Frontoparietal network integrity supports cognitive function in pre-symptomatic frontotemporal dementia: Multimodal analysis of brain function, structure, and perfusion.
atrophy
cerebral blood flow
frontotemporal dementia
functional network
multimodal neuroimaging
pre‐symptomatic dementia
Journal
Alzheimer's & dementia : the journal of the Alzheimer's Association
ISSN: 1552-5279
Titre abrégé: Alzheimers Dement
Pays: United States
ID NLM: 101231978
Informations de publication
Date de publication:
17 Oct 2024
17 Oct 2024
Historique:
revised:
14
08
2024
received:
01
03
2024
accepted:
10
09
2024
medline:
17
10
2024
pubmed:
17
10
2024
entrez:
17
10
2024
Statut:
aheadofprint
Résumé
Genetic mutation carriers of frontotemporal dementia can remain cognitively well despite neurodegeneration. A better understanding of brain structural, perfusion, and functional patterns in the pre-symptomatic stage could inform accurate staging and potential mechanisms. We included 207 pre-symptomatic genetic mutation carriers and 188 relatives without mutations. The gray matter volume, cerebral perfusion, and resting-state functional network maps were co-analyzed using linked independent component analysis (LICA). Multiple regression analysis was used to investigate the relationship of LICA components to genetic status and cognition. Pre-symptomatic mutation carriers showed an age-related decrease in the left frontoparietal network integrity, while non-carriers did not. Executive functions of mutation carriers became dependent on the left frontoparietal network integrity in older age. The frontoparietal network integrity of pre-symptomatic mutation carriers showed a distinctive relationship to age and cognition compared to non-carriers, suggesting a contribution of the network integrity to brain resilience. A multimodal analysis of structure, perfusion, and functional networks. The frontoparietal network integrity decreases with age in pre-symptomatic carriers only. Executive functions of pre-symptomatic carriers dissociated from non-carriers.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Cambridge Commonwealth, European and International Trust
Organisme : Dioraphte Foundation
ID : 09-02-03-00
Organisme : Netherlands Organization for Scientific Research
ID : HCMI 056-13-018
Organisme : Fundació Marató de TV3, Spain
ID : 20143810
Organisme : Swedish FTD Inititative-Schörling Foundation
Organisme : Alzheimer Foundation
Organisme : Brain Foundation
Organisme : Dementia Foundation
Organisme : Region Stockholm
ID : 733051042
Organisme : Mady Browaeys Fund
Organisme : Munich Cluster for Systems Neurology
ID : 390857198
Organisme : Federal Ministry of Education and Research
Organisme : Canadian Institute of Health Research
ID : 327387
Organisme : Weston Brain Institute
Organisme : Ontario Brain Institute
Organisme : Carlos III Health Institute
ID : PI19/01637
Organisme : MRC Clinician Scientist Fellowship
ID : MR/M008525/1
Organisme : European Reference Network for Rare Neurological Diseases
Organisme : Guarantors of Brain
ID : G101149
Organisme : Alzheimer's Society
ID : 602
Organisme : Wellcome Trust
ID : 103838
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 220258
Pays : United Kingdom
Organisme : Cambridge University Centre for Frontotemporal Dementia
Organisme : the Medical Research Council
ID : MC_UU_00030/14
Organisme : the Medical Research Council
ID : MR/T033371/1
Organisme : National Institute for Health Research Cambridge Biomedical Research Centre
ID : NIHR203312
Organisme : National Institute for Health Research Cambridge Biomedical Research Centre
ID : BRC-1215-20014
Organisme : Holt Fellowship
Organisme : EU Joint Programme-Neurodegenerative Disease Research
ID : 2019-02248
Informations de copyright
© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
Références
Bang J, Spina S, Miller BL. Frontotemporal dementia. Lancet. 2015;386:1672‐1682.
Rohrer JD, Guerreiro R, Vandrovcova J, et al. The heritability and genetics of frontotemporal lobar degeneration. Neurology. 2009;73:1451‐1456.
Kinnunen KM, Cash DM, Poole T, et al. Presymptomatic atrophy in autosomal dominant Alzheimer's disease: a serial magnetic resonance imaging study. Alzheimers Dement. 2018;14:43‐53.
Tsvetanov KA, Gazzina S, Jones PS, et al. Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia. Alzheimers Dement. 2021;17:500‐514.
Jack CR Jr, Knopman DS, Jagust WJ, et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 2010;9:119‐128.
Rohrer JD, Nicholas JM, Cash DM, et al. Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross‐sectional analysis. Lancet Neurol. 2015;14:253‐262.
Cash DM, Bocchetta M, Thomas DL, et al. Patterns of gray matter atrophy in genetic frontotemporal dementia: results from the GENFI study. Neurobiol Aging. 2018;62:191‐196.
Mutsaerts H, Mirza SS, Petr J, et al. Cerebral perfusion changes in presymptomatic genetic frontotemporal dementia: a GENFI study. Brain. 2019;142:1108‐1120.
Rittman T, Borchert R, Jones S, et al. Functional network resilience to pathology in presymptomatic genetic frontotemporal dementia. Neurobiol Aging. 2019;77:169‐177.
Whiteside DJ, Malpetti M, Jones PS, et al. Temporal dynamics predict symptom onset and cognitive decline in familial frontotemporal dementia. Alzheimers Dement. 2022;19(5):1947‐1962.
Miyagawa T, Brushaber D, Syrjanen J, et al. Utility of the global CDR((R)) plus NACC FTLD rating and development of scoring rules: data from the ARTFL/LEFFTDS Consortium. Alzheimers Dement. 2020;16:106‐117.
Gorno‐Tempini ML, Hillis AE, Weintraub S, et al. Classification of primary progressive aphasia and its variants. Neurology. 2011;76:1006‐1014.
Rascovsky K, Hodges JR, Knopman D, et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain. 2011;134:2456‐2477.
Knopman DS, Kramer JH, Boeve BF, et al. Development of methodology for conducting clinical trials in frontotemporal lobar degeneration. Brain. 2008;131:2957‐2968.
Wear HJ, Wedderburn CJ, Mioshi E, et al. The Cambridge Behavioural Inventory revised. Dement Neuropsychol. 2008;2:102‐107.
Morris JC, Weintraub S, Chui HC, et al. The Uniform Data Set (UDS): clinical and cognitive variables and descriptive data from Alzheimer Disease Centers. Alzheimer Dis Assoc Disord. 2006;20:210‐216.
Corrigan JD, Hinkeldey NS. Relationships between parts A and B of the Trail Making Test. J Clin Psychol. 1987;43:402‐409.
Delis DC, Kaplan E, Kramer JH. Delis Kaplan Executive Function System. San Antonio, TX: The Psychological Corporation; 2001.
Moore K, Convery R, Bocchetta M, et al. A modified Camel and Cactus Test detects presymptomatic semantic impairment in genetic frontotemporal dementia within the GENFI cohort. Appl Neuropsychol Adult. 2022;29:112‐119.
Tombaugh TN, Kozak J, Rees L. Normative data stratified by age and education for two measures of verbal fluency: FAS and animal naming. Arch Clin Neuropsychol. 1999;14:167‐177.
Poos JM, Russell LL, Peakman G, et al. Impairment of episodic memory in genetic frontotemporal dementia: a GENFI study. Alzheimers Dement (Amst). 2021;13:e12185.
Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38:95‐113.
Mutsaerts H, Petr J, Thomas DL, et al. Comparison of arterial spin labeling registration strategies in the multi‐center GENetic frontotemporal dementia initiative (GENFI). J Magn Reson Imaging. 2018;47:131‐140.
Mutsaerts H, Petr J, Groot P, et al. ExploreASL: an image processing pipeline for multi‐center ASL perfusion MRI studies. Neuroimage. 2020;219:117031.
Mutsaerts HJ, Petr J, Vaclavu L, et al. The spatial coefficient of variation in arterial spin labeling cerebral blood flow images. J Cereb Blood Flow Metab. 2017;37:3184‐3192.
Asllani I, Borogovac A, Brown TR. Regression algorithm correcting for partial volume effects in arterial spin labeling MRI. Magn Reson Med. 2008;60:1362‐1371.
Pasternak M, Mirza SS, Luciw N, et al. Longitudinal cerebral perfusion in presymptomatic genetic frontotemporal dementia: GENFI results. Alzheimers Dement. 2024;20:3525‐3542.
Li H, Smith SM, Gruber S, et al. Denoising scanner effects from multimodal MRI data using linked independent component analysis. Neuroimage. 2020;208:116388.
Chen J, Liu J, Calhoun VD, et al. Exploration of scanning effects in multi‐site structural MRI studies. J Neurosci Methods. 2014;230:37‐50.
Ashburner J, Friston KJ. Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation. Neuroimage. 2011;55:954‐967.
Shirzadi Z, Crane DE, Robertson AD, et al. Automated removal of spurious intermediate cerebral blood flow volumes improves image quality among older patients: a clinical arterial spin labeling investigation. J Magn Reson Imaging. 2015;42:1377‐1385.
Alsop DC, Detre JA, Golay X, et al. Recommended implementation of arterial spin‐labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn Reson Med. 2015;73:102‐116.
Cusack R, Vicente‐Grabovetsky A, Mitchell DJ, et al. Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML. Front Neuroinform. 2014;8:90.
Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA‐AROMA: a robust ICA‐based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015;112:267‐277.
Geerligs L, Tsvetanov KA, Cam C, Henson RN. Challenges in measuring individual differences in functional connectivity using fMRI: the case of healthy aging. Hum Brain Mapp. 2017;38:4125‐4156.
Calhoun VD, Adali T, Pearlson GD, Pekar JJ. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp. 2001;14:140‐151.
McKeown MJ, Makeig S, Brown GG, et al. Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp. 1998;6:160‐188.
Beckmann CF, DeLuca M, Devlin JT, Smith SM. Investigations into resting‐state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci. 2005;360:1001‐1013.
Damoiseaux JS, Rombouts SA, Barkhof F, et al. Consistent resting‐state networks across healthy subjects. Proc Natl Acad Sci U S A. 2006;103:13848‐13853.
Smith SM, Fox PT, Miller KL, et al. Correspondence of the brain's functional architecture during activation and rest. Proc Natl Acad Sci USA. 2009;106:13040‐13045.
Himberg J, Hyvarinen A. Icasso: software for investigating the reliability of ICA estimates by clustering and visualization. 2003 IEEE XIII Workshop on Neural Networks for Signal Processing. IEEE; 2003:259‐268. Cat No03TH8718.
Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD. Decoding subject‐driven cognitive states with whole‐brain connectivity patterns. Cereb Cortex. 2012;22:158‐165.
Liu X, Tyler LK, Cam CAN, Rowe JB, Tsvetanov KA. Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects. Hum Brain Mapp. 2022;43:5490‐5508.
Passamonti L, Tsvetanov KA, Jones PS, et al. Neuroinflammation and functional connectivity in Alzheimer's disease: interactive influences on cognitive performance. J Neurosci. 2019;39:7218‐7226.
Pievani M, de Haan W, Wu T, Seeley WW, Frisoni GB. Functional network disruption in the degenerative dementias. Lancet Neurol. 2011;10:829‐843.
Snyder W, Uddin LQ, Nomi JS. Dynamic functional connectivity profile of the salience network across the life span. Hum Brain Mapp. 2021;42:4740‐4749.
Groves AR, Beckmann CF, Smith SM, Woolrich MW. Linked independent component analysis for multimodal data fusion. Neuroimage. 2011;54:2198‐2217.
Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23(Suppl 1):S208‐S219.
Wilkinson GN, Rogers CE. Symbolic description of factorial models for analysis of variance. J R Stat Soc Ser C Appl Stat. 1973;22:392‐399.
Zhang N, Gordon ML, Goldberg TE. Cerebral blood flow measured by arterial spin labeling MRI at resting state in normal aging and Alzheimer's disease. Neurosci Biobehav Rev. 2017;72:168‐175.
Mokhber N, Shariatzadeh A, Avan A. Cerebral blood flow changes during aging process and in cognitive disorders: a review. Neuroradiol J. 2021;34:300‐307.
Damoiseaux JS, Beckmann CF, Arigita EJ, et al. Reduced resting‐state brain activity in the “default network” in normal aging. Cereb Cortex. 2008;18:1856‐1864.
Douaud G, Groves AR, Tamnes CK, et al. A common brain network links development, aging, and vulnerability to disease. Proc Natl Acad Sci USA. 2014;111:17648‐17653.
Kennedy KM, Raz N. Normal aging of the brain. In: Toga AW, ed. Brain Mapping. Academic Press; 2015:603‐617.
Fumagalli GG, Basilico P, Arighi A, et al. Distinct patterns of brain atrophy in Genetic Frontotemporal Dementia Initiative (GENFI) cohort revealed by visual rating scales. Alzheimers Res Ther. 2018;10:46.
Peelle JE, Cusack R, Henson RN. Adjusting for global effects in voxel‐based morphometry: gray matter decline in normal aging. Neuroimage. 2012;60:1503‐1516.
Zhou J, Greicius MD, Gennatas ED, et al. Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease. Brain. 2010;133:1352‐1367.
Whitwell JL, Josephs KA, Avula R, et al. Altered functional connectivity in asymptomatic MAPT subjects: a comparison to bvFTD. Neurology. 2011;77:866‐874.
Ferguson HJ, Brunsdon VEA, Bradford EEF. The developmental trajectories of executive function from adolescence to old age. Sci Rep. 2021;11:1382.
Salthouse T. Consequences of age‐related cognitive declines. Annu Rev Psychol. 2012;63:201‐226.
Poos JM, MacDougall A, van den Berg E, et al. Longitudinal cognitive changes in genetic frontotemporal dementia within the GENFI cohort. Neurology. 2022;99:e281‐e295.
Adnan A, Beaty R, Lam J, Spreng RN, Turner GR. Intrinsic default‐executive coupling of the creative aging brain. Soc Cogn Affect Neurosci. 2019;14:291‐303.
Kupis L, Goodman ZT, Kornfeld S, et al. Brain dynamics underlying cognitive flexibility across the lifespan. Cereb Cortex. 2021;31:5263‐5274.
Rohrer JD. Structural brain imaging in frontotemporal dementia. Biochim Biophys Acta. 2012;1822:325‐332.
Borroni B, Alberici A, Cercignani M, et al. Granulin mutation drives brain damage and reorganization from preclinical to symptomatic FTLD. Neurobiol Aging. 2012;33:2506‐2520.
Rohrer JD, Warren JD, Modat M, et al. Patterns of cortical thinning in the language variants of frontotemporal lobar degeneration. Neurology. 2009;72:1562‐1569.
Ramanan S, Halai AD, Garcia‐Penton L, et al. The neural substrates of transdiagnostic cognitive‐linguistic heterogeneity in primary progressive aphasia. Alzheimers Res Ther. 2023;15:219.
Mesulam MM. From sensation to cognition. Brain. 1998;121:1013‐1052.
Friedman NP, Robbins TW. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology. 2022;47:72‐89.
Murley AG, Rowe JB. Neurotransmitter deficits from frontotemporal lobar degeneration. Brain. 2018;141:1263‐1285.
Hedden T, Van Dijk KR, Becker JA, et al. Disruption of functional connectivity in clinically normal older adults harboring amyloid burden. J Neurosci. 2009;29:12686‐12694.
Gabrielyan M, Tisdall MD, Kammer C, Higgins C, Arratia PE, Detre JA. A perfusion phantom for ASL MRI based on impinging jets. Magn Reson Med. 2021;86:1145‐1158.
Adebimpe A, Bertolero M, Dolui S, et al. ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion. Nat Methods. 2022;19:683‐686.
Benussi A, Premi E, Grassi M, et al. Diagnostic accuracy of research criteria for prodromal frontotemporal dementia. Alzheimers Res Ther. 2024;16:10.
Tsvetanov KA, Ye Z, Hughes L, et al. Activity and connectivity differences underlying inhibitory control across the adult life span. J Neurosci. 2018;38:7887‐7900.