Coordinated neuronal ensembles in primary auditory cortical columns.

The synchronous activity of groups of neurons is increasingly thought to be important in cortical information processing and transmission. However, most studies of processing in the primary auditory cortex (AI) have viewed neurons as independent filters; little is known about how coordinated AI neuronal activity is expressed throughout cortical columns and how it might enhance the processing of auditory information. To address this, we recorded from populations of neurons in AI cortical columns of anesthetized rats and, using dimensionality reduction techniques, identified multiple coordinated neuronal ensembles (cNEs), which are groups of neurons with reliable synchronous activity. We show that cNEs reflect local network configurations with enhanced information encoding properties that cannot be accounted for by stimulus-driven synchronization alone. Furthermore, similar cNEs were identified in both spontaneous and evoked activity, indicating that columnar cNEs are stable functional constructs that may represent principal units of information processing in AI

Stability of spontaneous, correlated activity in mouse auditory cortex

Neural systems can be modeled as networks of functionally connected neural elements. The resulting network can be analyzed using mathematical tools from network science and graph theory to quantify the system's topological organization and to better understand its function. While the network-based approach is common in the analysis of large-scale neural systems probed by non-invasive neuroimaging, few studies have used network science to study the organization of networks reconstructed at the cellular level, and thus many very basic and fundamental questions remain unanswered. Here, we used two-photon calcium imaging to record spontaneous activity from the same set of cells in mouse auditory cortex over the course of several weeks. We reconstruct functional networks in which cells are linked to one another by edges weighted according to the correlation of their fluorescence traces. We show that the networks exhibit modular structure across multiple topological scales and that these multi-scale modules unfold as part of a hierarchy. We also show that, on average, network architecture becomes increasingly dissimilar over time, with similarity decaying monotonically with the distance (in time) between sessions. Finally, we show that a small fraction of cells maintain strongly-correlated activity over multiple days, forming a stable temporal core surrounded by a fluctuating and variable periphery. Our work provides a careful methodological blueprint for future studies of spontaneous activity measured by two-photon calcium imaging using cutting-edge computational methods and machine learning algorithms informed by explicit graphical models from network science. The methods are easily extended to additional datasets, opening the possibility of studying cellular level network organization of neural systems and how that organization is modulated by stimuli or altered in models of disease.Comment: 15 pages, 3 figure

Periodic patterns in human mobility

The recent rise of services and networks that rely on human mobility has prompted the need for tools that detect our patterns of visits to locations and encounters with other individuals. The widespread popularity of location- and encounter-aware mobile phones has given us a wealth of empirical mobility data and enabled many novel applications that benefit from automated detection of an individual’s mobility patterns. This thesis explores the presence and character of periodic patterns in the visits and encounters of human individuals. Novel tools for extracting and analysing periodic mobility patterns are proposed and evaluated on real-world data. We investigate these patterns in a range of datasets, including visits to public transport stations on a metropolitan scale, university campus WLAN access point transitions, online location-sharing service checkins, and Bluetooth encounters among university students. The methods developed in this thesis are designed for decentralised implementation to enable their real-world deployment. Analysing an individual’s visit and encounter events is a challenging problem since the data are often highly sparse. In order to study visit patterns we propose a novel inter-event interval (IEI) analysis approach, which is inspired by neural coding techniques. The resulting measure, IEI-irregularity, quantifies the weekly periodic patterns of an individual’s visits to a location. To detect encounter patterns we propose and compare methods based on IEI analysis and periodic subgraph mining. In particular, we introduce the novel concept of a periodic encounter community; that is, a collection of individuals that share the same periodic encounter pattern. The decentralised algorithms we develop for periodic encounter community detection are of particular relevance to human-based opportunistic communication networks. We explore these communities in terms of their opportunistic content sharing performance. Our findings show that periodic patterns are a prominent feature of human mobility and that these patterns are algorithmically detectabl

Information processing by coordinated neuronal ensembles in the primary auditory cortex

The primary auditory cortex (AI) is made up of highly interconnected populations of neurons that are responsible for integrating bottom-up auditory information from the lemniscal auditory pathway and top-down inputs from higher-order cortical areas. Despite that, most studies of information processing in AI focus on either single-unit spectro-temporal receptive field (STRF) estimation, or paired neuronal correlation analyses, and assume that AI neurons filter auditory information either as individual entities or pairs. Meanwhile, some recent studies have also shown how populations of AI neurons can also encode auditory behavior. Determining how AI encodes information will hence require an integrated approach that combines receptive field and multi-neuronal ensemble analyses. In this dissertation, I show that I can accurately detect coordinated neuronal ensembles (cNEs), which we define as groups of neurons that have reliable synchronous activity, in AI. These cNEs are meaningful constructs that are active in both spontaneous and evoked activity, and their synchronous evoked activity cannot be trivially explained by receptive field overlap. cNEs also come in two flavors – one of them enhances stimulus representation over single neurons or simultaneously recorded random groups of neurons of the same size, while the other does not represent spectro-temporal features at all, and might reflect internally generated neuronal activity. Since single neurons can participate in multiple cNEs over the course of a recording, I also show that neurons can multiplex information, and encode slightly different spectro-temporal information, if they encode spectro-temporal information at all, when associated with different cNEs. Meanwhile, the enhancement of information processing and the reliability of representation of the stimulus by cNEs suggest that cNEs should be considered the principal unit of information processing in AI

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

Reconstruction of neuronal activity and connectivity patterns in the zebrafish olfactory bulb

In the olfactory bulb (OB), odors evoke distributed patterns of activity across glomeruli that are reorganized by networks of interneurons (INs). This reorganization results in multiple computations including a decorrelation of activity patterns across the output neurons, the mitral cells (MCs). To understand the mechanistic basis of these computations it is essential to analyze the relationship between function and structure of the underlying circuit. I combined in vivo twophoton calcium imaging with dense circuit reconstruction from complete serial block-face electron microscopy (SBEM) stacks of the larval zebrafish OB (4.5 dpf) with a voxel size of 9x9x25nm. To address bottlenecks in the workflow of SBEM, I developed a novel embedding and staining procedure that effectively reduces surface charging in SBEM and enables to acquire SBEM stacks with at least a ten-fold increase in both, signal-to-noise as well as acquisition speed. I set up a high throughput neuron reconstruction pipeline with >30 professional tracers that is available for the scientific community (ariadne-service.com). To assure efficient and accurate circuit reconstruction, I developed PyKNOSSOS, a Python software for skeleton tracing and synapse annotation, and CORE, a skeleton consolidation procedure that combines redundant reconstruction with targeted expert input. Using these procedures I reconstructed all neurons (>1000) in the larval OB. Unlike in the adult OB, INs were rare and appeared to represent specific subtypes, indicating that different sub-circuits develop sequentially. MCs were uniglomerular whereas inter-glomerular projections of INs were complex and biased towards groups of glomeruli that receive input from common types of sensory neurons. Hence, the IN network in the OB exhibits a topological organization that is governed by glomerular identity. Calcium imaging revealed that the larval OB circuitry already decorrelates activity patterns evoked by similar odors. The comparison of inter-glomerular connectivity to the functional interactions between glomeruli indicates that pattern decorrelation depends on specific, non-random inter-glomerular IN projections. Hence, the topology of IN networks in the OB appears to be an important determinant of circuit function

Neural processes underpinning episodic memory

Episodic memory is the memory for our personal past experiences. Although numerous functional magnetic resonance imaging (fMRI) studies investigating its neural basis have revealed a consistent and distributed network of associated brain regions, surprisingly little is known about the contributions individual brain areas make to the recollective experience. In this thesis I address this fundamental issue by employing a range of different experimental techniques including neuropsychological testing, virtual reality environments, whole brain and high spatial resolution fMRI, and multivariate pattern analysis. Episodic memory recall is widely agreed to be a reconstructive process, one that is known to be critically reliant on the hippocampus. I therefore hypothesised that the same neural machinery responsible for reconstruction might also support ‘constructive’ cognitive functions such as imagination. To test this proposal, patients with focal damage to the hippocampus bilaterally were asked to imagine new experiences and were found to be impaired relative to matched control participants. Moreover, driving this deficit was a lack of spatial coherence in their imagined experiences, pointing to a role for the hippocampus in binding together the disparate elements of a scene. A subsequent fMRI study involving healthy participants compared the recall of real memories with the construction of imaginary memories. This revealed a fronto-temporo-parietal network in common to both tasks that included the hippocampus, ventromedial prefrontal, retrosplenial and parietal cortices. Based on these results I advanced the notion that this network might support the process of ‘scene construction’, defined as the generation and maintenance of a complex and coherent spatial context. Furthermore, I argued that this scene construction network might underpin other important cognitive functions besides episodic memory and imagination, such as navigation and thinking about the future. It is has been proposed that spatial context may act as the scaffold around which episodic memories are built. Given the hippocampus appears to play a critical role in imagination by supporting the creation of a rich coherent spatial scene, I sought to explore the nature of this hippocampal spatial code in a novel way. By combining high spatial resolution fMRI with multivariate pattern analysis techniques it proved possible to accurately determine where a subject was located in a virtual reality environment based solely on the pattern of activity across hippocampal voxels. For this to have been possible, the hippocampal population code must be large and non-uniform. I then extended these techniques to the domain of episodic memory by showing that individual memories could be accurately decoded from the pattern of activity across hippocampal voxels, thus identifying individual memory traces. I consider these findings together with other recent advances in the episodic memory field, and present a new perspective on the role of the hippocampus in episodic recollection. I discuss how this new (and preliminary) framework compares with current prevailing theories of hippocampal function, and suggest how it might account for some previously contradictory data