Subjects

Twenty-eight adolescents with IA – as diagnosed by the modified Young's Diagnostic Questionnaire (YDQ) for Internet Addiction52 – and 27 well-matched healthy subjects participated in this study. The IA subjects were outpatients of the Second Xiangya Hospital of Central South University while the controls were recruited from high schools in Changsha. All subjects (and one of their parents) received a structured clinical interview from two experienced psychiatrists, which was based on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). None of the participants in this study fulfilled any DSM-IV axis I disorders. Four healthy adolescents and 8 IA subjects were excluded from this study due to a failure to record behavioral data during the Go-Stop task. In addition, two IA subjects were removed from further analyses due to excessive head motion. Finally, 23 controls and 18 IA subjects were included in the analysis. There were no significant differences between the two groups in age (mean ± S.D., IA: 15.1 ± 1.4 years versus control: 15.2 ± 0.5 years), ethnicity or education (Table 2, p<0.05, two-sample t test). All subjects' caretakers gave written informed consents. The study was conducted according to the principles in the Declaration of Helsinki and approved by the Ethics Committee of Second Xiangya Hospital of Central South University, Changsha, China.

Table 2 Demographic characteristics of the participants Full size table

YDQ is a questionnaire proposed by Young to diagnose Internet addiction based on the DSM-IV criteria for pathological gambling4. YDQ consists of eight criteria (supplemental Table 1). Young asserted that those who fulfill five or more of the eight criteria should be considered Internet-dependent. The initial Young's YDQ criteria were later modified by Beard and Wolf52. They recommended that only those who met all of the first five criteria and at least one of the last three criteria should be considered Internet-dependent. The modified YDQ was used in the present study for the diagnosis of IA.

The Go-Stop Task

As shown in Fig. 5 this study used a block design. The scanning session began with a 70 s period of the Go-Stop task, which was followed by rest block that lasted for 20 s. The word ‘rest’ was fixated during the rest block, which was then followed by another task block. Rest and task blocks were repeated six times in each experiment and the scanning session lasted 9 min.

Figure 5 Task structure for the Go-Stop paradigm. Full size image

Go-Stop is a paradigm developed to assess the capacity to inhibit a response that has already been initiated. It requires participants to response to a series of five-digit numbers presented in black on a white background. There are three trial types: no-stop, stop and novel trials (Fig. 5). Participants are told to response with a button press to a number that is identical to the previous number presented in black and this is a no-stop trial. A stop trial presents a number that matches the one before it, but it changes unpredictably from black to red at some specified stimulus onset asynchrony (50, 150, 250, or 350 ms) after stimulus onset. No button is pressed during a stop trial. Novel trials present randomly generated non-matching numbers in black. In the current study, the intervals (50, 150, 250, or 350 ms) – during which the target remained black (no-stop) before turning red (stop) – occurred with equal probability. Number stimuli were presented on the screen for 500 ms once every 2 s. The participants were instructed to response to a no-stop trial before the number disappeared from the screen but withhold response to a stop trial or novel trial53,54.

Image acquisition

A PHILIPS 3.0T whole-body scanner was used for acquiring the functional MRI images. A T2*-weighted gradient-echo EPI sequence (TR/TE = 2000/30 ms, 4 mm slice thickness, typically 36 axial slices, 64 × 64 matrix size, 24 × 24 cm FOV, 90°-flip angle) was used to measure blood oxygen level-dependent (BOLD) responses.

Data preprocessing

Preprocessing of the fMRI data used SPM8 (http://www.fil.ion.ucl.ac.uk/spm). First, a six-parameter rigid body transformation (three rotations and three translations) was used to realign the functional images to the first image of each session. Then images were normalized to a MNI EPI template with affine registration followed by a nonlinear transformation, using a voxel size of 2 × 2 × 2 mm. Finally, an 8-mm FWHM Gaussian kernel was used to smooth the data.

Statistical parametric mapping

Subject-specific responses were modeled using a general linear model (GLM) and standard (statistical parametric mapping) procedures: No-stop, stop and novel trials were modeled after convolution with a canonical hemodynamic basis function. The six motion parameters were included to model the movement correlated effects. In the first-level (within subject) analyses, we determined the contrast “stop>baseline”, which allowed us to identify brain regions that were activated or deactivated when the subject tried to inhibit responses. The resulting contrast images were then entered into the second-level (between-subject) analyses. Statistical parametric maps based on one-sample t-tests were used to illustrate brain activation during response inhibition for each group, while two-sample t-tests were performed to determine group differences of brain activation in the Go-Stop task. As this study focused on the response inhibition network, small volume correction was employed to a priori regions of interest: the IFG, striatum and pre-SMA. Sphere ROIs (radius = 25 mm) centered at peak coordinates as reported by Aron et al.33 were constructed.

Dynamic causal modeling

Effective connectivity analysis was performed using DCM12. The basic idea behind DCM is to treat the brain as a dynamic input-state-output system that is driven by experimental inputs and produces outputs (BOLD responses). Each region has a (hidden) neuronal state corresponding to neuronal or synaptic activity and four (hidden) hemodynamic states representing a vasodilatory signal, blood flow, blood volume and deoxyhemoglobin content. At the neuronal level, the neuronal state equations describe how neuronal activity in one region is affected by neuronal activity in others and how these influences are modulated by experimental inputs. The neuronal state equations are supplemented with the hemodynamic state equations that transform the neuronal activity in each region to observed BOLD response51.

In this study, we used stochastic DCM. Stochastic DCM differs from conventional deterministic DCM by allowing for endogenous or random fluctuations in unobserved (hidden) neuronal and physiological states, known technically as system or state-noise54,55,56,57. Compared to deterministic DCM, stochastic DCM has been shown to provide more accurate parameter estimates54. Moreover, stochastic DCM can be used to study effective connectivity between brain regions in the resting state by exploiting spontaneous fluctuations in activity to estimate effective connectivity58.

In order to reduce the model complexity, we concatenated the three inputs (the no-stop, stop and novel trials) to one input for the DCM analysis. In this case, we performed a GLM analysis with regressor for the task condition and six regressors represented the movement effects. For each subject, we determined the contrast “task>rest”.

Based on the group analysis – and on previous studies of the neural network associated with response inhibition – three ROIs were defined: the right IFG, the right striatum and the left pre-SMA. Because subjects responded to visual stimuli (i.e., a series of five-digit numbers in black on a white background), activity within the motor system can be assumed to be driven by the visual system. Thus, we added a fourth region or node (V2) to our model. Because we did not detect significant activation of the STN during response inhibition in the subjects, this region was not included in the DCM. Subject-specific ROIs were centered on the local maximum of SPMs testing for “task minus rest”. For some subjects, the locations of the ROIs were slightly adjusted to make sure the same ROI for each subject was located within the same anatomical gyrus as the group maximum. For the pre-SMA in one IA subject and the V2 in one healthy subject, the coordinates of the group maximum were used because we did not detect significant activation of the region in these subjects. The time series for all ROIs were extracted from a sphere region (radius = 6 mm). Fig. 6 shows the locations and the time series of the four ROIs.

Figure 6 The locations and time series of the ROIs; IFG, inferior frontal gyrus; pre-SMA, pre-supplementary motor area. Full size image

For the DCM analysis, a fully connected model was first constructed. Specifically, visual input was modeled as a driving or exogenous input to V2 and subject-specific DCMs were fully and reciprocally connected (resulting in 12 connections among four nodes). Given our primary interest was to detect differences in effective connectivity between the groups; we did not model any bilinear or modulatory effects. In other words, we estimated the average connectivity under the task-set of response inhibition, assuming that endogenous fluctuations in neuronal activity (state noise) would model condition specific responses. The full connected model for each subject was inverted using generalized filtering as described previously59. A network discovery scheme was then used to identify the optimal model pooling over all subjects60. Subject specific parameter estimates (posterior means) under the optimal model were then taken to the second, between-subject level analysis using a classical random-effects analysis. This allowed us to summarize the findings from the subject-specific DCMs at the group level, using classical statistics (t-tests).