Marco Pagani1,2, Alessandro Giuliani3, Johanna Öberg4, Fabrizio De Carli5, Silvia Morbelli6, Nicola Girtler7,8, Dario Arnaldi7, Jennifer Accardo7, Matteo Bauckneht6, Francesca Bongioanni6, Andrea Chincarini9, Gianmario Sambuceti6, Cathrine Jonsson4, Flavio Nobili7. 1. Institute of Cognitive Sciences and Technologies, CNR, Rome, Italy marco.pagani@istc.cnr.it. 2. Department of Nuclear Medicine, Karolinska Hospital, Stockholm, Sweden. 3. Environment and Health Department, Istituto Superiore di Sanità, Rome, Italy. 4. Department of Hospital Physics, Karolinska Hospital, Stockholm, Sweden. 5. Institute of Molecular Bioimaging and Physiology, CNR, Genoa, Italy. 6. Departments of Nuclear Medicine and Health Science, University of Genoa, and IRCCS AOU San Martino-IST, Genoa, Italy. 7. Clinical Neurology, Department of Neuroscience, University of Genoa, and IRCCS AOU San Martino-IST, Genoa, Italy. 8. Clinical Psychology, IRCCS AOU San Martino-IST, Genoa, Italy; and. 9. National Institute of Nuclear Physics, Genoa, Italy.
Abstract
Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between predetermined regions or nodes. Selective breakdown of brain networks during progression from normal aging to Alzheimer disease dementia (AD) has also been observed. Methods: We implemented independent-component analysis of 18F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment (MCI) not converting or converting to AD-to disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups. Results: We could identify spatially distinct independent components in each group, with generation of local circuits increasing proportionally to the severity of the disease. AD-specific independent components first appeared in the late-MCI stage and could discriminate converting MCI and AD from nonconverting MCI with an accuracy of 83.5%. Progressive disintegration of the intrinsic networks from normal aging to MCI to AD was inversely proportional to the conversion time. Conclusion: Independent-component analysis of 18F-FDG PET data showed a gradual disruption of functional brain connectivity with progression of cognitive decline in AD. This information might be useful as a prognostic aid for individual patients and as a surrogate biomarker in intervention trials.
Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between predetermined regions or nodes. Selective breakdown of brain networks during progression from normal aging to Alzheimer disease dementia (AD) has also been observed. Methods: We implemented independent-component analysis of 18F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment (MCI) not converting or converting to AD-to disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups. Results: We could identify spatially distinct independent components in each group, with generation of local circuits increasing proportionally to the severity of the disease. AD-specific independent components first appeared in the late-MCI stage and could discriminate converting MCI and AD from nonconverting MCI with an accuracy of 83.5%. Progressive disintegration of the intrinsic networks from normal aging to MCI to AD was inversely proportional to the conversion time. Conclusion: Independent-component analysis of 18F-FDG PET data showed a gradual disruption of functional brain connectivity with progression of cognitive decline in AD. This information might be useful as a prognostic aid for individual patients and as a surrogate biomarker in intervention trials.
Authors: Claudia Bachmann; Heidi I L Jacobs; PierGianLuca Porta Mana; Kim Dillen; Nils Richter; Boris von Reutern; Julian Dronse; Oezguer A Onur; Karl-Josef Langen; Gereon R Fink; Juraj Kukolja; Abigail Morrison Journal: Front Neurosci Date: 2018-09-28 Impact factor: 4.677
Authors: Robert J Dawe; Lei Yu; Sue E Leurgans; Bryan D James; Victoria N Poole; Konstantinos Arfanakis; Julie A Schneider; David A Bennett; Aron S Buchman Journal: PLoS One Date: 2021-07-07 Impact factor: 3.240