Prediction of sleep side effects following methylphenidate treatment in ADHD youth
Objective: Sleep problems is the most common side effect of methylphenidate (MPH) treatment in ADHD youth and carry potential to negatively impact long-term self-regulatory functioning. This study aimed to examine whether applying machine learning approaches to pre-treatment demographic, clinical qu...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-01-01
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| Series: | NeuroImage: Clinical |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158219303808 |
| _version_ | 1818775782028214272 |
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| author | Jae Hyun Yoo Vinod Sharma Jae-Won Kim Dana L. McMakin Soon-Beom Hong Andrew Zalesky Bung-Nyun Kim Neal D. Ryan |
| author_facet | Jae Hyun Yoo Vinod Sharma Jae-Won Kim Dana L. McMakin Soon-Beom Hong Andrew Zalesky Bung-Nyun Kim Neal D. Ryan |
| author_sort | Jae Hyun Yoo |
| collection | DOAJ |
| description | Objective: Sleep problems is the most common side effect of methylphenidate (MPH) treatment in ADHD youth and carry potential to negatively impact long-term self-regulatory functioning. This study aimed to examine whether applying machine learning approaches to pre-treatment demographic, clinical questionnaire, environmental, neuropsychological, genetic, and neuroimaging features can predict sleep side effects following MPH administration. Method: The present study included 83 ADHD subjects as a training dataset. The participants were enrolled in an 8-week, open-label trial of MPH. The Barkley Stimulant Side Effects Rating Scale was used to determine the presence/absence of sleep problems at the 2nd week of treatment. Prediction of sleep side effects were performed with step-wise addition of variables measured at baseline: demographics (age, gender, IQ, height/weight) and clinical variables (ADHD Rating Scale-IV (ADHD-RS) and Disruptive Behavior Disorder rating scale) at stage 1, neuropsychological test (continuous performance test (CPT), Stroop color word test) and genetic/environmental variables (dopamine and norepinephrine receptor gene (DAT1, DRD4, ADRA2A, and SLC6A2) polymorphisms, blood lead, and urine cotinine level) at stage 2, and structural connectivities of frontostriatal circuits at stage 3. Three different machine learning algorithms ((Logistic Ridge Regression (LR), support vector machine (SVM), J48) were used for data analysis. Robustness of classifier model was validated in the independent dataset of 36 ADHD subjects. Results: Classification accuracy of LR was 95.5% (area under receiver operating characteristic curve (AUC) 0.99), followed by SVM (91.0%, AUC 0.85) and J48 (90.0%, AUC 0.87) at stage 3 for predicting sleep problems. The inattention symptoms of ADHD-RS, CPT response time variability, the DAT1, ADRA2A DraI, and SLC6A2 A-3081T polymorphisms, and the structural connectivities between frontal and striatal brain regions were identified as the most differentiating subset of features. Validation analysis achieved accuracy of 86.1% (AUC 0.92) at stage 3 with J48. Conclusions: Our results provide preliminary support to the combination of multimodal classifier, in particular, neuroimaging features, as an informative method that can assist in predicting MPH side effects in ADHD. |
| first_indexed | 2024-12-18T11:02:30Z |
| format | Article |
| id | doaj.art-501c957cafda42b494a879837d1c5aad |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-18T11:02:30Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-501c957cafda42b494a879837d1c5aad2022-12-21T21:10:11ZengElsevierNeuroImage: Clinical2213-15822020-01-0126Prediction of sleep side effects following methylphenidate treatment in ADHD youthJae Hyun Yoo0Vinod Sharma1Jae-Won Kim2Dana L. McMakin3Soon-Beom Hong4Andrew Zalesky5Bung-Nyun Kim6Neal D. Ryan7Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of KoreaDepartment of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, United StatesDivision of Child and Adolescent Psychiatry, Department of Neuropsychiatry, Seoul National University Hospital, 101 Daehak-no, Chongno-gu, Seoul, 03080, Republic of Korea; Corresponding author at: Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-no, Chongno-gu, Seoul, 03080, Republic of Korea.Department of Psychology, Florida International University, Miami, FloridaDivision of Child and Adolescent Psychiatry, Department of Neuropsychiatry, Seoul National University Hospital, 101 Daehak-no, Chongno-gu, Seoul, 03080, Republic of KoreaMelbourne Neuropsychiatry Centre, University of Melbourne, Victoria, AustraliaDivision of Child and Adolescent Psychiatry, Department of Neuropsychiatry, Seoul National University Hospital, 101 Daehak-no, Chongno-gu, Seoul, 03080, Republic of KoreaDepartment of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, United StatesObjective: Sleep problems is the most common side effect of methylphenidate (MPH) treatment in ADHD youth and carry potential to negatively impact long-term self-regulatory functioning. This study aimed to examine whether applying machine learning approaches to pre-treatment demographic, clinical questionnaire, environmental, neuropsychological, genetic, and neuroimaging features can predict sleep side effects following MPH administration. Method: The present study included 83 ADHD subjects as a training dataset. The participants were enrolled in an 8-week, open-label trial of MPH. The Barkley Stimulant Side Effects Rating Scale was used to determine the presence/absence of sleep problems at the 2nd week of treatment. Prediction of sleep side effects were performed with step-wise addition of variables measured at baseline: demographics (age, gender, IQ, height/weight) and clinical variables (ADHD Rating Scale-IV (ADHD-RS) and Disruptive Behavior Disorder rating scale) at stage 1, neuropsychological test (continuous performance test (CPT), Stroop color word test) and genetic/environmental variables (dopamine and norepinephrine receptor gene (DAT1, DRD4, ADRA2A, and SLC6A2) polymorphisms, blood lead, and urine cotinine level) at stage 2, and structural connectivities of frontostriatal circuits at stage 3. Three different machine learning algorithms ((Logistic Ridge Regression (LR), support vector machine (SVM), J48) were used for data analysis. Robustness of classifier model was validated in the independent dataset of 36 ADHD subjects. Results: Classification accuracy of LR was 95.5% (area under receiver operating characteristic curve (AUC) 0.99), followed by SVM (91.0%, AUC 0.85) and J48 (90.0%, AUC 0.87) at stage 3 for predicting sleep problems. The inattention symptoms of ADHD-RS, CPT response time variability, the DAT1, ADRA2A DraI, and SLC6A2 A-3081T polymorphisms, and the structural connectivities between frontal and striatal brain regions were identified as the most differentiating subset of features. Validation analysis achieved accuracy of 86.1% (AUC 0.92) at stage 3 with J48. Conclusions: Our results provide preliminary support to the combination of multimodal classifier, in particular, neuroimaging features, as an informative method that can assist in predicting MPH side effects in ADHD.http://www.sciencedirect.com/science/article/pii/S2213158219303808ADHDSleep problemsMachine learningMethylphenidatePredictionSide effects |
| spellingShingle | Jae Hyun Yoo Vinod Sharma Jae-Won Kim Dana L. McMakin Soon-Beom Hong Andrew Zalesky Bung-Nyun Kim Neal D. Ryan Prediction of sleep side effects following methylphenidate treatment in ADHD youth NeuroImage: Clinical ADHD Sleep problems Machine learning Methylphenidate Prediction Side effects |
| title | Prediction of sleep side effects following methylphenidate treatment in ADHD youth |
| title_full | Prediction of sleep side effects following methylphenidate treatment in ADHD youth |
| title_fullStr | Prediction of sleep side effects following methylphenidate treatment in ADHD youth |
| title_full_unstemmed | Prediction of sleep side effects following methylphenidate treatment in ADHD youth |
| title_short | Prediction of sleep side effects following methylphenidate treatment in ADHD youth |
| title_sort | prediction of sleep side effects following methylphenidate treatment in adhd youth |
| topic | ADHD Sleep problems Machine learning Methylphenidate Prediction Side effects |
| url | http://www.sciencedirect.com/science/article/pii/S2213158219303808 |
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