Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Alzheimer Research
  4. Volume 21, Issue 5
  5. Article
Volume 21, Issue 5
  • ISSN: 1567-2050
  • E-ISSN: 1875-5828
file format text download EPUB
file format pdf download PDF
side by side viewer icon HTML

Abstract

Background

Amnestic Mild Cognitive Impairment (aMCI) is a prodromal phase of Alzheimer's disease. Although recent studies have focused on cortical thickness as a key indicator, cortical complexity has not been exhaustively investigated.

Objectives

To investigate the altered patterns of cortical features in aMCI patients and their correlation with memory function for early identification.

Methods

25 aMCI patients and 54 normal controls underwent neuropsychological assessments and 3D-T1 MRI scans. Cortical thickness and complexity measures were calculated using CAT12 software. Differences between groups were analyzed using two-sample t-tests, and multiple linear regression was employed to identify features associated with memory function. A support vector machine (SVM) model was constructed using multidimensional structural indicators to evaluate diagnostic performance.

Results

aMCI patients exhibited extensive reductions in cortical thickness ( <0.05), with complexity reduction predominantly in the left parahippocampal, entorhinal, rostral anterior cingulate, fusiform, and orbitofrontal ( <0.05). Cortical indicators exhibited robust correlations with auditory verbal learning test (AVLT) scores. Specifically, the fractal dimension of the left medial orbitofrontal region was independently and positively associated with AVLT-short delayed score (=0.348, =0.002), while the gyrification index of the left rostral anterior cingulate region showed independent positive correlations with AVLT-long delayed and recognition scores (=0.408, =0.000; =0.332, =0.003). Finally, the SVM model integrating these cortical features achieved an AUC of 0.91, with 82.28% accuracy, 76% sensitivity, and 85.19% specificity.

Conclusion

Cortical morphological indicators provide important neuroimaging evidence for the early diagnosis of aMCI. Integrating multiple structural indicators significantly improves diagnostic accuracy.

© Bentham Science Publishers
© 2024 The Author(s). Published by Bentham Science Publisher. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
Loading

Article metrics loading...

/content/journals/car/10.2174/0115672050347905240918094644
2024-09-24
2025-04-04

  • From This Site

    /content/journals/car/10.2174/0115672050347905240918094644
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
The full text of this item is not currently available.

References

  1. PetersenR.C. SmithG.E. WaringS.C. IvnikR.J. TangalosE.G. KokmenE. Mild cognitive impairment: Clinical characterization and outcome.Arch. Neurol.199956330330810.1001/archneur.56.3.30310190820
     [Google Scholar]
  2. YaffeK. PetersenR.C. LindquistK. KramerJ. MillerB. Subtype of mild cognitive impairment and progression to dementia and death.Dement. Geriatr. Cogn. Disord.200622431231910.1159/00009542716940725
     [Google Scholar]
  3. EwersM. BuergerK. TeipelS.J. ScheltensP. SchröderJ. ZinkowskiR.P. BouwmanF.H. SchönknechtP. SchoonenboomN.S.M. AndreasenN. WallinA. DeBernardisJ.F. KerkmanD.J. HeindlB. BlennowK. HampelH. Multicenter assessment of CSF-phosphorylated tau for the prediction of conversion of MCI.Neurology200769242205221210.1212/01.wnl.0000286944.22262.ff18071141
     [Google Scholar]
  4. VanacoreN. Di PucchioA. LacorteE. BacigalupoI. MayerF. GrandeG. CesariM. CanevelliM. From mild cognitive impairment to dementia: What is the role of public health?.Recenti Prog. Med.2017108521121528643811
     [Google Scholar]
  5. ChaoL.L. PaJ. DuarteA. SchuffN. WeinerM.W. KramerJ.H. MillerB.L. FreemanK.M. JohnsonJ.K. Patterns of cerebral hypoperfusion in amnestic and dysexecutive MCI.Alzheimer Dis. Assoc. Disord.200923324525210.1097/WAD.0b013e318199ff4619812467
     [Google Scholar]
  6. PaJ. BoxerA. ChaoL.L. GazzaleyA. FreemanK. KramerJ. MillerB.L. WeinerM.W. NeuhausJ. JohnsonJ.K. Clinical‐neuroimaging characteristics of dysexecutive mild cognitive impairment.Ann. Neurol.200965441442310.1002/ana.2159119399879
     [Google Scholar]
  7. VenneriA. GorgoglioneG. ToraciC. NocettiL. PanzettiP. NichelliP. Combining neuropsychological and structural neuroimaging indicators of conversion to Alzheimer’s disease in amnestic mild cognitive impairment.Curr. Alzheimer Res.20118778979710.2174/15672051179763316022175662
     [Google Scholar]
  8. AmievaH. LetenneurL. DartiguesJ.F. Rouch-LeroyerI. SourgenC. D’Alchée-BiréeF. DibM. Barberger-GateauP. OrgogozoJ.M. FabrigouleC. Annual rate and predictors of conversion to dementia in subjects presenting mild cognitive impairment criteria defined according to a population-based study.Dement. Geriatr. Cogn. Disord.2004181879310.1159/00007781515087583
     [Google Scholar]
  9. WangP.N. HongC.J. LinK.N. LiuH.C. ChenW.T. APOE 4 increases the risk of progression from amnestic mild cognitive impairment to Alzheimer’s disease among ethnic Chinese in Taiwan.J. Neurol. Neurosurg. Psychiatry201182216516910.1136/jnnp.2010.20912220660919
     [Google Scholar]
  10. WangX. HuangW. SuL. XingY. JessenF. SunY. ShuN. HanY. Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer’s disease.Mol. Neurodegener.20201515510.1186/s13024‑020‑00395‑332962744
     [Google Scholar]
  11. KimH.J. LeeJ.E. ShinS.J. SohnY.H. LeeP.H. Analysis of the substantia innominata volume in patients with Parkinson’s disease with dementia, dementia with lewy bodies, and Alzheimer’s disease.J. Mov. Disord.201142687210.14802/jmd.1101424868398
     [Google Scholar]
  12. ParkH. YangJ. SeoJ. LeeJ. Dimensionality reduced cortical features and their use in the classification of Alzheimer’s disease and mild cognitive impairment.Neurosci. Lett.2012529212312710.1016/j.neulet.2012.09.01123000551
     [Google Scholar]
  13. MaZ. JingB. LiY. YanH. LiZ. MaX. ZhuoZ. WeiL. LiH. Alzheimer’s Disease Neuroimaging Initiative Identifying mild cognitive impairment with random forest by integrating multiple MRI morphological metrics.J. Alzheimers Dis.2020733991100210.3233/JAD‑19071531884464
     [Google Scholar]
  14. WangT. ShiF. JinY. JiangW. ShenD. XiaoS. Abnormal changes of brain cortical anatomy and the association with plasma microRNA107 level in amnestic mild cognitive impairment.Front. Aging Neurosci.2016811210.3389/fnagi.2016.0011227242521
     [Google Scholar]
  15. PetersenR.C. Mild cognitive impairment as a diagnostic entity.J. Intern. Med.2004256318319410.1111/j.1365‑2796.2004.01388.x15324362
     [Google Scholar]
  16. GaserC. DahnkeR. ThompsonP.M. KurthF. LudersE. The Alzheimer’s Disease Neuroimaging I. CAT: A computational anatomy toolbox for the analysis of structural MRI data.Gigascience202413
     [Google Scholar]
  17. AshburnerJ. A fast diffeomorphic image registration algorithm.Neuroimage20073819511310.1016/j.neuroimage.2007.07.00717761438
     [Google Scholar]
  18. TohkaJ. ZijdenbosA. EvansA. Fast and robust parameter estimation for statistical partial volume models in brain MRI.Neuroimage2004231849710.1016/j.neuroimage.2004.05.00715325355
     [Google Scholar]
  19. DahnkeR. YotterR.A. GaserC. Cortical thickness and central surface estimation.Neuroimage20136533634810.1016/j.neuroimage.2012.09.05023041529
     [Google Scholar]
  20. YotterR.A. DahnkeR. ThompsonP.M. GaserC. Topological correction of brain surface meshes using spherical harmonics.Hum. Brain Mapp.20113271109112410.1002/hbm.2109520665722
     [Google Scholar]
  21. YotterR.A. ThompsonP.M. GaserC. Algorithms to improve the reparameterization of spherical mappings of brain surface meshes.J. Neuroimaging2011212e134e14710.1111/j.1552‑6569.2010.00484.x20412393
     [Google Scholar]
  22. LenhartL. SeilerS. PirpamerL. GoebelG. PotrusilT. WagnerM. Dal BiancoP. RansmayrG. SchmidtR. BenkeT. ScherflerC. Anatomically standardized detection of MRI atrophy patterns in early-stage Alzheimer’s disease.Brain Sci.20211111149110.3390/brainsci1111149134827490
     [Google Scholar]
  23. NobleW.S. What is a support vector machine?Nat. Biotechnol.200624121565156710.1038/nbt1206‑156517160063
     [Google Scholar]
  24. HettwerM.D. LarivièreS. ParkB.Y. van den HeuvelO.A. SchmaalL. AndreassenO.A. ChingC.R.K. HoogmanM. BuitelaarJ. van RooijD. VeltmanD.J. SteinD.J. FrankeB. van ErpT.G.M. van RooijD. van den HeuvelO.A. van ErpT.G.M. JahanshadN. ThompsonP.M. ThomopoulosS.I. BethlehemR.A.I. BernhardtB.C. EickhoffS.B. ValkS.L. ENIGMA ADHD Working Group ENIGMA Autism Working Group ENIGMA Bipolar Disorder Working Group ENIGMA Major Depression Working Group ENIGMA OCD Working Group ENIGMA Schizophrenia Working Group Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders.Nat. Commun.2022131685110.1038/s41467‑022‑34367‑636369423
     [Google Scholar]
  25. Vidal-PineiroD. ParkerN. ShinJ. FrenchL. GrydelandH. JackowskiA.P. MowinckelA.M. PatelY. PausovaZ. SalumG. SørensenØ. WalhovdK.B. PausT. FjellA.M. Alzheimer’s Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle flagship study of ageing Cellular correlates of cortical thinning throughout the lifespan.Sci. Rep.20201012180310.1038/s41598‑020‑78471‑333311571
     [Google Scholar]
  26. Jiménez-BaladoJ. HabeckC. SternY. EichT. The relationship between cortical thickness and white matter hyperintensities in mid to late life.Neurobiol. Aging202414112913910.1016/j.neurobiolaging.2024.05.01438909430
     [Google Scholar]
  27. QuerbesO. AubryF. ParienteJ. LotterieJ.A. DémonetJ.F. DuretV. PuelM. BerryI. FortJ.C. CelsisP. Alzheimer’s Disease Neuroimaging Initiative Early diagnosis of Alzheimer’s disease using cortical thickness: Impact of cognitive reserve.Brain200913282036204710.1093/brain/awp10519439419
     [Google Scholar]
  28. JeremicD. Jiménez-DíazL. Navarro-LópezJ.D. Past, present and future of therapeutic strategies against amyloid-β peptides in Alzheimer’s disease: A systematic review.Ageing Res. Rev.20217210149610.1016/j.arr.2021.10149634687956
     [Google Scholar]
  29. KarranE. De StrooperB. The amyloid hypothesis in Alzheimer disease: New insights from new therapeutics.Nat. Rev. Drug Discov.202221430631810.1038/s41573‑022‑00391‑w35177833
     [Google Scholar]
  30. HampelH. HardyJ. BlennowK. ChenC. PerryG. KimS.H. VillemagneV.L. AisenP. VendruscoloM. IwatsuboT. MastersC.L. ChoM. LannfeltL. CummingsJ.L. VergalloA. The amyloid-β pathway in Alzheimer’s disease.Mol. Psychiatry202126105481550310.1038/s41380‑021‑01249‑034456336
     [Google Scholar]
  31. RenP. LoR.Y. ChapmanB.P. MapstoneM. PorsteinssonA. LinF. Alzheimer’s Disease Neuroimaging Initiative Longitudinal alteration of intrinsic brain activity in the striatum in mild cognitive impairment.J. Alzheimers Dis.2016541697810.3233/JAD‑16036827472880
     [Google Scholar]
  32. ConvitA. de AsisJ. de LeonM.J. TarshishC.Y. De SantiS. RusinekH. Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to Alzheimer’s disease.Neurobiol. Aging2000211192610.1016/S0197‑4580(99)00107‑410794844
     [Google Scholar]
  33. OcklenburgS. MundorfA. GerritsR. KarlssonE.M. Papadatou-PastouM. VingerhoetsG. Clinical implications of brain asymmetries.Nat. Rev. Neurol.202420738339410.1038/s41582‑024‑00974‑838783057
     [Google Scholar]
  34. ThompsonP.M. MoussaiJ. ZohooriS. Cortical variability and asymmetry in normal aging and Alzheimer's disease.Cerebral Cortex.199886492509Cortical variability and asymmetry in normal aging and Alzheimer’s disease.1998
     [Google Scholar]
  35. RoeJ.M. Vidal-PiñeiroD. SørensenØ. BrandmaierA.M. DüzelS. GonzalezH.A. KievitR.A. KnightsE. KühnS. LindenbergerU. MowinckelA.M. NybergL. ParkD.C. PudasS. RundleM.M. WalhovdK.B. FjellA.M. WesterhausenR. MastersC.L. BushA.I. FowlerC. DarbyD. PertileK. RestrepoC. RobertsB. RobertsonJ. RumbleR. RyanT. CollinsS. ThaiC. TrounsonB. LennonK. LiQ-X. UgarteF.Y. VolitakisI. VovosM. WilliamsR. BakerJ. RussellA. PerettiM. MilicicL. LimL. RodriguesM. TaddeiK. TaddeiT. HoneE. LimF. FernandezS. Rainey-SmithS. PedriniS. MartinsR. DoeckeJ. BourgeatP. FrippJ. GibsonS. LerouxH. HansonD. DoreV. ZhangP. BurnhamS. RoweC.C. VillemagneV.L. YatesP. PejoskaS.B. JonesG. AmesD. CyartoE. LautenschlagerN. BarnhamK. ChengL. HillA. KilleenN. MaruffP. SilbertB. BrownB. SohrabiH. SavageG. VacherM. Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer’s disease.Nat. Commun.202112172110.1038/s41467‑021‑21057‑y33526780
     [Google Scholar]
  36. KumforF. Landin-RomeroR. DevenneyE. HutchingsR. GrassoR. HodgesJ.R. PiguetO. On the right side? A longitudinal study of left- versus right-lateralized semantic dementia.Brain2016139398699810.1093/brain/awv38726811253
     [Google Scholar]
  37. ShiF. LiuB. ZhouY. YuC. JiangT. Hippocampal volume and asymmetry in mild cognitive impairment and Alzheimer’s disease: Meta‐analyses of MRI studies.Hippocampus200919111055106410.1002/hipo.2057319309039
     [Google Scholar]
  38. ZhaoQ. LuH. MetmerH. LiW.X.Y. LuJ. Evaluating functional connectivity of executive control network and frontoparietal network in Alzheimer’s disease.Brain Res.2018167826227210.1016/j.brainres.2017.10.02529079506
     [Google Scholar]
  39. RapakaD. TebogoM.O. MathewE.M. AdiukwuP.C. BitraV.R. Targeting papez circuit for cognitive dysfunction- insights into deep brain stimulation for Alzheimer’s disease.Heliyon2024109e3057410.1016/j.heliyon.2024.e3057438726200
     [Google Scholar]
  40. JangS.H. YeoS.S. Thalamocortical tract between anterior thalamic nuclei and cingulate gyrus in the human brain: Diffusion tensor tractography study.Brain Imaging Behav.20137223624110.1007/s11682‑013‑9222‑723371564
     [Google Scholar]
  41. RollsE.T. The cingulate cortex and limbic systems for emotion, action, and memory.Brain Struct. Funct.201922493001301810.1007/s00429‑019‑01945‑231451898
     [Google Scholar]
  42. DuJ. RollsE.T. ChengW. LiY. GongW. QiuJ. FengJ. Functional connectivity of the orbitofrontal cortex, anterior cingulate cortex, and inferior frontal gyrus in humans.Cortex202012318519910.1016/j.cortex.2019.10.01231869573
     [Google Scholar]
  43. DomenechP. RheimsS. KoechlinE. Neural mechanisms resolving exploitation-exploration dilemmas in the medial prefrontal cortex.Science20203696507eabb018410.1126/science.abb018432855307
     [Google Scholar]
  44. ChengW. RollsE.T. QiuJ. LiuW. TangY. HuangC.C. WangX. ZhangJ. LinW. ZhengL. PuJ. TsaiS.J. YangA.C. LinC.P. WangF. XieP. FengJ. Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression.Brain2016139123296330910.1093/brain/aww25527742666
     [Google Scholar]
  45. LongF. ChenY. ZhangQ. LiQ. WangY. WangY. LiH. ZhaoY. McNamaraR.K. DelBelloM.P. SweeneyJ.A. GongQ. LiF. Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: A meta-analysis.Mol. Psychiatry2024[Epub ahead of print].10.1038/s41380‑024‑02710‑639187625
     [Google Scholar]
  46. CataniM. Dell’AcquaF. Thiebaut de SchottenM. A revised limbic system model for memory, emotion and behaviour.Neurosci. Biobehav. Rev.20133781724173710.1016/j.neubiorev.2013.07.00123850593
     [Google Scholar]
  47. RamezaniM. BehzadipourS. FawcettA.J. JoghataeiM.T. Verbal Working Memory‐Balance program training alters the left fusiform gyrus resting‐state functional connectivity: A randomized clinical trial study on children with dyslexia.Dyslexia202329326428510.1002/dys.174737337459
     [Google Scholar]
  48. BrunyéT.T. MoranJ.M. HolmesA. MahoneyC.R. TaylorH.A. Non-invasive brain stimulation targeting the right fusiform gyrus selectively increases working memory for faces.Brain Cogn.2017113323910.1016/j.bandc.2017.01.00628107684
     [Google Scholar]
  49. VerfaillieS.C.J. SlotR.E. TijmsB.M. BouwmanF. BenedictusM.R. OverbeekJ.M. KoeneT. VrenkenH. ScheltensP. BarkhofF. van der FlierW.M. Thinner cortex in patients with subjective cognitive decline is associated with steeper decline of memory.Neurobiol. Aging20186123824410.1016/j.neurobiolaging.2017.09.00929029762
     [Google Scholar]
  50. ChételatG. LandeauB. EustacheF. MézengeF. ViaderF. de la SayetteV. DesgrangesB. BaronJ.C. Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: A longitudinal MRI study.Neuroimage200527493494610.1016/j.neuroimage.2005.05.01515979341
     [Google Scholar]
  51. RisacherS.L. ShenL. WestJ.D. KimS. McDonaldB.C. BeckettL.A. HarveyD.J. JackC.R.Jr WeinerM.W. SaykinA.J. Alzheimer’s Disease Neuroimaging Initiative (ADNI) Longitudinal MRI atrophy biomarkers: Relationship to conversion in the ADNI cohort.Neurobiol. Aging20103181401141810.1016/j.neurobiolaging.2010.04.02920620664
     [Google Scholar]
  52. LiS. YuanX. PuF. LiD. FanY. WuL. ChaoW. ChenN. HeY. HanY. Abnormal changes of multidimensional surface features using multivariate pattern classification in amnestic mild cognitive impairment patients.J. Neurosci.20143432105411055310.1523/JNEUROSCI.4356‑13.201425100588
     [Google Scholar]
/content/journals/car/10.2174/0115672050347905240918094644
Loading
Cortical Thickness and Complexity in aMCI Patients: Altered Pattern Analysis and Early Diagnosis
Curr Alzheimer Res 21, 342 (2024); https://doi.org/10.2174/0115672050347905240918094644
/content/journals/car/10.2174/0115672050347905240918094644
/content/journals/car/10.2174/0115672050347905240918094644
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Amnestic mild cognitive impairment; classification diagnosis; cortical complexity; cortical thickness; neuroimaging biomarkers; support vector machine; surface-based morphometry

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test