Understanding spatial and temporal patterning of astrocyte calcium transients via interactions between network transport and extracellular diffusion.

Astrocytes form interconnected networks in the brain and communicate via calcium signaling. We investigate how modes of coupling between astrocytes influence the spatio-temporal patterns of calcium signaling within astrocyte networks and specifically how these network interactions promote coordination within this group of cells. To investigate these complex phenomena, we study reduced cultured networks of astrocytes and neurons. We image the spatial temporal patterns of astrocyte calcium activity and quantify how perturbing the coupling between astrocytes influences astrocyte activity patterns. To gain insight into the pattern formation observed in these cultured networks, we compare the experimentally observed calcium activity patterns to the patterns produced by a reduced computational model, where we represent astrocytes as simple units that integrate input through two mechanisms: gap junction coupling (network transport) and chemical release (extracellular diffusion). We examine the activity patterns in the simulated astrocyte network and their dependence upon these two coupling mechanisms. We find that gap junctions and extracellular chemical release interact in astrocyte networks to modulate the spatiotemporal patterns of their calcium dynamics. We show agreement between the computational and experimental findings, which suggests that the complex global patterns can be understood as a result of simple local coupling mechanisms.

Testing the PROMIS® Depression measures for monitoring depression in a clinical sample outside the US.

The Patient Reported Outcomes Measurement Information System (PROMIS) was devised to facilitate assessment of patient self-reported health status, taking advantage of Item Response Theory. We aimed to assess measurement properties of the PROMIS Depression item bank and an 8-item static short form in a Spanish clinical sample. A three-month follow-up study of patients with active mood/anxiety symptoms (n = 218) was carried out. We assessed model unidimensionality (Confirmatory Item Factor Analysis), reliability (internal consistency and Item Information Curves), and validity (convergent-discriminant with correlations; known-groups with comparison of means and effect sizes; and criterion validity with Receiver operating Characteristics (ROC) analysis). We also assessed 3-month responsiveness to change (Cohen's effect sizes (d) in stable and recovered patients). The unidimensional model showed adequate fit (CFI = 0.97, RMSEA = 0.08). Information Curves had reliabilities over 0.90 throughout most of the score continuum. As expected, we observed high correlations with external self-reported depression, and moderate with self-reported anxiety and clinical measures. The item bank showed an increasing severity gradient from no disorder (mean = 48, SE = 0.6) to depression with comorbid anxiety (mean = 55.8, SE = 0.4). PROMIS detected depression disorder with great accuracy according to the area under the curve (AUC = 0.89). Both formats, item bank and short form, were highly responsive to change in recovered patients (d > 0.7) and had small changes in stable patients (d < 0.2). The good metric properties of the Spanish PROMIS Depression measures provide further evidence of their adequacy for monitoring depression levels of patients in clinical settings. This double check of quality (within countries and populations) supports the ability of PROMIS measures for guaranteeing fair comparisons across languages and countries in specific clinical populations.

Functional clustering in hippocampal cultures: relating network structure and dynamics.

In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures.