Evaluating the Accuracy of Diffusion MRI Models in White Matter

Models of diffusion MRI within a voxel are useful for making inferences about the properties of the tissue and inferring fiber orientation distribution used by tractography algorithms. A useful model must fit the data accurately. However, evaluations of model-accuracy of commonly used models have not been published before. Here, we evaluate model-accuracy of the two main classes of diffusion MRI models. The diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian distribution. Sparse fascicle models (SFM) summarize the signal as a sum of signals originating from a collection of fascicles oriented in different directions. We use cross-validation to assess model-accuracy at different gradient amplitudes (b-values) throughout the white matter. Specifically, we fit each model to all the white matter voxels in one data set and then use the model to predict a second, independent data set. This is the first evaluation of model-accuracy of these models. In most of the white matter the DTM predicts the data more accurately than test-retest reliability; SFM model-accuracy is higher than test-retest reliability and also higher than the DTM model-accuracy, particularly for measurements with (a) a b-value above 1000 in locations containing fiber crossings, and (b) in the regions of the brain surrounding the optic radiations. The SFM also has better parameter-validity: it more accurately estimates the fiber orientation distribution function (fODF) in each voxel, which is useful for fiber tracking.     

Local Diffusion Homogeneity (LDH): An Inter-Voxel Diffusion MRI Metric for Assessing Inter-Subject White Matter Variability

Many diffusion parameters and indices (e.g., fractional anisotropy [FA] and mean diffusivity [MD]) have been derived from diffusion magnetic resonance imaging (MRI) data. These parameters have been extensively applied as imaging markers for localizing white matter (WM) changes under various conditions (e.g., development, degeneration and disease). However, the vast majority of the existing parameters is derived from intra-voxel analyses and represents the diffusion properties solely within the voxel unit. Other types of parameters that characterize inter-voxel relationships have been largely overlooked. In the present study, we propose a novel inter-voxel metric referred to as the local diffusion homogeneity (LDH). This metric quantifies the local coherence of water molecule diffusion in a model-free manner. It can serve as an additional marker for evaluating the WM microstructural properties of the brain. To assess the distinguishing features between LDH and FA/MD, the metrics were systematically compared across space and subjects. As an example, both the LDH and FA/MD metrics were applied to measure age-related WM changes. The results indicate that LDH reveals unique inter-subject variability in specific WM regions (e.g., cerebral peduncle, internal capsule and splenium). Furthermore, there are regions in which measurements of age-related WM alterations with the LDH and FA/MD metrics yield discrepant results. These findings suggest that LDH and FA/MD have different sensitivities to specific WM microstructural properties. Taken together, the present study shows that LDH is complementary to the conventional diffusion-MRI markers and may provide additional insights into inter-subject WM variability. Further studies, however, are needed to uncover the neuronal mechanisms underlying the LDH.     

Estimating the Confidence Level of White Matter Connections Obtained with MRI Tractography

Background

Since the emergence of diffusion tensor imaging, a lot of work has been done to better understand the properties of diffusion MRI tractography. However, the validation of the reconstructed fiber connections remains problematic in many respects. For example, it is difficult to assess whether a connection is the result of the diffusion coherence contrast itself or the simple result of other uncontrolled parameters like for example: noise, brain geometry and algorithmic characteristics.

Methodology/Principal Findings

In this work, we propose a method to estimate the respective contributions of diffusion coherence versus other effects to a tractography result by comparing data sets with and without diffusion coherence contrast. We use this methodology to assign a confidence level to every gray matter to gray matter connection and add this new information directly in the connectivity matrix.

Conclusions/Significance

Our results demonstrate that whereas we can have a strong confidence in mid- and long-range connections obtained by a tractography experiment, it is difficult to distinguish between short connections traced due to diffusion coherence contrast from those produced by chance due to the other uncontrolled factors of the tractography methodology.     

Parametric Representation of Multiple White Matter Fascicles from Cube and Sphere Diffusion MRI

The characterization of the complex diffusion signal arising from the brain remains an open problem. Many representations focus on characterizing the global shape of the diffusion profile at each voxel and are limited to the assessment of connectivity. In contrast, Multiple Fascicle Models (MFM) seek to represent the contribution from each white matter fascicle and may be useful in the investigation of both white matter connectivity and diffusion properties of each individual fascicle. However, the most appropriate representation of multiple fascicles remains unclear. In particular, a multiple tensor representation of multiple fascicles has frequently been reported to be numerically challenging and unstable. We provide here the first analytical demonstration that when using a diffusion MRI acquisition with only one non-zero b-value, such as in conventional single-shell HARDI acquisition, a co-linearity in model parameters makes the precise model estimation impossible. Motivated by this theoretical result, we propose the novel CUSP (CUbe and SPhere) optimal acquisition scheme to achieve multiple non-zero b-values. It combines the gradients of a single-shell HARDI with gradients in its enclosing cube, in which varying b-values can be acquired by modulation of the gradient strength, without modifying the minimum echo time. Compared to a multi-shell HARDI acquisition, our scheme has significantly increased signal-to-noise ratio. We propose a novel estimation algorithm that enables efficient, robust and accurate estimation of the parameters of a multi-tensor model. In conjunction with a CUSP acquisition, it enables full estimation of the multi-tensor model. We present an evaluation of CUSP-MFM on both synthetic phantoms and invivo data. We report qualitative and quantitative experimental evaluations which demonstrate the ability of CUSP-MFM to characterize multiple fascicles from short duration acquisitions. CUSP-MFM enables rapid and effective investigation of multiple white matter fascicles, in both normal development and in disease and injury, in research and clinical practice.     

MRI Markers for Mild Cognitive Impairment: Comparisons between White Matter Integrity and Gray Matter Volume Measurements

The aim of the study was to evaluate the value of assessing white matter integrity using diffusion tensor imaging (DTI) for classification of mild cognitive impairment (MCI) and prediction of cognitive impairments in comparison to brain atrophy measurements using structural MRI. Fifty-one patients with MCI and 66 cognitive normal controls (CN) underwent DTI and T1-weighted structural MRI. DTI measures included fractional anisotropy (FA) and radial diffusivity (DR) from 20 predetermined regions-of-interest (ROIs) in the commissural, limbic and association tracts, which are thought to be involved in Alzheimer''s disease; measures of regional gray matter (GM) volume included 21 ROIs in medial temporal lobe, parietal cortex, and subcortical regions. Significant group differences between MCI and CN were detected by each MRI modality: In particular, reduced FA was found in splenium, left isthmus cingulum and fornix; increased DR was found in splenium, left isthmus cingulum and bilateral uncinate fasciculi; reduced GM volume was found in bilateral hippocampi, left entorhinal cortex, right amygdala and bilateral thalamus; and thinner cortex was found in the left entorhinal cortex. Group classifications based on FA or DR was significant and better than classifications based on GM volume. Using either DR or FA together with GM volume improved classification accuracy. Furthermore, all three measures, FA, DR and GM volume were similarly accurate in predicting cognitive performance in MCI patients. Taken together, the results imply that DTI measures are as accurate as measures of GM volume in detecting brain alterations that are associated with cognitive impairment. Furthermore, a combination of DTI and structural MRI measurements improves classification accuracy.     

Regional Variation in Brain White Matter Diffusion Index Changes following Chemoradiotherapy: A Prospective Study Using Tract-Based Spatial Statistics

Purpose

There is little known about how brain white matter structures differ in their response to radiation, which may have implications for radiation-induced neurocognitive impairment. We used diffusion tensor imaging (DTI) to examine regional variation in white matter changes following chemoradiotherapy.

Methods

Fourteen patients receiving two or three weeks of whole-brain radiation therapy (RT) ± chemotherapy underwent DTI pre-RT, at end-RT, and one month post-RT. Three diffusion indices were measured: fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD). We determined significant individual voxel changes of diffusion indices using tract-based spatial statistics, and mean changes of the indices within fourteen white matter structures of interest.

Results

Voxels of significant FA decreases and RD increases were seen in all structures (p<0.05), with the largest changes (20–50%) in the fornix, cingula, and corpus callosum. There were highly significant between-structure differences in pre-RT to end-RT mean FA changes (p<0.001). The inferior cingula had a mean FA decrease from pre-RT to end-RT significantly greater than 11 of the 13 other structures (p<0.00385).

Conclusions

Brain white matter structures varied greatly in their response to chemoradiotherapy as measured by DTI changes. Changes in FA and RD related to white matter demyelination were prominent in the cingula and fornix, structures relevant to radiation-induced neurocognitive impairment. Future research should evaluate DTI as a predictive biomarker of brain chemoradiotherapy adverse effects.     

Different Patterns of White Matter Degeneration Using Multiple Diffusion Indices and Volumetric Data in Mild Cognitive Impairment and Alzheimer Patients

Alzheimeŕs disease (AD) represents the most prevalent neurodegenerative disorder that causes cognitive decline in old age. In its early stages, AD is associated with microstructural abnormalities in white matter (WM). In the current study, multiple indices of diffusion tensor imaging (DTI) and brain volumetric measurements were employed to comprehensively investigate the landscape of AD pathology. The sample comprised 58 individuals including cognitively normal subjects (controls), amnestic mild cognitive impairment (MCI) and AD patients. Relative to controls, both MCI and AD subjects showed widespread changes of anisotropic fraction (FA) in the corpus callosum, cingulate and uncinate fasciculus. Mean diffusivity and radial changes were also observed in AD patients in comparison with controls. After controlling for the gray matter atrophy the number of regions of significantly lower FA in AD patients relative to controls was decreased; nonetheless, unique areas of microstructural damage remained, e.g., the corpus callosum and uncinate fasciculus. Despite sample size limitations, the current results suggest that a combination of secondary and primary degeneration occurrs in MCI and AD, although the secondary degeneration appears to have a more critical role during the stages of disease involving dementia.     

Altered Activation of Innate Immunity Associates with White Matter Volume and Diffusion in First-Episode Psychosis

First-episode psychosis (FEP) is associated with inflammatory and brain structural changes, but few studies have investigated whether systemic inflammation associates with brain structural changes in FEP. Thirty-seven FEP patients (median 27 days on antipsychotic medication), and 19 matched controls were recruited. Serum levels of 38 chemokines and cytokines, and cardiovascular risk markers were measured at baseline and 2 months later. We collected T1- and diffusion-weighted MRIs with a 3 T scanner from the patients at baseline. We analyzed the association of psychosis-related inflammatory markers with gray and white matter (WM) volume using voxel-based morphometry and WM diffusion using tract-based spatial statistics with whole-brain and region-of-interest (ROI) analyses. FEP patients had higher CCL22 and lower TGFα, CXCL1, CCL7, IFN-α2 and ApoA-I than controls. CCL22 decreased significantly between baseline and 2 months in patients but was still higher than in controls. The association between inflammatory markers and FEP remained significant after adjusting for age, sex, smoking and BMI. We did not observe a correlation of inflammatory markers with any symptoms or duration of antipsychotic treatment. Baseline CCL22 levels correlated negatively with WM volume and positively with mean diffusivity and radial diffusivity bilaterally in the frontal lobes in ROI analyses. Decreased serum level of ApoA-I was associated with smaller volume of the medial temporal WM. In whole-brain analyses, CCL22 correlated positively with mean diffusivity and radial diffusivity, and CXCL1 associated negatively with fractional anisotropy and positively with mean diffusivity and radial diffusivity in several brain regions. This is the first report to demonstrate an association between circulating chemokine levels and WM in FEP patients. Interestingly, CCL22 has been previously implicated in autoimmune diseases associated with WM pathology. The results suggest that an altered activation of innate immunity may contribute to WM damage in psychotic disorders.     

White Matter Changes in Patients with Amnestic Mild Cognitive Impairment Detected by Diffusion Tensor Imaging

Compared to normal aging adults, individuals with amnestic mild cognitive impairment (aMCI) have significantly increased risk for progressing into Alzheimer’s disease (AD). Autopsy studies found that most of the brains of aMCI cases showed anatomical features associated with AD pathology. The recent development of non-invasive neuroimaging technique, such as diffusion tensor imaging (DTI), makes it possible to investigate the microstructures of the cerebral white matter in vivo. We hypothesized that disrupted white matter (WM) integrity existed in aMCI. So we used DTI technique, by measuring fractional anisotropy (FA) and mean diffusivity (MD), to test the brain structures involved in patients with aMCI. DTI scans were collected from 40 patients with aMCI, and 28 normal controls (NC). Tract-based spatial statistics (TBSS) analyses of whole-brain FA and MD images in each individual and group comparisons were carried out. Compared to NC, aMCI patients showed significant FA reduction bilaterally, in the association and projection fibers of frontal, parietal, and temporal lobes, corpus callosum, bilateral corona radiation, right posterior thalamic radiation and right sagittal stratum. aMCI patients also showed significantly increased MD widespreadly in the association and projection fibers of frontal, parietal and temporal lobes, and corpus callosum. Assessment of the WM integrity of the frontal, parietal, temporal lobes, and corpus callosum by using DTI measures may aid early diagnosis of aMCI.     

Tritiated Water Vapour Exchange Method for the Evaluation of Whole Plant Diffusion Resistance

Water vapour exchange between plant and atmosphere was studiedby using THO as a tracer. Pulses of THO vapour were brieflyinjected into the airflow of an open circuit plant chamber.Water vapour exchange was calculated from the difference intotal quantity of THO escaping from the plant chamber with andwithout a plant The amount of THO exchanged was closely dependenton the product of total leaf area x mean leaf conductance. Themethod is suitable for measuring on the same plant short termvariations in leaf conductance following changes in environmentalconditions.     

Estimating Litter Decomposition Rate in Single-Pool Models Using Nonlinear Beta Regression

Litter decomposition rate (k) is typically estimated from proportional litter mass loss data using models that assume constant, normally distributed errors. However, such data often show non-normal errors with reduced variance near bounds (0 or 1), potentially leading to biased k estimates. We compared the performance of nonlinear regression using the beta distribution, which is well-suited to bounded data and this type of heteroscedasticity, to standard nonlinear regression (normal errors) on simulated and real litter decomposition data. Although the beta model often provided better fits to the simulated data (based on the corrected Akaike Information Criterion, AIC_c), standard nonlinear regression was robust to violation of homoscedasticity and gave equally or more accurate k estimates as nonlinear beta regression. Our simulation results also suggest that k estimates will be most accurate when study length captures mid to late stage decomposition (50–80% mass loss) and the number of measurements through time is ≥5. Regression method and data transformation choices had the smallest impact on k estimates during mid and late stage decomposition. Estimates of k were more variable among methods and generally less accurate during early and end stage decomposition. With real data, neither model was predominately best; in most cases the models were indistinguishable based on AIC_c, and gave similar k estimates. However, when decomposition rates were high, normal and beta model k estimates often diverged substantially. Therefore, we recommend a pragmatic approach where both models are compared and the best is selected for a given data set. Alternatively, both models may be used via model averaging to develop weighted parameter estimates. We provide code to perform nonlinear beta regression with freely available software.     

Perinatal Clinical Antecedents of White Matter Microstructural Abnormalities on Diffusion Tensor Imaging in Extremely Preterm Infants

Objective

To identify perinatal clinical antecedents of white matter microstructural abnormalities in extremely preterm infants.

Methods

A prospective cohort of extremely preterm infants (N = 86) and healthy term controls (N = 16) underwent diffusion tensor imaging (DTI) at term equivalent age. Region of interest-based measures of white matter microstructure - fractional anisotropy and mean diffusivity - were quantified in seven vulnerable cerebral regions and group differences assessed. In the preterm cohort, multivariable linear regression analyses were conducted to identify independent clinical factors associated with microstructural abnormalities.

Results

Preterm infants had a mean (standard deviation) gestational age of 26.1 (1.7) weeks and birth weight of 824 (182) grams. Compared to term controls, the preterm cohort exhibited widespread microstructural abnormalities in 9 of 14 regional measures. Chorioamnionitis, necrotizing enterocolitis, white matter injury on cranial ultrasound, and increasing duration of mechanical ventilation were adversely correlated with regional microstructure. Conversely, antenatal steroids, female sex, longer duration of caffeine therapy, and greater duration of human milk use were independent favorable factors. White matter injury on cranial ultrasound was associated with a five weeks or greater delayed maturation of the corpus callosum; every additional 10 days of human milk use were associated with a three weeks or greater advanced maturation of the corpus callosum.

Conclusions

Diffusion tensor imaging is sensitive in detecting the widespread cerebral delayed maturation and/or damage increasingly observed in extremely preterm infants. In our cohort, it also aided identification of several previously known or suspected perinatal clinical antecedents of brain injury, aberrant development, and neurodevelopmental impairments.     

The Use of the Lumbosacral Enlargement as an Intrinsic Imaging Biomarker: Feasibility of Grey Matter and White Matter Cross-Sectional Area Measurements Using MRI at 3T

Histopathological studies have demonstrated the involvement of spinal cord grey matter (GM) and white matter (WM) in several diseases and recent research has suggested the use of magnetic resonance imaging (MRI) as a promising tool for in vivo assessment of the upper spinal cord. However, many neurological conditions would benefit from quantitative assessment of tissue integrity at different levels and relatively little work has been done, mainly due to technical challenges associated with imaging the lower spinal cord. In this study, the value of the lumbosacral enlargement (LSE) as an intrinsic imaging biomarker was determined by exploring the feasibility of obtaining within it reliable GM and WM cross-sectional area (CSA) measurements by means of a commercially available MRI system at 3 tesla (T). 10 healthy volunteers (mean age 27.5 years, 6 female) gave written informed consent and high resolution images of the LSE were acquired and analysed using an optimised MRI acquisition and analysis protocol. GM and WM mean CSA measurements were obtained from a 15 mm section at the level of the LSE and the reproducibility of the measurements was determined by means of scan-rescan, intra- and inter-observer assessments. Mean (±SD) LSE cross-sectional area (LSE-CSA) was 62.3 (±4.1) mm² and mean (±SD) LSE grey matter cross-sectional area (LSE-GM-CSA) was 19.8 (±3.3) mm². The mean scan-rescan, intra- and inter-observer % coefficient of variation (COV) for measuring the LSE-CSA were 2%, 2% and 2.5%, respectively and for measuring the LSE-GM-CSA were 7.8%, 8% and 8.6%, respectively. This study has shown that the LSE can be used reliably as an intrinsic imaging biomarker. The method presented here can be potentially extended to study the LSE in the diseased state and could provide a solid foundation for subsequent multi-parametric MRI investigations.     

Assessment of Global and Regional Diffusion Changes along White Matter Tracts in Parkinsonian Disorders by MR Tractography

Purpose

The aim of the study was to determine the usefulness of diffusion tensor tractography (DTT) in parkinsonian disorders using a recently developed method for normalization of diffusion data and tract size along white matter tracts. Furthermore, the use of DTT in selected white matter tracts for differential diagnosis was assessed.

Methods

We quantified global and regional diffusion parameters in major white matter tracts in patients with multiple system atrophy (MSA), progressive nuclear palsy (PSP), idiopathic Parkinson’s disease (IPD) and healthy controls). Diffusion tensor imaging data sets with whole brain coverage were acquired at 3 T using 48 diffusion encoding directions and a voxel size of 2×2×2 mm³. DTT of the corpus callosum (CC), cingulum (CG), corticospinal tract (CST) and middle cerebellar peduncles (MCP) was performed using multiple regions of interest. Regional evaluation comprised projection of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and the apparent area coefficient (AAC) onto a calculated mean tract and extraction of their values along each structure.

Results

There were significant changes of global DTT parameters in the CST (MSA and PSP), CC (PSP) and CG (PSP). Consistent tract-specific variations in DTT parameters could be seen along each tract in the different patient groups and controls. Regional analysis demonstrated significant changes in the anterior CC (MD, RD and FA), CST (MD) and CG (AAC) of patients with PSP compared to controls. Increased MD in CC and CST, as well as decreased AAC in CG, was correlated with a diagnosis of PSP compared to IPD.

Conclusions

DTT can be used for demonstrating disease-specific regional white matter changes in parkinsonian disorders. The anterior portion of the CC was identified as a promising region for detection of neurodegenerative changes in patients with PSP, as well as for differential diagnosis between PSP and IPD.     

An Algorithm for Estimating Chlorophyll Content in Leaves Using a Video Camera

Plant leaf colour is commonly used as an index for nutrientdiagnosis. We have developed a low-cost diagnostic method thatis easy to use to assess the nutrient status of plants, basedon the estimation of chlorophyll content of leaves using a portablecolour video camera and a personal computer. Relationships betweenchlorophyll content and various functions derived from red,green and blue wavelengths are examined. Although red-blue andgreen-blue wavelengths show the highest correlation with chlorophyllcontent under a limited range of meteorological conditions,the normalized difference (red-blue)/(red+blue) is the mostapplicable function which can use data collected under differentmeteorological conditions. The accuracy in estimating chlorophyllcontent from video images could be improved by correcting withsolar radiation data. It is shown that, for practical purposes,the chlorophyll content of leaves can be estimated with sufficientaccuracy using a portable video camera.Copyright 1998 Annalsof Botany Company Chlorophyll content; leaf colour; video camera; image processing; nutrient diagnosis.     

Cortical Grey Matter and Subcortical White Matter Brain Microstructural Changes in Schizophrenia Are Localised and Age Independent: A Case-Control Diffusion Tensor Imaging Study

It is still unknown whether the structural brain impairments that characterize schizophrenia (SZ) worsen during the lifetime. Here, we aimed to describe age-related microstructural brain changes in cortical grey matter and subcortical white matter of patients affected by SZ. In this diffusion tensor imaging study, we included 69 patients diagnosed with SZ and 69 healthy control (HC) subjects, age and gender matched. We carried out analyses of covariance, with diagnosis as fixed factor and brain diffusion-related parameters as dependent variables, and controlled for the effect of education. White matter fractional anisotropy decreased in the entire age range spanned (18–65 years) in both SZ and HC and was significantly lower in younger patients with SZ, with no interaction (age by diagnosis) effect in fiber tracts including corpus callosum, corona radiata, thalamic radiations and external capsule. Also, grey matter mean diffusivity increased in the entire age range in both SZ and HC and was significantly higher in younger patients, with no age by diagnosis interaction in the left frontal operculum cortex, left insula and left planum polare and in the right temporal pole and right intracalcarine cortex. In individuals with SZ we found that localized brain cortical and white matter subcortical microstructural impairments appear early in life but do not worsen in the 18–65 year age range.     

Bayesian Hierarchical Model for Estimating Gene Expression Intensity Using Multiple Scanned Microarrays

We propose a method for improving the quality of signal from DNA microarrays by using several scans at varying scanner sen-sitivities. A Bayesian latent intensity model is introduced for the analysis of such data. The method improves the accuracy at which expressions can be measured in all ranges and extends the dynamic range of measured gene expression at the high end. Our method is generic and can be applied to data from any organism, for imaging with any scanner that allows varying the laser power, and for extraction with any image analysis software. Results from a self-self hybridization data set illustrate an improved precision in the estimation of the expression of genes compared to what can be achieved by applying standard methods and using only a single scan.     

Frontal White Matter Alterations in Short-Term Medicated Panic Disorder Patients without Comorbid Conditions: A Diffusion Tensor Imaging Study

The frontal cortex might play an important role in the fear network, and white matter (WM) integrity could be related to the pathophysiology of panic disorder (PD). A few studies have investigated alterations of WM integrity in PD. The aim of this study was to determine frontal WM integrity differences between patients with PD without comorbid conditions and healthy control (HC) subjects by using diffusion tensor imaging. Thirty-six patients with PD who had used medication within 1 week and 27 age- and sex-matched HC subjects participated in this study. Structural brain magnetic resonance imaging was performed on all participants. Panic Disorder Severity Scale and Beck Anxiety Inventory (BAI) scores were assessed. Tract-based spatial statistics (TBSS) was used for image analysis. TBSS analysis showed decreased fractional anisotropy (FA) in frontal WM and WM around the frontal lobe, including the corpus callosum of both hemispheres, in patients with PD compared to HC subjects. Moreover, voxel-wise correlation analysis revealed that the BAI scores for patients with PD were positively correlated with their FA values for regions showing group differences in the FA of frontal WM of both hemispheres. Altered integrity in frontal WM of patients with PD without comorbid conditions might represent the structural pathophysiology in these patients, and these changes could be related to clinical symptoms of PD.