On the sampling strategies and models for measuring diffusion exchange with a double diffusion encoding sequence

Water exchange between the different compartments of a heterogeneous specimen can be characterized via diffusion magnetic resonance imaging(dMRI).Many analysis frameworks using dMRI data have been proposed to describe exchange,often using a double diffusion encoding(DDE)stimulated echo sequence.Techniques such as diffusion exchange weighted imaging(DEWI)and the filter exchange and rapid exchange models,use a specific subset of the full space DDE signal.In this work,a general representation of the DDE signal was employed with different sampling schemes(namely constant b1,diagonal and anti-diagonal)from the data reduction models to estimate exchange.A near-uniform sampling scheme was proposed and compared with the other sampling schemes.The filter exchange and rapid exchange models were also applied to estimate exchange with their own subsampling schemes.These subsampling schemes and models were compared on both simulated data and experimental data acquired with a benchtop MR scanner.In synthetic data,the diagonal and near-uniform sampling schemes performed the best due to the consistency of their estimates with the ground truth.In experimental data,the shifted diagonal and near-uniform sampling schemes outperformed the others,yielding the most consistent estimates with the full space estimation.The results suggest the feasibility of measuring exchange using a general representation of the DDE signal along with variable sampling schemes.In future studies,algorithms could be further developed for the optimization of sampling schemes,as well as incorporating additional properties,such as geometry and diffusion anisotropy,into exchange frameworks.

Magnetotactic bacteria from the human gut microbiome associated with orientation and navigation regions of the brain

Magnetotactic bacteria(MTB),ubiquitous in soil and fresh and saltwater sources have been identified in the microbiome of humans and many animals.MTB endogenously produce magnetic nanocrystals enabling them to orient and navigate along geomagnetic fields.Similar magnetite deposits have been found throughout the tissues of the human brain,including brain regions associated with orientation such as the cerebellum and hippocampus,the origins of which remain unknown.Speculation over the role and source of MTB in humans,as well as any association with the brain,remain unanswered.We performed a metagenomic analysis of the gut microbiome of 34 healthy females as well as grey matter volume analysis in magnetite-rich brain regions associated with orientation and navigation with the goal of identifying specific MTB that could be associated with brain structure in orientation and navigation regions.We identified seven MTB in the human gut microbiome:Magnetococcus marinus,Magnetospira sp.QH-2,Magnetospirillum magneticum,Magnetospirillum sp.ME-1,Magnetospirillum sp.XM-1,Magnetospirillum gryphiswaldense,and Desulfovibrio magneticus.Our preliminary results show significant negative associations between multiple MTB with bilateral flocculonodular lobes of the cerebellum and hippocampus(adjusted for total intracranial volume,uncorrected P<0.05).These findings indicate that MTB in the gut are associated with grey matter volume in magnetite-rich brain regions related to orientation and navigation.These preliminary findings support MTB as a potential biogenic source for brain magnetite in humans.Further studies will be necessary to validate and elucidate the relationship between these bacteria,magnetite concentrations,and brain function.

Diffusivity-limited q-space trajectory imaging

Q-space trajectory imaging(QTI)allows non-invasive estimation of microstructural features of heterogeneous porous media via diffusion magnetic resonance imaging performed with generalised gradient waveforms.A recently proposed constrained estimation framework,called QTI+,improved QTI's resilience to noise and data sparsity,thus increasing the reliability of the method by enforcing relevant positivity constraints.In this work we consider expanding the set of constraints to be applied during the fitting of the QTI model.We show that the additional conditions,which introduce an upper bound on the diffusivity values,further improve the retrieved parameters on a publicly available human brain dataset as well as on data acquired from healthy volunteers using a scanner-ready protocol.

Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.