Tagged: Independent component analysis

Spatiotemporal properties of intracellular calcium signaling in osteocytic and osteoblastic cell networks under fluid flow

Mechanical stimuli can trigger intracellular calcium (Ca2 +) responses in osteocytes and osteoblasts. Successful construction of bone cell networks necessitates more elaborate and systematic analysis for the spatiotemporal properties of Ca2 + signaling in the networks. In the present study, an unsupervised algorithm based on independent component analysis (ICA) was employed to extract the Ca2 + signals of bone cells in the network. We demonstrated that the ICA-based technology could yield higher signal fidelity than the manual region of interest (ROI) method. Second, the spatiotemporal properties of Ca2 + signaling in osteocyte-like MLO-Y4 and osteoblast-like MC3T3-E1 cell networks under laminar and steady fluid flow stimulation were systematically analyzed and compared. MLO-Y4 cells exhibited much more active Ca2 + transients than MC3T3-E1 cells, evidenced by more Ca2 + peaks, less time to the 1st peak and less time between the 1st and 2nd peaks. With respect to temporal properties, MLO-Y4 cells demonstrated higher spike rate and Ca2 + oscillating frequency. The spatial intercellular synchronous activities of Ca2 + signaling in MLO-Y4 cell networks were higher than those in MC3T3-E1 cell networks and also negatively correlated with the intercellular distance, revealing faster Ca2 + wave propagation in MLO-Y4 cell networks. Our findings show that the unsupervised ICA-based technique results in more sensitive and quantitative signal extraction than traditional ROI analysis, with the potential to be widely employed in Ca2 + signaling extraction in the cell networks. The present study also revealed a dramatic spatiotemporal difference in Ca2 + signaling for osteocytic and osteoblastic cell networks in processing the mechanical stimulus. The higher intracellular Ca2 + oscillatory behaviors and intercellular coordination of MLO-Y4 cells provided further evidences that osteocytes may behave as the major mechanical sensor in bone modeling and remodeling processes.

Comparison of supervised and unsupervised linear methods for recovering task-relevant activity in EEG

In this paper we compare three linear methods, independent component analysis (ICA), common spatial patterns (CSP), and linear discrimination (LD) for recovering task relevant neural activity from high spatial density electroencephalography (EEG). Each linear method uses a different objective function to recover underlying source components by exploiting statistical structure across a large number of sensors. We test these methods using a dual-task event-related paradigm. While engaged in a primary task, subjects must detect infrequent changes in the visual display, which would be expected to evoke several well-known event-related potentials (ERPs), including the N2 and P3. We find that though each method utilizes a different objective function, they in fact yield similar components. We note that one advantage of the LD approach is that the recovered component is easily interpretable, namely it represents the component within a given time window which is most discriminating for the task, given a spatial integration of the sensors. Both ICA and CSP return multiple components, of which the most discriminating component may not be the first. Thus, for these methods, visual inspection or additional processing is required to determine the significance of these components for the task.