Microstructure Abnormalities in Adolescents with Internet Addiction Disorder. (2011)

Materials and Methods Top

All research procedures were approved by the West China Hospital Subcommittee on Human Studies and were conducted in accordance with the Declaration of Helsinki.

2.1 Subjects

According to the modified Young Diagnostic Questionnaire for Internet addiction (YDQ) criteria by Beard and Wolf [16], [29], eighteen freshman and sophomore students with IAD (12 males, mean age = 19.4±3.1 years, education 13.4±2.5 years) were engaged in our study. The YDQ criteria [16] consisted of the following eight “yes” or “no” questions which were: (1) Do you feel absorbed in the Internet (remember previous online activity or the desired next online session)? (2) Do you feel satisfied with Internet use if you increase your amount of online time? (3) Have you failed to control, reduce, or quit Internet use repeatedly? (4) Do you feel nervous, temperamental, depressed, or sensitive when trying to reduce or quit Internet use? (5) Do you stay online longer than originally intended? (6) Have you taken the risk of losing a significant relationship, job, educational or career opportunity because of the Internet? (7) Have you lied to your family members, therapist, or others to hide the truth of your involvement with the Internet? (8) Do you use the Internet as a way of escaping from problems or of relieving an anxious mood (e.g., feelings of helplessness, guilty, anxiety, or depression)? All of the eight questions were translated into Chinese. Young asserted that five or more “yes” responses to the eight questions indicated an internet-dependent user [16]. Later on, Beard and Wolf modified the YDQ criteria [29], and respondents who answered “yes” to questions 1 through 5 and at least to any one of the remaining three questions were classified as suffering from internet addiction, which was used for screening the subjects in the present study. The addiction was a gradual process, so we investigated whether or not there were any linear changes in the brain structure. The duration of the disease was estimated via a retrospective diagnosis. We asked the subjects to recall their life-style when they were initially addicted to the internet. To guarantee that they were suffering from internet addiction, we retested them with the YDQ criteria modified by Beard and Wolf. We also confirmed the reliability of the self-reports from the IAD subjects by talking with their parents via telephone. The IAD subjects spent 10.2±2.6 hours per day on online gaming. The days of internet use per week was 6.3±0.5. We also verified this information from the roommates and classmates of the IAD subjects that they often insisted being on the internet late at night, disrupting others’ lives despite the consequences. Eighteen age- and gender-matched (p>0.01) healthy controls (12 males, mean age = 19.5±2.8 years, education 13.3±2.0 years) with no personal or family history of psychiatric disorders also participated in our study. According to a previous IAD study [19], we chose healthy controls who spent less than 2 hours per day on the internet. The healthy controls were also tested with the YDQ criteria modified by Beard and Wolf to ensure they were not suffering from IAD. All recruited participants screened were native Chinese speakers, never used illegal substances, and were right-handed. Prior to magnetic resonance imaging (MRI) scanning, urine drug screening was performed on all subjects to exclude substance abuse. Exclusion criteria for both groups were (1) existence of a neurological disorder; (2) alcohol, nicotine or drug abuse; (3) pregnancy or menstrual period in women; and (4) any physical illness such as a brain tumor, hepatitis, or epilepsy as assessed according to clinical evaluations and medical records. Furthermore, the Self-Rating Anxiety Scale (SAS) and the Self-Rating Depression Scale (SDS) were used to evaluate the emotional states of all participants on the day of the scans. All patients and healthy controls gave written informed consent. More detailed demographic information was given in Table 1.

2.2 Brain Imaging Methodology and Data Analysis

2.2.1 Scanning parameters.

Imaging data was performed on a 3T Siemens scanner (Allegra; Siemens Medical System) at the Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China. A standard birdcage head coil was used, along with restraining foam pads to minimize head motion and to diminish scanner noise. Image sequences were acquired by means of diffusion weighted imaging with single-shot echo planar imaging in alignment with the anterior–posterior commissural plane. Diffusion tensor images were acquired with 2 averages. The diffusion sensitizing gradients were applied along 30 non-linear directions (b = 1000 s/mm²) together with an acquisition without diffusion weighting (b = 0 s/mm²). The imaging parameters were 45 continuous axial slices with a slice thickness of 3 mm and no gap, field of view = 240×240 mm², repetition time/echo time = 6800/93 ms, acquisition matrix = 128×128. In addition, the axial 3D T1-weighted images were obtained with a spoiled gradient recall sequence and the following parameters: TR = 1900 ms; TE = 2.26 ms; flip angle = 9⁰; in-plane matrix resolution = 256×256; slices = 176; field of view = 256 mm; voxel size = 1×1×1 mm.

2.2.2 VBM.

Structural data was processed with an FSL-VBM protocol [30], [31] with FSL 4.1 software [32]. First, all T1 images were brain-extracted using the brain extracting tool (BET) [33]. Next, tissue-type segmentation was carried out using FMRIB’s automated segmentation tool (FAST) V4.1 [34]. The resulting gray matter partial volume images were then aligned to MNI152 standard space using the FMRIB’s linear image registration tool (FLIRT) [35], [36], followed optionally by nonlinear registration using the FMRIB’s nonlinear image registration tool (FNIRT) [37], [38], which uses a b-spline representation of the registration warp field [39]. The resulting images were averaged to create a study-specific template, to which the native gray matter images were then non-linearly re-registered. The optimized protocol introduced a modulation for the contraction/enlargement due to the nonlinear component of the transformation: each voxel of the registered gray matter image was divided by the Jacobian of the warp field. Finally, in order to choose the best smoothing kernel, all 32 modulated, normalized gray matter volume images were smoothed with isotropic Gaussian kernels increasing in size (sigma = 2.5, 3, 3.5, and 4 mm, corresponding to a 6, 7, 8, and 9.2 mm FWHM respectively). Regional changes in gray matter were assessed using permutation-based non-parametric testing with 5000 random permutations [40]. Analysis of covariance (ANCOVA) was employed with age, gender effects and total intracranial volume as covariates. Total intracranial volume was calculated as the sum of gray matter, white matter, and cerebrospinal fluid volumes from FSL BET segmentations. Recently, Dong et al. found that depression and anxiety scores were significantly higher after the addiction compared to before the addiction in some college students, and they suggested that these were outcomes of IAD, hence SAS and SDS were not included as confounds [41]. Correction for multiple comparisons was carried out using a cluster-based thresholding method, with an initial cluster forming a threshold at t = 2.0. Results were considered significant for p<0.05. For the regions where IAD subjects showed significantly different gray matter volume from the controls, the gray matter volumes of these areas were extracted, averaged and regressed against the duration of internet addiction.

2.2.3 DTI.

We calculated the FA value for each voxel, which reflected the degree of diffusion anisotropy within a voxel (range 0–1, where smaller values indicated more isotropic diffusion and less coherence and large values indicated directional dependence of Brownian motion due to white matter tracts) [42]. FDT software in FSL 4.1 was used for FA calculation [32]. First of all, correction for eddy-currents and head motion was done by means of affine registration on the first no-diffusion weighted volume of each subject. FA images were created by fitting the diffusion tensor to the raw diffusion data after brain extraction using BET [33]. Then, a voxel-wise statistical analysis of the FA data was carried out using the tract-based spatial statistics (TBSS) V1.2 part of FSL [43], [44]. FA imges from all of the subjects (IAD subjects and healthy controls) were realigned into an FMRIB58_FA standard-space image by FNIRT [37], [38] using a b-spline representation of the registration warp field [39]. The mean FA image was then created and thinned to create a mean FA skeleton (threshold of 0.2) representing the centers of all of the tracts common to the group. Each subject’s aligned FA data was then projected back onto this skeleton. White matter FA value changes were assessed using permutation-based non-parametric testing [40] with 5000 random permutations. ANCOVA was employed with age and gender effects as covariates. Correction for multiple comparisons was carried out using a cluster-based thresholding method, with an initial cluster forming threshold of t = 2.0. Results were considered significant for p<0.05. For the clusters where internet addiction subjects showed significantly different FA values from the controls, the FA of these brain regions were extracted, averaged and regressed against the duration of internet addiction.

2.2.4 Interaction between gray matter and white matter abnormalities.

To investigate the interactions between gray matter changes and white matter alterations, a correlation analysis was performed between abnormal gray matter volumes and white matter FA values in the IAD group.

Results

3.1 VBM results

Regional gray matter volume changes were assessed non-parametrically using optimized VBM. Correction for multiple comparisons was carried out using a cluster-based thresholding method. VBM comparison between IAD subjects and matched healthy controls indicated decreased gray matter volume in several clusters, i.e. the bilateral DLPFC, the supplementary motor area (SMA), the OFC, the cerebellum and the left rostral ACC (rACC), after controlling for potential confounding variables including age, gender effects and total intracranial volume. Gray matter volumes of the right DLPFC, the left rACC and the right SMA showed a negative correlation with months of internet addiction (r1 = −0.7256, p1 < 0.005; r2 = −0.7409, p2 < 0.005; r3 = −0.6451, p3 < 0.005). No brain regions showed higher gray matter volume than healthy controls as shown in Figure 1 and Table 2

  

 

 

 

 

 

 

 

 

 

Figure 1. VBM results.

A. Reduced gray matter volume in IAD subjects, (1-p) corrected p-value images. The background image is the standard MNI152_T1_1mm_brain template in FSL. B. The gray matter volumes of the DLPFC, rACC and SMA were negatively correlated with the duration of internet addiction.

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 2. Regions that showed abnormal gray matter volume and white matter FA (fractional anisotropy) between subjects with internet addiction disorder (IAD) and healthy controls (p<0.05 corrected).

doi:10.1371/journal.pone.0020708.t002

3.2 DTI results

With regards to DTI data analysis, correction for multiple comparisons was carried out using the cluster-based thresholding method. Our TBSS results revealed enhanced FA value (IAD: 0.78±0.04; control: 0.56±0.02) of the left posterior limb of the internal capsule (PLIC) in IAD subjects compared to healthy controls and reduced FA value (IAD: 0.31±0.04 ; control: 0.48±0.03) in the white matter within the right parahippocampal gyrus (PHG) as shown in Figure 2 and Table 2. Furthermore, the FA tended to correlate positively with the duration of internet addiction in the left PLIC (r = 0.5869, p < 0.05), whereas no significant correlation was observed between the FA value of the right PHG and duration of internet addiction

  

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2. DTI results.

A. White matter structures showing abnormal FA in IAD subjects, (1-p) corrected p-value images. The background image is the standard FMRIB58_FA_1mm template in FSL. Red-Yellow voxels represent regions in which FA was decreased significantly in IAD relative to healthy controls. Blue-Light Blue voxels represent increased FA in IAD. B. The FA of the PLIC was positively correlated with the duration of internet addiction.

doi:10.1371/journal.pone.0020708.g002

3.3 Interaction between gray matter and white matter abnormalities

The interaction analysis between gray matter volumes and white matter FA values in IAD group revealed that there were no significant correlations between these two measures.

Discussion Top

IAD resulted in impaired individual psychological well-being, academic failure and reduced work performance among adolescents [12]–[18]. However, there is currently no standardized treatment for IAD. Developing effective methods for intervention and treatment of IAD will require first establishing a clear understanding of the mechanisms. Awareness of the brain structural abnormalities in IAD is critical for identifying possible pharmacotherapies to treat this disorder. In the present study, we discovered gray matter volume changes and white matter FA changes in adolescents with IAD. We also revealed the association between these structural abnormalities and duration of internet addiction. We suggested that IAD resulted in brain structural changes in the adolescents and these structural abnormalities were probably associated with functional impairments in cognitive control.

4.1 VBM results

Consistent with a previous VBM study [21], we found no brain regions showing increased gray matter volume in internet addiction subjects. Regional gray matter volume comparison indicated atrophy within several clusters for the entire group of internet addicts (p < 0.05, corrected), which were the bilateral DLPFC, SMA, cerebellum, OFC and the left rACC (as shown in Figure 1). Moreover, atrophy of the right DLPFC, the left rACC and the right SMA was negatively correlated with the duration of internet addiction, which Zhou et al. failed to detect [21]. These results demonstrated that as internet addiction persisted, brain atrophy of the DLPFC, rACC and SMA was more serious. Some results of brain atrophy in our study were different from previous findings [21], which may be due to the different data processing methods. In the present study, the possible confounding effects of age, gender and the whole brain volume were included as covariates, which the previous study failed to consider. The different processing methods possibly gave rise to the different findings.

According to previous drug addiction studies, long-term substance abuse [45], [46] and internet addiction [11], [20] will lead to impaired cognitive control. Cognitive control can be conceptualized as the capacity to suppress prepotent but incorrect responses and the ability to filter out irrelevant information within a stimulus set and allow appropriate actions to meet complicated task demands and adaptation to changing environments [47]. Numerous functional brain imaging studies have revealed that the DLPFC and rACC were centrally involved in cognitive control [48], [49]. Different neurocognitive studies have revealed that cognitive control is related to a specific cortico–subcortical circuit, including the rACC and the DLPFC [50], [51]. According to a prominent conflict-monitoring hypothesis [47], [52], the occurrence of response conflict is signaled by the rACC, leading to recruitment of the DLPFC for more cognitive control for subsequent performance. This important role of the DLPFC has been identified in neuroscience research with top-down regulatory processes of cognitive control [53]. Recent neuroimaging studies have also disclosed deactivation of the rACC in a GO/NOGO task in heroin-dependent individuals [54], [55] and cocaine users [45], indicating the critical role of the rACC in cognitive control [46].

The OFC is also thought to contribute to cognitive control of goal-directed behavior through the assessment of the motivational significance of stimuli and the selection of behavior to obtain desired outcomes [56]. The OFC has extensive connections with the striatum and limbic regions (such as the amygdala). As a result, the OFC is well situated to integrate the activity of several limbic and subcortical areas associated with motivational behavior and reward processing [57]. Some animal studies have demonstrated that damage to both of the OFC and the rat prelimbic cortex (the functional homologue of the human DLPFC) impaired the acquisition and modification of behavior guided by contingencies between responses and outcomes, indicating that these regions may be crucial for the cognitive control of goal-directed behavior [56], [58].

The SMA is critical for selection of appropriate behavior, whether selecting to execute an appropriate response or selecting to inhibit an inappropriate response [59]. Some researchers detected that both simple and complex GO/NOGO tasks were involved in the SMA and they revealed the important role of the SMA in mediating cognitive control [46], [60].

Several anatomical, physiological, and functional imaging studies suggest that the cerebellum contributes to higher-order cognitive functions [61]–[64], with discrete lesions to the cerebellum resulting in impairment of executive functions and working memory, even in personality changes such as disinhibited and inappropriate behavior.

Our results (Figure 1) of the reduced gray matter volume in the DLPFC, rACC, OFC, SMA and cerebellum may be, at least in part, associated with cognitive control and goal-directed behavior dysfunctions in internet addiction [15], [19], [20], [28], which may explain fundamental symptoms of internet addiction.

4.2 DTI results

We calculated the FA value in each white matter voxel for each subject, which quantified the strength of directionality of the local tract microstructure. The whole brain voxel-wise comparison over the white matter skeleton using permutation testing and stringent statistical thresholding indicated that IAD subjects had lower FA values in a cluster within the right PHG (p < 0.05, corrected). On the other hand, searching for increased FA in IAD subjects showed that IAD subjects had higher FA values in a cluster within the left PLIC (p < 0.05, corrected). Moreover, the FA value of the left PLIC was positively correlated with the duration of internet addiction (Figure 2).

The PHG is a brain region that surrounds the hippocampus and plays an important role in memory encoding and retrieval [65], [66]. The PHG provides the major poly-sensory input to the hippocampus through the entorhinal connections and is the recipient of different combinations of sensory information [67], [68], which are involved in cognition and emotional regulation [69]. Recently, some researchers suggested that the right PHG contributes to the formation and maintenance of bound information in working memory [70]. Working memory is devoted to the temporary storage and on-line manipulation of information and is critical for cognitive control [71]. The finding that the lower FA value of the PHG in IAD subjects demonstrated that abnormal white matter properties maybe the structural basis of functional deficits of working memory in IAD subjects [19]. Recently, Liu et al. [72] reported increased ReHo in the bilateral PHG in IAD college students compared with the controls and suggested this result reflected the functional change in the brain, possibly relating to the reward pathways. Evidently, more work is needed to understand the accurate role of the PHG in IAD.

Anatomically, the internal capsule is an area of white matter in the brain that separates the caudate nucleus and the thalamus from the lenticular nucleus, which contains both ascending and descending axons. In addition to corticospinal and corticopontine fibers, the internal capsule contains thalamocortical and corticofugal fibers [73], [74]. The posterior limb of the internal capsule contains corticospinal fibers, sensory fibers (including the medial lemniscus and the anterolateral system) from the body and a few corticobulbar fibers [73]–[76]. The primary motor cortex sends its axons through the posterior limb of the internal capsule and plays important roles in finger movement and motor imagery [77], [78]. The possible reason for the FA values in internal capsule enhancement was that the IAD subjects spent more time playing computer games and the repetitive motor actions such as clicking the mouse and keyboard typing changed the structure of the internal capsule. As the findings that training modified the brain structure in other studies [79]–[81], these long-term training probably changed the white matter structure of the PLIC. Information transmission between frontal and subcortical brain regions modulated higher cognitive functioning and human behaviors [82], [83], which relied on the white matter fiber tracts passing through the internal capsule [83], [84]. Structural abnormalities in the internal capsule could consequently interfere with the cognitive function and impair executive and memory functions [85]. The abnormal FA value of the left PLIC may influence the sensory information transfer and processing, and finally lead to impairments in cognitive control [86], [87]. Moreover, being addicted to the internet can cause physical discomfort or medical problems such as: carpal tunnel syndrome, dry eyes, backaches, and severe headaches [88]–[90]. The abnormal FA value of the left PLIC may explain the carpal tunnel syndrome in IAD subjects, which needs to be verified with a more sophisticated design in the future.

4.3 Interaction between gray matter and white matter abnormalities

We have investigated the relationship between gray matter and white matter alterations. Unfortunately, there were no significant correlations between these two measures. This phenomenon suggested that the morphological changes of IAD on the brain’s gray matter and white matter were not significantly linear correlated. There existed the possibility that the gray matter abnormalities linked the white matter alterations in some other way. However, our findings demonstrated that the structure characteristics of gray matter and white matter were abnormal in the adolescents with IAD.

There are some limitations of the current study. First of all, while our results have indicated that the gray matter and white matter changes may be the consequence of excessive internet use or IAD, we can’t exclude another possibility which addresses the structural difference between the normal controls and IAD that may be the cause for the over-use of the internet. The abnormal characteristics of these cognitive control-related brain regions in some adolescents make them relatively immature and allow them to be easily internet dependent. The cause and consequence issues should be investigated by a more comprehensive experimental design in the future study. However, we suggested that the findings in the present study were the consequence of IAD. Secondly, with regard to the relationship between the structural changes and duration of IAD, the months of IAD is a gross characterization by the recollection of the IAD subjects. We asked the subjects to recall their life-style when they were initially addicted to the internet. To guarantee that they were suffering from internet addiction, we retested them with the YDQ criteria modified by Beard and Wolf. We also confirmed the reliability of the self-reports from the IAD subjects by talking with their parents over the telephone. The brain structural changes in accordance with the addiction process may be more crucial in understanding the disease, hence the correlation between duration and the brain structural measures was carried out. These correlations suggested that cumulative effects were found in the reduced gray matter volume of the right DLPFC, the right SMA, the left rACC and increased white matter FA in the left PLIC. Finally, although we suggested that the structural abnormalities of the gray matter volume and white matter FA were associated with functional impairments in cognitive control in IAD, the biggest limitation of the present study is the lack of quantitative indication of the deficits in cognitive control in these adolescents with IAD. Although the relationships between these structural abnormalities and duration of internet addiction were verified in our current study, fully characterizing the nature of the underlying structural abnormalities in IAD is still needed to be researched in greater detail in the future, which is critical in understanding the impact of IAD on long-term functioning. In the future, we will integrate these structural findings with behavioral performances of cognitive tasks in subjects with IAD. Overall, the FA changes and gray matter volume changes as shown in the present study indicated an alteration in the brain on a microstructural level, which enhanced our understanding of IAD.

Conclusion

We provided evidences indicating that IAD subjects had multiple structural changes in the brain. The gray matter atrophy and white matter FA changes of some brain regions were significantly correlated with the duration of internet addiction. These results may be interpreted, at least partially, as the functional impairment of cognitive control in IAD. The prefrontal cortex abnormalities were consistent with previous substance abuse studies [23], [48], [80], [81], hence we suggested that there may exist partially overlapping mechanisms in IAD and substance use. We hoped that our results will enhance our understanding of IAD and aid in improving the diagnosis and prevention of IAD.

Acknowledgments Top

We would like to thank Qin Ouyang, Qizhu Wu and Junran Zhang for valuable technical assistance in conducting this research.

References Top

1. Ernst M, Pine D, Hardin M (2006) Triadic model of the neurobiology of motivated behavior in adolescence. Psychological Medicine 36: 299–312. Find this article online
2. Csikszentmihalyi M, Larson R, Prescott S (1977) The ecology of adolescent activity and experience. Journal of youth and adolescence 6: 281–294. Find this article online
3. Casey B, Tottenham N, Liston C, Durston S (2005) Imaging the developing brain: what have we learned about cognitive development? Trends in Cognitive Sciences 9: 104–110. Find this article online
4. Casey B, Galvan A, Hare T (2005) Changes in cerebral functional organization during cognitive development. Current opinion in neurobiology 15: 239–244. Find this article online
5. Ernst M, Nelson E, Jazbec S, McClure E, Monk C, et al. (2005) Amygdala and nucleus accumbens in responses to receipt and omission of gains in adults and adolescents. Neuroimage 25: 1279–1291. Find this article online
6. May J, Delgado M, Dahl R, Stenger V, Ryan N, et al. (2004) Event-related functional magnetic resonance imaging of reward-related brain circuitry in children and adolescents. Biological Psychiatry 55: 359–366.
7. Galvan A, Hare T, Parra C, Penn J, Voss H, et al. (2006) Earlier development of the accumbens relative to orbitofrontal cortex might underlie risk-taking behavior in adolescents. Journal of Neuroscience 26: 6885–6892. Find this article online
8. Steinberg L (2005) Cognitive and affective development in adolescence. Trends in Cognitive Sciences 9: 69–74. Find this article online
9. Pine D, Cohen P, Brook J (2001) Emotional reactivity and risk for psychopathology among adolescents. CNS spectrums 6: 27–35. Find this article online
10. Silveri M, Tzilos G, Pimentel P, Yurgelun-Todd D (2004) Trajectories of adolescent emotional and cognitive development: effects of sex and risk for drug use. Annals of the New York Academy of Sciences 1021: 363–370. Find this article online
11. Cao F, Su L, Liu T, Gao X (2007) The relationship between impulsivity and Internet addiction in a sample of Chinese adolescents. European Psychiatry 22: 466–471. Find this article online
12. Ko C, Yen J, Chen S, Yang M, Lin H, et al. (2009) Proposed diagnostic criteria and the screening and diagnosing tool of Internet addiction in college students. Comprehensive psychiatry 50: 378–384. Find this article online
13. Flisher C (2010) Getting plugged in: An overview of Internet addiction. Journal of Paediatrics and Child Health 46: 557–559. Find this article online
14. Christakis D (2010) Internet addiction: a 21 st century epidemic? BMC medicine 8: 61. Find this article online
15. Chou C, Condron L, Belland J (2005) A review of the research on Internet addiction. Educational Psychology Review 17: 363–388. Find this article online
16. Young K (1998) Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior 1: 237–244. Find this article online
17. Morahan-Martin J, Schumacher P (2000) Incidence and correlates of pathological Internet use among college students. Computers in Human Behavior 16: 13–29. Find this article online
18. Scherer K (1997) College life on-line: Healthy and unhealthy Internet use. Journal of College Student Development 38: 655–665. Find this article online
19. Ko C, Liu G, Hsiao S, Yen J, Yang M, et al. (2009) Brain activities associated with gaming urge of online gaming addiction. Journal of psychiatric research 43: 739–747. Find this article online
20. Dong G, Lu Q, Zhou H, Zhao X (2010) Impulse inhibition in people with Internet addiction disorder: electrophysiological evidence from a Go/NoGo study. Neuroscience Letters 485: 138–142. Find this article online
21. Zhou Y, Lin F, Du Y, Qin L, Zhao Z, et al. (2009) Gray Matter abnormalities in Internet addiction: A voxel-based morphometry study. European Journal of Radiology. doi:10.1016/j.ejrad.2009.1010.1025.
22. Jun L, Xue-ping G, Osunde I, Xin L, Shun-ke Z, et al. (2010) Increased regional homogeneity in internet addiction disorder: a resting state functional magnetic resonance imaging study. Chinese medical journal 123: 1904–1908. Find this article online
23. Yuan K, Qin W, Dong M, Liu J, Sun J, et al. (2010) Gray matter deficits and resting-state abnormalities in abstinent heroin-dependent individuals. Neuroscience Letters 482: 101–105. Find this article online
24. Yuan K, Qin W, Liu J, Guo Q, Dong M, et al. (2010) Altered small-world brain functional networks and duration of heroin use in male abstinent heroin-dependent individuals. Neuroscience Letters 477: 37–42. Find this article online
25. Yuan K, Qin W, Dong M, Liu J, Liu P, et al. (2010) Combining spatial and temporal information to explore resting-state networks changes in abstinent heroin-dependent individuals. Neuroscience Letters 475: 20–24. Find this article online
26. Liu J, Liang J, Qin W, Tian J, Yuan K, et al. (2009) Dysfunctional connectivity patterns in chronic heroin users: an fMRI study. Neuroscience Letters 460: 72–77. Find this article online
27. Volkow N, Fowler J, Wang G (2003) The addicted human brain: insights from imaging studies. Journal of Clinical Investigation 111: 1444–1451. Find this article online
28. Ko C, Hsiao S, Liu G, Yen J, Yang M, et al. (2010) The characteristics of decision making, potential to take risks, and personality of college students with Internet addiction. Psychiatry research 175: 121–125. Find this article online
29. Beard K, Wolf E (2001) Modification in the proposed diagnostic criteria for Internet addiction. CyberPsychology & Behavior 4: 377–383. Find this article online
30. Ashburner J, Friston K (2000) Voxel-based morphometry–the methods. Neuroimage 11: 805–821. Find this article online
31. Good C, Johnsrude I, Ashburner J, Henson R, Fristen K, et al. (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14: 21–36. Find this article online
32. Smith S, Jenkinson M, Woolrich M, Beckmann C, Behrens T, et al. (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23: 208–219. Find this article online
33. Smith S (2002) Fast robust automated brain extraction. Human Brain Mapping 17: 143–155. Find this article online
34. Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging 20: 45–57. Find this article online
35. Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Medical image analysis 5: 143–156. Find this article online
36. Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17: 825–841. Find this article online
37. Andersson J, Jenkinson M, Smith S (2007) Non-linear optimisation. FMRIB Analysis Group Technical Reports: TR07JA02 from www.fmrib.ox.ac.uk/analysis/techrep.
38. Andersson J, Jenkinson M, Smith S (2007) Non-linear registration, aka Spatial normalisation. FMRIB Analysis Group Technical Reports: TR07JA02 from www.fmrib.ox.ac.uk/analysis/techrep.
39. Rueckert D, Sonoda L, Hayes C, Hill D, Leach M, et al. (2002) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging 18: 712–721. Find this article online
40. Nichols T, Holmes A (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping 15: 1–25. Find this article online
41. Dong G, Lu Q, Zhou H, Zhao X, Miles J (2011) Precursor or Sequela: Pathological Disorders in People with Internet Addiction Disorder. PloS one 6: 306–307. Find this article online
42. Beaulieu C (2002) The basis of anisotropic water diffusion in the nervous system–a technical review. NMR in Biomedicine 15: 435–455. Find this article online
43. Smith S, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols T, et al. (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31: 1487–1505. Find this article online
44. Smith S, Johansen-Berg H, Jenkinson M, Rueckert D, Nichols T, et al. (2007) Acquisition and voxelwise analysis of multi-subject diffusion data with tract-based spatial statistics. Nature Protocols 2: 499–503. Find this article online
45. Kaufman J, Ross T, Stein E, Garavan H (2003) Cingulate hypoactivity in cocaine users during a GO-NOGO task as revealed by event-related functional magnetic resonance imaging. Journal of Neuroscience 23: 7839–7843. Find this article online
46. Li C, Sinha R (2008) Inhibitory control and emotional stress regulation: Neuroimaging evidence for frontal-limbic dysfunction in psycho-stimulant addiction. Neuroscience & Biobehavioral Reviews 32: 581–597. Find this article online
47. Botvinick M, Braver T, Barch D, Carter C, Cohen J (2001) Conflict monitoring and cognitive control. Psychological Review 108: 624–652. Find this article online
48. Krawczyk D (2002) Contributions of the prefrontal cortex to the neural basis of human decision making. Neuroscience & Biobehavioral Reviews 26: 631–664. Find this article online
49. Wilson S, Sayette M, Fiez J (2004) Prefrontal responses to drug cues: a neurocognitive analysis. Nature Neuroscience 7: 211–214. Find this article online
50. Barber A, Carter C (2005) Cognitive control involved in overcoming prepotent response tendencies and switching between tasks. Cerebral Cortex 15: 899–912. Find this article online
51. MacDonald A, Cohen J, Stenger V, Carter C (2000) Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science 288: 1835–1838. Find this article online
52. Botvinick M, Nystrom L, Fissell K, Carter C, Cohen J (1999) Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature 402: 179–180. Find this article online
53. Vanderhasselt M, De Raedt R, Baeken C (2009) Dorsolateral prefrontal cortex and Stroop performance: Tackling the lateralization. Psychonomic bulletin & review 16: 609–612. Find this article online
54. Forman S, Dougherty G, Casey B, Siegle G, Braver T, et al. (2004) Opiate addicts lack error-dependent activation of rostral anterior cingulate. Biological Psychiatry 55: 531–537. Find this article online
55. Fu L, Bi G, Zou Z, Wang Y, Ye E, et al. (2008) Impaired response inhibition function in abstinent heroin dependents: an fMRI study. Neuroscience Letters 438: 322–326. Find this article online
56. Rolls E (2000) The orbitofrontal cortex and reward. Cerebral Cortex 10: 284–294. Find this article online
57. Groenewegen H, Uylings H (2000) The prefrontal cortex and the integration of sensory, limbic and autonomic information. Progress in brain research 126: 3–28. Find this article online
58. Balleine B, Dickinson A (1998) Goal-directed instrumental action: contingency and incentive learning and their cortical substrates. Neuropharmacology 37: 407–419. Find this article online
59. Simmonds D, Pekar J, Mostofsky S (2008) Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated with response inhibition is task-dependent. Neuropsychologia 46: 224–232. Find this article online
60. Ray Li C, Huang C, Constable R, Sinha R (2006) Imaging response inhibition in a stop-signal task: neural correlates independent of signal monitoring and post-response processing. Journal of Neuroscience 26: 186–192. Find this article online
61. Raymond J, Lisberger S, Mauk M (1996) The cerebellum: a neuronal learning machine? Science 272: 1126–1131. Find this article online
62. Schmahmann J, Sherman J (1998) The cerebellar cognitive affective syndrome. Brain 121: 561–579. Find this article online
63. Desmond J (2001) Cerebellar involvement in cognitive function: evidence from neuroimaging. International Review of Psychiatry 13: 283–294. Find this article online
64. Heyder K, Suchan B, Daum I (2004) Cortico-subcortical contributions to executive control. Acta Psychologica 115: 271–289. Find this article online
65. Wagner A, Schacter D, Rotte M, Koutstaal W, Maril A, et al. (1998) Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. Science 281: 1188–1191. Find this article online
66. Tulving E, Markowitsch H, Craik F, Habib R, Houle S (1996) Novelty and familiarity activations in PET studies of memory encoding and retrieval. Cerebral Cortex 6: 71–79. Find this article online
67. Powell H, Guye M, Parker G, Symms M, Boulby P, et al. (2004) Noninvasive in vivo demonstration of the connections of the human parahippocampal gyrus. Neuroimage 22: 740–747. Find this article online
68. BURWELL R (2000) The parahippocampal region: corticocortical connectivity. Annals of the New York Academy of Sciences 911: 25–42. Find this article online
69. Zhu X, Wang X, Xiao J, Zhong M, Liao J, et al. (2010) Altered white matter integrity in first-episode, treatment-naive young adults with major depressive disorder: A tract-based spatial statistics study. Brain Research 1396: 223–229. Find this article online
70. Luck D, Danion J, Marrer C, Pham B, Gounot D, et al. (2010) The right parahippocampal gyrus contributes to the formation and maintenance of bound information in working memory. Brain and cognition 72: 255–263. Find this article online
71. Engle R, Kane M (2003) Executive attention, working memory capacity, and a two-factor theory of cognitive control. Psychology of Learning and Motivation 44: 145–199. Find this article online
72. Jun L, Xue-ping G, Osunde I, Xin L, Shun-ke Z, et al. Increased regional homogeneity in internet addiction disorder: a resting state functional magnetic resonance imaging study. Chinese medical journal 123: 1904–1908. Find this article online
73. Parent A, Carpenter M (1996) Carpenter’s human neuroanatomy: Williams & Wilkins.
74. Wakana S, Jiang H, Nagae-Poetscher L, van Zijl P, Mori S (2004) Fiber Tract–based Atlas of Human White Matter Anatomy1. Radiology 230: 77–87. Find this article online
75. Andersen R, Knight P, Merzenich M (1980) The thalamocortical and corticothalamic conections of AI, AII, and the anteriior auditory field (AFF) in the cat: Evidence ofr two largely sergregarted systems of connections. The Journal of Comparative Neurology 194: 663–701. Find this article online
76. Winer J, Diehl J, Larue D (2001) Projections of auditory cortex to the medial geniculate body of the cat. The Journal of Comparative Neurology 430: 27–55. Find this article online
77. Schnitzler A, Salenius S, Salmelin R, Jousm ki V, Hari R (1997) Involvement of primary motor cortex in motor imagery: a neuromagnetic study. Neuroimage 6: 201–208. Find this article online
78. Shibasaki H, Sadato N, Lyshkow H, Yonekura Y, Honda M, et al. (1993) Both primary motor cortex and supplementary motor area play an important role in complex finger movement. Brain 116: 1387–1398. Find this article online
79. Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, et al. (2004) Neuroplasticity: changes in grey matter induced by training. Nature 427: 311–312. Find this article online
80. Boyke J, Driemeyer J, Gaser C, Buchel C, May A (2008) Training-induced brain structure changes in the elderly. Journal of Neuroscience 28: 7031–7035. Find this article online
81. Scholz J, Klein MC, Behrens TEJ, Johansen-Berg H (2009) Training induces changes in white-matter architecture. Nature Neuroscience 12: 1370–1371. Find this article online
82. Cummings JL (1993) Frontal-subcortical circuits and human behavior. Archives of neurology 50: 873–880. Find this article online
83. Cummings JL (1995) Anatomic and Behavioral Aspects of Frontal-Subcortical Circuitsa. Annals of the New York Academy of Sciences 769: 1–14. Find this article online
84. Albin RL, Young AB, Penney JB (1989) The functional anatomy of basal ganglia disorders. Trends in neurosciences 12: 366–375. Find this article online
85. Levitt JJ, Kubicki M, Nestor PG, Ersner-Hershfield H, Westin C, et al. (2010) A diffusion tensor imaging study of the anterior limb of the internal capsule in schizophrenia. Psychiatry research 184: 143–150. Find this article online
86. Werring D, Clark C, Barker G, Miller D, Parker G, et al. (1998) The structural and functional mechanisms of motor recovery: complementary use of diffusion tensor and functional magnetic resonance imaging in a traumatic injury of the internal capsule. Journal of Neurology, Neurosurgery & Psychiatry 65: 863–869. Find this article online
87. Niogi S, Mukherjee P, Ghajar J, Johnson C, Kolster R, et al. (2008) Extent of microstructural white matter injury in postconcussive syndrome correlates with impaired cognitive reaction time: a 3T diffusion tensor imaging study of mild traumatic brain injury. American Journal of Neuroradiology 29: 967–973. Find this article online
88. Young K (1999) Internet addiction: symptoms, evaluation and treatment. Innovations in clinical practice: A source book 17: 19–31. Find this article online
89. Beard K (2005) Internet addiction: a review of current assessment techniques and potential assessment questions. CyberPsychology & Behavior 8: 7–14. Find this article online
90. Culver J, Gerr F, Frumkin H (1997) Medical information on the Internet. Journal of General Internal Medicine 12: 466–470.