Altered Resting Brain Function and Structure
in Professional Badminton Players
Xin Di,1,2Senhua Zhu,2,3Hua Jin,1Pin Wang,1Zhuoer Ye,1
Ke Zhou,4Yan Zhuo,4and Hengyi Rao 1–3
Abstract
Neuroimaging studies of professional athletic or musical training have demonstrated considerable practice-dependent plasticity in various brain structures,which may re?ect distinct training demands.In the present study,structural and functional brain alterations were examined in professional badminton players and com-pared with healthy controls using magnetic resonance imaging (MRI)and resting-state functional MRI.Gray mat-ter concentration (GMC)was assessed using voxel-based morphometry (VBM),and resting-brain functions were measured by amplitude of low-frequency ?uctuation (ALFF)and seed-based functional connectivity.Results showed that the athlete group had greater GMC and ALFF in the right and medial cerebellar regions,respectively.The athlete group also demonstrated smaller ALFF in the left superior parietal lobule and altered functional con-nectivity between the left superior parietal and frontal regions.These ?ndings indicate that badminton expertise is associated with not only plastic structural changes in terms of enlarged gray matter density in the cerebellum,but also functional alterations in fronto-parietal connectivity.Such structural and functional alterations may re?ect speci?c experiences of badminton training and practice,including high-capacity visuo-spatial processing and hand-eye coordination in addition to re?ned motor skills.
Key words:amplitude of low frequency ?uctuation;badminton athlete;cerebellum,fronto-parietal network;func-
tional connectivity;voxel-based morphometry
Introduction
T
he human brain shows remarkable plasticity in re-sponse to learning and experience,even after a relatively short time of training.For example,after 7days of juggling training,signi?cant changes in gray matter (GM)intensity could be detected in multiple brain regions,including the fronto-parietal attention network,the middle temporal (MT)area,and the hippocampus (Boyke et al.,2008;Dragan-ski et al.,2004;Driemeyer et al.,2008).Skilled professionals with multiple years of training and practice,such as athletes and musicians,have also demonstrated signi?cant changes in multiple brain regions (for reviews,see Nakata et al.,2010;Schlaug,2001;Yarrow et al.,2009).For example,the cerebel-lum has been shown to be larger in musicians (Gaser and Schlaug,2003;Han et al.,2009;Hutchinson et al.,2003)and basketball players (Park et al.,2009)compared with normal controls,which may re?ect re?ned motor skills in these pro-fessionals.Additional changes in multiple cortical areas in-volving motor,auditory,and visual-spatial processes have been observed in keyboard musicians (Gaser and Schlaug,2003;Han et al.,2009),which may be associated with more subtle and complex patterns of ?nger dexterity in music prac-tice.GM alterations in sensorimotor and premotor regions have also been revealed in professional divers (Wei et al.,
2009)and ballet dancers (Ha
¨nggi et al.,2010),while addi-tional parietal changes have been found in skilled golfers,which may re?ect more attention and motor control support-ing golf practice (Ja
¨ncke et al.,2009).These diverse ?ndings from athletes and musicians suggest that brain structure changes observed in professionals may depend on the spe-ci?c motor and cognitive processes involved in skill training over time.
Compared with structural changes in skilled professionals,evidence supporting functional brain alterations in elite ath-letes or musicians is relatively https://www.doczj.com/doc/254644565.html,ing functional mag-netic resonance imaging (fMRI),two recent studies examined neural activations during preshot routines of expert golfers
1Center for Studies of Psychological Application,South China Normal University,Guangzhou,China.2
Department of Psychology,Sun Yat-Sen University,Guangzhou,China.3
Department of Neurology,Center for Functional Neuroimaging,University of Pennsylvania,Philadelphia,Pennsylvania.4
State Key Laboratory of Brain and Cognitive Science,Institute of biophysics,Chinese Academy of Sciences,Beijing,China.
BRAIN CONNECTIVITY Volume 2,Number 4,2012aMary Ann Liebert,Inc.
DOI:10.1089/brain.2011.0050
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(Milton et al.,2007)and archers(Kim et al.,2008).The results demonstrated that professionals tended to recruit a more fo-cused and ef?cient organization of visual and motor net-works directly related to task requirements,whereas novices tended to engage more brain regions related to high-level cognitive control functions.However,?ndings from these task-related fMRI studies depend heavily on the task demands and speci?c cognitive or motor paradigms used in the studies,which makes the comparison of results across studies dif?cult.
The use of resting-state functional connectivity fMRI(fc-fMRI)that examines functional brain changes have been emerging since the?rst report of functional connectivity be-tween bilateral motor cortices(Biswal et al.,1995).Resting-state fc-fMRI measures the synchronization of low-frequency blood oxygen level dependent(BOLD)oscillations between spatially distinct brain regions in the absence of tasks.There-fore,it provides a complimentary method to the conventional task-related fMRI for detecting functional brain changes asso-ciated with training and experience.Since resting-state fc-fMRI does not involve any particular motor or cognitive task re-quirements,it may re?ect a cumulative effect of experience over time(Lewis et al.,2009).Recent fc-fMRI studies have sug-gested that motor training altered resting-state brain activity (Xiong et al.,2009;Albert et al.,2009).For example,Albert et al.(2009)examined resting-state functional brain changes during motor training using independent component analysis (ICA)and found that motor learning,but not motor perfor-mance,signi?cantly modulated resting-state functional con-nectivity in the fronto-parietal and cerebellar networks. However,to our knowledge,the effects of long-term training on resting-state brain function remain largely unknown.
In this study,we used MRI to examine both structural and resting-state functional brain alterations in professional rac-quet players,speci?cally badminton players.To the best of our knowledge,structural and functional characteristics of the badminton players’brain have not been explored using neuroimaging methods.Similar to other racquet sports,bad-minton requires re?ned hand-eye coordination and visuo-spatial ability(Ward et al.,2002;Shim et al.,2005),which may result in structural and functional brain changes that are different from other sports that have been studied,for ex-ample,golf,diving,or basketball.We predict that profes-sional badminton players will show signi?cant structural and functional alterations in brain regions related to visuo-spatial processing and hand-eye coordination.A group of professional badminton players and matched nonathlete con-trols were recruited in the present study.Standard voxel-based morphometry(VBM)(Ashburner and Friston,2000) was?rst conducted to examine the structural differences be-tween the two groups.The amplitudes of low-frequency?uc-tuations(ALFF;Zang et al.,2007)of resting-state fMRI were then used to measure regional properties of the brain’s intrin-sic neural activity and compared between the two groups. Finally,the seed-based functional connectivity analyses(Fox et al.,2005)of resting-state fMRI were conducted to character-ize functional integration,using regions that have been shown to be different between professional players and controls from the VBM and ALFF analyses.We chose to use the hypothesis-driven,seed-based functional connectivity analysis instead of data-driven methods such as ICA in order to determine the speci?c brain regions and networks that are related to culti-vated visuo-spatial processing and hand-eye coordination skills after long-term badminton training and practice. Methods
Subjects
Twenty badminton players(10males)were recruited for the study.All subjects were members of professional teams or professional university teams in Beijing and had completed at least3years of badminton training(mean=8.9years,range from3to16years).Subjects had either completed college-level education or were current college students during the study.The control group consisted of18age-and gender-matched college students(9men).No control subjects had formal training or experience in badminton or other racquet sports(Table1).All athletes and controls were right handed. Written informed consent was obtained from all subjects at the beginning of the study.
MRI scan
All subjects were scanned using a Siemens3T Trio scanner located in the Beijing Center for Magnetic Resonance Imaging, Beijing,China.Subjects?rst completed a task-independent, resting-state scan,during which time they were asked to open their eyes to look at a blank screen.No additional task was required.The resting scan lasted6min,with a TR of 2sec.As a result,180images were obtained for each subject. BOLD images were obtained using an echo-planar imaging se-quence with a12-channel coil.Acquisition parameters were as follows:TE=30ms;TR=2000ms;?ip angle=90o;32inter-leaved axial slices;slice thickness=4mm,no gap;image ma-trix=64·64;FOV=200·200mm;bandwidth=2232.At the end of the scan,a high spatial resolution,T1-weighted magne-tization prepared rapid gradient-echo(MP-RAGE)structural image was obtained.Scan parameters were as follows:TR= 2530ms;TE=1.64;?ip angle=7.0;176slices;slice thick-ness=1mm;image matrix=256·256;FOV=256·256mm. VBM analysis
Standard VBM analysis was conducted using SPM8soft-ware(www.?https://www.doczj.com/doc/254644565.html,/spm/,Wellcome Department of Cognitive Neurology,UK,implemented in Matlab 7.7,Math Works,Natick,MA).For each subject,the high-resolution structural image was?rst segmented into different brain tissue types using a standard template provided in the SPM software.The uni?ed segmentation model implemented in SPM8(Ashburner and Friston,2005)was used to segment the structural image into GM,white matter(WM),and cere-brospinal?uid(CSF)in standard Montreal Neurological Institute(MNI)space.The tissue probability template was the modi?ed version of the International Consortium for Brain Mapping tissue probabilistic atlases,which was https://www.doczj.com/doc/254644565.html,rmation for the Two Groups of Subjects
Athletes Controls Statistics No.of subjects(male)20(10)18(9)
Year of age(STD)22.5(4.57)20.7(4.25)p=0.234 Years of badminton
training
8.85(3.25)0
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provided by the SPM8new segment routine.We used the normalized GM images that were not modulated and repre-sented gray matter concentration (GMC).For computational simplicity,the image was re-sampled at a resolution of 1.5·1.5·1.5mm 3in the new segmentation step.The result-ing images were visually checked to make sure there were no misclassi?cations.Finally,the GMC images were smoothed using a Gaussian ?lter with a 6-mm full width at half maximum (FWHM)kernal.
Preprocessed GMC images were then entered into a sec-ond-level,two-sample t-test model to compare group level differences.Total Gray matter volume (GMV),gender,and age were added to the model as covariates.Since the smooth-ness of GMC images is nonstationary (Ashburner and Friston,2000),nonstationary cluster extent correction was adopted to correct multiple comparisons (Worsley et al.,1999).The am-plitude threshold was ?rst set at p <0.001,and then,the clus-ter extent was corrected at p <0.05using the nonstationarity toolbox (Hayasaka et al.,2004).The clusters identi?ed in the group analysis were labeled using the Talairach Daemon (Talairach and Tournoux,1988;Lancaster et al.,2000)after ac-counting for the discrepancy between MNI space and Talair-ach space (Lancaster et al.,2007).This analysis revealed two clusters in the right cerebellum that were de?ned as the seed regions for further functional connectivity analysis.Resting-state fMRI data preprocessing
Preprocessing of resting-state functional images was com-pleted using SPM8.First,the two initial resting-state images were discarded for each subject,leaving a total of 178images per subject.All functional images were motion corrected using the realign function.Functional images from two athletes and two controls were discarded,because at least one of the motion parameter estimates was larger than 3mm or 3de-
grees.As a result,images from 18athletes and 16controls were included in the subsequent analysis.For each subject,the functional images were ?rst registered to the subject’s own high-resolution structural image.The structural image was then normalized to the T1template.Next,all functional images were normalized to the standard MNI space using the parameters obtained from normalizing the subject’s struc-tural images.Finally,all normalized functional images were smoothed using a Gaussian ?lter with an 8mm FWHM kernal.
ALFF analysis
We used the ALFF to index regional properties of the brain’s intrinsic neural activity.ALFF analysis was completed using the resting-state fMRI data analysis toolkit,(REST)v1.4(https://www.doczj.com/doc/254644565.html,/forum/REST_V1.4,State Key Laboratory of Cognitive Neuroscience and Learning,Beijing,China;Song et al.,2011).For each subject,linear trends were removed from all the functional images.An ideal band-pass ?lter be-tween 0.01and 0.08Hz was used to ?lter the detrended func-tional images.An ALFF map was then calculated for each subject.A normalized ALFF map (mALFF)was also obtained by dividing the whole-brain mean from the original ALFF map.Finally,the mALFF maps were entered into a second-level,two-sample t-test model to compare group-level differ-ences using SPM8.Subject gender and age were added into the group model as covariates.An amplitude threshold was ?rst set at p <0.001,and the cluster extent was corrected at the false discovery rate,p <0.05.The clusters identi?ed in the group level analysis were also labeled using the Talairach Daemon (Lancaster et al.,2000;Talairach and Tournoux,1988)after accounting for the discrepancy between MNI space and Talairach space (Lancaster et al.,2007).This analy-sis revealed clusters in the medial cerebellum and left parietal lobe,which were also de?ned as the seed regions for further functional connectivity analysis.Functional connectivity analysis
Functional connectivity analysis was conducted using the functional connectivity toolbox,v.12.p (Chai et al.,2012;https://www.doczj.com/doc/254644565.html,/swg/software.htm).For each subject,four seed regions were de?ned,including the posterior cerebellum (Fig.3A)and the anterior cerebellum area (Fig.3C)from the VBM analysis,as well as the medial cerebellum (Fig.3E)and the left superior parietal lobule (Fig.3G)from the ALFF anal-ysis.Before the modeling,the ?fth-order eigenvector of WM and the ?rst ?fth-order eigenvector of CSF were re-moved from the preprocessed resting-state fMRI images to minimize physiological noise that was probably due to
FIG. https://www.doczj.com/doc/254644565.html,rger gray matter concentration in the athlete group than the control group.Threshold was set as cluster level false discovery rate (FDR)corrected p <0.05.L:left;R:right.
Table 2.Clusters Showed Larger Gray Matter Concentration in the Athlete Group than in the Control Group
MNI coordinates
Regions Cluster size
Corrected p Peak t x y z R.Cerebellum,Posterior Lobe,Cerebellar Tonsil 5970.001 5.39424à67à45R.Cerebellum,Posterior Lobe,Cerebellar Tonsil 4.76417à63à44R.Cerebellum,Anterior Lobe
141
0.022
4.92824à54à30R.
Cerebellum,
Anterior Lobe,Culmen
3.621
32
à49
à26
Threshold was p <0.001with a cluster-level nonstationary correction of p <0.05.R,right;L,left;MNI,Montreal Neurological Institute.
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respiratory and cardiac factors.Six rigid-body head-motion parameters were also regressed out using linear regression analyses.The imaging data were also temporally ?ltered using a 0.01–0.08Hz band-pass ?lter.Voxel-wise correla-tion analysis was then conducted,and a beta contrast map was generated for each seed region and each subject.These individual seed correlation maps were then entered into two types of group-level analyses.The ?rst group-level analysis used one-sample t-tests to determine the spe-ci?c intrinsic network connected to each of the seed regions.This is an important step,because subdivisions of a brain structure might be connected to different networks (e.g.,in cerebellum,Habas et al.,2009).The second group-level analysis used two-sample t-tests to compare differences be-tween the two groups.Since anti-correlation between the task-positive network and the default mode network re-mains controversial (Fox et al.,2009;Murphy et al.,2009),we restricted our results within the task-positive network by applying a positive correlation mask of the same seed re-gions.The masks were de?ned by using an ‘‘or’’operation on the two groups’correlation maps using a threshold of p <0.001.Results VBM results
The mean total GMV of the athlete group was 699.1mL (SD =60.2),while the mean total GMV of the control group was 717.7mL (SD =59.3).There was no difference in total GMV observed between the two groups (t =à0.96,p =0.35).
A voxel-wise comparison revealed greater GMC in two right cerebellum clusters for the athlete group than the con-trol group (Fig.1and Table 2).One cluster was located in the posterior right cerebellar tonsil (lobule 8),and the other region was located in the anterior right cerebellar hemisphere (lobule 6).However,no region showed greater GMC for the control group than the athlete group.
The voxel-wise comparison of GMV showed no differences between the two groups using a threshold of p <0.05with nonstationary correction.However,greater GMV in similar cerebellum clusters was observed using a more liberal thresh-old of p <0.001and cluster size >141voxels,(Table 3).ALFF results
Larger ALFFs were also found in the cerebellum for the athlete group than the control group (Fig.2and Table 4),in-cluding the anterior part of the culmen,and the medial por-tion of the anterior lobe (vermis lobule 6).In contrast,there was a cluster in the left superior parietal lube (BA 7/19)that revealed larger ALFFs in the control group than the ath-lete group.
Functional connectivity analysis
Functional connectivity analysis for all subjects revealed that seeding regions belonged to distinct functional networks (Fig.3).The right posterior cerebellum seed showed positive functional connectivity to the left dorsal-lateral prefrontal cor-tex,left parietal cortex,and left temporal lobe (Fig.3B);while the right anterior cerebellum and the medial part of the
Table 3.Clusters Showed Larger Gray Matter Volume in the Athlete Group
than in the Control Group (R:right;L:left)
MNI coordinates
Area
#voxels corrected p Peak t x y z R.Cerebellum,Posterior Lobe 2740.008 4.7124à64à44R.Cerebellum,Anterior Lobe 1490.031 4.6823à52à32R.Cerebellum,Anterior Lobe 3.9124à42à32L.Cerebellum,Posterior Lobe 2120.016 4.31à27à78à36L.Cerebellum,Posterior Lobe
3.74à30à81à27R.Superior Temporal Gyrus,BA 41182
0.021
4.1950à374R.Superior Temporal Gyrus,BA 13
4.09
42
à28
3
Threshold was p <0.001with a cluster extent of 141voxels.The clusters in bold corresponded to the clusters reported in Gray matter con-centration analysis (Table 2).
FIG.2.Different amplitude of low-frequency ?uctuation between the athlete group and the control group.Threshold was set as cluster level FDR corrected p <0.05.Red:athlete >control;Blue:control >athlete.L:left;R:right.
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FIG.3.Positive (red)and negative (blue)correlation map of all the subjects with the four seeding regions.(A,C,E),and G display the four seed regions,and (B,D,F,H)represent corresponding correlation maps.Threshold was set as cluster level FDR corrected p <0.05.L:left;R:right.
Table 4.Clusters Revealed Different Amplitude of Low-Frequency Fluctuation Between Groups
MNI coordinates
Label
Cluster size
Cluster p Peak t x y z Athlete >control No Gray Matter
3050.004 4.77à4à30à30L.Cerebellum,Anterior Lobe,Culmen 4.56à10à36à28No Gray Matter
4.51à10à22à30L.Cerebellum,Anterior Lobe 216
0.012
4.66à6à60à26R.Cerebellum,Anterior Lobe
4.2814à52à28R.Cerebellum,Anterior Lobe,Fastigium 4.166à58à24Control >athlete
L.Superior Parietal Lobule,BA 73540.001
5.59à38à6854L.Precuneus,BA 19
5.14à30à7448L.Middle Temporal Gyrus,BA 19
3.69
à42
à80
32
Threshold was p <0.001with a cluster-level false discovery rate correction of p <0.05.BA,Brodmann’s area.
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cerebellum seeds showed positive connectivity with bilateral sensorimotor areas (Fig.3D,F).The left superior parietal lob-ule seed showed connectivity with typical task-positive net-works,including the bilateral dorsal lateral prefrontal cortex,the bilateral parietal cortex,the bilateral MT lobe,and the bilateral posterior cerebellum (Fig.3H).
No differences were found in functional connectivity maps of both groups when the right posterior and the right anterior cerebellum regions were used as seeds,respectively.The me-dial cerebellum seed region revealed less functional connec-tivity with the right anterior cingulate cortex in the athlete group than the control group (Fig.4).However,this cluster was mainly located in the WM;therefore,it was not further discussed.
The left parietal seed region showed greater functional con-nectivity with the right anterior cingulate cortex (BA 32/24)and the left middle frontal cortex (BA 6),and weaker func-tional connectivity with the left inferior frontal cortex (BA 9)and the bilateral middle frontal cortex (BA 9)in the athlete group than the control group (Fig.5and Table 5).Using a positive correlation map of all subjects as an inclusive mask,we further con?rmed that the two left middle frontal
cortex clusters,which showed different functional connectiv-ity with the left superior parietal lobule between the two groups,belonged to the positive correlation network (Fig.6).Discussion
The two main ?ndings of the present study are as follows:First,badminton athletes demonstrated greater GMC in the right anterior and posterior lobes of the cerebellum and greater ALFFs in the medial portion of the cerebellum.Sec-ond,badminton athletes showed smaller ALFFs in the left su-perior parietal lobule and altered functional connectivity between the left superior parietal lobule and frontal regions.The role of the cerebellum
The greater cerebellum GMC in badminton players is con-sistent with other cross-sectional studies of athletes,such as basketball players (Park et al.,2009)and keyboard musicians (Hutchinson et al.,2003).Structural expansion may result from microstructural changes at the neuronal level (Anderson et al.,1994;Kleim et al.,1997;Kleim et al.,1998).Changes in the cerebellum were also observed in the ALFF analysis,which is consistent with the recent ?nding that short-term motor training enhances resting-state independent cerebellar component activity (Albert et al.,2009).There is also evidence that task-related neural responses,as measured by fMRI,may be shaped by motor training (Doyon et al.,2002;Jenkins et al.,1994;Jueptner et al.,1997).These ?ndings suggest that the cerebellum plays a key role in long-term professional bad-minton training and practice.However,the current cross-sectional study cannot rule out the alternative explanation that individuals with larger cerebellar density,and altered in-trinsic activity might be more likely to master the ?ne motor dexterity that is necessary for elite badminton skills.Future studies using a longitudinal design are needed to answer these questions.
It is noteworthy that Hutchinson et al.(2003)and Park et al.(2009)only measured the global volume of the cerebellum or cerebellar lobules.The voxel-wise method adopted in the present study enabled the determination of region-speci?c anatomical differences within the cerebellar https://www.doczj.com/doc/254644565.html,ing functional connectivity analysis,we further demonstrated that regions of the cerebellum which showed structural differ-ences with VBM analysis were not restricted solely to the
FIG. 4.Cluster showed reduced functional connectivity with the medial cerebellum seed region in the athlete group than in the control group.Threshold was set as cluster level FDR corrected p <0.05.L:left;R:right.
FIG.5.Cluster showed different functional connectivity with the left parietal seed region between groups.Threshold was set as cluster level FDR corrected p <0.05.Red:athlete >control;Blue:control >athlete.L:left;R:right.
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motor network.The right posterior cerebellum region is func-tionally connected to the left dorsal-lateral prefrontal cortex,the left parietal cortex,and the left temporal lobe,suggesting that the posterior cerebellum region belongs to the left fronto-parietal executive network (Habas et al.,2009).Therefore,the expansion of the cerebellum resulting from motor training may not only support motor control functions,but also in-volve other nonmotor functions such as visuo-motor coordi-nation and executive control.
It is also worthy to point out that the nonmodulated GMC from VBM analysis did not compensate for the effect of spa-tial normalization;therefore,it cannot re?ect absolute volume
changes (Mechelli et al.,2005).Instead,the GMC ?ndings might re?ect the differences of relative concentration or the density of cerebellum in the two groups.However,the GMV analysis after modulation showed very similar results,supporting the modulation of badminton training on the cere-bellum structure.
The role of the fronto-parietal network
The present study found,for the ?rst time,that the function of the fronto-parietal network is altered in athletes.First,re-gional ALFFs in the left superior parietal lobule were smaller in the athlete group.The superior parietal lobule is thought to act as a relay that passes information from visual-processing areas to motor-processing areas,regions which support visuomotor coordination (Caminiti et al.,1996;Wise et al.,1997).Enhanced neural activity was observed in the superior parietal lobule after short-term motor training (Jenkins et al.,1994;Jueptner et al.,1997;Sakai et al.,1998).However,lower ALFFs appear to be inconsistent with a recent ?nding that the strength of the fronto-parietal network increased after 11min of visuomotor training (Albert et al.,2009).We argue that this ?nding may re?ect different consequences of training phases between short-term training in Albert et al.(2009)and long-term athletic training in the present study.This notion is consistent with Xiong et al.(2009),who showed that region-speci?c brain activation increases ?rst,then decreases,during a longer period of motor training.
In addition to the amplitude of ?uctuation,our results re-veal altered connectivity within the fronto-parietal network.The fronto-parietal network,which is supported by the un-derlying superior longitudinal fasciculus (Makris et al.,2005),plays a key role in hand-eye coordination (Battaglia-Mayer et al.,2001;Marconi et al.,2001),visual-guided reach-ing (Battaglia-Mayer et al.,2003;Burnod et al.,1999),and grasping (Grol et al.,2007).This is consistent with the notion that badminton athletes have higher visuomotor skills than individuals who do not play racquet sports.In addition,this network is reported in key regions to support the antici-pation of badminton serving (Wright et al.,2010;Wright and Jackson 2007).
Our results suggest that functional connectivity has been rewired within the fronto-parietal network (Fig.6)in
Table 5.Clusters Revealed Different Functional Connectivity with the Parietal Seed Region Between Groups
MNI coordinates
Label
BA Cluster size
Cluster p Peak t x y z Athlete >control
R.Anterior Cingulate 32355<0.001 5.87103814R.Anterior Cingulate 32 4.8114426R.Anterior Cingulate 24 4.64123020R.Medial Frontal Gyrus 102020.004 5.72644à12L.Middle Frontal Gyrus 61140.025 4.47à362244R.Cingulate Gyrus 311200.025 4.28à2636Control >athlete
L.Inferior Frontal Gyrus 92960.001 5.25à461030L.Middle Frontal Gyrus 9 4.41à541432L.Middle Frontal Gyrus 9 3.78à462024R.Middle Frontal Gyrus
9
103
0.048
4.96
60
14
34
Threshold was p <0.001with a cluster-level false discovery rate correction of p <0.05.
FIG.6.Illustration of different functional connectivity be-tween the athlete group and controls within the left fronto-parietal network.Arrow in cyan represents enhanced functional connectivity between the left superior parietal lobule (green)and the left middle frontal gyrus (BA6)(red)in the athlete group than in the control group.Arrow in purple represents lower functional connectivity between the left superior parie-tal lobule (green)and the left middle frontal gyrus (BA9)(blue)in the athlete group than in the control group.
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badminton athletes but not controls.In the athlete group,the left superior parietal lobule revealed enhanced functional connectivity with the premotor cortex(BA6);whereas in the control group,the left superior parietal lobule revealed stron-ger functional connectivity with the middle frontal gyrus (BA9).Since the premotor cortex supports the sensory guid-ance of movement(Passingham,1985),this result indicates that the fronto-parietal network in the athlete group may in-volve superior motor-related processing in comparison with the control group,thus re?ecting a cumulative effect of the motor training or the prerequisition of superior visual-spatial capability in professional badminton athletes.
Conclusion
In conclusion,the present study revealed signi?cant struc-tural and functional alterations in professional badminton players compared with normal controls.Speci?cally,badmin-ton players showed altered intrinsic neural activity and func-tional integration in the fronto-parietal network in addition to structural changes in the cerebellum.These alterations may re?ect speci?c task demands and cognitive processes during badminton training,including re?ned visuo-spatial process-ing and hand-eye coordination in addition to motor skills. Acknowledgments
This research was supported in part by the National Nature Science Foundation of China Grants31070984, 30770730,30921064,31171082,90820307,and91124004;the National Basic Research Program of China2012CB720701; and NIH Grants R01HL102119,R21DA032022,and R03 DA027098.Tha authors thank Marc Korczykowski for his helpful comments.
Author Disclosure Statement
No competing?nancial interests exist.
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Address correspondence to:
Hengyi Rao
Department of Psychology
Sun Yat-Sen University
Guangzhou510275
China
E-mail:hengyi@https://www.doczj.com/doc/254644565.html,
Hua Jin
Center for Studies of Psychological Application
South China Normal University
Guangzhou510631
China
E-mail:jinhua@https://www.doczj.com/doc/254644565.html,
BADMINTON PLAYER’S BRAIN233
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函数说明: 1.TransformWindow(窗口句柄) 功能:转换窗口,对要取后台图色数据的窗口使用该函数后才能取后台图色数据。如果是DX图形绘图的窗口,DX绘图区域必须有部分移到屏幕外,否则无法使用。转换窗口后,有些窗口(特别是大多数游戏的)要等待一会儿才能用其它函数可靠地取到后台图色数据,等待的时间要大于画面两次刷新的时间间隔。转换后到取消转换前,可以无限次使用取到后台图色数据的命令,即通常只需要转换一次。 参数: 1)窗口句柄:整型数。 2.UnTransformWindow(窗口句柄) 功能:取消窗口转换,DX图形绘图的窗口,用过TransformWindow后,必须用UnTransformWindow取消窗口转换才能让DX绘图完全移到屏幕中,否则后很严重(不会损坏电脑的),自己试下就知道了。 参数: 1)窗口句柄:整型数。 3.GetPixelColor(窗口句柄,横坐标,纵坐标)[颜色值] 功能:获得指定点的颜色 参数: 1)窗口句柄:整型数。 2)横坐标:整型数,窗口客户区坐标。 3)纵坐标:整型数,窗口客户区坐标。 返回值: 颜色值:整型数。 例子: Plugin hwnd=Window.Foreground() Plugin Window.Move(hwnd,-30,10) Plugin BGCP2_02.TransformWindow(hwnd) Delay 200 Plugin color=BGCP2_02.GetPixelColor(hwnd,0,0) MsgBox CStr(Hex(color)),4096,"颜色" Plugin BGCP2_02.UnTransformWindow(hwnd) Plugin Window.Move(hwnd,10,10) 4.CmpColor(窗口句柄,横坐标,纵坐标,颜色,颜色最大偏差)[是否满足条件] 功能:判断指定点的颜色,后台的IfColor 参数: 1)窗口句柄:整型数。 2)横坐标:整型数,窗口客户区坐标。 3)纵坐标:整型数,窗口客户区坐标。 4)颜色:整型数。 5)颜色最大偏差:整型数。游戏中不同电脑上显示的颜色会有点偏差,这个参数用于兼容这种情况,它设置的是RGB各颜色分量偏差的最大允许值,取值范围是0-255,0是无颜色偏差。 返回值: 是否满足条件:布尔值,布尔值是用来表达是真是假的,指定点的颜色满足条件就返回真,否则返回假。 例子: Import "BGCP2_02.dll" Plugin hwnd=Window.Foreground() Plugin Window.Move(hwnd,-30,10) Plugin BGCP2_02.TransformWindow(hwnd) Delay 200 Plugin tj=BGCP2_02.CmpColor(hwnd,6,5,&HFF7F00,30) If tj=true MsgBox "满足条件",4096 Else MsgBox "不满足条件",4096 EndIf Plugin BGCP2_02.UnTransformWindow(hwnd) Plugin Window.Move(hwnd,10,10) 5.FindColor(窗口句柄,左边界,上边界,右边界,下边界,颜色,颜色最大偏差,查找方式,横坐标,纵坐标) 功能:找色 参数: 1)窗口句柄:整型数。 2)左边界,整型数,用于设置找色范围,找色区域左上角的横坐标(窗口客户区坐标)。 3)上边界,整型数,用于设置找色范围,找色区域左上角的纵坐标(窗口客户区坐标)。 4)右边界,整型数,用于设置找色范围,找色区域右下角的横坐标(窗口客户区坐标)。 5)下边界,整型数,用于设置找色范围,找色区域右下角的纵坐标(窗口客户区坐标)。
学习游戏脚本制作:按键精灵里的if语句教程 来源:按键学院【按键精灵】万万没有想到……有一天居然会栽在if语句手里。 First—小编的凄惨经历 小编今早写脚本,由于无意间将if语句中的end if错删了,弹出了这样的错误提示: “(错误码0)没有找到合法的符号。” 看到这个提示,小编以为是哪个逗号不小心写成中文逗号,没成想丢了个end if也是出现这样的提示。 好在代码不多,所以错误点容易找到,那……如果,代码多达几百上千条的时候呢?眼泪马上掉出来~ 今天在这里和童鞋们一起来了解下,按键里的夫妻组合,看看这些夫妻被分开之后都会出现什么样的可怕情况: Second—一夫一妻制 If……end if判断语句 If判断语句,有分为两种: 1、 if语句条(条模式) 当判断完之后,要执行的语句只有一条的时候,使用if语句条
例如: If 1 > 0 Then MessageBox"Hello~" // If语句条,不需要添加end if //条模式的时候,if语句还是单身,不是夫妻组合 2、 if语句块(块模式) 当判断完之后,要执行的语句有很多条的时候,使用if语句块 例如: If 1 > 0Then MessageBox"Hello~" MessageBox"Hello~" End If //块模式的时候,if语句是已婚状态,夫妻组合。如果这个时候缺少了end if 就会出现下面的错误提示: 拓展:if语句块中then 可以省略 例如: If 1 >0 MessageBox"Hello~" MessageBox"Hello~"
End If For……next循环语句 例子: For i=1 To 10 //这里的脚本可以循环10次 Next 拓展:如果,循环体里不需要用到循环次数值,例如,需要打开十个记事本,可以这样写: For 10 RunApp "Notepad.exe" Next 当for循环语句缺少next的时候,会出现下面的错误提示: Do……Loop 循环语句 Do……Loop循环语句分为两种情况: 1、前判断 Do While条件 Loop
C#中如何调用按键精灵插件 原来是为了在游戏外挂中发送键盘鼠标消息,自己写个sendmessage或者是postmessage又比较麻烦。于是google了一下,发现现在很多脚本工具都有这个功能,其中按键精灵的一个叫361度的插件已经有这个的实现,还验证过了。为什么不拿来己用呢?首先分析一下按键精灵插件的接口,发现: 插件的功能函数没有直接暴露出来,而是通过一个GetCommand的函数返回一个函数描述结构。 接下来看看这个结构: 上面这个结构我已经是转换成C#的对应结构了,原结构可以查看按键精灵提供的插件C++接口源代码。 这个结构里面的handlerFunction 实际上是指向函数的入口点,也就是一个函数指针,每个函数都一样是2个参数: typedef int (*QMPLUGIN_HANDLER)(char *lpszParamList, char *lpszRetVal); 转换为C#中相应的委托为: delegate void Invoker(string parameters, StringBuilder returnValue); 大家注意到,有两个参数,c++原型中都是char*类型,转换为C#的delegate后第一个为string,第二个为StringBuilder。这是因为parameters是in的,dll中不会对这个参数做修改,而returnValue是out的,dll返回时候要把返回值写入这个StringBuilder的缓冲区。 原本的想法是用C++写一个桥来调用dll,不过在.net 2.0 中,框架直接提供了
Marshal.GetDelegateForFunctionPointer 来转换一个函数指针为一个委托,这就方便多拉。请看下面代码,注意看BGKM_ExecuteCommand 这个函数里面的东西。 using System; using System.Collections.Generic; using System.Text; using System.Runtime.InteropServices; namespace WJsHome.Game.Utility { public class QMacro { [DllImport("BGKM5.dll", EntryPoint = "GetCommand")] static extern IntPtr BGKM_GetCommand(int commandNum); [StructLayout(LayoutKind.Sequential)] class QMPLUGIN_CMD_INFO { public string commandName; public string commandDescription; public IntPtr handlerFunction; public uint paramNumber; } delegate void Invoker(string parameters, StringBuilder returnValue); static string BuildParameters(params object[] parameters) { StringBuilder sb = new StringBuilder(); for (int i = 0; i < parameters.Length; i++) { sb.Append(parameters[i].ToString());
按键精灵脚本制作教程:HSV搞定偏色! 来源:按键学院【按键精灵】 院刊《如何识别渐变色或半透明的文字》中, 我们分享了如何通过设置偏色来查找渐变文字, 我们使用的是RGB方式,然后配合偏色计算器来计算出偏色的。 今天我们换个方式,不使用偏色计算器,依靠肉眼对颜色的感觉,看看能不能搞定偏色~ HSV颜色模型 了解HSV颜色模型前,我们先来看看RGB颜色模型 RGB颜色空间采用物理三基色表示:红、绿、蓝 任何一个颜色都是有三基色混合而成的。但是,人的视觉不适应这种颜色体制, 人的肉眼看颜色,不可能像机器一样,分析出颜色里含有多少比重的红、绿、蓝 肉眼看颜色,是通过由色相(Hue,简H),饱和度(Saturation,简S)和色明度(Value,简V)来识别我们看到的是什么颜色。
HSV就是用色相,饱和度和色明度来形容颜色,所以它适合人的视觉。 这个色彩缤纷的圆锥形就是HSV的色彩空间。 我们举个例子好好的理解下它。 例如,我们要找的颜色是,下图中红色点的颜色: 怎样才能描述这个颜色在圆锥里的位置呢? 首先要看圆锥的平面圆,这是一个被颜色块分割了的圆。(这个圆表示的是色相 H)图中为了便于查看,只分了几个大块,实际上,圆的360度每一度都表示着一种颜色。
我们看到了,我们要找的颜色它是在紫色的那一块。 接着我们看圆锥被切开的那个口子, 横向数进去,我们看到,红色点的颜色位于紫色块的第五个位置, 而且,我们发现,越靠近圆锥心,颜色就越淡,好像被掺和了水一样变得不纯洁了。这就是颜色的纯度,即饱和度S 。 最后,我们看圆锥被切开的口子,往圆锥底部而下的变化。 越往下颜色就越暗淡。 这就是颜色的亮度即色明度V 我们发现我们要找的点是在最亮的地方。 三步骤我们就确定了颜色的所在位置。 那么,真正应用到偏色里要怎么应用呢? 我们找个实例操作下~ 偏色处理
目录 插件命令面板 - BKgnd后台控制 (6) KeyPress 按键 (6) KeyDown 按下 (7) KeyUp 弹起 (8) LeftClick 左键单击 (9) LeftDoubleClick 左键双击 (10) LeftDown 左键按下 (11) LeftUp 左键弹起 (12) RightClick 右键单击 (13) RightDown 右键按下 (14) RightUp 右键弹起 (15) MiddleClick 中键单击 (16) SendString 发送字符串 (17) MoveTo 鼠标移动 (18) GetPixelColor 得到指定点颜色 (19) FindColor 区域找色 (20) FindColorEx 模糊找色 (21) FindCenterColor 中心找色 (22) 插件命令面板 - Color颜色 (23) ColorToRGB 颜色转RGB (23) GetRGB 得到RGB分量合并值 (23) ColorToHSL 颜色转HSL (24) CountColor 区域搜索颜色数量 (25) FindMutiColor 区域多点找色 (26) FindShape 区域多点找形状 (27) 插件命令面板 - Console控制台 (27) Open 打开 (28) Close 关闭 (29) ReadLine 读取一行 (29) WriteLine 写入一行 (29)
WaitKey 等待按键 (30) 插件命令面板 - Encrypt加解密 (30) Md5String 字符串MD5加密 (30) Md5File 文件MD5加密 (31) 插件命令面板 - File文件 (31) CloseFile 关闭文件 (31) CopyFile 复制文件 (31) CreateFolder 创建文件夹 (32) DeleteFile 删除文件 (32) DeleteFolder 删除文件夹 (33) ExistFile 判断文件(旧) (33) GetFileLength 得到文件长度 (33) IsFileExit 判断文件 (34) MoveFile 移动文件 (35) OpenFile 打开文件 (35) ReadFile 读取文件 (36) ReadFileEx 读取文件 (36) ReadINI 读取键值 (37) ReadLine 读取一行 (37) ReNameFile 重命名文件 (38) SeekFile 设置文件的当前读写位置 (38) SelectDirectory 弹出选择文件夹对话框 (39) SelectFile 弹出选择文件对话框 (39) SetAttrib 设置文件属性 (40) SetDate 设置文件日期时间 (41) WriteFile 写入文件 (41) WriteFileEx 写入文件 (41) WriteINI 写入键值 (42) WriteLine 写入一行 (42) 插件命令面板 - Media多媒体 (43) Beep 蜂鸣器 (43) Play 播放 (44)
1.什么是按键精灵的插件 按键精灵的插件是由按键精灵官方或用户自己提供的一种功能扩展。由于按键精灵本身只提供脚本制作过程中最常用的功能,而不可能面面俱到。所以,如果您稍懂一点Visual C++编写程序的知识,就可以通过自己写按键精灵插件,实现比较特殊、高级的功能,如文件读写、注册表访问,等等。如果您愿意,还可以把自己写的插件提交给我们,我们可以在按键精灵的最新版中捆绑您编写的插件,和大家共同分享您的智慧! 按键精灵的插件是通过动态链接库(DLL)的形式提供的。这些动态链接库必须满足一定的规范,并且放在按键精灵所在路径的plugin文件夹下。在按键精灵启动的时候,会自动加载plugin文件夹下的每个插件。每个插件可以包含多个“命令”,每个命令则可以看作是一个独立的函数或者子程序。比如我们提供的文件相关操作插件File.dll,就提供了ExistFile(判断文件是否存在)、CopyFile(复制一个文件)、DeleteFile(删除一个文件)等多个命令。 目前按键精灵的插件只能使用Visual C++编写。您不需要懂得很高深的Visual C++编程技巧,也不需要知道插件的技术细节。因为我们已经提供了一个“模板”插件,您只需要在这个模板上按照下文所述的步骤进行一点点修改,一个属于您自己的插件就完成了。我们推荐您使用Visual C++ 6.0,也可以用Visual C++.NET。 值得说明的是,由于技术原因,按键精灵的插件目前还不能用Visual Basic、Delphi、JBuilder等常见的开发工具编写。但是有聪明的用户使用VBScript脚本和ActiveX DLL的形式,同样实现了按键精灵的功能扩展,典型的例子如Ringfo大虾制作的QMBoost等等。严格说来,这种功能扩展不能称为按键精灵的插件,但是我们同样欢迎这种类型的功能扩展。 2. 如何制作一个插件 2.1.准备动手 为按键精灵写一个插件其实非常简单,只需要您有一点Visual C++编程的知识就够了。如果您懂Visual C++编程,就请跟我一步一步的来完成一个简单的插件。 首先得计划一下,我们的插件完成什么功能,再考虑一下这个插件都需要具有哪些命令。这里假设我们的插件是用于字符串操作的,名字就叫String.dll,这个插件目前暂时只有一个命令,名字叫StrLen,是用于得到字符串长度的。也就是说,用户通过使用我们提供的StrLen 命令,传入一个字符串,我们给他返回这个字符串的长度。 具体的说,用户可能将来会在按键精灵中这样调用我们的插件命令: Dim length as integer Plugin length=String.StrLen(“Hello, world”) 如果您熟悉按键精灵,那么对第一句话不会陌生,它的意思是定义一个叫length的整数变量。第二句的意思,我们来解析一下:
变量!神奇的小柜子 变量就是会变化的量。就像一个小柜子,我们可以在柜子里装载不同的东西,而当我们需要找到这些东西的时候,只要记住柜子的名字就可以了。 使用变量的方法是:先定义(给柜子起名)、再赋值(将物品放进柜子)、最后使用(根据柜子名字找到放在其中的物品)。 使用Dim命令定义变量,例如: Dim str1 //定义变量str1 Dim var1=22 //定义变量Var1,并且赋值为22 例子1:使用变量设置输出文字的内容 1、下面红色的是3行脚本,请把他复制到“源文件”当中 Dim str1 str1 = "你很聪明" SayString str1 2、Dim str1 就是定义变量,也就是说我们创建了一个小柜子,给他起名为str1 3、str1 = "你很聪明" 就是赋值,我们把"你很聪明"这几个字放到str1这个小柜子里 4、SayString str1 表示我们输出str1这个变量的内容,也就是说把str1这个小柜子里的内容拿出来交给SayString 这个命令去使用。 5、如果你希望修改喊话的内容,只要修改str1这个小柜子里的内容就可以了。 例子2:变量的一些用法 a=1 把数字1放进柜子a中。 b="你猜对了吗?" 把字符串你猜对了吗?放进柜子b中。字符串必须用""包含。 dc=3.14159265 把小数放进柜子dc中。 num1=1 num1=33 num2=55 sum=num1+num2 首先把33和55分别放入num1和num2中。然后把他们取出来,做加法操作(加法是由CPU来处理的),把结果放在sum中。结果sum等于88 num1=1 num1=33 num1被给值为1,然后又给值为33。此时,num1中存储是的33。1就被覆盖掉了。没有了:) sum=sum+1 这句不等同于数学的加法,也是初学者不容易理解的地方。我们只要想,把sum拿出来和1做加法,再放回sum中就可以了。sum原来的值是88,做完加法后,sum等于89。 pig=1 pig=pig*3+pig 能猜出pig最后等于几么?1*3+1。结果是4 例子3:使用变量输入1到100的数字 VBSCall RunApp("notepad") Delay 2000 a=1
学会用按键精灵制作游戏脚本之前后台坐标关联教程 来源:按键学院【按键精灵】 各位大大在切换前后台命令的时候,有没有遇到坐标切换呢~ 有没有发现前后台的命令,对同一个窗体内容,居然坐标不同!! 今天~院刊就跟大家普及下前台坐标与相对应的后台坐标知识~ 什么是前台坐标和后台坐标呢? 什么是前台坐标? 以屏幕左上角的坐标为起点(0,0,从而获取到的各个窗体的坐标,就是前台坐标。 什么是后台坐标? 以窗口左上角为起点(0,0,从而获取到的这个窗体内的相对坐标,就是后台坐标。 如图: 我们来举个栗子吧,例如txt文本里的输入文字的起始点。
至此,各位大大知道前后台坐标的联系了吧。一个是绝对坐标(前台),一个是相对坐标(后台)。 那么如何进行前后台坐标的切换呢 从上图里,聪明机智的小伙伴们就会发现:如果知道了前台坐标,也知道了窗口左上角的值。那么窗口客户区内的 任意后台的坐标,不是都可以通过以下计算来获得了: 后台x坐标=客户区前台x坐标-客户区左上角前台x坐标 后台y坐标=客户区前台y坐标-客户区左上角前台y坐标 如何获得客户区前台的x,y坐标呢? 我们使用按键精灵自带的窗体插件命令:GetWindowRect来获取。 命令名称: GetWindowRect 窗口边框大小 命令功能:得到窗口句柄的边框大小(包括标题栏 命令参数:参数1 整数型,窗口句柄
返回值:字符串型,边框大小(包括标题栏 注:返回为:边框窗口左角X坐标|边框窗口左上角Y坐标|边框窗口右下角X坐标 |边框窗口右下角Y坐标 //下面这句是得到窗口句柄的边框大小(包括标题栏 sRect = Plugin.Window.GetWindowRect(句柄 将你所要获取的窗口句柄填入括号内就可以啦~ 范例举例: 举个萌萌哒的例子:向记事本特定位置输入文字。 例如我要往“hello”和“按键精灵”中间插入文字: 2014-9-17 18:03 上传 下载附件(8 KB 思路: 每次打开记事本的位置,有可能会有变化。而我们又不能每次都要去获取它的坐标再改脚本,这样太费力了。所以呢,只要锁定了记事本,知道了目标在记事本中的相对位置就可以操作啦。 同理,寻找游戏里的物品目标,前台不稳定。后台命令也是基于相对坐标的。 1. 先找到目标窗体的左上角坐标 (通过窗体插件命令:GetWindowRect来获取) 2. 再找到目标窗体内,“hello”和“按键精灵”中间的坐标 (为了方便,我们用抓抓获取。在游戏中,可以通过找图找色来获取前台坐标)
学会用按键精灵制作脚本之RunApp 运行命令教程: 用脚本运行可执行程序 来源:按键学院【按键精灵】Runapp命令,看上去是不是觉得so easy ?不就是runapp 某个程序的路径,然后就可以打开这个程序了吗?老话怎么说来着,越简单的东西越是不简单。Runapp使用起来也是处处暗藏杀机滴。 Runapp命令是个啥? 命令名称RunApp 运行 命令功能启动一个程序或者打开一个文件 命令参数参数1 字符串型,要运行的程序或者文件 重头杀机——你所要启动的程序是带参数的,runapp 不支持启动带参数的程序。 首先,我们可以使用进程查看工具,查看下我们要启动的程序是否是带有参数的。
然后,我们打开进程查看工具,然后点击我们要查看的程序,例如QQ程序。 图1的是QQ的快捷键方式属性;图2是进程工具查看到的QQ程序信息;图3是进程工具界面如果是带有参数的程序,用进程工具打开之后,弹出的图2界面,在程序路径后面会出现参数。 例如:F:\桌面\程序目录\Not.exe $-fl$ 解决方法之一: 1. 鼠标右键,创建快捷方式 1)右击创建好的快捷方式——>属性: 2)“目标内容”填写目标文件路径及参数: 3)F:\桌面\程序目录\Not.exe $-fl$
4)“起始位置”填写目标文件夹: 5)F:\桌面\程序目录 (用进程查看工具查看,有的情况下会发现,程序所在的位置并不是程序的目录,这里要注意确认,一定要填写程序的其实位置,程序所在的目标文件夹的位置) 如图所示: 2. 使用RunApp启动这个快捷方式,例如在此快捷方式在桌面时。 Call RunApp("C:\Users\Death\Desktop\Not.exe.lnk") 经过上面的两步就可以达到预想的目的了。 解决方法之二:
手把手教你用“按键精灵”图文教程 类型:转载 按键精灵是一个可以模拟电脑操作的软件,您在电脑上的一切动作都可以让按键精灵模拟执行,完全解放您的双手。按键精灵可以帮你操作电脑,不需要任何编程知识就可以作出功能强大的脚本。 如果你还为一些枯燥、繁琐的电脑操作而烦恼,按键精灵绝对会是你最好的帮手。 那么,按键精灵具体能帮我们干什么呢?我们来列举几个例子来说明下。 * 网络游戏中可作脚本实现自动打怪,自动补血,自动说话等; * 办公族可用它自动处理表格、文档,自动收发邮件等; * 任何你觉得“有点烦”的电脑操作都可以替你完成。按键精灵第一个实现了“动动鼠标就可以制作出脚本”的功能。我们不希望为了使用一个小软件而去学习编程知识,考虑到这些,所以按键精灵完全界面操作就可以制作脚本。按键精灵的脚本是纯粹的TXT文件,即使是目前新增了插件功能,也引入了数字签名的机制。因此我们可以放心的使用网站上的脚本而不用担心会有病毒。 脚本就是一系列可以反复执行的命令.通过一些判断条件,可以让这些命令具有一定的智能效果.我们可以通过”录制”功能制作简单的脚本,还可通过”脚本编辑器”制作更加智能的脚本.今天我们就通过录制一个最简单的脚本,来手把手的教大家使用按键精灵。 上网一族一般开机后会先看看自己邮箱,或者看看自己博客;每天如此,可能都有些烦了。现在好了,把这些繁杂的事情交给按键精灵吧。今天我们就来录制一个自动登录博客,并对整个页面进行浏览的脚本。 首先,我们打开“按键精灵”。其运行界面如下(图1): 图1 运行界面 打开软件后点击工具栏上“新建”项(如图2);之后进入“脚本编译器”界面(如图3)。
WQM按键精灵插件说明书 1.插件简介 WQM按键精灵插件作为按键精灵的一个插件,为按键精灵提供对WQM的全方位的控制功能,同时也能够提供对WQM中网页的全面控制功能。 2.插件功能说明 WQM插件提供三类控制命令:WQM全局控制命令、WQM浏览控制命令、页面控制命令,后台键盘鼠标命令,后台找色命令,全局控制命令,JS扩展命令 2.1.全局控制命令 1)Bind(WQM进程名) 功能:绑定最后一个正在运行的WQM进程,如果没有找到就启动一个WQM进程并绑定 参数:WQM进程名 返回值:进程ID 2)Tips(字符串) 功能:在托盘区显示一个气泡提示信息; 参数:提示信息; 返回值:无 3)SetSize(窗口宽度,窗口高度) 功能:将WQM窗口设置为指定大小; 参数: 参数1:窗口宽度; 参数2:窗口高度; 返回值:无 4)Save(网页地址,保存的文件路径) 功能:将指定url保存为文件; 参数: 参数1:需要保存的网页地址; 参数2:需要保存的文件路径; 返回值:无 2.2.浏览控制命令 1)Go(网页地址) 功能:当前标签页打开Url指定的网页;此操作是一个阻塞操作,如果网页没有打开,脚本不能继续执行。如果超过全局超时设定,将导致脚本中止; 参数: 参数1:需要打开的网页地址 参数2 布尔型:是否强制从服务器读取,默认为读取页面,可能读取本地缓存。 返回值:无 2)Back() 功能:当前标签的网页浏览向后退, 参数:无; 返回值:无 3)Forward()
功能:当前标签页前进; 参数:无; 返回值:无 4)Refresh(指定是否强制刷) 功能:刷新当前标签页 参数:参数1:指定是否强制刷新当前标签页,0表示正常刷新,1表示强制刷新返回值:无 5)TabNew () 功能:在WQM中新建一个标签页,并跳转到该标签页上; 参数:无 返回值:无 6)TabGoto(标签页) 功能:跳转到WQM中指定需要的标签页上 参数:整数类型; 返回值:无 7)TabClose() 功能:关闭当前标签页 参数:无 返回值:无 8)ScrollTo(水平滚动条位置,垂直滚动条位置) 功能:将当前网页滚动到指定位置; 参数: 参数1:水平滚动条位置; 参数2:垂直滚动条位置; 返回值:无 9)ClearHistory() 功能:清除浏览器的历史记录,无需跳出确认对话框; 参数:无 返回值:无 10)ClearTemp() 功能:清空IE临时文件 参数:无 返回值:无 11)ClearCookie() 功能:清除IE所有的Cookie 参数:无 返回值:无 12)GetUrl() 功能:返回当前页面的URL地址 参数:无 返回值:字符串,当前页面的URL地址 2.3.页面控制命令 1)HtmlClick(网页元素特征串) 功能:点击网页中的按钮或链接,或者是其他元素,无ID请指定tag;
按键精灵自动化办公插件 来源:按键学院【按键精灵】“圣诞快乐~~” 在小编的印象里,圣诞节一般都是呵气成霜的日子。今年不一样哈,圣诞节天气还是十分暖和的。不知道童鞋们的城市是不是有在下雪,小编已经好久没有看见过雪花了~ 圣诞感言就到这里。今天小编要和大家分享的是一款办公插件。 懒人办公插件 在之前的院刊中,也分享过懒人办公插件。想必有不少童鞋都有使用过。 那么,是什么原因让小编忍不住再次要分享这个插件呢? 近期,懒人插件作者lxj1985 再次更新了插件,更新之后的插件更加给力,让小编忍不住想要分享给大家。 懒人插件更新内容 1、完善了EXCEL表格处理功能。 2、WORD类的处理功能重新编写,使其更适用于使用者。 3、ACCESS类不光可以操作ACCESS,还能对其他类别的数据库进行一定程度的操作。
4、编写了一份详细的的帮助文档,不光是在编程中用的上,在手动办公的过程中也能用的上这份帮助文档。 帮助文档的内容,作者大大写得非常的详细,每个命令的使用都附有例子说明: 使用方法 【调用懒人插件的方式有两种】 方法1: 1、将插件dll文件放在按键精灵安装目录下的plugin文件夹中。 2、打开按键精灵即可在按键精灵的插件命令中,找到新增的懒人插件命令,选择命令即可使用。
方法2: 如果插件(LazyOffice.dll)放在c:\test目录,使用以下代码对插件进行注册: Set ws = CreateObject("Wscript.Shell") ws.run "regsvr32 c:\test\LazyOffice.dll /s" 然后创建对象: //主脚本中使用插件命令,在脚本起始位置创建一次对象即可,如果有多开线程或者在控件事件中使用命令,则需在线程中控件事件中再次创建对象。 Set LazyExcel= CreateObject("Lazy.LxjExcel")//使用excel功能要创建的对象 Set LazyWord= CreateObject("Lazy.LxjWord")//使用word功能要创建的对象 Set LazyAccess= CreateObject("Lazy.LxjAccess")//使用Access功能要创建的对象 注意:帮助文档中的命令说明示例,是以第二种注册插件创建对象的方法。 第一种方法和第二种方法,代码上书写上的区别: Call https://www.doczj.com/doc/254644565.html,zyOffice.ExcelWrite(参数1,参数2,参数3,参数4,参数5)//第一种方法 Call 对象名.ExcelWrite(参数1,参数2,参数3,参数4,参数5)//第二种方法 单元格列名形式转换 ●○有的命令是以A1,A2 这样的形式来指定需要操作的区域,例如修改表格背景色的命令:Call LazyExcel.ExcelRange(1,"A1:D5","背景颜色",6,Index)
按键精灵经典教程 一、键盘命令(2~5页) 二、鼠标命令(5~13页) 三、控制命令(13~22页) 四、颜色/图像命令(22~28页) 五、其他命令(28~355页)五、网游脚本实例(35~最后)
一、键盘命令 命令名称:GetLastKey 检测上次按键命令功能:检测上次按键 命令参数:参数1 整数型,可选:变量名 返回值:无 脚本例子:(8.x语法) 复制代码 1.//脚本运行到这一行不会暂停,调用的时候立即返回,得到调用之前最后一次 按下的按键码保存在变量Key里。 2.Key=GetLastKey() 3.If Key = 13 Then 4. Msgbox "你上次按下了回车键" 5.End If 脚本例子:(7.x语法) 复制代码 1.//脚本运行到这一行不会暂停,调用的时候立即返回,得到调用之前最后一次 按下的按键码保存在变量Key里。 2.GetLastKey Key 3.If Key = 13 4. Msgbox "你上次按下了回车键" 5.EndIf 命令名称:KeyDown 按住 命令功能:键盘按住 命令参数:参数1 整数型,键盘虚拟码(8.X支持按键字符) 参数2 整数型,次数 返回值:无 脚本例子:(8.x语法) 复制代码 1.//KeyDown、KeyDownS、KeyDownH 2.//KeyDownS: 超级模拟方式,兼容性更强,对键盘和鼠标没有特别的要 求,PS2(圆口)和USB接口的键盘都可以使用 3.//KeyDownH: 硬件模拟方式,仅支持PS(圆口)的键盘点击查看使用硬件模
4.KeyDown 65,1 5.//65是A键的按键码,上面的语句表示按住A键1次 6. 7.KeyDown "A",1 8.//上面的支持按键字符,语句表示按住A键1次 脚本例子:(7.x语法) 复制代码 1.//KeyDown、KeyDownS、KeyDownH 2.//KeyDownS: 超级模拟方式,兼容性更强,对键盘和鼠标没有特别的要 求,PS2(圆口)和USB接口的键盘都可以使用 3.//KeyDownH: 硬件模拟方式,仅支持PS(圆口)的键盘点击查看使用硬件模 拟方式的注意事项 4.KeyDown 65,1 5.//65是A键的按键码,上面的语句表示按住A键1次 命令名称:KeyPress 按键 命令功能:键盘按键 命令参数:参数1 整数型,键盘虚拟码(8.X支持按键字符) 参数2 整数型,次数 返回值:无 脚本例子:(8.x语法) 复制代码 1.//KeyPress、KeyPressS、KeyPressH 2.//KeyPressS: 超级模拟方式,兼容性更强,对键盘和鼠标没有特别的要 求,PS2(圆口)和USB接口的键盘都可以使用 3.//KeyPressH: 硬件模拟方式,仅支持PS(圆口)的键盘点击查看使用硬件 模拟方式的注意事项 4.KeyPress 65,1 5.//65是A键的按键码,上面的语句表示按A键1次 6. 7.KeyPress "A",1 8.//上面的支持按键字符,语句表示按A键1次 脚本例子:(7.x语法) 复制代码 1.//KeyPress、KeyPressS、KeyPressH 2.//KeyPressS: 超级模拟方式,兼容性更强,对键盘和鼠标没有特别的要 求,PS2(圆口)和USB接口的键盘都可以使用 3.//KeyPressH: 硬件模拟方式,仅支持PS(圆口)的键盘点击查看使用硬件
按键精灵选项卡的使用 一、选项卡介绍 (1)创建选项 QUI的选项卡其实就是一个分页功能的控件和VB的SSTab控件的功能是一样的。 二、选项卡的属性 选项卡总共有11个属性,包含4个基本属性和5个位置属性,2个其他属性。 基本属性分别是:名称(Name)、显示(Visable)、有效(Enabled)、自定义。 位置属性分别是:左边(Left)、上边(Top)、宽度(Width)、高度(Height)、显示顺序(ZOrder)。 其他属性:字体、初始选项(Tab)。 三、选项页的使用 (1)创建选项 1.先创建一个选项TabControl1。
选项卡创建后默认有两个选项,分别是“选项页1”,“选项页2”。那我人如何来改变选项卡的数量和名称? 2.点击属性栏上的【自定义】的更多选项设置后面的三个小点点。 点击完成后就可以看到“选项卡控件设置页面”了。 在这里可以通过页面上面的“增加”、“删除”、“上移”、“下移”来控制标签页的数量和顺序,也可以双击标签名然后修改标签的名称。 3.点击增加,新增一个标签。默认名称是“选项页3”。后面增加的标签名称为“选项页4”,以此累加上去。
4.也可以删除标签,比如删除“选项页2”。或者是把“选项页3”移动到最顶端。 5.在标签页设置界面修改的时候,窗体上的标签页是不会改变的。如果想让它确实产生效果,修改完成后,点击界面下方的【确定】。 然后就可以看到窗体上的标签页按照我们修改的发生了变化。 以上就是修改标签页里面标签内容的方法了。
那它是否像我们之前所说的,能够达到分页的效果? 6.先选择"Tab3"。然后在"Tab3"中创建一个按钮。 创建完成后,再点击"Tab1",会看到Button1其实已经看不到了。 那在运行的时候是不是也是这样子的? 7.设置QUI为脚本界面,设置标签页的初始选项为0。也就是从左边数起第一个标签,也可以设置成1,这个下标的上限为当前标签页的标签数量。 然后保存脚本,进入调试,显示界面。 点击"Tab3","Tab1"。如果不出意外,看到的情况跟在编辑的时候是一样的。 (2)复制/剪贴/粘贴 既然分页里面可以创建控件,那可以把现成的控件直接拖到分页里面去?答案当然是否定的。 那有什么方法可以把已经创建好的控件拖到分页里面去? 最最常用的方法就是把控件剪贴,然后选择某一个标签页再粘贴进去。 粘贴成功后,这个控件就在会在这个标签的左上角。 比如,我们把一个输入框粘贴到其他中一个标签当中,粘贴后默认放在标签的左上角,然后再拖到我们想要放的那个位置去。
//~~~~~~~~~~{[做后台的准备]}~~~~~~~~~~~ //下面,用标题名来找父窗口 Hwnd = Plugin.Window.Find(0, "无标题- 记事本") //下面,根据父窗口来找子窗口(找对了标题名才有效) HwndEx = Plugin.Window.FindEx(Hwnd, 0, 0, 0) //下面,用程序名来找父窗口 Hwnd = Plugin.Window.Find("Notepad", 0) //下面,根据父窗口来找子窗口(找对了类名才有效) Hwnd = Plugin.Window.FindEx(Hwnd, 0, "Edit", 0) //~~~(也可以改指定程序的标题名来得来句柄)~~~ //下面,将指定程序名的标题名变为变量 Hwnd = Plugin.Window.Find("Notepad", 0) //改变窗口标题 Call Plugin.Window.SetText(Hwnd,"AJJL") //*********》之后可加第一种父子窗口就OK了《********* //~~~如果以上都不行,只剩下最后一种方法~~~~ //下面,鼠标指向的程序定为Hwnd这个变量(不过一定要加标记,否则变为假后台) hwnd=Plugin.Window.MousePoint() Rem star Goto star //区域模糊找色 //(XY,是坐标)(后面的是句柄,左X,上Y,右X,下Y,16位颜色,找的方式,相似度)XY = Plugin.Bkgnd.FindColorEx(Hwnd, 0, 0, 20, 20, "FFFFFF", 0, 0.9) //将XY坐标变为变量,再折分成X坐标和Y坐标。 MyArray = Split(XY, "|") X = Clng(MyArray(0)): Y = Clng(MyArray(1)) //后台判色 Color = Plugin.Bkgnd.GetPixelColor(hwndex,300,150) If color = "020503" Then //根据固定32位地址的值加红蓝(HP少于就。。。) Val = Plugin.Memory.Read32Bit(Hwnd, &H400000) //注意格式:符号&+字母H+8位地址 If clng(val)<=clng(HP)
脚本就是一系列可以反复执行的命令.通过一些判断条件,可以让这些命令具有一定的智能效果.我们可以通过”录制”功能制作简单的脚本,还可通过”脚本编辑器”制作更加智能的脚本.今天我们就通过录制一个最简单的脚本,来手把手的教大家使用按键精灵。 上网一族一般开机后会先看看自己邮箱,或者看看自己博客;每天如此,可能都有些烦了。现在好了,把这些繁杂的事情交给按键精灵吧。今天我们就来录制一个自动登录博客,并对整个页面进行浏览的脚本。 首先,我们打开“按键精灵”。其运行界面如下(图1): 打开软件后点击工具栏上“新建”项(如图2);之后进入“脚本编译器”界面(如图3)。现在就可以正式开始编译脚本了。
在脚本编译器界面上,左键点击工具栏上“录制”项,会出现这样的情况:进入桌面,并出现一个小的对话框(如图4)。 在这个小的对话框中,左侧红色圆按钮是录制的开始,第二个蓝色方按钮是录制结束,第三个是存储录制内容。在录制过程中该对话框可以随意移动,不会影响录制结果。 我们来点击红色圆形按钮开始录制(图5)
开始录制后我们把这个小的对话框移至窗口右下角,然后用鼠标点击左下角任务栏的IE浏览器标志,来打开浏览器(如图6)。 随后在地址栏输入博客地址(如图7)
进入博客主页(如图8) 用鼠标拖动滚动条,浏览整个页面。 看完后关闭页面,然后点击录制对话框的蓝色方形停止键(如图9) 之后点击第三个按钮,来保存录制动作并进入脚本编译界面。现在我们可以看到在编译界面的中部,有“按键精灵录制的内容”这句话显示。这句话的下面有
“鼠标移动”、“延时”、“按键动作”等记录的录制过程中的各个动作。 现在就让我们来检验下刚才的一系列动作是否已经记录好。点击工具栏的“调试”按钮(如图11)。 进入调试对话框(如图12)。
按键精灵可以帮你操作电脑。不需要任何编程知识就可以作出功能强大的脚本。只要您在电脑前用双手可以完成的动作,按键精灵都可以替您完成。 按键精灵官方网站是:https://www.doczj.com/doc/254644565.html,/cn/qmacro 从编程的角度看,集合一些插件的脚本编辑、调试工具。代码不公开。生成的小精灵,模式化界面,界面中有广告,以此盈利。 使用环境 操作系统:Windows 98/98SE/Me/2000/XP/2003/Vista 软件支持:支持绝大多数软件,部分网络游戏中可能失效,但可尝试"神盾"功能,提高按键精灵的兼容性 按键精灵能帮我做什么? * 网络游戏中可作脚本实现自动打怪,自动补血,自动说话等 * 办公族可用它自动处理表格、文档,自动收发邮件等 * 任何你觉得“有点烦”的电脑操作都可以替你完成 上手指南 按键精灵是一个容易上手,但精通较难的软件。第一次接触它,自然会希望尽快熟悉它,让它为您工作。但如何上手呢?我来提供一些技巧 1、试:提供了免费试用,下载试用版安装后就可体验自带的例子。 2、学:上网看按键宝典,教程、实例统统都有,不懂还可上论坛提问。 3、用:边用边学,作出自己第一个脚本,你就入门啦~ 4、精:操作电脑的不便,都用脚本来解决,你的脚本也可以越来越聪明! 什么是脚本? 脚本就是一系列可以反复执行的命令.通过一些判断条件,可以让这些命令具有一定的智能效果. 如何制作脚本? 初学者可以通过”录制”功能制作简单的脚本,还可通过”脚本编辑器”制作更加智能的脚本. 如何使用脚本? 使用步骤如下: 1. 制作脚本:按照个人需求从网上搜集脚本或者自己制作脚本。如果您的脚本是从别的地方收集的,请先把脚本文件拷贝到按键精灵文件夹下的script文件夹中,然后再运行按键精灵。 2.选择有效的窗口:建议您选择“对所有窗口有效”。如果您只需要脚本当某个窗口在前台时有效,请选择窗口名称,比如“龙族” 3.让需要执行的脚本“有效”,只有“有效”一栏中勾中的脚本才会执行。 4.进入游戏(或者其他需要使用按键精灵的软件),在需要使用脚本的时候按下脚本的“快捷键”,按键精灵就会忠实的为您工作了。 5.希望脚本暂停的时候按下中止热键,即可暂停脚本. 什么是简单游? 简单游是一个拥有上千个按键精灵游戏脚本的软件平台,是按键精灵的网络游戏版.上网看简单游 什么是按键宝典? 按键宝典是兄弟工作组提供给用户的帮助大全,包含使用手册、经典脚本、各种动画教程等等。上网看按键宝典 什么是按键小精灵?