Abstract
Background:
Our previous reports reflected some aspects of neuroplastic changes from long-term Chinese chess training but were mainly based on large-scale intrinsic connectivity. In contrast to functional connectivity among remote brain areas, synchronization of local intrinsic activity demonstrates functional connectivity among regional areas. Until now, local connectivity changes in professional Chinese chess players (PCCPs) have been reported only at specific hubs; whole-brain-based local connectivity and its relation to training profiles has not been revealed.
Objectives:
To investigate whole-brain local connectivity changes and their relation to training profiles in PCCPs.
Methods:
Regional homogeneity (ReHo) analysis of rs-fMRI data from 22 PCCPs versus 21 novices was performed to determine local connectivity changes and their relation to training profiles.
Results:
Compared to novices, PCCPs showed increased regional spontaneous activity in the posterior lobe of the left cerebellum, the left temporal pole, the right amygdala, and the brainstem but decreased ReHo in the right precentral gyrus. From a whole-brain perspective, local activity in areas such as the posterior lobe of the right cerebellum and the caudate correlated with training profiles.
Conclusion:
Regional homogeneity changes in PCCPs were consistent with the classical view of automaticity in motor control and learning. Related areas in the pattern indicated an enhanced capacity for emotion regulation, supporting cool and focused attention during gameplay. The possible participation of the basal ganglia-cerebellar-cerebral networks, as suggested by these correlation results, expands our present knowledge of the neural substrates of professional chess players. Meanwhile, ReHo change occurred in an area responsible for the pronunciation and reading of Chinese characters. Additionally, professional Chinese chess training was associated with change in a region that is affected by Alzheimer’s disease (AD).
Introduction
Chess serves studies in cognitive science as Drosophila serves studies in biological science. Many processes, such as perception (Sheridan and Reingold, 2017), memory (Gong et al., 2015), problem solving (Pereira et al., 2020), and empathy (Powell et al., 2017), become more apparent in the classical research paradigm of chess. With the application of non-invasive imaging techniques (Nichelli et al., 1994; Onofrj et al., 1995; Amidzic et al., 2001; Silva et al., 2018; Fuentes-García et al., 2019; Pereira et al., 2020) and especially functional magnetic resonance imaging (fMRI) (Atherton et al., 2003; Bilalić et al., 2011; Wan et al., 2011; Duan et al., 2014; Hänggi et al., 2014; Premi et al., 2020; Wang et al., 2020) in the human brain, neural substrates of cognitive processes have gradually been revealed. In studies investigating these substrates, cognitive research on expertise was a main research domain. Based on the theory of chunks in chess experts (Chase and Simon, 1973), the medial temporal lobe was initially revealed as the basis of long-term memory (LTM) in chess experts (Amidzic et al., 2001), and then the caudate region was comprehensively shown to be responsible for automatically producing the best next move in board games (Wan et al., 2011). Specific regions have long been believed to form the basis of cognitive expertise in board games. However, from the investigation of functional connectivity between the caudate and the default mode network (DMN) (Duan et al., 2012a), step-by-step exploration of specific region-based functional connections was applied (Duan et al., 2014; Sohn et al., 2017; Song et al., 2020; Wang et al., 2020). Recently, whole-brain-based detection of brain functional connectivity was reported as a dynamic functional network characteristic of Chinese chess experts (Premi et al., 2020).
Since the 1st China National Mind Sports Games in 2010 in Chengdu, China, the brain characteristics associated with cognitive expertise in Chinese chess have been discussed throughout the scientific world (e.g., Duan et al., 2012a,b, 2014; Premi et al., 2020; Song et al., 2020; Wang et al., 2020). Chinese chess (Xiangqi in Chinese) is a traditional board game originating from military strategies in ancient China. To the best of our knowledge, this game was first introduced in an English publication in 1895 (Platt, 1895). As in chess, the records of professional players in each competition are compiled to assign ranking points to each player, reflecting the person’s skill level. Xiangqi is remarkable among board games in that its famous endgame problems [such as “wild horses run on the farm” (Hung et al., 2017)], moving rules (such as “the horse moves in the shape of the character “RI” and “the elephant moves in the shape of the character “TIAN”)1 and combat strategies (such as “dāng tóu pào, m ǎ lái tiào”; See text footnote) are described and taught in vivid sentences (“rhymes” or “sayings”) or descriptive battle stories, which may explain the special relationship of this game to the cognitive processes of Chinese language cognition, sematic memory (SM) and episodic memory (EM). Changes in SM and EM are both early markers of Alzheimer’s disease (AD) (Marra et al., 2016; Gagliardi et al., 2019; Venneri et al., 2019). Recently, some reports have discussed the AD-preventive effects of board games (Nakao, 2019; Qureshi, 2019). For example, a previous randomized clinical trial of 147 AD patients reported that AD symptoms were reduced in patients who played the game of Go (Lin et al., 2015).
Our previous studies (Duan et al., 2012a,b, 2014) reported morphological changes in the caudate, the enhanced connectivity of the caudate to the DMN, and remote functional connectivity alterations including different global topological properties of the whole-brain functional networks and intrinsic brain networks. These reports reflected some aspects of neuroplastic changes from long-term Chinese chess training but were mainly based on large-scale intrinsic connectivity. In contrast to functional connectivity among remote brain areas, synchronization of local intrinsic activity demonstrates functional connectivity among regional areas (Jia et al., 2017). Until now, local connectivity changes in professional Chinese chess players (PCCPs) have been reported only at specific hubs (Song et al., 2020); whole-brain-based local connectivity and its relation to training profiles has not been revealed.
Regional homogeneity (ReHo), revealing the homogeneous characteristics of local brain activity, is one kind of postprocessing method of local spontaneous activity. ReHo is based on Kendall’s coefficient concordance (KCC) to measure the similarity of the time series of a given voxel to those of its nearest neighbors in a voxel-wise way in rs-fMRI analysis (Zang et al., 2004). Recently, some reports (e.g., Jiang et al., 2015; Jiang and Zuo, 2016) revealed the neurobiological relevance underlying ReHo, including anatomical morphology, brain development, and neurocognitive factors. Therefore, ReHo was confirmed to be a useful neuroimaging tool to understand human brain function (He et al., 2007; Wang et al., 2011; Wu et al., 2011; Dai et al., 2012; Tian et al., 2012; Dong et al., 2014; Liu et al., 2015; Lv et al., 2019). As one morphological change typical of PCCPs was detected in the caudate (Duan et al., 2012a), which subserves the associative phase of cognitive procedural learning (Chiu et al., 2017), we might expect ReHo changes to be mostly similar to their structural and functional equivalents in neuroimaging studies that have revealed training-specific areas.
In this study, ReHo analysis was performed based on rs-fMRI data to explore the whole-brain local functional connectivity changes in PCCPs and the relation of these changes to training profiles.
Materials and Methods
Participants
A total of 43 subjects were included in the present study. One group included 22 PCCPs (14 males and 8 females; age, 27.32 ± 8.31 years; years of education, 13.45 ± 2.37; rating points, 2410 ± 116; professional training years, 10 ± 9.32; and professional training hours per day, 4.25 ± 1.82). Another group included 21 novices (13 males and 8 females; age, 26.20 ± 8.17 years; years of education, 13.38 ± 3.37) who knew the rules of the game and simple strategies but with no game experience (Campitelli et al., 2005). PCCPs and novices were sex-, education- and age-matched. To further examine the difference between PCCPs and novices, both groups were tested by Raven’s Standard Progressive Matrices, and two groups did not differ on general intelligence (P = 0.63, two tailed t-test). All participants had normal or corrected-to-normal vision. Written informed consent was obtained from all subjects. The proposal was approved by the local Ethics Committee of Huaxi Hospital, Sichuan University.
Data Acquisition
Images were acquired using a 3.0T Siemens Magnetom Trio scanner in the Huaxi MR Research Center. Functional images were acquired using a single-shot, gradient-recalled echo-planar imaging sequence [repetition time (TR) = 2,000 ms, echo time (TE) = 30 ms and flip angle = 90^°]. Thirty transverse slices [field of view (FOV) = 24 cm, in-plane matrix = 64 × 64, slice thickness = 5 mm, without gap, voxel size = 3.8 × 3.8 × 5] and 205 volumes were obtained from each subject. The first five volumes were discarded to ensure steady-state longitudinal magnetization. During the scanning procedure, a standard head coil with foam padding was used to restrict head motion. Subjects were instructed simply to rest with their eyes closed, not to think of anything in particular and not to fall asleep.
Data Analysis
Image pre-processing was performed using SPM8 software.2 The first five volumes were discarded to ensure steady-state longitudinal magnetization. The remaining 200 volumes were first corrected for the temporal difference and head motion. In this study, the threshold for head motion was lower than ±1.5 mm or ±1.5^°. We calculated frame-wise displacement (FD) which reflected the head movement at every different time point by employing 6 displacements from the rigid body motion correction procedure (Power et al., 2012), and found no significant differences (P = 0.82) in FD between PCCPs (0.159 ± 0.16 mm) and novices (0.150 ± 0.07 mm) using two-sample t-tests. The resulting images were then normalized to the standard SPM8 echo-planar imaging template and resampled to a standard stereotaxic space at a resolution of 3 mm × 3 mm × 3 mm. Finally, the normalized images were temporally band-pass filtered (0.01 < f < 0.08 Hz) to reduce the effects of low-frequency drift and high-frequency physiology noise (Biswal et al., 1995); additionally, the linear trend was removed.
Regional Homogeneity Analysis
The KCC (Kendall and Gibbons, 1990) was calculated to measure the similarity of the time series of a defined cluster. In the present study, 27 nearest neighbor voxels were defined as a cluster. The KCC was given to the center voxel (Zang et al., 2004) as follows:
where W is the KCC among given voxels, ranging from 0 to 1; Ri is the sum rank of the ith time point; is the mean of the Ri values; K is the number of time series points within a measured cluster; and n is the number of ranks (here, n = 200 time points). REST software (Resting-state fMRI data analysis toolkit)3 was used to calculate individual ReHo values in a voxel-wise way. Each individual ReHo map was divided by that subject’s global mean KCC value within the brain mask. The ReHo maps were then spatially smoothed with a Gaussian filter of 8 mm of full width at half maximum (FWHM).
Second-Level Analysis
Group statistical analysis was performed in SPM8. The one-sample t-tests results from the two groups were combined to get a new map. By binarizing the map, a combined explicit mask was obtained. The significance threshold was set at P < 0.01, corrected by the false discovery rate (FDR) criterion (Genovese et al., 2002).
Then, two-sample t-tests were performed to show the between-group difference in ReHo. The t-map was created with a combined threshold of P < 0.005 and a minimum cluster size of 46 voxels using the AlphaSim program in REST software. This approach applied Monte Carlo simulation to calculate the probability of false positive detection by taking into consideration both the individual voxel probability threshold and cluster size.
Additionally, to explore whether ReHo correlates with the rating points, years of professional training and training intensity of PCCPs, a correlation analysis of ReHo versus these training profiles was performed in this group for each voxel of the whole brain. For the correlation analyses, we set the significance threshold at P < 0.05 (combined threshold of P < 0.001 and a minimum cluster size of 20 voxels, corrected by AlphaSim).
Results
Within-Group Results
In order to intuitively display ReHo results for Chinese chess novices and PCCPs, a ReHo map was calculated within each group and shown in Figure 1 (one-sample t-test; P < 0.01, corrected by FDR). For visual observation, areas in the DMN (Raichle et al., 2001) including the posterior cingulate cortex/precuneus (PCC/Pcu), medial prefrontal cortex (MPFC) and bilateral inferior parietal lobe (IPL) displayed significantly greater ReHo than other regions.
FIGURE 1
Between-Group Results
Compared with the novices, PCCPs revealed increased ReHo in the left cerebellum posterior lobe, left temporal pole, right amygdala, and brainstem and decreased ReHo in the right precentral gyrus (two-sample t-test, P < 0.05, corrected by AlphaSim; Figure 2 and Table 1).
FIGURE 2
TABLE 1
| Anatomical region | MNI (x,y,z)a | BA | Voxels | tb |
| Increased ReHo regions | ||||
| L cerebellum posterior lobe | −27, −57, −51 | – | 125 | 3.94 |
| L temporal pole | −48, 18, −27 | 38 | 104 | 4.45 |
| R amygdala | 27, 0, −18 | – | 79 | 4.04 |
| brainstem | 3, −21, −18 | 60 | 3.50 | |
| Decreased ReHo regions | ||||
| R precentral gyrus | 63, 3, 30 | 6 | 90 | −3.68 |
Comparison of regions with increased/decreased regional homogeneity (ReHo) in professional Chinese chess players (PCCPs) compared to novices.
MNI, Montreal Neurologic Institute; BA, Brodmann’s area; L, left; R, right.
ReHo, regional homogeneity; MNI, Montreal Neurologic Institute; BA, Brodmann’s area; L, left; R, right.
aCoordinates of primary peak locations in MNI space; bRepresents the statistical value of the peak voxel showing ReHo differences between groups. In the PCCP group, a positive t-value represents increased ReHo, and a negative t-value represents decreased ReHo.
Correlational Results
In the voxel-based group comparisons of ReHo maps, we chose a statistical threshold at voxel level P < 0.05 (AlphaSim corrected) with a minimum cluster size of 20 voxels to reduce Type I errors, resulting in a combined threshold of P < 0.001. The correlation of ReHo for each whole-brain voxel against rating points of PCCPs showed a significantly positive correlation in the right precentral gyrus and a significantly negative correlation in the left PCC/Pcu and right middle temporal gyrus (MTG). The professional training years of PCCPs were negatively correlated with the left SMA and right cerebellum posterior lobe. The ReHo of the right caudate was negatively correlated with training hours. Please see Figure 3 and Table 2 for more details.
FIGURE 3
TABLE 2
| Anatomical region (BA areas) | MNI (x,y,z)a | Voxels | tb |
| Significant correlation between ReHo and rating points | |||
| R precentral gyrus (6) | 39, −9, 45 | 35 | 5.11 |
| L PCC/precuneus (31) | −12, −54, 27 | 40 | −6.90 |
| R middle temporal gyrus (21) | 57, 0, −30 | 27 | −6.58 |
| Significant correlation between ReHo and professional training years | |||
| L supplementary motor area (SMA) (6) | −15, −12, 63 | 26 | −6.24 |
| R cerebellum posterior lobe | 15, −60, −42 | 79 | −6.20 |
| Significant correlation between ReHo and professional training hours per day | |||
| R caudate | 18, 12, 15 | 31 | −4.00 |
Significant correlations between ReHo and training profiles in PCCPs.
MNI, Montreal Neurologic Institute; BA, Brodmann’s area; L, left; R, right.
aCoordinates of primary peak locations in MNI space. bRepresents the peak statistical value of voxels showing ReHo correlated with training profiles. Positive and negative t-values indicate positive and negative correlations between ReHo and training profiles, respectively.
Discussion
Neuroscience studies have investigated the neural substrates of motor control and learning, which has shed light on the neural mechanism of expertise. From cognitive processes to associative processes and to automatic processes, changing patterns of skill performance are theoretically described; the main features of the automatic process are “rapid, smooth, effortless, demand little intentional capacity and difficult to consciously disrupt” (Yarrow et al., 2009). In the classical view of automaticity, it is believed that as long-term practice makes skills reflexive, subcortical structures primarily activate, whereas novel behaviors require attention and flexible thinking that depend on the cortex (Ashby et al., 2010). Pertinently, in reports on the brains of chess experts, this behavior was also described as follows: “the pattern of activation moves from frontal parts at the beginning of the process to posterior parts responsible for retrieval of domain specific knowledge around the final expertise stage” (Debarnot et al., 2014). In between-group analyses of this study, the ReHo of PCCPs decreased in the precentral gyrus but increased in the cerebellum, temporal pole, amygdala and brainstem; this pattern of activity was similar to the above-described changes.
A consensus has been reached on the relation between the cerebellum and emotion (Adamaszek et al., 2017). In addition to the cerebellum posterior lobe, we located areas of increased spontaneous brain activity in the temporal pole, amygdala and brainstem. The amygdala reflects emotion (Weymar and Schwabe, 2016), especially emotional regulation (Li et al., 2016; Morawetz et al., 2017) and cognitive reappraisal (d’Arbeloff et al., 2018). Notably, there is also a relation between the brainstem and emotion (Venkatraman et al., 2017). Also, the temporal pole is reported in the emotion of aggression (Breitschuh et al., 2018). As one dimension of personality, emotional expression control was once reported to incrementally contribute to the prediction of chess playing strength (Grabner et al., 2007). A randomized controlled trial protocol described a Go intervention programme designed to enhance elementary school students’ cognitive function and their capacity for emotional and behavioral control (Tachibana et al., 2012). In the current study, we deduce the possibility of enhanced emotion regulation function that results from long-term Chinese chess training, indicating superior emotion regulation ability that supports cool and focused attention in PCCPs during gameplay.
An ordinal consensus supports functional interactions between the basal ganglia and cortex and between the cerebellum and cortex. In the consensus view, the basal ganglia and the cerebellum are reported to form a densely interconnected network, namely, the basal ganglia-cerebellar-cerebral cortical networks, in which the caudate, different parts of the cerebellum and cortex form different networks supplying a neural basis for cognition (such as executive function) as well as for neuropsychiatric disorders (such as AD and anxiety) (Bostan and Strick, 2018). In addition to the negative correlation between the right caudate and training hours each day, we found a negative correlation between the right posterior lobe of the cerebellum and length of professional training and a positive correlation between the right precentral gyrus and rating points. All these findings demonstrate a similar pattern of concordant changes in some brain regions, such as the basal ganglia-cerebellar-cerebral networks in the right hemisphere, among PCCPs. These results add to our present knowledge of the neural substrates of professional chess players. Prior to this study, the caudate had been revealed initially as a neural substrate (Wan et al., 2011; Duan et al., 2012a,b), and the thalamus was recently reported as a neural substrate (as its mediation between the caudate and frontal cortex) (Wang et al., 2020). The present study reflects the participation of the basal ganglia-cerebellar-cerebral networks.
Previous studies reported that as motor skills became automatic, the activation of the SMA decreased (Poldrack et al., 2005; Puttemans et al., 2005). Reports from sequence training, task training and practice processes demonstrate a similar changing trend in the SMA (Nyberg et al., 2006; Hatanaka et al., 2009; Ma et al., 2010). Our result of a negative correlation between the SMA and training years is parallel to these conclusions. In addition to its role in training-specific plasticity, the left SMA plays an important role in phonological processing in Chinese language cognition (Kuo et al., 2004; Veroude et al., 2010); the left SMA also takes part in Chinese character reading (Tan et al., 2000; Kuo et al., 2003). Although the correlation between this training-specific area and language cognition is a novel finding in the study of the neural basis of board game experts, a previous report has already shown such a relationship in brain region mainly contributing to corresponding action to language understanding. For example, Beilock et al. (2008) reported that the left BA6 (dorsal lateral premotor cortex), as a region normally devoted to higher-level action selection and implementation, also supported specialized motor (sports) experience enhancing action-related language understanding even when there was no intention to perform a real action.
Chunking theory-induced neuroscience investigations have indicated that LTM chunks of domain-specific information are stored in the ventral areas of the temporal lobe, including the parahippocampal gyrus (PHG) and fusiform gyrus (Campitelli et al., 2007). The PHG plays an important role in EM (Di Paola et al., 2007; Gallagher and Koh, 2011); changes in EM serve as the earliest and “marker” cognitive function alterations in AD (Bäckman et al., 2001; Aretouli and Brandt, 2010; Gallagher and Koh, 2011; Marra et al., 2016; Gagliardi et al., 2019). Moreover, the connection of the PHG with the PCC/Pcu and MTG was positively correlated with the Mini-Mental State Examination (MMSE) score, indicating functional connectivity that reflects the progression of cognitive degeneration disease. Pertinently, the PHG is important in mediating the connectivity between the hippocampus and hubs of the DMN as well as the connection between the MTG memory system and the DMN (Liu et al., 2016). Both neuropathological (Van Hoesen et al., 2000) and structural MRI (Pantel et al., 2003) evidence have demonstrated that selective morphological brain changes and atrophy in the PHG represent a preclinical stage of AD. Recently, Jia et al. (2017) reported a link between local synchronization alterations in the PHG and APOE-related cerebral physiological heterogeneity. All related findings demonstrated the possibility that plastic ReHo changes in the PHG by professional Chinese chess training may indicate its role in AD prevention. A 5.1-year study of 469 elderly individuals, among whom dementia developed in 124 subjects (of which more than fifty percent developed AD), found that playing board games was associated with a reduced risk of dementia (Verghese et al., 2003). This protection was later explained by the cognitive reserve theory that the number and strength of neuronal connections in the brain could be increased by training; the more connections were built up, the larger reserves to counteract the rate at which neurons were disappearing from the brain as these neurons were destroyed by AD pathology (Marx, 2005). In this study, a negative correlation was found between ReHo of the right MTG and rating points, which demonstrates the relationship between the combined function of the PHG and fusiform gyrus and training-specific experiences among PCCPs.
Conclusion
Through the comparison of ReHo analysis of rs-fMRI data between PCCPs and novices, training-specific whole-brain local connectivity changes in PCCPs were revealed. Brain ReHo of PCCPs demonstrated a similar changing pattern described in the classical view of automaticity in motor control and learning. Besides, some ReHo changes occurred in an area responsible for Chinese character pronouncing and reading. Moreover, professional Chinese chess training induced ReHo changes located in AD-related areas, which suggested the possibility of AD preventive effects from Chinese chess training. Findings from this study demonstrate the feasibility of using ReHo as a research tool to monitor board game-induced brain plastic changes and shed light on the neural substrates underlying cognition in Chinese chess playing.
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Statements
Data availability statement
Data are available in a public, open access repository at the International Neuroimaging Data-Sharing Initiative or INDI (http://fcon_1000.projects.nitrc.org/index.html).
Ethics statement
The studies involving human participants were reviewed and approved by the Local Ethics Committee of Huaxi Hospital, Sichuan University. The participants provided their written informed consent to participate in this study.
Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.
Funding
This study was supported by the National Key Research and Development Project (2017YFB0403800), Natural Science Foundation of China (31560286), and China Scholarship Council (201906755035).
Acknowledgments
We are grateful to all the professional Chinese chess players and control volunteers who took part in the experiment. We especially thank Chinese Chess International Grandmaster Dahua Liu for his kindly support and insightful suggestions.
Conflict of interest
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.
References
1
AdamaszekM.D’AgataF.FerrucciR.HabasC.KeulenS.KirkbyK. C.et al (2017). Consensus paper: cerebellum and emotion.Cerebellum16552–576. 10.1007/s12311-016-0815-8
2
AmidzicO.RiehleH. J.FehrT.WienbruchC.ElbertT. (2001). Pattern of focal gamma-bursts in chess players.Nature412:603. 10.1038/35088119
3
AretouliE.BrandtJ. (2010). Episodic memory in dementia: characteristics of new learning that differentiate Alzheimer’s, Huntington’s, and Parkinson’s diseases.Arch. Clin. Neuropsychol.25396–409. 10.1093/arclin/acq038
4
AshbyF. G.TurnerB. O.HorvitzJ. C. (2010). Cortical and basal ganglia contributions to habit learning and automaticity.Trends Cogn. Sci.14208–215. 10.1016/j.tics.2010.02.001
5
AthertonM.ZhuangJ.BartW. M.HuX.HeS. (2003). A functional MRI study of high-level cognition. I. The game of chess.Brain Res. Cogn. Brain Res.1626–31. 10.1016/s0926-6410(02)00207-0
6
BäckmanL.SmallB. J.FratiglioniL. (2001). Stability of the preclinical episodic memory deficit in Alzheimer’s disease.Brain12496–102. 10.1093/brain/124.1.96
7
BeilockS. L.LyonsI. M.Mattarella-MickeA.NusbaumH. C.SmallS. L. (2008). Sports experience changes the neural processing of action language.Proc. Natl. Acad. Sci. U.S.A.10513269–13273. 10.1073/pnas.0803424105
8
BilalićM.LangnerR.UlrichR.GroddW. (2011). Many faces of expertise: fusiform face area in chess experts and novices.J. Neurosci.3110206–10214. 10.1523/JNEUROSCI.5727-10.2011
9
BiswalB.YetkinF. Z.HaughtonV. M.HydeJ. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.Magn. Reson. Med.34537–541. 10.1002/mrm.1910340409
10
BostanA. C.StrickP. L. (2018). The basal ganglia and the cerebellum: nodes in an integrated network.Nat. Rev. Neurosci.19338–350. 10.1038/s41583-018-0002-7
11
BreitschuhS.SchöneM.TozziL.KaufmannJ.StrumpfH.FenkerD.et al (2018). Aggressiveness of martial artists correlates with reduced temporal pole grey matter concentration.Psychiatry Res. Neuroimaging28124–30. 10.1016/j.pscychresns.2018.08.001
12
CampitelliG.GobetF.HeadK.BuckleyM.ParkerA. (2007). Brain localization of memory chunks in chessplayers.Int. J. Neurosci.1171641–1659. 10.1080/00207450601041955
13
CampitelliG.GobetF.ParkerA. (2005). Structure and stimulus familiarity: a study of memory in chess-players with functional magnetic resonance imaging.Span. J. Psychol.8238–245. 10.1017/s1138741600005126
14
ChaseW. G.SimonH. A. (1973). Perception in chess.Cogn. Psychol.455–81. 10.1016/0010-0285(73)90004-2
15
ChiuY. C.JiangJ.EgnerT. (2017). The caudate nucleus mediates learning of stimulus-control state associations.J. Neurosci.371028–1038. 10.1523/JNEUROSCI.0778-16.2016
16
DaiX. J.GongH. H.WangY. X.ZhouF. Q.MinY. J.ZhaoF.et al (2012). Gender differences in brain regional homogeneity of healthy subjects after normal sleep and after sleep deprivation: a resting-state fMRI study.Sleep Med.13720–727. 10.1016/j.sleep.2011.09.019
17
d’ArbeloffT. C.KimM. J.KnodtA. R.RadtkeS. R.BrigidiB. D.HaririA. R. (2018). Microstructural integrity of a pathway connecting the prefrontal cortex and amygdala moderates the association between cognitive reappraisal and negative emotions.Emotion18912–915. 10.1037/emo0000447
18
DebarnotU.SperdutiM.Di RienzoF.GuillotA. (2014). Experts bodies, experts minds: how physical and mental training shape the brain.Front. Hum. Neurosci.8:280. 10.3389/fnhum.2014.00280
19
Di PaolaM.MacalusoE.CarlesimoG. A.TomaiuoloF.WorsleyK. J.FaddaL.et al (2007). Episodic memory impairment in patients with Alzheimer’s disease is correlated with entorhinal cortex atrophy. A voxel-based morphometry study.J. Neurol.254774–781. 10.1007/s00415-006-0435-1
20
DongM.QinW.ZhaoL.YangX.YuanK.ZengF.et al (2014). Expertise modulates local regional homogeneity of spontaneous brain activity in the resting brain: an fMRI study using the model of skilled acupuncturists.Hum. Brain Mapp.351074–1084. 10.1002/hbm.22235
21
DuanX.HeS.LiaoW.LiangD.QiuL.WeiL.et al (2012a). Reduced caudate volume and enhanced striatal-DMN integration in chess experts.Neuroimage601280–1286. 10.1016/j.neuroimage.2012.01.047
22
DuanX.LiaoW.LiangD.QiuL.GaoQ.LiuC.et al (2012b). Large-scale brain networks in board game experts: insights from a domain-related task and task-free resting state.PLoS One7:e32532. 10.1371/journal.pone.0032532
23
DuanX.LongZ.ChenH.LiangD.QiuL.HuangX.et al (2014). Functional organization of intrinsic connectivity networks in Chinese-chess experts.Brain Res.155833–43. 10.1016/j.brainres.2014.02.033
24
Fuentes-GarcíaJ. P.PereiraT.CastroM. A.Carvalho SantosA.VillafainaS. (2019). Heart and brain responses to real versus simulated chess games in trained chess players: a quantitative EEG and HRV study.Int. J. Environ. Res Public Health16:5012. 10.3390/ijerph16245021
25
GagliardiG.EpelbaumS.HouotM.BakardjianH.BoukadidaL.RevillonM.et al (2019). Which episodic memory performance is associated with Alzheimer’s disease biomarkers in elderly cognitive complainers? Evidence from a longitudinal observational study with four episodic memory tests (Insight-PreAD).J. Alzheimers Dis.70811–824. 10.3233/JAD-180966
26
GallagherM.KohM. T. (2011). Episodic memory on the path to Alzheimer’s disease.Curr. Opin. Neurobiol.21929–934. 10.1016/j.conb.2011.10.021
27
GenoveseC. R.LazarN. A.NicholsT. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate.Neuroimage15870–878. 10.1006/nimg.2001.1037
28
GongY.EricssonK. A.MoxleyJ. H. (2015). Recall of briefly presented chess positions and its relation to chess skill.PLoS One10:e0118756. 10.1371/journal.pone.0118756
29
GrabnerR. H.SternE.NeubauerA. C. (2007). Individual differences in chess expertise: a psychometric investigation.Acta Psychol.124398–420. 10.1016/j.actpsy.2006.07.008
30
HänggiJ.BrütschK.SiegelA. M.JänckeL. (2014). The architecture of the chess player’s brain.Neuropsychologia62152–162. 10.1016/j.neuropsychologia.2014.07.019
31
HatanakaN.TokunoH.NambuA.TakadaM. (2009). Transdural doppler ultrasonography monitors cerebral blood flow changes in relation to motor tasks.Cereb. Cortex19820–831. 10.1093/cercor/bhn129
32
HeY.WangL.ZangY.TianL.ZhangX.LiK.et al (2007). Regional coherence changes in the early stages of Alzheimer’s disease: a combined structural and resting-state functional MRI study.Neuroimage35488–500. 10.1016/j.neuroimage.2006.11.042
33
HungC.GuoJ.SuK. (2017). Based on short motion paths and artificial intelligence method for Chinese chess game.J. Robot. Netw. Artif. Life4154–157. 10.2991/jrnal.2017.4.2.11
34
JiaX.ZhangY.ZangY.LiH. (2017). Frequency-dependent alterations in regional homogeneity in young carriers of the apolipoprotein E genotype.Sci. Bull.62654–655. 10.1016/j.scib.2017.03.019
35
JiangL.XuT.HeY.HouX. H.WangJ.CaoX. Y.et al (2015). Toward neurobiological characterization of functional homogeneity in the human cortex: regional variation, morphological association and functional covariance network organization.Brain Struct. Funct.2202485–2507. 10.1007/s00429-014-0795-8
36
JiangL.ZuoX. N. (2016). Regional homogeneity: a multimodal, multiscale neuroimaging marker of the human connectome.Neuroscientist22486–505. 10.1177/1073858415595004
37
KendallM.GibbonsJ. D. (1990). Rank Correlation Methods.London: Oxford University Press.
38
KuoW. J.YehT. C.LeeC. Y.YuW.ChouC. C.HoL. T.et al (2003). Frequency effects of Chinese character processing in the brain: an event-related fMRI study.Neuroimage18720–730. 10.1016/s1053-8119(03)00015-6
39
KuoW. J.YehT. C.LeeJ. R.ChenL. F.LeeP. L.ChenS. S.et al (2004). Orthographic and phonological processing of Chinese characters: an fMRI study.Neuroimage211721–1731. 10.1016/j.neuroimage.2003.12.007
40
LiZ.TongL.GuanM.HeW.WangL.BuH.et al (2016). Altered resting-state amygdala functional connectivity after real-time fMRI emotion self-regulation training.Biomed. Res. Int.2016:2719895. 10.1155/2016/2719895
41
LinQ.CaoY.GaoJ. (2015). The impacts of a GO-game (Chinese chess) intervention on Alzheimer disease in a Northeast Chinese population.Front. Aging Neurosci.7:163. 10.3389/fnagi.2015.00163
42
LiuC.ChenZ.WangT.TangD.HitchmanG.SunJ.et al (2015). Predicting stroop effect from spontaneous neuronal activity: a study of regional homogeneity.PLoS One10:e0124405. 10.1371/journal.pone.0124405
43
LiuJ.ZhangX.YuC.DuanY.ZhuoJ.CuiY.et al (2016). Impaired parahippocampus connectivity in mild cognitive impairment and Alzheimer’s disease.J. Alzheimers Dis.491051–1064. 10.3233/JAD-150727
44
LvC.WangQ.ChenC.QiuJ.XueG.HeQ. (2019). The regional homogeneity patterns of the dorsal medial prefrontal cortex predict individual differences in decision impulsivity.Neuroimage200556–561. 10.1016/j.neuroimage.2019.07.015
45
MaL.WangB.NarayanaS.HazeltineE.ChenX.RobinD. A.et al (2010). Changes in regional activity are accompanied with changes in inter-regional connectivity during 4 weeks motor learning.Brain Res.131864–76. 10.1016/j.brainres.2009.12.073
46
MarraC.GainottiG.FaddaL.PerriR.LacidognaG.ScaricamazzaE.et al (2016). Usefulness of an integrated analysis of different memory tasks to predict the progression from mild cognitive impairment to Alzheimer’s disease: the episodic memory score (EMS).J. Alzheimers Dis.5061–70. 10.3233/JAD-150613
47
MarxJ. (2005). Neuroscience. Preventing Alzheimer’s: a lifelong commitment.Science309864–866. 10.1126/science.309.5736.864
48
MorawetzC.BodeS.BaudewigJ.HeekerenH. R. (2017). Effective amygdala-prefrontal connectivity predicts individual differences in successful emotion regulation.Soc. Cogn. Affect. Neurosci.12569–585. 10.1093/scan/nsw169
49
NakaoM. (2019). Special series on “effects of board games on health education and promotion” board games as a promising tool for health promotion: a review of recent literature.Biopsychosoc. Med.13:5. 10.1186/s13030-019-0146-3
50
NichelliP.GrafmanJ.PietriniP.AlwayD.CartonJ. C.MiletichR. (1994). Brain activity in chess playing.Nature369:191. 10.1038/369191a0
51
NybergL.ErikssonJ.LarssonA.MarklundP. (2006). Learning by doing versus learning by thinking: an fMRI study of motor and mental training.Neuropsychologia44711–717. 10.1016/j.neuropsychologia.2005.08.006
52
OnofrjM.CuratolaL.ValentiniG.AntonelliM.ThomasA.FulgenteT. (1995). Non-dominant dorsal-prefrontal activation during chess problem solution evidenced by single photon emission computerized tomography (SPECT).Neurosci. Lett.198169–172. 10.1016/0304-3940(95)11985-6
53
PantelJ.KratzB.EssigM.SchröderJ. (2003). Parahippocampal volume deficits in subjects with aging-associated cognitive decline.Am. J. Psychiatry160379–382. 10.1176/appi.ajp.160.2.379
54
PereiraT.CastroM. A.VillafainaS.Carvalho SantosA.Fuentes-GarcíaJ. P. (2020). Dynamics of the prefrontal cortex during chess-based problem-solving tasks in competition-experienced chess players: an fNIR study.Sensors20:3917. 10.3390/s20143917
55
PlattJ. (1895). Chinese playing-cards.Notes Queries207:467. 10.1093/nq/s8-viii.207.467b
56
PoldrackR. A.SabbF. W.FoerdeK.TomS. M.AsarnowR. F.BookheimerS. Y.et al (2005). The neural correlates of motor skill automaticity.J. Neurosci.255356–5364. 10.1523/JNEUROSCI.3880-04.2005
57
PowellJ. L.GrossiD.CorcoranR.GobetF.García-FiñanaM. (2017). The neural correlates of theory of mind and their role during empathy and the game of chess: a functional magnetic resonance imaging study.Neuroscience355149–160. 10.1016/j.neuroscience.2017.04.042
58
PowerJ. D.BarnesK. A.SnyderA. Z.SchlaggarB. L.PetersenS. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.Neuroimage592142–2154. 10.1016/j.neuroimage.2011.10.018
59
PremiE.GazzinaS.DianoM.GirelliA.CalhounV. D.IrajiA.et al (2020). Enhanced dynamic functional connectivity (whole-brain chronnectome) in chess experts.Sci. Rep.10:7051. 10.1038/s41598-020-63984-8
60
PuttemansV.WenderothN.SwinnenS. P. (2005). Changes in brain activation during the acquisition of a multifrequency bimanual coordination task: from the cognitive stage to advanced levels of automaticity.J. Neurosci.254270–4278. 10.1523/JNEUROSCI.3866-04.2005
61
QureshiH. J. M. (2019). Phathophysiology of Alzheimer’s disease.J. Akhtar Saeed Med. Dent. College129–34.
62
RaichleM. E.MacLeodA. M.SnyderA. Z.PowersW. J.GusnardD. A.ShulmanG. L. (2001). A default mode of brain function.Proc. Natl. Acad. Sci. U.S.A.98676–682. 10.1073/pnas.98.2.676
63
SheridanH.ReingoldE. M. (2017). Chess players’ eye movements reveal rapid recognition of complex visual patterns: evidence from a chess-related visual search task.J. Vis.17:4. 10.1167/17.3.4
64
SilvaL. J.CesarF. H. G.RochaF. T.ThomazC. E. (2018). A combined eye-tracking and EEG analysis on chess moves.IEEE Latin Am. Trans. Latin Am. Trans.161288–1297. 10.1109/TLA.2018.8407099
65
SohnW. S.LeeT. Y.KwakS.YoonY. B.KwonJ. S. (2017). Higher extrinsic and lower intrinsic connectivity in resting state networks for professional Baduk (Go) players.Brain Behav.7:e00853. 10.1002/brb3.853
66
SongL.PengQ.LiuS.WangJ. (2020). Changed hub and functional connectivity patterns of the posterior fusiform gyrus in chess experts.Brain Imaging Behav.14797–805. 10.1007/s11682-018-0020-0
67
TachibanaY.YoshidaJ.IchinomiyaM.NouchiR.MiyauchiC.TakeuchiH.et al (2012). A GO intervention program for enhancing elementary school children’s cognitive functions and control abilities of emotion and behavior: study protocol for a randomized controlled trial.Trials13:8. 10.1186/1745-6215-13-8
68
TanL. H.SpinksJ. A.GaoJ. H.LiuH. L.PerfettiC. A.XiongJ.et al (2000). Brain activation in the processing of Chinese characters and words: a functional MRI study.Hum. Brain Mapp.1016–27. 10.1002/(SICI)1097-0193(200005)10:1<16::AID-HBM30>3.0.CO;2-M
69
TianL.RenJ.ZangY. (2012). Regional homogeneity of resting state fMRI signals predicts Stop signal task performance.Neuroimage60539–544. 10.1016/j.neuroimage.2011.11.098
70
Van HoesenG. W.AugustinackJ. C.DierkingJ.RedmanS. J.ThangavelR. (2000). The parahippocampal gyrus in Alzheimer’s disease. Clinical and preclinical neuroanatomical correlates.Ann. N. Y. Acad. Sci.911254–274. 10.1111/j.1749-6632.2000.tb06731.x
71
VenkatramanA.EdlowB. L.Immordino-YangM. H. (2017). The brainstem in emotion: a review.Front. Neuroanat.11:15. 10.3389/fnana.2017.00015
72
VenneriA.MitoloM.BeltrachiniL.VarmaS.Della PietàC.Jahn-CartaC.et al (2019). Beyond episodic memory: semantic processing as independent predictor of hippocampal/perirhinal volume in aging and mild cognitive impairment due to Alzheimer’s disease.Neuropsychology33523–533. 10.1037/neu0000534
73
VergheseJ.LiptonR. B.KatzM. J.HallC. B.DerbyC. A.KuslanskyG.et al (2003). Leisure activities and the risk of dementia in the elderly.N. Engl. J. Med.3482508–2516. 10.1056/NEJMoa022252
74
VeroudeK.NorrisD. G.ShumskayaE.GullbergM.IndefreyP. (2010). Functional connectivity between brain regions involved in learning words of a new language.Brain Lang.11321–27. 10.1016/j.bandl.2009.12.005
75
WanX.NakataniH.UenoK.AsamizuyaT.ChengK.TanakaK. (2011). The neural basis of intuitive best next-move generation in board game experts.Science331341–346. 10.1126/science.1194732
76
WangK. L.ZhangH. G.PingZ. L.HaiY. (2011). “Chinese chess character recognition with radial harmonic fourier moments,” in Proceedings of the 2011 International Conference on Document Analysis and Recognition, (Beijing: IEEE), 1369–1373.
77
WangY.ZuoC.WangD.TaoS.HaoL. (2020). Reduced thalamus volume and enhanced thalamus and fronto-parietal network integration in the chess experts.Cereb. Cortex305560–5569. 10.1093/cercor/bhaa140
78
WeymarM.SchwabeL. (2016). Amygdala and emotion: the bright side of it.Front. Neurosci.10:224. 10.3389/fnins.2016.00224
79
WuQ. Z.LiD. M.KuangW. H.ZhangT. J.LuiS.HuangX. Q.et al (2011). Abnormal regional spontaneous neural activity in treatment-refractory depression revealed by resting-state fMRI.Hum. Brain Mapp.321290–1299. 10.1002/hbm.21108
80
YarrowK.BrownP.KrakauerJ. W. (2009). Inside the brain of an elite athlete: the neural processes that support high achievement in sports.Nat. Rev. Neurosci.10585–596. 10.1038/nrn2672
81
ZangY.JiangT.LuY.HeY.TianL. (2004). Regional homogeneity approach to fMRI data analysis.Neuroimage22394–400. 10.1016/j.neuroimage.2003.12.030
Summary
Keywords
board games, Chinese chess, regional homogeneity, automaticity, Chinese language cognition, AD prevention
Citation
Liang D, Qiu L, Duan X, Chen H, Liu C and Gong Q (2022) Training-Specific Changes in Regional Spontaneous Neural Activity Among Professional Chinese Chess Players. Front. Neurosci. 16:877103. doi: 10.3389/fnins.2022.877103
Received
16 February 2022
Accepted
29 April 2022
Published
27 May 2022
Volume
16 - 2022
Edited by
Stephen J. Gotts, National Institute of Mental Health (NIH), United States
Reviewed by
Enrico Premi, University of Brescia, Italy; Yuzheng Hu, Zhejiang University, China
Updates
Copyright
© 2022 Liang, Qiu, Duan, Chen, Liu and Gong.
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*Correspondence: Chengyi Liu, liutcy@scnu.edu.cnQiyong Gong, qiyonggong@hmrrc.org.cn
†These authors have contributed equally to this work
This article was submitted to Neurodevelopment, a section of the journal Frontiers in Neuroscience
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