Microstructural Alterations in Hippocampal Subfields Mediate Age-Related Memory Decline in Humans.

Aging, even in the absence of clear pathology of dementia, is associated with cognitive decline. Neuroimaging, especially diffusion-weighted imaging, has been highly valuable in understanding some of these changes in live humans, non-invasively. Traditional tensor techniques have revealed that the integrity of the fornix and other white matter tracts significantly deteriorates with age, and that this deterioration is highly correlated with worsening cognitive performance. However, traditional tensor techniques are still not specific enough to indict explicit microstructural features that may be responsible for age-related cognitive decline and cannot be used to effectively study gray matter properties. Here, we sought to determine whether recent advances in diffusion-weighted imaging, including Neurite Orientation Dispersion and Density Imaging (NODDI) and Constrained Spherical Deconvolution, would provide more sensitive measures of age-related changes in the microstructure of the medial temporal lobe. We evaluated these measures in a group of young (ages 20-38 years old) and older (ages 59-84 years old) adults and assessed their relationships with performance on tests of cognition. We found that the fiber density (FD) of the fornix and the neurite density index (NDI) of the fornix, hippocampal subfields (DG/CA3, CA1, and subiculum), and parahippocampal cortex, varied as a function of age in a cross-sectional cohort. Moreover, in the fornix, DG/CA3, and CA1, these changes correlated with memory performance on the Rey Auditory Verbal Learning Test (RAVLT), even after regressing out the effect of age, suggesting that they were capturing neurobiological properties directly related to performance in this task. These measures provide more details regarding age-related neurobiological properties. For example, a change in fiber density could mean a reduction in axonal packing density or myelination, and the increase in NDI observed might be explained by changes in dendritic complexity or even sprouting. These results provide a far more comprehensive view than previously determined on the possible system-wide processes that may be occurring because of healthy aging and demonstrate that advanced diffusion-weighted imaging is evolving into a powerful tool to study more than just white matter properties

Comparing single-shell and multi-shell free-water fraction estimation algorithms

Tese de mestrado, Engenharia Biomédica e Biofísica , 2022, Universidade de Lisboa, Faculdade de CiênciasDiffusion Weighted (DW) MRI is a medical imaging modality which can be used to model the dis placement of water molecules as they diffuse through the brain, allowing the microstructural architecture of brain tissues to be explored in vivo. This technique has been widely applied to the study of many brain pathologies. However, the presence of extracellular free water affects the diffusion measurements, potentially leading to wrong interpretation about the underlying microstructural changes. Free-water elimination (FWE) is an alternative to more traditional approaches to model DWI data, which divides the signal into an extracellular compartment (which depending on tissue type can be isotropic) representing free water and another compartment representing tissue (usually anisotropic). A recent method by Neto Henriques, et al, to estimate free water fraction using multiple diffusion weighting shells has been shown to reduce the bias in parameter estimates. However, as clinical protocols often use a single diffusion-weighting (single-shell data) to reduce exam times, it becomes relevant to investigate if introducing prior knowledge in the estimation, as proposed in the work of Pasternak, et al, could enable reliable free water elimination when applied to single-shell data. The goal of this project is to compare the performance of these two free water elimination algorithms when applied to the same data. A large dataset of multi-shell DWI data was acquired as part of a longitudinal study led by the Norwegian University of Science and Technology (NTNU) in Trondheim. This dataset includes 78 healthy controls. The data was pre-processed and the multi-shell algorithm applied to eliminate free water contamination. In this project, the same data was processed after removal of the high diffusion-weighting shell, by applying an open-source implementation of the single-shell algorithm presented on the work of Golub, et al. The methods used are fully detailed in this work, including the participants, image acquisition and image preprocessing phases. Tract-based spatial statistics (TBSS) were used for registration and align ment of images for all studied parameters and voxelwise statistics was performed in order to learn which voxels were significantly different between images processed with the two a l gorithms. White matter, cerebral cortex and subcortical masks were used to understand how the algorithms behave at a regional level. The results include a comparison of the original data used, where differences can be observed and discussed. The statistical results - corrected p-value images for each parameter - are presented and dis cussed: considering the multishell algorithm as gold standard, for both fractional anysotropy (FA) and free water (FW) the singleshell algorithm seems to underestimate the white matter values and overes timate the gray matter values. For mean diffusivity (MD), it is the opposite: the single-shell algorithm seems to overestimate the white matter values and underestimate the gray matter values. These results are supported by further analysis, where each subject images for all parameters (FA, MD and FW) and for both algorithms (single and multi-shell) was used to get an average value of the voxels (excluding null values from this average), in order to understand how these values differ according to the algorithm used. These results, presented as boxplots for the regions being studied, also indicate that the values for both algorithms are significantly different in almost all regions and parameters. This study allows to understand the FWE-DTI application in single-shell data. The comparison with the multi-shell algorithm for FA and FW showed an underestimaion of WM values and overestimation of GM values. For MD, the values are conditioned by the prior and overestimated for all tissue type. Besides, the values obtained with the single-shell algorithm are considered significantly different from the ones obtained with multi-shell agorithm for both WM and GM in most parameters and regions

Diffusion Tensor Imaging as a Diagnostic and Research Tool: A Study on Preterm Infants

Diffusion tensor imaging (DTI) is an advanced magnetic resonance imaging (MRI) technique. DTI is based on free thermal motion (diffusion) of water molecules. The properties of diffusion can be represented using parameters such as fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, which are calculated from DTI data. These parameters can be used to study the microstructure in fibrous structure such as brain white matter. The aim of this study was to investigate the reproducibility of region-of-interest (ROI) analysis and determine associations between white matter integrity and antenatal and early postnatal growth at term age using DTI. Antenatal growth was studied using both the ROI and tract-based spatial statistics (TBSS) method and postnatal growth using only the TBSS method. The infants included to this study were born below 32 gestational weeks or birth weight less than 1,501 g and imaged with a 1.5 T MRI system at term age. Total number of 132 infants met the inclusion criteria between June 2004 and December 2006. Due to exclusion criteria, a total of 76 preterm infants (ROI) and 36 preterm infants (TBSS) were accepted to this study. The ROI analysis was quite reproducible at term age. Reproducibility varied between white matter structures and diffusion parameters. Normal antenatal growth was positively associated with white matter maturation at term age. The ROI analysis showed associations only in the corpus callosum. Whereas, TBSS revealed associations in several brain white matter areas. Infants with normal antenatal growth showed more mature white matter compared to small for gestational age infants. The gestational age at birth had no significant association with white matter maturation at term age. It was observed that good early postnatal growth associated negatively with white matter maturation at term age. Growth-restricted infants seemed to have delayed brain maturation that was not fully compensated at term, despite catchup growth.Diffuusiotensorikuvaus diagnostisena ja tutkimustyökaluna keskostutkimuksessa Diffuusiotensorikuvaus (DTI) on magneettikuvauksen erikoistekniikka. DTI perustuu veden vapaaseen lämpöliikkeeseen (diffuusioon). Diffuusion ominaisuuksia voidaan esittää DTI-datasta laskettavien parametrien avulla. Tällaisia parametreja ovat esimerkiksi fraktionaalinen anisotropia, keskimääräinen diffusiviteetti, aksiaalinen ja radiaalinen diffusiviteetti. Näitä parametrejä voidaan käyttää säikeisten rakenteiden esimerkiksi aivojen valkoisen aineen tutkimiseen. Tässä tutkimuksessa selvitettiin keskosten aivojen diffuusiotensorikuvista tehtyjen mielenkiintoalueisiin (ROI) perustuvien mittausten toistettavuutta sekä tutkittiin valkoisen aineen kypsyyden ja raskauden aikaisen sekä varhaisen postnataalisen kasvun välistä yhteyttä. Raskauden aikaisen kasvun vaikutusta tutkittiin käyttäen sekä ROI- että TBSS-tekniikoita. Postnataalista kasvua tarkasteltiin ainoastaan TBSS-tekniikalla. Tähän tutkimukseen otettiin mukaan keskoset, jotka syntyivät ennen 32 raskausviikkoa tai joiden syntymäpaino oli alle 1,501 g sekä MRI kuvaus oli tehty lasketunajan kohdalla. Tutkimukseen hyväksyttiin kesäkuun 2004 ja joulukuun 2006 välillä 132 keskosta. Poissulkukriteerien takia 76 keskosta (ROI) ja 36 (TBSS) hyväksyttiin tähän tutkimukseen. ROI-analyysi osoittautui melko toistettavaksi lasketun ajan iässä. Toistettavuus vaihteli sekä valkoisen aineen rakenteiden että diffuusioparametrien välillä. Normaali raskauden aikainen kasvu liittyi hyvään valkoisen aineen kehitykseen lasketunajan kohdalla. ROI-tekniikalla yhteys havaittiin corpus callosumin alueella. TBSS-menetelmä puolestaan näytti yhteyden usealla eri valkoisen aineen alueella. Syntymähetken gestaatioiällä ei havaittu yhteyttä valkoisen aineen kehitysasteeseen lasketun ajan kohdalla. Hyvän varhaisen vaiheen postnataalisen kasvun havaittiin liittyvän heikompaan valkoisen aineen kehitysasteeseen lasketunajan kohdalla. Saavutuskasvu ei ollut korjannut raskauden aikaisen kasvuhäiriön vaikutusta aivojen kypsyyteen laskettuun aikaan mennessä.Siirretty Doriast

Neurite orientation dispersion and density imaging of the healthy cervical spinal cord in vivo

Here we present the application of neurite orientation dispersion and density imaging (NODDI) to the healthy spinal cord in vivo. NODDI provides maps such as the intra-neurite tissue volume fraction (vin), the orientation dispersion index (ODI) and the isotropic volume fraction (viso), and here we investigate their potential for spinal cord imaging. We scanned five healthy volunteers, four of whom twice, on a 3 T MRI system with a ZOOM-EPI sequence. In accordance to the published NODDI protocol, multiple b-shells were acquired at cervical level and both NODDI and diffusion tensor imaging (DTI) metrics were obtained and analysed to: i) characterise differences in grey and white matter (GM/WM); ii) assess the scan–rescan reproducibility of NODDI; iii) investigate the relationship between NODDI and DTI; and iv) compare the quality of fit of NODDI and DTI. Our results demonstrated that: i) anatomical features can be identified in NODDI maps, such as clear contrast between GM and WM in ODI; ii) the variabilities of vin and ODI are comparable to that of DTI and are driven by biological differences between subjects for ODI, have similar contribution from measurement errors and biological variation for vin, whereas viso shows higher variability, driven by measurement errors; iii) NODDI identifies potential sources contributing to DTI indices, as in the brain; and iv) NODDI outperforms DTI in terms of quality of fit. In conclusion, this work shows that NODDI is a useful model for in vivo diffusion MRI of the spinal cord, providing metrics closely related to tissue microstructure, in line with findings in the brain

A robust deconvolution method to disentangle multiple water pools in diffusion MRI

The diffusion-weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo-diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal-to-noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data-driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro-compartments, which were consistent with hindered diffusion, free water and pseudo-diffusion. Taking free water and pseudo-diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co-existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels

Neurite Orientation Dispersion and Density Imaging in Psychiatric Disorders: A Systematic Literature Review and a Technical Note

While major psychiatric disorders lack signature diagnostic neuropathologies akin to dementias, classic postmortem studies have established microstructural involvement, i.e., cellular changes in neurons and glia, as a key pathophysiological finding. Advanced magnetic resonance imaging techniques allow mapping of cellular tissue architecture and microstructural abnormalities in vivo, which holds promise for advancing our understanding of the pathophysiology underlying psychiatric disorders. Here, we performed a systematic review of case-control studies using neurite orientation dispersion and density imaging (NODDI) to assess brain microstructure in psychiatric disorders and a selective review of technical considerations in NODDI. Of the 584 potentially relevant articles, 18 studies met the criteria to be included in this systematic review. We found a general theme of abnormal gray and white matter microstructure across the diagnostic spectrum. We also noted significant variability in patterns of neurite density and fiber orientation within and across diagnostic groups, as well as associations between brain microstructure and phenotypical variables. NODDI has been successfully used to detect subtle microstructure abnormalities in patients with psychiatric disorders. Given that NODDI indices may provide a more direct link to pathophysiological processes, this method may not only contribute to advancing our mechanistic understanding of disease processes, it may also be well positioned for next-generation biomarker development studies

Diffusion magnetic resonance imaging-based surrogate marker in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is the most prevalent type of motor neuron disease (MND) and is diagnosed with a delay from the first appearance of symptoms. Surrogate markers that may be used to detect pathological changes before a significant neuronal loss occurs and allow for early intervention with disease-modifying therapy techniques are desperately needed. Using water molecules that diffuse within the tissue and experience displacement on the micron scale, diffusion magnetic resonance imaging (MRI) is a promising technique that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, axonal density, order, and myelination. Diffusion tensor imaging (DTI) is the primary diffusion MRI technique used to evaluate the pathogenesis of ALS. Neurite orientation dispersion and density imaging (NODDI), diffusion kurtosis imaging (DKI), and free water elimination DTI (FWE-DTI) are only a few of the approaches that have been developed to overcome the shortcomings of the diffusion tensor technique. This article provides a summary of these methods and their potential as surrogate markers for detecting the onset of ALS at an early stage