publication . Other literature type . Preprint . 2020

Functional MRI applications for psychiatric disease subtyping: a review

Miranda, Lucas; Paul, Riya; Pütz, Benno; Müller-Myhsok, Bertram;
Open Access English
  • Published: 30 Jun 2020
  • Publisher: Zenodo
Abstract
Comment: 16 pages, 2 figures, 3 tables
Subjects
free text keywords: functional MRI, unsupervised learning, clustering, subtypes, machine learning, personalised medicine, translational psychiatry, artificial intelligence, mental health, clinical psychology, psychiatry, functional MRI, unsupervised learning, clustering, subtypes, machine learning, personalised medicine, translational psychiatry, artificial intelligence, mental health, clinical psychology, psychiatry, Quantitative Biology - Neurons and Cognition
Funded by
EC| MLFPM2018
Project
MLFPM2018
Machine Learning Frontiers in Precision Medicine
  • Funder: European Commission (EC)
  • Project Code: 813533
  • Funding stream: H2020 | MSCA-ITN-ETN
Download fromView all 4 versions
ZENODO
Preprint . 2020
Provider: ZENODO
Zenodo
Other literature type . 2020
Provider: Datacite
Zenodo
Other literature type . 2020
Provider: Datacite
55 references, page 1 of 4

1. Shorter E. The History of DSM. Making the DSM-5. 2013. pp. 3-19. doi:​10.1007/978-1-4614-6504-1_1

2. Spitzer RL. Research Diagnostic Criteria. Archives of General Psychiatry. 1979. p. 1381. doi:​10.1001/archpsyc.1979.01780120111013

3. Steel Z, Marnane C, Iranpour C, Chey T, Jackson JW, Patel V, et al. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980-2013. International Journal of Epidemiology. 2014. pp. 476-493. doi:​10.1093/ije/dyu038

Rush AJ, Ibrahim HM. Speculations on the Future of Psychiatric Diagnosis. J Nerv Ment Dis. 2018;206: 481-487.

Moran P, Stokes J, Marr J, Bock G, Desbonnet L, Waddington J, et al. Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models. Neural Plast. 2016;2016: 2173748.

6. Syvälahti EK. Biological factors in schizophrenia. Structural and functional aspects. Br J Psychiatry Suppl. 1994; 9-14.

7. Paul R, Andlauer TFM, Czamara D, Hoehn D, Lucae S, Pütz B, et al. Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models. Transl Psychiatry. 2019;9: 187.

8. Pandarakalam JP. Challenges of Treatment-resistant Depression. Psychiatr Danub. 2018;30: 273-284. [OpenAIRE]

9. Potkin SG, Kane JM, Correll CU, Lindenmayer J-P, Agid O, Marder SR, et al. The neurobiology of treatment-resistant schizophrenia: paths to antipsychotic resistance and a roadmap for future research. npj Schizophrenia. 2020. doi:​10.1038/s41537-019-0090-z [OpenAIRE]

10. Vilar A, Pérez-Sola V, Blasco MJ, Pérez-Gallo E, Ballester Coma L, Batlle Vila S, et al. Translational research in psychiatry: The Research Domain Criteria Project (RDoC). Rev Psiquiatr Salud Ment. 2019;12: 187-195.

11. Paykel ES. Classification of depressed patients: a cluster analysis derived grouping. Br J Psychiatry. 1971;118: 275-288.

12. Farmer AE, McGuffin P, Spitznagel EL. Heterogeneity in schizophrenia: a cluster-analytic approach. Psychiatry Res. 1983;8: 1-12.

13. Brainstorm Consortium, Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, et al. Analysis of shared heritability in common disorders of the brain. Science. 2018;360. doi:​10.1126/science.aap8757

14. Stroman PW. Essentials of Functional MRI. CRC Press; 2016. [OpenAIRE]

15. Poline J-B, Brett M. The general linear model and fMRI: does love last forever? Neuroimage. 2012;62: 871-880.

55 references, page 1 of 4
Abstract
Comment: 16 pages, 2 figures, 3 tables
Subjects
free text keywords: functional MRI, unsupervised learning, clustering, subtypes, machine learning, personalised medicine, translational psychiatry, artificial intelligence, mental health, clinical psychology, psychiatry, functional MRI, unsupervised learning, clustering, subtypes, machine learning, personalised medicine, translational psychiatry, artificial intelligence, mental health, clinical psychology, psychiatry, Quantitative Biology - Neurons and Cognition
Funded by
EC| MLFPM2018
Project
MLFPM2018
Machine Learning Frontiers in Precision Medicine
  • Funder: European Commission (EC)
  • Project Code: 813533
  • Funding stream: H2020 | MSCA-ITN-ETN
Download fromView all 4 versions
ZENODO
Preprint . 2020
Provider: ZENODO
Zenodo
Other literature type . 2020
Provider: Datacite
Zenodo
Other literature type . 2020
Provider: Datacite
55 references, page 1 of 4

1. Shorter E. The History of DSM. Making the DSM-5. 2013. pp. 3-19. doi:​10.1007/978-1-4614-6504-1_1

2. Spitzer RL. Research Diagnostic Criteria. Archives of General Psychiatry. 1979. p. 1381. doi:​10.1001/archpsyc.1979.01780120111013

3. Steel Z, Marnane C, Iranpour C, Chey T, Jackson JW, Patel V, et al. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980-2013. International Journal of Epidemiology. 2014. pp. 476-493. doi:​10.1093/ije/dyu038

Rush AJ, Ibrahim HM. Speculations on the Future of Psychiatric Diagnosis. J Nerv Ment Dis. 2018;206: 481-487.

Moran P, Stokes J, Marr J, Bock G, Desbonnet L, Waddington J, et al. Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models. Neural Plast. 2016;2016: 2173748.

6. Syvälahti EK. Biological factors in schizophrenia. Structural and functional aspects. Br J Psychiatry Suppl. 1994; 9-14.

7. Paul R, Andlauer TFM, Czamara D, Hoehn D, Lucae S, Pütz B, et al. Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models. Transl Psychiatry. 2019;9: 187.

8. Pandarakalam JP. Challenges of Treatment-resistant Depression. Psychiatr Danub. 2018;30: 273-284. [OpenAIRE]

9. Potkin SG, Kane JM, Correll CU, Lindenmayer J-P, Agid O, Marder SR, et al. The neurobiology of treatment-resistant schizophrenia: paths to antipsychotic resistance and a roadmap for future research. npj Schizophrenia. 2020. doi:​10.1038/s41537-019-0090-z [OpenAIRE]

10. Vilar A, Pérez-Sola V, Blasco MJ, Pérez-Gallo E, Ballester Coma L, Batlle Vila S, et al. Translational research in psychiatry: The Research Domain Criteria Project (RDoC). Rev Psiquiatr Salud Ment. 2019;12: 187-195.

11. Paykel ES. Classification of depressed patients: a cluster analysis derived grouping. Br J Psychiatry. 1971;118: 275-288.

12. Farmer AE, McGuffin P, Spitznagel EL. Heterogeneity in schizophrenia: a cluster-analytic approach. Psychiatry Res. 1983;8: 1-12.

13. Brainstorm Consortium, Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, et al. Analysis of shared heritability in common disorders of the brain. Science. 2018;360. doi:​10.1126/science.aap8757

14. Stroman PW. Essentials of Functional MRI. CRC Press; 2016. [OpenAIRE]

15. Poline J-B, Brett M. The general linear model and fMRI: does love last forever? Neuroimage. 2012;62: 871-880.

55 references, page 1 of 4
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