Comparison of DTI analysis methods for clinical research: influence of pre-processing and tract selection methods

Moving from phenomenological to predictive modelling: Progress and pitfalls of modelling brain stimulation in-silico

Brain stimulation is an increasingly popular neuromodulatory tool used in both clinical and research settings; however, the effects of brain stimulation, particularly those of non-invasive stimulation, are variable. This variability can be partially explained by an incomplete mechanistic understanding, coupled with a combinatorial explosion of possible stimulation parameters. Computational models constitute a useful tool to explore the vast sea of stimulation parameters and characterise their effects on brain activity. Yet the utility of modelling stimulation in-silico relies on its biophysical relevance, which needs to account for the dynamics of large and diverse neural populations and how underlying networks shape those collective dynamics. The large number of parameters to consider when constructing a model is no less than those needed to consider when planning empirical studies. This piece is centred on the application of phenomenological and biophysical models in non-invasive brain stimulation. We first introduce common forms of brain stimulation and computational models, and provide typical construction choices made when building phenomenological and biophysical models. Through the lens of four case studies, we provide an account of the questions these models can address, commonalities, and limitations across studies. We conclude by proposing future directions to fully realise the potential of computational models of brain stimulation for the design of personalized, efficient, and effective stimulation strategies.

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

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

Automation and standardization of subject-specific region-of-interest segmentation for investigation of diffusion imaging in clinical populations

Diffusion weighted imaging (DWI) is key in clinical neuroimaging studies. In recent years, DWI has undergone rapid evolution and increasing applications. Diffusion magnetic resonance imaging (dMRI) is widely used to analyse group-level differences in white matter (WM), but suffers from limitations that can be particularly impactful in clinical groups where 1) structural abnormalities may increase erroneous inter-subject registration and 2) subtle differences in WM microstructure between individuals can be missed. It also lacks standardization protocols for analyses at the subject level. Region of Interest (ROI) analyses in native diffusion space can help overcome these challenges, with manual segmentation still used as the gold standard. However, robust automated approaches for the analysis of ROI-extracted native diffusion characteristics are limited. Subject-Specific Diffusion Segmentation (SSDS) is an automated pipeline that uses pre-existing imaging analysis methods to carry out WM investigations in native diffusion space, while overcoming the need to interpolate diffusion images and using an intermediate T1 image to limit registration errors and guide segmentation. SSDS is validated in a cohort of healthy subjects scanned three times to derive test-retest reliability measures and compared to other methods, namely manual segmentation and tract-based spatial statistics as an example of group-level method. The performance of the pipeline is further tested in a clinical population of patients with traumatic brain injury and structural abnormalities. Mean FA values obtained from SSDS showed high test-retest and were similar to FA values estimated from the manual segmentation of the same ROIs (p-value > 0.1). The average dice similarity coefficients (DSCs) comparing results from SSDS and manual segmentations was 0.8 ± 0.1. Case studies of TBI patients showed robustness to the presence of significant structural abnormalities, indicating its potential clinical application in the identification and diagnosis of WM abnormalities. Further recommendation is given regarding the tracts used with SSDS.

A Mega-analytic Study of White Matter Microstructural Differences Across 5 Cohorts of Youths With Attention-Deficit/Hyperactivity Disorder

BACKGROUND

While attention-deficit/hyperactivity disorder (ADHD) has been associated with differences in the structural connections formed by the brain's white matter tracts, studies of such differences have yielded inconsistent findings, likely reflecting small sample sizes. Thus, we conducted a mega-analysis on in vivo measures of white matter microstructure obtained through diffusion tensor imaging of more than 6000 participants from 5 cohorts.

METHODS

In a mega-analysis, linear mixed models were used to test for associations between the fractional anisotropy of 42 white matter tracts and ADHD traits and diagnosis. Contrasts were made against measures of mood, anxiety, and other externalizing problems.

RESULTS

Overall, 6993 participants (ages 6-18 years, mean age 10.62 years [SD 1.99]; 3368 girls, 3625 boys; 764 African American, 4146 non-Hispanic White, and 2083 other race/ethnicities) had measures of ADHD and other emotional/behavioral symptoms (N = 6933) and/or enough clinical data to allow a diagnosis of ADHD (n = 951) or its absence (n = 4884). Both the diagnosis and symptoms of ADHD were associated with lower fractional anisotropy of the inferior longitudinal and left uncinate fasciculi (at a false discovery rate-adjusted p < .05). Associated effect sizes were small (the strongest association with ADHD traits had an effect size of partial r = -0.14, while the largest case-control difference was associated with an effect size of d = -0.3). Similar microstructural anomalies were not present for anxiety, mood, or externalizing problems. Findings held when ADHD cases and control subjects were matched on in-scanner motion.

CONCLUSIONS

While present across cohorts, ADHD-associated microstructural differences had small effects, underscoring the limited clinical utility of this imaging modality used in isolation.

Diffusion tensor imaging pipeline measures of cerebral white matter integrity: An overview of recent advances and prospects

Cerebral small vessel disease (CSVD) is a leading cause of age-related microvascular cognitive decline, resulting in significant morbidity and decreased quality of life. Despite a progress on its key pathophysiological bases and general acceptance of key terms from neuroimaging findings as observed on the magnetic resonance imaging (MRI), key questions on CSVD remain elusive. Enhanced relationships and reliable lesion studies, such as white matter tractography using diffusion-based MRI (dMRI) are necessary in order to improve the assessment of white matter architecture and connectivity in CSVD. Diffusion tensor imaging (DTI) and tractography is an application of dMRI that provides data that can be used to non-invasively appraise the brain white matter connections via fiber tracking and enable visualization of individual patient-specific white matter fiber tracts to reflect the extent of CSVD-associated white matter damage. However, due to a lack of standardization on various sets of software or image pipeline processing utilized in this technique that driven mostly from research setting, interpreting the findings remain contentious, especially to inform an improved diagnosis and/or prognosis of CSVD for routine clinical use. In this minireview, we highlight the advances in DTI pipeline processing and the prospect of this DTI metrics as potential imaging biomarker for CSVD, even for subclinical CSVD in at-risk individuals.

Individualized Prognostic Prediction of the Long-Term Functional Trajectory in Pediatric Acquired Brain Injury

In pediatric acquired brain injury, heterogeneity of functional response to specific rehabilitation treatments is a key confound to medical decisions and outcome prediction. We aimed to identify patient subgroups sharing comparable trajectories, and to implement a method for the early prediction of the long-term recovery course from clinical condition at first discharge. 600 consecutive patients with acquired brain injury (7.4 years ± 5.2; 367 males; median GCS = 6) entered a standardized rehabilitation program. Functional Independent Measure scores were measured yearly, until year 7. We classified the functional trajectories in clusters, through a latent class model. We performed single-subject prediction of trajectory membership in cases unseen during model fitting. Four trajectory types were identified (post.prob. > 0.95): high-start fast (N = 92), low-start fast (N = 168), slow (N = 130) and non-responders (N = 210). Fast responders were older (chigh = 1.8; clow = 1.1) than non-responders and suffered shorter coma (chigh = -14.7; clow = -4.3). High-start fast-responders had shorter length of stay (c = -1.6), and slow responders had lower incidence of epilepsy (c = -1.4), than non-responders (p < 0.001). Single-subject trajectory could be predicted with high accuracy at first discharge (accuracy = 0.80). In conclusion, we stratified patients based on the evolution of their response to a specific treatment program. Data at first discharge predicted the response over 7 years. This method enables early detection of the slow responders, who show poor post-acute functional gains, but achieve recovery comparable to fast responders by year 7. Further external validation in other rehabilitation programs is warranted.

White matter abnormalities of right hemisphere attention networks contribute to visual hallucinations in dementia with Lewy bodies

OBJECTIVE

Functional alterations of the visual attention networks in a setting of impaired visual information processing have a role in the genesis of visual hallucinations (VH) in dementia with Lewy bodies (DLB). This multimodal MRI study aims at exploring structural and functional basis of VH.

METHODS

23 DLB patients (10 with and 13 without VH) and 13 healthy controls were studied. They underwent MRI with T1-w sequences to measure cortical thickness, DTI for whole-brain and single tract microstructural properties and rs-fMRI of the default mode, dorsal and ventral attention, and visual networks.

RESULTS

In DLB with VH, whole-brain DTI revealed a lower fractional anisotropy and a greater mean diffusivity in the right frontal and temporo-parietal white matter tracts. Tracts dissection showed lower fractional anisotropy in the right inferior and superior (ventral part) longitudinal fasciculi (ILF and SLF) (p < .05, corrected), and greater mean diffusivity (p < .05). The extent of white matter microstructural alterations involving the right ILF and SLF correlated with the severity of VH (r = .55, p < .01; r = .42, p < .05, respectively), and with performance in the visual attention task (r = -.56 and r = -.61; p < .01, respectively). Cortical thickness in the projection areas of the right SLF was significantly reduced (p < .05). Patients with VH also showed an altered functional connectivity in the ventral attention network, connected by the ventral portion of the SLF (p < .05).

CONCLUSIONS

Our findings suggest that a combination of microstructural and functional alterations involving the attention networks in the right hemisphere may be important in the genesis of VH.

Dynamic changes in white matter following traumatic brain injury and how diffuse axonal injury relates to cognitive domain

Objective: The goal is to evaluate longitudinally with diffusion tensor imaging (DTI) the integrity of cerebral white matter in patients with moderate and severe DAI and to correlate the DTI findings with cognitive deficits.Methods: Patients with DAI (n = 20) were scanned at three timepoints (2, 6 and 12 months) after trauma. A healthy control group (n = 20) was evaluated once with the same high-field MRI scanner. The corpus callosum (CC) and the bilateral superior longitudinal fascicles (SLFs) were assessed by deterministic tractography with ExploreDTI. A neuropschychological evaluation was also performed.Results: The CC and both SLFs demonstrated various microstructural abnormalities in between-groups comparisons. All DTI parameters demonstrated changes across time in the body of the CC, while FA (fractional anisotropy) increases were seen on both SLFs. In the splenium of the CC, progressive changes in the mean diffusivity (MD) and axial diffusivity (AD) were also observed. There was an improvement in attention and memory along time. Remarkably, DTI parameters demonstrated several correlations with the cognitive domains.Conclusions: Our findings suggest that microstructural changes in the white matter are dynamic and may be detectable by DTI throughout the first year after trauma. Likewise, patients also demonstrated improvement in some cognitive skills.

XTRACT - Standardised protocols for automated tractography in the human and macaque brain

We present a new software package with a library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain. Using in vivo data from the Human Connectome Project (HCP) and the UK Biobank and ex vivo data for the macaque brain datasets, we obtain white matter atlases, as well as atlases for tract endpoints on the white-grey matter boundary, for both species. We illustrate that our protocols are robust against data quality, generalisable across two species and reflect the known anatomy. We further demonstrate that they capture inter-subject variability by preserving tract lateralisation in humans and tract similarities stemming from twinship in the HCP cohort. Our results demonstrate that the presented toolbox will be useful for generating imaging-derived features in large cohorts, and in facilitating comparative neuroanatomy studies. The software, tractography protocols, and atlases are publicly released through FSL, allowing users to define their own tractography protocols in a standardised manner, further contributing to open science.

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A new software package for standardised and automated cross-species tractography.

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Homologous white matter bundles in the human and macaque brain.

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Human white matter tract atlases generated from large datasets (1000 subjects).

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Tractography protocols are standardised, but preserve individual variability.

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Generalisability across datasets shown using the HCP and the UK Biobank data.

Magnetic resonance imaging markers reflect cognitive outcome after rehabilitation in children with acquired brain injury

PURPOSE

To test markers from conventional and diffusion Magnetic Resonance Imaging (MRI) as possible predictors of cognitive outcome following rehabilitation therapy in children with acquired brain injury (ABI).

METHODS

Twenty-one children (10 boys, mean age 11.6 years, range 7.1-19.4) with stroke or traumatic brain injury underwent MRI including Diffusion Tensor Imaging (DTI) before admission to the rehabilitation centre. The conventional images were scored according to a standardised injury scoring system, and mean Fractional Anisotropy (FA) was determined within the Corpus Callosum (CC), as this structure is hypothesised to play an important role in cognition. Both conventional MRI injury scores and mean FA of the CC and its sub-regions were compared with standard functional cognitive outcome scores. Relationships between MRI indices and cognitive outcome scores were assessed using multiple regression and receiver operating characteristic (ROC) analyses.

RESULTS

A backwards regression analysis revealed that the mean FA of the CC body and genu and the supratentorial injury score appear to represent the best predictors of outcome, together with the age at rehabilitation and time in rehabilitation. In the ROC analysis, the mean FA values of the CC body and genu and the infratentorial injury score provided the highest sensitivity, while the mean FA of the CC splenium showed the highest specificity for outcome.

CONCLUSIONS

The conventional MRI injury scores and DTI metrics from the CC reflect cognitive outcomes following rehabilitation. Neuroimaging methods such as MRI with DTI may therefore provide important markers for cognitive recovery after brain injury.