Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging

Microglia: The Drunken Gardeners of Early Adversity

Early life adversity (ELA) is a heterogeneous group of negative childhood experiences that can lead to abnormal brain development and more severe psychiatric, neurological, and medical conditions in adulthood. According to the immune hypothesis, ELA leads to an abnormal immune response characterized by high levels of inflammatory cytokines. This abnormal immune response contributes to more severe negative health outcomes and a refractory response to treatment in individuals with a history of ELA. Here, we examine this hypothesis in the context of recent rodent studies that focus on the impact of ELA on microglia, the resident immune cells in the brain. We review recent progress in our ability to mechanistically link molecular alterations in microglial function during a critical period of development with changes in synaptic connectivity, cognition, and stress reactivity later in life. We also examine recent research showing that ELA induces long-term alterations in microglial inflammatory response to "secondary hits" such as traumatic brain injury, substance use, and exposure to additional stress in adulthood. We conclude with a discussion on future directions and unresolved questions regarding the signals that modify microglial function and the clinical significance of rodent studies for humans.

Early adversity causes sex-specific deficits in perforant pathway connectivity and contextual memory in adolescent mice

BACKGROUND

Early life adversity impairs hippocampal development and function across diverse species. While initial evidence indicated potential variations between males and females, further research is required to validate these observations and better understand the underlying mechanisms contributing to these sex differences. Furthermore, most of the preclinical work in rodents was performed in adult males, with only few studies examining sex differences during adolescence when such differences appear more pronounced. To address these concerns, we investigated the impact of limited bedding (LB), a mouse model of early adversity, on hippocampal development in prepubescent and adolescent male and female mice.

METHODS

RNA sequencing, confocal microscopy, and electron microscopy were used to evaluate the impact of LB and sex on hippocampal development in prepubescent postnatal day 17 (P17) mice. Additional studies were conducted on adolescent mice aged P29-36, which included contextual fear conditioning, retrograde tracing, and ex vivo diffusion magnetic resonance imaging (dMRI).

RESULTS

More severe deficits in axonal innervation and myelination were found in the perforant pathway of prepubescent and adolescent LB males compared to LB female littermates. These sex differences were due to a failure of reelin-positive neurons located in the lateral entorhinal cortex (LEC) to innervate the dorsal hippocampus via the perforant pathway in males, but not LB females, and were strongly correlated with deficits in contextual fear conditioning.

CONCLUSIONS

LB impairs the capacity of reelin-positive cells located in the LEC to project and innervate the dorsal hippocampus in LB males but not female LB littermates. Given the critical role that these projections play in supporting normal hippocampal function, a failure to establish proper connectivity between the LEC and the dorsal hippocampus provides a compelling and novel mechanism to explain the more severe deficits in myelination and contextual freezing found in adolescent LB males.

Early adversity changes the economic conditions of mouse structural brain network organization

Early adversity can change educational, cognitive, and mental health outcomes. However, the neural processes through which early adversity exerts these effects remain largely unknown. We used generative network modeling of the mouse connectome to test whether unpredictable postnatal stress shifts the constraints that govern the organization of the structural connectome. A model that trades off the wiring cost of long-distance connections with topological homophily (i.e., links between regions with shared neighbors) generated simulations that successfully replicate the rodent connectome. The imposition of early life adversity shifted the best-performing parameter combinations toward zero, heightening the stochastic nature of the generative process. Put simply, unpredictable postnatal stress changes the economic constraints that reproduce rodent connectome organization, introducing greater randomness into the development of the simulations. While this change may constrain the development of cognitive abilities, it could also reflect an adaptive mechanism that facilitates effective responses to future challenges.

Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI

Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.

Using mesoscopic tract-tracing data to guide the estimation of fiber orientation distributions in the mouse brain from diffusion MRI

Diffusion MRI (dMRI) tractography is the only tool for non-invasive mapping of macroscopic structural connectivity over the entire brain. Although it has been successfully used to reconstruct large white matter tracts in the human and animal brains, the sensitivity and specificity of dMRI tractography remained limited. In particular, the fiber orientation distributions (FODs) estimated from dMRI signals, key to tractography, may deviate from histologically measured fiber orientation in crossing fibers and gray matter regions. In this study, we demonstrated that a deep learning network, trained using mesoscopic tract-tracing data from the Allen Mouse Brain Connectivity Atlas, was able to improve the estimation of FODs from mouse brain dMRI data. Tractography results based on the network generated FODs showed improved specificity while maintaining sensitivity comparable to results based on FOD estimated using a conventional spherical deconvolution method. Our result is a proof-of-concept of how mesoscale tract-tracing data can guide dMRI tractography and enhance our ability to characterize brain connectivity.

Virtual mouse brain histology from multi-contrast MRI via deep learning

¹H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from magnetic resonance imaging (MRI) findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we show that deep convolutional neural networks, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each voxel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimic target histology and enhanced sensitivity and specificity compared to conventional MRI markers. Furthermore, the relative contribution of each MRI contrast within the networks can be used to optimize multi-contrast MRI acquisition. We anticipate our method to be a starting point for translation of MRI results into easy-to-understand virtual histology for neurobiologists and provide resources for validating novel MRI techniques.