Stability effects on results of diffusion tensor imaging analysis by reduction of the number of gradient directions due to motion artifacts: an application to presymptomatic Huntington's disease

Longitudinal monitoring of amyotrophic lateral sclerosis by diffusion tensor imaging: Power calculations for group studies

Introduction

Diffusion tensor imaging (DTI) can be used to map disease progression in amyotrophic lateral sclerosis (ALS) and therefore is a promising candidate for a biomarker in ALS. To this end, longitudinal study protocols need to be optimized and validated regarding group sizes and time intervals between visits. The objective of this study was to assess the influences of sample size, the schedule of follow-up measurements, and measurement uncertainties on the statistical power to optimize longitudinal DTI study protocols in ALS.

Patients and methods

To estimate the measurement uncertainty of a tract-of–interest-based DTI approach, longitudinal test-retest measurements were applied first to a normal data set. Then, DTI data sets of 80 patients with ALS and 50 healthy participants were analyzed in the simulation of longitudinal trajectories, that is, longitudinal fractional anisotropy (FA) values for follow-up sessions were simulated for synthetic patient and control groups with different rates of FA decrease in the corticospinal tract. Monte Carlo simulations of synthetic longitudinal study groups were used to estimate the statistical power and thus the potentially needed sample sizes for a various number of scans at one visit, different time intervals between baseline and follow-up measurements, and measurement uncertainties.

Results

From the simulation for different longitudinal FA decrease rates, it was found that two scans per session increased the statistical power in the investigated settings unless sample sizes were sufficiently large and time intervals were appropriately long. The positive effect of a second scan per session on the statistical power was particularly pronounced for FA values with high measurement uncertainty, for which the third scan per session increased the statistical power even further.

Conclusion

With more than one scan per session, the statistical power of longitudinal DTI studies can be increased in patients with ALS. Consequently, sufficient statistical power can be achieved even with limited sample sizes. An improved longitudinal DTI study protocol contributes to the detection of small changes in diffusion metrics and thereby supports DTI as an applicable and reliable non-invasive biomarker in ALS.

A multivariate Bayesian classification algorithm for cerebral stage prediction by diffusion tensor imaging in amyotrophic lateral sclerosis

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Novel DTI-based classification of ALS disease stages by a Bayesian approach is applied.

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Bayesian classification algorithm improves threshold-based staging method.

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Step forward in MRI-based patient stratification in ALS in vivo.

Background and Objective

Diffusion tensor imaging (DTI) can be used to tract-wise map correlates of the sequential disease progression and, therefore, to assess disease stages of amyotrophic lateral sclerosis (ALS) in vivo. According to a threshold-based sequential scheme, a classification of ALS patients into disease stages is possible, however, several patients cannot be staged for methodological reasons. This study aims to implement a multivariate Bayesian classification algorithm for disease stage prediction at an individual ALS patient level based on DTI metrics of involved tract systems to improve disease stage mapping.

Methods

The analysis of fiber tracts involved in each stage of ALS was performed in 325 ALS patients and 130 age- and gender-matched healthy controls. Based on Bayes’ theorem and in accordance with the sequential disease progression, a multistage classifier was implemented. Patients were categorized into in vivo DTI stages using the threshold-based method and the Bayesian algorithm. By the margin of confidence, the reliability of the Bayesian categorizations was accessible.

Results

Based on the Bayesian multistage classifier, 88% of all ALS patients could be assigned into an ALS stage compared to 77% using the threshold-based staging scheme. Additionally, the confidence of all classifications could be estimated.

Conclusions

By the application of the multi-stage Bayesian classifier, an individualized in vivo cerebral staging of ALS patients was possible based on the sequentially involved tract systems and, furthermore, the reliability of the respective classifications could be determined. The Bayesian classification algorithm is an improvement of the threshold-based staging method and could provide a framework for extending the DTI-based in vivo cerebral staging in ALS.

Severe white matter damage in SHANK3 deficiency: a human and translational study

Objective

Heterozygous SHANK3 mutations or partial deletions of the long arm of chromosome 22, also known as Phelan–McDermid syndrome, result in a syndromic form of the autism spectrum as well as in global developmental delay, intellectual disability, and several neuropsychiatric comorbidities. The exact pathophysiological mechanisms underlying the disease are still far from being deciphered but studies of SHANK3 models have contributed to the understanding of how the loss of the synaptic protein SHANK3 affects neuronal function.

Methods and results

Diffusion tensor imaging‐based and automatic volumetric brain mapping were performed in 12 SHANK3‐deficient participants (mean age 19 ± 15 years) versus 14 age‐ and gender‐matched controls (mean age 29 ± 5 years). Using whole brain–based spatial statistics, we observed a highly significant pattern of white matter alterations in participants with SHANK3 mutations with focus on the long association fiber tracts, particularly the uncinate tract and the inferior fronto‐occipital fasciculus. In contrast, only subtle gray matter volumetric abnormalities were detectable. In a back‐translational approach, we observed similar white matter alterations in heterozygous isoform–specific Shank3 knockout (KO) mice. Here, in the baseline data sets, the comparison of Shank3 heterozygous KO vs wildtype showed significant fractional anisotropy reduction of the long fiber tract systems in the KO model. The multiparametric Magnetic Resonance Imaging (MRI) analysis by DTI and volumetry demonstrated a pathology pattern with severe white matter alterations and only subtle gray matter changes in the animal model.

Interpretation

In summary, these translational data provide strong evidence that the SHANK3‐deficiency–associated pathomechanism presents predominantly with a white matter disease. Further studies should concentrate on the role of SHANK3 during early axonal pathfinding/wiring and in myelin formation.

Longitudinal diffusion tensor magnetic resonance imaging analysis at the cohort level reveals disturbed cortical and callosal microstructure with spared corticospinal tract in the TDP-43 G298S ALS mouse model

Background

In vivo diffusion tensor imaging (DTI) of the mouse brain was used to identify TDP-43 associated alterations in a mouse model for amyotrophic lateral sclerosis (ALS).

Methods

Ten mice with TDP-43 ^G298S overexpression under control of the Thy1.2 promoter and 10 wild type (wt) underwent longitudinal DTI scans at 11.7 T, including one baseline and one follow-up scan with an interval of about 5 months. Whole brain-based spatial statistics (WBSS) of DTI-based parameter maps was used to identify longitudinal alterations of TDP-43 ^G298S mice compared to wt at the cohort level. Results were supplemented by tractwise fractional anisotropy statistics (TFAS) and histological evaluation of motor cortex for signs of neuronal loss.

Results

Alterations at the cohort level in TDP-43 ^G298S mice were observed cross-sectionally and longitudinally in motor areas M1/M2 and in transcallosal fibers but not in the corticospinal tract. Neuronal loss in layer V of motor cortex was detected in TDP-43 ^G298S at the later (but not at the earlier) timepoint compared to wt.

Conclusion

DTI mapping of TDP-43 ^G298S mice demonstrated progression in motor areas M1/M2. WBSS and TFAS are useful techniques to localize TDP-43 ^G298S associated alterations over time in this ALS mouse model, as a biological marker.

Developing a Neuroimaging Biomarker for Amyotrophic Lateral Sclerosis: Multi-Center Data Sharing and the Road to a "Global Cohort"

Neuroimaging in Amyotrophic Lateral Sclerosis (ALS) has steadily evolved from an academic exercise to a powerful clinical tool for detecting and following pathological change. Nevertheless, significant challenges need to be addressed for the translation of neuroimaging as a robust outcome-metric and biomarker in quality-of-care assessments and pharmaceutical trials. Studies have been limited by small sample sizes, poor replication, incomplete patient characterization, and substantial differences in data collection and processing. This has been further exacerbated by the substantial heterogeneity associated with ALS. Multi-center transnational collaborations are needed to address these methodological limitations and achieve representation of rare phenotypes. This review will use the example of the Neuroimaging Society in ALS (NiSALS) to discuss the set-up of a multi-center data sharing ecosystem and the flow of information between various stakeholders. NiSALS' founding objective was to establish best practices for the acquisition and processing of MRI data and establish a structure that allows continuous data sharing and therefore augments the ability to fully describe patients. The practical challenges associated with such a system, including quality control, legal, ethical, and logistical constraints, will be discussed, as will be recommendations for future collaborative endeavors. We posit that “global cohorts” of well-characterized sub-populations within the disease spectrum are needed to fully understand the complex interplay between neuroimaging and other clinical metrics used to study ALS.

Structural Magnetic Resonance Imaging in Huntington's Disease

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder, caused by expansion of the CAG repeat in the huntingtin gene. HD is characterized clinically by progressive motor, cognitive and neuropsychiatric symptoms. There are currently no disease modifying treatments available for HD, and there is a great need for biomarkers to monitor disease progression and identify new targets for therapeutic intervention. Neuroimaging techniques provide a powerful tool for assessing disease pathology and progression in premanifest stages, before the onset of overt motor symptoms. Structural magnetic resonance imaging (MRI) is non-invasive imaging techniques which have been employed to study structural and microstructural changes in premanifest and manifest HD gene carriers. This chapter described structural imaging techniques and analysis methods employed across HD MRI studies. Current evidence for structural MRI abnormalities in HD, and associations between atrophy, structural white matter changes, iron deposition and clinical performance are discussed; together with the use of structural MRI measures as a diagnostic tool, to assess longitudinal changes, and as potential biomarkers and endpoints for clinical trials.

MRI-Based Mapping of Cerebral Propagation in Amyotrophic Lateral Sclerosis

Neuropathological studies revealed the propagation of amyotrophic lateral sclerosis (ALS) in a sequence of four separate disease-related regional patterns. Diffusion tensor imaging (DTI)-based analysis was established for the individual mapping of sequential disease spreading in ALS as the in vivo transfer to neuroimaging. The aim of this review is to summarize cross-sectional and longitudinal results of these technical approaches in ALS as an in vivo tool to image ALS propagation stages. This concept was also applied to restricted phenotypes of ALS, e.g., lower motor neuron disease (LMND) or primary lateral sclerosis (PLS). In summary, the regional disease patterns in the course of ALS have been successfully mapped by DTI in vivo both cross-sectionally and longitudinally so that this technique might have the potential as a read-out in clinical trials.

Imaging the pathoanatomy of amyotrophic lateral sclerosis in vivo: targeting a propagation-based biological marker

OBJECTIVE

Neuropathological studies in amyotrophic lateral sclerosis (ALS) have shown a dissemination in a regional sequence in four anatomically defined patterns. The aim of this retrospective study was to see whether longitudinal diffusion tensor imaging (DTI) data support the pathological findings.

METHODS

The application of DTI analysis to fibre structures that are prone to be involved at each neuropathological pattern of ALS was performed in a monocentre sample of 67 patients with ALS and 31 controls that obtained at least one follow-up scan after a median of 6 months.

RESULTS

At the group level, longitudinal ALS data showed significant differences for the stage-related tract systems. At the individual level, 27% of the longitudinally scanned patients with ALS showed an increase in ALS stage, while the remaining were stable or were at the highest ALS stage. Longitudinal fractional anisotropy changes in the respective tract systems correlated significantly with the slope of the revised ALS functional rating scale.

INTERPRETATION

The DTI-based protocol was able to image the disease patterns of ALS in vivo cross-sectionally and longitudinally, in support of DTI as a technical marker to image ALS stages.

A large-scale multicentre cerebral diffusion tensor imaging study in amyotrophic lateral sclerosis

OBJECTIVE

Damage to the cerebral tissue structural connectivity associated with amyotrophic lateral sclerosis (ALS), which extends beyond the motor pathways, can be visualised by diffusion tensor imaging (DTI). The effective translation of DTI metrics as biomarker requires its application across multiple MRI scanners and patient cohorts. A multicentre study was undertaken to assess structural connectivity in ALS within a large sample size.

METHODS

442 DTI data sets from patients with ALS (N=253) and controls (N=189) were collected for this retrospective study, from eight international ALS-specialist clinic sites. Equipment and DTI protocols varied across the centres. Fractional anisotropy (FA) maps of the control participants were used to establish correction matrices to pool data, and correction algorithms were applied to the FA maps of the control and ALS patient groups.

RESULTS

Analysis of data pooled from all centres, using whole-brain-based statistical analysis of FA maps, confirmed the most significant alterations in the corticospinal tracts, and captured additional significant white matter tract changes in the frontal lobe, brainstem and hippocampal regions of the ALS group that coincided with postmortem neuropathological stages. Stratification of the ALS group for disease severity (ALS functional rating scale) confirmed these findings.

INTERPRETATION

This large-scale study overcomes the challenges associated with processing and analysis of multiplatform, multicentre DTI data, and effectively demonstrates the anatomical fingerprint patterns of changes in a DTI metric that reflect distinct ALS disease stages. This success paves the way for the use of DTI-based metrics as read-out in natural history, prognostic stratification and multisite disease-modifying studies in ALS.

Microstructure of the Midbrain and Cervical Spinal Cord in Idiopathic Restless Legs Syndrome: A Diffusion Tensor Imaging Study

STUDY OBJECTIVES

Diffusion tensor imaging (DTI) allows the study of white matter microstructure in the central nervous system. The aim of this study was to examine the DTI metrics of the cervical spinal cord and the brainstem up to the midbrain in patients with idiopathic restless legs (RLS) compared to matched healthy controls.

METHODS

DTI analysis of the cervical spinal cord and the brainstem up into the midbrain was performed in 25 patients with idiopathic RLS and 25 matched healthy controls. Data analysis in the brain was performed by voxelwise comparison of fractional anisotropy (FA) maps at group level. Cervical spinal cord data analysis was performed by slicewise analysis of averaged FA values in axial slices along the spinal cord.

RESULTS

Voxelwise comparison of FA maps in the brainstem showed significant microstructural alterations in two clusters in the midbrain bilaterally. Slicewise comparison of the FA maps in the cervical spinal cord showed a trend for lower FA values at the level of the second and third vertebra area in the patient sample.

CONCLUSIONS

The imaging data suggest that significant alterations in the midbrain in RLS can be visualized by DTI and might correlate to a macroscopically subtle process with changes of the tissue microstructure in the corresponding tracts. An additional area of interest is regionally clustered in the upper cervical spinal cord with a tendency toward altered diffusion metrics. These results might be addressed by further studies, e.g., at higher magnetic field strengths.

The effects of hemorrhagic parenchymal infarction on the establishment of sensori-motor structural and functional connectivity in early infancy

INTRODUCTION

The objective of the study was to characterize alterations of structural and functional connectivity within the developing sensori-motor system in infants with focal perinatal brain injury and at high risk of cerebral palsy.

METHODS

Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data were used to study the developing functional and structural connectivity framework in six infants born prematurely at term equivalent age. This was first characterised in three infants without focal pathology, which was then compared to that derived from three infants with unilateral haemorrhagic parenchymal infarction and a subsequent focal periventricular white matter lesion who developed later haemiparesis.

RESULTS

Functional responses to passive hand movement were in the contralateral perirolandic cortex, regardless of focal pathology. In infants with unilateral periventricular injury, afferent thalamo-cortical tracts appeared to have developed compensatory trajectories which circumvented areas of damage. In contrast, efferent corticospinal tracts showed marked asymmetry at term equivalent age following focal brain injury. Sensori-motor network analysis suggested that inter-hemispheric functional connectivity is largely preserved despite pathology and that impairment may be associated with adverse neurodevelopmental outcome.

CONCLUSION

Following focal perinatal brain injury, altered structural and functional connectivity is already present and can be characterized with MRI at term equivalent age. The results of this small case series suggest that these techniques may provide valuable new information about prognosis and the pathophysiology underlying cerebral palsy.

Intrinsic functional connectivity networks in healthy elderly subjects: a multiparametric approach with structural connectivity analysis

Intrinsic functional connectivity magnetic resonance imaging (iFCMRI) provides an encouraging approach for mapping large-scale intrinsic connectivity networks (ICNs) in the “resting” brain. Structural connections as measured by diffusion tensor imaging (DTI) are a major constraint on the identified ICNs. This study aimed at the combined investigation of ten well-defined ICNs in healthy elderly subjects at single subject level as well as at the group level, together with the underlying structural connectivity. IFCMRI and DTI data were acquired in twelve subjects (68 ± 7 years) at a 3T scanner and were studied using the tensor imaging and fiber tracking software package. The seed-based iFCMRI analysis approach was comprehensively performed with DTI analysis, following standardized procedures including an 8-step processing of iFCMRI data. Our findings demonstrated robust ICNs at the single subject level and conclusive brain maps at the group level in the healthy elderly sample, supported by the complementary fiber tractography. The findings demonstrated here provide a methodological framework for future comparisons of pathological (e.g., neurodegenerative) conditions with healthy controls on the basis of multiparametric functional connectivity mapping.

Acute nicotine administration effects on fractional anisotropy of cerebral white matter and associated attention performance

Introduction: Nicotinic acetylcholine receptors are present in the cerebral white matter (WM). We hypothesized that WM response to nicotine can be detected by diffusion tensor imaging (DTI); and that such responses may be associated with nicotine-led cognitive enhancement in sustained attention.

Methods: A randomized, nicotine-placebo patch, crossover, double-blind clinical trial in two non-overlapping cohorts of smokers was used to test the hypothesis. The discovery cohort consisted of 39 subjects (N = 20/19 controls/schizophrenic patients, age = 36.8 ± 10.1 years) and the replication cohorts consisted of 38 healthy smokers (31.7 ± 10.5 years). WM integrity was measured by fractional anisotropy (FA) values for the whole brain and nine preselected WM tracts using tract-based-spatial-statistics.

Results: Nicotine significantly enhanced FA values for the genu of corpus callosum compared with placebo (ΔFA_genu) (p = 0.01) in smokers with low recent smoking exposure as measured by low average cotinine level. This finding was replicated in the second cohort (p = 0.02). ΔFA_genu values explained 22% of variance in performance of a sustained attention task during the nicotine session (p = 0.006). However, this effect was limited to schizophrenia patients (r = 0.62 and 0.09; p = 0.003 and 0.7 for patients and controls, respectively).

Conclusion: Acute pharmacological influence of nicotine patch on WM integrity appeared present, but was dependent on nicotine intake from recent smoking. Change in the WM integrity in the genu of corpus callosum was associated with a significant proportion of variability of nicotine-led changes in sustained attention/working memory of the smokers. Further studies will be necessary to understand biophysical underpinning of the nicotine-related changes in FA.

Diffusion tensor magnetic resonance imaging in the analysis of neurodegenerative diseases

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels. In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.

Diffusion tensor magnetic resonance imaging of the brain in APP transgenic mice: a cohort study

INTRODUCTION

Fast in-vivo high resolution diffusion tensor imaging (DTI) of the mouse brain has recently been shown to enable cohort studies by the combination of appropriate pulse sequences and cryogenically cooled resonators (CCR). The objective of this study was to apply this DTI approach at the group level to β-amyloid precursor protein (APP) transgenic mice.

METHODS

Twelve mice (5 wild type, 7 APP transgenic tg2576) underwent DTI examination at 156(2) × 250 µm(3) spatial resolution with a CCR at ultrahigh field (11.7 T). Diffusion images were acquired along 30 gradient directions plus 5 references without diffusion encoding with a total acquisition time of 35 minutes. Fractional anisotropy (FA) maps were statistically compared by whole brain-based spatial statistics (WBSS) at the group level vs. wild type controls.

RESULTS

FA-map comparison showed characteristic regional patterns of differences between the groups with localizations associated with Alzheimer's disease in humans, such as the hippocampus, the entorhinal cortex, and the caudoputamen.

CONCLUSION

In this proof-of-principle study, regions associated with amyloid-β deposition could be identified by WBSS of FA maps in APP transgenic mice vs. wild type mice. Thus, DTI in the mouse brain acquired at 11.7 T by use of a CCR was demonstrated to be feasible for cohort studies.

DTI Quality Control Assessment via Error Estimation From Monte Carlo Simulations

Diffusion Tensor Imaging (DTI) is currently the state of the art method for characterizing microscopic tissue structure in the white matter in normal or diseased brain in vivo. DTI is estimated from a series of Diffusion Weighted Imaging (DWI) volumes. DWIs suffer from a number of artifacts which mandate stringent Quality Control (QC) schemes to eliminate lower quality images for optimal tensor estimation. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we have devised a more sophisticated, Monte-Carlo simulation based method for the assessment of resulting tensor properties. This allows for a consistent, error-based threshold definition in order to reject/accept the DWI-QC data. Specifically, we propose the estimation of two error metrics related to directional distribution bias of Fractional Anisotropy (FA) and the Principal Direction (PD). The bias is modeled from the DWI-QC gradient information and a Rician noise model incorporating the loss of signal due to the DWI exclusions. Our simulations further show that the estimated bias can be substantially different with respect to magnitude and directional distribution depending on the degree of spatial clustering of the excluded DWIs. Thus, determination of diffusion properties with minimal error requires an evenly distributed sampling of the gradient directions before and after QC.