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Multilevel diffusion tensor imaging classification technique for characterizing neurobehavioral disorders

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Abstract

This proposed novel method consists of three levels of analyses of diffusion tensor imaging data: 1) voxel level analysis of fractional anisotropy of white matter tracks, 2) connection level analysis, based on fiber tracks between specific brain regions, and 3) network level analysis, based connections among multiple brain regions. Machine-learning techniques of (Fisher score) feature selection, (Support Vector Machine) pattern classification, and (Leave-one-out) cross-validation are performed, for recognition of the neural connectivity patterns for diagnostic purposes. For validation proposes, this multilevel approach achieved an average classification accuracy of 90% between Alzheimer’s disease and healthy controls, 83% between Alzheimer’s disease and mild cognitive impairment, and 83% between mild cognitive impairment and healthy controls. The results indicate that the multilevel diffusion tensor imaging approach used in this analysis is a potential diagnostic tool for clinical evaluations of brain disorders. The presented pipeline is now available as a tool for scientifically applications in a broad range of studies from both clinical and behavioral spectrum, which includes studies about autism, dyslexia, schizophrenia, dementia, motor body performance, among others.

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Acknowledgements

The senior author of this study was supported by the Indigo Project FKZ 01DQ13004, and Fondecyt Regular projects number 1171313 and number 1171320.

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Authors and Affiliations

  1. Brain and Language Lab, Department of Clinical Neuroscience, University of Geneva, Geneva, Switzerland

    Josué Luiz Dalboni da Rocha

  2. Department of Biomedical Engineering, University of Florida, Gainesville, USA

    Josué Luiz Dalboni da Rocha

  3. D’Or Institute for Research and Education, Rio de Janeiro, Brazil

    Gabriel Coutinho, Ivanei Bramati & Fernanda Tovar Moll

  4. Federal Univerisity of Rio de Janeiro, Rio de Janeiro, Brazil

    Fernanda Tovar Moll

  5. Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile

    Ranganatha Sitaram

  6. Department of Psychiatry and Section of Neuroscience, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile

    Ranganatha Sitaram

  7. Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile

    Ranganatha Sitaram

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  1. Josué Luiz Dalboni da Rocha
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  2. Gabriel Coutinho
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  3. Ivanei Bramati
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  4. Fernanda Tovar Moll
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  5. Ranganatha Sitaram
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Correspondence to Ranganatha Sitaram.

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Dalboni da Rocha, J.L., Coutinho, G., Bramati, I. et al. Multilevel diffusion tensor imaging classification technique for characterizing neurobehavioral disorders. Brain Imaging and Behavior 14, 641–652 (2020). https://doi.org/10.1007/s11682-018-0002-2

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