87 research outputs found
Dense 4D nanoscale reconstruction of living brain tissue
Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structureâfunction relationships of the brainâs complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue
Designing Deep Learning Frameworks for Plant Biology
In recent years the parallel progress in high-throughput microscopy and deep learning drastically widened the landscape of possible research avenues in life sciences.
In particular, combining high-resolution microscopic images and automated imaging pipelines powered by deep learning dramatically reduced the manual annotation work required for quantitative analysis.
In this work, we will present two deep learning frameworks tailored to the needs of life scientists in the context of plant biology.
First, we will introduce PlantSeg, a software for 2D and 3D instance segmentation. The PlantSeg pipeline contains several pre-trained models for different microscopy modalities and multiple popular graph-based instance segmentation algorithms.
In the second part, we will present CellTypeGraph, a benchmark for quantitatively evaluating graph neural networks. The benchmark is designed to test the ability of machine learning methods to classify the types of cells in an \textit{Arabidopsis thaliana} ovules. CellTypeGraph's prime aim is to give a valuable tool to the geometric learning community, but at the same time it also offers a framework for plant biologists to perform fast and accurate cell type inference on new data
Learning Instance Segmentation from Sparse Supervision
Instance segmentation is an important task in many domains of automatic image processing, such as self-driving cars, robotics and microscopy data analysis. Recently, deep learning-based algorithms have brought image segmentation close to human performance. However, most existing models rely on dense groundtruth labels for training, which are expensive, time consuming and often require experienced annotators to perform the labeling. Besides the annotation burden, training complex high-capacity neural networks depends upon non-trivial expertise in the choice and tuning of hyperparameters, making the adoption of these models challenging for researchers in other fields.
The aim of this work is twofold. The first is to make the deep learning segmentation methods accessible to non-specialist. The second is to address the dense annotation problem by developing instance segmentation methods trainable with limited groundtruth data.
In the first part of this thesis, I bring state-of-the-art instance segmentation methods closer to non-experts by developing PlantSeg: a pipeline for volumetric segmentation of light microscopy images of biological tissues into cells. PlantSeg comes with a large repository of pre-trained models and delivers highly accurate results on a variety of samples and image modalities. We exemplify its usefulness to answer biological questions in several collaborative research projects.
In the second part, I tackle the dense annotation bottleneck by introducing SPOCO, an instance segmentation method, which can be trained from just a few annotated objects. It demonstrates strong segmentation performance on challenging natural and biological benchmark datasets at a very reduced manual annotation cost and delivers state-of-the-art results on the CVPPP benchmark.
In summary, my contributions enable training of instance segmentation models with limited amounts of labeled data and make these methods more accessible for non-experts, speeding up the process of quantitative data analysis
Detailing the Genetic and Environmental Influences Shared between Conventional and Electronic Cigarette Use Across Measures of Initiation and Past 12-Month Use
Introduction. Tobacco use is a public health crisis with nearly 500,000 Americans suffering premature mortality attributable to tobacco use in 2014. New development efforts have created new nicotine delivery systems whose health consequences are not yet fully understood such as electronic cigarettes (ECIG). It is possible there are shared genetic and environmental factors that influence an individualâs liability to initiate cigarette (CIG) or ECIG use, as both systems are designed to deliver nicotine.
Methods. Four study designs were used to resolve the genetic and environmental influences that underlie CIG and ECIG initiation. A twin study, scoping review, genome-wide association study (GWAS), and moderation model examined these potential sources of variation.
Results. The twin study suggested there were shared genetic factors between CIG and ECIG initiation. Univariate GWAS analysis of ECIG found no genome-wide significant hits among self-identified white participants. Genome-wide polygenic scores also showed no association between CIG and ECIG initiation. Statistical evidence of a weak interaction between ECIG coupon receipt, income level, and CIG use was reported. A review of tobacco use measures in genetically informative samples found that how individual studies measured different aspects of tobacco use lead to different genome-wide significant results.
Conclusions. These analyses suggest there are shared genetic and environmental influences between CIG and ECIG. Low sample sizes may have contributed to non-significant findings of measured molecular genetic effects, though genome-wide suggestive findings indicate further research is needed. Further, aggregating genome-wide association study results by biological function may increase the consistency of findings
Beyond language: The unspoken sensory-motor representation of the tongue in non-primates, non-human and human primates
The English idiom âon the tip of my tongueâ commonly acknowledges that something is known, but it cannot be immediately brought to mind. This phrase accurately describes sensorimotor functions of the tongue, which are fundamental for many tongue-related behaviors (e.g., speech), but often neglected by scientific research. Here, we review a wide range of studies conducted on non-primates, non-human and human primates with the aim of providing a comprehensive description of the cortical representation of the tongue's somatosensory inputs and motor outputs across different phylogenetic domains. First, we summarize how the properties of passive non-noxious mechanical stimuli are encoded in the putative somatosensory tongue area, which has a conserved location in the ventral portion of the somatosensory cortex across mammals. Second, we review how complex self-generated actions involving the tongue are represented in more anterior regions of the putative somato-motor tongue area. Finally, we describe multisensory response properties of the primate and non-primate tongue area by also defining how the cytoarchitecture of this area is affected by experience and deafferentation
Imaging the subthalamic nucleus in Parkinsonâs disease
This thesis is comprised of a set of work that aims to visualize and quantify the anatomy, structural variability, and connectivity of the subthalamic nucleus (STN) with optimized neuroimaging methods. The study populations include both healthy cohorts and individuals living with Parkinson's disease (PD). PD was chosen specifically due to the involvement of the STN in the pathophysiology of the disease. Optimized neuroimaging methods were primarily obtained using ultra-high field (UHF) magnetic resonance imaging (MRI). An additional component of this thesis was to determine to what extent UHF-MRI can be used in a clinical setting, specifically for pre-operative planning of deep brain stimulation (DBS) of the STN for patients with advanced PD. The thesis collectively demonstrates that i, MRI research, and clinical applications must account for the different anatomical and structural changes that occur in the STN with both age and PD. ii, Anatomical connections involved in preparatory motor control, response inhibition, and decision-making may be compromised in PD. iii. The accuracy of visualizing and quantifying the STN strongly depends on the type of MR contrast and voxel size. iv, MRI at a field strength of 3 Tesla (T) can under certain circumstances be optimized to produce results similar to that of 7 T at the expense of increased acquisition time
Magnetoencephalography for the investigation and diagnosis of Mild Traumatic Brain Injury
Mild Traumatic Brain Injury (mTBI), (or concussion), is the most common type of brain injury. Despite this, it often goes undiagnosed and can cause long term disabilityâmost likely caused by the disruption of axonal connections in the brain. Objective methods for diagnosis and prognosis are needed but clinically available neuroimaging modalities rarely show structural abnormalities, even when patients suffer persisting functional deficits. In the past three decades, new powerful techniques to image brain structure and function have shown promise in detecting mTBI related changes. Magnetoencephalography (MEG), which measures electrical brain activity by detecting magnetic fields outside the head generated by neural currents, is particularly sensitive and has therefore gained interest from researchers. Numerous studies are proposing abnormal low-frequency neural oscillations and functional connectivityâthe statistical interdependency of signals from separate brain regionsâas potential biomarkers for mTBI. However, typically small sample sizes, the lack of replication between groups, the heterogeneity of the cohorts studied, and the lack of longitudinal studies impedes the adoption of MEG as a clinical tool in mTBI management. In particular, little is known about the acute phase of mTBI.
In this thesis, some of these gaps will be addressed by analysing MEG data from individuals with mTBI, using novel as well as conventional methods. The potential future of MEG in mTBI research will also be addressed by testing the capabilities of a wearable MEG system based on optically pumped magnetometers (OPMs).
The thesis contains three main experimental studies. In study 1, we investigated the signal dynamics underlying MEG abnormalities, found in a cohort of subjects scanned within three months of an mTBI, using a Hidden Markov Model (HMM), as growing evidence suggests that neural dynamics are (in part) driven by transient bursting events. Applying the HMM to resting-state data, we show that previously reported findings of diminished intrinsic beta amplitude and connectivity in individuals with mTBI (compared to healthy controls) can be explained by a reduction in the beta-band content of pan-spectral bursts and a loss in the temporal coincidence of bursts respectively. Using machine learning, we find the functional connections driving group differences and achieve classification accuracies of 98%. In a motor task, mTBI resulted in reduced burst amplitude, altered modulation of burst probability during movement and decreased connectivity in the motor network.
In study 2, we further test our HMM-based method in a cohort of subjects with mTBI and non-head traumaâscanned within two weeks of injuryâto ensure specificity of any observed effects to mTBI and replicate our previous finding of reduced connectivity and high classification accuracy, although not the reduction in burst amplitude. Burst statistics were stable over both studiesâdespite data being acquired at different sites, using different scanners. In the same cohort, we applied a more conventional analysis of delta-band power. Although excess low-frequency power appears to be a promising candidate marker for persistently symptomatic mTBI, insufficient data exist to confirm this pattern in acute mTBI. We found abnormally high delta power to be a sensitive measure for discriminating mTBI subjects from healthy controls, however, similarly elevated delta amplitude was found in the cohort with non-head trauma, suggesting that excess delta may not be specific to mTBI, at least in the acute stage of injury.
Our work highlights the need for longitudinal assessment of mTBI. In addition, there appears to be a need to investigate naturalistic paradigms which can be tailored to induce activity in symptom-relevant brain networks and consequently are likely to be more sensitive biomarkers than the resting state scans used to date. Wearable OPM-MEG makes naturalistic scanning possible and may offer a cheaper and more accessible alternative to cryogenic MEG, however, before deploying OPMs clinically, or in pitch-side assessment for athletes, for example, the reliability of OPM-derived measures needs to be verified. In the third and final study, we performed a repeatability study using a novel motor task, estimating a series of common MEG measures and quantifying the reliability of both activity and connectivity derived from OPM-MEG data. These initial findingsâpresently limited to a small sample of healthy controlsâdemonstrate the utility of OPM-MEG and pave the way for this technology to be deployed on patients with mTBI
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