Bayesian network classifiers for categorizing cortical gABAergic interneurons

Abstract An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1–F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts’ assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell’s label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1–F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52 % accuracy, and single out the number of branches at 180 µm from the soma, the convex hull 2D area, and axonal features F1–F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons

New insights into the classification and nomenclature of cortical GABAergic interneurons.

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Abstract Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features

Brain Network Connectivity in Anaesthesia and Disorders of Consciousness

Until recently, understanding the nature of consciousness was considered a philosophical pursuit. However, technological developments in brain imaging have allowed the study of consciousness as a natural, neurobiological phenomenon. The neurobiology of consciousness has been studied using cognitive and behavioural testing in healthy volunteers and by examining how brain function and connectivity is altered in various clinical settings. The focus of this thesis is to use two of these clinical settings, pharmacologically-induced sedation and disorders of consciousness (DOC), as experimental models for measuring changes in connectivity patterns associated with alterations in consciousness. Experiment 1 presents a method for improving functional magnetic resonance imaging (fMRI) data pre-processing to measure brain network connectivity more accurately. This pre-processing method is then applied to the analyses in the remainder of the thesis. Experiment 2 focuses on a fMRI dataset in which healthy volunteers were administered propofol, an anaesthetic drug known to act on inhibitory GABAergic interneurons. Using a novel multimodal analysis, changes in functional brain network connectivity in default mode, salience, and frontoparietal control networks were found to correlate with the cortical distribution of parvalbumin-expressing GABAergic interneurons. Using the same dataset, Experiment 3 identified a relationship between structural and functional networks in connections between default mode and salience networks. Similar results have been reported in non-human primate models, however, this is the first study to find network-specific structure-function relationships during sedation in humans. These findings informed the remainder of the thesis, which focused on developing network-based machine learning methods for examining brain connectivity in patients with DOC. Experiment 4 developed and validated a graph convolutional neural network (GCNN) classifier using fMRI data and functional connectivity from healthy volunteers performing a volitional mental imagery task. Experiment 5 applied the GCNN to patients with DOC and found frontoparietal control network connectivity measured at rest to be most important in classifying patients capable of performing the mental imagery task. Taken together, these results contribute to the improvement of brain network analysis techniques, the understanding of the neurobiology of propofol-induced sedation, and the development of machine learning algorithms to identify DOC patients with preserved covert volitional capacity. This work demonstrates the utility of clinical models in deepening our understanding of the neurobiology of consciousness

Statistical analysis of neuronal data: Development of quantitative frameworks and application to microelectrode array analysis and cell type classification

With increasing amounts of data being collected in various fields of neuroscience, there is a growing need for robust techniques for the analysis of this information. This thesis focuses on the evaluation and development of quantitative frameworks for the analysis and classification of neuronal data from a variety of contexts. Firstly, I investigate methods for analysing spontaneous neuronal network activity recorded on microelectrode arrays (MEAs). I perform an unbiased evaluation of the existing techniques for detecting ‘bursts’ of neuronal activity in these types of recordings, and provide recommendations for the robust analysis of bursting activity in a range of contexts using both existing and adapted burst detection methods. These techniques are then used to analyse bursting activity in novel recordings of human induced pluripotent stem cell-derived neuronal networks. Results from this review of burst analysis methods are then used to inform the development of a framework for characterising the activity of neuronal networks recorded on MEAs, using properties of bursting as well as other common features of spontaneous activity. Using this framework, I examine the ontogeny of spontaneous network activity in in vitro neuronal networks from various brain regions, recorded on both single and multi-well MEAs. I also develop a framework for classifying these recordings according to their network type, based on quantitative features of their activity patterns. Next, I take a multi-view approach to classifying neuronal cell types using both the morphological and electrophysiological features of cells. I show that a number of multi-view clustering algorithms can more reliably differentiate between neuronal cell types in two existing data sets, compared to single-view clustering techniques applied to either the morphological or electrophysiological ‘view’ of the data, or a concatenation of the two views. To close, I examine the properties of the cell types identified by these methods.Supported by a Wellcome Trust PhD Studentship and a National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre Studentshi

Epilepsy

Epilepsy is the most common neurological disorder globally, affecting approximately 50 million people of all ages. It is one of the oldest diseases described in literature from remote ancient civilizations 2000-3000 years ago. Despite its long history and wide spread, epilepsy is still surrounded by myth and prejudice, which can only be overcome with great difficulty. The term epilepsy is derived from the Greek verb epilambanein, which by itself means to be seized and to be overwhelmed by surprise or attack. Therefore, epilepsy is a condition of getting over, seized, or attacked. The twelve very interesting chapters of this book cover various aspects of epileptology from the history and milestones of epilepsy as a disease entity, to the most recent advances in understanding and diagnosing epilepsy

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

NOVEL COMPUTATIONAL ELECTROENCEPHALOGRAPHIC (EEG) METHODOLOGIES FOR AUTISM MANAGEMENT AND EPILEPTIC SEIZURE PREDICTION

The doctoral thesis deals with a novel methodology of looking and processing electroencephalographic (EEG) data. The first part deals with real-time brain stimulation in the form of a sonified neurofeedback therapy derived from a clinically comparable portable, 4-channel EEG system. The therapy aims to provide an effective management for symptoms of the Autism Spectrum Disorder (ASD). ASD is characterized with a high level of delta electroencephalographic waveform levels, while alpha and beta prove to be present at lower levels especially in the frontal-temporal regions. The treatment aims at lowering delta waves and promoting alpha and beta waveforms. The second part of the thesis focuses on using EEG data in the prediction of epileptic seizures. With the help of custom built algorithms and neural networks, an effective prediction of an epileptic seizure can be achieved

Machine Learning As Tool And Theory For Computational Neuroscience

Computational neuroscience is in the midst of constructing a new framework for understanding the brain based on the ideas and methods of machine learning. This is effort has been encouraged, in part, by recent advances in neural network models. It is also driven by a recognition of the complexity of neural computation and the challenges that this poses for neuroscience’s methods. In this dissertation, I first work to describe these problems of complexity that have prompted a shift in focus. In particular, I develop machine learning tools for neurophysiology that help test whether tuning curves and other statistical models in fact capture the meaning of neural activity. Then, taking up a machine learning framework for understanding, I consider theories about how neural computation emerges from experience. Specifically, I develop hypotheses about the potential learning objectives of sensory plasticity, the potential learning algorithms in the brain, and finally the consequences for sensory representations of learning with such algorithms. These hypotheses pull from advances in several areas of machine learning, including optimization, representation learning, and deep learning theory. Each of these subfields has insights for neuroscience, offering up links for a chain of knowledge about how we learn and think. Together, this dissertation helps to further an understanding of the brain in the lens of machine learning