The Kӓrger model and its derivatives have been widely used to incorporate transcytolemmal water exchange rate, an essential characteristic of living cells, into analyses of diffusion MRI (dMRI) signals from tissues. The Kӓrger model consists of two homogeneous exchanging components coupled by an exchange rate constant and assumes measurements are made with sufficiently long diffusion time and slow water exchange. Despite successful applications, it remains unclear whether these assumptions are generally valid for practical dMRI sequences and biological tissues. In particular, barrier-induced restrictions to diffusion produce inhomogeneous magnetization distributions in relatively large-sized compartments such as cancer cells, violating the above assumptions. The effects of this inhomogeneity are usually overlooked. We performed computer simulations to quantify how restriction effects, which in images produce edge enhancements at compartment boundaries, influence different variants of the Kӓrger-model. The results show that the edge enhancement effect will produce larger, time-dependent estimates of exchange rates in e.g., tumors with relatively large cell sizes (>10 μm), resulting in overestimations of water exchange as previously reported. Moreover, stronger diffusion gradients, longer diffusion gradient durations, and larger cell sizes, all cause more pronounced edge enhancement effects. This helps us to better understand the feasibility of the Kärger model in estimating water exchange in different tissue types and provides useful guidance on signal acquisition methods that may mitigate the edge enhancement effect. This work also indicates the need to correct the overestimated transcytolemmal water exchange rates obtained assuming the Kärger-model.

Brain Age Gap Estimation (BrainAGE) is an estimate of the gap between a person\'s chronological age (CA) and a measure of their brain\'s \'biological age\' (BA). This metric is often used as a marker of accelerated aging, albeit with some caveats. Age prediction models trained on brain structural and functional MRI have been employed to derive BrainAGE biomarkers, for predicting the risk of neurodegeneration. While voxel-based and along-tract microstructural maps from diffusion MRI have been used to study brain aging, no studies have evaluated along-tract microstructure for computing BrainAGE. In this study, we train machine learning models to predict a person\'s age using along-tract microstructural profiles from diffusion tensor imaging. We were able to demonstrate differential aging patterns across different white matter bundles and microstructural measures. The novel Bundle Age Gap Estimation (BundleAGE) biomarker shows potential in quantifying risk factors for neurodegenerative diseases and aging, while incorporating finer scale information throughout white matter bundles.

OBJECTIVE: To enable diffusion weighted imaging in prostate patients with metallic total hip replacements in clinically feasible scan times for prostate cancer screening, and avoid distortion and dropout artifacts present in the conventionally used Echo Planar Imaging (EPI).
METHODS: A reduced field of view (FOV) diffusion-prepared sequence that is robust to the B 0 $$ {\\kern0em }_0 $$ inhomogeneities produced by total hip replacements was achieved using high radiofrequency (RF) bandwidth pulses and manipulation for stimulated echo pathways. The reduced FOV along the A/P direction was obtained using slice-select gradient reversal, and the prepared magnetization was imaged with a three-dimensional RF-spoiled gradient echo readout. The sequence was validated in phantom experiments, in vivo in healthy volunteers with and without total hip replacements, and in vivo in patients undergoing a standard MRI prostate exam.
RESULTS: The proposed sequence is robust to shading and distortion artifacts that are encountered by standard diffusion-weighted EPI in the presence of moderate off-resonance. Apparent diffusion coefficient estimates obtained by the proposed sequence were comparable to those obtained with diffusion-weighted EPI.
CONCLUSIONS: Acquisition of distortionless diffusion weighted images of the prostate is feasible in patients with total hip replacements on conventional, whole-body 3T MRI, using a b-value of 800 s / mm 2 $$ \\mathrm{s}/{\\mathrm{mm}}^2 $$ and nominal resolution of 1.7 × $$ \\times $$ 1.7 × $$ \\times $$ 4 mm3 in scan times of 6 min.

Multi-site diffusion MRI data is often acquired on different scanners and with distinct protocols. Differences in hardware and acquisition result in data that contains site dependent information, which confounds connectome analyses aiming to combine such multi-site data. We propose a data-driven solution that isolates site-invariant information whilst maintaining relevant features of the connectome. We construct a latent space that is uncorrelated with the imaging site and highly correlated with patient age and a connectome summary measure. Here, we focus on network modularity. The proposed model is a conditional, variational autoencoder with three additional prediction tasks: one for patient age, and two for modularity trained exclusively on data from each site. This model enables us to 1) isolate site-invariant biological features, 2) learn site context, and 3) re-inject site context and project biological features to desired site domains. We tested these hypotheses by projecting 77 connectomes from two studies and protocols (Vanderbilt Memory and Aging Project (VMAP) and Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) to a common site. We find that the resulting dataset of modularity has statistically similar means (p-value <0.05) across sites. In addition, we fit a linear model to the joint dataset and find that positive correlations between age and modularity were preserved.

Subject head motion during the acquisition of diffusion-weighted imaging (DWI) of the brain induces artifacts and affects image quality. Information about the frequency and extent of motion could reveal which aspects of motion correction are most necessary. Therefore, we investigate the extent of translation and rotation among participants, and how the motion changes during the scan acquisition. We analyze 5,380 DWI scans from 1,034 participants. We measure the rotations and translations in the sagittal, coronal and transverse planes needed to align the volumes to the first and previous volumes, as well as the displacement. The different types of motion are compared with each other and compared over time. The largest rotation (per minute) is around the right - left axis (median 0.378 °/min, range 0.000 - 11.466°) and the largest translation (per minute) is along the anterior - posterior axis (median 1.867 mm/min, range 0.000 - 10.944 mm). We additionally observe that spikes in movement occur at the beginning of the scan, particularly in anterior - posterior translation. The results show that all scans are affected by subtle head motion, which may impact subsequent image analysis.

BACKGROUND: Our comprehension of the interplay of cognition and the brain remains constrained. While functional imaging studies have identified cognitive brain regions, structural correlates of cognitive functions remain underexplored. Advanced methods like Diffusion Magnetic Resonance Imaging (DMRI) facilitate the exploration of brain connectivity and White Matter (WM) tract microstructure. Therefore, we conducted connectometry method on DMRI data, to reveal WM tracts associated with cognition.
METHODS: 125 healthy participants from the National Institute of Mental Health Intramural Healthy Volunteer Dataset were recruited. Multiple regression analyses were conducted between DMRI-derived Quantitative Anisotropy (QA) values within WM tracts and scores of participants in Flanker Inhibitory Control and Attention Test (attention), Dimensional Change Card Sort (executive function), Picture Sequence Memory Test (episodic memory), and List Sorting Working Memory Test (working memory) tasks from National Institute of Health toolbox. The significance level was set at False Discovery Rate (FDR)<0.05.
RESULTS: We identified significant positive correlations between the QA of WM tracts within the left cerebellum and bilateral fornix with attention, executive functioning, and episodic memory (FDR=0.018, 0.0002, and 0.0002, respectively), and a negative correlation between QA of WM tracts within bilateral cerebellum with attention (FDR=0.028). Working memory demonstrated positive correlations with QA of left inferior longitudinal and left inferior fronto-occipital fasciculi (FDR=0.0009), while it showed a negative correlation with QA of right cerebellar tracts (FDR=0.0005).
CONCLUSIONS: Our results underscore the intricate link between cognitive performance and WM integrity in frontal, temporal, and cerebellar regions, offering insights into early detection and targeted interventions for cognitive disorders.

Noonan syndrome and neurofibromatosis type 1 are genetic conditions linked to pathogenic variants in genes of the Ras-mitogen-activated protein kinase signalling pathway. Both conditions hyper-activate signalling of the Ras-mitogen-activated protein kinase pathway and exhibit a high prevalence of neuropsychiatric disorders. Further, animal models of Noonan syndrome and neurofibromatosis type 1 and human imaging studies show white matter abnormalities in both conditions. While these findings suggest Ras-mitogen-activated protein kinas pathway hyper-activation effects on white matter, it is unknown whether these effects are syndrome-specific or pathway-specific. To characterize the effect of Noonan syndrome and neurofibromatosis type 1 on human white matter\'s microstructural integrity and discern potential syndrome-specific influences on microstructural integrity of individual tracts, we collected diffusion-weighted imaging data from children with Noonan syndrome (n = 24), neurofibromatosis type 1 (n = 28) and age- and sex-matched controls (n = 31). We contrasted the clinical groups (Noonan syndrome or neurofibromatosis type 1) and controls using voxel-wise, tract-based and along-tract analyses. Outcomes included voxel-wise, tract-based and along-tract fractional anisotropy, axial diffusivity, radial diffusivity and mean diffusivity. Noonan syndrome and neurofibromatosis type 1 showed similar patterns of reduced fractional anisotropy and increased axial diffusivity, radial diffusivity, and mean diffusivity on white matter relative to controls and different spatial patterns. Noonan syndrome presented a more extensive spatial effect than neurofibromatosis type 1 on white matter integrity as measured by fractional anisotropy. Tract-based analysis also demonstrated differences in effect magnitude with overall lower fractional anisotropy in Noonan syndrome compared to neurofibromatosis type 1 (d = 0.4). At the tract level, Noonan syndrome-specific effects on fractional anisotropy were detected in association tracts (superior longitudinal, uncinate and arcuate fasciculi; P < 0.012), and neurofibromatosis type 1-specific effects were detected in the corpus callosum (P < 0.037) compared to controls. Results from along-tract analyses aligned with results from tract-based analyses and indicated that effects are pervasive along the affected tracts. In conclusion, we find that pathogenic variants in the Ras-mitogen-activated protein kinase pathway are associated with white matter abnormalities as measured by diffusion in the developing brain. Overall, Noonan syndrome and neurofibromatosis type 1 show common effects on fractional anisotropy and diffusion scalars, as well as specific unique effects, namely, on temporoparietal-frontal tracts (intra-hemispheric) in Noonan syndrome and on the corpus callosum (inter-hemispheric) in neurofibromatosis type 1. The observed specific effects not only confirm prior observations from independent cohorts of Noonan syndrome and neurofibromatosis type 1 but also inform on syndrome-specific susceptibility of individual tracts. Thus, these findings suggest potential targets for precise, brain-focused outcome measures for existing medications, such as MEK inhibitors, that act on the Ras-mitogen-activated protein kinase pathway.

Diffusion-weighted MRI (dMRI) is universally recommended for the detection and classification of prostate cancer (PCa), with PI-RADS recommendations to acquire b-values of ≥1.4 ms/μm2. However, clinical dMRI suffers from a low signal-to-noise ratio (SNR) as the consequence of prolonged echo times (TEs) attributable to the limited gradient power in the range of 40-80 mT/m. To overcome this, MRI systems with strong gradients have been designed but so far have mainly been applied in the brain. The aim of this work was to assess the feasibility, data quality, SNR and contrast-to-noise ratio (CNR) of measurements in PCa with a 300 mT/m whole-body system. A cohort of men without and with diagnosed PCa were imaged on a research-only 3T Connectom Siemens MRI system equipped with a gradient amplitude of 300 mT/m. dMRI at high b-values were acquired using high gradient amplitudes and compared with gradient capabilities mimicking clinical systems. Data artefacts typically amplified with stronger gradients were assessed and their correction evaluated. The SNR gains and lesion-to-healthy tissue CNR were statistically tested investigating the effect of protocol and b-value. The diagnostic quality of the images for different dMRI protocols was assessed by an experienced radiologist using a 5-point Likert scale and an adapted PI-QUAL scoring system. The strong gradients for prostate dMRI allowed a significant gain in SNR per unit time compared with clinical gradients. Furthermore, a 1.6-2.1-fold increase in CNR was observed. Despite the more pronounced artefacts typically associated with strong gradients, a satisfactory correction could be achieved. Smoother and less biased parameter maps were obtained with protocols at shorter TEs. The results of this study show that dMRI in PCa with a whole-body 300-mT/m scanner is feasible without a report of physiological effects, SNR and CNR can be improved compared with lower gradient strengths, and artefacts do not negate the benefits of strong gradients and can be ameliorated. This assessment provides the first essential step towards unveiling the full potential of cutting-edge scanners, now increasingly becoming available, to advance early detection and diagnostic precision.

Head motion is a major confounding variable for magnetic resonance imaging (MRI) analysis, and is commonly seen in individuals with neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). This study investigated the trajectory of change in head motion in typically developing children and children with ADHD, and examined possible altered trajectories in head motion between children with remitted and persistent ADHD. 105 children with ADHD and 84 controls completed diffusion and resting-state functional MRI scans at up to three waves over ages 9-14 years. In-scanner head motion was calculated using framewise displacement, and longitudinal trajectories analyzed using generalized additive mixed modelling. Results revealed a significant age effect on framewise displacement where head motion decreased as age increased during both diffusion (p < .001) and resting-state functional MRI (p < .001). A significant effect of group was also observed; children with ADHD displayed greater framewise displacement than controls over the age range (diffusion MRI p = .036, functional MRI p = .004). Further analyses revealed continued elevation in head motion in children in remission from ADHD (diffusion MRI p = .020, functional MRI p = .011) compared to controls. Rates of change in head motion did not significantly differ between diagnostic groups. Findings indicate a critical link between in-scanner head motion and developmental age within children regardless of ADHD diagnosis, important to consider in studies of neurodevelopment. Findings also suggest change in head motion with age does not differ between individuals with remitted and persistent ADHD, adding further evidence that behavioral manifestations of ADHD may continue despite clinical remission.

Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data-driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure-function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP to a large-scale dataset from the Human Connectome Project Young Adult study (n = 1065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure-function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure-function saliency representation, clustering algorithm, and cluster count. We found that a 1D convolutional neural network architecture and a transformer network architecture both performed comparably for the multitask prediction of endurance, strength, reading decoding, and vocabulary comprehension, with both architectures outperforming a fully connected network architecture. Quantitative experiments demonstrated that a proposed low-dimensional saliency representation with an explicit measure of motor versus cognitive category bias achieved the best parcellation results, while a parcel count of four was most successful according to standard cluster quality metrics. Our results suggested that motor and cognitive saliencies are distributed across the cerebellar white matter pathways. Inspection of the final k = 4 parcellation revealed that the highest-saliency parcel was most salient for the prediction of both motor and cognitive performance scores and included parts of the middle and superior cerebellar peduncles. Our proposed saliency-based parcellation framework, DeepMSP, enables multimodal, data-driven tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure-function relationships of the cerebellar pathways.