Neuroimaging-Based Biomarkers in Psychiatry: Clinical Opportunities of a Paradigm Shift

Article English OPEN
Fu, Cynthia H.Y. ; Costafreda, Sergi G. (2013)
  • Publisher: Canadian Psychiatric Association

Neuroimaging research has substantiated the functional and structural abnormalities\ud underlying psychiatric disorders but has, thus far, failed to have a significant impact on\ud clinical practice. Recently, neuroimaging-based diagnoses and clinical predictions derived\ud from machine learning analysis have shown significant potential for clinical translation.\ud This review introduces the key concepts of this approach, including how the multivariate\ud integration of patterns of brain abnormalities is a crucial component. We survey recent\ud findings that have potential application for diagnosis, in particular early and differential\ud diagnoses in Alzheimer disease and schizophrenia, and the prediction of clinical response\ud to treatment in depression. We discuss the specific clinical opportunities and the challenges\ud for developing biomarkers for psychiatry in the absence of a diagnostic gold standard. We\ud propose that longitudinal outcomes, such as early diagnosis and prediction of treatment\ud response, offer definite opportunities for progress. We propose that efforts should be\ud directed toward clinically challenging predictions in which neuroimaging may have added\ud value, compared with the existing standard assessment. We conclude that diagnostic and\ud prognostic biomarkers will be developed through the joint application of expert psychiatric\ud knowledge in addition to advanced methods of analysis.
  • References (83)
    83 references, page 1 of 9

    1. Biomarkers Definitions Working Group NIH. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69(3):89-95.

    2. Kraemer HC, Kupfer DJ, Clarke DE, et al. DSM-5: how reliable is reliable enough? Am J Psychiatry. 2012;169(1):13-15.

    3. Kupfer DJ, First MB, Regier DA, editors. Neuroscience research agenda to guide development of a pathophysiologically based classification system. A research agenda for DSM-V. Washington (DC): American Psychiatric Press; 2002.

    4. Insel T, Cuthbert B, Garvey M, et al. Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167(7):748-751.

    5. Clarke R, Ressom HW, Wang A, et al. The properties of highdimensional data spaces: implications for exploring gene and protein expression data. Nat Rev Cancer. 2008;8(1):37-49.

    6. Duin RPW. Classifiers in almost empty spaces. 15th International Conference on Pattern Recognition; Barcelona, Spain; September 3-7, 2000. p 1-7.

    7. Noble WS. What is a support vector machine? Nat Biotechnol. 2006;24(12):1565-1567.

    8. Mitchell TM. The discipline of machine learning. Technical report. Pittsburgh (PA): Carnegie Mellon University, School of Computer Science, Machine Learning Department; 2006. Contract number CMU-ML-06-108.

    9. Pereira F, Mitchell T, Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage. 2009;45(1 Suppl 1):S199-S209.

    10. Costafreda SG. Pooling fMRI data: meta-analysis, mega-analysis and multi-center studies. Front Neuroinform. 2009;3:33.

  • Related Research Results (1)
  • Metrics
    0
    views in OpenAIRE
    0
    views in local repository
    219
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    ROAR at University of East London - IRUS-UK 0 219
Share - Bookmark