Relationship between daily rated depression symptom severity and the retrospective self-report on PHQ-9: A prospective ecological momentary assessment study on 80 psychiatric outpatients.

BACKGROUND: Depression-related negative bias in emotional processing and memory may bias accuracy of recall of temporally distal symptoms. We tested the hypothesis that when responding to the Patient Health Questionnaire (PHQ-9) the responses reflect more accurately temporally proximal than distal mood states. METHODS: Currently, depressed psychiatric outpatients (N = 80) with depression confirmed in semi-structured interviews had the Aware application installed on their smartphones for ecological momentary assessment (EMA). The severity of "low mood", "hopelessness", "low energy", "anhedonia", and "wish to die" was assessed on a Likert scale five times daily during a 12-day period, and thereafter, the PHQ-9 questionnaire was completed. We used auto- and cross-correlation analyses and linear mixed-effects multilevel models (LMM) to investigate the effect of time lag on the association between EMA of depression symptoms and the PHQ-9. RESULTS: Autocorrelations of the EMA of depressive symptom severity at two subsequent days were strong (r varying from 0.7 to 0.9; p < 0.001). "Low mood" was the least and "wish to die" the most temporally stable symptom. The correlations between EMA of depressive symptoms and total scores of the PHQ-9 were temporally stable (r from 0.3 to 0.6; p < 0.001). No effect of assessment time on the association between EMA data and the PHQ-9 emerged in the LMM. LIMITATIONS: Altogether 11.5 % of observations were missing. CONCLUSIONS: Despite fluctuations in severity of some of the depressive symptoms, patients with depression accurately recollect their most dominant symptoms, without a significant recall bias favouring the most recent days, when responding to the PHQ-9.

A collection of public transport network data sets for 25 cities.

Various public transport (PT) agencies publish their route and timetable information with the General Transit Feed Specification (GTFS) as the standard open format. Timetable data are commonly used for PT passenger routing. They can also be used for studying the structure and organization of PT networks, as well as the accessibility and the level of service these networks provide. However, using raw GTFS data is challenging as researchers need to understand the details of the GTFS data format, make sure that the data contain all relevant modes of public transport, and have no errors. To lower the barrier for using GTFS data in research, we publish a curated collection of 25 cities' public transport networks in multiple easy-to-use formats including network edge lists, temporal network event lists, SQLite databases, GeoJSON files, and the GTFS data format. This collection promotes the study of how PT is organized across the globe, and also provides a testbed for developing tools for PT network analysis and PT routing algorithms.

Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype.

BACKGROUND: Mental and behavioral disorders are the main cause of disability worldwide. However, their diagnosis is challenging due to a lack of reliable biomarkers; current detection is based on structured clinical interviews which can be biased by the patient's recall ability, affective state, changing in temporal frames, etc. While digital platforms have been introduced as a possible solution to this complex problem, there is little evidence on the extent of usability and usefulness of these platforms. Therefore, more studies where digital data is collected in larger scales are needed to collect scientific evidence on the capacities of these platforms. Most of the existing platforms for digital psychiatry studies are designed as monolithic systems for a certain type of study; publications from these studies focus on their results, rather than the design features of the data collection platform. Inevitably, more tools and platforms will emerge in the near future to fulfill the need for digital data collection for psychiatry. Currently little knowledge is available from existing digital platforms for future data collection platforms to build upon. OBJECTIVE: The objective of this work was to identify the most important features for designing a digital platform for data collection for mental health studies, and to demonstrate a prototype platform that we built based on these design features. METHODS: We worked closely in a multidisciplinary collaboration with psychiatrists, software developers, and data scientists and identified the key features which could guarantee short-term and long-term stability and usefulness of the platform from the designing stage to data collection and analysis of collected data. RESULTS: The key design features that we identified were flexibility of access control, flexibility of data sources, and first-order privacy protection. We also designed the prototype platform Non-Intrusive Individual Monitoring Architecture (Niima), where we implemented these key design features. We described why each of these features are important for digital data collection for psychiatry, gave examples of projects where Niima was used or is going to be used in the future, and demonstrated how incorporating these design principles opens new possibilities for studies. CONCLUSIONS: The new methods of digital psychiatry are still immature and need further research. The design features we suggested are a first step to design platforms which can adapt to the upcoming requirements of digital psychiatry.

Detection of timescales in evolving complex systems.

Most complex systems are intrinsically dynamic in nature. The evolution of a dynamic complex system is typically represented as a sequence of snapshots, where each snapshot describes the configuration of the system at a particular instant of time. This is often done by using constant intervals but a better approach would be to define dynamic intervals that match the evolution of the system's configuration. To this end, we propose a method that aims at detecting evolutionary changes in the configuration of a complex system, and generates intervals accordingly. We show that evolutionary timescales can be identified by looking for peaks in the similarity between the sets of events on consecutive time intervals of data. Tests on simple toy models reveal that the technique is able to detect evolutionary timescales of time-varying data both when the evolution is smooth as well as when it changes sharply. This is further corroborated by analyses of several real datasets. Our method is scalable to extremely large datasets and is computationally efficient. This allows a quick, parameter-free detection of multiple timescales in the evolution of a complex system.

Benchmark model to assess community structure in evolving networks.

Detecting the time evolution of the community structure of networks is crucial to identify major changes in the internal organization of many complex systems, which may undergo important endogenous or exogenous events. This analysis can be done in two ways: considering each snapshot as an independent community detection problem or taking into account the whole evolution of the network. In the first case, one can apply static methods on the temporal snapshots, which correspond to configurations of the system in short time windows, and match afterward the communities across layers. Alternatively, one can develop dedicated dynamic procedures so that multiple snapshots are simultaneously taken into account while detecting communities, which allows us to keep memory of the flow. To check how well a method of any kind could capture the evolution of communities, suitable benchmarks are needed. Here we propose a model for generating simple dynamic benchmark graphs, based on stochastic block models. In them, the time evolution consists of a periodic oscillation of the system's structure between configurations with built-in community structure. We also propose the extension of quality comparison indices to the dynamic scenario.

Triadic closure as a basic generating mechanism of communities in complex networks.

Most of the complex social, technological, and biological networks have a significant community structure. Therefore the community structure of complex networks has to be considered as a universal property, together with the much explored small-world and scale-free properties of these networks. Despite the large interest in characterizing the community structures of real networks, not enough attention has been devoted to the detection of universal mechanisms able to spontaneously generate networks with communities. Triadic closure is a natural mechanism to make new connections, especially in social networks. Here we show that models of network growth based on simple triadic closure naturally lead to the emergence of community structure, together with fat-tailed distributions of node degree and high clustering coefficients. Communities emerge from the initial stochastic heterogeneity in the concentration of links, followed by a cycle of growth and fragmentation. Communities are the more pronounced, the sparser the graph, and disappear for high values of link density and randomness in the attachment procedure. By introducing a fitness-based link attractivity for the nodes, we find a phase transition where communities disappear for high heterogeneity of the fitness distribution, but a different mesoscopic organization of the nodes emerges, with groups of nodes being shared between just a few superhubs, which attract most of the links of the system.

Improving the performance of algorithms to find communities in networks.

Most algorithms to detect communities in networks typically work without any information on the cluster structure to be found, as one has no a priori knowledge of it, in general. Not surprisingly, knowing some features of the unknown partition could help its identification, yielding an improvement of the performance of the method. Here we show that, if the number of clusters was known beforehand, standard methods, like modularity optimization, would considerably gain in accuracy, mitigating the severe resolution bias that undermines the reliability of the results of the original unconstrained version. The number of clusters can be inferred from the spectra of the recently introduced nonbacktracking and flow matrices, even in benchmark graphs with realistic community structure. The limit of such a two-step procedure is the overhead of the computation of the spectra.

Community detection in networks: Structural communities versus ground truth.

Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (nontopological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.

Dynamical heterogeneity in lattice glass models.

In this paper, we consider in detail the properties of dynamical heterogeneity in lattice glass models (LGMs). LGMs are lattice models whose dynamical rules are based on thermodynamic, as opposed to purely kinetic, considerations. We devise a LGM that is not prone to crystallization and displays properties of a fragile glass-forming liquid. Particle motion in this model tends to be locally anisotropic on intermediate time scales even though the rules governing the model are isotropic. The model demonstrates violations of the Stokes-Einstein relation and the growth of various length scales associated with dynamical heterogeneity. We discuss future avenues of research comparing the predictions of LGMs and kinetically constrained models to atomistic systems.

Mechanistic elements of protein cold denaturation.

Globular proteins undergo structural transitions to denatured states when sufficient thermodynamic state or chemical perturbations are introduced to their native environment. Cold denaturation is a somewhat counterintuitive phenomenon whereby proteins lose their compact folded structure as a result of a temperature drop. The currently accepted explanation for cold denaturation is based on an associated favorable change in the contact free energy between water and nonpolar groups at colder temperatures which would weaken the hydrophobic interaction and is thought to eventually allow polymer entropy to disrupt protein tertiary structure. In this paper we explore how this environmental perturbation leads to changes in the protein hydration and local motions in apomyoglobin. We do this by analyzing changes in protein hydration and protein motion from molecular dynamics simulation trajectories initially at 310 K, followed by a temperature drop to 278 K. We observe an increase in the number of solvent contacts around the protein and, in particular, distinctly around nonpolar atoms. Further analysis shows that the fluctuations of some protein atoms increase with decreasing temperature. This is accompanied by an observed increase in the isothermal compressibility of the protein, indicating an increase in the protein interior interstitial space. Closer inspection reveals that atoms with increased compressibility and larger-than-expected fluctuations are localized within the protein core regions. These results provide insight into a description of the mechanism of cold denaturation. That is, the lower temperature leads to solvent-induced packing defects at the protein surface, and this more favorable water-protein interaction in turn destabilizes the overall protein structure.

Origins of nonexponential decay in single molecule measurements of rotational dynamics.

Recent reports have demonstrated that the correlation function of the fluorescence dichroism signal, measured as a probe of single molecule rotational dynamics, should not manifest a single exponential decay even for isotropic diffusion. This has called into question the attribution of observed nonexponential behavior in supercooled fluids and polymer systems to dynamical heterogeneity. We show here that, for the case of a high numerical aperture objective, the dichroism decay becomes indistinguishable from a single exponential. As a consequence, observed nonexponential decays can be associated with complex rotational dynamics. These effects are illustrated via simulated rotational trajectories for isotropic diffusion of a dipole.