Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging

Assessment of Cerebral White Matter Involvement in Amyotrophic Lateral Sclerosis Patients With Disease Progression and Cognitive Impairment by Fixel-Based Analysis and Neurite Orientation Dispersion and Density Imaging

BACKGROUND

Previous studies using emerging diffusion MRI techniques have revealed damage to the white matter (WM) microstructure in amyotrophic lateral sclerosis (ALS), particularly the influence of crossed fibers, but there is a lack of subgroup analyses.

PURPOSE

To detect WM microstructural changes in ALS patients using fixel-based analysis (FBA) and neurite orientation dispersion and density imaging (NODDI) MRI.

STUDY TYPE

Prospective.

POPULATION

Thirty-six ALS patients (aged 60.50 ± 9.5 years) and 25 healthy controls (HCs) (aged 58.90 ± 8.1 years).

FIELD STRENGTH/SEQUENCE

3 T; NODDI and FBA (b-values = 0, 1000, and 2500 seconds/mm2).

ASSESSMENT

Subgroups were performed according to progression rate and cognition, including fast and slow progression (FP/SP), ALS with and without cognitive impairment (ALS-ci/ALS-nci). Fiber density (FD), fiber-bundle cross-section (FC), combined fiber density and cross-section (FDC), neurite density index (NDI), orientation dispersion index (ODI), isotropic volume fraction (ISO), and fractional anisotropy (FA) were calculated and their correlation with clinical variables examined.

STATISTICAL TESTING

Chi-square test, Mann-Whitney U test, two-sample t test, partial correlation analysis, and false discovery rate (FDR) corrected. A P-value <0.05 was considered significant.

RESULTS

ALS patients had lower FD and FDC values predominantly in the corticospinal tract (CST) and corpus callosum (CC) regions, as well as lower NDI value in the CC, radial crown, and internal capsule compared to HCs. Subgroup analysis based on progression rate and cognitive function showed significant differences in FBA results. The FC in the right CST region was significantly lower in the FP than SP, and the FD in the CC region was significantly lower in the ALS-ci than ALS-nci. Furthermore, a negative correlation was found between the mean FC value and the rate of progression in ALS patients (r = -0.408).

DATA CONCLUSION

FBA is a powerful tool for detecting complex cerebral WM microstructural damage for evaluating ALS cognition and disease progression.

Comparison of T1-weighted landmark placement and ROI transfer onto diffusion-weighted EPI sequences for targeted tractography tasks in the optic nerve

Diffusion-based tractography in the optic nerve requires sampling strategies assisted by anatomical landmark information (regions of interest [ROIs]). We aimed to investigate the feasibility of expert-placed, high-resolution T1-weighted ROI-data transfer onto lower spatial resolution diffusion-weighted images. Slab volumes from 20 volunteers were acquired and preprocessed including distortion bias correction and artifact reduction. Constrained spherical deconvolution was used to generate a directional diffusion information grid (fibre orientation distribution-model [FOD]). Three neuroradiologists marked landmarks on both diffusion imaging variants and structural datasets. Structural ROI information (volumetric interpolated breath-hold sequence [VIBE]) was respectively registered (linear with 6/12 degrees of freedom [DOF]) onto single-shot EPI (ss-EPI) and readout-segmented EPI (rs-EPI) volumes, respectively. All eight ROI/FOD-combinations were compared in a targeted tractography task of the optic nerve pathway. Inter-rater reliability for placed ROIs among experts was highest in VIBE images (lower confidence interval 0.84 to 0.97, mean 0.91) and lower in both ss-EPI (0.61 to 0.95, mean 0.79) and rs-EPI (0.59 to 0.86, mean 0.70). Tractography success rate based on streamline selection performance was highest in VIBE-drawn ROIs registered (6-DOF) onto rs-EPI FOD (70.0% over 5%-threshold, capped to failed ratio 39/16) followed by both 12-DOF-registered (67.5%; 41/16) and nonregistered VIBE (67.5%; 40/23). On ss-EPI FOD, VIBE-ROI-datasets obtained fewer streamlines overall with each at 55.0% above 5%-threshold and with lower capped to failed ratio (6-DOF: 35/36; 12-DOF: 34/34, nonregistered 33/36). The combination of VIBE-placed ROIs (highest inter-rater reliability) with 6-DOF registration onto rs-EPI targets (best streamline selection performance) is most suitable for white matter template generation required in group studies.

Characteristics of the Structural Connectivity in Patients with Brain Injury and Chronic Health Symptoms: A Pilot Study

Diffusion properties from diffusion tensor imaging (DTI) are exquisitely sensitive to white matter abnormalities incurred during traumatic brain injury (TBI), especially for those patients with chronic post-TBI symptoms such as headaches, dizziness, fatigue, etc. The evaluation of structural and functional connectivity using DTI has become a promising method for identifying subtle alterations in brain connectivity associated with TBI that are otherwise not visible with conventional imaging. This study assessed whether TBI patients with (n = 17) or without (n = 16) chronic symptoms (TBIcs/TBIncs) exhibit any changes in structural connectivity (SC) and mean fractional anisotropy (mFA) of intra- and inter-hemispheric connections when compared to a control group (CG) (n = 13). Reductions in SC and mFA were observed for TBIcs compared to CG, but not for TBIncs. More connections were found to have mFA reductions than SC reductions. On the whole, SC is dominated by ipsilateral connections for all the groups after the comparison of contralateral and ipsilateral connections. More contra-ipsi reductions of mFA were found for TBIcs than TBIncs compared to CG. These findings suggest that TBI patients with chronic symptoms not only demonstrate decreased global and regional mFA but also reduced structural network connectivity.

Hippocampal hub failure is linked to long-term memory impairment in anti-NMDA-receptor encephalitis: insights from structural connectome graph theoretical network analysis

BACKGROUND

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is characterized by distinct structural and functional brain alterations, predominantly affecting the medial temporal lobes and the hippocampus. Structural connectome analysis with graph-based investigations of network properties allows for an in-depth characterization of global and local network changes and their relationship with clinical deficits in NMDAR encephalitis.

METHODS

Structural networks from 61 NMDAR encephalitis patients in the post-acute stage (median time from acute hospital discharge: 18 months) and 61 age- and sex-matched healthy controls (HC) were analyzed using diffusion-weighted imaging (DWI)-based probabilistic anatomically constrained tractography and volumetry of a selection of subcortical and white matter brain volumes was performed. We calculated global, modular, and nodal graph measures with special focus on default-mode network, medial temporal lobe, and hippocampus. Pathologically altered metrics were investigated regarding their potential association with clinical course, disease severity, and cognitive outcome.

RESULTS

Patients with NMDAR encephalitis showed regular global graph metrics, but bilateral reductions of hippocampal node strength (left: p = 0.049; right: p = 0.013) and increased node strength of right precuneus (p = 0.013) compared to HC. Betweenness centrality was decreased for left-sided entorhinal cortex (p = 0.042) and left caudal middle frontal gyrus (p = 0.037). Correlation analyses showed a significant association between reduced left hippocampal node strength and verbal long-term memory impairment (p = 0.021). We found decreased left (p = 0.013) and right (p = 0.001) hippocampal volumes that were associated with hippocampal node strength (left p = 0.009; right p < 0.001).

CONCLUSIONS

Focal network property changes of the medial temporal lobes indicate hippocampal hub failure that is associated with memory impairment in NMDAR encephalitis at the post-acute stage, while global structural network properties remain unaltered. Graph theory analysis provides new pathophysiological insight into structural network changes and their association with persistent cognitive deficits in NMDAR encephalitis.

Parameter estimation in a whole-brain network model of epilepsy: Comparison of parallel global optimization solvers

The Virtual Epileptic Patient (VEP) refers to a computer-based representation of a patient with epilepsy that combines personalized anatomical data with dynamical models of abnormal brain activities. It is capable of generating spatio-temporal seizure patterns that resemble those recorded with invasive methods such as stereoelectro EEG data, allowing for the evaluation of clinical hypotheses before planning surgery. This study highlights the effectiveness of calibrating VEP models using a global optimization approach. The approach utilizes SaCeSS, a cooperative metaheuristic algorithm capable of parallel computation, to yield high-quality solutions without requiring excessive computational time. Through extensive benchmarking on synthetic data, our proposal successfully solved a set of different configurations of VEP models, demonstrating better scalability and superior performance against other parallel solvers. These results were further enhanced using a Bayesian optimization framework for hyperparameter tuning, with significant gains in terms of both accuracy and computational cost. Additionally, we added a scalable uncertainty quantification phase after model calibration, and used it to assess the variability in estimated parameters across different problems. Overall, this study has the potential to improve the estimation of pathological brain areas in drug-resistant epilepsy, thereby to inform the clinical decision-making process.

NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions

PURPOSE

Diffusion magnetic resonance imaging (dMRI) is a widely used non-invasive method for investigating brain anatomical structures. Conventional techniques for estimating fiber orientation distribution (FOD) from dMRI data often neglect voxel-level spatial relationships, leading to ambiguous associations between target voxels and their neighbors, which, in turn, adversely impacts FOD accuracy. This study aims to address this issue by introducing a novel neural network, the neighboring voxel attention mechanism network (NVAM-Net), designed to reconstruct high-quality FOD images.

METHODS

The NVAM-Net leverages a Transformer architecture and incorporates two innovative attention mechanisms: voxel attention and surface attention. These mechanisms are specifically designed to capture overlooked features among neighboring voxels. The processed features are subsequently passed through two fully connected layers, further enhancing FOD estimation accuracy by separately estimating spherical harmonics (SH) coefficients of varying orders.

RESULTS

The experimental findings, based on the Human Connectome Project (HCP) dataset, reveal that the reconstructed super-resolution FOD images achieve results comparable to those obtained through more advanced dMRI acquisition protocols. These results underscore the NVAM-Net's robust performance in reconstructing multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD).

CONCLUSION

In summary, this research underscores the NVAM-Net's advantages and practical feasibility in reconstructing high-quality FOD images. It provides a reliable reference point for clinical applications in the field of diffusion magnetic resonance imaging.

Dynamic changes in subplate and cortical plate microstructure at the onset of cortical folding in vivo

Cortical gyrification takes place predominantly during the second to third trimester, alongside other fundamental developmental processes, such as the development of white matter connections, lamination of the cortex and formation of neural circuits. The mechanistic biology that drives the formation cortical folding patterns remains an open question in neuroscience. In our previous work, we modelled the in utero diffusion signal to quantify the maturation of microstructure in transient fetal compartments, identifying patterns of change in diffusion metrics that reflect critical neurobiological transitions occurring in the second to third trimester. In this work, we apply the same modelling approach to explore whether microstructural maturation of these compartments is correlated with the process of gyrification. We quantify the relationship between sulcal depth and tissue anisotropy within the cortical plate (CP) and underlying subplate (SP), key transient fetal compartments often implicated in mechanistic hypotheses about the onset of gyrification. Using in utero high angular resolution multi-shell diffusion-weighted imaging (HARDI) from the Developing Human Connectome Project (dHCP), our analysis reveals that the anisotropic, tissue component of the diffusion signal in the SP and CP decreases immediately prior to the formation of sulcal pits in the fetal brain. By back-projecting a map of folded brain regions onto the unfolded brain, we find evidence for cytoarchitectural differences between gyral and sulcal areas in the late second trimester, suggesting that regional variation in the microstructure of transient fetal compartments precedes, and thus may have a mechanistic function, in the onset of cortical folding in the developing human brain.

Feasibility study to unveil the potential: considerations of constrained spherical deconvolution tractography with unsedated neonatal diffusion brain MRI data

Purpose

The study aimed to (1) assess the feasibility constrained spherical deconvolution (CSD) tractography to reconstruct crossing fiber bundles with unsedated neonatal diffusion MRI (dMRI), and (2) demonstrate the impact of spatial and angular resolution and processing settings on tractography and derived quantitative measures.

Methods

For the purpose of this study, the term-equivalent dMRIs (single-shell b800, and b2000, both 5 b0, and 45 gradient directions) of two moderate-late preterm infants (with and without motion artifacts) from a local cohort [Brain Imaging in Moderate-late Preterm infants (BIMP) study; Calgary, Canada] and one infant from the developing human connectome project with high-quality dMRI (using the b2600 shell, comprising 20 b0 and 128 gradient directions, from the multi-shell dataset) were selected. Diffusion tensor imaging (DTI) and CSD tractography were compared on b800 and b2000 dMRI. Varying image resolution modifications, (pre-)processing and tractography settings were tested to assess their impact on tractography. Each experiment involved visualizing local modeling and tractography for the corpus callosum and corticospinal tracts, and assessment of morphological and diffusion measures.

Results

Contrary to DTI, CSD enabled reconstruction of crossing fibers. Tractography was susceptible to image resolution, (pre-) processing and tractography settings. In addition to visual variations, settings were found to affect streamline count, length, and diffusion measures (fractional anisotropy and mean diffusivity). Diffusion measures exhibited variations of up to 23%.

Conclusion

Reconstruction of crossing fiber bundles using CSD tractography with unsedated neonatal dMRI data is feasible. Tractography settings affected streamline reconstruction, warranting careful documentation of methods for reproducibility and comparison of cohorts.

Developmental surface dyslexia and dysgraphia in a child with corpus callosum agenesis: an approach to diagnosis and treatment

We present a case study detailing cognitive performance, functional neuroimaging, and effects of a hypothesis-driven treatment in a 10-year-old girl diagnosed with complete, isolated corpus callosum agenesis. Despite having average overall intellectual abilities, the girl exhibited profound surface dyslexia and dysgraphia. Spelling treatment significantly and persistently improved her spelling of trained irregular words, and this improvement generalized to reading accuracy and speed of trained words. Diffusion weighted imaging revealed strengthened intrahemispheric white matter connectivity of the left temporal cortex after treatment and identified interhemispheric connectivity between the occipital lobes, likely facilitated by a pathway crossing the midline via the posterior commissure. This case underlines the corpus callosum's critical role in lexical reading and writing. It demonstrates that spelling treatment may enhance interhemispheric connectivity in corpus callosum agenesis through alternative pathways, boosting the development of a more efficient functional organization of the visual word form area within the left temporo-occipital cortex.

Recovering high-quality fiber orientation distributions from a reduced number of diffusion-weighted images using a model-driven deep learning architecture

PURPOSE

The aim of this study was to develop a model-based deep learning architecture to accurately reconstruct fiber orientation distributions (FODs) from a reduced number of diffusion-weighted images (DWIs), facilitating accurate analysis with reduced acquisition times.

METHODS

Our proposed architecture, Spherical Deconvolution Network (SDNet), performed FOD reconstruction by mapping 30 DWIs to fully sampled FODs, which have been fit to 288 DWIs. SDNet included DWI-consistency blocks within the network architecture, and a fixel-classification penalty within the loss function. SDNet was trained on a subset of the Human Connectome Project, and its performance compared with FOD-Net, and multishell multitissue constrained spherical deconvolution.

RESULTS

SDNet achieved the strongest results with respect to angular correlation coefficient and sum of squared errors. When the impact of the fixel-classification penalty was increased, we observed an improvement in performance metrics reliant on segmenting the FODs into the correct number of fixels.

CONCLUSION

Inclusion of DWI-consistency blocks improved reconstruction performance, and the fixel-classification penalty term offered increased control over the angular separation of fixels in the reconstructed FODs.

Detailed mapping of the complex fiber structure and white matter pathways of the chimpanzee brain

Long-standing questions about human brain evolution may only be resolved through comparisons with close living evolutionary relatives, such as chimpanzees. This applies in particular to structural white matter (WM) connectivity, which continuously expanded throughout evolution. However, due to legal restrictions on chimpanzee research, neuroscience research currently relies largely on data with limited detail or on comparisons with evolutionarily distant monkeys. Here, we present a detailed magnetic resonance imaging resource to study structural WM connectivity in the chimpanzee. This open-access resource contains (1) WM reconstructions of a postmortem chimpanzee brain, using the highest-quality diffusion magnetic resonance imaging data yet acquired from great apes; (2) an optimized and validated method for high-quality fiber orientation reconstructions; and (3) major fiber tract segmentations for cross-species morphological comparisons. This dataset enabled us to identify phylogenetically relevant details of the chimpanzee connectome, and we anticipate that it will substantially contribute to understanding human brain evolution.

Longitudinal microstructural alterations surrounding subcortical ischemic stroke lesions detected by free-water imaging

In this study we explore the spatio-temporal trajectory and clinical relevance of microstructural white matter changes within and beyond subcortical stroke lesions detected by free-water imaging. Twenty-seven patients with subcortical infarct with mean age of 66.73 (SD 11.57) and median initial NIHSS score of 4 (IQR 3-7) received diffusion MRI 3-5 days, 1 month, 3 months, and 12 months after symptom-onset. Extracellular free-water and fractional anisotropy of the tissue (FAT) were averaged within stroke lesions and the surrounding tissue. Linear models showed increased free-water and decreased FAT in the white matter of patients with subcortical stroke (lesion [free-water/FAT, mean relative difference in %, ipsilesional vs. contralesional hemisphere at 3-5 days, 1 month, 3 months, and 12 months after symptom-onset]: +41/-34, +111/-37, +208/-26, +251/-18; perilesional tissue [range in %]: +[5-24]/-[0.2-7], +[2-20]/-[3-16], +[5-43]/-[2-16], +[10-110]/-[2-12]). Microstructural changes were most prominent within the lesion and gradually became less pronounced with increasing distance from the lesion. While free-water elevations continuously increased over time and peaked after 12 months, FAT decreases were most evident 1 month post-stroke, gradually returning to baseline values thereafter. Higher perilesional free-water and higher lesional FAT at baseline were correlated with greater reductions in lesion size (rho = -0.51, p = .03) in unadjusted analyses only, while there were no associations with clinical measures. In summary, we find a characteristic spatio-temporal pattern of extracellular and cellular alterations beyond subcortical stroke lesions, indicating a dynamic parenchymal response to ischemia characterized by vasogenic edema, cellular damage, and white matter atrophy.

Sparse Blind Spherical Deconvolution of diffusion weighted MRI

Diffusion-weighted magnetic resonance imaging provides invaluable insights into in-vivo neurological pathways. However, accurate and robust characterization of white matter fibers microstructure remains challenging. Widely used spherical deconvolution algorithms retrieve the fiber Orientation Distribution Function (ODF) by using an estimation of a response function, i.e., the signal arising from individual fascicles within a voxel. In this paper, an algorithm of blind spherical deconvolution is proposed, which only assumes the axial symmetry of the response function instead of its exact knowledge. This algorithm provides a method for estimating the peaks of the ODF in a voxel without any explicit response function, as well as a method for estimating signals associated with the peaks of the ODF, regardless of how those peaks were obtained. The two stages of the algorithm are tested on Monte Carlo simulations, as well as compared to state-of-the-art methods on real in-vivo data for the orientation retrieval task. Although the proposed algorithm was shown to attain lower angular errors than the state-of-the-art constrained spherical deconvolution algorithm on synthetic data, it was outperformed by state-of-the-art spherical deconvolution algorithms on in-vivo data. In conjunction with state-of-the art methods for axon bundles direction estimation, the proposed method showed its potential for the derivation of per-voxel per-direction metrics on synthetic as well as in-vivo data.

Brain structure and connectivity mediate the association between lifestyle and cognition: The Maastricht Study

Life-course exposure to risk and protective factors impacts brain macro- and micro-structure, which in turn affects cognition. The concept of brain-age gap assesses brain health by comparing an individual's neuroimaging-based predicted age with their calendar age. A higher BAG implies accelerated brain ageing and is expected to be associated with worse cognition. In this study, we comprehensively modelled mutual associations between brain health and lifestyle factors, brain age and cognition in a large, middle-aged population. For this study, cognitive test scores, lifestyle and 3T MRI data for n = 4881 participants [mean age (± SD) = 59.2 (±8.6), 50.1% male] were available from The Maastricht Study, a population-based cohort study with extensive phenotyping. Whole-brain volumes (grey matter, cerebrospinal fluid and white matter hyperintensity), cerebral microbleeds and structural white matter connectivity were calculated. Lifestyle factors were combined into an adapted LIfestyle for BRAin health weighted sum score, with higher score indicating greater dementia risk. Cognition was calculated by averaging z-scores across three cognitive domains (memory, information processing speed and executive function and attention). Brain-age gap was calculated by comparing calendar age to predictions from a neuroimaging-based multivariable regression model. Paths between LIfestyle for BRAin health tertiles, brain-age gap and cognitive function were tested using linear regression and structural equation modelling, adjusting for sociodemographic and clinical confounders. The results show that cerebrospinal fluid, grey matter, white matter hyperintensity and cerebral microbleeds best predicted brain-age gap (R 2 = 0.455, root mean squared error = 6.44). In regression analysis, higher LIfestyle for BRAin health scores (greater dementia risk) were associated with higher brain-age gap (standardized regression coefficient β = 0.126, P < 0.001) and worse cognition (β = -0.046, P = 0.013), while higher brain-age gap was associated with worse cognition (β=-0.163, P < 0.001). In mediation analysis, 24.7% of the total difference in cognition between the highest and lowest LIfestyle for BRAin health tertile was mediated by brain-age gap (β indirect = -0.049, P < 0.001; β total = -0.198, P < 0.001) and an additional 3.8% was mediated via connectivity (β indirect = -0.006, P < 0.001; β total = -0.150, P < 0.001). Findings suggest that associations between health- and lifestyle-based risk/protective factors (LIfestyle for BRAin health) and cognition can be partially explained by structural brain health markers (brain-age gap) and white matter connectivity markers. Lifestyle interventions targeted at high-risk individuals in mid-to-late life may be effective in promoting and preserving cognitive function in the general public.

A Hybrid Routing Pattern in Human Brain Structural Network Revealed By Evolutionary Computation

The human brain functional connectivity network (FCN) is constrained and shaped by the communication processes in the structural connectivity network (SCN). The underlying communication mechanism thus becomes a critical issue for understanding the formation and organization of the FCN. A number of communication models supported by different routing strategies have been proposed, with shortest path (SP), random diffusion (DIF), and spatial navigation (NAV) as the most typical, respectively requiring network global knowledge, local knowledge, and both for path seeking. Yet these models all assumed every brain region to use one routing strategy uniformly, ignoring convergent evidence that supports the regional heterogeneity in both terms of biological substrates and functional roles. In this regard, the current study developed a hybrid communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB). The HYB was found to outperform the three typical routing strategies in predicting FCN and facilitating robust communication. Analyses on HYB further revealed that brain regions in lower-order functional modules inclined to route signals using global knowledge, while those in higher-order functional modules preferred DIF that requires only local knowledge. Compared to regions that used global knowledge for routing, regions using DIF had denser structural connections, participated in more functional modules, but played a less dominant role within modules. Together, our findings further evidenced that hybrid routing underpins efficient SCN communication and locally heterogeneous structure-function coupling.

Comparative validation of automated presurgical tractography based on constrained spherical deconvolution and diffusion tensor imaging with direct electrical stimulation

OBJECTIVES

Accurate presurgical brain mapping enables preoperative risk assessment and intraoperative guidance. This cross-sectional study investigated whether constrained spherical deconvolution (CSD) methods were more accurate than diffusion tensor imaging (DTI)-based methods for presurgical white matter mapping using intraoperative direct electrical stimulation (DES) as the ground truth.

METHODS

Five different tractography methods were compared (three DTI-based and two CSD-based) in 22 preoperative neurosurgical patients undergoing surgery with DES mapping. The corticospinal tract (CST, N = 20) and arcuate fasciculus (AF, N = 7) bundles were reconstructed, then minimum distances between tractograms and DES coordinates were compared between tractography methods. Receiver-operating characteristic (ROC) curves were used for both bundles. For the CST, binary agreement, linear modeling, and posthoc testing were used to compare tractography methods while correcting for relative lesion and bundle volumes.

RESULTS

Distance measures between 154 positive (functional response, pDES) and negative (no response, nDES) coordinates, and 134 tractograms resulted in 860 data points. Higher agreement was found between pDES coordinates and CSD-based compared to DTI-based tractograms. ROC curves showed overall higher sensitivity at shorter distance cutoffs for CSD (8.5 mm) compared to DTI (14.5 mm). CSD-based CST tractograms showed significantly higher agreement with pDES, which was confirmed by linear modeling and posthoc tests (PFWE < .05).

CONCLUSIONS

CSD-based CST tractograms were more accurate than DTI-based ones when validated using DES-based assessment of motor and sensory function. This demonstrates the potential benefits of structural mapping using CSD in clinical practice.

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.

Subtle microstructural alterations in white matter tracts involved in socio-emotional processing after very preterm birth

Children born very preterm (VPT, < 32 weeks of gestation) have an increased risk of developing socio-emotional difficulties. Possible neural substrates for these socio-emotional difficulties are alterations in the structural connectivity of the social brain due to premature birth. The objective of the current study was to study microstructural white matter integrity in VPT versus full-term (FT) born school-aged children along twelve white matter tracts involved in socio-emotional processing. Diffusion MRI scans were obtained from a sample of 35 VPT and 38 FT 8-to-12-year-old children. Tractography was performed using TractSeg, a state-of-the-art neural network-based approach, which offers investigation of detailed tract profiles of fractional anisotropy (FA). Group differences in FA along the tracts were investigated using both a traditional and complementary functional data analysis approach. Exploratory correlations were performed between the Social Responsiveness Scale (SRS-2), a parent-report questionnaire assessing difficulties in social functioning, and FA along the tract. Both analyses showed significant reductions in FA for the VPT group along the middle portion of the right SLF I and an anterior portion of the left SLF II. These group differences possibly indicate altered white matter maturation due to premature birth and may contribute to altered functional connectivity in the Theory of Mind network which has been documented in earlier work with VPT samples. Apart from reduced social motivation in the VPT group, there were no significant group differences in reported social functioning, as assessed by SRS-2. We found that in the VPT group higher FA values in segments of the left SLF I and right SLF II were associated with better social functioning. Surprisingly, the opposite was found for segments in the right IFO, where higher FA values were associated with worse reported social functioning. Since no significant correlations were found for the FT group, this relationship may be specific for VPT children. The current study overcomes methodological limitations of previous studies by more accurately segmenting white matter tracts using constrained spherical deconvolution based tractography, by applying complementary tractometry analysis approaches to estimate changes in FA more accurately, and by investigating the FA profile along the three components of the SLF.

Effect of Magnetic Resonance Image Quality on Structural and Functional Brain Connectivity: The Maastricht Study

In population-based cohort studies, magnetic resonance imaging (MRI) is vital for examining brain structure and function. Advanced MRI techniques, such as diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI), provide insights into brain connectivity. However, biases in MRI data acquisition and processing can impact brain connectivity measures and their associations with demographic and clinical variables. This study, conducted with 5110 participants from The Maastricht Study, explored the relationship between brain connectivity and various image quality metrics (e.g., signal-to-noise ratio, head motion, and atlas-template mismatches) that were obtained from dMRI and rs-fMRI scans. Results revealed that in particular increased head motion (R2 up to 0.169, p < 0.001) and reduced signal-to-noise ratio (R2 up to 0.013, p < 0.001) negatively impacted structural and functional brain connectivity, respectively. These image quality metrics significantly affected associations of overall brain connectivity with age (up to -59%), sex (up to -25%), and body mass index (BMI) (up to +14%). Associations with diabetes status, educational level, history of cardiovascular disease, and white matter hyperintensities were generally less affected. This emphasizes the potential confounding effects of image quality in large population-based neuroimaging studies on brain connectivity and underscores the importance of accounting for it.

Association between altered white matter networks and post operative ventricle volume in shunt-treated pediatric hydrocephalus

OBJECTIVE

The objective of this study was to use probabilistic tractography in combination with white matter microstructure metrics to characterize differences in white matter networks between shunt-treated pediatric hydrocephalus patients relative to healthy controls. We were also able to explore the relationship between these white matter networks and postoperative ventricle volume.

METHODS

Network-based statistics was used in combination with whole-brain probabilistic tractography to determine dysregulated white matter networks in a sample of patients with pediatric hydrocephalus (n = 8), relative to controls (n = 36). Metrics such as streamline count (SC), as well as the mean of the fractional anisotropy along a tract, axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) were assessed. In networks that were found to be significantly different for patients with hydrocephalus, tracts were evaluated to assess their relationship with postoperative lateral ventricle volume.

RESULTS

Patients with pediatric hydrocephalus had various networks that were either upregulated or downregulated relative to controls across all white matter measures. Predominately, network dysregulation occurred in tracts involving structures located outside of the frontal lobe. Furthermore tracts with values suggesting decreased white matter integrity were not only found between subcortical structures, but also cortical structures. While there were various tracts with white matter metrics that were initially predicted by lateral ventricle volume, only two tracts remained significant following multiple comparisons.

CONCLUSIONS

This cross-sectional study in pediatric patients with hydrocephalus and healthy controls demonstrated using whole-brain probabilistic tractography that there are various networks with dysregulated white matter integrity in hydrocephalus patients relative to controls. These dysregulated networks have tracts connecting structures throughout the brain, and the regions were predominately located centrally and posteriorly. Postoperative ventricle volume did not predict the white matter integrity of many tracts. Future studies with larger sample sizes are needed to further understand these results.

Abnormal wiring of the structural connectome in adults with ADHD

Current knowledge of white matter changes in large-scale brain networks in adult attention-deficit/hyperactivity disorder (ADHD) is scarce. We collected diffusion-weighted magnetic resonance imaging data in 40 adults with ADHD and 36 neurotypical controls and used constrained spherical deconvolution-based tractography to reconstruct whole-brain structural connectivity networks. We used network-based statistic (NBS) and graph theoretical analysis to investigate differences in these networks between the ADHD and control groups, as well as associations between structural connectivity and ADHD symptoms assessed with the Adult ADHD Self-Report Scale or performance in the Conners Continuous Performance Test 2 (CPT-2). NBS revealed decreased connectivity in the ADHD group compared to the neurotypical controls in widespread unilateral networks, which included subcortical and corticocortical structures and encompassed dorsal and ventral attention networks and visual and somatomotor systems. Furthermore, hypoconnectivity in a predominantly left-frontal network was associated with higher amount of commission errors in CPT-2. Graph theoretical analysis did not reveal topological differences between the groups or associations between topological properties and ADHD symptoms or task performance. Our results suggest that abnormal structural wiring of the brain in adult ADHD is manifested as widespread intrahemispheric hypoconnectivity in networks previously associated with ADHD in functional neuroimaging studies.

Whole-brain modeling of the differential influences of amyloid-beta and tau in Alzheimer's disease

BACKGROUND

Alzheimer's disease is a neurodegenerative condition associated with the accumulation of two misfolded proteins, amyloid-beta (A[Formula: see text]) and tau. We study their effect on neuronal activity, with the aim of assessing their individual and combined impact.

METHODS

We use a whole-brain dynamic model to find the optimal parameters that best describe the effects of A[Formula: see text] and tau on the excitation-inhibition balance of the local nodes.

RESULTS

We found a clear dominance of A[Formula: see text] over tau in the early disease stages (MCI), while tau dominates over A[Formula: see text] in the latest stages (AD). We identify crucial roles for A[Formula: see text] and tau in complex neuronal dynamics and demonstrate the viability of using regional distributions to define models of large-scale brain function in AD.

CONCLUSIONS

Our study provides further insight into the dynamics and complex interplay between these two proteins, opening the path for further investigations on biomarkers and candidate therapeutic targets in-silico.

Subject-Specific Automatic Reconstruction of White Matter Tracts

MRI-based tractography is still underexploited and unsuited for routine use in brain tumor surgery due to heterogeneity of methods and functional-anatomical definitions and above all, the lack of a turn-key system. Standardization of methods is therefore desirable, whereby an objective and reliable approach is a prerequisite before the results of any automated procedure can subsequently be validated and used in neurosurgical practice. In this work, we evaluated these preliminary but necessary steps in healthy volunteers. Specifically, we evaluated the robustness and reliability (i.e., test-retest reproducibility) of tractography results of six clinically relevant white matter tracts by using healthy volunteer data (N = 136) from the Human Connectome Project consortium. A deep learning convolutional network-based approach was used for individualized segmentation of regions of interest, combined with an evidence-based tractography protocol and appropriate post-tractography filtering. Robustness was evaluated by estimating the consistency of tractography probability maps, i.e., averaged tractograms in normalized space, through the use of a hold-out cross-validation approach. No major outliers were found, indicating a high robustness of the tractography results. Reliability was evaluated at the individual level. First by examining the overlap of tractograms that resulted from repeatedly processed identical MRI scans (N = 10, 10 iterations) to establish an upper limit of reliability of the pipeline. Second, by examining the overlap for subjects that were scanned twice at different time points (N = 40). Both analyses indicated high reliability, with the second analysis showing a reliability near the upper limit. The robust and reliable subject-specific generation of white matter tracts in healthy subjects holds promise for future validation of our pipeline in a clinical population and subsequent implementation in brain tumor surgery.

Rich-club reorganization of white matter structural network in schizophrenia patients with auditory verbal hallucinations following 1 Hz rTMS treatment

The human brain comprises a large-scale structural network of regions and interregional pathways, including a selectively defined set of highly central and interconnected hub regions, often referred to as the "rich club", which may play a pivotal role in the integrative processes of the brain. A quintessential symptom of schizophrenia, auditory verbal hallucinations (AVH) have shown a decrease in severity following low-frequency repetitive transcranial magnetic stimulation (rTMS). However, the underlying mechanism of rTMS in treating AVH remains elusive. This study investigated the effect of low-frequency rTMS on the rich-club organization within the brain in patients diagnosed with schizophrenia who experience AVH using diffusion tensor imaging data. Through by constructing structural connectivity networks, we identified several critical rich hub nodes, which constituted a rich-club subnetwork, predominantly located in the prefrontal cortices. Notably, our findings revealed enhanced connection strength and density within the rich-club subnetwork following rTMS treatment. Furthermore, we found that the decreased connectivity within the subnetwork components, including the rich-club subnetwork, was notably enhanced in patients following rTMS treatment. In particular, the increased connectivity strength of the right median superior frontal gyrus, which functions as a critical local bridge, with the right postcentral gyrus exhibited a significant correlation with improvements in both positive symptoms and AVH. These findings provide valuable insights into the role of rTMS in inducing reorganizational changes within the rich-club structural network in schizophrenia and shed light on potential mechanisms through which rTMS may alleviate AVH.

Fiber-Specific White Matter Alterations in Parkinson's Disease Patients with GBA Gene Mutations

BACKGROUND

Patients with Parkinson's disease (PD) carrying GBA gene mutations (GBA-PD) have a more aggressive disease course than those with idiopathic PD (iPD).

OBJECTIVE

The objective of this study was to investigate fiber-specific white matter (WM) differences in nonmedicated patients with early-stage GBA-PD and iPD using fixel-based analysis, a novel technique to assess tract-specific WM microstructural and macrostructural features comprehensively.

METHODS

Fixel-based metrics, including microstructural fiber density (FD), macrostructural fiber-bundle cross section (FC), and a combination of FD and FC (FDC), were compared among 30 healthy control subjects, 16 patients with GBA-PD, and 35 patients with iPD. Associations between FDC and clinical evaluations were also explored using multiple linear regression analyses.

RESULTS

Patients with GBA-PD showed significantly lower FD in the fornix and superior longitudinal fasciculus than healthy control subjects, and lower FC in the corticospinal tract (CST) and lower FDC in the CST, middle cerebellar peduncle, and striatal-thalamo-cortical pathways than patients with iPD. Contrarily, patients with iPD showed significantly higher FC and FDC in the CST and striatal-thalamo-cortical pathways than healthy control subjects. In addition, lower FDC in patients with GBA-PD was associated with reduced glucocerebrosidase enzyme activity, lower cerebrospinal fluid total α-synuclein levels, lower Montreal Cognitive Assessment scores, lower striatal binding ratio, and higher Unified Parkinson's Disease Rating Scale Part III scores.

CONCLUSIONS

We report reduced fiber-specific WM density and bundle cross-sectional size in patients with GBA-PD, suggesting neurodegeneration linked to glucocerebrosidase deficiency, α-synuclein accumulation, and poorer cognition and motor functions. Conversely, patients with iPD showed increased fiber bundle size, likely because of WM reorganization. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Probing bundle-wise abnormalities in patients infected with human immunodeficiency virus using fixel-based analysis: new insights into neurocognitive impairments

BACKGROUND

Changes in white matter (WM) underlie the neurocognitive damages induced by a human immunodeficiency virus (HIV) infection. This study aimed to examine using a bundle-associated fixel-based analysis (FBA) pipeline for investigating the microstructural and macrostructural alterations in the WM of the brain of HIV patients.

METHODS

This study collected 93 HIV infected patients and 45 age/education/handedness matched healthy controls (HCs) at the Beijing Youan Hospital between January 1, 2016 and December 30, 2016.All HIV patients underwent neurocognitive evaluation and laboratory testing followed by magnetic resonance imaging (MRI) scanning. In order to detect the bundle-wise WM abnormalities accurately, a specific WM bundle template with 56 tracts of interest was firstly generated by an automated fiber clustering method using a subset of subjects. Fixel-based analysis was used to investigate bundle-wise differences between HIV patients and HCs in three perspectives: fiber density (FD), fiber cross-section (FC), and fiber density and cross-section (FDC). The between-group differences were detected by a two-sample t -test with the false discovery rate (FDR) correction ( P <0.05). Furthermore, the covarying relationship in FD, FC and FDC between any pair of bundles was also accessed by the constructed covariance networks, which was subsequently compared between HIV and HCs via permutation t -tests. The correlations between abnormal WM metrics and the cognitive functions of HIV patients were explored via partial correlation analysis after controlling age and gender.

RESULTS

Among FD, FC and FDC, FD was the only metric that showed significant bundle-wise alterations in HIV patients compared to HCs. Increased FD values were observed in the bilateral fronto pontine tract, corona radiata frontal, left arcuate fasciculus, left corona radiata parietal, left superior longitudinal fasciculus III, and right superficial frontal parietal (SFP) (all FDR P <0.05). In bundle-wise covariance network, HIV patients displayed decreased FD and increased FC covarying patterns in comparison to HC ( P <0.05) , especially between associated pathways. Finally, the FCs of several tracts exhibited a significant correlation with language and attention-related functions.

CONCLUSIONS

Our study demonstrated the utility of FBA on detecting the WM alterations related to HIV infection. The bundle-wise FBA method provides a new perspective for investigating HIV-induced microstructural and macrostructural WM-related changes, which may help to understand cognitive dysfunction in HIV patients thoroughly.

High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions

Diffusion-weighted Imaging (DWI) is a non-invasive imaging technique based on Magnetic Resonance Imaging (MRI) principles to measure water diffusivity and reveal details of the underlying brain micro-structure. By fitting a tensor model to quantify the directionality of water diffusion a Diffusion Tensor Image (DTI) can be derived and scalar measures, such as fractional anisotropy (FA), can then be estimated from the DTI to summarise quantitative microstructural information for clinical studies. In particular, FA has been shown to be a useful research metric to identify tissue abnormalities in neurological disease (e.g. decreased anisotropy as a proxy for tissue damage). However, time constraints in clinical practice lead to low angular resolution diffusion imaging (LARDI) acquisitions that can cause inaccurate FA value estimates when compared to those generated from high angular resolution diffusion imaging (HARDI) acquisitions. In this work, we propose High Angular DTI Estimation Network (HADTI-Net) to estimate an enhanced DTI model from LARDI with a set of minimal and evenly distributed diffusion gradient directions. Extensive experiments have been conducted to show the reliability and generalisation of HADTI-Net to generate high angular DTI estimation from any minimal evenly distributed diffusion gradient directions and to explore the feasibility of applying a data-driven method for this task. The code repository of this work and other related works can be found at https://mri-synthesis.github.io/.

A Mega-analytic Study of White Matter Microstructural Differences Across 5 Cohorts of Youths With Attention-Deficit/Hyperactivity Disorder

BACKGROUND

While attention-deficit/hyperactivity disorder (ADHD) has been associated with differences in the structural connections formed by the brain's white matter tracts, studies of such differences have yielded inconsistent findings, likely reflecting small sample sizes. Thus, we conducted a mega-analysis on in vivo measures of white matter microstructure obtained through diffusion tensor imaging of more than 6000 participants from 5 cohorts.

METHODS

In a mega-analysis, linear mixed models were used to test for associations between the fractional anisotropy of 42 white matter tracts and ADHD traits and diagnosis. Contrasts were made against measures of mood, anxiety, and other externalizing problems.

RESULTS

Overall, 6993 participants (ages 6-18 years, mean age 10.62 years [SD 1.99]; 3368 girls, 3625 boys; 764 African American, 4146 non-Hispanic White, and 2083 other race/ethnicities) had measures of ADHD and other emotional/behavioral symptoms (N = 6933) and/or enough clinical data to allow a diagnosis of ADHD (n = 951) or its absence (n = 4884). Both the diagnosis and symptoms of ADHD were associated with lower fractional anisotropy of the inferior longitudinal and left uncinate fasciculi (at a false discovery rate-adjusted p < .05). Associated effect sizes were small (the strongest association with ADHD traits had an effect size of partial r = -0.14, while the largest case-control difference was associated with an effect size of d = -0.3). Similar microstructural anomalies were not present for anxiety, mood, or externalizing problems. Findings held when ADHD cases and control subjects were matched on in-scanner motion.

CONCLUSIONS

While present across cohorts, ADHD-associated microstructural differences had small effects, underscoring the limited clinical utility of this imaging modality used in isolation.

Evaluating Fiber Orientation Dispersion Measures Computed From Single-Shell Diffusion MRI

Fiber orientation dispersion is one of the fundamental features that can be estimated from diffusion magnetic resonance imaging (dMRI) of the brain. Several approaches have been proposed to estimate dispersion from single- and multi-shell dMRI acquisitions. Here, we derive solutions to bring these proposed methods to a standard orientation dispersion index (ODI) with the goal of making them comparable across different dMRI acquisitions. To illustrate the utility of the measures in studying brain aging, we further examined the age-dependent trajectory of the different single- and multi-shell ODI estimates in the white matter across the lifespan.Clinical Relevance- This work computes metrics of brain microstructure that can be adapted for large neuroimaging initiatives that aim to study the brain's development and aging, and to identify deviations that may serve as biomarkers of brain disease.

Structural connectome alterations in anxious dogs: a DTI-based study

Anxiety and fear are dysfunctional behaviors commonly observed in domesticated dogs. Although dogs and humans share psychopathological similarities, little is known about how dysfunctional fear behaviors are represented in brain networks in dogs diagnosed with anxiety disorders. A combination of diffusion tensor imaging (DTI) and graph theory was used to investigate the underlying structural connections of dysfunctional anxiety in anxious dogs and compared with healthy dogs with normal behavior. The degree of anxiety was assessed using the Canine Behavioral Assessment & Research Questionnaire (C-BARQ), a widely used, validated questionnaire for abnormal behaviors in dogs. Anxious dogs showed significantly decreased clustering coefficient ([Formula: see text]), decreased global efficiency ([Formula: see text]), and increased small-worldness (σ) when compared with healthy dogs. The nodal parameters that differed between the anxious dogs and healthy dogs were mainly located in the posterior part of the brain, including the occipital lobe, posterior cingulate gyrus, hippocampus, mesencephalon, and cerebellum. Furthermore, the nodal degree ([Formula: see text]) of the left cerebellum was significantly negatively correlated with "excitability" in the C-BARQ of anxious dogs. These findings could contribute to the understanding of a disrupted brain structural connectome underlying the pathological mechanisms of anxiety-related disorders in dogs.

Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging

Compressed sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions, but it is not widely used in preclinical settings yet. In this study, we optimized and compared several CS reconstruction methods for diffusion imaging. Different undersampling patterns and two reconstruction approaches were evaluated: conventional CS, based on Berkeley Advanced Reconstruction Toolbox (BART-CS) toolbox, and a new kernel low-rank (KLR)-CS, based on kernel principal component analysis and low-resolution-phase (LRP) maps. 3D CS acquisitions were performed at 9.4T using a 4-element cryocoil on mice (wild type and a MAP6 knockout). Comparison metrics were error and structural similarity index measure (SSIM) on fractional anisotropy (FA) and mean diffusivity (MD), as well as reconstructions of the anterior commissure and fornix. Acceleration factors (AF) up to 6 were considered. In the case of retrospective undersampling, the proposed KLR-CS outperformed BART-CS up to AF = 6 for FA and MD maps and tractography. For instance, for AF = 4, the maximum errors were, respectively, 8.0% for BART-CS and 4.9% for KLR-CS, considering both FA and MD in the corpus callosum. Regarding undersampled acquisitions, these maximum errors became, respectively, 10.5% for BART-CS and 7.0% for KLR-CS. This difference between simulations and acquisitions arose mainly from repetition noise, but also from differences in resonance frequency drift, signal-to-noise ratio, and in reconstruction noise. Despite this increased error, fully sampled and AF = 2 yielded comparable results for FA, MD and tractography, and AF = 4 showed minor faults. Altogether, KLR-CS based on LRP maps seems a robust approach to accelerate preclinical diffusion MRI and thereby limit the effect of the frequency drift.

Fiber-specific white matter alterations in Parkinson's disease patients with freezing of gait

Freezing of gait (FOG) is a gait disorder that usually occurs in advanced stages of Parkinson's disease (PD). Understanding the underlying mechanism of FOG is important for treatment and prevention. Previous studies have investigated white matter (WM) structure to explore the pathology of FOG. However, the pathology is still unclear, possibly due to the methodological limitation in identifying specific fiber tracts. This study aimed to investigate tract-specific WM structural changes in FOG patients and their relationships with clinical characteristics. We enrolled 19 PD patients with FOG (PD-FOG), 19 without FOG (PD-woFOG) and 21 controls. Fixel-based analysis is a novel framework to avoid the effect of crossing fibers, which provides the metrics to assess WM morphology. By combining a method for segmenting fibers, we identified abnormalities in the specific fiber tracts. Compared to PD-woFOG, PD-FOG showed significant increased fiber-bundle cross-section (FC) in the corpus callosum (CC), fornix (FX), inferior longitudinal fasciculus (ILF), striato-premotor (ST_PREM), superior thalamic radiation (STR), thalamo-premotor (T_PREM), increased fiber density and cross-section (FDC) in the STR, and decreased fiber density (FD) in the CC and ILF. Additionally, the ILF was correlated with motor, cognition and memory, the CC was correlated with anxiety, and the T_PREM was also correlated with cognition. In conclusion, in addition to impairments of WM found in PD-FOG, we found enhancements in WM, which may imply compensatory mechanisms. Furthermore, multiple fiber tracts were correlated with clinical characteristics, especially the ILF, validating the involvement of transmission circuits of multiple distinct information in mechanisms of FOG.

Mammillary body abnormalities and cognitive outcomes in children cooled for neonatal encephalopathy

AIM

To evaluate mammillary body abnormalities in school-age children without cerebral palsy treated with therapeutic hypothermia for neonatal hypoxic-ischaemic encephalopathy (cases) and matched controls, and associations with cognitive outcome, hippocampal volume, and diffusivity in the mammillothalamic tract (MTT) and fornix.

METHOD

Mammillary body abnormalities were scored from T1-weighted magnetic resonance imaging (MRI) in 32 cases and 35 controls (median age [interquartile range] 7 years [6 years 7 months-7 years 7 months] and 7 years 4 months [6 years 7 months-7 years 7 months] respectively). Cognition was assessed using the Wechsler Intelligence Scale for Children, Fourth Edition. Hippocampal volume (normalized by total brain volume) was measured from T1-weighted MRI. Radial diffusivity and fractional anisotropy were measured in the MTT and fornix, from diffusion-weighted MRI using deterministic tractography.

RESULTS

More cases than controls had mammillary body abnormalities (34% vs 0%; p < 0.001). Cases with abnormal mammillary bodies had lower processing speed (p = 0.016) and full-scale IQ (p = 0.028) than cases without abnormal mammillary bodies, and lower scores than controls in all cognitive domains (p < 0.05). Cases with abnormal mammillary bodies had smaller hippocampi (left p = 0.016; right p = 0.004) and increased radial diffusivity in the right MTT (p = 0.004) compared with cases without mammillary body abnormalities.

INTERPRETATION

Cooled children with mammillary body abnormalities at school-age have reduced cognitive scores, smaller hippocampi, and altered MTT microstructure compared with those without mammillary body abnormalities, and matched controls.

WHAT THIS PAPER ADDS

Cooled children are at higher risk of mammillary body abnormalities than controls. Abnormal mammillary bodies are associated with reduced cognitive scores and smaller hippocampi. Abnormal mammillary bodies are associated with altered mammillothalamic tract diffusivity.

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.

Spatiotemporal tissue maturation of thalamocortical pathways in the human fetal brain

The development of connectivity between the thalamus and maturing cortex is a fundamental process in the second half of human gestation, establishing the neural circuits that are the basis for several important brain functions. In this study, we acquired high-resolution in utero diffusion magnetic resonance imaging (MRI) from 140 fetuses as part of the Developing Human Connectome Project, to examine the emergence of thalamocortical white matter over the second to third trimester. We delineate developing thalamocortical pathways and parcellate the fetal thalamus according to its cortical connectivity using diffusion tractography. We then quantify microstructural tissue components along the tracts in fetal compartments that are critical substrates for white matter maturation, such as the subplate and intermediate zone. We identify patterns of change in the diffusion metrics that reflect critical neurobiological transitions occurring in the second to third trimester, such as the disassembly of radial glial scaffolding and the lamination of the cortical plate. These maturational trajectories of MR signal in transient fetal compartments provide a normative reference to complement histological knowledge, facilitating future studies to establish how developmental disruptions in these regions contribute to pathophysiology.

Abnormal Structural Network Communication Reflects Cognitive Deficits in Schizophrenia

Schizophrenia has long been thought to be a disconnection syndrome and several previous studies have reported widespread abnormalities in white matter tracts in individuals with schizophrenia. Furthermore, reductions in structural connectivity may also impair communication between anatomically unconnected pairs of brain regions, potentially impacting global signal traffic in the brain. Therefore, we used different communication models to examine direct and indirect structural connections (polysynaptic) communication in large-scale brain networks in schizophrenia. Diffusion-weighted magnetic resonance imaging scans were acquired from 62 patients diagnosed with schizophrenia and 35 controls. In this study, we used five network communication models including, shortest paths, navigation, diffusion, search information and communicability to examine polysynaptic communication in large-scale brain networks in schizophrenia. We showed less efficient communication between spatially widespread brain regions particulary encompassing cortico-subcortical basal ganglia network in schizophrenia group relative to controls. Then, we also examined whether reduced communication efficiency was related to clinical symptoms in schizophrenia group. Among different measures of communication efficiency, only navigation efficiency was associated with global cognitive impairment across multiple cognitive domains including verbal learning, processing speed, executive functions and working memory, in individuals with schizophrenia. We did not find any association between communication efficiency measures and positive or negative symptoms within the schizophrenia group. Our findings are important for improving our mechanistic understanding of neurobiological process underlying cognitive symptoms in schizophrenia.

Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context

Diffusion MRI (dMRI) streamline tractography is the gold-standard for in vivo estimation of white matter (WM) pathways in the brain. However, the high angular resolution dMRI acquisitions capable of fitting the microstructural models needed for tractography are often time-consuming and not routinely collected clinically, restricting the scope of tractography analyses. To address this limitation, we build on recent advances in deep learning which have demonstrated that streamline propagation can be learned from dMRI directly without traditional model fitting. Specifically, we propose learning the streamline propagator from T1w MRI to facilitate arbitrary tractography analyses when dMRI is unavailable. To do so, we present a novel convolutional-recurrent neural network (CoRNN) trained in a teacher-student framework that leverages T1w MRI, associated anatomical context, and streamline memory from data acquired for the Human Connectome Project. We characterize our approach under two common tractography paradigms, WM bundle analysis and structural connectomics, and find approximately a 5-15% difference between measures computed from streamlines generated with our approach and those generated using traditional dMRI tractography. When placed in the literature, these results suggest that the accuracy of WM measures computed from T1w MRI with our method is on the level of scan-rescan dMRI variability and raise an important question: is tractography truly a microstructural phenomenon, or has dMRI merely facilitated its discovery and implementation?

Statistical power in network neuroscience

Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies.

Applications of advanced diffusion MRI in early brain development: a comprehensive review

Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.

Visual outcomes after anterior temporal lobectomy and transsylvian selective amygdalohippocampectomy: A quantitative comparison of clinical and diffusion data

OBJECTIVE

Anterior temporal lobectomy (ATL) and transsylvian selective amygdalohippocampectomy (tsSAHE) are effective treatment strategies for intractable temporal lobe epilepsy but may cause visual field deficits (VFDs) by damaging the optic radiation (OpR). Due to the OpR's considerable variability and because it is indistinguishable from surrounding tissue without further technical guidance, it is highly vulnerable to iatrogenic injury. This imaging study uses a multimodal approach to assess visual outcomes after epilepsy surgery.

METHODS

We studied 62 patients who underwent ATL (n = 32) or tsSAHE (n = 30). Analysis of visual outcomes was conducted in four steps, including the assessment of (1) perimetry outcome (VFD incidence/extent, n = 44/40), (2) volumetric OpR tractography damage (n = 55), and the (3) relation of volumetric OpR tractography damage and perimetry outcome (n = 35). Furthermore, (4) fixel-based analysis (FBA) was performed to assess micro- and macrostructural changes within the OpR following surgery (n = 36).

RESULTS

Altogether, 56% of all patients had postoperative VFDs (78.9% after ATL, 36.36% after tsSAHE, p = .011). VFDs and OpR tractography damage tended to be more severe within the ATL group (ATL vs. tsSAHE, integrity of contralateral upper quadrant: 65% vs. 97%, p = .002; OpR tractography damage: 69.2 mm³ vs. 3.8 mm³ , p = .002). Volumetric OpR tractography damage could reliably predict VFD incidence (86% sensitivity, 78% specificity) and could significantly explain VFD extent (R²  = .47, p = .0001). FBA revealed a more widespread decline of fibre cross-section within the ATL group.

SIGNIFICANCE

In the context of controversial visual outcomes following epilepsy surgery, this study provides clinical as well as neuroimaging evidence for a higher risk and greater severity of postoperative VFDs after ATL compared to tsSAHE. Volumetric OpR tractography damage is a feasible parameter to reliably predict this morbidity in both treatment groups and may ultimately support personalized planning of surgical candidates. Advanced diffusion analysis tools such as FBA offer a structural explanation of surgically induced visual pathway damage, allowing noninvasive quantification and visualization of micro- and macrostructural tract affection.

Tissue optimization strategies for high-quality ex vivo diffusion imaging

Ex vivo diffusion imaging can be used to study healthy and pathological tissue microstructure in the rodent brain with high resolution, providing a link between in vivo MRI and ex vivo microscopy techniques. Major challenges for the successful acquisition of ex vivo diffusion imaging data however are changes in the relaxivity and diffusivity of brain tissue following perfusion fixation. In this study we address this question by examining the combined effects of tissue preparation factors that influence signal-to-noise ratio (SNR) and consequently image quality, including fixative concentration, contrast agent concentration and tissue rehydration time. We present an optimization strategy combining these factors to manipulate theT 1 andT 2 of fixed tissue and maximize SNR efficiency. We apply this strategy in the rat brain, for a diffusion-weighted spin echo protocol with TE = 27 ms on a 9.4 T scanner with a 39 mm volume coil and 660 mT/m 114 mm gradient insert. We used a reduced fixative concentration of 2% paraformaldehyde (PFA), rehydration time more than 20 days, 15 mM Gd-DTPA in perfusate and TR 250 ms. This resulted in a doubling of SNR and an increase in SNR per unit time of 135% in cortical grey matter and 88% in white matter compared with 4% PFA and no contrast agent. This improved SNR efficiency enabled the acquisition of excellent-quality high-resolution (78 μ m isotropic voxel size) diffusion data with b = 4000 s/mm2 , 30 diffusion directions and a field of view of 40 × 13 × 18 mm3 in less than 4 days. It was also possible to achieve comparable data quality for a standard resolution (150 μ m) diffusion dataset in 2 1 4 h. In conclusion, the tissue optimization strategy presented here may be used to improve SNR, increase spatial resolution and/or allow faster acquisitions in preclinical ex vivo diffusion MRI experiments.

Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators

Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models of abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders. However, the calculation of likelihood function at whole-brain scale is often intractable. Thus, likelihood-free algorithms are required to efficiently estimate the parameters pertaining to the hypothetical areas, ideally including the uncertainty. In this study, we introduce the simulation-based inference for the virtual epileptic patient model (SBI-VEP), enabling us to amortize the approximate posterior of the generative process from a low-dimensional representation of whole-brain epileptic patterns. The state-of-the-art deep learning algorithms for conditional density estimation are used to readily retrieve the statistical relationships between parameters and observations through a sequence of invertible transformations. We show that the SBI-VEP is able to efficiently estimate the posterior distribution of parameters linked to the extent of the epileptogenic and propagation zones from sparse intracranial electroencephalography recordings. The presented Bayesian methodology can deal with non-linear latent dynamics and parameter degeneracy, paving the way for fast and reliable inference on brain disorders from neuroimaging modalities.

Integrated diffusion image operator (iDIO): A pipeline for automated configuration and processing of diffusion MRI data

The preprocessing of diffusion magnetic resonance imaging (dMRI) data involve numerous steps, including the corrections for head motion, susceptibility distortion, low signal-to-noise ratio, and signal drifting. Researchers or clinical practitioners often need to configure different preprocessing steps depending on disparate image acquisition schemes, which increases the technical threshold for dMRI analysis for nonexpert users. This could cause disparities in data processing approaches and thus hinder the comparability between studies. To make the dMRI data processing steps transparent and adapt to various dMRI acquisition schemes for researchers, we propose a semi-automated pipeline tool for dMRI named integrated diffusion image operator or iDIO. This pipeline integrates features from a wide range of advanced dMRI software tools and targets at providing a one-click solution for dMRI data analysis, via adaptive configuration for a set of suggested processing steps based on the image header of the input data. Additionally, the pipeline provides options for post-processing, such as estimation of diffusion tensor metrics and whole-brain tractography-based connectomes reconstruction using common brain atlases. The iDIO pipeline also outputs an easy-to-interpret quality control report to facilitate users to assess the data quality. To keep the transparency of data processing, the execution log and all the intermediate images produced in the iDIO's workflow are accessible. The goal of iDIO is to reduce the barriers for clinical or nonspecialist users to adopt the state-of-art dMRI processing steps.

Fixel-based analysis of the diffusion properties of the patients with brain injury and chronic health symptoms

The diffusion properties from diffusion tensor imaging (DTI) are sensitive to white matter (WM) abnormalities and could serve as indicators of diffuse axonal damages incurred during a traumatic brain injury (TBI). Analyses of diffusion metrics in the regions of interest (ROIs) were used to compare the differences in the 18 major fiber tracts in 46 participants, between TBI participants with (n = 17) or without (n = 16) chronic symptoms (CS) and a control group (CG, n = 13). In addition to the widely used diffusion metrics, such as fractional anisotropy (FA), mean (MD), axial (AD) and radial (RD) diffusivities, apparent fiber density (AFD), complexity (CX) and fixel number (FN) derived from Mrtrix3 software package were used to characterize WM tracts and compare between participant groups in the ROIs defined by the fixel numbers. Significant differences were found in FA, AFD, MD, RD and CX in ROIs with different FNs in the corpus callosum forceps minor, left and right inferior longitudinal fasciculus, and left and right uncinate fasciculus for both TBI groups compared to controls. Diffusion properties in ROIs with different FNs can serve as detailed biomarkers of WM abnormalities, especially for individuals with chronic TBI related symptoms.

White matter microstructure predicts measures of clinical symptoms in chronic back pain patients

Chronic back pain (CBP) has extensive clinical and social implications for its sufferers and is a major source of disability. Chronic pain has previously been shown to have central neural factors underpinning it, including the loss of white matter (WM), however traditional methods of analyzing WM microstructure have produced mixed and unclear results. To better understand these factors, we assessed the WM microstructure of 50 patients and 40 healthy controls (HC) using diffusion-weighted imaging. The data were analyzed using fixel-based analysis (FBA), a higher-order diffusion modelling technique applied to CBP for the first time here. Subjects also answered questionnaires relating to pain, disability, catastrophizing, and mood disorders, to establish the relationship between fixelwise metrics and clinical symptoms. FBA determined that, compared to HC, CBP patients had: 1) lower fibre density (FD) in several tracts, specifically the right anterior and bilateral superior thalamic radiations, right spinothalamic tract, right middle cerebellar peduncle, and the body and splenium of corpus callosum; 2) higher FD in the genu of corpus callosum; and 3) lower FDC - a combined fibre density and cross-section measure - in the bilateral spinothalamic tracts and right anterior thalamic radiation. Exploratory correlations showed strong negative relationships between fixelwise metrics and clinical questionnaire scores, especially pain catastrophizing. These results have important implications for the intake and processing of sensory data in CBP that warrant further investigation.

Topological Structural Brain Connectivity Alterations in Aspartylglucosaminuria: A Case-Control Study

BACKGROUND AND PURPOSE

We investigated global and local properties of the structural brain connectivity networks in aspartylglucosaminuria, an autosomal recessive and progressive neurodegenerative lysosomal storage disease. Brain connectivity in aspartylglucosaminuria has not been investigated before, but previous structural MR imaging studies have shown brain atrophy, delayed myelination, and decreased thalamic and increased periventricular WM T2 signal intensity.

MATERIALS AND METHODS

We acquired diffusion MR imaging and T1-weighted data from 12 patients with aspartylglucosaminuria (mean age, 23 [SD, 8] years; 5 men), and 30 healthy controls (mean age, 25 [SD, 10] years; 13 men). We performed whole-brain constrained spherical deconvolution tractography, which enables the reconstruction of neural tracts through regions with complex fiber configurations, and used graph-theoretical analysis to investigate the structural brain connectivity networks.

RESULTS

The integration of the networks was decreased, as demonstrated by a decreased normalized global efficiency and an increased normalized characteristic path length. In addition, the average strength of the networks was decreased. In the local analyses, we found decreased strength in 11 nodes, including, for example, the right thalamus, right putamen, and, bilaterally, several occipital and temporal regions.

CONCLUSIONS

We found global and local structural connectivity alterations in aspartylglucosaminuria. Biomarkers related to the treatment efficacy are needed, and brain network properties may provide the means for long term follow-up.

A comparison of diffusion tensor imaging tractography and constrained spherical deconvolution with automatic segmentation in traumatic brain injury

Detection of microstructural white matter injury in traumatic brain injury (TBI) requires specialised imaging methods, of which diffusion tensor imaging (DTI) has been extensively studied. Newer fibre alignment estimation methods, such as constrained spherical deconvolution (CSD), are better than DTI in resolving crossing fibres that are ubiquitous in the brain and may improve the ability to detect microstructural injuries. Furthermore, automatic tract segmentation has the potential to improve tractography reliability and accelerate workflow compared to the manual segmentation commonly used. In this study, we compared the results of deterministic DTI based tractography and manual tract segmentation with CSD based probabilistic tractography and automatic tract segmentation using TractSeg. 37 participants with a history of TBI (with Glasgow Coma Scale 13-15) and persistent symptoms, and 41 healthy controls underwent deterministic DTI-based tractography with manual tract segmentation and probabilistic CSD-based tractography with TractSeg automatic segmentation.Fractional anisotropy (FA) and mean diffusivity of corpus callosum and three bilateral association tracts were measured. FA and MD values derived from both tractography methods were generally moderately to strongly correlated. CSD with TractSeg differentiated the groups based on FA, while DTI did not. CSD and TractSeg-based tractography may be more sensitive in detecting microstructural changes associated with TBI than deterministic DTI tractography. Additionally, CSD with TractSeg was found to be applicable at lower b-value and number of diffusion-encoding gradients data than previously reported.

Optimization of quasi-diffusion magnetic resonance imaging for quantitative accuracy and time-efficient acquisition

PURPOSE

Quasi-diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mspace/> <mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> in mm²  s^-1 and a fractional exponent, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> , defining the non-Gaussianity of the diffusion signal decay. Here, the b-value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized.

METHODS

Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi-b-value reference (MbR) dataset comprising 29 diffusion-sensitized images arrayed between <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>b</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn></mml:mrow> <mml:annotation>$$ b=0 $$</mml:annotation></mml:semantics> </mml:math> and 5000 s mm^-2 . The effects of varying maximum b-value ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> </mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}} $$</mml:annotation></mml:semantics> </mml:math> ), number of b-value shells, and the effects of Rician noise were investigated.

RESULTS

QDTI measures showed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> </mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}} $$</mml:annotation></mml:semantics> </mml:math> dependence, most significantly for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> in white matter, which monotonically decreased with higher <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> </mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}} $$</mml:annotation></mml:semantics> </mml:math> leading to improved tissue contrast. Optimized 2 b-value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mspace/> <mml:mspace/> <mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ \kern0.50em {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> and underestimation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> in white matter, and overestimation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> <mml:mo>=</mml:mo> <mml:mn>5000</mml:mn></mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}}=5000 $$</mml:annotation></mml:semantics> </mml:math>  s mm^-2 , and 4 b-value shells at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> <mml:mo>=</mml:mo> <mml:mn>3960</mml:mn></mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}}=3960 $$</mml:annotation></mml:semantics> </mml:math>  s mm^-2 , providing minimal bias in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> compared to the MbR.

CONCLUSION

A highly detailed optimization of non-Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non-Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.

Hyperconnectivity and altered interactions of a nucleus accumbens network in post-stroke depression

Post-stroke depression is a common complication of stroke. To date, no consistent locus of injury is associated with this complication. Here, we probed network dynamics and structural alterations in post-stroke depression in four functional circuits linked to major depressive disorder and a visual network, which served as a control network. Forty-four participants with recent stroke (mean age = 69.03, standard deviation age = 8.59, age range = 51–86 and gender: female = 10) and 16 healthy volunteers (mean age = 71.53, standard deviation age = 10.62, age range = 51–84 and gender: female = 11) were imaged with 3-Tesla structural, diffusion and resting-state functional MRI. The Geriatric Depression Scale was administered to measure depression severity. Associations between depression severity and functional connectivity were investigated within networks seeded from nucleus accumbens, amygdala, dorsolateral prefrontal cortex and primary visual cortex. In addition, the default mode network was identified by connectivity with medial prefrontal cortex and posterior cingulate cortex. Circuits that exhibited altered activity associated with depression severity were further investigated by extracting within-network volumetric and microstructural measures from structural images. In the stroke group, functional connectivity within the nucleus accumbens-seeded network (left hemisphere: P = 0.001; and right hemisphere: P = 0.004) and default mode network (cluster one: P &lt; 0.001; and cluster two: P &lt; 0.001) correlated positively with depressive symptoms. Normal anticorrelations between these two networks were absent in patients with post-stroke depression. Grey matter volume of the right posterior cingulate cortex (Pearson correlation coefficient = −0.286, P = 0.03), as well as microstructural measures in the posterior cingulate cortex (right: Pearson correlation coefficient = 0.4, P = 0.024; and left: Pearson correlation coefficient = 0.3, P = 0.048), right medial prefrontal cortex (Pearson correlation coefficient = 0.312, P = 0.039) and the medial forebrain bundle (Pearson correlation coefficient = 0.450, P = 0.003), a major projection pathway interconnecting the nucleus accumbens-seeded network and linking to medial prefrontal cortex, were associated with depression severity. Depression after stroke is marked by reduced mutual inhibition between functional circuits involving nucleus accumbens and default mode network as well as volumetric and microstructural changes within these networks. Aberrant network dynamics present in patients with post-stroke depression are therefore likely to be influenced by secondary, pervasive alterations in grey and white matter, remote from the site of injury.

Mutation-related magnetization-transfer, not axon density, drives white matter differences in premanifest Huntington disease: Evidence from in vivo ultra-strong gradient MRI

White matter (WM) alterations have been observed in Huntington disease (HD) but their role in the disease-pathophysiology remains unknown. We assessed WM changes in premanifest HD by exploiting ultra-strong-gradient magnetic resonance imaging (MRI). This allowed to separately quantify magnetization transfer ratio (MTR) and hindered and restricted diffusion-weighted signal fractions, and assess how they drove WM microstructure differences between patients and controls. We used tractometry to investigate region-specific alterations across callosal segments with well-characterized early- and late-myelinating axon populations, while brain-wise differences were explored with tract-based cluster analysis (TBCA). Behavioral measures were included to explore disease-associated brain-function relationships. We detected lower MTR in patients' callosal rostrum (tractometry: p = .03; TBCA: p = .03), but higher MTR in their splenium (tractometry: p = .02). Importantly, patients' mutation-size and MTR were positively correlated (all p-values < .01), indicating that MTR alterations may directly result from the mutation. Further, MTR was higher in younger, but lower in older patients relative to controls (p = .003), suggesting that MTR increases are detrimental later in the disease. Finally, patients showed higher restricted diffusion signal fraction (FR) from the composite hindered and restricted model of diffusion (CHARMED) in the cortico-spinal tract (p = .03), which correlated positively with MTR in the posterior callosum (p = .033), potentially reflecting compensatory mechanisms. In summary, this first comprehensive, ultra-strong gradient MRI study in HD provides novel evidence of mutation-driven MTR alterations at the premanifest disease stage which may reflect neurodevelopmental changes in iron, myelin, or a combination of these.