Classifying Children with ADHD Based on Prefrontal Functional Near-infrared Spectroscopy Using Machine Learning

키워드

참고문헌 (33)

1. Austerman J, "ADHD and behavioral disorders : Assessment, management, and an update from DSM-5", Cleve Clin J Med, 82(11 Suppl 1), pp.S2~S7 (2015)
2. Drechsler R, "The course of neuropsychological functions in children with attention deficit hyperactivity disorder from late childhood to early adolescence", J Child Psychol Psychiatry, 46, pp.824~836 (2005)
3. Drechsler R, "ADHD : Current concepts and treatments in children and adolescents", Neuropediatrics, 51, pp.315~335 (2020)
4. Uddin LQ, "Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit", hyperactivity disorder, 2017, pp.7, e1218~
5. Grazioli S, "Light up ADHD : II. Neuropharmacological effects measured by near infrared spectroscopy : Is there a biomarker?", J Affect Disord, 244, pp.100~106 (2019)
6. Shao L, "Classification of ADHD with fMRI data and multi-objective optimization", Comput Methods Programs Biomed, 196, pp.105676~ (2020)
7. Kautzky A, "Machine learning classification of ADHD and HC by multimodal serotonergic data", Transl Psychiatry, 10, pp.104~ (2020)
8. Weyandt L, "Neuroimaging and ADHD : fMRI, PET, DTI findings, and methodological limitations", Dev Neuropsychol, 38, pp.211~225 (2013)
9. Gossé LK, "Functional near-infrared spectroscopy in developmental psychiatry : A review of attention deficit hyperactivity disorder", Eur Arch Psychiatry Clin Neurosci, 272, pp.273~290 (2022)
10. Monden Y, "Individual classification of ADHD children by right prefrontal hemodynamic responses during a go", no-go task as assessed by fNIRS, 2015, pp.9, 1~12
11. Hendrikx D, "Measurement of neurovascular coupling in neonates", Front Physiol, 10, pp.65~ (2019)
12. Cutini S, "Unleashing the future potential of functional near-infrared spectroscopy in brain sciences", J Neurosci Methods, 232, pp.152~156 (2014)
13. Mauri M, "Hemodynamic and behavioral peculiarities in response to emotional stimuli in children with attention deficit hyperactivity disorder : An fNIRS study", J Affect Disord, 277, pp.671~680 (2020)
14. Wang M, "Disrupted functional brain connectivity networks in children with attention-deficit", hyperactivity disorder : Evidence from restingstate functional near-infrared spectroscopy, 2020, pp.7, 015012~
15. Sutoko S, "Atypical dynamic-connectivity recruitment in attention-deficit", hyperactivity disorder children : An insight into task-based dynamic connectivity through an fNIRS study, 2020, pp.14, 3~
16. Nielsen AN, "Machine learning with neuroimaging : Evaluating its applications in psychiatry", Biol Psychiatry Cogn Neurosci Neuroimaging, 5, pp.791~798 (2020)
17. McTeague LM, "Identification of common neural circuit disruptions in cognitive control across psychiatric disorders", Am J Psychiatry, 174, pp.676~685 (2017)
18. Goodkind M, "Identification of a common neurobiological substrate for mental illness", JAMA Psychiatry, 72, pp.305~315 (2015)
19. Koppe G, "Deep learning for small and big data in psychiatry", Neuropsychopharmacology, 46, pp.176~190 (2021)
20. Eslami T, "Machine learning methods for diagnosing autism spectrum disorder and attention-deficit", hyperactivity disorder using functional and structural MRI : A survey, 2021, pp.14, 575999~
21. Zhang Z, "Application of artificial intelligence in the MRI classification task of human brain neurological and psychiatric diseases: A scoping review", Diagnostics(Basel), 11, pp.1402~ (2021)
22. Yasumura A, "Applied machine learning method to predict children with ADHD using prefrontal cortex activity : A multicenter study in Japan", J Atten Disord, 24, pp.2012~2020 (2020)
23. DuPaul GJ, "Parent and teacher ratings of ADHD symptoms : Psychometric properties in a community-based sample", J Clin Child Psychol, 20, pp.245~253 (1991)
24. 소유경, "한국어판 부모, 교사 ADHD 평가 척도의 신뢰도와 타당도 연구", 신경정신의학, 41(2), pp.283~289 (2002), 대한신경정신의학회
25. 김영신, "한국어판 부모 및 교사용 ADHD 평가 척도(K-ARS)의 규준연구", 신경정신의학, 42(3) (2003), 대한신경정신의학회
26. Kocsis L, "The modified Beer-Lambert law revisited", Phys Med Biol, 51, pp.N91~N98 (2006)
27. Šimundić AM, "Measures of diagnostic accuracy : basic definitions", EJIFCC, 19, pp.203~211 (2009)
28. Dai D, "Classification of ADHD children through multimodal magnetic resonance imaging", Front Syst Neurosci, 6, pp.63~ (2012)
29. Jourdan Moser S, "Right prefrontal brain activation due to Stroop interference is altered in attention-deficit hyperactivity disorde-a functional near-infrared spectroscopy study", Psychiatry Res, 173, pp.190~195 (2009)
30. Negoro H, "Prefrontal dysfunction in attention-deficit", hyperactivity disorder as measured by near-infrared spectroscopy, 2010, pp.41, 193~203
31. Suzuki K, "Excessive hemodynamic activity in the superior frontal cortex during the flanker task in children with attention deficit hyperactivity disorder", Neuroreport, 28, pp.828~832 (2017)
32. Rubia K, "Cognitive neuroscience of attention deficit hyperactivity disorder(ADHD)and its clinical translation", Front Hum Neurosci, 12, pp.100~ (2018)
33. Pulini AA, "Classification accuracy of neuroimaging biomarkers in attention-deficit", hyperactivity disorder : Effects of sample size and circular analysis, 2019, pp.4, 108~120

관련 논문AI추천