Login Register Cart 0
Generic placeholder image

Current Alzheimer Research

Editor-in-Chief

ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe Translate in Chinese
Research Article

An Effective Brain Imaging Biomarker for AD and aMCI: ALFF in Slow-5 Frequency Band

Author(s): Luoyu Wang, Qi Feng, Mei Wang, Tingting Zhu, Enyan Yu, Jialing Niu, Xiuhong Ge, Dewang Mao, Yating Lv* and Zhongxiang Ding*

Volume 18, Issue 1, 2021

Published on: 24 March, 2021

Page: [45 - 55] Pages: 11

DOI: 10.2174/1567205018666210324130502

Price: $65

Abstract

Background: As a potential brain imaging biomarker, amplitude of low frequency fluctuation (ALFF) has been used as a feature to distinguish patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) from normal controls (NC). However, it remains unclear whether the frequency-dependent pattern of ALFF alterations can effectively distinguish the different phases of the disease.

Methods: In the present study, 52 AD and 50 aMCI patients were enrolled together with 43 NC in total. The ALFF values were calculated in the following three frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) for the three different groups. Subsequently, the local functional abnormalities were employed as features to examine the effect of classification among AD, aMCI and NC using a support vector machine (SVM).

Results: We found that the among-group differences of ALFF in the different frequency bands were mainly located in the left hippocampus (HP), right HP, bilateral posterior cingulate cortex (PCC) and bilateral precuneus (PCu), left angular gyrus (AG) and left medial prefrontal cortex (mPFC). When the local functional abnormalities were employed as features, we identified that the ALFF in the slow-5 frequency band showed the highest accuracy to distinguish among the three groups.

Conclusion: These findings may deepen our understanding of the pathogenesis of AD and suggest that slow-5 frequency band may be helpful to explore the pathogenesis and distinguish the phases of this disease.

Keywords: Alzheimer’s disease, amnestic mild cognitive impairment, resting-state functional magnetic resonance imaging, amplitude of low frequency fluctuation, slow-5 frequency band, support vector machine.

« Previous Next »
[1]
Dubois B, Hampel H, Feldman HH, et al. Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria. Alzheimers Dement 2016; 2(3): 292-323.
[2]
Bae JB, Han JW, Kwak KP, et al. Is dementia more fatal than previously estimated? A population-based prospective cohort study. Aging Dis 2019; 10(1): 1-11.
[http://dx.doi.org/10.14336/AD.2018.0123] [PMID: 30705763]
[3]
Sun BL, Li WW, Zhu C, et al. Clinical research on Alzheimer’s disease: Progress and perspectives. Neurosci Bull 2018; 34(6): 1111-8.
[http://dx.doi.org/10.1007/s12264-018-0249-z] [PMID: 29956105]
[4]
Scheltens P, Blennow K, Breteler MMB, et al. Alzheimer’s disease. Lancet 2016; 388(10043): 505-17.
[http://dx.doi.org/10.1016/S0140-6736(15)01124-1] [PMID: 26921134]
[5]
McDade E, Bateman RJ. Stop Alzheimer’s before it starts. Nature 2017; 547(7662): 153-5.
[http://dx.doi.org/10.1038/547153a] [PMID: 28703214]
[6]
Sperling RA, Aisen PS, Beckett LA, et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7(3): 280-92.
[http://dx.doi.org/10.1016/j.jalz.2011.03.003] [PMID: 21514248]
[7]
Bradfield NI, Ellis KA, Savage G, et al. Baseline amnestic severity predicts progression from amnestic mild cognitive impairment to Alzheimer disease dementia at 3 years. Alzheimer Dis Assoc Disord 2018; 32(3): 190-6.
[http://dx.doi.org/10.1097/WAD.0000000000000252] [PMID: 29561277]
[8]
Murayama N, Tagaya H, Ota K, et al. Neuropsychological detection of the early stage of amnestic mild cognitive impairment without objective memory impairment. Dement Geriatr Cogn Disord 2013; 35(1-2): 98-105.
[http://dx.doi.org/10.1159/000346286] [PMID: 23392179]
[9]
Zippo AG, Castiglioni I. Integration of 18FDG-PET metabolic and functional connectomes in the early diagnosis and prognosis of the Alzheimer’s disease. Curr Alzheimer Res 2016; 13(5): 487-97.
[http://dx.doi.org/10.2174/1567205013666151116142451] [PMID: 26567731]
[10]
Feng Q, Ding Z. MRI radiomics classification and prediction in Alzheimer’s disease and mild cognitive impairment: A review. Curr Alzheimer Res 2020; 17(3): 297-309.
[http://dx.doi.org/10.2174/1567205017666200303105016] [PMID: 32124697]
[11]
Feng Q, Chen Y, Liao Z, et al. Corpus callosum radiomics-based classification model in Alzheimer’s disease: A case-control study. Front Neurol 2018; 9(JUL): 618.
[http://dx.doi.org/10.3389/fneur.2018.00618] [PMID: 30093881]
[12]
Wang J, Liu J, Wang Z, Sun P, Li K, Liang P. Dysfunctional interactions between the default mode network and the dorsal attention network in subtypes of amnestic mild cognitive impairment. Aging (Albany NY) 2019; 11(20): 9147-66.
[http://dx.doi.org/10.18632/aging.102380] [PMID: 31645482]
[13]
Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: Evidence from functional MRI. Proc Natl Acad Sci USA 2004; 101(13): 4637-42.
[http://dx.doi.org/10.1073/pnas.0308627101] [PMID: 15070770]
[14]
Cavanna AE, Trimble MR. The precuneus: A review of its functional anatomy and behavioural correlates. Brain 2006; 129(Pt 3): 564-83.
[http://dx.doi.org/10.1093/brain/awl004] [PMID: 16399806]
[15]
Zhang HY, Wang SJ, Xing J, et al. Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer’s disease. Behav Brain Res 2009; 197(1): 103-8.
[http://dx.doi.org/10.1016/j.bbr.2008.08.012] [PMID: 18786570]
[16]
Binnewijzend MAA, Schoonheim MM, Sanz-Arigita E, et al. Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging 2012; 33(9): 2018-28.
[http://dx.doi.org/10.1016/j.neurobiolaging.2011.07.003] [PMID: 21862179]
[17]
Sun Y, Bi Q, Wang X, et al. Prediction of conversion from amnestic mild cognitive impairment to Alzheimer’s disease based on the brain structural connectome. Front Neurol 2019; 9(JAN): 1178.
[http://dx.doi.org/10.3389/fneur.2018.01178] [PMID: 30687226]
[18]
Cha J, Jo HJ, Kim HJ, et al. Functional alteration patterns of default mode networks: comparisons of normal aging, amnestic mild cognitive impairment and Alzheimer’s disease. Eur J Neurosci 2013; 37(12): 1916-24.
[http://dx.doi.org/10.1111/ejn.12177] [PMID: 23773060]
[19]
Hu WT, Wang Z, Lee VMY, Trojanowski JQ, Detre JA, Grossman M. Distinct cerebral perfusion patterns in FTLD and AD. Neurology 2010; 75(10): 881-8.
[http://dx.doi.org/10.1212/WNL.0b013e3181f11e35] [PMID: 20819999]
[20]
Zang YF, He Y, Zhu CZ, et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev 2007; 29(2): 83-91.
[http://dx.doi.org/10.1016/j.braindev.2006.07.002] [PMID: 16919409]
[21]
Yang L, Yan Y, Wang Y, et al. Gradual disturbances of the Amplitude of Low-Frequency Fluctuations (ALFF) and fractional ALFF in Alzheimer Spectrum. Front Neurosci 2018; 12: 975.
[http://dx.doi.org/10.3389/fnins.2018.00975] [PMID: 30618593]
[22]
Zhao Z, Lu J, Jia X, et al. Selective changes of resting-state brain oscillations in aMCI: An fMRI study using ALFF. BioMed Res Int 2014; 2014: 920902.
[http://dx.doi.org/10.1155/2014/920902] [PMID: 24822220]
[23]
Liu X, Wang S, Zhang X, Wang Z, Tian X, He Y. Abnormal amplitude of low-frequency fluctuations of intrinsic brain activity in Alzheimer’s disease. J Alzheimers Dis 2014; 40(2): 387-97.
[http://dx.doi.org/10.3233/JAD-131322] [PMID: 24473186]
[24]
Qi Z, Wu X, Wang Z, et al. Impairment and compensation coexist in amnestic MCI default mode network. Neuroimage 2010; 50(1): 48-55.
[http://dx.doi.org/10.1016/j.neuroimage.2009.12.025] [PMID: 20006713]
[25]
Han Y, Wang J, Zhao Z, et al. Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: A resting-state fMRI study. Neuroimage 2011; 55(1): 287-95.
[http://dx.doi.org/10.1016/j.neuroimage.2010.11.059] [PMID: 21118724]
[26]
Mao S, Zhang C, Gao N, Wang Y, Yang Y, Guo X, et al. A study of feature extraction for Alzheimer’s disease based on resting-state fMRI. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS. 517-20.
[http://dx.doi.org/10.1109/EMBC.2017.8036875]
[27]
Long Z, Jing B, Yan H, et al. A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging. Neuroscience 2016; 331(June): 169-76.
[http://dx.doi.org/10.1016/j.neuroscience.2016.06.025] [PMID: 27343830]
[28]
Bu X, Hu X, Zhang L, et al. Investigating the predictive value of different resting-state functional MRI parameters in obsessive-compulsive disorder. Transl Psychiatry 2019; 9(1): 17.
[http://dx.doi.org/10.1038/s41398-018-0362-9] [PMID: 30655506]
[29]
Wang Z. A hybrid SVM-GLM approach for fMRI data analysis. Neuroimage 2009; 46(3): 608-15.
[http://dx.doi.org/10.1016/j.neuroimage.2009.03.016] [PMID: 19303449]
[30]
Zuo X-N, Di Martino A, Kelly C, et al. The oscillating brain: Complex and reliable. NIH Public Access 2009; 12(5): 736-40.
[PMID: 19782143]
[31]
Oscillations N, Networks C. Neuronal oscillations in cortical networks. Science (80- ) 2004; 304: 1926-9.
[32]
Wang Z, Yan C, Zhao C, et al. Spatial patterns of intrinsic brain activity in mild cognitive impairment and Alzheimer’s disease: A resting-state functional MRI study. Hum Brain Mapp 2011; 32(10): 1720-40.
[http://dx.doi.org/10.1002/hbm.21140] [PMID: 21077137]
[33]
Dubois B, Feldman HH, Jacova C, et al. Research criteria for the diagnosis of Alzheimer’s disease: Revising the NINCDS-ADRDA criteria. Lancet Neurol 2007; 6(8): 734-46.
[http://dx.doi.org/10.1016/S1474-4422(07)70178-3] [PMID: 17616482]
[34]
Yan CG, Wang XD, Zuo XN, Zang YF. DPABI: Data processing & analysis for (Resting-State) brain imaging. Neuroinformatics 2016; 14(3): 339-51.
[http://dx.doi.org/10.1007/s12021-016-9299-4] [PMID: 27075850]
[35]
Chang C-C, Lin C-J. Libsvm. ACM Trans Intell Syst Technol 2011; 2(3): 1-27.
[http://dx.doi.org/10.1145/1961189.1961199]
[36]
Ojala M, Garriga GC. Permutation tests for studying classifier performance. Proc - IEEE Int Conf Data Mining. ICDM. 908-13.
[http://dx.doi.org/10.1109/ICDM.2009.108]
[37]
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 1988; 44(3): 837-45.
[http://dx.doi.org/10.2307/2531595] [PMID: 3203132]
[38]
Licher S, van der Willik KD, Vinke EJ, et al. Alzheimer’s disease as a multistage process: An analysis from a population-based cohort study. Aging (Albany NY) 2019; 11(4): 1163-76.
[http://dx.doi.org/10.18632/aging.101816] [PMID: 30811346]
[39]
Pan P, Zhu L, Yu T, et al. Aberrant spontaneous low-frequency brain activity in amnestic mild cognitive impairment: A meta-analysis of resting-state fMRI studies. Ageing Res Rev 2017; 35: 12-21.
[http://dx.doi.org/10.1016/j.arr.2016.12.001] [PMID: 28017880]
[40]
Yang L, Yan Y, Li Y, et al. Frequency-dependent changes in fractional amplitude of low-frequency oscillations in Alzheimer’s disease: A resting-state fMRI study. Brain Imaging Behav 2020; 14(6): 2187-201.
[http://dx.doi.org/10.1007/s11682-019-00169-6] [PMID: 31478145]
[41]
Cai S, Chong T, Peng Y, et al. Altered functional brain networks in amnestic mild cognitive impairment: A resting-state fMRI study. Brain Imaging Behav 2017; 11(3): 619-31.
[http://dx.doi.org/10.1007/s11682-016-9539-0] [PMID: 26972578]
[42]
Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci USA 2001; 98(2): 676-82.
[http://dx.doi.org/10.1073/pnas.98.2.676] [PMID: 11209064]
[43]
Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proc Natl Acad Sci USA 2003; 100(1): 253-8.
[http://dx.doi.org/10.1073/pnas.0135058100] [PMID: 12506194]
[44]
Uddin LQ, Supekar K, Amin H, et al. Dissociable connectivity within human angular gyrus and intraparietal sulcus: Evidence from functional and structural connectivity. Cereb Cortex 2010; 20(11): 2636-46.
[http://dx.doi.org/10.1093/cercor/bhq011] [PMID: 20154013]
[45]
Thakral PP, Madore KP, Schacter DL. A role for the left angular gyrus in episodic simulation and memory. J Neurosci 2017; 37(34): 8142-9.
[http://dx.doi.org/10.1523/JNEUROSCI.1319-17.2017] [PMID: 28733357]
[46]
Jonides J, Schumacher EH, Smith EE, et al. The role of parietal cortex in verbal working memory. J Neurosci 1998; 18(13): 5026-34.
[http://dx.doi.org/10.1523/JNEUROSCI.18-13-05026.1998] [PMID: 9634568]
[47]
Bokde ALW, Karmann M, Born C, et al. Altered brain activation during a verbal working memory task in subjects with amnestic mild cognitive impairment. J Alzheimers Dis 2010; 21(1): 103-18.
[http://dx.doi.org/10.3233/JAD-2010-091054] [PMID: 20413893]
[48]
Spaniol J, Davidson PSR, Kim ASN, Han H, Moscovitch M, Grady CL. Event-related fMRI studies of episodic encoding and retrieval: Meta-analyses using activation likelihood estimation. Neuropsychologia 2009; 47(8-9): 1765-79.
[http://dx.doi.org/10.1016/j.neuropsychologia.2009.02.028] [PMID: 19428409]
[49]
Lepage M, Habib R, Tulving E. Hippocampal PET activations of memory encoding and retrieval: The HIPER model. Hippocampus 1998; 8(4): 313-22.
[http://dx.doi.org/10.1002/(SICI)1098-1063(1998)8:4<313::AID-HIPO1>3.0.CO;2-I] [PMID: 9744418]
[50]
Chen J, Zhang Z, Li S. Can multi-modal neuroimaging evidence from hippocampus provide biomarkers for the progression of amnestic mild cognitive impairment? Neurosci Bull 2015; 31(1): 128-40.
[http://dx.doi.org/10.1007/s12264-014-1490-8] [PMID: 25595368]
[51]
Huijbers W, Mormino EC, Schultz AP, et al. Amyloid-β deposition in mild cognitive impairment is associated with increased hippocampal activity, atrophy and clinical progression. Brain 2015; 138(Pt 4): 1023-35.
[http://dx.doi.org/10.1093/brain/awv007] [PMID: 25678559]
[52]
Xue SW, Li D, Weng XC, Northoff G, Li DW. Different neural manifestations of two slow frequency bands in resting functional magnetic resonance imaging: A systemic survey at regional, interregional, and network levels. Brain Connect 2014; 4(4): 242-55.
[http://dx.doi.org/10.1089/brain.2013.0182] [PMID: 24456196]
[53]
Stogmann E, Moser D, Klug S, et al. Activities of daily living and depressive symptoms in patients with subjective cognitive decline, mild cognitive impairment, and Alzheimer’s disease. J Alzheimers Dis 2016; 49(4): 1043-50.
[http://dx.doi.org/10.3233/JAD-150785] [PMID: 26577522]

Rights & Permissions Print Cite
Article Metrics
28
© 2024 Bentham Science Publishers | Privacy Policy