{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:59:04Z","timestamp":1782835144841,"version":"3.54.5"},"reference-count":66,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"project \u201cSmart Express Cost Optimization System for Small and Micro Enterprises\u201d","award":["C80JX254004"],"award-info":[{"award-number":["C80JX254004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer\u2019s disease (AD), comprises three potential trajectories: reversion, stability, or progression. Accurate prediction of these trajectories is crucial for disease modeling and early intervention. We propose a novel analytical framework that integrates a healthy control\u2013AD difference template (HAD) with a large-scale Granger causality algorithm based on long short-term memory networks (LSTM-lsGC) to construct effective connectivity (EC) networks. By applying principal component analysis for dimensionality reduction, modeling dynamic sequences with LSTM, and estimating EC matrices through Granger causality, the framework captures both symmetrical and asymmetrical connectivity, providing a refined characterization of the network alterations underlying MCI progression and reversion. Leveraging graph-theoretical features, our method achieved an MCI subtype classification accuracy of 84.92% (AUC = 0.84) across three subgroups and 90.86% when distinguishing rMCI from pMCI. Moreover, key brain regions, including the precentral gyrus, hippocampus, and cerebellum, were identified as being associated with MCI progression. Overall, by developing a symmetry-aware effective connectivity framework that simultaneously investigates both MCI progression and reversion, this study bridges a critical gap and offers a promising tool for early detection and dynamic disease characterization.<\/jats:p>","DOI":"10.3390\/sym17101754","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T07:33:50Z","timestamp":1760686430000},"page":"1754","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Symmetry-Aware LSTM-Based Effective Connectivity Framework for Identifying MCI Progression and Reversion with Resting-State fMRI"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8255-3917","authenticated-orcid":false,"given":"Bowen","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Intelligent Technology, Shanghai Institute of Echnology, Shanghai 201418, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengqi","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2587-9440","authenticated-orcid":false,"given":"Ziyu","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Engineering, Durham University, Durham DH1 3LE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tongpo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Monteiro, A.R., Barbosa, D.J., Remi\u00e3o, F., and Silva, R. (2023). Alzheimer\u2019s disease: Insights and new prospects in disease pathophysiology, biomarkers and disease-modifying drugs. Biochem. Pharmacol., 211.","DOI":"10.1016\/j.bcp.2023.115522"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cintoli, S., Favilli, L., Morganti, R., Siciliano, G., Ceravolo, R., and Tognoni, G. (2024). Verbal fluency patterns associated with the amnestic conversion from mild cognitive impairment to dementia. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-52562-x"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, L., Du, T., Zhao, L., Shi, Y., and Zeng, W. (2023). Research on the lateralization of brain functional complexity in mild cognitive impairment-Alzheimer\u2019s disease progression based on multiscale lateralized brain entropy. Biomed. Signal Process. Control, 86.","DOI":"10.1016\/j.bspc.2023.105216"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1007\/s11357-023-00733-5","article-title":"The combination of hyperventilation test and graph theory parameters to characterize EEG changes in mild cognitive impairment (MCI) condition","volume":"45","author":"Miraglia","year":"2023","journal-title":"Geroscience"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bolla, G., Berente, D.B., Andr\u00e1ssy, A., Zsuffa, J.A., Hidasi, Z., Csibri, E., Csukly, G., Kamondi, A., Kiss, M., and Horvath, A.A. (2023). Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-49461-y"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.jns.2016.07.055","article-title":"Does mild cognitive impairment always lead to dementia? A review","volume":"369","author":"Pandya","year":"2016","journal-title":"J. Neurol. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Grassi, M., Rouleaux, N., Caldirola, D., Loewenstein, D., and Perna, G. (2019). A novel ensemble-based machine learning algorithm to predict the conversion from mild cognitive impairment to Alzheimer\u2019s disease using socio-demographic characteristics, clinical information, and neuropsychological measures. Front. Neurol., 10.","DOI":"10.3389\/fneur.2019.00756"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zheng, W., Yao, Z., Li, Y., Zhang, Y., Hu, B., Wu, D., and Alzheimer\u2019s Disease Neuroimaging Initiative (2019). Brain Connectivity Based Prediction of Alzheimer\u2019s Disease in Patients With Mild Cognitive Impairment Based on Multi-Modal Images. Front. Hum. Neurosci., 13.","DOI":"10.3389\/fnhum.2019.00399"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, Y., Ma, L., Zhang, H., and Liao, H. (2023, January 24\u201327). Double-attention Assisted Multi-task Learning for the Alzheimer\u2019s Disease Prediction from Mild Cognitive Impairment. Proceedings of the 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia.","DOI":"10.1109\/EMBC40787.2023.10340815"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lu, D., Popuri, K., Ding, G.W., Balachandar, R., Beg, M.F., and Alzheimer\u2019s Disease Neuroimaging Initiative (2018). Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer\u2019s Disease using structural MR and FDG-PET images. Sci. Rep., 8.","DOI":"10.1016\/j.media.2018.02.002"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3141","DOI":"10.1109\/JBHI.2021.3053568","article-title":"Auto-Metric Graph Neural Network Based on a Meta-learning Strategy for the Diagnosis of Alzheimer\u2019s disease","volume":"25","author":"Song","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"S91","DOI":"10.1016\/j.neurobiolaging.2014.05.040","article-title":"Thickness network features for prognostic applications in dementia","volume":"36","author":"Raamana","year":"2015","journal-title":"Neurobiol. Aging"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chechkin, A., Pleshakova, E., and Gataullin, S. (2025). A Hybrid KAN-BiLSTM Transformer with Multi-Domain Dynamic Attention Model for Cybersecurity. Technologies, 13.","DOI":"10.3390\/technologies13060223"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Poorna, S., Menon, V., and Gopalan, S. (2025). Hybrid CNN-BiLSTM architecture with multiple attention mechanisms to enhance speech emotion recognition. Biomed. Signal Process. Control, 100.","DOI":"10.1016\/j.bspc.2024.106967"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, P., Shi, Z., Cui, Y., Wang, R., and Wu, D. (2023\u20131, January 28). Spatiotemporal Graph Transformer Network Based on Adversarial Training for AD Diagnosis. Proceedings of the ICC 2023\u2014IEEE International Conference on Communications, Rome, Italy.","DOI":"10.1109\/ICC45041.2023.10279559"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Peng, W., Li, C., Ma, Y., Dai, W., Fu, D., Liu, L., Liu, L., Yu, N., and Liu, J. (2025). Integrating Time and Frequency Domain Features of fMRI Time Series for Alzheimer\u2019s Disease Classification Using Graph Neural Networks. Interdiscip. Sci. Comput. Life Sci., 1\u201317.","DOI":"10.1007\/s12539-025-00759-7"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, N., Zeng, W., Shi, Y., and Yan, H. (2017). Brain Functional Plasticity Driven by Career Experience: A Resting-State fMRI Study of the Seafarer. Front. Psychol., 8.","DOI":"10.3389\/fpsyg.2017.01786"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shi, Y., Zeng, W., Wang, N., Wang, S., and Huang, Z. (2015). Early warning for human mental sub-health based on fMRI data analysis: An example from a seafarers\u2019 resting-data study. Front. Psychol., 6.","DOI":"10.3389\/fpsyg.2015.01030"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1002\/hbm.25369","article-title":"Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer\u2019s disease and mild cognitive impairment: A systematic review","volume":"42","author":"Ibrahim","year":"2021","journal-title":"Hum. Brain Mapp."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1109\/TNSRE.2020.2984519","article-title":"Kernel Granger Causality Based on Back Propagation Neural Network Fuzzy Inference System on fMRI Data","volume":"28","author":"Guo","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jneumeth.2017.03.006","article-title":"Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM","volume":"282","author":"Hojjati","year":"2017","journal-title":"J. Neurosci. Methods"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"118503","DOI":"10.1016\/j.neuroimage.2021.118503","article-title":"Advances in resting state fMRI acquisitions for functional connectomics","volume":"243","author":"Raimondo","year":"2021","journal-title":"NeuroImage"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.compbiomed.2018.09.004","article-title":"Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI","volume":"102","author":"Hojjati","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gupta, Y., Kim, J.I., Kim, B.C., and Kwon, G.R. (2020). Classification and graphical analysis of Alzheimer\u2019s disease and its prodromal stage using multimodal features from structural, diffusion, and functional neuroimaging data and the APOE genotype. Front. Aging Neurosci., 12.","DOI":"10.3389\/fnagi.2020.00238"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1007\/s11548-022-02620-4","article-title":"Predicting conversion from MCI to AD by integration of rs-fMRI and clinical information using 3D-convolutional neural network","volume":"17","author":"Ghafoori","year":"2022","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, T., Liao, Q., Zhang, D., Zhang, C., Yan, J., Ngetich, R., Zhang, J., Jin, Z., and Li, L. (2021). Predicting MCI to AD Conversation Using Integrated sMRI and rs-fMRI: Machine Learning and Graph Theory Approach. Front. Aging Neurosci., 13.","DOI":"10.3389\/fnagi.2021.688926"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5171618","DOI":"10.1155\/2021\/5171618","article-title":"Aberrant brain functional connectivity strength and effective connectivity in patients with type 2 diabetes mellitus","volume":"2021","author":"Guo","year":"2021","journal-title":"J. Diabetes Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1002\/hbm.25683","article-title":"Brain functional and effective connectivity based on electroencephalography recordings: A review","volume":"43","author":"Cao","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.neuroimage.2013.07.071","article-title":"Identifying the default mode network structure using dynamic causal modeling on resting-state functional magnetic resonance imaging","volume":"86","author":"Xin","year":"2014","journal-title":"Neuroimage"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.4249\/scholarpedia.1667","article-title":"Granger causality","volume":"2","author":"Seth","year":"2007","journal-title":"Scholarpedia"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.conb.2012.11.010","article-title":"Analysing connectivity with Granger causality and dynamic causal modelling","volume":"23","author":"Friston","year":"2013","journal-title":"Curr. Opin. Neurobiol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gao, X., Huang, W., Liu, Y., Zhang, Y., Zhang, J., Li, C., Bore, J.C., Wang, Z., Si, Y., and Tian, Y. (2023). A novel robust Student\u2019s t-based Granger causality for EEG based brain network analysis. Biomed. Signal Process. Control, 80.","DOI":"10.1016\/j.bspc.2022.104321"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, L., Zeng, W., Shi, Y., and Nie, W. (2022). Dynamic effective connectivity network based on change points detection. Biomed. Signal Process. Control, 72.","DOI":"10.1016\/j.bspc.2021.103274"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mohammadian, F., Noroozian, M., Sadeghi, A.Z., Malekian, V., Saffar, A., Talebi, M., Hashemi, H., Mobarak Salari, H., Samadi, F., and Sodaei, F. (2023). Effective connectivity evaluation of resting-state brain networks in Alzheimer\u2019s disease, amnestic mild cognitive impairment, and normal aging: An exploratory study. Brain Sci., 13.","DOI":"10.3390\/brainsci13020265"},{"key":"ref_35","unstructured":"Zhou, Y. (2021). Imaging and Multiomic Biomarker Applications: Advances in Early Alzheimer\u2019s Disease, Nova Science Publishers."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1016\/j.jagp.2013.02.015","article-title":"Mild Cognitive Impairment, Incidence, Progression, and Reversion: Findings from a Community-Based Cohort of Elderly African Americans","volume":"22","author":"Gao","year":"2014","journal-title":"Am. J. Geriatr. Psychiatry"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hu, Q., Wang, Q., Li, Y., Xie, Z., Lin, X., Huang, G., Zhan, L., Jia, X., and Zhao, X. (2022). Intrinsic brain activity alterations in patients with mild cognitive impairment-to-normal reversion: A resting-state functional magnetic resonance imaging study from voxel to whole-brain level. Front. Aging Neurosci., 13.","DOI":"10.3389\/fnagi.2021.788765"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1212\/WNL.0000000000000055","article-title":"Higher risk of progression to dementia in mild cognitive impairment cases who revert to normal","volume":"82","author":"Roberts","year":"2013","journal-title":"Neurology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.jad.2024.03.009","article-title":"Predicting the reversion from mild cognitive impairment to normal cognition based on magnetic resonance imaging, clinical, and neuropsychological examinations","volume":"353","author":"Yu","year":"2024","journal-title":"J. Affect. Disord."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s13195-023-01167-z","article-title":"Hippocampus-centred grey matter covariance networks predict the development and reversion of mild cognitive impairment","volume":"15","author":"Dang","year":"2023","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e70263","DOI":"10.1002\/alz.70263","article-title":"Characterizing bidirectional transitions in mild cognitive impairment and post-reversion based on longitudinal neuroimaging and cognitive assessments","volume":"21","author":"Qin","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.neucom.2020.03.006","article-title":"Enhancing the feature representation of multi-modal MRI data by combining multi-view information for MCI classification","volume":"400","author":"Liu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.jneumeth.2013.10.018","article-title":"The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference","volume":"223","author":"Barnett","year":"2014","journal-title":"J. Neurosci. Methods"},{"key":"ref_44","first-page":"1377","article-title":"DPARSF: A MATLAB Toolbox for \u201cPipeline\u201d Data Analysis of Resting-State fMRI","volume":"4","author":"Yan","year":"2010","journal-title":"Front. Syst. Neurosci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1006\/nimg.2001.0978","article-title":"Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain","volume":"15","author":"Landeau","year":"2002","journal-title":"Neuroimage"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.jneumeth.2017.06.007","article-title":"Exploring connectivity with large-scale Granger causality on resting-state functional MRI","volume":"287","author":"Dsouza","year":"2017","journal-title":"J. Neurosci. Methods"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"424","DOI":"10.2307\/1912791","article-title":"Investigating Causal Relations by Econometric Models and Cross-spectral Methods","volume":"37","author":"Granger","year":"1969","journal-title":"Econometrica"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Roso\u0142, M., M\u0142y\u0144czak, M., and Cybulski, G. (2022). Granger causality test with nonlinear neural-network-based methods: Python package and simulation study. Comput. Methods Programs Biomed., 216.","DOI":"10.1016\/j.cmpb.2022.106669"},{"key":"ref_49","unstructured":"Pester, B., Schmidt, C., Schmid-Hertel, N., Witte, H., and Leistritz, L. (, January 25\u2013August). Identification of whole-brain network modules based on a large scale Granger Causality approach. Proceedings of the Engineering in Medicine & Biology Society, Milan, Italy."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Pester, B., de la Cruz, F., Bar, K.J., Witte, H., and Leistritz, L. (2016, January 16\u201320). Detecting spatially highly resolved network modules: A multi subject approach. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591967"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sanz-Arigita, E.J., Schoonheim, M.M., Damoiseaux, J.S., Rombouts, S.A., Maris, E., Barkhof, F., Scheltens, P., and Stam, C.J. (2010). Loss of \u2018small-world\u2019networks in Alzheimer\u2019s disease: Graph analysis of FMRI resting-state functional connectivity. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0013788"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6614520","DOI":"10.1155\/2021\/6614520","article-title":"Extraction and analysis of dynamic functional connectome patterns in migraine sufferers: A resting-state fMRI study","volume":"2021","author":"Nie","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Achard, S., and Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Comput. Biol., 3.","DOI":"10.1371\/journal.pcbi.0030017"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.irbm.2019.10.006","article-title":"A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models","volume":"41","author":"Ergen","year":"2020","journal-title":"IRBM"},{"key":"ref_55","unstructured":"Kohavi, R. (1995, January 20\u201325). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the International. Joint Conference on Artificial Intelligence, Montreal, QC, Canada."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1093\/biomet\/93.3.491","article-title":"Adaptive linear step-up procedures that control the false discovery rate","volume":"93","author":"Benjamini","year":"2006","journal-title":"Biometrika"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1080\/00401706.1964.10490181","article-title":"Multiple comparisons using rank sums","volume":"6","author":"Dunn","year":"1964","journal-title":"Technometrics"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Abrol, A., Fu, Z., Du, Y., and Calhoun, V.D. (2019, January 23\u201327). Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer\u2019s Disease Progression. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856500"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e065955","DOI":"10.1002\/alz.065955","article-title":"Functional connectivity of basal nucleus of Meynert, locus coeruleus and ventral tegmental area in MCI and mild dementia in Alzheimer\u2019s disease","volume":"18","author":"Balthazar","year":"2022","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1093\/brain\/awh166","article-title":"\u2018In the course of time\u2019: A PET study of the cerebral substrates of autobiographical amnesia in Alzheimer\u2019s disease","volume":"127","author":"Eustache","year":"2004","journal-title":"Brain"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1400211","DOI":"10.1109\/JTEHM.2020.2985022","article-title":"The Identification of Alzheimer\u2019s Disease Using Functional Connectivity Between Activity Voxels in Resting-State fMRI Data","volume":"8","author":"Shi","year":"2020","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Liu, X.C., Qi, X.H., Fang, H., Zhou, K.Q., Wang, Q.S., and Chen, G.H. (2021). Increased MANF expression in the inferior temporal gyrus in patients with Alzheimer disease. Front. Aging Neurosci., 13.","DOI":"10.3389\/fnagi.2021.639318"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.neuron.2010.02.005","article-title":"Functional-Anatomic Fractionation of the Brain\u2019s Default Network","volume":"65","author":"Reidler","year":"2010","journal-title":"Neuron"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1002\/hbm.24871","article-title":"The effect of white matter signal abnormalities on default mode network connectivity in mild cognitive impairment","volume":"41","author":"Wang","year":"2020","journal-title":"Hum. Brain Mapping"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Yang, X., Gao, M., Shi, J., Ye, H., and Chen, S. (2017). Modulating the activity of the DLPFC and OFC has distinct effects on risk and ambiguity decision-making: A tDCS study. Front. Psychol., 8.","DOI":"10.3389\/fpsyg.2017.01417"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ghafoorian, M., Mehrtash, A., Kapur, T., Karssemeijer, N., Marchiori, E., Pesteie, M., Guttmann, C.R., De Leeuw, F.E., Tempany, C.M., and Van Ginneken, B. (2017). Transfer learning for domain adaptation in MRI: Application in brain lesion segmentation. Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-66179-7_59"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/10\/1754\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T07:53:39Z","timestamp":1760687619000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/10\/1754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"references-count":66,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["sym17101754"],"URL":"https:\/\/doi.org\/10.3390\/sym17101754","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,17]]}}}