Studies have shown that the brain is constituted by anatomically segregated and functionally specific regions working in synergy as a complex network. In this context, the brain at rest does not passively retrieve environmental information and respond but instead it maintains an active representation modulated by sensory information. Using independent component analysis (ICA) over resting state recordings a discrete set of resting state networks (RSNs) has been found, which proven to be systematically present across individuals and to be modified by the state of consciousness and also in disease. ICA's main drawback is that its output consists of a series of 3D z-score maps where noise and physiological components are randomly mixed. In this work we present a computational method composed by an ICA-based noise filtering preprocessing pipeline and a template-based identification algorithm that combines spatial comparison metrics through a voting system developed to find RSNs in a subject-by-subject basis. To validate it, we use a publicly available dataset consisting of 75 resting state fMRI sessions from 25 participants scanned three different times each one. For most common RSNs the correct candidate won the voting 93% of the times and it was voted at least once in 99%. Then we probe within-subject consistency in detected RSNs by showing augmented correlation in networks from the same subject. Finally, by comparing obtained mean RSNs with the ones from nearly 30,000 participants we show that our method constitutes a personalized-medicine oriented approach to shorten the gap between RSN research and clinical applications.
The investigation of unsteady aerodynamics is becoming a more attractive topic of research in enhancing flight capabilities. Natural flyers such as birds and insects can undergo flight maneuvers that are very difficult or impossible to accomplish with man-made flyers and current classical aerodynamic theory. Modeling the unsteady phenomena produced by flapping wings is important to the understanding of these maneuvers, with possible applications to aircraft flight. We investigate numerically simulating the unsteady aerodynamics generated by flapping wings using the two seperate approaches of rotational lift and dynamic stall. A low order quasi-steady model based on rotational lift and a revised version based on dynamic stall are presented. Both concepts are analyzed using simulated results, with experimental data produced with matching kinematics as a basis of comparison. The numerically generated force curves are used to explore the characteristics and distinguishing features of both approaches, as well as how well they capture the salient features of the experimentally produced forces.