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Multivariate Pattern Recognition for Diagnosis and Prognosis in Clinical Neuroimaging: State of the Art, Current Challenges and Future Trends

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Abstract

Many diseases are associated with systematic modifications in brain morphometry and function. These alterations may be subtle, in particular at early stages of the disease progress, and thus not evident by visual inspection alone. Group-level statistical comparisons have dominated neuroimaging studies for many years, proving fascinating insight into brain regions involved in various diseases. However, such group-level results do not warrant diagnostic value for individual patients. Recently, pattern recognition approaches have led to a fundamental shift in paradigm, bringing multivariate analysis and predictive results, notably for the early diagnosis of individual patients. We review the state-of-the-art fundamentals of pattern recognition including feature selection, cross-validation and classification techniques, as well as limitations including inter-individual variation in normal brain anatomy and neurocognitive reserve. We conclude with the discussion of future trends including multi-modal pattern recognition, multi-center approaches with data-sharing and cloud-computing.

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Abbreviations

AD:

Alzheimer disease

ADNI:

Alzheimer disease neuroimaging initiative

DTI:

Diffusion tensor imaging

fMRI:

Functional magnetic resonance imaging

MCI:

Mild cognitive impairment

MRI:

Magnetic resonance imaging

SVM:

Support vector machines

VBM:

Voxel based morphometry

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Acknowledgments

This work was supported in parts by the Swiss National Science Foundation (SNF) grants 320030_147126/1 and PP00P2-146318

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No conflicts of interest.

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Authors and Affiliations

  1. Department of Imaging and Medical Informatics, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland

    Sven Haller & Karl-Olof Lovblad

  2. Department of Mental Health and Psychiatry, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland

    Panteleimon Giannakopoulos

  3. Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland

    Dimitri Van De Ville

  4. Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Dimitri Van De Ville

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  1. Sven Haller
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  2. Karl-Olof Lovblad
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  3. Panteleimon Giannakopoulos
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  4. Dimitri Van De Ville
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Correspondence to Sven Haller.

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Haller, S., Lovblad, KO., Giannakopoulos, P. et al. Multivariate Pattern Recognition for Diagnosis and Prognosis in Clinical Neuroimaging: State of the Art, Current Challenges and Future Trends. Brain Topogr 27, 329–337 (2014). https://doi.org/10.1007/s10548-014-0360-z

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