A technique for quantifying regional blood-brain barrier (BBB) water exchange rates using contrast-enhanced arterial spin labelling (CE-ASL) is presented and evaluated in simulations and in vivo. The two-compartment ASL model describes the water exchange rate from blood to tissue, kb, but to estimate kbin practice it is necessary to separate the intra- and extravascular signals. This is challenging in standard ASL data owing to the small difference in T1values. Here, a gadolinium-based contrast agent is used to increase this T1difference and enable the signal components to be disentangled. The optimal post-contrast blood T1(T1post) at 3 T was determined in a sensitivity analysis, and the accuracy and precision of the method quantified using Monte Carlo simulations. Proof-of-concept data were acquired in six healthy volunteers (five female, age range 24-46 years). The sensitivity analysis identified the optimal T1postat 3 T as 0.8 s. Simulations showed kbcould be estimated in individual cortical regions with a relative error ε < 1 % and coefficient of variation CoV = 30 %; however, a high dependence on blood T1was also observed. In volunteer data, mean parameter values in grey matter were: arterial transit time tA= 1.15±0.49 s, cerebral blood flow f = 58.0±14.3 ml blood / min / 100 ml tissue, water exchange rate kb= 2.32±2.49 s-1. CE-ASL can provide regional BBB water exchange rate estimates; however, the clinical utility of the technique is dependent on the achievable accuracy of measured T1values.

Structural network-based approaches can assess white matter connections revealing topological alterations in multiple sclerosis (MS). However, principal network (pn) organisation and its clinical relevance in MS has not been explored yet. Here, structural networks were reconstructed from diffusion data in 58 relapsing-remitting MS (RRMS), 28 primary progressive MS (PPMS), 36 secondary progressive (SPMS) and 51 healthy controls (HCs). Network hubs' strengths were compared with HCs. Then, PN analysis was performed in each clinical subtype. Regression analysis was applied to investigate the associations between nodal strength derived from the first and second PNs (PN1 and PN2) in MS, with clinical disability. compared with Hcs, MS patients had preserved hub number, but some hubs exhibited reduced strength. PN1 comprised 10 hubs in HCs, RRMS and PPMS but did not include the right thalamus in SPMS. PN2 comprised 10 hub regions with intra-hemispheric connections in HCs. in MS, this subnetwork did not include the right putamen whilst in SpMS the right thalamus was also not included. Decreased nodal strength of the right thalamus and putamen from the pns correlated strongly with higher clinical disability. these pn analyses suggest distinct patterns of disruptions in MS subtypes which are clinically relevant Multiple sclerosis (MS) is an inflammatory, demyelinating and neurodegenerative disease of the central nervous system (CNS) 1. Conventional whole brain magnetic resonance imaging (MRI) measures do not necessarily reflect processes of brain reorganisation in pathology and poorly reflect the long-term course of the disease 2 meaning that additional biomarkers for disease progression and treatment effects are needed. Structural network analysis provides a framework to study whole brain connectivity patterns and their disruptions, incorporating data beyond focal pathology (i.e. lesions). In this approach, grey matter regions are modelled as nodes connected by structural pathways known as edges derived from diffusion data. The pairwise connection between nodes can be represented in a connectivity matrix and graph theory is applied to quantify differences in connectivity patterns in pathology 3. The application of network-based approaches in MS has shown interesting findings. Previous studies demonstrated that network measures were different between MS patients and controls 4-6 or between clinical profiles 7,8. Additionally, structural network measures were associated with clinical disability 9 and lesion load 6 and with cognitive deficits 4. Interestingly, structural network metrics explained physical disability and cognitive impairment over and above non-network measures 8 highlighting the clinical relevance of these studies in MS. Network-based techniques have the potential to not only allow the quantitative characterisation of global connectivity patterns but also to provide a framework to elucidate important topological features. For instance, studies have identified the existence of a number of highly connected regio...

Purpose To evaluate potential modeling paradigms and the impact of relaxation time effects on human blood‐brain barrier (BBB) water exchange measurements using FEXI (BBB‐FEXI), and to quantify the accuracy, precision, and repeatability of BBB‐FEXI exchange rate estimates at 3 T$$ \mathrm{T} $$. Methods Three modeling paradigms were evaluated: (i) the apparent exchange rate (AXR) model; (ii) a two‐compartment model (2CM$$ 2\mathrm{CM} $$) explicitly representing intra‐ and extravascular signal components, and (iii) a two‐compartment model additionally accounting for finite compartmental normalT1$$ {\mathrm{T}}_1 $$ and normalT2$$ {\mathrm{T}}_2 $$ relaxation times (2CMr$$ 2{\mathrm{CM}}_r $$). Each model had three free parameters. Simulations quantified biases introduced by the assumption of infinite relaxation times in the AXR and 2CM$$ 2\mathrm{CM} $$ models, as well as the accuracy and precision of all three models. The scan–rescan repeatability of all paradigms was quantified for the first time in vivo in 10 healthy volunteers (age range 23–52 years; five female). Results The assumption of infinite relaxation times yielded exchange rate errors in simulations up to 42%/14% in the AXR/2CM$$ 2\mathrm{CM} $$ models, respectively. Accuracy was highest in the compartmental models; precision was best in the AXR model. Scan–rescan repeatability in vivo was good for all models, with negligible bias and repeatability coefficients in grey matter of RCAXR=0.43$$ {\mathrm{RC}}_{\mathrm{AXR}}=0.43 $$ s0.3emprefix−1$$ {\mathrm{s}}^{-1} $$, RC2CM=0.51$$ {\mathrm{RC}}_{2\mathrm{CM}}=0.51 $$ s0.3emprefix−1$$ {\mathrm{s}}^{-1} $$, and RC2CMr=0.61$$ {\mathrm{RC}}_{2{\mathrm{CM}}_r}=0.61 $$ s0.3emprefix−1$$ {\mathrm{s}}^{-1} $$. Conclusion Compartmental modelling of BBB‐FEXI signals can provide accurate and repeatable measurements of BBB water exchange; however, relaxation time and partial volume effects may cause model‐dependent biases.

We propose compartmental modelling of blood-brain barrier (BBB) water exchange measurements using diffusion-filtered exchange imaging (FEXI), with the aim of providing greater biophysical insight into BBB function than is possible using the apparent exchange rate (AXR) approach. As relaxation time differences between blood and extravascular tissue have not yet been accounted for in FEXI-based BBB permeability measurements, we use simulations to quantify potential biases in exchange rates from both the AXR and compartmental approaches. Finally, we evaluate the repeatability of the AXR and compartmental models in a cohort of healthy subjects.