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Tian L, Zheng H, Zhang K, Qiu J, Song X, Li S, Zeng Z, Ran B, Deng X, Cai J. Structural or/and functional MRI-based machine learning techniques for attention-deficit/hyperactivity disorder diagnosis: A systematic review and meta-analysis. J Affect Disord 2024; 355:459-469. [PMID: 38580035 DOI: 10.1016/j.jad.2024.03.111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/21/2024] [Accepted: 03/23/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND The aim of this study was to investigate the diagnostic value of ML techniques based on sMRI or/and fMRI for ADHD. METHODS We conducted a comprehensive search (from database creation date to March 2024) for relevant English articles on sMRI or/and fMRI-based ML techniques for diagnosing ADHD. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve and area under the curve (AUC) were calculated to assess the diagnostic value of sMRI or/and fMRI-based ML techniques. The I2 test was used to assess heterogeneity and the source of heterogeneity was investigated by performing a meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test. RESULTS Forty-three studies were included in the systematic review, 27 of which were included in our meta-analysis. The pooled sensitivity and specificity of sMRI or/and fMRI-based ML techniques for the diagnosis of ADHD were 0.74 (95 % CI 0.65-0.81) and 0.75 (95 % CI 0.67-0.81), respectively. SROC curve showed that AUC was 0.81 (95 % CI 0.77-0.84). Based on these findings, the sMRI or/and fMRI-based ML techniques have relatively good diagnostic value for ADHD. LIMITATIONS Our meta-analysis specifically focused on ML techniques based on sMRI or/and fMRI studies. Since EEG-based ML techniques are also used for diagnosing ADHD, further systematic analyses are necessary to explore ML methods based on multimodal medical data. CONCLUSION sMRI or/and fMRI-based ML technique is a promising objective diagnostic method for ADHD.
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Affiliation(s)
- Lu Tian
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Helin Zheng
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Ke Zhang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Jiawen Qiu
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Xuejuan Song
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Siwei Li
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Zhao Zeng
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Baosheng Ran
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Xin Deng
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Jinhua Cai
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China.
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Cao M, Martin E, Li X. Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms. Transl Psychiatry 2023; 13:236. [PMID: 37391419 DOI: 10.1038/s41398-023-02536-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/02/2023] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder in children and has a high chance of persisting in adulthood. The development of individualized, efficient, and reliable treatment strategies is limited by the lack of understanding of the underlying neural mechanisms. Diverging and inconsistent findings from existing studies suggest that ADHD may be simultaneously associated with multivariate factors across cognitive, genetic, and biological domains. Machine learning algorithms are more capable of detecting complex interactions between multiple variables than conventional statistical methods. Here we present a narrative review of the existing machine learning studies that have contributed to understanding mechanisms underlying ADHD with a focus on behavioral and neurocognitive problems, neurobiological measures including genetic data, structural magnetic resonance imaging (MRI), task-based and resting-state functional MRI (fMRI), electroencephalogram, and functional near-infrared spectroscopy, and prevention and treatment strategies. Implications of machine learning models in ADHD research are discussed. Although increasing evidence suggests that machine learning has potential in studying ADHD, extra precautions are still required when designing machine learning strategies considering the limitations of interpretability and generalization.
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Affiliation(s)
- Meng Cao
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | | | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA.
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Yao F, Chen K, Zhuang Y, Shen X, Wang X. Mid-Luteal Olfactory Abilities Reveal Healthy Women’s Emotional and Cognitive Functions. Front Neurosci 2022; 16:826547. [PMID: 35173576 PMCID: PMC8841682 DOI: 10.3389/fnins.2022.826547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/10/2022] [Indexed: 11/24/2022] Open
Abstract
Ovarian hormones modulate women’s physical and psychological states periodically. Although the olfactory function is increasingly recognized as a reflection of physical and mental health conditions in the clinic, the role of olfaction in emotional and cognitive functions for healthy individuals has yet to be elucidated, especially when taking the menstrual cycle into account. We carried out a comprehensive investigation to explore whether the menstrual cycle could modulate women’s olfactory function and whether healthy women’s emotional symptoms and behavioral impulsivity could be characterized by their olfactory abilities at a specific menstrual cycle stage. Twenty-nine healthy young women were evaluated repeatedly using a standard olfactory test battery during the late follicular and mid-luteal phases. Their emotional symptoms and behavioral impulsivity were separately quantified via psychometric scales and a stop-signal task. We observed enhanced olfactory discrimination performance during the mid-luteal phase than the late follicular phase. We also found that women’s better olfactory discrimination and worse olfactory threshold in the mid-luteal phase predicted fewer individual emotional symptoms and lower behavioral impulsivity, respectively. These relationships were nonetheless absent in the late follicular phase. Our data extend previous clinical observations of the coexistence of olfactory deficits and neuropsychiatric disorders, providing new insights into the significance of olfaction and ovarian hormones for emotional and cognitive functions.
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Affiliation(s)
- Fangshu Yao
- School of Psychology, Shanghai University of Sport, Shanghai, China
| | - Kepu Chen
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Yiyun Zhuang
- School of Psychology, Shanghai University of Sport, Shanghai, China
| | - Xueer Shen
- School of Psychology, Shanghai University of Sport, Shanghai, China
| | - Xiaochun Wang
- School of Psychology, Shanghai University of Sport, Shanghai, China
- *Correspondence: Xiaochun Wang,
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Si FF, Liu L, Li HM, Sun L, Cao QJ, Lu H, Wang YF, Qian QJ. Cortical Morphometric Abnormality and Its Association with Working Memory in Children with Attention-Deficit/Hyperactivity Disorder. Psychiatry Investig 2021; 18:679-687. [PMID: 34340276 PMCID: PMC8328834 DOI: 10.30773/pi.2020.0333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 05/02/2021] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adolescents. The present study investigated the cortical morphology features and their relationship with working memory (WM). METHODS In the present study, a total of 36 medication naïve children with ADHD (aged from 8 to 15 years) and 36 age- and gendermatched healthy control (HC) children were included. The digit span test was used to evaluate WM. The magnetic resonance imaging (MRI) was used to examine the characteristics of cortical morphology. Firstly, we compared the cortical morphology features between two groups to identify the potential structural alterations of cortical volume, surface, thickness, and curvature in children with ADHD. Then, the correlation between the brain structural abnormalities and WM was further explored in children with ADHD. RESULTS Compared with the HC children, the children with ADHD showed reduced cortical volumes in the left lateral superior temporal gyrus (STG) (p=6.67×10-6) and left anterior cingulate cortex (ACC) (p=3.88×10-4). In addition, the cortical volume of left lateral STG was positively correlated with WM (r=0.36, p=0.029). CONCLUSION Though preliminary, these findings suggest that the reduced cortical volumes of left lateral STG may contribute to the pathogenesis of ADHD and correlate with WM in children with ADHD.
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Affiliation(s)
- Fei-Fei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Lu Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Hai-Mei Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Li Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Qing-Jiu Cao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Hanna Lu
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China
| | - Yu-Feng Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Qiu-Jin Qian
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
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7
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Jiang W, Rootes-Murdy K, Duan K, Schoenmacker G, Hoekstra PJ, Hartman CA, Oosterlaan J, Heslenfeld D, Franke B, Sprooten E, Buitelaar J, Arias-Vasquez A, Liu J, Turner JA. Discrepancies of polygenic effects on symptom dimensions between adolescents and adults with ADHD. Psychiatry Res Neuroimaging 2021; 311:111282. [PMID: 33780745 PMCID: PMC8058322 DOI: 10.1016/j.pscychresns.2021.111282] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 03/12/2021] [Accepted: 03/18/2021] [Indexed: 02/05/2023]
Abstract
A significant proportion of individuals with attention-deficit/hyperactivity disorder (ADHD) show persistence into adulthood. The genetic and neural correlates of ADHD in adolescents versus adults remain poorly characterized. We investigated ADHD polygenic risk score (PRS) in relation to previously identified gray matter (GM) patterns, neurocognitive, and symptom findings in the same ADHD sample (462 adolescents & 422 adults from the NeuroIMAGE and IMpACT cohorts). Significant effects of ADHD PRS were found on hyperactivity and impulsivity symptoms in adolescents, hyperactivity symptom in adults, but not GM volume components. A distinct PRS effect between adolescents and adults on individual ADHD symptoms is suggested.
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Affiliation(s)
- Wenhao Jiang
- Department of Psychology, Georgia State University, United States; Department of Psychosomatics and Psychiatry, Zhongda Hospital, Institute of Psychosomatics, Medical School, Southeast University, Nanjing, China
| | | | - Kuaikuai Duan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, USA
| | - Gido Schoenmacker
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Pieter J Hoekstra
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
| | - Catharina A Hartman
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
| | - Jaap Oosterlaan
- Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Emma Neuroscience Group, department of Pediatrics, Amsterdam Reproduction & Development, Amsterdam, The Netherlands; Vrije Universiteit, Clinical Neuropsychology section, Van der Boechortstraat 7, 1081 BT Amsterdam, Netherlands
| | - Dirk Heslenfeld
- Vrije Universiteit, Clinical Neuropsychology section, Van der Boechortstraat 7, 1081 BT Amsterdam, Netherlands
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Emma Sprooten
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jan Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Alejandro Arias-Vasquez
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, USA; Department of Computer Science, Georgia State University, USA
| | - Jessica A Turner
- Department of Psychology, Georgia State University, United States; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, USA; Neuroscience Institute, Georgia State University, USA
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8
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Chauvin RJ, Buitelaar JK, Sprooten E, Oldehinkel M, Franke B, Hartman C, Heslenfeld DJ, Hoekstra PJ, Oosterlaan J, Beckmann CF, Mennes M. Task-generic and task-specific connectivity modulations in the ADHD brain: an integrated analysis across multiple tasks. Transl Psychiatry 2021; 11:159. [PMID: 33750765 PMCID: PMC7943764 DOI: 10.1038/s41398-021-01284-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 01/27/2021] [Accepted: 02/19/2021] [Indexed: 11/23/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is associated with altered functioning in multiple cognitive domains and neural networks. This paper offers an overarching biological perspective across these. We applied a novel strategy that extracts functional connectivity modulations in the brain across one (Psingle), two (Pmix) or three (Pall) cognitive tasks and compared the pattern of modulations between participants with ADHD (n-89), unaffected siblings (n = 93) and controls (n = 84; total N = 266; age range = 8-27 years). Participants with ADHD had significantly fewer Pall connections (modulated regardless of task), but significantly more task-specific (Psingle) connectivity modulations than the other groups. The amplitude of these Psingle modulations was significantly higher in ADHD. Unaffected siblings showed a similar degree of Pall connectivity modulation as controls but a similar degree of Psingle connectivity modulation as ADHD probands. Pall connections were strongly reproducible at the individual level in controls, but showed marked heterogeneity in both participants with ADHD and unaffected siblings. The pattern of reduced task-generic and increased task-specific connectivity modulations in ADHD may be interpreted as reflecting a less efficient functional brain architecture due to a reduction in the ability to generalise processing pathways across multiple cognitive domains. The higher amplitude of unique task-specific connectivity modulations in ADHD may index a more "effortful" coping strategy. Unaffected siblings displayed a task connectivity profile in between that of controls and ADHD probands, supporting an endophenotype view. Our approach provides a new perspective on the core neural underpinnings of ADHD.
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Affiliation(s)
- Roselyne J. Chauvin
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands ,grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands ,grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St Louis, USA
| | - Jan K. Buitelaar
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands ,grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands ,grid.461871.d0000 0004 0624 8031Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, The Netherlands
| | - Emma Sprooten
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands ,grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Marianne Oldehinkel
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands ,grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands ,grid.1002.30000 0004 1936 7857School of Psychological Sciences, Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, VIC Australia
| | - Barbara Franke
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands ,grid.10417.330000 0004 0444 9382Departments of Human Genetics and Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Catharina Hartman
- grid.4494.d0000 0000 9558 4598University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, The Netherlands
| | - Dirk J. Heslenfeld
- Amsterdam UMC, University of Amsterdam & Vrije Universiteit Amsterdam, Emma Neuroscience Group at Emma Children’s Hospital, department of Pediatrics, Amsterdam Reproduction & Development, Amsterdam, The Netherlands
| | - Pieter J. Hoekstra
- grid.4494.d0000 0000 9558 4598University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, The Netherlands
| | - Jaap Oosterlaan
- Amsterdam UMC, University of Amsterdam & Vrije Universiteit Amsterdam, Emma Neuroscience Group at Emma Children’s Hospital, department of Pediatrics, Amsterdam Reproduction & Development, Amsterdam, The Netherlands ,grid.12380.380000 0004 1754 9227Clinical Neuropsychology section, Vrije Universiteit, Van der Boechortstraat 7, 1081 BT Amsterdam, The Netherlands
| | - Christian F. Beckmann
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands ,grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands ,grid.4991.50000 0004 1936 8948Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Maarten Mennes
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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10
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Jiang W, Duan K, Rootes-Murdy K, Hoekstra PJ, Hartman CA, Oosterlaan J, Heslenfeld D, Franke B, Buitelaar J, Arias-Vasquez A, Liu J, Turner JA. Structural brain alterations and their association with cognitive function and symptoms in Attention-deficit/Hyperactivity Disorder families. Neuroimage Clin 2020; 27:102273. [PMID: 32387850 PMCID: PMC7210582 DOI: 10.1016/j.nicl.2020.102273] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 03/26/2020] [Accepted: 04/21/2020] [Indexed: 11/25/2022]
Abstract
Gray matter disruptions have been found consistently in Attention-deficit/Hyperactivity Disorder (ADHD). The organization of these alterations into brain structural networks remains largely unexplored. We investigated 508 participants (281 males) with ADHD (N = 210), their unaffected siblings (N = 108), individuals with subthreshold ADHD (N = 49), and unrelated healthy controls (N = 141) with an age range from 7 to 18 years old from 336 families in the Dutch NeuroIMAGE project. Source based morphometry was used to examine structural brain network alterations and their association with symptoms and cognitive performance. Two networks showed significant reductions in individuals with ADHD compared to unrelated healthy controls after False Discovery Rate correction. Component A, mainly located in bilateral Crus I, showed a ADHD/typically developing difference with subthreshold cases being intermediate between ADHD and typically developing controls. The unaffected siblings were similar to controls. After correcting for IQ and medication status, component A showed a negative correlation with inattention symptoms across the entire sample. Component B included a maximum cluster in the bilateral insula, where unaffected siblings, similar to individuals with ADHD, showed significantly reduced loadings compared to controls; but no relationship with individual symptoms or cognitive measures was found for component B. This multivariate approach suggests that areas reflecting genetic liability within ADHD are partly separate from those areas modulating symptom severity.
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Affiliation(s)
- Wenhao Jiang
- Department of Psychology, Georgia State University, USA
| | - Kuaikuai Duan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, USA
| | | | - Pieter J Hoekstra
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, The Netherlands
| | - Catharina A Hartman
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, The Netherlands
| | - Jaap Oosterlaan
- Department of Clinical Neuropsychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Dirk Heslenfeld
- Department of Clinical Neuropsychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jan Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Alejandro Arias-Vasquez
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jingyu Liu
- Department of Computer Science, TReNDS Center, Georgia State University, Atlanta, USA
| | - Jessica A Turner
- Department of Psychology, Georgia State University, USA; Neuroscience Institute, Georgia State University, USA.
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Wolfers T, Beckmann CF, Hoogman M, Buitelaar JK, Franke B, Marquand AF. Individual differences v. the average patient: mapping the heterogeneity in ADHD using normative models. Psychol Med 2020; 50:314-323. [PMID: 30782224 PMCID: PMC7083555 DOI: 10.1017/s0033291719000084] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 01/04/2019] [Accepted: 01/08/2019] [Indexed: 12/26/2022]
Abstract
BACKGROUND The present paper presents a fundamentally novel approach to model individual differences of persons with the same biologically heterogeneous mental disorder. Unlike prevalent case-control analyses, that assume a clear distinction between patient and control groups and thereby introducing the concept of an 'average patient', we describe each patient's biology individually, gaining insights into the different facets that characterize persistent attention-deficit/hyperactivity disorder (ADHD). METHODS Using a normative modeling approach, we mapped inter-individual differences in reference to normative structural brain changes across the lifespan to examine the degree to which case-control analyses disguise differences between individuals. RESULTS At the level of the individual, deviations from the normative model were frequent in persistent ADHD. However, the overlap of more than 2% between participants with ADHD was only observed in few brain loci. On average, participants with ADHD showed significantly reduced gray matter in the cerebellum and hippocampus compared to healthy individuals. While the case-control differences were in line with the literature on ADHD, individuals with ADHD only marginally reflected these group differences. CONCLUSIONS Case-control comparisons, disguise inter-individual differences in brain biology in individuals with persistent ADHD. The present results show that the 'average ADHD patient' has limited informative value, providing the first evidence for the necessity to explore different biological facets of ADHD at the level of the individual and practical means to achieve this end.
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Affiliation(s)
- Thomas Wolfers
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Christian F. Beckmann
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
- Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Martine Hoogman
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Jan K. Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
- Karakter Child and Adolescent Psychiatry University Center, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK
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Marx I, Reis O, Berger C. Perceptual timing in children with attention-deficit/hyperactivity disorder (ADHD) as measured by computer-based experiments versus real-life tasks: protocol for a cross-sectional experimental study in an ambulatory setting. BMJ Open 2019; 9:e027651. [PMID: 31028043 PMCID: PMC6502000 DOI: 10.1136/bmjopen-2018-027651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION The goal of this study is to get a better understanding of the fundamentals of perceptual timing deficits, that is, difficulties with estimating durations of explicitly attended temporal intervals, in children with attention-deficit/hyperactivity disorder (ADHD). Whereas these deficits were repeatedly demonstrated in laboratory studies using computer-based timing tasks, we will additionally implement a more practical task reflecting real-life activity. In doing so, the research questions of the planned study follow a hierarchically structured path 'from lab to life': Are the timing abilities of children with ADHD really disturbed both in the range of milliseconds and in the range of seconds? What causes these deficits? Do children with ADHD rather display a global perceptual timing deficit, or do different 'timing types' exist? Are timing deficits present during real-life activities as well, and are they based on the same mechanisms as in computerised tasks? METHODS AND ANALYSES A quasi-experimental study with two groups of male children aged 8-12 years (ADHD; controls) and with a cross-sectional design will be used to address our research questions. Statistical analyses of the dependent variables will comprise (repeated) measures analyses of variance, stepwise multiple regression analyses and latent class models. With an estimated dropout rate of 25%, power analysis indicated a sample size of 140 subjects (70 ADHD, 70 controls) to detect medium effect sizes. ETHICS AND DISSEMINATION Ethics approval was obtained from the ethics committee of the Faculty of Medicine, University of Rostock. Results will be disseminated to researcher, clinician and patient communities in peer-reviewed journals and at scientific conferences, at a meeting of the local ADHD competence network and on our web page which will summarise the study results in an easily comprehensible manner. TRIAL REGISTRATION NUMBER DRKS00015760.
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Affiliation(s)
- Ivo Marx
- Department of Child and Adolescent Psychiatry, Neurology, Psychosomatics and Psychotherapy, University Medicine Rostock, Rostock, Germany
| | - Olaf Reis
- Department of Child and Adolescent Psychiatry, Neurology, Psychosomatics and Psychotherapy, University Medicine Rostock, Rostock, Germany
| | - Christoph Berger
- Department of Child and Adolescent Psychiatry, Neurology, Psychosomatics and Psychotherapy, University Medicine Rostock, Rostock, Germany
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13
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Pulini AA, Kerr WT, Loo SK, Lenartowicz A. Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:108-120. [PMID: 30064848 PMCID: PMC6310118 DOI: 10.1016/j.bpsc.2018.06.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 06/15/2018] [Accepted: 06/18/2018] [Indexed: 11/21/2022]
Abstract
BACKGROUND Motivated by an inconsistency between reports of high diagnosis-classification accuracies and known heterogeneity in attention-deficit/hyperactivity disorder (ADHD), this study assessed classification accuracy in studies of ADHD as a function of methodological factors that can bias results. We hypothesized that high classification results in ADHD diagnosis are inflated by methodological factors. METHODS We reviewed 69 studies (of 95 studies identified) that used neuroimaging features to predict ADHD diagnosis. Based on reported methods, we assessed the prevalence of circular analysis, which inflates classification accuracy, and evaluated the relationship between sample size and accuracy to test if small-sample models tend to report higher classification accuracy, also an indicator of bias. RESULTS Circular analysis was detected in 15.9% of ADHD classification studies, lack of independent test set was noted in 13%, and insufficient methodological detail to establish its presence was noted in another 11.6%. Accuracy of classification ranged from 60% to 80% in the 59.4% of reviewed studies that met criteria for independence of feature selection, model construction, and test datasets. Moreover, there was a negative relationship between accuracy and sample size, implying additional bias contributing to reported accuracies at lower sample sizes. CONCLUSIONS High classification accuracies in neuroimaging studies of ADHD appear to be inflated by circular analysis and small sample size. Accuracies on independent datasets were consistent with known heterogeneity of the disorder. Steps to resolve these issues, and a shift toward accounting for sample heterogeneity and prediction of future outcomes, will be crucial in future classification studies in ADHD.
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Affiliation(s)
| | - Wesley T Kerr
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles; Department of Biomathematics, University of California, Los Angeles, Los Angeles; Department of Internal Medicine, Eisenhower Medical Center, Rancho Mirage, California
| | - Sandra K Loo
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles
| | - Agatha Lenartowicz
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles.
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Herman AM, Critchley H, Duka T. Decreased olfactory discrimination is associated with impulsivity in healthy volunteers. Sci Rep 2018; 8:15584. [PMID: 30349020 PMCID: PMC6197201 DOI: 10.1038/s41598-018-34056-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 10/05/2018] [Indexed: 01/09/2023] Open
Abstract
In clinical populations, olfactory abilities parallel executive function, implicating shared neuroanatomical substrates within the ventral prefrontal cortex. In healthy individuals, the relationship between olfaction and personality traits or certain cognitive and behavioural characteristics remains unexplored. We therefore tested if olfactory function is associated with trait and behavioural impulsivity in nonclinical individuals. Eighty-three healthy volunteers (50 females) underwent quantitative assessment of olfactory function (odour detection threshold, discrimination, and identification). Each participant was rated for trait impulsivity index using the Barratt Impulsiveness Scale and performed a battery of tasks to assess behavioural impulsivity (Stop Signal Task, SST; Information Sampling Task, IST; Delay Discounting). Lower odour discrimination predicted high ratings in non-planning impulsivity (Barratt Non-Planning impulsivity subscale); both, lower odour discrimination and detection threshold predicted low inhibitory control (SST; increased motor impulsivity). These findings extend clinical observations to support the hypothesis that deficits in olfactory ability are linked to impulsive tendencies within the healthy population. In particular, the relationship between olfactory abilities and behavioural inhibitory control (in the SST) reinforces evidence for functional overlap between neural networks involved in both processes. These findings may usefully inform the stratification of people at risk of impulse-control-related problems and support planning early clinical interventions.
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Affiliation(s)
- Aleksandra M Herman
- Behavioural and Clinical Neuroscience, School of Psychology, University of Sussex, Brighton, BN1 9QH, UK.
| | - Hugo Critchley
- Psychiatry, Department of Neuroscience, Brighton and Sussex Medical School (BSMS), University of Sussex, Brighton, UK.,Sackler Centre for Consciousness Science, University of Sussex, Brighton, UK
| | - Theodora Duka
- Behavioural and Clinical Neuroscience, School of Psychology, University of Sussex, Brighton, BN1 9QH, UK.,Sussex Addiction and Intervention Centre, University of Sussex, Brighton, BN1 9QH, UK
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15
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Wolfers T, Arenas AL, Onnink AMH, Dammers J, Hoogman M, Zwiers MP, Buitelaar JK, Franke B, Marquand AF, Beckmann CF. Refinement by integration: aggregated effects of multimodal imaging markers on adult ADHD. J Psychiatry Neurosci 2017; 42:386-394. [PMID: 28832320 PMCID: PMC5662460 DOI: 10.1503/jpn.160240] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is biologically heterogeneous, with different biological predispositions - mediated through developmental processes - converging upon a common clinical phenotype. Brain imaging studies have variably shown altered brain structure, activity and connectivity in children and adults with ADHD. Recent methodological developments allow for the integration of information across imaging modalities, potentially yielding a more coherent view regarding the biology underlying the disorder. METHODS We analyzed a sample of adults with persistent ADHD and healthy controls using an advanced multimodal linked independent component analysis approach. Diffusion and structural MRI data were fused to form imaging markers reflecting independent components that explain variation across modalities. We included these markers as predictors into logistic regression models on adult ADHD and put those into context with predictions of estimated intelligence, age and sex. RESULTS We included 87 adults with ADHD and 93 controls in our analysis. Participants' courses associated with all imaging markers explained 27.86% of the variance in adult ADHD. No single imaging modality dominated this result. Instead, it was explained by aggregation of relatively small effects across several modalities and markers. One of the top markers for adult ADHD was multimodal and linked to morphological and microstructural effects within anterior temporal brain regions; another was linked to cortical thickness. Several markers were also influenced by estimated intelligence, age and/or sex. LIMITATIONS Although complex analytical approaches, such as the one applied here, provide insight into otherwise hidden mechanisms, they also increase the complexity of interpretations. CONCLUSION No dominant imaging modality or marker characterizes structural brain phenotypes in adults with ADHD, but we can refine our characterization of the disorder by the integration of small effects across modalities.
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Affiliation(s)
- Thomas Wolfers
- Correspondence to: T. Wolfers, Donders Centre for Cognitive Neuroimaging, Radboud University, PO Box 9101, 6500 HB Nijmegen, the Netherlands;
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