A probabilistic atlas of fiber crossings for variability reduction of anisotropy measures

Characterization of the orientation dependence of magnetization transfer measures in single and crossing-fiber white matter

PURPOSE

To fully characterize the orientation dependence of magnetization transfer (MT) and inhomogeneous MT (ihMT) measures in the whole white matter (WM), for both single-fiber and crossing-fiber voxels.

METHODS

A characterization method was developed using the fiber orientation obtained from diffusion MRI (dMRI) with diffusion tensor imaging (DTI) and constrained spherical deconvolution. This allowed for characterization of the orientation dependence of measures in all of WM, regardless of the number of fiber orientation in a voxel. Furthermore, the orientation dependence inside 31 different WM bundles was characterized to evaluate the homogeneity of the effect. Variation of the results within and between-subject was assessed from a 12-subject dataset.

RESULTS

Previous results for single-fiber voxels were reproduced and a novel characterization was produced in voxels of crossing fibers, which seems to follow trends consistent with single-fiber results. Heterogeneity of the orientation dependence across bundles was observed, but homogeneity within similar bundles was also highlighted. Differences in behavior between MT and ihMT measures, as well as the ratio and saturation versions of these, were noted.

CONCLUSION

Orientation dependence characterization was proven possible over the entirety of WM. The vast range of effects and subtleties of the orientation dependence on MT measures showed the need for, but also the challenges of, a correction method.

Menstrual cycle-driven hormone concentrations co-fluctuate with white and gray matter architecture changes across the whole brain

Cyclic fluctuations in hypothalamic-pituitary-gonadal axis (HPG-axis) hormones exert powerful behavioral, structural, and functional effects through actions on the mammalian central nervous system. Yet, very little is known about how these fluctuations alter the structural nodes and information highways of the human brain. In a study of 30 naturally cycling women, we employed multidimensional diffusion and T1-weighted imaging during three estimated menstrual cycle phases (menses, ovulation, and mid-luteal) to investigate whether HPG-axis hormone concentrations co-fluctuate with alterations in white matter (WM) microstructure, cortical thickness (CT), and brain volume. Across the whole brain, 17β-estradiol and luteinizing hormone (LH) concentrations were directly proportional to diffusion anisotropy (μFA; 17β-estradiol: β1 = 0.145, highest density interval (HDI) = [0.211, 0.4]; LH: β1 = 0.111, HDI = [0.157, 0.364]), while follicle-stimulating hormone (FSH) was directly proportional to CT (β1 = 0 .162, HDI = [0.115, 0.678]). Within several individual regions, FSH and progesterone demonstrated opposing relationships with mean diffusivity (Diso) and CT. These regions mainly reside within the temporal and occipital lobes, with functional implications for the limbic and visual systems. Finally, progesterone was associated with increased tissue (β1 = 0.66, HDI = [0.607, 15.845]) and decreased cerebrospinal fluid (CSF; β1 = -0.749, HDI = [-11.604, -0.903]) volumes, with total brain volume remaining unchanged. These results are the first to report simultaneous brain-wide changes in human WM microstructure and CT coinciding with menstrual cycle-driven hormone rhythms. Effects were observed in both classically known HPG-axis receptor-dense regions (medial temporal lobe, prefrontal cortex) and in other regions located across frontal, occipital, temporal, and parietal lobes. Our results suggest that HPG-axis hormone fluctuations may have significant structural impacts across the entire brain.

Cognitive training and promoting a healthy lifestyle for individuals with isolated REM sleep behavior disorder: study protocol of the delayed-start randomized controlled trial CogTrAiL-RBD

BACKGROUND

Isolated REM sleep behavior disorder (iRBD) is an early α-synucleinopathy often accompanied by incipient cognitive impairment. As executive dysfunctions predict earlier phenotypic conversion from iRBD to Parkinson's disease and Lewy body dementia, cognitive training focusing on executive functions could have disease-modifying effects for individuals with iRBD.

METHODS

The study CogTrAiL-RBD investigates the short- and long-term effectiveness and the feasibility and underlying neural mechanisms of a cognitive training intervention for individuals with iRBD. The intervention consists of a 5-week digital cognitive training accompanied by a module promoting a healthy, active lifestyle. In this monocentric, single-blinded, delayed-start randomized controlled trial, the intervention's effectiveness will be evaluated compared to an initially passive control group that receives the intervention in the second, open-label phase of the study. Eighty individuals with iRBD confirmed by polysomnography will be consecutively recruited from the continuously expanding iRBD cohort at the University Hospital Cologne. The evaluation will focus on cognition and additional neuropsychological and motor variables. Furthermore, the study will examine the feasibility of the intervention, effects on physical activity assessed by accelerometry, and interrogate the intervention's neural effects using magnetic resonance imaging and polysomnography. Besides, a healthy, age-matched control group (HC) will be examined at the first assessment time point, enabling a cross-sectional comparison between individuals with iRBD and HC.

DISCUSSION

This study will provide insights into whether cognitive training and psychoeducation on a healthy, active lifestyle have short- and long-term (neuro-)protective effects for individuals with iRBD.

TRIAL REGISTRATION

The study was prospectively registered in the German Clinical Trial Register (DRKS00024898) on 2022-03-11, https://drks.de/search/de/trial/DRKS00024898 .

PROTOCOL VERSION

V5 2023-04-24.

Interhemispheric Structural Connectivity Underlies Motor Recovery after Stroke

OBJECTIVE

Although ample evidence highlights that the ipsilesional corticospinal tract (CST) plays a crucial role in motor recovery after stroke, studies on cortico-cortical motor connections remain scarce and provide inconclusive results. Given their unique potential to serve as structural reserve enabling motor network reorganization, the question arises whether cortico-cortical connections may facilitate motor control depending on CST damage.

METHODS

Diffusion spectrum imaging (DSI) and a novel compartment-wise analysis approach were used to quantify structural connectivity between bilateral cortical core motor regions in chronic stroke patients. Basal and complex motor control were differentially assessed.

RESULTS

Both basal and complex motor performance were correlated with structural connectivity between bilateral premotor areas and ipsilesional primary motor cortex (M1) as well as interhemispheric M1 to M1 connectivity. Whereas complex motor skills depended on CST integrity, a strong association between M1 to M1 connectivity and basal motor control was observed independent of CST integrity especially in patients who underwent substantial motor recovery. Harnessing the informational wealth of cortico-cortical connectivity facilitated the explanation of both basal and complex motor control.

INTERPRETATION

We demonstrate for the first time that distinct aspects of cortical structural reserve enable basal and complex motor control after stroke. In particular, recovery of basal motor control may be supported via an alternative route through contralesional M1 and non-crossing fibers of the contralesional CST. Our findings help to explain previous conflicting interpretations regarding the functional role of the contralesional M1 and highlight the potential of cortico-cortical structural connectivity as a future biomarker for motor recovery post-stroke. ANN NEUROL 2023;94:785-797.

The role of corticospinal and extrapyramidal pathways in motor impairment after stroke

Anisotropy of descending motor pathways has repeatedly been linked to the severity of motor impairment following stroke-related damage to the corticospinal tract. Despite promising findings consistently tying anisotropy of the ipsilesional corticospinal tract to motor outcome, anisotropy is not yet utilized as a biomarker for motor recovery in clinical practice as several methodological constraints hinder a conclusive understanding of degenerative processes in the ipsilesional corticospinal tract and compensatory roles of other descending motor pathways. These constraints include estimating anisotropy in voxels with multiple fibre directions, sampling biases and confounds due to ageing-related atrophy. The present study addressed these issues by combining diffusion spectrum imaging with a novel compartmentwise analysis approach differentiating voxels with one dominant fibre direction (one-directional voxels) from voxels with multiple fibre directions. Compartmentwise anisotropy for bihemispheric corticospinal and extrapyramidal tracts was compared between 25 chronic stroke patients, 22 healthy age-matched controls, and 24 healthy young controls and its associations with motor performance of the upper and lower limbs were assessed. Our results provide direct evidence for Wallerian degeneration along the entire length of the ipsilesional corticospinal tract reflected by decreased anisotropy in descending fibres compared with age-matched controls, while ageing-related atrophy was observed more ubiquitously across compartments. Anisotropy of descending ipsilesional corticospinal tract voxels showed highly robust correlations with various aspects of upper and lower limb motor impairment, highlighting the behavioural relevance of Wallerian degeneration. Moreover, anisotropy measures of two-directional voxels within bihemispheric rubrospinal and reticulospinal tracts were linked to lower limb deficits, while anisotropy of two-directional contralesional rubrospinal voxels explained gross motor performance of the affected hand. Of note, the relevant extrapyramidal structures contained fibres crossing the midline, fibres potentially mitigating output from brain stem nuclei, and fibres transferring signals between the extrapyramidal system and the cerebellum. Thus, specific parts of extrapyramidal pathways seem to compensate for impaired gross arm and leg movements incurred through stroke-related corticospinal tract lesions, while fine motor control of the paretic hand critically relies on ipsilesional corticospinal tract integrity. Importantly, our findings suggest that the extrapyramidal system may serve as a compensatory structural reserve independent of post-stroke reorganization of extrapyramidal tracts. In summary, compartment-specific anisotropy of ipsilesional corticospinal tract and extrapyramidal tracts explained distinct aspects of motor impairment, with both systems representing different pathophysiological mechanisms contributing to motor control post-stroke. Considering both systems in concert may help to develop diffusion imaging biomarkers for specific motor functions after stroke.

White matter structural and network topological changes in moyamoya disease with limb paresthesia: A study based on diffusion kurtosis imaging

Purpose

To investigate the structural and network topological changes in the white matter (WM) in MMD patients with limb paresthesia by performing diffusion kurtosis imaging (DKI).

Materials and methods

A total of 151 MMD patients, including 46 with left-limb paresthesia (MLP), 52 with right-limb paresthesia (MRP), and 53 without paresthesia (MWP), and 28 healthy controls (HCs) underwent whole-brain DKI, while the surgical patients were reexamined 3-4 months after revascularization. The data were preprocessed to calculate the fractional anisotropy (FA) and mean kurtosis (MK) values. Voxel-wise statistics for FA and MK images were obtained by using tract-based spatial statistics (TBSS). Next, the whole-brain network was constructed, and global and local network parameters were analyzed using graph theory. All parameters were compared among the HC, MWP, MLP, and MRP groups, and changes in the MMD patients before and after revascularization were also compared.

Results

The TBSS analysis revealed significant reductions in FA and MK in extensive WM regions in the three patient groups. In comparison with the MWP group, the MLP group showed reductions in FA and MK in both right and left WM, mainly in the right WM, while the MRP group mainly showed a reduction in FA in the left WM region and demonstrated no significant change in MK. The graph theoretical analysis showed decreased global network efficiency, increased characteristic path length, and increased sigma in the MWP, MRP, and MLP groups in comparison with the HC group. Among local network parameters, the nodal efficiency decreased in the bilateral MFG and IFGtriang, while the degree decreased in the MFG.L and bilateral IFGtriang. Patients with right-limb paresthesia showed the lowest nodal efficiency and degree in MFG.L and IFGtriang.L, while those with left-limb paresthesia showed the lowest nodal efficiency in MFG.R and IFGtriang.R and the lowest degree in IFGtriang.R.

Conclusion

A DKI-based whole-brain structural and network analysis can be used to detect changes in WM damage and network topological changes in MMD patients with limb paresthesia. FA is more sensitive than MK in detecting WM injury, while MFG and IFGtriang are the key nodes related to the development of acroparesthesia.

Swallowing function in the chronic stage following stroke is associated with white matter integrity of the callosal tract between the interhemispheric S1 swallowing representation areas

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Swallowing performance was tested in dysphagic patients following stroke.

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M1 and S1 callosal tracts relevant for swallowing was mapped in the HCP-dataset.

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S1 and M1 swallowing tracts were overlapping between in house and HCP datasets.

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Swallowing specific callosal tracts showed lower FA for patients compared to HCs.

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Integrity of S1 callosal fibres (FA) was associated with swallowing performance.

Sensorimotor representations of swallowing in pre- and postcentral gyri of both cerebral hemispheres are interconnected by callosal tracts. We were interested in (1) the callosal location of fibers interconnecting the precentral gyri (with the primary motor cortex; M1) and the postcentral gyri (with the primary somatosensory cortex; S1) relevant for swallowing, and (2) the importance of their integrity given the challenges of swallowing compliance after recovery of dysphagia following stroke. We investigated 17 patients who had almost recovered from dysphagia in the chronic stage following stroke and age-matched and gender-matched healthy controls. We assessed their swallowing compliance, investigating swallowing of a predefined bolus in one swallowing movement in response to a ‘go’ signal when in a lying position. A somatotopic representation of swallowing was mapped for the pre- and postcentral gyrus, and callosal tract location between these regions was compared to results for healthy participants. We applied multi-directional diffusion-weighted imaging of the brain in patients and matched controls to calculate fractional anisotropy (FA) as a tract integrity marker for M1/S1 callosal fibers. Firstly, interconnecting callosal tract maps were well spatially separated for M1 and S1, but were overlapped for somatotopic differentiation within M1 and S1 in healthy participants’ data (HCP: head/face representation; in house dataset: fMRI-swallowing representation in healthy volunteers). Secondly, the FA for both callosal tracts, connecting M1 and S1 swallowing representations, were decreased for patients when compared to healthy volunteers. Thirdly, integrity of callosal fibers interconnecting S1 swallowing representation sites was associated with effective swallowing compliance. We conclude that somatosensory interaction between hemispheres is important for effective swallowing in the case of a demanding task undertaken by stroke survivors with good swallowing outcome from dysphagia.

MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI

PURPOSE

Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge.

METHODS

To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length.

RESULTS

We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability.

CONCLUSIONS

This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.

Early Microstructure Changes of White Matter Fiber Bundles in Patients with Amnestic Mild Cognitive Impairment Predicts Progression of Mild Cognitive Impairment to Alzheimer's Disease

BACKGROUND

Amnestic mild cognitive impairment (aMCI) is the transitional stage between normal aging and Alzheimer's disease (AD). Some aMCI patients will progress into AD eventually, whereas others will not. If the trajectory of aMCI can be predicted, it would enable early diagnosis and early therapy of AD.

OBJECTIVE

To explore the development trajectory of aMCI patients, we used diffusion tensor imaging to analyze the white matter microstructure changes of patients with different trajectories of aMCI.

METHODS

We included three groups of subjects:1) aMCI patients who convert to AD (MCI-P); 2) aMCI patients who remain in MCI status (MCI-S); 3) normal controls (NC). We analyzed the fractional anisotropy and mean diffusion rate of brain regions, and we adopted logistic binomial regression model to predicate the development trajectory of aMCI.

RESULTS

The fraction anisotropy value is significantly reduced, the mean diffusivity value is significantly increased in the two aMCI patient groups, and the MCI-P patients presented greater changes. Significant changes are mainly located in the cingulum, fornix, hippocampus, and uncinate fasciculus. These changed brain regions significantly correlated with the patient's Mini-Mental State Examination scores.

CONCLUSION

The study predicted the disease trajectory of different types of aMCI patients based on the characteristic values of the above-mentioned brain regions. The prediction accuracy rate can reach 90.2%, and the microstructure characteristics of the right cingulate band and the right hippocampus may have potential clinical application value to predict the disease trajectory.

Leveraging multi-shell diffusion for studies of brain development in youth and young adulthood

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Multi-shell imaging sequences may improve sensitivity to developmental effects.

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Models that leverage multi-shell information are often less sensitive to the confounding effects of motion.

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Multi-shell sequences and models that leverage this data may be of particular utility for studying the developing brain.

Diffusion weighted imaging (DWI) has advanced our understanding of brain microstructure evolution over development. Recently, the use of multi-shell diffusion imaging sequences has coincided with advances in modeling the diffusion signal, such as Neurite Orientation Dispersion and Density Imaging (NODDI) and Laplacian-regularized Mean Apparent Propagator MRI (MAPL). However, the relative utility of recently-developed diffusion models for understanding brain maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound for studies of development, the vulnerability of metrics derived from contemporary models to in-scanner motion has not been described. Accordingly, in a sample of 120 youth and young adults (ages 12–30) we evaluated metrics derived from diffusion tensor imaging (DTI), NODDI, and MAPL for associations with age and in-scanner head motion at multiple scales. Specifically, we examined mean white matter values, white matter tracts, white matter voxels, and connections in structural brain networks. Our results revealed that multi-shell diffusion imaging data can be leveraged to robustly characterize neurodevelopment, and demonstrate stronger age effects than equivalent single-shell data. Additionally, MAPL-derived metrics were less sensitive to the confounding effects of head motion. Our findings suggest that multi-shell imaging data and contemporary modeling techniques confer important advantages for studies of neurodevelopment.

A population-based atlas of the human pyramidal tract in 410 healthy participants

With the advances in diffusion MRI and tractography, numerous atlases of the human pyramidal tract (PyT) have been proposed, but the inherent limitation of tractography to resolve crossing bundles within the centrum semiovale has so far prevented the complete description of the most lateral PyT projections. Here, we combined a precise manual positioning of individual subcortical regions of interest along the descending pathway of the PyT with a new bundle-specific tractography algorithm. This later is based on anatomical priors to improve streamlines tracking in crossing areas. We then extracted both left and right PyT in a large cohort of 410 healthy participants and built a population-based atlas of the whole-fanning PyT with a complete description of its most corticolateral projections. Clinical applications are envisaged, the whole-fanning PyT atlas being likely a better marker of corticospinal integrity metrics than those currently used within the frame of prediction of poststroke motor recovery. The present population-based PyT, freely available, provides an interesting tool for clinical applications to locate specific PyT damage and its impact to the short- and long-term motor recovery after stroke.

Whole mouse brain structural connectomics using magnetic resonance histology

Diffusion tensor histology holds great promise for quantitative characterization of structural connectivity in mouse models of neurological and psychiatric conditions. There has been extensive study in both the clinical and preclinical domains on the complex tradeoffs between the spatial resolution, the number of samples in diffusion q-space, scan time, and the reliability of the resultant data. We describe here a method for accelerating the acquisition of diffusion MRI data to support quantitative connectivity measurements in the whole mouse brain using compressed sensing (CS). The use of CS allows substantial increase in spatial resolution and/or reduction in scan time. Compared to the fully sampled results at the same scan time, the subtle anatomical details of the brain, such as cortical layers, dentate gyrus, and cerebellum, were better visualized using CS due to the higher spatial resolution. Compared to the fully sampled results at the same spatial resolution, the scalar diffusion metrics, including fractional anisotropy (FA) and mean diffusivity (MD), showed consistently low error across the whole brain (< 6.0%) even with 8.0 times acceleration. The node properties of connectivity (strength, cluster coefficient, eigenvector centrality, and local efficiency) demonstrated correlation of better than 95.0% between accelerated and fully sampled connectomes. The acceleration will enable routine application of this technology to a wide range of mouse models of neurologic diseases.