TY - JOUR
T1 - Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging
AU - Bouts, Mark J R J
AU - Möller, Christiane
AU - Hafkemeijer, Anne
AU - van Swieten, John C
AU - Dopper, Elise
AU - van der Flier, Wiesje M
AU - Vrenken, Hugo
AU - Wink, Alle Meije
AU - Pijnenburg, Yolande A L
AU - Scheltens, Philip
AU - Barkhof, Frederik
AU - Schouten, Tijn M
AU - de Vos, Frank
AU - Feis, Rogier A
AU - van der Grond, Jeroen
AU - de Rooij, Mark
AU - Rombouts, Serge A R B
PY - 2018
Y1 - 2018
N2 - 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.
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.
KW - Alzheimer’s disease
KW - behavioral variant frontotemporal dementia
KW - classification
KW - differential diagnosis
KW - diffusion tensor imaging
KW - functional MRI
KW - machine learning
UR - http://www.mendeley.com/research/single-subject-classification-alzheimers-disease-behavioral-variant-frontotemporal-dementia-using-an
U2 - 10.3233/JAD-170893
DO - 10.3233/JAD-170893
M3 - Article
C2 - 29614652
VL - 62
SP - 1827
EP - 1839
JO - Journal of Alzheimer's Disease
JF - Journal of Alzheimer's Disease
SN - 1387-2877
IS - 4
ER -