Dataset Information

Active State Organization of Spontaneous Behavioral Patterns.

ABSTRACT: We report the development and validation of a principled analytical approach to reveal the manner in which diverse mouse home cage behaviors are organized. We define and automate detection of two mutually-exclusive low-dimensional spatiotemporal units of behavior: "Active" and "Inactive" States. Analyses of these features using a large multimodal 16-strain behavioral dataset provide a series of novel insights into how feeding, drinking, and movement behaviors are coordinately expressed in Mus Musculus. Moreover, we find that patterns of Active State expression are exquisitely sensitive to strain, and classical supervised machine learning incorporating these features provides 99% cross-validated accuracy in genotyping animals using behavioral data alone. Altogether, these findings advance understanding of the organization of spontaneous behavior and provide a high-throughput phenotyping strategy with wide applicability to behavioral neuroscience and animal models of disease.

SUBMITTER: Hillar C

PROVIDER: S-EPMC5773533 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Active State Organization of Spontaneous Behavioral Patterns.

Hillar C C Onnis G G Rhea D D Tecott L L

Scientific reports 20180118 1

We report the development and validation of a principled analytical approach to reveal the manner in which diverse mouse home cage behaviors are organized. We define and automate detection of two mutually-exclusive low-dimensional spatiotemporal units of behavior: "Active" and "Inactive" States. Analyses of these features using a large multimodal 16-strain behavioral dataset provide a series of novel insights into how feeding, drinking, and movement behaviors are coordinately expressed in Mus Mu ...[more]

PMID: 29348406

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Project description:The characterization of topological architecture of complex brain networks is one of the most challenging issues in neuroscience. Slow (<0.1 Hz), spontaneous fluctuations of the blood oxygen level dependent (BOLD) signal in functional magnetic resonance imaging are thought to be potentially important for the reflection of spontaneous neuronal activity. Many studies have shown that these fluctuations are highly coherent within anatomically or functionally linked areas of the brain. However, the underlying topological mechanisms responsible for these coherent intrinsic or spontaneous fluctuations are still poorly understood. Here, we apply modern network analysis techniques to investigate how spontaneous neuronal activities in the human brain derived from the resting-state BOLD signals are topologically organized at both the temporal and spatial scales. We first show that the spontaneous brain functional networks have an intrinsically cohesive modular structure in which the connections between regions are much denser within modules than between them. These identified modules are found to be closely associated with several well known functionally interconnected subsystems such as the somatosensory/motor, auditory, attention, visual, subcortical, and the "default" system. Specifically, we demonstrate that the module-specific topological features can not be captured by means of computing the corresponding global network parameters, suggesting a unique organization within each module. Finally, we identify several pivotal network connectors and paths (predominantly associated with the association and limbic/paralimbic cortex regions) that are vital for the global coordination of information flow over the whole network, and we find that their lesions (deletions) critically affect the stability and robustness of the brain functional system. Together, our results demonstrate the highly organized modular architecture and associated topological properties in the temporal and spatial brain functional networks of the human brain that underlie spontaneous neuronal dynamics, which provides important implications for our understanding of how intrinsically coherent spontaneous brain activity has evolved into an optimal neuronal architecture to support global computation and information integration in the absence of specific stimuli or behaviors.

Dataset Information

Active State Organization of Spontaneous Behavioral Patterns.

Publications

Active State Organization of Spontaneous Behavioral Patterns.

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