Unsupervised deep learning of human brain diffusion magnetic resonance imaging tractography data

L'imagerie par résonance magnétique de diffusion est une technique non invasive permettant de connaître la microstructure organisationnelle des tissus biologiques. Les méthodes computationnelles qui exploitent la préférence orientationnelle de la diffusion dans des structures restreintes pour révéler les voies axonales de la matière blanche du cerveau sont appelées tractographie. Ces dernières années, diverses méthodes de tractographie ont été utilisées avec succès pour découvrir l'architecture de la matière blanche du cerveau. Pourtant, ces techniques de reconstruction souffrent d'un certain nombre de défauts dérivés d'ambiguïtés fondamentales liées à l'information orientationnelle. Cela a des conséquences dramatiques, puisque les cartes de connectivité de la matière blanche basées sur la tractographie sont dominées par des faux positifs. Ainsi, la grande proportion de voies invalides récupérées demeure un des principaux défis à résoudre par la tractographie pour obtenir une description anatomique fiable de la matière blanche. Des approches méthodologiques innovantes sont nécessaires pour aider à résoudre ces questions. Les progrès récents en termes de puissance de calcul et de disponibilité des données ont rendu possible l'application réussie des approches modernes d'apprentissage automatique à une variété de problèmes, y compris les tâches de vision par ordinateur et d'analyse d'images. Ces méthodes modélisent et trouvent les motifs sous-jacents dans les données, et permettent de faire des prédictions sur de nouvelles données. De même, elles peuvent permettre d'obtenir des représentations compactes des caractéristiques intrinsèques des données d'intérêt. Les approches modernes basées sur les données, regroupées sous la famille des méthodes d'apprentissage profond, sont adoptées pour résoudre des tâches d'analyse de données d'imagerie médicale, y compris la tractographie. Dans ce contexte, les méthodes deviennent moins dépendantes des contraintes imposées par les approches classiques utilisées en tractographie. Par conséquent, les méthodes inspirées de l'apprentissage profond conviennent au changement de paradigme requis, et peuvent ouvrir de nouvelles possibilités de modélisation, en améliorant ainsi l'état de l'art en tractographie. Dans cette thèse, un nouveau paradigme basé sur les techniques d'apprentissage de représentation est proposé pour générer et analyser des données de tractographie. En exploitant les architectures d'autoencodeurs, ce travail tente d'explorer leur capacité à trouver un code optimal pour représenter les caractéristiques des fibres de la matière blanche. Les contributions proposées exploitent ces représentations pour une variété de tâches liées à la tractographie, y compris (i) le filtrage et (ii) le regroupement efficace sur les résultats générés par d'autres méthodes, ainsi que (iii) la reconstruction proprement dite des fibres de la matière blanche en utilisant une méthode générative. Ainsi, les méthodes issues de cette thèse ont été nommées (i) FINTA (Filtering in Tractography using Autoencoders), (ii) CINTA (Clustering in Tractography using Autoencoders), et (iii) GESTA (Generative Sampling in Bundle Tractography using Autoencoders), respectivement. Les performances des méthodes proposées sont évaluées par rapport aux méthodes de l'état de l'art sur des données de diffusion synthétiques et des données de cerveaux humains chez l'adulte sain in vivo. Les résultats montrent que (i) la méthode de filtrage proposée offre une sensibilité et spécificité supérieures par rapport à d'autres méthodes de l'état de l'art; (ii) le regroupement des tractes dans des faisceaux est fait de manière consistante; et (iii) l'approche générative échantillonnant des tractes comble mieux l'espace de la matière blanche dans des régions difficiles à reconstruire. Enfin, cette thèse révèle les possibilités des autoencodeurs pour l'analyse des données des fibres de la matière blanche, et ouvre la voie à fournir des données de tractographie plus fiables.Abstract : Diffusion magnetic resonance imaging is a non-invasive technique providing insights into the organizational microstructure of biological tissues. The computational methods that exploit the orientational preference of the diffusion in restricted structures to reveal the brain's white matter axonal pathways are called tractography. In recent years, a variety of tractography methods have been successfully used to uncover the brain's white matter architecture. Yet, these reconstruction techniques suffer from a number of shortcomings derived from fundamental ambiguities inherent to the orientation information. This has dramatic consequences, since current tractography-based white matter connectivity maps are dominated by false positive connections. Thus, the large proportion of invalid pathways recovered remains one of the main challenges to be solved by tractography to obtain a reliable anatomical description of the white matter. Methodological innovative approaches are required to help solving these questions. Recent advances in computational power and data availability have made it possible to successfully apply modern machine learning approaches to a variety of problems, including computer vision and image analysis tasks. These methods model and learn the underlying patterns in the data, and allow making accurate predictions on new data. Similarly, they may enable to obtain compact representations of the intrinsic features of the data of interest. Modern data-driven approaches, grouped under the family of deep learning methods, are being adopted to solve medical imaging data analysis tasks, including tractography. In this context, the proposed methods are less dependent on the constraints imposed by current tractography approaches. Hence, deep learning-inspired methods are suit for the required paradigm shift, may open new modeling possibilities, and thus improve the state of the art in tractography. In this thesis, a new paradigm based on representation learning techniques is proposed to generate and to analyze tractography data. By harnessing autoencoder architectures, this work explores their ability to find an optimal code to represent the features of the white matter fiber pathways. The contributions exploit such representations for a variety of tractography-related tasks, including efficient (i) filtering and (ii) clustering on results generated by other methods, and (iii) the white matter pathway reconstruction itself using a generative method. The methods issued from this thesis have been named (i) FINTA (Filtering in Tractography using Autoencoders), (ii) CINTA (Clustering in Tractography using Autoencoders), and (iii) GESTA (Generative Sampling in Bundle Tractography using Autoencoders), respectively. The proposed methods' performance is assessed against current state-of-the-art methods on synthetic data and healthy adult human brain in vivo data. Results show that the (i) introduced filtering method has superior sensitivity and specificity over other state-of-the-art methods; (ii) the clustering method groups streamlines into anatomically coherent bundles with a high degree of consistency; and (iii) the generative streamline sampling technique successfully improves the white matter coverage in hard-to-track bundles. In summary, this thesis unlocks the potential of deep autoencoder-based models for white matter data analysis, and paves the way towards delivering more reliable tractography data

Eguzki-sistemako planeta erraldoien atmosfera

Haize-korronte izugarriak dira, Eguzki-sistemako planeta erraldoien (Jupiter, Saturno, Urano eta Neptuno) atmosferen ezaugarri nagusia. Saturnoren kasuan, 500 m/s-ko abiadura izatera hel daitezke korronte hauek. Hauen jatorria ez da ondo ezagutzen oraindik eta izatez beraiek dira astrofisikarientzako erronkarik handienetako bat da. Lan honetan, planeta erraldoien atmosfera aztertuko da hodei mailan gertatzen diren haizeen zirkulazio orokorra kontuan hartuz

Evaluation of the theoretical, technical and economic potential of industrial waste heat recovery in the Basque Country

Industrial waste heat recovery shows significant potential for increasing energy efficiency in industry. However, to design strategies that exploit this potential, it is necessary to have data about the quantity and characteristics of industrial waste heat flows. This information is not always readily available and many companies do not even have a systematic record of these energy flows. Hence, bottom-up methodologies to estimate that recovery potential by means of key transfer figures are useful tools within this field. In the present article, four different methods are applied to determine the industrial waste heat recovery potential in the Autonomous Community of the Basque Country (northern Spain), an energy-intensive industrial region with large energy dependency from the outside. Besides, the analysis of the economic viability of the industrial waste heat recovery is essential, because it determines the final adoption of energy efficiency measures. For that aim, the authors develop an easy-to-apply bottom-up methodology to carry out an assessment for the economic potential of the estimated industrial waste heat at different temperature levels. This method is applied to 129 companies, whose potentials are characterized and discussed. The obtained results show that, for waste heat streams above 400 ?C, more than 90% of the studied companies present payback periods below five years. For those industries with waste heat temperatures below 200 ?C, the ratio decreases to around 40%, still a noticeable value. The estimations show a significant opportunity to implement solutions to recover this wasted energy, especially in the iron and steel sector and the petrochemical industry. The development of public policies that encourage these measurements would be also beneficial.The authors would like to acknowledge the Spanish Ministry of Science and Innovation (MICINN) for funding through the SweetTES research project (RTI 2018099557BC22)

Convective storms in closed cyclones in Jupiter: (II) numerical modeling

On May 31, 2020 a convective storm appeared in one small cyclone in the South Temperate Belt (STB) of Jupiter. The storm, nicknamed as Clyde's Spot, had an explosive start and quickly diminished in activity in a few days. However, it left a highly turbulent cyclone as a remnant that evolved to become a turbulent segment of the STB in a time-scale of one year. A very similar storm erupted on August 7, 2021 in another cyclone of the STB with a similar initial phenomenology. In both cases, the outbreaks started in cyclones that were the result of the merger of pre-existing vortices. In a previous paper we presented an observational study of these storms compared with a similar cyclonic convective system observed during the Voyager 2 flyby [Hueso et al., Convective storms in closed cyclones in Jupiter's South Temperate Belt: (I) Observations, Icarus, 380, 2022]. Here we present numerical simulations of these vortices and storms with the Explicit Planetary Isentropic-Coordinate (EPIC) numerical model. We simulate mergers of cyclones in Jupiter's STB and investigate the deep structure of the resulting cyclone and its capability to uplift material from the water condensation level. Convection is introduced in the model imposing heating sources whose vertical extent, horizontal size and duration are free parameters that we explore. Our simulations reproduce the cloud field of both storms after short episodes of a few hours of intense con-vection. The evolution of the morphology of the convective cyclone after the convective pulse stopped shows a strong relation between the convective energy released and the initial vorticity in the cyclone. Similar results are obtained for the cyclonic storm observed during the Voyager 2 flyby. We also compare our simulations of these storms with numerical simulations of a storm that developed in the STB in 2018 inside an elongated cyclone known as the South Temperate Belt Ghost [Inurrigarro et al., Observations and numerical modelling of a convective disturbance in a large-scale cyclone in Jupiter's South Temperate Belt, Icarus, 336, 2020]. In addition, we also simulate one of the large-scale storms that develop in the South Equatorial Belt comparing our simulations with Voyager 1 observations of one of those events. From these simulations, we establish a relative scale of energies associated to these convective storms. As coherent cyclones isolate the local atmosphere from their surroundings, we propose that the availability of condensables inside closed cyclones limits the duration of active convection, allowing larger convective outbursts in larger cyclones. Our simulations of the short and intense convective pulse associated to the 2020 and 2021 STB suggest a minimum local water abundance of 1.0-1.2 times solar at the location of the storms. The lower number considers a significant contribution of ammonia condensation, and the larger number considers only water moist convection with a negligible role of ammonia.This work has been supported by Grant PID2019-109467GB-I00 funded by MCIN/AEI/10.13039/501100011033/ and by Grupos Gobierno Vasco IT1366-19. PI acknowledges a PhD scholarship from Gobierno Vasco

A large active wave trapped in Jupiter's equator

Context. A peculiar atmospheric feature was observed in the equatorial zone (EZ) of Jupiter between September and December 2012 in ground-based and Hubble Space Telescope (HST) images. This feature consisted of two low albedo Y-shaped cloud structures (Y1 and Y2) oriented along the equator and centred on it (latitude 0.5°-1°N). Aims. We wanted to characterize these features, and also tried to find out their properties and understand their nature. Methods. We tracked these features to obtain their velocity and analyse their cloud morphology and the interaction with their surroundings. We present numerical simulations of the phenomenon based on one- and two-layer shallow water models under a Gaussian pulse excitation. Results. Each Y feature had a characteristic zonal length of ~15° (18¿000 km) and a meridional width (distance between the north-south extremes of the Y) of 5° (6000 km), and moved eastward with a speed of around 20-40 m¿s-1 relative to Jupiter’s mean flow. Their lifetime was 90 and 60 days for Y1 and Y2, respectively. In November, both Y1 and Y2 exhibited outbursts of rapidly evolving bright spots emerging from the Y vertex. The Y features were not visible at wavelengths of 255 or 890 nm, which suggests that they were vertically shallow and placed in altitude between the upper equatorial hazes and the main cloud deck. Numerical simulations of the dynamics of the Jovian equatorial region generate Kelvin and Rossby waves, which are similar to those in the Matsuno-Gill model for Earth’s equatorial dynamics, and reproduce the observed cloud morphology and the main properties the main properties of the Y features.Peer ReviewedPostprint (author's final draft

A planetary-scale disturbance in a long living three vortex coupled system in Saturn's atmosphere

The zonal wind profile of Saturn has a unique structure at 60°N with a double-peaked jet that reaches maximum zonal velocities close to 100 ms−1. In this region, a singular group of vortices consisting of a cyclone surrounded by two anticyclones was active since 2012 until the time of this report. Our observation demonstrates that vortices in Saturn can be long-lived. The three-vortex system drifts at u = 69.0 ± 1.6 ms−1, similar to the speed of the local wind. Local motions reveal that the relative vorticity of the vortices comprising the system is ∼2–3 times the ambient zonal vorticity. In May 2015, a disturbance developed at the location of the triple vortex system, and expanded eastwards covering in two months a third of the latitudinal circle, but leaving the vortices essentially unchanged. At the time of the onset of the disturbance, a fourth vortex was present at 55°N, south of the three vortices and the evolution of the disturbance proved to be linked to the motion of this vortex. Measurements of local motions of the disturbed region show that cloud features moved essentially at the local wind speeds, suggesting that the disturbance consisted of passively advecting clouds generated by the interaction of the triple vortex system with the fourth vortex to the south. Nonlinear simulations are able to reproduce the stability and longevity of the triple vortex system under low vertical wind shear and high static stability in the upper troposphere of Saturn.This work was supported by the Spanish MICIIN projects AYA2015-65041-P (MINECO/FEDER, UE), Grupos Gobierno Vasco IT-765-13, and UFI11/55 from UPV/EHU. EGM is supported by the Serra Hunter Programme, Generalitat de Catalunya. A. Simon, K. Sayanagi and M.H. Wong were supported by a NASA Cassini Data Analysisgrant (NNX15AD33G and NNX15AD34G). We acknowledge the three orbits assigned by the Director Discretionary time from HST for this research (DD Program 14064, IP A. Sánchez-Lavega). We are very grateful to amateur astronomers contributing with their images to open databases such as PVOL (http://pvol2.ehu.eus/) and ALPO-Japan (http://alpo-j.asahikawa-med.ac.jp/)

A complex storm system in Saturn's north polar atmosphere in 2018

Saturn’s convective storms usually fall in two categories. One consists of mid-sized storms ~2,000¿km wide, appearing as irregular bright cloud systems that evolve rapidly, on scales of a few days. The other includes the Great White Spots, planetary-scale giant storms ten times larger than the mid-sized ones, which disturb a full latitude band, enduring several months, and have been observed only seven times since 1876. Here we report a new intermediate type, observed in 2018 in the north polar region. Four large storms with east–west lengths ~4,000–8,000¿km (the first one lasting longer than 200 days) formed sequentially in close latitudes, experiencing mutual encounters and leading to zonal disturbances affecting a full latitude band ~8,000¿km wide, during at least eight months. Dynamical simulations indicate that each storm required energies around ten times larger than mid-sized storms but ~100 times smaller than those necessary for a Great White Spot. This event occurred at about the same latitude and season as the Great White Spot in 1960, in close correspondence with the cycle of approximately 60 years hypothesized for equatorial Great White Spots.Peer ReviewedPostprint (author's final draft

Tractostorm 2 : Optimizing tractography dissection reproducibility with segmentation protocol dissemination

The segmentation of brain structures is a key component of many neuroimaging studies. Consistent anatomical definitions are crucial to ensure consensus on the position and shape of brain structures, but segmentations are prone to variation in their interpretation and execution. White-matter (WM) pathways are global structures of the brain defined by local landmarks, which leads to anatomical definitions being difficult to convey, learn, or teach. Moreover, the complex shape of WM pathways and their representation using tractography (streamlines) make the design and evaluation of dissection protocols difficult and time-consuming. The first iteration of Tractostorm quantified the variability of a pyramidal tract dissection protocol and compared results between experts in neuroanatomy and nonexperts. Despite virtual dissection being used for decades, in-depth investigations of how learning or practicing such protocols impact dissection results are nonexistent. To begin to fill the gap, we evaluate an online educational tractography course and investigate the impact learning and practicing a dissection protocol has on interrater (groupwise) reproducibility. To generate the required data to quantify reproducibility across raters and time, 20 independent raters performed dissections of three bundles of interest on five Human Connectome Project subjects, each with four timepoints. Our investigation shows that the dissection protocol in conjunction with an online course achieves a high level of reproducibility (between 0.85 and 0.90 for the voxel-based Dice score) for the three bundles of interest and remains stable over time (repetition of the protocol). Suggesting that once raters are familiar with the software and tasks at hand, their interpretation and execution at the group level do not drastically vary. When compared to previous work that used a different method of communication for the protocol, our results show that incorporating a virtual educational session increased reproducibility. Insights from this work may be used to improve the future design of WM pathway dissection protocols and to further inform neuroanatomical definitions.Peer reviewe

A planetary-scale disturbance in a long-living three-vortex coupled system in Saturn's atmosphere

The zonal wind profile of Saturn has a singular structure in the latitude range 50ºN-65ºN planetocentric, with a double peak that reaches maximum zonal velocities close to 100ms-1[1]. A survey of Cassini ISS images shows that a system of three vortices formed in this latitudinal region in 2012 and has remained active until present, confirming that vortices in Saturn can be long lived [2]. In May 2015 a disturbance started to develop at the location of the triple vortex. Since at the time Cassini orbits were not favorable to the observation of the region, we were granted Director Discretionary Time of the Hubble Space Telescope to observe the region before the perturbation faded away. Here we report the dynamics and vertical structure of the three-vortex system and of the disturbance that developed at its location, based on HST and Cassini images. We also present results of numerical models to explain the stability of vortices in the region.Peer ReviewedPostprint (published version