Publication

The Alzheimer's Disease-Related Glucose Metabolic Brain Pattern

Teune, L. K., Strijkert, F., Renken, R. J., Izaks, G. J., de Vries, J. J., Segbers, M., Roerdink, J. B. T. M., Dierckx, R. A. J. O. & Leenders, K. L., 2014, In : Current alzheimer research. 11, 8, p. 725-732 8 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Teune, L. K., Strijkert, F., Renken, R. J., Izaks, G. J., de Vries, J. J., Segbers, M., ... Leenders, K. L. (2014). The Alzheimer's Disease-Related Glucose Metabolic Brain Pattern. Current alzheimer research, 11(8), 725-732. https://doi.org/10.2174/156720501108140910114230

Author

Teune, Laura K. ; Strijkert, Fijanne ; Renken, Remco J. ; Izaks, Gerbrand J. ; de Vries, Jeroen J. ; Segbers, Marcel ; Roerdink, Jos B. T. M. ; Dierckx, Rudi A. J. O. ; Leenders, Klaus L. / The Alzheimer's Disease-Related Glucose Metabolic Brain Pattern. In: Current alzheimer research. 2014 ; Vol. 11, No. 8. pp. 725-732.

Harvard

Teune, LK, Strijkert, F, Renken, RJ, Izaks, GJ, de Vries, JJ, Segbers, M, Roerdink, JBTM, Dierckx, RAJO & Leenders, KL 2014, 'The Alzheimer's Disease-Related Glucose Metabolic Brain Pattern', Current alzheimer research, vol. 11, no. 8, pp. 725-732. https://doi.org/10.2174/156720501108140910114230

Standard

The Alzheimer's Disease-Related Glucose Metabolic Brain Pattern. / Teune, Laura K.; Strijkert, Fijanne; Renken, Remco J.; Izaks, Gerbrand J.; de Vries, Jeroen J.; Segbers, Marcel; Roerdink, Jos B. T. M.; Dierckx, Rudi A. J. O.; Leenders, Klaus L.

In: Current alzheimer research, Vol. 11, No. 8, 2014, p. 725-732.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Teune LK, Strijkert F, Renken RJ, Izaks GJ, de Vries JJ, Segbers M et al. The Alzheimer's Disease-Related Glucose Metabolic Brain Pattern. Current alzheimer research. 2014;11(8):725-732. https://doi.org/10.2174/156720501108140910114230


BibTeX

@article{98788f1fc7ba4152a9e9c39d805d0210,
title = "The Alzheimer's Disease-Related Glucose Metabolic Brain Pattern",
abstract = "Purpose: [F-18] fluorodeoxyglucose (FDG) PET imaging of the brain can be used to assist in the differential diagnosis of dementia. Group differences in glucose uptake between patients with dementia and controls are well-known. However, a multivariate analysis technique called scaled subprofile model, principal component analysis (SSM/PCA) aiming at identifying diagnostic neural networks in diseases, have been applied less frequently. We validated an Alzheimer's Disease-related (AD) glucose metabolic brain pattern using the SSM/PCA analysis and applied it prospectively in an independent confirmation cohort. Methods: We used FDG-PET scans of 18 healthy controls and 15 AD patients (identification cohort) to identify an AD-related glucose metabolic covariance pattern. In the confirmation cohort (n=15), we investigated the ability to discriminate between probable AD and non-probable AD (possible AD, mild cognitive impairment (MCI) or subjective complaints). Results: The AD-related metabolic covariance pattern was characterized by relatively decreased metabolism in the temporoparietal regions and relatively increased metabolism in the subcortical white matter, cerebellum and sensorimotor cortex. Receiver-operating characteristic (ROC) curves showed at a cut-off value of z=1.23, a sensitivity of 93{\%} and a specificity of 94{\%} for correct AD classification. In the confirmation cohort, subjects with clinically probable AD diagnosis showed a high expression of the AD-related pattern whereas in subjects with a non-probable AD diagnosis a low expression was found. Conclusion: The Alzheimer's disease-related cerebral glucose metabolic covariance pattern identified by SSM/PCA analysis was highly sensitive and specific for Alzheimer's disease. This method is expected to be helpful in the early diagnosis of Alzheimer's disease in clinical practice.",
keywords = "Alzheimer's Disease, clinical diagnosis, disease-specific metabolic brain patterns, FDG-PET imaging, neuropsychological profiles, SSM-PCA method, MILD COGNITIVE IMPAIRMENT, FDG-PET, FRONTOTEMPORAL DEMENTIA, F-18-FDG PET, LEWY BODIES, WORK GROUP, DIAGNOSIS, MULTIVARIATE, DISCRIMINATION, PERFUSION",
author = "Teune, {Laura K.} and Fijanne Strijkert and Renken, {Remco J.} and Izaks, {Gerbrand J.} and {de Vries}, {Jeroen J.} and Marcel Segbers and Roerdink, {Jos B. T. M.} and Dierckx, {Rudi A. J. O.} and Leenders, {Klaus L.}",
year = "2014",
doi = "10.2174/156720501108140910114230",
language = "English",
volume = "11",
pages = "725--732",
journal = "Current alzheimer research",
issn = "1567-2050",
publisher = "BENTHAM SCIENCE PUBL LTD",
number = "8",

}

RIS

TY - JOUR

T1 - The Alzheimer's Disease-Related Glucose Metabolic Brain Pattern

AU - Teune, Laura K.

AU - Strijkert, Fijanne

AU - Renken, Remco J.

AU - Izaks, Gerbrand J.

AU - de Vries, Jeroen J.

AU - Segbers, Marcel

AU - Roerdink, Jos B. T. M.

AU - Dierckx, Rudi A. J. O.

AU - Leenders, Klaus L.

PY - 2014

Y1 - 2014

N2 - Purpose: [F-18] fluorodeoxyglucose (FDG) PET imaging of the brain can be used to assist in the differential diagnosis of dementia. Group differences in glucose uptake between patients with dementia and controls are well-known. However, a multivariate analysis technique called scaled subprofile model, principal component analysis (SSM/PCA) aiming at identifying diagnostic neural networks in diseases, have been applied less frequently. We validated an Alzheimer's Disease-related (AD) glucose metabolic brain pattern using the SSM/PCA analysis and applied it prospectively in an independent confirmation cohort. Methods: We used FDG-PET scans of 18 healthy controls and 15 AD patients (identification cohort) to identify an AD-related glucose metabolic covariance pattern. In the confirmation cohort (n=15), we investigated the ability to discriminate between probable AD and non-probable AD (possible AD, mild cognitive impairment (MCI) or subjective complaints). Results: The AD-related metabolic covariance pattern was characterized by relatively decreased metabolism in the temporoparietal regions and relatively increased metabolism in the subcortical white matter, cerebellum and sensorimotor cortex. Receiver-operating characteristic (ROC) curves showed at a cut-off value of z=1.23, a sensitivity of 93% and a specificity of 94% for correct AD classification. In the confirmation cohort, subjects with clinically probable AD diagnosis showed a high expression of the AD-related pattern whereas in subjects with a non-probable AD diagnosis a low expression was found. Conclusion: The Alzheimer's disease-related cerebral glucose metabolic covariance pattern identified by SSM/PCA analysis was highly sensitive and specific for Alzheimer's disease. This method is expected to be helpful in the early diagnosis of Alzheimer's disease in clinical practice.

AB - Purpose: [F-18] fluorodeoxyglucose (FDG) PET imaging of the brain can be used to assist in the differential diagnosis of dementia. Group differences in glucose uptake between patients with dementia and controls are well-known. However, a multivariate analysis technique called scaled subprofile model, principal component analysis (SSM/PCA) aiming at identifying diagnostic neural networks in diseases, have been applied less frequently. We validated an Alzheimer's Disease-related (AD) glucose metabolic brain pattern using the SSM/PCA analysis and applied it prospectively in an independent confirmation cohort. Methods: We used FDG-PET scans of 18 healthy controls and 15 AD patients (identification cohort) to identify an AD-related glucose metabolic covariance pattern. In the confirmation cohort (n=15), we investigated the ability to discriminate between probable AD and non-probable AD (possible AD, mild cognitive impairment (MCI) or subjective complaints). Results: The AD-related metabolic covariance pattern was characterized by relatively decreased metabolism in the temporoparietal regions and relatively increased metabolism in the subcortical white matter, cerebellum and sensorimotor cortex. Receiver-operating characteristic (ROC) curves showed at a cut-off value of z=1.23, a sensitivity of 93% and a specificity of 94% for correct AD classification. In the confirmation cohort, subjects with clinically probable AD diagnosis showed a high expression of the AD-related pattern whereas in subjects with a non-probable AD diagnosis a low expression was found. Conclusion: The Alzheimer's disease-related cerebral glucose metabolic covariance pattern identified by SSM/PCA analysis was highly sensitive and specific for Alzheimer's disease. This method is expected to be helpful in the early diagnosis of Alzheimer's disease in clinical practice.

KW - Alzheimer's Disease

KW - clinical diagnosis

KW - disease-specific metabolic brain patterns

KW - FDG-PET imaging

KW - neuropsychological profiles

KW - SSM-PCA method

KW - MILD COGNITIVE IMPAIRMENT

KW - FDG-PET

KW - FRONTOTEMPORAL DEMENTIA

KW - F-18-FDG PET

KW - LEWY BODIES

KW - WORK GROUP

KW - DIAGNOSIS

KW - MULTIVARIATE

KW - DISCRIMINATION

KW - PERFUSION

U2 - 10.2174/156720501108140910114230

DO - 10.2174/156720501108140910114230

M3 - Article

VL - 11

SP - 725

EP - 732

JO - Current alzheimer research

JF - Current alzheimer research

SN - 1567-2050

IS - 8

ER -

ID: 16102109