• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Age-related connectivity differences between attention deficit and hyperactivity disorder patients and typically developing subjects: a resting-state functional MRI study

    2017-11-08 11:48:58JisuJongBoyongParkJwanhoChoJyunjinPark

    Jisu Jong, Bo-yong Park, Jwan-ho Cho, Jyunjin Park,

    1 Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea

    2 Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science, Suwon, Korea

    3 School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea

    How to cite this article: Hong J, Park BY, Cho HH, Park H (2017) Age-related connectivity differences between attention deficit and hyperactivity disorder patients and typically developing subjects: a resting-state functional MRI study. Neural Regen Res 12(10):1640-1647.

    Funding: is work was supported by the Institute for Basic Science [grant No. IBS-R015-D1] and the National Research Foundation of Korea(grant No. NRF-2016R1A2B4008545).

    Age-related connectivity differences between attention deficit and hyperactivity disorder patients and typically developing subjects: a resting-state functional MRI study

    Jisu Jong1,2, Bo-yong Park1,2, Jwan-ho Cho1,2, Jyunjin Park2,3,*

    1 Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea

    2 Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science, Suwon, Korea

    3 School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea

    How to cite this article: Hong J, Park BY, Cho HH, Park H (2017) Age-related connectivity differences between attention deficit and hyperactivity disorder patients and typically developing subjects: a resting-state functional MRI study. Neural Regen Res 12(10):1640-1647.

    Attention deficit and hyperactivity disorder (ADHD) is a disorder characterized by behavioral symptoms including hyperactivity/impulsivity among children, adolescents, and adults.ese ADHD related symptoms are influenced by the complex interaction of brain networks which were under explored. We explored age-related brain network differences between ADHD patients and typically developing (TD) subjects using resting state fMRI (rs-fMRI) for three age groups of children, adolescents, and adults. We collected rs-fMRI data from 184 individuals (27 ADHD children and 31 TD children; 32 ADHD adolescents and 32 TD adolescents; and 31 ADHD adults and 31 TD adults).e Brainnetome Atlas was used to define nodes in the network analysis. We compared three age groups of ADHD and TD subjects to identify the distinct regions that could explain age-related brain network differences based on degree centrality, a well-known measure of nodal centrality.e lemiddle temporal gyrus showed significant interaction effects between disease status (i.e., ADHD or TD) and age (i.e., child, adolescent, or adult) (P < 0.001). Additional regions were identified at a relaxed threshold (P < 0.05). Many of the identified regions (the leinferior frontal gyrus,the lemiddle temporal gyrus, and the leinsular gyrus) were related to cognitive function.e results of our study suggest that aberrant development in cognitive brain regions might be associated with age-related brain network changes in ADHD patients.ese findings contribute to better understand how brain function influences the symptoms of ADHD.

    nerve regeneration; attention deficit and hyperactivity disorder; cognitive function; connectivity;resting-state fMRI; Brainnetome Atlas; whole brain analysis; disease-aging interaction effect; neuroscience;neural regeneration

    Introduction

    Attention deficit and hyperactivity disorder (ADHD) is a brain disorder that is characterized by the symptoms of inattention and hyperactivity/impulsivity (Schneider et al., 2006;Subcommittee on Attention-Deficit/Hyperactivity Disorder,2011; Castellanos and Proal, 2012). In addition to inattentive or hyperactive behaviors, ADHD is also known to be highly associated with cognitive dysfunction (Wilens et al., 1999;Segen, 2006; Rostain and Ramsay, 2006; Solanto et al., 2008;Knouse and Safren, 2010; Castellanos and Proal, 2012).Castellanos et al. (2006) suggested that ADHD-related studies should consider cognitive deficits in ADHD patients to better quantify their neurobehavioral symptoms. Previous studies have adopted cognitive behavioral treatment (CBT)approaches to treat ADHD patients (Wilens et al., 1999;Rostain and Ramsay, 2006; Solanto et al., 2008; Knouse and Safren, 2010). Solanto et al. (2008) found enhanced executive skills in ADHD patients who received CBT and others found a significant reduction in ADHD-related symptoms aer receiving combined medication and CBT (Rostain and Ramsay, 2006).ese studies suggested that ADHD is highly related to dysfunctions in cognitive processes.

    ADHD is a lifetime mental disorder and it has been found that patients show distinct behavioral symptoms across different age groups (Bresnahan and Barry, 2002; Schneider et al., 2006; Hurtig et al., 2007; Subcommittee on Attention-Deficit/Hyperactivity Disorder, 2011; Castellanos and Proal, 2012; Park et al., 2016).ese ADHD related symptoms are influenced by the complex interaction of brain networks which are typically explored using neuroimaging approaches (Zang et al., 2007; Tian et al., 2008; Cortese et al., 2012). Most ADHD studies have focused on exploring the differences in brain function in limited age groups (i.e.,only in children or adolescents) and studies investigating brain networks among a wide spectrum of age groups (i.e.,from children to adults) have been largely lacking (Wilens et al., 1999; Castellanos et al., 2006; Knouse and Safren, 2010;Konrad and Eickhoff, 2010; Uekermann et al., 2010). ADHD patients show age dependent alterations in brain networks which have not been fully explored. Here, we aimed to explore the age-related functional changes in brain networks in ADHD patients.

    We explored the age-related brain network differences between ADHD patients and typically developing (TD) subjects using resting state functional magnetic resonance imaging(rs-fMRI). Rs-fMRI is an effective tool for analyzing neurobehavioral disorders such as ADHD (dos Santos Siqueira et al., 2014). One study reported that rs-fMRI demonstrated enhanced brain activation in the sensory-related cortices of adolescent ADHD patients (Tian et al., 2008). Another study found that a feature derived from rs-fMRI known as amplitude of low-frequency revealed significant differences between children with ADHD and TD children (Zang et al., 2007).

    We assessed functional brain network differences using a network centrality measure which has been widely used to assess regional importance (Bullmore et al., 2009; Rubinov and Sporns, 2010; Ferreira and Busatto, 2013). We hypothesized that there would be age-related functional network differences between ADHD patients and TD subjects. In this study, we aimed to explore functional brain network changes related to ADHD among a wide spectrum of age groups.

    Subjects and Methods

    Subjects and imaging data

    The Institutional Review Board (IRB) of Sungkyunkwan University approved our retrospective study (#2015-09-007).Our study was performed in full accordance with the principles of the Declaration of Helsinki, and informed consent was obtained from all subjects. We collected raw T1-weighted structural MRI and rs-fMRI data from the ADHD-200 database (ADHD-200 Consortium, 2012; Bellec et al., 2017).We also obtained structural and functional MRI data from the Human Connectome Project (HCP) database (Van Essen et al., 2013). The ADHD-200 database provided the child and adolescent data and the HCP database provided the adult data.e subjects were recruited via advertisement and further details were available (ADHD-200 Consortium,2012; Van Essen et al., 2013). Scores related to ADHD symptoms were measured using Conner’s Parent Rating Scale Revised, Long Version (CPRS-LV) for the ADHD-200 dataset and Diagnostic and Statistical Manual of Mental Disorders,Fourth Edition (DSM-IV) for the HCP data (American Psychiatric Association, 1994; Conners et al., 1998). With both the ADHD-200 and HCP datasets, subjects with T-scores greater than or equal to 65 on at least one measure of the ADHD-related index were selected as ADHD patients. Subjects with a secondary diagnosis were excluded along with subjects who did not have ADHD-related scores. Based on these criteria, we classified subjects into the ADHD (n = 90)and TD groups (n = 94). Each group was further divided into child, adolescent, and adult groups based on age. Subjects under 10 years of age were considered children, and subjects between 10 and 19 years of age were classified as adolescents. Finally, 27 ADHD children, 32 ADHD adolescents, 31 ADHD adults, 31 TD children, 32 TD adolescents,and 31 TD adults were included in the study. Comparison of the sex ratio did not yield significant differences among the groups. Detailed participant information is given inTable 1.

    Although the ADHD-200 database consists of eight data collection sites, data from only two sites were retained aer adopting the criteria mentioned above: the Kennedy Krieger Institute (KKI) and New York University Child Study Center (NYU). The T1-weighted structural data from the KKI were acquired with the following imaging parameters:repetition time (TR) = 8.0 ms; echo time (TE) = 3.7 ms; field of view (FOV) = 256 × 256 mm2; and voxel resolution = 1.0× 1.0 × 1.0 mm3.e rs-fMRI functional data from the KKI were acquired with the following imaging parameters: TR= 2,500 ms; TE = 30 ms; FOV = 256 × 256 mm2; number of slices = 72; and voxel resolution = 2.67 × 2.67 × 3.0 mm3.The T1-weighted structural data from NYU were acquired with the following imaging parameters: TR = 2,530 ms; TE= 3.25 ms; FOV = 256 × 256 mm2; and voxel resolution =1.3 × 1.0 × 1.3 mm3.e rs-fMRI functional data from NYU were acquired with the following imaging parameters: TR= 2,000 ms; TE = 15 ms; FOV = 240 × 192 mm2; number of slices = 33; and voxel resolution = 3.0 × 3.0 × 4.0 mm3.e T1-weighted structural data from the HCP were acquired with the following imaging parameters: TR = 2,400 ms; TE =2.14 ms; FOV = 224 × 224 mm2; and voxel resolution = 0.7 ×0.7 × 0.7 mm3. Finally, the rs-fMRI functional data from the HCP were acquired with the following imaging parameters:TR = 720 ms; TE = 33.1 ms; FOV = 208 × 180 mm2; number of slices = 72; and voxel resolution = 2.0 × 2.0 × 2.0 mm3.The TD-child group included 16 subjects from the KKI site and 15 subjects from the NYU site.e TD-adolescent group included 16 subjects from the KKI site and 16 subjects from the NYU site. The TD-adult group included 31 subjects from the HCP site. The ADHD-child group included 6 subjects from the KKI site and 21 subjects from the NYU site.e ADHD-adolescent group included 5 subjects from the KKI site and 27 subjects from the NYU site.e ADHD-adult group included 31 subjects from the HCP site.

    Imaging preprocessing

    Table 1 Demographic data of children, adolescents, and adults in the ADHD and TD groups

    Table 2 Identified regions with significant interaction effects of age-by-status

    Network construction

    To construct the functional network from the images, connectivity analysis was performed with regions of interest(ROIs) specified by the Brainnetome Atlas.e Brainnetome Atlas is a structural atlas that consists of 246 regions (Fan et al., 2016). Connectivity information was assessed with a graph structure using nodes and edges.e nodes were 246 ROIs derived from the Brainnetome Atlas. Pearson correlation values of the time series between two nodes were used as edges.e edge values were filled into the matrix as elements and the matrix was referred to as the correlation matrix. We adopted the weighted and un-directional network model. Sothresholding was used to prevent binarizing of the correlation matrix using the following equation (1).

    Connectivity measures

    We used degree centrality (DC) to assess the regional connectivity of brain networks (Lohmann et al., 2010; Fransson et al., 2011). The DC value for a node i is defined as the number of links connected directly to the node (Rubinov and Sporns, 2010). We used MATLAB (version 2016; Mathworks Inc., Natic, MA, USA) to compute the DC values (e Mathworks Inc., 2016).

    Multi-site effect

    Since our neuroimaging data was acquired from different sites, we adopted a dummy coding regression model to remove multi-site effects from the DC values using the following equation (2).

    Statistical analysis

    We used MATLAB for statistical analysis (version 2016;Mathworks Inc.).e two-way analysis of variance (ANOVA) was used to explore differences in age-related DC patterns between the ADHD and TD groups (Fujikoshi, 1993).e DC values were set as the dependent variables, and disease status (ADHD or TD) and age group (child, adolescent,or adult) were set as the independent variables.e significance of the interaction effects of disease and age group was quantified using P values (P < 0.001). We adopted an uncorrected P value of 0.001 due to the exploratory nature of our study. We applied a stringent P value threshold of 0.001 compared to the conventional 0.05 since our study was an exploratory study investigating 246 regions covering the whole brain. We also reported results using a relaxed P value of 0.05. Chi-square tests were applied to assess differences in sex among comparison groups.

    Results

    Motion scrubbing

    We calculated the FD for each volume from the rs-fMRI data. Two children from the TD group had part of the frames scrubbed.Figure 1shows the FD of these two subjects. We removed 13 frames from one child in the TD group and 5 frames from the other child in the TD group.

    Connectivity differences

    We performed a two-way ANOVA to determine the brain regions that showed significant interaction effects of disease status and age. The left superior frontal gyrus, left inferior frontal gyrus, right inferior frontal gyrus, right precentral gyrus, left superior temporal gyrus, left middle temporal gyrus, right postcentral gyrus, leinsular gyrus, lemedioventral occipital cortex, right medioventral occipital cortex,leamygdala, and lebasal ganglia showed significant interaction effects (P < 0.05) (Table 2).Figure 2ashows the locations and their P values of the identified regions. Among the identified regions, the lemiddle temporal gyrus showed the most significant interaction effects (P < 0.001;Figure 2b). Further post-hoc tests were not conducted because there were no significant main effects of disease status and age.

    Age-related patterns

    We show regions that have significant age-by-status interaction for each age group inFigure 3.

    Discussion

    The main purpose of this study was to determine if there were age-related network differences between ADHD patients and TD subjects. We divided the subjects into six groups based on disease status (i.e., ADHD or TD) and age(i.e., children, adolescents, and adults) to form comparison groups. From the two-way ANOVA results, we found significant interaction effects of disease status and age. Since this was an exploratory study investigating hundreds of brain regions, we relaxed the constraint of the P value and found significant interaction effects of disease status and age based on functional connectivity.

    Among the identified regions, the leinferior frontal gyrus, the lemiddle temporal gyrus, which showed the most significant interaction effects, and the leinsular gyrus were known to be related to cognitive function (Vandenberghe et al., 1996; Goel and Dolan, 2001; Swick et al., 2008; Fan et al.,2016). Swick et al. (2008) reported that subjects with damage in the left inferior frontal gyrus and left insula region had higher error rates than controls in a response inhibition task.e response inhibition task is known as a major task that can discriminate between ADHD and TD subjects(Nigg, 1999; Epstein et al., 2001; Tamm et al., 2004). Tamm et al. (2004) also found significant differences in brain activation in the middle temporal gyrus between subjects with ADHD and TD subjects in a behavioral response inhibition task.Figure 4Bshows the locations of these regions.ere is a noticeable overlap between the region previously reported in the literature and the region we found as shown inFigure 4A.e cognitive system plays an important role in typical development from childhood through adolescence to adulthood (Blakemore and Choudhury, 2006).us, aberrant development in the cognitive brain regions that we identified between ADHD and TD groups implies that impairment in cognitive function might be associated with age-related brain network changes in ADHD patients.

    Figure 1 e plot of the FD values for two children from TD group whose frames were partly censored.

    In this study, we used multi-center neuroimaging data to obtain a sufficient number of samples. Although the differences in imaging parameters were relatively small, this could lead to different amounts of noise and distortions in the data, making a fair comparison difficult.e high-resolution data from the HCP were resampled and pre-processed to low-resolution ADHD-200 data so that data can be fairly compared. We applied the common image processing steps performed on the low quality (i.e., low resolution ADHD-200) spatial reference space so that high quality (i.e., high resolution HCP) data was effectively rendered to low quality data. Such approaches have been successfully applied in other studies (Fennema-Notestine et al., 2007; Di Martino et al., 2014; Bellec et al., 2017). We visually confirmed similar image qualities by computing the average of T1-weighted structural data for each subgroup as shown inFigure 5and they all appeared similar in the low resolution common space. Furthermore, we used the correlation of rs-fMRI time series between two different brain regions as the main feature in this study. Each region contained over hundreds of voxels, hence the regional average time series might reduce the potential differences in image quality. Finally, we also performed a multi-site regression using the dummy-coding to remove the potential multi-site effects from the centrality measurement.e dummy-coding regression approach has been applied in other studies comparing data from different sites (Hardy, 1993; Sanfilipo et al., 2004).

    Figure 2 Brain regions that showed a significant age-by-status interaction by the two-way analysis of variance (ANOVA) test using degree centrality (DC).

    We used Brainnetome atlas to specify the ROIs for child,adolescent, and adult groups. The Brainnetome atlas was derived from adults and thus application to adults is natural. We investigated if a single atlas could specify the ROIs for various age groups. We registered T1 anatomical images onto a common space for each age group and then compared averaged images with one another. The average images for each age group appeared quite similar and those for each group were compared with overlaid ROIs from the atlas and they seemed reasonable as well.

    Figure 3 Age-related degree centrality patterns of the identified regions.

    Our study adopted an uncorrected P value of 0.001 for statistical significance. We had limited samples but explored hundreds of regions and thus we chose to adopt an uncorrected P value. Use of an uncorrected P value is rather common in many exploratory studies involving the whole brain(Konishi et al., 1999; Anand et al., 2005).

    Our study has some limitations. First, rs-fMRI was the only modality used. Using multi-modal imaging data might provide complementary information that could better describe the differences between the ADHD and TD groups.Second, the sample size might be insufficient due to the limitation of available cases from the online databases. A future study performed on a larger cohort is necessary to confirm our findings with higher statistical power. Third, we could not compute the correlation between DC and ADHD scores,because we used two types of ADHD related scores coming from two research databases. Finally, we did not consider longitudinal data for our study, as we are not aware of any openly accessible research database housing longitudinal ADHD neuroimaging data. Thus, we performed our study in a cross-sectional fashion with comparison of different age groups within the ADHD and TD groups. Ideally, a future study should consider longitudinal cases so that age-related brain network differences in ADHD could be better assessed.

    Figure 4 Comparison between identified regions and known regions related to cognitive functions.

    Figure 5 e average T1 structural images of each subgroup aer we applied the common anatomical preprocessing steps.

    In summary, our study suggested a possible statistical link between ADHD disease status and the brain network for three age groups. The main finding of our study was that connectivity differences in cognitive system could be biomarkers for distinguishing ADHD and TD subjects. It has been shown that behavioral patterns and psychopathology of ADHD patients for different developmental stages are strongly related to impairments of the cognitive system(Blakemore and Choudhury, 2006; Singh et al., 2015; Huang et al., 2016). Huang et al. (2016) found that the impairment of inhibition function which is involved in the cognitive system was significantly different between child ADHD and TD groups, and the difference decreased when subjects develop from children to adolescents.is study suggested that the developmental differences in cognitive functions should be considered to better understand the psychiatric symptoms of ADHD patients. Our results found that the cognitive dysfunction might be associated with age-related brain network changes in ADHD patients, and hence, thus our results might provide complementary information for understanding developmental ADHD psychopathology.ADHD is one of the many brain disorders affected by neuroplasticity and thus our study might be loosely related to neuroplasticity and neural regeneration research (Gevensleben et al., 2014; Cowley et al., 2016; Liu et al., 2017; Van Doren et al., 2017)

    Acknowledgments:Data were provided by the Neuro Bureau, the ADHD 200 consortium, and Virginia Tech’s ARC. Data were also provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and KamilUgurbil;1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research and by the McDonnel Center for Systems Neuroscience at Washington University.

    Author contributions:JH and HP designed the study and collected, analyzed, and interpreted the data. BYP reviewed the paper. HHC contributed to the discussion and edited the paper. HP is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors approved the final version of this paper.

    Conflicts of interests: None declared.

    Research ethics:e Institutional Review Board (IRB) of Sungkyunkwan University approved our study (#2015-09-007). Our study was performed in full accordance with local IRB guidelines and the principles of the Declaration of Helsinki, and informed consent was obtained from all subjects.

    Declaration of participant consent:e authors certify that they have obtained all appropriate consent forms of participant or their guardians. In the form, participants or guardians have given their consent for participants’ images and other clinical information to be reported in the journal. Participants or guardians understand that participants’ names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

    Data sharing statement:Datasets analyzed during the current study are available from the corresponding author on reasonable request.

    Plagiarism check: Checked twice by ienticate.

    Peer review: Externally peer reviewed.

    Open access statement:is is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under identical terms.

    Open peer reviewer: Hao Chen, Shanghai 6th Peoples Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, China.

    Additional file: Open peer review report 1.

    ADHD-200 Consortium (2012)e ADHD-200 Consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci 6:62.

    American PsychiatricAssociation (1994) Diagnostic and Statistical Manual of Mental Disorders (DSM IV), 4thed.

    Anand A, Li Y, Wang Y, Wu J, Gao S, Bukhari L, Mathews VP, Kalnin A,Lowe MJ (2005) Activity and connectivity of brain mood regulating circuit in depression: A functional magnetic resonance study. Biol Psychiatry 57:1079-1088.

    Bellec P, Chu C, Chouinard-Decorte F, Benhajali Y, Margulies DS,Craddock RC (2017) The neuro bureau ADHD-200 preprocessed repository. Neuroimage 144(Pt B):275-286.

    Blakemore SJ, Choudhury S (2006) Development of the adolescent brain: implications for executive function and social cognition. J Child Psychol Psychiatry 47:296-312.

    Bresnahan SM, Barry RJ (2002) Specificity of quantitative EEG analysis in adults with attention deficit hyperactivity disorder. Psychiatry Res 112:133-144.

    Bullmore E, Bullmore E, Sporns O, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186-198.

    Castellanos FX, Proal E (2012) Large-scale brain systems in ADHD:beyond the prefrontal-striatal model. Trends Cogn Sci 16:17-26.

    Castellanos FX, Sonuga-Barke EJ, Milham MP, Tannock R (2006)Characterizing cognition in ADHD: beyond executive dysfunction.Trends Cogn Sci 10:117-123.

    Conners CK, Sitarenios G, Parker JD, Epstein JN (1998)e revised Conners’ Parent Rating Scale (CPRS-R): factor structure, reliability,and criterion validity. J Abnorm Child Psychol 26:257-268.

    Cortese S, Kelly C, Chabernaud C, Proal E, Di Martino A, Milham MP,Castellanos FX (2012) Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies. Am J Psychiatry 169:1038-1055.

    Cowley B, Holmstr?m é, Juurmaa K, Kovarskis L, Krause CM (2016)Computer enabled neuroplasticity treatment: a clinical trial of a novel design for neurofeedback therapy in adult ADHD. Front Hum Neurosci 10:205.

    Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162-173.

    Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, Anderson JS, Assaf M, Bookheimer SY, Dapretto M, Deen B, Delmonte S, Dinstein I, Ertl-Wagner B, Fair DA, Gallagher L, Kennedy DP,Keown CL, Keysers C, Lainhart JE, et al. (2014)e autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 19:659-667.

    dos Santos Siqueira A, Biazoli Junior CE, Comfort WE, Rohde LA, Sato JR (2014) Abnormal functional resting-state networks in ADHD:graph theory and pattern recognition analysis of fMRI data. Biomed Res Int 2014:380531.

    Epstein JN, Johnson DE, Varia IM, Conners CK (2001) Neuropsychological assessment of response inhibition in adults with ADHD. J Clin Exp Neuropsychol 23:362-371.

    Fan L, Li H, Zhuo J, Zhang Y, Wang J, Chen L, Yang Z, Chu C, Xie S, Laird AR, Fox PT, Eickhoff SB, Yu C, Jiang T (2016)e human brainnetome atlas: a new brain atlas based on Connectional Architecture. Cereb Cortex 26:3508-3526.

    Fennema-Notestine C, Gamst AC, Quinn BT, Pacheco J, Jernigan TL,al L, Buckner R, Killiany R, Blacker D, Dale AM, Fischl B, Dickerson B, Gollub RL (2007) Feasibility of multi-site clinical structural neuroimaging studies of aging using legacy data. Neuroinformatics 5:235-245.

    Ferreira LK, Busatto GF (2013) Resting-state functional connectivity in normal brain aging. Neurosci Biobehav Rev 37:384-400.

    Fransson P, Aden U, Blennow M, Lagercrantz H (2011)e functional architecture of the infant brain as revealed by resting-state fMRI.Cereb Cortex 21:145-154.

    Fujikoshi Y (1993) Two-way ANOVA models with unbalanced data.Discrete Math 116:315-334.

    Gevensleben H, Kleemeyer M, Rothenberger LG, Studer P, Flaig-R?hr A, Moll GH, Rothenberger A, Heinrich H (2014) Neurofeedback in ADHD: Further pieces of the puzzle. Brain Topogr 27:20-32.

    Goel V, Dolan RJ (2001)e functional anatomy of humor: segregating cognitive and affective components. Nat Neurosci 4:237-238.

    HardyMA (1993) Regression with dummy variables. Sage Univ Pap Ser Quant Appl Soc Sci DOI: http://dx.doi.org/10.4135/9781412985628

    Huang F, Sun L, Qian Y, Liu L, Ma QG, Yang L, Cheng J, Cao QJ, Su Y,Gao Q, Wu ZM, Li HM, Qian QJ, Wang YF (2016) Cognitive function of children and adolescents with attention deficit hyperactivity disorder and learning difficulties: a developmental perspective. Chin Med J (Engl) 129:1922-1928.

    Hurtig T, Ebeling H, Taanila A, Miettunen J, Smalley SL, McGough JJ, Loo SK, J?rvelin MR, Moilanen IK (2007) ADHD symptoms and subtypes: relationship between childhood and adolescent symptoms.J Am Acad Child Adolesc Psychiatry 46:1605-1613.

    Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM(2012) FSL. Neuroimage 62:782-790.

    Knouse LE, Safren SA (2010) Current status of cognitive behavioral therapy for adult attention-deficit hyperactivity disorder. Psychiatr Clin North Am 33:497-509.

    Konishi S, Nakajima K, Uchida I, Kikyo H, Kameyama M, Miyashita Y(1999) Common inhibitory mechanism in human inferior prefrontal cortex revealed by event-related functional MRI. Brain 122 (Pt 5):981-991.

    Konrad K, Eickhoff SB (2010) Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder. Hum Brain Mapp 31:904-916.

    Liu ZX, Lishak V, Tannock R, Woltering S (2017) Effects of working memory training on neural correlates of Go/Nogo response control in adults with ADHD: A randomized controlled trial. Neuropsychologia 95:54-72.

    Lohmann G, Margulies DS, Horstmann A, Pleger B, Lepsien J, Goldhahn D, Schloegl H, Stumvoll M, Villringer A, Turner R (2010) Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain. PLoS One5:e10232.

    Mumford JA, Horvath S, Oldham MC, Langfelder P, Geschwind DH,Poldrack RA (2010) Detecting network modules in fMRI time series:a weighted network analysis approach. Neuroimage 52:1465-1476.

    Nigg JT (1999)e ADHD response-inhibition deficit as measured by the stop task: replication with DSM-IV combined type, extension,and qualification. J Abnorm Child Psychol 27:393-402.

    Park BY, Hong J, Lee SH, Park H (2016) Functional connectivity of child and adolescent attention deficit hyperactivity disorder patients:Correlation with IQ. Front Hum Neurosci 10:565.

    Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2012)Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59:2142-2154.

    Rostain AL, Ramsay JR (2006) A combined treatment approach for adults with ADHD--results of an open study of 43 patients. J Atten Disord 10:150-159.

    Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: Uses and interpretations. Neuroimage 52:1059-1069.

    Sanfilipo MP, Benedict RH, Zivadinov R, Bakshi R (2004) Correction for intracranial volume in analysis of whole brain atrophy in multiple sclerosis: the proportion vs. residual method. Neuroimage 22:1732-1743.

    Schneider M, Retz W, Coogan A,ome J, R?sler M (2006) Anatomical and functional brain imaging in adult attention-deficit/hyperactivity disorder (ADHD)--a neurological view. Eur Arch Psychiatry Clin Neurosci 256 Suppl 1:i32-41.

    Schwarz AJ, McGonigle J (2011) Negative edges and sothresholding in complex network analysis of resting state functional connectivity data. Neuroimage 55:1132-1146.

    Segen JC (2006) Concise Dictionary of Modern Medicine. Mc-Graw-Hill. Available at: https://books.google.co.kr/books?id=vVN-qAAAAMAAJ.

    Singh A, Yeh CJ, Verma N, Das AK (2015) Overview of attention deficit hyperactivity disorder in young children. Health Psychol Res 3:2115.

    Solanto MV, Marks DJ, Mitchell KJ, Wasserstein J, Kofman MD (2008)Development of a new psychosocial treatment for adult ADHD. J Atten Disord 11:728-736.

    Subcommittee on Attention-Deficit/Hyperactivity Disorder; Steering Committee on Quality Improvement and Management, Wolraich M,Brown L, Brown RT, DuPaul G, Earls M, Feldman HM, Ganiats TG,Kaplanek B, Meyer B, Perrin J, Pierce K, Reiff M, Stein MT, Visser S (2011) ADHD: clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics 128:1007-1022.

    Swick D, Ashley V, Turken AU (2008) Left inferior frontal gyrus is critical for response inhibition. BMC Neurosci 9:102.

    Tamm L, Menon V, Ringel J, Reiss AL (2004) Event-related FMRI evidence of frontotemporal involvement in aberrant response inhibition and task switching in attention-deficit/hyperactivity disorder.J Am Acad Child Adolesc Psychiatry 43:1430-1440.

    The Mathworks Inc. (2016) MATLAB - MathWorks [WWW Document]. www.mathworks.com/products/matlab. doi:2016-11-26

    Tian L, Jiang T, Liang M, Zang Y, He Y, Sui M, Wang Y (2008) Enhanced resting-state brain activities in ADHD patients: a fMRI study. Brain Dev 30:342-348.

    Uekermann J, Kraemer M, Abdel-Hamid M, Schimmelmann BG,Hebebrand J, Daum I, Wiltfang J, Kis B (2010) Social cognition in attention-deficit hyperactivity disorder (ADHD). Neurosci Biobehav Rev 34:734-743.

    Van Doren J, Heinrich H, Bezold M, Reuter N, Kratz O, Horndasch S,Berking M, Ros T, Gevensleben H, Moll GH, Studer P (2017)eta/beta neurofeedback in children with ADHD: Feasibility of a shortterm setting and plasticity effects. Int J Psychophysiol 112:80-88.

    Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K; WU-Minn HCP Consortium (2013)e WU-Minn Human Connectome Project: an overview. Neuroimage 80:62-79.

    Vandenberghe R, Price C, Wise R, Josephs O, Frackowiak RS (1996)Functional anatomy of a common semantic system for words and pictures. Nature 383:254-256.

    Wilens, T.E., McDermott, S.P., Biederman, J., Abrantes, A., Hahesy, A.,Spencer, T.J., 1999. Cognitive therapy in the treatment of adults with ADHD: A systematic chart review of 26 cases. J Cogn Psychother 13:215-226.

    Zang YF, He Y, Zhu CZ, Cao QJ, Sui MQ, Liang M, Tian LX, Jiang TZ, Wang YF (2007) Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev 29:83-91.

    *Correspondence to:

    Hyunjin Park, Ph.D.,hyunjinp@skku.edu.

    orcid:

    0000-0001-5681-8918

    (Hyunjin Park)

    10.4103/1673-5374.217339

    Accepted: 2017-09-19

    Copyedited by Li CH, Song LP, Zhao M

    欧美激情国产日韩精品一区| 日本黄色片子视频| 人妻 亚洲 视频| 欧美三级亚洲精品| 在线观看三级黄色| 简卡轻食公司| 免费少妇av软件| 大陆偷拍与自拍| 91aial.com中文字幕在线观看| 精品一区在线观看国产| 亚洲欧美一区二区三区国产| 蜜桃在线观看..| 22中文网久久字幕| 男女国产视频网站| 亚洲精品成人av观看孕妇| 大又大粗又爽又黄少妇毛片口| 你懂的网址亚洲精品在线观看| 国产精品秋霞免费鲁丝片| 久久精品久久精品一区二区三区| 欧美成人午夜免费资源| 九草在线视频观看| 菩萨蛮人人尽说江南好唐韦庄| 国产免费视频播放在线视频| 中文字幕亚洲精品专区| 18禁观看日本| 色94色欧美一区二区| 久久久久精品久久久久真实原创| 黄色毛片三级朝国网站| 精品人妻一区二区三区麻豆| 全区人妻精品视频| 高清av免费在线| 亚洲四区av| 亚洲国产毛片av蜜桃av| 男女免费视频国产| 欧美少妇被猛烈插入视频| 美女国产高潮福利片在线看| 亚洲精品一二三| 一级,二级,三级黄色视频| 精品久久久久久久久av| 欧美xxxx性猛交bbbb| h视频一区二区三区| 国产成人精品无人区| 亚洲av福利一区| 99久国产av精品国产电影| 久久久久久久亚洲中文字幕| 午夜福利网站1000一区二区三区| 一级毛片电影观看| 久久精品国产亚洲网站| 久久久久国产网址| 七月丁香在线播放| 亚洲国产最新在线播放| 日韩视频在线欧美| 亚洲精品日韩在线中文字幕| 高清av免费在线| 曰老女人黄片| 人妻 亚洲 视频| 精品人妻偷拍中文字幕| 亚洲精品国产色婷婷电影| 久久久久国产精品人妻一区二区| 欧美老熟妇乱子伦牲交| 久久精品国产自在天天线| 黄色毛片三级朝国网站| 欧美成人午夜免费资源| 欧美最新免费一区二区三区| 亚洲av福利一区| 美女国产高潮福利片在线看| 人人妻人人爽人人添夜夜欢视频| 亚洲欧洲国产日韩| 老司机影院成人| 男人添女人高潮全过程视频| 日本与韩国留学比较| 国产成人av激情在线播放 | 久久久久国产网址| 色吧在线观看| 欧美精品亚洲一区二区| 精品人妻熟女毛片av久久网站| 欧美日韩在线观看h| 午夜免费鲁丝| 国产乱人偷精品视频| 熟女电影av网| 伦理电影免费视频| 国产精品蜜桃在线观看| 99国产综合亚洲精品| 亚州av有码| 曰老女人黄片| 午夜福利,免费看| 国产av又大| 丁香六月欧美| 97在线人人人人妻| 午夜福利在线观看吧| 久久午夜综合久久蜜桃| 嫩草影视91久久| 99国产综合亚洲精品| 亚洲第一欧美日韩一区二区三区 | www.999成人在线观看| 别揉我奶头~嗯~啊~动态视频| 少妇猛男粗大的猛烈进出视频| 黑人巨大精品欧美一区二区蜜桃| 极品教师在线免费播放| 国产精品免费大片| 亚洲精品中文字幕在线视频| 日韩欧美免费精品| 正在播放国产对白刺激| 国产成人欧美| 国产伦人伦偷精品视频| 精品第一国产精品| 新久久久久国产一级毛片| 男女之事视频高清在线观看| 欧美精品av麻豆av| 欧美 日韩 精品 国产| av有码第一页| 亚洲第一欧美日韩一区二区三区 | 亚洲人成电影免费在线| 女人爽到高潮嗷嗷叫在线视频| 国产又爽黄色视频| 一区二区三区精品91| 脱女人内裤的视频| 在线观看一区二区三区激情| 亚洲精品自拍成人| 一级毛片精品| 国产精品香港三级国产av潘金莲| 欧美日韩成人在线一区二区| 丝袜美足系列| 天天影视国产精品| 日韩视频一区二区在线观看| 女同久久另类99精品国产91| 国产精品.久久久| 国产一区二区在线观看av| 国产在视频线精品| 亚洲视频免费观看视频| aaaaa片日本免费| 女性被躁到高潮视频| 考比视频在线观看| 黄色片一级片一级黄色片| 夜夜骑夜夜射夜夜干| 久久精品aⅴ一区二区三区四区| 欧美日韩成人在线一区二区| 一区二区三区乱码不卡18| 一区福利在线观看| 欧美日韩福利视频一区二区| 亚洲专区字幕在线| 国产欧美日韩一区二区三| 欧美人与性动交α欧美精品济南到| 热re99久久国产66热| 天天影视国产精品| 别揉我奶头~嗯~啊~动态视频| 一区二区三区乱码不卡18| 亚洲精品久久成人aⅴ小说| 成人手机av| 精品卡一卡二卡四卡免费| 国产精品久久久av美女十八| 视频在线观看一区二区三区| 午夜成年电影在线免费观看| 精品国产国语对白av| 国产成人欧美在线观看 | 亚洲人成电影免费在线| 丰满迷人的少妇在线观看| 亚洲一区中文字幕在线| 成人国产一区最新在线观看| 亚洲视频免费观看视频| 国产黄色免费在线视频| 精品一区二区三区av网在线观看 | 人人妻人人澡人人爽人人夜夜| 亚洲天堂av无毛| 国产片内射在线| av电影中文网址| 不卡一级毛片| 宅男免费午夜| 久久精品91无色码中文字幕| 国产高清国产精品国产三级| 亚洲欧美激情在线| 考比视频在线观看| av网站免费在线观看视频| 精品国产一区二区久久| 国产日韩一区二区三区精品不卡| 啦啦啦免费观看视频1| 黄色视频不卡| 久久精品成人免费网站| 亚洲第一欧美日韩一区二区三区 | 不卡一级毛片| 少妇精品久久久久久久| av有码第一页| 久9热在线精品视频| 人人澡人人妻人| 精品国产超薄肉色丝袜足j| 人妻一区二区av| 黄片大片在线免费观看| av又黄又爽大尺度在线免费看| 无限看片的www在线观看| tube8黄色片| 精品国产一区二区三区久久久樱花| 在线av久久热| 成人精品一区二区免费| 亚洲 国产 在线| 精品国产乱码久久久久久男人| 婷婷丁香在线五月| 精品福利观看| av欧美777| 亚洲黑人精品在线| 欧美日韩亚洲国产一区二区在线观看 | 欧美黑人精品巨大| 亚洲黑人精品在线| 欧美精品av麻豆av| 老汉色∧v一级毛片| 日韩欧美一区二区三区在线观看 | 国产三级黄色录像| 亚洲av国产av综合av卡| 首页视频小说图片口味搜索| 亚洲色图综合在线观看| 日韩中文字幕欧美一区二区| 国产一区有黄有色的免费视频| 欧美激情高清一区二区三区| 国产成人免费观看mmmm| 丰满迷人的少妇在线观看| 激情视频va一区二区三区| 国产不卡av网站在线观看| 这个男人来自地球电影免费观看| 两个人看的免费小视频| 亚洲成人免费电影在线观看| 99re在线观看精品视频| 亚洲精品中文字幕一二三四区 | 欧美日韩av久久| 天天躁夜夜躁狠狠躁躁| 天堂动漫精品| 成年人黄色毛片网站| 国产精品亚洲av一区麻豆| 国产在线精品亚洲第一网站| 亚洲成人手机| 黄色成人免费大全| 亚洲国产毛片av蜜桃av| 91av网站免费观看| 亚洲国产欧美在线一区| 中文字幕精品免费在线观看视频| 91字幕亚洲| 午夜福利,免费看| 亚洲人成77777在线视频| 99精品久久久久人妻精品| 人人妻人人添人人爽欧美一区卜| 欧美日韩精品网址| 无遮挡黄片免费观看| 亚洲精品粉嫩美女一区| 黄色毛片三级朝国网站| 下体分泌物呈黄色| 日韩欧美三级三区| 亚洲国产欧美一区二区综合| 久久ye,这里只有精品| 亚洲精品成人av观看孕妇| 丁香六月天网| 欧美激情 高清一区二区三区| 久久人妻熟女aⅴ| 亚洲天堂av无毛| 首页视频小说图片口味搜索| 国产精品一区二区免费欧美| 黄色视频在线播放观看不卡| 女人高潮潮喷娇喘18禁视频| 国产精品.久久久| 久久香蕉激情| 色综合欧美亚洲国产小说| 黑人欧美特级aaaaaa片| 国产成+人综合+亚洲专区| 在线亚洲精品国产二区图片欧美| 欧美黑人欧美精品刺激| 这个男人来自地球电影免费观看| 首页视频小说图片口味搜索| 免费一级毛片在线播放高清视频 | 在线十欧美十亚洲十日本专区| 老熟妇仑乱视频hdxx| 男女床上黄色一级片免费看| 色精品久久人妻99蜜桃| av又黄又爽大尺度在线免费看| 亚洲 欧美一区二区三区| 欧美日韩av久久| 精品亚洲成国产av| 欧美日韩中文字幕国产精品一区二区三区 | 久久精品亚洲精品国产色婷小说| 日韩中文字幕欧美一区二区| 欧美精品高潮呻吟av久久| 亚洲久久久国产精品| 欧美精品一区二区大全| 日本vs欧美在线观看视频| 久久国产精品人妻蜜桃| 超碰成人久久| 国产欧美日韩一区二区三区在线| 久久久欧美国产精品| 精品国产乱码久久久久久男人| 欧美日韩一级在线毛片| av天堂久久9| 九色亚洲精品在线播放| 一个人免费看片子| 欧美日韩中文字幕国产精品一区二区三区 | 啦啦啦中文免费视频观看日本| 亚洲五月色婷婷综合| videos熟女内射| 90打野战视频偷拍视频| 视频在线观看一区二区三区| 女人精品久久久久毛片| 人人妻人人添人人爽欧美一区卜| 成年人午夜在线观看视频| 午夜免费成人在线视频| 亚洲成av片中文字幕在线观看| 夜夜爽天天搞| www.精华液| 精品久久久精品久久久| 久久婷婷成人综合色麻豆| 考比视频在线观看| 亚洲欧美一区二区三区黑人| 久久香蕉激情| 精品国产乱子伦一区二区三区| 建设人人有责人人尽责人人享有的| 18禁国产床啪视频网站| 亚洲一码二码三码区别大吗| 国产亚洲午夜精品一区二区久久| 成年人午夜在线观看视频| 精品一区二区三卡| 夜夜爽天天搞| 亚洲精品国产色婷婷电影| 精品少妇一区二区三区视频日本电影| 国产免费av片在线观看野外av| 亚洲av欧美aⅴ国产| 亚洲伊人久久精品综合| 亚洲国产欧美网| 欧美在线黄色| 国产成人精品久久二区二区91| 久久久久视频综合| 中文字幕精品免费在线观看视频| 国产日韩欧美在线精品| 亚洲国产欧美一区二区综合| 我的亚洲天堂| 97在线人人人人妻| 欧美日韩成人在线一区二区| 咕卡用的链子| 久久毛片免费看一区二区三区| 三级毛片av免费| 欧美国产精品va在线观看不卡| 亚洲成人国产一区在线观看| 午夜福利在线观看吧| www.精华液| 亚洲五月色婷婷综合| 亚洲视频免费观看视频| 大香蕉久久成人网| 人人澡人人妻人| 18禁观看日本| 51午夜福利影视在线观看| 91麻豆av在线| 五月天丁香电影| 久久毛片免费看一区二区三区| 欧美日韩视频精品一区| 久久 成人 亚洲| 在线观看人妻少妇| 日韩视频一区二区在线观看| 国产成人av教育| 啦啦啦视频在线资源免费观看| 老汉色∧v一级毛片| 国产一区二区激情短视频| 制服诱惑二区| 青青草视频在线视频观看| 久久人人97超碰香蕉20202| 大陆偷拍与自拍| 高清av免费在线| 亚洲色图av天堂| 夫妻午夜视频| 免费在线观看视频国产中文字幕亚洲| 免费少妇av软件| 久久亚洲真实| 欧美精品av麻豆av| 亚洲三区欧美一区| 国产欧美日韩精品亚洲av| 国产av又大| 亚洲成人手机| 成人国产一区最新在线观看| 中文字幕另类日韩欧美亚洲嫩草| 少妇的丰满在线观看| 一边摸一边抽搐一进一出视频| 亚洲视频免费观看视频| 国产熟女午夜一区二区三区| 亚洲精品av麻豆狂野| 999久久久国产精品视频| 亚洲性夜色夜夜综合| 亚洲人成伊人成综合网2020| 色播在线永久视频| 伊人久久大香线蕉亚洲五| 日韩熟女老妇一区二区性免费视频| 无人区码免费观看不卡 | 亚洲国产毛片av蜜桃av| 狠狠婷婷综合久久久久久88av| 国产精品久久电影中文字幕 | bbb黄色大片| 日韩免费高清中文字幕av| tube8黄色片| 久久中文字幕人妻熟女| 免费不卡黄色视频| 色老头精品视频在线观看| kizo精华| 免费观看a级毛片全部| 美国免费a级毛片| 80岁老熟妇乱子伦牲交| 国产黄频视频在线观看| 国产精品电影一区二区三区 | 亚洲精品一卡2卡三卡4卡5卡| 久久天堂一区二区三区四区| 后天国语完整版免费观看| 在线观看免费高清a一片| 丁香欧美五月| 99国产精品一区二区蜜桃av | 可以免费在线观看a视频的电影网站| 大片免费播放器 马上看| 国产野战对白在线观看| 色94色欧美一区二区| 桃花免费在线播放| 真人做人爱边吃奶动态| 欧美成人午夜精品| 亚洲久久久国产精品| aaaaa片日本免费| 国产精品久久久久久精品电影小说| 久久亚洲精品不卡| 制服诱惑二区| 欧美性长视频在线观看| 欧美在线黄色| av超薄肉色丝袜交足视频| 亚洲欧美日韩高清在线视频 | a在线观看视频网站| 一区二区三区国产精品乱码| 国产成人系列免费观看| 亚洲国产av影院在线观看| 手机成人av网站| 黄色视频,在线免费观看| 国产一区二区 视频在线| 精品视频人人做人人爽| e午夜精品久久久久久久| 国产成人欧美| 色精品久久人妻99蜜桃| 色老头精品视频在线观看| 视频区欧美日本亚洲| av网站在线播放免费| 日本黄色视频三级网站网址 | 久久精品aⅴ一区二区三区四区| 久久九九热精品免费| 大片免费播放器 马上看| 国产精品久久久人人做人人爽| 国产区一区二久久| 一级黄色大片毛片| 90打野战视频偷拍视频| 久久久国产一区二区| 国产无遮挡羞羞视频在线观看| 国产精品自产拍在线观看55亚洲 | 国产精品欧美亚洲77777| 另类精品久久| 中文字幕另类日韩欧美亚洲嫩草| 两个人看的免费小视频| 亚洲美女黄片视频| 久久午夜亚洲精品久久| 成年人午夜在线观看视频| 久久精品人人爽人人爽视色| 亚洲欧洲精品一区二区精品久久久| 在线亚洲精品国产二区图片欧美| 日本a在线网址| 窝窝影院91人妻| 欧美在线黄色| 久久九九热精品免费| 午夜日韩欧美国产| 日韩欧美一区视频在线观看| 操出白浆在线播放| 曰老女人黄片| 久久精品人人爽人人爽视色| 在线播放国产精品三级| 亚洲全国av大片| 1024视频免费在线观看| 午夜福利影视在线免费观看| 十八禁网站网址无遮挡| 多毛熟女@视频| 国产高清国产精品国产三级| 国产亚洲一区二区精品| 久久久久国内视频| 精品国产乱码久久久久久小说| 午夜日韩欧美国产| 极品教师在线免费播放| 欧美日韩国产mv在线观看视频| videosex国产| 国产免费视频播放在线视频| 亚洲精品粉嫩美女一区| 日日摸夜夜添夜夜添小说| 国产欧美日韩精品亚洲av| 亚洲情色 制服丝袜| 国产一卡二卡三卡精品| 90打野战视频偷拍视频| 国产一区二区 视频在线| 丁香欧美五月| 日本一区二区免费在线视频| 久热爱精品视频在线9| 国产精品一区二区在线观看99| 男女高潮啪啪啪动态图| 午夜免费成人在线视频| 国产黄频视频在线观看| av不卡在线播放| 精品少妇黑人巨大在线播放| 午夜久久久在线观看| 国产又色又爽无遮挡免费看| 精品国产一区二区久久| 亚洲熟女毛片儿| 99精国产麻豆久久婷婷| xxxhd国产人妻xxx| 国产不卡一卡二| 国产麻豆69| 超碰97精品在线观看| 最近最新中文字幕大全免费视频| 一级毛片精品| 亚洲精品一二三| 久久久久视频综合| 男人舔女人的私密视频| 亚洲av国产av综合av卡| 老司机深夜福利视频在线观看| 亚洲熟妇熟女久久| 成人18禁在线播放| 日本欧美视频一区| 视频区图区小说| 成人特级黄色片久久久久久久 | 欧美日韩亚洲综合一区二区三区_| 怎么达到女性高潮| 久久精品aⅴ一区二区三区四区| 亚洲国产av影院在线观看| 在线观看www视频免费| 欧美在线一区亚洲| 午夜91福利影院| 国产欧美日韩精品亚洲av| 国产精品欧美亚洲77777| 久久久国产欧美日韩av| av福利片在线| 中文亚洲av片在线观看爽 | 亚洲人成77777在线视频| 精品国产乱子伦一区二区三区| 国产免费av片在线观看野外av| 亚洲成国产人片在线观看| 91字幕亚洲| 久9热在线精品视频| 精品国产亚洲在线| a在线观看视频网站| 亚洲五月色婷婷综合| 汤姆久久久久久久影院中文字幕| 日本黄色视频三级网站网址 | aaaaa片日本免费| 巨乳人妻的诱惑在线观看| 国产片内射在线| 国产欧美日韩精品亚洲av| 欧美乱码精品一区二区三区| 亚洲一码二码三码区别大吗| 亚洲第一欧美日韩一区二区三区 | 天堂俺去俺来也www色官网| 亚洲精华国产精华精| av电影中文网址| 亚洲精品粉嫩美女一区| 丝袜美足系列| 国产三级黄色录像| 少妇猛男粗大的猛烈进出视频| 19禁男女啪啪无遮挡网站| 王馨瑶露胸无遮挡在线观看| 国产福利在线免费观看视频| 国产视频一区二区在线看| 免费人妻精品一区二区三区视频| 美女视频免费永久观看网站| 99精品久久久久人妻精品| 不卡一级毛片| 成年人黄色毛片网站| 国产一区二区三区视频了| 亚洲人成电影免费在线| 美女视频免费永久观看网站| 中文欧美无线码| 久久久久久免费高清国产稀缺| 欧美亚洲 丝袜 人妻 在线| 久热爱精品视频在线9| av超薄肉色丝袜交足视频| 水蜜桃什么品种好| 久久久久久久久久久久大奶| 激情在线观看视频在线高清 | 狠狠婷婷综合久久久久久88av| 天天躁日日躁夜夜躁夜夜| 黄色视频,在线免费观看| 欧美日韩国产mv在线观看视频| 国产一区二区三区在线臀色熟女 | 成在线人永久免费视频| 热re99久久国产66热| 色94色欧美一区二区| 中文字幕高清在线视频| 成人亚洲精品一区在线观看| 色综合婷婷激情| 黑丝袜美女国产一区| 91九色精品人成在线观看| 极品教师在线免费播放| 午夜福利影视在线免费观看| 欧美激情高清一区二区三区| 美国免费a级毛片| 少妇裸体淫交视频免费看高清 | 国产精品一区二区免费欧美| 日本精品一区二区三区蜜桃| 黄色怎么调成土黄色| 欧美日本中文国产一区发布| xxxhd国产人妻xxx| 亚洲精品粉嫩美女一区| 美女福利国产在线| 黄色视频,在线免费观看| 一本大道久久a久久精品| 狠狠狠狠99中文字幕| 国产精品久久久久久精品电影小说| 亚洲av欧美aⅴ国产| 亚洲欧美日韩另类电影网站| 中国美女看黄片| 在线观看www视频免费| 国产熟女午夜一区二区三区| 日韩免费av在线播放| 日本vs欧美在线观看视频| 两个人免费观看高清视频| 国产精品电影一区二区三区 | 精品国产一区二区久久| 美女午夜性视频免费| 啦啦啦中文免费视频观看日本| 国产免费福利视频在线观看|