{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T11:24:10Z","timestamp":1787052250640,"version":"build-2736575974"},"reference-count":23,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004829","name":"Science and Technology Department of Sichuan Province","doi-asserted-by":"publisher","award":["2026YFHZ0021"],"award-info":[{"award-number":["2026YFHZ0021"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]},{"award":["2026YFHZ0021"],"award-info":[{"award-number":["2026YFHZ0021"]}],"id":[{"id":"https:\/\/ror.org\/04323m874","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a novel Topological Data Analysis (TDA) pipeline to extract robust structural features from functional near-infrared spectroscopy (fNIRS) signals for the classification of Alzheimer\u2019s Disease (AD) stages. Alzheimer\u2019s disease is increasingly understood as a disconnection syndrome, where the disruption of functional brain networks precedes gross anatomical atrophy. However, traditional graph-theoretic approaches rely on arbitrary connectivity thresholds, which can obscure critical multi-scale topological information, and are sensitive to noise. To address this, our framework leverages Persistent Homology (PH) to analyse the topological evolution of brain networks across a continuous range of scales. By modeling 48-channel hemoglobin concentration time-series as high-dimensional point clouds via Granger causality metrics, we construct filtration sequences of Vietoris\u2013Rips complexes. The resulting topological invariants, including 0\u2014dimensional connected components, 1\u2014dimensional loops, and 2\u2014dimensional voids, are first examined through Persistence Diagrams. For classification, significant H0 and H1 features are converted into Persistence Images using Gaussian kernel smoothing, while H2 features are retained for qualitative topological interpretation. This transformation enables the integration of complex topological features into standard machine learning workflows. Our experimental results were evaluated on a subject-level held-out test set consisting only of original, non-augmented recordings. Data augmentation was applied only to the training set to alleviate class imbalance. The proposed topology-driven feature extraction method achieved 86% accuracy in multi-class diagnosis (NC vs. MCI vs. AD). This study validates the efficacy of TDA as a sophisticated signal processing tool for revealing intrinsic neurodegenerative patterns in hemodynamic data, offering an exploratory methodological proof-of-concept for AD stage classification.<\/jats:p>","DOI":"10.3390\/s26165221","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T10:22:52Z","timestamp":1787048572000},"page":"5221","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Topological Data Analysis-Driven fNIRS Signal Processing for Alzheimer\u2019s Disease Stage Identification"],"prefix":"10.3390","volume":"26","author":[{"given":"Siyuan","family":"Liu","sequence":"first","affiliation":[{"name":"Pittsburgh Institute, Sichuan University, Chengdu 610207, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hangcheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Pittsburgh Institute, Sichuan University, Chengdu 610207, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Sun","sequence":"additional","affiliation":[{"name":"Pittsburgh Institute, Sichuan University, Chengdu 610207, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanbin","family":"Qiu","sequence":"additional","affiliation":[{"name":"Pittsburgh Institute, Sichuan University, Chengdu 610207, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoliang","family":"Wu","sequence":"additional","affiliation":[{"name":"West China School of Clinical Medicine, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yucong","family":"Wei","sequence":"additional","affiliation":[{"name":"Pittsburgh Institute, Sichuan University, Chengdu 610207, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9425-2967","authenticated-orcid":false,"given":"Yang","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6827-8289","authenticated-orcid":false,"given":"Zheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Pittsburgh Institute, Sichuan University, Chengdu 610207, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Nawaz, A., Anwar, S.M., Liaqat, R., Iqbal, J., Bagci, U., and Majid, M. (2020, January 5\u20137). Deep Convolutional Neural Network Based Classification of Alzheimer\u2019s Disease Using MRI Data. Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan.","DOI":"10.1109\/INMIC50486.2020.9318172"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17266","DOI":"10.1109\/TNNLS.2023.3301456","article-title":"Brain Network Classification for Accurate Detection of Alzheimer\u2019s Disease via Manifold Harmonic Discriminant Analysis","volume":"35","author":"Cai","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1007\/s10462-023-10644-8","article-title":"Deep Learning Based Computer Aided Diagnosis of Alzheimer\u2019s Disease: A Snapshot of Last 5 Years, Gaps, and Future Directions","volume":"57","author":"Bhandarkar","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s13195-017-0283-5","article-title":"On the Path to 2025: Understanding the Alzheimer\u2019s Disease Continuum","volume":"9","author":"Aisen","year":"2017","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1038\/mp.2013.178","article-title":"An Anemia of Alzheimer\u2019s Disease","volume":"19","author":"Faux","year":"2014","journal-title":"Mol. Psychiatry"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5697","DOI":"10.1038\/s41598-018-22871-z","article-title":"Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer\u2019s Disease Using Structural MR and FDG-PET Images","volume":"8","author":"Lu","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1111\/nyas.13948","article-title":"The Present and Future Use of Functional Near-Infrared Spectroscopy (fNIRS) for Cognitive Neuroscience","volume":"1464","author":"Pinti","year":"2020","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xie, L., Liu, Y., Gao, Y., and Zhou, J. (2024). Functional Near-Infrared Spectroscopy in Neurodegenerative Disease: A Review. Front. Neurosci., 18.","DOI":"10.3389\/fnins.2024.1469903"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.neuroimage.2013.05.004","article-title":"A Review on Continuous Wave Functional Near-Infrared Spectroscopy and Imaging Instrumentation and Methodology","volume":"85","author":"Scholkmann","year":"2014","journal-title":"NeuroImage"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"537","DOI":"10.3233\/JAD-2010-101485","article-title":"Neuronal Hemoglobin Is Reduced in Alzheimer\u2019s Disease, Argyrophilic Grain Disease, Parkinson\u2019s Disease, and Dementia with Lewy Bodies","volume":"23","author":"Ferrer","year":"2011","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"14069","DOI":"10.1109\/TPAMI.2023.3308391","article-title":"A Survey of Vectorization Methods in Topological Data Analysis","volume":"45","author":"Ali","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"047410","DOI":"10.1063\/1.4978997","article-title":"Persistent Homology of Time-Dependent Functional Networks Constructed from Coupled Time Series","volume":"27","author":"Stolz","year":"2017","journal-title":"Chaos"},{"key":"ref_13","first-page":"583","article-title":"Deep Learning-Based Feature Representation for AD\/MCI Classification","volume":"Volume 8150","author":"Mori","year":"2013","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2013"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1093\/brain\/awm319","article-title":"Automatic Classification of MR Scans in Alzheimer\u2019s Disease","volume":"131","author":"Stonnington","year":"2008","journal-title":"Brain"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., and Feng, D. (May, January 29). Early Diagnosis of Alzheimer\u2019s Disease with Deep Learning. Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, China.","DOI":"10.1109\/ISBI.2014.6868045"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Korolev, S., Safiullin, A., Belyaev, M., and Dodonova, Y. (2017, January 18\u201321). Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, Australia.","DOI":"10.1109\/ISBI.2017.7950647"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"24277","DOI":"10.1007\/s11042-023-16379-6","article-title":"A Novel Machine Learning Based Technique for Classification of Early-Stage Alzheimer\u2019s Disease Using Brain Images","volume":"83","author":"Hazarika","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.neuroimage.2014.06.077","article-title":"Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD\/MCI Diagnosis","volume":"101","author":"Suk","year":"2014","journal-title":"NeuroImage"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1109\/TBME.2014.2372011","article-title":"Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer\u2019s Disease","volume":"62","author":"Liu","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"095018","DOI":"10.1088\/1361-6560\/accac8","article-title":"Multi-Level Fusion Network for Mild Cognitive Impairment Identification Using Multi-Modal Neuroimages","volume":"68","author":"Xu","year":"2023","journal-title":"Phys. Med. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.neuroimage.2010.02.059","article-title":"Wiener\u2013Granger Causality: A Well Established Methodology","volume":"58","author":"Bressler","year":"2011","journal-title":"NeuroImage"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1038\/s41467-018-03664-4","article-title":"Towards a New Approach to Reveal Dynamical Organization of the Brain Using Topological Data Analysis","volume":"9","author":"Saggar","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/978-3-030-00755-3_8","article-title":"Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains","volume":"Volume 11083","author":"Anderson","year":"2018","journal-title":"Connectomics in NeuroImaging"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5221\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T10:24:39Z","timestamp":1787048679000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,18]]},"references-count":23,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26165221"],"URL":"https:\/\/doi.org\/10.3390\/s26165221","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,18]]}}}