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. 2019 Jan 10;12:1021. doi: 10.3389/fnins.2018.01021

Shared and Distinct Neural Bases of Large- and Small-Scale Spatial Ability: A Coordinate-Based Activation Likelihood Estimation Meta-Analysis

Yuan Li 1,2,*, Feng Kong 1,2, Ming Ji 1,2, Yangmei Luo 1,2, Jijun Lan 1,2, Xuqun You 1,2,*
PMCID: PMC6335367  PMID: 30686987

Abstract

Background: Spatial ability is vital for human survival and development. However, the relationship between large-scale and small-scale spatial ability remains poorly understood. To address this issue from a novel perspective, we performed an activation likelihood estimation (ALE) meta-analysis of neuroimaging studies to determine the shared and distinct neural bases of these two forms of spatial ability.

Methods: We searched Web of Science, PubMed, PsycINFO, and Google Scholar for studies regarding “spatial ability” published within the last 20 years (January 1988 through June 2018). A final total of 103 studies (Table 1) involving 2,085 participants (male = 1,116) and 2,586 foci were incorporated into the meta-analysis.

Results: Large-scale spatial ability was associated with activation in the limbic lobe, posterior lobe, occipital lobe, parietal lobe, right anterior lobe, frontal lobe, and right sub-lobar area. Small-scale spatial ability was associated with activation in the parietal lobe, occipital lobe, frontal lobe, right posterior lobe, and left sub-lobar area. Furthermore, conjunction analysis revealed overlapping regions in the sub-gyrus, right superior frontal gyrus, right superior parietal lobule, right middle occipital gyrus, right superior occipital gyrus, left inferior occipital gyrus, and precuneus. The contrast analysis demonstrated that the parahippocampal gyrus, left lingual gyrus, culmen, right middle temporal gyrus, left declive, left superior occipital gyrus, and right lentiform nucleus were more strongly activated during large-scale spatial tasks. In contrast, the precuneus, right inferior frontal gyrus, right precentral gyrus, left inferior parietal lobule, left supramarginal gyrus, left superior parietal lobule, right inferior occipital gyrus, and left middle frontal gyrus were more strongly activated during small-scale spatial tasks. Our results further indicated that there is no absolute difference in the cognitive strategies associated with the two forms of spatial ability (egocentric/allocentric).

Conclusion: The results of the present study verify and expand upon the theoretical model of spatial ability proposed by Hegarty et al. Our analysis revealed a shared neural basis between large- and small-scale spatial abilities, as well as specific yet independent neural bases underlying each. Based on these findings, we proposed a more comprehensive version of the behavioral model.

Keywords: large-scale spatial ability, small-scale spatial ability, activation likelihood estimation, meta-analysis, behavioral model

Introduction

Spatial ability is a core cognitive function that plays a significant role in individual intelligence. In general, spatial ability has been defined as the ability to understand the relationships among different positions in space or imagined movements of two- and three-dimensional objects (Clements, 1999; Wang et al., 2014). Specifically, spatial ability can be divided into two major categories: large-scale spatial ability and small-scale spatial ability. Large-scale spatial ability refers to the ability of individuals to carry out cognitive processing of spatial information in the large-scale environment. During this process, the viewer's perspective changes with respect to the larger environment, but the spatial relationships among individual objects remain the same (Hegarty and Waller, 2004; Wang et al., 2014). Representative examples of large-scale spatial ability include navigation and spatial orientation abilities (Jansen, 2009; Höffler, 2010; Wang et al., 2014). Navigation ability refers to the ability to navigate within a large-scale environment in which the spatial relationships among landmarks cannot be fully apprehended from a single vantage point (Wang and Carr, 2014), while spatial orientation refers to the ability to imagine objects from different perspectives (Yilmaz, 2009; Turgut, 2015).

Small-scale spatial ability can be defined as the ability to mentally represent and transform two- and three-dimensional images that can typically be apprehended from a single vantage point (Wang and Carr, 2014). Small-scale spatial ability mainly refers to spatial visualization and spatial relations capabilities (Jansen, 2009; Höffler, 2010). Spatial visualization is defined as the ability to manipulate complex spatial information involving the configurations of shapes (e.g., image folding or movement) or to mentally transform a two-dimensional object into three-dimensional object (Linn and Petersen, 1985; Yang and Chen, 2010). Spatial relations ability refers to the ability to recognize the relationships among the visual components of an object (Bosnyak and Nagy-Kondor, 2008; Turgut, 2015).

Currently, the relationship between large- and small-scale spatial ability can be explained using four models: (1) The “unitary model” assumes that spatial abilities along the two scales exhibit complete overlap, (2) while the “partial dissociation model” proposes that the two types of spatial ability exhibit similarities and differences. (3) In contrast, the “total dissociation model” assumes that the two sets of abilities are distinct. Lastly, (4) the “mediation model” assumes that small- and large-scale spatial abilities can be dissociated, but are determined by a third variable (Hegarty et al., 2006; Jansen, 2009). That is, while Models 1 and 2 propose that the two forms of spatial ability are related, Models 3, and 4 propose that they are not.

Each of the aforementioned models has been verified to some extent in previous studies. For instance, Hegarty and Waller (2004) observed a dissociative relationship between mental rotation and perspective-taking spatial abilities. A meta-analysis by Wang et al. (2014) further suggested that small- and large-scale spatial abilities are best characterized as separate entities. However, other studies have yielded conflicting results. Kozhevnikov et al. (2006) demonstrated that spatial navigation performance can be predicted based on mental rotation ability. In addition, Malinowski (2001) reported that mental rotation skills are significantly correlated with wayfinding performance during an orienteering task.

Notably, in a study that examined spatial abilities, spatial updating, verbal abilities, and working memory in 221 participants, Hegarty et al. (2006) reported results that were consistent with the partial dissociation model only. Moreover, they specified the degree of overlap between small-scale and large-scale abilities, providing new insights regarding the similarities, and differences between these abilities (see Figure 1). Although their work is of great significance, the partial dissociation model remains problematic for two reasons: The model is currently incomplete, and evidence for the neural basis of such a model requires further and more comprehensive verification.

Figure 1.

Figure 1

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Model characterizing the relationship between large- and small-scale spatial abilities, as proposed by Hegarty et al. (2006).

The rapid development of neuroimaging techniques in recent years has prompted extensive investigation of large- and small-scale spatial abilities using functional magnetic resonance imaging (FMRI) and positron emission tomography (PET). In an effort to verify and expand the model of spatial ability proposed by Hegarty et al. (2006), the present study aimed to elucidate the neural basis of large- and small-scale spatial abilities via activation likelihood estimation (ALE) analysis (Eickhoff et al., 2012).

Materials and Methods

Literature Search and Study Selection

We searched Web of Science, PubMed, PsycINFO, and Google Scholar for studies regarding “spatial ability” published within the last 20 years (January 1988 through June 2018). In order to retrieve the maximum number of relevant articles, we classified the search keywords into the following four series based on the type of spatial ability, yielding a total of 76 groups:

  1. Spatial ability/Spatial cognition/Spatial perception/Spatial information processing + FMRI/PET.

  2. Large-scale Spatial ability/Small-scale Spatial ability + FMRI/PET.

  3. Navigation/Spatial orientation/Spatial visualization/ Spatial relations + FMRI/PET.

  4. Navigation task/test + FMRI/PET; Draw maps task/test + FMRI/PET; Way-finding task/test + FMRI/PET; Map learning task/test + FMRI/PET; Spatial orientation task/test + FMRI/PET; Perspective taking task/test + FMRI/PET; Spatial visualization task/test + FMRI/PET; Mental rotations task/test + FMRI/PET; Paper folding task/test + FMRI/PET; Spatial relations task/test + FMRI/PET; Water level task/test + FMRI/PET; Card rotation task/test + FMRI/PET; Figures task/test + FMRI/PET; Differential aptitude task/test + FMRI/PET.

After four rounds of the above search process, a total of 826 documents were identified. We then examined each of the documents in full, incorporating those with the following characteristics into our meta-analysis:

  1. All participants were healthy individuals.

  2. The study must have included whole-brain rather than region-of-interest analyses, and the data reported must have been standardized (Montreal Neurological Institute (MNI) or Talairach space).

  3. The study must have utilized behavioral experimental methods, and the articles must have included brain imaging data obtained from individuals following independent experimental tasks.

  4. If an experimental result was reported in multiple papers, only one instance was utilized for the meta-analysis.

Following this screening process, a total of 103 studies (Table 1 and Figure 2; see Supplementary Table 1 for PRISMA Checklist)) involving 2,085 participants (male = 1,116) and 2,586 foci were incorporated into the meta-analysis (see Supplementary Table 2). Among these foci, 1,372 were associated with large-scale spatial ability, while 1,214 were associated with small-scale spatial ability.

Table 1.

Summary of studies included in the present meta-analysis.

Study N(Male) Mean age Country Task and Contrast Foci
Large-scale spatial ability
Janzen and Weststeijn (2007) 15(8) 22.6 Netherlands Decision point items>non-decision point items in object location and route direction task, in-route items>against-route items 18
Nemmi et al. (2013) 19(11) 25.05 Italy Route task>control 13
Ohnishi et al. (2006) 246(133) 30.2 Japan maze task>control 22
Ino et al. (2002) 16(16) 32.3 Japan Mental navigation task>control 10
Hartley and Maguire (2003) 16(16) 28.9 UK Good navigation performance>poor navigation performance, wayfinding>trail following, wayfinding>route following 23
Baumann et al. (2010) 17(17) 31.6 Australia Navigation task>control, good navigators>poor navigators 64
Janzen and Jansen (2010) 20(10) 24.75 Netherlands Objects seen once at a decision point in wayfinding task>once at a non-decision point 7
Xu et al. (2010) 20(20) 24.2 Norway The conditions of Normal, Without and Blocked in navigation task>Line following 83
Schinazi and Epstein (2010) 16(5) 23 USA Decision point in real-world route learning>non-decision point, in-route>against-route 16
Latini-Corazzini et al. (2010) 16(16) 21.2 Italy Route task>control, survey task>control, route task>survey task 31
Iaria et al. (2008) 10(5) 23.08 Canada Highly familiar pathway in navigation task>control, bottom-up attention mechanisms>control, direct events>action events and perceptual event 86
Rosenbaum et al. (2004) 10(10) 26.4 Canadian Mental navigation task>control 35
Rauchs et al. (2008) 16(8) 22.1 Canada Common navigation task>control 62
Wolbers et al. (2007) 13(7) 21–29 USA Path integration>control 16
Wolbers and Büchel (2005) 11(11) 19–28 Germany Learning, performance, and change phase in navigation task>control 17
Hirshhorn et al. (2011) 13(5) 26.7 Unknown Distance judgment, Proximity judgment, blocked route problem solving, and landmark sequencing>control 33
Rosenbaum et al. (2007) 7(4) 46.57 Canada House recognition task>control 9
Brown et al. (2010) 20(9) 21.36 USA Overlapping>non-overlapping 69
Iaria et al. (2007) 9(4) 24.9 Canada Acquisition of the cognitive map and using the cognitive map>control 35
Grön et al. (2000) 24(12) 26 Germany Navigation task>control 18
Weniger et al. (2010) 19(11) 25 Switzerland Decide point during navigation in a virtual maze>control 17
Pine et al. (2002) 20(5) 13.9–28.8 USA memory-guided navigation task and arrow- guided navigation task>control 27
Iaria et al. (2003) 14(7) 25.3 Canada Place-learning task>control 17
Shelton and Gabrieli (2002) 12(6) 23.1 USA Route encoding>fixation, survey encoding>fixation, route encoding>survey encoding 52
Maguire et al. (1997) 11(11) 45 UK Routes, landmarks, film plots, and film frames tasks>control 35
IglóiI et al. (2010) 19(19) 24.3 UK Training trials in navigation>control trials, allocentric and egocentric responses trials>control trials, allocentric responses trials>egocentric responses trials 95
Marsh et al. (2010) 25(21) 32.5 USA Spatial learning>control 27
Rodriguez (2010) 11(5) 24–39 USA Allocentric test>cue-place test, encoding>test phases 51
Orban et al. (2006) 24(12) 23.5 Belgium Navigation task>control 24
Moffat et al. (2006) 30(15) 27 Unknown Younger adults and older adults>control 46
Lambrey et al. (2012) 18(9) 21 UK Self-rotation conditions in perspective taking task>table rotation conditions, self-rotation conditions>array rotation conditions, good performance>poor performance 13
Ino et al. (2007) 1(1) 55 Japan Navigation task>control 7
Whittingstall et al. (2014) 18(5) 20–28 Canada Visuospatial imagery task>control 14
Kaiser et al. (2008) 24(12) 28.15 Germany Third-person-perspective>first-person-perspective 14
Hirshhorn et al. (2012) 16(6) 26.43 Canada Navigation task>control 12
Pintzka et al. (2016) 53(0) 22.5 Norway Successful>failed navigation 23
Jandl et al. (2015) 26(13) 63.7 Germany Navigation trials>visual memory trials 12
Ledoux et al. (2013) 22(16) 30.48 Canada Wayfinding task>control 13
Powell et al. (2012) 82(32) 21.6 UK Landmark task>control 28
Ganesh et al. (2015) 23(11) 23 Netherlands Egocentric mental spatial transformations>allocentric mental spatial transformations 8
Etchamendy et al. (2012) 30(12) 26.53 Canada Concurrent spatial discrimination learning task>control 12
Clemente et al. (2013) 14(0) 21.64 Spain Navigation>video, navigation>photographs 10
Mazzarella et al. (2013) 20(9) 27.7 Unknown Altercentric condition in perspective taking task>control, egocentric condition>control 94
Lee et al. (2005) 10(10) 22–25 Hong Kong Spatial orientation task>control 39
Lux et al. (2003) 14(14) 26.8 Germany Spatial orientation task>control 10
Kesler et al. (2004) 13 14.5 USA Spatial orientation task>control, difficult>control 8
SMALL-SCALE SPATIAL ABILITY
Hugdahl et al. (2006) 11(6) 30 Norway Mental rotation task>control 4
Schöning et al. (2007) 34(14) 32 Germany Mental rotation task>control 95
Gogos et al. (2010) 10(0) 55.4 Australia Mental rotation task>control 16
Weiss et al. (2009) 16(16) 20–39 Germany Mental rotatio>stimulus categorization, mirrored presentation>non-mirrored presentation 59
Suchan et al. (2002) 10(4) 28.9 Germany Mental rotation task>control 27
Wraga et al. (2010) 18(8) 22 USA Perspective task>object-in-hand task, rotation task>fixation 50
Johnston et al. (2004) 9(5) 25.8 UK Different orientation>same orientation 3
Suchan et al. (2006) 11(6) 27 Germany Simultaneous matrix rotation>simultaneous 3-D cube rotation, simultaneous 3-D cube rotation>simultaneous matrix rotation, successive matrix rotation>successive 3-D cube rotation 19
Jordan et al. (2001) 9(1) 21 Germany Three mental rotation conditions>control 36
Podzebenko et al. (2002) 10(5) 28.3 Australia Mental rotation task>control 14
Seurinck et al. (2005) 24(0) 23 Belgium Mental rotation task>control 36
Creem-Regehr et al. (2007) 16(7) 21 USA Hand and viewer rotation tasks>control 35
Sluming et al. (2007) 10(10) 41 UK Mental rotation task>control 14
Wolbers et al. (2003) 13(9) Unknown Germany Mental rotation task>control 11
Keehner et al. (2006) 14(7) Unknown Australia Different degrees of rotation>control 10
Lamm et al. (2007) 13(13) 23–31 Austria Location and orientation condition during mental rotation>control 16
Vingerhoets et al. (2002) 13(13) 29 Belgium Rotated hands and figures>control 31
Wraga et al. (2005) 11(7) 25 USA Object rotation task>control, self-rotation task>control 51
Corradi-Dell'Acqua et al. (2009) 17(17) 28.31 Germany Body schema and body structural rotation>control, stimulus strategy>control 5
Halari et al. (2006) 19(9) 25.78 UK Mental rotation task>control 27
Creem et al. (2001) 12(6) 24 USA Mental rotation task>control 15
Thomsen et al. (2000) 11(6) 30 Norway Mental rotation task>control 4
Vingerhoets et al. (2001) 10(5) 26 Belgium Mental rotation task>control 5
de Lange et al. (2005) 6(6) 25 Netherlands Mental rotation task>control 10
Logie et al. (2011) 21(7) 30–35 UK Mental rotation>control, high imagers>low imagers 16
Levin et al. (2005) 12(6) 20.67 USA Mental rotation task>control 18
De Lange et al. (2006) 17(17) 24 Netherlands mental rotation task>control 7
Ferri et al. (2014) 18(9) 24 Italy Mental rotation of one's own right hand>mental rotation of one's own left and other's hand, angular orientation>control 25
Kawamichi et al. (2007) 14(14) 18–33 Japan Mental rotation task>control 40
Jordan et al. (2002) 24(10) 23.17 Germany Three mental rotation conditions (3d, abstract, letter)>control, 3D-condition>the ABSTRACT- and LETTER-conditions 36
Lamm et al. (2001) 13(13) 24.5 Austria Mental rotation task>control 11
Paschke et al. (2012) 10(10) 25 Germany mental rotation task>control 3
Milivojevic et al. (2008) 14(8) 26.21 New Zealand Stimulus rotation during a mirror-normal parity judgment task>letter–digit category judgment task 15
Ng et al. (2001) 12(12) 29.25 UK Line orientation experiment>control, mental rotation experiment>control 15
Stoodley et al. (2012) 9(9) 25 USA Rotated letters>upright letters 18
Wilson and Farah (2006) 7(3) 18–23 USA Letter and object mental rotation>control 11
Papeo et al. (2012) 18(0) 22–28 USA Motor strategy and visuospatial strategy>control 15
Schendan and Stern (2007) 16(7) 21.2 USA Mental rotation task>control 26
Kucian et al. (2006) 20(10) 27.2 Switzerland Mental rotation task>control 13
Zacks et al. (2002) 24(9) 19–31 USA Spatial transformation tasks>control 36
Seurinck et al. (2011) 16(16) 24 Netherlands Mental rotation task>control 16
Seurinck et al. (2004) 22(11) 25.4 Belgium Mental rotation task>control 46
Vanrie et al. (2002) 6(3) 25.2 Belgium Mental rotation task>control 60
Carrillo et al. (2010) 42(23) 31.89 Spain Mental rotation task>control 2
O'Boyle et al. (2005) 6(6) 14.3 Australia Mental rotation task>control 7
Ecker et al. (2006) 10(0) 20–30 UK Mental rotation task>control 15
Baumann et al. (2012) 14(7) 21.5 Australia Spatial relations task>control 4
Bodin et al. (2010) 11(5) 20–35 Sweden Mental rotation task>control 20
Gao et al. (2017) 30(15) 19–25 China Mental rotation task>control 10
Prescott et al. (2010) 8(8) 14.2 Australia Mental rotation task>control 18
Goh et al. (2013) 97(50) 22.6 USA Visuo-spatial judgments>control 7
Wolbers et al. (2006) 16(16) 19–29 Germany Spatial visualization task>control 6
Ebisch et al. (2012) 22(0) 20–24 Italy Induction–visualization>induction–spatial relationships, visualization–induction>visualization–spatial relationships 8
Blacker and Courtney (2016) 32(8) 18–30 USA Spatial relations>location 36
Newman et al. (2016) 36(21) 8 USA Mental rotation task>control 8
Kucian et al. (2005) 22(10) 25.9 Switzerland Mental rotation task>control 37
Elizabeth and Paul (2011) 16(4) 18–28 USA Matching in embedded figures task>searching 16
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Figure 2.

Figure 2

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Procedure of data selection (PRISMA 2009 Flow Diagram).

Activation Likelihood Estimation (ALE)

ALE, which treats activation foci reported in neuroimaging studies not as single points but as spatial probability distributions centered at the given coordinates, is the most common algorithm for coordinate-based meta-analysis (Eickhoff et al., 2012). In the present study, we utilized the revised algorithm for ALE analysis proposed by Eickhoff et al. (2009). This algorithm models the spatial uncertainty—and thus the probability distribution—of each focus using an estimation of the inter-individual and inter-laboratory variability typically observed in neuroimaging experiments, rather than using a pre-specified full-width at half maximum (FWHM) for all experiments as originally proposed. The modified permutation procedure reflects the null-distribution of a random spatial association between studies (i.e., random-effects analysis), rather than between foci (i.e., fixed-effects analysis; Eickhoff et al., 2016, 2017). The “modeled activation” (MA) map is computed by the following procedure. First, all foci reported for a given study are modeled as Gaussian probability distributions. The information provided by the foci of a given study is then merged into a single 3D-volume. To this end, the modeled probabilities are combined over all foci reported in that experiment by taking the voxel-wise union of their probability values (Eickhoff et al., 2009). The respective activation probabilities (the values of the MA maps) are then recorded, yielding as many values as there had been studies included in the current meta-analysis. These values correspond to MA values, that were sampled from random, spatially independent locations. The union of these activation probabilities is then computed in the same manner as done for the meta-analysis itself in order to yield an ALE score under the null-hypothesis of spatial independence (Eickhoff et al., 2009, 2012).

The ALE method is advantageous in that it may help to resolve several issues in current brain imaging research. First, the number of participants involved in single brain imaging studies is generally low, often providing unstable results. Second, single-brain imaging results are likely influenced by certain experimental conditions (e.g., scan parameters). Third, interpretations regarding the function of a given brain region derived from a single study are often based on a small number of experimental tasks.

Ginger ALE software (version 2.3; http://www.brainmap.org/ale) was used to conduct the current meta-analysis, including the conversion of Talairach coordinates into MNI space. Loci in both cerebral and cerebellar regions were all focused by the current research. The resulting p-value maps were threshold using cluster-level family-wise error (FWE) correction at p < 0.05, with a cluster-defining threshold of p < 0.005 and 5,000 permutations (Eickhoff et al., 2012). The results were overlaid onto an anatomical template (Colin27 T1 seg MNI.nii; http://www.brainmap.org/ale) and displayed using Mango software (http://rii.uthscsa.edu/mango).

Results

Our ALE analysis identified 16 clusters of consistent activation related to large-scale spatial ability. The largest two clusters of activation in the bilateral limbic lobe were centered in the bilateral parahippocampal gyrus. Other clusters were primarily located in the bilateral posterior lobe, bilateral occipital lobe, bilateral parietal lobe, right anterior lobe, bilateral frontal lobe, and right sub-lobar area (Table 2, Figure 3). Clusters exhibiting consistent activation in the analysis of small-scale spatial ability were concentrated mainly within the bilateral parietal lobe, bilateral occipital lobe, bilateral frontal lobe, right posterior lobe, and left sub-lobar area (Table 3 and Figure 3).

Table 2.

ALE meta-analysis results for large-scale spatial ability.

Cluster No. Hemisphere Brain regions BA x y z ALE(10−2) Cluster size (mm3)
1 R Parahippocampal gyrus 36 26 −38 −10 0.102188 9912
R Declive 26 −60 −12 0.032387
R Parahippocampal gyrus 36 −22 −22 0.026213
2 L Parahippocampal gyrus 36 −24 −46 −6 0.079631 5784
3 R Superior occipital gyrus 19 42 −78 32 0.051903 4400
R Middle occipital gyrus 19 40 −80 22 0.046983
R Precuneus 7 26 −76 46 0.034177
R Precuneus 7 26 −66 36 0.024326
4 L Posterior cingulate 30 −16 −56 18 0.07205 4224
5 R Posterior cingulate 30 18 −52 16 0.072787 3512
R Culmen 10 −46 2 0.037102
6 L Lingual gyrus 18 −6 −80 −2 0.050804 3384
R Lingual gyrus 18 18 −68 0 0.036607
R Culmen 6 −72 −2 0.034144
L Declive −16 −76 −8 0.027501
7 R Sub-gyral 6 28 6 54 0.049654 2856
8 R Precuneus 7 18 −62 58 0.048159 2752
L Precuneus 7 −2 −66 54 0.031828
9 R Superior frontal gyrus 6 4 14 50 0.04669 2096
10 L Middle occipital gyrus 19 −32 −86 24 0.057178 1896
L Superior occipital gyrus 19 −32 −84 38 0.025026
L Cuneus 17 −20 −80 20 0.02431
11 L Superior parietal lobule 7 −18 −62 62 0.044397 1832
12 L Sub-gyral 6 −26 0 58 0.048783 1752
13 L Inferior occipital gyrus 19 −42 −78 0 0.043239 1504
L Middle occipital gyrus 19 −44 −82 12 0.025768
14 R Lentiform nucleus 18 −4 −12 0.0353 1376
R lentiform nucleus 26 −6 −12 0.034406
R Parahippocampal gyrus 22 −10 −16 0.031555
15 L Parahippocampal gyrus −26 −20 −18 0.028272 1256
16 R Claustrum 32 24 −4 0.052402 1112
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BA, Brodmann area; R, right; L, left; ALE, activation likelihood estimation.

Figure 3.

Figure 3

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ALE meta-analysis of neuroimaging studies regarding large-scale spatial ability (A) and small-scale spatial ability (B). Coordinates are presented in millimeters (mm). ALE, activation likelihood estimation.

Table 3.

ALE meta-analysis results for small-scale spatial ability.

Cluster No. Hemisphere Brain regions BA x y z ALE(10−2) Cluster size (mm3)
1 R Precuneus 7 26 −66 48 0.089947 24816
R Precuneus 7 26 −56 56 0.075651
R Precuneus 31 34 −74 26 0.049883
R Inferior parietal lobule 40 36 −40 42 0.048361
R Middle occipital gyrus 18 34 −82 2 0.042117
2 L Superior parietal lobule 7 −26 −64 50 0.059831 22584
L Inferior parietal lobule 40 −38 −40 46 0.056222
L Inferior parietal lobule 40 −46 −36 46 0.048675
L Precuneus 7 −16 −62 54 0.047647
L Precuneus 7 −20 −74 48 0.045286
L Inferior occipital gyrus 19 −42 −72 −4 0.043441
L Inferior occipital gyrus 19 −36 −78 0 0.041091
L Middle occipital gyrus 18 −30 −90 12 0.039092
L Inferior occipital gyrus 18 −36 −86 −2 0.037764
L Middle occipital gyrus 19 −34 −78 18 0.024928
L Precuneus 31 −26 −78 24 0.024692
3 R Middle frontal gyrus 6 28 −2 56 0.086822 4968
4 L Middle frontal gyrus 6 −24 −4 54 0.073822 4880
5 R Inferior frontal gyrus 9 52 8 26 0.070489 4696
6 L Superior frontal gyrus 6 2 12 50 0.072562 4608
R Medial frontal gyrus 8 6 26 46 0.030976
7 L Inferior frontal gyrus 9 −48 8 28 0.057083 3160
8 R Tuber 42 −62 −32 0.040321 2944
R Declive 44 −64 −16 0.036898
9 L Insula 13 −32 26 0 0.039269 976
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We then compared the ALE results for large- and small-scale spatial abilities to identify common and distinct neural regions associated with each ability. Conjunction analysis revealed that bilateral sub-gyrus, right superior frontal gyrus (SFG), right superior parietal lobule (SPL), right middle occipital gyrus (MOG), right superior occipital gyrus (SOG), left inferior occipital gyrus (IOG), and bilateral precuneus were activated for both large- and small-scale spatial abilities (Table 4 and Figure 4). In addition, large- and small-scale spatial ability contrasts demonstrated correspondence in the bilateral parahippocampal gyrus, left lingual gyrus, bilateral culmen, right middle temporal gyrus (MTG), left declive, left SOG, and right lentiform nucleus (Table 5 and Figure 4). Conversely, small- and large-scale spatial ability contrasts converged in the bilateral precuneus, right inferior frontal gyrus (IFG), right precentral gyrus, left inferior parietal lobule (IPL), left supramarginal gyrus, left SPL, right IOG, and left middle frontal gyrus (MFG; Table 5 and Figure 4).

Table 4.

Results of conjunction analysis for large- and small-scale spatial ability.

Cluster No. Hemisphere Brain regions BA x y z ALE(10−2) Cluster size (mm3)
1 R Sub-Gyral 6 26 4 56 0.049543 1760
2 R Superior frontal gyrus 6 4 14 50 0.04669 1704
3 R Superior parietal lobule 7 20 −62 58 0.045159 1616
4 L Sub-Gyral 6 −26 0 58 0.048783 1440
5 R Middle occipital gyrus 19 34 −78 20 0.035724 1136
R Superior occipital gyrus 19 36 −74 30 0.03098
6 L Inferior occipital gyrus 19 −42 −76 −2 0.036802 664
7 L Precuneus 7 −18 −62 58 0.036471 640
8 R Precuneus 7 26 −74 48 0.032807 464
9 R Precuneus 7 26 −66 36 0.024326 120
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Figure 4.

Figure 4

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Results of conjunction and contrast analyses. (A) The common regions associated with large- and small-scale spatial ability. (B) Brain regions exhibiting greater activation for large-scale spatial ability than for small-scale spatial ability. (C) Brain regions exhibiting greater activation for small-scale spatial ability than for large-scale spatial ability. Coordinates are presented in millimeters (mm).

Table 5.

Results of contrast analysis for large- and small-scale spatial ability.

Cluster No. Hemisphere Brain regions BA X Y Z ALE(10−2) Cluster size (mm3)
LARGE-SCALE SPATIAL ABILITY>SMALL-SCALE SPATIAL ABILITY
1 R Parahippocampal gyrus 35 25 −36.8 −10.5 0.09013 6368
R parahippocampal gyrus 35 32 −23.3 −22 0.021
2 L Lingual gyrus −14.7 −56.5 14.6 0.05872 4016
3 L Culmen −24.3 −43 −7.5 0.05677 3536
L Culmen −20.3 −42.3 −17.7 0.02638
4 R Culmen 17.4 −52.2 14.6 0.069 3392
5 R Middle temporal gyrus 19 41 −76 35.4 0.0448 936
6 L Culmen 0 −3.5 −73.2 −1.7 0.0239 616
L lingual gyrus 18 −10 −76 −6 0.02136
L Declive −14.7 −77.3 −8.7 0.02416
7 L Superior occipital gyrus 19 −36.2 −83.8 30 0.03538 464
8 R Lentiform nucleus 15 −2.7 −12.2 0.0278 440
R Lentiform nucleus 20 −3 −10 0.02655
R Lentiform nucleus 22 −8 −10 0.02424
9 L Parahippocampal gyrus −28 −24 −16 0.02198 128
L Parahippocampal gyrus −26 −18 −17 0.00386
SMALL-SCALE SPATIAL ABILITY>LARGE-SCALE SPATIAL ABILITY
1 R Precuneus 7 26.6 −63.3 48.7 0.0659 3008
R Precuneus 7 25.3 −54.7 59 0.05862
2 R Inferior frontal gyrus 9 55.1 10.9 28.7 0.03762 1352
R Precentral gyrus 6 58 8 30 0.02762
R inferior frontal gyrus 44 55 12 19 0.02476
R Precentral gyrus 6 43 9.3 21.3 0.02805
R Precentral gyrus 6 44 4 22 0.01912
R Inferior frontal gyrus 9 47 14 20 0.02064
3 L Inferior parietal lobule 40 −48 −40 46 0.03136 968
L Inferior parietal lobule 40 −47 −43 49 0.02126
L Supramarginal gyrus 40 −46.7 −36.7 41.3 0.0275
L Inferior parietal lobule 40 −38 −48 58.5 0.02253
4 L Superior parietal lobule 7 −25 −74 52 0.0222 936
L Precuneus 7 −21.7 −61.4 45.7 0.02749
L Precuneus 7 −23 −53 55 0.02721
5 R Inferior occipital gyrus 19 37.5 −79.4 6.2 0.03082 888
6 L Middle frontal gyrus 6 −20.3 −6.6 52.9 0.0387 568
L Middle frontal gyrus 6 −22 −9.2 62 0.02362
7 R Precuneus 7 30 −42 42 0.025 504
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Discussion

In the present study, we conducted an ALE meta-analysis to elucidate the neural basis of large- and small-scale spatial abilities. Our results indicated that large-scale spatial tasks were associated with increased activation in the parahippocampal gyrus, SOG, MOG, precuneus, posterior cingulate, lingual gyrus, sub-gyrus, right SFG, left cuneus, and left SPL. This result is consistent with the findings of Boccia et al. (2014) and Kühn and Gallinat (2014) regarding individual navigational capabilities. Furthermore, cognitive processing of individual large-scale spatial information was associated with activation in the declive, culmen, left IOG, right lentiform nucleus, and right claustrum. These findings are in accordance with those of several previous studies, which have also suggested that the parahippocampal gyrus plays a key role in large-scale spatial ability (Connor and Knierim, 2017; Cullen and Taube, 2017; Epstein et al., 2017; Lisman et al., 2017; Moser et al., 2017).

Our analysis also revealed that small-scale spatial tasks were associated with higher activation in the IPL, MOG, left SPL, left IOG, MFG, and IFG. These results are consistent with the findings of Tomasino and Gremese (2016) regarding individual mental rotation capabilities. Furthermore, cognitive processing of individual small-scale spatial information was also associated with activation in the precuneus, right MFG, right tuber, right declive, and left insula.

Notably, our ALE conjunction analysis revealed that the sub-gyrus, right SFG, right SPL, right MOG, right SOG, left IOG, and precuneus participate in the cognitive processing of both large- and small-scale spatial information. These results are in accordance with the model proposed by Hegarty et al. (2006), which argues that there is a partial intersection/association between individual large-scale and small-scale spatial abilities. Hegarty et al. (2006) speculated that this association arises from the fact that spatial ability is not a completely independent cognitive function, as both large- and small-scale spatial ability involve cognitive processes such as visual processing and working memory. Indeed, our results support this notion, suggesting a shared neural basis for large- and small-scale spatial ability. Extensive research has demonstrated that the sub-gyrus, right SPL, right MOG, right SOG, left IOG, and precuneus are associated with the processing of visual spatial information (Fink et al., 2003; Price and Devlin, 2003; Bristow et al., 2005; Konen et al., 2005; Slotnick and Moo, 2006; Deutschländer et al., 2008; Choi et al., 2010; Seo et al., 2012). Additional studies have indicated that the sub-gyrus, right SFG, and precuneus are significantly associated with working memory tasks (Mitchell, 2007; Chein et al., 2011; Fassbender et al., 2011; Ma et al., 2012; Opitz et al., 2014). Furthermore, the right sub-gyrus, right SFG, right MOG, and precuneus have been associated with attention (Wu et al., 2007; Kelley et al., 2008; Thakral and Slotnick, 2009; Li et al., 2012; Majerus et al., 2012), while the sub-gyrus and left precuneus have been associated with the planning process (Wagner et al., 2006; Jankowski et al., 2009; Crescentini et al., 2012). The sub-gyrus has also been implicated in executive function (Kerstin et al., 2005; Schubotz and von Cramon, 2009; Jin et al., 2012; Dambacher et al., 2014).

Taken together, these findings suggest that both large- and small-scale spatial ability involve visual processing and working memory of spatial information, as well as cognitive processes such as attention, planning, and executive control. These processes may represent vital cognitive and behavioral foundations for the overlap and interaction between large- and small-scale spatial ability. In this context, an individual first relies on attention to capture visual spatial information, following which working memory aids in the preparation and processing of the input. The individual then develops a detailed plan to finish the current spatial cognitive task. The planning process is often embodied in the corresponding task execution strategy for large-scale spatial tasks. For small-scale spatial tasks, it is often embodied in the operational scheme developed by the participant according to his/her own operational capabilities. Execution of the plan and behavioral responses during the experimental task are under the control of executive function.

We further analyzed the specific neural activity involved in large- and small-scale spatial abilities. Our analysis indicated that large-scale spatial tasks were associated with more intense activation of the parahippocampal gyrus, left lingual gyrus, culmen, right MTG, left declive, left SOG, and right lentiform nucleus. Indeed, previous studies have indicated that the parahippocampal gyrus, left lingual gyrus, culmen, and right MTG are significantly associated with the processing of information related to virtual environments (Weniger et al., 2009, 2013; Mellet et al., 2010; Nieuwland, 2012; Fairhurst et al., 2013). Additional studies have demonstrated that the parahippocampal gyrus, left lingual gyrus, culmen, and left SOG are associated with the processing of information related to construction, scene, and place (Suzuki et al., 2005; Henderson et al., 2008; Mullally et al., 2012; Rémy, 2014; Sulpizio et al., 2014; Zeidman et al., 2015). The parahippocampal gyrus, left lingual gyrus, and culmen have also been implicated in autobiographical memory (Maguire and Dhassabis, 2010; Whalley et al., 2012; Wilbers et al., 2012). Several studies have further reported that the parahippocampal gyrus, culmen, and left SOG are associated with episodic memory (Suzuki et al., 2009; Reggev et al., 2011; Demaster and Ghetti, 2013), while the right parahippocampal gyrus, left lingual gyrus, left culmen, and left declive are associated with semantic memory (Grossman et al., 2003; Assaf et al., 2005; Kodama et al., 2015). Recent research has also demonstrated that the right lentiform nucleus is associated with emotional processing (Goldin et al., 2008; Telzer et al., 2014; Wardle et al., 2014; Touroutoglou et al., 2015). Taken together, these findings suggest that, relative to small-scale spatial ability, large-scale spatial ability relies more heavily on the cognitive processing of spatial information related to aspects of the virtual environment (e.g., construction, scene, place, etc.), and on the autobiographical, episodic, and semantic aspects of memory. Our findings further suggest that large-scale spatial ability is associated with greater emotional responses.

In contrast, small-scale spatial tasks were associated with more intense activation in the precuneus, right IFG, right precentral gyrus, left IPL, left supramarginal gyrus, left SPL, right IOG, and left MFG. Previous studies have revealed that the precuneus, right IFG, right precentral gyrus, left IPL, left SPL, right IOG, and left MFG are associated with motor imagery (Kaladjian et al., 2007; De Lange, 2008; Chen et al., 2009; Thompson and Baccus, 2012; Chinier et al., 2014; Lissek et al., 2014). Additional studies have demonstrated that the precuneus, right IFG, right precentral gyrus, left supramarginal gyrus, left IPL, and left MFG are associated with control of the finger and hand, particular for grasping and tapping movements (Culham et al., 2003; Vandermeeren et al., 2003; Müller et al., 2011; Schweisfurth et al., 2011; Akhlaghi et al., 2012; Lavrysen et al., 2012; Stefanics and Czigler, 2012). Moreover, the right precentral gyrus, left IPL, and left supramarginal gyrus have been associated with working memory (Mitchell, 2007; Burgess and Braver, 2010; Chein et al., 2011; Fassbender et al., 2011). In addition, several studies have indicated that the precuneus, right IFG, right precentral gyrus, left IPL, and left supramarginal gyrus are involved in arithmetic and calculation (Gruber et al., 2001; Fehr et al., 2007; Benn et al., 2013). Some studies have also reported that the right precentral gyrus and right IFG are assocaited with matching activities (Darcy Burgund et al., 2005; Soulières et al., 2009). Taken together, these findings indicate that, relative to large-scale spatial ability, small-scale spatial ability relies more heavily on motor imagery, mental processes associated with finger and hand movements, and cognitive processes such as working memory, calculation, and matching.

However, it is noteworthy that although there are brain regions related to individuals' working memory in the common part of large- and small-scale spatial ability and in the unique part of the small-scale spatial ability, these brain regions are still different with respect to the cognitive process and the functions they are specifically involved in. First of all, the cognitive process shows that the working memory includes such three processes as encoding, maintenance, and retrieval; however, the sub-gyrus in the common part is involved in the encoding process, precuneus is involved in the maintenance process, and SFG is involved in the retrieval process; whereas the precentral gyrus in the unique part is involved in the encoding and maintenance processes, IPL is involved in the retrieval process, and supramarginal gyrus is involved in the encoding and retrieval processes (Langel et al., 2014). Then, the function of the working memory shows that these brain regions serve as the significant neural foundations of the central executive system in individuals' working memory. The central execution system, as the core component of the working memory, has such important functions as selection, inhibition, updating, and conversion (Collette and Linden, 2002). In the common part, however, SFG is mainly responsible for updating in the central executive system, and precuneus is mainly responsible for selection. In addition, IPL and supramarginal gyrus in the unique part are also crucial brain regions for updating (Bledowski et al., 2009; Borst and Anderson, 2013). To sum up, we believe that although both large- and small-scale spatial ability involves the cognitive process of encoding, maintenance, and retrieval in individuals' working memory, small-scale spatial ability also involves working memory processes that more precuneus, IPL and supramarginal gyrus are involved in. It can also be understood this way: although working memory is involved in the cognitive processing of large- and small-scale spatial information of individuals at the same time, the involvement in the latter is wider and deeper. On the other hand, the selection and updating functions of the central executive system in the working memory also constitute the common basis of individuals' large- and small-scale spatial ability, but the small-scale spatial ability, compared with the large-scale spatial ability, is more dependent on the updating function. Therefore, the result that this working memory is present in the common part of the large- and small-scale spatial ability and in the unique part of the small-scale spatial ability is not contradictory. Moreover, this result, to a certain extent, also mutually supports the phenomenon that the working memory training as found by many researchers in the domain of cognitive training can lead to a significant transfer influence on individuals' spatial ability (Alloway et al., 2013; Borella et al., 2017; Nilsson et al., 2017).

Although large- and small-scale spatial ability are to some extent associated at both the neural and behavioral levels, they are profoundly different in many ways. Large-scale spatial ability reflects more of the individual's ability to process spatial information related to actual or virtual situations. In such cases, regions associated with the cognitive processing of environmental information (e.g., construction, scene, place, etc.) will be activated more intensely, following which brain regions corresponding to autobiographical and episodic memory will be engaged. In addition, as large-scale spatial tasks often require more detailed explanation, semantic memory load will be greater for such tasks than for those associated with small-scale spatial ability. Interestingly, we also observed activation of emotion-related regions during large-scale spatial tasks. One explanation for this finding was that experimental tasks in general might elicit emotional responses. Individuals are more prone to such negative emotions as anxiety and fear when completing large-scale spatial tasks. For instance, Lawton (1994) and Malinowski and Gillespie (2001) found that wayfinding performance was negatively correlated with anxiety. Lawton and Kallai (2002) further discovered that women showed greater anxiety than men in large-scale spatial tasks. In addition, many scholars focus on the stress responses of individuals in navigation tasks (Duncko et al., 2007; Schwabe et al., 2007, 2009). Therefore, when compared with small-scale spatial ability, the large-scale spatial ability of individuals is more often studied by being linked to emotional factors. Consistent with these studies, our findings also showed that individuals have actually activated the emotion-related brain regions when completing large-scale spatial tasks. Alternatively, experimental materials depicting beautiful scenery, architecture, or familiar scenes may evoke various emotional responses among individual participants. Such task elements as the stimulus materials and the experimental scenarios for the large-scale spatial tasks at this stage are much more plentiful than smaller-scale space tasks. It is for sure that this also prompts them to have more elements affecting individual emotions potentially. So, can the improved and enriched small-scale spatial tasks or research paradigms affect individuals' activities and emotion-related brain regions? We believe that this question still warrants validation of more studies.

In contrast, our findings indicated that small-scale spatial ability was more strongly associated with brain regions involved in motor imagery, grasping, calculation, and matching, suggesting that this form of spatial ability reflects an individual's ability to process spatial information for specific objects. In this case, judgments are made based on imagination and mental representations. Thus, grasping, calculation, and matching functions may aid individuals in operating upon mental representations. Because imagination and operation abilities reflect working memory function, our findings suggest that small-scale spatial ability relies more heavily on working memory than other aspects of memory function. Given these findings, we propose a new model, as shown in Figure 5.

Figure 5.

Figure 5

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Model of the relationship between large- and small-scale spatial ability based on the current findings.

The current model was proposed on basis of Hegarty et al. (2006)'s model. We validated and supplemented their model from the neuroimaging level. We know that each region of our brain has many corresponding functions, and we proposed the current model mainly based on the following three considerations: first, based on our own literature accumulation and experience and taking into account the functional intelligence of the relevant brain regions from such database as Neurosynth (http://neurosynth.org/) and Brainnetome (http://atlas.brainnetome.org/bnatlas.html), we determined the aspects of cognitive function that brain regions mainly involve in the current ALE results; then, we examined whether these functions were consistent or directly associated with the cognitive activity components of the various components of Hegarty et al. (2006)'s model, thereby determining whether our findings can support Hegarty et al. (2006)'s model; lastly, by summarizing the work in the previous two steps, findings of previous studies, and our understanding of the individual spatial ability, we proposed the current model that would supplement and deepen Hegarty et al. (2006)'s model finally.

In general, the findings of the present research supported and extended the model proposed by Hegarty et al. (2006). With regard to the large-scale spatial ability, first, Hegarty et al. summed up the cognitive components specific to individuals' large-scale spatial ability based on his own studies, including addressing and spatial learning based on sensing of body motions. In fact, both “updating” and “learning” refer to the process of encoding the spatial stimulus information by individuals, and this spatial stimulus information is on basis of sensing of body motions. According to Hegarty et al. (2006), on the one hand, these stimuli were derived from the real environment; on the other hand, these stimulus messages also contained information regarding the “self.” Our model based on the current ALE results also supported such a view. According to our model, individuals' large-scale spatial ability not only involves individuals' processing of such real-world spatial stimulus information as constructions, scenes and places, but also contains individuals' cognitive processing of the virtual context space stimulus information. Moreover, we have also found that the brain regions associated with individuals' autobiographical memory, episodic memory, semantic memory and emotional responses are also deeply involved in individuals' large-scale spatial cognition, among which the first two items are associated with individuals' “self” information. Secondly, Hegarty et al. (2006) also thought that individuals' large- and small-scale spatial ability involves individuals' encoding of the visual spatial stimulus information, maintenance of spatial representations in the working memory and process of inference on the basis of these spatial representations. Not only does our model support the above conclusions fully, but also we further propose that all the processes of encoding, maintenance and retrieval, and the core central execution system components in the working memory are important for individuals' spatial ability. In addition, we believe that the final inference process involves mainly the ability of individuals' planning and executive control. Finally, as mentioned earlier (also as Figure 5), another important addition and extension of the current research to Hegarty et al. (2006)'s model was that we attempted to present the cognitive ability base specific to individuals' small-scale spatial ability.

Notably, we observed no distinct differences in the cognitive processing strategies utilized during large- and small-scale spatial tasks. Currently, strategies for processing spatial information are divided into three main categories: (1) egocentric-allocentric (Galati et al., 2000), (2) whole-part (Li and O'Boyle, 2011; Janssen and Geiser, 2012), and (3) motor imagination-visual processing (Tomasino and Gremese, 2016). However, the basis and theory of these three categories are quite similar, and we speculate that the latter two represent variations upon the first category. Whole-part strategies are likely to be utilized during the processing of large-scale spatial information, while motor imagination-visual processing strategies are likely to be utilized during the processing of small-scale spatial information. Some researchers have hypothesized that large-scale spatial tasks primarily require egocentric strategies, while small-scale tasks primarily require allocentric strategies (Malinowski, 2001; Zacks et al., 2001; Peña et al., 2008).

A meta-analysis by Boccia et al. (2014) revealed that navigation tasks were associated with greater activation of the right SOG, right angular gyrus, and right precuneus when egocentric rather than allocentric strategies are adopted. A subsequent meta-analysis by Tomasino and Gremese (2016) revealed that small-scale spatial tasks were associated with greater activation in the right postcentral gyrus, left IPL, left postcentral gyrus, left SPL, and right angular gyrus when egocentric strategies are adopted. In contrast, allocentric strategies were associated with greater activation of the right precuneus, right SFG, right SOG, left MOG, left SPL, left inferior temporal gyrus (ITG), left IOG, left MOG, and right posterior medial frontal gyrus. Interestingly, the right precuneus, right SOG, and left SPL reflect opposing processing strategies for large- and small-scale spatial tasks. Taken together, these findings suggest that there is no absolute difference in cognitive processing strategies between large- and small-scale spatial ability, and that either egocentric or allocentric strategies may be utilized in both task types. Differences in strategy selection may be related to individual differences such as the interaction between gender and verbal working memory (VWM)/visual spatial working memory (VSWM) ratio (Wang and Carr, 2014).

The present study possesses several limitations of note. First, unlike meta-analyses performed in other fields of research, ALE calculations based on neuroimaging do not consider the size of an effect; consequently, they cannot include evidence for the absence of an effect (i.e., so-called null results). Furthermore, ALE analyses cannot illuminate the temporal dynamics of cognitive processes (Winlove et al., 2018). Secondly, as most participants in the included studies were relatively young, we were unable to explore the effect of life-long development on the relationship between large- and small-scale spatial ability. Finally, we investigated spatial ability in healthy individuals only. Further studies are required to determine whether similar results can be obtained in patients with major depressive disorder, schizophrenia, attention deficit hyperactivity disorder, autism, and Alzheimer's disease.

Conclusion

The results of the present study verify and expand upon the theoretical model of spatial ability proposed by Hegarty et al. (2006). Our analysis revealed a shared neural basis between large- and small-scale spatial abilities, as well as specific yet independent neural bases underlying each. Based on these findings, we proposed a more comprehensive version of the behavioral model (Figure 5). Our results further suggest that there is no absolute difference in cognitive strategies adopted during large- and small-scale spatial tasks.

Ethics Statement

This study was approved by the Human Research Ethics Committee of Shaanxi Normal University.

Author Contributions

YL, FK, MJ, YL, JL, and XY design of the study. YL, FK, MJ, and YL data collection, coding, and data analysis. YL, FK, MJ, and XY paper writing and revising.

Conflict of Interest Statement

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.

Acknowledgments

This research was supported by the Fundamental Research Funds for the Central Universities (2016CBZ010; 2017TS115), Key research and development programs in Shaanxi (2017SF-271) and the Major Project of Medicine Science and Technology of PLA (AWS17J012).

Footnotes

1Papers with asterisk were included in the meta-analysis.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2018.01021/full#supplementary-material

Supplementary Table 1

PRISMA 2009 Checklist.

Click here for additional data file. (64KB, doc)
Supplementary Table 2

Samples of coordinate.

Click here for additional data file. (39.5KB, docx)

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Associated Data

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Supplementary Materials

Supplementary Table 1

PRISMA 2009 Checklist.

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Supplementary Table 2

Samples of coordinate.

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