Diffusion Tensor Data Research Articles

Multimodal brain age indicators of internalising problems in early adolescence: A longitudinal investigation

Adolescence is a time of increased risk for the onset of internalizing problems, particularly in females. However, how individual differences in brain maturation are related to the increased vulnerability for internalizing problems in adolescence remains poorly understood due to a scarcity of longitudinal studies. Using ABCD (Adolescent Brain Cognitive Development) Study data, we examined longitudinal associations between multimodal brain age and youth internalizing problems. Brain age models were trained, validated, and tested independently on T1-weighted imaging (n= 9523), diffusion tensor imaging (n= 8834), and resting-state functional magnetic resonance imaging (n= 8233) data at baseline (meanage= 9.9 years) and 2-year follow-up (meanage= 11.9 years). Self-reported internalizing problems were measured at 3-year follow-up (meanage= 12.9 years) using the Brief Problem Monitor. Latent change score models demonstrated that although brain age gap (BAG) at baseline was not related to later internalizing problems, an increase in BAG between time points was positively associated with internalizing problems at 3-year follow-up in females but not males. This association between an increasing BAG and higher internalizing problems was observed in the T1-weighted imaging (β= 0.067, SE= 0.050, false discovery rate [FDR]-corrected p = .020) and resting-state functional magnetic resonance imaging (β= 0.090, SE= 0.025, pFDR= .007) models but not diffusion tensor imaging (β=-0.002, SE= 0.053, pFDR= .932) and remained significant when accounting for earlier internalizing problems. A greater increase in BAG in early adolescence may reflect the heightened vulnerability shown by female youth to internalizing problems. Longitudinal research is necessary to understand whether this increasing BAG signifies accelerated brain development and its relationship to the trajectory of internalizing problems throughout adolescence.

Structure-function coupling in white matter uncovers the hypoconnectivity in autism spectrum disorder

BackgroundAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder associated with alterations in structural and functional coupling in gray matter. However, despite the detectability and modulation of brain signals in white matter, the structure-function coupling in white matter in autism remains less explored.MethodsIn this study, we investigated structural-functional coupling in white matter (WM) regions, by integrating diffusion tensor data that contain fiber orientation information from WM tracts, with functional connectivity tensor data that reflect local functional anisotropy information. Using functional and diffusion magnetic resonance images, we analyzed a cohort of 89 ASD and 63 typically developing (TD) individuals from the Autism Brain Imaging Data Exchange II (ABIDE-II). Subsequently, the associations between structural-functional coupling in WM regions and ASD severity symptoms assessed by Autism Diagnostic Observation Schedule-2 were examined via supervised machine learning in an independent test cohort of 29 ASD individuals. Furthermore, we also compared the performance of multi-model features (i.e. structural-functional coupling) with single-model features (i.e. functional or structural models alone).ResultsIn the discovery cohort (ABIDE-II), individuals with ASD exhibited widespread reductions in structural-functional coupling in WM regions compared to TD individuals, particularly in commissural tracts (e.g. corpus callosum), association tracts (sagittal stratum), and projection tracts (e.g. internal capsule). Notably, supervised machine learning analysis in the independent test cohort revealed a significant correlation between these alterations in structural-functional coupling and ASD severity scores. Furthermore, compared to single-model features, the integration of multi-model features (i.e., structural-functional coupling) performed best in predicting ASD severity scores.ConclusionThis work provides novel evidence for atypical structural-functional coupling in ASD in white matter regions, further refining our understanding of the critical role of WM networks in the pathophysiology of ASD.

Structural and functional connectome alterations across King’s stages in amyotrophic lateral sclerosis (S33.006)

<h3>Objective:</h3> To explore the rearrangements of structural and functional connectivity within and among brain networks underlying the clinical spreading of amyotrophic lateral sclerosis (ALS), as described by the King’s staging system, in order to suggest objective, continuous measures mirroring disease progression. <h3>Background:</h3> The identification of quantitative and reproducible markers of disease progression in ALS is of paramount importance for study design and inclusion of homogenous patient cohorts into clinical trials, as there is currently no validated disease-stage biomarker for ALS. <h3>Design/Methods:</h3> 104 patients with ALS and 61 age- and sex-matched healthy controls underwent clinical and brain magnetic resonance imaging (MRI) on a 3T scanner. Patients were stratified into four groups, according to the King’s staging system. No patient had comorbid frontotemporal dementia. Structural and functional connectivity values within and between different anatomical brain regions were obtained using diffusion tensor and resting-state functional MRI data, respectively. Comparisons between groups were performed using age- and sex-adjusted ANOVA models. Correlations were tested using Pearson’s coefficient. <h3>Results:</h3> Compared with controls, a significant, progressive reduction of structural connectivity within brain nodes of the sensorimotor network was observed in ALS patients across King’s stages 2, 3 and 4 (p&lt;0.006). Patients in stages 3 and 4 also showed significant loss of structural connectivity between frontal and sensorimotor regions (p=0.001). A significant disruption of functional connectivity within frontotemporal regions was found only in stage 4 (p=0.025). Sensorimotor structural connectivity showed a strong correlation with ALSFRS-r scores (r=0.31, p=0.001). <h3>Conclusions:</h3> Brain MRI allows to demonstrate and quantify increasing disruption of structural connectivity involving the sensorimotor and frontal networks in ALS, mirroring disease progression. Frontotemporal functional disconnection seems to characterize only advanced disease stages. Our findings demonstrate the utility of MRI connectomics to stratify patients and stage brain pathology in ALS in a reproducible way, which mirrors clinical progression. <b>Disclosure:</b> Dr. Spinelli has nothing to disclose. Miss Ghirelli has nothing to disclose. Silvia Basaia has nothing to disclose. Ms. Cividini has nothing to disclose. Nilo Riva has nothing to disclose. Dr. Russo has nothing to disclose. The institution of Elisa Canu has received research support from Italian Ministry of Health . Dr. Castelnovo has nothing to disclose. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Springer Nature. The institution of Dr. Filippi has received research support from Biogen Idec, Merck-Serono, Novartis, Roche, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA). Federica Agosta has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Philips. Federica Agosta has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Elsevier INC.

Spatial profiles provide sensitive MRI measures of the midbrain micro- and macrostructure

The midbrain is the rostral-most part of the brainstem. It contains numerous nuclei and white matter tracts, which are involved in motor, auditory and visual processing, and changes in their structure and function have been associated with aging, as well as neurodegenerative disorders. Current tools for estimating midbrain subregions and their structure with MRI require high resolution and multi-parametric quantitative MRI measures. We propose an approach that relies on morphology to calculate profiles along the midbrain and show these profiles are sensitive to the underlying macrostructure of the midbrain. First, we show that the midbrain structure can be sampled, within subject space, along three main axes of the left and right midbrain, producing profiles that are similar across subjects. We use two data sets with different field strengths, that contain R1, R2* and QSM maps and show that the profiles are highly correlated both across subjects and between datasets. Next, we compare profiles of the midbrain that sample ROIs, and show that the profiles along the first two axes sample the midbrain in a way that reliably separates the main structures, i.e., the substantia nigra, the red nucleus, and periaqueductal gray. We further show that age differences which are localized to specific nuclei, are reflected in the profiles. Finally, we generalize the same approach to calculate midbrain profiles on a third clinically relevant dataset using HCP subjects, with metrics such as the diffusion tensor and semi-quantitative data such as T1w/T2w maps. Our results suggest that midbrain profiles, both of quantitative and semi-quantitative estimates are sensitive to the underlying macrostructure of the midbrain. The midbrain profiles are calculated in native space, and rely on simple measurements. We show that it is robust and can be easily expanded to different datasets, and as such we hope that it will be of great use to the community and to the study of the midbrain in particular.

Exploiting Spatial Information to Enhance DTI Segmentations via Spatial Fuzzy c-Means with Covariance Matrix Data and Non-Euclidean Metrics

A diffusion tensor models the covariance of the Brownian motion of water at a voxel and is required to be symmetric and positive semi-definite. Therefore, image processing approaches, designed for linear entities, are not effective for diffusion tensor data manipulation, and the existence of artefacts in diffusion tensor imaging acquisition makes diffusion tensor data segmentation even more challenging. In this study, we develop a spatial fuzzy c-means clustering method for diffusion tensor data that effectively segments diffusion tensor images by accounting for the noise, partial voluming, magnetic field inhomogeneity, and other imaging artefacts. To retain the symmetry and positive semi-definiteness of diffusion tensors, the log and root Euclidean metrics are used to estimate the mean diffusion tensor for each cluster. The method exploits spatial contextual information and provides uncertainty information in segmentation decisions by calculating the membership values for assigning a diffusion tensor at one voxel to different clusters. A regularisation model that allows the user to integrate their prior knowledge into the segmentation scheme or to highlight and segment local structures is also proposed. Experiments on simulated images and real brain datasets from healthy and Spinocerebellar ataxia 2 subjects showed that the new method was more effective than conventional segmentation methods.

Evaluation and categorisation of individual patients based on white matter profiles: Single-patient diffusion data interpretation in neurodegeneration

The majority of radiology studies in neurodegenerative conditions infer group-level imaging traits from group comparisons. While this strategy is helpful to define phenotype-specific imaging signatures for academic use, the meaningful interpretation of single scans of individual subjects is more important in everyday clinical practice. Accordingly, we present a computational method to evaluate individual subject diffusion tensor data to highlight white matter integrity alterations. Fifty white matter tracts were quantitatively evaluated in 132 patients with amyotrophic lateral sclerosis (ALS) with respect to normative values from 100 healthy subjects. Fractional anisotropy and radial diffusivity alterations were assessed individually in each patient. The approach was validated against standard tract-based spatial statistics and further scrutinised by the assessment of 78 additional data sets with a blinded diagnosis. Our z-score-based approach readily detected white matter degeneration in individual ALS patients and helped to categorise single subjects with a 'blinded diagnosis' as likely 'ALS' or 'control'. The group-level inferences from the z-score-based approach were analogous to the standard TBSS output maps. The benefit of the z-score-based strategy is that it enables the interpretation of single DTI datasets as well as the comparison of study groups. Outputs can be summarised either visually by highlighting the affected tracts, or, listing the affected tracts in a text file with reference to normative data, making it particularly useful for clinical applications. While individual diffusion data cannot be visually appraised, our approach provides a viable framework for single-subject imaging data interpretation.

Acute thalamic damage as a prognostic biomarker for post-traumatic epileptogenesis.

To identify magnetic resonance imaging (MRI) biomarkers for post-traumatic epilepsy. The EPITARGET (Targets and biomarkers for antiepileptogenesis, epitarget.eu) animal cohort completing T2 relaxation and diffusion tensor MRI follow-up and 1-month-long video-electroencephalography monitoring included 98 male Sprague-Dawley rats with traumatic brain injury and 18 controls. T2 imaging was performed on day (D) 2, D7, and D21 and diffusion tensor imaging (DTI) on D7 and D21 using a 7-Tesla Bruker PharmaScan MRI scanner. The mean and standard deviation (SD) of the T2 relaxation rate, multiple diffusivity measures, and diffusion anisotropy at each time-point within the ventroposterolateral and ventroposteromedial thalamus were used as predictor variables in multi-variable logistic regression models to distinguish rats with and without epilepsy. Twenty-nine percent (28/98) of the rats with traumatic brain injury (TBI) developed epilepsy. The best-performing logistic regression model utilized the D2 and D7 T2 relaxation time as well as the D7 diffusion tensor data. The model distinguished rats with and without epilepsy (Bonferroni-corrected p-value<.001) with a cross-validated concordance statistic of 0.74 (95% confidence interval [CI] 0.60-0.84). In a cross-validated classification test, the model exhibited 54% sensitivity and 91% specificity, enriching the epilepsy rate within the study population from the expected 29% to 71%. A model using the D2 T2 data only resulted in a 73% enriched epilepsy rate (regression p-value .007, cross-validated concordance 0.70, 95% CI 0.56-0.80, sensitivity 29%, specificity 96%). An MRI parameter set reporting on acute and subacute neuropathologic changes common to experimental and human TBI presents a diagnostic biomarker for post-traumatic epileptogenesis. Significant enrichment of the study population was achieved even when using a single time-point measurement, producing an expected epilepsy rate of 73%.

Distinguishing persistent post-traumatic headache from migraine: Classification based on clinical symptoms and brain structural MRI data.

Persistent post-traumatic headache most commonly has symptoms that overlap those of migraine. In some cases, it can be clinically difficult to differentiate persistent post-traumatic headache with a migraine phenotype from migraine. The objective of this study was to develop a classification model based on questionnaire data and structural neuroimaging data that distinguishes individuals with migraine from those with persistent post-traumatic headache. Questionnaires assessing headache characteristics, sensory hypersensitivities, cognitive functioning, and mood, as well as T1-weighted magnetic resonance imaging and diffusion tensor data from 34 patients with migraine and 48 patients with persistent post-traumatic headache attributed to mild traumatic brain injury were included for analysis. The majority of patients with persistent post-traumatic headache had a migraine/probable migraine phenotype (77%). A machine-learning leave-one-out cross-validation algorithm determined the average accuracy for distinguishing individual migraine patients from individual patients with persistent post-traumatic headache. Based on questionnaire data alone, the average classification accuracy for determining whether an individual person had migraine or persistent post-traumatic headache was 71.9%. Adding imaging data features to the model improved the classification accuracy to 78%, including an average accuracy of 97.1% for identifying individual migraine patients and an average accuracy of 64.6% for identifying individual patients with persistent post-traumatic headache. The most important clinical features that contributed to the classification accuracy included questions related to anxiety and decision making. Cortical brain features and fibertract data from the following regions or tracts most contributed to the classification accuracy: Bilateral superior temporal, inferior parietal and posterior cingulate; right lateral occipital, uncinate, and superior longitudinal fasciculus. A post-hoc analysis showed that compared to incorrectly classified persistent post-traumatic headache patients, those who were correctly classified as having persistent post-traumatic headache had more severe physical, autonomic, anxiety and depression symptoms, were more likely to have post-traumatic stress disorder, and were more likely to have had mild traumatic brain injury attributed to blasts. A classification model that included a combination of questionnaire data and structural imaging parameters classified individual patients as having migraine versus persistent post-traumatic headache with good accuracy. The most important clinical measures that contributed to the classification accuracy included questions on mood. Regional brain structures and fibertracts that play roles in pain processing and pain integration were important brain features that contributed to the classification accuracy. The lower classification accuracy for patients with persistent post-traumatic headache compared to migraine may be related to greater heterogeneity of patients in the persistent post-traumatic headache cohort regarding their traumatic brain injury mechanisms, and physical, emotional, and cognitive symptoms.

Low-frequency conductivity tensor imaging with a single current injection using DT-MREIT

Diffusion tensor-magnetic resonance electrical impedance tomography (DT-MREIT) is an imaging modality to obtain low-frequency anisotropic conductivity distribution employing diffusion tensor imaging and MREIT techniques. DT-MREIT is based on the linear relationship between the conductivity and water self-diffusion tensors in a porous medium, like the brain white matter. Several DT-MREIT studies in the literature provide cross-sectional anisotropic conductivity images of tissue phantoms, canine brain, and the human brain. In these studies, the conductivity tensor images are reconstructed using the diffusion tensor and current density data acquired by injecting two linearly independent current patterns. In this study, a novel reconstruction algorithm is devised for DT-MREIT to reconstruct the conductivity tensor images using a single current injection. Therefore, the clinical applicability of DT-MREIT can be improved by reducing the total acquisition time, the number of current injection cables, and contact electrodes to half by decreasing the number of current injection patterns to one. The proposed method is evaluated utilizing simulated measurements and physical experiments. The results obtained show the successful reconstruction of the anisotropic conductivity distribution using the proposed single current DT-MREIT.

Multimodal assessment shows misalignment of structural and functional thalamocortical connectivity in children and adolescents born very preterm

Thalamocortical connections are altered following very preterm birth but it is unknown whether structural and functional alterations are linked and how they contribute to neurodevelopmental deficits. We used a multimodal approach in 27 very preterm and 35 term-born children and adolescents aged 10-16 years: Structural thalamocortical connectivity was quantified with two measures derived from probabilistic tractography of diffusion tensor data, namely the volume of thalamic segments with cortical connections and mean fractional anisotropy (FA) within the respective segments. High-density sleep EEG was recorded and sleep spindles were identified at each electrode. Sleep spindle density and integrated spindle activity (ISA) were calculated to quantify functional thalamocortical connectivity. In term-born participants, the volume of the global thalamic segment with cortical connections was strongly related to sleep spindles across the entire head (mean r​=​.53​± .10; range​=​0.35 to 0.78). Regionally, the volume of the thalamic segment connecting to frontal brain regions correlated with sleep spindle density in two clusters of electrodes over fronto-temporal brain regions (.42​± .06; 0.35 to 0.51 and 0.43​± .08; 0.35 to 0.62) and the volume of the thalamic segment connecting to parietal brain regions correlated with sleep spindle density over parietal brain regions (mean r​=​.43​± .07; 0.35 to 0.61). In very preterm participants, the volume of the thalamic segments was not associated with sleep spindles. In the very preterm group, mean FA within the global thalamic segment was negatively correlated with ISA over a cluster of frontal and temporo-occipital brain regions (mean r​=​-.53​± .07; -.41 to -.72). No association between mean FA and ISA was found in the term-born group. With this multimodal study protocol, we identified a potential misalignment between structural and functional thalamocortical connectivity in children and adolescents born very preterm. Eventually, this may shed further light on the neuronal mechanisms underlying neurodevelopmental sequelae of preterm birth.

Brain connectivity markers for the identification of effective contacts in subthalamic nucleus deep brain stimulation.

The clinical benefit of deep brain stimulation (DBS) for Parkinson's disease (PD) is relevant to the tracts adjacent to the stimulation site, but it remains unclear what connectivity pattern is associated with effective DBS. The aim of this study was to identify clinically effective electrode contacts on the basis of brain connectivity markers derived from diffusion tensor tractography. We reviewed 77 PD patients who underwent bilateral subthalamic nucleus DBS surgery. The patients were assigned into the training (n = 58) and validation (n = 19) groups. According to the therapeutic window size, all contacts were classified into effective and ineffective groups. The whole‐brain connectivity of each contact's volume of tissue activated was estimated using tractography with preoperative diffusion tensor data. Extracted connectivity features were put into an all‐relevant feature selection procedure within cross‐validation loops, to identify features with significant discriminative power for contact classification. A total of 616 contacts on 154 DBS leads were discriminated, with 388 and 228 contacts being classified as effective and ineffective ones, respectively. After the feature selection, the connectivity of contacts with the thalamus, pallidum, hippocampus, primary motor area, supplementary motor area and superior frontal gyrus was identified to significantly contribute to contact classification. Based on these relevant features, the random forest model constructed from the training group achieved an accuracy of 84.9% in the validation group, to discriminate effective contacts from the ineffective. Our findings advanced the understanding of the specific brain connectivity patterns associated with clinical effective electrode contacts, which potentially guided postoperative DBS programming.

Potential of a statistical approach for the standardization of multicenter diffusion tensor data: A phantom study

Diffusion tensor imaging (DTI) parameters, such as fractional anisotropy (FA), allow examining the structural integrity of the brain. However, the true value of these parameters may be confounded by variability in MR hardware, acquisition parameters, and image quality. To examine the effects of confounding factors on FA and to evaluate the feasibility of statistical methods to model and reduce multicenter variability. Longitudinal multicenter study. DTI single strand phantom (HQ imaging). 3T diffusion tensor imaging. Thirteen European imaging centers participated. DTI scans were acquired every 6 months and whenever maintenance or upgrades to the system were performed. A total of 64 scans were acquired in 2 years, obtained by three scanner vendors, using six individual head coils, and 12 software versions. The variability in FA was assessed by the coefficients of variation (CoV). Several linear mixed effects models (LMEM) were developed and compared by means of the Akaike Information Criterion (AIC). The CoV was 2.22% for mean FA and 18.40% for standard deviation of FA. The variables "site" (P = 9.26 × 10-5 ), "vendor" (P = 2.18 × 10-5 ), "head coil" (P = 9.00 × 10-4 ), "scanner drift," "bandwidth" (P = 0.033), "TE" (P = 8.20 × 10-6 ), "SNR" (P = 0.029) and "mean residuals" (P = 6.50 × 10-4 ) had a significant effect on the variability in mean FA. The variables "site" (P = 4.00 × 10-4 ), "head coil" (P = 2.00 × 10-4 ), "software" (P = 0.014), and "mean voxel outlier intensity count" (P = 1.10 × 10-4 ) had a significant effect on the variability in standard deviation of FA. The mean FA was best predicted by an LMEM that included "vendor" and the interaction term of "SNR" and "head coil" as model factors (AIC -347.98). In contrast, the standard deviation of FA was best predicted by an LMEM that included "vendor," "bandwidth," "TE," and the interaction term between "SNR" and "head coil" (AIC -399.81). Our findings suggest that perhaps statistical models seem promising to model the variability in quantitative DTI biomarkers for clinical routine and multicenter studies. 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:955-965.

Structural network topology relates to tissue properties in multiple sclerosis

ObjectiveAbnormalities in segregative and integrative properties of brain networks have been observed in multiple sclerosis (MS) and are related to clinical functioning. This study aims to investigate the micro-scale correlates of macro-scale network measures of segregation and integration in MS.MethodsEight MS patients underwent post-mortem in situ whole-brain diffusion tensor (DT) imaging and subsequent brain dissection. Macro-scale structural network topology was derived from DT data using graph theory. Clustering coefficient and mean white matter (WM) fiber length were measures of nodal segregation and integration. Thirty-three tissue blocks were collected from five cortical brain regions. Using immunohistochemistry micro-scale tissue properties were evaluated, including, neuronal size, neuronal density, axonal density and total cell density. Nodal network properties and tissue properties were correlated.ResultsA negative correlation between clustering coefficient and WM fiber length was found. Higher clustering coefficient was associated with smaller neuronal size and lower axonal density, and vice versa for fiber length. Higher whole-brain WM lesion load was associated with higher whole-brain clustering, shorter whole-brain fiber length, lower neuronal size and axonal density.ConclusionStructural network properties on MRI associate with neuronal size and axonal density, suggesting that macro-scale network measures may grasp cortical neuroaxonal degeneration in MS.

Disconnection due to white matter hyperintensities is associated with lower cognitive scores

Previous studies have linked global burden of age-related white matter hyperintensities (WMHs) to cognitive impairment. We aimed to determine how WMHs in individual white matter connections relate to measures of cognitive function relative to measures of connectivity which do not take WMHs into account. Brain connectivity and WMH-related disconnectivity were derived from 3714 participants of the population-based Rotterdam Study. Connectivity was represented by the structural connectome, which was defined using diffusion tensor data, whereas the disconnectome represented disconnectivity due to WMH. The relationship between (dis)connectivity and cognitive measures was estimated using linear regression. We found that lower disconnectivity and higher connectivity corresponded to better cognitive function. There were many more significant associations with cognitive function in the disconnectome than in the connectome. Most connectome associations attenuated when disconnection was included in the model. WMH-related disconnectivity was especially related to worse executive functioning. Better cognitive speed corresponded to higher connectivity in specific connections independent of WMH presence. We conclude that WMH-related disconnectivity explains more variation in cognitive function than does connectivity. Efficient wiring in specific connections is important to information processing speed independent of WMH presence.

Occulomotor Neural Integrator Dysfunction in Multiple Sclerosis: Insights From Neuroimaging.

Background: Magnetic resonance imaging is a key diagnostic and monitoring tool in multiple Sclerosis (MS). While the substrates of motor and neuropsychological symptoms in MS have been extensively investigated, nystagmus-associated imaging signatures are relatively under studied. Accordingly, the objective of this study is the comprehensive characterisation of cortical, subcortical, and brainstem involvement in a cohort of MS patients with gaze-evoked nystagmus.Methods: Patients were recruited from a specialist MS clinic and underwent multimodal neuroimaging including high-resolution structural and diffusion tensor data acquisitions. Morphometric analyses were carried out to evaluate patterns of cortical, subcortical, brainstem, and cerebellar gray matter pathology. Volumetric analyses were also performed to further characterize subcortical gray matter degeneration. White matter integrity was evaluated using axial-, mean-, and radial diffusivity as well as fractional anisotropy.Results: Whole-brain morphometry highlighted considerable brainstem and cerebellar gray matter atrophy, and the tract-wise evaluation of white matter metrics revealed widespread pathology in frontotemporal and parietal regions. Nystagmus-associated gray matter degeneration was identified in medial cerebellar, posterior medullar, central pontine, and superior collicular regions. Volume reductions were identified in the putamen, thalamus and hippocampus.Conclusions: Multiple sclerosis is associated with widespread gray matter pathology which is not limited to cortical regions but involves striatal, thalamic, cerebellar, and hippocampal foci. The imaging signature of gaze-evoked nystagmus in MS confirms the degeneration of key structures of the neural integrator network.

Spatially resolved kinetics of skeletal muscle exercise response and recovery with multiple echo diffusion tensor imaging (MEDITI): a feasibility study.

We describe measurement of skeletal muscle kinetics with multiple echo diffusion tensor imaging (MEDITI). This approach allows characterization of the microstructural dynamics in healthy and pathologic muscle. In a Siemens 3-T Skyra scanner, MEDITI was used to collect dynamic DTI with a combination of rapid diffusion encoding, radial imaging, and compressed sensing reconstruction in a multi-compartment agarose gel rotation phantom and within in vivo calf muscle. An MR-compatible ergometer (Ergospect Trispect) was employed to enable in-scanner plantar flexion exercise. In a HIPAA-compliant study with written informed consent, post-exercise recovery of DTI metrics was quantified in eight volunteers. Exercise response of DTI metrics was compared with that of T2-weighted imaging and characterized by a gamma variate model. Phantom results show quantification of diffusivities in each compartment over its full dynamic rotation. In vivo calf imaging results indicate larger radial than axial exercise response and recovery in the plantar flexion-challenged gastrocnemius medialis (fractional response: nT2w = 0.385 ± 0.244, nMD = 0.163 ± 0.130, nλ1 = 0.110 ± 0.093, nλrad = 0.303 ± 0.185). Diffusion and T2-weighted response magnitudes were correlated (e.g., r = 0.792, p = 0.019 for nMD vs. nT2w). We have demonstrated the feasibility of MEDITI for capturing spatially resolved diffusion tensor data in dynamic systems including post-exercise skeletal muscle recovery following in-scanner plantar flexion.

The retinal ganglion cell layer predicts normal-appearing white matter tract integrity in multiple sclerosis: A combined diffusion tensor imaging and optical coherence tomography approach.

We investigated the relationship between retinal layers and normal-appearing white matter (WM) integrity in the brain of patients with relapsing-remitting multiple sclerosis (MS), using a combined diffusion tensor imaging and high resolution optical coherence tomography approach. Fifty patients and 62 controls were recruited. The patients were divided into two groups according to presence (n=18) or absence (n=32) of optic neuritis. Diffusion tensor data were analyzed with a voxel-wise whole brain analysis of diffusion metrics in WM with tract-based spatial statistics. Thickness measurements were obtained for each individual retinal layer. Partial correlation and multivariate regression analyses were performed, assessing the association between individual retinal layers and diffusion metrics across all groups. Region-based analysis was performed, by focusing on tracts associated with the visual system. Receiver operating characteristic (ROC) curves were computed to compare the biomarker potential for the diagnosis of MS, using the thickness of each retinal layer and diffusion metrics. In patients without optic neuritis, both ganglion cell layer (GCL) and inner plexiform layer thickness correlated with the diffusion metrics within and outside the visual system. GCL thickness was a significant predictor of diffusion metrics in the whole WM skeleton, unlike other layers. No association was observed for either controls or patients with a history of optic neuritis. ROC analysis showed that the biomarker potential for the diagnosis of MS based on the GCL was high when compared to other layers. We conclude that GCL integrity is a predictor of whole-brain WM disruption in MS patients without optic neuritis.

Group-Level Progressive Alterations in Brain Connectivity Patterns Revealed by Diffusion-Tensor Brain Networks across Severity Stages in Alzheimer's Disease.

Alzheimer’s disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer’s Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1 = 36, healthy control subjects, Control), G2 (N2 = 36, early mild cognitive impairment, EMCI), G3 (N3 = 36, late mild cognitive impairment, LMCI) and G4 (N4 = 36, AD). Diffusion-tensor brain networks were compared across three disease stages: stage I (Control vs. EMCI), stage II (Control vs. LMCI) and stage III (Control vs. AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle brain functional networks, but here applied to diffusion-tensor data. The results were threefold: First, no significant differences were found in stage I. Second, significant differences were found in stage II in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting more strongly the same memory subnetwork appearing in stage II, plus the other new subnetworks, including the default mode network, medial visual network, frontoparietal regions and striatum. Our results are consistent with a scenario where progressive alterations of connectivity arise as the disease severity increases and provide the brain areas possibly involved in such a degenerative process. Further studies applying the same strategy to longitudinal data are needed to fully confirm this scenario.

A robust diffusion tensor model for clinical applications of MRI to cartilage.

Diffusion tensor imaging (DTI) of articular cartilage is a promising technique for the early diagnosis of osteoarthritis (OA). However, in vivo diffusion tensor (DT) measurements suffer from low signal-to-noise ratio (SNR) that can result in bias when estimating the six parameters of the full DT, thus reducing sensitivity. This study seeks to validate a simplified four-parameter DT model (zeppelin) for obtaining more robust and sensitive in vivo DTI biomarkers of cartilage. We use simulations in a substrate to mimic changes during OA; and analytic simulations of the DT drawn from a range of fractional anisotropies (FA) measured with high-quality DT data from ex vivo human cartilage. We also use in vivo data from the knees of a healthy subject and two OA patients with Kellgren-Lawrence (KL) grades 1 and 2. For simulated in vivo cartilage SNR (∼25) and anisotropy levels, the estimated mean values of MD from the DT and zeppelin models were identical to the ground truth values. However, zeppelin's FA is more accurate in measuring water restriction. More specifically, the FA estimations of the DT model were additionally biased by between +2% and +48% with respect to zeppelin values. Additionally, both mean diffusivity (MD) and FA of the zeppelin had lower parameter variance compared to the full DT (F-test, P < 0.05). We observe the same trends from in vivo values of patient data. The zeppelin is more robust than the full DT for cartilage diffusion anisotropy and SNR at levels typically encountered in clinical applications of articular cartilage. Magn Reson Med 79:1157-1164, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Abstract WP366: Quantitative Diffusion Tensor Tractography of the Corticospinal Tract as a Predictive Outcome Parameter in Patients With Intracerebral Hemorrhage

Introduction: Intracerebral hemorrhage (ICH) is a devastating disease with poor outcome. Established predictive markers include initial hematoma size, clinical status, intraventricular bleeding and age. However, prognostic assessment is often difficult which additionally burdens patients and caregivers and complicates rehabilitation. Functional outcome especially relies on motor function which is correlated with the integrity of the Corticospinal Tract (CST). Diffusion Tensor (DT) Tractography allows visualization of the CST. However, sufficient data about the prognostic impact of quantitative CST assessment is lacking. Methods: We prospectively enrolled patients with spontaneous supratentorial ICH admitted between 08/2012 und 08/2015. Informed consent and ethical approval was obtained. MRI scan was performed on day 5±1. DT data was normalized to MNI Space and Q-Space diffeomorphic reconstruction was performed using DSI-studio. CST was reconstructed based on quantitative anisotropy (QA) using the CST region (JHU white matter atlas) as seeding region and cerebral peduncle as region of interest (ROI). Fractional Anisotropy (FA) and QA were analyzed in the seeding and ROI region and in the posterior limb of the internal capsule (PLIC). Dichotomized modified Rankin Scale on day 90 (favorable outcome = mRS 0-2) was assessed as primary outcome variable. Results: 33 patients, mean age 70.7 y (standard deviation (SD) 12.9), 12 male, 21 with lobar hemorrhage, mean ICH volume on admission 16.5 (SD 11.3) cm 3 were included. 16 patients had a favorable outcome on day 90 (median mRS 3 (IQR 1-3.5). Mean number of reconstructed CST fiber pathways ipsilateral to ICH was higher in patients with favorable outcome (11343 (SD 8201)) than in patients with unfavorable outcome (4868 (SD 3221), p=0.008). Median QA and FA values in the PLIC ipsilateral to ICH were also higher in patients with favorable outcome (QA: 18.9 (IQR 16.2-23.3) vs. 14.6 (IQR 11.7 - 17.7), p=0.016, FA: 0.49 (IQR 0.45-0.53) vs. 0.41 (IQR 0.38-0.49), p=0.026). Conclusion: Higher FA- and QA-values in the PLIC and higher numbers of CST fiber pathways ipsilateral to ICH seem to be associated with a favorable outcome. DT Imaging may turn out as a useful quantitative predictive marker in the acute phase of ICH.