Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex.

Neuronal representations change as associations are learned between sensory stimuli and behavioral actions. However, it is poorly understood whether representations for learned associations stabilize in cortical association areas or continue to change following learning. We tracked the activity of posterior parietal cortex neurons for a month as mice stably performed a virtual-navigation task. The relationship between cells' activity and task features was mostly stable on single days but underwent major reorganization over weeks. The neurons informative about task features (trial type and maze locations) changed across days. Despite changes in individual cells, the population activity had statistically similar properties each day and stable information for over a week. As mice learned additional associations, new activity patterns emerged in the neurons used for existing representations without greatly affecting the rate of change of these representations. We propose that dynamic neuronal activity patterns could balance plasticity for learning and stability for memory.

Chronic imaging of cortical sensory map dynamics using a genetically encoded calcium indicator.

In vivo optical imaging can reveal the dynamics of large-scale cortical activity, but methods for chronic recording are limited. Here we present a technique for long-term investigation of cortical map dynamics using wide-field ratiometric fluorescence imaging of the genetically encoded calcium indicator (GECI) Yellow Cameleon 3.60. We find that wide-field GECI signals report sensory-evoked activity in anaesthetized mouse somatosensory cortex with high sensitivity and spatiotemporal precision, and furthermore, can be measured repeatedly in separate imaging sessions over multiple weeks. This method opens new possibilities for the longitudinal study of stability and plasticity of cortical sensory representations.

The Spatial Structure of Neural Encoding in Mouse Posterior Cortex during Navigation.

Navigation engages many cortical areas, including visual, parietal, and retrosplenial cortices. These regions have been mapped anatomically and with sensory stimuli and studied individually during behavior. Here, we investigated how behaviorally driven neural activity is distributed and combined across these regions. We performed dense sampling of single-neuron activity across the mouse posterior cortex and developed unbiased methods to relate neural activity to behavior and anatomical space. Most parts of the posterior cortex encoded most behavior-related features. However, the relative strength with which features were encoded varied across space. Therefore, the posterior cortex could be divided into discriminable areas based solely on behaviorally relevant neural activity, revealing functional structure in association regions. Multimodal representations combining sensory and movement signals were strongest in posterior parietal cortex, where gradients of single-feature representations spatially overlapped. We propose that encoding of behavioral features is not constrained by retinotopic borders and instead varies smoothly over space within association regions.

HNCcorr: A Novel Combinatorial Approach for Cell Identification in Calcium-Imaging Movies.

Calcium imaging is a key method in neuroscience for investigating patterns of neuronal activity in vivo. Still, existing algorithms to detect and extract activity signals from calcium-imaging movies have major shortcomings. We introduce the HNCcorr algorithm for cell identification in calcium-imaging datasets that addresses these shortcomings. HNCcorr relies on the combinatorial clustering problem HNC (Hochbaum's Normalized Cut), which is similar to the Normalized Cut problem of Shi and Malik, a well known problem in image segmentation. HNC identifies cells as coherent clusters of pixels that are highly distinct from the remaining pixels. HNCcorr guarantees a globally optimal solution to the underlying optimization problem as well as minimal dependence on initialization techniques. HNCcorr also uses a new method, called "similarity squared", for measuring similarity between pixels in calcium-imaging movies. The effectiveness of HNCcorr is demonstrated by its top performance on the Neurofinder cell identification benchmark. We believe HNCcorr is an important addition to the toolbox for analysis of calcium-imaging movies.

The nuclear transport receptor Importin-11 is a tumor suppressor that maintains PTEN protein.

Phosphatase and tensin homologue (PTEN) protein levels are critical for tumor suppression. However, the search for a recurrent cancer-associated gene alteration that causes PTEN degradation has remained futile. In this study, we show that Importin-11 (Ipo11) is a transport receptor for PTEN that is required to physically separate PTEN from elements of the PTEN degradation machinery. Mechanistically, we find that the E2 ubiquitin-conjugating enzyme and IPO11 cargo, UBE2E1, is a limiting factor for PTEN degradation. Using in vitro and in vivo gene-targeting methods, we show that Ipo11 loss results in degradation of Pten, lung adenocarcinoma, and neoplasia in mouse prostate with aberrantly high levels of Ube2e1 in the cytoplasm. These findings explain the correlation between loss of IPO11 and PTEN protein in human lung tumors. Furthermore, we find that IPO11 status predicts disease recurrence and progression to metastasis in patients choosing radical prostatectomy. Thus, our data introduce the IPO11 gene as a tumor-suppressor locus, which is of special importance in cancers that still retain at least one intact PTEN allele.