journal article Feb 23, 2016

Computational psychiatry as a bridge from neuroscience to clinical applications

Topics

No keywords indexed for this article. Browse by subject →

References
148
[1]
Kapur, S., Phillips, A.G. & Insel, T.R. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Mol. Psychiatry 17, 1174–1179 (2012). 10.1038/mp.2012.105
[2]
Maia, T.V. & Cano-Colino, M. The role of serotonin in orbitofrontal function and obsessive-compulsive disorder. Clin. Psychol. Sci. 3, 460–482 (2015). 10.1177/2167702614566809
[3]
Huys, Q.J.M., Moutoussis, M. & Williams, J. Are computational models of any use to psychiatry? Neural Netw. 24, 544–551 (2011). 10.1016/j.neunet.2011.03.001
[4]
Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis

Klaas E Stephan, Dominik R Bach, Paul C Fletcher et al.

The Lancet Psychiatry 2015 10.1016/s2215-0366(15)00361-2
[5]
Gene–environment interactions in psychiatry: joining forces with neuroscience

Avshalom Caspi, Terrie E. Moffitt

Nature Reviews Neuroscience 2006 10.1038/nrn1925
[6]
Williams, L.M. et al. International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol. Trials 12, 4 (2011). 10.1186/1745-6215-12-4
[7]
Mennes, M., Biswal, B.B., Castellanos, F.X. & Milham, M.P. Making data sharing work: the FCP/INDI experience. Neuroimage 82, 683–691 (2013). 10.1016/j.neuroimage.2012.10.064
[8]
Maia, T.V. Introduction to the series on computational psychiatry. Clin. Psychol. Sci. 3, 374–377 (2015). 10.1177/2167702614567350
[9]
Maia, T.V. & Frank, M.J. From reinforcement learning models to psychiatric and neurological disorders. Nat. Neurosci. 14, 154–162 (2011). 10.1038/nn.2723
[10]
Computational psychiatry

P. Read Montague, Raymond J. Dolan, Karl J. Friston et al.

Trends in Cognitive Sciences 2012 10.1016/j.tics.2011.11.018
[11]
Wang, X.J. & Krystal, J.H. Computational psychiatry. Neuron 84, 638–654 (2014). 10.1016/j.neuron.2014.10.018
[12]
Wiecki, T.V., Poland, J. & Frank, M.J. Model-based cognitive neuroscience approaches to computational psychiatry clustering and classification. Clin. Psychol. Sci. 3, 378–399 (2015). 10.1177/2167702614565359
[13]
Maia, T.V. & McClelland, J.L. A neurocomputational approach to obsessive-compulsive disorder. Trends Cogn. Sci. 16, 14–15 (2012). 10.1016/j.tics.2011.11.011
[14]
Stephan, K.E. & Mathys, C. Computational approaches to psychiatry. Curr. Opin. Neurobiol. 25, 85–92 (2014). 10.1016/j.conb.2013.12.007
[15]
Huys, Q.J.M., Daw, N.D. & Dayan, P. Depression: a decision-theoretic analysis. Annu. Rev. Neurosci. 38, 1–23 (2015). 10.1146/annurev-neuro-071714-033928
[16]
Stephan, K.E., Iglesias, S., Heinzle, J. & Diaconescu, A.O. Translational perspectives for computational neuroimaging. Neuron 87, 716–732 (2015). 10.1016/j.neuron.2015.07.008
[17]
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5 R) (American Psychiatric Publishing, 2013). 10.1176/appi.books.9780890425596
[18]
World Health Organization. International Classification of Diseases (World Health Organization Press, 1990).
[19]
Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders

Thomas Insel, Bruce Cuthbert, Marjorie Garvey et al.

American Journal of Psychiatry 2010 10.1176/appi.ajp.2010.09091379
[20]
MacKay, D.J. Information Theory, Inference and Learning Algorithms (CUP, Cambridge, 2003).
[21]
Lee, S.H. et al.; Cross-Disorder Group of the Psychiatric Genomics Consortium; International Inflammatory Bowel Disease Genetics Consortium (IIBDGC). Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013). 10.1038/ng.2805
[22]
Huys, Q.J.M., Pizzagalli, D.A., Bogdan, R. & Dayan, P. Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis. Biol. Mood Anxiety Disord. 3, 12 (2013). 10.1186/2045-5380-3-12
[23]
Cunningham, J.P. & Yu, B.M. Dimensionality reduction for large-scale neural recordings. Nat. Neurosci. 17, 1500–1509 (2014). 10.1038/nn.3776
[24]
Brodersen, K.H. et al. Dissecting psychiatric spectrum disorders by generative embedding. Neuroimage Clin. 4, 98–111 (2014). 10.1016/j.nicl.2013.11.002
[25]
Harlé, K.M. et al. Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use. Brain 138, 3413–3426 (2015). 10.1093/brain/awv246
[26]
Orrù, G., Pettersson-Yeo, W., Marquand, A.F., Sartori, G. & Mechelli, A. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. 36, 1140–1152 (2012). 10.1016/j.neubiorev.2012.01.004
[27]
Wolfers, T., Buitelaar, J.K., Beckmann, C.F., Franke, B. & Marquand, A.F. From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci. Biobehav. Rev. 57, 328–349 (2015). 10.1016/j.neubiorev.2015.08.001
[28]
Borsboom, D., Cramer, A.O.J., Schmittmann, V.D., Epskamp, S. & Waldorp, L.J. The small world of psychopathology. PLoS One 6, e27407 (2011). 10.1371/journal.pone.0027407
[29]
Kessler, R.C. et al. Comorbidity of DSM-III-R major depressive disorder in the general population: results from the US National Comorbidity Survey. Br. J. Psychiatry Suppl. 30, 17–30 (1996). 10.1192/s0007125000298371
[30]
Fairburn, C.G. & Bohn, K. Eating disorder NOS (EDNOS): an example of the troublesome “not otherwise specified” (NOS) category in DSM-IV. Behav. Res. Ther. 43, 691–701 (2005). 10.1016/j.brat.2004.06.011
[31]
Kessler, R.C., Zhao, S., Blazer, D.G. & Swartz, M. Prevalence, correlates, and course of minor depression and major depression in the National Comorbidity Survey. J. Affect. Disord. 45, 19–30 (1997). 10.1016/s0165-0327(97)00056-6
[32]
Freedman, R. et al. The initial field trials of DSM-5: new blooms and old thorns. Am. J. Psychiatry 170, 1–5 (2013). 10.1176/appi.ajp.2012.12091189
[33]
Silva, R.F. et al. The tenth annual MLSP competition: schizophrenia classification challenge. IEEE Int. Workshop Mach. Learn. Signal Process. 1–6 (2014). 10.1109/mlsp.2014.6958889
[34]
Solin, A. & Sarkka, S. The tenth annual MLSP competition: first place. in IEEE Int. Workshop Mach. Learn. Signal Process. 1–6 (2014). 10.1109/mlsp.2014.6958886
[35]
Sabuncu, M.R. & Konukoglu, E. Alzheimer's Disease Neuroimaging Initiative. Clinical prediction from structural brain MRI scans: a large-scale empirical study. Neuroinformatics 13, 31–46 (2015). 10.1007/s12021-014-9238-1
[36]
Hahn, T. et al. Integrating neurobiological markers of depression. Arch. Gen. Psychiatry 68, 361–368 (2011). 10.1001/archgenpsychiatry.2010.178
[37]
A Fast Learning Algorithm for Deep Belief Nets

Geoffrey E. Hinton, Simon Osindero, Yee-Whye Teh

Neural Computation 2006 10.1162/neco.2006.18.7.1527
[38]
Peng, X., Lin, P., Zhang, T. & Wang, J. Extreme learning machine-based classification of ADHD using brain structural MRI data. PLoS One 8, e79476 (2013). 10.1371/journal.pone.0079476
[40]
Watanabe, T., Kessler, D., Scott, C., Angstadt, M. & Sripada, C. Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine. Neuroimage 96, 183–202 (2014). 10.1016/j.neuroimage.2014.03.067
[41]
Costafreda, S.G. et al. Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder. BMC Psychiatry 11, 18 (2011). 10.1186/1471-244x-11-18
[42]
Pereira, F., Mitchell, T. & Botvinick, M. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45 (suppl.) S199–S209 (2009). 10.1016/j.neuroimage.2008.11.007
[43]
Lubke, G.H. et al. Subtypes versus severity differences in attention-deficit/hyperactivity disorder in the Northern Finnish Birth Cohort. J. Am. Acad. Child Adolesc. Psychiatry 46, 1584–1593 (2007). 10.1097/chi.0b013e31815750dd
[44]
The p Factor

Avshalom Caspi, Renate M. Houts, Daniel W. Belsky et al.

Clinical Psychological Science 2014 10.1177/2167702613497473
[45]
Ruiz, F.J.R., Valera, I., Blanco, C. & Perez-Cruz, F. Bayesian nonparametric comorbidity analysis of psychiatric disorders. J. Mach. Learn. Res. 15, 1215–1247 (2014).
[46]
The Diagnosis of Mental Disorders: The Problem of Reification

Steven E. Hyman

Annual Review of Clinical Psychology 2010 10.1146/annurev.clinpsy.3.022806.091532
[47]
Koutsouleris, N. et al. Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch. Gen. Psychiatry 66, 700–712 (2009). 10.1001/archgenpsychiatry.2009.62
[48]
Schmaal, L. et al. Predicting the naturalistic course of major depressive disorder using clinical and multimodal neuroimaging information: A multivariate pattern recognition study. Biol. Psychiatry 78, 278–286 (2015). 10.1016/j.biopsych.2014.11.018
[49]
The Brain’s Response to Reward Anticipation and Depression in Adolescence: Dimensionality, Specificity, and Longitudinal Predictions in a Community-Based Sample

Argyris Stringaris, Pablo Vidal-Ribas Belil, Eric Artiges et al.

American Journal of Psychiatry 2015 10.1176/appi.ajp.2015.14101298
[50]
Whelan, R. et al.; IMAGEN Consortium. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 512, 185–189 (2014). 10.1038/nature13402

Showing 50 of 148 references

Metrics
892
Citations
148
References
Details
Published
Feb 23, 2016
Vol/Issue
19(3)
Pages
404-413
License
View
Cite This Article
Quentin J M Huys, Tiago V Maia, Michael J Frank (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19(3), 404-413. https://doi.org/10.1038/nn.4238
Related

You May Also Like