Two-photon imaging and analysis of neural network dynamics

The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behaviour. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so called 'microcircuits') remains comparably poor. In large parts, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near- millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.Comment: 36 pages, 4 figures, accepted for publication in Reports on Progress in Physic

Structural basis for the role of inhibition in facilitating adult brain plasticity

Although inhibition has been implicated in mediating plasticity in the adult brain, the underlying mechanism remains unclear. Here we present a structural mechanism for the role of inhibition in experience-dependent plasticity. Using chronic in vivo two-photon microscopy in the mouse neocortex, we show that experience drives structural remodeling of superficial layer 2/3 interneurons in an input- and circuit-specific manner, with up to 16% of branch tips undergoing remodeling. Visual deprivation initially induces dendritic branch retractions, and this is accompanied by a loss of inhibitory inputs onto neighboring pyramidal cells. The resulting decrease in inhibitory tone, also achievable pharmacologically using the antidepressant fluoxetine, provides a permissive environment for further structural adaptation, including addition of new synapse-bearing branch tips. Our findings suggest that therapeutic approaches that reduce inhibition, when combined with an instructive stimulus, could facilitate restructuring of mature circuits impaired by damage or disease, improving function and perhaps enhancing cognitive abilities

Toward a comprehensive account of orientation selectivity in the retina.

Retinal Ganglion Cells (RGCs) form functionally distinct signaling channels that selectively encode features of the visual input including direction of motion, contrast polarity, size, and color. A highly conserved visual channel amongst vertebrates conveys orientation selectivity, i.e., the selective firing of neuronal cells in response to elongated stimuli along a preferred orientation. Orientation selectivity is an apparent critical computation and several studies have reported aspects of it, including cell type identity in anatomical reconstructions, and functional characterization of at least four different identified RGC types. But how cell types in the different studies relate is not well resolved; the mechanisms that generate the orientation selective responses in mice remain incompletely understood; and the retinofugal projections of OS RGC types are unknown. The goal of this study was to comprehensively characterize Orientation Selective (OS) RGC types in the mouse retina, and to elucidate the mechanisms that contribute to their tuning properties. We used population calcium imaging and hierarchical clustering to identify orientation selective RCGs in retinal explants. We then targeted these cells for detailed morphological and electrophysiological study. Our survey of RGC populations and subsequent morphological analysis distinguished 10 morphological types with apparent OS tuning. Electrophysiological analysis of 5 types identified specific tuning mechanisms, including a type with tuned excitation and inhibition, and a type with just tuned inhibition. Retrograde tracing from dLGN indicates that OS cells project to the shell region of the dorsal Lateral Geniculate Nucleus (dLGN), indicating that at least some OS RGC types contribute to dLGN OS tuning. This work provides new insight into the morphology and function of RGC types that exhibit OS properties. Additional studies will be necessary to further solidify the full complement of OS types in the retina and resolve their detailed circuit-level mechanisms, synaptic partners, molecular profiles, and retinofugal projections

Decoding Pixel-Level Image Features from Two-Photon Calcium Signals of Macaque Visual Cortex

Images of visual scenes comprise essential features important for visual cognition of the brain. The complexity of visual features lies at different levels, from simple artificial patterns to natural images with different scenes. It has been a focus of using stimulus images to predict neural responses. However, it remains unclear how to extract features from neuronal responses. Here we address this question by leveraging two-photon calcium neural data recorded from the visual cortex of awake macaque monkeys. With stimuli including various categories of artificial patterns and diverse scenes of natural images, we employed a deep neural network decoder inspired by image segmentation technique. Consistent with the notation of sparse coding for natural images, a few neurons with stronger responses dominated the decoding performance, whereas decoding of ar tificial patterns needs a large number of neurons. When natural images using the model pretrained on artificial patterns are decoded, salient features of natural scenes can be extracted, as well as the conventional category information. Altogether, our results give a new perspective on studying neural encoding principles using reverse-engineering decoding strategies

Functional connectivity and dendritic integration of feedback in visual cortex

A fundamental question in neuroscience is how different brain regions communicate with each other. Sensory processing engages distributed circuits across many brain areas and involves information flow in the feedforward and feedback direction. While feedforward processing is conceptually well understood, feedback processing has remained mysterious. Cortico-cortical feedback axons are enriched in layer 1, where they form synapses with the apical dendrites of pyramidal neurons. The organization and dendritic integration of information conveyed by these axons, however, are unknown. This thesis describes my efforts to link the circuit-level and dendritic-level organization of cortico-cortical feedback in the mouse visual system. First, using cellular resolution all-optical interrogation across cortical areas, I characterized the functional connectivity between the lateromedial higher visual area (LM) and primary visual cortex (V1). Feedback influence had both facilitating and suppressive effects on visually-evoked activity in V1 neurons, and was spatially organized: retinotopically aligned feedback was relatively more suppressive, while retinotopically offset feedback was relatively more facilitating. Second, to examine how feedback inputs are integrated in apical dendrites, I optogenetically stimulated presynaptic neurons in LM while using 2-photon calcium imaging to map feedback-recipient spines in the apical tufts of layer 5 neurons in V1. Activation of a single feedback-providing input was sufficient to boost calcium signals and recruit branch-specific local events in the recipient dendrite, suggesting that feedback can engage dendritic nonlinearities directly. Finally, I measured the recruitment of apical dendrites during visual stimulus processing. Surround visual stimuli, which should recruit relatively more facilitating feedback, drove local calcium events in apical tuft branches. Moreover, global dendritic event size was not purely determined by somatic activity but modulated by visual stimuli and behavioural state, in a manner consistent with the spatial organization of feedback. In summary, these results point toward a possible involvement of active dendritic processing in the integration of feedback signals. Active dendrites could thus provide a biophysical substrate for the integration of essential top-down information streams, including contextual or predictive processing

Anatomical and functional characterization of neocortical circuits involved in transforming whisker sensory processing into goal-directed licking

The choice of an action upon perception of an external stimulus, arriving at a sensory organ of an animal, depends on previous experiences and outcomes throughout its life. In the rodent brain, the underlying mechanisms involved in simple sensorimotor transformations, such as the detection of a whisker stimulus through goal-directed licking, still remain largely unknown. In this thesis, using as a model the mouse somatosensory system, I explored the anatomical and functional properties of neuronal circuits at different stages of this cortical processing. To start with, using state-of-the-art viral tracing techniques, I investigated the thalamocortical circuits relaying sensory signals to the primary and secondary whisker somatosensory cortices (wS1, wS2). Challenging the "classical" views, the results indicated two streams of information carrying whisker-selective tactile signals. The principal trigeminal nucleus (Pr5) innervates the ventral posterior medial nucleus of the thalamus (VPM) and finally reaching layer 4 of wS1 while the spinal trigeminal nucleus (Sp5) through the rostral part of the posterior medial (POm) thalamus drives the layer 4 of wS2. Finally, a caudal part of the POm, which does not receive brainstem input, innervates layer 1 and layer 5A. Apart from their anatomical differences, those pathways conveyed distinct whisker sensory signals during goal-directed behaviors. Afterwards, I studied the cortical control of jaw and tongue movements during licking for rewards, using multisensory and multimotor whisker detection tasks. The data revealed a frontal tongue-jaw primary motor area (tjM1) which is necessary and encodes for directional licking, independently of the sensory stimulus type, shedding light on how the neocortex orchestrates the main motor output of the animal. Subsequently, I focused on changes in the L2/3 neuronal networks of wS1 after learning of a whisker stimulus. Using as a benchmark a novel "fast" learning and reward-dependent whisker detection task, I carried out inactivations of wS1 during different stages of learning and chronic two-photon (2P) calcium imaging in the L2/3 of the C2 barrel column. The inactivation results indicated that wS1 is indispensable for the acquisition of the novel stimulus and the execution of the task at expert levels. Moreover, the neural data suggested a learning-induced and "long-lasting" enhancement in the whisker sensory responses even when animals were unmotivated to lick. At a network level, a re-organization of the neuronal circuits was observed at different timescales with some of the alterations accompanying the rapid changes in the animal behavior. Additionally, the changes in the whisker sensory responses of neurons in wS1, after learning, were projection-pathway specific with wS2-projecting neurons showing higher whisker responses than whisker primary motor cortex (wM1)-projecting ones. In the final part, acknowledging the importance of a better characterization of the cortical-cortical communication of wS1, I described recent technical advancements in neuronal reconstructions. In vivo single-cell electroporation combined with 2P tomography and registration to a digital atlas, demonstrated the diversity of the projection targets of neurons in the L2/3 of wS1. Overall, I presented different results which contribute to a pre-existing body of research and help to decipher fundamentals and yet highly complex neural computations of the mammalian brain

A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data

Deducing the structure of neural circuits is one of the central problems of modern neuroscience. Recently-introduced calcium fluorescent imaging methods permit experimentalists to observe network activity in large populations of neurons, but these techniques provide only indirect observations of neural spike trains, with limited time resolution and signal quality. In this work we present a Bayesian approach for inferring neural circuitry given this type of imaging data. We model the network activity in terms of a collection of coupled hidden Markov chains, with each chain corresponding to a single neuron in the network and the coupling between the chains reflecting the network's connectivity matrix. We derive a Monte Carlo Expectation--Maximization algorithm for fitting the model parameters; to obtain the sufficient statistics in a computationally-efficient manner, we introduce a specialized blockwise-Gibbs algorithm for sampling from the joint activity of all observed neurons given the observed fluorescence data. We perform large-scale simulations of randomly connected neuronal networks with biophysically realistic parameters and find that the proposed methods can accurately infer the connectivity in these networks given reasonable experimental and computational constraints. In addition, the estimation accuracy may be improved significantly by incorporating prior knowledge about the sparseness of connectivity in the network, via standard L$_1$ penalization methods.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS303 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Cortex-wide response mode of VIP-expressing inhibitory neurons by reward and punishment

Neocortex is classically divided into distinct areas, each specializing in different function, but all could benefit from reinforcement feedback to inform and update local processing. Yet it remains elusive how global signals like reward and punishment are represented in local cortical computations. Previously, we identified a cortical neuron type, vasoactive intestinal polypeptide (VIP)-expressing interneurons, in auditory cortex that is recruited by behavioral reinforcers and mediates disinhibitory control by inhibiting other inhibitory neurons. As the same disinhibitory cortical circuit is present virtually throughout cortex, we wondered whether VIP neurons are likewise recruited by reinforcers throughout cortex. We monitored VIP neural activity in dozens of cortical regions using three-dimensional random access two-photon microscopy and fiber photometry while mice learned an auditory discrimination task. We found that reward and punishment during initial learning produce rapid, cortex-wide activation of most VIP interneurons. This global recruitment mode showed variations in temporal dynamics in individual neurons and across areas. Neither the weak sensory tuning of VIP interneurons in visual cortex nor their arousal state modulation was fully predictive of reinforcer responses. We suggest that the global response mode of cortical VIP interneurons supports a cell-type-specific circuit mechanism by which organism-level information about reinforcers regulates local circuit processing and plasticity

Cortical circuits for visual processing and epileptic activity propagation

The thesis focuses on the relationship between cortical connectivity and cortical function. The first part investigates how the fine scale connectivity between visual neurons determines their functional responses during physiological sensory processing. The second part ascertains how the mesoscopic scale connectivity between brain areas constrains the spread of abnormal activity during the propagation of focal cortical seizures. Part 1: Neurons in the primary visual cortex (V1) are tuned to retinotopic location, orientation and direction of motion. Such selectivity stems from the integration of inputs from hundreds of presynaptic neurons distributed across cortical layers. Yet, the functional principles that organize such presynaptic networks have only begun to be understood. To uncover them, I used monosynaptic rabies virus tracing to target a single pyramidal neuron in L2/3 (starter neuron) and trace its presynaptic partners. I combined this approach with two-photon microscopy in V1 to investigate the relationship between the activity of the starter cell, its presynaptic neurons and the surrounding excitatory population across cortical layers in awake animals. Part 2: Focal epilepsy involves excessive and synchronous cortical activity that propagates both locally and distally. Does this propagation follow the same functional circuits as normal cortical activity? I induced focal seizures in primary visual cortex (V1) of awake mice, and compared their propagation to the retinotopic organization of V1 and higher visual areas. I measured activity through simultaneous local field potential recordings and widefield calcium imaging, and observed prolonged seizures that were orders of magnitude larger than normal visual responses. I demonstrate that seizure start as standing waves (synchronous elevated activity in the focal V1 region and in corresponding retinotopic locations in higher areas) and then propagate both locally and into distal regions. These regions matched each other in retinotopy. I conclude that seizure propagation respects the connectivity underlying normal visual processing