Brain Metabolism and Amyloid Load in Individuals With Subjective Cognitive Decline or Pre-Mild Cognitive Impairment.


Journal

Neurology
ISSN: 1526-632X
Titre abrégé: Neurology
Pays: United States
ID NLM: 0401060

Informations de publication

Date de publication:
29 Apr 2022
Historique:
received: 04 09 2021
accepted: 21 02 2022
entrez: 29 4 2022
pubmed: 30 4 2022
medline: 30 4 2022
Statut: aheadofprint

Résumé

Multicenter study aiming at investigating the characteristics of cognitive decline, neuropsychiatric symptoms, and brain imaging in individuals with subjective cognitive decline (SCD) and subtle cognitive decline (pre-Mild Cognitive Impairment, pre-MCI). Data were obtained from the Network-AD project (NET-2011-02346784). The included subjects underwent baseline cognitive and neurobehavioral evaluation, FDG-PET, and, amyloid-PET. We used Principal Component Analysis (PCA) to identify independent neuropsychological and neuropsychiatric dimensions and their association with brain metabolism. A total of 105 subjects (SCD=49, pre-MCI=56) were included. FDG-PET was normal in 45% of subjects and revealed brain hypometabolism in 55%, with a frontal-like pattern as the most frequent finding (28%). Neuropsychiatric symptoms emerging from the Neuropsychiatric Inventory and the Starkstein Apathy Scale were highly prevalent in the whole sample (78%). An abnormal amyloid load was detected in the 18% of the subjects that underwent amyloid-PET (n=60). PCA resulted in three neuropsychological factors: 1) executive/visuo-motor, correlating with hypometabolism in frontal, occipital cortices and basal ganglia; 2) memory, correlating with hypometabolism in temporo-parietal regions; 3) visuo-spatial/constructional, correlating with hypometabolism in fronto-parietal cortices. Two factors emerged from the neuropsychiatric PCA: 1) affective, correlating with hypometabolism in orbito-frontal, cingulate cortex, insula; 2) hyperactive/psychotic, correlating with hypometabolism in frontal, temporal and parietal regions. FDG-PET evidence suggests either normal brain function or different patterns of brain hypometabolism in SCD and pre-MCI subjects. These results indicate that SCD and pre-MCI represent heterogeneous populations. Consistently, different neuropsychological and neuropsychiatric profiles emerged, which correlated with neuronal dysfunction in specific brain regions. Long-term follow-up studies are needed to assess the risk of progression to dementia in these conditions.

Sections du résumé

BACKGROUND AND OBJECTIVE OBJECTIVE
Multicenter study aiming at investigating the characteristics of cognitive decline, neuropsychiatric symptoms, and brain imaging in individuals with subjective cognitive decline (SCD) and subtle cognitive decline (pre-Mild Cognitive Impairment, pre-MCI).
METHODS METHODS
Data were obtained from the Network-AD project (NET-2011-02346784). The included subjects underwent baseline cognitive and neurobehavioral evaluation, FDG-PET, and, amyloid-PET. We used Principal Component Analysis (PCA) to identify independent neuropsychological and neuropsychiatric dimensions and their association with brain metabolism.
RESULTS RESULTS
A total of 105 subjects (SCD=49, pre-MCI=56) were included. FDG-PET was normal in 45% of subjects and revealed brain hypometabolism in 55%, with a frontal-like pattern as the most frequent finding (28%). Neuropsychiatric symptoms emerging from the Neuropsychiatric Inventory and the Starkstein Apathy Scale were highly prevalent in the whole sample (78%). An abnormal amyloid load was detected in the 18% of the subjects that underwent amyloid-PET (n=60). PCA resulted in three neuropsychological factors: 1) executive/visuo-motor, correlating with hypometabolism in frontal, occipital cortices and basal ganglia; 2) memory, correlating with hypometabolism in temporo-parietal regions; 3) visuo-spatial/constructional, correlating with hypometabolism in fronto-parietal cortices. Two factors emerged from the neuropsychiatric PCA: 1) affective, correlating with hypometabolism in orbito-frontal, cingulate cortex, insula; 2) hyperactive/psychotic, correlating with hypometabolism in frontal, temporal and parietal regions.
DISCUSSION CONCLUSIONS
FDG-PET evidence suggests either normal brain function or different patterns of brain hypometabolism in SCD and pre-MCI subjects. These results indicate that SCD and pre-MCI represent heterogeneous populations. Consistently, different neuropsychological and neuropsychiatric profiles emerged, which correlated with neuronal dysfunction in specific brain regions. Long-term follow-up studies are needed to assess the risk of progression to dementia in these conditions.

Identifiants

pubmed: 35487700
pii: WNL.0000000000200351
doi: 10.1212/WNL.0000000000200351
pmc: PMC9302934
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2022 American Academy of Neurology.

Références

Alzheimers Dement. 2018 Apr;14(4):535-562
pubmed: 29653606
Alzheimers Dement. 2014 Nov;10(6):844-52
pubmed: 24798886
Neuroimage Clin. 2014 Oct 24;6:445-54
pubmed: 25389519
Ann Clin Transl Neurol. 2019 May 24;6(6):1113-1120
pubmed: 31211176
Lancet Neurol. 2013 Feb;12(2):207-16
pubmed: 23332364
Neuroimage Clin. 2018 Jan 28;18:167-177
pubmed: 29387532
Eur J Nucl Med Mol Imaging. 2016 Mar;43(3):499-508
pubmed: 26341365
Front Pharmacol. 2019 Nov 22;10:1398
pubmed: 31824321
Neuroimage Clin. 2014 Dec 05;7:187-94
pubmed: 25610780
Dement Geriatr Cogn Disord. 2007;24(6):457-63
pubmed: 17986816
Neuropsychologia. 2018 Jul 1;115:78-87
pubmed: 29596856
Neurology. 2020 Sep 1;95(9):e1134-e1143
pubmed: 32636322
PLoS One. 2014 Jan 31;9(1):e87747
pubmed: 24498184
Neurology. 2012 Sep 25;79(13):1332-9
pubmed: 22914828
J Int Neuropsychol Soc. 2012 Jan;18(1):144-50
pubmed: 22114843
Brain. 2011 Sep;134(Pt 9):2456-77
pubmed: 21810890
Neurology. 2006 Aug 8;67(3):467-73
pubmed: 16894109
Dement Geriatr Cogn Disord. 1999 Mar-Apr;10(2):130-8
pubmed: 10026387
Alzheimers Dement. 2011 May;7(3):270-9
pubmed: 21514249
Lancet Neurol. 2021 Jun;20(6):484-496
pubmed: 33933186
Prog Neurobiol. 2014 Jun;117:20-40
pubmed: 24548606
Nat Rev Neurol. 2014 Nov;10(11):634-42
pubmed: 25266297
Alzheimers Res Ther. 2013 Jan 16;5(1):4
pubmed: 23324163
Eur J Nucl Med Mol Imaging. 2009 Dec;36(12):2103-10
pubmed: 19838705
Alzheimers Dement. 2016 Feb;12(2):195-202
pubmed: 26096665
Am J Geriatr Psychiatry. 2016 Nov;24(11):1095-1104
pubmed: 27426238
Mov Disord. 2010 Oct 30;25(14):2395-404
pubmed: 20669302
J Affect Disord. 2014 Feb;155:266-72
pubmed: 24355647
Neurology. 2013 Jan 29;80(5):496-503
pubmed: 23359374
Am J Geriatr Psychiatry. 2010 Aug;18(8):701-10
pubmed: 21491631
Hum Brain Mapp. 2016 Dec;37(12):4234-4247
pubmed: 27412866
Handb Clin Neurol. 2018;151:331-348
pubmed: 29519467
Neurology. 2017 May 2;88(18):1759-1767
pubmed: 28381517
Neurology. 2019 Jul 23;93(4):e322-e333
pubmed: 31289148
Neuroimage. 2003 Jul;19(3):1233-9
pubmed: 12880848
Eur J Neurol. 2021 Apr;28(4):1123-1133
pubmed: 33185922
Am J Geriatr Psychiatry. 2011 Nov;19(11):951-60
pubmed: 21422909
Neuroimage Clin. 2018 Jul 19;20:153-160
pubmed: 30094164
Neurology. 2020 Jul 7;95(1):e46-e58
pubmed: 32522798
Alzheimers Dement. 2019 Aug;15(8):1081-1103
pubmed: 31230910
Neuroinformatics. 2017 Apr;15(2):151-163
pubmed: 28063108
Annu Rev Clin Psychol. 2017 May 8;13:369-396
pubmed: 28482688
J Nucl Med. 2016 Nov;57(11):1740-1745
pubmed: 27363836
J Alzheimers Dis. 2015;46(1):63-73
pubmed: 25697700
Mol Neurodegener. 2020 Sep 22;15(1):55
pubmed: 32962744
Lancet Neurol. 2020 Mar;19(3):271-278
pubmed: 31958406
Alzheimers Dement. 2011 May;7(3):280-92
pubmed: 21514248
Neurology. 2017 May 9;88(19):1814-1821
pubmed: 28404803
JAMA. 2015 May 19;313(19):1924-38
pubmed: 25988462

Auteurs

Giacomo Tondo (G)

Vita-Salute San Raffaele University, Milan, Italy.
In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.

Cecilia Boccalini (C)

Vita-Salute San Raffaele University, Milan, Italy.
In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.

Emilia Giovanna Vanoli (EG)

Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy.

Luca Presotto (L)

In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy.

Cristina Muscio (C)

Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy.

Valentina Ciullo (V)

Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.

Nerisa Banaj (N)

Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.

Federica Piras (F)

Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.

Graziella Filippini (G)

Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy.

Pietro Tiraboschi (P)

Unit of Neurology and Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy.

Fabrizio Tagliavini (F)

Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy.

Giovanni Battista Frisoni (GB)

IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland.

Stefano F Cappa (SF)

ICoN, Scuola Universitaria Superiore IUSS Pavia, Pavia, Italy.
IRCCS Mondino Foundation, Pavia, Italy.

Gianfranco Spalletta (G)

Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.

Daniela Perani (D)

Vita-Salute San Raffaele University, Milan, Italy perani.daniela@hsr.it.
In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy.

Classifications MeSH