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image of Integration of Neuroimaging and Molecular Biomarkers in the Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia: The Promise of fMRI

Abstract

Introduction

Dementia is a set of acquired and progressive neuropsychiatric disorders. The most common types of dementia include Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD). Early intravital diagnosis of both types of dementia is difficult. Both molecular and neuroimaging markers are important for the diagnosis of different types of dementia.

Methods

This review employed freely accessible databases, including PubMed, Google Scholar, and ScienceDirect, using keywords such as molecular parameters, neuroimaging factors, dementia, FTD, Alzheimer’s disease, and fMRI.

Results

Among the molecular markers of dementia, there are parameters common to its various types and enabling their differentiation. These parameters include both genetic and biochemical factors. Markers include genetic factors that help differentiate AD () from FTD (). Simultaneously, there are important biochemical parameters differentiating AD (amyloid-beta (Aβ), neurofibrillary tangles) from FTD (TDP-43, FUS, and different forms of tau protein aggregates). Currently, there is growing interest in neuroimaging studies in the differential diagnosis of dementia. Positron Emission Tomography (PET) imaging enables the quantification and localization of Aβ deposits in the brain through the selective binding of the Pittsburgh Compound-B (PiB) ligand. This method has become the standard in AD diagnostics. In the context of magnetic resonance imaging studies, it is worth noting the search for structural differences between AD (mainly affecting the temporal lobe, including the hippocampus and entorhinal cortex, and the parietal lobe) and FTD (primarily involving the prefrontal cortex, anterior temporal lobes, and subcortical structures, as well as exhibiting an anteroposterior gradient of atrophy). However, the method of the future appears to be functional Magnetic Resonance Imaging (fMRI), especially since functional changes precede structural changes in the development of dementia.

Discussion

The review encompasses the basic diagnostic criteria for AD and FTD dementia, as well as molecular and neuroimaging parameters important for the intravital diagnosis of these dementias. It seems that the use of fMRI can contribute to both early diagnosis and early introduction of targeted treatment in developing dementia. Although it is not yet widely used clinically, its diagnostic value is increasingly recognized.

Conclusion

The benefits of fMRI studies complementing molecular markers in the diagnosis of dementia were highlighted.

© 2025 Bentham Science Publishers
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2025-07-31
2025-09-10

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References

  1. Villemagne V.L. Barkhof F. Garibotto V. Landau S.M. Nordberg A. van Berckel B.N.M. Molecular imaging approaches in dementia. Radiology 2021 298 3 517 530 10.1148/radiol.2020200028 33464184
    [Google Scholar]
  2. Villa C. Lavitrano M. Salvatore E. Combi R. Molecular and imaging biomarkers in Alzheimer’s disease: A focus on recent insights. J. Pers. Med. 2020 10 3 61 10.3390/jpm10030061 32664352
    [Google Scholar]
  3. Huang Y. Mucke L. Alzheimer mechanisms and therapeutic strategies. Cell 2012 148 6 1204 1222 10.1016/j.cell.2012.02.040 22424230
    [Google Scholar]
  4. Jack C.R. Jr Bennett D.A. Blennow K. Carrillo M.C. Feldman H.H. Frisoni G.B. Hampel H. Jagust W.J. Johnson K.A. Knopman D.S. Petersen R.C. Scheltens P. Sperling R.A. Dubois B. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 2016 87 5 539 547 10.1212/WNL.0000000000002923 27371494
    [Google Scholar]
  5. Bang J. Spina S. Miller B.L. Frontotemporal dementia. Lancet 2015 386 10004 1672 1682 10.1016/S0140‑6736(15)00461‑4 26595641
    [Google Scholar]
  6. Boeve B.F. Boxer A.L. Kumfor F. Pijnenburg Y. Rohrer J.D. Advances and controversies in frontotemporal dementia: Diagnosis, biomarkers, and therapeutic considerations. Lancet Neurol. 2022 21 3 258 272 10.1016/S1474‑4422(21)00341‑0 35182511
    [Google Scholar]
  7. Khan S. Barve K.H. Kumar M.S. Recent advancements in pathogenesis, diagnostics and treatment of Alzheimer’s disease. Curr. Neuropharmacol. 2020 18 11 1106 1125 10.2174/1570159X18666200528142429 32484110
    [Google Scholar]
  8. Ray N.R. Ayodele T. Jean-Francois M. Baez P. Fernandez V. Bradley J. Crane P.K. Dalgard C.L. Kuzma A. Nicaretta H. Sims R. Williams J. Cuccaro M.L. Pericak-Vance M.A. Mayeux R. Wang L.S. Schellenberg G.D. Cruchaga C. Beecham G.W. Reitz C. The early-onset Alzheimer’s disease whole-genome sequencing project: Study design and methodology. Alzheimers Dement. 2023 19 9 4187 4195 10.1002/alz.13370 37390458
    [Google Scholar]
  9. Zhang N.K. Zhang S.K. Zhang L.I. Tao H.W. Zhang G.W. The neural basis of neuropsychiatric symptoms in Alzheimer’s disease. Front. Aging Neurosci. 2024 16 1487875 10.3389/fnagi.2024.1487875 39703925
    [Google Scholar]
  10. Ismail Z. Creese B. Aarsland D. Kales H.C. Lyketsos C.G. Sweet R.A. Ballard C. Psychosis in Alzheimer disease — mechanisms, genetics and therapeutic opportunities. Nat. Rev. Neurol. 2022 18 3 131 144 10.1038/s41582‑021‑00597‑3 34983978
    [Google Scholar]
  11. Angelucci F. Spalletta G. Iulio F. Ciaramella A. Salani F. Varsi A. Gianni W. Sancesario G. Caltagirone C. Bossu P. Bossù P. Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) patients are characterized by increased BDNF serum levels. Curr. Alzheimer Res. 2010 7 1 15 20 10.2174/156720510790274473 20205668
    [Google Scholar]
  12. Scheltens P. De Strooper B. Kivipelto M. Holstege H. Chételat G. Teunissen C.E. Cummings J. van der Flier W.M. Alzheimer’s disease. Lancet 2021 397 10284 1577 1590 10.1016/S0140‑6736(20)32205‑4 33667416
    [Google Scholar]
  13. Słowikowski B. Owecki W. Jeske J. Jezierski M. Draguła M. Goutor U. Jagodziński P.P. Kozubski W. Dorszewska J. Epigenetics and the neurodegenerative process. Epigenomics 2024 16 7 473 491 10.2217/epi‑2023‑0416 38511224
    [Google Scholar]
  14. Liu E. Zhang Y. Wang J.Z. Updates in Alzheimer’s disease: From basic research to diagnosis and therapies. Transl. Neurodegener. 2024 13 1 45 10.1186/s40035‑024‑00432‑x 39232848
    [Google Scholar]
  15. Ulugut H. Pijnenburg Y.A.L. Frontotemporal dementia: Past, present, and future. Alzheimers Dement. 2023 19 11 5253 5263 10.1002/alz.13363 37379561
    [Google Scholar]
  16. Miller B. Llibre Guerra J.J. Frontotemporal dementia. Handb. Clin. Neurol. 2019 165 33 45 10.1016/B978‑0‑444‑64012‑3.00003‑4 31727221
    [Google Scholar]
  17. Rascovsky K. Hodges J.R. Knopman D. Mendez M.F. Kramer J.H. Neuhaus J. van Swieten J.C. Seelaar H. Dopper E.G.P. Onyike C.U. Hillis A.E. Josephs K.A. Boeve B.F. Kertesz A. Seeley W.W. Rankin K.P. Johnson J.K. Gorno-Tempini M.L. Rosen H. Prioleau-Latham C.E. Lee A. Kipps C.M. Lillo P. Piguet O. Rohrer J.D. Rossor M.N. Warren J.D. Fox N.C. Galasko D. Salmon D.P. Black S.E. Mesulam M. Weintraub S. Dickerson B.C. Diehl-Schmid J. Pasquier F. Deramecourt V. Lebert F. Pijnenburg Y. Chow T.W. Manes F. Grafman J. Cappa S.F. Freedman M. Grossman M. Miller B.L. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 2011 134 9 2456 2477 10.1093/brain/awr179 21810890
    [Google Scholar]
  18. Antonioni A. Raho E.M. Lopriore P. Pace A.P. Latino R.R. Assogna M. Mancuso M. Gragnaniello D. Granieri E. Pugliatti M. Di Lorenzo F. Koch G. Frontotemporal dementia, where do we stand? A narrative review. Int. J. Mol. Sci. 2023 24 14 11732 10.3390/ijms241411732 37511491
    [Google Scholar]
  19. Gorno-Tempini M.L. Hillis A.E. Weintraub S. Kertesz A. Mendez M. Cappa S.F. Ogar J.M. Rohrer J.D. Black S. Boeve B.F. Manes F. Dronkers N.F. Vandenberghe R. Rascovsky K. Patterson K. Miller B.L. Knopman D.S. Hodges J.R. Mesulam M.M. Grossman M. Classification of primary progressive aphasia and its variants. Neurology 2011 76 11 1006 1014 10.1212/WNL.0b013e31821103e6 21325651
    [Google Scholar]
  20. Yu Q. Mai Y. Ruan Y. Luo Y. Zhao L. Fang W. Cao Z. Li Y. Liao W. Xiao S. Mok V.C.T. Shi L. Liu J. National Alzheimer’s Coordinating Center, the Alzheimer’s Disease Neuroimaging Initiative Frontotemporal Lobar Degeneration Neuroimaging Initiative An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer’s disease. Alzheimers Res. Ther. 2021 13 1 23 10.1186/s13195‑020‑00757‑5 33436059
    [Google Scholar]
  21. Chouliaras L. Thomas A. Malpetti M. Donaghy P. Kane J. Mak E. Savulich G. Prats-Sedano M.A. Heslegrave A.J. Zetterberg H. Su L. Rowe J.B. O’Brien J.T. Differential levels of plasma biomarkers of neurodegeneration in Lewy body dementia, Alzheimer’s disease, frontotemporal dementia and progressive supranuclear palsy. J. Neurol. Neurosurg. Psychiatry 2022 93 6 651 658 10.1136/jnnp‑2021‑327788 35078917
    [Google Scholar]
  22. Nacmias B. Piaceri I. Bagnoli S. Tedde A. Piacentini S. Sorbi S. Genetics of Alzheimer’s disease and frontotemporal dementia. Curr. Mol. Med. 2014 14 8 993 1000 10.2174/1566524014666141010152143 25323872
    [Google Scholar]
  23. Bekris L.M. Yu C.E. Bird T.D. Tsuang D.W. Genetics of Alzheimer disease. J. Geriatr. Psychiatry Neurol. 2010 23 4 213 227 10.1177/0891988710383571 21045163
    [Google Scholar]
  24. Pires M. Rego A.C. Apoe4 and Alzheimer’s disease pathogenesis-mitochondrial deregulation and targeted therapeutic strategies. Int. J. Mol. Sci. 2023 24 1 778 10.3390/ijms24010778 36614219
    [Google Scholar]
  25. Rahman A. Jackson H. Hristov H. Isaacson R.S. Saif N. Shetty T. Etingin O. Henchcliffe C. Brinton R.D. Mosconi L. Sex and gender driven modifiers of Alzheimer’s: The role for estrogenic control across age, race, medical, and lifestyle risks. Front. Aging Neurosci. 2019 11 315 10.3389/fnagi.2019.00315 31803046
    [Google Scholar]
  26. Park C.H. Lee H. Lee Y.M. Lee B.D. Moon E. Suh H. Kim K. Kim H.J. Shim H. Pak K. Choi K.U. Kim C.S. Sex-specific effects of apolipoprotein E ε4 genotype on longitudinal hippocampal atrophy in amnestic mild cognitive impairment over a 2-year evaluation period. J. Alzheimers Dis. 2025 103 4 1269 1276 10.1177/13872877241313066 39924830
    [Google Scholar]
  27. Goedert M. Ghetti B. Spillantini M.G. Frontotemporal dementia: Implications for understanding Alzheimer disease. Cold Spring Harb. Perspect. Med. 2012 2 2 a006254 10.1101/cshperspect.a006254 22355793
    [Google Scholar]
  28. Root J. Merino P. Nuckols A. Johnson M. Kukar T. Lysosome dysfunction as a cause of neurodegenerative diseases: Lessons from frontotemporal dementia and amyotrophic lateral sclerosis. Neurobiol. Dis. 2021 154 105360 10.1016/j.nbd.2021.105360 33812000
    [Google Scholar]
  29. Barber R.C. The genetics of Alzheimer’s disease. Scientifica 2012 2012 1 14 10.6064/2012/246210 24278680
    [Google Scholar]
  30. Liu C.C. Kanekiyo T. Xu H. Bu G. Bu G. Apolipoprotein E and Alzheimer disease: Risk, mechanisms and therapy. Nat. Rev. Neurol. 2013 9 2 106 118 10.1038/nrneurol.2012.263 23296339
    [Google Scholar]
  31. Strang K.H. Golde T.E. Giasson B.I. MAPT mutations, tauopathy, and mechanisms of neurodegeneration. Lab. Invest. 2019 99 7 912 928 10.1038/s41374‑019‑0197‑x 30742061
    [Google Scholar]
  32. Kao A.W. McKay A. Singh P.P. Brunet A. Huang E.J. Progranulin, lysosomal regulation and neurodegenerative disease. Nat. Rev. Neurosci. 2017 18 6 325 333 10.1038/nrn.2017.36 28435163
    [Google Scholar]
  33. Hill S.M. Wrobel L. Ashkenazi A. Fernandez-Estevez M. Tan K. Bürli R.W. Rubinsztein D.C. VCP/p97 regulates Beclin-1-dependent autophagy initiation. Nat. Chem. Biol. 2021 17 4 448 455 10.1038/s41589‑020‑00726‑x 33510452
    [Google Scholar]
  34. Benussi A. Padovani A. Borroni B. Phenotypic heterogeneity of monogenic frontotemporal dementia. Front. Aging Neurosci. 2015 7 171 10.3389/fnagi.2015.00171 26388768
    [Google Scholar]
  35. McEachin Z.T. Parameswaran J. Raj N. Bassell G.J. Jiang J. RNA-mediated toxicity in C9orf72 ALS and FTD. Neurobiol. Dis. 2020 145 105055 10.1016/j.nbd.2020.105055 32829028
    [Google Scholar]
  36. Grobler C. van Tongeren M. Gettemans J. Kell D.B. Pretorius E. Alzheimer’s disease: A systems view provides a unifying explanation of its development. J. Alzheimers Dis. 2023 91 1 43 70 10.3233/JAD‑220720 36442193
    [Google Scholar]
  37. Gibbons L. Rollinson S. Thompson J.C. Robinson A. Davidson Y.S. Richardson A. Neary D. Pickering-Brown S.M. Snowden J.S. Mann D.M.A. Plasma levels of progranulin and interleukin-6 in frontotemporal lobar degeneration. Neurobiol. Aging 2015 36 3 1603.e1 1603.e4 10.1016/j.neurobiolaging.2014.10.023 25435337
    [Google Scholar]
  38. Forgrave L.M. Ma M. Best J.R. DeMarco M.L. The diagnostic performance of neurofilament light chain in CSF and blood for Alzheimer’s disease, frontotemporal dementia, and amyotrophic lateral sclerosis: A systematic review and meta-analysis. Alzheimers Dement. 2019 11 1 730 743 10.1016/j.dadm.2019.08.009 31909174
    [Google Scholar]
  39. Gómez-Tortosa E. Agüero-Rabes P. Ruiz-González A. Wagner-Reguero S. Téllez R. Mahillo I. Ruiz-Calvo A. Sainz M.J. Nystrom A.L. del Ser T. Sánchez-Juan P. Plasma biomarkers in the distinction of Alzheimer’s disease and frontotemporal dementia. Int. J. Mol. Sci. 2025 26 3 1231 10.3390/ijms26031231 39940998
    [Google Scholar]
  40. Kozubski W. Ong K. Waleszczyk W. Zabel M. Dorszewska J. Molecular factors mediating neural cell plasticity changes in dementia brain diseases. Neural Plast. 2021 2021 1 20 10.1155/2021/8834645 33854544
    [Google Scholar]
  41. Li L.M. Che P. Liu D. Wang Y. Li J. He D. Liu T. Zhang N. Diagnostic and discriminative accuracy of plasma phosphorylated tau 217 for symptomatic Alzheimerʼs disease in a Chinese cohort. J. Prev. Alzheimers Dis. 2025 12 5 100092 10.1016/j.tjpad.2025.100092 39948000
    [Google Scholar]
  42. Visser P.J. Reus L.M. Gobom J. Jansen I. Dicks E. van der Lee S.J. Tsolaki M. Verhey F.R.J. Popp J. Martinez-Lage P. Vandenberghe R. Lleó A. Molinuevo J.L. Engelborghs S. Freund-Levi Y. Froelich L. Sleegers K. Dobricic V. Lovestone S. Streffer J. Vos S.J.B. Bos I. Smit A.B. Blennow K. Scheltens P. Teunissen C.E. Bertram L. Zetterberg H. Tijms B.M. Smit A.B. Blennow K. Scheltens P. Teunissen C.E. Bertram L. Zetterberg H. Tijms B.M. ADNI Cerebrospinal fluid tau levels are associated with abnormal neuronal plasticity markers in Alzheimer’s disease. Mol. Neurodegener. 2022 17 1 27 10.1186/s13024‑022‑00521‑3 35346299
    [Google Scholar]
  43. Abu-Rumeileh S. Mometto N. Bartoletti-Stella A. Polischi B. Oppi F. Poda R. Stanzani-Maserati M. Cortelli P. Liguori R. Capellari S. Parchi P. Cerebrospinal fluid biomarkers in patients with frontotemporal dementia spectrum: A single-center study. J. Alzheimers Dis. 2018 66 2 551 563 10.3233/JAD‑180409 30320576
    [Google Scholar]
  44. Foiani M.S. Woollacott I.O.C. Heller C. Bocchetta M. Heslegrave A. Dick K.M. Russell L.L. Marshall C.R. Mead S. Schott J.M. Fox N.C. Warren J.D. Zetterberg H. Rohrer J.D. Plasma tau is increased in frontotemporal dementia. J. Neurol. Neurosurg. Psychiatry 2018 89 8 804 807 10.1136/jnnp‑2017‑317260 29440230
    [Google Scholar]
  45. Dorszewska J. Lahiri D.K. Diversity of molecular factors in Alzheimer’s disease. Curr. Alzheimer Res. 2020 17 3 205 207 10.2174/156720501703200518081524 32442077
    [Google Scholar]
  46. Szymanowicz O. Pawlak S. Potocka E. Goutor U. Kozubski W. Dorszewska J. Molecular basis of dementia. J. Multiscale Neurosci. 2024 3 1 53 63 10.56280/1605703412
    [Google Scholar]
  47. Palmqvist S. Tideman P. Mattsson-Carlgren N. Schindler S.E. Smith R. Ossenkoppele R. Calling S. West T. Monane M. Verghese P.B. Braunstein J.B. Blennow K. Janelidze S. Stomrud E. Salvadó G. Hansson O. Blood biomarkers to detect Alzheimer disease in primary care and secondary care. JAMA 2024 332 15 1245 1257 10.1001/jama.2024.13855 39068545
    [Google Scholar]
  48. Piekut T. Hurła M. Banaszek N. Szejn P. Dorszewska J. Kozubski W. Prendecki M. Infectious agents and Alzheimer’s disease. J. Integr. Neurosci. 2022 21 2 73 10.31083/j.jin2102073 35364661
    [Google Scholar]
  49. Dorszewska J. Hurła M. Banaszek N. Kobylarek D. Piekut T. Kozubski W. From infection to inoculation: Expanding the microbial hypothesis of Alzheimer’s disease. Curr. Alzheimer Res. 2023 19 13 849 853 10.2174/1567205020666230202155404 36740797
    [Google Scholar]
  50. Janelidze S. Zetterberg H. Mattsson N. Palmqvist S. Vanderstichele H. Lindberg O. van Westen D. Stomrud E. Minthon L. Blennow K. Hansson O. Swedish BioFINDER study group CSF A β 42/A β 40 and A β 42/A β 38 ratios: Better diagnostic markers of Alzheimer disease. Ann. Clin. Transl. Neurol. 2016 3 3 154 165 10.1002/acn3.274 27042676
    [Google Scholar]
  51. Khalafi M. Dartora W.J. McIntire L.B.J. Butler T.A. Wartchow K.M. Hojjati S.H. Razlighi Q.R. Shirbandi K. Zhou L. Chen K. Xi K. Banerjee S. Foldi N. Pahlajani S. Glodzik L. Li Y. de Leon M.J. Chiang G.C. Diagnostic accuracy of phosphorylated tau217 in detecting Alzheimer’s disease pathology among cognitively impaired and unimpaired: A systematic review and meta-analysis. Alzheimers Dement. 2025 21 2 e14458 10.1002/alz.14458 39711334
    [Google Scholar]
  52. Liampas I. Kyriakoulopoulou P. Karakoida V. Kavvoura P.A. Sgantzos M. Bogdanos D.P. Stamati P. Dardiotis E. Siokas V. Blood-based biomarkers in frontotemporal dementia: A narrative review. Int. J. Mol. Sci. 2024 25 21 11838 10.3390/ijms252111838 39519389
    [Google Scholar]
  53. Forlenza O.V. Radanovic M. Talib L.L. Aprahamian I. Diniz B.S. Zetterberg H. Gattaz W.F. Cerebrospinal fluid biomarkers in Alzheimer’s disease: Diagnostic accuracy and prediction of dementia. Alzheimers Dement. 2015 1 4 455 463 10.1016/j.dadm.2015.09.003 27239524
    [Google Scholar]
  54. Vijverberg E.G.B. Dols A. Krudop W.A. Del Campo Milan M. Kerssens C.J. Gossink F. Prins N.D. Stek M.L. Scheltens P. Teunissen C.E. Pijnenburg Y.A.L. Cerebrospinal fluid biomarker examination as a tool to discriminate behavioral variant frontotemporal dementia from primary psychiatric disorders. Alzheimers Dement. 2017 7 1 99 106 10.1016/j.dadm.2017.01.009 28337476
    [Google Scholar]
  55. Rabinovici G. Lehmann M. Rosen H. Ghosh P. Cohn-Sheehy B. Trojanowski J. Mendez M. Vinters H. Dickson D. Gorno-Tempini M. Boxer A. Kramer J. Miller B. Grinberg L. Seeley W. Jagust W. Diagnostic accuracy of amyloid and FDG PET in pathologically-confirmed dementia (S8.005). Neurology 2014 82 10_supplement S8.005 10.1212/WNL.82.10_supplement.S8.005
    [Google Scholar]
  56. Lesman-Segev O.H. La Joie R. Iaccarino L. Lobach I. Rosen H.J. Seo S.W. Janabi M. Baker S.L. Edwards L. Pham J. Olichney J. Boxer A. Huang E. Gorno-Tempini M. DeCarli C. Hepker M. Hwang J.H.L. Miller B.L. Spina S. Grinberg L.T. Seeley W.W. Jagust W.J. Rabinovici G.D. Diagnostic accuracy of amyloid versus 18 F-fluorodeoxyglucose positron emission tomography in autopsy-confirmed dementia. Ann. Neurol. 2021 89 2 389 401 10.1002/ana.25968 33219525
    [Google Scholar]
  57. Ward J. Ly M. Raji C.A. Brain PET Imaging. PET Clin. 2023 18 1 123 133 10.1016/j.cpet.2022.09.010 36442960
    [Google Scholar]
  58. Mendez M.F. Shapira J.S. McMurtray A. Licht E. Miller B.L. Accuracy of the clinical evaluation for frontotemporal dementia. Arch. Neurol. 2007 64 6 830 835 10.1001/archneur.64.6.830 17562930
    [Google Scholar]
  59. Alarjani M.S. Almarri B.A. Brain functional connectivity analysis of fMRI-based Alzheimer’s disease data. Front. Med. 2025 12 1540297 10.3389/fmed.2025.1540297 40046917
    [Google Scholar]
  60. Ibrahim B. Suppiah S. Ibrahim N. Mohamad M. Hassan H.A. Nasser N.S. Saripan M.I. Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer’s disease and mild cognitive impairment: A systematic review. Hum. Brain Mapp. 2021 42 9 2941 2968 10.1002/hbm.25369 33942449
    [Google Scholar]
  61. Cohen A.D. Klunk W.E. Early detection of Alzheimer’s disease using PiB and FDG PET. Neurobiol. Dis. 2014 72 Pt A 117 122 10.1016/j.nbd.2014.05.001 24825318
    [Google Scholar]
  62. Shen C. Wang Z. Chen H. Bai Y. Li X. Liang D. Liu X. Zheng H. Wang M. Yang Y. Wang H. Sun T. Identifying mild Alzheimer’s disease with first 30-Min 11C-PiB PET Scan. Front. Aging Neurosci. 2022 14 785495 10.3389/fnagi.2022.785495 35450057
    [Google Scholar]
  63. Rabinovici G. Lehmann M. Rosen H. Ghosh P. Cohn-Sheehy B. Trojanowski J. Mendez M. Vinters H. Dickson D. Gorno-Tempini M. Boxer A. Kramer J. Miller B. Grinberg L. Seeley W. Jagust W. Diagnostic accuracy of amyloid and FDG PET in pathologically-confirmed dementia (S8.005). Neurology 2014 82 10_supplement S8.005 10.1212/WNL.82.10_supplement.S8.005
    [Google Scholar]
  64. Fleisher A.S. Pontecorvo M.J. Devous M.D. Sr Lu M. Arora A.K. Truocchio S.P. Aldea P. Flitter M. Locascio T. Devine M. Siderowf A. Beach T.G. Montine T.J. Serrano G.E. Curtis C. Perrin A. Salloway S. Daniel M. Wellman C. Joshi A.D. Irwin D.J. Lowe V.J. Seeley W.W. Ikonomovic M.D. Masdeu J.C. Kennedy I. Harris T. Navitsky M. Southekal S. Mintun M.A. A16 study investigators. Positron emission tomography imaging with [18F]flortaucipir and postmortem assessment of Alzheimer disease neuropathologic changes. JAMA Neurol. 2020 77 7 829 839 10.1001/jamaneurol.2020.0528 32338734
    [Google Scholar]
  65. Tian M. Civelek A.C. Carrio I. Watanabe Y. Kang K.W. Murakami K. Garibotto V. Prior J.O. Barthel H. Zhou R. Hou H. Dou X. Jin C. Zuo C. Zhang H. Molecular Imaging-based Precision Medicine Task Group of A3 (China-Japan-Korea) Foresight Program International consensus on the use of tau PET imaging agent 18F-flortaucipir in Alzheimer’s disease. Eur. J. Nucl. Med. Mol. Imaging 2022 49 3 895 904 10.1007/s00259‑021‑05673‑w 34978595
    [Google Scholar]
  66. Tsai R.M. Bejanin A. Lesman-Segev O. LaJoie R. Visani A. Bourakova V. O’Neil J.P. Janabi M. Baker S. Lee S.E. Perry D.C. Bajorek L. Karydas A. Spina S. Grinberg L.T. Seeley W.W. Ramos E.M. Coppola G. Gorno-Tempini M.L. Miller B.L. Rosen H.J. Jagust W. Boxer A.L. Rabinovici G.D. 18F-flortaucipir (AV-1451) tau PET in frontotemporal dementia syndromes. Alzheimers Res. Ther. 2019 11 1 13 10.1186/s13195‑019‑0470‑7 30704514
    [Google Scholar]
  67. Bastin C. Bahri M.A. Bernard C. Hustinx R. Salmon E. Frontal hypometabolism in neurocognitive disorder with behavioral disturbance. J. Nucl. Med. 2021 62 12 1783 1788 10.2967/jnumed.120.260497 33789936
    [Google Scholar]
  68. Migliaccio R. Agosta F. Possin K.L. Canu E. Filippi M. Rabinovici G.D. Rosen H.J. Miller B.L. Gorno-Tempini M.L. Mapping the progression of atrophy in early - and late - onset Alzheimer’s Disease. J. Alzheimers Dis. 2015 46 2 351 364 10.3233/JAD‑142292 25737041
    [Google Scholar]
  69. Manera A.L. Dadar M. Collins D.L. Ducharme S. Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) Alzheimer’s Disease Neuroimaging Initiative (ADNI) Ventricular features as reliable differentiators between bvFTD and other dementias. Neuroimage Clin. 2022 33 102947 10.1016/j.nicl.2022.102947 35134704
    [Google Scholar]
  70. Halliday G. Pathology and hippocampal atrophy in Alzheimer’s disease. Lancet Neurol. 2017 16 11 862 864 10.1016/S1474‑4422(17)30343‑5 29029840
    [Google Scholar]
  71. Rao YL Ganaraja B Murlimanju BV Joy T Krishnamurthy A Agrawal A Hippocampus and its involvement in Alzheimer's disease: A review. 3 Biotech 2022 12 2 55
    [Google Scholar]
  72. Eldaief M.C. Brickhouse M. Katsumi Y. Rosen H. Carvalho N. Touroutoglou A. Dickerson B.C. Atrophy in behavioural variant frontotemporal dementia spans multiple large-scale prefrontal and temporal networks. Brain 2023 146 11 4476 4485 10.1093/brain/awad167 37201288
    [Google Scholar]
  73. Hurley R.S. Lapin B. Jones S.E. Crawford A. Leverenz J.B. Bonner-Jackson A. Pillai J.A. Hemispheric asymmetries in hippocampal volume related to memory in left and right temporal variants of frontotemporal degeneration. Front. Neurol. 2024 15 1374827 10.3389/fneur.2024.1374827 38742046
    [Google Scholar]
  74. van de Pol L.A. Hensel A. van der Flier W.M. Visser P.J. Pijnenburg Y.A. Barkhof F. Gertz H.J. Scheltens P. Hippocampal atrophy on MRI in frontotemporal lobar degeneration and Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 2006 77 4 439 442 10.1136/jnnp.2005.075341 16306153
    [Google Scholar]
  75. Wu J. Zhao K. Li Z. Wang D. Ding Y. Wei Y. Zhang H. Liu Y. A systematic analysis of diagnostic performance for Alzheimer’s disease using structural MRI. Psychoradiology 2022 2 1 1 9 10.1093/psyrad/kkac001 38665142
    [Google Scholar]
  76. Lombardi G. Crescioli G. Cavedo E. Lucenteforte E. Casazza G. Bellatorre A.G. Lista C. Costantino G. Frisoni G. Virgili G. Filippini G. Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer’s disease in people with mild cognitive impairment. Cochrane Libr. 2020 3 3 CD009628 10.1002/14651858.CD009628.pub2 32119112
    [Google Scholar]
  77. Casagrande C.C. Rempe M.P. Springer S.D. Wilson T.W. Comprehensive review of task-based neuroimaging studies of cognitive deficits in Alzheimer’s disease using electrophysiological methods. Ageing Res. Rev. 2023 88 101950 10.1016/j.arr.2023.101950 37156399
    [Google Scholar]
  78. Agosta F. Pievani M. Geroldi C. Copetti M. Frisoni G.B. Filippi M. Resting state fMRI in Alzheimer’s disease: Beyond the default mode network. Neurobiol. Aging 2012 33 8 1564 1578 10.1016/j.neurobiolaging.2011.06.007 21813210
    [Google Scholar]
  79. Passamonti L. Tsvetanov K.A. Jones P.S. Bevan-Jones W.R. Arnold R. Borchert R.J. Mak E. Su L. O’Brien J.T. Rowe J.B. Neuroinflammation and functional connectivity in Alzheimer’s disease: Interactive influences on cognitive performance. J. Neurosci. 2019 39 36 7218 7226 10.1523/JNEUROSCI.2574‑18.2019 31320450
    [Google Scholar]
  80. Huang J. Jung J.Y. Nam C.S. Estimating effective connectivity in Alzheimer’s disease progression: A dynamic causal modeling study. Front. Hum. Neurosci. 2022 16 1060936 10.3389/fnhum.2022.1060936 36590062
    [Google Scholar]
  81. Dickerson B.C. Salat D.H. Greve D.N. Chua E.F. Rand-Giovannetti E. Rentz D.M. Bertram L. Mullin K. Tanzi R.E. Blacker D. Albert M.S. Sperling R.A. Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD. Neurology 2005 65 3 404 411 10.1212/01.wnl.0000171450.97464.49 16087905
    [Google Scholar]
  82. Wu X. Li R. Fleisher A.S. Reiman E.M. Guan X. Zhang Y. Chen K. Yao L. Altered default mode network connectivity in Alzheimer’s disease—A resting functional MRI and bayesian network study. Hum. Brain Mapp. 2011 32 11 1868 1881 10.1002/hbm.21153 21259382
    [Google Scholar]
  83. Ng A.S.L. Wang J. Ng K.K. Chong J.S.X. Qian X. Lim J.K.W. Tan Y.J. Yong A.C.W. Chander R.J. Hameed S. Ting S.K.S. Kandiah N. Zhou J.H. Distinct network topology in Alzheimer’s disease and behavioral variant frontotemporal dementia. Alzheimers Res. Ther. 2021 13 1 13 10.1186/s13195‑020‑00752‑w 33407913
    [Google Scholar]
  84. Rytty R. Nikkinen J. Paavola L. Abou Elseoud A. Moilanen V. Visuri A. Tervonen O. Renton A.E. Traynor B.J. Kiviniemi V. Remes A.M. GroupICA dual regression analysis of resting state networks in a behavioral variant of frontotemporal dementia. Front. Hum. Neurosci. 2013 7 461 10.3389/fnhum.2013.00461 23986673
    [Google Scholar]
  85. Whitwell J.L. FTD spectrum: Neuroimaging across the FTD spectrum. Prog. Mol. Biol. Transl. Sci. 2019 165 187 223 10.1016/bs.pmbts.2019.05.009 31481163
    [Google Scholar]
  86. Tavares T.P. Mitchell D.G.V. Coleman K.K.L. Finger E. Neural correlates of reversal learning in frontotemporal dementia. Cortex 2021 143 92 108 10.1016/j.cortex.2021.06.016 34399309
    [Google Scholar]
  87. Rombouts S.A.R.B. van Swieten J.C. Pijnenburg Y.A.L. Goekoop R. Barkhof F. Scheltens P. Loss of frontal fMRI activation in early frontotemporal dementia compared to early AD. Neurology 2003 60 12 1904 1908 10.1212/01.WNL.0000069462.11741.EC 12821731
    [Google Scholar]
  88. Sadeghi M.A. Stevens D. Kundu S. Sanghera R. Dagher R. Yedavalli V. Jones C. Sair H. Luna L.P. Alzheimer’s Disease Neuroimaging Initiative and the Frontotemporal Lobar Degeneration Neuroimaging Initiative Detecting Alzheimer’s disease stages and frontotemporal dementia in time courses of resting-state fMRI data using a machine learning approach. J. Imaging Inform. Med. 2024 37 6 2768 2783 10.1007/s10278‑024‑01101‑1 38780666
    [Google Scholar]
  89. Xue C. Kowshik S.S. Lteif D. Puducheri S. Jasodanand V.H. Zhou O.T. Walia A.S. Guney O.B. Zhang J.D. Poésy S. Kaliaev A. Andreu-Arasa V.C. Dwyer B.C. Farris C.W. Hao H. Kedar S. Mian A.Z. Murman D.L. O’Shea S.A. Paul A.B. Rohatgi S. Saint-Hilaire M.H. Sartor E.A. Setty B.N. Small J.E. Swaminathan A. Taraschenko O. Yuan J. Zhou Y. Zhu S. Karjadi C. Alvin Ang T.F. Bargal S.A. Plummer B.A. Poston K.L. Ahangaran M. Au R. Kolachalama V.B. AI-based differential diagnosis of dementia etiologies on multimodal data. Nat. Med. 2024 30 10 2977 2989 10.1038/s41591‑024‑03118‑z 38965435
    [Google Scholar]
  90. Karlsson L. Vogel J. Arvidsson I. Åström K. Strandberg O. Seidlitz J. Bethlehem R.A.I. Stomrud E. Ossenkoppele R. Ashton N.J. Zetterberg H. Blennow K. Palmqvist S. Smith R. Janelidze S. La Joie R. Rabinovici G.D. Binette A.P. Mattsson-Carlgren N. Hansson O. Machine learning prediction of tau-PET in Alzheimer’s disease using plasma, MRI, and clinical data. Alzheimers Dement. 2025 21 2 e14600 10.1002/alz.14600 39985487
    [Google Scholar]
  91. Wahul R.M. Ambadekar S. Dhanvijay D.M. Dhanvijay M.M. Dudhedia M.A. Gaikwad V. Kanawade B. Pansare J.R. Bodkhe B. Gawande S.H. Multimodal approaches and AI-driven innovations in dementia diagnosis: A systematic review. Discover Artificial Intelligence 2025 5 1 96 10.1007/s44163‑025‑00358‑x
    [Google Scholar]
  92. Maity R. Raja Sankari V.M. Snekhalatha U. Velu S. Alahmadi T.J. Alhababi Z.A. Alkahtani H.K. Early detection of Alzheimer’s disease in structural and functional MRI. Front. Med. 2024 11 1520878 10.3389/fmed.2024.1520878 39726682
    [Google Scholar]
  93. Hojjati S.H. Ebrahimzadeh A. Babajani-Feremi A. Identification of the early stage of Alzheimer’s disease using structural MRI and resting-state fMRI. Front. Neurol. 2019 10 904 10.3389/fneur.2019.00904 31543860
    [Google Scholar]
  94. Nakamura A. Cuesta P. Kato T. Arahata Y. Iwata K. Yamagishi M. Kuratsubo I. Kato K. Bundo M. Diers K. Fernández A. Maestú F. Ito K. Early functional network alterations in asymptomatic elders at risk for Alzheimer’s disease. Sci. Rep. 2017 7 1 6517 10.1038/s41598‑017‑06876‑8 28747760
    [Google Scholar]
  95. Ereira S. Waters S. Razi A. Marshall C.R. Early detection of dementia with default-mode network effective connectivity. Nat. Ment. Health 2024 2 7 787 800 10.1038/s44220‑024‑00259‑5
    [Google Scholar]
  96. Sheline Y.I. Raichle M.E. Resting state functional connectivity in preclinical Alzheimer’s disease. Biol. Psychiatry 2013 74 5 340 347 10.1016/j.biopsych.2012.11.028 23290495
    [Google Scholar]
  97. Alarjani M.S. Almarri B.A. Brain functional connectivity analysis of fMRI-based Alzheimer’s disease data. Front. Med. 2025 12 1540297 10.3389/fmed.2025.1540297 40046917
    [Google Scholar]
  98. Yousem D.M. The economics of functional magnetic resonance imaging: Clinical and research. Neuroimaging Clin. N. Am. 2014 24 4 717 724 10.1016/j.nic.2014.07.007 25441510
    [Google Scholar]
  99. Najafpour Z. Fatemi A. Goudarzi Z. Goudarzi R. Shayanfard K. Noorizadeh F. Cost-effectiveness of neuroimaging technologies in management of psychiatric and insomnia disorders: A meta-analysis and prospective cost analysis. J. Neuroradiol. 2021 48 5 348 358 10.1016/j.neurad.2020.12.003 33383065
    [Google Scholar]
  100. Balachandrasekaran A. Cohen A.L. Afacan O. Warfield S.K. Gholipour A. Reducing the effects of motion artifacts in fMRI: A structured matrix completion approach. IEEE Trans. Med. Imaging 2022 41 1 172 185 10.1109/TMI.2021.3107829 34432631
    [Google Scholar]
  101. Bullock M. Jackson G.D. Abbott D.F. Artifact reduction in simultaneous EEG-fMRI: A systematic review of methods and contemporary usage. Front. Neurol. 2021 12 622719 10.3389/fneur.2021.622719 33776886
    [Google Scholar]
  102. Schneider J.A. Arvanitakis Z. Bang W. Bennett D.A. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology 2007 69 24 2197 2204 10.1212/01.wnl.0000271090.28148.24 17568013
    [Google Scholar]
  103. Forrest S.L. Kovacs G.G. Current concepts of mixed pathologies in neurodegenerative diseases. Can. J. Neurol. Sci. 2023 50 3 329 345 10.1017/cjn.2022.34 35356856
    [Google Scholar]
  104. Rahimi J. Kovacs G.G. Prevalence of mixed pathologies in the aging brain. Alzheimers Res. Ther. 2014 6 9 82 10.1186/s13195‑014‑0082‑1 25419243
    [Google Scholar]
  105. Fierini F. Mixed dementia: Neglected clinical entity or nosographic artifice? J. Neurol. Sci. 2020 410 116662 10.1016/j.jns.2019.116662 31911281
    [Google Scholar]
  106. McAleese K.E. Colloby S.J. Attems J. Thomas A.J. Francis P.T. Mixed brain pathologies account for most dementia in the UK’s Brains for Dementia Research cohort. Alzheimers Dement. 2020 16 S2 e043354 10.1002/alz.043354
    [Google Scholar]
  107. Alafuzoff I. Libard S. Mixed brain pathology is the most common cause of cognitive impairment in the elderly. J. Alzheimers Dis. 2020 78 1 453 465 10.3233/JAD‑200925 33016922
    [Google Scholar]
  108. Liang Y. Bo K. Meyyappan S. Ding M. Decoding fMRI data with support vector machines and deep neural networks. J. Neurosci. Methods 2024 401 110004 10.1016/j.jneumeth.2023.110004 37914001
    [Google Scholar]
/content/journals/car/10.2174/0115672050390340250716061313
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Integration of Neuroimaging and Molecular Biomarkers in the Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia: The Promise of fMRI
Bentham Science Publishers ; https://doi.org/10.2174/0115672050390340250716061313
/content/journals/car/10.2174/0115672050390340250716061313
/content/journals/car/10.2174/0115672050390340250716061313
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  • Article Type:     Review Article
Keywords: Molecular parameters ; fMRI ; neuroimaging factors ; dementia ; FTD ; Alzheimer’s disease

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