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To overcome the necessity of presuming a specific model for the hemodynamic response, we introduce a semi-supervised automatic detection (SAD) method.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>The proposed\u00a0SAD\u00a0method employs a Bi-LSTM neural network to classify high temporal resolution fMRI data. Network training utilized an fMRI dataset with 75-ms temporal resolution in an iterative scheme. Classification performance was evaluated on a second fMRI dataset from the same participant, collected on a different day. Comparative analysis with the standard GLM approach was conducted to evaluate the cooperative\u00a0effectiveness of the SAD method.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The SAD method performed well based on the classification scores: true-positive rate\u2009=\u20090.961, area under the receiver operating curve\u2009=\u20090.998, true-negative rate\u2009=\u20090.99, F1-score\u2009=\u20090.979, False-negative rate\u2009=\u20090.038, false-discovery rate\u2009=\u20090.002, false-positive rate\u2009=\u20090.002 at 75-ms temporal resolution.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>SAD can detect hemodynamic responses at 75-ms temporal resolution without relying on a specific shape of an HRF. Future work could expand the use cases to include more participants and different fMRI paradigms.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s10334-024-01197-0","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T09:03:04Z","timestamp":1724922184000},"page":"1031-1046","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SAD: semi-supervised automatic detection of BOLD activations in high temporal resolution fMRI data"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0135-6335","authenticated-orcid":false,"given":"Tim","family":"Schmidt","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zolt\u00e1n","family":"Nagy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,29]]},"reference":[{"key":"1197_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0270(94)90191-0","author":"EA Deyoe","year":"1994","unstructured":"Deyoe EA, Bandettini P, Neitz J, Miller D, Winans P (1994) Functional magnetic resonance imaging (FMRI) of the human brain. 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