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Cited by 35Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsAdvanced Neuroimaging Techniques and Applications

Multimodal magnetic resonance imaging in Alzheimer's disease patients at prodromal stage

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Eustache, Pierre, Nemmi, Federico, Saint-Aubert, Laure, Pariente, Jeremie, Péran, Patrice·2016·Journal of Alzheimer’s Disease
DOI 10.3233/jad-150353PMC PMC4927932
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Abstract

One objective of modern neuroimaging is to identify markers that can aid in diagnosis, monitor disease progression, and impact long-term drug analysis. In this study, physiopathological modifications in seven subcortical structures of patients with mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) were characterized by simultaneously measuring quantitative magnetic resonance parameters that are sensitive to complementary tissue characteristics (e.g., volume atrophy, shape changes, microstructural damage, and iron deposition). Fourteen MCI patients and fourteen matched, healthy subjects underwent 3T-magnetic resonance imaging with whole-brain, T1-weighted, T2 * -weighted, and diffusion-tensor imaging scans. Volume, shape, mean R2 * , mean diffusivity (MD), and mean fractional anisotropy (FA) in the thalamus, hippocampus, putamen, amygdala, caudate nucleus, pallidum, and accumbens were compared between MCI patients and healthy subjects. Comparisons were then performed using voxel-based analyses of R2 * , MD, FA maps, and voxel-based morphometry to determine which subregions showed the greatest difference for each parameter. With respect to the micro- and macro-structural patterns of damage, our results suggest that different and distinct physiopathological processes are present in the prodromal phase of AD. MCI patients had significant atrophy and microstructural changes within their hippocampi and amygdalae, which are known to be affected in the prodromal stage of AD. This suggests that the amygdala is affected in the same, direct physiopathological process as the hippocampus. Conversely, atrophy alone was observed within the thalamus and putamen, which are not directly involved in AD pathogenesis. This latter result may reflect another mechanism, whereby atrophy is linked to indirect physiopathological processes.

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  • Previous studies reported increases of hippocampal mean diffusivity and whole brain gray matter mean diffusivity in subjects with mild cognitive impairment compared to healthy controls.

    “Previous studies on GM MD in AD reported increases of hippocampal MD and whole brain GM MD in subjects with mild cognitive impairment (MCI) as compared to healthy controls (Cherubini et al., 2010, Eustache et al., 2016, Fellgiebel et al., 2004, Müller et al., 2005, Scola et al., 2010)”

    From Mean diffusivity in cortical gray matter in Alzheimer's disease: The importance of partial volume correction (2018) · cites this paper as Eustache et al., 2016

    The Results (Region-based analysis and Voxel-based analyses; page not available) report higher hippocampal mean diffusivity in patients with MCI due to AD than in healthy controls. The whole-brain analysis found localized increases, mainly in the hippocampi and right amygdala; it does not establish an increase in mean diffusivity across the whole brain’s gray matter. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Left and right hippocampi and the posterior cingulate cortex were chosen as regions of interest following the literature on peak differences in regional mean diffusivity and volumes in Alzheimer's disease.

    “Left and right hippocampi as well as the posterior cingulate cortex (PCC) were chosen as regions of interest following the literature on peak differences in regional MD and volumes in AD (Cherubini et al., 2010, Eustache et al., 2016, Rose et al., 2008).”

    From Mean diffusivity in cortical gray matter in Alzheimer's disease: The importance of partial volume correction (2018) · cites this paper as Eustache et al., 2016

    The Results sections on mean diffusivity and volumetry report higher MD and reduced volume in both hippocampi in patients with MCI due to AD compared with controls. The paper’s region-based analysis examined seven segmented structures, but not the posterior cingulate cortex; it therefore supports the hippocampal rationale, not the PCC part of the claim. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Most studies using diffusion tensor imaging data of healthy older and Alzheimer's disease participants did not consider partial volume effects at all.

    “However, most of the studies using DTI data of healthy older and AD participants did not consider PVE at all (Cherubini et al., 2010, Douaud et al., 2013, Eustache et al., 2016, Müller et al., 2007).”

    From Mean diffusivity in cortical gray matter in Alzheimer's disease: The importance of partial volume correction (2018) · cites this paper as Eustache et al., 2016

    The Materials and Methods sections on imaging and voxel-based analysis describe how the paper processed DTI-derived maps, but do not state whether partial volume effects were considered. The Introduction discusses prior DTI studies without assessing how many accounted for those effects. Searches for “partial volume,” “PVE,” and related correction methods found no passage that settles the claim. (LLM, conf=0.68)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 66

  1. [1]Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E (1999) Mild cognitive impairment: Clinical characterization and outcome. Arch Neurol 56, 303– 308. 10190820
  2. [2]Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CH (2011) 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 Dement 7, 270– 279. 21514249 PMC3312027
  3. [3]Apostolova LG, Dutton RA, Dinov ID, Hayashi KM, Toga AW, Cummings JL, Thompson PM (2006) Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch Neurol 63, 693– 699. 16682538
  4. [4]Wright IC, Rabe-Hesketh S, Woodruff PW, David AS, Murray RM, Bullmore ET (2000) Meta-analysis of regional brain volumes in schizophrenia. Am J Psychiatry 157, 16– 25. 10618008
  5. [5]Scher AI, Xu Y, Korf ESC, Hartley SW, Witter MP, Scheltens P, White LR, Thompson PM, Toga AW, Valentino DJ, Launer LJ (2011) Hippocampal morphometry in population-based incident Alzheimer’s disease and vascular dementia: The HAAS. J Neurol Neurosurg Psychiatry 82, 373– 376. 20826877 PMC3192810
  6. [6]van de Pol L, Gertz H-J, Scheltens P, Wolf H (2011) Hippocampal atrophy in subcortical vascular dementia. Neurodegener Dis 8, 465– 469. 21613775
  7. [7]Chételat G, Desgranges B, De La Sayette V, Viader F, Eustache F, Baron J-C (2002) Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. Neuroreport 13, 1939– 1943. 12395096
  8. [8]Bell-McGinty S, Lopez OL, Meltzer CC, Scanlon JM, Whyte EM, Dekosky ST, Becker JT (2005) Differential cortical atrophy in subgroups of mild cognitive impairment. Arch Neurol 62, 1393– 1397. 16157746
  9. [9]Klein-Koerkamp Y, Heckemann RA, Ramdeen KT, Moreaud O, Keignart S, Krainik A, Hammers A, Baciu M, Hot P, for the Alzheimer’s Disease Neuroimaging, Initiative (2014) Amygdalar atrophy in early Alzheimer’s disease. Curr Alzheimer Res 11, 239– 252. 24484275
  10. [10]Nemmi F, Sabatini U, Rascol O, Péran P (2015) Parkinson’s disease and local atrophy in subcortical nuclei: Insight from shape analysis. Neurobiol Aging 36, 424– 433. 25174648
  11. [11]Ferrarini L1, Frisoni GB, Pievani M, Reiber JH, Ganzola R, Milles J (2009) Morphological hippocampal markers for automated detection of Alzheimer’s disease and mild cognitive impairment converters in magnetic resonance images. J Alzheimers Dis 17, 643– 659. 19433888
  12. [12]Shen K, Fripp J, Mériaudeau F, Chételat G, Salvado O, Bourgeat P, Alzheimer’s Disease Neuroimaging Initiative (2012) Detecting global and local hippocampal shape changes in Alzheimer’s disease using statistical shape models. Neuroimage 59, 2155– 2166. 22037419
  13. [13]Qiu A, Fennema-Notestine C, Dale AM, Miller MI, Alzheimer’s Disease Neuroimaging Initiative (2009) Regional shape abnormalities in mild cognitive impairment and Alzheimer’s disease. Neuroimage 45, 656– 661. 19280688 PMC2847795
  14. [14]Syková E (2004) Extrasynaptic volume transmission and diffusion parameters of the extracellular space. Neuroscience 129, 861– 876. 15561404
  15. [15]Kantarci K, Avula R, Senjem ML, Samikoglu AR, Zhang B, Weigand SD, Przybelski SA, Edmonson HA, Vemuri P, Knopman DS, Ferman TJ, Boeve BF, Petersen RC, Jack CR (2010) Dementia with Lewy bodies and Alzheimer disease: Neurodegenerative patterns characterized by DTI. Neurology 74, 1814– 1821. 20513818 PMC2882217
  16. [16]Cherubini A, Péran P, Caltagirone C, Sabatini U, Spalletta G (2009) Aging of subcortical nuclei: Microstructural, mineralization and atrophy modifications measured in vivo using MRI. Neuroimage 48, 29– 36. 19540925
  17. [17]Péran P, Cherubini A, Assogna F, Piras F, Quattrocchi C, Peppe A, Celsis P, Rascol O, Démonet J-F, Stefani A, Pierantozzi M, Pontieri FE, Caltagirone C, Spalletta G, Sabatini U (2010) Magnetic resonance imaging markers of Parkinson’s disease nigrostriatal signature. Brain 133, 3423– 3433. 20736190
  18. [20]Fellgiebel A, Wille P, Müller MJ, Winterer G, Scheurich A, Vucurevic G, Schmidt LG, Stoeter P (2004) Ultrastructural hippocampal and white matter alterations in mild cognitive impairment: A diffusion tensor imaging study. Dement Geriatr Cogn Disord 18, 101– 108. 15087585
  19. [21]Scola E, Bozzali M, Agosta F, Magnani G, Franceschi M, Sormani MP, Cercignani M, Pagani E, Falautano M, Filippi M, Falini A (2010) A diffusion tensor MRI study of patients with MCI and AD with a 2-year clinical follow-up. J Neurol Neurosurg Psychiatry 81, 798– 805. 20392973
  20. [22]Hanyu H, Sakurai H, Iwamoto T, Takasaki M, Shindo H, Abe K (1998) Diffusion-weighted MR imaging of the hippocampus and temporal white matter in Alzheimer’s disease. J Neurol Sci 156, 195– 200. 9588857
  21. [23]Naggara O, Oppenheim C, Rieu D, Raoux N, Rodrigo S, Dalla Barba G, Meder J-F (2006) Diffusion tensor imaging in early Alzheimer’s disease. Psychiatry Res 146, 243– 249. 16520023
  22. [24]Zecca L, Youdim MBH, Riederer P, Connor JR, Crichton RR (2004) Iron, brain ageing and neurodegenerative disorders. Nat Rev Neurosci 5, 863– 873. 15496864
  23. [25]Moon W-J, Kim H-J, Roh HG, Choi JW, Han S-H (2012) Fluid-attenuated inversion recovery hypointensity of the pulvinar nucleus of patients with Alzheimer disease: Its possible association with iron accumulation as evidenced by the t2(*) map. Korean J Radiol 13, 674– 683. 23118565 PMC3484287
  24. [26]Zhu W-Z, Zhong W, Wang W, Zhan C-J, Wang C-Y, Qi J-P, Wang J-Z, Lei T (2009) Quantitative MR phase-corrected imaging to investigate increased brain iron deposition of patients with Alzheimer disease. Radiology 253, 497– 504. 19709998
  25. [28]Li Y, He H, Dong H, Feng X, Xie G, Zhang L (2013) Discriminative analysis of early-stage Alzheimer’s disease and normal aging with automatic segmentation technique in subcortical gray matter structures: A multicenter in vivo MRI volumetric and DTI study. Acta Radiol 54, 1191– 1200. 23878359
  26. [29]Liu Y, Paajanen T, Zhang Y, Westman E, Wahlund L-O, Simmons A, Tunnard C, Sobow T, Mecocci P, Tsolaki M, Vellas B, Muehlboeck S, Evans A, Spenger C, Lovestone S, Soininen H, AddNeuroMed Consortium (2010) Analysis of regional MRI volumes and thicknesses as predictors of conversion from mild cognitive impairment to Alzheimer’s disease. Neurobiol. Aging 31, 1375– 1385. 20447732
  27. [30]Yi H-A, Möller C, Dieleman N, Bouwman FH, Barkhof F, Scheltens P, van der Flier WM, Vrenken H (2015) Relation between subcortical grey matter atrophy and conversion from mild cognitive impairment to Alzheimer’s disease. J Neurol Neurosurg Psychiatry, doi: 10.1136/jnnp-2014-309105 25904810 · DOI
  28. [31]Sarazin M, Berr C, De Rotrou J, Fabrigoule C, Pasquier F, Legrain S, Michel B, Puel M, Volteau M, Touchon J, Verny M, Dubois B (2007) Amnestic syndrome of the medial temporal type identifies prodromal AD: A longitudinal study. Neurology 69, 1859– 1867. 17984454
  29. [32]Saint-Aubert L, Barbeau EJ, Péran P, Nemmi F, Vervueren C, Mirabel H, Payoux P, Hitzel A, Bonneville F, Gramada R, Tafani M, Vincent C, Puel M, Dechaumont S, Chollet F, Pariente J (2013) Cortical florbetapir-PET amyloid load in prodromal Alzheimer’s disease patients. EJNMMI Res 3, 43. 23731789 PMC3733998
  30. [33]Strauss E, Sherman E, Spreen O (2006) A Compendium of Neuropsychological Tests, Oxford University Press, Oxford.
  31. [34]Patenaude B (2007) Bayesian statistical models of shape and appearance for subcortical brain segmentation. D.Phil. Thesis, University of Oxford, UK.
  32. [35]Patenaude B, Smith SM, Kennedy DN, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56, 907– 922. 21352927 PMC3417233
  33. [36]Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM (2012) FSL. Neuroimage 62, 782– 790. 21979382
  34. [37]Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Beckmann C, Jenkinson M, Smith SM (2009) Bayesian analysis of neuroimaging data in FSL. Neuroimage 45, S173– S186. 19059349
  35. [38]Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl 1), S208– S219. 15501092
  36. [39]Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17, 143– 155. 12391568 PMC6871816
  37. [40]Douaud G, Smith S, Jenkinson M, Behrens T, Johansen-Berg H, Vickers J, James S, Voets N, Watkins K, Matthews PM, James A (2007) Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain J Neurol 130, 2375– 2386. 17698497
  38. [41]Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14, 21– 36. 11525331
  39. [43]Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: A primer with examples. Hum Brain Mapp 15, 1– 25. 11747097 PMC6871862
  40. [44]Hayasaka S (2003) Validating cluster size inference: random field and permutation methods. Neuroimage 20, 2343– 2356. 14683734
  41. [45]Baron JC, Chételat G, Desgranges B, Perchey G, Landeau B, de la Sayette V, Eustache F (2001) in vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer’s Disease. Neuroimage 14, 298– 309. 11467904
  42. [46]Krasuski JS, Alexander GE, Horwitz B, Daly EM, Murphy DG, Rapoport SI, Schapiro MB (1998) Volumes of medial temporal lobe structures in patients with Alzheimer’s disease and mild cognitive impairment (and in healthy controls). Biol Psychiatry 43, 60– 68. 9442345
  43. [49]Apostolova LG, Dinov ID, Dutton RA, Hayashi KM, Toga AW, Cummings JL, Thompson PM (2006) 3D comparison of hippocampal atrophy in amnestic mild cognitive impairment and Alzheimer’s disease. Brain 129, 2867– 2873. 17018552
  44. [50]Becker JT, Davis SW, Hayashi KM, Meltzer CC, Toga AW, Lopez OL, Thompson PM, Imaging Methods, Analysis in Geriatrics Research, Group (2006) Three-dimensional patterns of hippocampal atrophy in mild cognitive impairment. Arch Neurol 63, 97– 101. 16401741
  45. [51]Cavedo E, Boccardi M, Ganzola R, Canu E, Beltramello A, Caltagirone C, Thompson PM, Frisoni GB (2011) Local amygdala structural differences with 3T MRI in patients with Alzheimer disease. Neurology 76, 727– 733. 21339500 PMC3053328
  46. [52]Cavedo E, Pievani M, Boccardi M, Galluzzi S, Bocchetta M, Bonetti M, Thompson PM, Frisoni GB (2014) Medial temporal atrophy in early and late-onset Alzheimer’s disease. Neurobiol Aging 35, 2004– 2012. 24721821 PMC4053814
  47. [53]Miller MI, Younes L, Ratnanather JT, Brown T, Trinh H, Lee DS, Tward D, Mahon PB, Mori S, Albert M, BIOCARD Research Team (2015) Amygdalar atrophy in symptomatic Alzheimer’s disease based on diffeomorphometry: The BIOCARD cohort. Neurobiol Aging 36 (Suppl 1), S3– S10. 25444602 PMC4271320
  48. [54]Fellgiebel A, Dellani PR, Greverus D, Scheurich A, Stoeter P, Müller MJ (2006) Predicting conversion to dementia in mild cognitive impairment by volumetric and diffusivity measurements of the hippocampus. Psychiatry Res 146, 283– 287. 16530394
  49. [55]Ray KM, Wang H, Chu Y, Chen Y-F, Bert A, Hasso AN, Su M-Y (2006) Mild cognitive impairment: Apparent diffusion coefficient in regional gray matter and white matter structures. Radiology 241, 197– 205. 16990677
  50. [56]Ramani A, Jensen JH, Helpern JA (2006) Quantitative MR imaging in Alzheimer disease. Radiology 241, 26– 44. 16990669
  51. [57]Raven EP, Lu PH, Tishler TA, Heydari P, Bartzokis G (2013) Increased iron levels and decreased tissue integrity in hippocampus of Alzheimer’s disease detected in vivo with magnetic resonance imaging. J Alzheimers Dis 37, 127– 136. 23792695
  52. [58]Yao B, Li T-Q, Gelderen P, van Shmueli K, de Zwart JA, Duyn JH (2009) Susceptibility contrast in high field MRI of human brain as a function of tissue iron content. Neuroimage 44, 1259– 1266. 19027861 PMC2670442
  53. [59]Luccichenti G, Giugni E, Péran P, Cherubini A, Barba C, Bivona U, Formisano R, Sabatini U (2010) 3 Tesla is twice as sensitive as 1.5 Tesla magnetic resonance imaging in the assessment of diffuse axonal injury in traumatic brain injury patients. Funct Neurol 25, 109– 114. 20923609
  54. [60]Deibel MA, Ehmann WD, Markesbery WR (1996) Copper, iron, and zinc imbalances in severely degenerated brain regions in Alzheimer’s disease: Possible relation to oxidative stress. J Neurol Sci 143, 137– 142. 8981312
  55. [61]Braak H, Braak E, Yilmazer D, de Vos RA, Jansen EN, Bohl J (1996) Pattern of brain destruction in Parkinson’s and Alzheimer’s diseases. J Neural Transm 103, 455– 490. 9617789
  56. [62]Poulin SP, Dautoff R, Morris JC, Barrett LF, Dickerson BC, Alzheimer’s Disease Neuroimaging Initiative (2011) Amygdala atrophy is prominent in early Alzheimer’s disease and relates to symptom severity. Psychiatry Res 194, 7– 13. 21920712 PMC3185127
  57. [63]Scott SA, DeKosky ST, Scheff SW (1991) Volumetric atrophy of the amygdala in Alzheimer’s disease: Quantitative serial reconstruction. Neurology 41, 351– 356. 2006000
  58. [64]Price JL, Davis PB, Morris JC, White DL (1991) The distribution of tangles, plaques and related immunohistochemical markers in healthy aging and Alzheimer’s disease. Neurobiol Aging 12, 295– 312. 1961359
  59. [65]Arriagada PV, Growdon JH, Hedley-Whyte ET, Hyman BT (1992) Neurofibrillary tangles but not senile plaques parallel duration and severity of Alzheimer’s disease. Neurology 42, 631– 639. 1549228
  60. [66]Price JL, Morris JC (1999) Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Ann Neurol 45, 358– 368. 10072051
  61. [67]Pedro T, Weiler M, Yasuda CL, D’Abreu A, Damasceno BP, Cendes F, Balthazar MLF (2012) Volumetric brain changes in thalamus, corpus callosum and medial temporal structures: mild Alzheimer’s disease compared with amnestic mild cognitive impairment. Dement Geriatr Cogn Disord 34, 149– 155. 22986782
  62. [68]Desgranges B, Baron JC, de la Sayette V, Petit-Taboué MC, Benali K, Landeau B, Lechevalier B, Eustache F (1998) The neural substrates of memory systems impairment in Alzheimer’s disease. A PET study of resting brain glucose utilization. Brain J Neurol 121(Pt 4), 611– 631. 9577389
  63. [69]Rose SE, Janke AL, Chalk JB (2008) Gray and white matter changes in Alzheimer’s disease: A diffusion tensor imaging study. J Magn Reson Imaging JMRI 27, 20– 26. 18050329
  64. [70]Canu E1, McLaren DG, Fitzgerald ME, Bendlin BB, Zoccatelli G, Alessandrini F, Pizzini FB, Ricciardi GK, Beltramello A, Johnson SC, Frisoni GB (2011) Mapping the structural brain changes in Alzheimer’s disease: The independent contribution of two imaging modalities. J Alzheimers Dis 26(Suppl. 3), 263– 274. 21971466 PMC3267543
  65. [71]Scott SA, DeKosky ST, Sparks DL, Knox CA, Scheff SW (1992) Amygdala cell loss and atrophy in Alzheimer’s disease. Ann. Neurol 32, 555– 563. 1456740
  66. [72]Papez JW (1995) A proposed mechanism of emotion. 1937. J Neuropsychiatry Clin Neurosci 7, 103– 112. 7711480

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Authors
Eustache, P., Nemmi, F., Saint-Aubert, L., Pariente, J., & Péran, P.
Affiliations
  • Université Toulouse III - Paul Sabatier (FR)
  • Karolinska Institutet (SE)
  • Translational Alzheimer Neurobiology, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
  • Inserm, imagerie cérébrale et handicaps neurologiques, UMR 825; CHU Purpan – Pavillon Baudot, Place du Dr Baylac, Toulouse, France
Corresponding author
  • Eustache, Pierre
  • Péran, Patrice
ORCID
Venue
Journal of Alzheimer’s Disease
Publisher
SAGE Publications
Published
2016-01-23
Volume
50
Issue
4
Pages
1035-1050
ISSN
  • 1387-2877 (ISSN-L)
  • 1875-8908
DOI
10.3233/jad-150353
PMC
PMC4927932
OpenAlex
W2269298413
Cited by
35
References
5
Citations by year
2026: 2 · 2025: 2 · 2024: 4 · 2023: 2 · 2022: 5 · 2021: 1 · 2020: 7 · 2019: 2 · 2018: 3 · 2017: 5 · 2016: 2
FWCI
3.55
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Alzheimer s Disease (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • HAL (submittedVersion)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
Alzheimer's disease, mild cognitive impairment
MeSH terms
Aged, Alzheimer Disease, Brain, Female, Humans, Magnetic Resonance Imaging, Male, Mental Status Schedule, Neuropsychological Tests, Organ Size, Biomarkers, Radiopharmaceuticals, Fluorodeoxyglucose F18, Positron-Emission Tomography, Diffusion Tensor Imaging, Cognitive Dysfunction, Prodromal Symptoms, Multimodal Imaging

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