Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Phân loại đối tượng và dự đoán theo thời gian dựa trên các đặc điểm kết nối chức năng và cấu trúc vi chất trắng trong mô hình chuột trong bệnh Alzheimer sử dụng học máy
Tóm tắt
Quá trình bệnh lý của bệnh Alzheimer (AD) thường mất hàng thập kỷ từ khi khởi phát đến khi có triệu chứng lâm sàng. Những thay đổi sớm ở não trong AD bao gồm các đặc điểm có thể đo lường bằng MRI như thay đổi kết nối chức năng (FC) và thoái hóa chất trắng. Tuy nhiên, khả năng của những đặc điểm này trong việc phân biệt giữa các đối tượng không có chẩn đoán, hoặc giá trị tiên lượng của chúng, vẫn chưa được xác lập. Cơ chế kích thích chính của AD vẫn đang được tranh luận, mặc dù sự rối loạn chuyển hóa glucose não đang đóng một vai trò ngày càng trung tâm. Ở đây, chúng tôi sử dụng một mô hình chuột của AD sporadic, dựa trên sự rối loạn chuyển hóa glucose não được gây ra bởi tiêm streptozotocin (STZ) vào não thất. Chúng tôi đã xác định những thay đổi trong FC và vi cấu trúc chất trắng theo chiều dọc sử dụng MRI chức năng và khuếch tán. Những biện pháp thu được từ MRI đã được sử dụng để phân loại chuột STZ và chuột đối chứng bằng cách sử dụng học máy, và tầm quan trọng của từng biện pháp cá nhân đã được định lượng bằng các phương pháp trí tuệ nhân tạo giải thích được. Nhìn chung, việc kết hợp tất cả các chỉ số FC và chất trắng theo cách tập hợp là chiến lược tốt nhất để phân biệt chuột STZ, với độ chính xác nhất quán trên 0.85. Tuy nhiên, độ chính xác tốt nhất ở giai đoạn đầu đạt được bằng cách sử dụng các đặc điểm vi cấu trúc chất trắng, và sau đó là FC. Điều này cho thấy rằng sự tổn thương nhất quán trong chất trắng ở nhóm STZ có thể xảy ra trước FC. Đối với dự đoán theo thời gian, các đặc điểm vi cấu trúc cũng có hiệu suất cao nhất trong khi, ngược lại, hiệu suất của FC giảm do mô hình động lực của nó thay đổi từ kết nối vượt mức ở giai đoạn đầu đến kết nối giảm mức ở giai đoạn muộn. Nghiên cứu của chúng tôi làm nổi bật những biện pháp thu được từ MRI tốt nhất phân biệt chuột STZ so với chuột đối chứng trong giai đoạn đầu của bệnh, với tiềm năng chuyển giao sang người.
Từ khóa
#Alzheimer #kết nối chức năng #vi cấu trúc chất trắng #chuột #học máyTài liệu tham khảo
Acosta-Cabronero J, Alley S, Williams GB, Pengas G, Nestor PJ. Diffusion tensor metrics as biomarkers in Alzheimer's disease. PLoS One. 2012;7(11):e49072. https://doi.org/10.1371/journal.pone.0049072.
Agosta F, Pievani M, Geroldi C, Copetti M, Frisoni GB, Filippi M. Resting state fMRI in Alzheimer’s disease: beyond the default mode network. Neurobiol Aging. 2012;33:1564–78. https://doi.org/10.1016/j.neurobiolaging.2011.06.007.
Agosta F, Pievani M, Sala S, Geroldi C, Galluzzi S, Frisoni GB, Filippi M. White matter damage in Alzheimer disease and its relationship to gray matter atrophy. Radiology. 2011;258:853–63. https://doi.org/10.1148/radiol.10101284.
Agrawal R, Tyagi E, Shukla R, Nath C. Insulin receptor signaling in rat hippocampus: A study in STZ (ICV) induced memory deficit model. Eur Neuropsychopharmacol. 2011;21:261–73. https://doi.org/10.1016/j.euroneuro.2010.11.009.
Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063–78. https://doi.org/10.1016/j.neuroimage.2015.10.019.
Araque Caballero MÁ, Suárez-Calvet M, Duering M, Franzmeier N, Benzinger T, Fagan AM, Bateman RJ, Jack CR, Levin J, Dichgans M, Jucker M, Karch C, Masters CL, Morris JC, Weiner M, Rossor M, Fox NC, Lee J-H, Salloway S, Danek A, Goate A, Yakushev I, Hassenstab J, Schofield PR, Haass C, Ewers M. White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease. Brain. 2018;141:3065–80. https://doi.org/10.1093/brain/awy229.
Avants B, Epstein C, Grossman M, Gee J. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal. 2008;12:26–41. https://doi.org/10.1016/j.media.2007.06.004.
Avants B, Tustison N, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage. 2011;54:2033–44. https://doi.org/10.1016/j.neuroimage.2010.09.025.
Bali J, Gheinani AH, Zurbriggen S, Rajendran L. Role of genes linked to sporadic Alzheimer’s disease risk in the production of β-amyloid peptides. Proc Natl Acad Sci. 2012;109:15307–11. https://doi.org/10.1073/pnas.1201632109.
Biasibetti R, Almeida Dos Santos JP, Rodrigues L, Wartchow KM, Suardi LZ, Nardin P, Selistre NG, Vázquez D, Gonçalves C-A. Hippocampal changes in STZ-model of Alzheimer’s disease are dependent on sex. Behav Brain Res. 2017;316:205–14. https://doi.org/10.1016/j.bbr.2016.08.057.
Billeci L, Badolato A, Bachi L, Tonacci A. Machine learning for the classification of Alzheimer’s disease and its prodromal stage using brain diffusion tensor imaging data: a systematic review. Processes. 2020;8:1071. https://doi.org/10.3390/pr8091071.
Binnewijzend MAA, Schoonheim MM, Sanz-Arigita E, Wink AM, van der Flier WM, Tolboom N, Adriaanse SM, Damoiseaux JS, Scheltens P, van Berckel BNM, Barkhof F. Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging. 2012;33:2018–28. https://doi.org/10.1016/j.neurobiolaging.2011.07.003.
Breijyeh Z, Karaman R. Comprehensive review on Alzheimer’s disease: causes and treatment. Molecules. 2020;25:5789. https://doi.org/10.3390/molecules25245789.
Brewer AA, Barton B. Visual cortex in aging and Alzheimer’s disease: changes in visual field maps and population receptive fields. Front Psychol. 2014;5:74. https://doi.org/10.3389/fpsyg.2014.00074.
Brier MR, Thomas JB, Snyder AZ, Benzinger TL, Zhang D, Raichle ME, Holtzman DM, Morris JC, Ances BM. Loss of intranetwork and internetwork resting state functional connections with Alzheimer’s disease progression. J Neurosci. 2012;32:8890–9. https://doi.org/10.1523/JNEUROSCI.5698-11.2012.
Buxbaum JN. Animal models of human amyloidoses: are transgenic mice worth the time and trouble? FEBS Lett. 2009;583:2663–73. https://doi.org/10.1016/j.febslet.2009.07.031.
Carneiro L, Geller S, Fioramonti X, Hébert A, Repond C, Leloup C, Pellerin L. Evidence for hypothalamic ketone body sensing: impact on food intake and peripheral metabolic responses in mice. Am J Physiol Endocrinol Metab. 2016;310:E103–15. https://doi.org/10.1152/ajpendo.00282.2015.
Castanho, I., Lunnon, K., 2019. Epigenetic processes in Alzheimer’s disease, in: Binda, O. (Ed.), Chromatin Signaling and Neurological Disorders, Translational Epigenetics. Academic Press, pp. 153–180. https://doi.org/10.1016/B978-0-12-813796-3.00008-0
Chang Y-L, Chen T-F, Shih Y-C, Chiu M-J, Yan S-H, Tseng W-YI. Regional cingulum disruption, not gray matter atrophy, detects cognitive changes in amnestic mild cognitive impairment subtypes. JAD. 2015;44:125–38. https://doi.org/10.3233/JAD-141839.
Choo IH, Lee DY, Oh JS, Lee JS, Lee DS, Song IC, Youn JC, Kim SG, Kim KW, Jhoo JH, Woo JI. Posterior cingulate cortex atrophy and regional cingulum disruption in mild cognitive impairment and Alzheimer’s disease. Neurobiol Aging. 2010;31:772–9. https://doi.org/10.1016/j.neurobiolaging.2008.06.015.
Correia SC, Santos RX, Perry G, Zhu X, Moreira PI, Smith MA. Insulin-resistant brain state: the culprit in sporadic Alzheimer’s disease? Ageing research reviews. Longevity Consortium. 2011;10:264–73. https://doi.org/10.1016/j.arr.2011.01.001.
Damoiseaux JS, Prater KE, Miller BL, Greicius MD. Functional connectivity tracks clinical deterioration in Alzheimer's disease. Neurobiol Aging. 2012;33(4):828–e19. https://doi.org/10.1016/j.neurobiolaging.2011.06.024.
De-Paula, V.J., Radanovic, M., Diniz, B.S., Forlenza, O.V., 2012. Alzheimer’s Disease, in: Harris, J.R. (Ed.), Protein Aggregation and Fibrillogenesis in Cerebral and Systemic Amyloid Disease, Subcellular Biochemistry. Springer Netherlands, Dordrecht, pp. 329–352. https://doi.org/10.1007/978-94-007-5416-4_14
Diao Y, Jelescu I. Parameter estimation for WMTI-Watson model of white matter using encoder–decoder recurrent neural network. Magn Reson Med. 2023;89:1193–206. https://doi.org/10.1002/mrm.29495.
Diao, Y., Yin, T., Gruetter, R., Jelescu, I.O., 2021. PIRACY: An optimized pipeline for functional connectivity analysis in the rat brain. Front. Neurosci. 15. https://doi.org/10.3389/fnins.2021.602170
Dickerson BC, Salat DH, Greve DN, Chua EF, Rand-Giovannetti E, Rentz DM, Bertram L, Mullin K, Tanzi RE, Blacker D, Albert MS, Sperling RA. Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD. Neurology. 2005;65:404. https://doi.org/10.1212/01.wnl.0000171450.97464.49.
Do K, Laing BT, Landry T, Bunner W, Mersaud N, Matsubara T, Li P, Yuan Y, Lu Q, Huang H. The effects of exercise on hypothalamic neurodegeneration of Alzheimer’s disease mouse model. PLoS ONE. 2018;13:e0190205. https://doi.org/10.1371/journal.pone.0190205.
Doan NT, Engvig A, Persson K, Alnæs D, Kaufmann T, Rokicki J, Córdova-Palomera A, Moberget T, Brækhus A, Barca ML, Engedal K, Andreassen OA, Selbæk G, Westlye LT. Dissociable diffusion MRI patterns of white matter microstructure and connectivity in Alzheimer’s disease spectrum. Sci Rep. 2017;7:45131. https://doi.org/10.1038/srep45131.
Dong JW, Jelescu IO, Ades-Aron B, Novikov DS, Friedman K, Babb JS, Osorio RS, Galvin JE, Shepherd TM, Fieremans E. Diffusion MRI biomarkers of white matter microstructure vary nonmonotonically with increasing cerebral amyloid deposition. Neurobiol Aging. 2020;89:118–28. https://doi.org/10.1016/j.neurobiolaging.2020.01.009.
Du L-L, Xie J-Z, Cheng X-S, Li X-H, Kong F-L, Jiang X, Ma Z-W, Wang J-Z, Chen C, Zhou X-W. Activation of sirtuin 1 attenuates cerebral ventricular streptozotocin-induced tau hyperphosphorylation and cognitive injuries in rat hippocampi. Age. 2014;36:613–23. https://doi.org/10.1007/s11357-013-9592-1.
Du X, Wang X, Geng M. Alzheimer’s disease hypothesis and related therapies. Transl Neurodegener. 2018;7:2. https://doi.org/10.1186/s40035-018-0107-y.
Fieremans E, Jensen JH, Helpern JA. White matter characterization with diffusional kurtosis imaging. Neuroimage. 2011;58:177–88. https://doi.org/10.1016/j.neuroimage.2011.06.006.
Foll CL, Dunn-Meynell AA, Miziorko HM, Levin BE. Regulation of hypothalamic neuronal sensing and food intake by ketone bodies and fatty acids. Diabetes. 2014;63:1259–69. https://doi.org/10.2337/db13-1090.
Franzmeier, N., Ren, J., Damm, A., Monté-Rubio, G., Boada, M., Ruiz, A., Ramirez, A., Jessen, F., Düzel, E., Rodríguez Gómez, O., Benzinger, T., Goate, A., Karch, C.M., Fagan, A.M., McDade, E., Buerger, K., Levin, J., Duering, M., Dichgans, M., Suárez-Calvet, M., Haass, C., Gordon, B.A., Lim, Y.Y., Masters, C.L., Janowitz, D., Catak, C., Wolfsgruber, S., Wagner, M., Milz, E., Moreno-Grau, S., Teipel, S., Grothe, M.J., Kilimann, I., Rossor, M., Fox, N., Laske, C., Chhatwal, J., Falkai, P., Perneczky, R., Lee, J.-H., Spottke, A., Boecker, H., Brosseron, F., Fliessbach, K., Heneka, M.T., Nestor, P., Peters, O., Fuentes, M., Menne, F., Priller, J., Spruth, E.J., Franke, C., Schneider, A., Westerteicher, C., Speck, O., Wiltfang, J., Bartels, C., Araque Caballero, M.Á., Metzger, C., Bittner, D., Salloway, S., Danek, A., Hassenstab, J., Yakushev, I., Schofield, P.R., Morris, J.C., Bateman, R.J., Ewers, M., 2019. The BDNFVal66Met SNP modulates the association between beta-amyloid and hippocampal disconnection in Alzheimer’s disease. Mol Psychiatr. https://doi.org/10.1038/s41380-019-0404-6.
Furman BL. Streptozotocin-induced diabetic models in mice and rats. Curr Protoc Pharmacol. 2015;70:5.47.1-5.47.20. https://doi.org/10.1002/0471141755.ph0547s70.
Gano LB, Patel M, Rho JM. Ketogenic diets, mitochondria, and neurological diseases. J Lipid Res. 2014;55:2211–28. https://doi.org/10.1194/jlr.R048975.
Gispert JD, Rami L, Sánchez-Benavides G, Falcon C, Tucholka A, Rojas S, Molinuevo JL. Nonlinear cerebral atrophy patterns across the Alzheimer’s disease continuum: impact of APOE4 genotype. Neurobiol Aging. 2015;36:2687–701. https://doi.org/10.1016/j.neurobiolaging.2015.06.027.
Grandjean J, Desrosiers-Gregoire G, Anckaerts C, Angeles-Valdez D, Ayad F, Barrière DA, Blockx I, Bortel A, Broadwater M, Cardoso BM, Célestine M, Chavez-Negrete JE, Choi S, Christiaen E, Clavijo P, Colon-Perez L, Cramer S, Daniele T, Dempsey E, Diao Y, Doelemeyer A, Dopfel D, Dvořáková L, Falfán-Melgoza C, Fernandes FF, Fowler CF, Fuentes-Ibañez A, Garin CM, Gelderman E, Golden CEM, Guo CCG, Henckens MJAG, Hennessy LA, Herman P, Hofwijks N, Horien C, Ionescu TM, Jones J, Kaesser J, Kim E, Lambers H, Lazari A, Lee S-H, Lillywhite A, Liu Y, Liu YY, López-Castro A, López-Gil X, Ma Z, MacNicol E, Madularu D, Mandino F, Marciano S, McAuslan MJ, McCunn P, McIntosh A, Meng X, Meyer-Baese L, Missault S, Moro F, Naessens DMP, Nava-Gomez LJ, Nonaka H, Ortiz JJ, Paasonen J, Peeters LM, Pereira M, Perez PD, Pompilus M, Prior M, Rakhmatullin R, Reimann HM, Reinwald J, Del Rio RT, Rivera-Olvera A, Ruiz-Pérez D, Russo G, Rutten TJ, Ryoke R, Sack M, Salvan P, Sanganahalli BG, Schroeter A, Seewoo BJ, Selingue E, Seuwen A, Shi B, Sirmpilatze N, Smith JAB, Smith C, Sobczak F, Stenroos PJ, Straathof M, Strobelt S, Sumiyoshi A, Takahashi K, Torres-García ME, Tudela R, van den Berg M, van der Marel K, van Hout ATB, Vertullo R, Vidal B, Vrooman RM, Wang VX, Wank I, Watson DJG, Yin T, Zhang Y, Zurbruegg S, Achard S, Alcauter S, Auer DP, Barbier EL, Baudewig J, Beckmann CF, Beckmann N, Becq GJPC, Blezer ELA, Bolbos R, Boretius S, Bouvard S, Budinger E, Buxbaum JD, Cash D, Chapman V, Chuang K-H, Ciobanu L, Coolen BF, Dalley JW, Dhenain M, Dijkhuizen RM, Esteban O, Faber C, Febo M, Feindel KW, Forloni G, Fouquet J, Garza-Villarreal EA, Gass N, Glennon JC, Gozzi A, Gröhn O, Harkin A, Heerschap A, Helluy X, Herfert K, Heuser A, Homberg JR, Houwing DJ, Hyder F, Ielacqua GD, Jelescu IO, Johansen-Berg H, Kaneko G, Kawashima R, Keilholz SD, Keliris GA, Kelly C, Kerskens C, Khokhar JY, Kind PC, Langlois J-B, Lerch JP, López-Hidalgo MA, Manahan-Vaughan D, Marchand F, Mars RB, Marsella G, Micotti E, Muñoz-Moreno E, Near J, Niendorf T, Otte WM, Pais-Roldán P, Pan W-J, Prado-Alcalá RA, Quirarte GL, Rodger J, Rosenow T, Sampaio-Baptista C, Sartorius A, Sawiak SJ, Scheenen TWJ, Shemesh N, Shih Y-YI, Shmuel A, Soria G, Stoop R, Thompson GJ, Till SM, Todd N, Van Der Linden A, van der Toorn A, van Tilborg GAF, Vanhove C, Veltien A, Verhoye M, Wachsmuth L, Weber-Fahr W, Wenk P, Yu X, Zerbi V, Zhang N, Zhang BB, Zimmer L, Devenyi GA, Chakravarty MM, Hess A. A consensus protocol for functional connectivity analysis in the rat brain. Nat Neurosci. 2023;26:673–81. https://doi.org/10.1038/s41593-023-01286-8.
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;101:4637. https://doi.org/10.1073/pnas.0308627101.
Grieb P. Intracerebroventricular streptozotocin injections as a model of Alzheimer’s disease: in search of a relevant mechanism. Mol Neurobiol. 2016;53:1741–52. https://doi.org/10.1007/s12035-015-9132-3.
Hammond TC, Xing X, Wang C, Ma D, Nho K, Crane PK, Elahi F, Ziegler DA, Liang G, Cheng Q, Yanckello LM. β-amyloid and tau drive early Alzheimer’s disease decline while glucose hypometabolism drives late decline. Commun Biol. 2020;3(1):352. https://doi.org/10.1038/s42003-020-1079-x.
Henson R, Buechel C, Josephs O, Friston K. The slice-timing problem in event-related fMRI. In: 5th International Conference on Functional Mapping of the Human Brain (HBM'99) and Educational Brain Mapping Course. Düsseldorf. 1999.
Heo J-H, Lee S-R, Lee S-T, Lee K-M, Oh J-H, Jang D-P, Chang K-T, Cho Z-H. Spatial distribution of glucose hypometabolism induced by intracerebroventricular streptozotocin in monkeys. JAD. 2011;25:517–23. https://doi.org/10.3233/JAD-2011-102079.
Hiller AJ, Ishii M. Disorders of body weight, sleep and circadian rhythm as manifestations of hypothalamic dysfunction in Alzheimer’s disease. Front Cell Neurosci. 2018;12. https://doi.org/10.3389/fncel.2018.00471.
Hojjati SH, 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. https://doi.org/10.3389/fneur.2019.00904.
Hölscher C. Insulin Signaling Impairment in the Brain as a Risk Factor in Alzheimer’s Disease. Front Aging Neurosci. 2019;11. https://doi.org/10.3389/fnagi.2019.00088.
Ibrahim B, Suppiah S, Ibrahim N, Mohamad M, Hassan HA, Nasser NS, Saripan MI. 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;42:2941–68. https://doi.org/10.1002/hbm.25369.
Ishii M, Iadecola C. Metabolic and non-cognitive manifestations of Alzheimer’s disease: the hypothalamus as both culprit and target of pathology. Cell Metab. 2015;22:761–76. https://doi.org/10.1016/j.cmet.2015.08.016.
Jelescu IO, Budde MD. 2017. Design and validation of diffusion MRI models of white matter. Front Phys. 5. https://doi.org/10.3389/fphy.2017.00061
Jelescu IO, Palombo M, Bagnato F, Schilling KG. Challenges for biophysical modeling of microstructure. J Neurosci Methods. 2020;344:108861. https://doi.org/10.1016/j.jneumeth.2020.108861.
Jelescu, I.O., Shepherd, T.M., Novikov, D.S., Ding, Y.-S., Ades-Aron, B., Smith, J., Vahle, T., Babb, J.S., Friedman, K.P., de Leon, M.J., Golomb, J.B., Galvin, J.E., Fieremans, E., 2018. Spatial relationships between white matter degeneration, amyloid load and cortical volume in amnestic mild cognitive impairment. bioRxiv. 441840. https://doi.org/10.1101/441840
Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17:825–41. https://doi.org/10.1006/nimg.2002.1132.
Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005;53:1432–40. https://doi.org/10.1002/mrm.20508.
Jespersen SN, Olesen JL, Hansen B, Shemesh N. Diffusion time dependence of microstructural parameters in fixed spinal cord. Neuroimage. 2018;182:329–42. https://doi.org/10.1016/j.neuroimage.2017.08.039.
Jitsuishi T, Yamaguchi A. Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data. Sci Rep. 2022;12:4284. https://doi.org/10.1038/s41598-022-08231-y.
Kametani F, Hasegawa M. Reconsideration of amyloid hypothesis and tau hypothesis in Alzheimer’s disease. Front Neurosci. 2018;12:25. https://doi.org/10.3389/fnins.2018.00025.
Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med. 2016;76:1574–81. https://doi.org/10.1002/mrm.26054.
Khatri U, Kwon GR. Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI. Front Aging Neurosci. 2022;14. https://doi.org/10.3389/fnagi.2022.818871.
King A. The search for better animal models of Alzheimer’s disease. Nature. 2018;559:S13–5. https://doi.org/10.1038/d41586-018-05722-9.
Knezovic A, Osmanovic-Barilar J, Curlin M, Hof PR, Simic G, Riederer P, Salkovic-Petrisic M. Staging of cognitive deficits and neuropathological and ultrastructural changes in streptozotocin-induced rat model of Alzheimer’s disease. J Neural Transm (Vienna). 2015;122:577–92. https://doi.org/10.1007/s00702-015-1394-4.
Kraska A, Santin MD, Dorieux O, Joseph-Mathurin N, Bourrin E, Petit F, Jan C, Chaigneau M, Hantraye P, Lestage P, Dhenain M. In vivo cross-sectional characterization of cerebral alterations induced by intracerebroventricular administration of streptozotocin. PLoS ONE. 2012;7:e46196. https://doi.org/10.1371/journal.pone.0046196.
Kuehn BM. In Alzheimer research, glucose metabolism moves to center stage. JAMA. 2020;323:297–9. https://doi.org/10.1001/jama.2019.20939.
Lanz B, Poitry-Yamate C, Gruetter R. Image-derived input function from the vena cava for 18F-FDG PET studies in rats and mice. J Nuclear Med. 2014;55:1380–8. https://doi.org/10.2967/jnumed.113.127381.
Le Foll C. Hypothalamic fatty acids and ketone bodies sensing and role of FAT/CD36 in the regulation of food intake. Front Physiol. 2019;10:1036. https://doi.org/10.3389/fphys.2019.01036.
Lei, D., Qin, K., Pinaya, W.H.L., Young, J., van Amelsvoort, T., Marcelis, M., Donohoe, G., Mothersill, D.O., Corvin, A., Vieira, S., Lui, S., Scarpazza, C., Arango, C., Bullmore, E., Gong, Q., McGuire, P., Mechelli, A., 2022. Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia. Schizophrenia Bulletin. sbac047. https://doi.org/10.1093/schbul/sbac047
Lester-Coll N, Rivera EJ, Soscia SJ, Doiron K, Wands JR, de la Monte SM. Intracerebral streptozotocin model of type 3 diabetes: relevance to sporadic Alzheimer’s disease. J Alzheimers Dis. 2006;9:13–33.
Liu P-P, Xie Y, Meng X-Y, Kang J-S. History and progress of hypotheses and clinical trials for Alzheimer’s disease. Sig Transduct Target Ther. 2019;4:1–22. https://doi.org/10.1038/s41392-019-0063-8.
Long JM, Holtzman DM. Alzheimer disease: an update on pathobiology and treatment strategies. Cell. 2019;179:312–39. https://doi.org/10.1016/j.cell.2019.09.001.
Lundberg SM, Lee S-I. A Unified Approach to Interpreting Model Predictions, In: 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach. 2017.
Luo C, Li M, Qin R, Chen H, Yang D, Huang L, Liu R, Xu Y, Bai F, Zhao H. White matter microstructural damage as an early sign of subjective cognitive decline. Front Aging Neurosci. 2020;11:378. https://doi.org/10.3389/fnagi.2019.00378.
Marchitelli R, Aiello M, Cachia A, Quarantelli M, Cavaliere C, Postiglione A, Tedeschi G, Montella P, Milan G, Salvatore M, Salvatore E. Simultaneous resting-state FDG-PET/fMRI in Alzheimer disease: relationship between glucose metabolism and intrinsic activity. Neuroimage. 2018;176:246–58. https://doi.org/10.1016/j.neuroimage.2018.04.048.
Mayo CD, Mazerolle EL, Ritchie L, Fisk JD, Gawryluk JR. Longitudinal changes in microstructural white matter metrics in Alzheimer's disease. NeuroImage Clin. 2017;13:330–8. https://doi.org/10.1016/j.nicl.2016.12.012.
Mitchell AG, Rossit S, Pal S, Hornberger M, Warman A, Kenning E, Williamson L, Shapland R, McIntosh RD. Peripheral reaching in Alzheimer’s disease and mild cognitive impairment. Cortex. 2022;149:29–43. https://doi.org/10.1016/j.cortex.2022.01.003.
Montero-Odasso M, Pieruccini-Faria F, Ismail Z, Li K, Lim A, Phillips N, Kamkar N, Sarquis-Adamson Y, Speechley M, Theou O, Verghese J, Wallace L, Camicioli R. CCCDTD5 recommendations on early non cognitive markers of dementia: a Canadian consensus. Alzheimers Dement. 2020;6:e12068. https://doi.org/10.1002/trc2.12068.
Moreira-Silva D, Carrettiero DC, Oliveira ASA, Rodrigues S, dos Santos-Lopes J, Canas PM, et al. Anandamide effects in a streptozotocin-induced Alzheimer’s disease-like sporadic dementia in rats. Front Neurosci. 2018;12. https://doi.org/10.3389/fnins.2018.00653.
Mousa D, Zayed N, Yassine IA. Alzheimer disease stages identification based on correlation transfer function system using resting-state functional magnetic resonance imaging. PLoS ONE. 2022;17:e0264710. https://doi.org/10.1371/journal.pone.0264710.
Mu Y, Gage FH. Adult hippocampal neurogenesis and its role in Alzheimer’s disease. Mol Neurodegener. 2011;6:85. https://doi.org/10.1186/1750-1326-6-85.
Novikov DS, Fieremans E, Jespersen SN, Kiselev VG. Quantifying brain microstructure with diffusion MRI: theory and parameter estimation. NMR Biomed. 2019;32:e3998. https://doi.org/10.1002/nbm.3998.
Nowrangi MA, Lyketsos CG, Leoutsakos JM, Oishi K, Albert M, Mori S, Mielke MM. Longitudinal, region-specific course of diffusion tensor imaging measures in mild cognitive impairment and Alzheimer’s disease. Alzheimers Dement. 2013;9(5):519–28. https://doi.org/10.1016/j.jalz.2012.05.2186.
O’brien JL, O’keefe KM, LaViolette PS, DeLuca AN, Blacker D, Dickerson BC, Sperling R. Longitudinal fMRI in elderly reveals loss of hippocampal activation with clinical decline. Neurology. 2010;74(24):1969–76. https://doi.org/10.1212/WNL.0b013e3181e3966e.
Opitz D, Maclin R. Popular ensemble methods: an empirical study. J Artif Intell Res. 1999;11:169–98. https://doi.org/10.1613/jair.614.
Parker CS, Weston PS, Zhang H, Oxtoby NP. 2023. White matter microstructural abnormality precedes cortical volumetric decline in Alzheimer’s disease: evidence from data-driven disease progression modelling. bioRxiv. 2022.07.12.499784. https://doi.org/10.1101/2022.07.12.499784
Pawela CP, Biswal BB, Hudetz AG, Schulte ML, Li R, Jones SR, Cho YR, Matloub HS, Hyde JS. A protocol for use of medetomidine anesthesia in rats for extended studies using task-induced BOLD contrast and resting-state functional connectivity. Neuroimage. 2009;46:1137–47. https://doi.org/10.1016/j.neuroimage.2009.03.004.
Pegueroles J, Vilaplana E, Montal V, Sampedro F, Alcolea D, Carmona-Iragui M, Clarimon J, Blesa R, Lleó A, Fortea J. Longitudinal brain structural changes in preclinical Alzheimer’s disease. Alzheimers Dement. 2017;13:499–509. https://doi.org/10.1016/j.jalz.2016.08.010.
Reynaud O, da Silva AR, Gruetter R, Jelescu IO. Multi-slice passband bSSFP for human and rodent fMRI at ultra-high field. J Magn Reson. 2019;305:31–40. https://doi.org/10.1016/j.jmr.2019.05.010.
Rocha DS, Dentz MV, Model JFA, Vogt EL, Ohlweiler R, Lima MV, de Souza SK, Kucharski LC. 2022. Female Wistar rats present particular glucose flux when submitted to classic protocols of experimental diabetes. Biomed J. https://doi.org/10.1016/j.bj.2022.05.004.
Rokach L. Ensemble-based classifiers. Artif Intell Rev. 2010;33:1–39. https://doi.org/10.1007/s10462-009-9124-7.
Schultz AP, Chhatwal JP, Hedden T, Mormino EC, Hanseeuw BJ, Sepulcre J, Huijbers W, LaPoint M, Buckley RF, Johnson KA, Sperling RA. Phases of hyperconnectivity and hypoconnectivity in the default mode and salience networks track with amyloid and tau in clinically normal individuals. J Neurosci. 2017;37:4323–31. https://doi.org/10.1523/JNEUROSCI.3263-16.2017.
Setti SE, Hunsberger HC, Reed MN. Alterations in hippocampal activity and Alzheimer’s disease. Transl Issues Psychol Sci. 2017;3:348–56. https://doi.org/10.1037/tps0000124.
Shafer AT, Williams OA, Perez E, An Y, Landman BA, Ferrucci L, Resnick SM. Accelerated decline in white matter microstructure in subsequently impaired older adults and its relationship with cognitive decline. Brain Commun. 2022;4(2):fcac051. https://doi.org/10.1093/braincomms/fcac051.
Shoham S, Bejar C, Kovalev E, Weinstock M. Intracerebroventricular injection of streptozotocin causes neurotoxicity to myelin that contributes to spatial memory deficits in rats. Exp Neurol. 2003;184:1043–52. https://doi.org/10.1016/j.expneurol.2003.08.015.
Silva SSL, Tureck LV, Souza LC, Mello-Hortega JV, Piumbini AL, Teixeira MD, Furtado-Alle L, Vital MABF, Souza RLR. Animal model of Alzheimer’s disease induced by streptozotocin: new insights about cholinergic pathway. Brain Res. 2023;1799:148175. https://doi.org/10.1016/j.brainres.2022.148175.
Smerdov, A., Kiskun, A., Shaniiazov, R., Somov, A., Burnaev, E., 2019. Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario, in: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). pp. 973–978. https://doi.org/10.1109/WF-IoT.2019.8767295
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. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23(Suppl 1):S208-219. https://doi.org/10.1016/j.neuroimage.2004.07.051.
Souza LC, Andrade MK, Azevedo EM, Ramos DC, Bail EL, Vital MABF. Andrographolide Attenuates short-term spatial and recognition memory impairment and neuroinflammation induced by a streptozotocin rat model of Alzheimer’s disease. Neurotox Res. 2022;40:1440–54. https://doi.org/10.1007/s12640-022-00569-5.
Teipel SJ, Meindl T, Wagner M, Stieltjes B, Reuter S, Hauenstein KH, Filippi M, Ernemann U, Reiser MF, Hampel H. Longitudinal changes in fiber tract integrity in healthy aging and mild cognitive impairment: a DTI follow-up study. J Alzheimers Dis. 2010;22(2):507–22. https://doi.org/10.3233/JAD-2010-100234.
Tristão Pereira C, Diao Y, Yin T, da Silva AR, Lanz B, Pierzchala K, Poitry-Yamate C, Jelescu IO. Synchronous nonmonotonic changes in functional connectivity and white matter integrity in a rat model of sporadic Alzheimer’s disease. Neuroimage. 2021;225:117498. https://doi.org/10.1016/j.neuroimage.2020.117498.
Tsurugizawa T, Djemai B, Zalesky A. The impact of fasting on resting state brain networks in mice. Sci Rep. 2019;9:1–12. https://doi.org/10.1038/s41598-019-39851-6.
van Dyck, C.H., Swanson, C.J., Aisen, P., Bateman, R.J., Chen, C., Gee, M., Kanekiyo, M., Li, D., Reyderman, L., Cohen, S., Froelich, L., Katayama, S., Sabbagh, M., Vellas, B., Watson, D., Dhadda, S., Irizarry, M., Kramer, L.D., Iwatsubo, T., 2022. Lecanemab in Early Alzheimer’s Disease. New England J Med. 0, null. https://doi.org/10.1056/NEJMoa2212948
Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. Neuroimage. 2016;142:394–406. https://doi.org/10.1016/j.neuroimage.2016.08.016.
Veraart J, Sijbers J, Sunaert S, Leemans A, Jeurissen B. Weighted linear least squares estimation of diffusion MRI parameters: strengths, limitations, and pitfalls. Neuroimage. 2013;81:335–46. https://doi.org/10.1016/j.neuroimage.2013.05.028.
Vidoni ED, Thomas GP, Honea RA, Loskutova N, Burns JM. Evidence of altered corticomotor system connectivity in early-stage Alzheimer’s disease. J Neurol Phys Ther. 2012;36:8–16. https://doi.org/10.1097/NPT.0b013e3182462ea6.
Wang Z, Zheng Y, Zhu DC, Bozoki AC, Li T. Classification of Alzheimer’s disease, mild cognitive impairment and normal control subjects using resting-state fmri based network connectivity analysis. IEEE J Transl Eng Health Med. 2018;6:1–9. https://doi.org/10.1109/JTEHM.2018.2874887.
Weber R, Ramos-Cabrer P, Wiedermann D, van Camp N, Hoehn M. A fully noninvasive and robust experimental protocol for longitudinal fMRI studies in the rat. Neuroimage. 2006;29:1303–10. https://doi.org/10.1016/j.neuroimage.2005.08.028.
Wisch JK, Roe CM, Babulal GM, Schindler SE, Fagan AM, Benzinger TL, Morris JC, Ances BM. Resting state functional connectivity signature differentiates cognitively normal from individuals who convert to symptomatic Alzheimer disease. J Alzheimers Dis. 2020;74:1085–95. https://doi.org/10.3233/JAD-191039.
Wu L, Zhang X, Zhao L. Human ApoE isoforms differentially modulate brain glucose and ketone body metabolism: implications for Alzheimer’s disease risk reduction and early intervention. J Neurosci. 2018;38:6665–81. https://doi.org/10.1523/JNEUROSCI.2262-17.2018.
Yakushev I, Gerhard A, Müller MJ, Lorscheider M, Buchholz HG, Schermuly I, Weibrich C, Hammers A, Stoeter P, Schreckenberger M, Fellgiebel A. Relationships between hippocampal microstructure, metabolism, and function in early Alzheimer’s disease. Brain Struct Funct. 2011;216:219–26. https://doi.org/10.1007/s00429-011-0302-4.
Ying, R., Bourgeois, D., You, J., Zitnik, M., Leskovec, J., 2019. GNNExplainer: Generating Explanations for Graph Neural Networks. arXiv:1903.03894 [cs, stat].
Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage. 2010;53:1197–207. https://doi.org/10.1016/j.neuroimage.2010.06.041.
Zamani J, Sadr A, Javadi A-H. Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer’s disease neuroimaging initiative. PLoS ONE. 2022;17:e0267608. https://doi.org/10.1371/journal.pone.0267608.
Zhang M, Sun W, Guan Z, Hu J, Li B, Ye G, Meng H, Huang X, Lin X, Wang J, Liu J. Simultaneous PET/fMRI detects distinctive alterations in functional connectivity and glucose metabolism of precuneus subregions in Alzheimer’s disease. Front Aging Neurosci. 2021;13:737002. https://www.frontiersin.org/articles/10.3389/fnagi.2021.737002.
Zhang T, Zhao Z, Zhang C, Zhang J, Jin Z, Li L. 2019. Classification of early and late mild cognitive impairment using functional brain network of resting-state fMRI. Front Psychiatr 10.
Zhou, H., He, L., Zhang, Y., Shen, L., Chen, B., 2022. Interpretable Graph Convolutional Network Of Multi-Modality Brain Imaging For Alzheimer’s Disease Diagnosis, in: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). pp. 1–5. https://doi.org/10.1109/ISBI52829.2022.9761449
Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M. Graph neural networks: a review of methods and applications. AI Open. 2020;1:57–81. https://doi.org/10.1016/j.aiopen.2021.01.001.
Zimny A, Bladowska J, Macioszek A, Szewczyk P, Trypka E, Wojtynska R, Noga L, Leszek J, Sasiadek M. Evaluation of the posterior cingulate region with FDG-PET and advanced MR techniques in patients with amnestic mild cognitive impairment: comparison of the methods. J Alzheimers Dis. 2015;44(1):329–38. https://doi.org/10.3233/JAD-132138.
