Knowing where and getting there: A human navigation network

Science

Volumn 280, Issue 5365, 1998, Pages 921-924

  Maguire, Eleanor A ^a   Burgess, Neil ^b   Donnett, James G ^b   Frackowiak, Richard S J ^a   Frith, Christopher D ^a   O'Keefe, John ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CAUDATE NUCLEUS; COGNITION; FRONTAL CORTEX; HIPPOCAMPUS; HUMAN; HUMAN EXPERIMENT; NORMAL HUMAN; PARIETAL LOBE; POSITRON EMISSION TOMOGRAPHY; PRIORITY JOURNAL;

BRAIN MAPPING; CAUDATE NUCLEUS; CUES; FRONTAL LOBE; HIPPOCAMPUS; HUMANS; MALE; MEMORY; NEURAL PATHWAYS; ORIENTATION; PARIETAL LOBE; PSYCHOMOTOR PERFORMANCE; REGIONAL BLOOD FLOW; SPACE PERCEPTION; TOMOGRAPHY, EMISSION-COMPUTED;

MAMMALIA;

EID: 0032496356     PISSN: 00368075     EISSN: None     Source Type: Journal    
DOI: 10.1126/science.280.5365.921     Document Type: Article

References (45)

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14. 15O, each 8 min apart. The integrated radioactivity counts that accumulated over the 90-s acquisition period, corrected for background, were used as an index of regional cerebral blood flow. Attenuation correction was computed with a transmission scan before emission scan acquisition. Images were reconstructed into 128 pixels by 128 pixels in 63 planes with an in-plane resolution of 6.5 mm. In addition, high-resolution magnetic resonance imaging (MRI) scans were obtained with a 2.0-7 Vision system (Siemens GmbH, Erlangen, Germany) using a T1-weighted 3D gradient echo sequence. The image dimensions were 256 voxels by 256 voxels by 256 voxels. The voxel size was 1 mm by 1 mm by 2 mm. Images were analyzed with Statistical Parametric Mapping (SPM'96, Wellcome Department of Cognitive Neurology, London, UK; www.fil.ion.ucl.ac.uk) executed in MATLAB (Mathworks, Sherborn, MA). All scans were automatically realigned to the first scan and then normalized using a nonlinear deformation [K. J. Friston et al., Hum. Brain Mapp. 2, 189 (1995)] into standard stereotactic space [J. Talairach and P. Tournoux, Co-Planar Stereotactic Atlas of the Human Brain (Thieme, Stuttgart, Germany, 1988)] using a template from the Montreal Neurological Institute [A. C. Evans et al., in Proceedings of the IEEE-Nuclear Science Symposium and Medical Imaging Conference, San Francisco, CA, 31 October to 6 November 1993, L. A. Klaisner, Ed. (IEEE Service Center, Piscataway, NJ, 1993), pp. 1813-1817]. The structural MRI scans were normalized into the same space to allow for the superimposition of PET activations onto an averaged structural image. Images were smoothed using an isotropic Gaussian kernel of 16 mm (full width at half maximum) to optimize the signal-to-noise ratio and to adjust for intersubject differences in gyral anatomy. Global variance between conditions was removed, using analysis of covariance (ANCOVA). For each pixel in stereotactic space, condition-specific adjusted rCBF values with an associated adjusted error variance were generated. Areas of significant change in brain activity were then determined, using appropriately weighted contrasts between the task-specific scans and the t statistic. The resulting sets of t values constituted the statistical parametric map (SPM). Significance levels were set at P < 0.001 (uncorrected).
15. 15O, each 8 min apart. The integrated radioactivity counts that accumulated over the 90-s acquisition period, corrected for background, were used as an index of regional cerebral blood flow. Attenuation correction was computed with a transmission scan before emission scan acquisition. Images were reconstructed into 128 pixels by 128 pixels in 63 planes with an in-plane resolution of 6.5 mm. In addition, high-resolution magnetic resonance imaging (MRI) scans were obtained with a 2.0-7 Vision system (Siemens GmbH, Erlangen, Germany) using a T1-weighted 3D gradient echo sequence. The image dimensions were 256 voxels by 256 voxels by 256 voxels. The voxel size was 1 mm by 1 mm by 2 mm. Images were analyzed with Statistical Parametric Mapping (SPM'96, Wellcome Department of Cognitive Neurology, London, UK; www.fil.ion.ucl.ac.uk) executed in MATLAB (Mathworks, Sherborn, MA). All scans were automatically realigned to the first scan and then normalized using a nonlinear deformation [K. J. Friston et al., Hum. Brain Mapp. 2, 189 (1995)] into standard stereotactic space [J. Talairach and P. Tournoux, Co-Planar Stereotactic Atlas of the Human Brain (Thieme, Stuttgart, Germany, 1988)] using a template from the Montreal Neurological Institute [A. C. Evans et al., in Proceedings of the IEEE-Nuclear Science Symposium and Medical Imaging Conference, San Francisco, CA, 31 October to 6 November 1993, L. A. Klaisner, Ed. (IEEE Service Center, Piscataway, NJ, 1993), pp. 1813-1817]. The structural MRI scans were normalized into the same space to allow for the superimposition of PET activations onto an averaged structural image. Images were smoothed using an isotropic Gaussian kernel of 16 mm (full width at half maximum) to optimize the signal-to-noise ratio and to adjust for intersubject differences in gyral anatomy. Global variance between conditions was removed, using analysis of covariance (ANCOVA). For each pixel in stereotactic space, condition-specific adjusted rCBF values with an associated adjusted error variance were generated. Areas of significant change in brain activity were then determined, using appropriately weighted contrasts between the task-specific scans and the t statistic. The resulting sets of t values constituted the statistical parametric map (SPM). Significance levels were set at P < 0.001 (uncorrected).
16. 15O, each 8 min apart. The integrated radioactivity counts that accumulated over the 90-s acquisition period, corrected for background, were used as an index of regional cerebral blood flow. Attenuation correction was computed with a transmission scan before emission scan acquisition. Images were reconstructed into 128 pixels by 128 pixels in 63 planes with an in-plane resolution of 6.5 mm. In addition, high-resolution magnetic resonance imaging (MRI) scans were obtained with a 2.0-7 Vision system (Siemens GmbH, Erlangen, Germany) using a T1-weighted 3D gradient echo sequence. The image dimensions were 256 voxels by 256 voxels by 256 voxels. The voxel size was 1 mm by 1 mm by 2 mm. Images were analyzed with Statistical Parametric Mapping (SPM'96, Wellcome Department of Cognitive Neurology, London, UK; www.fil.ion.ucl.ac.uk) executed in MATLAB (Mathworks, Sherborn, MA). All scans were automatically realigned to the first scan and then normalized using a nonlinear deformation [K. J. Friston et al., Hum. Brain Mapp. 2, 189 (1995)] into standard stereotactic space [J. Talairach and P. Tournoux, Co-Planar Stereotactic Atlas of the Human Brain (Thieme, Stuttgart, Germany, 1988)] using a template from the Montreal Neurological Institute [A. C. Evans et al., in Proceedings of the IEEE-Nuclear Science Symposium and Medical Imaging Conference, San Francisco, CA, 31 October to 6 November 1993, L. A. Klaisner, Ed. (IEEE Service Center, Piscataway, NJ, 1993), pp. 1813-1817]. The structural MRI scans were normalized into the same space to allow for the superimposition of PET activations onto an averaged structural image. Images were smoothed using an isotropic Gaussian kernel of 16 mm (full width at half maximum) to optimize the signal-to-noise ratio and to adjust for intersubject differences in gyral anatomy. Global variance between conditions was removed, using analysis of covariance (ANCOVA). For each pixel in stereotactic space, condition-specific adjusted rCBF values with an associated adjusted error variance were generated. Areas of significant change in brain activity were then determined, using appropriately weighted contrasts between the task-specific scans and the t statistic. The resulting sets of t values constituted the statistical parametric map (SPM). Significance levels were set at P < 0.001 (uncorrected).
17. A commercially available computer game (Duke Nukem 3D, 3D Realms Entertainment, Apogee Software Ltd., Garland, TX) was used to present the virtual reality town on a 120-MHz Pentium-based personal computer, showing a color, 3D, fully textured first-person view. The town was created using the editor provided (BUILD, Ken Silverman, 3D Realms Entertainment). The game's record and playback functions were used to store subjects' actions and replay them for subsequent analysis. The town had four streets and contained shops, bars, a cinema, church, bank, train station, and video games arcade (Fig. 1A). Subjects could enter into and navigate through the buildings, as each room had at least two entrances. The town contained small screens on the walls at various locations. Approaching a screen and switching it on caused it to display a view of another part of the town. Subjects controlled their movement within the environment by using a keypad with backward, forward, left turn, and right turn buttons. A firth button served to activate screens. Before scanning acquisition, subjects spent up to 60 min exploring the environment until they felt that they had learned the spatial layout of streets and building interiors. A trail of arrows on the floor was present during exploration and in all conditions, but was only relevant in the arrows condition. Subjects were scanned under four conditions. (i) nav1: subjects switch on a screen and navigate through the town to the destination displayed. When the destination is reached, the subject activates the screen found there, which displays the next destination, and so on; (ii) nav2: identical to nav1, except that some doors have been closed, and a barrier has been moved to block a different street; (iii) arrows: subjects move through the town following a trail of arrows on the floor. Subjects activated the screens encountered during the task, but the views of the town displayed had no relevance to their task; (iv) scenes: static scenes from the town are presented every 2 s, subjects respond according to whether there is a screen in the scene or not.
18. d|〉 as a measure of accuracy of heading. Similar analysis was not applied to the nav2 detour condition, because subjects did not know beforehand which doors would be closed or where the barriers would be, and so could not be expected to plan an optimal route. Theoretically, accuracy scores may vary from 0 (always moving directly away from the current destination), through 90° (moving randomly) to 180° (always moving directly toward the current destination). In practice, an accuracy of 160° is hard to exceed because of the cluttered nature of the environment (the accuracy of one very well-practiced author. N.B., in the three trials varied between 144.3° and 157.4°). This measure agrees with our subjective assessment of trials, was independent of the speed of navigation, and is consistent with models of how the hippocampus directs navigation in rodents (17).
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45. E.A.M., R.S.J.F., and C.D.F. are supported by the Wellcome Trust, J.O'K. and J.G.D. by the Medical Research Council, and N.B. by the Royal Society. We thank K. Friston, C. Price, and C. Buchel for helpful comments.