Data-Driven Motion Detection and Characterization in PET Brain Scans Using List Mode

Head motion during a Positron Emission Tomography (PET) brain scan can considerably degrade image quality. External motion-tracking devices have proven successful in minimizing this effect, but the associated time, maintenance, and workflow changes inhibit their widespread clinical use. List-mode PET acquisition allows for the retroactive analysis of coincidence events on any time scale throughout a scan, and therefore potentially offers a data-driven motion detection and characterization technique. An algorithm was developed to parse list-mode data, divide the full acquisition into short scan intervals, and calculate the line-of-response (LOR) midpoint average for each interval. These LOR midpoint averages, known as “radioactivity centroids,” were presumed to represent the center of the radioactivity distribution in the scanner, and it was thought that changes in this metric over time would correspond to intra-scan motion.

Several scans were taken of the 3D Hoffman brain phantom on a GE Discovery IQ PET/CT scanner to test the ability of the radioactivity to indicate intra-scan motion. Each scan incrementally surveyed motion in a different degree of freedom (2 translational and 2 rotational). The radioactivity centroids calculated from these scans correlated linearly to phantom positions/orientations. Centroid measurements over 1-second intervals performed on scans with ~1mCi of activity in the center of the field of view had standard deviations of 0.026 cm in the x- and y-dimensions and 0.020 cm in the z-dimension, which demonstrates high precision and repeatability in this metric. Radioactivity centroids are thus shown to successfully represent discrete motions on the submillimeter scale. It is also shown that while the radioactivity centroid can precisely indicate the amount of motion during an acquisition, it fails to distinguish what type of motion occurred.

Mechanism of Shear Thickening in Reversibly Cross-linked Supramolecular Polymer Networks.

We report here the nonlinear rheological properties of metallo-supramolecular networks formed by the reversible cross-linking of semi-dilute unentangled solutions of poly(4-vinylpyridine) (PVP) in dimethyl sulfoxide (DMSO). The reversible cross-linkers are bis-Pd(II) or bis-Pt(II) complexes that coordinate to the pyridine functional groups on the PVP. Under steady shear, shear thickening is observed above a critical shear rate, and that critical shear rate is experimentally correlated with the lifetime of the metal-ligand bond. The onset and magnitude of the shear thickening depend on the amount of cross-linkers added. In contrast to the behavior observed in most transient networks, the time scale of network relaxation is found to increase during shear thickening. The primary mechanism of shear thickening is ascribed to the shear-induced transformation of intrachain cross-linking to interchain cross-linking, rather than nonlinear high tension along polymer chains that are stretched beyond the Gaussian range.

Deformation of stem cell nuclei by nanotopographical cues.

Cells sense cues in their surrounding microenvironment. These cues are converted into intracellular signals and transduced to the nucleus in order for the cell to respond and adapt its function. Within the nucleus, structural changes occur that ultimately lead to changes in the gene expression. In this study, we explore the structural changes of the nucleus of human mesenchymal stem cells as an effect of topographical cues. We use a controlled nanotopography to drive shape changes to the cell nucleus, and measure the changes with both fluorescence microscopy and a novel light scattering technique. The nucleus changes shape dramatically in response to the nanotopography, and in a manner dependent on the mechanical properties of the substrate. The kinetics of the nuclear deformation follows an unexpected trajectory. As opposed to a gradual shape change in response to the topography, once the cytoskeleton attains an aligned and elongation morphology on the time scale of several hours, the nucleus changes shape rapidly and intensely.

The evolution of airplanes

The prevailing view is that we cannot witness biological evolution because it occurred on a time scale immensely greater than our lifetime. Here, we show that we can witness evolution in our lifetime by watching the evolution of the flying human-and-machine species: the airplane. We document this evolution, and we also predict it based on a physics principle: the constructal law. We show that the airplanes must obey theoretical allometric rules that unite them with the birds and other animals. For example, the larger airplanes are faster, more efficient as vehicles, and have greater range. The engine mass is proportional to the body size: this scaling is analogous to animal design, where the mass of the motive organs (muscle, heart, lung) is proportional to the body size. Large or small, airplanes exhibit a proportionality between wing span and fuselage length, and between fuel load and body size. The animal-design counterparts of these features are evident. The view that emerges is that the evolution phenomenon is broader than biological evolution. The evolution of technology, river basins, and animal design is one phenomenon, and it belongs in physics. © 2014 AIP Publishing LLC.

Activity in descending dopaminergic neurons represents but is not required for leg movements in the fruit fly Drosophila.

Modulatory descending neurons (DNs) that link the brain to body motor circuits, including dopaminergic DNs (DA-DNs), are thought to contribute to the flexible control of behavior. Dopamine elicits locomotor-like outputs and influences neuronal excitability in isolated body motor circuits over tens of seconds to minutes, but it remains unknown how and over what time scale DA-DN activity relates to movement in behaving animals. To address this question, we identified DA-DNs in the Drosophila brain and developed an electrophysiological preparation to record and manipulate the activity of these cells during behavior. We find that DA-DN spike rates are rapidly modulated during a subset of leg movements and scale with the total speed of ongoing leg movements, whether occurring spontaneously or in response to stimuli. However, activating DA-DNs does not elicit leg movements in intact flies, nor do acute bidirectional manipulations of DA-DN activity affect the probability or speed of leg movements over a time scale of seconds to minutes. Our findings indicate that in the context of intact descending control, changes in DA-DN activity are not sufficient to influence ongoing leg movements and open the door to studies investigating how these cells interact with other descending and local neuromodulatory inputs to influence body motor output.

Stress Relaxation for Granular Materials near Jamming under Cyclic Compression.

We have explored isotropically jammed states of semi-2D granular materials through cyclic compression. In each compression cycle, systems of either identical ellipses or bidisperse disks transition between jammed and unjammed states. We determine the evolution of the average pressure P and structure through consecutive jammed states. We observe a transition point ϕ_{m} above which P persists over many cycles; below ϕ_{m}, P relaxes slowly. The relaxation time scale associated with P increases with packing fraction, while the relaxation time scale for collective particle motion remains constant. The collective motion of the ellipses is hindered compared to disks because of the rotational constraints on elliptical particles.

Calcium/Calmodulin-Dependent Protein Kinase II Serves as a Biochemical Integrator of Calcium Signals for the Induction of Synaptic Plasticity

Repetitive Ca2+ transients in dendritic spines induce various forms of synaptic plasticity by transmitting information encoded in their frequency and amplitude. CaMKII plays a critical role in decoding these Ca2+ signals to initiate long-lasting synaptic plasticity. However, the properties of CaMKII that mediate Ca2+ decoding in spines remain elusive. Here, I measured CaMKII activity in spines using fast-framing two-photon fluorescence lifetime imaging. Following each repetitive Ca2+ elevations, CaMKII activity increased in a stepwise manner. This signal integration, at the time scale of seconds, critically depended on Thr286 phosphorylation. In the absence of Thr286 phosphorylation, only by increasing the frequency of repetitive Ca2+ elevations could high peak CaMKII activity or plasticity be induced. In addition, I measured the association between CaMKII and Ca2+/CaM during spine plasticity induction. Unlike CaMKII activity, association of Ca2+/CaM to CaMKII plateaued at the first Ca2+ elevation event. This result indicated that integration of Ca2+ signals was initiated by the binding of Ca2+/CaM and amplified by the subsequent increases in Thr286-phosphorylated form of CaMKII. Together, these findings demonstrate that CaMKII functions as a leaky integrator of repetitive Ca2+ signals during the induction of synaptic plasticity, and that Thr286 phosphorylation is critical for defining the frequencies of such integration.

Transient scaling and resurgence of chimera states in networks of Boolean phase oscillators.

We study networks of nonlocally coupled electronic oscillators that can be described approximately by a Kuramoto-like model. The experimental networks show long complex transients from random initial conditions on the route to network synchronization. The transients display complex behaviors, including resurgence of chimera states, which are network dynamics where order and disorder coexists. The spatial domain of the chimera state moves around the network and alternates with desynchronized dynamics. The fast time scale of our oscillators (on the order of 100ns) allows us to study the scaling of the transient time of large networks of more than a hundred nodes, which has not yet been confirmed previously in an experiment and could potentially be important in many natural networks. We find that the average transient time increases exponentially with the network size and can be modeled as a Poisson process in experiment and simulation. This exponential scaling is a result of a synchronization rate that follows a power law of the phase-space volume.