Form and function in systems neuroscience

From cognitive maps to spatial schemas

A schema refers to a structured body of prior knowledge that captures common patterns across related experiences. Schemas have been studied separately in the realms of episodic memory and spatial navigation across different species and have been grounded in theories of memory consolidation, but there has been little attempt to integrate our understanding across domains, particularly in humans. We propose that experiences during navigation with many similarly structured environments give rise to the formation of spatial schemas (for example, the expected layout of modern cities) that share properties with but are distinct from cognitive maps (for example, the memory of a modern city) and event schemas (such as expected events in a modern city) at both cognitive and neural levels. We describe earlier theoretical frameworks and empirical findings relevant to spatial schemas, along with more targeted investigations of spatial schemas in human and non-human animals. Consideration of architecture and urban analytics, including the influence of scale and regionalization, on different properties of spatial schemas may provide a powerful approach to advance our understanding of spatial schemas.

Mapping individual differences across brain network structure to function and behavior with connectome embedding

The connectome, a comprehensive map of the brain’s anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes “context” is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n = 542; Cam-CAN: n = 601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping, are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.

Elimination of glutamatergic transmission from Hb9 interneurons does not impact treadmill locomotion

The spinal cord contains neural circuits that can produce the rhythm and pattern of locomotor activity. It has previously been postulated that a population of glutamatergic neurons, termed Hb9 interneurons, contributes to locomotor rhythmogenesis. These neurons were identified by their expression of the homeobox gene, Hb9, which is also expressed in motor neurons. We developed a mouse line in which Cre recombinase activity is inducible in neurons expressing Hb9. We then used this line to eliminate vesicular glutamate transporter 2 from Hb9 interneurons, and found that there were no deficits in treadmill locomotion. We conclude that glutamatergic neurotransmission by Hb9 interneurons is not required for locomotor behaviour. The role of these neurons in neural circuits remains elusive.

Households’ Electricity Consumption in Hungarian Urban Areas

The aim of this study is to examine the factors influencing the electricity consumption of urban households and to prove these with statistically significant results. The study includes 46 small and medium-sized towns in Hungary. The methodology of the study is mainly provided by a model that can be used for this purpose; however, the results obtained with the traditional regression method are compared with the results of another, more complex estimation method, the artificial neural network, which has the advantage of being able to use different types of models. The focus of our article is on methodological alignment, not necessarily the discovery of new results. Certain demographic characteristics significantly determine the energy demand of a household sector in a municipality. In this case, as the ratio of people aged 60 or over within a city rises by 1%, the urban household average energy consumption decreases by 61 kilowatt hours, and when it rises by 1%, the amount of pollutants expelled from urban households’ average energy consumption may decrease by 22.8745 kg. The research area of our paper was greatly influenced by the availability of the statistical data. The results can be used in the planning of urban developments.

Effect of Circuit Structure on Odor Representation in the Insect Olfactory System

In neuroscience, the structure of a circuit has often been used to intuit function—an inversion of Louis Kahn’s famous dictum, “Form follows function” (Kristan and Katz, 2006). However, different brain networks may use different network architectures to solve the same problem. The olfactory circuits of two insects, the locust, Schistocerca americana, and the fruit fly, Drosophila melanogaster, serve the same function—to identify and discriminate odors. The neural circuitry that achieves this shows marked structural differences. Projection neurons (PNs) in the antennal lobe innervate Kenyon cells (KCs) of the mushroom body. In locust, each KC receives inputs from ∼50% of PNs, a scheme that maximizes the difference between inputs to any two of ∼50,000 KCs. In contrast, in Drosophila, this number is only 5% and appears suboptimal. Using a computational model of the olfactory system, we show that the activity of KCs is sufficiently high-dimensional that it can separate similar odors regardless of the divergence of PN–KC connections. However, when temporal patterning encodes odor attributes, dense connectivity outperforms sparse connections. Increased separability comes at the cost of reliability. The disadvantage of sparse connectivity can be mitigated by incorporating other aspects of circuit architecture seen in Drosophila. Our simulations predict that Drosophila and locust circuits lie at different ends of a continuum where the Drosophila gives up on the ability to resolve similar odors to generalize across varying environments, while the locust separates odor representations but risks misclassifying noisy variants of the same odor.

Network Neuroscience and Personality

Personality and individual differences originate from the brain. Despite major advances in the affective and cognitive neurosciences, however, it is still not well understood how personality and single personality traits are represented within the brain. Most research on brain-personality correlates has focused either on morphological aspects of the brain such as increases or decreases in local gray matter volume, or has investigated how personality traits can account for individual differences in activation differences in various tasks. Here, we propose that personality neuroscience can be advanced by adding a network perspective on brain structure and function, an endeavor that we label personality network neuroscience. With the rise of resting-state functional magnetic resonance imaging (MRI), the establishment of connectomics as a theoretical framework for structural and functional connectivity modeling, and recent advancements in the application of mathematical graph theory to brain connectivity data, several new tools and techniques are readily available to be applied in personality neuroscience. The present contribution introduces these concepts, reviews recent progress in their application to the study of individual differences, and explores their potential to advance our understanding of the neural implementation of personality. Trait theorists have long argued that personality traits are biophysical entities that are not mere abstractions of and metaphors for human behavior. Traits are thought to actually exist in the brain, presumably in the form of conceptual nervous systems. A conceptual nervous system refers to the attempt to describe parts of the central nervous system in functional terms with relevance to psychology and behavior. We contend that personality network neuroscience can characterize these conceptual nervous systems on a functional and anatomical level and has the potential do link dispositional neural correlates to actual behavior.

Unraveling a locomotor network, many neurons at a time

In this issue of Neuron, Bruno et al. (2015) use large-scale recordings in Aplysia, and apply novel dimensionality-reduction techniques to define dynamical building blocks involved in locomotor behavior. These techniques open new avenues to the study of neuronal networks.

Strategies for delineating spinal locomotor rhythm-generating networks and the possible role of Hb9 interneurones in rhythmogenesis

Despite significant advances in our understanding of pattern generation in invertebrates and lower vertebrates, there have been barriers to the application of the principles learned to the definition of networks underlying mammalian locomotion. Major difficulties have arisen in identifying spinal interneurones in preparations which allow study of neuronal intrinsic properties and the role of identified interneurones in locomotor networks. Recent genetic technologies in which selective expression of fluorescent proteins in specific populations of mouse spinal neurones have provided new avenues of investigation. In this review, we focus on the generation of locomotor rhythm and outline criteria that rhythm-generating neurones might be expected to fulfill. We then examine the extent to which a recently identified population of spinal interneurones, Hb9 interneurones, fulfill these criteria. Finally, we suggest that Hb9 interneurones could be involved in an asymmetric model of locomotor rhythmogenesis through projections of electrotonically coupled rhythm-generating modules to flexor pattern formation half-centres. The principles learned from studying this population of interneurones have led to strategies to systematically evaluate neurones that may be involved in locomotor rhythmogenesis.