Synthesized b0 for diffusion distortion correction (Synb0-DisCo)

Higher blood pressure variability during hospitalisation is associated with lower cerebral white matter integrity in COVID-19 patients

BACKGROUND

High blood pressure variability (BPV) is associated with cerebrovascular damage and dementia, but it is unknown whether short-term BPV during hospitalisation is also associated with cerebral white matter (WM) damage. We examined whether BPV, measured in-hospital using continuous monitoring, is associated with WM microstructural integrity in COVID-19 patients.

METHODS

We included hospitalised COVID-19 patients from the CORONavirus and Ischemic Stroke (CORONIS) study who underwent continuous vital signs monitoring using a wearable device during hospital admission and had an MRI shortly after discharge. Systolic BPV was calculated as Average Real Variability (ARV) and Coefficient of Variation (CV) with 1-, 5- and 20-minute intervals. We used diffusion tensor imaging to assess fractional anisotropy (FA) and peak width of skeletonised mean diffusivity (PSMD) as markers of WM integrity. Associations between BPV and WM integrity were examined with linear regression adjusted for age, mean systolic blood pressure (BP), number of BP measurements and type of respiratory support.

RESULTS

We included 47 COVID-19 patients (mean age: 59.6 years). BP was measured 6306 ± 4343 times per patient (median admission: 11 days (Interquartile Range [IQR] 7.5-15.0). Both higher ARV and CV were associated with lower WM microstructural integrity, reflected by lower FA (ARV: β = -0.40, p = .010; CV: β = -0.33, p = 0.026) and higher PSMD (CV: β = 0.28, p = .038) after adjustment for confounders. Correction for WM hyperintensities did not change these results.

CONCLUSIONS

High BPV during hospitalisation is associated with lower WM integrity in COVID-19 patients, although causality needs to be demonstrated. Our findings need validation in hospitalised patients without COVID-19 to examine generalisability.

Subventricular Zone Microstructure in Pediatric-Onset Multiple Sclerosis

OBJECTIVE

The aim of this study was to explore the microstructural dynamics of the subventricular zone (SVZ) with aging and their associations with clinical disability and brain structural damage in pediatric-onset multiple sclerosis (MS) patients.

METHODS

One-hundred and forty-one pediatric-onset MS patients (67 pediatric and 74 adults with pediatric-onset) and 233 healthy controls (HC) underwent neurological and 3.0 T MRI assessment. Fractional anisotropy (FA) and mean diffusivity (MD) were extracted from the SVZ and the thalamus (as control region).

RESULTS

In HC, SVZ FA was higher until age 40 then declined, whereas MD was lower until age 35 before rising (false discovery rate p value [pFDR] ≤ 0.008). Thalamic FA was higher until age 30 and then declined, whereas MD was higher until age 50 (pFDR ≤ 0.007). Pediatric MS patients showed significantly higher SVZ FA than pediatric HC (pFDR < 0.001), while adult patients showed no differences compared to adult HC (pFDR ≤ 0.724). Adult patients had lower thalamic FA and higher MD (pFDR < 0.001). Adults had lower SVZ FA and MD, but higher thalamic MD compared to pediatric patients (pFDR < 0.001). In pediatric MS, higher SVZ FA and MD were associated with higher white matter (WM) lesion volume (LV) and choroid plexus volume and lower brain and thalamic volumes (pFDR ≤ 0.047). In adult patients, higher SVZ MD associated with higher WM LV, lower brain volumes, and lower z-SDMT (pFDR≤0.019). Thalamic microstructural abnormalities were associated with more severe disability and brain damage in both groups (pFDR ≤ 0.018).

INTERPRETATION

Our findings suggest that microstructural changes in the SVZ occur early in pediatric MS and are associated with brain structural damage but not with clinical impairment. ANN NEUROL 2025;97:979-992.

White matter characterization in regions of edema surrounding meningioma brain tumor using diffusion MRI: A comparative study of DTI and NODDI

White matter (WM) tract detection is critical in presurgical planning of tumor resection; however, standard-of-care diffusion tensor imaging (DTI) often fails to characterize white matter tracts through regions of edema. This is because the presence of edema has the effect of increasing the isotropic volume fraction within a voxel and thus marginalizing the anisotropic volume fraction associated with white matter presence and directionality. More recent biophysical models of diffusion, such as neurite orientation dispersion and density imaging (NODDI), account for isotropic and anisotropic volume fractions within voxels by compartmentalizing the diffusion signal based on an assumed tissue microenvironment, e.g., "free water" (cerebrospinal fluid (CSF), interstitial fluid (ISF), edema), "intra-neurite", and "extra-neurite" tissue, as a sphere, stick, and tensor, respectively. We hypothesize that a low fractional anisotropy (FA), low orientation dispersion index (ODI) value and high fractional isotropic volume (FISO) would be observed in white matter regions containing edema but a high FA, low ODI value and low FISO would be observed in healthy-appearing contralateral white matter. In our study, we test this hypothesis using multi-shell diffusion MRI data collected from patients bearing meningioma brains tumors. Brains bearing meningioma tumors are selected in this study as meningiomas rarely invade the brain parenchyma and we can thus assume that our analyses of edematous regions are not confounded by infiltrating tumor cells. Here, we show that NODDI-based characterization of white matter is more sensitive than that of standard-of-care DTI through regions of edema. Future studies will focus on implementation of biophysical model-based tractography in cases of glioma and translation of biophysical model-based tractography to the operating room.

Connectome harmonic decomposition tracks the presence of disconnected consciousness during ketamine-induced unresponsiveness

BACKGROUND

Ketamine, in doses suitable to induce anaesthesia in humans, gives rise to a unique state of unresponsiveness accompanied by vivid experiences and sensations, making it possible to disentangle the correlated but distinct concepts of conscious awareness and behavioural responsiveness. This distinction is often overlooked in the study of consciousness.

METHODS

The mathematical framework of connectome harmonic decomposition (CHD) was used to view functional magnetic resonance imaging (fMRI) signals during ketamine-induced unresponsiveness as distributed patterns across spatial scales. The connectome harmonic signature of this particular state was mapped onto signatures of other states of consciousness for comparison.

RESULTS

An increased prevalence of fine-grained connectome harmonics was found in fMRI signals obtained during ketamine-induced unresponsiveness, indicating higher granularity. After statistical assessment, the ketamine sedation harmonic signature showed alignment with signatures of LSD-induced (fixed effect =0.0113 [0.0099, 0.0127], P<0.001) or ketamine-induced (fixed effect =0.0087 [0.0071, 0.0103], P<0.001) psychedelic states, and misalignment with signatures seen in unconscious individuals owing to propofol sedation (fixed effect =-0.0213 [-0.0245, -0.0181], P<0.001) or brain injury (fixed effect =-0.0205 [-0.0234, -0.0178], P<0.001).

CONCLUSIONS

The CHD framework, which only requires resting-state fMRI data and can be applied retrospectively, has the ability to track alterations in conscious awareness in the absence of behavioural responsiveness on a group level. This is possible because of ketamine's unique property of decoupling these two facets, and is important for consciousness and anaesthesia research.

The effect of Alzheimer's disease genetic factors on limbic white matter microstructure

INTRODUCTION

White matter (WM) microstructure is essential for brain function but deteriorates with age and in neurodegenerative conditions such as Alzheimer's disease (AD). Diffusion MRI, enhanced by advanced bi-tensor models accounting for free water (FW), enables in vivo quantification of WM microstructural differences.

METHODS

To evaluate how AD genetic risk factors affect limbic WM microstructure - crucial for memory and early impacted in disease - we conducted linear regression analyses in a cohort of 2,614 non-Hispanic White aging adults (aged 50.12 to 100.85 years). The study evaluated 36 AD risk variants across 26 genes, the association between AD polygenic scores (PGSs) and WM metrics, and interactions with cognitive status.

RESULTS

AD PGSs, variants in TMEM106B, PTK2B, WNT3, and apolipoprotein E (APOE), and interactions involving MS4A6A were significantly linked to WM microstructure.

DISCUSSION

These findings implicate AD-related genetic factors related to neurodevelopment (WNT3), lipid metabolism (APOE), and inflammation (TMEM106B, PTK2B, MS4A6A) that contribute to alternations in WM microstructure in older adults.

HIGHLIGHTS

AD risk variants in TMEM106B, PTK2B, WNT3, and APOE genes showed distinct associations with limbic FW-corrected WM microstructure metrics. Interaction effects were observed between MS4A6A variants and cognitive status. PGS for AD was associated with higher FW content in the limbic system.

White-matter alterations in dysthyroid optic neuropathy: a diffusion kurtosis imaging study using tract-based spatial statistics

PURPOSE

So far, there is no gold standard to diagnosis dysthyroid optic neuropathy (DON). Diffusion kurtosis imaging (DKI) has the potential to provide imaging biomarkers for the timely and accurate diagnosis of DON. This study aimed to explore the white matter (WM) alterations in thyroid-associated ophthalmopathy (TAO) patients with and without DON using DKI with tract-based spatial statistics method.

MATERIALS AND METHODS

Fifty-three TAO patients (21 DON and 32 non-DON) and 30 healthy controls (HCs) were recruited in this cross-sectional study. DKI data were analyzed and compared among groups. The correlations between diffusion parameters and clinical variables were assessed. Receiver-operating characteristic curve analysis was used to evaluate the feasibility of using DKI parameters to distinguish DON and non-DON.

RESULTS

Compared with HCs, both DON and non-DON groups exhibited significantly decreased radial kurtosis (RK), mean kurtosis (MK), axial kurtosis (AK), kurtosis fractional anisotropy, and fractional anisotropy values in several WM tracts. No significant differences were observed in mean diffusivity values among groups. Meanwhile, DON patients exhibited lower RK, MK, and AK values than non-DON patients mainly in the visual system. Significant correlations were observed between RK values of posterior thalamic radiation (PTR) and best-corrected visual acuity. For distinguishing DON, the RK values of PTR exhibited decent diagnostic performance.

CONCLUSION

Microstructural abnormalities in WM, especially in the visual system, could provide novel insights into the potential neural mechanisms of the disease, thereby contributing to the timely diagnosis of DON and the development of neuroprotective therapy.

Mapping Along-Tract White Matter Microstructural Differences in Autism

Previous diffusion magnetic resonance imaging (dMRI) research has indicated altered white matter microstructure in autism, but the implicated regions are highly inconsistent across studies. Such prior work has largely used conventional dMRI analysis methods, including the traditional microstructure model, based on diffusion tensor imaging (DTI). However, these methods are limited in their ability to precisely map microstructural differences and accurately resolve complex fiber configurations. In our study, we investigated white matter microstructure alterations in autism using the refined along-tract analytic approach, BUndle ANalytics (BUAN), and an advanced microstructure model, the tensor distribution function (TDF). We analyzed dMRI data from 365 autistic and neurotypical participants (5-24 years; 34% female) from 10 cohorts to examine commissural and association tracts. Autism was associated with lower fractional anisotropy and higher diffusivity in localized portions of nearly every commissural and association tract examined; these tracts inter-connected a wide range of brain regions, including frontal, temporal, parietal, and occipital. Taken together, BUAN and TDF allow robust and spatially precise mapping of microstructural properties in autism. Our findings rigorously demonstrate that white matter microstructure alterations in autism may be greater within specific regions of individual tracts, and that the implicated tracts are distributed across the brain.

Connectivity related to major brain functions in Alzheimer disease progression: microstructural properties of the cingulum bundle and its subdivision using diffusion-weighted MRI

BACKGROUND

The cingulum bundle is a brain white matter fasciculus associated with the cingulate gyrus. It connects areas from the temporal to the frontal lobe. It is composed of fibers with different terminations, lengths, and structural properties, related to specific brain functions. We aimed to automatically reconstruct this fasciculus in patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) and to assess whether trajectories have different microstructural properties in relation to dementia progression.

METHODS

Multi-shell high angular resolution diffusion imaging-HARDI image datasets from the "Alzheimer's Disease Neuroimaging Initiative"-ADNI repository of 10 AD, 18 MCI, and 21 cognitive normal (CN) subjects were used to reconstruct three subdivisions of the cingulum bundle, using a probabilistic approach, combined with measurements of diffusion tensor and neurite orientation dispersion and density imaging metrics in each subdivision.

RESULTS

The subdivisions exhibit different pathways, terminations, and structural characteristics. We found differences in almost all the diffusivity metrics among the subdivisions (p < 0.001 for all the metrics) and between AD versus CN and MCI versus CN subjects for mean diffusivity (p = 0.007-0.038), radial diffusivity (p = 0.008-0.049) and neurite dispersion index (p = 0.005-0.049).

CONCLUSION

Results from tractography analysis of the subdivisions of the cingulum bundle showed an association in the role of groups of fibers with their functions and the variance of their properties in relation to dementia progression.

RELEVANCE STATEMENT

The cingulum bundle is a complex tract with several pathways and terminations related to many cognitive functions. A probabilistic automatic approach is proposed to reconstruct its subdivisions, showing different microstructural properties and variations. A larger sample of patients is needed to confirm results and elucidate the role of diffusion parameters in characterizing alterations in brain function and progression to dementia.

KEY POINTS

The microstructure of the cingulum bundle is related to brain cognitive functions. A probabilistic automatic approach is proposed to reconstruct the subdivisions of the cingulum bundle by diffusion-weighted images. The subdivisions showed different microstructural properties and variations in relation to the progression of dementia.

Diffusion- and Tractography-Based Characterization of Tissue Damage Within and Surrounding Paramagnetic Rim Lesions in Multiple Sclerosis

BACKGROUND AND PURPOSE

Paramagnetic rim lesions (PRLs) are an imaging biomarker of chronic inflammation in MS that are associated with more aggressive disease. However, the precise tissue characteristics and extent of their damage, particularly with regard to connected axonal tracts, are incompletely understood. Quantitative diffusion tissue measurements and fiber tractography can provide a more complete picture of these phenomena.

MATERIALS AND METHODS

One hundred fifteen people with MS were enrolled in this study. Quantitative susceptibility mapping and DWI were acquired on a 3T MRI scanner. PRLs were identified in 49 (43%) subjects. Diffusion tractography was then used to identify nearby PRL-connected versus non-PRL connected tracts and PRL-connected versus nonconnected surrounding tracts. DWI metrics, including fractional anisotropy (FA), quantitative anisotropy (QA), mean diffusivity, axial diffusivity, radial diffusivity, isotropy, and restricted diffusion imaging, were compared between these tracts and within PRLs and non-PRL lesions themselves.

RESULTS

Tissue within PRLs had significantly lower FA than tissue within non-PRL T2 lesions (P = .04). Tracts connected to PRLs exhibited significantly lower FA (P < .001), higher restricted diffusion imaging (P = .02, and higher Iso values (P = .007) than tracts connected to non-PRL T2 lesions. Only QA was different between tracts connected to PRLs and nonconnected surrounding tracts (P = .003).

CONCLUSIONS

PRLs are more destructive both within themselves and to surrounding tissue. This damage appears more spatially than axonally mediated.

White matter injuries mediate brain age effects on cognitive function in cerebral small vessel disease

PURPOSE

This study aims to investigate the potential effect of compromised structural integrity on cerebral aging and cognitive function in cerebral small vessel disease (CSVD).

METHODS

Fifty-five CSVD patients and 42 controls underwent three-dimensional T1-weighted imaging and diffusion tensor imaging. Relative brain age (RBA) was computed to assess cerebral aging. Variables of structural integrity included cortical thickness, cortical volume, white matter hyperintensity (WMH) volume, peak width of skeletonized mean diffusivity (PSMD), ventricular volume, and choroid plexus volume. Mini-Mental State Examination (MMSE) was conducted to assess general cognition. Trail Making Test (TMT) and Auditory Verbal Learning Test were administered to evaluate executive function and episodic memory, respectively. Mediation analysis and multivariate linear regression with interaction terms were performed to explore the differential impacts of RBA on cognitive function and structural integrity between CSVD patients and controls.

RESULTS

RBA was significantly increased in CSVD patients compared to controls (p < 0.001). White matter injuries as assessed with PSMD (mediation magnitude: 41.1%) and WMH volume (mediation magnitude: 56.9%) significantly mediated the relationship between CSVD pathologies and RBA (p < 0.001). Higher RBA was significantly correlated with poorer scores of MMSE, TMT-A, and TMT-B in CSVD patients (p < 0.01). Additionally, PSMD (mediation magnitude: 57.8% in MMSE, 48.3% in TMT-A, and 28.8% in TMT-B) and WMH volume (mediation magnitude: 55.1% in MMSE) significantly mediated the relationship between RBA and cognitive function (p < 0.05).

CONCLUSION

White matter injuries play a critical role in the cerebral aging and cognitive decline in CSVD patients.

Distinct White Matter Fiber Density Patterns in Bipolar and Depressive Disorders: Insights from Fixel-Based Analysis

Background

Differentiating Bipolar (BD) and depressive (DD) disorders remains challenging in clinical practice due to overlapping symptoms. Our study employs fixel-based analysis (FBA) to examine fiber-specific white matter differences in BD and DD and gain insights into the ability of FBA metrics to predict future spectrum mood symptoms.

Methods

163 individuals between 18 and 45 years with BD, DD, and healthy controls (HC) underwent Diffusion Magnetic Resonance Imaging. FBA was used to assess fiber density (FD), fiber cross-section (FC), and fiber density cross-section (FDC) in major white matter tracts. A longitudinal follow-up evaluated whether FBA measures predicted future spectrum depressive and hypomanic symptom trajectories over six months.

Results

Direct comparisons between BD and DD indicated lower FD in the right superior longitudinal and uncinate fasciculi and left thalamo-occipital tract in BD versus DD. Individuals with DD exhibited lower FD in the left arcuate fasciculus than those with BD. Compared to HC, both groups showed lower FD in the splenium of the corpus callosum and left striato-occipital and optic radiation tracts. FD in these tracts predicted future spectrum symptom severity. Exploratory analyses revealed associations between FD, medication use, and marijuana exposure.

Conclusions

Our findings highlight distinct and overlapping white matter alterations in BD and DD. Furthermore, FD in key tracts may serve as a predictor of future symptom trajectories, supporting the potential clinical utility of FD as a biomarker for mood disorder prognosis. Future longitudinal studies are needed to explore the impact of treatment and disease progression on white matter microstructure.

Lifespan cardiometabolic exposures are associated with midlife white matter microstructure: The Bogalusa Heart Study

INTRODUCTION

Compartment model analysis of diffusion MRI data provides unique information on the microstructural properties of white matter. However, studies relating compartment model microstructural measures to longitudinal cardiometabolic health data are rare.

METHODS

130 cognitively healthy participants in the Bogalusa Heart Study completed diffusion MRI scans. Compartment model analysis was performed, and summary metrics were measured in organized and diffuse white matter. Multiple linear regression models were used to relate the white matter microstructure metrics to demographics and cardiometabolic risk factors.

RESULTS

In both organized and diffuse white matter, age was associated with worse diffusion metrics, women had better diffusion metrics than men, and African American participants had worse diffusion metrics compared to White participants. Greater blood pressure in pre-adulthood was associated with worse diffusion metrics in midlife.

DISCUSSION

Summary metrics from compartment model analyses of diffusion MRI data were associated cardiometabolic risk factors from youth to midlife as well as demographic factors.

Structural connectome construction using constrained spherical deconvolution in multi-shell diffusion-weighted magnetic resonance imaging

Connectional neuroanatomical maps can be generated in vivo by using diffusion-weighted magnetic resonance imaging (dMRI) data, and their representation as structural connectome (SC) atlases adopts network-based brain analysis methods. We explain the generation of high-quality SCs of brain connectivity by using recent advances for reconstructing long-range white matter connections such as local fiber orientation estimation on multi-shell dMRI data with constrained spherical deconvolution, which yields both increased sensitivity to detecting crossing fibers compared with competing methods and the ability to separate signal contributions from different macroscopic tissues, and improvements to streamline tractography such as anatomically constrained tractography and spherical-deconvolution informed filtering of tractograms, which have increased the biological accuracy of SC creation. Here, we provide step-by-step instructions to creating SCs by using these methods. In addition, intermediate steps of our procedure can be adapted for related analyses, including region of interest-based tractography and quantification of local white matter properties. The associated software MRtrix3 implements the relevant tools for easy application of the protocol, with specific processing tasks deferred to components of the FSL software. The protocol is suitable for users with expertise in dMRI and neuroscience and requires between 2 h and 13 h to complete, depending on the available computational system.

Structural Development of Speech Networks in Young Children

Characterizing the structural development of the neural speech network in early childhood is important to understand speech acquisition. To investigate speech in the developing brain, 94 children aged 4-7-years-old were scanned using diffusion weighted imaging (DWI) magnetic resonance imaging (MRI). In order to increase sample size and performance variability, we included children who were diagnosed with attention-deficit hyperactivity disorder (ADHD) from a larger ongoing study. Additionally, each child completed the Syllable Repetition Task (SRT), a validated measure of phoneme articulation. The DWI data were modeled using restriction spectrum imaging (RSI) to measure restricted and hindered diffusion properties in both grey and white matter. Consequently, we analyzed the diffusion data using both whole brain analysis, and automated fiber quantification (AFQ) analysis to establish tract profiles for each of six fiber pathways thought to be important for supporting speech development. In the whole brain analysis, we found that SRT performance was associated with restricted diffusion in bilateral inferior frontal gyrus, pars opercularis , right pre-supplementary and supplementary motor area, and bilateral cerebellar grey matter ( p < .005). Age moderated these associations in left pars opercularis and frontal aslant tract (FAT). However, in both cases only the cerebellar findings survived a cluster correction. We also found associations between SRT performance and restricted diffusion in cortical association fiber pathways, especially left FAT, and in the cerebellar peduncles. Analyses using automated fiber quantification (AFQ) highlighted differences in high and low performing children along specific tract profiles, most notably in left but not right FAT, in bilateral SLFIII, and in the cerebellar peduncles. These findings suggest that individual differences in speech performance are reflected in structural grey and white matter differences as measured by restricted and hindered diffusion metrics, and offer important insights into developing brain networks supporting speech in very young children.

Insomnia Subtypes Have Differentiating Deviations in Brain Structural Connectivity

BACKGROUND

Insomnia disorder is the most common sleep disorder. A better understanding of insomnia-related deviations in the brain could inspire better treatment. Insufficiently recognized heterogeneity within the insomnia population could obscure detection of involved brain circuits. In the current study, we investigated whether structural brain connectivity deviations differed between recently discovered and validated insomnia subtypes.

METHODS

Structural and diffusion-weighted 3T magnetic resonance imaging data from 4 independent studies were harmonized. The sample consisted of 73 control participants without sleep complaints and 204 participants with insomnia who were grouped into 5 insomnia subtypes based on their fingerprint of mood and personality traits assessed with the Insomnia Type Questionnaire. Linear regression correcting for age and sex was used to evaluate group differences in structural connectivity strength, indicated by fractional anisotropy, streamline volume density, and mean diffusivity and evaluated within 3 different atlases.

RESULTS

Insomnia subtypes showed differentiating profiles of deviating structural connectivity that were concentrated in different functional networks. Permutation testing against randomly drawn heterogeneous subsamples indicated significant specificity of deviation profiles in 4 of the 5 subtypes: highly distressed, moderately distressed reward sensitive, slightly distressed low reactive, and slightly distressed high reactive. Connectivity deviation profile significance ranged from p = .001 to p = .049 for different resolutions of brain parcellation and connectivity weight.

CONCLUSIONS

Our results provide an initial indication that different insomnia subtypes exhibit distinct profiles of deviations in structural brain connectivity. Subtyping insomnia may be essential for a better understanding of brain mechanisms that contribute to insomnia vulnerability.

Brain network alterations in anorexia Nervosa: A Multi-Center structural connectivity study

Anorexia nervosa (AN) is a severe eating disorder characterized by intense fear of weight gain, distorted body image, and extreme food restriction. This research employed advanced diffusion MRI techniques including single-shell 3-tissue constrained spherical deconvolution, anatomically constrained tractography, and spherical deconvolution informed filtering of tractograms to analyze brain network alterations in AN. Diffusion MRI data from 81 AN patients and 98 healthy controls were obtained. The structural brain connectome was constructed based on nodes set in 84 brain regions, and graph theory analysis was conducted. Results showed that AN patients exhibited significantly higher clustering coefficient and local efficiency in several brain regions, including the left fusiform gyrus, bilateral orbitofrontal cortex, right entorhinal cortex, right lateral occipital gyrus, right superior temporal gyrus, and right insula. A trend towards higher global efficiency and small-worldness was also observed in AN patients, although not statistically significant. These findings suggest increased local connectivity and efficiency within regions associated with behavioral rigidity, emotional regulation, and disturbed body image among AN patients. This study contributes to the understanding of the neurological basis of AN by highlighting structural connectivity alterations in specific brain regions.

Microstructural mapping of neural pathways in Alzheimer's disease using macrostructure-informed normative tractometry

INTRODUCTION

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

METHODS

We propose a novel macrostructure-informed normative tractometry (MINT) framework to investigate how white matter (WM) microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compared MINT-derived metrics with univariate diffusion tensor imaging (DTI) metrics to examine how fiber geometry may impact the interpretation of microstructure.

RESULTS

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

DISCUSSION

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

HIGHLIGHTS

Changes in diffusion tensor imaging metrics may be due to macroscopic changes. Normative models encode normal variability of diffusion metrics in healthy controls. Variational autoencoder applied on tractography can learn patterns of fiber geometry. WM microstructure and macrostructure are modeled with multivariate methods. Transfer learning uses pretraining and fine-tuning for increased efficiency.

Sex and APOE ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer's disease

INTRODUCTION

The effects of sex and apolipoprotein E (APOE)-Alzheimer's disease (AD) risk factors-on white matter microstructure are not well characterized.

METHODS

Diffusion magnetic resonance imaging data from nine well-established longitudinal cohorts of aging were free water (FW)-corrected and harmonized. This dataset included 4741 participants (age = 73.06 ± 9.75) with 9671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FAFWcorr) were used to assess differences in white matter microstructure by sex and APOE ε4 carrier status.

RESULTS

Sex differences in FAFWcorr in projection tracts and APOE ε4 differences in FW limbic and occipital transcallosal tracts were most pronounced.

DISCUSSION

There are prominent differences in white matter microstructure by sex and APOE ε4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted.

HIGHLIGHTS

Sex and apolipoprotein E (APOE) ε4 carrier status relate to white matter microstructural integrity. Females generally have lower free water-corrected fractional anisotropy compared to males. APOE ε4 carriers tended to have higher free water than non-carriers.

Interactive mining of neural pathways to preoperative neurosurgical planning

BACKGROUND AND OBJECTIVE

Preoperative understanding of white matter anatomy, including its spatial relationship with pathology and superficial landmarks, is vital for effective surgical planning. The ability to interactively synthesize neural pathways from diffusion data and dynamically discern neuroanatomy-referenced fiber patterns enables neurosurgeons to construct detailed mental models of the patient's brain and assess surgical risks. We present a novel interactive software designed for real-time mining of neural pathways from diffusion-weighted magnetic resonance imaging (DW-MRI) data. This software leverages a user-guided approach, integrating curvilinear reformatting and surgeon expertise with diffusion tensor imaging (DTI) data, and employs a finite-state machine interaction model to facilitate intuitive use through a windows, icons, menus, and pointers (WIMP) interface.

METHODS

The proposed system merges user analytical skills with neuroanatomy-referenced DTI data, including scalar maps, tensor glyphs, and streamlines, within a visually interactive environment. Key features of the system include optimized GPU-based rendering for enhanced graphical representation and the proposed finite-state machine model that enables seamless interaction through intuitive controls. This approach allows for real-time manipulation of DTI data and dynamic generation of depth maps for each frame, facilitating practical exploration and analysis.

RESULTS

After testing seven control volumes, our system demonstrates tract reconstruction capabilities comparable to MRTrix software's. The evaluation of GPU-based fiber tracking and rendering performance, using NVIDIA Nsight Visual Studio Edition, confirms the system's interactive responsiveness. Preliminary results indicate that the environment effectively extracts critical fibers and evaluates their spatial relationships with surgical targets and landmarks. This functionality provides valuable insights for refining preoperative planning, optimizing surgical approaches, and minimizing potential functional damage.

CONCLUSION

Our WIMP-based interactive environment empowers surgeons with enhanced capabilities for real-time manipulation of neuroanatomy-referenced DTI data. Integrating curvilinear reformatting and finite-state machine interaction enhances user experience significantly, making it a valuable tool for improving surgical safety and precision. This low-cost, accessible approach has the potential to facilitate minimally invasive procedures, accurate landmark identification, and reduced functional damage, particularly in resource-limited settings.

Sex-Specific White Matter Abnormalities Across the Dynamic Pain Connectome in Neuropathic Pain: A Fixel-Based Analysis Study

A fundamental issue in neuroscience is a lack of understanding regarding the relationship between brain function and the white matter architecture that supports it. Individuals with chronic neuropathic pain (NP) exhibit functional abnormalities throughout brain networks collectively termed the "dynamic pain connectome" (DPC), including the default mode network (DMN), salience network, and ascending nociceptive and descending pain modulation systems. These functional abnormalities are often observed in a sex-dependent fashion. However, the enigmatic white matter structural features underpinning these functional networks and the relationship between structure and function/dysfunction in NP remain poorly understood. Here we used fixel-based analysis of diffusion weighted imaging data in 80 individuals (40 with NP [21 female, 19 male] and 40 sex- and age-matched healthy controls [HCs]) to evaluate white matter microstructure (fiber density [FD]), macrostructure (fiber bundle cross section) and combined microstructure and macrostructure (fiber density and cross section) within anatomical connections that support the DPC. We additionally examined whether there are sex-specific abnormalities in NP white matter structure. We performed fixel-wise and connection-specific mean analyses and found three main ways in which individuals with NP differed from HCs: (1) people with NP exhibited abnormally low FD and FDC within the corona radiata consistent with the ascending nociceptive pathway between the sensory thalamus and primary somatosensory cortex (S1). Furthermore, the entire sensory thalamus-S1 pathway exhibited abnormally low FD and FDC in people with NP, and this effect was driven by the females with NP; (2) females, but not males, with NP had abnormally low FD within the cingulum consistent with the right medial prefrontal cortex-posterior cingulate cortex DMN pathway; and (3) individuals with NP had higher connection-specific mean FDC than HCs in the anterior insula-temporoparietal junction and sensory thalamus-posterior insula pathways. However, sex-specific analyses did not corroborate these connection-specific findings in either females or males with NP. Our findings suggest that females with NP exhibit microstructural and macrostructural white matter abnormalities within the DPC networks including the ascending nociceptive system and DMN. We propose that aberrant white matter structure contributes to or is driven by functional abnormalities associated with NP. Our sex-specific findings highlight the utility and importance of using sex-disaggregated analyses to identify white matter abnormalities in clinical conditions such as chronic pain.

Traumatic brain injury, posttraumatic stress disorder, and vascular risk are independently associated with white matter aging in Vietnam-Era veterans

OBJECTIVE

Traumatic brain injury (TBI), mental health conditions (e.g., posttraumatic stress disorder [PTSD]), and vascular comorbidities (e.g., hypertension, diabetes) are highly prevalent in the Veteran population and may exacerbate age-related changes to cerebral white matter (WM). Our study examined (1) relationships between health conditions-TBI history, PTSD, and vascular risk-and cerebral WM micro- and macrostructure, and (2) associations between WM measures and cognition.

METHOD

We analyzed diffusion tensor images from 183 older male Veterans (mean age = 69.18; SD = 3.61) with (n = 95) and without (n = 88) a history of TBI using tractography. Generalized linear models examined associations between health conditions and diffusion metrics. Total WM hyperintensity (WMH) volume was calculated from fluid-attenuated inversion recovery images. Robust regression examined associations between health conditions and WMH volume. Finally, elastic net regularized regression examined associations between WM measures and cognitive performance.

RESULTS

Veterans with and without TBI did not differ in severity of PTSD or vascular risk (p's >0.05). TBI history, PTSD, and vascular risk were independently associated with poorer WM microstructural organization (p's <0.5, corrected), however the effects of vascular risk were more numerous and widespread. Vascular risk was positively associated with WMH volume (p = 0.004, β=0.200, R2 = 0.034). Higher WMH volume predicted poorer processing speed (R2 = 0.052).

CONCLUSIONS

Relative to TBI history and PTSD, vascular risk may be more robustly associated with WM micro- and macrostructure. Furthermore, greater WMH burden is associated with poorer processing speed. Our study supports the importance of vascular health interventions in mitigating negative brain aging outcomes in Veterans.

Deep learning methods for 3D magnetic resonance image denoising, bias field and motion artifact correction: a comprehensive review

Magnetic resonance imaging (MRI) provides detailed structural information of the internal body organs and soft tissue regions of a patient in clinical diagnosis for disease detection, localization, and progress monitoring. MRI scanner hardware manufacturers incorporate various post-acquisition image-processing techniques into the scanner's computer software tools for different post-processing tasks. These tools provide a final image of adequate quality and essential features for accurate clinical reporting and predictive interpretation for better treatment planning. Different post-acquisition image-processing tasks for MRI quality enhancement include noise removal, motion artifact reduction, magnetic bias field correction, and eddy electric current effect removal. Recently, deep learning (DL) methods have shown great success in many research fields, including image and video applications. DL-based data-driven feature-learning approaches have great potential for MR image denoising and image-quality-degrading artifact correction. Recent studies have demonstrated significant improvements in image-analysis tasks using DL-based convolutional neural network techniques. The promising capabilities and performance of DL techniques in various problem-solving domains have motivated researchers to adapt DL methods to medical image analysis and quality enhancement tasks. This paper presents a comprehensive review of DL-based state-of-the-art MRI quality enhancement and artifact removal methods for regenerating high-quality images while preserving essential anatomical and physiological feature maps without destroying important image information. Existing research gaps and future directions have also been provided by highlighting potential research areas for future developments, along with their importance and advantages in medical imaging.

White matter integrity and cognitive performance in the subacute phase after ischemic stroke in young adults

INTRODUCTION

Reduced white matter integrity outside the stroke lesion may be a potential contributor of post-stroke cognitive impairment. We aimed to investigate how a stroke lesion affects the integrity of surrounding white matter, and whether the integrity of the non-lesioned part of white matter tracts is associated with cognitive performance after ischemic stroke in young adults.

METHODS

Patients from the ODYSSEY study, aged 18-49 years, with a first-ever ischemic stroke, underwent 3T MRI and cognitive assessment within six months after the index event. Using TractSeg and free water imaging, we analyzed free water corrected fractional anisotropy (FAT), free water corrected mean diffusivity (MDT), and free water (FW) of all white matter tracts outside the stroke lesion. We calculated FAT and FW in the lesioned white matter tracts at 2 mm incremental distances from the lesion, extending up to 10 mm, represented as Z-scores using the diffusion measures of controls. We categorized patients as no/mild or major vascular cognitive disorder (VCD) and compared with a stroke-free control group (n = 23). Group differences in diffusion measures were examined. We investigated associations between FAT, FW and cognitive performance across seven domains.

RESULTS

Among 66 patients (median age 40.3 years (IQR 31.3-46.2); 54.5 % women), 22 had major VCD. In the different lesion expansions, we found differences in FAT (p = 0.009) and FW (p = 0.049). Patients with major VCD had lower FAT [range of Cohen's d (0.65; 1.65)] and higher FW [Cohen's d (-1.40; -0.64)] values compared to controls, both in the hemisphere affected by the lesion and the unaffected hemisphere. Performance in processing speed correlated with FAT across eight tracts in the affected hemisphere [range of R2adj (0.30; 0.37)], and with FW in four tracts in the affected and three in the unaffected hemisphere [R2adj (0.28; 0.38)].

DISCUSSION

In the first months after a stroke, we observed a trend of microstructural changes remote from the lesion that diminish as the distance from the lesion increases. Tissue changes in the white matter outside the lesion are present in both hemispheres, but are more pronounced in the hemisphere affected by the stroke, and may contribute to worse cognitive performance.

Diffusion MRI with Machine Learning

Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high inter-session and inter-scanner variability in the data, as well as inter-subject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

White matter organization abnormalities in adults with 47,XXX: A 7 Tesla MRI study

47,XXX (Triple X syndrome) is a sex chromosome aneuploidy characterized by the presence of a supernumerary X chromosome in affected females, and has been associated with a variable cognitive, behavioral, and psychiatric phenotype. Alterations in brain gray matter structure and function have been reported, but less is known about white matter (WM) organization in 47,XXX. Therefore, we conducted 7 T diffusion tensor imaging and characterized fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity of 22 adult women with 47,XXX and 22 age-matched typically developing females using tract-based spatial statistics. Relationships between phenotypic traits and WM organization characteristics in 47,XXX were also investigated. Adults with 47,XXX showed lower axial diffusivity in the body of the corpus callosum and the right superior longitudinal fasciculus. WM organization variability was not associated with IQ and social cognition and social functioning deficits in 47,XXX. Our findings indicate an effect of a supernumerary X chromosome in adult women on axonal integrity of the body of the corpus callosum and the right superior longitudinal fasciculus. These findings provide additional insight into the role of the X chromosome on WM organization. Future research is warranted to explore the clinical significant impact of altered WM organization in 47,XXX.

"Pscore": A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics

BACKGROUND

Quantitative magnetic resonance imaging (MRI) metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed.

PURPOSE

To confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, "Pscore" to address this issue.

STUDY TYPE

Retrospective cohort.

POPULATION

Nine hundred and sixty-one healthy young adults (age: 22-35 years, females: 53%) from the Human Connectome Project.

FIELD STRENGTH/SEQUENCE

3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE.

ASSESSMENT

The dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate "Pscores"-which normalized the difference between the median and a participant's value with the corresponding difference between the median and the 5th/95th percentile values.

STATISTICAL TESTS

ROI-wise distributions were assessed using log transformations, Zscore, and the "Pscore" methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV>95(%), PEV<5(%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (N = 100) using 100 iterations.

RESULTS

The dMRI metric distributions were systematically non-Gaussian, including positively skewed (eg, mean and radial diffusivity) and negatively skewed (eg, fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV>95 ≠ 5%, PEV<5 ≠ 5%) whereas "Pscore" distributions were symmetric and balanced (PEV>95 = PEV<5 = 5%); even for small bootstrapped samples (averagePEV > 95 ¯ = PEV < 5 ¯ = 5 ± 0 % [SD]).

DATA CONCLUSION

The inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed "Pscore" method may help estimating individual deviations more accurately in skewed normative data, even from small datasets.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 1.

Human Claustrum Connections: Robust In Vivo Detection by DWI-Based Tractography in Two Large Samples

Despite substantial neuroscience research in the last decade revealing the claustrum's prominent role in mammalian forebrain organization, as evidenced by its extraordinarily widespread connectivity pattern, claustrum studies in humans are rare. This is particularly true for studies focusing on claustrum connections. Two primary reasons may account for this situation: First, the intricate anatomy of the human claustrum located between the external and extreme capsule hinders straightforward and reliable structural delineation. In addition, the few studies that used diffusion-weighted-imaging (DWI)-based tractography could not clarify whether in vivo tractography consistently and reliably identifies claustrum connections in humans across different subjects, cohorts, imaging methods, and connectivity metrics. To address these issues, we combined a recently developed deep-learning-based claustrum segmentation tool with DWI-based tractography in two large adult cohorts: 81 healthy young adults from the human connectome project and 81 further healthy young participants from the Bavarian longitudinal study. Tracts between the claustrum and 13 cortical and 9 subcortical regions were reconstructed in each subject using probabilistic tractography. Probabilistic group average maps and different connectivity metrics were generated to assess the claustrum's connectivity profile as well as consistency and replicability of tractography. We found, across individuals, cohorts, DWI-protocols, and measures, consistent and replicable cortical and subcortical ipsi- and contralateral claustrum connections. This result demonstrates robust in vivo tractography of claustrum connections in humans, providing a base for further examinations of claustrum connectivity in health and disease.

Diffusion tensor imaging in Behcet's disease with and without neurological involvement patients: evaluation of microstructural white matter abnormality with a tract-based spatial statistical analysis

OBJECTIVE

This study aims to assess the microstructural abnormalities in white matter (WM) among Behcet's disease (BD) patients, both with and without neurological involvement, utilising tract-based spatial statistics (TBSS) to elucidate the underlying causes of WM microstructural changes.

METHODS

This prospective study comprised 43 BD patients without neurological involvement, 15 neuro-Behcet's disease (NBD) patients with normal conventional MRI, and 54 healthy controls matched for age and sex. TBSS was applied in this diffusion tensor imaging study to conduct a whole-brain voxel-wise analysis of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) of WM.

RESULTS

Compared to the control group, BD patients exhibited decreased FA and increased MD and RD in nearly all WM tracts, along with increased AD in the left corticospinal tract (CST), left inferior longitudinal fasciculus (ILF), and left superior longitudinal fasciculus (SLF). NBD patients also showed a widespread decrease in FA and increased MD and RD, similar to BD patients without neurological involvement. Additionally, NBD patients had increased AD in the left CST, left ILF, left SLF, left inferior fronto-occipital fasciculus (IFOF), and right CST. Compared to BD patients without neurological involvement, NBD patients exhibited a greater reduction in FA and an increase in MD and RD in WM tracts, with no significant differences in AD.

CONCLUSION

These results suggest that the main mechanism of microstructural changes in the WM of BD patients may be related to impaired fibre integrity, demyelination, and decreased myelin sheath integrity.

ADVANCES IN KNOWLEDGE

This study demonstrated BD patients without neurological involvement and NBD patients a decrease in FA and an increase in MD and RD were observed in larger areas of major WM tracts, while an increase in AD values was observed in fewer tracts. Our findings may be useful in understanding the pathophysiology underlying subclinical parenchymal involvement and neurological dysfunction in BD patients and the management of BD patients.

Temporal evolution of microstructural integrity in cerebellar peduncles in Parkinson's disease: Stage-specific patterns and dopaminergic correlates

BACKGROUND

Previous research revealed differences in cerebellar white matter integrity by disease stages, indicating a compensatory role in Parkinson's disease (PD). However, the temporal evolution of cerebellar white matter microstructure in patients with PD (PwPD) remains unclear.

OBJECTIVE

To unravel temporal evolution of cerebellar white matter and its dopaminergic correlates in PD.

METHODS

We recruited 124 PwPD from the PPMI study. The participants were divided into two subsets: Subset 1 (n = 41) had three MRI scans (baseline, 2 years, and 4 years), and Subset 2 (n = 106) had at least two MRI scans at baseline, 1 year, and/or 2 years. Free water-corrected diffusion metrics were used to measure the microstructural integrity in cerebellar peduncles (CP), the main white matter tracts connecting to and from the cerebellum. The ACAPULCO processing pipeline was used to assess cerebellar lobules volumes. Linear mixed-effect models were used to study longitudinal changes. We also examined the relationships between microstructural integrity in CP, striatal dopamine transporter specific binding ratio (SBR), and clinical symptoms.

RESULTS

Microstructural changes in CP showed a non-linear pattern in PwPD. Free water-corrected fractional anisotropy (FAt) increased in the first two years but declined from 2 to 4 years, while free water-corrected mean diffusivity exhibited the opposite trend. The initial increased FAt in CP correlated with cerebellar regional volume atrophy, striatal dopaminergic SBR decline, and worsening clinical symptoms, but this correlation varied across disease stages.

CONCLUSIONS

Our findings suggest a non-linear evolution of microstructural integrity in CP throughout the course of PD, indicating the adaptive structural reorganization of the cerebellum simultaneously with progressive striatal dopaminergic degeneration in PD.

Glymphatic system dysfunction in mood disorders: Evaluation by diffusion magnetic resonance imaging

The glymphatic system, an expansive cerebral waste-disposal network, harbors myriad enigmatic facets necessitating elucidation of their nexus with diverse pathologies. Murine investigations have revealed a relationship between the glymphatic system and affective disorders. This study aimed to illuminate the interplay between bipolar disorder and the glymphatic system. Fifty-eight individuals afflicted with bipolar disorder were identified through meticulous psychiatric assessment. These individuals were juxtaposed with a cohort of 66 comparably aged and sex-matched, mentally stable subjects. Subsequent analysis entailed the application of covariance analysis to evaluate along with the perivascular space (ALPS) index, a novel magnetic resonance imaging method for assessing brain interstitial fluid dynamics via diffusion tensor imaging within the bipolar and control cohorts. We also evaluated the correlation between the ALPS index and clinical parameters, which included the Hamilton Depression scale scores, disease duration, and other clinical assessments. Moreover, partial correlation analyses, incorporating age and sex as covariates, were performed to investigate the relationships between the ALPS index and clinical measures within the two cohorts. A noteworthy adverse correlation was observed between the ALPS index and illness duration. A free-water imaging analysis revealed a substantial elevation in the free-water index within the white-matter tracts, prominently centered on the corpus callosum, within the bipolar cohort relative to that in the control group. In analogous cerebral regions, a conspicuous affirmative correlation was observed between the free-water-corrected radial diffusivity and depression rating scales. Our results showed that the protracted course of bipolar disorder concomitantly exacerbated glymphatic system dysregulation.

Improved Dementia Prediction in Cerebral Small Vessel Disease Using Deep Learning-Derived Diffusion Scalar Maps From T1

BACKGROUND

Cerebral small vessel disease is the most common pathology underlying vascular dementia. In small vessel disease, diffusion tensor imaging is more sensitive to white matter damage and better predicts dementia risk than conventional magnetic resonance imaging sequences, such as T1 and fluid attenuation inversion recovery, but diffusion tensor imaging takes longer to acquire and is not routinely available in clinical practice. As diffusion tensor imaging-derived scalar maps-fractional anisotropy (FA) and mean diffusivity (MD)-are frequently used in clinical settings, one solution is to synthesize FA/MD from T1 images.

METHODS

We developed a deep learning model to synthesize FA/MD from T1. The training data set consisted of 4998 participants with the highest white matter hyperintensity volumes in the UK Biobank. Four external validations data sets with small vessel disease were included: SCANS (St George's Cognition and Neuroimaging in Stroke; n=120), RUN DMC (Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Imaging Cohort; n=502), PRESERVE (Blood Pressure in Established Cerebral Small Vessel Disease; n=105), and NETWORKS (n=26), along with 1000 normal controls from the UK Biobank.

RESULTS

The synthetic maps resembled ground-truth maps (structural similarity index >0.89 for MD maps and >0.80 for FA maps across all external validation data sets except for SCANS). The prediction accuracy of dementia using whole-brain median MD from the synthetic maps is comparable to the ground truth (SCANS ground-truth c-index, 0.822 and synthetic, 0.821; RUN DMC ground truth, 0.816 and synthetic, 0.812) and better than white matter hyperintensity volume (SCANS, 0.534; RUN DMC, 0.710).

CONCLUSIONS

We have developed a fast and generalizable method to synthesize FA/MD maps from T1 to improve the prediction accuracy of dementia in small vessel disease when diffusion tensor imaging data have not been acquired.

Pretreatment Brain White Matter Integrity Associated With Neuropathic Pain Relief and Changes in Temporal Summation of Pain Following Ketamine

Neuropathic pain (NP) is a prevalent condition often associated with heightened pain responsiveness suggestive of central sensitization. Neuroimaging biomarkers of treatment outcomes may help develop personalized treatment strategies, but white matter (WM) properties have been underexplored for this purpose. Here we assessed whether WM pathways of the default mode network (DMN: medial prefrontal cortex [mPFC], posterior cingulate cortex, and precuneus) and descending pain modulation system (periaqueductal gray [PAG]) are associated with ketamine analgesia and attenuated temporal summation of pain (TSP, reflecting central sensitization) in NP. We used a fixel-based analysis of diffusion-weighted imaging data to evaluate WM microstructure (fiber density [FD]) and macrostructure (fiber bundle cross-section) within the DMN and mPFC-PAG pathways in 70 individuals who underwent magnetic resonance imaging and TSP testing; 35 with NP who underwent ketamine treatment and 35 age- and sex-matched pain-free individuals. Individuals with NP were assessed before and 1 month after treatment; those with ≥30% pain relief were considered responders (n = 18), or otherwise as nonresponders (n = 17). We found that WM structure within the DMN and mPFC-PAG pathways did not differentiate responders from nonresponders. However, pretreatment FD in the anterior limb of the internal capsule correlated with pain relief (r=.48). Moreover, pretreatment FD in the DMN (left mPFC-precuneus/posterior cingulate cortex; r=.52) and mPFC-PAG (r=.42) negatively correlated with changes in TSP. This suggests that WM microstructure in the DMN and mPFC-PAG pathway is associated with the degree to which ketamine reduces central sensitization. Thus, fixel metrics of WM structure may hold promise to predict ketamine NP treatment outcomes. PERSPECTIVE: We used advanced fixel-based analyses of MRI diffusion-weighted imaging data to identify pretreatment WM microstructure associated with ketamine outcomes, including analgesia and markers of attenuated central sensitization. Exploring associations between brain structure and treatment outcomes could contribute to a personalized approach to treatment for individuals with NP.

Design and validation of the ADNI MR protocol

Phase four of the Alzheimer's Disease Neuroimaging Initiative (ADNI4) magnetic resonance imaging (MRI) protocols aim to maintain longitudinal consistency across two decades of data acquisition, while adopting new technologies. Here we describe and justify the study's design and targeted biomarkers. The ADNI4 MRI protocol includes nine MRI sequences. Some sequences require the latest hardware and software system upgrades and are continuously rolled out as they become available at each site. The main sequence additions/changes in ADNI4 are: (1) compressed sensing (CS) T1-weighting, (2) pseudo-continuous arterial spin labeling (ASL) on all three vendors (GE, Siemens, Philips), (3) multiple-post-labeling-delay ASL, (4) 1 mm3 isotropic 3D fluid-attenuated inversion recovery, and (5) CS 3D T2-weighted. ADNI4 aims to help the neuroimaging community extract valuable imaging biomarkers and provide a database to test the impact of advanced imaging strategies on diagnostic accuracy and disease sensitivity among individuals lying on the cognitively normal to impaired spectrum. HIGHLIGHTS: A summary of MRI protocols for phase four of the Alzheimer's Disease Neuroimaging Initiative (ADNI 4). The design and justification for the ADNI 4 MRI protocols. Compressed sensing and multi-band advances have been applied to improve scan time. ADNI4 protocols aim to streamline safety screening and therapy monitoring. The ADNI4 database will be a valuable test bed for academic research.

Exploring White Matter Microstructure with Symptom Severity and Outcomes Following Deep Brain Stimulation in Tremor Syndromes

Background

Essential tremor (ET) and dystonic tremor (DT) are movement disorders that cause debilitating symptoms, significantly impacting daily activities and quality of life. A poor understanding of their pathophysiology, as well as the mediators of clinical outcomes following deep brain stimulation (DBS), highlights the need for biomarkers to accurately characterise and optimally treat patients.

Objectives

We assessed the white matter microstructure of pathways implicated in the pathophysiology and therapeutic intervention in a retrospective cohort of patients with DT (n = 17) and ET (n = 19). We aimed to identity associations between white matter microstructure, upper limb tremor severity, and tremor improvement following DBS.

Methods

A fixel-based analysis pipeline was implemented to investigate white matter microstructural metrics in the whole brain, cerebello-thalamic pathways and tracts connected to stimulation volumes following DBS. Associations with preoperative and postoperative severity were analysed within each disorder group and across combined disorder groups.

Results

DBS led to significant improvements in both groups. No group differences in stimulation positions were identified. When white matter microstructural data was aligned according to the maximally affected upper limb, increased fiber density, and combined fiber density & cross-section of fixels in the left cerebellum were associated with greater tremor severity across DT and ET patients. White matter microstructure did not show associations with postoperative changes in cerebello-thalamic pathways, or tracts connected to stimulation volumes.

Discussion

Diffusion changes of the cerebellum are associated with the severity of upper limb tremor and appear to overlap in essential or dystonic tremor disorders.

A practical guide for combining functional regions of interest and white matter bundles

Diffusion-weighted imaging (DWI) is the primary method to investigate macro- and microstructure of neural white matter in vivo. DWI can be used to identify and characterize individual-specific white matter bundles, enabling precise analyses on hypothesis-driven connections in the brain and bridging the relationships between brain structure, function, and behavior. However, cortical endpoints of bundles may span larger areas than what a researcher is interested in, challenging presumptions that bundles are specifically tied to certain brain functions. Functional MRI (fMRI) can be integrated to further refine bundles such that they are restricted to functionally-defined cortical regions. Analyzing properties of these Functional Sub-Bundles (FSuB) increases precision and interpretability of results when studying neural connections supporting specific tasks. Several parameters of DWI and fMRI analyses, ranging from data acquisition to processing, can impact the efficacy of integrating functional and diffusion MRI. Here, we discuss the applications of the FSuB approach, suggest best practices for acquiring and processing neuroimaging data towards this end, and introduce the FSuB-Extractor, a flexible open-source software for creating FSuBs. We demonstrate our processing code and the FSuB-Extractor on an openly-available dataset, the Natural Scenes Dataset.

White matter integrity in hospitalized COVID-19 patients is not associated with short- and long-term clinical outcomes

Objectives

SARS-CoV-2 infection is associated with a decline in functional outcomes; many patients experience persistent symptoms, while the underlying pathophysiology remains unclear. This study investigated white matter (WM) integrity on brain MRI in hospitalized COVID-19 patients and its associations with clinical outcomes, including long COVID.

Materials and methods

We included hospitalized COVID-19 patients and controls from CORONavirus and Ischemic Stroke (CORONIS), an observational cohort study, who underwent MRI-DWI imaging at baseline shortly after discharge (<3 months after positive PCR) and 3 months after baseline scanning. We assessed WM integrity using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) and performed comparisons between groups and within patients. Clinical assessment was conducted at 3 and 12 months with functional outcomes such as modified Rankin Scale (mRS), Post-COVID-19 Functional Status scale (PCFS), Visual Analogue Scale (VAS), and long COVID, cognitive assessment was conducted by the Modified Telephone Interview for Cognitive Status (TICS-M), and the Hospital Anxiety and Depression Scale (HADS) was used to assess mood disorder. Associations between WM integrity and clinical outcomes were evaluated using logistic regression and linear regression.

Results

A total of 49 patients (mean age 59.5 years) showed higher overall peak width of skeletonized mean diffusivity (PSMD) (p = 0.030) and lower neurite density index (NDI) in several WM regions compared with 25 controls at the baseline (p < 0.05; FWE-corrected) but did not remain statistically significant after adjusting for WM hyperintensities. Orientation dispersion index (ODI) increased after 3-month follow-up in several WM regions within patients (p < 0.05), which remained significant after correction for changes in WMH volume. Patients exhibited worse clinical outcomes compared with controls. Low NDI at baseline was associated with worse performance on the Post-COVID-19 Functional Status scale after 12 months (p = 0.018).

Conclusion

After adjusting for WMH, hospitalized COVID-19 patients no longer exhibited lower WM integrity compared with controls. WM integrity was generally not associated with clinical assessments as measured shortly after discharge, suggesting that factors other than underlying WM integrity play a role in worse clinical outcomes or long COVID.

Greater cortical thinning and microstructural integrity loss in myotonic dystrophy type 1 compared to myotonic dystrophy type 2

BACKGROUND

Myotonic dystrophy is a multisystem disorder characterized by widespread organic involvement including central nervous system symptoms. Although myotonic dystrophy disease types 1 (DM1) and 2 (DM2) cover a similar spectrum of symptoms, more pronounced clinical and brain alterations have been described in DM1. Here, we investigated brain volumetric and white matter alterations in both disease types and compared to healthy controls (HC).

METHODS

MRI scans were obtained from 29 DM1, 27 DM2, and 56 HC. We assessed macro- and microstructural brain changes by surface-based analysis of cortical thickness of anatomical images and tract-based spatial statistics of fractional anisotropy (FA) obtained by diffusion-weighted imaging, respectively. Global MRI measures were related to clinical and neuropsychological scores to evaluate their clinical relevance.

RESULTS

Cortical thickness was reduced in both patient groups compared to HC, showing similar patterns of regional distribution in DM1 and DM2 (occipital, temporal, frontal) but more pronounced cortical thinning for DM1. Similarly, FA values showed a widespread decrease in DM1 and DM2 compared to HC. Interestingly, FA was significantly lower in DM1 compared to DM2 within most parts of the brain.

CONCLUSION

Comparisons between DM1 and DM2 indicate a more pronounced cortical thinning of grey matter and a widespread reduction in microstructural integrity of white matter in DM1. Future studies are required to unravel the underlying and separating mechanisms for the disease courses of the two types and their neuropsychological symptoms.

White matter changes after Gamma Knife Capsulotomy in patients with intractable obsessive-compulsive disorder

Background

Anterior capsulotomy is one of the therapeutic options for refractory obsessive-compulsive disorder (OCD). Safety and efficacy of Gamma Knife Capsulotomy (GKC) have been demonstrated in the past.

Objective

To characterize changes induced by GKC using a fixel-based analysis (FBA) and possible predictors of efficacy.

Methods

Patients with OCD refractory to other therapies underwent bilateral GKC with 120 Gy as a maximum dose on the anterior limb of the internal capsule (ALIC). The clinical outcome was percent reduction in Yale- Brown Obsessive-Compulsive Scale (Y-BOCS). White Matter changes were analyzed using fixel-based analysis (FBA) for fibre density (FD), fibre-bundle cross-section (FC) and the combination of the two (FDC).

Results

Seven patients underwent GKC. Median follow-up was 13 months (range 12-58 months). Mean (±SD) decrease in Y-BOCS score at last follow-up was 61 % ± 35 % with five patients considered as responders. FBA showed a symmetric FD reduction in the ALIC with extension to the anterior fronto-thalamic radiation; a reduction of FC along the superior longitudinal fasciculus (SLF) in both hemispheres with a predominance in the left one. Reductions in FDC were detected predominantly in the right hemisphere, with a similar pattern to FD reductions and associated with a positive correlation (p < 0.05) between Y-BOCS reduction and fibres passing in the ventral part.

Conclusions

GKC is safe and efficient in reducing OCD severity in selected patients. Changes induced in white matter by GKC extend over the ALIC. Reduction of fibres passing the ventral part of the right sided ALIC correlates with better results.

Field-of-view extension for brain diffusion MRI via deep generative models

Purpose

In brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data.

Approach

We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV.

Results

For evaluating the imputed slices, on the Wisconsin Registry for Alzheimer's Prevention (WRAP) dataset, the proposed framework achievedPSNR b 0 = 22.397 ,SSIM b 0 = 0.905 ,PSNR b 1300 = 22.479 , andSSIM b 1300 = 0.893 ; on the National Alzheimer's Coordinating Center (NACC) dataset, it achievedPSNR b 0 = 21.304 ,SSIM b 0 = 0.892 ,PSNR b 1300 = 21.599 , andSSIM b 1300 = 0.877 . The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts ( p < 0.001 ) on both the WRAP and NACC datasets.

Conclusions

Results suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's disease.

Artificial intelligence for neuro MRI acquisition: a review

OBJECT

To review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts.

MATERIALS AND METHODS

A comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods.

RESULTS

The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency.

DISCUSSION

The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.

Sex Differences in the Brain's White Matter Microstructure during Development assessed using Advanced Diffusion MRI Models

Typical sex differences in white matter (WM) microstructure during development are incompletely understood. Here we evaluated sex differences in WM microstructure during typical brain development using a sample of neurotypical individuals across a wide developmental age (N=239, aged 5-22 years). We used the conventional diffusion-weighted MRI (dMRI) model, diffusion tensor imaging (DTI), and two advanced dMRI models, the tensor distribution function (TDF) and neurite orientation dispersion density imaging (NODDI) to assess WM microstructure. WM microstructure exhibited significant, regionally consistent sex differences across the brain during typical development. Additionally, the TDF model was most sensitive in detecting sex differences. These findings highlight the importance of considering sex in neurodevelopmental research and underscore the value of the advanced TDF model.

Diffusion Model-based FOD Restoration from High Distortion in dMRI

Fiber orientation distributions (FODs) is a popular model to represent the diffusion MRI (dMRI) data. However, imaging artifacts such as susceptibility-induced distortion in dMRI can cause signal loss and lead to the corrupted reconstruction of FODs, which prohibits successful fiber tracking and connectivity analysis in affected brain regions such as the brain stem. Generative models, such as the diffusion models, have been successfully applied in various image restoration tasks. However, their application on FOD images poses unique challenges since FODs are 4-dimensional data represented by spherical harmonics (SPHARM) with the 4-th dimension exhibiting order-related dependency. In this paper, we propose a novel diffusion model for FOD restoration that can recover the signal loss caused by distortion artifacts. We use volume-order encoding to enhance the ability of the diffusion model to generate individual FOD volumes at all SPHARM orders. Moreover, we add cross-attention features extracted across all SPHARM orders in generating every individual FOD volume to capture the order-related dependency across FOD volumes. We also condition the diffusion model with low-distortion FODs surrounding high-distortion areas to maintain the geometric coherence of the generated FODs. We trained and tested our model using data from the UK Biobank (n = 1315). On a test set with ground truth (n = 43), we demonstrate the high accuracy of the generated FODs in terms of root mean square errors of FOD volumes and angular errors of FOD peaks. We also apply our method to a test set with large distortion in the brain stem area (n = 1172) and demonstrate the efficacy of our method in restoring the FOD integrity and, hence, greatly improving tractography performance in affected brain regions.

Sex, racial, and APOE-ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer's disease

INTRODUCTION

The effects of sex, race, and Apolipoprotein E (APOE) - Alzheimer's disease (AD) risk factors - on white matter integrity are not well characterized.

METHODS

Diffusion MRI data from nine well-established longitudinal cohorts of aging were free-water (FW)-corrected and harmonized. This dataset included 4,702 participants (age=73.06 ± 9.75) with 9,671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FAFWcorr) were used to assess differences in white matter microstructure by sex, race, and APOE-ε4 carrier status.

RESULTS

Sex differences in FAFWcorr in association and projection tracts, racial differences in FAFWcorr in projection tracts, and APOE-ε4 differences in FW limbic and occipital transcallosal tracts were most pronounced.

DISCUSSION

There are prominent differences in white matter microstructure by sex, race, and APOE-ε4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted.

Cognitive Impairment Is Related to Glymphatic System Dysfunction in Pediatric Multiple Sclerosis

OBJECTIVE

The aim of this study was to investigate whether, compared to pediatric healthy controls (HCs), the glymphatic system is impaired in pediatric multiple sclerosis (MS) patients according to their cognitive status, and to assess its association with clinical disability and MRI measures of brain structural damage.

METHODS

Sixty-five pediatric MS patients (females = 62%; median age = 15.5 [interquartile range, IQR = 14.5;17.0] years) and 23 age- and sex-matched HCs (females = 44%; median age = 14.1 [IQR = 11.8;16.2] years) underwent neurological, neuropsychological and 3.0 Tesla MRI assessment, including conventional and diffusion tensor imaging (DTI). We calculated the diffusion along the perivascular space (DTI-ALPS) index, a proxy of glymphatic function. Cognitive impairment (Co-I) was defined as impairment in at least 2 cognitive domains.

RESULTS

No significant differences in DTI-ALPS index were found between HCs and cognitively preserved (Co-P) pediatric MS patients (estimated mean difference [EMD] = -0.002 [95% confidence interval = -0.069; 0.065], FDR-p = 0.956). Compared to HCs and Co-P patients, Co-I pediatric MS patients (n = 20) showed significantly lower DTI-ALPS index (EMD = -0.136 [95% confidence interval = -0.214; -0.058], FDR-p ≤ 0.004). In HCs, no associations were observed between DTI-ALPS index and normalized brain, cortical and thalamic volumes, and normal-appearing white matter (NAWM) fractional anisotropy (FA) and mean diffusivity (MD) (FDR-p ≥ 0.348). In pediatric MS patients, higher brain WM lesion volume (LV), higher NAWM MD, lower normalized thalamic volume, and lower NAWM FA were associated with lower DTI-ALPS index (FDR-p ≤ 0.016). Random Forest selected lower DTI-ALPS index (relative importance [RI] = 100%), higher brain WM LV (RI = 59.5%) NAWM MD (RI = 57.1%) and intelligence quotient (RI = 51.3%) as informative predictors of cognitive impairment (out-of-bag area under the curve = 0.762).

INTERPRETATION

Glymphatic system dysfunction occurs in pediatric MS, is associated with brain focal lesions, irreversible tissue loss accumulation and cognitive impairment. ANN NEUROL 2024;95:1080-1092.

Erythrocyte membrane fatty acid concentrations and myelin integrity in young people at ultra-high risk of psychosis

Decreased white matter (WM) integrity and disturbance in fatty acid composition have been reported in individuals at ultra-high risk of psychosis (UHR). The current study is the first to investigate both WM integrity and erythrocyte membrane polyunsaturated fatty acid (PUFA) levels as potential risk biomarkers for persistent UHR status, and global functioning in UHR individuals. Forty UHR individuals were analysed at baseline for erythrocyte membrane PUFA concentrates. Tract-based spatial statistics (TBSS) was used to analyse fractional anisotropy (FA) and diffusivity measures. Measures of global functioning and psychiatric symptoms were evaluated at baseline and at 12-months. Fatty acids and WM indices did not predict functional outcomes at baseline or 12-months. Significant differences were found in FA between UHR remitters and non-remitters (individuals who no longer met UHR criteria versus those who continued to meet criteria at 12-months). Docosahexaenoic acid (DHA) was found to be a significant predictor of UHR status at 12-months, as was the interaction between the sum of ώ-3 and whole brain FA, and the interaction between the right anterior limb of the internal capsule and the sum of ώ-3. The results confirm that certain fatty acids have a unique relationship with WM integrity in UHR individuals.

Perivascular Spaces, Diffusivity Along Perivascular Spaces, and Free Water in Cerebral Small Vessel Disease

BACKGROUND AND OBJECTIVES

Previous studies have linked the MRI measures of perivascular spaces (PVSs), diffusivity along the perivascular spaces (DTI-ALPS), and free water (FW) to cerebral small vessel disease (SVD) and SVD-related cognitive impairments. However, studies on the longitudinal associations between the three MRI measures, SVD progression, and cognitive decline are lacking. This study aimed to explore how PVS, DTI-ALPS, and FW contribute to SVD progression and cognitive decline.

METHODS

This is a cohort study that included participants with SVD who underwent neuroimaging and cognitive assessment, specifically measuring Mini-Mental State Examination (MMSE), cognitive index, and processing speed, at 2 time points. Three MRI measures were quantified: PVS in basal ganglia (BG-PVS) volumes, FW fraction, and DTI-ALPS. We performed a latent change score model to test inter-relations between the 3 MRI measures and linear regression mixed models to test their longitudinal associations with the changes of other SVD MRI markers and cognitive performances.

RESULTS

In baseline assessment, we included 289 participants with SVD, characterized by a median age of 67.0 years and 42.9% women. Of which, 220 participants underwent the follow-up assessment, with a median follow-up time of 3.4 years. Baseline DTI-ALPS was associated with changes in BG-PVS volumes (β = -0.09, p = 0.030), but not vice versa (β = -0.08, p = 0.110). Baseline BG-PVS volumes were associated with changes in white matter hyperintensity (WMH) volumes (β = 0.33, p-corrected < 0.001) and lacune numbers (β = 0.28, p-corrected < 0.001); FW fraction was associated with changes in WMH volumes (β = 0.30, p-corrected < 0.001), lacune numbers (β = 0.28, p-corrected < 0.001), and brain volumes (β = -0.45, p-corrected < 0.001); DTI-ALPS was associated with changes in WMH volumes (β = -0.20, p-corrected = 0.002) and brain volumes (β = 0.23, p-corrected < 0.001). Furthermore, baseline FW fraction was associated with decline in MMSE score (β = -0.17, p-corrected = 0.006); baseline FW fraction and DTI-ALPS were associated with changes in cognitive index (FW fraction: β = -0.25, p-corrected < 0.001; DTI-ALPS: β = 0.20, p-corrected = 0.001) and processing speed over time (FW fraction: β = -0.29, p-corrected < 0.001; DTI-ALPS: β = 0.21, p-corrected < 0.001).

DISCUSSION

Our results showed that increased BG-PVS volumes, increased FW fraction, and decreased DTI-ALPS are related to progression of MRI markers of SVD, along with SVD-related cognitive decline over time. These findings may suggest that the glymphatic dysfunction is related to SVD progression, but further studies are needed.

Influence of preprocessing, distortion correction and cardiac triggering on the quality of diffusion MR images of spinal cord

Diffusion MRI of the spinal cord (SC) is susceptible to geometric distortion caused by field inhomogeneities, and prone to misalignment across time series and signal dropout caused by biological motion. Several modifications of image acquisition and image processing techniques have been introduced to overcome these artifacts, but their specific benefits are largely unproven and warrant further investigations. We aim to evaluate two specific aspects of image acquisition and processing that address image quality in diffusion studies of the spinal cord: susceptibility corrections to reduce geometric distortions, and cardiac triggering to minimize motion artifacts. First, we evaluate 4 distortion preprocessing strategies on 7 datasets of the cervical and lumbar SC and find that while distortion correction techniques increase geometric similarity to structural images, they are largely driven by the high-contrast cerebrospinal fluid, and do not consistently improve the geometry within the cord nor improve white-to-gray matter contrast. We recommend at a minimum to perform bulk-motion correction in preprocessing and posit that improvements/adaptations are needed for spinal cord distortion preprocessing algorithms, which are currently optimized and designed for brain imaging. Second, we design experiments to evaluate the impact of removing cardiac triggering. We show that when triggering is foregone, images are qualitatively similar to triggered sequences, do not have increased prevalence of artifacts, and result in similar diffusion tensor indices with similar reproducibility to triggered acquisitions. When triggering is removed, much shorter acquisitions are possible, which are also qualitatively and quantitatively similar to triggered sequences. We suggest that removing cardiac triggering for cervical SC diffusion can be a reasonable option to save time with minimal sacrifice to image quality.

In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI

Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency,ω $$ \omega $$ , in addition to the diffusion tensor,D $$ \mathbf{D} $$ , and relaxation,R 1 $$ {R}_1 $$ ,R 2 $$ {R}_2 $$ , correlations. AD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on theirD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.

Sex Differences in the Brain's White Matter Microstructure during Development assessed using Advanced Diffusion MRI Models

Typical sex differences in white matter (WM) microstructure during development are incompletely understood. Here we evaluated sex differences in WM microstructure during typical brain development using a sample of neurotypical individuals across a wide developmental age (N=239, aged 5-22 years). We used the conventional diffusion-weighted MRI (dMRI) model, diffusion tensor imaging (DTI), and two advanced dMRI models, the tensor distribution function (TDF) and neurite orientation dispersion density imaging (NODDI) to assess WM microstructure. WM microstructure exhibited significant, regionally consistent sex differences across the brain during typical development. Additionally, the TDF model was most sensitive in detecting sex differences. These findings highlight the importance of considering sex in neurodevelopmental research and underscore the value of the advanced TDF model.