The present study used data acquired from participants for a previous study examining univariate differences in fMRI activations related to spatial disambiguation in humans (Brown et al., 2010
). Twenty-two participants (ages 19-31, mean age ± s.d.: 21.36 ± 3.43; nine male) with normal or corrected-to-normal vision were recruited from the Boston University community. Two participants were eliminated from the analysis due to excessive motion during scanning, four because of poor behavioral performance, and two because of signal drop-out in the orbitofrontal cortex. Informed consent was obtained from each participant in a manner approved by the Partners Human Research Committee and the Boston University Institutional Review Board.
2.2 Virtual Environments
Twelve virtual mazes (see ) were constructed using POV-Ray Version 3.6 (http://www.povray.org/
), a 3D ray-tracer modeling program. Participants navigated the mazes from a ground-level first-person perspective and behavioral data were recorded using E-Prime 2.0 (Psychology Software Tools, Inc., Pittsburgh, PA). The virtual mazes were presented as a series of images rendered in POV-Ray. Every maze was comprised of five hallways, each containing unique objects which served as distinguishing features between the locations and were clearly identifiable and distinguishable from one another.
Figure 1 Virtual maze design.(a) Overhead layout of the 12 mazes used in the task. The overlapping mazes (OL) are depicted on the left and the non-overlapping mazes (NOL) are depicted on the right. Mazes began at the “^” and end at the “X.” (more ...)
Participants began each maze at a unique starting location (termed the “first hall”) and traveled down each hallway to an intersection. There were four intersections per maze. Using a button box, participants could choose to turn left, right, or continue straight ahead at each intersection. The correct choice was the next hall in the sequence of spatial locations comprising a maze. When participants made their navigational responses in a maze, they were auto-piloted down that hall to the next intersection. The navigational responses at the end of the halls were counterbalanced across conditions.
The twelve mazes were divided into two conditions. Six of the mazes comprised a “non-overlapping” condition, which did not share any hallways with each other and were therefore completely distinct (NOL1-NOL6). The other six mazes comprised the “overlapping” condition. In the overlapping condition, the six mazes were split into three pairs in which each route began and ended at distinct, non-overlapping locations, but converged in the middle to share one, two, or three hallways with the other maze (OL1, OL2, and OL3 pairs, respectively).
Navigational demands were matched between both conditions: all mazes were the same length and the number of left, right, and straight choices counterbalanced across the mazes and conditions (see also the Experimental Task section below). By contrasting one navigational condition (the overlapping condition) with another closely matched navigational condition (the non-overlapping condition) the present study was designed to remove effects due to spatial navigation alone, allowing us to examine changes in functional connectivity specifically related to processes supporting spatial disambiguation.
After an incorrect choice at the end of a given hall, participants were turned in the selected direction, text reading “Wrong way” in red letters was overlaid on the scene, and a green arrow appeared indicating the correct direction. Participants were then rotated in the correct direction and sent down the correct hallway. To further control the timing of the task, participants were allowed a maximum of five seconds to respond at the end of each hallway. In the case of a “no response,” text reading “Respond” was overlaid on the scene and participants were provided with the same feedback arrows and correctional movement as with an incorrect response. “No response” trials were treated as incorrect for both the training and testing periods of the task. Error feedback was provided during all components of the study.
2.4 Pre-scan Training
Participants were trained to a criterion of 100% correct on all 12 mazes the day before scanning. Participants were initially guided by the experimenter through a sample pair of overlapping mazes (different from those used in the actual task) to ensure participants understood the mechanics of the navigational task and to explain how feedback for incorrect navigational choices worked. Participants were made aware that some mazes would share hallways with other mazes, but that they would all begin and end at distinct locations from one-another. Participants were instructed to attend to the starting hallway as it was the cue for which maze they were following in a given trial, and to attend to the landmark objects to aid in knowing where they were in the mazes.
When learning the mazes, participants would repeatedly navigate one maze until they met a training criterion of four perfect consecutive trials. When criterion was met for one maze, participants would learn the next maze. The order in which mazes were learned was randomized for each participant. Following individual training on all the mazes, participants performed four training runs in which all twelve mazes were presented in an interleaved, randomized order, just as they would be presented the following day during scanning. The final three training runs were required to be error-free to ensure participants had mastered the mazes and task contingencies.
2.5 Experimental Task
Participants were scanned the day after training took place. Before scanning, participants were given a warm-up run through all twelve mazes in an interleaved, randomized order. Within the scanner, participants performed 12 runs of the experiment. Each run contained all 12 mazes presented in a counterbalanced order across runs. The order of the runs was randomized across subjects. Each maze began with a 2 second instructional cue image, in which participants viewed the starting perspective of the first hallway of the maze without moving. Overlaid on the cue image were the instructions “Navigate to the end of the maze.” Following the instructional cue image, participants were automatically piloted down the unique starting hallway to the first intersection. At the intersection, participants responded with a button press of 1 to turn left, 2 to continue straight ahead, or 3 to turn right. Following a correct navigational choice, participants were automatically piloted down the next hallway to the subsequent intersection. Incorrect navigational choices were met with the feedback described above. Turns were made in two simulated steps, with each step incorporating 45 degrees of rotation, such that the participant would come out of the turn centered and facing directly down the next hallway.
In the non-overlapping condition, each hallway was always followed by the same navigational choice. In the overlapping condition, both the non-overlapping and overlapping hallways were also always associated with the same navigational choices except for the “critical halls” (). The critical halls were the last hall within an overlapping segment before the two mazes diverged. These hallways were termed critical halls because the navigational choice at the end of the hallways differed depending on which route was being followed. Because every maze began at a distinct location, correctly navigating beyond a critical hall required knowledge of the starting point and the hallways traveled before having entered the overlapping component. Importantly, the critical halls were constructed no differently from any other hallway, ending at an intersection with three possible navigational choices leading to hallways containing uniquely identifiable objects. Only knowledge of the routes distinguished the critical halls as important. Accuracy and reaction times were recorded for each navigational choice made.
Each hallway was comprised of 9 POV-Ray generated images, presented to participants as virtual steps in E-Prime. Each image was presented for 0.25 seconds, so that each hallway took 2.25 seconds to traverse. Following the navigational response at an intersection, translation or rotation through the intersection to the start of the next hallway took 1 second. The exact timing of behavioral responses as well as the image presentation was logged in E-Prime to allow accurate modeling of the task. The total duration of a maze varied with the response times at each intersection. Each maze was followed by an 8 second inter-trial interval (ITI) in which participants viewed a fixation point in the center of a black screen.
2.6 Post-scan interview
After scanning, participants were interviewed about their experience with the mazes, including their use of the landmark objects, how they identified the mazes, and their strategy for accurately navigating the periods of overlap.
2.7 Image Acquisition
Images were acquired using a 3 T Siemens MAGNETOM TrioTim scanner (Siemens AG, Medical Solutions, Erlangen, Germany) with a 12-channel Tim Matrix head coil. Two high-resolution T1-weighted multiplanar rapidly acquired gradient echo (MP-RAGE) structural scans were acquired using generalized autocalibrating partially parallel acquisitions (GRAPPA) (TR = 2,530 ms; TE = 3.44 ms; flip angle = 7; slices = 176, field of view = 256; resolution = 1 mm × 1 mm × 1 mm). Functional T2*-weighted BOLD images were acquired using an echo planar imaging (EPI) sequence (TR = 2 s; TE = 30 ms; flip angle = 90°; acquisition matrix = 64 × 64, field of view = 256; slices = 32, resolution = 4.0 mm isotropic). Slices were aligned along the anterior/posterior commissure line.
2.8 fMRI Pre-Processing
Imaging analysis was conducted using SPM8 (Wellcome Department of Cognitive Neurology, London, UK). All BOLD images were reoriented so the origin (i.e., coordinate xyz= [0 0 0]) was at the anterior commissure. Images were then slice-time corrected to the first slice acquired in time. Motion correction was conducted and included realigning and unwarping the BOLD images (Andersson et al., 2001
). The high-resolution structural images were then coregistered with the mean BOLD image from motion correction and segmented into gray and white matter images. The bias-corrected structural images and the coregistered BOLD images were spatially normalized into standard MNI space using the parameters derived during segmentation with resampling of the BOLD images to 2 mm3
isotropic voxels and then smoothed using a 6 mm full-width at half maximum Gaussian kernel.
2.9 Behavioral data analysis
Separate paired-sample t-tests were used to assess differences in the fMRI task between the overlapping and non-overlapping conditions for percent accuracy and reaction time for the first halls and critical halls. Because the study was designed to assess functional connectivity effects in well-learned environments, a stringent criterion was applied such that participants were excluded from the study if they made more than three errors at any intersection in either condition (yielding less than 75% accuracy for that navigational choice).
Training participants to stable 100% performance on all mazes prior to scanning helped ensure that differential learning effects between the overlapping and non-overlapping conditions would not be a confound for interpreting the fMRI functional connectivity data. To demonstrate that behavioral performance in the scanner did not change across time differently for the two conditions as a result of continued practice, accuracies and reaction times were examined across runs for both the first hall and critical hall periods. Accuracies for the first halls and critical halls in both conditions remained markedly stable across runs at near 100%. Since reaction times can demonstrate practice effects even when accuracy is invariant, the individual reaction times of participants across trials were entered into a repeated-measures general linear model (GLM) analysis testing for influences of condition and run number on performance.