An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges
-
Walter Antonio Campos-Ugaz
,
Jessica Paola Palacios Garay
,
Oriana Rivera-Lozada
,
Mitchell Alberto Alarcón Diaz
,
Doris Fuster-Guillén
,
Arístides Alfonso Tejada Arana
Abstract
Objective: Automatic diagnosis of psychiatric disorders such as bipolar disorder (BD) through machine learning techniques has attracted substantial attention from psychiatric and artificial intelligence communities. These approaches mostly rely on various biomarkers extracted from electroencephalogram (EEG) or magnetic resonance imaging (MRI)/functional MRI (fMRI) data. In this paper, we provide an updated overview of existing machine learning-based methods for bipolar disorder (BD) diagnosis using MRI and EEG data.
Method: This study is a short non-systematic review with the aim of describing the current situation in automatic diagnosis of BD using machine learning methods. Therefore, an appropriate literature search was conducted via relevant keywords for original EEG/MRI studies on distinguishing BD from other conditions, particularly from healthy peers, in PubMed, Web of Science, and Google Scholar databases.
Results: We reviewed 26 studies, including 10 EEG studies and 16 MRI studies (including structural and functional MRI), that used traditional machine learning methods and deep learning algorithms to automatically detect BD. The reported accuracies for EEG studies is about 90%, while the reported accuracies for MRI studies remains below the minimum level for clinical relevance, i.e. about 80% of the classification outcome for traditional machine learning methods. However, deep learning techniques have generally achieved accuracies higher than 95%.
Conclusion: Research utilizing machine learning applied to EEG signals and brain images has provided proof of concept for how this innovative technique can help psychiatrists distinguish BD patients from healthy people. However, the results have been somewhat contradictory and we must keep away from excessive optimistic interpretations of the findings. Much progress is still needed to reach the level of clinical practice in this field.
1. Mohammadi MR, Ahmadi N, Khaleghi A, Mostafavi SA, Kamali K, Rahgozar M, et al. Prevalence and Correlates of Psychiatric Disorders in a National Survey of Iranian Children and Adolescents. Iran J Psychiatry. 2019;14(1):1-15.
2. Khaleghi A, Mohammadi MR, Zandifar A, Ahmadi N, Alavi SS, Ahmadi A, et al. Epidemiology of psychiatric disorders in children and adolescents; in Tehran, 2017. Asian J Psychiatr. 2018;37:146-53.
3. Talepasand S, Mohammadi MR, Alavi SS, Khaleghi A, Sajedi Z, Akbari P, et al. Psychiatric disorders in children and adolescents: Prevalence and sociodemographic correlates in Semnan Province in Iran. Asian J Psychiatr. 2019;40:9-14.
4. Rosa AR, González-Ortega I, González-Pinto A, Echeburúa E, Comes M, Martínez-Àran A, et al. One-year psychosocial functioning in patients in the early vs. late stage of bipolar disorder. Acta Psychiatr Scand. 2012;125(4):335-41.
5. Berk M, Dodd S, Callaly P, Berk L, Fitzgerald P, de Castella AR, et al. History of illness prior to a diagnosis of bipolar disorder or schizoaffective disorder. J Affect Disord. 2007;103(1-3):181-6.
6. Goi PD, Vianna-Sulzbach M, Silveira L, Grande I, Chendo I, Sodré LA, et al. Treatment delay is associated with more episodes and more severe illness staging progression in patients with bipolar disorder. Psychiatry Res. 2015;227(2-3):372-3.
7. Alda M, Manchia M. Personalized management of bipolar disorder. Neurosci Lett. 2018;669:3-9.
8. Giridharan VV, Sayana P, Pinjari OF, Ahmad N, da Rosa MI, Quevedo J, et al. Postmortem evidence of brain inflammatory markers in bipolar disorder: a systematic review. Mol. Psychiatry. 2020;25(1):94-113.
9. Clark L, Sahakian BJ. Cognitive neuroscience and brain imaging in bipolar disorder. Dialogues in clinical neuroscience. 2022.
10. Khaleghi A, Sheikhani A, Mohammadi MR, Moti Nasrabadi A. Evaluation of Cerebral Cortex Function in Clients with Bipolar Mood Disorder I (BMD I) Compared With BMD II Using QEEG Analysis. Iran J Psychiatry. 2015;10(2):93-9.
11. Scaini G, Valvassori SS, Diaz AP, Lima CN, Benevenuto D, Fries GR, et al. Neurobiology of bipolar disorders: a review of genetic components, signaling pathways, biochemical changes, and neuroimaging findings. Braz J Psychiatry. 2020;42(5):536-51.
12. Wang X, Luo Q, Tian F, Cheng B, Qiu L, Wang S, et al. Brain grey-matter volume alteration in adult patients with bipolar disorder under different conditions: a voxel-based meta-analysis. J Psychiatry Neurosci. 2019;44(2):89-101.
13. Xiao W, Manyi G, Khaleghi A. Deficits in auditory and visual steady-state responses in adolescents with bipolar disorder. J Psychiatr Res. 2022;151:368-76.
14. Moeini M, Khaleghi A, Mohammadi MR. Characteristics of Alpha Band Frequency in Adolescents with Bipolar II Disorder: A Resting-State QEEG Study. Iran J Psychiatry. 2015;10(1):8-12.
15. Hibar DP, Westlye LT, Doan NT, Jahanshad N, Cheung JW, Ching CRK, et al. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol Psychiatry. 2018;23(4):932-42.
16. Hibar DP, Westlye LT, van Erp TG, Rasmussen J, Leonardo CD, Faskowitz J, et al. Subcortical volumetric abnormalities in bipolar disorder. Mol Psychiatry. 2016;21(12):1710-6.
17. Khaleghi A, Mohammadi MR, Shahi K, Nasrabadi AM. Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches. Clin Psychopharmacol Neurosci. 2022;20(1):26-36.
18. Yasin S, Hussain SA, Aslan S, Raza I, Muzammel M, Othmani A. EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks:A review. Comput Methods Programs Biomed. 2021;202:106007.
19. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett. 2016;6(2):66-73.
20. Zarafshan H, Khaleghi A, Mohammadi MR, Moeini M, Malmir N. Electroencephalogram complexity analysis in children with attention-deficit/hyperactivity disorder during a visual cognitive task. J Clin Exp Neuropsychol. 2016;38(3):361-9.
21. Murphy KP. Probabilistic machine learning: an introduction: MIT press; 2022.
22. Ayodele TO. Machine learning overview. Mach Lear. 2010;2:9-18.
23. Claude LA, Houenou J, Duchesnay E, Favre P. Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions. Bipolar Disord. 2020;22(4):334-55.
24. Passos IC, Mwangi B, Cao B, Hamilton JE, Wu MJ, Zhang XY, et al. Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach. J Affect Disord. 2016;193:109-16.
25. Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Bürger C, et al. Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data. JAMA Psychiatry. 2016;73(6):557-64.
26. Librenza-Garcia D, Kotzian BJ, Yang J, Mwangi B, Cao B, Pereira Lima LN, et al. The impact of machine learning techniques in the study of bipolar disorder: A systematic review. Neurosci Biobehav Rev. 2017;80:538-54.
27. Passos IC, Ballester PL, Barros RC, Librenza-Garcia D, Mwangi B, Birmaher B, et al. Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force. Bipolar Disord. 2019;21(7):582-94.
28. Sadatnezhad K, Boostani R, Ghanizadeh A. Classification of BMD and ADHD patients using their EEG signals. Expert Systems with Applications. 2011;38(3):1956-63.
29. Alimardani F, Boostani R, Azadehdel M, Ghanizadeh A, Rastegar K. Presenting a new search strategy to select synchronization values for classifying bipolar mood disorders from schizophrenic patients. Eng Appl Artif Intell. 2013;26(2):913-23.
30. Nazhvani AD, Boostani R, Afrasiabi S, Sadatnezhad K. Classification of ADHD and BMD patients using visual evoked potential. Clin Neurol Neurosurg. 2013;115(11):2329-35.
31. Khaleghi A, Sheikhani A, Mohammadi MR, Nasrabadi AM, Vand SR, Zarafshan H, et al. EEG classification of adolescents with type I and type II of bipolar disorder. Australas Phys Eng Sci Med. 2015;38(4):551-9.
32. Erguzel TT, Sayar GH, Tarhan N. Artificial intelligence approach to classify unipolar and bipolar depressive disorders. Neural Comput Appl. 2016;27(6):1607-16.
33. Metin SZ, Erguzel TT, Ertan G, Salcini C, Kocarslan B, Cebi M, et al. The Use of Quantitative EEG for Differentiating Frontotemporal Dementia From Late-Onset Bipolar Disorder. Clin EEG Neurosci. 2018;49(3):171-6.
34. Khaleghi A, Mohammadi MR, Moeini M, Zarafshan H, Fadaei Fooladi M. Abnormalities of Alpha Activity in Frontocentral Region of the Brain as a Biomarker to Diagnose Adolescents With Bipolar Disorder. Clin EEG Neurosci. 2019;50(5):311-8.
35. Mateo-Sotos J, Torres A, Santos J, Quevedo O, Basar C. A Machine Learning-Based Method to Identify Bipolar Disorder Patients. Analog Integr Circuits Signal Process. 2022;41(4):2244-65.
36. Metin B, Uyulan Ç, Ergüzel TT, Farhad S, Çifçi E, Türk Ö, et al. The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data. Clin EEG Neurosci. 2022:15500594221137234.
37. Lei Y, Belkacem AN, Wang X, Sha S, Wang C, Chen C. A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders. Biomed Signal Process Control. 2022;72:103370.
38. Besga A, Termenon M, Graña M, Echeveste J, Pérez JM, Gonzalez-Pinto A. Discovering Alzheimer's disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features. Neurosci Lett. 2012;520(1):71-6.
39. Anticevic A, Cole MW, Repovs G, Murray JD, Brumbaugh MS, Winkler AM, et al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cereb Cortex. 2014;24(12):3116-30.
40. Jie NF, Zhu MH, Ma XY, Osuch EA, Wammes M, Théberge J, et al. Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa: Efficient Feature Selection With Multimodal Brain Imaging Data. IEEE Trans Auton Ment Dev. 2015;7(4):320-31.
41. Mwangi B, Wu MJ, Bauer IE, Modi H, Zeni CP, Zunta-Soares GB, et al. Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines. Psychiatry Res. 2015;234(2):265-71.
42. Sacchet MD, Livermore EE, Iglesias JE, Glover GH, Gotlib IH. Subcortical volumes differentiate Major Depressive Disorder, Bipolar Disorder, and remitted Major Depressive Disorder. J Psychiatr Res. 2015;68:91-8.
43. Mwangi B, Wu MJ, Cao B, Passos IC, Lavagnino L, Keser Z, et al. Individualized Prediction and Clinical Staging of Bipolar Disorders using Neuroanatomical Biomarkers. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(2):186-94.
44. Salvador R, Radua J, Canales-Rodríguez EJ, Solanes A, Sarró S, Goikolea JM, et al. Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis. PLoS One. 2017;12(4):e0175683.
45. Li M, Das T, Deng W, Wang Q, Li Y, Zhao L, et al. Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression. Acta Psychiatr Scand. 2017;136(3):288-99.
46. Frangou S, Dima D, Jogia J. Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder. Neuroimage. 2017;145(Pt B):230-7.
47. Doan NT, Kaufmann T, Bettella F, Jørgensen KN, Brandt CL, Moberget T, et al. Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders. Neuroimage Clin. 2017;15:719-31.
48. Bürger C, Redlich R, Grotegerd D, Meinert S, Dohm K, Schneider I, et al. Differential Abnormal Pattern of Anterior Cingulate Gyrus Activation in Unipolar and Bipolar Depression: an fMRI and Pattern Classification Approach. Neuropsychopharmacology. 2017;42(7):1399-408.
49. Jie N-F, Osuch EA, Zhu M-H, Wammes M, Ma X-Y, Jiang T-Z, et al. Discriminating bipolar disorder from major depression using whole-brain functional connectivity: a feature selection analysis with SVM-FoBa algorithm. J Signal Process Syst. 2018;90(2):259-71.
50. Matsuo K, Harada K, Fujita Y, Okamoto Y, Ota M, Narita H, et al. Distinctive Neuroanatomical Substrates for Depression in Bipolar Disorder versus Major Depressive Disorder. Cereb Cortex. 2019;29(1):202-14.
51. Schwarz E, Doan NT, Pergola G, Westlye LT, Kaufmann T, Wolfers T, et al. Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder. Transl Psychiatry. 2019;9(1):12.
52. Nunes A, Schnack HG, Ching CRK, Agartz I, Akudjedu TN, Alda M, et al. Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group. Mol Psychiatry. 2020;25(9):2130-43.
53. Li Z, Li W, Wei Y, Gui G, Zhang R, Liu H, et al. Deep learning based automatic diagnosis of first-episode psychosis, bipolar disorder and healthy controls. Comput Med Imaging Graph. 2021;89:101882.
54. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Bmj. 2015;350:g7594.
55. Varoquaux G. Cross-validation failure: Small sample sizes lead to large error bars. Neuroimage. 2018;180(Pt A):68-77.
56. Shim M, Lee SH, Hwang HJ. Inflated prediction accuracy of neuropsychiatric biomarkers caused by data leakage in feature selection. Sci Rep. 2021;11(1):7980.
57. Kernbach JM, Staartjes VE. Foundations of Machine Learning-Based Clinical Prediction Modeling: Part II-Generalization and Overfitting. Acta Neurochir Suppl. 2022;134:15-21.
2. Khaleghi A, Mohammadi MR, Zandifar A, Ahmadi N, Alavi SS, Ahmadi A, et al. Epidemiology of psychiatric disorders in children and adolescents; in Tehran, 2017. Asian J Psychiatr. 2018;37:146-53.
3. Talepasand S, Mohammadi MR, Alavi SS, Khaleghi A, Sajedi Z, Akbari P, et al. Psychiatric disorders in children and adolescents: Prevalence and sociodemographic correlates in Semnan Province in Iran. Asian J Psychiatr. 2019;40:9-14.
4. Rosa AR, González-Ortega I, González-Pinto A, Echeburúa E, Comes M, Martínez-Àran A, et al. One-year psychosocial functioning in patients in the early vs. late stage of bipolar disorder. Acta Psychiatr Scand. 2012;125(4):335-41.
5. Berk M, Dodd S, Callaly P, Berk L, Fitzgerald P, de Castella AR, et al. History of illness prior to a diagnosis of bipolar disorder or schizoaffective disorder. J Affect Disord. 2007;103(1-3):181-6.
6. Goi PD, Vianna-Sulzbach M, Silveira L, Grande I, Chendo I, Sodré LA, et al. Treatment delay is associated with more episodes and more severe illness staging progression in patients with bipolar disorder. Psychiatry Res. 2015;227(2-3):372-3.
7. Alda M, Manchia M. Personalized management of bipolar disorder. Neurosci Lett. 2018;669:3-9.
8. Giridharan VV, Sayana P, Pinjari OF, Ahmad N, da Rosa MI, Quevedo J, et al. Postmortem evidence of brain inflammatory markers in bipolar disorder: a systematic review. Mol. Psychiatry. 2020;25(1):94-113.
9. Clark L, Sahakian BJ. Cognitive neuroscience and brain imaging in bipolar disorder. Dialogues in clinical neuroscience. 2022.
10. Khaleghi A, Sheikhani A, Mohammadi MR, Moti Nasrabadi A. Evaluation of Cerebral Cortex Function in Clients with Bipolar Mood Disorder I (BMD I) Compared With BMD II Using QEEG Analysis. Iran J Psychiatry. 2015;10(2):93-9.
11. Scaini G, Valvassori SS, Diaz AP, Lima CN, Benevenuto D, Fries GR, et al. Neurobiology of bipolar disorders: a review of genetic components, signaling pathways, biochemical changes, and neuroimaging findings. Braz J Psychiatry. 2020;42(5):536-51.
12. Wang X, Luo Q, Tian F, Cheng B, Qiu L, Wang S, et al. Brain grey-matter volume alteration in adult patients with bipolar disorder under different conditions: a voxel-based meta-analysis. J Psychiatry Neurosci. 2019;44(2):89-101.
13. Xiao W, Manyi G, Khaleghi A. Deficits in auditory and visual steady-state responses in adolescents with bipolar disorder. J Psychiatr Res. 2022;151:368-76.
14. Moeini M, Khaleghi A, Mohammadi MR. Characteristics of Alpha Band Frequency in Adolescents with Bipolar II Disorder: A Resting-State QEEG Study. Iran J Psychiatry. 2015;10(1):8-12.
15. Hibar DP, Westlye LT, Doan NT, Jahanshad N, Cheung JW, Ching CRK, et al. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol Psychiatry. 2018;23(4):932-42.
16. Hibar DP, Westlye LT, van Erp TG, Rasmussen J, Leonardo CD, Faskowitz J, et al. Subcortical volumetric abnormalities in bipolar disorder. Mol Psychiatry. 2016;21(12):1710-6.
17. Khaleghi A, Mohammadi MR, Shahi K, Nasrabadi AM. Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches. Clin Psychopharmacol Neurosci. 2022;20(1):26-36.
18. Yasin S, Hussain SA, Aslan S, Raza I, Muzammel M, Othmani A. EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks:A review. Comput Methods Programs Biomed. 2021;202:106007.
19. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett. 2016;6(2):66-73.
20. Zarafshan H, Khaleghi A, Mohammadi MR, Moeini M, Malmir N. Electroencephalogram complexity analysis in children with attention-deficit/hyperactivity disorder during a visual cognitive task. J Clin Exp Neuropsychol. 2016;38(3):361-9.
21. Murphy KP. Probabilistic machine learning: an introduction: MIT press; 2022.
22. Ayodele TO. Machine learning overview. Mach Lear. 2010;2:9-18.
23. Claude LA, Houenou J, Duchesnay E, Favre P. Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions. Bipolar Disord. 2020;22(4):334-55.
24. Passos IC, Mwangi B, Cao B, Hamilton JE, Wu MJ, Zhang XY, et al. Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach. J Affect Disord. 2016;193:109-16.
25. Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Bürger C, et al. Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data. JAMA Psychiatry. 2016;73(6):557-64.
26. Librenza-Garcia D, Kotzian BJ, Yang J, Mwangi B, Cao B, Pereira Lima LN, et al. The impact of machine learning techniques in the study of bipolar disorder: A systematic review. Neurosci Biobehav Rev. 2017;80:538-54.
27. Passos IC, Ballester PL, Barros RC, Librenza-Garcia D, Mwangi B, Birmaher B, et al. Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force. Bipolar Disord. 2019;21(7):582-94.
28. Sadatnezhad K, Boostani R, Ghanizadeh A. Classification of BMD and ADHD patients using their EEG signals. Expert Systems with Applications. 2011;38(3):1956-63.
29. Alimardani F, Boostani R, Azadehdel M, Ghanizadeh A, Rastegar K. Presenting a new search strategy to select synchronization values for classifying bipolar mood disorders from schizophrenic patients. Eng Appl Artif Intell. 2013;26(2):913-23.
30. Nazhvani AD, Boostani R, Afrasiabi S, Sadatnezhad K. Classification of ADHD and BMD patients using visual evoked potential. Clin Neurol Neurosurg. 2013;115(11):2329-35.
31. Khaleghi A, Sheikhani A, Mohammadi MR, Nasrabadi AM, Vand SR, Zarafshan H, et al. EEG classification of adolescents with type I and type II of bipolar disorder. Australas Phys Eng Sci Med. 2015;38(4):551-9.
32. Erguzel TT, Sayar GH, Tarhan N. Artificial intelligence approach to classify unipolar and bipolar depressive disorders. Neural Comput Appl. 2016;27(6):1607-16.
33. Metin SZ, Erguzel TT, Ertan G, Salcini C, Kocarslan B, Cebi M, et al. The Use of Quantitative EEG for Differentiating Frontotemporal Dementia From Late-Onset Bipolar Disorder. Clin EEG Neurosci. 2018;49(3):171-6.
34. Khaleghi A, Mohammadi MR, Moeini M, Zarafshan H, Fadaei Fooladi M. Abnormalities of Alpha Activity in Frontocentral Region of the Brain as a Biomarker to Diagnose Adolescents With Bipolar Disorder. Clin EEG Neurosci. 2019;50(5):311-8.
35. Mateo-Sotos J, Torres A, Santos J, Quevedo O, Basar C. A Machine Learning-Based Method to Identify Bipolar Disorder Patients. Analog Integr Circuits Signal Process. 2022;41(4):2244-65.
36. Metin B, Uyulan Ç, Ergüzel TT, Farhad S, Çifçi E, Türk Ö, et al. The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data. Clin EEG Neurosci. 2022:15500594221137234.
37. Lei Y, Belkacem AN, Wang X, Sha S, Wang C, Chen C. A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders. Biomed Signal Process Control. 2022;72:103370.
38. Besga A, Termenon M, Graña M, Echeveste J, Pérez JM, Gonzalez-Pinto A. Discovering Alzheimer's disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features. Neurosci Lett. 2012;520(1):71-6.
39. Anticevic A, Cole MW, Repovs G, Murray JD, Brumbaugh MS, Winkler AM, et al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cereb Cortex. 2014;24(12):3116-30.
40. Jie NF, Zhu MH, Ma XY, Osuch EA, Wammes M, Théberge J, et al. Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa: Efficient Feature Selection With Multimodal Brain Imaging Data. IEEE Trans Auton Ment Dev. 2015;7(4):320-31.
41. Mwangi B, Wu MJ, Bauer IE, Modi H, Zeni CP, Zunta-Soares GB, et al. Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines. Psychiatry Res. 2015;234(2):265-71.
42. Sacchet MD, Livermore EE, Iglesias JE, Glover GH, Gotlib IH. Subcortical volumes differentiate Major Depressive Disorder, Bipolar Disorder, and remitted Major Depressive Disorder. J Psychiatr Res. 2015;68:91-8.
43. Mwangi B, Wu MJ, Cao B, Passos IC, Lavagnino L, Keser Z, et al. Individualized Prediction and Clinical Staging of Bipolar Disorders using Neuroanatomical Biomarkers. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(2):186-94.
44. Salvador R, Radua J, Canales-Rodríguez EJ, Solanes A, Sarró S, Goikolea JM, et al. Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis. PLoS One. 2017;12(4):e0175683.
45. Li M, Das T, Deng W, Wang Q, Li Y, Zhao L, et al. Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression. Acta Psychiatr Scand. 2017;136(3):288-99.
46. Frangou S, Dima D, Jogia J. Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder. Neuroimage. 2017;145(Pt B):230-7.
47. Doan NT, Kaufmann T, Bettella F, Jørgensen KN, Brandt CL, Moberget T, et al. Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders. Neuroimage Clin. 2017;15:719-31.
48. Bürger C, Redlich R, Grotegerd D, Meinert S, Dohm K, Schneider I, et al. Differential Abnormal Pattern of Anterior Cingulate Gyrus Activation in Unipolar and Bipolar Depression: an fMRI and Pattern Classification Approach. Neuropsychopharmacology. 2017;42(7):1399-408.
49. Jie N-F, Osuch EA, Zhu M-H, Wammes M, Ma X-Y, Jiang T-Z, et al. Discriminating bipolar disorder from major depression using whole-brain functional connectivity: a feature selection analysis with SVM-FoBa algorithm. J Signal Process Syst. 2018;90(2):259-71.
50. Matsuo K, Harada K, Fujita Y, Okamoto Y, Ota M, Narita H, et al. Distinctive Neuroanatomical Substrates for Depression in Bipolar Disorder versus Major Depressive Disorder. Cereb Cortex. 2019;29(1):202-14.
51. Schwarz E, Doan NT, Pergola G, Westlye LT, Kaufmann T, Wolfers T, et al. Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder. Transl Psychiatry. 2019;9(1):12.
52. Nunes A, Schnack HG, Ching CRK, Agartz I, Akudjedu TN, Alda M, et al. Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group. Mol Psychiatry. 2020;25(9):2130-43.
53. Li Z, Li W, Wei Y, Gui G, Zhang R, Liu H, et al. Deep learning based automatic diagnosis of first-episode psychosis, bipolar disorder and healthy controls. Comput Med Imaging Graph. 2021;89:101882.
54. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Bmj. 2015;350:g7594.
55. Varoquaux G. Cross-validation failure: Small sample sizes lead to large error bars. Neuroimage. 2018;180(Pt A):68-77.
56. Shim M, Lee SH, Hwang HJ. Inflated prediction accuracy of neuropsychiatric biomarkers caused by data leakage in feature selection. Sci Rep. 2021;11(1):7980.
57. Kernbach JM, Staartjes VE. Foundations of Machine Learning-Based Clinical Prediction Modeling: Part II-Generalization and Overfitting. Acta Neurochir Suppl. 2022;134:15-21.
| Files | ||
| Issue | Vol 18 No 2 (2023) | |
| Section | Short Communication(s) | |
| DOI | https://doi.org/10.18502/ijps.v18i2.12372 | |
| Keywords | ||
| Bipolar Disorder Electroencephalogram Magnetic Resonance Imaging Machine Learning | ||
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How to Cite
1.
Campos-Ugaz WA, Palacios Garay JP, Rivera-Lozada O, Alarcón Diaz MA, Fuster-Guillén D, Tejada Arana AA. An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges. Iran J Psychiatry. 2023;18(2):237-247.
