SUMMARY
A major challenge in understanding the cellular diversity of the brain has been linking activity during behavior with standard cellular typology. For example, it has not been possible to determine whether principal neurons in prefrontal cortex active during distinct experiences represent separable cell types, and it is not known whether these differentially active cells exert distinct causal influences on behavior. Here, we develop quantitative hydrogel-based technologies to connect activity in cells reporting on behavioral experience with measures for both brain-wide wiring and molecular phenotype. We find that positive and negative-valence experiences in prefrontal cortex are represented by cell populations that differ in their causal impact on behavior, long-range wiring, and gene expression profiles, with the major discriminant being expression of the adaptation-linked gene NPAS4. These findings illuminate cellular logic of prefrontal cortex information processing and natural adaptive behavior and may point the way to cell-type-specific understanding and treatment of disease-associated states.
3D histology, slice-based connectivity atlases, and diffusion MRI are common techniques to map brain wiring. While there are many modality-specific tools to process these data, there is a lack of integration across modalities. We develop an automated resource that combines histologically cleared volumes with connectivity atlases and MRI, enabling the analysis of histological features across multiple fiber tracts and networks, and their correlation with in-vivo biomarkers. We apply our pipeline in a murine stroke model, demonstrating not only strong correspondence between MRI abnormalities and CLARITY-tissue staining, but also uncovering acute cellular effects in areas connected to the ischemic core. We provide improved maps of connectivity by quantifying projection terminals from CLARITY viral injections, and integrate diffusion MRI with CLARITY viral tracing to compare connectivity maps across scales. Finally, we demonstrate tract-level histological changes of stroke through this multimodal integration. This resource can propel investigations of network alterations underlying neurological disorders.
Purpose
The goal of this work is to propose a motion robust reconstruction method for diffusion‐weighted MRI that resolves shot‐to‐shot phase mismatches without using phase estimation.
Methods
Assuming that shot‐to‐shot phase variations are slowly varying, spatial‐shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low‐rank constraint on the spatial‐shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method.
Results
The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase‐estimation‐based methods to achieve even higher image quality.
Conclusion
We introduced the shot–locally low‐rank method, a reconstruction technique for multishot diffusion‐weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.
Despite the widespread use of magnetic resonance imaging (MRI) of the brain, the relative contribution of different biological components (e.g. lipids and proteins) to structural MRI contrasts (e.g., T1, T2, T2*, proton density, diffusion) remains incompletely understood. This limitation can undermine the interpretation of clinical MRI and hinder the development of new contrast mechanisms. Here, we determine the respective contribution of lipids and proteins to MRI contrast by removing lipids and preserving proteins in mouse brains using CLARITY. We monitor the temporal dynamics of tissue clearance via NMR spectroscopy, protein assays and optical emission spectroscopy. MRI of cleared brain tissue showed: 1) minimal contrast on standard MRI sequences; 2) increased relaxation times; and 3) diffusion rates close to free water. We conclude that lipids, present in myelin and membranes, are a dominant source of MRI contrast in brain tissue.
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