Functional Neuroanatomical Correlates of The Frontal Assessment Battery Performance in Alzheimer Disease: A FDG-PET Study

被引:11
|
作者
Lee, Jun Ho [1 ]
Byun, Min Soo [1 ,2 ]
Sohn, Bo Kyung [2 ,3 ]
Choe, Young Min [1 ,2 ]
Yi, Dahyun [1 ]
Han, Ji Young [1 ]
Choi, Hyo Jung [1 ,2 ]
Baek, Hyewon [1 ]
Woo, Jong Inn [4 ]
Lee, Dong Young [1 ,2 ]
机构
[1] Seoul Natl Univ Hosp, Dept Neuropsychiat, Seoul 110744, South Korea
[2] Seoul Natl Univ, Coll Med, Dept Psychiat, Seoul, South Korea
[3] Seoul Natl Univ, Boramae Med Ctr, Dept Psychiat, Seoul Metropolitan Govt, Seoul, South Korea
[4] Seoul Natl Univ, Med Res Ctr, Inst Human Behav Med, Seoul, South Korea
关键词
Alzheimer disease; Frontal Assessment Battery; FDG-PET; MILD COGNITIVE IMPAIRMENT; NEUROPSYCHOLOGICAL ASSESSMENT; FRONTOTEMPORAL DEMENTIA; KOREAN VERSION; BRAIN; DYSFUNCTION; DIAGNOSIS; CERAD; CONSORTIUM; ESTABLISH;
D O I
10.1177/0891988715573533
中图分类号
R592 [老年病学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 100203 ;
摘要
Background/Objectives: We aimed to elucidate the functional neuroanatomical correlates of Frontal Assessment Battery (FAB) performances by applying [F-18]fluorodeoxyglucose positron emission tomography (FDG-PET) to a large population of patients with Alzheimer disease (AD). Methods: The FAB was administered to 177 patients with AD, and regional cerebral glucose metabolism (rCMglc) was measured by FDG-PET scan. Correlations between FAB scores and rCMglc were explored using both region-of-interest-based (ROI-based) and voxel-based approaches. Results: The ROI-based analysis showed that FAB scores correlated with the rCMglc of the dorsolateral prefrontal cortices. Voxel-based approach revealed significant positive correlations between FAB scores and rCMglc which were in various cortical regions including the temporal and parietal cortices as well as frontal regions, independent of age, gender, and education. After controlling the effect of global disease severity with Mini-Mental State Examination score, significant positive correlation was found only in the bilateral prefrontal regions. Conclusions: Although FAB scores are influenced by temporoparietal dysfunction due to the overall progression of AD, it likely reflects prefrontal dysfunction specifically regardless of global cognitive state or disease severity in patients with AD.
引用
收藏
页码:184 / 192
页数:9
相关论文
共 50 条
  • [41] Heuristic scoring method utilizing FDG-PET statistical parametric mapping in the evaluation of suspected Alzheimer disease and frontotemporal lobar degeneration
    Ford, Jeremy N.
    Sweeney, Elizabeth M.
    Skafida, Myrto
    Glynn, Shannon
    Amoashiy, Michael
    Lange, Dale J.
    Lin, Eaton
    Chiang, Gloria C.
    Osborne, Joseph R.
    Pahlajani, Silky
    de Leon, Mony J.
    Ivanidze, Jana
    AMERICAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2021, 11 (04): : 313 - 326
  • [42] [18F]FDG-PET in patients with Alzheimer's disease:: Marker of disease spread
    Bittner, D
    Grön, G
    Schirrmeister, H
    Reske, SN
    Riepe, MW
    DEMENTIA AND GERIATRIC COGNITIVE DISORDERS, 2005, 19 (01) : 24 - 30
  • [43] FDG-PET as an independent biomarker for Alzheimer's biological diagnosis: a longitudinal study
    Ou, Ya-Nan
    Xu, Wei
    Li, Jie-Qiong
    Guo, Yu
    Cui, Mei
    Chen, Ke-Liang
    Huang, Yu-Yuan
    Dong, Qiang
    Tan, Lan
    Yu, Jin-Tai
    ALZHEIMERS RESEARCH & THERAPY, 2019, 11 (1)
  • [44] Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers
    Gupta, Yubraj
    Lama, Ramesh Kumar
    Kwon, Goo-Rak
    FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2019, 13
  • [45] Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease
    Gray, Katherine R.
    Wolz, Robin
    Heckemann, Rolf A.
    Aljabar, Paul
    Hammers, Alexander
    Rueckert, Daniel
    NEUROIMAGE, 2012, 60 (01) : 221 - 229
  • [46] Multilevel Feature Representation of FDG-PET Brain Images for Diagnosing Alzheimer's Disease
    Pan, Xiaoxi
    Adel, Mouloud
    Fossati, Caroline
    Gaidon, Thierry
    Guedj, Eric
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2019, 23 (04) : 1499 - 1506
  • [47] Diagnosis and Grading of Alzheimer's Disease via Automatic Classification of FDG-PET Scans
    Benton, Ryan G.
    Choubey, Suresh
    Clark, David G.
    Johnsten, Tom
    Raghavan, Vijay V.
    BRAIN AND HEALTH INFORMATICS, 2013, 8211 : 266 - 276
  • [48] Alzheimer’s disease markers from structural MRI and FDG-PET brain images
    Andrea Chincarini
    Paolo Bosco
    Gianluca Gemme
    Silvia Morbelli
    Dario Arnaldi
    Francesco Sensi
    Ilaria Solano
    Nicola Amoroso
    Sabina Tangaro
    Renata Longo
    Sandro Squarcia
    Flavio Nobili
    The European Physical Journal Plus, 127
  • [49] Functional imaging of Hodgkin's disease with FDG-PET and gallium-67
    Willkomm, P
    Palmedo, H
    Grunwald, F
    Ruhlmann, J
    Biersack, HJ
    NUKLEARMEDIZIN, 1998, 37 (07) : 251 - 253
  • [50] Predicting the transition from normal aging to Alzheimer's disease: A statistical mechanistic evaluation of FDG-PET data
    Pagani, Marco
    Giuliani, Alessandro
    Oberg, Johanna
    Chincarini, Andrea
    Morbelli, Silvia
    Brugnolo, Andrea
    Arnaldi, Dario
    Picco, Agnese
    Bauckneht, Matteo
    Buschiazzo, Ambra
    Sambuceti, Gianmario
    Nobili, Flavio
    NEUROIMAGE, 2016, 141 : 282 - 290