High Angular Resolution Diffusion Imaging Research Articles

A framework for multi-component analysis of diffusion MRI data over the neonatal period

We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.

Linking structural and effective brain connectivity: structurally informed Parametric Empirical Bayes (si-PEB)

Despite the potential for better understanding functional neuroanatomy, the complex relationship between neuroimaging measures of brain structure and function has confounded integrative, multimodal analyses of brain connectivity. This is particularly true for task-related effective connectivity, which describes the causal influences between neuronal populations. Here, we assess whether measures of structural connectivity may usefully inform estimates of effective connectivity in larger scale brain networks. To this end, we introduce an integrative approach, capitalising on two recent statistical advances: Parametric Empirical Bayes, which provides group-level estimates of effective connectivity, and Bayesian model reduction, which enables rapid comparison of competing models. Crucially, we show that structural priors derived from high angular resolution diffusion imaging on a dynamic causal model of a 12-region network—based on functional MRI data from the same subjects—substantially improve model evidence (posterior probability 1.00). This provides definitive evidence that structural and effective connectivity depend upon each other in mediating distributed, large-scale interactions in the brain. Furthermore, this work offers novel perspectives for understanding normal brain architecture and its disintegration in clinical conditions.

Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging

Mapping non-invasively the complex microstructural architecture of the living human brain, diffusion magnetic resonance imaging (dMRI) is one of the core imaging modalities in current population studies. For the application in longitudinal population imaging, the dMRI protocol should deliver reliable data with maximum potential for future analysis. With the recent introduction of novel MRI hardware, advanced dMRI acquisition strategies can be applied within reasonable scan time. In this work we conducted a pilot study based on the requirements for high resolution dMRI in a long-term and high throughput population study. The key question was: can diffusion spectrum imaging accelerated by compressed sensing theory (CS-DSI) be used as an advanced imaging protocol for microstructure dMRI in a long-term population imaging study? As a minimum requirement we expected a high level of agreement of several diffusion metrics derived from both CS-DSI and a 3-shell high angular resolution diffusion imaging (HARDI) acquisition, an established imaging strategy used in other population studies. A wide spectrum of state-of-the-art diffusion processing and analysis techniques was applied to the pilot study data including quantitative diffusion and microstructural parameter mapping, fiber orientation estimation and white matter fiber tracking. When considering diffusion weighted images up to the same maximum diffusion weighting for both protocols, group analysis across 20 subjects indicates that CS-DSI performs comparable to 3-shell HARDI in the estimation of diffusion and microstructural parameters. Further, both protocols provide similar results in the estimation of fiber orientations and for local fiber tracking. CS-DSI provides high radial resolution while maintaining high angular resolution and it is well-suited for analysis strategies that require high b-value acquisitions, such as CHARMED modeling and biomarkers from the diffusion propagator.

Functional tractography of white matter by high angular resolution functional-correlation imaging (HARFI).

Functional magnetic resonance imaging with BOLD contrast is widely used for detecting brain activity in the cortex. Recently, several studies have described anisotropic correlations of resting-state BOLD signals between voxels in white matter (WM). These local WM correlations have been modeled as functional-correlation tensors, are largely consistent with underlying WM fiber orientations derived from diffusion MRI, and appear to change during functional activity. However, functional-correlation tensors have several limitations. The use of only nearest-neighbor voxels makes functional-correlation tensors sensitive to noise. Furthermore, adjacent voxels tend to have higher correlations than diagonal voxels, resulting in orientation-related biases. Finally, the tensor model restricts functional correlations to an ellipsoidal bipolar-symmetric shape, and precludes the ability to detect complex functional orientation distributions (FODs). We introduce high-angular-resolution functional-correlation imaging (HARFI) to address these limitations. In the same way that high-angular-resolution diffusion imaging (HARDI) techniques provide more information than diffusion tensors, we show that the HARFI model is capable of characterizing complex FODs expected to be present in WM. We demonstrate that the unique radial and angular sampling strategy eliminates orientation biases present in tensor models. We further show that HARFI FODs are able to reconstruct known WM pathways. Finally, we show that HARFI allows asymmetric "bending" and "fanning" distributions, and propose asymmetric and functional indices which may increase fiber tracking specificity, or highlight boundaries between functional regions. The results suggest the HARFI model could be a robust, new way to evaluate anisotropic BOLD signal changes in WM.

The role of the arcuate and middle longitudinal fasciculi in speech perception in noise in adulthood.

In this article, we used High Angular Resolution Diffusion Imaging (HARDI) with advanced anatomically constrained particle filtering tractography to investigate the role of the arcuate fasciculus (AF) and the middle longitudinal fasciculus (MdLF) in speech perception in noise in younger and older adults. Fourteen young and 15 elderly adults completed a syllable discrimination task in the presence of broadband masking noise. Mediation analyses revealed few effects of age on white matter (WM) in these fascicles but broad effects of WM on speech perception, independently of age, especially in terms of sensitivity and criterion (response bias), after controlling for individual differences in hearing sensitivity and head size. Indirect (mediated) effects of age on speech perception through WM microstructure were also found, after controlling for individual differences in hearing sensitivity and head size, with AF microstructure related to sensitivity, response bias and phonological priming, and MdLF microstructure more strongly related to response bias. These findings suggest that pathways of the perisylvian region contribute to speech processing abilities, with relatively distinct contributions for the AF (sensitivity) and MdLF (response bias), indicative of a complex contribution of both phonological and cognitive processes to age-related speech perception decline. These results provide new and important insights into the roles of these pathways as well as the factors that may contribute to elderly speech perception deficits. They also highlight the need for a greater focus to be placed on studying the role of WM microstructure to understand cognitive aging.

Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI

Spherical deconvolution methods are widely used to estimate the brain's white-matter fiber orientations from diffusion MRI data. In this study, eight spherical deconvolution algorithms were implemented and evaluated. These included two model selection techniques based on the extended Bayesian information criterion (i.e., best subset selection and the least absolute shrinkage and selection operator), iteratively reweighted l2- and l1-norm approaches to approximate the l0-norm, sparse Bayesian learning, Cauchy deconvolution, and two accelerated Richardson-Lucy algorithms. Results from our exhaustive evaluation show that there is no single optimal method for all different fiber configurations, suggesting that further studies should be conducted to find the optimal way of combining solutions from different methods. We found l0-norm regularization algorithms to resolve more accurately fiber crossings with small inter-fiber angles. However, in voxels with very dominant fibers, algorithms promoting more sparsity are less accurate in detecting smaller fibers. In most cases, the best algorithm to reconstruct fiber crossings with two fibers did not perform optimally in voxels with one or three fibers. Therefore, simplified validation systems as employed in a number of previous studies, where only two fibers with similar volume fractions were tested, should be avoided as they provide incomplete information. Future studies proposing new reconstruction methods based on high angular resolution diffusion imaging data should validate their results by considering, at least, voxels with one, two, and three fibers, as well as voxels with dominant fibers and different diffusion anisotropies.

Joint spatial-angular sparse coding for dMRI with separable dictionaries.

Diffusion MRI (dMRI) provides the ability to reconstruct neuronal fibers in the brain, in vivo, by measuring water diffusion along angular gradient directions in q-space. High angular resolution diffusion imaging (HARDI) can produce better estimates of fiber orientation than the popularly used diffusion tensor imaging, but the high number of samples needed to estimate diffusivity requires longer patient scan times. To accelerate dMRI, compressed sensing (CS) has been utilized by exploiting a sparse dictionary representation of the data, discovered through sparse coding. The sparser the representation, the fewer samples are needed to reconstruct a high resolution signal with limited information loss, and so an important area of research has focused on finding the sparsest possible representation of dMRI. Current reconstruction methods however, rely on an angular representation per voxel with added spatial regularization, and so, for non-zero signals, one is required to have at least one non-zero coefficient per voxel. This means that the global level of sparsity must be greater than the number of voxels. In contrast, we propose a joint spatial-angular representation of dMRI that will allow us to achieve levels of global sparsity that are below the number of voxels. A major challenge, however, is the computational complexity of solving a global sparse coding problem over large-scale dMRI. In this work, we present novel adaptations of popular sparse coding algorithms that become better suited for solving large-scale problems by exploiting spatial-angular separability. Our experiments show that our method achieves significantly sparser representations of HARDI than is possible by the state of the art.

Awakening Neuropsychiatric Research Into the Stria Medullaris: Development of a Diffusion-Weighted Imaging Tractography Protocol of This Key Limbic Structure.

The Stria medullaris (SM) Thalami is a discrete white matter tract that directly connects frontolimbic areas to the habenula, allowing the forebrain to influence midbrain monoaminergic output. Habenular dysfunction has been shown in various neuropsychiatric conditions. However, there exists a paucity of research into the habenula’s principal afferent tract, the SM. Diffusion-weighted tractography may provide insights into the properties of the SM in vivo, opening up investigation of this tract in conditions of monoamine dysregulation such as depression, schizophrenia, addiction and pain. We present a reliable method for reconstructing the SM using diffusion-weighted imaging, and examine the effects of age and gender on tract diffusion metrics. We also investigate reproducibility of the method through inter-rater comparisons. In consultation with neuroanatomists, a Boolean logic gate protocol was developed for use in ExploreDTI to extract the SM from constrained spherical deconvolution based whole brain tractography. Particular emphasis was placed on the reproducibility of the tract, attention to crossing white matter tract proximity and anatomical consistency of anterior and posterior boundaries. The anterior commissure, pineal gland and mid point of the thalamus were defined as anatomical fixed points used for reconstruction. Fifty subjects were scanned using High Angular Resolution Diffusion Imaging (HARDI; 61 directions, b-value 1500 mm3). Following constrained spherical deconvolution whole brain tractography, two independent raters isolated the SM. Each output was checked, examined and cleaned for extraneous streamlines inconsistent with known anatomy of the tract by the rater and a neuroanatomist. A second neuroanatomist assessed tracts for face validity. The SM was reconstructed with excellent inter-rater reliability for dimensions and diffusion metrics. Gender had no effect on the dimensions or diffusion metrics, however radial diffusivity (RD) showed a positive correlation with age. Reliable identification and quantification of diffusion metrics of the SM invites further exploration of this key habenula linked structure in neuropsychiatric disorders such as depression, anxiety, chronic pain and addiction. The accurate anatomical localization of the SM may also aid preoperative stereotactic localization of the tract for deep brain stimulation (DBS) treatment.

High angular resolution diffusion imaging correlates of depression in Parkinson’s disease: a connectometry study

Depression is a significant disabling feature in Parkinson's disease (PD). However, the neuropathology of this comorbidity is still unclear. In fact, few studies have tried to elucidate the neural correlates of depression in PD and have mostly examined specific regions of interest. In this study, we applied diffusion MRI connectometry, a powerful complementary approach to investigate alterations in whole white matter pathways regarding the severity of depressive symptoms. Using a multiple regression model, the correlation of severity of depressive symptoms assessed by the Hospital Anxiety and Depression Scale (HADS) with white matter connectivity was surveyed in 27 non-demented PD patients related to 26 age, sex, and educational level-matched healthy subjects. Results revealed areas, where white matter quantitative anisotropy (QA) was correlated with depression score in PD patients, without any significant association in healthy controls. The analysis showed a significant negative association (false discovery rate < 0.05) between scores on depression subscale of HADS in PD patients and QA of left Cingulum, Genu, and Splenium of the Corpus Callosum, and anterior and posterior limbs of the right internal capsule. This finding might improve our understanding of the neural basis of depression and its severity in PD.

White matter degeneration profile in the cognitive cortico-subcortical tracts in Parkinson's disease.

In Parkinson's disease cognitive impairment is an early nonmotor feature, but it is still unclear why some patients are able to maintain their cognitive performance at normal levels, as quantified by neuropsychological tests, whereas others cannot. The objectives of this study were to perform a cross-sectional study and analyze the white matter changes in the cognitive and motor bundles in patients with Parkinson's disease. Sixteen Parkinson's disease patients with normal cognitive performance, 19 with mild cognitive impairment (based on their performance of 1.5 standard deviations below the healthy population mean), and 16 healthy controls were compared with respect to their tractography patterns between the cortical cognitive / motor regions and subcortical structures, using high angular resolution diffusion imaging and constrained spherical deconvolution computation. Motor bundles showed decreased apparent fiber density in both PD groups, associated with a significant increase in diffusivity metrics, number of reconstructed streamlines, and track volumes, compared with healthy controls. By contrast, in the cognitive bundles, decreased fiber density in both Parkinson's groups was compounded by the absence of changes in diffusivity in patients with normal cognition, whereas patients with cognitive impairment had increased diffusivity metrics, lower numbers of reconstructed streamlines, and lower track volumes. Both PD groups showed similar patterns of white matter neurodegeneration in the motor bundles, whereas cognitive bundles showed a distinct pattern: Parkinson's patients with normal cognition had white matter diffusivity metrics similar to healthy controls, whereas in patients with cognitive impairment white matter showed a neurodegeneration pattern. © 2018 International Parkinson and Movement Disorder Society.

O6.1. HIPPOCAMPAL VOLUME IN ADOLESCENTS WITH PERSISTENT PSYCHOTIC EXPERIENCES: A LONGITUDINAL POPULATION-BASED MRI STUDY

BackgroundIndividuals with schizophrenia show significant brain morphological abnormalities. The ENIGMA consortium identified that patients with schizophrenia had smaller hippocampus, amygdala, thalamus, accumbens and intracranial volumes.1 Reduced hippocampal volume is one of the most consistent findings in schizophrenia research.2–4 Also, Previous research has reported differences in hippocampal volume and white matter integrity in young adolescents who report psychotic experiences.5,6 However there has been little longitudinal research to investigate the developmental trajectory of these regions in adolescence with an increased susceptibility to psychotic disorders.Aimsto investigate two-year longitudinal changes in hippocampal volume in a sample of adolescents who reported psychotic experiences relative to their peers. To investigate the role of presence of co-morbid DSM IV mental disorders and stressful life events in influencing hippocampal volume and study the differences in hippocampus volume between adolescents who were having persistent symptoms versus adolescents with remitting symptoms.MethodsA longitudinal case-control study of 50 community-based adolescents aged 13–16 years (25 with psychotic experiences and a matched sample of 25 without psychotic experiences), compared hippocampal volume. All participants were assessed at baseline and two years follow up. T1 weighted anatomical high-resolution imaging and high angular resolution diffusion imaging data were used to conduct quantitative anatomical volumetric evaluations of global hippocampal volume. Clinical interviews also provided information on psychotic experiences, co-morbid disorders and adverse life events.ResultsThere were significant differences in the Right and Left Whole hippocampus between PE and Control group at baseline and 2-year follow up (p≤ 0.05). There were significant differences between PE persist and Control group in the left and right whole hippocampus (p≤ 0.05).DiscussionThe differences identified in our study suggest that early hippocampal reductions, may play a role in increasing vulnerability to psychosis.

T170. THE SUPERIOR LONGITUDINAL FASCICULUS: CAN CSD BASED TRACT DELINEATION AND NODAL ANALYSIS CLARIFY THE PRESENCE OF TARGETED DIVERGENT DEVELOPMENTAL STRUCTURAL CONNECTIVITY IN ADOLESCENTS REPORTING PSYCHOTIC EXPERIENCES

BackgroundSuperior Longitudinal Fasciculus (SLF) differences are consistently reported in psychotic disorders. The SLF is a complex large bundle of association white matter fibers that bidirectionally connect caudal, temporal cortex and inferior parietal cortex to locations in the frontal lobe. Advances in tractography methodologies detail four discrete subdivisions (SLF I, II, III and IV, more often referred to as the arcuate fasciculus (AF)). Greater specificity of the SLF subdivisions and associated cognitive networks may clarify the mechanisms of divergent tract developmental and functional aspects associated with psychotic experience symptomology in population based samples of adolescents within the extended psychosis continuumMethodsA case-control sample of 25 adolescents reporting psychotic experiences versus 25 controls (mean age 13.7 years). We employed High Angular Resolution Diffusion Imaging (HARDI) based data with constrained spherical deconvolution (CSD) based fibre tractography to delineate the discrete subdivisions of the SLF including the arcuate fasciculous. Following tract identification, standard diffusion metrics, (fractional anisotrophy (FA), and Diffusivity measures MD, AD and RD), were assessed. A secondary supportive “along-tract” analysis to ascertain more subtle patterns of variation of tract integrity over the tract length was applied. White matter nodal analysis exploring the structural connectivity was applied to investigate additional functional networks recruited by and interconnected via the SFL. We investigate the ability of tractography of the SLF subdivisions and nodal analysis to identify possible differences between adolescents experiencing subclinical psychotic like experiences and those who don’t.ResultsOur results agree with recent studies of the SLF I and AF (Fernandes-Miranda et al 2015) revealing a pattern of asymmetry of these tracts with more extensive tract bundles being consistently identified in the left hemisphere compared to the right. Along-tract analysis revealed subtle patterns of change in discrete subdivisions of the SLF while nodal analysis shows promise in its ability to define precise organisational networksDiscussionDelineating the SLF subdivisions may clarify potential developmental trajectories between frontal and parieto-temporal speech-related areas contributing to the pathogenesis of psychotic like experiences. These results reveal the presence of aberrant structural connectivity in young adolescents with psychotic experiences.

Structural connectome with high angular resolution diffusion imaging MRI: assessing the impact of diffusion weighting and sampling on graph-theoretic measures

PurposeAdvances in computational network analysis have enabled the characterization of topological properties of human brain networks (connectomics) from high angular resolution diffusion imaging (HARDI) MRI structural measurements. In this study, the effect of changing the diffusion weighting (b value) and sampling (number of gradient directions) was investigated in ten healthy volunteers, with specific focus on graph theoretical network metrics used to characterize the human connectome.MethodsProbabilistic tractography based on the Q-ball reconstruction of HARDI MRI measurements was performed and structural connections between all pairs of regions from the automated anatomical labeling (AAL) atlas were estimated, to compare two HARDI schemes: low b value (b = 1000) and low direction number (n = 32) (LBLD); high b value (b = 3000) and high number (n = 54) of directions (HBHD).ResultsLBLD and HBHD data sets produced connectome images with highly overlapping hub structure. Overall, the HBHD scheme yielded significantly higher connection probabilities between cortical and subcortical sites and allowed detecting more connections. Small worldness and modularity were reduced in HBHD data. The clustering coefficient was significantly higher in HBHD data indicating a higher level of segregation in the resulting connectome for the HBHD scheme.ConclusionOur results demonstrate that the HARDI scheme as an impact on structural connectome measures which is not automatically implied by the tractography outcome. As the number of gradient directions and b values applied may introduce a bias in the assessment of network properties, the choice of a given HARDI protocol must be carefully considered when comparing results across connectomic studies.

SHARD: Spherical Harmonic-based Robust Outlier Detection for HARDI Methods.

High Angular Resolution Diffusion Imaging (HARDI) models are used to capture complex intra-voxel microarchitectures. The magnetic resonance imaging sequences that are sensitized to diffusion are often highly accelerated and prone to motion, physiologic, and imaging artifacts. In diffusion tensor imaging, robust statistical approaches have been shown to greatly reduce these adverse factors without human intervention. Similar approaches would be possible with HARDI methods, but robust versions of each distinct HARDI approach would be necessary. To avoid the computational and pragmatic burdens of creating individual robust HARDI analysis variants, we propose a robust outlier imputation model to mitigate outliers prior to traditional HARDI analysis. This model uses a weighted spherical harmonic fit of diffusion weighted magnetic resonance imaging scans to estimate the values which had been corrupted during acquisition to restore them. Briefly, spherical harmonics of 6th order were used to generate basis function which were weighted by diffusion signal for detection of outliers. For validation, a single healthy volunteer was scanned for a single session comprising of two scans one without head movement and the other with deliberate head movement at a b-value of 3000 s/mm2 with 64 diffusion weighted directions with a single b0 (5 averages) per scan. The deliberate motion from the volunteer created natural artifacts in the acquisition of one of the scans. The imputation model shows reduction in root mean squared error of the raw signal intensities and improvement for the HARDI method Q-ball in terms of the Angular Correlation Coefficient. The results reveal that there is quantitative and qualitative improvement. The proposed model can be used as general pre-processing model before implementing any HARDI model in general to restore the artifacts which are created because of the outlier diffusion signal in certain gradient volumes.

Evaluation of inter-site bias and variance in diffusion-weighted MRI.

An understanding of the bias and variance of diffusion weighted magnetic resonance imaging (DW-MRI) acquisitions across scanners, study sites, or over time is essential for the incorporation of multiple data sources into a single clinical study. Studies that combine samples from various sites may be introducing confounding due to site-specific artifacts and patterns. Differences in bias and variance across sites may render the scans incomparable, and, without correction, any inferences obtained from these data are misleading. We present an analysis of the bias and variance of scans of the same subjects across different sites and evaluate their impact on statistical analyses. In previous work, we presented a simulation extrapolation (SIMEX) technique for bias estimation as well as a wild bootstrap technique for variance estimation in metrics obtained from a Q-ball imaging (QBI) reconstruction of empirical high angular resolution diffusion imaging (HARDI) data. We now apply those techniques to data acquired from 5 healthy volunteers on 3 independent scanners under closely matched acquisition protocols. The bias and variance of GFA measurements were estimated on a voxel-wise basis for each scan and compared across study sites to identify site-specific differences. Further, we provide model recommendations that can be used to determine the extent of the impact of bias and variance as well as aspects of the analysis to account for these differences. We include a decision tree to help researchers determine if model adjustments are necessary based on the bias and variance results.

Early blindness is associated with increased volume of the uncinate fasciculus.

Growing evidence demonstrates dramatic structural and functional neuroplastic changes in individuals born with early-onset blindness. For example, cross-modal sensory processing at the level of the occipital cortex appears to be associated with adaptive behaviors in the blind. However, detailed studies examining the structural properties of key white matter pathways in other regions of the brain remain limited. Given that blind individuals rely heavily on their sense of hearing, we examined the structural properties of two important pathways involved with auditory processing, namely the uncinate and arcuate fasciculi. High angular resolution diffusion imaging (HARDI) tractography was used to examine structural parameters (i.e., tract volume and quantitative anisotropy, or QA) of these two fasciculi in a sample of 13 early blind individuals and 14 normally sighted controls. Compared to controls, early blind individuals showed a significant increase in the volume of the left uncinate fasciculus. A small area of increased QA was also observed halfway along the right arcuate fasciculus in the blind group. These findings contribute to our knowledge regarding the broad neuroplastic changes associated with profound early blindness.

Empirical single sample quantification of bias and variance in Q-ball imaging.

The bias and variance of high angular resolution diffusion imaging methods have not been thoroughly explored in the literature and may benefit from the simulation extrapolation (SIMEX) and bootstrap techniques to estimate bias and variance of high angular resolution diffusion imaging metrics. The SIMEX approach is well established in the statistics literature and uses simulation of increasingly noisy data to extrapolate back to a hypothetical case with no noise. The bias of calculated metrics can then be computed by subtracting the SIMEX estimate from the original pointwise measurement. The SIMEX technique has been studied in the context of diffusion imaging to accurately capture the bias in fractional anisotropy measurements in DTI. Herein, we extend the application of SIMEX and bootstrap approaches to characterize bias and variance in metrics obtained from a Q-ball imaging reconstruction of high angular resolution diffusion imaging data. The results demonstrate that SIMEX and bootstrap approaches provide consistent estimates of the bias and variance of generalized fractional anisotropy, respectively. The RMSE for the generalized fractional anisotropy estimates shows a 7% decrease in white matter and an 8% decrease in gray matter when compared with the observed generalized fractional anisotropy estimates. On average, the bootstrap technique results in SD estimates that are approximately 97% of the true variation in white matter, and 86% in gray matter. Both SIMEX and bootstrap methods are flexible, estimate population characteristics based on single scans, and may be extended for bias and variance estimation on a variety of high angular resolution diffusion imaging metrics.

Post-mortem inference of the human hippocampal connectivity and microstructure using ultra-high field diffusion MRI at 11.7 T

The human hippocampus plays a key role in memory management and is one of the first structures affected by Alzheimer’s disease. Ultra-high magnetic resonance imaging provides access to its inner structure in vivo. However, gradient limitations on clinical systems hinder access to its inner connectivity and microstructure. A major target of this paper is the demonstration of diffusion MRI potential, using ultra-high field (11.7 T) and strong gradients (750 mT/m), to reveal the extra- and intra-hippocampal connectivity in addition to its microstructure. To this purpose, a multiple-shell diffusion-weighted acquisition protocol was developed to reach an ultra-high spatio-angular resolution with a good signal-to-noise ratio. The MRI data set was analyzed using analytical Q-Ball Imaging, Diffusion Tensor Imaging (DTI), and Neurite Orientation Dispersion and Density Imaging models. High Angular Resolution Diffusion Imaging estimates allowed us to obtain an accurate tractography resolving more complex fiber architecture than DTI models, and subsequently provided a map of the cross-regional connectivity. The neurite density was akin to that found in the histological literature, revealing the three hippocampal layers. Moreover, a gradient of connectivity and neurite density was observed between the anterior and the posterior part of the hippocampus. These results demonstrate that ex vivo ultra-high field/ultra-high gradients diffusion-weighted MRI allows the mapping of the inner connectivity of the human hippocampus, its microstructure, and to accurately reconstruct elements of the polysynaptic intra-hippocampal pathway using fiber tractography techniques at very high spatial/angular resolutions.

Age-related changes in fiber tracts in healthy adult brains: A generalized q-sampling and connectometry study.

Generalized q-sampling (GQI) and connectometry analysis provide new indices, i.e., quantitative anisotropy (QA) and spin distribution function (SDF) in comparison with diffusion tensor imaging (DTI). They may provide more age-related changes in white matter (WM) in aging. To investigate the feasibility of using GQI and connectometry analysis to determine WM properties changes in aging. Cross-cohort research. Fifty normal adults (27 females) aged 21-71 years. T1 -weighted images (T1 WI) and high angular resolution diffusion imaging (HARDI) images were acquired at 1.5T. HARDI data were analyzed using DTI and GQI to obtain fractional anisotropy (FA), QA, fiber numbers, and fiber lengths for tract analysis and using q-space diffeomorphic reconstruction (QSDR) for the connectometry analysis. We compared differences of DTI, GQI, and connectometry analysis to reflect WM changes in aging. Associations between FA, QA, and fiber numbers and lengths and age were analyzed using Pearson's correlation coefficients. The connectometry analysis was conducted using a multiple linear regression analysis, including age and gender as factors. Uncorrected P-value/false discovery rate (FDR) (corrected for multiple comparisons) < 0.05 was considered statistically significant. More regional changes were detected in FA related to age than changes in QA (17 > 6 regions, P < 0.05). Fewer regional changes in fiber numbers and more changes of fiber lengths were observed for DTI than for GQI (5 < 8/10 > 7 regions, P < 0.05). However, DTI and GQI analyses revealed consistent results in some regions, including the genu of the corpus callosum (GCC), body of the corpus callosum (BCC), fornix (Fx), and anterior coronal radiation (ACR) (P < 0.05). The connectometry analysis showed more tract changes associated with age at an FDR of 0.05, which partially overlapped with the FA and QA. GQI and connectometry provide more information about age-related tracts and complement the DTI findings. 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018;48:369-381.

Connectivity derived thalamic segmentation in deep brain stimulation for tremor.

The ventral intermediate nucleus (VIM) of the thalamus is an established surgical target for stereotactic ablation and deep brain stimulation (DBS) in the treatment of tremor in Parkinson's disease (PD) and essential tremor (ET). It is centrally placed on a cerebello-thalamo-cortical network connecting the primary motor cortex, to the dentate nucleus of the contralateral cerebellum through the dentato-rubro-thalamic tract (DRT). The VIM is not readily visible on conventional MR imaging, so identifying the surgical target traditionally involved indirect targeting that relies on atlas-defined coordinates. Unfortunately, this approach does not fully account for individual variability and requires surgery to be performed with the patient awake to allow for intraoperative targeting confirmation. The aim of this study is to identify the VIM and the DRT using probabilistic tractography in patients that will undergo thalamic DBS for tremor. Four male patients with tremor dominant PD and five patients (three female) with ET underwent high angular resolution diffusion imaging (HARDI) (128 diffusion directions, 1.5 mm isotropic voxels and b value = 1500) preoperatively. Patients received VIM-DBS using an MR image guided and MR image verified approach with indirect targeting. Postoperatively, using parallel Graphical Processing Unit (GPU) processing, thalamic areas with the highest diffusion connectivity to the primary motor area (M1), supplementary motor area (SMA), primary sensory area (S1) and contralateral dentate nucleus were identified. Additionally, volume of tissue activation (VTA) corresponding to active DBS contacts were modelled. Response to treatment was defined as 40% reduction in the total Fahn-Tolosa-Martin Tremor Rating Score (FTMTRS) with DBS-ON, one year from surgery. Three out of nine patients had a suboptimal, long-term response to treatment. The segmented thalamic areas corresponded well to anatomically known counterparts in the ventrolateral (VL) and ventroposterior (VP) thalamus. The dentate-thalamic area, lay within the M1-thalamic area in a ventral and lateral location. Streamlines corresponding to the DRT connected M1 to the contralateral dentate nucleus via the dentate-thalamic area, clearly crossing the midline in the mesencephalon. Good response was seen when the active contact VTA was in the thalamic area with highest connectivity to the contralateral dentate nucleus. Non-responders had active contact VTAs outside the dentate-thalamic area. We conclude that probabilistic tractography techniques can be used to segment the VL and VP thalamus based on cortical and cerebellar connectivity. The thalamic area, best representing the VIM, is connected to the contralateral dentate cerebellar nucleus. Connectivity based segmentation of the VIM can be achieved in individual patients in a clinically feasible timescale, using HARDI and high performance computing with parallel GPU processing. This same technique can map out the DRT tract with clear mesencephalic crossing.