The power of neuroimaging biomarkers for screening frontotemporal dementia

Authors

  • Corey T. McMillan,

    Corresponding author
    1. Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
    • Correspondence to: Corey T. McMillan, Department of Neurology, University of Pennsylvania Perelman School of Medicine, 3400 Spruce Street, 3 West Gates, Philadelphia, PA 19104. E-mail: mcmillac@mail.med.upenn.edu

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  • Brian B. Avants,

    1. Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Philip Cook,

    1. Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Lyle Ungar,

    1. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania
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  • John Q. Trojanowski,

    1. Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania
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  • Murray Grossman

    1. Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
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Abstract

Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative disease that can result from either frontotemporal lobar degeneration (FTLD) or Alzheimer's disease (AD) pathology. It is critical to establish statistically powerful biomarkers that can achieve substantial cost-savings and increase the feasibility of clinical trials. We assessed three broad categories of neuroimaging methods to screen underlying FTLD and AD pathology in a clinical FTD series: global measures (e.g., ventricular volume), anatomical volumes of interest (VOIs) (e.g., hippocampus) using a standard atlas, and data-driven VOIs using Eigenanatomy. We evaluated clinical FTD patients (N = 93) with cerebrospinal fluid, gray matter (GM) magnetic resonance imaging (MRI), and diffusion tensor imaging (DTI) to assess whether they had underlying FTLD or AD pathology. Linear regression was performed to identify the optimal VOIs for each method in a training dataset and then we evaluated classification sensitivity and specificity in an independent test cohort. Power was evaluated by calculating minimum sample sizes required in the test classification analyses for each model. The data-driven VOI analysis using a multimodal combination of GM MRI and DTI achieved the greatest classification accuracy (89% sensitive and 89% specific) and required a lower minimum sample size (N = 26) relative to anatomical VOI and global measures. We conclude that a data-driven VOI approach using Eigenanatomy provides more accurate classification, benefits from increased statistical power in unseen datasets, and therefore provides a robust method for screening underlying pathology in FTD patients for entry into clinical trials. Hum Brain Mapp 35:4827–4840, 2014. © 2014 Wiley Periodicals, Inc.

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