scholarly journals MRI-Based Radiomic Machine-Learning Model May Accurately Distinguish between Subjects with Internet Gaming Disorder and Healthy Controls

2021 ◽  
Vol 12 (1) ◽  
pp. 44
Author(s):  
Xu Han ◽  
Lei Wei ◽  
Yawen Sun ◽  
Ying Hu ◽  
Yao Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffusion Tensor ◽  
Internet Gaming Disorder ◽  
Learning Model ◽  
Brain Morphology ◽  
Healthy Controls ◽  
Gaming Disorder ◽  
Internet Gaming ◽  
Machine Learning Model ◽  
And Diffusion

Purpose To identify cerebral radiomic features related to the diagnosis of Internet gaming disorder (IGD) and construct a radiomics-based machine-learning model for IGD diagnosis. Methods A total of 59 treatment-naïve subjects with IGD and 69 age- and sex-matched healthy controls (HCs) were recruited and underwent anatomic and diffusion-tensor magnetic resonance imaging (MRI). The features of the morphometric properties of gray matter and diffusion properties of white matter were extracted for each participant. After excluding the noise feature with single-factor analysis of variance, the remaining 179 features were included in an all-relevant feature selection procedure within cross-validation loops to identify features with significant discriminative power. Random forest classifiers were constructed and evaluated based on the identified features. Results No overall differences in the total brain volume (1,555,295.64 ± 152316.31 mm3 vs. 154,491.19 ± 151,241.11 mm3), total gray (709,119.83 ± 59,534.46 mm3 vs. 751,018.21 ± 58,611.32 mm3) and white (465,054.49 ± 51,862.65 mm3 vs. 470,600.22 ± 47,006.67 mm3) matter volumes, and subcortical region volume (63,882.71 ± 5110.42 mm3 vs. 64,764.36 ± 4332.33 mm3) between the IGD and HC groups were observed. The mean classification accuracy was 73%. An altered cortical shape in the bilateral fusiform, left rostral middle frontal (rMFG), left cuneus, left parsopercularis (IFG), and regions around the right uncinate fasciculus (UF) and left internal capsule (IC) contributed significantly to group discrimination. Conclusions: Our study found the brain morphology alterations between IGD subjects and HCs through a radiomics-based machine-learning method, which may help revealing underlying IGD-related neurobiology mechanisms.

scholarly journals The neural features in the precentral gyrus predict the severity of internet game disorder: results from the multi-voxel pattern analyses

2020 ◽  
Author(s):  
Shuer Ye ◽  
Min Wang ◽  
Qun Yang ◽  
Haohao Dong ◽  
Guang-Heng Dong
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Resting State ◽  
Internet Gaming Disorder ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Diagnostic Study ◽  
Precentral Gyrus ◽  
Gaming Disorder ◽  
Internet Gaming

AbstractImportanceFinding the neural features that could predict internet gaming disorder severity is important in finding the targets for potential interventions using brain modulation methods.ObjectiveTo determine whether resting-state neural patterns can predict individual variations of internet gaming disorder by applying machine learning method and further investigate brain regions strongly related to IGD severity.DesignThe diagnostic study lasted from December 1, 2013, to November 20, 2019. The data were analyzed from December 31, 2019, to July 10, 2020.SettingThe resting-state fMRI data were collected at East China Normal University, Shanghai.ParticipantsA convenience sample consisting of 402 college students with diverse IGD severityMain Outcomes and MeasuresThe neural patterns were represented by regional homogeneity (ReHo) and the amplitude of low-frequency fluctuation (ALFF). Predictive model performance was assessed by Pearson correlation coefficient and standard mean squared error between the predicted and true IGD severity. The correlations between IGD severity and topological features (i.e., degree centrality (DC), betweenness centrality (BC), and nodal efficiency (NE)) of consensus highly weighted regions in predictive models were examined.ResultsThe final dataset consists of 402 college students (mean [SD] age, 21.43 [2.44] years; 239 [59.5%] male). The predictive models could significantly predict IGD severity (model based on ReHo: r = 0.11, p(r) = 0.030, SMSE = 3.73, p(SMSE) = 0.033; model based on ALFF: r=0.19, p(r) = 0.002, SMSE = 3.58, p(SMSE) = 0.002). The highly weighted brain regions that contributed to both predictive models were the right precentral gyrus and the left postcentral gyrus. Moreover, the topological properties of the right precentral gyrus were significantly correlated with IGD severity (DC: r = 0.16, p = 0.001; BC: r = 0.14, p = 0.005; NE: r = 0.15, p = 0.003) whereas no significant result was found for the left postcentral gyrus (DC: r = 0.02, p = 0.673; BC: r = 0.04, p = 0.432; NE: r = 0.02, p = 0.664).Conclusions and RelevanceThe machine learning models could significantly predict IGD severity from resting-state neural patterns at the individual level. The predictions of IGD severity deepen our understanding of the neural mechanism of IGD and have implications for clinical diagnosis of IGD. In addition, we propose precentral gyrus as a potential target for physiological treatment interventions for IGD.Key PointsQuestionCan machine learning algorithms predict internet gaming disorder (IGD) from resting-state neural patterns?FindingsThis diagnostic study collected resting-state fMRI data from 402 subjects with diverse IGD severity. We found that machine learning models based on resting-state neural patterns yielded significant predictions of IGD severity. In addition, the topological neural features of precentral gyrus, which is a consensus highly weighted region, is significantly correlated with IGD severity.MeaningThe study found that IGD is a distinctive disorder and its dependence severity could be predicted by brain features. The precentral gyrus and its connection with other brain regions could be view as targets for potential IGD intervention, especially using brain modulation methods.

scholarly journals Differences in resting-state functional connectivity according to the level of impulsiveness in patients with internet gaming disorder

2021 ◽  
Vol 10 (1) ◽  
pp. 88-98
Author(s):  
Soo-Jeong Kim ◽  
Min-Kyeong Kim ◽  
Yu-Bin Shin ◽  
Hesun Erin Kim ◽  
Jun Hee Kwon ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Resting State ◽  
Inferior Frontal Gyrus ◽  
Internet Gaming Disorder ◽  
Healthy Controls ◽  
Gaming Disorder ◽  
Resting State Functional Connectivity ◽  
Brain Functions ◽  
Internet Gaming

AbstractBackground and aimsImpulsiveness is an important factor in the pathophysiology of Internet gaming disorder (IGD), and regional brain functions can be different depending on the level of impulsiveness. This study aimed to demonstrate that different brain mechanisms are involved depending on the level of impulsiveness among patients with IGD.MethodsResting-state functional MRI data were obtained from 23 IGD patients with high impulsivity, 27 IGD patients with low impulsivity, and 22 healthy controls, and seed-based functional connectivity was compared among the three groups. The seed regions were the ventromedial prefrontal cortex (vmPFC), dorsolateral prefrontal cortex, nucleus accumbens (NAcc), and amygdala.ResultsConnectivity of the vmPFC with the left temporo-parietal junction (TPJ) and NAcc-left insula connectivity were significantly decreased in the patients with high impulsivity, compared with the patients with low impulsivity and healthy controls. On the other hand, amygdala-based connectivity with the left inferior frontal gyrus showed decreases in both patient groups, compared with the healthy controls.ConclusionThese findings may suggest a potential relationship between impulsivity and deficits in reward-related social cognition processes in patients with IGD. In particular, certain interventions targeted at vmPFC-TPJ connectivity, found to be impulsivity-specific brain connectivity, are likely to help with addiction recovery among impulsive patients with IGD.

scholarly journals White matter brain aging In Relationship to Schizophrenia and Its Cognitive Deficit

2020 ◽  
Author(s):  
Jingtao Wang ◽  
Peter Kochunov ◽  
Hemalatha Sampath ◽  
Kathryn S. Hatch ◽  
Meghann C. Ryan ◽  
...  
Keyword(s):  
Machine Learning ◽  
White Matter ◽  
Brain Aging ◽  
Diffusion Tensor ◽  
Life Stage ◽  
Temporal Trends ◽  
Learning Model ◽  
Chronological Age ◽  
Imaging Features ◽  
Machine Learning Model

AbstractWe hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age (Δage) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. Followed feature selection, a machine learning model was trained with fractional anisotropy values in 34 of 43 tracts on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on Δage and whether this effect was moderated by chronological age using the regression spline model. The results showed that Δage was significantly elevated in the age >30 group in patients (p < 0.001) but not in age ⩽ 30 group (p = 0.364). Δage in patients was significantly and negatively associated with both working memory (β = −0.176, p = 0.007) and processing speed (β = −0.519, p = 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the Δage is elevated in SZs and become significantly from middle life stage; the increase of Δage in SZs is associated with the decline neurocognitive performance.

scholarly journals Automatic Detection of Cognitive Impairments through Acoustic Analysis of Speech

2020 ◽  
Vol 17 (1) ◽  
pp. 60-68 ◽  
Author(s):  
Ryosuke Nagumo ◽  
Yaming Zhang ◽  
Yuki Ogawa ◽  
Mitsuharu Hosokawa ◽  
Kengo Abe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Acoustic Analysis ◽  
Cognitive Impairments ◽  
Learning Model ◽  
Community Dwelling ◽  
Healthy Controls ◽  
Acoustic Features ◽  
Machine Learning Model

Background: Early detection of mild cognitive impairment is crucial in the prevention of Alzheimer’s disease. The aim of the present study was to identify whether acoustic features can help differentiate older, independent community-dwelling individuals with cognitive impairment from healthy controls. Methods: A total of 8779 participants (mean age 74.2 ± 5.7 in the range of 65-96, 3907 males and 4872 females) with different cognitive profiles, namely healthy controls, mild cognitive impairment, global cognitive impairment (defined as a Mini Mental State Examination score of 20-23), and mild cognitive impairment with global cognitive impairment (a combined status of mild cognitive impairment and global cognitive impairment), were evaluated in short-sentence reading tasks, and their acoustic features, including temporal features (such as duration of utterance, number and length of pauses) and spectral features (F0, F1, and F2), were used to build a machine learning model to predict their cognitive impairments. Results: The classification metrics from the healthy controls were evaluated through the area under the receiver operating characteristic curve and were found to be 0.61, 0.67, and 0.77 for mild cognitive impairment, global cognitive impairment, and mild cognitive impairment with global cognitive impairment, respectively. Conclusion: Our machine learning model revealed that individuals’ acoustic features can be employed to discriminate between healthy controls and those with mild cognitive impairment with global cognitive impairment, which is a more severe form of cognitive impairment compared with mild cognitive impairment or global cognitive impairment alone. It is suggested that language impairment increases in severity with cognitive impairment.

scholarly journals Monetary Reward Discounting, Inhibitory Control, and Trait Impulsivity in Young Adults With Internet Gaming Disorder and Nicotine Dependence

2021 ◽  
Vol 12 ◽  
Author(s):  
Wan-Sen Yan ◽  
Ruo-Ting Chen ◽  
Meng-Meng Liu ◽  
Dan-Hui Zheng
Keyword(s):  
Young Adults ◽  
Nicotine Dependence ◽  
Inhibitory Control ◽  
Delay Discounting ◽  
Internet Gaming Disorder ◽  
Reward Processing ◽  
Probability Discounting ◽  
Healthy Controls ◽  
Gaming Disorder ◽  
Internet Gaming

Internet Gaming Disorder (IGD) has been considered a potential behavioral or non-substance addiction that requires further investigation. Recognition of the commonalities between IGD and Substance Use disorders (SUD) would be of great help to better understand the basic mechanisms of addictive behaviors and excessive Internet gaming. However, little research has targeted a straightforward contrast between IGD and SUD on neuropsychological aspects. The present study thus aimed to explore the associations of reward processing and inhibitory control with IGD and nicotine dependence (ND) in young adults. Fifty-eight IGD and 53 ND individuals, as well as 57 age- and gender-matched healthy controls, were assessed with a series of measurements including the Delay-discounting Test (DDT), Probability Discounting Test (PDT), the Stroop Color-Word Task, a revised Go/No Go Task, and the Barratt Impulsiveness Scale (BIS-11). Multivariate analysis of variance (mANOVA) models revealed that both IGD and ND groups scored higher than healthy controls on the BIS-11 attentional, motor, and non-planning impulsiveness (Cohen's d = 0.41–1.75). Higher degrees of delay discounting on the DDT were also found in IGD and ND groups compared to healthy controls (Cohen's d = 0.53–0.69). Although IGD group did not differ from healthy controls on the PDT, ND group had a lower degree of probability discounting than healthy controls (Cohen's d = 0.55), suggesting a reduction in risk aversion. Furthermore, ND subjects showed a lower correct accuracy in the incongruent trials of the Stroop task than healthy controls (Cohen's d = 0.61). On the Go/No Go task, both IGD and ND groups had a lower correct accuracy in the No-Go trials than healthy controls (Cohen's d = 1.35–1.50), indicating compromised response inhibition. These findings suggested that IGD was linked to both anomalous reward discounting and dysfunctional inhibitory control, which was comparable with one typical SUD category (i.e., ND). This study might promote a better understanding of the pathogenesis of IGD as a potential addictive disorder similar to SUD.

Internet Gaming Disorder Scale--Short Form; Polish Version

2018 ◽  
Author(s):  
Bruno Schivinski ◽  
Magdalena Brzozowska-Woś ◽  
Erin M. Buchanan ◽  
Mark D. Griffiths ◽  
Halley M. Pontes
Keyword(s):  
Short Form ◽  
Internet Gaming Disorder ◽  
Gaming Disorder ◽  
Internet Gaming ◽  
Polish Version
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