Quantification of water exchange across the blood-brain barrier using noncontrast MR fingerprinting.

PURPOSE: A method is proposed to quantify cerebral blood volume ( v b $$ {v}_b $$ ) and intravascular water residence time ( τ b $$ {\tau}_b $$ ) using MR fingerprinting (MRF), applied using a spoiled gradient echo sequence without the need for contrast agent. METHODS: An in silico study optimized an acquisition protocol to maximize the sensitivity of the measurement to v b $$ {v}_b $$ and τ b $$ {\tau}_b $$ changes. Its accuracy in the presence of variations in T 1 , t $$ {\mathrm{T}}_{1,t} $$ , T 1 , b $$ {\mathrm{T}}_{1,b} $$ , and B 1 $$ {\mathrm{B}}_1 $$ was evaluated. The optimized protocol (scan time of 19 min) was then tested in a exploratory healthy volunteer study (10 volunteers, mean age 24 ± $$ \pm $$ 3, six males) at 3 T with a repeat scan taken after repositioning to allow estimation of repeatability. RESULTS: Simulations show that assuming literature values for T 1 , b $$ {\mathrm{T}}_{1,b} $$ and T 1 , t $$ {\mathrm{T}}_{1,t} $$ , no variation in B 1 $$ {\mathrm{B}}_1 $$ , while fitting only v b $$ {v}_b $$ and τ b $$ {\tau}_b $$ , leads to large errors in quantification of v b $$ {v}_b $$ and τ b $$ {\tau}_b $$ , regardless of noise levels. However, simulations also show that matching T 1 , t $$ {\mathrm{T}}_{1,t} $$ , T 1 , b $$ {\mathrm{T}}_{1,b} $$ , B 1 + $$ {\mathrm{B}}_1^{+} $$ , v b $$ {v}_b $$ and τ b $$ {\tau}_b $$ , simultaneously is feasible at clinically achievable noise levels. Across the healthy volunteers, all parameter quantifications fell within the expected literature range. In addition, the maps show good agreement between hemispheres suggesting physiologically relevant information is being extracted. Expected differences between white and gray matter T 1 , t $$ {\mathrm{T}}_{1,t} $$ (p < 0.0001) and v b $$ {v}_b $$ (p < 0.0001) are observed, T 1 , b $$ {\mathrm{T}}_{1,b} $$ and τ b $$ {\tau}_b $$ show no significant differences, p = 0.4 and p = 0.6, respectively. Moderate to excellent repeatability was seen between repeat scans: mean intra-class correlation coefficient of T 1 , t : 0 . 91 $$ {\mathrm{T}}_{1,t}:0.91 $$ , T 1 , b : 0 . 58 $$ {\mathrm{T}}_{1,b}:0.58 $$ , v b : 0 . 90 $$ {v}_b:0.90 $$ , and τ b : 0 . 96 $$ {\tau}_b:0.96 $$ . CONCLUSION: We demonstrate that regional simultaneous quantification of v b $$ {v}_b $$ , τ b $$ {\tau}_b $$ , T 1 , b , T 1 , t $$ {\mathrm{T}}_{1,b},{T}_{1,t} $$ , and B 1 + $$ {\mathrm{B}}_1^{+} $$ using MRF is feasible in vivo.

Systemic inflammation associates with and precedes cord atrophy in progressive multiple sclerosis.

In preclinical models of multiple sclerosis, systemic inflammation has an impact on the compartmentalized inflammatory process within the central nervous system and results in axonal loss. It remains to be shown whether this is the case in humans, specifically whether systemic inflammation contributes to spinal cord or brain atrophy in multiple sclerosis. Hence, an observational longitudinal study was conducted to delineate the relationship between systemic inflammation and atrophy using magnetic resonance imaging: the SIMS (Systemic Inflammation in Multiple Sclerosis) study. Systemic inflammation and progression were assessed in people with progressive multiple sclerosis (n = 50) over two and a half years. Eligibility criteria included: (i) primary or secondary progressive multiple sclerosis; (ii) age ≤ 70; and (iii) Expanded Disability Status Scale ≤ 6.5. First morning urine was collected weekly to quantify systemic inflammation by measuring the urinary neopterin-to-creatinine ratio using a validated ultra-performance liquid chromatography mass spectrometry technique. The urinary neopterin-to-creatinine ratio temporal profile was characterized by short-term responses overlaid on a background level of inflammation, so these two distinct processes were considered as separate variables: background inflammation and inflammatory response. Participants underwent MRI at the start and end of the study, to measure cervical spinal cord and brain atrophy. Brain and cervical cord atrophy occurred on the study, but the most striking change was seen in the cervical spinal cord, in keeping with the corticospinal tract involvement that is typical of progressive disease. Systemic inflammation predicted cervical cord atrophy. An association with brain atrophy was not observed in this cohort. A time lag between systemic inflammation and cord atrophy was evident, suggesting but not proving causation. The association of the inflammatory response with cord atrophy depended on the level of background inflammation, in keeping with experimental data in preclinical models where the effects of a systemic inflammatory challenge on tissue injury depended on prior exposure to inflammation. A higher inflammatory response was associated with accelerated cord atrophy in the presence of background systemic inflammation below the median for the study population. Higher background inflammation, while associated with cervical cord atrophy itself, subdued the association of the inflammatory response with cord atrophy. Findings were robust to sensitivity analyses adjusting for potential confounders and excluding cases with new lesion formation. In conclusion, systemic inflammation associates with, and precedes, multiple sclerosis progression. Further work is needed to prove causation since targeting systemic inflammation may offer novel treatment strategies for slowing neurodegeneration in multiple sclerosis.

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study.

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject's sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.

UPDATE trial: investigating the effects of ultra-processed versus minimally processed diets following UK dietary guidance on health outcomes: a protocol for an 8-week community-based cross-over randomised controlled trial in people with overweight or obesity, followed by a 6-month behavioural intervention.

INTRODUCTION: Obesity increases the risk of morbidity and mortality. A major driver has been the increased availability of ultra-processed food (UPF), now the main UK dietary energy source. The UK Eatwell Guide (EWG) provides public guidance for a healthy balanced diet but offers no UPF guidance. Whether a healthy diet can largely consist of UPFs is unclear. No study has assessed whether the health impact of adhering to dietary guidelines depends on food processing. Furthermore, our study will assess the impact of a 6-month behavioural support programme aimed at reducing UPF intake in people with overweight/obesity and high UPF intakes. METHODS AND ANALYSIS: UPDATE is a 2×2 cross-over randomised controlled trial with a 6-month behavioural intervention. Fifty-five adults aged ≥18, with overweight/obesity (≥25 to <40 kg/m2), and ≥50% of habitual energy intake from UPFs will receive an 8-week UPF diet and an 8-week minimally processed food (MPF) diet delivered to their home, both following EWG recommendations, in a random order, with a 4-week washout period. All food/drink will be provided. Participants will then receive 6 months of behavioural support to reduce UPF intake. The primary outcome is the difference in weight change between UPF and MPF diets from baseline to week 8. Secondary outcomes include changes in diet, waist circumference, body composition, heart rate, blood pressure, cardiometabolic risk factors, appetite regulation, sleep quality, physical activity levels, physical function/strength, well-being and aspects of behaviour change/eating behaviour at 8 weeks between UPF/MPF diets, and at 6-month follow-up. Quantitative assessment of changes in brain MRI functional resting-state connectivity between UPF/MPF diets, and qualitative analysis of the behavioural intervention for feasibility and acceptability will be undertaken. ETHICS AND DISSEMINATION: Sheffield Research Ethics Committee approved the trial (22/YH/0281). Peer-reviewed journals, conferences, PhD thesis and lay media will report results. TRIAL REGISTRATION NUMBER: NCT05627570.

Visual Snow Syndrome Improves With Modulation of Resting-State Functional MRI Connectivity After Mindfulness-Based Cognitive Therapy: An Open-Label Feasibility Study.

BACKGROUND: Visual snow syndrome (VSS) is associated with functional connectivity (FC) dysregulation of visual networks (VNs). We hypothesized that mindfulness-based cognitive therapy, customized for visual symptoms (MBCT-vision), can treat VSS and modulate dysfunctional VNs. METHODS: An open-label feasibility study for an 8-week MBCT-vision treatment program was conducted. Primary (symptom severity; impact on daily life) and secondary (WHO-5; CORE-10) outcomes at Week 9 and Week 20 were compared with baseline. Secondary MRI outcomes in a subcohort compared resting-state functional and diffusion MRI between baseline and Week 20. RESULTS: Twenty-one participants (14 male participants, median 30 years, range 22-56 years) recruited from January 2020 to October 2021. Two (9.5%) dropped out. Self-rated symptom severity (0-10) improved: baseline (median [interquartile range (IQR)] 7 [6-8]) vs Week 9 (5.5 [3-7], P = 0.015) and Week 20 (4 [3-6], P < 0.001), respectively. Self-rated impact of symptoms on daily life (0-10) improved: baseline (6 [5-8]) vs Week 9 (4 [2-5], P = 0.003) and Week 20 (2 [1-3], P < 0.001), respectively. WHO-5 Wellbeing (0-100) improved: baseline (median [IQR] 52 [36-56]) vs Week 9 (median 64 [47-80], P = 0.001) and Week 20 (68 [48-76], P < 0.001), respectively. CORE-10 Distress (0-40) improved: baseline (15 [12-20]) vs Week 9 (12.5 [11-16.5], P = 0.003) and Week 20 (11 [10-14], P = 0.003), respectively. Within-subject fMRI analysis found reductions between baseline and Week 20, within VN-related FC in the i) left lateral occipital cortex (size = 82 mL, familywise error [FWE]-corrected P value = 0.006) and ii) left cerebellar lobules VIIb/VIII (size = 65 mL, FWE-corrected P value = 0.02), and increases within VN-related FC in the precuneus/posterior cingulate cortex (size = 69 mL, cluster-level FWE-corrected P value = 0.02). CONCLUSIONS: MBCT-vision was a feasible treatment for VSS, improved symptoms and modulated FC of VNs. This study also showed proof-of-concept for intensive mindfulness interventions in the treatment of neurological conditions.

2D sodium MRI of the human calf using half-sinc excitation pulses and compressed sensing.

PURPOSE: Sodium MRI can be used to quantify tissue sodium concentration (TSC) in vivo; however, UTE sequences are required to capture the rapidly decaying signal. 2D MRI enables high in-plane resolution but typically has long TEs. Half-sinc excitation may enable UTE; however, twice as many readouts are necessary. Scan time can be minimized by reducing the number of signal averages (NSAs), but at a cost to SNR. We propose using compressed sensing (CS) to accelerate 2D half-sinc acquisitions while maintaining SNR and TSC. METHODS: Ex vivo and in vivo TSC were compared between 2D spiral sequences with full-sinc (TE = 0.73 ms, scan time ≈ 5 min) and half-sinc excitation (TE = 0.23 ms, scan time ≈ 10 min), with 150 NSAs. Ex vivo, these were compared to a reference 3D sequence (TE = 0.22 ms, scan time ≈ 24 min). To investigate shortening 2D scan times, half-sinc data was retrospectively reconstructed with fewer NSAs, comparing a nonuniform fast Fourier transform to CS. Resultant TSC and image quality were compared to reference 150 NSAs nonuniform fast Fourier transform images. RESULTS: TSC was significantly higher from half-sinc than from full-sinc acquisitions, ex vivo and in vivo. Ex vivo, half-sinc data more closely matched the reference 3D sequence, indicating improved accuracy. In silico modeling confirmed this was due to shorter TEs minimizing bias caused by relaxation differences between phantoms and tissue. CS was successfully applied to in vivo, half-sinc data, maintaining TSC and image quality (estimated SNR, edge sharpness, and qualitative metrics) with ≥50 NSAs. CONCLUSION: 2D sodium MRI with half-sinc excitation and CS was validated, enabling TSC quantification with 2.25 × 2.25 mm2 resolution and scan times of ≤5 mins.

Structural and connectivity parameters reveal spared connectivity in young patients with non-progressive compared to slow-progressive cerebellar ataxia.

Introduction: Within Pediatric Cerebellar Ataxias (PCAs), patients with non-progressive ataxia (NonP) surprisingly show postural motor behavior comparable to that of healthy controls, differently to slow-progressive ataxia patients (SlowP). This difference may depend on the building of compensatory strategies of the intact areas in NonP brain network. Methods: Eleven PCAs patients were recruited: five with NonP and six with SlowP. We assessed volumetric and axonal bundles alterations with a multimodal approach to investigate whether eventual spared connectivity between basal ganglia and cerebellum explains the different postural motor behavior of NonP and SlowP patients. Results: Cerebellar lobules were smaller in SlowP patients. NonP patients showed a lower number of streamlines in the cerebello-thalamo-cortical tracts but a generalized higher integrity of white matter tracts connecting the cortex and the basal ganglia with the cerebellum. Discussion: This work reveals that the axonal bundles connecting the cerebellum with basal ganglia and cortex demonstrate a higher integrity in NonP patients. This evidence highlights the importance of the cerebellum-basal ganglia connectivity to explain the different postural motor behavior of NonP and SlowP patients and support the possible compensatory role of basal ganglia in patients with stable cerebellar malformation.

Longitudinal Metabolite Changes in Progressive Multiple Sclerosis: A Study of 3 Potential Neuroprotective Treatments.

BACKGROUND: 1 H-magnetic resonance spectroscopy (1 H-MRS) may provide a direct index for the testing of medicines for neuroprotection and drug mechanisms in multiple sclerosis (MS) through measures of total N-acetyl-aspartate (tNAA), total creatine (tCr), myo-inositol (mIns), total-choline (tCho), and glutamate + glutamine (Glx). Neurometabolites may be associated with clinical disability with evidence that baseline neuroaxonal integrity is associated with upper limb function and processing speed in secondary progressive MS (SPMS). PURPOSE: To assess the effect on neurometabolites from three candidate drugs after 96-weeks as seen by 1 H-MRS and their association with clinical disability in SPMS. STUDY-TYPE: Longitudinal. POPULATION: 108 participants with SPMS randomized to receive neuroprotective drugs amiloride [mean age 55.4 (SD 7.4), 61% female], fluoxetine [55.6 (6.6), 71%], riluzole [54.6 (6.3), 68%], or placebo [54.8 (7.9), 67%]. FIELD STRENGTH/SEQUENCE: 3-Tesla. Chemical-shift-imaging 2D-point-resolved-spectroscopy (PRESS), 3DT1. ASSESSMENT: Brain metabolites in normal appearing white matter (NAWM) and gray matter (GM), brain volume, lesion load, nine-hole peg test (9HPT), and paced auditory serial addition test were measured at baseline and at 96-weeks. STATISTICAL TESTS: Paired t-test was used to analyze metabolite changes in the placebo arm over 96-weeks. Metabolite differences between treatment arms and placebo; and associations between baseline metabolites and upper limb function/information processing speed at 96-weeks assessed using multiple linear regression models. P-value<0.05 was considered statistically significant. RESULTS: In the placebo arm, tCho increased in GM (mean difference = -0.32 IU) but decreased in NAWM (mean difference = 0.13 IU). Compared to placebo, in the fluoxetine arm, mIns/tCr was lower (ß = -0.21); in the riluzole arm, GM Glx (ß = -0.25) and Glx/tCr (ß = -0.29) were reduced. Baseline tNAA(ß = 0.22) and tNAA/tCr (ß = 0.23) in NAWM were associated with 9HPT scores at 96-weeks. DATA CONCLUSION: 1 H-MRS demonstrated altered membrane turnover over 96-weeks in the placebo group. It also distinguished changes in neuro-metabolites related to gliosis and glutaminergic transmission, due to fluoxetine and riluzole, respectively. Data show tNAA is a potential marker for upper limb function. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 4.

Genetic influences on disease course and severity, 30 years after a clinically isolated syndrome.

Multiple sclerosis risk has a well-established polygenic component, yet the genetic contribution to disease course and severity remains unclear and difficult to examine. Accurately measuring disease progression requires long-term study of clinical and radiological outcomes with sufficient follow-up duration to confidently confirm disability accrual and multiple sclerosis phenotypes. In this retrospective study, we explore genetic influences on long-term disease course and severity; in a unique cohort of clinically isolated syndrome patients with homogenous 30-year disease duration, deep clinical phenotyping and advanced MRI metrics. Sixty-one clinically isolated syndrome patients [41 female (67%): 20 male (33%)] underwent clinical and MRI assessment at baseline, 1-, 5-, 10-, 14-, 20- and 30-year follow-up (mean age ± standard deviation: 60.9 ± 6.5 years). After 30 years, 29 patients developed relapsing-remitting multiple sclerosis, 15 developed secondary progressive multiple sclerosis and 17 still had a clinically isolated syndrome. Twenty-seven genes were investigated for associations with clinical outcomes [including disease course and Expanded Disability Status Scale (EDSS)] and brain MRI (including white matter lesions, cortical lesions, and brain tissue volumes) at the 30-year follow-up. Genetic associations with changes in EDSS, relapses, white matter lesions and brain atrophy (third ventricular and medullary measurements) over 30 years were assessed using mixed-effects models. HLA-DRB1*1501-positive (n = 26) patients showed faster white matter lesion accrual [+1.96 lesions/year (0.64-3.29), P = 3.8 × 10-3], greater 30-year white matter lesion volumes [+11.60 ml, (5.49-18.29), P = 1.27 × 10-3] and higher annualized relapse rates [+0.06 relapses/year (0.005-0.11), P = 0.031] compared with HLA-DRB1*1501-negative patients (n = 35). PVRL2-positive patients (n = 41) had more cortical lesions (+0.83 [0.08-1.66], P = 0.042), faster EDSS worsening [+0.06 points/year (0.02-0.11), P = 0.010], greater 30-year EDSS [+1.72 (0.49-2.93), P = 0.013; multiple sclerosis cases: +2.60 (1.30-3.87), P = 2.02 × 10-3], and greater risk of secondary progressive multiple sclerosis [odds ratio (OR) = 12.25 (1.15-23.10), P = 0.031] than PVRL2-negative patients (n = 18). In contrast, IRX1-positive (n = 30) patients had preserved 30-year grey matter fraction [+0.76% (0.28-1.29), P = 8.4 × 10-3], lower risk of cortical lesions [OR = 0.22 (0.05-0.99), P = 0.049] and lower 30-year EDSS [-1.35 (-0.87,-3.44), P = 0.026; multiple sclerosis cases: -2.12 (-0.87, -3.44), P = 5.02 × 10-3] than IRX1-negative patients (n = 30). In multiple sclerosis cases, IRX1-positive patients also had slower EDSS worsening [-0.07 points/year (-0.01,-0.13), P = 0.015] and lower risk of secondary progressive multiple sclerosis [OR = 0.19 (0.04-0.92), P = 0.042]. These exploratory findings support diverse genetic influences on pathological mechanisms associated with multiple sclerosis disease course. HLA-DRB1*1501 influenced white matter inflammation and relapses, while IRX1 (protective) and PVRL2 (adverse) were associated with grey matter pathology (cortical lesions and atrophy), long-term disability worsening and the risk of developing secondary progressive multiple sclerosis.

A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics.

Mean-field (MF) models are computational formalism used to summarize in a few statistical parameters the salient biophysical properties of an inter-wired neuronal network. Their formalism normally incorporates different types of neurons and synapses along with their topological organization. MFs are crucial to efficiently implement the computational modules of large-scale models of brain function, maintaining the specificity of local cortical microcircuits. While MFs have been generated for the isocortex, they are still missing for other parts of the brain. Here we have designed and simulated a multi-layer MF of the cerebellar microcircuit (including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells) and validated it against experimental data and the corresponding spiking neural network (SNN) microcircuit model. The cerebellar MF was built using a system of equations, where properties of neuronal populations and topological parameters are embedded in inter-dependent transfer functions. The model time constant was optimised using local field potentials recorded experimentally from acute mouse cerebellar slices as a template. The MF reproduced the average dynamics of different neuronal populations in response to various input patterns and predicted the modulation of the Purkinje Cells firing depending on cortical plasticity, which drives learning in associative tasks, and the level of feedforward inhibition. The cerebellar MF provides a computationally efficient tool for future investigations of the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions.

Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias.

Introduction: Neural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. The Virtual Brain (TVB) modeling, by exploiting structural and functional magnetic resonance imaging (MRI), yields mesoscopic parameters of connectivity and synaptic transmission. Methods: We used TVB to simulate brain networks, which are key for human brain function, in Alzheimer's disease (AD) and frontotemporal dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks. Results: The pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles. Discussion: These TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.

Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network.

PURPOSE: The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. METHODS: Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A "traveling brains" dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. RESULTS: The results show an inter-center and inter-subject variability of <10%, except for "clustering coefficient" (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners' hardware. CONCLUSIONS: The results show low variability of connectivity topological metrics across sites running a harmonised protocol.

Cardiovascular risk factors in secondary progressive multiple sclerosis: A cross-sectional analysis from the MS-STAT2 randomized controlled trial.

BACKGROUND AND PURPOSE: There is increasing evidence that cardiovascular risk (CVR) contributes to disability progression in multiple sclerosis (MS). CVR is particularly prevalent in secondary progressive MS (SPMS) and can be quantified through validated composite CVR scores. The aim was to examine the cross-sectional relationships between excess modifiable CVR, whole and regional brain atrophy on magnetic resonance imaging, and disability in patients with SPMS. METHODS: Participants had SPMS, and data were collected at enrolment into the MS-STAT2 trial. Composite CVR scores were calculated using the QRISK3 software. Prematurely achieved CVR due to modifiable risk factors was expressed as QRISK3 premature CVR, derived through reference to the normative QRISK3 dataset and expressed in years. Associations were determined with multiple linear regressions. RESULTS: For the 218 participants, mean age was 54 years and median Expanded Disability Status Scale was 6.0. Each additional year of prematurely achieved CVR was associated with a 2.7 mL (beta coefficient; 95% confidence interval 0.8-4.7; p = 0.006) smaller normalized whole brain volume. The strongest relationship was seen for the cortical grey matter (beta coefficient 1.6 mL per year; 95% confidence interval 0.5-2.7; p = 0.003), and associations were also found with poorer verbal working memory performance. Body mass index demonstrated the strongest relationships with normalized brain volumes, whilst serum lipid ratios demonstrated strong relationships with verbal and visuospatial working memory performance. CONCLUSIONS: Prematurely achieved CVR is associated with lower normalized brain volumes in SPMS. Future longitudinal analyses of this clinical trial dataset will be important to determine whether CVR predicts future disease worsening.

Quality assessment, variability and reproducibility of anatomical measurements derived from T1-weighted brain imaging: The RIN-Neuroimaging Network case study.

Initiatives for the collection of harmonized MRI datasets are growing continuously, opening questions on the reliability of results obtained in multi-site contexts. Here we present the assessment of the brain anatomical variability of MRI-derived measurements obtained from T1-weighted images, acquired according to the Standard Operating Procedures, promoted by the RIN-Neuroimaging Network. A multicentric dataset composed of 77 brain T1w acquisitions of young healthy volunteers (mean age = 29.7 ± 5.0 years), collected in 15 sites with MRI scanners of three different vendors, was considered. Parallelly, a dataset of 7 "traveling" subjects, each undergoing three acquisitions with scanners from different vendors, was also used. Intra-site, intra-vendor, and inter-site variabilities were evaluated in terms of the percentage standard deviation of volumetric and cortical thickness measures. Image quality metrics such as contrast-to-noise and signal-to-noise ratio in gray and white matter were also assessed for all sites and vendors. The results showed a measured global variability that ranges from 11% to 19% for subcortical volumes and from 3% to 10% for cortical thicknesses. Univariate distributions of the normalized volumes of subcortical regions, as well as the distributions of the thickness of cortical parcels appeared to be significantly different among sites in 8 subcortical (out of 17) and 21 cortical (out of 68) regions of i nterest in the multicentric study. The Bland-Altman analysis on "traveling" brain measurements did not detect systematic scanner biases even though a multivariate classification approach was able to classify the scanner vendor from brain measures with an accuracy of 0.60 ± 0.14 (chance level 0.33).

Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset.

Introduction: Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns. Methods: In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself. Results and discussion: Average classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks.

Feasibility of in vivo multi-parametric quantitative magnetic resonance imaging of the healthy sciatic nerve with a unified signal readout protocol.

Magnetic resonance neurography (MRN) has been used successfully over the years to investigate the peripheral nervous system (PNS) because it allows early detection and precise localisation of neural tissue damage. However, studies demonstrating the feasibility of combining MRN with multi-parametric quantitative magnetic resonance imaging (qMRI) methods, which provide more specific information related to nerve tissue composition and microstructural organisation, can be invaluable. The translation of emerging qMRI methods previously validated in the central nervous system to the PNS offers real potential to characterise in patients in vivo the underlying pathophysiological mechanisms involved in a plethora of conditions of the PNS. The aim of this study was to assess the feasibility of combining MRN with qMRI to measure diffusion, magnetisation transfer and relaxation properties of the healthy sciatic nerve in vivo using a unified signal readout protocol. The reproducibility of the multi-parametric qMRI protocol as well as normative qMRI measures in the healthy sciatic nerve are reported. The findings presented herein pave the way to the practical implementation of joint MRN-qMRI in future studies of pathological conditions affecting the PNS.

Aberrant olfactory network functional connectivity in people with olfactory dysfunction following COVID-19 infection: an exploratory, observational study.

Background: Olfactory impairments and anosmia from COVID-19 infection typically resolve within 2-4 weeks, although in some cases, symptoms persist longer. COVID-19-related anosmia is associated with olfactory bulb atrophy, however, the impact on cortical structures is relatively unknown, particularly in those with long-term symptoms. Methods: In this exploratory, observational study, we studied individuals who experienced COVID-19-related anosmia, with or without recovered sense of smell, and compared against individuals with no prior COVID-19 infection (confirmed by antibody testing, all vaccine naïve). MRI Imaging was carried out between the 15th July and 17th November 2020 at the Queen Square House Clinical Scanning Facility, UCL, United Kingdom. Using functional magnetic resonance imaging (fMRI) and structural imaging, we assessed differences in functional connectivity (FC) between olfactory regions, whole brain grey matter (GM) cerebral blood flow (CBF) and GM density. Findings: Individuals with anosmia showed increased FC between the left orbitofrontal cortex (OFC), visual association cortex and cerebellum and FC reductions between the right OFC and dorsal anterior cingulate cortex compared to those with no prior COVID-19 infection (p < 0.05, from whole brain statistical parametric map analysis). Individuals with anosmia also showed greater CBF in the left insula, hippocampus and ventral posterior cingulate when compared to those with resolved anosmia (p < 0.05, from whole brain statistical parametric map analysis). Interpretation: This work describes, for the first time to our knowledge, functional differences within olfactory areas and regions involved in sensory processing and cognitive functioning. This work identifies key areas for further research and potential target sites for therapeutic strategies. Funding: This study was funded by the National Institute for Health and Care Research and supported by the Queen Square Scanner business case.

Multimodal Analysis of the Visual Pathways in Friedreich's Ataxia Reveals Novel Biomarkers.

BACKGROUND: Optic neuropathy is a near ubiquitous feature of Friedreich's ataxia (FRDA). Previous studies have examined varying aspects of the anterior and posterior visual pathways but none so far have comprehensively evaluated the heterogeneity of degeneration across different areas of the retina, changes to the macula layers and combined these with volumetric MRI studies of the visual cortex and frataxin level. METHODS: We investigated 62 genetically confirmed FRDA patients using an integrated approach as part of an observational cohort study. We included measurement of frataxin protein levels, clinical evaluation of visual and neurological function, optical coherence tomography to determine retinal nerve fibre layer thickness and macular layer volume and volumetric brain MRI. RESULTS: We demonstrate that frataxin level correlates with peripapillary retinal nerve fibre layer thickness and that retinal sectors differ in their degree of degeneration. We also shown that retinal nerve fibre layer is thinner in FRDA patients than controls and that this thinning is influenced by the AAO and GAA1. Furthermore we show that the ganglion cell and inner plexiform layers are affected in FRDA. Our MRI data indicate that there are borderline correlations between retinal layers and areas of the cortex involved in visual processing. CONCLUSION: Our study demonstrates the uneven distribution of the axonopathy in the retinal nerve fibre layer and highlight the relative sparing of the papillomacular bundle and temporal sectors. We show that thinning of the retinal nerve fibre layer is associated with frataxin levels, supporting the use the two biomarkers in future clinical trials design. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Italian, European, and international neuroinformatics efforts: An overview.

Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.

MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms.

PURPOSE: Generating big-data is becoming imperative with the advent of machine learning. RIN-Neuroimaging Network addresses this need by developing harmonized protocols for multisite studies to identify quantitative MRI (qMRI) biomarkers for neurological diseases. In this context, image quality control (QC) is essential. Here, we present methods and results of how the RIN performs intra- and inter-site reproducibility of geometrical and image contrast parameters, demonstrating the relevance of such QC practice. METHODS: American College of Radiology (ACR) large and small phantoms were selected. Eighteen sites were equipped with a 3T scanner that differed by vendor, hardware/software versions, and receiver coils. The standard ACR protocol was optimized (in-plane voxel, post-processing filters, receiver bandwidth) and repeated monthly. Uniformity, ghosting, geometric accuracy, ellipse's ratio, slice thickness, and high-contrast detectability tests were performed using an automatic QC script. RESULTS: Measures were mostly within the ACR tolerance ranges for both T1- and T2-weighted acquisitions, for all scanners, regardless of vendor, coil, and signal transmission chain type. All measurements showed good reproducibility over time. Uniformity and slice thickness failed at some sites. Scanners that upgraded the signal transmission chain showed a decrease in geometric distortion along the slice encoding direction. Inter-vendor differences were observed in uniformity and geometric measurements along the slice encoding direction (i.e. ellipse's ratio). CONCLUSIONS: Use of the ACR phantoms highlighted issues that triggered interventions to correct performance at some sites and to improve the longitudinal stability of the scanners. This is relevant for establishing precision levels for future multisite studies of qMRI biomarkers.