Article – Page 12 – Soft Matter Lab

Optimal Search Strategy in Complex Topography published in PNAS

The topography of the environment alters the optimal search strategy for active particles
Giorgio Volpe & Giovanni Volpe
Proceedings of the National Academy of Science USA 114(43), 11350—11355 (2017)
DOI: 10.1073/pnas.1711371114
arXiv: 1706.07785

In environments with scarce resources, adopting the right search strategy can make the difference between succeeding and failing, even between life and death. At different scales, this applies to molecular encounters in the cell cytoplasm, to animals looking for food or mates in natural landscapes, to rescuers during search-and- rescue operations in disaster zones, and to genetic computer algo- rithms exploring parameter spaces. When looking for sparse targets in a homogeneous environment, a combination of ballistic and diffusive steps is considered optimal; in particular, more ballistic Lévy flights with exponent α ≤ 1 are generally believed to optimize the search process. However, most search spaces present complex to- pographies. What is the best search strategy in these more realistic scenarios? Here we show that the topography of the environment significantly alters the optimal search strategy towards less ballistic and more Brownian strategies. We consider an active particle performing a blind cruise search for non-regenerating sparse targets in a two-dimensional space with steps drawn from a Lévy distribution with exponent varying from α = 1 to α = 2 (Brownian). We demon- strate that, when boundaries, barriers and obstacles are present, the optimal search strategy depends on the topography of the environ- ment with α assuming intermediate values in the whole range under consideration. We interpret these findings using simple scaling arguments and discuss their robustness to varying searcher’s size. Our results are relevant for search problems at different length scales, from animal and human foraging, to microswimmers’ taxis, to bio- chemical rates of reaction.

Abnormal Structural Brain Connectome in Preclinical Alzheimer published in Cerebral Cortex

Abnormal structural brain connectome in individuals with preclinical Alzheimer’s disease
Joana B. Pereira, Danielle van Westen, Erik Stomrud, Tor Olof Strandberg, Giovanni Volpe, Eric Westman & Oskar Hansson
Cerebral Cortex 28(10), 3638—3649 (2018)
DOI: 10.1093/cercor/bhx236

Alzheimer’s disease has a long preclinical phase during which amyloid pathology and neurodegeneration accumulate in the brain without producing overt cognitive deficits. It is currently unclear whether these early disease stages are associated with a progressive disruption in the communication between brain regions that subsequently leads to cognitive decline and dementia. In this study we assessed the organization of structural networks in cognitively normal (CN) individuals harboring amyloid pathology (A+N−), neurodegeneration (A−N+), or both (A+N+) from the prospective and longitudinal Swedish BioFINDER study. We combined graph theory with diffusion tensor imaging to investigate integration, segregation, and centrality measures in the brain connectome in the previous groups. At baseline, our findings revealed a disrupted network topology characterized by longer paths, lower efficiency, increased clustering and modularity in CN A−N+ and CN A+N+, but not in CN A+N−. After 2 years, CN A+N+ showed significant abnormalities in all global network measures, whereas CN A−N+ only showed abnormalities in the global efficiency. Network connectivity and organization were associated with memory in CN A+N+ individuals. Altogether, our findings suggest that amyloid pathology is not sufficient to disrupt structural network topology, whereas neurodegeneration is.

Featured in “Nuke med helps diagnose early Alzheimer’s from amyloid network topology”, HealthImaging, 14 Nov 2017

BRAPH published in Plos ONE

BRAPH: A graph theory software for the analysis of brain connectivity
Mite Mijalkov, Ehsan Kakaei, Joana B. Pereira, Eric Westman & Giovanni Volpe
PLoS ONE 12(8), e0178798 (2017)
DOI: 10.1371/journal.pone.0178798
bioRxiv: 106625

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkin- son’s patients with mild cognitive impairment.

2D-Nature of Active Brownian Motion at Interfaces published in New J. Phys.

Two-dimensional nature of the active Brownian motion of catalytic microswimmers at solid and liquid interfaces
Kilian Dietrich, Damian Renggli, Michele Zanini, Giovanni Volpe, Ivo Buttinoni & Lucio Isa
New Journal of Physics 19, 065008 (2017)
DOI: 10.1088/1367-2630/aa7126

Colloidal particles equipped with platinum patches can establish chemical gradients in H₂O2-enriched solutions and undergo self-propulsion due to local diffusiophoretic migration. In bulk (3D), this class of active particles swim in the direction of the surface heterogeneities introduced by the patches and consequently reorient with the characteristic rotational diffusion time of the colloids. In this article, we present experimental and numerical evidence that planar 2D confinements defy this simple picture. Instead, the motion of active particles both on solid substrates and at flat liquid–liquid interfaces is captured by a 2D active Brownian motion model, in which rotational and translational motion are constrained in the xy-plane. This leads to an active motion that does not follow the direction of the surface heterogeneities and to timescales of reorientation that do not match the free rotational diffusion times. Furthermore, 2D-confinement at fluid–fluid interfaces gives rise to a unique distribution of swimming velocities: the patchy colloids uptake two main orientations leading to two particle populations with velocities that differ up to one order of magnitude. Our results shed new light on the behavior of active colloids in 2D, which is of interest for modeling and applications where confinements are present.

Langevin Equation on a Manifold published in Ann. Henri Poincaré

Small Mass Limit of a Langevin Equation on a Manifold
Jeremiah Birrell, Scott Hottovy, Giovanni Volpe & Jan Wehr
Annales Henri Poincaré 18(2), 707—755 (2017)
DOI: 10.1007/s00023-016-0508-3
arXiv: 1604.04819

We study damped geodesic motion of a particle of mass m on a Riemannian manifold, in the presence of an external force and noise. Lifting the resulting stochastic differential equation to the orthogonal frame bundle, we prove that, as m → 0, its solutions converge to solutions of a limiting equation which includes a noise-induced drift term. A very special case of the main result presents Brownian motion on the manifold as a limit of inertial systems.

Non-Boltzmann Distributions and Non-Equilibrium Relations in Active Baths published in Phys. Rev. E

Non-Boltzmann stationary distributions and non-equilibrium relations in active baths
Aykut Argun, Ali-Reza Moradi, Erçağ Pinçe, Gokhan Baris Bagci, Alberto Imparato & Giovanni Volpe
Physical Review E 94(6), 062150 (2016)
DOI: 10.1103/PhysRevE.94.062150

Most natural and engineered processes, such as biomolecular reactions, protein folding, and population dynamics, occur far from equilibrium and therefore cannot be treated within the framework of classical equilibrium thermodynamics. Here we experimentally study how some fundamental thermodynamic quantities and relations are affected by the presence of the nonequilibrium fluctuations associated with an active bath. We show in particular that, as the confinement of the particle increases, the stationary probability distribution of a Brownian particle confined within a harmonic potential becomes non-Boltzmann, featuring a transition from a Gaussian distribution to a heavy-tailed distribution. Because of this, nonequilibrium relations (e.g., the Jarzynski equality and Crooks fluctuation theorem) cannot be applied. We show that these relations can be restored by using the effective potential associated with the stationary probability distribution. We corroborate our experimental findings with theoretical arguments.

Review on Active Matter published in Rev. Mod. Phys.

Active Brownian particles in complex and crowded environments (Invited review)
Clemens Bechinger, Roberto Di Leonardo, Hartmut Löwen, Charles Reichhardt, Giorgio Volpe & Giovanni Volpe
Reviews of Modern Physics 88(4), 045006 (2016)
DOI: 10.1103/RevModPhys.88.045006
arXiv: 1602.00081

Differently from passive Brownian particles, active particles, also known as self-propelled Brownian particles or microswimmers and nanoswimmers, are capable of taking up energy from their environment and converting it into directed motion. Because of this constant flow of energy, their behavior can be explained and understood only within the framework of nonequilibrium physics. In the biological realm, many cells perform directed motion, for example, as a way to browse for nutrients or to avoid toxins. Inspired by these motile microorganisms, researchers have been developing artificial particles that feature similar swimming behaviors based on different mechanisms. These man-made micromachines and nanomachines hold a great potential as autonomous agents for health care, sustainability, and security applications. With a focus on the basic physical features of the interactions of self-propelled Brownian particles with a crowded and complex environment, this comprehensive review will provide a guided tour through its basic principles, the development of artificial self-propelling microparticles and nanoparticles, and their application to the study of nonequilibrium phenomena, as well as the open challenges that the field is currently facing.

Stochastic Differential Delay Equations with Colored State-Dependent Noise published in Markov Processes and Related Fields

An SDE approximation for stochastic differential delay equations with colored state-dependent noise
Austin McDaniel, Ozer Duman, Giovanni Volpe & Jan Wehr
Markov Processes and Related Fields 22(3), 595-628 (2016)
arXiv: 1406.7287

We consider a general multidimensional stochastic differential delay equation (SDDE) with colored state-dependent noises. We approxi-mate it by a stochastic differential equation (SDE) system and calcu- late its limit as the time delays and the correlation times of the noises go to zero. The main result is proven using a theorem of convergence of stochastic integrals developed by Kurtz and Protter. The result formalizes and extends a method that has been used in the analysis of a noisy electrical circuit with delayed state-dependent noise, and may be further used as a working SDE approximation of an SDDE system modeling a real system, where noises are correlated in time and whose response to stimuli is delayed.

Disrupted Network Topology in Alzheimer published in Cerebral Cortex

Disrupted Network Topology in Patients with Stable and Progressive Mild Cognitive Impairment and Alzheimer’s Disease
Joana B. Pereira, Mite Mijalkov, Ehsan Kakaei, Patricia Mecocci, Bruno Vellas, Magda Tsolaki, Iwona Kłoszewska, Hilka Soininen, Christian Spenger, Simmon Lovestone, Andrew Simmons, Lars-Olof Wahlund, Giovanni Volpe & Eric Westman, AddNeuroMed consortium, for the Alzheimer’s Disease Neuroimaging Initiative
Cerebral Cortex 26(8), 3476—3493 (2016)
DOI: 10.1093/cercor/bhw128

Recent findings suggest that Alzheimer’s disease (AD) is a disconnection syndrome characterized by abnormalities in large- scale networks. However, the alterations that occur in network topology during the prodromal stages of AD, particularly in patients with stable mild cognitive impairment (MCI) and those that show a slow or faster progression to dementia, are still poorly understood. In this study, we used graph theory to assess the organization of structural MRI networks in stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients from 2 large multicenter cohorts: ADNI and AddNeuroMed. Our findings showed an abnormal global network organization in all patient groups, as reflected by an increased path length, reduced transitivity, and increased modularity compared with controls. In addition, lMCIc, eMCIc, and AD patients showed a decreased path length and mean clustering compared with the sMCI group. At the local level, there were nodal clustering decreases mostly in AD patients, while the nodal closeness centrality detected abnormalities across all patient groups, showing overlapping changes in the hippocampi and amygdala and nonoverlapping changes in parietal, entorhinal, and orbitofrontal regions. These findings suggest that the prodromal and clinical stages of AD are associated with an abnormal network topology.

Better Stability with Measurement Errors published in J. Stat. Phys.

Better stability with measurement errors
Aykut Argun & Giovanni Volpe
Journal of Statistical Physics 163(6), 1477—1485 (2016)
DOI: 10.1007/s10955-016-1518-8
arXiv: 1608.08461

Often it is desirable to stabilize a system around an optimal state. This can be effectively accomplished using feedback control, where the system deviation from the desired state is measured in order to determine the magnitude of the restoring force to be applied. Contrary to conventional wisdom, i.e. that a more precise measurement is expected to improve the system stability, here we demonstrate that a certain degree of measurement error can improve the system stability. We exemplify the implications of this finding with numerical examples drawn from various fields, such as the operation of a temperature controller, the confinement of a microscopic particle, the localization of a target by a microswimmer, and the control of a population.