Reconstruction of 3D deformation from 2D MR velocity mapping with incompressibility constraints

This paper presents a new method for calculating 3D myocardial deformation from multislice 2D magnetic resonance velocity mapping. The method first involves the rectification of in-plane velocity distribution with a variational vector restoration method. This restored 2D velocity is then used to estimate the through-plane velocity component by applying a local incompressibility constraint. A global optimization procedure was then used to derive the velocity distribution that conforms to the incompressibility constraint. The proposed method was validated by using a simulation phantom with different levels of noise. The derived velocity field permits a full 3D deformation analysis of the myocardium

NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity

In Diffusion Weighted MR Imaging (DWI), the signal is affected by the biophysical properties of neuronal cells and their relative placement, as well as extra-cellular tissue compartments. Typically, microstructural indices, such as fractional anisotropy (FA) and mean diffusivity (MD), are based on a tensor model that cannot disentangle the influence of these parameters. Recently, Neurite Orientation Dispersion and Density Imaging (NODDI) has exploited multi-shell acquisition protocols to model the diffusion signal as the contribution of three tissue compartments. NODDI microstructural indices, such as intra-cellular volume fraction (ICVF) and orientation dispersion index (ODI) are directly related to neuronal density and orientation dispersion, respectively. One way of examining the neurophysiological role of these microstructural indices across neuronal fibres is to look into how they relate to brain function. Here we exploit a statistical framework based on sparse Canonical Correlation Analysis (sCCA) and randomised Lasso to identify structural connections that are highly correlated with resting-state functional connectivity measured with simultaneous EEG-fMRI. Our results reveal distinct structural fingerprints for each microstructural index that also reflect their inter-relationships

Consensus of state of the art mortality prediction models: From all-cause mortality to sudden death prediction

Worldwide, many millions of people die suddenly and unexpectedly each year, either with or without a prior history of cardiovascular disease. Such events are sparse (once in a lifetime), many victims will not have had prior investigations for cardiac disease and many different definitions of sudden death exist. Accordingly, sudden death is hard to predict. This analysis used NHS Electronic Health Records (EHRs) for people aged $\geq$50 years living in the Greater Glasgow and Clyde (GG\&C) region in 2010 (n = 380,000) to try to overcome these challenges. We investigated whether medical history, blood tests, prescription of medicines, and hospitalisations might, in combination, predict a heightened risk of sudden death. We compared the performance of models trained to predict either sudden death or all-cause mortality. We built six models for each outcome of interest: three taken from state-of-the-art research (BEHRT, Deepr and Deep Patient), and three of our own creation. We trained these using two different data representations: a language-based representation, and a sparse temporal matrix. We used global interpretability to understand the most important features of each model, and compare how much agreement there was amongst models using Rank Biased Overlap. It is challenging to account for correlated variables without increasing the complexity of the interpretability technique. We overcame this by clustering features into groups and comparing the most important groups for each model. We found the agreement between models to be much higher when accounting for correlated variables. Our analysis emphasises the challenge of predicting sudden death and emphasises the need for better understanding and interpretation of machine learning models applied to healthcare applications

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands.

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity

Variable echo time imaging for detecting the short T2* components of the sciatic nerve: a validation study

OBJECTIVE: The aim of this study was to develop and validate an MRI protocol based on a variable echo time (vTE) sensitive to the short T2* components of the sciatic nerve. MATERIALS AND METHODS: 15 healthy subjects (M/F: 9/6; age: 21-62) were scanned at 3T targeting the sciatic nerve at the thigh bilaterally, using a dual echo variable echo time (vTE) sequence (based on a spoiled gradient echo acquisition) with echo times of 0.98/5.37 ms. Apparent T2* (aT2*) values of the sciatic nerves were calculated with a mono-exponential fit and used for data comparison. RESULTS: There were no significant differences in aT2* related to side, sex, age, and BMI, even though small differences for side were reported. Good-to-excellent repeatability and reproducibility were found for geometry of ROIs (Dice indices: intra-rater 0.68-0.7; inter-rater 0.70-0.72) and the related aT2* measures (intra-inter reader ICC 0.95-0.97; 0.66-0.85) from two different operators. Side-related signal-to-noise-ratio non-significant differences were reported, while contrast-to-noise-ratio measures were excellent both for side and echo. DISCUSSION: Our study introduces a novel MR sequence sensitive to the short T2* components of the sciatic nerve and may be used for the study of peripheral nerve disorders

Pilot Study on Quantitative Cervical Cord and Muscular MRI in Spinal Muscular Atrophy: Promising Biomarkers of Disease Evolution and Treatment?

Introduction: Nusinersen is a recent promising therapy approved for the treatment of spinal muscular atrophy (SMA), a rare disease characterized by the degeneration of alpha motor neurons (αMN) in the spinal cord (SC) leading to progressive muscle atrophy and dysfunction. Muscle and cervical SC quantitative magnetic resonance imaging (qMRI) has never been used to monitor drug treatment in SMA. The aim of this pilot study is to investigate whether qMRI can provide useful biomarkers for monitoring treatment efficacy in SMA. Methods: Three adult SMA 3a patients under treatment with nusinersen underwent longitudinal clinical and qMRI examinations every 4 months from baseline to 21-month follow-up. The qMRI protocol aimed to quantify thigh muscle fat fraction (FF) and water-T2 (w-T2) and to characterize SC volumes and microstructure. Eleven healthy controls underwent the same SC protocol (single time point). We evaluated clinical and imaging outcomes of SMA patients longitudinally and compared SC data between groups transversally. Results: Patient motor function was stable, with only Patient 2 showing moderate improvements. Average muscle FF was already high at baseline (50%) and progressed over time (57%). w-T2 was also slightly higher than previously published data at baseline and slightly decreased over time. Cross-sectional area of the whole SC, gray matter (GM), and ventral horns (VHs) of Patients 1 and 3 were reduced compared to controls and remained stable over time, while GM and VHs areas of Patient 2 slightly increased. We found altered diffusion and magnetization transfer parameters in SC structures of SMA patients compared to controls, thus suggesting changes in tissue microstructure and myelin content. Conclusion: In this pilot study, we found a progression of FF in thigh muscles of SMA 3a patients during nusinersen therapy and a concurrent slight reduction of w-T2 over time. The SC qMRI analysis confirmed previous imaging and histopathological studies suggesting degeneration of αMN of the VHs, resulting in GM atrophy and demyelination. Our longitudinal data suggest that qMRI could represent a feasible technique for capturing microstructural changes induced by SMA in vivo and a candidate methodology for monitoring the effects of treatment, once replicated on a larger cohort

Infants in Control: Rapid Anticipation of Action Outcomes in a Gaze-Contingent Paradigm

Infants' poor motor abilities limit their interaction with their environment and render studying infant cognition notoriously difficult. Exceptions are eye movements, which reach high accuracy early, but generally do not allow manipulation of the physical environment. In this study, real-time eye tracking is used to put 6- and 8-month-old infants in direct control of their visual surroundings to study the fundamental problem of discovery of agency, i.e. the ability to infer that certain sensory events are caused by one's own actions. We demonstrate that infants quickly learn to perform eye movements to trigger the appearance of new stimuli and that they anticipate the consequences of their actions in as few as 3 trials. Our findings show that infants can rapidly discover new ways of controlling their environment. We suggest that gaze-contingent paradigms offer effective new ways for studying many aspects of infant learning and cognition in an interactive fashion and provide new opportunities for behavioral training and treatment in infants

Linear viscoelasticity - bone volume fraction relationships of bovine trabecular bone

Trabecular bone has been previously recognized as time-dependent (viscoelastic) material, but the relationships of its viscoelastic behaviour with bone volume fraction (BV/TV) have not been investigated so far. Therefore, the aim of the present study was to quantify the time-dependent viscoelastic behaviour of trabecular bone and relate it to BV/TV. Uniaxial compressive creep experiments were performed on cylindrical bovine trabecular bone samples ([Formula: see text] ) at loads corresponding to physiological strain level of 2000 [Formula: see text] . We assumed that the bone behaves in a linear viscoelastic manner at this low strain level and the corresponding linear viscoelastic parameters were estimated by fitting a generalized Kelvin–Voigt rheological model to the experimental creep strain response. Strong and significant power law relationships ([Formula: see text] ) were found between time-dependent creep compliance function and BV/TV of the bone. These BV/TV-based material properties can be used in finite element models involving trabecular bone to predict time-dependent response. For users’ convenience, the creep compliance functions were also converted to relaxation functions by using numerical interconversion methods and similar power law relationships were reported between time-dependent relaxation modulus function and BV/TV

Sheets of vertically aligned BaTiO₃ nanotubes reduce cell proliferation but not viability of NIH-3T3 cells

All biomaterials initiate a tissue response when implanted in living tissues. Ultimately this reaction causes fibrous encapsulation and hence isolation of the material, leading to failure of the intended therapeutic effect of the implant. There has been extensive bioengineering research aimed at overcoming or delaying the onset of encapsulation. Nanotechnology has the potential to address this problem by virtue of the ability of some nanomaterials to modulate interactions with cells, thereby inducing specific biological responses to implanted foreign materials. To this effect in the present study, we have characterised the growth of fibroblasts on nano-structured sheets constituted by BaTiO3, a material extensively used in biomedical applications. We found that sheets of vertically aligned BaTiO3 nanotubes inhibit cell cycle progression - without impairing cell viability - of NIH-3T3 fibroblast cells. We postulate that the 3D organization of the material surface acts by increasing the availability of adhesion sites, promoting cell attachment and inhibition of cell proliferation. This finding could be of relevance for biomedical applications designed to prevent or minimize fibrous encasement by uncontrolled proliferation of fibroblastic cells with loss of material-tissue interface underpinning long-term function of implants