Diffusion weighted image denoising using overcomplete local PCA

Structural and functional connectivity in relation to executive functions in antipsychotic-naïve patients with first episode schizophrenia and levels of glutamatergic metabolites

Patients with schizophrenia exhibit structural and functional dysconnectivity but the relationship to the well-documented cognitive impairments is less clear. This study investigates associations between structural and functional connectivity and executive functions in antipsychotic-naïve patients experiencing schizophrenia. Sixty-four patients with schizophrenia and 95 matched controls underwent cognitive testing, diffusion weighted imaging and resting state functional magnetic resonance imaging. In the primary analyses, groupwise interactions between structural connectivity as measured by fixel-based analyses and executive functions were investigated using multivariate linear regression analyses. For significant structural connections, secondary analyses examined whether functional connectivity and associations with executive functions also differed for the two groups. In group comparisons, patients exhibited cognitive impairments across all executive functions compared to controls (p < 0.001), but no group difference were observed in the fixel-based measures. Primary analyses revealed a groupwise interaction between planning abilities and fixel-based measures in the left anterior thalamic radiation (p = 0.004), as well as interactions between cognitive flexibility and fixel-based measures in the isthmus of corpus callosum and cingulum (p = 0.049). Secondary analyses revealed increased functional connectivity between grey matter regions connected by the left anterior thalamic radiation (left thalamus with pars opercularis p = 0.018, and pars orbitalis p = 0.003) in patients compared to controls. Moreover, a groupwise interaction was observed between cognitive flexibility and functional connectivity between contralateral regions connected by the isthmus (precuneus p = 0.028, postcentral p = 0.012), all p-values corrected for multiple comparisons. We conclude that structural and functional connectivity appear to associate with executive functions differently in antipsychotic-naïve patients with schizophrenia compared to controls.

Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling

Brain structural connectivity, capturing the white matter fiber tracts among brain regions inferred by diffusion MRI (dMRI), provides a unique characterization of brain anatomical organization. One fundamental question to address with structural connectivity is how to properly summarize and perform statistical inference for a group-level connectivity architecture, for instance, under different sex groups, or disease cohorts. Existing analyses commonly summarize group-level brain connectivity by a simple entry-wise sample mean or median across individual brain connectivity matrices. However, such a heuristic approach fully ignores the associations among structural connections and the topological properties of brain networks. In this project, we propose a latent space-based generative network model to estimate group-level brain connectivity. Within our modeling framework, we incorporate the anatomical information of brain regions as the attributes of nodes to enhance the plausibility of our estimation and improve biological interpretation. We name our method the attributes-informed brain connectivity (ABC) model, which compared with existing group-level connectivity estimations, (1) offers an interpretable latent space representation of the group-level connectivity, (2) incorporates the anatomical knowledge of nodes and tests its co-varying relationship with connectivity and (3) quantifies the uncertainty and evaluates the likelihood of the estimated group-level effects against chance. We devise a novel Bayesian MCMC algorithm to estimate the model. We evaluate the performance of our model through extensive simulations. By applying the ABC model to study brain structural connectivity stratified by sex among Alzheimer's Disease (AD) subjects and healthy controls incorporating the anatomical attributes (volume, thickness and area) on nodes, our method shows superior predictive power on out-of-sample structural connectivity and identifies meaningful sex-specific network neuromarkers for AD.

Improved cognition and preserved hippocampal fractional anisotropy in subjects undergoing carotid endarterectomy "CEA preserves cognition & hippocampal structure"

OBJECTIVES

A growing body of data indicates that extracranial carotid artery disease (ECAD) can contribute to cognitive impairment. However, there have been mixed reports regarding the benefit of carotid endarterectomy (CEA) as it relates to preserving cognitive function. In this work, diffusion magnetic resonance imaging (dMRI) and neurocognitive testing are used to provide insight into structural and functional brain changes that occur in subjects with significant carotid artery stenosis, as well as changes that occur in response to CEA.

MATERIALS AND METHODS

The study design was a prospective, non-randomized, controlled study that enrolled patients with asymptomatic carotid stenosis. Thirteen subjects had severe ECAD (≥70% stenosis in at least one carotid artery) and were scheduled to undergo surgery. Thirteen had asymptomatic ECAD with <70% stenosis, therefore not requiring surgery. All subjects underwent neurocognitive testing using the Montreal Cognitive Assessment test (MoCA) and high angular resolution, multi-shell diffusion magnetic resonance imaging (dMRI) of the brain at baseline and at four-six months follow-up. Changes in MoCA scores as well as in Fractional anisotropy (FA) along the hippocampus were compared at baseline and follow-up.

RESULTS

At baseline, FA was significantly lower along the ipsilateral hippocampus in subjects with severe ECAD compared to subjects without severe ECAD. MoCA scores were lower in these individuals, but this did not reach statistical significance. At follow-up, MoCA scores increased significantly in subjects who underwent CEA and remained statistically equal in control subjects that did not have CEA. FA remained unchanged in the CEA group and decreased in the control group.

CONCLUSIONS

This study suggests that CEA improves cognition and preserves hippocampal white matter structure compared to control subjects not undergoing CEA.

Amyloid, Tau, and APOE in Alzheimer's Disease: Impact on White Matter Tracts

Alzheimer's disease (AD) is characterized by cognitive decline and memory loss due to the abnormal accumulation of amyloid-beta (Aβ) plaques and tau tangles in the brain; its onset and progression also depend on genetic factors such as the apolipoprotein E (APOE) genotype. Understanding how these factors affect the brain's neural pathways is important for early diagnostics and interventions. Tractometry is an advanced technique for 3D quantitative assessment of white matter tracts, localizing microstructural abnormalities in diseased populations in vivo. In this work, we applied BUAN (Bundle Analytics) tractometry to 3D diffusion MRI data from 730 participants in ADNI3 (phase 3 of the Alzheimer's Disease Neuroimaging Initiative; age range: 55-95 years, 349M/381F, 214 with mild cognitive impairment, 69 with AD, and 447 cognitively healthy controls). Using along-tract statistical analysis, we assessed the localized impact of amyloid, tau, and APOE genetic variants on the brain's neural pathways. BUAN quantifies microstructural properties of white matter tracts, supporting along-tract statistical analyses that identify factors associated with brain microstructure. We visualize the 3D profile of white matter tract associations with tau and amyloid burden in Alzheimer's disease; strong associations near the cortex may support models of disease propagation along neural pathways. Relative to the neutral genotype, APOE ϵ3/ϵ3, carriers of the AD-risk conferring APOE ϵ4 genotype show microstructural abnormalities, while carriers of the protective ϵ2 genotype also show subtle differences. Of all the microstructural metrics, mean diffusivity (MD) generally shows the strongest associations with AD pathology, followed by axial diffusivity (AxD) and radial diffusivity (RD), while fractional anisotropy (FA) is typically the least sensitive metric. Along-tract microstructural metrics are sensitive to tau and amyloid accumulation, showing the potential of diffusion MRI to track AD pathology and map its impact on neural pathways.

Relationships between quantitative magnetic resonance imaging measures at the time of return to sport and clinical outcomes following acute hamstring strain injury

Hamstring strain injuries (HSI) are a common occurrence in athletics and complicated by high rates of reinjury. Evidence of remaining injury observed on magnetic resonance imaging (MRI) at the time of return to sport (RTS) may be associated with strength deficits and prognostic for reinjury, however, conventional imaging has failed to establish a relationship. Quantitative measure of muscle microstructure using diffusion tensor imaging (DTI) may hold potential for assessing a possible association between injury-related structural changes and clinical outcomes. The purpose of this study was to determine the association of RTS MRI-based quantitative measures, such as edema volume, muscle volume, and DTI metrics, with clinical outcomes (i.e., strength and reinjury) following HSI. Spearman's correlations and Firth logistic regressions were used to determine relationships in between-limb imaging measures and between-limb eccentric strength and reinjury status, respectively. Twenty injuries were observed, with four reinjuries. At the time of RTS, between-limb differences in eccentric hamstring strength were significantly associated with principal effective diffusivity eigenvalue λ1 (r = -0.64, p = 0.003) and marginally associated with mean diffusivity (r = -0.46, p = 0.056). Significant relationships between other MRI-based measures of morphology and eccentric strength were not detected, as well as between any MRI-based measure and reinjury status. In conclusion, this preliminary evidence indicates DTI may track differences in hamstring muscle microstructure, not captured by conventional imaging at the whole muscle level, that relate to eccentric strength.

Tensor denoising of quantitative multi-parameter mapping

PURPOSE

Quantitative multi-parameter mapping (MPM) provides maps of physical quantities representing physiologically meaningful tissue characteristics, which allows to investigate microstructure-function relationships reflecting normal or pathologic processes in the brain. However, the achievable spatial resolution and stability of MPM for basic research or clinical applications is severely constrained by SNR limits of the MR acquisition process, resulting in relatively long acquisition times. To increase SNR, we denoise MPM acquisitions using principal component analysis along tensors exploiting the Marchenko-Pastur law (tMPPCA).

METHODS

tMPPCA denoising was applied to three sets of MPM raw data before the quantification of maps of proton density, magnetization transfer saturation, R1, and R2*. The regional SNR gain for high-resolution MPM was investigated as well as reproducibility gains for clinically optimized protocols with moderate and high acceleration factors at different image resolutions.

RESULTS

Substantial noise reduction in raw data was achieved, resulting in reduced noise for quantitative mapping up to sixfold without introducing bias of mean values (below 1%). Scan-rescan fluctuations were reduced up to threefold. Denoising allowed to decrease the voxel volume fourfold at the same scan time or reduce the scan time twofold at same voxel volume without loss of sensitivity.

CONCLUSIONS

tMPPCA denoising can (a) improve of fine spatial and temporal patterns, (b) considerably reduce scan time for clinical applications, or (c) increase resolution to potentially push cutting-edge MPM protocols from the upper to the lower limit of the mesoscopic scale.

Examining the associations between microglia genetic capacity, early life exposures and white matter development at the level of the individual

There are inter-individual differences in susceptibility to the influence of early life experiences for which the underlying neurobiological mechanisms are poorly understood. Microglia play a role in environmental surveillance and may influence individual susceptibility to environmental factors. As an index of neurodevelopment, we estimated individual slopes of mean white matter fractional anisotropy (WM-FA) across three time-points (age 4.5, 6.0, and 7.5 years) for 351 participants. Individual variation in microglia reactivity was derived from an expression-based polygenic score(ePGS) comprised of Single Nucleotide Polymorphisms (SNPs) functionally related to the expression of microglia-enriched genes.A higher ePGS denotes an increased genetic capacity for the expression of microglia-related genes, and thus may confer a greater capacity to respond to the early environment and to influence brain development. We hypothesized that this ePGS would associate with the WM-FA index of neurodevelopment and moderate the influence of early environmental factors.Our findings show sex dependency, where a significant association between WM-FA and microglia ePGS was only obtained for females.We then examined associations with perinatal factors known to decrease (optimal birth outcomes and familial conditions) or increase (systemic inflammation) the risk for later mental health problems.In females, individuals with high microglia ePGS showed a negative association between systemic inflammation and WM-FA and a positive association between more advantageous environmental conditions and WM-FA. The microglia ePGS in females thus accounted for variations in the influence of the quality of the early environment on WM-FA.Finally, WM-FA slopes mediated the association of microglia ePGS with interpersonal problems and social hostility in females. Our findings suggest the genetic capacity for microglia function as a potential factor underlying differential susceptibility to early life exposuresthrough influences on neurodevelopment.

The effect of elimination of gibbs ringing, noise and systematic errors on the DTI metrics and tractography in a rat brain

Diffusion tensor imaging (DTI) metrics and tractography can be biased due to low signal-to-noise ratio (SNR) and systematic errors resulting from image artifacts and imperfections in magnetic field gradients. The imperfections include non-uniformity and nonlinearity, effects caused by eddy currents, and the influence of background and imaging gradients. We investigated the impact of systematic errors on DTI metrics of an isotropic phantom and DTI metrics and tractography of a rat brain measured at high resolution. We tested denoising and Gibbs ringing removal methods combined with the B matrix spatial distribution (BSD) method for magnetic field gradient calibration. The results showed that the performance of the BSD method depends on whether Gibbs ringing is removed and the effectiveness of stochastic error removal. Region of interest (ROI)-based analysis of the DTI metrics showed that, depending on the size of the ROI and its location in space, correction methods can remove systematic bias to varying degrees. The preprocessing pipeline proposed and dedicated to this type of data together with the BSD method resulted in an even - 90% decrease in fractional anisotropy (FA) (globally and locally) in the isotropic phantom and - 45% in the rat brain. The largest global changes in the rat brain tractogram compared to the standard method without preprocessing (sDTI) were noticed after denoising. The direction of the first eigenvector obtained from DTI after denoising, Gibbs ringing removal and BSD differed by an average of 56 and 10 degrees in the ROI from sDTI and from sDTI after denoising and Gibbs ringing removal, respectively. The latter can be identified with the amount of improvement in tractography due to the elimination of systematic errors related to imperfect magnetic field gradients. Based on the results, the systematic bias for high resolution data mainly depended on SNR, but the influence of non-uniform gradients could also be seen. After denoising, the BSD method was able to further correct both the metrics and tractography of the diffusion tensor in the rat brain by taking into account the actual distribution of magnetic field gradients independent of the examined object and uniquely dependent on the scanner and sequence. This means that in vivo studies are also subject to this type of errors, which should be taken into account when processing such data.

Diffusion tensor imaging of hamstring muscles after acute strain injury and throughout recovery in collegiate athletes

OBJECTIVE

To identify the region of interest (ROI) to represent injury and observe between-limb diffusion tensor imaging (DTI) microstructural differences in muscle following hamstring strain injury.

MATERIALS AND METHODS

Participants who sustained a hamstring strain injury prospectively underwent 3T-MRI of bilateral thighs using T1, T2, and diffusion-weighted imaging at time of injury (TOI), return to sport (RTS), and 12 weeks after RTS (12wks). ROIs were using the hyperintense region on a T2-weighted sequence: edema, focused edema, and primary muscle injured excluding edema (no edema). Linear mixed-effects models were used to compare diffusion parameters between ROIs and timepoints and limbs and timepoints.

RESULTS

Twenty-four participants (29 injuries) were included. A significant ROI-by-timepoint interaction was detected for all diffusivity measures. The edema and focused edema ROIs demonstrated increased diffusion at TOI compared to RTS for all diffusivity measures (p-values < 0.006), except λ1 (p-values = 0.058-0.12), and compared to 12wks (p-values < 0.02). In the no edema ROI, differences in diffusivity measures were not observed (p-values > 0.82). At TOI, no edema ROI diffusivity measures were lower than the edema ROI (p-values < 0.001) but not at RTS or 12wks (p-values > 0.69). A significant limb-by-timepoint interaction was detected for all diffusivity measures with increased diffusion in the involved limb at TOI (p-values < 0.001) but not at RTS or 12wks (p-values > 0.42). Significant differences in fractional anisotropy over time or between limbs were not detected.

CONCLUSION

Hyperintensity on T2-weighted imaging used to define the injured region holds promise in describing muscle microstructure following hamstring strain injury by demonstrating between-limb differences at TOI but not at follow-up timepoints.

Mapping the impact of age and APOE risk factors for late onset Alzheimer's disease on long range brain connections through multiscale bundle analysis

Alzheimer's disease currently has no cure and is usually detected too late for interventions to be effective. In this study we have focused on cognitively normal subjects to study the impact of risk factors on their long-range brain connections. To detect vulnerable connections, we devised a multiscale, hierarchical method for spatial clustering of the whole brain tractogram and examined the impact of age and APOE allelic variation on cognitive abilities and bundle properties including texture e.g., mean fractional anisotropy, variability, and geometric properties including streamline length, volume, and shape, as well as asymmetry. We found that the third level subdivision in the bundle hierarchy provided the most sensitive ability to detect age and genotype differences associated with risk factors. Our results indicate that frontal bundles were a major age predictor, while the occipital cortex and cerebellar connections were important risk predictors that were heavily genotype dependent, and showed accelerated decline in fractional anisotropy, shape similarity, and increased asymmetry. Cognitive metrics related to olfactory memory were mapped to bundles, providing possible early markers of neurodegeneration. In addition, physiological metrics such as diastolic blood pressure were associated with changes in white matter tracts. Our novel method for a data driven analysis of sensitive changes in tractography may differentiate populations at risk for AD and isolate specific vulnerable networks.

Distinct grey and white matter changes are associated with the phenomenology of visual hallucinations in Lewy Body Disease

Visual hallucinations in Lewy body disease (LBD) can be differentiated based on phenomenology into minor phenomena (MVH) and complex hallucinations (CVH). MVH include a variety of phenomena, such as illusions, presence and passage hallucinations occurring at early stages of LBD. The neural mechanisms of visual hallucinations are largely unknown. The hodotopic model posits that the hallucination state is due to abnormal activity in specialized visual areas, that occurs in the context of wider network connectivity alterations and that phenomenology of VH, including content and temporal characteristics, may help identify brain regions underpinning these phenomena. Here we investigated both the topological and hodological neural basis of visual hallucinations integrating grey and white matter imaging analyses. We studied LBD patients with VH and age matched healthy controls (HC). VH were assessed using a North-East-Visual-Hallucinations-Interview that captures phenomenological detail. Then we applied voxel-based morphometry and tract based spatial statistics approaches to identify grey and white matter changes. First, we compared LBD patients and HC. We found a reduced grey matter volume and a widespread damage of white tracts in LBD compared to HC. Then we tested the association between CVH and MVH and grey and white matter indices. We found that CVH duration was associated with decreased grey matter volume in the fusiform gyrus suggesting that LBD neurodegeneration-related abnormal activity in this area is responsible for CVH. An unexpected finding was that MVH severity was associated with a greater integrity of white matter tracts, specifically those connecting dorsal, ventral attention networks and visual areas. Our results suggest that networks underlying MVH need to be partly intact and functional for MVH experiences to occur, while CVH occur when cortical areas are damaged. The findings support the hodotopic view and the hypothesis that MVH and CVH relate to different neural mechanisms, with wider implications for the treatment of these symptoms in a clinical context.

From Voxels to Physiology: A Review of Diffusion Magnetic Resonance Imaging Applications in Skeletal Muscle

Skeletal muscle has a classic structure function relationship; both skeletal muscle microstructure and architecture are directly related to force generating capacity. Biopsy, the gold standard for evaluating muscle microstructure, is highly invasive, destructive to muscle, and provides only a small amount of information about the entire volume of a muscle. Similarly, muscle fiber lengths and pennation angles, key features of muscle architecture predictive of muscle function, are traditionally studied via cadaveric dissection. Noninvasive techniques such as diffusion magnetic resonance imaging (dMRI) offer quantitative approaches to study skeletal muscle microstructure and architecture. Despite its prevalence in applications for musculoskeletal research, clinical adoption is hindered by a lack of understanding regarding its sensitivity to clinically important biomarkers such as muscle fiber cross-sectional area. This review aims to elucidate how dMRI has been utilized to study skeletal muscle, covering fundamentals of muscle physiology, dMRI acquisition techniques, dMRI modeling, and applications where dMRI has been leveraged to noninvasively study skeletal muscle changes in response to disease, aging, injury, and human performance. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Calculation of intravoxel incoherent motion parameter maps using a kernelized total difference-based method

Quantitative analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for many clinical applications since its development. In particular, the intravoxel incoherent motion (IVIM) model for DW-MRI has been commonly utilized in various organs. However, because of the presence of excessive noise, the IVIM parameter maps obtained from pixel-wise fitting are often unreliable. In this study, we propose a kernelized total difference-based curve-fitting method to estimate the IVIM parameters. Simulated DW-MRI data at five signal-to-noise ratios (i.e., 10, 20, 30, 50, and 100) and real abdominal DW-MRI data acquired on a 1.5-T MRI scanner with nine b-values (i.e., 0, 10, 25, 50, 100, 200, 300, 400, and 500 s/mm2) and six diffusion-encoding gradient directions were used to evaluate the performance of the proposed method. The results were compared with those obtained by three existing methods: trust-region reflective (TRR) algorithm, Bayesian probability (BP), and deep neural network (DNN). Our simulation results showed that the proposed method outperformed the other three comparing methods in terms of root-mean-square error. Moreover, the proposed method could preserve small details in the estimated IVIM parameter maps. The experimental results showed that, compared with the TRR method, the proposed method as well as the BP (and DNN) method could reduce the overestimation of the pseudodiffusion coefficient and improve the quality of IVIM parameter maps. For all studied abdominal organs except the pancreas, both the proposed method and the BP method could provide IVIM parameter estimates close to the reference values; the former had higher precision. The kernelized total difference-based curve-fitting method has the potential to improve the reliability of IVIM parametric imaging.

Image denoising and model-independent parameterization for IVIM MRI

Objective. To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versusb-value curve.Approach. IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method's ability to recover artificial 'pseudokernels'. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis.Main results. Image denoising improved blind image quality metrics, smoothed the signal versusb-value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising.AUCparameters were more correlated with dose and had higher relative importance than exponential model parameters.Significance. IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versusb-value curve with model-independent parameters like theAUCand preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.

Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry

Introduction

Diffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry.

Methods

Here, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure.

Results

In two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics' sensitivity to dementia.

Discussion

We show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways.

Optimal shrinkage denoising breaks the noise floor in high-resolution diffusion MRI

The spatial resolution attainable in diffusion magnetic resonance (MR) imaging is inherently limited by noise. The weaker signal associated with a smaller voxel size, especially at a high level of diffusion sensitization, is often buried under the noise floor owing to the non-Gaussian nature of the MR magnitude signal. Here, we show how the noise floor can be suppressed remarkably via optimal shrinkage of singular values associated with noise in complex-valued k-space data from multiple receiver channels. We explore and compare different low-rank signal matrix recovery strategies to utilize the inherently redundant information from multiple channels. In combination with background phase removal, the optimal strategy reduces the noise floor by 11 times. Our framework enables imaging with substantially improved resolution for precise characterization of tissue microstructure and white matter pathways without relying on expensive hardware upgrades and time-consuming acquisition repetitions, outperforming other related denoising methods.

Assessment of fitting methods and variability of IVIM parameters in muscles of the lumbar spine at rest

Intravoxel incoherent motion (IVIM) MRI provides insight into tissue diffusion and perfusion. Here, estimates of perfusion fraction ( f ), pseudo-diffusion coefficient (D * ), and diffusion coefficient ( D ) obtained via different fitting methods are compared to ascertain (1) the optimal analysis strategy for muscles of the lumbar spine and (2) repeatability of IVIM parameters in skeletal muscle at rest. Diffusion-weighted images were acquired in the lumbar spine at rest in 15 healthy participants. Data were fit to the bi-exponential IVIM model to estimate f , D * and D using three variably segmented approaches based on non-linear least squares fitting, and a Bayesian fitting method. Assuming that perfusion and diffusion are temporally stable in skeletal muscle at rest, and spatially uniform within a spinal segment, the optimal analysis strategy was determined as the approach with the lowest temporal or spatial variation and smallest residual between measured and fit data. Inter-session repeatability of IVIM parameters was evaluated in a subset of 11 people. Finally, simulated IVIM signal at varying signal to noise ratio were evaluated to understand precision and bias. Experimental results showed that IVIM parameter values differed depending on the fitting method. A three-step non-linear least squares fitting approach, where D , f , andD * were estimated sequentially, generally yielded the lowest spatial and temporal variation. Solving all parameters simultaneously yielded the lowest residual between measured and fit data, however there was substantial spatial and temporal variability. Results obtained by Bayesian fitting had high spatial and temporal variability in addition to a large residual between measured and fit data. Simulations showed that all fitting methods can fit the IVIM data at signal to noise ratios >35, and thatD * was the most challenging to accurately obtain. Overall, this study motivates use of a three-step non-linear least squares fitting strategy to quantify IVIM parameters in skeletal muscle.

Romer-EPTI: rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale dMRI and microstructure imaging

Purpose

To overcome the major challenges in dMRI acquisition, including low SNR, distortion/blurring, and motion vulnerability.

Methods

A novel Romer-EPTI technique is developed to provide distortion-free dMRI with significant SNR gain, high motion-robustness, sharp spatial resolution, and simultaneous multi-TE imaging. It introduces a ROtating-view Motion-robust supEr-Resolution technique (Romer) combined with a distortion/blurring-free EPTI encoding. Romer enhances SNR by a simultaneous multi-thick-slice acquisition with rotating-view encoding, while providing high motion-robustness through a motion-aware super-resolution reconstruction, which also incorporates slice-profile and real-value diffusion, to resolve high-isotropic-resolution volumes. The in-plane encoding is performed using distortion/blurring-free EPTI, which further improves effective spatial resolution and motion robustness by preventing not only T2/T2*-blurring but also additional blurring resulting from combining encoded volumes with inconsistent geometries caused by dynamic distortions. Self-navigation was incorporated to enable efficient phase correction. Additional developments include strategies to address slab-boundary artifacts, achieve minimal TE for SNR gain at 7T, and achieve high robustness to strong phase variations at high b-values.

Results

Using Romer-EPTI, we demonstrate distortion-free whole-brain mesoscale in-vivo dMRI at both 3T (500-μm-iso) and 7T (485-μm-iso) for the first time, with high SNR efficiency (e.g., 25 × ), and high image quality free from distortion and slab-boundary artifacts with minimal blurring. Motion experiments demonstrate Romer-EPTI's high motion-robustness and ability to recover sharp images in the presence of motion. Romer-EPTI also demonstrates significant SNR gain and robustness in high b-value (b=5000s/mm2) and time-dependent dMRI.

Conclusion

Romer-EPTI significantly improves SNR, motion-robustness, and image quality, providing a highly efficient acquisition for high-resolution dMRI and microstructure imaging.

Diffusion MRI harmonization via personalized template mapping

One fundamental challenge in diffusion magnetic resonance imaging (dMRI) harmonization is to disentangle the contributions of scanner-related effects from the variable brain anatomy for the observed imaging signals. Conventional harmonization methods rely on establishing an atlas space to resolve anatomical variability and generate a unified inter-site mapping function. However, this approach is limited in accounting for the misalignment of neuroanatomy that still widely persists even after registration, especially in regions close to cortical boundaries. To overcome this challenge, we propose a personalized framework in this paper to more effectively address the confounding from the misalignment of neuroanatomy in dMRI harmonization. Instead of using a common template representing site-effects for all subjects, the main novelty of our method is the adaptive computation of personalized templates for both source and target scanning sites to estimate the inter-site mapping function. We integrate our method with the rotation invariant spherical harmonics (RISH) features to achieve the harmonization of dMRI signals between sites. In our experiments, the proposed approach is applied to harmonize the dMRI data acquired from two scanning platforms: Siemens Prisma and GE MR750 from the Adolescent Brain Cognitive Development dataset and compared with a state-of-the-art method based on RISH features. Our results indicate that the proposed harmonization framework achieves superior performance not only in reducing inter-site variations due to scanner differences but also in preserving sex-related biological variability in original cohorts. Moreover, we assess the impact of harmonization on the estimation of fiber orientation distributions and show the robustness of the personalized harmonization procedure in preserving the fiber orientation of original dMRI signals.

Brain fiber structure estimation based on principal component analysis and RINLM filter

Diffusion magnetic resonance imaging is a technique for non-invasive detection of microstructure in the white matter of the human brain, which is widely used in neuroscience research of the brain. However, diffusion-weighted images(DWI) are sensitive to noise, which affects the subsequent reconstruction of fiber orientation direction, microstructural parameter estimation and fiber tracking. In order to better eliminate the noise in diffusion-weighted images, this study proposes a noise reduction method combining Marchenko-Pastur principal component analysis(MPPCA) and rotation-invariant non-local means filter(RINLM) to further remove residual noise and preserve the image texture detail information. In this study, the algorithm is applied to the fiber structure and the prevailing microstructural models within the human brain voxels based on simulated and real human brain datasets. Experimental comparisons between the proposed method and the state-of-the-art methods are performed in single-fiber, multi-fiber, crossed and curved-fiber regions as well as in different microstructure estimation models. Results demonstrated the superior performance of the proposed method in denoising DWI data, which can reduce the angular error in fiber orientation reconstruction to obtain more valid fiber structure estimation and enable more complete fiber tracking trajectories with higher coverage. Meanwhile, the method reduces the estimation errors of various white matter microstructural parameters and verifies the performance of the method in white matter microstructure estimation.

Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease

This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro-and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT's generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNT's capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimer's disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.

Cortical microstructural associations with CSF amyloid and pTau

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aβ1-42 than pTau181 and better distinguished Aβ+ from Aβ- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline.

Joint diffusional kurtosis magnetic resonance imaging analysis of white matter and the thalamus to identify subcortical ischemic vascular disease

Identifying subcortical ischemic vascular disease (SIVD) in older adults is important but challenging. Growing evidence suggests that diffusional kurtosis imaging (DKI) can detect SIVD-relevant microstructural pathology, and a systematic assessment of the discriminant power of DKI metrics in various brain tissue microstructures is urgently needed. Therefore, the current study aimed to explore the value of DKI and diffusion tensor imaging (DTI) metrics in detecting early-stage SIVD by combining multiple diffusion metrics, analysis strategies, and clinical-radiological constraints. This prospective study compared DKI with diffusivity and macroscopic imaging evaluations across the aging spectrum including SIVD, Alzheimer's disease (AD), and cognitively normal (NC) groups. Using a white matter atlas and segregated thalamus analysis with considerations of the pre-identified macroscopic pathology, the most effective diffusion metrics were selected and then examined using multiple clinical-radiological constraints in a two-group or three-group paradigm. A total of 122 participants (mean age, 74.6 ± 7.38 years, 72 women) including 42 with SIVD, 50 with AD, and 30 NC were evaluated. Fractional anisotropy, mean kurtosis, and radial kurtosis were critical metrics in detecting early-stage SIVD. The optimal selection of diffusion metrics showed 84.4-100% correct classification of the results in a three-group paradigm, with an area under the curve of .909-.987 in a two-group paradigm related to SIVD detection (all P < .001). We therefore concluded that greatly resilient to the effect of pre-identified macroscopic pathology, the combination of DKI/DTI metrics showed preferable performance in identifying early-stage SIVD among adults across the aging spectrum.

Accelerated Brain Atrophy, Microstructural Decline and Connectopathy in Age-Related Macular Degeneration

Age-related macular degeneration (AMD) has recently been linked to cognitive impairment. We hypothesized that AMD modifies the brain aging trajectory, and we conducted a longitudinal diffusion MRI study on 40 participants (20 with AMD and 20 controls) to reveal the location, extent, and dynamics of AMD-related brain changes. Voxel-based analyses at the first visit identified reduced volume in AMD participants in the cuneate gyrus, associated with vision, and the temporal and bilateral cingulate gyrus, linked to higher cognition and memory. The second visit occurred 2 years after the first and revealed that AMD participants had reduced cingulate and superior frontal gyrus volumes, as well as lower fractional anisotropy (FA) for the bilateral occipital lobe, including the visual and the superior frontal cortex. We detected faster rates of volume and FA reduction in AMD participants in the left temporal cortex. We identified inter-lingual and lingual-cerebellar connections as important differentiators in AMD participants. Bundle analyses revealed that the lingual gyrus had a lower streamline length in the AMD participants at the first visit, indicating a connection between retinal and brain health. FA differences in select inter-lingual and lingual cerebellar bundles at the second visit showed downstream effects of vision loss. Our analyses revealed widespread changes in AMD participants, beyond brain networks directly involved in vision processing.

Association of quantitative diffusion tensor imaging measures with time to return to sport and reinjury incidence following acute hamstring strain injury

Hamstring strain injuries (HSI) are a common occurrence in athletics and complicated by limited prognostic indicators and high rates of reinjury. Assessment of injury characteristics at the time of injury (TOI) may be used to manage athlete expectations for time to return to sport (RTS) and mitigate reinjury risk. Magnetic resonance imaging (MRI) is routinely used in soft tissue injury management, but its prognostic value for HSI is widely debated. Recent advancements in musculoskeletal MRI, such as diffusion tensor imaging (DTI), have allowed for quantitative measures of muscle microstructure assessment. The purpose of this study was to determine the association of TOI MRI-based measures, including the British Athletic Muscle Injury Classification (BAMIC) system, edema volume, and DTI metrics, with time to RTS and reinjury incidence. Negative binomial regressions and generalized estimating equations were used to determine relationships between imaging measures and time to RTS and reinjury, respectively. Twenty-six index injuries were observed, with five recorded reinjuries. A significant association was not detected between BAMIC score and edema volume at TOI with days to RTS (p-values ≥ 0.15) or reinjury (p-values ≥ 0.13). Similarly, a significant association between DTI metrics and days to RTS was not detected (p-values ≥ 0.11). Although diffusivity metrics are expected to increase following injury, decreased values were observed in those who reinjured (mean diffusivity, p = 0.016; radial diffusivity, p = 0.02; principal effective diffusivity eigenvalues, p-values = 0.007-0.057). Additional work to further understand the directional relationship observed between DTI metrics and reinjury status and the influence of external factors is warranted.

Structural Network Efficiency Predicts Conversion to Incident Parkinsonism in Patients With Cerebral Small Vessel Disease

BACKGROUND

To investigate whether structural network disconnectivity is associated with parkinsonian signs and their progression, as well as with an increased risk of incident parkinsonism.

METHODS

In a prospective cohort (Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort study) consisting of 293 participants with small vessel disease (SVD), we assessed parkinsonian signs and incident parkinsonism over an 8-year follow-up. In addition, we reconstructed the white matter network followed by graph-theoretical analyses to compute the network metrics. Conventional magnetic resonance imaging markers for SVD were assessed.

RESULTS

We included 293 patients free of parkinsonism at baseline (2011), with a mean age 68.8 (standard deviation [SD] 8.4) years, and 130 (44.4%) were men. Nineteen participants (6.5%) developed parkinsonism during a median (SD) follow-up time of 8.3 years. Compared with participants without parkinsonism, those with all-cause parkinsonism had higher Unified Parkinson's Disease Rating scale (UPDRS) scores and lower global efficiency at baseline. Baseline global efficiency was associated with UPDRS motor scores in 2011 (β = -0.047, p < .001) and 2015 (β = -0.84, p < .001), as well as with the changes in UPDRS scores during the 4-year follow-up (β = -0.63, p = .004). In addition, at the regional level, we identified an inter-hemispheric disconnected network associated with an increased UPDRS motor score. Besides, lower global efficiency was associated with an increased risk of all-cause and vascular parkinsonism independent of SVD markers.

CONCLUSIONS

Our findings suggest that global network efficiency is associated with a gradual decline in motor performance, ultimately leading to incident parkinsonism in the elderly with SVD. Global network efficiency may have the added value to serve as a useful marker to capture changes in motor signs.

Arachidonic and docosahexaenoic acid supplementation and brain maturation in preterm infants; a double blind RCT

BACKGROUND

Arachidonic acid (ARA) and docosahexaenoic acid (DHA) are important structural components of neural cellular membranes and possess anti-inflammatory properties. Very preterm infants are deprived of the enhanced placental supply of these fatty acids, but the benefit of postnatal supplementation on brain development is uncertain. The aim of this study was to test the hypothesis that early enteral supplementation with ARA and DHA in preterm infants improves white matter (WM) microstructure assessed by diffusion-weighted MRI at term equivalent age.

METHODS

In this double-blind, randomized controlled trial, infants born before 29 weeks gestational age were allocated to either 100 mg/kg ARA and 50 mg/kg DHA (ARA:DHA group) or medium chain triglycerides (control). Supplements were started on the second day of life and provided until 36 weeks postmenstrual age. The primary outcome was brain maturation assessed by diffusion tensor imaging (DTI) using Tract-Based Spatial Statistics (TBSS) analysis.

RESULTS

We included 120 infants (60 per group) in the trial; mean (range) gestational age was 26+3 (22+6 - 28+6) weeks and postmenstrual age at scan was 41+3 (39+1 - 47+0) weeks. Ninety-two infants underwent MRI imaging, and of these, 90 had successful T1/T2 weighted MR images and 74 had DTI data of acceptable quality. TBSS did not show significant differences in mean or axial diffusivity between the groups, but demonstrated significantly higher fractional anisotropy in several large WM tracts in the ARA:DHA group, including corpus callosum, the anterior and posterior limb of the internal capsula, inferior occipitofrontal fasciculus, uncinate fasciculus, and the inferior longitudinal fasciculus. Radial diffusivity was also significantly lower in several of the same WM tracts in the ARA:DHA group.

CONCLUSION

This study suggests that supplementation with ARA and DHA at doses matching estimated fetal accretion rates improves WM maturation compared to control treatment, but further studies are needed to ascertain any functional benefit.

CLINICAL TRIAL REGISTRATION

www.

CLINICALTRIALS

gov; ID:NCT03555019.

Neuroimaging Methods for MRI Analysis in CSF Biomarkers Studies

Among others, the existence of pathophysiological biomarkers such as cerebrospinal fluid (CSF) Aβ-42, t-tau, and p-tau preceding the onset of Alzheimer's disease (AD) symptomatology has shifted the conceptualization of AD as a continuum. In addition, magnetic resonance imaging (MRI) enables the study of structural and functional cross-sectional correlates and longitudinal changes in vivo, and therefore, the combination of CSF data and imaging analyses emerges as a synergistic approach to understand the structural correlates related with specific AD-related biomarkers. In this chapter, we describe the methods used in neuroimaging that will allow researchers to combine data on CSF metabolites with imaging analyses.

Exploring Radial Asymmetry in MR Diffusion Tensor Imaging and Its Impact on the Interpretation of Glymphatic Mechanisms

BACKGROUND

Diffusion imaging holds great potential for the non-invasive assessment of the glymphatic system in humans. One technique, diffusion tensor imaging along the perivascular space (DTI-ALPS), has introduced the ALPS-index, a novel metric for evaluating diffusivity within the perivascular space. However, it still needs to be established whether the observed reduction in the ALPS-index reflects axonal changes, a common occurrence in neurodegenerative diseases.

PURPOSE

To determine whether axonal alterations can influence change in the ALPS-index.

STUDY TYPE

Retrospective.

POPULATION

100 participants (78 cognitively normal and 22 with mild cognitive impairments) aged 50-90 years old.

FIELD STRENGTH/SEQUENCE

3T; diffusion-weighted single-shot spin-echo echo-planar imaging sequence, T1-weighted images (MP-RAGE).

ASSESSMENT

The ratio of two radial diffusivities of the diffusion tensor (i.e., λ2/λ3) across major white matter tracts with distinct venous/perivenous anatomy that fulfill (ALPS-tracts) and do not fulfill (control tracts) ALPS-index anatomical assumptions were analyzed.

STATISTICAL TESTS

To investigate the correlation between λ2/λ3 and age/cognitive function (RAVLT) while accounting for the effect of age, linear regression was implemented to remove the age effect from each variable. Pearson correlation analysis was conducted on the residuals obtained from the linear regression. Statistical significance was set at p < 0.05.

RESULTS

λ2 was ~50% higher than λ3 and demonstrated a consistent pattern across both ALPS and control tracts. Additionally, in both ALPS and control tracts a reduction in the λ2/λ3 ratio was observed with advancing age (r = -0.39, r = -0.29, association and forceps tract, respectively) and decreased memory function (r = 0.24, r = 0.27, association and forceps tract, respectively).

DATA CONCLUSIONS

The results unveil a widespread radial asymmetry of white matter tracts that changes with aging and neurodegeration. These findings highlight that the ALPS-index may not solely reflect changes in the diffusivity of the perivascular space but may also incorporate axonal contributions.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Investigation of perfusion impairment in degenerative cervical myelopathy beyond the site of cord compression

The aim of this study was to determine tissue-specific blood perfusion impairment of the cervical cord above the compression site in patients with degenerative cervical myelopathy (DCM) using intravoxel incoherent motion (IVIM) imaging. A quantitative MRI protocol, including structural and IVIM imaging, was conducted in healthy controls and patients. In patients, T2-weighted scans were acquired to quantify intramedullary signal changes, the maximal canal compromise, and the maximal cord compression. T2*-weighted MRI and IVIM were applied in all participants in the cervical cord (covering C1-C3 levels) to determine white matter (WM) and grey matter (GM) cross-sectional areas (as a marker of atrophy), and tissue-specific perfusion indices, respectively. IVIM imaging resulted in microvascular volume fraction ([Formula: see text]), blood velocity ([Formula: see text]), and blood flow ([Formula: see text]) indices. DCM patients additionally underwent a standard neurological clinical assessment. Regression analysis assessed associations between perfusion parameters, clinical outcome measures, and remote spinal cord atrophy. Twenty-nine DCM patients and 30 healthy controls were enrolled in the study. At the level of stenosis, 11 patients showed focal radiological evidence of cervical myelopathy. Above the stenosis level, cord atrophy was observed in the WM (- 9.3%; p = 0.005) and GM (- 6.3%; p = 0.008) in patients compared to healthy controls. Blood velocity (BV) and blood flow (BF) indices were decreased in the ventral horns of the GM (BV: - 20.1%, p = 0.0009; BF: - 28.2%, p = 0.0008), in the ventral funiculi (BV: - 18.2%, p = 0.01; BF: - 21.5%, p = 0.04) and lateral funiculi (BV: - 8.5%, p = 0.03; BF: - 16.5%, p = 0.03) of the WM, across C1-C3 levels. A decrease in microvascular volume fraction was associated with GM atrophy (R = 0.46, p = 0.02). This study demonstrates tissue-specific cervical perfusion impairment rostral to the compression site in DCM patients. IVIM indices are sensitive to remote perfusion changes in the cervical cord in DCM and may serve as neuroimaging biomarkers of hemodynamic impairment in future studies. The association between perfusion impairment and cervical cord atrophy indicates that changes in hemodynamics caused by compression may contribute to the neurodegenerative processes in DCM.

Fibre density and fibre-bundle cross-section of the corticospinal tract are distinctly linked to psychosis-specific symptoms in antipsychotic-naïve patients with first-episode schizophrenia

Multiple lines of research support the dysconnectivity hypothesis of schizophrenia. However, findings on white matter (WM) alterations in patients with schizophrenia are widespread and non-specific. Confounding factors from magnetic resonance image (MRI) processing, clinical diversity, antipsychotic exposure, and substance use may underlie some of the variability. By application of refined methodology and careful sampling, we rectified common confounders investigating WM and symptom correlates in a sample of strictly antipsychotic-naïve first-episode patients with schizophrenia. Eighty-six patients and 112 matched controls underwent diffusion MRI. Using fixel-based analysis (FBA), we extracted fibre-specific measures such as fibre density and fibre-bundle cross-section. Group differences on fixel-wise measures were examined with multivariate general linear modelling. Psychopathology was assessed with the Positive and Negative Syndrome Scale. We separately tested multivariate correlations between fixel-wise measures and predefined psychosis-specific versus anxio-depressive symptoms. Results were corrected for multiple comparisons. Patients displayed reduced fibre density in the body of corpus callosum and in the middle cerebellar peduncle. Fibre density and fibre-bundle cross-section of the corticospinal tract were positively correlated with suspiciousness/persecution, and negatively correlated with delusions. Fibre-bundle cross-section of isthmus of corpus callosum and hallucinatory behaviour were negatively correlated. Fibre density and fibre-bundle cross-section of genu and splenium of corpus callosum were negative correlated with anxio-depressive symptoms. FBA revealed fibre-specific properties of WM abnormalities in patients and differentiated associations between WM and psychosis-specific versus anxio-depressive symptoms. Our findings encourage an itemised approach to investigate the relationship between WM microstructure and clinical symptoms in patients with schizophrenia.

3D CEST MRI with an unevenly segmented RF irradiation scheme: A feasibility study in brain tumor imaging

PURPOSE

To integrate 3D CEST EPI with an unevenly segmented RF irradiation module and preliminarily demonstrate it in the clinical setting.

METHODS

A CEST MRI with unevenly segmented RF saturation was implemented, including a long primary RF saturation to induce the steady-state CEST effect, maintained with repetitive short secondary RF irradiation between readouts. This configuration reduces relaxation-induced blur artifacts during acquisition, allowing fast 3D spatial coverage. Numerical simulations were performed to select parameters such as flip angle (FA), short RF saturation duration (Ts2), and the number of readout segments. The sequence was validated experimentally with data from a phantom, healthy volunteers, and a brain tumor patient.

RESULTS

Based on the numerical simulation and l-carnosine gel phantom experiment, FA, Ts2, and the number of segments were set to 20°, 0.3 s, and the range from 4 to 8, respectively. The proposed method minimized signal modulation in the human brain images in the kz direction during the acquisition and provided the blur artifacts-free CEST contrast over the whole volume. Additionally, the CEST contrast in the tumor tissue region is higher than in the contralateral normal tissue region.

CONCLUSIONS

It is feasible to implement a highly accelerated 3D EPI CEST imaging with unevenly segmented RF irradiation.

Locally low-rank denoising in transform domains

Purpose

To develop an extension to locally low rank (LLR) denoising techniques based on transform domain processing that reduces the number of images required in the MR image series for high-quality denoising.

Theory and Methods

LLR methods with random matrix theory-based thresholds are successfully used in the denoising of MR image series in a number of applications. The performance of these methods depend on how well the LLR assumption is satisfied, which deteriorates with few numbers of images, as is commonly encountered in quantitative MRI applications. We propose a transform-domain approach for denoising of MR image series to represent the underlying signal with higher fidelity when using a locally low rank approximation. The efficacy of the method is demonstrated for fully-sampled k-space, undersampled k-space, DICOM images, and complex-valued SENSE-1 images in quantitative MRI applications with as few as 4 images.

Results

For both MSK and brain applications, the transform domain denoising preserves local subtle variability, whereas the quantitative maps based on image domain LLR methods tend to be locally more homogeneous.

Conclusion

A transform domain extension to LLR denoising produces high quality images and is compatible with both raw k-space data and vendor reconstructed data. This allows for improved imaging and more accurate quantitative analyses and parameters obtained therefrom.

DENOISING DIFFUSION MRI: CONSIDERATIONS AND IMPLICATIONS FOR ANALYSIS

Development of diffusion MRI (dMRI) denoising approaches has experienced considerable growth over the last years. As noise can inherently reduce accuracy and precision in measurements, its effects have been well characterised both in terms of uncertainty increase in dMRI-derived features and in terms of biases caused by the noise floor, the smallest measurable signal given the noise level. However, gaps in our knowledge still exist in objectively characterising dMRI denoising approaches in terms of both of these effects and assessing their efficacy. In this work, we reconsider what a denoising method should and should not do and we accordingly define criteria to characterise the performance. We propose a comprehensive set of evaluations, including i) benefits in improving signal quality and reducing noise variance, ii) gains in reducing biases and the noise floor and improving, iii) preservation of spatial resolution, iv) agreement of denoised data against a gold standard, v) gains in downstream parameter estimation (precision and accuracy), vi) efficacy in enabling noise-prone applications, such as ultra-high-resolution imaging. We further provide newly acquired complex datasets (magnitude and phase) with multiple repeats that sample different SNR regimes to highlight performance differences under different scenarios. Without loss of generality, we subsequently apply a number of exemplar patch-based denoising algorithms to these datasets, including Non-Local Means, Marchenko-Pastur PCA (MPPCA) in the magnitude and complex domain and NORDIC, and compare them with respect to the above criteria and against a gold standard complex average of multiple repeats. We demonstrate that all tested denoising approaches reduce noise-related variance, but not always biases from the elevated noise floor. They all induce a spatial resolution penalty, but its extent can vary depending on the method and the implementation. Some denoising approaches agree with the gold standard more than others and we demonstrate challenges in even defining such a standard. Overall, we show that dMRI denoising performed in the complex domain is advantageous to magnitude domain denoising with respect to all the above criteria.

Coronal As Well As Sagittal Fascicle Dynamics Can Bring About a Gearing Effect in Muscle Elongation by Passive Lengthening

PURPOSE

The amount of muscle belly elongation induced by passive lengthening is often assumed to be equal to that of fascicles. But these are different if fascicles shorter than the muscle belly rotate around their attachment sites. Such discrepancy between fascicles and muscle belly length changes can be considered as gearing. As the muscle fascicle arrangement is 3D, the fascicle rotation by passive lengthening may occur in the coronal as well as the sagittal planes. Here we examined the fascicle 3D dynamics and resultant gearing during passive elongation of human medial gastrocnemius in vivo .

METHODS

For 16 healthy adults, we reconstructed fascicles three-dimensionally using diffusion tensor imaging and evaluated the change in fascicle length and angles in the sagittal and coronal planes during passive ankle dorsiflexion (from 20° plantar flexion to 20° dorsiflexion).

RESULTS

Whole muscle belly elongation during passive ankle dorsiflexion was 38% greater than the fascicle elongation. Upon passive lengthening, the fascicle angle in the sagittal plane in all regions (-5.9°) and that in the coronal plane in the middle-medial (-2.7°) and distal-medial (-4.3°) regions decreased significantly. Combining the fascicle coronal and sagittal rotation significantly increased the gearing effects in the middle-medial (+10%) and distal-medial (+23%) regions. The gearing effect by fascicle sagittal and coronal rotations corresponded to 26% of fascicle elongation, accounting for 19% of whole muscle belly elongation.

CONCLUSIONS

Fascicle rotation in the coronal and sagittal planes is responsible for passive gearing, contributing to the whole muscle belly elongation. Passive gearing can be favorable for reducing fascicle elongation for a given muscle belly elongation.

Dopaminergic denervation and associated MRI microstructural changes in the nigrostriatal projection in early Parkinson's disease patients

Loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) and a profound reduction of striatal dopamine are two hallmarks of Parkinson's disease (PD). However, it's unclear whether degeneration starts at the neuronal soma or the striatal presynaptic terminals, and how microstructural degeneration is linked to dopaminergic loss is also uncertain. In this study, thirty de novo PD patients and twenty healthy subjects (HS) underwent 6-[18F]-fluoro-L-dopa (FDOPA) PET and MRI studies no later than 12 months from clinical diagnosis. FDOPA uptake rate (Ki), fractional volume of free-water (FW), and iron-sensitive R2* relaxometry were quantified within nigrostriatal regions. Inter-group differences (PD vs HS) were studied using non-parametric statistics and complemented with Cohen's d effect sizes and Bayesian statistics. Correlation analyses were performed exploring biomarker dependencies and their association with bradykinesia scores. PD patients exhibited a significant decline in nigrostriatal dopaminergic activity, being post-commissural putamen (-67%) and posterolateral SNc (-11.7%) the most affected subregions within striatum and SNc respectively. Microstructural alterations (FW) were restricted to the hemisphere corresponding to the most affected side and followed similar spatial gradients as FDOPA Ki (+20% in posterior putamen and +11% in posterolateral SNc). R2* revealed no relevant significant changes. FDOPA and FW were correlated within the posterolateral SNc, and clinical severity was associated with FDOPA Ki loss. The asymmetry between striatal and SNc changes for both dopaminergic depletion and microstructural degeneration biomarkers is consistent with a neurodegenerative process that begins in the striatal terminals before progressing toward the cell bodies in the SNc.

Self-supervised structural similarity-based convolutional neural network for cardiac diffusion tensor image denoising

BACKGROUND

Diffusion tensor imaging (DTI) is a promising technique for non-invasively investigating the myocardial fiber structures of human heart. However, low signal-to-noise ratio (SNR) has been a major limit of cardiac DTI to prevent us from detecting myocardium structure accurately. Therefore, it is important to remove the effect of noise on diffusion weighted (DW) images.

PURPOSE

Although the conventional and deep learning-based denoising methods have shown the potential to deal with effectively the noise in DW images, most of them are redundant information dependent or require the noise-free images as golden standard. In addition, the existed DW image denoising methods often suffer from problems of over-smoothing. To address these issues, we propose a self-supervised learning model, structural similarity based convolutional neural network with edge-weighted loss (SSECNN), to remove the noise effectively in cardiac DTI.

METHODS

Considering that the DW images acquired along different diffusion directions have structural similarity, and the noise in these DW images is independent and identically distributed, the structural similarity-based matching algorithm is proposed to search for the most similar DW images. Such similar noisy DW image pairs are then used as the input and target of the denoising network SSECNN, which consists of several convolutional and residual blocks. Through the self-supervised training with these image pairs, the network can restore the clean DW images and retain the correlations between the denoised DW images along different directions. To avoid the over-smoothing problem, we design a novel edge-weighted loss which enables the network to adaptively adjust the loss weights with iterations and therefore to improve the detail preserve ability of the model. To verify the superiority of the proposed method, comparisons with state-of-the-art (SOTA) denoising methods are performed on both synthetic and real acquired DTI datasets.

RESULTS

Experimental results show that SSECNN can effectively reduce the noise in the DW images while preserving detailed texture and edge information and therefore achieve better performance in DTI reconstruction. For synthetic dataset, compared to the SOTA method, the root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structure similarity index measure (SSIM) between the denoised DW images obtained with SSECNN and noise-free DW images are improved by 6.94%, 1.98%, and 0.76% respectively when the noise level is 10%. As for the acquired cardiac DTI dataset, the SSECNN method could significantly improve SNR and contrast to noise ratio (CNR) of cardiac DW images and achieve more regular helix angle (HA) and transverse angle (TA) maps. The ablation experimental results validate that using the structure similarity-based method to search the similar DW image pairs yield the smallest loss, and with the help of the edge-weighted loss, the denoised DW images and diffusion metric maps can preserve more details.

CONCLUSIONS

The proposed SSECNN method can fully explore the similarity between the DW images along different diffusion directions. Using such similarity and an edge-weighted loss enable us to denoise cardiac DTI effectively in a self-supervised manner. Our method can overcome the redundancy information dependence and over-smoothing problem of the SOTA methods.

Functional activity of the caudate mediates the relation between early childhood microstructural variations and elevated metabolic syndrome scores

BACKGROUND

Metabolic syndrome score in children assesses the risk of developing cardiovascular disease in future. We aim to probe the role of the caudate in relation to the metabolic syndrome score. Furthermore, using both functional and structural neuroimaging, we aim to examine the interplay between functional and structural measures.

METHODS

A longitudinal birth cohort study with functional and structural neuroimaging data obtained at 4.5, 6.0 and 7.5 years and metabolic syndrome scores at 8.0 years was used. Pearson correlation and linear regression was used to test for correlation fractional anisotropy (FA) and fractional amplitude of low frequency fluctuations (fALFF) of the caudate with metabolic syndrome scores. Mediation analysis was used to test if later brain measures mediated the relation between earlier brain measures and metabolic syndrome scores. Inhibitory control was also tested as a mediator of the relation between caudate brain measures and metabolic syndrome scores.

RESULTS

FA at 4.5 years and fALFF at 7.5 years of the left caudate was significantly correlated with metabolic syndrome scores. Post-hoc mediation analysis showed that fALFF at 7.5 years fully mediated the relation between FA at 4.5 years and metabolic syndrome scores. Inhibitory control was significantly correlated with fALFF at 7.5 years, but did not mediate the relation between fALFF at 7.5 years and metabolic syndrome scores.

CONCLUSIONS

We found that variations in caudate microstructure at 4.5 years predict later variation in functional activity at 7.5 years. This later variation in functional activity fully mediates the relation between microstructural changes in early childhood and metabolic syndrome scores at 8.0 years.

BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data

We present BundleCleaner, an unsupervised multi-step framework that can filter, denoise and subsample bundles derived from diffusion MRI-based whole-brain tractography. Our approach considers both the global bundle structure and local streamline-wise features. We apply BundleCleaner to bundles generated from single-shell diffusion MRI data in an independent clinical sample of older adults from India using probabilistic tractography and the resulting 'cleaned' bundles can better align with the atlas bundles with reduced overreach. In a downstream tractometry analysis, we show that the cleaned bundles, represented with less than 20% of the original set of points, can robustly localize along-tract microstructural differences between 32 healthy controls and 34 participants with Alzheimer's disease ranging in age from 55 to 84 years old. Our approach can help reduce memory burden and improving computational efficiency when working with tractography data, and shows promise for large-scale multi-site tractometry.

Diffusion tensor imaging tractography in the one-humped camel (Camelus dromedarius) brain

Introduction

Tractography is a technique used to trace the pathways of the brain using noninvasive diffusion tensor imaging (DTI) data. It is becoming increasingly popular for investigating the brains of domestic mammals and other animals with myelinated fibers but the principle of DTI can also apply for those with unmyelinated fibers. In the case of camels, DTI tractography is a promising method for enhancing current knowledge of the brain's structural connectivity and identifying white-matter tract changes potentially linked to neurodegenerative pathologies. The present study was therefore designed to describe representative white-matter tracts in the one-humped camel DTI tractography.

Methods

Post mortem DTI was used to obtain images of two one-humped camel brains using a 3 Tesla system. T2-weighted images were also acquired to identify regions of interest for each fiber tract and a fiber dissection technique was used to complement the DT images. The main association, commissural, and projection fibers were reconstructed and superimposed on T2-weighted images or fractional anisotropy maps.

Results

The results of the present study show the reconstruction of the most representative tracts, ie the cingulum, the corpus callosum and the internal capsule, in the one-humped camel brain using DTI data acquired post mortem. These DTI results were compared to those from fiber dissection.

Discussion

Anatomy of the cingulum, corpus callosum and internal capsule correlates well with the description in anatomical textbooks and appears to be similar to fibers describe in large animals. Further research will be required to improve and validate these findings and to generate a tractography atlas based on MRI and histological data, as such an atlas would be a valuable resource for future neuroimaging research.

Preparing Cities for Future Pandemics: Unraveling the Influence of Urban and Housing Variables on COVID-19 Incidence in Santiago de Chile

In this study, we analyzed how urban, housing, and socioeconomic variables are related to COVID-19 incidence. As such, we have analyzed these variables along with demographic, education, employment, and COVID-19 data from 32 communes in Santiago de Chile between March and August of 2020, before the release of the vaccines. The results of our Principal Component Analysis (PCA) confirmed that those communes with more economic, social, organizational, and infrastructural resources were overall less affected by COVID-19. As the dimensions affecting COVID-19 are based on structural variables, this study discusses to what extent our cities can be prepared for the next pandemic. Recommendations for local decision-makers in controlling illegal immigration and investing in housing and urban parks are drawn.

Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology

Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement.

Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting

White matter tracts generated from whole brain tractography are often processed using automatic segmentation methods with standard atlases. Atlases are generated from hundreds of subjects, which becomes time-consuming to create and difficult to apply to all populations. In this study, we extended our prior work on using a deep generative model - a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space given a limited sample size of 50 subjects from the ADNI3 dataset, to generate synthetic population-specific bundle templates using Kernel Density Estimation (KDE) on streamline embeddings. We conducted a quantitative shape analysis by calculating bundle shape metrics, and found that our bundle templates better capture the shape distribution of the bundles than the atlas data used in the original segmentation derived from young healthy adults. We further demonstrated the use of our framework for direct bundle segmentation from whole-brain tractograms.

Use of Support Vector Machines Approach via ComBat Harmonized Diffusion Tensor Imaging for the Diagnosis and Prognosis of Mild Traumatic Brain Injury: A CENTER-TBI Study

The prediction of functional outcome after mild traumatic brain injury (mTBI) is challenging. Conventional magnetic resonance imaging (MRI) does not do a good job of explaining the variance in outcome, as many patients with incomplete recovery will have normal-appearing clinical neuroimaging. More advanced quantitative techniques such as diffusion MRI (dMRI), can detect microstructural changes not otherwise visible, and so may offer a way to improve outcome prediction. In this study, we explore the potential of linear support vector classifiers (linearSVCs) to identify dMRI biomarkers that can predict recovery after mTBI. Simultaneously, the harmonization of fractional anisotropy (FA) and mean diffusivity (MD) via ComBat was evaluated and compared for the classification performances of the linearSVCs. We included dMRI scans of 179 mTBI patients and 85 controls from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI), a multi-center prospective cohort study, up to 21 days post-injury. Patients were dichotomized according to their Extended Glasgow Outcome Scale (GOSE) scores at 6 months into complete (n = 92; GOSE = 8) and incomplete (n = 87; GOSE <8) recovery. FA and MD maps were registered to a common space and harmonized via the ComBat algorithm. LinearSVCs were applied to distinguish: (1) mTBI patients from controls and (2) mTBI patients with complete from those with incomplete recovery. The linearSVCs were trained on (1) age and sex only, (2) non-harmonized, (3) two-category-harmonized ComBat, and (4) three-category-harmonized ComBat FA and MD images combined with age and sex. White matter FA and MD voxels and regions of interest (ROIs) within the John Hopkins University (JHU) atlas were examined. Recursive feature elimination was used to identify the 10% most discriminative voxels or the 10 most discriminative ROIs for each implementation. mTBI patients displayed significantly higher MD and lower FA values than controls for the discriminative voxels and ROIs. For the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD voxel-wise linearSVC provided significantly higher classification scores (81.4% accuracy, 93.3% sensitivity, 80.3% F1-score, and 0.88 area under the curve [AUC], p < 0.05) compared with the classification based on age and sex only and the ROI approaches (accuracies: 59.8% and 64.8%, respectively). Similar to the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD maps voxelwise approach yields statistically significant prediction scores between mTBI patients with complete and those with incomplete recovery (71.8% specificity, 66.2% F1-score and 0.71 AUC, p < 0.05), which provided a modest increase in the classification score (accuracy: 66.4%) compared with the classification based on age and sex only and ROI-wise approaches (accuracy: 61.4% and 64.7%, respectively). This study showed that ComBat harmonized FA and MD may provide additional information for diagnosis and prognosis of mTBI in a multi-modal machine learning approach. These findings demonstrate that dMRI may assist in the early detection of patients at risk of incomplete recovery from mTBI.

Effects of acute alcohol exposure and chronic alcohol use on neurite orientation dispersion and density imaging (NODDI) parameters

RATIONALE

Little is known about how acute and chronic alcohol exposure may alter the in vivo membrane properties of neurons.

OBJECTIVES

We employed neurite orientation dispersion and density imaging (NODDI) to examine acute and chronic effects of alcohol exposure on neurite density.

METHODS

Twenty-one healthy social drinkers (CON) and thirteen nontreatment-seeking individuals with alcohol use disorder (AUD) underwent a baseline multi-shell diffusion magnetic resonance imaging (dMRI) scan. A subset (10 CON, 5 AUD) received dMRI during intravenous infusions of saline and alcohol during dMRI. NODDI parametric images included orientation dispersion (OD), isotropic volume fraction (ISOVF), and corrected intracellular volume fraction (cICVF). Diffusion tensor imaging metrics of fractional anisotropy and mean, axial, and radial diffusivity (FA, MD, AD, RD) were also computed. Average parameter values were extracted from white matter (WM) tracts defined by the Johns Hopkins University atlas.

RESULTS

There were group differences in FA, RD, MD, OD, and cICVF, primarily in the corpus callosum. Both saline and alcohol had effects on AD and cICVF in WM tracts proximal to the striatum, cingulate, and thalamus. This is the first work to indicate that acute fluid infusions may alter WM properties, which are conventionally believed to be insensitive to acute pharmacological challenges. It also suggests that the NODDI approach may be sensitive to transient changes in WM. The next steps should include determining if the effect on neurite density differs with solute or osmolality, or both, and translational studies to assess how alcohol and osmolality affect the efficiency of neurotransmission.

Influence of adaptive denoising on Diffusion Kurtosis Imaging at 3T and 7T

BACKGROUND AND OBJECTIVE

Choosing the most appropriate denoising method to improve the quality of diagnostic images maximally is key in pre-processing of diffusion MRI images. Recent advancements in acquisition and reconstruction techniques have questioned traditional noise estimation methods favoring adaptive denoising frameworks, circumventing the need to know a priori information that is hardly available in a clinical setting. In this observational study, we compared two innovative adaptive techniques sharing some features, Patch2Self and Nlsam, through application on reference adult data at 3T and 7T. The primary aim was identifying the most effective method in case of Diffusion Kurtosis Imaging (DKI) data - particularly susceptible to noise and signal fluctuations - at 3T and 7T fields. A side goal consisted of investigating the dependence of kurtosis metrics' variability with respect to the magnetic field on the adopted denoising methodology.

METHODS

For comparison purposes, we focused on qualitative and quantitative analysis of DKI data and related microstructural maps before and after applying the two denoising approaches. Specifically, we assessed computational efficiency, preservation of anatomical details via perceptual metrics, consistency of microstructure model fitting, alleviation of degeneracies in model estimation, and joint variability with varying field strength and denoising method.

RESULTS

Accounting for all these factors, Patch2Self framework has turned out to be specifically suitable for DKI data, with improving performance at 7T. Nlsam method is more robust in alleviating degenerate black voxels while introducing some blurring, which in turn is reflected in an overall loss of image sharpness. Regarding the impact of denoising on field-dependent variability, both methods have been shown to make variations from standard to Ultra-High Field more concordant with theoretical evidence, claiming that kurtosis metrics are sensitive to susceptibility-induced background gradients, directly proportional to the magnetic field strength and sensitive to the microscopic distribution of iron and myelin.

CONCLUSIONS

This study serves as a proof-of-concept stressing the need for an accurate choice of a denoising methodology, specifically tailored for the data under analysis and allowing higher spatial resolution acquisition within clinically compatible timings, with all the potential benefits that improving suboptimal quality of diagnostic images entails.

CrOssing fiber Modeling in the Peritumoral Area using dMRI (COMPARI)

Visualization of fiber tracts around the tumor is critical for neurosurgical planning and preservation of crucial structural connectivity during tumor resection. Biophysical modeling approaches estimate fiber tract orientations from differential water diffusivity information of diffusion MRI. However, the presence of edema and tumor infiltration presents a challenge to visualize crossing fiber tracts in the peritumoral region. Previous approaches proposed free water modeling to compensate for the effect of water diffusivity in edema, but those methods were limited in estimating complex crossing fiber tracts. We propose a new cascaded multi-compartment model to estimate tissue microstructure in the presence of edema and pathological contaminants in the area surrounding brain tumors. In our model (COMPARI), the isotropic components of diffusion signal, including free water and hindered water, were eliminated, and the fiber orientation distribution (FOD) of the remaining signal was estimated. In simulated data, COMPARI accurately recovered fiber orientations in the presence of extracellular water. In a dataset of 23 patients with highly edematous brain tumors, the amplitudes of FOD and anisotropic index distribution within the peritumoral region were higher with COMPARI than with a recently proposed multi-compartment constrained deconvolution model. In a selected patient with metastatic brain tumor, we demonstrated COMPARI's ability to effectively model and eliminate water from the peritumoral region. The white matter bundles reconstructed with our model were qualitatively improved compared to those of other models, and allowed the identification of crossing fibers. In conclusion, the removal of isotropic components as proposed with COMPARI improved the bio-physical modeling of dMRI in edema, thus providing information on crossing fibers, thereby enabling improved tractography in a highly edematous brain tumor. This model may improve surgical planning tools to help achieve maximal safe resection of brain tumors.

Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting

White matter tracts generated from whole brain tractography are often processed using automatic segmentation methods with standard atlases. Atlases are generated from hundreds of subjects, which becomes time-consuming to create and difficult to apply to all populations. In this study, we extended our prior work on using a deep generative model a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space given a limited sample size of 50 subjects from the ADNI3 dataset, to generate synthetic population-specific bundle templates using Kernel Density Estimation (KDE) on streamline embeddings. We conducted a quantitative shape analysis by calculating bundle shape metrics, and found that our bundle templates better capture the shape distribution of the bundles than the atlas data used in the original segmentation derived from young healthy adults. We further demonstrated the use of our framework for direct bundle segmentation from whole-brain tractograms.

Network structure-function coupling and neurocognition in cerebral small vessel disease

BACKGROUND

Cerebral small vessel disease is a leading cause of cognitive decline and vascular dementia. Small vessel disease pathology changes structural brain networks, but its impact on functional networks remains poorly understood. Structural and functional networks are closely coupled in healthy individuals, and decoupling is associated with clinical symptoms in other neurological conditions. We tested the hypothesis that structural-functional network coupling is related to neurocognitive outcomes in 262 small vessel disease patients.

METHODS

Participants underwent multimodal magnetic resonance imaging and cognitive assessment in 2011 and 2015. Structural connectivity networks were reconstructed using probabilistic diffusion tractography, while functional connectivity networks were estimated from resting-state functional magnetic resonance imaging. Structural and functional networks were then correlated to calculate a measure of structural-functional network coupling for each participant.

RESULTS

Lower whole-brain coupling was associated with reduced processing speed and greater apathy both cross-sectionally and longitudinally. In addition, coupling within the cognitive control network was associated with all cognitive outcomes, suggesting that neurocognitive outcomes in small vessel disease may be related to the functioning of this intrinsic connectivity network.

CONCLUSIONS

Our work demonstrates the influence of structural-functional connectivity network decoupling in small vessel disease symptomatology. Cognitive control network function may be investigated in future studies.

MP-PCA denoising of fMRI time-series data can lead to artificial activation "spreading"

MP-PCA denoising has become the method of choice for denoising MRI data since it provides an objective threshold to separate the signal components from unwanted thermal noise components. In rodents, thermal noise in the coils is an important source of noise that can reduce the accuracy of activation mapping in fMRI. Further confounding this problem, vendor data often contains zero-filling and other post-processing steps that may violate MP-PCA assumptions. Here, we develop an approach to denoise vendor data and assess activation "spreading" caused by MP-PCA denoising in rodent task-based fMRI data. Data was obtained from N = 3 mice using conventional multislice and ultrafast acquisitions (1 s and 50 ms temporal resolution, respectively), during visual stimulation. MP-PCA denoising produced SNR gains of 64% and 39% and Fourier Spectral Amplitude (FSA) increases in BOLD maps of 9% and 7% for multislice and ultrafast data, respectively, when using a small [2 2] denoising window. Larger windows provided higher SNR and FSA gains with increased spatial extent of activation that may or may not represent real activation. Simulations showed that MP-PCA denoising can incur activation "spreading" with increased false positive rate and smoother functional maps due to local "bleeding" of principal components, and that the optimal denoising window for improved specificity of functional mapping, based on Dice score calculations, depends on the data's tSNR and functional CNR. This "spreading" effect applies also to another recently proposed low-rank denoising method (NORDIC), although to a lesser degree. Our results bode well for enhancing spatial and/or temporal resolution in future fMRI work, while taking into account the sensitivity/specificity trade-offs of low-rank denoising methods.