Mice display signs of fear when neurons that express cFos during fear conditioning are artificially reactivated. This finding gave rise to the notion that cFos marks neurons that encode specific memories. Here we show that cFos expression patterns in the mouse dentate gyrus (DG) change dramatically from day to day in a water maze spatial learning paradigm, regardless of training level. Optogenetic inhibition of neurons that expressed cFos on the first training day affected performance days later, suggesting that these neurons continue to be important for spatial memory recall. The mechanism preventing repeated cFos expression in DG granule cells involves accumulation of ΔFosB, a long-lived splice variant of FosB. CA1 neurons, in contrast, repeatedly expressed cFos. Thus, cFos-expressing granule cells may encode new features being added to the internal representation during the last training session. This form of timestamping is thought to be required for the formation of episodic memories.

Information within the brain travels from neuron to neuron across billions of synapses. At any given moment, only a small subset of neurons and synapses are active, but finding the active synapses in brain tissue has been a technical challenge. Here we introduce SynTagMA to tag active synapses in a user-defined time window. Upon 395–405 nm illumination, this genetically encoded marker of activity converts from green to red fluorescence if, and only if, it is bound to calcium. Targeted to presynaptic terminals, preSynTagMA allows discrimination between active and silent axons. Targeted to excitatory postsynapses, postSynTagMA creates a snapshot of synapses active just before photoconversion. To analyze large datasets, we show how to identify and track the fluorescence of thousands of individual synapses in an automated fashion. Together, these tools provide an efficient method for repeatedly mapping active neurons and synapses in cell culture, slice preparations, and in vivo during behavior.

Abstract. Magnetic resonance imaging is a technique for the diagnosis and classification of brain tumors. Discrete compactness is a morphological feature of two-dimensional and three-dimensional objects. This measure determines the compactness of a discretized object depending on the sum of the areas of the connected voxels and has been used for understanding the morphology of nonbrain tumors. We hypothesized that regarding brain tumors, we may improve the malignancy grade classification. We analyzed the values in 20 patients with different subtypes of primary brain tumors: astrocytoma, oligodendroglioma, and glioblastoma multiforme subdivided into the contrast-enhanced and the necrotic tumor regions. The preliminary results show an inverse relationship between the compactness value and the malignancy grade of gliomas. Astrocytomas exhibit a mean of 973 AE 14, whereas oligodendrogliomas exhibit a mean of 942 AE 21. In contrast, the contrast-enhanced region of the glioblastoma presented a mean of 919 AE 43, and the necrotic region presented a mean of 869 AE 66. However, the volume and area of the enclosing surface did not show a relationship with the malignancy grade of the gliomas. Discrete compactness appears to be a stable characteristic between primary brain tumors of different malignancy grades, because similar values were obtained from different patients with the same type of tumor.

Information within the brain travels from neuron to neuron across billions of synapses. At any given moment, only a small subset of neurons and synapses are active, but finding the active synapses in brain tissue has been a technical challenge. To tag active synapses in a user-defined time window, we developed SynTagMA. Upon 405 nm illumination, this genetically encoded marker of activity converts from green to red fluorescence if, and only if, it is bound to calcium. Targeted to presynaptic terminals, preSynTagMA allows discrimination between active and silent axons. Targeted to excitatory postsynapses, postSynTagMA creates a snapshot of synapses active just before photoconversion. To analyze large datasets, we developed software to identify and track the fluorescence of thousands of individual synapses in tissue. Together, these tools provide a high throughput method for repeatedly mapping active neurons and synapses in cell culture, slice preparations, and in vivo during behavior.