter fibers; however, clinical application of DWI after DBS implantation is challenging due to the strong susceptibility artifacts caused by implanted devices. This study aims to evaluate the feasibility of generating meaningful white matter reconstructions after DBS implantation; and to subsequently quantify the degree to which these tracts are affected by post-operative device-related artifacts. DWI was safely performed before and after implanting electrodes for DBS in 9 PD patients. Differences within each subject between pre- and post-implantation FA, MD, and RD values for 123 regions of interest (ROIs) were calculated. While differences were noted globally, they were larger in regions directly affected by the artifact. White matter tracts were generated from each ROI with probabilistic tractography, revealing significant differences in the reconstruction of several white matter structures after DBS. Tracts pertinent to PD, such as regions of the substantia nigra and nigrostriatal tracts, were largely unaffected. The aim of this study was to demonstrate the feasibility and clinical applicability of acquiring and processing DWI post-operatively in PD patients after DBS implantation. The presence of global differences provides an impetus for acquiring DWI shortly after implantation to establish a new baseline against which longitudinal changes in brain connectivity in DBS patients can be compared. Understanding that post-operative fiber tracking in patients is feasible on a clinically-relevant scale has significant implications for increasing our current understanding of the pathophysiology of movement disorders, and may provide insights into better defining the pathophysiology and therapeutic effects of DBS.

Microstructural correlates of Edinburgh Cognitive and Behavioural ALS Screen (ECAS) changes in amyotrophic lateral sclerosis

Edinburgh Cognitive and Behavioural ALS Screen (ECAS) was designed for testing patients with amyotrophic lateral sclerosis (ALS), a multi-system neurodegenerative disease characterized by progressive physical disability. In this study, we aim to explore the potential brain microstructural substrates associated with performance on ECAS in the early stages of ALS, using a whole-brain tract-based spatial statistics diffusion tensor imaging approach. Thirty-six non-demented ALS patients, assessed using ECAS, and 35 age-, sex- and education-matched healthy controls underwent magnetic resonance imaging at 3 Tesla. The ALS patients showed decreased fractional anisotropy (FA) in the cortico-spinal tracts and corpus callosum (CC) and significant association between verbal fluency score, among ALS-specific ECAS scores, and FA measures in several long association fiber tracts in the frontal, temporal and parietal lobes. Furthermore, the ALS non-specific total score was inversely related to axial diffusivity (AD) in the mediodorsal nucleus of the thalamus, with more extended areas of correlation in the CC, when considering only the memory subscore. Our results point towards microstructural degeneration across motor and extra-motor areas in ALS, underlining that alterations in verbal fluency performances may be related to impairment of frontotemporal connectivity, while alterations of memory may be associated with damage of thalamocortical circuits.

The impact of in-scanner head motion on structural connectivity derived from diffusion MRI

Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in-scanner head motion on structural connectivity using a sample of 949 participants (ages 8-23 years old) who passed a rigorous quality assessment protocol for diffusion magnetic resonance imaging (dMRI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in-scanner head motion significantly impacted the strength of structural connectivity in a consistency- and length-dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for network edges with high inter-subject consistency, which included both short- and long-range connections. In contrast, motion inflated estimates of structural connectivity for low-consistency network edges that were primarily shorter-range. Finally, we demonstrate that age-related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion-related confounds in studies of structural brain network development.

Structural brain development: A review of methodological approaches and best practices

Continued advances in neuroimaging technologies and statistical modelling capabilities have improved our knowledge of structural brain development in children and adolescents. While this has provided an increasingly nuanced understanding of brain development, the field is still plagued by inconsistent findings. This review highlights the methodological diversity in existing longitudinal magnetic resonance imaging (MRI) studies on structural brain development during childhood and adolescence, and addresses how such variation might contribute to inconsistencies in the literature. We discuss the impact of method choices at multiple decision points across the research process, from study design and sample selection, to image processing and statistical analysis. We also highlight the extent to which different methodological considerations have been empirically examined, drawing attention to specific areas that would benefit from future investigation. Where appropriate, we recommend certain best practices that would be beneficial for the field to adopt, including greater completeness and transparency in reporting methods, in order to ultimately develop an accurate and detailed understanding of normative child and adolescent brain development.

Assessing the performance of different DTI motion correction strategies in the presence of EPI distortion correction

Diffusion tensor imaging (DTI) is susceptible to several artifacts due to eddy currents, echo planar imaging (EPI) distortion and subject motion. While several techniques correct for individual distortion effects, no optimal combination of DTI acquisition and processing has been determined. Here, the effects of several motion correction techniques are investigated while also correcting for EPI distortion: prospective correction, using navigation; retrospective correction, using two different popular packages (FSL and TORTOISE); and the combination of both methods. Data from a pediatric group that exhibited incidental motion in varying degrees are analyzed. Comparisons are carried while implementing eddy current and EPI distortion correction. DTI parameter distributions, white matter (WM) maps and probabilistic tractography are examined. The importance of prospective correction during data acquisition is demonstrated. In contrast to some previous studies, results also show that the inclusion of retrospective processing also improved ellipsoid fits and both the sensitivity and specificity of group tractographic results, even for navigated data. Matches with anatomical WM maps are highest throughout the brain for data that have been both navigated and processed using TORTOISE. The inclusion of both prospective and retrospective motion correction with EPI distortion correction is important for DTI analysis, particularly when studying subject populations that are prone to motion. Hum Brain Mapp 37:4405-4424, 2016. © 2016 Wiley Periodicals, Inc.

Abnormal white matter structural networks characterize heroin-dependent individuals: a network analysis

Neuroimaging studies suggested that drug addiction is linked to abnormal brain functional connectivity. However, little is known about the alteration of brain white matter (WM) connectivity in addictive drug users and nearly no study has been performed to examine the alterations of brain WM connectivity in heroin-dependent individuals (HDIs). Diffusion tensor imaging (DTI) offers a comprehensive technique to map the whole brain WM connectivity in vivo. In this study, we acquired DTI datasets from 20 HDIs and 18 healthy controls and constructed their brain WM structural networks using a deterministic fibre tracking approach. Using graph theoretical analysis, we explored the global and nodal topological parameters of brain network for both groups and adopted a network-based statistic (NBS) approach to assess between-group differences in inter-regional WM connections. Statistical analysis indicated the global efficiency and network strength were significantly increased, but the characteristic path length was significantly decreased in the HDIs compared with the controls. We also found that in the HDIs, the nodal efficiency was significantly increased in the left prefrontal cortex, bilateral orbital frontal cortices and left anterior cingulate gyrus. Moreover, the NBS analysis revealed that in the HDIs, the significant increased connections were located in the paralimbic, orbitofrontal, prefrontal and temporal regions. Our results may reflect the disruption of whole brain WM structural networks in the HDIs. Our findings suggest that mapping brain WM structural network may be helpful for better understanding the neuromechanism of heroin addiction.

Head Motion and Inattention/Hyperactivity Share Common Genetic Influences: Implications for fMRI Studies of ADHD

Head motion (HM) is a well known confound in analyses of functional MRI (fMRI) data. Neuroimaging researchers therefore typically treat HM as a nuisance covariate in their analyses. Even so, it is possible that HM shares a common genetic influence with the trait of interest. Here we investigate the extent to which this relationship is due to shared genetic factors, using HM extracted from resting-state fMRI and maternal and self report measures of Inattention and Hyperactivity-Impulsivity from the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour (SWAN) scales. Our sample consisted of healthy young adult twins (N = 627 (63% females) including 95 MZ and 144 DZ twin pairs, mean age 22, who had mother-reported SWAN; N = 725 (58% females) including 101 MZ and 156 DZ pairs, mean age 25, with self reported SWAN). This design enabled us to distinguish genetic from environmental factors in the association between head movement and ADHD scales. HM was moderately correlated with maternal reports of Inattention (r = 0.17, p-value = 7.4E-5) and Hyperactivity-Impulsivity (r = 0.16, p-value = 2.9E-4), and these associations were mainly due to pleiotropic genetic factors with genetic correlations [95% CIs] of rg = 0.24 [0.02, 0.43] and rg = 0.23 [0.07, 0.39]. Correlations between self-reports and HM were not significant, due largely to increased measurement error. These results indicate that treating HM as a nuisance covariate in neuroimaging studies of ADHD will likely reduce power to detect between-group effects, as the implicit assumption of independence between HM and Inattention or Hyperactivity-Impulsivity is not warranted. The implications of this finding are problematic for fMRI studies of ADHD, as failing to apply HM correction is known to increase the likelihood of false positives. We discuss two ways to circumvent this problem: censoring the motion contaminated frames of the RS-fMRI scan or explicitly modeling the relationship between HM and Inattention or Hyperactivity-Impulsivity.

Diffusion tensor imaging findings in semi-acute mild traumatic brain injury

The past 10 years have seen a rapid increase in the use of diffusion tensor imaging to identify biomarkers of traumatic brain injury (TBI). Although the literature generally indicates decreased anisotropic diffusion at more chronic injury periods and in more severe injuries, considerable debate remains regarding the direction (i.e., increased or decreased) of anisotropic diffusion in the acute to semi-acute phase (here defined as less than 3 months post-injury) of mild TBI (mTBI). A systematic review of the literature was therefore performed to (1) determine the prevalence of different anisotropic diffusion findings (increased, decreased, bidirectional, or null) during the semi-acute injury phase of mTBI and to (2) identify clinical (e.g., age of injury, post-injury scan time, etc.) and experimental factors (e.g., number of unique directions, field strength) that may influence these findings. Results from the literature review indicated 31 articles with independent samples of semi-acute mTBI patients, with 13 studies reporting decreased anisotropic diffusion, 11 reporting increased diffusion, 2 reporting bidirectional findings, and 5 reporting null findings. Chi-squared analyses indicated that the total number of diffusion-weighted (DW) images was significantly associated with findings of either increased (DW ≥ 30) versus decreased (DW ≤ 25) anisotropic diffusion. Other clinical and experimental factors were not statistically significant for direction of anisotropic diffusion, but these results may have been limited by the relatively small number of studies within each domain (e.g., pediatric studies). In summary, current results indicate roughly equivalent number of studies reporting increased versus decreased anisotropic diffusion during semi-acute mTBI, with the number of unique diffusion images being statistically associated with the direction of findings.

Individual differences in impulsivity predict head motion during magnetic resonance imaging

Magnetic resonance imaging (MRI) provides valuable data for understanding the human mind and brain disorders, but in-scanner head motion introduces systematic and spurious biases. For example, differences in MRI measures (e.g., network strength, white matter integrity) between patient and control groups may be due to the differences in their head motion. To determine whether head motion is an important variable in itself, or just simply a confounding variable, we explored individual differences in psychological traits that may predispose some people to move more than others during an MRI scan. In the first two studies, we demonstrated in both children (N  =  245) and adults (N  =  581) that head motion, estimated from resting-state functional MRI and diffusion tensor imaging, was reliably correlated with impulsivity scores. Further, the difference in head motion between children with attention deficit hyperactivity disorder (ADHD) and typically developing children was largely due to differences in impulsivity. Finally, in the third study, we confirmed the observation that the regression approach, which aims to deal with motion issues by regressing out motion in the group analysis, would underestimate the effect of interest. Taken together, the present findings provide empirical evidence that links in-scanner head motion to psychological traits.

Association between in-scanner head motion with cerebral white matter microstructure: a multiband diffusion-weighted MRI study

Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) has emerged as the most popular neuroimaging technique used to depict the biological microstructural properties of human brain white matter. However, like other MRI techniques, traditional DW-MRI data remains subject to head motion artifacts during scanning. For example, previous studies have indicated that, with traditional DW-MRI data, head motion artifacts significantly affect the evaluation of diffusion metrics. Actually, DW-MRI data scanned with higher sampling rate are important for accurately evaluating diffusion metrics because it allows for full-brain coverage through the acquisition of multiple slices simultaneously and more gradient directions. Here, we employed a publicly available multiband DW-MRI dataset to investigate the association between motion and diffusion metrics with the standard pipeline, tract-based spatial statistics (TBSS). The diffusion metrics used in this study included not only the commonly used metrics (i.e., FA and MD) in DW-MRI studies, but also newly proposed inter-voxel metric, local diffusion homogeneity (LDH). We found that the motion effects in FA and MD seems to be mitigated to some extent, but the effect on MD still exists. Furthermore, the effect in LDH is much more pronounced. These results indicate that researchers shall be cautious when conducting data analysis and interpretation. Finally, the motion-diffusion association is discussed.

Test-Retest Reliability of Diffusion Tensor Imaging in Huntington's Disease

Diffusion tensor imaging (DTI) has shown microstructural abnormalities in patients with Huntington's Disease (HD) and work is underway to characterise how these abnormalities change with disease progression. Using methods that will be applied in longitudinal research, we sought to establish the reliability of DTI in early HD patients and controls. Test-retest reliability, quantified using the intraclass correlation coefficient (ICC), was assessed using region-of-interest (ROI)-based white matter atlas and voxelwise approaches on repeat scan data from 22 participants (10 early HD, 12 controls). T1 data was used to generate further ROIs for analysis in a reduced sample of 18 participants. The results suggest that fractional anisotropy (FA) and other diffusivity metrics are generally highly reliable, with ICCs indicating considerably lower within-subject compared to between-subject variability in both HD patients and controls. Where ICC was low, particularly for the diffusivity measures in the caudate and putamen, this was partly influenced by outliers. The analysis suggests that the specific DTI methods used here are appropriate for cross-sectional research in HD, and give confidence that they can also be applied longitudinally, although this requires further investigation. An important caveat for DTI studies is that test-retest reliability may not be evenly distributed throughout the brain whereby highly anisotropic white matter regions tended to show lower relative within-subject variability than other white or grey matter regions.

Neuroinformatics challenges to the structural, connectomic, functional and electrophysiological multimodal imaging of human traumatic brain injury

Throughout the past few decades, the ability to treat and rehabilitate traumatic brain injury (TBI) patients has become critically reliant upon the use of neuroimaging to acquire adequate knowledge of injury-related effects upon brain function and recovery. As a result, the need for TBI neuroimaging analysis methods has increased in recent years due to the recognition that spatiotemporal computational analyses of TBI evolution are useful for capturing the effects of TBI dynamics. At the same time, however, the advent of such methods has brought about the need to analyze, manage, and integrate TBI neuroimaging data using informatically inspired approaches which can take full advantage of their large dimensionality and informational complexity. Given this perspective, we here discuss the neuroinformatics challenges for TBI neuroimaging analysis in the context of structural, connectivity, and functional paradigms. Within each of these, the availability of a wide range of neuroimaging modalities can be leveraged to fully understand the heterogeneity of TBI pathology; consequently, large-scale computer hardware resources and next-generation processing software are often required for efficient data storage, management, and analysis of TBI neuroimaging data. However, each of these paradigms poses challenges in the context of informatics such that the ability to address them is critical for augmenting current capabilities to perform neuroimaging analysis of TBI and to improve therapeutic efficacy.

Assessment of cardiac motion effects on the fiber architecture of the human heart in vivo

The use of diffusion tensor imaging (DTI) for studying the human heart in vivo is very challenging due to cardiac motion. This paper assesses the effects of cardiac motion on the human myocardial fiber architecture. To this end, a model for analyzing the effects of cardiac motion on signal intensity is presented. A Monte-Carlo simulation based on polarized light imaging data is then performed to calculate the diffusion signals obtained by the displacement of water molecules, which generate diffusion weighted (DW) images. Rician noise and in vivo motion data obtained from DENSE acquisition are added to the simulated cardiac DW images to produce motion-induced datasets. An algorithm based on principal components analysis filtering and temporal maximum intensity projection (PCATMIP) is used to compensate for motion-induced signal loss. Diffusion tensor parameters derived from motion-reduced DW images are compared to those derived from the original simulated DW images. Finally, to assess cardiac motion effects on in vivo fiber architecture, in vivo cardiac DTI data processed by PCATMIP are compared to those obtained from one trigger delay (TD) or one single phase acquisition. The results showed that cardiac motion produced overestimated fractional anisotropy and mean diffusivity as well as a narrower range of fiber angles. The combined use of shifted TD acquisitions and postprocessing based on image registration and PCATMIP effectively improved the quality of in vivo DW images and subsequently, the measurement accuracy of fiber architecture properties. This suggests new solutions to the problems associated with obtaining in vivo human myocardial fiber architecture properties in clinical conditions.

Reproducibility of corticospinal diffusion tensor tractography in normal subjects and hemiparetic stroke patients

PURPOSE

The reproducibility of corticospinal diffusion tensor tractography (DTT) for a guideline is important before longitudinal monitoring of the therapy effects in stroke patients. This study aimed to establish the reproducibility of corticospinal DTT indices in healthy subjects and chronic hemiparetic stroke patients.

MATERIALS AND METHODS

Written informed consents were obtained from 10 healthy subjects (mean age 25.8 ± 6.8 years), who underwent two scans in one session plus the third scan one week later, and from 15 patients (mean age 47.5 ± 9.1 years, 6-60 months after the onset of stroke, NIHSS scores between 9 and 20) who were scanned thrice on separate days within one month. Diffusion-tensor imaging was performed at 3T with 25 diffusion directions. Corticospinal tracts were reconstructed using fiber assignment by continuous tracking without and with motion/eddy-current corrections. Intra- and inter-rater as well as intra- and inter-session variations of the DTT derived indices (fiber number, apparent diffusion coefficient (ADC), and fractional anisotropy (FA)) were assessed.

RESULTS

Intra-session and inter-session coefficients of variations (CVs) are small for FA (1.13-2.09%) and ADC (0.45-1.64%), but much larger for fiber number (8.05-22.4%). Inter-session CVs in the stroke side of patients (22.4%) are higher than those in the normal sides (18.0%) and in the normal subjects (14.7%). Motion/eddy-current correction improved inter-session reproducibility only for the fiber number of the infarcted corticospinal tract (CV reduced from 22.4% to 14.1%).

CONCLUSION

The fiber number derived from corticospinal DTT shows substantially lower precision than ADC and FA, with infarcted tracts showing lower reproducibility than the healthy tissues.

Neural tract development of infants born to methadone-maintained mothers

The early cerebral connective tract development of infants born to methadone-maintained mothers and comparison infants was examined using diffusion tensor imaging. Drawing on animal models, we hypothesized higher mean diffusivity in methadone-exposed infants, corresponding to the delayed or altered maturation of neural connective tracts. Thirteen methadone-exposed infants and seven comparison infants were scanned within 13-44 days after birth. Mean diffusivity was compared across groups voxelwise throughout a common white matter skeleton defined for the sample, and in probabilistically defined tracts of interest overlapping the skeleton, i.e., the superior and inferior longitudinal fasciculi. Higher mean diffusivity (P < 0.05) in methadone-exposed infants was evident in the superior longitudinal fasciculus regionally by voxelwise analysis and whole-tract analysis. These results are preliminary, given the small sample. However, all observed effects were in the hypothesized direction, with methadone-exposed infants exhibiting higher mean diffusivity, suggesting altered maturation of connective tracts. Such differences may underlie some of the increased risk for cognitive and behavioral difficulties in children born to mothers using opioids. These findings highlight the need for further assessments of the effects of prenatal methadone exposure on neural development.

Diffusion tensor imaging in attention deficit/hyperactivity disorder: a systematic review and meta-analysis

Diffusion tensor imaging (DTI) allows in vivo examination of the microstructural integrity of white matter brain tissue. A systematic review and quantitative meta-analysis using GingerALE were undertaken to compare current DTI findings in patients with ADHD and healthy controls to further unravel the neurobiological underpinnings of the disorder. Online databases were searched for DTI studies comparing white matter integrity between ADHD patients and healthy controls. Fifteen studies met inclusion criteria. Alterations in white matter integrity were found in widespread areas, most consistently so in the right anterior corona radiata, right forceps minor, bilateral internal capsule, and left cerebellum, areas previously implicated in the pathophysiology of the disorder. Current literature is critically discussed in terms of its important methodological limitations and challenges, and guidelines for future DTI research are provided. While more research is needed, DTI proves to be a promising technique, providing new prospects and challenges for future research into the pathophysiology of ADHD.

Prospective and retrospective motion correction in diffusion magnetic resonance imaging of the human brain

Diffusion-weighting in magnetic resonance imaging (MRI) increases the sensitivity to molecular Brownian motion, providing insight in the micro-environment of the underlying tissue types and structures. At the same time, the diffusion weighting renders the scans sensitive to other motion, including bulk patient motion. Typically, several image volumes are needed to extract diffusion information, inducing also inter-volume motion susceptibility. Bulk motion is more likely during long acquisitions, as they appear in diffusion tensor, diffusion spectrum and q-ball imaging. Image registration methods are successfully used to correct for bulk motion in other MRI time series, but their performance in diffusion-weighted MRI is limited since diffusion weighting introduces strong signal and contrast changes between serial image volumes. In this work, we combine the capability of free induction decay (FID) navigators, providing information on object motion, with image registration methodology to prospectively--or optionally retrospectively--correct for motion in diffusion imaging of the human brain. Eight healthy subjects were instructed to perform small-scale voluntary head motion during clinical diffusion tensor imaging acquisitions. The implemented motion detection based on FID navigator signals is processed in real-time and provided an excellent detection performance of voluntary motion patterns even at a sub-millimetre scale (sensitivity≥92%, specificity>98%). Motion detection triggered an additional image volume acquisition with b=0 s/mm2 which was subsequently co-registered to a reference volume. In the prospective correction scenario, the calculated motion-parameters were applied to perform a real-time update of the gradient coordinate system to correct for the head movement. Quantitative analysis revealed that the motion correction implementation is capable to correct head motion in diffusion-weighted MRI to a level comparable to scans without voluntary head motion. The results indicate the potential of this method to improve image quality in diffusion-weighted MRI, a concept that can also be applied when highest diffusion weightings are performed.

Head injury or head motion? Assessment and quantification of motion artifacts in diffusion tensor imaging studies

The relationship between head motion and diffusion values such as fractional anisotropy (FA) and mean diffusivity (MD) is currently not well understood. Simulation studies suggest that head motion may introduce either a positive or negative bias, but this has not been quantified in clinical studies. Moreover, alternative measures for removing bias as result of head motion, such as the removal of problematic gradients, has been suggested but not carefully evaluated. The current study examined the impact of head motion on FA and MD across three common pipelines (tract-based spatial statistics, voxelwise, and region of interest analyses) and determined the impact of removing diffusion weighted images. Our findings from a large cohort of healthy controls indicate that while head motion was associated with a positive bias for both FA and MD, the effect was greater for MD. The positive bias was observed across all three analysis pipelines and was present following established protocols for data processing, suggesting that current techniques (i.e., correction of both image and gradient table) for removing motion bias are likely insufficient. However, the removal of images with gross artifacts did not fundamentally change the relationship between motion and DTI scalar values. In addition, Monte Carlo simulations suggested that the random removal of images increases the bias and reduces the precision of both FA and MD. Finally, we provide an example of how head motion can be quantified across different neuropsychiatric populations, which should be implemented as part of any diffusion tensor imaging quality assurance protocol.

Functional connectivity in mild traumatic brain injury

OBJECTIVES

Research suggests that the majority of mild traumatic brain injury (mTBI) patients exhibit both cognitive and emotional dysfunction within the first weeks of injury, followed by symptom resolution 3-6 months postinjury. The neuronal correlates of said dysfunction are difficult to detect with standard clinical neuroimaging, complicating differential diagnosis and early identification of patients who may not recover. This study examined whether resting state functional magnetic resonance imaging (fMRI) provides objective markers of injury and predicts cognitive, emotional, and somatic complaints in mTBI patients semiacutely (<3 weeks postinjury) and in late recovery (3-5 month) phases.

METHODS

Twenty-seven semiacute mTBI patients and 26 gender, age, and education-matched controls were studied. Fifteen of 27 patients returned for a follow-up visit 3-5 months postinjury. The main dependent variables were spontaneous fluctuations (temporal correlation) in the default-mode (DMN) and fronto-parietal task-related networks as measured by fMRI.

RESULTS

Significant differences in self-reported cognitive, emotional, and somatic complaints were observed (all P < 0.05), despite normal clinical (T1 and T2) imaging and neuropsychological testing results. Mild TBI patients demonstrated decreased functional connectivity within the DMN and hyper-connectivity between the DMN and lateral prefrontal cortex. Measures of functional connectivity exhibited high levels of sensitivity and specificity for patient classification and predicted cognitive complaints in the semi-acute injury stage. However, no changes in functional connectivity were observed across a 4-month recovery period.

CONCLUSIONS

Abnormal connectivity between the DMN and frontal cortex may provide objective biomarkers of mTBI and underlie cognitive impairment.

Diffusion tensor imaging (DTI) of the kidney at 3 tesla-feasibility, protocol evaluation and comparison to 1.5 Tesla

PURPOSE

The purpose of this study was to evaluate the feasibility of diffusion tensor imaging of the kidney at a field strength of 3T. We assessed fractional anisotropy (FA) and apparent diffusion coefficients (ADC) of various acquisition protocols and determined the reproducibility of these measurements. FA, ADC, signal-to-noise ratios (SNR), and contrast-to-noise ratios (CNR) were compared with those acquired at 1.5T.

MATERIAL AND METHODS

Ten healthy volunteers were examined with a respiratory-triggered echo-planar imaging sequence (TR: 1800 ms, TE: 58 ms, b = 0, 300 s/mm(2)) on a 3-Tesla whole-body MR scanner. Protocol variations included diffusion measurements during free-breathing, in 6 or 12 directions, and an additional b-value of 50 s/mm(2). A breath-hold protocol was also integrated (TR: 820 ms, TE: 58 ms, b = 0, 300 s/mm(2)). Measurements with 2 b-values and 6 diffusion directions were also acquired at 1.5 T. SNR was calculated with the difference-image method. Statistical analysis was performed with Wilcoxon signed-rank tests. Intrareader correlation was assessed with weighted kappa coefficients and reproducibility with the root-mean-square-average and the Bland-Altman-method.

RESULTS

At 3T, SNR of cortex and medulla and CNR of cortex/medulla were significantly higher than at 1.5T, leading to improved corticomedullary discrimination. There were no significant FA- and ADC differences with 2 b-values and 6 diffusion directions between measurements at 1.5T and 3T. FA of the medulla was significantly higher than that of the cortex in all measurements. Tractography visualized a typical radial diffusion direction in the medulla. Best image quality was achieved with a respiratory triggered protocol with 12 acquisition directions. Measurements with 3 b-values led to decreased ADCs. Acquisition in 12 directions resulted in decreased cortical FA. FA and ADC of breath-hold and free-breathing acquisitions were significantly higher than that of the respiratory-triggered protocol. Intrareader correlation ranged from kappa 0.60 to 0.96. Variance of the respiratory-triggered protocol was smaller than that of breath-hold and free-breathing protocols. Variance was highest for medullary FA in all protocols with reproducibility coefficients ranging from 0.36 to 0.46.

CONCLUSION

Diffusion tensor imaging of the kidney at 3T is feasible and yields significantly higher SNR and CNR. FA and ADCs do not significantly differ from 1.5T. Number of b-values influences ADC-values. Acquisitions in 12 directions provide lower cortical FA-values. We recommend a respiratory-triggered protocol because of improved image quality and reproducibility.

The effect and reproducibility of different clinical DTI gradient sets on small world brain connectivity measures

Advances in computational network analysis have enabled the characterization of topological properties in large scale networks including the human brain. Information on structural networks in the brain can be obtained in-vivo by performing tractography on diffusion tensor imaging (DTI) data. However, little is known about the reproducibility of network properties derived from whole brain tractography data, which has important consequences for minimally detectable abnormalities or changes over time. Moreover, acquisition parameters, such as the number of gradient directions and gradient strength, possibly influence network metrics and the corresponding reproducibility derived from tractography data. The aim of the present study is twofold: (i) to determine the effect of several clinically available DTI sampling schemes, differing in number of gradient directions and gradient amplitude, on small world metrics and (ii) to evaluate the interscan reproducibility of small world metrics. DTI experiments were conducted on six healthy volunteers scanned twice. Probabilistic tractography was performed to reconstruct structural connections between regions defined from an anatomical atlas. The observed reproducibility of the network measures was high, reflected by low values for the coefficient of variation (<3.8%), advocating the use of graph theoretical measurements to study neurological diseases. Small world metrics were dependent on the choice of DTI gradient scheme and showed stronger connectivity with increasing directional resolution. The interscan reproducibility was not dependent on the gradient scheme. These findings should be considered when comparing results across studies using different gradient schemes or designing new studies.

Quantitative assessment of motion correction for high angular resolution diffusion imaging

Several methods have been proposed for motion correction of high angular resolution diffusion imaging (HARDI) data. There have been few comparisons of these methods, partly due to a lack of quantitative metrics of performance. We compare two motion correction strategies using two figures of merit: displacement introduced by the motion correction and the 95% confidence interval of the cone of uncertainty of voxels with prolate tensors. What follows is a general approach for assessing motion correction of HARDI data that may have broad application for quality assurance and optimization of postprocessing protocols. Our analysis demonstrates two important issues related to motion correction of HARDI data: (1) although neither method we tested was dramatically superior in performance, both were dramatically better than performing no motion correction, and (2) iteration of motion correction can improve the final results. Based on the results demonstrated here, iterative motion correction is strongly recommended for HARDI acquisitions.