Edited by: Srikantan S. Nagarajan, University of California, San Francisco, United States
Reviewed by: Arun Bokde, Trinity College, Dublin, Ireland; Feng Liu, Tianjin Medical University General Hospital, China; Kamalini G. Ranasinghe, University of California, San Francisco, United States
†These authors have contributed equally to this work
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Humans differ in their individual navigational performance, in part because successful navigation relies on several diverse abilities. One such navigational capability is
Humans differ considerably in their individual navigational abilities, and successful navigation relies on several different skills and capabilities (Wolbers and Hegarty,
The goal of this study was to examine the relationship between path integration abilities and functional connectivity to canonical intrinsic brain networks. Intrinsic networks within the brain reflect past inputs and communication (Damoiseaux et al.,
Specifically, we were interested in intrinsic functional communication between navigation brain regions and the default mode network (DMN) and between navigation brain regions and the central executive network (CEN). The DMN and CEN were chosen
Previous research in both animals and humans suggest that the medial temporal lobe (MTL) regions of hippocampus, parahippocampal cortex (PHC) and entorhinal cortex are likely candidates to support path integration abilities, as are RSC and mPFC. Rodent models have found several cellular fundamentals for path integration, including place cells in the hippocampus (O’Keefe and Nadel,
Path integration often involves tracking a start or home location and we previously found task-based functional imaging evidence in support of a homing signal in the human brain (Chrastil et al.,
Thirty-one participants were recruited for this study from the Boston University community as part of previous studies (Chrastil et al.,
Complex path integration and self-motion processing involve tracking location, often the start or home location. This paradigm required participants to track self-motion during videos shown from a first-person perspective. Briefly, in the complex path integration task (loop closure task), participants viewed a single video of movement that traveled in a circle in a sparse environment (Figure
Loop closure task.
The virtual environment was developed using POV-Ray v.3.6
In the loop closure task, the camera movement in the video traveled in a circular pattern. Once the video ended, participants had to indicate whether the movement in the video ended at the same location in which it started, at the home location. Half of the videos ended in the home location (“match,” a full 360° traversal of the loop), and half were non-matches, ending at another point along the circle. Half of the non-matches were undershoots, such that the movement only traversed partway around the circle (225° of the loop). The other half were overshoots, such that movement went past the home location and went partway around a second loop (495° of the loop). Participants were given clear instructions that overshoots were considered non-matches, and that it was important to determine whether the end point itself was the same as the start location. Three different radii of curvature (2.0, 3.0 and 4.5 virtual units) and two different travel speeds (1.5 and 2.0 virtual units/s) were used in the loop task, crossed to yield six angular speeds (0.33, 0.44, 0.50, 0.67, 0.75 and 1.00 radians/s). The length of the videos for the loop task ranged between approximately 4–25 s, with an average of 11.5 s. After the video, a response screen was presented, and participants had up to 2 s to respond whether the loop returned to the home location. A 6 s intertrial interval (ITI) began as soon as the response was recorded, thus the duration of the response was based on participants’ reaction time. Loops turned both to the right and to the left in equal numbers; we combined over left and right turning direction for analysis.
The functional imaging of interest took place during a resting state scan that occurred after the test runs of the path integration task. During the resting state scan, participants were instructed to keep their eyes open and look at a fixation cross, but they could think about whatever they liked. One 6:12 min long resting state scan was acquired after the experimental task scan runs.
Participants were trained outside the scanner the day prior to scanning. Participants were given a general description of movement in the environment and shown a short example. In addition to the loop closure task, participants were trained on additional tasks not presented here (loop, distance, angle, curve and static image change; see Chrastil et al.,
While the structural scans were being acquired, participants were given a practice run with feedback using examples from the training, with eight trials per task block. Following practice, there were six functional test runs, randomized across participants, for a total of 36 trials per condition. Each of the test runs consisted of one block each of the experimental tasks (loop, distance, angle, curve and static image mentioned in the section on pre-scan training). Each block contained six trials of the task, with match and non-match trials counterbalanced across runs. The task order of each block was counterbalanced across runs. Length and direction of movement, as well as speed of travel, were counterbalanced across conditions and runs. Because the ITI began as soon as participants made their responses, the scan time for each of the six runs varied somewhat, but generally lasted just under 10 min. Total scan time for the experimental task was approximately 1 h. Following the experimental task runs, the 6:12 min resting-state scan was acquired.
Images were acquired at the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital in Charlestown, MA, USA using a 3 Tesla Siemens MAGNETOM TrioTim scanner with a 32-channel Tim Matrix head coil. High-resolution T1-weighted multi-planar rapidly acquired gradient echo (MP-RAGE) structural scans were acquired using Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA; TR = 2530 ms; TE = 3.31 ms; flip angle = 7°; slices = 176; resolution = 1 mm isotropic). T2*-weighted BOLD images were acquired for the resting state scan using an echo planar imaging (EPI) sequence (TR = 2,000 ms; TE = 30 ms; flip angle = 85°; slices = 33, resolution = 3.0 × 3.0 × 3.44 mm, interslice gap of 0.5 mm). Functional image slices were aligned parallel to the long axis of the hippocampus.
The primary outcome measure of path integration ability was the proportion of correct trials. Behavioral performance was assessed using MatLab (MathWorks) and SPSS20 (IBM). A one-sample
Resting state BOLD images were reoriented in SPM8 (Statistical Parametric Mapping, Wellcome Department of Cognitive Neurology, London) so that the origin (coordinate x, y, z = [0, 0, 0]) was the anterior commissure. The remainder of the preprocessing was done with FSL (FMRIB, Oxford, UK; FSL version 5.0.6) using the MELODIC preprocessing stream (Jenkinson et al.,
Functional connectivity analysis was used to uncover the relationship between performance on the loop closure task and network connectivity. The regression analysis correlated performance with the strength of network connectivity. The significant effects shown in each voxel in the results indicate connectivity with the network of interest that varied by performance at that voxel. We conducted a whole-brain analysis of this question. Thus, this analysis tests whether the strength of connectivity between any given voxel in the brain and the CEN or DMN increased with accuracy in the loop task.
BrainMap 20 templates (Filippini et al.,
Network connectivity results of the central executive network (CEN).
Network connectivity results of the default mode network (DMN).
Dual regression was performed using the pre-defined BrainMap 20 templates (Filippini et al.,
We note that our results could show regions both outside of the network of interest and regions within the network that were significantly connected related to performance because our whole-brain analysis examines all voxels in the brain. For example, the RSC is part of the DMN, and a significant finding in RSC in the DMN contrast would indicate that RSC has significantly greater connectivity to other parts of the DMN in people who did better at the task. Thus, some of our results could be within-network, although they are not explicitly stated as such.
To conduct this whole-brain analysis for significant connectivity to the three complete networks that was related to accuracy in the loop closure task, we used
Behavioral performance has been described in depth elsewhere (Chrastil et al.,
We analyzed resting state connectivity using previously defined networks, testing whether the strength of connectivity to these networks increased with accuracy in the loop task. We examined the relationship between accuracy in the loop closure task and connectivity to three
Our whole-brain analysis looked for areas that showed increasing connectivity to a network as a function of accuracy in the path integration task. This analysis revealed significant intrinsic connectivity between the right CEN and the left hippocampus tail (xyz: −20, −38, −2;
Brain regions where greater accuracy in the path integration task was associated with increased connectivity to the right central executive network (CEN).
Cluster size (k) | Brain region | Left MNI |
Right MNI |
||
---|---|---|---|---|---|
3275 | White matter extending into | 0.036 | 28, −68, 6 | ||
Thalamus | 0.034 | 16, −28, 8 | |||
Caudate | 0.04 | 18, 6, 18 | |||
Cingulate | 0.02 | 14, −26, 32 | |||
Parahippocampal Cortex | 0.038 | 18, −30, −10 | |||
87 | Hippocampus Tail | 0.016 | −20, −38, −2 | ||
56 | Middle Temporal Gyrus/Superior Temporal Sulcus | 0.024 | −52, −32, −8 | ||
39 | Cerebellum | 0.032 | −2, −56, −4 | 0.04 | 2, −56, −4 |
20 | Entorhinal Cortex | 0.04 | 28, −14, −32 | ||
7 | Cingulate Sulcus | 0.048 | 12, 14, 38 | ||
7 | Cerebellum | 0.048 | −8, −48, −14 |
For the DMN, we found a significant relationship related to accuracy with PHC (xyz: 22, −32, −10;
Brain regions where greater accuracy in the path integration task was associated with increased connectivity to the default mode network (DMN).
Cluster size (k) | Brain region | Left MNI |
Right MNI |
||
---|---|---|---|---|---|
1838 | Precentral Gyrus | 0.016 | 30, −18, 64 | ||
Postcentral Gyrus | 0.01 | 30, −38, 64 | |||
Superior Parietal Lobule | 0.044 | 30, −54, 68 | |||
123 | Precuneus | 0.026 | −6, −54, 56 | ||
102 | Collateral Sulcus | 0.034 | −32, −28, −24 | ||
88 | Cingulate Sulcus | 0.03 | −18, −26, 38 | ||
81 | Temporo-Occipital Gyrus | 0.034 | 40, −32, −24 | ||
72 | Temporo-Occipital Gyrus | 0.044 | −32, −6, −44 | ||
69 | Inferior Temporal Gyrus | 0.046 | −48, −10, −36 | ||
59 | Cerebellum | 0.044 | −24, −46, −26 | ||
32 | Superior Temproal Sulcus | 0.044 | 46, −20, −10 | ||
31 | Cerebellum | 0.048 | −2, −58, −22 | ||
22 | Precentral Gyrus | 0.046 | 60, −2, 34 | ||
19 | Temporo-Occipital Gyrus | 0.048 | 34, −18, −34 | ||
14 | Parahippocampal Cortex | 0.044 | 22, −32, −10 | ||
10 | Temporo-Occipital Gyrus | 0.048 | −38, −34, −20 |
In this experiment, we combined behavioral accuracy in a loop closure task, which provided a measurement of path integration ability, and resting state fMRI analysis (rsMRI). We found that better performance in the loop closure task was associated with increased functional connectivity between the right CEN and hippocampus tail, PHC and entorhinal cortex. We also found that functional connectivity between the DMN and PHC was associated with better loop closure task performance. The results suggest that interactions between MTL regions and both the CEN and DMN are important for navigation. In particular, both CEN and DMN have major network nodes in PFC, indicating a link between individual navigational abilities and executive function, working memory and episodic memory processes.
Our first major finding is that increased intrinsic connectivity between MTL regions and the right CEN is predictive of navigational ability. The CEN is important for adaptive implementation of shifting task demands and other executive control functions (Dosenbach et al.,
Surprisingly, we did not find any significant connectivity with the left CEN that was related to accuracy in the loop closure task. It is possible that the left networks connected equally well to all navigators, or that lateralization of this network plays a significant role. Although the left hemisphere has generally been more closely associated with executive functioning, the right hemisphere tends to be more associated with spatial processing (e.g., Smith and Jonides,
The CEN showed intrinsic connectivity with several navigational brain regions. Specifically, we found increased connectivity between regions within the right CEN and the hippocampus, entorhinal cortex and PHC in better navigators. These MTL regions are vital to path integration, and experiments in both animals and humans, as well as computational models, have demonstrated that these areas are important for the updating of spatial location. Grid cells in rodent entorhinal cortex demonstrate firing patterns that code spatial arrays, facilitating the updating of spatial location (Fyhn et al.,
Our second major finding was that better navigators have increased intrinsic connectivity between PHC and the DMN. Although the DMN was originally viewed as a task-negative network, it has since been linked to many cognitive processes, including episodic memory and representations of oneself (Buckner and Carroll,
Many of the regions commonly observed in navigation tasks are hubs of the DMN (Maguire et al.,
Regions of the PFC are nodes in both the CEN and DMN. Dorsal mPFC, dlPFC and vlPFC are nodes in the CEN (Seeley et al.,
Notably, we did not observe any connectivity effects involving RSC or mPFC, regions in which we previously found structural variation corresponding to individual path integration ability on this same task (Chrastil et al.,
We found other notable differences between our previous structural results (Chrastil et al.,
Finally, we should note some limitations for our study. Although there was substantial variation in behavioral performance, the sample size was limited. The sample size could reduce our power to distinguish true effects. In addition, the resting state scan was completed after the task, which could influence resting state function (Waites et al.,
In conclusion, we found evidence for functional communication between brain regions in the MTL that are vital for navigation and both the CEN and DMN, two cortical networks that are important for memory, self-referential processing and executive function. Individuals with greater communication between MTL regions and both the CEN and DMN had greater accuracy in the loop closure task. These results suggest that the strength of communication between navigation regions and primary memory and executive function networks is important for successful navigation. The results of this study suggest that in the future a broader examination into working memory and executive functions will be necessary to understand the breadth of human navigational abilities.
EC and SI contributed equally to this work. SI conducted the analysis and wrote the article. EC designed the research, wrote the article, collected data, and conducted analysis. CS designed the research and wrote the article.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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