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Water management is a major contemporary and future challenge. In an increasing water demand scenario related to climate change, a water distribution system must ensure equal access to water for all users. In this context, a reliable short-term water demand forecasting system is crucial for reliable water management. However, despite the abundance of studies in the scientific literature, few examples highlight complete tools for providing such models to real water utilities and water managers. This study presents AI-Forecast, an innovative tool developed to predict water demand with state-of-the-art models. Such tool is based on the data-driven logic, and it is designed to provide a complete data-driven chain that starts from the data and arrives to the short-term water demand prediction. AI-Forecast can import data, properly manage them, and assess tasks like outlier detection and missing data imputation. Eventually, it can implement state-of-the-art forecasting models and provide the forecasts. The prediction is shown through an intuitive web interface, which is designed to highlight the major information related to the prediction accuracy. Although this tool does not provide a new prediction algorithm, it proposes a complete data-driven chain that is practically designed to take such models in practice to real water utilities. HIGHLIGHTS This study proposes a complete data-driven chain that is practically designed to consider such models in practice to real water utilities.; The results highlight how such practical tool can deliver reliable prediction.; AI-Forecast is designed to host state-of-the-art methods based on the data-driven formulation.;

Abstract During long-duration spaceflight, astronauts experience headward fluid shifts and expansion of the cerebral perivascular spaces (PVS). A major limitation to our understanding of the changes in brain structure and physiology induced by spaceflight stems from the logistical difficulties of studying astronauts. The current study aimed to determine whether PVS changes also occur on Earth with the spaceflight analog head-down tilt bed rest (HDBR). We examined how the number and morphology of magnetic resonance imaging-visible PVS (MV-PVS) are affected by HDBR with and without elevated carbon dioxide (CO2). These environments mimic the headward fluid shifts, body unloading, and elevated CO2 observed aboard the International Space Station. Additionally, we sought to understand how changes in MV-PVS are associated with signs of Spaceflight Associated Neuro-ocular Syndrome (SANS), ocular structural alterations that can occur with spaceflight. Participants were separated into two bed rest campaigns: HDBR (60 days) and HDBR + CO2 (30 days with elevated ambient CO2). Both groups completed multiple magnetic resonance image acquisitions before, during, and post-bed rest. We found that at the group level, neither spaceflight analog affected MV-PVS quantity or morphology. However, when taking into account SANS status, persons exhibiting signs of SANS showed little or no MV-PVS changes, whereas their No-SANS counterparts showed MV-PVS morphological changes during the HDBR + CO2 campaign. These findings highlight spaceflight analogs as models for inducing changes in MV-PVS and implicate MV-PVS dynamic compliance as a mechanism underlying SANS. These findings may lead to countermeasures to mitigate health risks associated with human spaceflight.

Introduction: Vascular cognitive impairment (VCI) is heterogeneous in brain atrophy patterns, clinical symptoms, and possibly in the factors affecting resilience to symptoms. The objective of this study was to investigate the heterogeneity of VCI by estimating different atrophy-driven subtypes and use those for identifying resilience factors in each subtype. Methods: We used cross-sectional data from the Trace-VCI cohort comprising of memory-clinic patients with vascular brain injury on MRI (n=361 all-cause dementia, n=190 MCI, and n=188 SCD). White matter hyper-intensities (WMH) were segmented, and SLF toolbox was used to refill them on the MRIs for accurate parcellation. Freesurfer volumes were used for identifying subtypes with non-negative matrix factorization. Subtype-specific pseudo-timelines of progression were estimated using a previously validated discriminative event-based model. Severity of atrophy (SA) in patients was estimated using cross-validation based on their position on the pseudo-timeline. Using linear-regression, cognition and disability (MMSE, Global deterioration scale, disability assessment for dementia) were modelled to be dependent on SA and its interactions with genetic factors, vascular markers and risk-factors, and co-pathology independently (variables in Figure-3). Resilience factors were identified by testing if the model with the interaction explains the symptoms significantly more than the one without. Results: The algorithm identified three atrophy-based VCI subtypes: Frontal, Subcortical/Temporal, and Parietal subtype. Their prevalence, vascular and clinical presentations are summarized in Figure-1. The pseudo-timelines of atrophy are shown in Figure-2. SA's interaction with education positively influenced cognition in all subtypes, but negatively influenced disability in subcortical/temporal subtype. In frontal and parietal subtypes, APOE ε4, AD co-pathology resulted in more SA without worsening symptoms. SA's interaction with smoking and microbleeds negatively influenced disability. In the subcortical/temporal subtype, SA's interaction with WMH, lacunes, infarcts negatively impacted disability. In parietal subtype, men have more cognitive resilience than women. SA's interaction with hypercholesterolemia, and smoking were significantly negative for cognition. Lacunes were associated with more SA without affecting cognition. Figure-3 summarizes the interactions for all subtypes. Discussion: We identified three atrophy-based VCI subtypes where the risk-factors have different influence on atrophy and symptoms highlighting differences in resilience. These results could aid in prognosis and in personalizing patients’ intervention strategy.

Introduction: Lesion-symptom mapping (LSM) is used to capture the impact of lesions on cognitive performance while accounting for their location in the brain and is highly relevant for vascular cognitive impairment [1], [2]. Current LSM methods only consider a single lesion type and single cognitive score at a time. Neural networks (NNs) allow for multiple inputs (lesions) and outputs (cognitive domains), which might take interrelations of vascular lesions and cognitive subscores into account. Explainable AI (XAI) can be used to compute attribution maps reflecting which image locations are deemed important for a NN, even at an individual level. We explore the feasibility of NNs and XAI for LSM by comparing two NNs with current gold standard, support vector regression (SVR) [3], [4]. Methods: White matter hyperintensity segmentations from 821 patients in the TRACE-VCI dataset were used to develop a simulation study similar to [3]. Three regions of interest (ROIs) were defined within the lesion prevalence map. Lesion volume fractions within each ROI were calculated and summed to create an artificial cognitive score with a known source location. A linear NN, a convolutional NN (CNN), and SVR were used to predict the artificial scores and determine responsible ROI locations. Predictive performance was quantified using the coefficient of determination, while ROI identification was evaluated using the precision-recall metric based on the attribution maps of each method: SVR's β-map, the linear NN's weight map, and the CNN XAI saliency map. The XAI saliency map was computed by occluding parts of the image: the predicted outcome changes considerably for relevant locations and remains unchanged when background is occluded [5]. Results: SVR and both NNs have similar predictive performance, all reaching R^2>0.9 (Fig. 1). However, attribution maps (Fig. 2) show differences in ROI location determination, which is reflected by the precision-recall curves. The curves in Fig. 3 show that SVR has overall better precision and recall (AUC=0.761), followed by the CNN (AUC=0.582) and the linear NN (AUC=0.203). Discussion: In this first exploration, the CNN with XAI did not outperform SVR, but it proved able to detect relevant lesion locations, thereby showing potential for LSM.

Introduction: Enlarged perivascular spaces (EPVS) that are visible on conventional MRI sequences are considered a marker of small vessel disease (SVD)[1]. To date, the evaluation of PVS has mostly relied on semi-quantitative evaluation from expert raters, which is time consuming and suffers of intra-rater variability. In this study, we evaluate a previously validated automated PVS segmentation pipeline [2] in a multi-center SVD cohort. Methods: We considered data from the TRACE-VCI cohort[3] (n=356; 251/105 from Amsterdam/Utrecht). T1w, T2w and FLAIR MRI scans were visually assessed by an expert in midbrain, basal ganglia (BG) and centrum semiovale (CSO). The visual rating was according to the Potter scale[4]. Automated EPVS segmentations were performed on T1w images only, given the insufficient resolution of T2w and FLAIR scans. EPVS segmentation optimization consisted of tuning: filter type (Frangi vs RORPO[5]); segmentation threshold(δ). The default value (0.5) of the hyperparameters α (tube vs plate-like structures) and β (tube vs blob-like structures) was used for the Frangi filter. First, we optimized the hyperparameters separately in the two cohorts in a random subset of 50 subjects, and evaluated the spearman correlation between automated EPVS count and the corresponding expert ratings. Second, the pipeline was run with the optimized hyperparameters on all subjects. Results: Fig. 1 shows the results of the threshold optimization for the FRANGI filter. For the midbrain, only modest correlations were observed (

Key points Strategies for segmenting sarcoma tumors vary widely throughout literature. Details of the segmentation strategy are often not reported. Including necrotic or cystic areas in the segmentation affects diffusion measurements. Varying the slice of a single-slice segmentation can drastically impact diffusion measurements.

Introduction: In the pediatric brain tumor surgery setting, intraoperative MRI (ioMRI) provides “real-time” imaging, allowing for evaluation of the extent of resection and detection of complications. The use of advanced MRI sequences could potentially provide additional physiological information that may aid in the preservation of healthy brain regions. This review aims to determine the added value of advanced imaging in ioMRI for pediatric brain tumor surgery compared to conventional imaging.Methods: Our systematic literature search identified relevant articles on PubMed using keywords associated with pediatrics, ioMRI, and brain tumors. The literature search was extended using the snowball technique to gather more information on advanced MRI techniques, their technical background, their use in adult ioMRI, and their use in routine pediatric brain tumor care.Results: The available literature was sparse and demonstrated that advanced sequences were used to reconstruct fibers to prevent damage to important structures, provide information on relative cerebral blood flow or abnormal metabolites, or to indicate the onset of hemorrhage or ischemic infarcts. The explorative literature search revealed developments within each advanced MRI field, such as multi-shell diffusion MRI, arterial spin labeling, and amide-proton transfer-weighted imaging, that have been studied in adult ioMRI but have not yet been applied in pediatrics. These techniques could have the potential to provide more accurate fiber tractography, information on intraoperative cerebral perfusion, and to match gadolinium-based T1w images without using a contrast agent.Conclusion: The potential added value of advanced MRI in the intraoperative setting for pediatric brain tumors is to prevent damage to important structures, to provide additional physiological or metabolic information, or to indicate the onset of postoperative changes. Current developments within various advanced ioMRI sequences are promising with regard to providing in-depth tissue information.

AimThis study aims to assess the integrity of white matter in various segments of the corpus callosum in Alzheimer's disease (AD) by using metrics derived from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI) and white matter tract integrity model (WMTI) and compare these findings to healthy controls (HC).MethodsThe study was approved by the institutional ethics board. 12 AD patients and 12 HC formed the study population. All AD patients were recruited from a tertiary neurology memory clinic. A standardized battery of neuropsychological assessments was administered to the study participants by a trained rater. MRI scans were performed with a Philips Ingenia 3.0T scanner equipped with a 32-channel head coil. The protocol included a T1-weighted sequence, FLAIR and a dMRI acquisition. The dMRI scan included a total of 71 volumes, 8 at b = 0 s/mm2, 15 at b = 1,000 s/mm2 and 48 at b = 2,000 s/mm2. Diffusion data fit was performed using DKI REKINDLE and WMTI models.Results and discussionWe detected changes suggesting demyelination and axonal degeneration throughout the corpus callosum of patients with AD, most prominent in the mid-anterior and mid-posterior segments of CC. Axial kurtosis was the most significantly altered metric, being reduced in AD patients in almost all segments of corpus callosum. Reduced axial kurtosis in the CC segments correlated with poor cognition scores in AD patients in the visuospatial, language and attention domains.

Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions.

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

Friedreich's ataxia (FRDA) is an inherited neurodegenerative movement disorder with early onset, widespread cerebral and cerebellar pathology, and no cure still available. Functional MRI (fMRI) studies, although currently limited in number, have provided a better understanding of brain changes in people with FRDA. This systematic review aimed to provide a critical overview of the findings and methodologies of all fMRI studies conducted in genetically confirmed FRDA so far, and to offer recommendations for future study designs. About 12 cross-sectional and longitudinal fMRI studies, included 198 FRDA children and young adult patients and, 205 healthy controls (HCs), according to the inclusion criteria. Details regarding GAA triplet expansion and demographic and clinical severity measures were widely reported. fMRI designs included motor and cognitive task paradigms, and resting-state studies, with widespread changes in functionally activated areas and extensive variability in study methodologies. These studies highlight a mixed picture of both hypoactivation and hyperactivation in different cerebral and cerebellar brain regions depending on fMRI design and cohort characteristics. Functional changes often correlate with clinical variables. In aggregate, the findings provide support for cerebro-cerebellar loop damage and the compensatory mechanism hypothesis. Current literature indicates that fMRI is a valuable tool for gaining in vivo insights into FRDA pathology, but addressing that its limitations would be a key to improving the design, interpretation, and generalizability of studies in the future.

Purpose: To investigate if network thresholding and raw data harmonization improve consistency of diffusion MRI (dMRI)-based brain networks while also increasing precision and sensitivity to detect disease effects in multicentre datasets. Methods: Brain networks were reconstructed from dMRI of five samples with cerebral small vessel disease (SVD; 629 patients, 166 controls), as a clinically relevant exemplar condition for studies on network integrity. We evaluated consistency of network architecture in age-matched controls, by calculating cross-site differences in connection probability and fractional anisotropy (FA). Subsequently we evaluated precision and sensitivity to disease effects by identifying connections with low FA in sporadic SVD patients relative to controls, using more severely affected patients with a pure form of genetically defined SVD as reference. Results: In controls, thresholding and harmonization improved consistency of network architecture, minimizing cross-site differences in connection probability and FA. In patients relative to controls, thresholding improved precision to detect disrupted connections by removing false positive connections (precision, before: 0.09–0.19; after: 0.38–0.70). Before harmonization, sensitivity was low within individual sites, with few connections surviving multiple testing correction (k = 0–25 connections). Harmonization and pooling improved sensitivity (k = 38), while also achieving higher precision when combined with thresholding (0.97). Conclusion: We demonstrated that network consistency, precision and sensitivity to detect disease effects in SVD are improved by thresholding and harmonization. We recommend introducing these techniques to leverage large existing multicentre datasets to better understand the impact of disease on brain networks.

While the diagnosis of high-grade glioma (HGG) is still associated with a considerably poor prognosis, neurosurgical tumor resection provides an opportunity for prolonged survival and improved quality of life for affected patients. However, successful tumor resection is dependent on a proper surgical planning to avoid surgery-induced functional deficits whilst achieving a maximum extent of resection (EOR). With diffusion magnetic resonance imaging (MRI) providing insight into individual white matter neuroanatomy, the challenge remains to disentangle that information as correctly and as completely as possible. In particular, due to the lack of sensitivity and accuracy, the clinical value of widely used diffusion tensor imaging (DTI)-based tractography is increasingly questioned. We evaluated whether the recently developed multi-level fiber tracking (MLFT) technique can improve tractography of the corticospinal tract (CST) in patients with motor-eloquent HGGs. Forty patients with therapy-naïve HGGs (mean age: 62.6 ± 13.4 years, 57.5% males) and preoperative diffusion MRI [repetition time (TR)/echo time (TE): 5000/78 ms, voxel size: 2x2x2 mm3, one volume at b=0 s/mm2, 32 volumes at b=1000 s/mm2] underwent reconstruction of the CST of the tumor-affected and unaffected hemispheres using MLFT in addition to deterministic DTI-based and deterministic constrained spherical deconvolution (CSD)-based fiber tractography. The brain stem was used as a seeding region, with a motor cortex mask serving as a target region for MLFT and a region of interest (ROI) for the other two algorithms. Application of the MLFT method substantially improved bundle reconstruction, leading to CST bundles with higher radial extent compared to the two other algorithms (delineation of CST fanning with a wider range; median radial extent for tumor-affected vs. unaffected hemisphere – DTI: 19.46° vs. 18.99°, p=0.8931; CSD: 30.54° vs. 27.63°, p=0.0546; MLFT: 81.17° vs. 74.59°, p=0.0134). In addition, reconstructions by MLFT and CSD-based tractography nearly completely included respective bundles derived from DTI-based tractography, which was however favorable for MLFT compared to CSD-based tractography (median coverage of the DTI-based CST for affected vs. unaffected hemispheres – CSD: 68.16% vs. 77.59%, p=0.0075; MLFT: 93.09% vs. 95.49%; p=0.0046). Thus, a more complete picture of the CST in patients with motor-eloquent HGGs might be achieved based on routinely acquired diffusion MRI data using MLFT.

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

The digitalisation of water supply systems is essential to support crucial activities, ranging from the hydraulic modelling and calibration of distribution systems to the optimal planning of renewal measures. Digital Twins is the current challenge that aims to join accurate hydraulic models with Artificial Intelligence (AI) tools that employ a faithful digital copy of the original system and smart infrastructures. Thus, the aqueduct Digital Twin model is intended to be the core of future integrated water management systems to support efficient and sustainable management. In this paper, a process of implementing a digital model for a mountain aqueduct with its associated applications is proposed.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

The cardiac cycle induces blood volume pulsations in the cerebral microvasculature that cause subtle deformation of the surrounding tissue. These tissue deformations are highly relevant as a potential source of information on the brain's microvasculature as well as of tissue condition. Besides, cyclic brain tissue deformations may be a driving force in clearance of brain waste products. We have developed a high-field magnetic resonance imaging (MRI) technique to capture these tissue deformations with full brain coverage and sufficient signal-to-noise to derive the cardiac-induced strain tensor on a voxel by voxel basis, that could not be assessed non-invasively before. We acquired the strain tensor with 3 mm isotropic resolution in 9 subjects with repeated measurements for 8 subjects. The strain tensor yielded both positive and negative eigenvalues (principle strains), reflecting the Poison effect in tissue. The principle strain associated with expansion followed the known funnel shaped brain motion pattern pointing towards the foramen magnum. Furthermore, we evaluate two scalar quantities from the strain tensor: the volumetric strain and octahedral shear strain. These quantities showed consistent patterns between subjects, and yielded repeatable results: the peak systolic volumetric strain (relative to end-diastolic strain) was 4.19⋅10−4 ± 0.78⋅10−4 and 3.98⋅10−4 ± 0.44⋅10−4 (mean ± standard deviation for first and second measurement, respectively), and the peak octahedral shear strain was 2.16⋅10−3 ± 0.31⋅10−3 and 2.31⋅10−3 ± 0.38⋅10−3, for the first and second measurement, respectively. The volumetric strain was typically highest in the cortex and lowest in the periventricular white matter, while anisotropy was highest in the subcortical white matter and basal ganglia. This technique thus reveals new, regional information on the brain's cardiac-induced deformation characteristics, and has the potential to advance our understanding of the role of microvascular pulsations in health and disease.

Objectives: Acquisition-related differences in diffusion magnetic resonance imaging (dMRI) hamper pooling of multicentre data to achieve large sample sizes. A promising solution is to harmonize the raw diffusion signal using rotation invariant spherical harmonic (RISH) features, but this has not been tested in elderly subjects. Here we aimed to establish if RISH harmonization effectively removes acquisition-related differences in multicentre dMRI of elderly subjects with cerebral small vessel disease (SVD), while preserving sensitivity to disease effects. Methods: Five cohorts of patients with SVD (N = 397) and elderly controls (N = 175) with 3 Tesla MRI on different systems were included. First, to establish effectiveness of harmonization, the RISH method was trained with data of 13 to 15 age and sex-matched controls from each site. Fractional anisotropy (FA) and mean diffusivity (MD) were compared in matched controls between sites using tract-based spatial statistics (TBSS) and voxel-wise analysis, before and after harmonization. Second, to assess sensitivity to disease effects, we examined whether the contrast (effect sizes of FA, MD and peak width of skeletonized MD - PSMD) between patients and controls within each site remained unaffected by harmonization. Finally, we evaluated the association between white matter hyperintensity (WMH) burden, FA, MD and PSMD using linear regression analyses both within individual cohorts as well as with pooled scans from multiple sites, before and after harmonization. Results: Before harmonization, significant differences in FA and MD were observed between matched controls of different sites (p

Background: Cerebral small vessel diseases (SVDs) are a major cause of stroke and dementia. Yet, specific treatment strategies are lacking in part because of a limited understanding of the underlying disease processes. There is therefore an urgent need to study SVDs at their core, the small vessels themselves. Objective: This paper presents the rationale and design of the ZOOM@SVDs study, which aims to establish measures of cerebral small vessel dysfunction on 7T MRI as novel disease markers of SVDs. Methods: ZOOM@SVDs is a prospective observational cohort study with two years follow-up. ZOOM@SVDs recruits participants with Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL, N = 20), sporadic SVDs (N = 60), and healthy controls (N = 40). Participants undergo 7T brain MRI to assess different aspects of small vessel function including small vessel reactivity, cerebral perforating artery flow, and pulsatility. Extensive work-up at baseline and follow-up further includes clinical and neuropsychological assessment as well as 3T brain MRI to assess conventional SVD imaging markers. Measures of small vessel dysfunction are compared between patients and controls, and related to the severity of clinical and conventional MRI manifestations of SVDs. Discussion: ZOOM@SVDs will deliver novel markers of cerebral small vessel function in patients with monogenic and sporadic forms of SVDs, and establish their relation with disease burden and progression. These small vessel markers can support etiological studies in SVDs and may serve as surrogate outcome measures in future clinical trials to show target engagement of drugs directed at the small vessels.

In diffusion MRI, spherical deconvolution approaches can estimate local white matter (WM) fiber orientation distributions (FOD) which can be used to produce fiber tractography reconstructions. The applicability of spherical deconvolution to gray matter (GM), however, is still limited, despite its critical role as start/endpoint of WM fiber pathways. The advent of multi-shell diffusion MRI data offers additional contrast to model the GM signal but, to date, only isotropic models have been applied to GM. Evidence from both histology and high-resolution diffusion MRI studies suggests a marked anisotropic character of the diffusion process in GM, which could be exploited to improve the description of the cortical organization. In this study, we investigated whether performing spherical deconvolution with tissue specific models of both WM and GM can improve the characterization of the latter while retaining state-of-the-art performances in WM. To this end, we developed a framework able to simultaneously accommodate multiple anisotropic response functions to estimate multiple, tissue-specific, fiber orientation distributions (mFODs). As proof of principle, we used the diffusion kurtosis imaging model to represent the WM signal, and the neurite orientation dispersion and density imaging (NODDI) model to represent the GM signal. The feasibility of the proposed approach is shown with numerical simulations and with data from the Human Connectome Project (HCP). The performance of our method is compared to the current state of the art, multi-shell constrained spherical deconvolution (MSCSD). The simulations show that with our new method we can accurately estimate a mixture of two FODs at SNR≥50. With HCP data, the proposed method was able to reconstruct both tangentially and radially oriented FODs in GM, and performed comparably well to MSCSD in computing FODs in WM. When performing fiber tractography, the trajectories reconstructed with mFODs reached the cortex with more spatial continuity and for a longer distance as compared to MSCSD and allowed to reconstruct short trajectories tangential to the cortical folding. In conclusion, we demonstrated that our proposed method allows to perform spherical deconvolution of multiple anisotropic response functions, specifically improving the performances of spherical deconvolution in GM tissue.

Spherical deconvolution is a widely used approach to quantify the fiber orientation distribution (FOD) from diffusion MRI data of the brain. The damped Richardson-Lucy (dRL) is an algorithm developed to perform robust spherical deconvolution on single-shell diffusion MRI data while suppressing spurious FOD peaks due to noise or partial volume effects. Due to recent progress in acquisition hardware and scanning protocols, it is becoming increasingly common to acquire multi-shell diffusion MRI data, which allows for the modelling of multiple tissue types beyond white matter. While the dRL algorithm could, in theory, be directly applied to multi-shell data, it is not optimised to exploit its information content to model the signal from multiple tissue types. In this work, we introduce a new framework based on dRL – dubbed generalized Richardson-Lucy (GRL) – that uses multi-shell data in combination with user-chosen tissue models to disentangle partial volume effects and increase the accuracy in FOD estimation. Further, GRL estimates signal fraction maps associated to each user-selected model, which can be used during fiber tractography to dissect and terminate the tracking without need for additional structural data. The optimal weighting of multi-shell data in the fit and the robustness to noise and to partial volume effects of GRL was studied with synthetic data. Subsequently, we investigated the performance of GRL in comparison to dRL and to multi-shell constrained spherical deconvolution (MSCSD) on a high-resolution diffusion MRI dataset from the Human Connectome Project and on an MRI dataset acquired at 3T on a clinical scanner. In line with previous studies, we described the signal of the cerebrospinal-fluid and of the grey matter with isotropic diffusion models, whereas four diffusion models were considered to describe the white matter. With a third dataset including small diffusion weightings, we studied the feasibility of including intra-voxel incoherent motion effects due to blood pseudo-diffusion in the modelling. Further, the reliability of GRL was demonstrated with a test-retest scan of a subject acquired at 3T. Results of simulations show that GRL can robustly disentangle different tissue types at SNR above 20 with respect to the non-weighted image, and that it improves the angular accuracy of the FOD estimation as compared to dRL. On real data, GRL provides signal fraction maps that are physiologically plausible and consistent with those obtained with MSCSD, with correlation coefficients between the two methods up to 0.96. When considering IVIM effects, a high blood pseudo-diffusion fraction is observed in the medial temporal lobe and in the sagittal sinus. In comparison to dRL and MSCSD, GRL provided sharper FODs and less spurious peaks in presence of partial volume effects, but the FOD reconstructions are also highly dependent on the chosen modelling of white matter. When performing fiber tractography, GRL allows to terminate fiber tractography using the signal fraction maps, which results in a better tract termination at the grey-white matter interface or at the outer cortical surface. In terms of inter-scan reliability, GRL performed similarly to or better than compared methods. In conclusion, GRL offers a new modular and flexible framework to perform spherical deconvolution of multi-shell data.

Transcutaneous auricular Vagal Nerve Stimulation (taVNS) is a non-invasive brain stimulation technique associated with possible modulation of norepinephrinergic (NE) activity. NE is suspected to contribute to generation of the P3 event-related potential. Recent evidence has produced equivocal evidence whether taVNS influences the P3 in healthy individuals during oddball tasks. We examined the effect of taVNS on P3 amplitudes using a novel visual Bayesian oddball task, which presented 200 sequences of three stimuli. The three consecutive stimuli in each sequence are labelled Draw 1, Draw 2 and Draw 3. In total, 47 Subjects completed this visual Bayesian oddball task under randomised sham and active taVNS stimulation in parallel with an electroencephalographic (EEG) recording. We conducted exploratory analyses of the effect of taVNS on P3 amplitudes separately for Draws. We found typical oddball effects on P3 amplitudes at Draws 1 and 2, but not Draw 3. At Draw 2, the oddball effect was enhanced during active compared to sham taVNS stimulation. These data provide evidence that taVNS influences parietal P3 amplitudes under specific circumstances. Only P3 amplitudes at Draw 2 were affected, which may relate to closure of Bayesian inference after Draw 2. Our findings seemingly support previously reported links between taVNS and the NE system.

Friedreich's ataxia (FRDA) is a rare hereditary neurodegenerative disorder caused by a GAA repeat expansion in the FXN gene. There is still no cure or quantitative biomarkers reliaby correlating with the progression rate and disease severity. Investigation of functional and structural alterations characterizing white (WM) and gray matter (GM) in FRDA are needed prerequisite to monitor progression and response to treatment. Here we report the results of a multimodal cross-sectional MRI study of FRDA including Voxel-Based Morphometry (VBM), diffusion-tensor imaging (DTI), functional MRI (fMRI), and a correlation analysis with clinical severity scores. Twenty-one early-onset FRDA patients and 18 age-matched healthy controls (HCs) were imaged at 3T. All patients underwent a complete cognitive and clinical assessment with ataxia scales. VBM analysis showed GM volume reduction in FRDA compared to HCs bilaterally in lobules V, VI, VIII (L>R), as well as in the crus of cerebellum, posterior lobe of the vermis, in the flocculi and in the left tonsil. Voxel-wise DTI analysis showed a diffuse fractional anisotropy reduction and mean, radial, axial (AD) diffusivity increase in both infratentorial and supratentorial WM. ROI-based analysis confirmed the results showing differences of the same DTI metrics in cortico-spinal-tracts, forceps major, corpus callosum, posterior thalamic radiations, cerebellar penduncles. Additionally, we observed increased AD in superior (SCP) and middle cerebellar peduncles. The WM findings correlated with age at onset (AAO), short-allelle GAA, and disease severity. The intragroup analysis of fMRI data from right-handed 14 FRDA and 15 HCs showed similar findings in both groups, including activation in M1, insula and superior cerebellar hemisphere (lobules V–VIII). Significant differences emerged only during the non-dominant hand movement, with HCs showing a stronger activation in the left superior cerebellar hemisphere compared to FRDA. Significant correlations were found between AAO and the fMRI activation in cerebellar anterior and posterior lobes, insula and temporal lobe. Our multimodal neuroimaging protocol suggests that MRI is a useful tool to document the extension of the neurological impairment in FRDA.

We describe the results of a functional and structural brain connectivity analysis comparing a homogeneous group of 10 young adults with Williams Syndrome (WS; 3 females, age 20. 7 ± 3.7 years, age range 17.4–28.7 years) to a group of 18 controls of similar age (3 females, age 23.9 ± 4.4 years, age range 16.8–30.2), with the aim to increase knowledge of the structure – function relationship in WS. Subjects underwent a 3T brain MRI exam including anatomical, functional (resting state) and structural (diffusion MRI) sequences. We found convergent anomalies in structural and functional connectivity in the WS group. Altered Fractional Anisotropy (FA) values in parieto-occipital regions were associated with increased connectivity in the antero-posterior pathways linking parieto-occipital with frontal regions. The analysis of resting state data showed altered functional connectivity in the WS group in main brain networks (default mode, executive control and dorsal attention, sensori-motor, fronto—parietal, ventral stream). The combined analysis of functional and structural connectivity displayed a different pattern in the two groups: in controls the highest agreement was found in frontal and visual areas, whereas in WS patients in posterior regions (parieto-occipital and temporal areas). These preliminary findings may reflect an altered “wiring” of the brain in WS, which can be driven by hyper-connectivity of the posterior regions as opposed to disrupted connectivity in the anterior areas, supporting the hypothesis that a different brain (organization) could be associated with a different (organization of) behavior in Williams Syndrome.