Db Omer

Independent component analysis of high-resolution imaging data identifies distinct functional domains

In the vertebrate brain external stimuli are often represented in distinct functional domains distributed across the cortical surface. Fast imaging techniques used to measure patterns of population activity record movies with many pixels and many frames, i.e., data sets with high dimensionality. Here we demonstrate that principal component analysis (PCA) followed by spatial independent component analysis (sICA), can be exploited to reduce the dimensionality of data sets recorded in the olfactory bulb and the somatosensory cortex of mice as well as the visual cortex of monkeys, without loosing the stimulus-specific responses. Different neuronal populations are separated based on their stimulus-specific spatiotemporal activation. Both, spatial and temporal response characteristics can be objectively obtained, simultaneously. In the olfactory bulb, groups of glomeruli with different response latencies can be identified. This is shown for recordings of olfactory receptor neuron input measured with a calcium-sensitive axon tracer and for network dynamics measured with the voltage-sensitive dye RH 1838. In the somatosensory cortex, barrels responding to the stimulation of single whiskers can be automatically detected. In the visual cortex orientation columns can be extracted. In all cases artifacts due to movement, heartbeat or respiration were separated from the functional signal by sICA and could be removed from the data set. sICA following PCA is therefore a powerful technique for data compression, unbiased analysis and dissection of imaging data of population activity, collected with high spatial and temporal resolution.

Embedding of Cortical Representations by the Superficial Patch System

Pyramidal cells in layers 2 and 3 of the neocortex of many species collectively form a clustered system of lateral axonal projections (the superficial patch system—Lund JS, Angelucci A, Bressloff PC. 2003. Anatomical substrates for functional columns in macaque monkey primary visual cortex. Cereb Cortex. 13:15–24. or daisy architecture—Douglas RJ, Martin KAC. 2004. Neuronal circuits of the neocortex. Annu Rev Neurosci. 27:419–451.), but the function performed by this general feature of the cortical architecture remains obscure. By comparing the spatial configuration of labeled patches with the configuration of responses to drifting grating stimuli, we found the spatial organizations both of the patch system and of the cortical response to be highly conserved between cat and monkey primary visual cortex. More importantly, the configuration of the superficial patch system is directly reflected in the arrangement of function across monkey primary visual cortex. Our results indicate a close relationship between the structure of the superficial patch system and cortical responses encoding a single value across the surface of visual cortex (self-consistent states). This relationship is consistent with the spontaneous emergence of orientation response–like activity patterns during ongoing cortical activity (Kenet T, Bibitchkov D, Tsodyks M, Grinvald A, Arieli A. 2003. Spontaneously emerging cortical representations of visual attributes. Nature. 425:954–956.). We conclude that the superficial patch system is the physical encoding of self-consistent cortical states, and that a set of concurrently labeled patches participate in a network of mutually consistent representations of cortical input.

Social place-cells in the bat hippocampus

The representation of others in space Different sets of neurons encode the spatial position and orientation of an organism. However, social animals need to know the position of other individuals for social interactions, observational learning, and group navigation. Surprisingly, very little is known about how the position of other animals is represented in the brain. Danjo et al. and Omer et al. now report the discovery of a subgroup of neurons in hippocampal area CA1 that encodes the presence of conspecifics in rat and bat brains, respectively. Science, this issue p. 213, p. 218 A subpopulation of bat hippocampal CA1 neurons represents the spatial position of another bat. Social animals have to know the spatial positions of conspecifics. However, it is unknown how the position of others is represented in the brain. We designed a spatial observational-learning task, in which an observer bat mimicked a demonstrator bat while we recorded hippocampal dorsal-CA1 neurons from the observer bat. A neuronal subpopulation represented the position of the other bat, in allocentric coordinates. About half of these “social place-cells” represented also the observer’s own position—that is, were place cells. The representation of the demonstrator bat did not reflect self-movement or trajectory planning by the observer. Some neurons represented also the position of inanimate moving objects; however, their representation differed from the representation of the demonstrator bat. This suggests a role for hippocampal CA1 neurons in social-spatial cognition.

Arousal increases the representational capacity of cortical tissue

Arousal patently transforms the faculties of complex organisms. Although typical changes in cortical activity such as seen in EEG and LFP measurements are associated with change in state of arousal, it remains unclear what in the constitution of such state dependent activity enables this profound enhancement of ability. We put forward the hypothesis that arousal modulates cortical activity by rendering it more fit to represent information. We argue that representational capacity is of a dual nature—it requires not only that cortical tissue generate complex activity (i.e. spatiotemporal neuronal events), but also a complex cortical activity space (which is comprised of such spatiotemporal events). We explain that the topological notion of complexity—homology—is the pertinent measure of the complexity of neuronal activity spaces, as homological structure indicates not only the degree to which underlying activity is inherently clustered but also registers the effective dimensionality of the configurations formed by such clusters. Changes of this sort in the structure of cortical activity spaces can serve as the basis of the enhanced capacity to make perceptual/behavioral distinctions brought about by arousal. To show the feasibility of these ideas, we analyzed voltage sensitive dye imaging (VSDI) data acquired from primate visual cortex in disparate states of arousal. Our results lend some support to the theory: first as arousal increased so did the complexity of activity (that is the complexity of VSDI movies). Moreover, the complexity of structure of activity space (that is VSDI movie space) as measured by persistent homology—a multi scale topological measure of complexity—increased with arousal as well.

Voltage-Sensitive Dye Imaging of Neocortical Activity

Neural computations underlying sensory perception, cognition, and motor control are performed by populations of neurons at different anatomical and temporal scales. Few techniques are currently available for exploring the dynamics of local and large range populations. Voltage-sensitive dye imaging (VSDI), based on organic voltage probes, reveals neural population activity in areas ranging from a few tens of micrometers to a couple of centimeters, or two areas up to ~10 cm apart. VSDI provides a submillisecond temporal resolution and a spatial resolution of ~50 µm. The dye signal emphasizes subthreshold synaptic potentials. VSDI has been applied in the mouse, rat, gerbil, ferret, tree shrew, cat, and monkey cortices to explore the lateral spread of retinotopic or somatotopic activation; the dynamic spatiotemporal pattern resulting from sensory activation, including the somatosensory, olfactory, auditory, and visual modalities; and motor preparation and the properties of spontaneously occurring population activity. In this introduction, we focus on VSDI in vivo and review results obtained mostly in the visual system in our laboratory.

Removal of spatial biological artifacts in functional maps by local similarity minimization

Functional maps obtained by various technologies, including optical imaging techniques, f-MRI, PET, and others, may be contaminated with biological artifacts such as vascular patterns or large patches of parenchyma. These artifacts originate mostly from changes in the microcirculation that result from either activity-dependent changes in volume or from oximetric changes that do not co-localize with neuronal activity per se. Standard methods do not always suffice to reduce such artifacts, in which case conspicuous spatial artifacts mask details of the underlying activity patterns. Here we propose a simple algorithm that efficiently removes spatial biological artifacts contaminating high-resolution functional maps. We validated this procedure by applying it to cortical maps resulting from optical imaging, based either on voltage-sensitive dye signals or on intrinsic signals. To remove vascular spatial patterns we first constructed a template of typical artifacts (vascular/cardiac pulsation/vasomotion), using principle components derived from baseline information obtained in the absence of stimulation. Next, we modified this template by means of local similarity minimization (LSM), achieved by measuring neighborhood similarity between contaminated data and the artifact template and then abolishing the similarity. LSM thus removed spatial patterns originating from the cortical vasculature components, including large fields of capillary parenchyma, helping to unveil details of neuronal activity patterns that were otherwise masked by these vascular artifacts. Examples obtained from our imaging experiments with anaesthetized cats and behaving monkeys showed that the LSM method is both general and reproducible, and is often superior to other available procedures.

Imaging the Neocortex Functional Architecture Using Multiple Intrinsic Signals: Implications for Hemodynamic-Based Functional Imaging

Optical imaging based on intrinsic signals has provided a new level of understanding of the principles underlying cortical development, organization, and function, providing a spatial resolution of up to 20 µm for mapping cortical columns in vivo. This introduction briefly reviews the development of this technique, the types of applications that have been pursued, and the general implications of some findings for other neuroimaging techniques based on hemodynamic responses (e.g., functional magnetic resonance imaging).

Imaging the Dynamics of Mammalian Neocortical Population Activity In-Vivo

Neural computations underlying sensory perception, cognition, and motor control are performed by populations of neurons at different anatomical and temporal scales. Few techniques are currently available for exploring dynamics of local and large range populations. Voltage-sensitive dye imaging (VSDI) reveals neural population activity in areas ranging from a few tens of microns to a couple of centimeters, or two areas up to ∼10 cm apart. VSDI provides a sub-millisecond temporal resolution, and a spatial resolution of about 50 µm. The dye signal emphasizes subthreshold synaptic potentials. VSDI has been applied in the mouse, rat, gerbil, ferret, tree shrew, cat, and monkey cortices, in order to explore lateral spread of retinotopic or somatotopic activation, the dynamic spatiotemporal pattern resulting from sensory activation, including the somatosensory, olfactory, auditory, and visual modalities, as well as motor preparation and the properties of spontaneously occurring population activity. In this chapter we focus on VSDI in vivo and review the results obtained mostly in the visual system in our laboratory.

The representational capacity of cortical tissue

The ability to make distinctions is one of the fundamental capacities underlying cognition, from perception through abstract (categorical) thought. The distinctions a cognitive system is capable of making, should be manifested in its neural activity. Given a set of distinctions, the natural question that arises is whether this imposes constraints on the activity spaces which could embed such a set. We hypothesize that an activity space can embed a given set of distinctions only if its structure corresponds in some sense to the set of distinctions (that is it does not cause collapse of distinctions or undue elaborations within domains or clusters). Thus, we reason that the homology of an activity space approximates the rough structure of the underlying set of distinctions that is realized by the systems activity. Therefore, we refer to the structure of a given activity space as its representational capacity. Thus we hypothesize that there will be a disparity in representational capacity between different states of arousal (for example wakefulness as compared to sleep). In other words, that the structure of activity spaces becomes progressively more complex as arousal increases. To test this hypothesis we analyzed voltage sensitive dye imaging [1] data obtained from the primary visual cortex of behaving primates: 1) Instances of activity were registered at different states of vigilance (anesthesia/covered eyes/visual stimulation). We conjecture that what constitutes a state in terms of activity is similarity (invariance) in the structure of instances of activity. Thus, real (structure sensitive) functions could be utilized to classify activity according to state. 2) The level sets of the typical value corresponding to a state were calculated explicitly within a boundary of e from the set of measurements 3) Finally, the persistent Betty numbers of such level sets, which give the rank of the corresponding homology groups, and the corresponding statistics were computed following [2-5]. Indeed, it was found that activity is an invariant of state – activity becomes less random, more regularly distributed in space and time, more correlated, and has typical distribution of spectral energy in specific spatial-temporal bands, as arousal increases. These phenomena are very robust and thus allow not only perfect classification of activity according to state, but also noticeable confidence margins. Moreover the representational capacity of the imaged cortical tissue increased with arousal – that is the structure of activity space tends to be more complex as arousal increases.

Dynamic Patterns of Spontaneous Ongoing Activity in the Visual Cortex of Anesthetized and Awake Monkeys are Different

&NA; Ongoing internal cortical activity plays a major role in perception and behavior both in animals and humans. Previously we have shown that spontaneous patterns resembling orientation‐maps appear over large cortical areas in the primary visual‐cortex of anesthetized cats. However, it remains unknown 1) whether spontaneous‐activity in the primate also displays similar patterns and 2) whether a significant difference exists between cortical ongoing‐activity in the anesthetized and awake primate. We explored these questions by combining voltage‐sensitive‐dye imaging with multiunit and local‐field‐potential recordings. Spontaneously emerging orientation and ocular‐dominance maps, spanning up to 6 × 6 mm2, were readily observed in anesthetized but not in awake monkeys. Nevertheless, spontaneous correlated‐activity involving orientation‐domains was observed in awake monkeys. Under both anesthetized and awake conditions, spontaneous correlated‐activity coincided with traveling waves. We found that spontaneous activity resembling orientation‐maps in awake animals spans smaller cortical areas in each instance, but over time it appears across all of V1. Furthermore, in the awake monkey, our results suggest that the synaptic strength had been completely reorganized including connections between dissimilar elements of the functional architecture. These findings lend support to the notion that ongoing‐activity has many more fast switching representations playing an important role in cortical function and behavior.