Interacting Turing-Hopf Instabilities Drive Symmetry-Breaking Transitions in a Mean-Field Model of the Cortex: A Mechanism for the Slow Oscillation

Electrical recordings of brain activity during the transition from wake to anesthetic coma show temporal and spectral alterations that are correlated with gross changes in the underlying brain state. Entry into anesthetic unconsciousness is signposted by the emergence of large, slow oscillations of electrical activity (≲1  Hz) similar to the slow waves observed in natural sleep. Here we present a two-dimensional mean-field model of the cortex in which slow spatiotemporal oscillations arise spontaneously through a Turing (spatial) symmetry-breaking bifurcation that is modulated by a Hopf (temporal) instability. In our model, populations of neurons are densely interlinked by chemical synapses, and by interneuronal gap junctions represented as an inhibitory diffusive coupling. To demonstrate cortical behavior over a wide range of distinct brain states, we explore model dynamics in the vicinity of a general-anesthetic-induced transition from “wake” to “coma.” In this region, the system is poised at a codimension-2 point where competing Turing and Hopf instabilities coexist. We model anesthesia as a moderate reduction in inhibitory diffusion, paired with an increase in inhibitory postsynaptic response, producing a coma state that is characterized by emergent low-frequency oscillations whose dynamics is chaotic in time and space. The effect of long-range axonal white-matter connectivity is probed with the inclusion of a single idealized point-to-point connection. We find that the additional excitation from the long-range connection can provoke seizurelike bursts of cortical activity when inhibitory diffusion is weak, but has little impact on an active cortex. Our proposed dynamic mechanism for the origin of anesthetic slow waves complements—and contrasts with—conventional explanations that require cyclic modulation of ion-channel conductances. We postulate that a similar bifurcation mechanism might underpin the slow waves of natural sleep and comment on the possible consequences of chaotic dynamics for memory processing and learning

Neural connectivity predicts spreading of alpha-synuclein pathology in fibril-injected mouse models: Involvement of retrograde and anterograde axonal propagation.

In Parkinson's disease, some of the first alpha-synuclein aggregates appear in the olfactory system and the dorsal motor nucleus of the vagus nerve before spreading to connected brain regions. We previously demonstrated that injection of alpha-synuclein fibrils unilaterally into the olfactory bulb of wild type mice leads to widespread synucleinopathy in brain regions directly and indirectly connected to the injection site, consistently, over the course of periods longer than 6 months. Our previously reported observations support the idea that alpha-synuclein inclusions propagates between brain region through neuronal networks. In the present study, we further defined the pattern of propagation of alpha-synuclein inclusions and developed a mathematical model based on known mouse brain connectivity. Using this model, we first predicted the pattern of alpha-synuclein inclusions propagation following an injection of fibrils into the olfactory bulb. We then analyzed the fitting of these predictions to our published histological data. Our results demonstrate that the pattern of propagation we observed in vivo is consistent with axonal transport of alpha-synuclein aggregate seeds, followed by transsynaptic transmission. By contrast, simple diffusion of alpha-synuclein fits very poorly our in vivo data. We also found that the spread of alpha-synuclein inclusions appeared to primarily follow neural connections retrogradely until 9 months after injection into the olfactory bulb. Thereafter, the pattern of spreading was consistent with anterograde propagation mathematical models. Finally, we applied our mathematical model to a different, previously published, dataset involving alpha-synuclein fibril injections into the striatum, instead of the olfactory bulb. We found that the mathematical model accurately predicts the reported progressive increase in alpha-synuclein neuropathology also in that paradigm. In conclusion, our findings support that the progressive spread of alpha-synuclein inclusions after injection of protein fibrils follows neural networks in the mouse connectome

Cell-cell communication enhances the capacity of cell ensembles to sense shallow gradients during morphogenesis

Collective cell responses to exogenous cues depend on cell-cell interactions. In principle, these can result in enhanced sensitivity to weak and noisy stimuli. However, this has not yet been shown experimentally, and, little is known about how multicellular signal processing modulates single cell sensitivity to extracellular signaling inputs, including those guiding complex changes in the tissue form and function. Here we explored if cell-cell communication can enhance the ability of cell ensembles to sense and respond to weak gradients of chemotactic cues. Using a combination of experiments with mammary epithelial cells and mathematical modeling, we find that multicellular sensing enables detection of and response to shallow Epidermal Growth Factor (EGF) gradients that are undetectable by single cells. However, the advantage of this type of gradient sensing is limited by the noisiness of the signaling relay, necessary to integrate spatially distributed ligand concentration information. We calculate the fundamental sensory limits imposed by this communication noise and combine them with the experimental data to estimate the effective size of multicellular sensory groups involved in gradient sensing. Functional experiments strongly implicated intercellular communication through gap junctions and calcium release from intracellular stores as mediators of collective gradient sensing. The resulting integrative analysis provides a framework for understanding the advantages and limitations of sensory information processing by relays of chemically coupled cells.Comment: paper + supporting information, total 35 pages, 15 figure

Predicting Bankruptcy with Support Vector Machines

The purpose of this work is to introduce one of the most promising among recently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to default probability estimation is proposed. A survey of practically applied methods is given. This work shows that support vector machines are capable of extracting useful information from financial data, although extensive data sets are required in order to fully utilize their classification power.support vector machine, classification method, statistical learning theory, electric load prediction, optical character recognition, predicting bankruptcy, risk classification

Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in McLaughlin, G. A., Langdon, E. M., Crutchley, J. M., Holt, L. J., Forest, M. G., Newby, J. M., & Gladfelter, A. S. (2020). Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking. Molecular Biology of the Cell, 31(14), 1498-1511, doi:10.1091/mbc.E20-03-0210.The spatial structure and physical properties of the cytosol are not well understood. Measurements of the material state of the cytosol are challenging due to its spatial and temporal heterogeneity. Recent development of genetically encoded multimeric nanoparticles (GEMs) has opened up study of the cytosol at the length scales of multiprotein complexes (20-60 nm). We developed an image analysis pipeline for 3D imaging of GEMs in the context of large, multinucleate fungi where there is evidence of functional compartmentalization of the cytosol for both the nuclear division cycle and branching. We applied a neural network to track particles in 3D and then created quantitative visualizations of spatially varying diffusivity. Using this pipeline to analyze spatial diffusivity patterns, we found that there is substantial variability in the properties of the cytosol. We detected zones where GEMs display especially low diffusivity at hyphal tips and near some nuclei, showing that the physical state of the cytosol varies spatially within a single cell. Additionally, we observed significant cell-to-cell variability in the average diffusivity of GEMs. Thus, the physical properties of the cytosol vary substantially in time and space and can be a source of heterogeneity within individual cells and across populations.We would like to thank the 2016 Physiology course and Christina Termini at the Marine Biological Laboratory in Woods Hole, MA, Gregory Brittingham, and Marcus Roper for initial experiments and perspectives on pipeline. We thank David Adalsteinsson for help with DataTank software and many conversations about image analysis on large datasets. We thank Emmanual Levy (Weizmann Institute) for providing plasmids encoding synthetic phase separating peptides. This work was supported by Google Cloud, the National Science Foundation (NSF), the National Institutes of Health (NIH), and the Natural Sciences and Engineering Research Council of Canada (NSERC). ASG, EML, and GAM were supported by the NSF (RoLs: 1840273), HHMI faculty scholar award and the NIH (R01GM081506). JMN was supported by the NSERC (RGPIN-2019-06435, RGPAS-2019-00014, DGECR-2019-00321) and the NSF (DMS-171474). MGF was supported by the NSF (DMS-1816630, DMS-1664645). LJH was supported by the NIH (R01GM132447)

Novel contrasts in photoacoustic tomography

Photoacoustic tomography (PAT) combines rich optical contrast and high ultrasonic resolution in optically scattering tissue at depths. Taking advantage of its 100% sensitivity to optical absorption, PAT has been widely applied to structural, functional and molecular imaging, with both endogenous and exogenous contrasts, at superior depths than pure optical methods. This dissertation explores novel absorption contrast mechanisms of PAT based on optical/thermal patterns, endogenous cellular chromophores, nanoparticles, small-molecule dyes and genetically-encoded proteins. With these novel contrasts, the proof-of-concept applications of PAT have been extended to include homogenous flow measurements, targeted angiogenesis imaging and therapy, label-free white blood cell imaging, 3D-whole-organ cell nuclei imaging with a subcellular resolution, and in vivo neural activity imaging with voltage/calcium-sensitive indicators. Specifically, Chapter 1 introduces photoacoustic microscopy (PAM) and photoacoustic computed tomography (PACT) systems and discuss the motivation of the dissertation. Chapter 2 describes two photoacoustic (PA) flow measurement methods with optical and thermal patterns, which are applicable to homogenous flowing medium. In the first method, a Doppler frequency shift in PA signals of the flow was detected and used to calculate flow speeds. In the second method, unique features in an externally imposed thermal pattern of the flow, captured by repeated B-scans along the flow direction with a PAM system, revealed different flow speeds. Chapter 3 explores the unique PA contrast of macrophages, an important type of white blood cells. Macrophages were imaged by PAM without any label, and their measured PA spectrum was distinctive from the hemoglobin spectrum, so they can be potentially differentiated from red blood cells in the blood stream. Next, with a microtomy-assisted PAM system, cell nuclei distribution in whole organs, including mouse brain and mouse lung, were imaged with subcellular resolution. Chapter 4 introduces a type of target copper nanoparticles, which are less expensive and more biocompatible than its counterpart gold nanoparticles. The PA signals of neovasculature in the mouse flank were enhanced by the ___3-targeted copper nanoparticles. Moreover, the work shows the first example of a systemically targeted antiangiogenic drug delivery with a photoacoustic contrast nanoparticle in vivo. Chapter 5 demonstrates the voltage imaging capability of PA. A voltage sensitive dye with sufficient signal change was discovered and used as a PA voltage indicator for the first time. The mechanism was characterized through both PA imaging and spectroscopic methods. Its use was explored in a mouse epilepsy model and cortical electrical stimulation model in vivo. Finally, the deep imaging potential of PA was realized by imaging the voltage response of cells under 4.5 mm thick slice of rat brain tissue using a PACT system. Chapter 6 proves the neural calcium imaging capability of PA with a genetically encoded calcium indicator. In a fly model, I ambiguously demonstrated for the first time that PA can be used to imaging neural activities in the fly brain without the interference signals from hemoglobin. In the a live-mouse-brain-slice model, I successfully demonstrated the deep imaging capability of PA for calcium imaging by imaging through a 2-mm-thick scattering medium with a PACT system

Odor coding and memory traces in the antennal lobe of honeybee

In dieser Arbeit werden zwei wesentliche neue Ergebnisse vorgestellt. Das erste bezieht sich auf die olfaktorische Kodierung und das zweite auf das sensorische Gedaechtnis. Beide Phaenomene werden am Beispiel des Gehirns der Honigbiene untersucht. In Bezug auf die olfaktorische Kodierung zeige ich, dass die neuronale Dynamik waehrend der Stimulation im Antennallobus duftspezifische Trajektorien beschreibt, die in duftspezifischen Attraktoren enden. Das Zeitinterval, in dem diese Attraktoren erreicht werden, betraegt unabhaengig von der Identitaet und der Konzentration des Duftes ungefaehr 800 ms. Darueber hinaus zeige ich, dass Support-Vektor Maschinen, und insbesondere Perzeptronen, ein realistisches und biologisches Model der Wechselwirkung zwischen dem Antennallobus (dem kodierenden Netwerk) und dem Pilzkoerper (dem dekodierenden Netzwerk) darstellen. Dieses Model kann sowohl Reaktionszeiten von ca. 300 ms als auch die Invarianz der Duftwahrnehmung gegenueber der Duftkonzentration erklaeren. In Bezug auf das sensorische Gedaechtnis zeige ich, dass eine einzige Stimulation ohne Belohnung dem Hebbschen Postulat folgend Veraenderungen der paarweisen Korrelationen zwischen Glomeruli induziert. Ich zeige, dass diese Veranderungen der Korrelationen bei 2/3 der Bienen ausreichen, um den letzten Stimulus zu bestimmen. In der zweiten Minute nach der Stimulation ist eine erfolgreiche Bestimmung des Stimulus nur bei 1/3 der Bienen moeglich. Eine Hauptkomponentenanalyse der spontanen Aktivitaet laesst erkennen, dass das dominante Muster des Netzwerks waehrend der spontanen Aktivitaet nach, aber nicht vor der Stimulation das duftinduzierte Aktivitaetsmuster bei 2/3 der Bienen nachbildet. Man kann deshalb die duftinduzierten (Veraenderungen der) Korrelationen als Spuren eines Kurzzeitgedaechtnisses bzw. als Hebbsche "Reverberationen" betrachtet werden.Two major novel results are reported in this work. The first concerns olfactory coding and the second concerns sensory memory. Both phenomena are investigated in the brain of the honeybee as a model system. Considering olfactory coding I demonstrate that the neural dynamics in the antennal lobe describe odor-specific trajectories during stimulation that converge to odor-specific attractors. The time interval to reach these attractors is, regardless of odor identity and concentration, approximately 800 ms. I show that support-vector machines and, in particular perceptrons provide a realistic and biological model of the interaction between the antennal lobe (coding network) and the mushroom body (decoding network). This model can also account for reaction-times of about 300 ms and for concentration invariance of odor perception. Regarding sensory memory I show that a single stimulation without reward induces changes of pairwise correlation between glomeruli in a Hebbian-like manner. I demonstrate that those changes of correlation suffice to retrieve the last stimulus presented in 2/3 of the bees studied. Succesful retrieval decays to 1/3 of the bees within the second minute after stimulation. In addition, a principal-component analysis of the spontaneous activity reveals that the dominant pattern of the network during the spontaneous activity after, but not before stimulation, reproduces the odor-induced activity pattern in 2/3 of the bees studied. One can therefore consider the odor-induced (changes of) correlation as traces of a short-term memory or as Hebbian reverberations