Automated imaging system for fast quantitation of neurons, cell morphology and neurite morphometry in vivo and in vitro

Producción CientíficaQuantitation of neurons using stereologic approaches reduces bias and systematic error, but is time-consuming and labor-intensive. Accurate methods for quantifying neurons in vitro are lacking; conventional methodologies are limited in reliability and application. The morphological properties of the soma and neurites are a key aspect of neuronal phenotype and function, but the assays commonly used in such evaluations are beset with several methodological drawbacks. Herein we describe automated techniques to quantify the number and morphology of neurons (or any cell type, e.g., astrocytes) and their processes with high speed and accuracy. Neuronal quantification from brain tissue using a motorized stage system yielded results that were statistically comparable to those generated by stereology. The approach was then adapted for in vitro neuron and neurite outgrowth quantification. To determine the utility of our methods, rotenone was used as a neurotoxicant leading to morphological changes in neurons and cell death, astrocytic activation, and loss of neurites. Importantly, our technique counted about 8 times as many neurons in less than 5-10% of the time taken by manual stereological analysis

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

Subcellular distributions of peroxisomes and endoplasmic reticulum in hippocampal neurons

In summary, the work described in the present thesis focused on analysis of polarized positioning of neuronal constituents in different compartments, including GABAergic synapses, CO (cisternal organelle), and POs (peroxisomes). The potential correlation between the heterogeneity of neuronal morphology and polarized distribution of subcellular compartments was discussed. The results imply that the differential distribution of inhibitory GABAergic terminals at the AIS - which are highly relevant to control AP generation – could play a role in homeostasis of neuronal excitability potentially relevant for neuronal network function in vivo. The AIS-associated GABA-terminals are found in proximity to a distinct type of axonal ER – the so-called CO. The distribution of such CO does correlate with morphological variation of AIS (AcD vs. nonAcD). A tight control in the distribution of other major organelles, such as POs, is equally vital for the normal function of a neuron. Organelle contacts of POs most likely serve as a major factor in regulating their distribution and motility in distinct cell types. Hence, the newly identified PO tether ACBD5 was used to evaluate the impact of organelle contacts on the positioning of POs in hippocampal neurons. Eventually, the alteration of ACBD5 expression in neurons leads to significant redistribution and decreased motility of POs, strongly suggesting that ACBD5 plays an important role in regulating motility and distribution of neuronal POs

MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale

Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States

Investigating temporal variability of functional connectivity is an emerging field in connectomics. Entering dynamic functional connectivity by applying sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged from this topic. We introduce frequency-resolved dynamic functional connectivity (frdFC) by means of multivariate empirical mode decomposition (MEMD) followed up by filter-bank investigations. In general, we find that MEMD is capable of generating time courses to perform frdFC and we discover that the structure of connectivity-states is robust over frequency scales and even becomes more evident with decreasing frequency. This scale-stability varies with the number of extracted clusters when applying k-means. We find a scale-stability drop-off from k = 4 to k = 5 extracted connectivity-states, which is corroborated by null-models, simulations, theoretical considerations, filter-banks, and scale-adjusted windows. Our filter-bank studies show that filter design is more delicate in the rs-fMRI than in the simulated case. Besides offering a baseline for further frdFC research, we suggest and demonstrate the use of scale-stability as a possible quality criterion for connectivity-state and model selection. We present first evidence showing that connectivity-states are both a multivariate, and a multiscale phenomenon. A data repository of our frequency-resolved time-series is provided

Improved split fluorescent proteins for endogenous protein labeling.

Self-complementing split fluorescent proteins (FPs) have been widely used for protein labeling, visualization of subcellular protein localization, and detection of cell-cell contact. To expand this toolset, we have developed a screening strategy for the direct engineering of self-complementing split FPs. Via this strategy, we have generated a yellow-green split-mNeonGreen21-10/11 that improves the ratio of complemented signal to the background of FP1-10-expressing cells compared to the commonly used split GFP1-10/11; as well as a 10-fold brighter red-colored split-sfCherry21-10/11. Based on split sfCherry2, we have engineered a photoactivatable variant that enables single-molecule localization-based super-resolution microscopy. We have demonstrated dual-color endogenous protein tagging with sfCherry211 and GFP11, revealing that endoplasmic reticulum translocon complex Sec61B has reduced abundance in certain peripheral tubules. These new split FPs not only offer multiple colors for imaging interaction networks of endogenous proteins, but also hold the potential to provide orthogonal handles for biochemical isolation of native protein complexes.Split fluorescent proteins (FPs) have been widely used to visualise proteins in cells. Here the authors develop a screen for engineering new split FPs, and report a yellow-green split-mNeonGreen2 with reduced background, a red split-sfCherry2 for multicolour labeling, and its photoactivatable variant for super-resolution use

Estimating the subjective perception of object size and position through brain imaging and psychophysics

Perception is subjective and context-dependent. Size and position perception are no exceptions. Studies have shown that apparent object size is represented by the retinotopic location of peak response in V1. Such representation is likely supported by a combination of V1 architecture and top-down driven retinotopic reorganisation. Are apparent object size and position encoded via a common mechanism? Using functional magnetic resonance imaging and a model-based reconstruction technique, the first part of this thesis sets out to test if retinotopic encoding of size percepts can be generalised to apparent position representation and whether neural signatures could be used to predict an individual’s perceptual experience. Here, I present evidence that static apparent position – induced by a dot-variant Muller-Lyer illusion – is represented retinotopically in V1. However, there is mixed evidence for retinotopic representation of motion-induced position shifts (e.g. curveball illusion) in early visual areas. My findings could be reconciled by assuming dual representation of veridical and percept-based information in early visual areas, which is consistent with the larger framework of predictive coding. The second part of the thesis sets out to compare different psychophysical methods for measuring size perception in the Ebbinghaus illusion. Consistent with the idea that psychophysical methods are not equally susceptible to cognitive factors, my experiments reveal a consistent discrepancy in illusion magnitude estimates between a traditional forced choice (2AFC) task and a novel perceptual matching (PM) task – a variant of a comparison-of-comparisons (CoC) task, a design widely seen as the gold standard in psychophysics. Further investigation reveals the difference was not driven by greater 2AFC susceptibility to cognitive factors, but a tendency for PM to skew illusion magnitude estimates towards the underlying stimulus distribution. I show that this dependency can be largely corrected using adaptive stimulus sampling

Velocimetry-based pressure information for spray analysis – novel experimental, processing and evaluation strategies

In der vorliegenden Arbeit wurde der Spraytransport von komplexen Benzindirekteinspritzungssprays (GDI) mittels auf Geschwindigkeitmessung basierter Druckauswertung untersucht. Für diesen Zweck wurden neue Versuchs-, Verarbeitungs- und Auswertestrategien eingeführt, um eine Druckauswertung der Spray-induzierten Strömung zu befähigen und deren Möglichkeiten auszuweiten. Dies umfasst unter anderem ein statistisches Verfahren auf Basis der Unsteady Reynolds-Averaged Navier-Stokes (URANS) Gleichungen und Ensemble-Mittelung, welche die Druckauswertung transienter, statistisch stationärer Strömungen mittels konventioneller Particle Image Velocimetry (PIV) ermöglicht. Darüber hinaus wurde eine neuartige Technik namens Dual-Plane-Stereo-Astigmatismus (DPSA) entwickelt, die die Auswertung momentaner Druckfelder und damit die Analyse einzelner Einspritzereignisse unter Verwendung eines stereoskopischen Aufbaus und einer einzigen Lichtquelle ermöglicht. Abschließend wurde die Methode der Physics-Informed Neural Networks (PINNs) erfolgreich aus dem Bereich des Deep Learnings in die experimentelle Strömungsmechanik und Spray-Analyse übertragen. Das PINN-Verfahren weitet die Möglichkeiten der bisherigen auf Geschwindigkeitsmessung basierenden Druckauswertung aus und ermöglicht die Auswertung von bislang nicht auswertbaren Strömungsbereichen, sowohl in Raum und Zeit. Unter Verwendung der beschriebenen Methoden wurde die Wechselwirkung zwischen Spray und Umgebungsgasströmung für unterschiedliche Betriebsbedingungen und Sprayauslegungen untersucht. Es zeigte sich, dass der Impulsaustausch mit höherem Einspritzdruck, Gasdichte, Kraftstofftemperatur, größerer Relativgeschwindigkeit, Spray-Gas-Grenzfläche, Sprayexpansion und stärkerer Zerstäubung bzw. Flash-Boiling zunimmt. Als eine wesentliche Erkenntnis wurde festgestellt, dass die Ablenkung von Sprays bzw. das Phänomen der Strahl-zu-Strahl-Wechselwirkung und Spraykontraktion auf einen Nettoimpuls zurückzuführen ist, der auf einzelne Spraykeulen infolge von induzierten Druckkräften wirkt. In diesem Zusammenhang wurde das Vorhandensein eines Niederdruckgebiets im Zentrum von Mehrlochsprays experimentell bestätigt. Es wurde aufgezeigt, dass das Ausmaß der Strahl-zu-Strahl-Wechselwirkung und der Spraykontraktion durch eine enge Spritzlochanordnung und -ausrichtung, eine starke Zerstäubung und ein erhöhtes Tropfen-Folgeverhalten begünstigt wird