The generation of myelin-forming oligodendrocytes persists throughout life and is regulated by neural activity. Here we tested whether experience-driven changes in oligodendrogenesis are important for memory consolidation. We found that water maze learning promotes oligodendrogenesis and de novo myelination in the cortex and associated white matter tracts. Preventing these learning-induced increases in oligodendrogenesis without affecting existing oligodendrocytes impaired memory consolidation of water maze, as well as contextual fear, memories. These results suggest that de novo myelination tunes activated circuits, promoting coordinated activity that is important for memory consolidation. Consistent with this, contextual fear learning increased the coupling of hippocampal sharp wave ripples and cortical spindles, and these learning-induced increases in ripple-spindle coupling were blocked when oligodendrogenesis was suppressed. Our results identify a non-neuronal form of plasticity that remodels hippocampal-cortical networks following learning and is required for memory consolidation.

Classically, model-based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi-atlas-based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel-by-voxel label-voting procedure. In this article, we demonstrate how the multi-atlas approach can be extended to work with input atlases that are unique and extremely time consuming to construct by generating a library of multiple automatically generated templates of different brains (MAGeT Brain). We demonstrate the efficacy of our method for the mouse and human using two different nonlinear registration algorithms (ANIMAL and ANTs). The input atlases consist a high-resolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data. MAGeT Brain segmentation improves the identification of the mouse anterior commissure (mean Dice Kappa values (κ = 0.801), but may be encountering a ceiling effect for hippocampal segmentations. Applying MAGeT Brain to human subcortical structures improves segmentation accuracy for all structures compared to regular model-based techniques (κ = 0.845, 0.752, and 0.861 for the striatum, globus pallidus, and thalamus, respectively). Experiments performed with three manually derived input templates suggest that MAGeT Brain can approach or exceed the accuracy of multi-atlas label-fusion segmentation (κ = 0.894, 0.815, and 0.895 for the striatum, globus pallidus, and thalamus, respectively).

No abstract

Behavior depends on coordinated activity across multiple brain regions. Within such networks, highly connected hub regions are assumed to disproportionately influence behavioral output, although this hypothesis has not been systematically evaluated. Previously, by mapping brain-wide expression of the activity-regulated gene c-fos, we identified a network of brain regions co-activated by fear memory. To test the hypothesis that hub regions are more important for network function, here, we simulated node deletion in silico in this behaviorally defined functional network. Removal of high degree nodes produced the greatest network disruption (e.g., reduction in global efficiency). To test these predictions in vivo, we examined the impact of post-training chemogenetic silencing of different network nodes on fear memory consolidation. In a series of independent experiments encompassing 25% of network nodes (i.e., 21/84 brain regions), we found that node degree accurately predicted observed deficits in memory consolidation, with silencing of highly connected hubs producing the largest impairments.