{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:58:55Z","timestamp":1787032735177,"version":"build-2736575974"},"reference-count":169,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia","award":["Grant No. 4362"],"award-info":[{"award-number":["Grant No. 4362"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Alzheimer\u2019s disease is a common brain disorder affecting many people worldwide. It is the primary cause of dementia and memory loss. The early diagnosis of Alzheimer\u2019s disease is essential to provide timely care to AD patients and prevent the development of symptoms of this disease. Various non-invasive techniques can be utilized to diagnose Alzheimer\u2019s in its early stages. These techniques include functional magnetic resonance imaging, electroencephalography, positron emission tomography, and diffusion tensor imaging. They are mainly used to explore functional and structural connectivity of human brains. Functional connectivity is essential for understanding the co-activation of certain brain regions co-activation. This systematic review scrutinizes various works of Alzheimer\u2019s disease detection by analyzing the learning from functional connectivity of fMRI datasets that were published between 2018 and 2024. This work investigates the whole learning pipeline including data analysis, standard preprocessing phases of fMRI, feature computation, extraction and selection, and the various machine learning and deep learning algorithms that are used to predict the occurrence of Alzheimer\u2019s disease. Ultimately, the paper analyzed results on AD and highlighted future research directions in medical imaging. There is a need for an efficient and accurate way to detect AD to overcome the problems faced by patients in the early stages.<\/jats:p>","DOI":"10.7717\/peerj-cs.2302","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T05:40:33Z","timestamp":1729057233000},"page":"e2302","source":"Crossref","is-referenced-by-count":9,"title":["fMRI-based Alzheimer\u2019s disease detection via functional connectivity analysis: a systematic 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