Imaging laminar structures in the gray matter with diffusion MRI

Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain

Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation-diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo-times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation-diffusion distributions where contributions from different sub-voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre-specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation-specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre-tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways.

Post-learning micro- and macro-structural neuroplasticity changes with time and sleep

Neuroplasticity refers to the fact that our brain can partially modify both structure and function to adequately respond to novel environmental stimulations. Neuroplasticity mechanisms are not only operating during the acquisition of novel information (i.e., online) but also during the offline periods that take place after the end of the actual learning episode. Structural brain changes as a consequence of learning have been consistently demonstrated on the long term using non-invasive neuroimaging methods, but short-term changes remained more elusive. Fortunately, the swift development of advanced MR methods over the last decade now allows tracking fine-grained cerebral changes on short timescales beyond gross volumetric modifications stretching over several days or weeks. Besides a mere effect of time, post-learning sleep mechanisms have been shown to play an important role in memory consolidation and promote long-lasting changes in neural networks. Sleep was shown to contribute to structural modifications over weeks of prolonged training, but studies evidencing more rapid post-training sleep structural effects linked to memory consolidation are still scarce in human. On the other hand, animal studies convincingly show how sleep might modulate synaptic microstructure. We aim here at reviewing the literature establishing a link between different types of training/learning and the resulting structural changes, with an emphasis on the role of post-training sleep and time in tuning these modifications. Open questions are raised such as the role of post-learning sleep in macrostructural changes, the links between different MR structural measurement-related modifications and the underlying microstructural brain processes, and bidirectional influences between structural and functional brain changes.

Substantially thinner internal granular layer and reduced molecular layer surface in the cerebellar cortex of the Tc1 mouse model of down syndrome - a comprehensive morphometric analysis with active staining contrast-enhanced MRI

Down Syndrome is a chromosomal disorder that affects the development of cerebellar cortical lobules. Impaired neurogenesis in the cerebellum varies among different types of neuronal cells and neuronal layers. In this study, we developed an imaging analysis framework that utilizes gadolinium-enhanced ex vivo mouse brain MRI. We extracted the middle Purkinje layer of the mouse cerebellar cortex, enabling the estimation of the volume, thickness, and surface area of the entire cerebellar cortex, the internal granular layer, and the molecular layer in the Tc1 mouse model of Down Syndrome. The morphometric analysis of our method revealed that a larger proportion of the cerebellar thinning in this model of Down Syndrome resided in the inner granule cell layer, while a larger proportion of the surface area shrinkage was in the molecular layer.

Microstructural Integrity of the Hippocampus During Childhood: Relations With Age and Source Memory

The hippocampus is a brain structure known to be important for memory. However, studies examining relations between hippocampal volume and memory across development yield mixed results. This may be due in part to the fact that volume is a coarser measure of hippocampal composition. Studies have begun to examine measures of diffusion, which capture characteristics of the microstructure of the hippocampus, and thus may provide additional information about the integrity of the underlying neural circuits. The present study applied this approach to a developmental period characterized by dramatic changes in both hippocampal microstructure and memory behavior - early childhood. Specifically, measures of hippocampal microstructural integrity were related to age and source memory performance in 93 children aged 4-8 years. Results revealed significant negative associations between hippocampal mean diffusivity and both age and memory, even after controlling for differences in hippocampal volume. These results suggest that hippocampal diffusion may provide additional, independent information about hippocampal integrity compared to volume, particularly during early childhood when important developmental changes have been proposed.

Cytoarchitecture of the mouse brain by high resolution diffusion magnetic resonance imaging

MRI has been widely used to probe the neuroanatomy of the mouse brain, directly correlating MRI findings to histology is still challenging due to the limited spatial resolution and various image contrasts derived from water relaxation or diffusion properties. Magnetic resonance histology has the potential to become an indispensable research tool to mitigate such challenges. In the present study, we acquired high spatial resolution MRI datasets, including diffusion MRI (dMRI) at 25 ​μm isotropic resolution and quantitative susceptibility mapping (QSM) at 21.5 ​μm isotropic resolution to validate with conventional mouse brain histology. Diffusion weighted images (DWIs) show better delineation of cortical layers and glomeruli in the olfactory bulb than fractional anisotropy (FA) maps. However, among all the image contrasts, including quantitative susceptibility mapping (QSM), T1/T2∗ images and DTI metrics, FA maps highlight unique laminar architecture in sub-regions of the hippocampus, including the strata of the dentate gyrus and CA fields of the hippocampus. The mean diffusivity (MD) and axial diffusivity (AD) yield higher correlation with DAPI (0.62 and 0.71) and NeuN (0.78 and 0.74) than with NF-160 (-0.34 and -0.49). The correlations between FA and DAPI, NeuN, and NF-160 are 0.31, -0.01, and -0.49, respectively. Our findings demonstrate that MRI at microscopic resolution deliver a three-dimensional, non-invasive and non-destructive platform for characterization of fine structural detail in both gray matter and white matter of the mouse brain.

SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI

This work introduces a compartment-based model for apparent cell body (namely soma) and neurite density imaging (SANDI) using non-invasive diffusion-weighted MRI (DW-MRI). The existing conjecture in brain microstructure imaging through DW-MRI presents water diffusion in white (WM) and gray (GM) matter as restricted diffusion in neurites, modelled by infinite cylinders of null radius embedded in the hindered extra-neurite water. The extra-neurite pool in WM corresponds to water in the extra-axonal space, but in GM it combines water in the extra-cellular space with water in soma. While several studies showed that this microstructure model successfully describe DW-MRI data in WM and GM at b ​≤ ​3,000 ​s/mm² (or 3 ​ms/μm²), it has been also shown to fail in GM at high b values (b≫3,000 ​s/mm² or 3 ​ms/μm²). Here we hypothesise that the unmodelled soma compartment (i.e. cell body of any brain cell type: from neuroglia to neurons) may be responsible for this failure and propose SANDI as a new model of brain microstructure where soma of any brain cell type is explicitly included. We assess the effects of size and density of soma on the direction-averaged DW-MRI signal at high b values and the regime of validity of the model using numerical simulations and comparison with experimental data from mouse (b_max ​= ​40,000 ​s/mm², or 40 ​ms/μm²) and human (b_max ​= ​10,000 ​s/mm², or 10 ​ms/μm²) brain. We show that SANDI defines new contrasts representing complementary information on the brain cyto- and myelo-architecture. Indeed, we show maps from 25 healthy human subjects of MR soma and neurite signal fractions, that remarkably mirror contrasts of histological images of brain cyto- and myelo-architecture. Although still under validation, SANDI might provide new insight into tissue architecture by introducing a new set of biomarkers of potential great value for biomedical applications and pure neuroscience.

In Vivo Imaging of Gray Matter Microstructure in Major Psychiatric Disorders: Opportunities for Clinical Translation

Postmortem studies reveal that individuals with major neuropsychiatric disorders such as schizophrenia and autism spectrum disorder have gray matter microstructural abnormalities. These include abnormalities in neuropil organization, expression of proteins supporting neuritic and synaptic integrity, and myelination. Genetic and postmortem studies suggest that these changes may be causally linked to the pathogenesis of these disorders. Advances in diffusion-weighted magnetic resonance image (dMRI) acquisition techniques and biophysical modeling allow for the quantification of gray matter microstructure in vivo. While several biophysical models for imaging microstructural properties are available, one in particular, neurite orientation dispersion and density imaging (NODDI), holds great promise for clinical applications. NODDI can be applied to both gray and white matter and requires only a single extra shell beyond a standard dMRI acquisition. Since its development only a few years ago, the NODDI algorithm has been used to characterize gray matter microstructure in schizophrenia, Alzheimer's disease, healthy aging, and development. These investigations have shown that microstructural findings in vivo, using NODDI, align with postmortem findings. Not only do NODDI and other advanced dMRI-based modeling methods provide a window into the brain previously only available postmortem, but they may be more sensitive to certain brain changes than conventional magnetic resonance imaging approaches. This opens up exciting new possibilities for clinicians to more rapidly detect disease signatures and allows earlier intervention in the course of the disease. Given that neurites and gray matter microstructure have the capacity to rapidly remodel, these novel dMRI-based methods represent an opportunity to noninvasively monitor neuroplastic changes posttherapy within much shorter time scales.

Transferring principles of solid-state and Laplace NMR to the field of in vivo brain MRI

Magnetic resonance imaging (MRI) is the primary method for noninvasive investigations of the human brain in health, disease, and development but yields data that are difficult to interpret whenever the millimeter-scale voxels contain multiple microscopic tissue environments with different chemical and structural properties. We propose a novel MRI framework to quantify the microscopic heterogeneity of the living human brain as spatially resolved five-dimensional relaxation-diffusion distributions by augmenting a conventional diffusion-weighted imaging sequence with signal encoding principles from multidimensional solid-state nuclear magnetic resonance (NMR) spectroscopy, relaxation-diffusion correlation methods from Laplace NMR of porous media, and Monte Carlo data inversion. The high dimensionality of the distribution space allows resolution of multiple microscopic environments within each heterogeneous voxel as well as their individual characterization with novel statistical measures that combine the chemical sensitivity of the relaxation rates with the link between microstructure and the anisotropic diffusivity of tissue water. The proposed framework is demonstrated on a healthy volunteer using both exhaustive and clinically viable acquisition protocols.

Higher body mass index is linked to altered hypothalamic microstructure

Animal studies suggest that obesity-related diets induce structural changes in the hypothalamus, a key brain area involved in energy homeostasis. Whether this translates to humans is however largely unknown. Using a novel multimodal approach with manual segmentation, we here show that a higher body mass index (BMI) selectively predicted higher proton diffusivity within the hypothalamus, indicative of compromised microstructure in the underlying tissue, in a well-characterized population-based cohort (n1 = 338, 48% females, age 21-78 years, BMI 18-43 kg/m²). Results were independent from confounders and confirmed in another independent sample (n2 = 236). In addition, while hypothalamic volume was not associated with obesity, we identified a sexual dimorphism and larger hypothalamic volumes in the left compared to the right hemisphere. Using two large samples of the general population, we showed that a higher BMI specifically relates to altered microstructure in the hypothalamus, independent from confounders such as age, sex and obesity-associated co-morbidities. This points to persisting microstructural changes in a key regulatory area of energy homeostasis occurring with excessive weight. Our findings may help to better understand the pathomechanisms of obesity and other eating-related disorders.

Brain structure changes induced by attention bias modification training

Attention bias modification (ABM) therapy aims to reduce anxiety by changing threat-related attention patterns using computerized training tasks. We examined changes in brain microstructure following ABM training. Thirty-two participants were randomly assigned to one of two training conditions: active ABM training shifting attention away from threat or attention control training involving no attention modification. Participants completed six lab visits, including five training sessions and three diffusion tensor imaging scans: immediately before and after the first training session, and at the end of the training series. Indices of local and global changes in microstructure and connectivity were measured. Significant longitudinal differences in fractional anisotropy (FA) between the active and control training regimens occurred in inferior temporal cortex. Changes in FA occurred across groups within ventromedial prefrontal cortex and middle occipital gyrus. These results indicate specific effects of active ABM on brain structure. Such changes could relate to clinical effects of ABM.

Fractional Anisotropy of Thalamic Nuclei Is Associated With Verticality Misperception After Extra-Thalamic Stroke

Verticality misperception after stroke is a frequent neurological deficit that leads to postural imbalance and a higher risk of falls. The posterior thalamic nuclei are described to be involved with verticality perception, but it is unknown if extra-thalamic lesions can have the same effect via diaschisis and degeneration of thalamic nuclei. We investigated the relationship between thalamic fractional anisotropy (FA, a proxy of structural integrity), and verticality perception, in patients after stroke with diverse encephalic extra-thalamic lesions. We included 11 first time post-stroke patients with extra-thalamic primary lesions, and compared their region-based FA to a group of 25 age-matched healthy controls. For the patient sample, correlation and regression analyses evaluated the relationship between thalamic nuclei FA and error of postural vertical (PV) and haptic vertical (HV) in the roll (PVroll/HVroll) and pitch planes (PVpitch/HVpitch). Relative to controls, patients showed decreased FA of anterior, ventral anterior, ventral posterior lateral, dorsal, and pulvinar thalamic nuclei, despite the primary lesions being extra-thalamic. We found a significant correlation between HVroll, and FA in the anterior and dorsal nuclei, and PVroll with FA in the anterior nucleus. FA in the anterior, ventral anterior, ventral posterior lateral, dorsal and pulvinar nuclei predicted PV, and FA in the ventral anterior, ventral posterior lateral and dorsal nuclei predicted HV. While prior studies indicate that primary lesions of the thalamus can result in verticality misperception, here we present evidence supporting that secondary degeneration of thalamic nuclei via diaschisis can also be associated with verticality misperception after stroke.

Neurite orientation dispersion and density imaging of mouse brain microstructure

Advanced biophysical models like neurite orientation dispersion and density imaging (NODDI) have been developed to estimate the microstructural complexity of voxels enriched in dendrites and axons for both in vivo and ex vivo studies. NODDI metrics derived from high spatial and angular resolution diffusion MRI using the fixed mouse brain as a reference template have not yet been reported due in part to the extremely long scan time required. In this study, we modified the three-dimensional diffusion-weighted spin-echo pulse sequence for multi-shell and undersampling acquisition to reduce the scan time. This allowed us to acquire several exhaustive datasets that would otherwise not be attainable. NODDI metrics were derived from a complex 8-shell diffusion (1000-8000 s/mm²) dataset with 384 diffusion gradient-encoding directions at 50 µm isotropic resolution. These provided a foundation for exploration of tradeoffs among acquisition parameters. A three-shell acquisition strategy covering low, medium, and high b values with at least angular resolution of 64 is essential for ex vivo NODDI experiments. The good agreement between neurite density index (NDI) and the orientation dispersion index (ODI) with the subsequent histochemical analysis of myelin and neuronal density highlights that NODDI could provide new insight into the microstructure of the brain. Furthermore, we found that NDI is sensitive to microstructural variations in the corpus callosum using a well-established demyelination cuprizone model. The study lays the ground work for developing protocols for routine use of high-resolution NODDI method in characterizing brain microstructure in mouse models.

Structural neural predictors of Farsi-English bilingualism

The neurobiology of bilingualism is hotly debated. The present study examines whether normalized cortical measurements can be used to reliably classify monolinguals versus bilinguals in a structural MRI dataset of Farsi-English bilinguals and English monolinguals. A decision tree classifier classified bilinguals with an average correct classification rate of 85%, and monolinguals with a rate of 71.4%. The most relevant regions for classification were the right supramarginal gyrus, left inferior temporal gyrus and left inferior frontal gyrus. Larger studies with carefully matched monolingual and bilingual samples are needed to confirm that features of these regions can reliably categorize monolingual and bilingual brains. Nonetheless, the present findings suggest that a single structural MRI scan, analyzed with measures readily available using default procedures in a free open-access software (Freesurfer), can be used to reliably predict an individual's language experience using a decision tree classifier, and that Farsi-English bilingualism implicates regions identified in previous group-level studies of bilingualism in other languages.