Subjective cognitive decline (SCD) refers to individuals who perceive a decrease in their cognitive functioning despite no detectable deficits on neuropsychological tests, and may represent the earliest clinical stage of Alzheimer's disease (AD).
Objective
The aim of this study was to assess grey matter (GM) volumes and cortical folding patterns in individuals with SCD compared with the broader AD spectrum.
Methods
A total of 107 participants were enrolled: 31 with AD, 23 with amnestic mild cognitive impairment (aMCI), 25 with SCD and 28 healthy participants (HS). All participants underwent neuropsychological assessment and 3T MRI to acquire T1-weighted images, which were processed using CAT-12 for voxel-based morphometry (VBM) and surface-based morphometry (SBM).
Results
VBM revealed expected atrophic patterns in AD and aMCI patients compared to SCD and HS, with no volumetric differences found between SCD individuals and HS. Notably, AD patients showed cortical surface abnormalities in all SBM measures. aMCI patients showed reduced cortical thickness and gyrification compared to SCD and HS. In contrast, SCD individuals showed reduced gyrification and sulcal depth compared to HS.
Conclusions
These findings suggest that cortical surface abnormalities may manifest early in the AD continuum, whereas volumetric changes occur later. The use of multiparametric MRI techniques may be beneficial in detecting subtle changes within the AD spectrum, highlighting the potential for early identification of structural changes associated with cognitive decline in SCD individuals. Further research is warranted to explore these relationships and their implications for early intervention strategies in AD.
JessenFAmariglioREvan BoxtelM, et al.A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimers Dement2014; 10: 844–852.
2.
JessenFAmariglioREBuckleyRF, et al.The characterisation of subjective cognitive decline. Lancet Neurol2020; 19: 271–278.
3.
KaupARNettiksimmonsJLeBlancES, et al.Memory complaints and risk of cognitive impairment after nearly 2 decades among older women. Neurology2015; 85: 1852–1858.
4.
KuhnEPerrotinATomadessoC, et al.Subjective cognitive decline: opposite links to neurodegeneration across the Alzheimer's continuum. Brain Commun2021; 3: fcab199.
5.
MitchellAJBeaumontHFergusonD, et al.Risk of dementia and mild cognitive impairment in older people with subjective memory complaints: meta-analysis. Acta Psychiatr Scand2014; 130: 439–451.
6.
AnRGaoYHuangX, et al.Predictors of progression from subjective cognitive decline to objective cognitive impairment: a systematic review and meta-analysis of longitudinal studies. Int J Nurs Stud2024; 149: 104629.
7.
SerraLBonarotaSDi DomenicoC, et al.Preclinical brain network abnormalities in patients with subjective cognitive decline. J Alzheimers Dis2023; 95: 1119–1131.
8.
PavisicIMLuKKeussSE, et al.Subjective cognitive complaints at age 70: associations with amyloid and mental health. J Neurol Neurosurg Psychiatry2021; 92: 1215–1221.
9.
ListaSMolinuevoJLCavedoE, et al.Evolving evidence for the value of neuroimaging methods and biological markers in subjects categorized with subjective cognitive decline. J Alzheimers Dis2015; 48: S171–S191.
10.
Fernández-BlázquezMAÁvila-VillanuevaMMaestúF, et al.Specific features of subjective cognitive decline predict faster conversion to mild cognitive impairment. J Alzheimers Dis2016; 52: 271–281.
11.
TsutsumimotoKMakizakoHDoiT, et al.Subjective memory complaints are associated with incident dementia in cognitively intact older people, but not in those with cognitive impairment: a 24-month prospective cohort study. Am J Geriatr Psychiatry2017; 25: 607–616.
12.
SiTXingGHanY. Subjective cognitive decline and related cognitive deficits. Front Neurol2020; 11: 247.
13.
RibaldiFRolandiEVaccaroR, et al.The clinical heterogeneity of subjective cognitive decline: a data-driven approach on a population-based sample. Age Ageing2022; 51: afac209.
14.
StriepensNScheefLWindA, et al.Volume loss of the medial temporal lobe structures in subjective memory impairment. Dement Geriatr Cogn Disord2010; 29: 75–81.
15.
PeterJScheefLAbdulkadirA, et al.Gray matter atrophy pattern in elderly with subjective memory impairment. Alzheimers Dement2014; 10: 99–108.
16.
PiniLWennbergAM. Structural imaging outcomes in subjective cognitive decline: community vs. clinical-based samples. Exp Gerontol2021; 145: 111216.
17.
WangXHuangWSuL, et al.Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease. Mol Neurodegener2020; 15: 55.
18.
ArrondoPElía-ZudaireÓMartí-AndrésG, et al.Grey matter changes on brain MRI in subjective cognitive decline: a systematic review. Alzheimers Res Ther2022; 14: 98.
19.
HafkemeijerAAltmann-SchneiderIOleksikAM, et al.Increased functional connectivity and brain atrophy in elderly with subjective memory complaints. Brain Connect2013; 3: 353–362.
20.
LiangLZhaoLWeiY, et al.Structural and functional hippocampal changes in subjective cognitive decline from the community. Front Aging Neurosci2020; 17: 12.
21.
PerrotinAde FloresRLambertonF, et al.Hippocampal subfield volumetry and 3D surface mapping in subjective cognitive decline. J Alzheimers Dis2015; 48: S141–S150.
22.
SaykinAJWishartHARabinLA, et al.Older adults with cognitive complaints show brain atrophy similar to that of amnestic MCI. Neurology2006; 67: 834–842.
23.
ScheefLSpottkeADaerrM, et al.Glucose metabolism, gray matter structure, and memory decline in subjective memory impairment. Neurology2012; 79: 1332–1339.
24.
CedresNDiaz-GalvanPDiaz-FloresL, et al.The interplay between gray matter and white matter neurodegeneration in subjective cognitive decline. Aging2021; 13: 19963–19977.
25.
ChételatGVillemagneVLBourgeatP, et al.Relationship between atrophy and beta-amyloid deposition in Alzheimer disease. Ann Neurol2010; 67: 317–324.
26.
ChoiYYoonB-NChoiSH, et al.Reduced gray matter volume in subjective cognitive decline: a voxel-based morphometric study. Dement Neurocognitive Disord2015; 14: 143.
27.
HongYJYoonBShimYS, et al.Gray and white matter degenerations in subjective memory impairment: comparisons with normal controls and mild cognitive impairment. J Korean Med Sci2015; 30: 1652.
28.
DongGYangLLiC, et al.Dynamic network connectivity predicts subjective cognitive decline: the Sino-Longitudinal Cognitive impairment and dementia study. Brain Imaging Behav2020; 14: 2692–2707.
29.
ErkSSpottkeAMeisenA, et al.Evidence of neuronal compensation during episodic memory in subjective memory impairment. Arch Gen Psychiatry2011; 68: 845–852.
30.
ParkerAFSmartCMScarapicchiaV, et al.Identification of earlier biomarkers for Alzheimer’s disease: a multimodal neuroimaging study of individuals with subjective cognitive decline. J Alzheimers Dis2020; 77: 1067–1076.
31.
PerrotinALa JoieRde La SayetteV, et al.Subjective cognitive decline in cognitively normal elders from the community or from a memory clinic: differential affective and imaging correlates. Alzheimers Dement2017; 13: 550–560.
32.
SunYDaiZLiY, et al.Subjective cognitive decline: mapping functional and structural brain changes - a combined resting-state functional and structural MR imaging study. Radiology2016; 281: 185–192.
33.
XueCSunHHuG, et al.Disrupted patterns of rich-club and diverse-club organizations in subjective cognitive decline and amnestic mild cognitive impairment. Front Neurosci2020; 14: 575652.
34.
TopiwalaASuriSAllanC, et al.Subjective cognitive complaints given in questionnaire: relationship with brain structure, cognitive performance and self-reported depressive symptoms in a 25-year retrospective cohort study. Am J Geriatr Psychiatry2021; 29: 217–226.
35.
CanteroJLIglesiasJEVan LeemputK, et al.Regional hippocampal atrophy and higher levels of plasma amyloid-beta are associated with subjective memory complaints in nondemented elderly subjects. J Gerontol Ser A Biol Sci Med Sci2016; 71: 1210–1215.
36.
YueLWangTWangJ, et al.Asymmetry of hippocampus and amygdala defect in subjective cognitive decline among the community dwelling Chinese. Front Psychiatry2018; 9: 226.
37.
ZhaoWLuoYZhaoL, et al.Automated brain MRI volumetry differentiates early stages of Alzheimer’s disease from normal aging. J Geriatr Psychiatry Neurol2019; 32: 354–364.
38.
ZajacLKooB-BTripodisY, et al.Hippocampal resting-state functional connectivity patterns are more closely associated with severity of subjective memory decline than whole hippocampal and subfield volumes. Cereb Cortex Commun2020; 1: tgaa019.
39.
CongSRisacherSLWestJD, et al.Volumetric comparison of hippocampal subfields extracted from 4-minute accelerated vs. 8-minute high-resolution T2-weighted 3T MRI scans. Brain Imaging Behav2018; 12: 1583–1595.
40.
SchultzSAOhJMKoscikRL, et al.Subjective memory complaints, cortical thinning, and cognitive dysfunction in middle-age adults at risk of AD. Alzheimers Dement (Amst)2015; 1: 33–40.
41.
JessenFFeyenLFreymannK, et al.Volume reduction of the entorhinal cortex in subjective memory impairment. Neurobiol Aging2006; 27: 1751–1756.
42.
RyuSYLimEYNaS, et al.Hippocampal and entorhinal structures in subjective memory impairment: a combined MRI volumetric and DTI study. Int Psychogeriatrics2017; 29: 785–792.
43.
CaillaudMHudonCBollerB, et al.Evidence of a relation between hippocampal volume, white matter hyperintensities, and cognition in subjective cognitive decline and mild cognitive impairment. J Gerontol Ser B Psychol Sci Soc Sci2020; 75: 1382–1392.
44.
FanLYLaiYMChenTF, et al.Diminution of context association memory structure in subjects with subjective cognitive decline. Hum Brain Mapp2018; 39: 2549–2562.
45.
MarcotteCPotvinOCollinsDL, et al.Brain atrophy and patch-based grading in individuals from the CIMA-Q study: a progressive continuum from subjective cognitive decline to AD. Sci Rep2019; 9: 13532.
46.
RisacherSLWuDunnDTallmanEF, et al.Visual contrast sensitivity is associated with the presence of cerebral amyloid and tau deposition. Brain Commun2020; 2: fcaa019.
47.
EliassenCFReinvangISelnesP, et al.Biomarkers in subtypes of mild cognitive impairment and subjective cognitive decline. Brain Behav2017; 7: e00776.
48.
MeiberthDScheefLWolfsgruberS, et al.Cortical thinning in individuals with subjective memory impairment. J Alzheimers Dis2015; 45: 139–146.
49.
LauriolaMEspositoRDelli PizziS, et al.Sleep changes without medial temporal lobe or brain cortical changes in community-dwelling individuals with subjective cognitive decline. Alzheimers Dement2017; 13: 783–791.
50.
NiemantsverdrietERibbensABastinC, et al.A retrospective Belgian multi-center MRI biomarker study in Alzheimer’s disease (REMEMBER). J Alzheimers Dis2018; 63: 1509–1522.
PettigrewCSoldanAZhuY, et al.Cortical thickness in relation to clinical symptom onset in preclinical AD. Neuroimage Clin2016; 12: 116–122.
53.
ZillesKArmstrongESchleicherA, et al.The human pattern of gyrification in the cerebral cortex. Anat Embryol (Berl)1988; 179: 173–179.
54.
MeregalliVAlbertiFMadanCR, et al.Cortical complexity estimation using fractal dimension: a systematic review of the literature on clinical and nonclinical samples. Eur J Neurosci2022; 55: 1547–1583.
55.
DahnkeRYotterRAGaserC. Cortical thickness and central surface estimation. Neuroimage2013; 65: 336–348.
56.
JackCRJrAndrewsJSBeachTG, et al.Revised criteria for diagnosis and staging of Alzheimer's disease: alzheimer's Association Workgroup. Alzheimers Dement2024; 20: 5143–5169.
57.
AlbertMSDeKoskySTDicksonD, et al.The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement2011; 7: 270–279.
58.
American Psychiatric Association (APA). Diagnostic and statistical manual of mental disorders. 5th edn. Milano: American Psychiatric Association, 2013.
59.
HughesCPBergLDanzigerWL, et al.A new clinical scale for the staging of dementia. Br J Psychiatry1982; 140: 566–572.
60.
FolsteinMFFolsteinSEMcHughPR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res1975; 12: 189–198.
61.
MeassoGCavarzeranFZappalàG, et al.The Mini-Mental State Examination: normative study of an Italian random sample. Dev Neuropsychol1993; 9: 77–85.
62.
ScheltensPLaunerLJBarkhofF, et al.Visual assessment of medial temporal lobe atrophy on magnetic resonance imaging: interobserver reliability. J Neurol1995; 242: 557–560.
63.
HachinskiVCIliffLDZilhkaE, et al.Cerebral blood flow in dementia. Arch Neurol1975; 32: 632–637.
64.
BüschDHagemannNBenderN. The dimensionality of the Edinburgh Handedness Inventory: an analysis with models of the item response theory. Laterality2010; 15: 610–628.
65.
MioshiEDawsonKMitchellJ, et al.The Addenbrooke's Cognitive Examination Revised (ACE-R): a brief cognitive test battery for dementia screening. Int J Geriatr Psychiatry2006; 21: 1078–1085.
66.
PigliautileMChiesiFRossettiS, et al.Normative data for the ACE-R in an Italian population sample. Neurol Sci2015; 36: 2185–2190.
67.
ManjónJVCoupéPMartí-BonmatíL, et al.Adaptive non-local means denoising of MR images with spatially varying noise levels. J Magn Reson Imaging2010; 31: 192–203.
GaserCDahnkeRThompsonPM, et al.CAT: a computational anatomy toolbox for the analysis of structural MRI data. Gigascience2024; 13: giae049.
70.
RajapakseJCGieddJNRapoportJL. Statistical approach to segmentation of single channel cerebral MR images. IEEE Trans Med Imaging1997; 16: 176–186.
71.
TohkaJZijdenbosAEvansA. Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage2004; 23: 84–97.
72.
AshburnerJFristonKJ. Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. Neuroimage2011; 55: 954–967.
73.
YotterRADahnkeRThompsonPM, et al.Topological correction of brain surface meshes using spherical harmonics. Hum Brain Mapp2011; 32: 1109–1124.
74.
YotterRANenadicIZieglerG, et al.Local cortical surface complexity maps from spherical harmonic reconstructions. Neuroimage2011; 56: 961–973.
75.
PereiraJBCavallinLSpulberG, et al.Influence of age, disease onset and ApoE4 on visual medial temporal lobe atrophy cut-offs. Intern Med2014; 2: 317–330.
76.
RiedelBCThompsonPMBrintonRD. Age, APOE and sex: triad of risk of Alzheimer's disease. J Steroid Biochem Mol Biol2016; 160: 134–147.
77.
Arenaza-UrquijoEMBoyleRCasalettoK, et al.Reserve, resilience and protective factors professional interest area, sex and gender professional interest area and the ADDRESS! Special interest group. Sex and gender differences in cognitive resilience to aging and Alzheimer's disease. Alzheimers Dement2024; 20: 5695–5719.
78.
Sánchez-BenavidesGGrau-RiveraOCacciagliaR. Distinct cognitive and brain morphological features in healthy subjects unaware of informant-reported cognitive decline. J Alzheimers Dis2018; 65: 181–191.
79.
JohnsEKPhillipsNABellevilleS. The profile of executive functioning in amnestic mild cognitive impairment: disproportionate deficits in inhibitory control. J Int Neuropsychol Soc2012; 18: 541–555.
80.
ZhengDDongXSunH, et al.The overall impairment of core executive function components in patients with amnestic mild cognitive impairment: a cross-sectional study. BMC Neurol2012; 12: 138.
81.
SchmahmannJD. The cerebellum and cognition. Neurosci Lett2019; 688: 62–75.
82.
OlivitoGSerraLMarraC, et al.Cerebellar dentate nucleus functional connectivity with cerebral cortex in Alzheimer's disease and memory: a seed-based approach. Neurobiol Aging2020; 89: 32–40.
83.
TonioloSSerraLOlivitoG, et al.Cerebellar white matter disruption in Alzheimer's disease patients: a diffusion tensor imaging study. J Alzheimers Dis2020; 74: 615–624.
84.
SerraLD'AmelioMDi DomenicoC, et al.In vivo mapping of brainstem nuclei functional connectivity disruption in Alzheimer's disease. Neurobiol Aging2018; 72: 72–82.
85.
TonioloSSerraLOlivitoG, et al.Patterns of cerebellar gray matter atrophy across Alzheimer's disease progression. Front Cell Neurosci2018; 12: 430.
86.
LiuYPaajanenTZhangY, et al.Analysis of regional MRI volumes and thicknesses as predictors of conversion from mild cognitive impairment to Alzheimer's disease. Neurobiol Aging2010; 31: 1375–1385.
87.
HuangHZhengSYangZ, et al.Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer's disease based on cerebral gray matter changes. Cereb Cortex2023; 33: 754–763.
88.
HogstromLJWestlyeLTWalhovdKB. The structure of the cerebral cortex across adult life: age-related patterns of surface area, thickness, and gyrification. Cereb Cortex2013; 11: 2521–2530.
89.
EmchMvon BastianCCKochK. Neural correlates of verbal working memory: an fMRI meta-analysis. Front Hum Neurosci2019; 13: 180.
90.
DickersonBCFeczkoEAugustinackJC, et al.Differential effects of aging and Alzheimer's disease on medial temporal lobe cortical thickness and surface area. Neurobiol Aging2009; 30: 432–440.
91.
KingRDBrownBHwangM, et al.Fractal dimension analysis of the cortical ribbon in mild Alzheimer's disease. Neuroimage2010; 53: 471–479.
92.
FrisoniGBFoxNCJackCRJr, et al.The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol2010; 6: 67–77.
93.
ImKLeeJMYoonU, et al.Fractal dimension in human cortical surface: multiple regression analysis with cortical thickness, sulcal depth, and folding area. Hum Brain Mapp2006; 27: 994–1003.
94.
van der KouweAJWBennerTSalatDH, et al.Brain morphometry with multiecho MPRAGE. Neuroimage2008; 40: 559–569.
95.
KimJHLeeJWKimGH, et al.Cortical asymmetries in normal, mild cognitive impairment, and Alzheimer's disease. Neurobiol Aging2012; 33: 1959–1966.
96.
HirnsteinMLeaskSRoseJ, et al.Disentangling the relationship between hemispheric asymmetry and cognitive performance. Brain Cogn2010; 73: 119–127.
97.
NadeauSE. Hemispheric asymmetry: what, why, and at what cost?J Int Neuropsychol Soc2010; 16: 593–595.
98.
MasliahEMalloryMHansenL, et al.Patterns of aberrant sprouting in Alzheimer's disease. Neuron1991; 6: 729–739.
99.
ArcherDBMooreEEPamidimukkalaU, et al.The relationship between white matter microstructure and self-perceived cognitive decline. Neuroimage Clin2021; 32: 102794.
100.
UchidaYOndaKNishimakiK, et al.Longitudinal changes in brain diffusion characteristics associated with cognition and vascular risk factors: the ARIC-NCS Study. Neurology2025; 105: e213867.
101.
UchidaYNishimakiKSoldanA, et al.Change points for dynamic biomarkers in the Alzheimer's disease pathological cascade: a 30-year cohort study. Alzheimers Dement2025; 8: e70544.
102.
UchidaYNishimakiKSoldanA, et al.Acceleration of brain atrophy and progression from normal cognition to mild cognitive impairment. JAMA Netw Open2024; 7: e2441505.
103.
UchidaYHouZGomez-IsazaL, et al.Quantification of perforant path fibers for early detection of Alzheimer's disease. Alzheimers Dement2025; 21: e70142.
