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Front Hum Neurosci. 2014 Apr 29;8:263. doi: 10.3389/fnhum.2014.00263. eCollection 2014.

Differential neural network configuration during human path integration.

Author information

1
NeuroLab, Department of Psychology, University of Calgary Calgary, AB, Canada ; Hotchkiss Brain Institute, University of Calgary Calgary, AB, Canada.
2
Departments of Radiology and Psychiatry, University of Calgary Calgary, AB, Canada ; Alberta Children's Hospital Research Institute, University of Calgary Calgary, AB, Canada.
3
Faculty of Environmental Design, University of Calgary Calgary, AB, Canada.
4
NeuroLab, Department of Psychology, University of Calgary Calgary, AB, Canada ; Hotchkiss Brain Institute, University of Calgary Calgary, AB, Canada ; Alberta Children's Hospital Research Institute, University of Calgary Calgary, AB, Canada ; Department of Clinical Neurosciences, University of Calgary Calgary, AB, Canada.

Abstract

Path integration is a fundamental skill for navigation in both humans and animals. Despite recent advances in unraveling the neural basis of path integration in animal models, relatively little is known about how path integration operates at a neural level in humans. Previous attempts to characterize the neural mechanisms used by humans to visually path integrate have suggested a central role of the hippocampus in allowing accurate performance, broadly resembling results from animal data. However, in recent years both the central role of the hippocampus and the perspective that animals and humans share similar neural mechanisms for path integration has come into question. The present study uses a data driven analysis to investigate the neural systems engaged during visual path integration in humans, allowing for an unbiased estimate of neural activity across the entire brain. Our results suggest that humans employ common task control, attention and spatial working memory systems across a frontoparietal network during path integration. However, individuals differed in how these systems are configured into functional networks. High performing individuals were found to more broadly express spatial working memory systems in prefrontal cortex, while low performing individuals engaged an allocentric memory system based primarily in the medial occipito-temporal region. These findings suggest that visual path integration in humans over short distances can operate through a spatial working memory system engaging primarily the prefrontal cortex and that the differential configuration of memory systems recruited by task control networks may help explain individual biases in spatial learning strategies.

KEYWORDS:

dead reckoning; navigation; neural network; partial least squares; spatial memory

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