To explore the differences in brain structure network properties and related characteristic parameters among individuals with different spatial navigation abilities. In this study, we included 50 subjects (25 good navigators and 25 poor navigators) who underwent Magnetic Resonance Imaging (MRI) examination and Santa Barbara Sense of Direction Scale (SBSOD) test. The T-threshold was the number of fibers (FN) between each pair of brain regions. The brain structure network was constructed using the deterministic fiber tracking algorithm and graph theory model. We calculated its small worldness (Sigma, σ), global efficiency (Eg), local efficiency (El), node efficiency, and Hub nodes with GRETNA. We found significant statistical differences between the two groups' spatial navigation abilities, Eg and El. The efficiency of the 16 nodes was statistically significant across all brain regions; the different nodes were found in the core network dominated by the middle temporal lobe and the dilated region consisting of the parietal lobe and frontal lobe. We did not find statistically significant differences in groups of Hub nodes at which information was exchanged and exchanged. Individual network structures with different spatial navigation capabilities have different properties. Based on this approach, we can screen for spatial navigation capabilities and monitor changes in spatial navigation capabilities with age.
Research Article
Study on Brain Structure Network of Individuals with Different Space Navigation Ability
https://doi.org/10.21203/rs.3.rs-2333831/v1
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spatial navigation
DTI
brain structure network
small worldness
node efficiency
Spatial navigation is one of the most fundamental advanced cognitive functions of all living things, involving various cognitive processes such as sensory integration, visuospatial coding, memory, and decision-making [1]. Decline in spatial navigation ability is one of the earliest indicators of cognitive decline, with varying degrees of impact on different populations[2]. For the average person, a decline in space navigation can reduce their sense of direction, and heavier people can affect basic life skills, such as being unable to find their way home. For professionals who require more space navigation, such as pilots, a decline in space navigation can lead to serious flight accidents.
Numerous studies have suggested that spatial navigation requires the involvement of several brain regions, such as the hippocampus(HIP), entorhinal cortex(EC), parahippocampal gyrus(PHG), retrosplenial complex(RSC), and thalamus(THA)[3]. Previous studies have shown that the parahippocampal gyrus(PHG) and retrosplenial complex(RSC) have scene recognition, the hippocampus(HIP) and entorhinal cortex(EC) have cognitive map formation and thalamus has motor orientation[4, 5].Current research on the role of brain regions in spatial navigation functions focuses on individual brain regions, and little is known about how these regions support flexible navigation as brain networks (or navigational networks)[6]. Brain networks can systematically analyze structurally unrelated but functionally connected brain regions. The localization of brain network injury can be helpful for the diagnosis of disease, the degree of brain network injury can predict the severity of clinical symptoms, and differences in topological properties of brain network can distinguish the advantages of individual spatial navigation ability[2, 7].
There are many researches into animal spatial navigation. Humans and rodents differ in perception, cognition, movement, and the connection between brain structure and function. Therefore, it remains impossible to accurately infer human findings from animal models[8]. Medical imaging opens the door to studying the function of brain networks. Functional magnetic resonance imaging (fMRI) and weighted magnetic resonance imaging (DTI) are two common methods of functional imaging and structural imaging. fMRI mimics animal experiments by measuring changes in signals related to blood oxygen levels in different regions of the brain, solving a major problem in human studies. On the other hand, structural connections are stable for short periods and are more suitable for studying differences in a single spatial navigation network without uniform training. Structural networks can be evaluated by diffusion-weighted MRI (DTI). DTI is highly sensitive to the Brownian motion of water molecules in voxels, especially in white matter[9]. Li[10]found that reduced spatial navigation in patients with mild cognitive impairment was associated with white matter lesions. A recent case study of developmental topographic disorientation reported no morphological changes in MRI. However, DTI results suggested that etiology is associated with disrupted fiber connections and decreased FA values, both of which occur in the prefrontal and prefrontal lobe-motor pathways[11].
Breakthroughs have been made in spatial navigation, white matter signaling, and structural brain networks. However, most of these studies have focused on changes in spatial navigation abilities related to age and cognitive impairment[12, 13]. It also looked at differences in gray matter volume associated with spatial navigation in the brain. However, studies of all white matter fiber in young brains have not been reported[14]. Some studies have linked white matter fibers to differences in one's emotional memory. As with emotional memory, spatial navigation abilities vary widely between individuals[1]. However, whether white matter fiber connections differ across the brain in subjects with different spatial navigation abilities in exceptionally healthy young adults is not known.
In this study, we used the DTI technique combined with a graph theory-based analysis to construct a structural network of white matter fibers in the brains of young people with differences in spatial navigation ability. We analyzed whether individuals with excellent spatial orientation ability had higher Sigma (σ), global efficiency (Eg), local efficiency, local efficiency (El) and node efficiency than the control group.
Participants and Assessments:
100 male students from a university in Xi'an were recruited. SBSOD was used to evaluate spatial navigation ability. SBSOD is a fast and effective self-evaluation method of sense of direction developed by Hegarty. the scale has good reliability (α = 0.88) and test-retest reliability (r = 0.91)[15]. A series of studies have confirmed that SBSOD scores are closely related to the accuracy of identifying surface locations in the environment, the ability to learn new environments, and the objective measurement of spatial navigation skills[16–20].SBSOD has 15 questions, and the scoring method uses the Likert 7-point scale and is tried 1–7 (1 totally agree, 4 unclear, 7 totally disagree) to evaluate their identity to each topic; questions 1, 3, 4, 5, 7, 9, 14 are positive scores, and questions 2, 6, 8, 10, 11, 12, 13, 15 are reverse scores. The total score of 15 questions is obtained. The lower the score is, the better the spatial navigation ability is[15]. Finally, 25 subjects with excellent spatial orientation ability (Good Navigator, GN) and 25 with extremely poor spatial orientation (Poor Navigator, PG) were selected for MRI scanning. All the participants were right-handed, Han nationality, and no contraindications of MRI.
Image Acquisition and Preprocessing:
The imaging quality of the 3.0T Shanghai Joint Picture (uMR780) magnetic resonance scanner, 32-channel head phased front coil, and uMR780 magnetic resonance scanner has been recognized internally and externally[21, 22]. See the FDA certification document(uWS-MR, United Imaging Healthcare; Shanghai, China). High-resolution T1-weighted images were obtained using a three-dimensional fast spoiled gradient recalled pulse sequence with the following scanning parameters: repetition time(TR)= 6.9 ms, echo time༈TE༉= 3.0 ms, flip angle = 9◦, field of view༈FOV༉= 256×256 mm2, matrix size = 230×256, slice thickness = 1.0 mm. DTI scanning parameters were as follows: TR = 12255 ms, TE = 84.3 ms, FOV = 230x250 mm2, matrix size = 118 × 128, voxel size = 1.95x1.95x1.8 mm3, b-value = 1000 s/mm2, slice thickness = 1.8 mm, no slice gap.
Network Construction:
The structural network was constructed using PANDA[23], a pipeline toolbox for analyzing brain diffusion images. PANDA was developed based on MATLAB under the Ubuntu operating system and integrated several data processing tools such as FSL[24], Pipeline System for Octave and Matlab (PSOM)[25], Diffusion Toolkit [26] and MRIcron (http://www.mccauslandcenter.sc.edu/mricro/mricron/). In brief, the overall preprocessing pipeline comprised the following steps: 1 converting DICOM files into NIfTI files; 2 Estimating the brain mask; 3 Non-brain tissue was removed; 4 Eddy current correction; 5 The brain of each space is divided into 116 network nodes based on AAL template; 6 The fractional anisotropy matrix is obtained by deterministic fiber tracking. The termination condition of fiber tracking is set as FA < 0.2, and the rotation angle between two connected steps is > 45 °. Network nodes and edges are defined according to the results of fiber tracking; 7 The individual brain structure network is constructed, and the network matrix is obtained based on the number of fibers between two nodes.
According to the fiber number matrix data obtained above, using Gretna software[27] network analysis, taking the number of fibers between each pair of brain regions (FN) as the threshold T, setting the value range of 1 ≤ T ≤ 5 and the step size as 1, calculate the global attributes σ, Eg, El and node efficiency of local attributes and Hub nodes.
Statistical Analysis:
The statistics of demographic and SBSOD scores were conducted using Statistical Product and Service Solutions (SPSS, version 26.0) by two-sample t-test. The structural network parameters corresponding to each value of the PN and GN groups at 1 ≤ T ≤ 5 were analyzed using the GRETNA software with two-sample t-test and Bonferroni's correction. p < .05 was considered statistically significant.
Demographic and Clinical Characteristics:
There was no significant difference in age and years of education between GN group and PN group. The SBSOD score of GN group was significantly lower than that of PN group (31.60 ± 6.04 VS 55.08 ± 8.91P < 0.01)(see Fig. 1, Table 1 for details).
Table 1. The demographic and clinical characteristics of all participants.
Global Topological Properties of the Structural Networks:
The brain structural networks of the two groups were both σ > 1; They had small-world characteristics, and there was a significant correlation between the SBSOD score of navigation ability and the small-world σ of navigation network (r = 0.60). The better navigation performance was related to the increase in the small worldness of the navigation network, and the average σ value of the GN group was higher than that of the PN group (3.57 VS 3.51). In 1 ≤ T ≤ 5 GN group, the Eg (0.36—0.45) and El (0.55—0.67) were higher than those of PN (0.34–0.44) and (0.50–0.66). There were statistical differences in Eg values at T = 3, 4, and 5, and El values at all T values (see Fig. 2 for details).
Node-Based Analysis of the Structural Networks:
There were statistical differences in the efficiency of all nodes, and the different brain regions were mainly located in the core and extended spatial navigation networks (see Table 2, Fig. 3 for details). There were significant differences in Left middle frontal gyrus(MFG.L), Left Insula(INS.L), Right superior temporal gyrus(STG.R)(p < 0.01), and Left superior frontal gyrus, dorsolateral (SFGdor.L), Left superior frontal gyrus, orbital part(ORBsup.L), Right Superior frontal gyrus, orbital part(ORBsup.R), Left anterior cingulate, paracingulate gyri(ACG.L), Right hippocampus(HIP.R), Left parahippocampus gyrus (PHG.L), Left calcarine fissure and surrounding cortex (CAL.L), Right middle occipital gyrus (MOG.R), Right fusiform gyrus (FFG.R), Right heschl gyrus(HES.R), Right superior temporal gyrus (STG.R), Left temporal pole: superior temporal gyrus(TPOsup.L), and Left temporal pole: middle temporal gyrus(TPOmid.L)(P < 0.05). There are 11 common Hub nodes between the two groups. Left superior frontal gyrus, dorsolateral(SFGdor.L) is the unique hub node in the GN group, and Left thalamus (THA.L) is the unique hub node in the PN group (see Table 3, Fig. 4 for details).
|
Index |
Regions |
Abbreviation |
Node efficiency GN group PN group |
t |
P |
|
|---|---|---|---|---|---|---|
|
3 |
Left superior frontal gyrus, dorsolateral |
SFGdor.L |
0.47 ± 0.03 |
0.44 ± 0.03 |
2.26 |
0.03 |
|
5 |
Left superior frontal gyrus, orbital part |
ORBsup.L |
0.42 ± 0.03 |
0.39 ± 0.05 |
2.13 |
0.04 |
|
6 |
Right Superior frontal gyrus, orbital part |
ORBsup.R |
0.43 ± 0.02 |
0.41 ± 0.03 |
2.08 |
0.04 |
|
7 |
Left middle frontal gyrus |
MFG.L |
0.40 ± 0.03 |
0.37 ± 0.02 |
3.31 |
0.00 |
|
29 |
Left Insula |
INS.L |
0.42 ± 0.02 |
0.40 ± 0.03 |
3.00 |
0.00 |
|
31 |
Left anterior cingulate and paracingulate gyri |
ACG.L |
0.43 ± 0.02 |
0.41 ± 0.02 |
2.28 |
0.03 |
|
34 |
Right median cingulate and paracingulate gyri |
DCG.R |
0.45 ± 0.02 |
0.44 ± 0.02 |
2.71 |
0.01 |
|
38 |
Right hippocampus |
HIP.R |
0.43 ± 0.03 |
0.40 ± 0.04 |
2.41 |
0.02 |
|
39 |
Left parahippocampus pocampal gyrus |
PHG.L |
0.39 ± 0.03 |
0.38 ± 0.02 |
2.59 |
0.01 |
|
43 |
Left calcarine fissure and surrounding cortex |
CAL.L |
0.45 ± 0.03 |
0.43 ± 0.03 |
2.42 |
0.02 |
|
52 |
Right middle occipital gyrus |
MOG.R |
0.40 ± 0.03 |
0.38 ± 0.03 |
2.14 |
0.04 |
|
56 |
Right fusiform gyrus |
FFG.R |
0.42 ± 0.02 |
0.40 ± 0.02 |
2.20 |
0.03 |
|
80 |
Right heschl gyrus |
HES.R |
0.30 ± 0.02 |
0.27 ± 0.08 |
2.16 |
0.04 |
|
81 |
Right superior temporal gyrus |
STG.R |
0.41 ± 0.04 |
0.37 ± 0.04 |
3.47 |
0.00 |
|
83 |
Left temporal pole:superior temporal gyrus |
TPOsup.L |
0.41 ± 0.03 |
0.39 ± 0.02 |
2.21 |
0.03 |
|
87 |
Left temporal pole: middle temporal gyrus |
TPOmid.L |
0.38 ± 0.03 |
0.35 ± 0.04 |
2.75 |
0.01 |
|
Index |
Regions |
Abbreviation |
Node efficiency GN group PN group |
|
|---|---|---|---|---|
|
1 |
Left precental gyrus |
PreCG.L |
0.46 |
0.47 |
|
2 |
Right precental gyrus |
PreCG.R |
0.48 |
0.47 |
|
3 |
Left superior frontal gyrus,dorsolateral |
SFGdor.L |
0.47* |
|
|
4 |
Right superior frontal gyrus,dorsolateral |
SFGdor.R |
0.48 |
0.48 |
|
57 |
Left postcentral gyrus |
PoCG.L |
0.46 |
0.46 |
|
58 |
Right postcentral gyrus |
PoCG.R |
0.48 |
0.48 |
|
59 |
Left superior parietal gyrus |
SPG.L |
0.46 |
0.46 |
|
60 |
Right superior parietal gyrus |
SPG.R |
0.47 |
0.47 |
|
67 |
Left precuneus |
PCUN.L |
0.51 |
0.50 |
|
68 |
Right precuneus |
PCUN.R |
0.52 |
0.51 |
|
73 |
Left lenticular nucleus,putamen |
PUT.L |
0.49 |
0.48 |
|
74 |
Right lenticular nucleus,putamen |
PUT.R |
0.49 |
0.49 |
|
77 |
Left thalamus |
THA.L |
0.45** |
|
*unique hub node of GN group
* * unique hub node of PN group
The white matter in the brain provides a bridge for communication and information exchange with gray matter. The amount, length, and network structure of white matter affect brain information transmission, and spatial navigation as an advanced cognitive function is no exception. This study systematically analyzes fiber bundle connections' network structure and structural characteristics. The results show that the small worldness of the two networks is more significant than 1, which indicates that the two networks have high clustering, short distance, and small worldness. In other words, in healthy young people, spatial navigation networks are economical and support complex spatial navigation. This also shows that the two groups can achieve optimal balance in functional integration and separation of information processing, consistent with the study by Sharma[28]. Eg is the total efficiency of information transmission between all nodes through multiple paths; increasing Eg means less interference in information transmission and robust synchronization between different cortical regions. In this study, Eg is consistent with other studies on the relations hippocampus(HIP) between global efficiency and the cognitive ability of brain networks; that is, global efficiency is an essential predictor of working memory performance[29].
When assessing differences in an individual's ability to navigate, significant changes in cortical functions are localized rather than whole-brain. To further differentiate differences in spatial navigation capabilities, we use node efficiency to estimate the activity of local brain regions and examine the brain regions that play a crucial role in structural networks based on Hub nodes. The results show a statistical difference in nodal efficiency between the two groups in 16 brain regions, but the differences between the nodes have not been studied. However, there is much agreement that spatial navigation functions are performed by a core network dominated by the medial temporal lobe and expanded area composed of the parietal lobe and the frontal lob[28, 30]. The core network region is the cerebral cortex, primarily responsible for spatial navigation functions, including hippocampus(HIP), entorhinal cortex(EC), parahippocampal gyrus(PHG), retrosplenial complex(RSC), and thalamus(THA). There have been few studies on the expansion of the network area in the past. More and more research has focused on the role of the parietal lobe and frontal lobe in spatial navigation, speculating that the parietal lobe is involved in spatial image and directional decision-making, while the frontal lobe is involved in sensory information processing during navigation[3, 6, 31]. In more than half of the 16 different brain regions, hippocampus༈HIP༉, parahippocampal gyrus(PHG), Left Insula(INS), fusiform gyrus (FFG), heschl gyrus(HES), superior temporal gyrus (STG),temporal pole:superior temporal gyrus(TPOsup) and temporal pole: middle temporal gyrus (TPOmid) were located in the core area. It is shown that the GN group spatial navigation core region nodes have better connectivity and communication efficiency[32]. Previous studies on spatial positioning have focused on the medial temporal lobe but have tended to ignore the importance of extended regions such as the frontal-parietal lobe[33]. We found that there were differences in the nodes of the bilateral superior frontal gyrus(SFG), middle frontal gyrus(MFG), and cingulate gyrus(CG), especially the middle frontal gyrus (MFG), and Right median cingulate and paracingulate gyri (DCG.R). Some studies have observed the discharge sequence of cells and found that hippocampal activity driven by the anterior cingulate gyrus(ACG) increases episodic memory. The prefrontal cortex can reduce the interference of other signals to the hippocampus༈HIP༉ in spatial orientation tasks. Although the frontal lobe node is not the core area of spatial navigation, it can assist the core area in carrying out multiple cognitive processes related to spatial navigation, such as target planning, decision-making, memory, coding, and so on[4, 5]. However, the occipital lobe is not classified as the core and extended region of spatial guidance. Recent studies have verified that the occipital lobe perceives the spatial position and judges the sources and characteristics of the external environment through optical transmission[34]. The occipital nodes in the Left calcarine fissure and surrounding cortex(CAL.L) (0.45 ± 0.03 vs 0.43 ± 0.03,p = 0.02) and Right middle occipital gyrus(MOG.R) (0.40 ± 0.03 vs 0.38 ± 0.03,p = 0.04) were significantly different between the two groups. There is no statistical difference between the two groups' number and efficiency of Hub nodes. These nodes are located in areas associated with spatial navigation function[12], contrary to the result of Sharma[28]. Sharma found that people with better spatial orientation had more hub nodes. Speculate the reason:1 there is no difference between the two groups in the critical nodes of spatial navigation information processing, indicating that the two groups of subjects can communicate efficiently between different structures through these key node[35]. 2 this study is male, excluding gender differences.
To summarize, this study constructs individual structural networks based on DTI technology to explore their structural network connection mode differences. Overall efficiency shows that both groups of structural brain networks were small-world. In contrast, the PN group had a lower topological nature, meaning brain networks could not transmit and process information. Local node efficiency reflects the spatial localization ability of critical regions of the brain. It provides a quantitative analysis method for differentiating the differences in brain tissue structure of different spatial navigation abilities groups, which benefits the selection of particular occupational groups. It also provides a critical imaging basis for the early prevention of spatial disorientation.
In the test method, only the SBSOD of supervisor self-evaluation is selected, and the objective test method is not listed. This study the effect of white matter fiber connection on spatial navigation function, and the difference of gray matter is not measured. Grey matter is the processing center of the brain's advanced cognitive activity, and its volume and cortical thickness influence spatial navigation ability. Future research needs to study further the differences in gray matter volume and cortical thickness among individuals with different spatial orientation abilities and the whole brain region and analyze the gray matter and white matter comprehensively. The subjects in this paper are all male. First, considering eliminating the influence of gender on spatial navigation ability, male spatial navigation ability is better than female [36]; moreover, for occupations such as pilots with high spatial orientation ability, most of them are male. The study of this subject can provide essential theoretical support for the selection of pilots.
Prospection
Spatial navigation is a highly integrated capability that involves multiple cognitive processes, including multi-sensory integration, visuospatial coding, memory, and decision-making. A comprehensive study can be conducted if every cognitive process can be broken down and analyzed in detail. Research into space navigation capabilities will be more accurate.
The prospective longitudinal study of the diagnosis and intervention of disease progression based on the degree of impairment of spatial navigation capabilities still needs to be improved. Therefore, longitudinal studies are urgently needed to confirm the results of horizontal studies. As a result, researchers can better understand navigational behaviors associated with neural responses and cognitive processes. According to the results, we can select individuals with hyperspace navigation ability for particular occupations, such as pilots, based on the differences in brain network structure.
GN groups' spatial navigation abilities, global efficiency and local efficiency are better than PN groups. the different nodes are found in the core network dominated by the middle temporal lobe and the dilated region consisting of the parietal lobe and frontal lobe. Individual network structures with different spatial navigation capabilities have different properties. Based on this approach, we can screen for spatial navigation capabilities and monitor changes in spatial navigation capabilities with age.
Acknowledgments We thank the school for their help with participant recruitment.
Availability of data and material Not applicable.
Code availability Not applicable.
Authors’ contributions Study conceptualization and design (WHH, ZYH, and CLY). Data collection (WHH, CLLand BWQ). Data analysis and interpretation (WHH, ZYH and CLY). Supervision of the study procedures(WHH, CLL and BWQ). Drafting the manuscript: WHH. All authors contributed to the final version of the manuscript.
Funding This study was supported through the grants from the Air Force Medicine.
Compliance with ethical standards
Conflict of interest Authors HuihuiWang, YanhaiZhang, Linli Chang, Wanqi Bai , Liyi Chi declare that they have no conflict of interest.
Author Liyi Chi received grant funding from the Air Force Medicine.
Ethical approval The study was approved by the Ethics Committee of Air Force 986 Hospital.
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No competing interests reported.