TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy

We introduce TempoCave, a novel visualization application for analyzing dynamic brain networks, or connectomes. TempoCave provides a range of functionality to explore metrics related to the activity patterns and modular affiliations of different regions in the brain. These patterns are calculated by processing raw data retrieved functional magnetic resonance imaging (fMRI) scans, which creates a network of weighted edges between each brain region, where the weight indicates how likely these regions are to activate synchronously. In particular, we support the analysis needs of clinical psychologists, who examine these modular affiliations and weighted edges and their temporal dynamics, utilizing them to understand relationships between neurological disorders and brain activity, which could have a significant impact on the way in which patients are diagnosed and treated. We summarize the core functionality of TempoCave, which supports a range of comparative tasks, and runs both in a desktop mode and in an immersive mode. Furthermore, we present a real-world use case that analyzes pre- and post-treatment connectome datasets from 27 subjects in a clinical study investigating the use of cognitive behavior therapy to treat major depression disorder, indicating that TempoCave can provide new insight into the dynamic behavior of the human brain

Automatic generation of user interfaces from rigorous domain and use case models

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

Improved Method to Select the Lagrange Multiplier for Rate-Distortion Based Motion Estimation in Video Coding

The motion estimation (ME) process used in the H.264/AVC reference software is based on minimizing a cost function that involves two terms (distortion and rate) that are properly balanced through a Lagrangian parameter, usually denoted as lambda(motion). In this paper we propose an algorithm to improve the conventional way of estimating lambda(motion) and, consequently, the ME process. First, we show that the conventional estimation of lambda(motion) turns out to be significantly less accurate when ME-compromising events, which make the ME process to perform poorly, happen. Second, with the aim of improving the coding efficiency in these cases, an efficient algorithm is proposed that allows the encoder to choose between three different values of lambda(motion) for the Inter 16x16 partition size. To be more precise, for this partition size, the proposed algorithm allows the encoder to additionally test lambda(motion) = 0 and lambda(motion) arbitrarily large, which corresponds to minimum distortion and minimum rate solutions, respectively. By testing these two extreme values, the algorithm avoids making large ME errors. The experimental results on video segments exhibiting this type of ME-compromising events reveal an average rate reduction of 2.20% for the same coding quality with respect to the JM15.1 reference software of H.264/AVC. The algorithm has been also tested in comparison with a state-of-the-art algorithm called context adaptive Lagrange multiplier. Additionally, two illustrative examples of the subjective performance improvement are provided.This work has been partially supported by the National Grant TEC2011-26807 of the Spanish Ministry of Science and Innovation.Publicad

Privacy-Friendly Mobility Analytics using Aggregate Location Data

Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates -- i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201

Investigation on Genetic Modifiers of Age at Onset of Major Depressive Disorder

Major Depressive Disorder (MDD) is a complex multifactorial disorder, which would lead to disability. Environmental and genetic factors are involved in MDD etiology. The aim of this project was to identify loci modifying age at onset (AAO) of MDD using survival models after adjusting for Childhood Sexual Abuse (CSA). To achieve this aim, a dataset was made available by the China Oxford and VCU Experimental Research on Genetic Epidemiology (CONVERGE) consortium. The study population had 5,220 controls and 5,282 cases with MDD. We performed two univariate association analyses using Cox Proportional Hazard (Cox PH) models. These two are Full Sample (FS), cases and controls, and only the Case Cohort (CC). No genome-wide significant associations were found in univariate analyses. Subsequent gene set enrichment analysis showed that there were significant enrichments in neurological Gene Ontology terms and some novel non-neural pathways. These findings may allow us to better understand MDD pathology

Closed-aperture unbounded acoustics experimentation using multidimensional deconvolution

In physical acoustic laboratories, wave propagation experiments often suffer from unwanted reflections at the boundaries of the experimental setup. We propose using multidimensional deconvolution (MDD) to post-process recorded experimental data such that the scattering imprint related to the domain boundary is completely removed and only the Green's functions associated with a scattering object of interest are obtained. The application of the MDD method requires in/out wavefield separation of data recorded along a closed surface surrounding the object of interest, and we propose a decomposition method to separate such data for arbitrary curved surfaces. The MDD results consist of the Green's functions between any pair of points on the closed recording surface, fully sampling the scattered field. We apply the MDD algorithm to post-process laboratory data acquired in a two-dimensional acoustic waveguide to characterize the wavefield scattering related to a rigid steel block while removing the scattering imprint of the domain boundary. The experimental results are validated with synthetic simulations, corroborating that MDD is an effective and general method to obtain the experimentally desired Green's functions for arbitrary inhomogeneous scatterers

Using a Dynamic Domain-Specific Modeling Language for the Model-Driven Development of Cross-Platform Mobile Applications

There has been a gradual but steady convergence of dynamic programming languages with modeling languages. One area that can benefit from this convergence is modeldriven development (MDD) especially in the domain of mobile application development. By using a dynamic language to construct a domain-specific modeling language (DSML), it is possible to create models that are executable, exhibit flexible type checking, and provide a smaller cognitive gap between business users, modelers and developers than more traditional model-driven approaches. Dynamic languages have found strong adoption by practitioners of Agile development processes. These processes often rely on developers to rapidly produce working code that meets business needs and to do so in an iterative and incremental way. Such methodologies tend to eschew “throwaway” artifacts and models as being wasteful except as a communication vehicle to produce executable code. These approaches are not readily supported with traditional heavyweight approaches to model-driven development such as the Object Management Group’s Model-Driven Architecture approach. This research asks whether it is possible for a domain-specific modeling language written in a dynamic programming language to define a cross-platform model that can produce native code and do so in a way that developer productivity and code quality are at least as effective as hand-written code produced using native tools. Using a prototype modeling tool, AXIOM (Agile eXecutable and Incremental Objectoriented Modeling), we examine this question through small- and mid-scale experiments and find that the AXIOM approach improved developer productivity by almost 400%, albeit only after some up-front investment. We also find that the generated code can be of equal if not better quality than the equivalent hand-written code. Finally, we find that there are significant challenges in the synthesis of a DSML that can be used to model applications across platforms as diverse as today’s mobile operating systems, which point to intriguing avenues of subsequent research

An investigation of the neural circuitry that mediates inhibitory signalling within the lateral habenula

Previously held under moratorium from 3 March 2020 until 3 March 2022Background: The lateral habenula (LHb) is a brain structure which is known to be pathologically hyperactive in depression, thus providing enhanced inhibitory input to the brains’ reward circuitry. As such, inhibition of the LHb has an antidepressant effect, via disinhibition of the reward circuitry. However, the neural circuity which mediates inhibitory signalling within the LHb remains to be fully described. Hence, the overarching aim of this project was to study inhibitory signalling within the LHb, by studying the circuitry formed by neurons expressing one of three transgenic markers classically considered to be associated with inhibitory neurons: Neuron-derived neurotrophic factor (Ndnf), parvalbumin (PV) and somatostatin (SOM). Methods: Circuitry was studied in vitro using patch-clamp electrophysiology in combination with optogenetic manipulations of neurons expressing the above molecular markers. Additionally, immunohistochemistry and confocal microscopy were used to assess the fraction of neurons expressing these markers which were also GABAergic. Results: This work identifies three sources of inhibitory input to the LHb, arising from both local PV-positive neurons, and those in the medial dorsal thalamic nucleus; and from SOM-positive neurons in the ventral pallidum. Additionally, we find that within the LHb, these markers are not confined to exclusively inhibitory populations. Rather, we identify physiologically distinct.Background: The lateral habenula (LHb) is a brain structure which is known to be pathologically hyperactive in depression, thus providing enhanced inhibitory input to the brains’ reward circuitry. As such, inhibition of the LHb has an antidepressant effect, via disinhibition of the reward circuitry. However, the neural circuity which mediates inhibitory signalling within the LHb remains to be fully described. Hence, the overarching aim of this project was to study inhibitory signalling within the LHb, by studying the circuitry formed by neurons expressing one of three transgenic markers classically considered to be associated with inhibitory neurons: Neuron-derived neurotrophic factor (Ndnf), parvalbumin (PV) and somatostatin (SOM). Methods: Circuitry was studied in vitro using patch-clamp electrophysiology in combination with optogenetic manipulations of neurons expressing the above molecular markers. Additionally, immunohistochemistry and confocal microscopy were used to assess the fraction of neurons expressing these markers which were also GABAergic. Results: This work identifies three sources of inhibitory input to the LHb, arising from both local PV-positive neurons, and those in the medial dorsal thalamic nucleus; and from SOM-positive neurons in the ventral pallidum. Additionally, we find that within the LHb, these markers are not confined to exclusively inhibitory populations. Rather, we identify physiologically distinct