Deciphering the microstructure of hippocampal subfields with in vivo DTI and NODDI: Applications to experimental multiple sclerosis
Graphical abstract
Introduction
The hippocampus is involved in declarative memory and hippocampal damage has been correlated to memory decline in several neurological disorders (Kandel et al., 2014). The hippocampus is not a unitary and homogeneous entity but a complex and highly connected archeocortical structure. Indeed, it is composed of distinct hippocampal subfields and layers which are very different in their morphological, molecular, electrophysiological and functional profiles (Kandel et al., 2014). Disorders affecting the hippocampus, such as Alzheimer, schizophrenia, post-traumatic stress disorder, hypoxia and multiple sclerosis among others, can start by differentially targeting one specific hippocampal subfield, depending on the underlying pathological process: a principle known as differential vulnerability (Small, 2014). Mapping such vulnerability and capturing subtle histological abnormalities in a particular subfield at the early stage of the disease is highly desirable in order to develop and monitor future neuroprotective strategies.
Diffusion tensor imaging (DTI), the most commonly used model of water diffusion in tissue (Le Bihan, 2013), could be able to pinpoint the hippocampal subfield most affected early in the course of the disease. DTI is based on one Gaussian model of water diffusion for both intra and extra-cellular compartments in order to reflect brain microstructure through numeric indices called fractional anisotropy (FA), axial diffusivity (AD, water diffusion along tracts), radial diffusivity (RD, water diffusion perpendicular to tracts) and mean diffusivity (MD) (Le Bihan, 2013). In rodents, DTI can individualize the layered hippocampal anatomy (Laitinen et al., 2010, Shepherd et al., 2006, Zhang et al., 2002). Furthermore, it has been recently demonstrated that DTI is able to capture early dendritic alterations selectively present within the molecular layer of the dentate gyrus and responsible for memory deficit prior to measurable hippocampal atrophy at the early stage of experimental multiple sclerosis (Planche et al., 2017).
Although DTI could be a promising early in vivo marker of hippocampal differential vulnerability in multiple sclerosis (and probably other conditions), DTI is the simplest model of water molecule diffusion that does not take into account restricted and hindered diffusion. The link between tissue architecture and DTI parameters is therefore indirect and DTI parameters lack specificity as they can be altered similarly by several diseases. Furthermore, DTI measurements are hardly analyzable in areas of partial-volume, crossing, kissing or fanning axons and dendrites as they estimate FA and diffusivity indexes poorly.
Consequently, an important field of research in diffusion magnetic resonance imaging (MRI) consists in developing alternative model-based strategies that describe the cerebral microstructure more accurately. One of these strategies is NODDI (for Neurite Orientation Dispersion and Density Imaging), which has proved promising because the acquisition protocol for this type of model is typically applicable in clinical settings (Zhang et al., 2012). The model is built with three diffusion-compartments: (i) the cerebrospinal fluid (CSF) compartment with Gaussian isotropic diffusion; (ii) the intra neurite compartment, which is modeled as a set of cylinders of zero radius to capture highly restricted diffusion perpendicular to the neurites and unhindered diffusion along them, and which takes into account numerous possibilities of orientation dispersion; and (iii) the extra neurite compartment, which refers to the space occupied by somas, glial cells and the space around neurites and which is modeled with Gaussian anisotropic diffusion.
NODDI provides volume fraction of isotropic diffusivity (isoVF), neurite density index (NDI) and orientation dispersion index (ODI) maps which could reflect the morphology of axons and dendrites and their branching complexity. NDI and ODI might disentangle factors contributing to DTI parameters (Zhang et al., 2012) and become more specific surrogate biomarkers of neurological diseases. NODDI has already been applied to patients with multiple sclerosis presenting altered ODI and NDI in normal appearing white matter (Schneider et al., 2017), and to the whole hippocampus of an Alzheimer's mouse model (Colgan et al., 2016). However, NODDI has never been used to precisely analyze the hippocampus micro-anatomy at the level of the subfields and layers in which diseases can start. Furthermore, NODDI has been built to be clinically feasible and thus relies on assumptions (fixed diffusivities for each compartment and Watson distribution of the orientations of fiber segments) that might not hold true in pathological conditions.
Thus, we wanted to test whether or not NODDI could be a step forward compared to DTI and provide additional and more specific information on early hippocampal microstructural changes responsible for cognitive impairment. To test this hypothesis, we developed a high-resolution diffusion MRI acquisition procedure capable of revealing hippocampal architecture, first ex vivo, and then in live mice in an acceptable timeframe, in order to evaluate the capacity of DTI and NODDI to identify the hippocampal subfield microstructure. Then, we assessed the capacity of these two techniques to pinpoint early regional hippocampal vulnerability in mice with experimental multiple sclerosis (experimental autoimmune encephalomyelitis, EAE) by performing MRI-histological correlations with a large panel of immunofluorescence staining for neurites, glial cells and myelin.
Section snippets
Animals and Experimental Autoimmune Encephalomyelitis (EAE)
We used EAE which is the most widely accepted animal model of multiple sclerosis (t Hart et al., 2011). Briefly, 7-to-9 week-old female C57BL6/J mice (Janvier Labs) were injected subcutaneously at the base of the tail with 200 μg of Myelin Oligodendrocyte Glycoprotein peptide 35-55 (MOG35-55, Anaspec) emulsified in 200 μL of Complete Freund's Adjuvant (CFA, Difco) containing 6 mg/mL of desiccated Mycobacterium Tuberculosis (H37Ra, Difco). Animals received intraperitoneal injections of Pertussis
Immunostaining
To allow MRI-to-histology correlations, animals were sacrificed immediately following MRI. Mice were deeply anesthetized with pentobarbital and perfused transcardially with PBS containing 2% PFA and 0.2% picric acid, for 20 min (7–8 mL/min). The brain was then removed and transferred into a Tris-buffered saline (TBS) solution containing 30% sucrose for cryoprotection and 0.05% sodium azide for conservation and left at 4 °C until use. A 0.8 mm block containing the dorsal hippocampus (i.e; from
Statistical analyses
Data are presented as mean ± SEM. The Gaussian distribution of the data was tested with the Shapiro-Wilk normality test. First, we looked at the hippocampal internal anatomy and we compared MRI data (DTI and NODDI parameters) between the 3 main hippocampal layers in control mice (CFA) using ANOVA (and Tukey's multiple comparisons test for post-hoc multiple comparisons) or the nonparametric Friedman test (and Dunn's multiple comparison test) as appropriate. We also tested whether each layer
Results
Our optimized protocol was successfully implemented on the 7 T and 4.7 T MR-systems. For in vivo acquisitions, 39 diffusion-weighted images distributed among 5 mice (1.59% of the total number of diffusion-weighted images collected from the 36 mice) were scored as artifacted data during the quality control procedure, due to movement, blurring and ghosting effects. Consequently, these 5 mice were excluded from the analysis conducted on the 31 remaining mice (15 control CFA and 16 EAE mice) with
Discussion
In this study, we challenged the capacity of the NODDI model to pinpoint the differential vulnerability of a specific hippocampal layer at the early stage of a neurodegenerative disease such as multiple sclerosis. We showed that high-resolution ODI mapping could highlight each of the three main hippocampal layers and that the quantitative signature of an individual layer can be identified by combining DTI and NODDI indices. However, at the early stage of experimental multiple sclerosis, NODDI
Funding
This work was supported by public grants from the French Agence Nationale de la Recherche within the context of the Investments for the Future program referenced ANR-10-LABX-57 named TRAIL (project GM-COG), ANR-10-LABX-43 named BRAIN (Project MEMO-MS) and ARSEP foundation “Fondation pour l'aide à la recherche sur la sclérose en plaques”. The 4.7 T animal scanner was supported by FLI (ANR-11-INBS-0006). AC received a grant in the framework of the Master 2 Bioscience of the Ecole Normale
Acknowledgements
The microscopy experiments were performed in the Bordeaux Imaging Center, a unit of the CNRS-INSERM and Bordeaux University, and member of the national France BioImaging infrastructure. We gratefully thank Nicolas Renaud (INCIA, CNRS UMR5287) for his important technical support with MRI post processing.
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Dr Hiba and Prof. Tourdias jointly directed this work and share co-senior authorship.