조현병과 양극성 장애 진단을 위한 확산 텐서 영상 기반 기계학습 모델 개발 연구
Development of a Machine Learning Model for Diagnosing Schizophrenia and Bipolar Disorder Based on Diffusion Tensor Imaging: A Preliminary Study
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Objectives: This study aimed to develop a machine learning model for diagnosing schizophrenia (SZ) and bipolar disorder (BD) based on diffusion tensor imaging (DTI) data. Methods: We used 3T-magnetic resonance imaging to examine SZ, BD, healthy control (HC) subjects (aged 20-50 years, n=65 in each group). Applying Support Vector Machine (SVM) to fractional anisotropy (FA) values, we built classification models of SZ and HC, BD and HC, and SZ and BD. Features of white matter (WM) tracts were selected through recursive feature elimination, and 5-fold cross validation was performed. Results: The SVM models classified SZ and BD from HC with a mean accuracy of 83.5% and 75.4%, respectively. The SZ-BD classification model archived 75.0% accuracy. These classification models used FA values in 15-18 WM tracts as features, including the retrolenticular part of the internal capsule, superior corona radiata, cingulum, and superior fronto-occipital fasciculus. Conclusions: This study presented a preliminary machine learning model to diagnose SZ and BD based on DTI data. Our findings also suggest that there might be a specific pattern of abnormalities in WM integrity that can differentiate the two psychotic disorders.
Abstract
Objectives: This study aimed to develop a machine learning model for diagnosing schizophrenia (SZ) and bipolar disorder (BD) based on diffusion tensor imaging (DTI) data. Methods: We used 3T-magnetic resonance imaging to examine SZ, BD, healthy control (HC) subjects (aged 20-50 years, n=65 in each group). Applying Support Vector Machine (SVM) to fractional anisotropy (FA) values, we built classification models of SZ and HC, BD and HC, and SZ and BD. Features of white matter (WM) tracts were selected through recursive feature elimination, and 5-fold cross validation was performed. Results: The SVM models classified SZ and BD from HC with a mean accuracy of 83.5% and 75.4%, respectively. The SZ-BD classification model archived 75.0% accuracy. These classification models used FA values in 15-18 WM tracts as features, including the retrolenticular part of the internal capsule, superior corona radiata, cingulum, and superior fronto-occipital fasciculus. Conclusions: This study presented a preliminary machine learning model to diagnose SZ and BD based on DTI data. Our findings also suggest that there might be a specific pattern of abnormalities in WM integrity that can differentiate the two psychotic disorders.
참고문헌 (28)
- van Bergen AH, "The characteristics of psychotic features in bipolar disorder", Psychol Med, 49, pp.2036~2048 (2019)
- Altamura AC, "Differential diagnoses and management strategies in patients with schizophrenia and bipolar disorder", Neuropsychiatr Dis Treat, 4, pp.311~317 (2008)
- Craddock N, "Psychiatric diagnosis: impersonal, imperfect and important", Br J Psychiatry, 204, pp.93~95 (2014)
- Kim JS, "Diagnostic stability of first-episode psychosis and predictors of diagnostic shift from non-affective psychosis to bipolar disorder: a retrospective evaluation after recurrence", Psychiatry Res, 188, pp.29~33 (2011)
- 이선우, "양극성 장애에서 항정신병약물", 생물치료정신의학, 4(1), pp.61~70 (1998), 대한생물치료정신의학회
- Prata D, "Clinically meaningful biomarkers for psychosis: a systematic and quantitative review", Neurosci Biobehav Rev, 45, pp.134~141 (2014)
- Clementz BA, "Identification of distinct psychosis biotypes using brain-based biomarkers", Am J Psychiatry, 173, pp.373~384 (2016)
- Dwyer DB, "Machine learning approaches for clinical psychology and psychiatry", Annu Rev Clin Psychol, 14, pp.91~118 (2018)
- Iniesta R, "Machine learning, statistical learning and the future of biological research in psychiatry", Psychol Med, 46, pp.2455~2465 (2016)
- Oquendo MA, "Machine learning and data mining: strategies for hypothesis generation", Mol Psychiatry, 17, pp.956~959 (2012)
- Nielsen AN, "Machine learning with neuroimaging: evaluating its applications in psychiatry", Biol Psychiatry Cogn Neurosci Neuroimaging, 5, pp.791~798 (2020)
- Janssen RJ, "Making individual prognoses in psychiatry using neuroimaging and machine learning", Biol Psychiatry Cogn Neurosci Neuroimaging, 3, pp.798~808 (2018)
- Claude LA, "Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions", Bipolar Disord, 22, pp.334~355 (2020)
- de Filippis R, "Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review", Neuropsychiatr Dis Treat, 15, pp.1605~1627 (2019)
- Sheehan DV, "The mini-international neuropsychiatric interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10", J Clin Psychiatry, 59(Suppl 20), pp.22~33 (1998)
- Yoo SW, "Validity of Korean version of the mini-international neuropsychiatric interview", Anxiety Mood, 2, pp.50~55 (2006)
- Overall JE, "The brief psychiatric rating scale (BPRS): recent developments in ascertainment and scaling", Psychopharmacol Bull, 24, pp.97~99 (1988)
- Schnack HG, "Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects", Neuroimage, 84, pp.299~306 (2014)
- Salvador R, "Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis", PLoS One, 12, pp.e0175683~ (2017)
- Sutcubasi B, "Anatomical connectivity changes in bipolar disorder and schizophrenia investigated using wholebrain tract-based spatial statistics and machine learning approaches", Neural Comput & Applic, 31, pp.4983~4992 (2019)
- Savitz JB, "Clinical application of brain imaging for the diagnosis of mood disorders: the current state of play", Mol Psychiatry, 18, pp.528~539 (2013)
- Quaak M, "Deep learning applications for the classification of psychiatric disorders using neuroimaging data: systematic review and meta-analysis", Neuroimage Clin, 30, pp.102584~ (2021)
- Nunes A, "Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group", Mol Psychiatry, 25, pp.2130~2143 (2020)
- Vieira S, "Using machine learning and structural neuroimaging to detect first episode psychosis: reconsidering the evidence", Schizophr Bull, 46, pp.17~26 (2020)
- Altman N, "The curse(s) of dimensionality", Nat Methods, 15, pp.399~400 (2018)
- Lee DK, "Common gray and white matter abnormalities in schizophrenia and bipolar disorder", PLoS One, 15, pp.e0232826~ (2020)
- Skudlarski P, "Diffusion tensor imaging white matter endophenotypes in patients with schizophrenia or psychotic bipolar disorder and their relatives", Am J Psychiatry, 170, pp.886~898 (2013)
- Squarcina L, "Similar white matter changes in schizophrenia and bipolar disorder: a tract-based spatial statistics study", PLoS One, 12, pp.e0178089~ (2017)