Machine learning models to predict neuropsychiatric disorders in various brain tumors

References

• Bunevicius A, Tamasauskas S, Deltuva V, et al. Predictors of health-related quality of life in neurosurgical brain tumor patients: focus on patient-centered perspective. Acta Neurochir. 2014;156(2):367–374.
   PubMed Web of Science ®Google Scholar
• Pelletier G, Verhoef MJ, Khatri N, et al. Quality of life in brain tumor patients: the relative contributions of depression, fatigue, emotional distress, and existential issues. J Neurooncol. 2002;57(1):41–49.
   PubMed Web of Science ®Google Scholar
• Moise D, Madhusoodanan S. Psychiatric symptoms associated with brain tumors: a clinical enigma. CNS Spectr. 2006;11(1):28–31.
   PubMed Web of Science ®Google Scholar
• Giovagnoli AR. Investigation of cognitive impairments in people with brain tumors. J Neurooncol. 2012;108(2):277–283.
   PubMed Web of Science ®Google Scholar
• Brown PD, Ballman KV, Rummans TA, et al. Prospective study of quality of life in adults with newly diagnosed high-grade gliomas. J Neurooncol. 2006;76(3):283–291.
   PubMed Web of Science ®Google Scholar
• Wellisch DK, Kaleita TA, Freeman D, et al. Predicting major depression in brain tumor patients. Psychooncology. 2002;11(3):230–238.
   PubMed Web of Science ®Google Scholar
• Arnold SD, Forman LM, Brigidi BD, et al. Evaluation and characterization of generalized anxiety and depression in patients with primary brain tumors. Neuro Oncol. 2008;10(2):171–181.
   PubMed Web of Science ®Google Scholar
• Kocher R, Linder M, Stula D. Primary brain tumors in psychiatry. Schweiz Arch Neurol Neurochir Psychiatr. 1984;135(2):217–227.
   PubMedGoogle Scholar
• Gupta RK, Kumar R. Benign brain tumours and psychiatric morbidity: a 5-years retrospective data analysis. Aust N Z J Psychiatry. 2004;38(5):316–319.
   PubMed Web of Science ®Google Scholar
• Rooney AG, Carson A, Grant R. Depression in cerebral glioma patients: a systematic review of observational studies. J Natl Cancer Inst. 2011;103(1):61–76.
   PubMedGoogle Scholar
• Belyi B. Mental impairment in unilateral frontal tumours: role of the laterality of the lesion. Int J Neurosci. 1987;32(3-4):799–810.
   PubMed Web of Science ®Google Scholar
• Craig AH, Cummings JL, Fairbanks L, et al. Cerebral blood flow correlates of apathy in Alzheimer disease. Arch Neurol. 1996;53(11):1116–1120.
   PubMed Web of Science ®Google Scholar
• Cummings JL, Mendez MF. Secondary mania with focal cerebrovascular lesions. Am J Psychiatry. 1984;141(9):1084–1087.
   Web of Science ®Google Scholar
• Madhusoodanan S, Ting MB, Farah T, et al. Psychiatric aspects of brain tumors: a review. World J Psychiatry. 2015;5(3):273–285.
   PubMedGoogle Scholar
• Korali Z, Wittchen H, Pfister H, et al. Are patients with pituitary adenomas at an increased risk of mental disorders? Acta Psychiatr Scand. 2003;107(1):60–68.
   PubMed Web of Science ®Google Scholar
• Cornblath EJ, Lydon-Staley DM, Bassett DS. Harnessing networks and machine learning in neuropsychiatric care. Curr Opin Neurobiol. 2019;55:32–39.
   PubMed Web of Science ®Google Scholar
• Walsh CG, Ribeiro JD, Franklin JC. Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science. 2017;5(3):457–469.
   Web of Science ®Google Scholar
• Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cognitive Neurosci Neuroimag. 2018;3(3):223–230.
   PubMed Web of Science ®Google Scholar
• Mitchell TM. Machine learning. New York: McGraw-hill; 1997.
   Google Scholar
• Senders JT, Staples PC, Karhade AV, et al. Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg. 2018;109:476–486.
   PubMed Web of Science ®Google Scholar
• Davatzikos C, Zacharaki EI, Gooya A, et al. Multi-parametric analysis and registration of brain tumors: constructing statistical atlases and diagnostic tools of predictive value. In2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2011. IEEE. p. 6979–6981.
   Google Scholar
• Gautam R, Sharma M. Prevalence and diagnosis of neurological disorders using different deep learning techniques: a meta-analysis. J Med Syst. 2020;44(2):1–24.
   Web of Science ®Google Scholar
• Gill S, Mouches P, Hu S, et al. Using machine learning to predict dementia from neuropsychiatric symptom and neuroimaging data. J Alzheimers Dis. 2020;75(1):277–288.
   PubMed Web of Science ®Google Scholar
• Mallo SC, Valladares-Rodriguez S, Facal D, et al. Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach. Int Psychogeriatr. 2020;32(3):381–392.
   PubMed Web of Science ®Google Scholar
• So A, Hooshyar D, Park KW, et al. Early diagnosis of dementia from clinical data by machine learning techniques. Appl Sci. 2017;7(7):651.
   Google Scholar
• Vaccaro MG, Sarica A, Quattrone A, et al. Neuropsychological assessment could distinguish among different clinical phenotypes of progressive supranuclear palsy: a machine learning approach. J Neuropsychol. 2021;15(3):301–318. Sep
   PubMed Web of Science ®Google Scholar
• Battista P, Salvatore C, Castiglioni I. Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment classification: a machine learning study. Behav Neurol. 2017;2017:1850909.
   PubMed Web of Science ®Google Scholar
• Breiman L. Random forests. Statistics department. Berkeley, CA: University of California; 2001. 4720.
   Google Scholar
• Askland KD, Garnaat S, Sibrava NJ, et al. Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy. Int J Methods Psychiatr Res. 2015;24(2):156–169.
   PubMed Web of Science ®Google Scholar
• Breiman L. Random forests. Machine Learning. 2001;45(1):5–32.
   Web of Science ®Google Scholar
• Quinlan JR, editor. Combining instance-based and model-based learning. Proceedings of the tenth international conference on machine learning; 1993.
   Google Scholar
• Quinlan JR. Constructing decision tree. C4. 1993;5:17–26.
   Google Scholar
• Quinlan JR. C4. 5: Programming for machine learning. Morgan Kauffmann. 1993;38:48.
   Google Scholar
• Salzberg SL. Book Review: C4.5: Programs for machine learning by J Ross Quinlan. M0rgan Kaufmann Publishers, Inc., 1993. Machine Learning. Boston: Kluwer Academic Publishers. 1994;16:235–240.
   Google Scholar
• Hulten G, Spencer L, Domingos P, editors. Mining time-changing data streams. Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining; 2001.
   Google Scholar
• Dwyer DB, Falkai P, Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol. 2018;14:91–118.
   PubMed Web of Science ®Google Scholar
• Stupp R, Mason WP, Van Den Bent MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987–996.
   PubMed Web of Science ®Google Scholar
• Hart MG, Garside R, Rogers G, et al. Temozolomide for high grade glioma. Cochrane Database Syst Rev. 2013;2013(4):CD007415.
   Web of Science ®Google Scholar
• Seif G. A guide to decision trees for machine learning and data science; 2018. Available from: https://towardsdatascience.com/a-guide-to-decision-trees-for-machine-learning-and-data-science-fe2607241956.
   Google Scholar
• Huys QJ, Maia TV, Frank MJ. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci. 2016;19(3):404–413.
   PubMed Web of Science ®Google Scholar
• Arbabshirani MR, Plis S, Sui J, et al. Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. Neuroimage. 2017;145(Pt B):137–165.
   PubMed Web of Science ®Google Scholar
• Kambeitz J, Kambeitz-Ilankovic L, Leucht S, et al. Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies. Neuropsychopharmacology. 2015;40(7):1742–1751.
   PubMed Web of Science ®Google Scholar
• Koutsouleris N, Borgwardt S, Meisenzahl EM, et al. Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study. Schizophr Bull. 2012;38(6):1234–1246.
   PubMed Web of Science ®Google Scholar
• Hofmann SG, Asnaani A, Vonk IJ, et al. The efficacy of cognitive behavioral therapy: a review of meta-analyses. Cognit Ther Res. 2012;36(5):427–440.
   PubMed Web of Science ®Google Scholar
• Rush AJ, Trivedi MH, Wisniewski SR, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR* D report. AJP. 2006;163(11):1905–1917.
   Web of Science ®Google Scholar
• Wong EH, Yocca F, Smith MA, et al. Challenges and opportunities for drug discovery in psychiatric disorders: the drug hunters' perspective. Int J Neuropsychopharmacol. 2010;13(9):1269–1284.
   PubMed Web of Science ®Google Scholar
• Wunderink L, Sytema S, Nienhuis FJ, et al. Clinical recovery in first-episode psychosis. Schizophr Bull. 2009;35(2):362–369.
   PubMed Web of Science ®Google Scholar
• Barak-Corren Y, Castro VM, Javitt S, et al. Predicting suicidal behavior from longitudinal electronic health records. Am J Psychiatry. 2017;174(2):154–162.
   PubMed Web of Science ®Google Scholar
• Duda M, Ma R, Haber N, et al. Use of machine learning for behavioral distinction of autism and ADHD. Transl Psychiatry. 2016;6(2):e732.
   PubMed Web of Science ®Google Scholar
• Klöppel S, Stonnington CM, Barnes J, et al. Accuracy of dementia diagnosis: a direct comparison between radiologists and a computerized method. Brain. 2008;131(Pt 11):2969–2974.
   PubMed Web of Science ®Google Scholar
• Fu CH, Mourao-Miranda J, Costafreda SG, et al. Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol Psychiatry. 2008;63(7):656–662.
   PubMed Web of Science ®Google Scholar
• Davatzikos C, Fan Y, Wu X, et al. Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol Aging. 2008;29(4):514–523.
   PubMed Web of Science ®Google Scholar
• Lueken U, Straube B, Yang Y, et al. Separating depressive comorbidity from panic disorder: a combined functional magnetic resonance imaging and machine learning approach. J Affect Disord. 2015;184:182–192.
   PubMed Web of Science ®Google Scholar
• Visser RM, Haver P, Zwitser RJ, et al. First steps in using multi-voxel pattern analysis to disentangle neural processes underlying generalization of spider fear. Front Hum Neurosci. 2016;10:222.
   PubMed Web of Science ®Google Scholar
• Just MA, Pan L, Cherkassky VL, et al. Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nat Hum Behav. 2017;1(12):911–919.
   PubMed Web of Science ®Google Scholar
• Oh J, Yun K, Hwang J-H, et al. Classification of suicide attempts through a machine learning algorithm based on multiple systemic psychiatric scales. Front Psychiatry. 2017;8:192.
   PubMed Web of Science ®Google Scholar
• Poulin C, Shiner B, Thompson P, et al. Predicting the risk of suicide by analyzing the text of clinical notes. PloS One. 2014;9(1):e85733.
   PubMed Web of Science ®Google Scholar
• Velupillai S, Hadlaczky G, Baca-Garcia E, et al. Risk assessment tools and data-driven approaches for predicting and preventing suicidal behavior. Front Psychiatry. 2019;10:36.
   PubMed Web of Science ®Google Scholar
• Wu M-J, Mwangi B, Bauer IE, et al. Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning. Neuroimage. 2017;145(Pt B):254–264.
   PubMed Web of Science ®Google Scholar
• Tucha O, Smely C, Preier M, et al. Cognitive deficits before treatment among patients with brain tumors. Neurosurgery. 2000;47(2):324–334.
   PubMed Web of Science ®Google Scholar
• Pringle A, Taylor R, Whittle I. Anxiety and depression in patients with an intracranial neoplasm before and after tumour surgery. Br J Neurosurg. 1999;13(1):46–51.
   PubMed Web of Science ®Google Scholar
• Grant R. Overview: brain tumour diagnosis and management/royal college of physicians guidelines. J Neurol Neurosurg Psychiatry. 2004;75(suppl_2):ii18–ii23.
   PubMed Web of Science ®Google Scholar
• Benson DF, Geschwind N. Psychiatric conditions associated with focal lesions of the Central nervous system. Am Handbook Psychiatry. 1975;4:208–243.
   Google Scholar
• Madhusoodanan S, Opler MG, Moise D, et al. Brain tumor location and psychiatric symptoms: is there any association? A meta-analysis of published case studies. Expert Rev Neurother. 2010;10(10):1529–1536.
   PubMed Web of Science ®Google Scholar
• Gregg N, Arber A, Ashkan K, et al. Neurobehavioural changes in patients following brain tumour: patients and relatives perspective. Support Care Cancer. 2014;22(11):2965–2972.
   PubMed Web of Science ®Google Scholar
• Whiting D, Koh E, Simpson G. Addressing the behavioural and cognitive sequelae of adults with Brain Tumour: Trialling a Behavioural Consultancy Model. 2010. Cancer Institute NSW. http://www cancerinstitute org au/media/25260/2009-09_nswog_neurooncology_project_final_report pdf.
   Google Scholar
• Yetimalar Y, Iyidogan E, Basoglu M. Secondary mania after pontin cavernous angioma. J Neuropsychiatry Clin Neurosci. 2007;19(3):344–345.
   PubMed Web of Science ®Google Scholar
• Parisis D, Poulios I, Karkavelas G, et al. Peduncular hallucinosis secondary to brainstem compression by cerebellar metastases. Eur Neurol. 2003;50(2):107–109.
   PubMed Web of Science ®Google Scholar
• Hoffmann K, Kretschmar B, Buller V, et al. Craniopharyngioma resulting in pituitary gland insufficiency and coma in an adult with intellectual disability and severe challenging behavior. J Neuropsychiatry Clin Neurosci. 2010;22(4):451–e19.
   PubMed Web of Science ®Google Scholar
• Filley CM, Kleinschmidt-DeMasters BK. Neurobehavioral presentations of brain neoplasms. Western J Med. 1995;163(1):19.
   PubMedGoogle Scholar
• Ghaziuddin N, DeQuardo JR, Ghaziuddin M, et al. Electroconvulsive treatment of a bipolar adolescent postcraniotomy for brain stem astrocytoma. J Child Adolesc Psychopharmacol. 1999;9(1):63–69.
   PubMed Web of Science ®Google Scholar
• Fulton J, Duncan G, Caird F. Psychiatric presentation of intracranial tumour in the elderly. Int J Geriat Psychiatry. 1992;7(6):411–418.
   Web of Science ®Google Scholar
• Maiuri F, Iaconetta G, Sardo L, et al. Peduncular hallucinations associated with large posterior fossa meningiomas. Clin Neurol Neurosurg. 2002;104(1):41–43.
   PubMed Web of Science ®Google Scholar
• Binder RL. Neurologically silent brain tumors in psychiatric hospital admissions: three cases and a review. J Clin Psychiatry. 1983;44(3):94–97.
   PubMed Web of Science ®Google Scholar
• Dunn DW, Weisberg LA, Nadell J. Peduncular hallucinations caused by brainstem compression. Neurology. 1983;33(10):1360–1361.
   PubMed Web of Science ®Google Scholar
• Assefa D, Haque FN, Wong AH. Case report: anxiety and fear in a patient with meningioma compressing the left amygdala. Neurocase. 2012;18(2):91–94.
   PubMed Web of Science ®Google Scholar
• Madhusoodanan S, Danan D, Brenner R, et al. Brain tumor and psychiatric manifestations: a case report and brief review. Ann Clin Psychiatry. 2004;16(2):111–113.
   PubMedGoogle Scholar
• Sone D, Beheshti I. Clinical application of machine learning models for brain imaging in epilepsy: a review. Front Neurosci. 2021;15:684825.
   PubMed Web of Science ®Google Scholar
• Akeret K, Stumpo V, Staartjes VE, et al. Topographic brain tumor anatomy drives seizure risk and enables machine learning based prediction. Neuroimage Clin. 2020;28:102506.
   PubMed Web of Science ®Google Scholar
• Liu S, Cai W, Liu S, et al. Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Inform. 2015;2(3):167–180.
   PubMedGoogle Scholar
• Rasheed K, Qayyum A, Qadir J, et al. Machine learning for predicting epileptic seizures using EEG signals: a review. IEEE Rev Biomed Eng. 2021;14:139–155.
   PubMedGoogle Scholar