Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging.

Background/objective: Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid...

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Published in:Journal of Alzheimer's Disease Vol. 62; no. 4; pp. 1827 - 1840
Main Authors: Zhang, Bouts, Mark J.R.J., Möller, Christiane, Hafkemeijer, Anne, Schouten, Tijn. M., de Vos, Frank, Rombouts, Serge A.R.B., de Rooij, Mark, van der Grond, Jeroen, Feis, Rogier A., van Swieten, John C., Dopper, Elise, Pijnenburg, Yolande A.L., Scheltens, Philip, van der Flier, Wiesje M., Wink, Alle Meije, Barkhof, Frederik, Vrenken, Hugo
Format: research Journal Article
Published: Sage Publications Inc. 2018
Online Access:View this record in EBSCOhost
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      dt: 2018
      vid: 62
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        10.3233/JAD-170893
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        atl: Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging.
      aug:
        au:
          Zhang
          Bouts, Mark J.R.J.
          Möller, Christiane
          Hafkemeijer, Anne
          Schouten, Tijn. M.
          de Vos, Frank
          Rombouts, Serge A.R.B.
          de Rooij, Mark
          van der Grond, Jeroen
          Feis, Rogier A.
          van Swieten, John C.
          Dopper, Elise
          Pijnenburg, Yolande A.L.
          Scheltens, Philip
          van der Flier, Wiesje M.
          Wink, Alle Meije
          Barkhof, Frederik
          Vrenken, Hugo
        affil: Institute of Psychology, Leiden University, Leiden, The Netherlands
      sug:
        subj:
          Magnetic Resonance Imaging
          Brain
          Alzheimer's Disease
          Frontotemporal Dementia
          Brain Physiopathology
          Relaxation
          Diagnosis, Differential
          Pharmacokinetics
          Male
          ROC Curve
          Human
          Aged
          Frontotemporal Dementia Physiopathology
          Retrospective Design
          Middle Age
          Female
          Image Interpretation, Computer Assisted
          Alzheimer's Disease Physiopathology
          Validation Studies
          Comparative Studies
          Evaluation Research
          Multicenter Studies
          Aged: 65+ years
          Middle Aged: 45-64 years
          Male
          Female
      ab: Background/objective: Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known.Methods: Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC).Results: Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41).Conclusion: Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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