Brain Microstructure: Impact of the Permeability on Diffusion MRI

Diffusion Magnetic Resonance Imaging (dMRI) enables a non invasive in-vivo characterization of the brain tissue. The disentanglement of each microstructural property reflected on the total dMRI signal is one of the hottest topics in the field. The dMRI reconstruction techniques ground on assumptions on the signal model and consider the neurons axons as impermeable cylinders. Nevertheless, interactions with the environment is characteristic of the biological life and diffusional water exchange takes place through cell membranes. Myelin wraps axons with multiple layers constitute a barrier modulating exchange between the axon and the extracellular tissue. Due to the short transverse relaxation time (T2) of water trapped between sheets, myelin contribution to the diffusion signal is often neglected. This thesis aims to explore how the exchange influences the dMRI signal and how this can be informative on myelin structure. We also aimed to explore how recent dMRI signal reconstruction techniques could be applied in clinics proposing a strategy for investigating the potential as biomarkers of the derived tissue descriptors. The first goal of the thesis was addressed performing Monte Carlo simulations of a system with three compartments: intra-axonal, spiraling myelin and extra-axonal. The experiments showed that the exchange time between intra- and extra-axonal compartments was on the sub-second level (and thus possibly observable) for geometries with small axon diameter and low number of wraps such as in the infant brain and in demyelinating diseases. The second goal of the thesis was reached by assessing the indices derived from three dimensional simple harmonics oscillator-based reconstruction and estimation (3D-SHORE) in stroke disease. The tract-based analysis involving motor networks and the region-based analysis in grey matter (GM) were performed. 3D-SHORE indices proved to be sensitive to plasticity in both white matter (WM) and GM, highlighting their viability as biomarkers in ischemic stroke. The overall study could be considered the starting point for a future investigation of the interdependence of different phenomena like exchange and relaxation related to the established dMRI indices. This is valuable for the accurate dMRI data interpretation in heterogeneous tissues and different physiological conditions

On the Viability of Diffusion MRI-Based Microstructural Biomarkers in Ischemic Stroke

Recent tract-based analyses provided evidence for the exploitability of 3D-SHORE microstructural descriptors derived from diffusion MRI (dMRI) in revealing white matter (WM) plasticity. In this work, we focused on the main open issues left: (1) the comparative analysis with respect to classical tensor-derived indices, i.e., Fractional Anisotropy (FA) and Mean Diffusivity (MD); and (2) the ability to detect plasticity processes in gray matter (GM). Although signal modeling in GM is still largely unexplored, we investigated their sensibility to stroke-induced microstructural modifications occurring in the contralateral hemisphere. A more complete picture could provide hints for investigating the interplay of GM and WM modulations. Ten stroke patients and ten age/gender-matched healthy controls were enrolled in the study and underwent diffusion spectrum imaging (DSI). Acquisitions at three and two time points (tp) were performed on patients and controls, respectively. For all subjects and acquisitions, FA and MD were computed along with 3D-SHORE-based indices [Generalized Fractional Anisotropy (GFA), Propagator Anisotropy (PA), Return To the Axis Probability (RTAP), Return To the Plane Probability (RTPP), and Mean Square Displacement (MSD)]. Tract-based analysis involving the cortical, subcortical and transcallosal motor networks and region-based analysis in GM were successively performed, focusing on the contralateral hemisphere to the stroke. Reproducibility of all the indices on both WM and GM was quantitatively proved on controls. For tract-based, longitudinal group analyses revealed the highest significant differences across the subcortical and transcallosal networks for all the indices. The optimal regression model for predicting the clinical motor outcome at tp3 included GFA, PA, RTPP, and MSD in the subcortical network in combination with the main clinical information at baseline. Region-based analysis in the contralateral GM highlighted the ability of anisotropy indices in discriminating between groups mainly at tp1, while diffusivity indices appeared to be altered at tp2. 3D-SHORE indices proved to be suitable in probing plasticity in both WM and GM, further confirming their viability as a novel family of biomarkers in ischemic stroke in WM and revealing their potential exploitability in GM. Their combination with tensor-derived indices can provide more detailed insights of the different tissue modulations related to stroke pathology

Microstructural MRI Correlates of Cognitive Impairment in Multiple Sclerosis: The Role of Deep Gray Matter

Although cognitive impairment (CI) is frequently observed in people with multiple sclerosis (pwMS), its pathogenesis is still controversial. Conflicting results emerged concerning the role of microstructural gray matter (GM) damage especially when involving the deep GM structures. In this study, we aimed at evaluating whether differences in cortical and deep GM structures between apparently cognitively normal (ACN) and CI pwMS (36 subjects in total) are present, using an extensive set of diffusion MRI (dMRI) indices and conventional morphometry measures. The results revealed increased anisotropy and restriction over several deep GM structures in CI compared with ACN pwMS, while no changes in volume were present in the same areas. Conversely, reduced anisotropy/restriction values were detected in cortical regions, mostly the pericalcarine cortex and precuneus, combined with reduced thickness of the superior frontal gyrus and insula. Most of the dMRI metrics but none of the morphometric indices correlated with the Symbol Digit Modality Test. These results suggest that deep GM microstructural damage can be a strong anatomical substrate of CI in pwMS and might allow identifying pwMS at higher risk of developing CI

Monte Carlo Simulations of Water Exchange Through Myelin Wraps: Implications for Diffusion MRI

Diffusion magnetic resonance imaging (dMRI) yields parameters sensitive to brain tissue microstructure. A structurally important aspect of this microstructure is the myelin wrapping around the axons. This paper investigated the forward problem concerning whether water exchange via the spiraling structure of the myelin can meaningfully contribute to the signal in dMRI. Monte Carlo simulations were performed in a system with intra-axonal, myelin, and extra-axonal compartments. Diffusion in the myelin was simulated as a spiral wrapping the axon, with a custom number of wraps. Exchange (or intra-axonal residence) times were analyzed for various number of wraps and axon diameters. Pulsed gradient sequences were employed to simulate the dMRI signal, which was analyzed using different methods. Diffusional kurtosis imaging analysis yielded the radial diffusivity (RD) and radial kurtosis (RK), while the two-compartment Karger model yielded estimates the intra-axonal volume fraction (v(ic)) and exchange time (tau). Results showed that tau was on the sub-second level for geometrieswith axon diameters below 1.0 mu m and less than eight wraps. Otherwise, exchangewas negligible compared to typical experimental durations, with tau of seconds or longer. In situations where exchange influenced the signal, estimates of RK and v(ic) increasedwith the number of wraps, while RD decreased. tau estimates from simulated signals were in agreement with predicted ones. In conclusion, exchange through spiraling myelin permits sub-second tau for small diameters and low number of wraps. Such conditions may arise in the developing brain or in neurodegenerative disease, and thus the results could aid the interpretation of dMRI studies

Assessing tissue heterogeneity by non-Gaussian measures in a permeable environment

In diffusion MRI, the deviation of the Ensemble Average Propagator (EAP) from Gaussianity conveys information about the microstructural heterogeneity within an imaging voxel. Different measures have been proposed for assessing this heterogeneity. This paper assesses the performance of the Diffusional Kurtosis Imaging (DKI) and Simple Harmonics Oscillator Reconstruction and Estimation (SHORE) approaches using Monte Carlo simulations of water diffusion within synthetic axons with a permeable myelin sheath. The aim was also to understand the impact of myelin features such as its number of wrappings and relaxation (T2) rate on MR-observable parameters. To this end, a substrate consisting of parallel cylinders coated by a multi-layer sheet was considered, and simulations were used to generate the synthetic diffusion-weighted signal. Results show that myelin features affects the parameters quantified by both DKI and SHORE. A strong agreement was found between DKI and SHORE parameters, highlighting the consistency of the methods in characterising the diffusion-weighted signal

Assessing Tissue Heterogeneity by non-Gaussian Measures in a Permeable Environment

In diffusion MRI, the deviation of the Ensemble Average Propagator (EAP) from Gaussianity conveys information about the microstructural heterogeneity within an imaging voxel. Different measures have been proposed for assessing this heterogeneity. This paper assess the performance of the Diffusional Kurtosis Imaging (DKI) and Simple Harmonics Oscillator Reconstruction and Estimation (SHORE) approaches using Monte Carlo simulations of water diffusion within synthetic axons with a permeable myelin sheath. The aim was also to understand the impact of myelin features such as its number of wrappings and relaxation (T2) rate on MR-observable parameters. To this end, a substrate consisting of parallel cylinders coated by a multi-layer sheet was considered, and simulations were used to generate the synthetic diffusion-weighted signal. Results show that myelin features affects the parameters quantified by both DKI and SHORE. A strong agreement was found between DKI and SHORE parameters, highlighting the consistency of the methods in characterising the diffusion-weighted signal

Microstructural description of cerebral tissues from Diffusion Spectrum Imaging data

Micro-structural indexes based on a novel reconstructionmethod for diffusion MRI data have recently beenproposed. Nevertheless, the validation of such descriptors is stillon the way and no data are currently available in the literatureon human subjects. In this work we derive such descriptors on aa group of human Diffusion Spectrum Imaging (DSI) data. Firstresults show that such measures allow to discriminate betweenwhite and gray matter by statistical analysis

Motor-imagery EEG signal decoding using multichannel-empirical wavelet transform for brain computer interfaces

Motor-imagery (MI) electroencephalography (EEG) signal decomposition is an emerging technique for improving the performance of brain computer interfaces (BCIs), We proposed a multichannel-empirical wavelet transform (EWT) representation combined with a scattering convolution network (SCN) to efficiently decode the brain activity and extract relevant wave patterns for MI-based BCI. Two different preprocessing steps were tested: the first (PM1) included a bandpass Butterworth filter (1–40 Hz) and the independent component analysis (ICA), the second one (PM2) consisted only of a bandpass Butterworth filter (8–30 Hz). A binary support vector machine (SVM) classifier was used and the performance was evaluated in terms of classification accuracy. The proposed framework was assessed using the BCI competition IV dataset IIa, which contains EEG from 9 healthy subjects. PMI presented a maximum mean accuracy over all subjects of 82.05% in the classification of the tongue and the left-hand MI tasks. PM2 achieved an average accuracy over all subjects of 88.40% and a standard deviation of 3.01 outperforming other state of the art methods in classifying right-hand and left-hand MI tasks. Finally, we observed that the best channels, intended as the channels holding the highest discrimination power between two MI tasks, were highly subject-specific and thus enabling task-based channel selection is crucial

Diffusion MRI characterization of stroke lesions using 3D-SHORE microstructural indices

Recently, several reconstruction methods of diffusion MRI signal have been introduced in order to overcome Diffusion Tensor Imaging (DTI) limitations. One of these models is the 3D Simple Harmonic Oscillator based Reconstruction and Estimation1 (3D-SHORE) which enables the reconstruction of fiber crossings via the calculation of the Orientation Distribution Function (ODF). From 3D-SHORE it is also possible to derive new diffusion indices such as the Return To the Origin Probability (RTOP), Return To the Axis Probability (RTAP), and Return To the Plane Probability (RTPP)1, in addition to well-established Fractional Anisotropy (FA), and Mean Diffusivity (MD)